DOI: 10.3897/jucs.207783

Authors: Shaofeng Ming, Ying Ling, Xin Li, Songming Han, Zhengwang Miao, Xizheng Pan

Abstract: This paper addresses key issues in current malicious code detection technologies, such as insufficient feature modeling, lack of high-order correlation, and poor adaptability. We propose a novel method for detecting malicious code based on abstract syntax tree (AST) multi-dimensional feature fusion and dynamic graph optimization. This approach innovatively constructs an association-aware graph-enhanced convolution mechanism. First, we use an AST parser to build a code syntax tree, integrating syntactic node type features with word vector semantic features to generate rich contextual multi-dimensional fused embeddings. Next, we introduce a dynamic edge weight adjustment algorithm that adaptively optimizes the graph adjacency matrix based on node attribute similarity, effectively enhancing the representation capability of high-order attack patterns such as cross-process and cross-module attacks. Finally, we design a dynamic loss function with regularization terms to optimize the graph convolution network parameters, improving model generalization performance. Experimental results on the standard Ember dataset show that our method achieves an F1 score of 97.87%, outperforming traditional GCN and GAT baseline models by 3.25%-4.16%. This method effectively overcomes the reliance of traditional detection techniques on static feature libraries, demonstrating significant advantages in complex scenarios such as APT attack chain detection and concurrent behavior analysis in multi-core environments. It provides an efficient and scalable solution for real-time malicious code defense in cloud computing and IoT environments.

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DOI: 10.3897/jucs.166944

Authors: Gema Gutierrez, Moisés Rodríguez, Javier Garzás, Mario Piattini

Abstract: As organizations advance their digital transformation efforts, the strategic importance of data quality becomes critical. Legal and security aspects, along with the economic value of data, further emphasize the need for high-quality, reliable, and trustworthy datasets. In particular, the effectiveness of artificial intelligence techniques heavily depends on the integrity of the underlying data. However, organizations often lack structured methods to assess and improve data quality in alignment with business goals. This paper addresses this gap by introducing OKR4DQ, a methodology that leverages Objectives and Key Results (OKRs) to systematically improve data quality. The approach combines quality assessments based on the ISO/IEC 25012 standard with the definition and implementation of OKRs targeting specific quality characteristics requiring enhancement.We present the full methodology and its application in a real-world case study, demonstrating measurable improvements in data quality and offering practical insights into the challenges and benefits of aligning data quality initiatives with business performance objectives.

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DOI: 10.3897/jucs.168955

Authors: Davut Çulha

Abstract: Large Language Models (LLMs) have shown impressive capabilities in natural language understanding and reasoning; however, their internal conceptual organization remains largely opaque. This study introduces a novel prompt engineering approach to explore the latent conceptual spaces of LLMs by identifying maximally divergent conceptual regions. It draws on the conceptual spaces framework, in which knowledge is represented as geometric regions defined by quality dimensions. Within this framework, a proxy metric referred to as the conceptual divergence count is proposed. This metric represents the number of maximally divergent conceptual regions identified through structured prompts. Although the metric does not measure dimensionality in the geometric or architectural sense, it serves as an indicator of a model’s conceptual diversity. The method is applied to a range of LLMs, including models from the Gemini, GPT, and Claude families, as well as additional models such as Llama-3.3-70B-Instruct, Mistral-7B-Instruct-v0.3, Gemma-3-12B-IT, and Grok-3. The results show substantial variation in diversity, with gemini-2.5-flash-preview-05-20 achieving the highest value of 91 and claude-3-7-sonnet-20250219 recording the lowest value of 7. These findings suggest that diversity, as measured by this method, may provide insights into the internal organization of conceptual representations. While the metric is to be an indicator of a model’s factual knowledge structure, providing complementary insight into its internal organization, it does not directly measure knowledge accuracy. The proposed approach contributes a geometric perspective to the evaluation of LLM knowledge, complementing existing benchmarks and supporting interpretability.

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DOI: 10.3897/jucs.141277

Authors: Kausthav Pratim Kalita, Debojit Boro, Dhruba Kumar Bhattacharyya

Abstract: Purpose: Big data applications enable organizations to derive actionable insights that inform strategic decision making and enhance operational efficiency in real time. Hadoop’s architecture features a distributed file system that stores voluminous data across multiple machines within a cluster. However, the management and control of access to this data can be viewed as centralized, as Hadoop relies on a central coordination system to manage tasks and resources across the cluster.Design / methodology / approach: To address the limitation in Hadoop, this paper proposes integrating blockchain technology to establish strict authentication procedures through smart contracts, enabling controlled access to the Hadoop platform. The proposed platform allows organizations to access Hadoop clusters through participation in a blockchain network, enabling efficient data storage mechanisms and model training capabilities.Findings: The performance of this integrated system is evaluated through simulations leveraging Ethereum based smart contracts. The findings suggest that implementing appropriate indexing mechanisms and hashing techniques can enable sufficient access control, thereby facilitating controlled access to Hadoop clusters. The paper presents the simulation results in terms of execution cost and execution time.Originality/value: This paper addresses the identified need for a transparent and reliable access control system that leverages blockchain’s smart contracts to enable controlled and restricted access to Hadoop clusters.

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DOI: 10.3897/jucs.142860

Authors: Banani Ghose, Zeenat Rehena

Abstract: Due to the advancement of industrialization and urbanization, air pollution become a serious issue in recent decades. To get rid of this problem Air Quality Monitoring Stations (AQMSs) are established that can asses the air quality and provide some measures to control it. These AQMSs capture the time series data through sensors and form an IoT based network to send the data to the cloud for further analysis. These IoT devices are paired with low capacity batteries and limited memory, transmission, and computational components. Transmitting the high volume data to the cloud for successive analytical purposes demands high energy dissipation and higher bandwidth. Moreover, for storing the big volumed data the system needs larger storage space. The single solution for all these constraints is data compression. Data compression reduces the volume of the datasets. The reduced volume of data saves energy and it is easier to transmit the compressed data through the limited bandwidth. In this paper, a lossless data compression algorithm using Genetic Algorithms is proposed. On successful implementation of the algorithm, the air quality time series data is compressed with absolutely no data loss. To evaluate the efficiency of the algorithm, its performance is compared with some classical and several state of the art compression schemes. The experimental results show that the proposed compression algorithm outperforms the classical as well as state of the art models concerning the performance evaluating parameters like Compression Ratio, Compression Factor, and most importantly Power Saving.

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DOI: 10.3897/jucs.164694

Authors: Aram Adamyan, Hovhannes Hovhannisyan, Daniel Radrigan, Nelson Baloian, Ashot Harutyunyan

Abstract: This study presents DSClustering, a novel algorithm that merges clustering validity with interpretability using the Dempster-Shafer theory. Traditional clustering methods like K-means, DBSCAN, and agglomerative clustering, while widely used for their efficiency and accuracy, often fall short in transparency, creating barriers in critical fields such as healthcare, finance, and consumer analytics where decision-making requires clear, interpretable insights. DSClustering aims to bridge this gap by assigning clusters based on belief functions from Dempster-Shafer theory, which allows it to generate rule-based explanations for each data point’s cluster assignment. Through detailed experiments on real-world datasets, including consumer behavior and airline satisfaction data, we evaluate DSClustering against traditional algorithms using key metrics such as Silhouette score, Rand index and Dunn’s index for clustering validity. The results indicate that DSClustering not only performs competitively but also offers a clear interpretative layer, making it suitable for applications where understanding model outputs is as essential as the accuracy of the outputs themselves. This work underscores the increasing importance of interpretability in machine learning, particularly in unsupervised learning, where transparency is typically challenging to achieve. DSClustering demonstrates a promising approach for balancing robust clustering with user-oriented interpretability, potentially encouraging broader adoption of interpretable clustering models in data-critical industries.

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DOI: 10.3897/jucs.164671

Authors: Claudio Gutiérrez-Soto, Marco A. Palomino, Patricio Galdames

Abstract: In recent years, we have witnessed the proliferation of applications that generate thousands of terabytes of data per day, due to the explosive increase in storage capacity across various devices. As a consequence, a new concept called Data Deluge has emerged. Data deluge refers to the situation where the quantity of data generated exceeds the processing power available, and spatio-temporal data is no exception to this phenomenon. In this context, the efficient processing of spatio-temporal queries becomes crucial to address this challenge, as slow query processing can result in obsolete answers, which may lead to errors. Considering this dynamic context of storage and processing, we explore a new online workload algorithm in a distributed parallel environment using hybrid spatio-temporal indexes. This algorithm is able to update the indexes with the most appropriate data, aiming to achieve more efficient query processing. To measure the efficiency of this algorithm, we present its time complexity along with an empirical evaluation of its performance, considering processing time, number of accessed nodes, and communication costs. The empirical results show a significant reduction in processing time, communication costs, and number of accessed nodes.

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DOI: 10.3897/jucs.165358

Authors: Mohini Sharma, Raghav Sandhane, Jaydeep Rajeshkumar Katariya

Abstract: The metaverse seamlessly integrates physical and digital spaces, enabling AI-driven innovations in virtual interactions, autonomous avatars, and real-time experiences. However, increased reliance on AI brings sweeping cybersecurity challenges, such as adversarial attacks, deep fake impersonation, and AI-driven phishing campaigns. The security of the metaverse is vital for the sustainability of user trust and system integrity. As AI assumes a larger role in virtual environments, proactive cybersecurity measures must be taken to counter emerging threats. This paper introduces DAI-TIRS, a holistic security framework designed to proactively secure the metaverse. DAI-TIRS is the integration of machine learning-based anomaly detection, dynamic honeypots, and predictive threat modeling that detect, classify, and mitigate AI-driven threats in real-time. By utilizing MITRE ATT&CK and the PyTM framework, it constantly learns new emerging threats through advanced behavioral analytics and keeps pace with the adversarial AI model’s evolution. The experimental results from a simulated metaverse environment demonstrate that DAI-TIRS achieves 93% accuracy in threat detection, 90% precision in classifying the severity, and a 36.9% faster threat mitigation response time than the average performance of baseline models, as detailed in the paper. These findings underscore the critical need for adaptive AI-based cybersecurity solutions that will enhance the resilience, trust, and integrity of metaverse ecosystems. This research establishes DAI-TIRS as an advanced cybersecurity framework that has demonstrated its adaptability and effectiveness in countering AI-driven threats across multiple sectors. The source code for DAI-TIRS is available on GitHub: [https://github.com/sharmamohini762/DAI-TIRS-Code]

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DOI: 10.3897/jucs.167412

Authors: Yanling Chen

Abstract: In this paper, we show the problem of two-party privacy-preserving record linkage (PPRL) can be seen as an identification problem in Information Theory. We propose to apply the identification codes that are designed for identification via channels to the problem of PPRL, due to their advantage in the performance analysis, especially on a quantitative evaluation of the privacy. Note that for the PPRL, linkage quality is typically evaluated experimentally, whilst for privacy, there are so far no commonly accepted privacy measures available that allow an objective evaluation. Our approach of identification code provides an objective evaluation on both linkage quality and privacy based on parameters of employed identification codes.

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DOI: 10.3897/jucs.164654

Authors: Wolfram Luther, A. J. Han Vinck

Abstract: This volume presents a conference paper selection from the 4th Workshop on Collaborative Technologies and Data Science in Smart City Applications (CODASSCA 2024): Data Science and Reliable Machine Learning, held in Yerevan, Armenia, October 3-6, 2024, https://codassca2024.aua.am/. The special issues guest editors invited five groups of authors from Armenia, Chile, Germany, the UK, and the USA to submit enlarged versions of their CODASSCA 2024 papers There was also a J.UCS open call so that any author could submit papers on the highlighted subjects. The invitation to review the 16 contributions received was accepted by 16 experts, and, after three rounds, seven articles were finally accepted for publication in the special issue.

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DOI: 10.3897/jucs.129515

Authors: Juan-Sebastián González-Sanabria, Cristian Pinto, Jhon Zuñiga, Hugo Ordoñez, Xiomara Blanco

Abstract: Gender-based violence remains a persistent social challenge in Colombia. Despite efforts to address it, statistics show a steady increase year after year. This study addresses the need for predictive solutions by introducing a Machine Learning model using XGBoost, chosen for its high performance in classification tasks with complex datasets. The model is trained on data collected from the department of Santander, Colombia, aiming to predict gender-based violence incidents based on specific socio-demographic and situational features. The motivation behind using XGBoost lies in its ability to handle diverse data types and produce accurate, interpretable results. Key influential features in the model’s predictions were identified, including the context of the incidents and the relationship between victim and the perpetrator, underscoring the importance of situational as well as individual factors. The model achieved promising results, with an accuracy, precision, recall, and F1 score exceeding 84% demonstrating its potential to effectively predict and contribute to preventing gender-based violence in the region. This approach not only represents a proactive response to a critical social challenge but also offers a framework that could be applied in similar contexts at the national and international levels.

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DOI: 10.3897/jucs.128450

Authors: Ji Woong Yoo, Kyoung Jun Lee, Arum Park

Abstract: The advancement of AI has heightened the significance of ethical concerns, particularly in managing negative user feedback like malicious comments, necessitating thoughtful deliberation. The focus of this research is to explore the potential of deep learning techniques in addressing these issues and enhancing the ethical nature of AI systems. Specifically, we investigated the collection and processing of news comment data using Long Short-Term Memory (LSTM) algorithm and Word2Vec model. The primary objective was to evaluate how deep learning techniques can improve the quality of data obtained from news comments. Our findings demonstrate that deep learning models surpass CleanBot in accuracy and block rates for handling negative user comments, including malicious ones, enabling organizations to effectively manage such comments in online communities using AI-based methods. This study adds to the existing research by showing how advanced deep learning techniques can effectively identify and classify harmful comments by analyzing complex language patterns.

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DOI: 10.3897/jucs.124087

Authors: Michele Berti, Matheus Camilo da Silva, Sebastiano Saccani, Sylvio Barbon Junior

Abstract: Synthetic data generation is a promising alternative to traditional data anonymization, with Variational Autoencoders (VAEs) excelling at generating high-quality synthetic tabular datasets. However, VAE hyperparameter selection is often computationally expensive or suboptimal. We propose a meta-learning (MtL) method for hyperparameter recommendation, which achieves competitive performance to state-of-the-art Bayesian Optimization (BO) with median AUC values of 0.660 ± 0.038 (MtL) and 0.650 ± 0.041 (BO), showing no statistically significant difference. Notably, our approach reduces configuration time to under three minutes, compared to BO’s multi-hour requirement, while also enabling incremental improvements through new data integration. This combination of efficiency, adaptability, and performance establishes MtL as a practical solution for hyperparameter tuning in synthetic data generation.

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DOI: 10.3897/jucs.122380

Authors: Maroua Benleulmi, Ibtissem Gasmi, Nabiha Azizi, Nilanjan Dey

Abstract: Recommender systems have a pivotal function in delivering customized and pertinent suggestions to clients on the basis of their preferences and activities. The present paper presents a thorough overview of deep learning-based recommender systems, explores their application to enhance performance, and overcomes limitations. The survey encompasses fundamental models of recommender systems; moreover, it also delves into key deep learning models. This discussion focuses on the effective integration of deep learning techniques into recommender systems. Real-world applications highlight the effectiveness of these approaches in capturing complex and nonlinear patterns from large-scale data. This paper concludes by reflecting on challenges encountered in this research field and outlines potential future directions, offering valuable insights for academics and professionals in the field of recommender systems based on deep learning.

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DOI: 10.3897/jucs.115121

Authors: Mehtap Ülker, A. Bedri Özer

Abstract: With the development of deep learning techniques, many models have been proposed for abstractive text summarization. However, the problem of summarizing source documents while preserving their integrity persists due to token restrictions and the inability to adequately extract semantic word relations between different sentences. To overcome this problem, a fine-tuning BART-based model was proposed, which generates a scientific summary by selecting important words contained in the text in the input document. The input text consists of terminology and keywords from the source document. The proposed model is based on the working principle of graph-based methods. Thus, the proposed model can summarize the source document with as few words as possible that are relevant to the content. The proposed model was compared with baseline models and the results of human evaluation. The experimental results demonstrate that the proposed model outperforms the baseline methods with a 37.60 ROUGE-L score.

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DOI: 10.3897/jucs.114607

Authors: Marija Kuštelega, Renata Mekovec, Ahmed Shareef

Abstract: As technology advances and becomes more extensively used, digital twins are likely to play an increasingly crucial role in defining the future of industry, trade, and society. Despite the stated advantages and potential of digital twin technology, certain research and implementation gaps exist, which have hindered the adoption and advancement of digital twins. This study investigates how current research on the digital twin implementations has been positioned in front of practical challenges focused on privacy and security issues. The research method adopted was a systematic literature review, employing the PRISMA approach. A total of 47 publications were identified and analyzed. The results indicate that the privacy and security challenges for digital twin implementation are complicated and may be divided into six primary groups: (1) data privacy, (2) data security, (3) data management, (4) data infrastructure and standardization, (5) ethical and moral issues, (6) legal and social issues.

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DOI: 10.3897/jucs.109788

Authors: Lojenaa Navanesan, Nhien-An Le-Khac, Yossi Oren, Kasun De Zoysa, Asanka P. Sayakkara

Abstract: The possession of smart devices has ingrained itself into daily life. Therefore, smart devices, such as IoT and smartphones, are crucial sources of evidence in instances where criminal activity occurs. Due to the challenges in traditional digital forensic techniques involving smart devices, it has been recently proposed in the literature to leverage electromagnetic side-channel analysis (EM-SCA) for the purpose. This paper identifies and discusses an important barrier that exists in the application of EM-SCA for digital forensics that hinders its successful use, namely, the issue of cross-device portability of machine learning (ML) models that are used for EM-SCA. Firstly, the paper empirically evaluates the possibility of using trained ML models to extract forensic insights from EM radiation data of IoT devices. During this empirical study, the inability to reuse a trained ML model across different devices is identified. Secondly, the paper surveys the literature in search of related work that has studied the use of EM-SCA to gather information from smart devices. The purpose of the survey is to identify whether any existing work has been able to introduce potential approaches to enable cross-device portability of ML models in EM-SCA. The findings of this survey point to the fact that the identified problem still exists and requires further studies opening the door to future research.

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DOI: 10.3897/jucs.134762

Authors: Lasse Nitz, Avikarsha Mandal

Abstract: The use of machine learning has shown to benefit a wide range of applications, especially for classification tasks. As such, the detection of algorithmically generated domains to identify corrupted machines has proven itself to be a mature use case with good classification performance. The use of privacy and security sensitive data, however, raises concerns in scenarios that require interaction with external parties. As one of such scenarios, we consider the training of domain generation algorithm detection classifiers in a Machine-Learning-as-a-Service (MLaaS) scenario. We evaluate the use of a Bloom encoding approach from the area of privacy-preserving record linkage to prevent the MLaaS provider from getting to know the exact classification task as well as the data samples transmitted for training and classification. We investigate the threat associated with pattern mining attacks by performing a privacy analysis for two versions of these encodings (basic and randomized). We further identify sets of parameter values which we find to provide an adequate level of protection against these attacks. We see the potential for this approach in machine learning use cases dealing with sensitive data or tasks, especially for MLaaS scenarios dealing with short data samples that lack a clear structure.

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DOI: 10.3897/jucs.132132

Authors: Dagmar Gesmann-Nuissl, Ines Maria Tacke, Stefanie Meyer

Abstract: As part of the modern Internet, Internet of Things (IoT) devices are particularly indispensable. The reason these devices fit so seamlessly into our everyday lives is that they constantly generate, process and evaluate data (using the Internet) and can react smoothly to circumstances – such as the refrigerator that automatically reorders missing food or a maintenance monitory system in smart homes that assigns digital maintenance orders to the responsible tradesmen. These data flows and underlying information are the subject of a wide variety of legal projects: Europe aims to become a pioneer of the data economy by providing access to available data accessible for further value and business models. Subject matter includes both personal data and non-personal data, such as IoT machine data. To achieve these politicaleconomic goals, but also to ensure the interests of users and especially manufacturers in confidentiality and fair competition, we introduce the person of the neutral data intermediary: the data innovator.

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DOI: 10.3897/jucs.108811

Authors: Carina Heßeling, Sebastian Litzinger, Jörg Keller

Abstract: With the advent of the Internet-of-Things, sensor data streams are widespread and thus a promising target for network steganography including covert channels. Therefore, we present an approach where the covert sender and receiver first build an archive of values that occur in the stream over a time interval, and then encode bits of the secret message via sensor stream values belonging to the class of seen values or not. If a sensor value’s category does not match the required bit value, it is adapted to switch its category. The chosen value minimizes the distance to the original sensor value to improve stealthiness. Furthermore, we present an application scenario and an analysis of the channel’s detectability, in particular how detectability and steganographic bandwidth can be traded against each other. We have tested the approach and the proposed detec-tion metrics via simulation experiments.

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DOI: 10.3897/jucs.109080

Authors: F. Didem Alay, Nagehan İlhan, M. Tahir Güllüoğlu

Abstract: Photovoltaic (PV) energy systems are a leading type of renewable energy systems globally. Predicting PV energy production accurately is crucial for maintaining efficient energy grids, making informed decisions in the energy market, and reducing maintenance costs. To ensure high accuracy and optimal production, it is essential to monitor and analyze these variables regularly. Solar radiation and temperature are two meteorological variables that directly affect the quantity of PV energy generated in PV facilities. The Performance Ratio (PR) is a critical parameter for assessing PV plant performance. A comprehensive model was constructed in this study to forecast solar radiation and temperature using multiple machine learning methods, including Instance-Based K-Nearest Neighbor Algorithm (IBK), Linear Regression, Random Forests, Random Tree, Multilayer Perceptron (MLP), and MLP Regression. Moreover, we used time series approaches, such as Simple Exponential Smoothing (SES), Error-Trend-Seasonality (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt Winter's Seasonal Method (HWES) models for PV systems prediction. Initially, we conducted daily forecasts as well as 1-step ahead forecasts at 5-minute intervals for both solar radiation and temperature. It is crucial to subject both variables to the same methodology in order to construct precise models for forecasting PV. Secondly, we compared the predicted values of solar radiation and temperature with the actual energy yield of the power plant to calculate energy production. Subsequently, a relative analysis of data mining models and time series models have been performed depending on the statistical error criteria like RMSE, MAPE, MABE, MAE, MSE, and direction accuracy (DAC).

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DOI: 10.3897/jucs.105363

Authors: G.V.Vidya Lakshmi, S. Gopikrishnan

Abstract: Data in the Internet of Things (IoT) domain may be missing due to connectivity errors, environmental extremes, sensor malfunctions, and human errors. Despite the many approaches for imputing missing values, the most significant difficulty in terms of imputation precision or compute complexity for larger missing sub-sequences in uni-variate series is still being explored. This work introduced IMD-MP (Imputation of Missing Data using Matrix Profile), a new technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations using a novel distance metric Matrix Profile Distance (MPD). Our method preserves spatial correlation by grouping the sensors present in the network (using grouping algorithm-GA) to impute the missing data of the failed sensor node. After grouping, similar sensor nodes to the failed sensor node are identified using the Node Similarity Algorithm (NSF). From its similar sensor data, a certain number of sub-sequences that are most similar to the one preceding the failed node’s missing values are gathered. These sub-sequences heights are optimized to ensure temporal correlation in the imputed data. To find the optimal imputation sequence, the current research uses MPD and similarity scores. Numerical findings using sensor data from real-time environmental mon-itoring and Intel data sets demonstrate the algorithm’s effectiveness compared to other benchmarks.

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DOI: 10.3897/jucs.112977

Authors: Mauricio Solar, Pablo Aguirre

Abstract: The idea of using X–rays and Computed Tomography (CT) images as diagnostic method has been explored in several studies. Most of these studies work with slices of CT image in 2D, requiring less computational capacity and less time to process them than 3D. The processing of volumetric data (the complete CT images in 3D) adds an extra dimension of information. However, the magnitude of the data is considerably larger than working with slices in 2D, so extra computational processing is required. In this study a model capable of performing a classification of a 3D input that represents the volume of the CT scan is proposed. The model is able to classify the 3D input between COVID–19 and Non–COVID–19, but reducing the use of resources when performing the classification. The proposed model is the ResNet–50 model with a new dimension of information added, which is a simple autoencoder. This autoencoder is trained on the same dataset, and a vector representation of each exam is generated and used together with the exams to feed the ResNet–50. To validate the proposal, the same proposed model is compared with and without the autoencoder module that provides more information to the proposed model. The proposed model obtains better metrics than the same model without the autoencoder, confirming that extracting relevant features from the dataset helps improve the performance of the model.

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DOI: 10.3897/jucs.108550

Authors: Ayoub Charef, Zahi Jarir, Mohamed Quafafou

Abstract: Traffic violations by motorcycle riders pose a significant risk to urban road safety. In this paper, we present a novel approach that utilizes computer vision algorithms to detect and quantify traffic violations committed by motorcycle riders. These violations include non-compliance with helmet regulations, illegal lane changing, wrong way driving, weaving between vehicles, and running red lights. To enhance the awareness of motorcycle riders regarding the infractions they commit and the potential hazards these pose to road safety, we have developed a mobile application. This application not only provides riders with valuable feedback but also encourages them to be more conscientious and responsible for their actions. A series of experiments was conducted in the city of Marrakech, Morocco, demonstrating the system's effectiveness in positively influencing the behavior of motorcyclists on urban roads.

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DOI: 10.3897/jucs.108445

Authors: Emre Sadıkoğlu, İrfan Kösesoy, Murat Gök

Abstract: With the increasing popularity and usage of artificial intelligence systems, it has become crucial to address their vulnerability to cyber-attacks. In this study, we propose a novel gradient descent-based method to generate fake data that can be accepted as positive by a targeted machine learning model. Our method is designed to generate a large number of positive samples with a minimal number of probes to the model, making it difficult to detect by security systems. Additionally, we develop an alternative model to the attacked model using a reverse engineering approach, trained on a dataset composed of the samples generated by our method. We evaluate the success of our proposed method and the alternative model through a series of experiments. We conducted experiments on six distinct datasets, each of which was trained using three separate machine-learning algorithms. This resulted in a total of eighteen unique models that were evaluated and compared in our analysis. In the evaluation of results, the most commonly used metrics in the literature, including effective attack rate (EAR), accuracy, precision, recall, and F1 score, were employed. Focusing particularly on EAR-oriented assessments, our method demonstrates its effectiveness with a notably high EAR of 97% in the combination of the kNN method and the Cancer dataset. According to the results of our experiments, the proposed method demonstrates high effectiveness as a data-driven attack method.

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DOI: 10.3897/jucs.99223

Authors: Ana Cristina Alves de Oliveira, Marco Aurélio Spohn, Christof Fetzer, Le Quoc Do, André Martin

Abstract: Data-as-a-Service (DaaS) is a branch of cloud computing that supports “querying the Web”. Due to its ultrahigh scale, it is essential to establish rules when defining resources’ costs and guidelines for infrastructure investments. Those decisions should prioritize minimizing the incidence of agreement breaches that compromise the performance of cloud services and optimize resources’ usage and services’ cost. This article aims to address the cost problem of DaaS by developing a model that optimizes the costs of querying distributed data sources over virtual machines spread across multisite data centers. We have designed and analyzed a cost model for DaaS, besides implementing a scheduling system to perform a cost-based VM assignment. To validate our model, we have studied and characterized a real-world DaaS system’s network and processing workloads. On average, our cost-based scheduling performs at least twice as well as the traditional round-robin approach. Our model also supports load balancing and infrastructure scalability when combined with an adaptive cost scheme that prioritizes VM allocation within the underutilized data centers and avoids sending VMs to data centers in the eminence of becoming over-utilized.

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DOI: 10.3897/jucs.112797

Authors: Lorenz Gillner, Ekaterina Auer

Abstract: In this paper, we describe the requirements for traceable open-source data retrieval in the context of computation of BRCA1/2 mutation probabilities (mutations in two tumor-suppressor genes responsible for hereditary BReast or/and ovarian CAncer). We show how such data can be used to develop a Dempster-Shafer model for computing the probability of BRCA1/2 mutations enhanced by taking into account the actual age of a patient or a family member in an appropriate way even if it is not known exactly. The model is compared with PENN II and BOADICEA (based on undisclosed data), two established platforms for this purpose accessible online, as well as with our own previous models. A proof-of-concept implementation shows that set-based techniques are able to provide better information about mutation probabilities, simultaneously highlighting the necessity for ground truth data of high quality.

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DOI: 10.3897/jucs.112913

Authors: Mariam Haroutunian, Parandzem Hakobyan, Arman Avetisyan

Abstract: We study the information-theoretic model of stegosystem with an active adversary, where unlike a passive adversary he can not only read but also write. The legitimate sender as well as the adversary can embed or not a message in the sending data. The receiver’s first task is to decide whether the communication is a covertext, data with no hidden message, or a stegotext, modified data with a hidden secret message. In case of stegotext, the receiver’s second task is to decide whether the message was sent by a legitimate sender or from an adversary. For this purpose an authenticated encryption from the legitimate sender is considered.In this paper we suggest two-stage statistical hypothesis testing approach from the receivers point of view. We propose the logarithmically asymptotically optimal testing for this model. As a result the functional dependence of reliabilities of the first and second kind of errors in both stages is constructed. A comparison of overall error probabilities with the situation of one stage hypotheses testing is discussed and the behaviour of functional dependences of reliabilities are illustrated.

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DOI: 10.3897/jucs.98789

Authors: Samar Bouazizi, Emna Benmohamed, Hela Ltifi

Abstract: Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion classification and feature extraction. Furthermore, we feed the selected features to a grouped ESN for recognizing emotions. Our approach is validated on the well-known DEAP benchmark, which includes the EEG data from 32 participants. The proposed model achieved 89.32% accuracy for H/L Arousal and 91.21% accuracy for H/L Valence on the DEAP dataset. The obtained results demonstrate the effectiveness of our approach, which yields good performance compared to existing models of emotion analysis based on EEG.

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DOI: 10.3897/jucs.98824

Authors: Jaeyoung Yang, Sooin Kim, Sangwoo Lee, Won-gyum Kim, Donghoon Kim, Doosung Hwang

Abstract: The increased usage of the internet and ICT has posed a significant challenge to protect copyrighted content due to advanced image forgery techniques that make image authentication extremely difficult. The aim of this paper is to establish a binary classification method for determining copyright images from copyright-free ones. A deep hashing model is introduced for an image authentication system, which uses deep learning-based perceptual hashing. Hash codes from a deep hashing model trained with a copyright image dataset are used to identify images. The deep learning model is able to learn features that represent the implicit meaning or structural information of an image. The copyright dataset, which lacks class labels, is trained with deep hashing models with self-supervision. The proposed model is based on an autoencoder or variational autoencoder model and is improved by including convolutional filters, residual blocks, and vision transformer blocks. Experimental results show that the proposed model performs a one-to-one mapping with most stored images and can retrieve related images using image features in hash collisions. The model can find the query image among the top 5 images with comparable hash codes. The results indicate that the proposed deep hashing approach is robust and applicable.

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DOI: 10.3897/jucs.96013

Authors: Monika Lamba, Seema Verma, Pardeep Kumar

Abstract: Safety has become the primary concern for the air transportation system nowadays primarily due to increasing air traffic throughout the world. Various regulatory bodies have been maintaining enormous amount of aviation accidental data repositories. This past data is highly complex because of its many temporal and geographical components along with multiple variables. To be able to analyze this past data, there is always a need of user friendly and GUI based System. This article has proposed an intelligent vision-based decision-making system for the exploration of past aviation accidents and incidents dataset. The proposed visual query-based model is capable to analyse the major factors like flight phases, human factors, weather conditions and faulty components in particular aircraft models which are responsible for those unsafe events and may claim life of many passengers who are traveling and crew personnels. This model enables the users to express “what” visuals should be created instead of “how” to create them. Various case studies conducted through visual queries have proved that the system will be highly able to improve situational awareness regarding flight conditions to the crew members and air traffic controllers along with aviation authorities so that they are able to take timely decisions and deciding on what kind of training staff members need to reduce the consequences of such accidents and incidents.

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DOI: 10.3897/jucs.99169

Authors: Abubakhari Sserwadda, Alper Ozcan, Yusuf Yaslan

Abstract: Several real-world phenomena, including social, communication, transportation, and biological networks, can be efficiently expressed as graphs. This enables the deployment of graph algorithms to infer information from such complex network interactions to enhance graph applications’ accuracy, including link prediction, node classification, and clustering. However, the large size and complexity of the network data limit the efficiency of the learning algorithms in making decisions from such graph datasets. To overcome these limitations, graph embedding techniques are usually adopted. However, many studies not only assume static networks but also pay less attention to preserving the network topological and centrality information, which information is key in analyzing networks. In order to fill these gaps, we propose a novel end-to-end unified Topological Similarity and Centrality driven Hybrid Deep Learning model for Temporal Link Prediction (TSC-TLP). First, we extract topological similarity and centrality-based features from the raw networks. Next, we systematically aggregate these topological and centrality features to act as inputs for the encoder. In addition, we leverage the long short-term memory (LSTM) layer to learn the underlying temporal information in the graph snapshots. Lastly, we impose topological similarity and centrality constraints on the model learning to enforce preserving of topological structure and node centrality role of the input graphs in the learned embeddings. The proposed TSC-TLP is tested on 3 real-world temporal social networks. On average, it exhibits a 4% improvement in link prediction accuracy and a 37% reduction in MSE on centrality prediction over the best benchmark.

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DOI: 10.3897/jucs.94514

Authors: Fernando Terroso-Saenz, Andres Muñoz

Abstract: Predicting human mobility is a key element in the development of intelligent transport systems. Current digital technologies enable capturing a wealth of data on mobility flows between geographic areas, which are then used to train machine learning models to predict these flows. However, most works have only considered a single data source for building these models or different sources but covering the same spatial area. In this paper we propose to augment a macro open-data mobility study based on cellular phones with data from a road traffic sensor located within a specific motorway of one of the mobility areas in the study. The results show that models trained with the fusion of both types of data, especially long short-term memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, provide a more reliable prediction than models based only on the open data source. These results show that it is possible to predict the traffic entering a particular city in the next 30 minutes with an absolute error less than 10%. Thus, this work is a further step towards improving the prediction of human mobility in interurban areas by fusing open data with data from IoT systems.

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DOI: 10.3897/jucs.90346

Authors: Seyedeh Mahsa Mirhoseini-Moghaddam, Mohammad Reza Yamaghani, Adel Bakhshipour

Abstract: In this study, a combined system of electronic nose (e-nose) and computer vision was developed for the detection of adulteration in extra virgin olive oil (EVOO). The canola oil was blended with the pure EVOO to provide adulterations at four levels of 5, 10, 15, and 20%. Data collection was carried out using an e-nose system containing 13 metal oxide gas sensors, and a computer vision system. Applying principal component analysis (PCA) on the e-nose-extracted features showed that 93% and 92% of total data variance was covered by the three first PCs generated from Maximum Sensor Response (MSR), Area Under Curve (AUC) features, respectively. Cluster analysis verified that the pure and impure EVOO samples can be categorized by e-nose properties. PCA-Quadratic Discriminant Analysis (PCA-QDA) classified the EVOOs with an accuracy of 100%. Multiple Linear Regression (MLR) was able to estimate the adulteration percentage with the R2 of 0.8565 and RMSE of 2.7125 on the validation dataset. Moreover, factor analysis using Partial Least Square (PLS) introduced the MQ3 and TGS2620 sensors as the most important e-nose sensors for EVOO adulteration monitoring. Application of Response Surface Methodology (RSM) on RGB, HSV, L*,a*, and b* as color parameters of the EVOO images revealed that the color parameters are at their optimal state in the case up to 0.1% of canola impurity, where the obtained desirability index was 97%. Results of this study demonstrated the high capability of e-nose and computer vision systems for accurate, fast and non-destructive detection of adulteration in EVOO and detection of food adulteration may be more reliable using these artificial senses.

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DOI: 10.3897/jucs.81681

Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

Abstract: Malaria is a huge public health concern around the world. The conventional method of diagnosing malaria is for qualified technicians to visually examine blood smears for parasite-infected red blood cells under a microscope. This procedure is ineffective. It takes time and requires the expertise of a skilled specialist. The diagnosis is dependent on the individual performing the examination’s experience and understanding. This article offers a new and robust deep learning model for automatically classifying malaria cells as infected or uninfected. This approach is based on a convolutional neural network (CNN). It improved by the regularization method on a publicly available dataset which contains 27, 558 cell images with equal instances of parasitized and uninfected cells from the National Institute of health. The performance of our proposed model is 99.70% of accuracy and 0.0476 loss value.

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DOI: 10.3897/jucs.94155

Authors: V D Ambeth Kumar, Vijayakumar Varadarajan, Mukesh Kumar Gupta, Joel J. P. C Rodrigues, Neha Janu

Abstract: We proposed the idea of editing a special issue that would compile the fruitful research that resulted from the stimulating discussions that occurred during the workshop that was held during the 5th International Conference on Intelligent Computing, Chennai on 25th & 26th March 2022. The objective of this special issue is to call for high-quality papers covering the latest data analytic concepts and technologies of big data and artificial intelligence. This special issue serves as a forum for researchers across the globe to discuss their work and recent advances in this field. The best papers from Artificial intelligence and Big Data Analytics (BAM) in the domains of Product, Finance, Health, and Environment were invited, peer-reviewed. The best high-quality papers were selected based on the innovativeness and relevance of the theme. The amount of data being generated and stored in various fields such as education, energy, environment, healthcare, fraud detection, and traffic is increasing exponentially in the modern era of Big Data. Simultaneously, there is a significant paradigm shift in business and society worldwide due to rapid advancements in fields such as artificial intelligence, machine learning, deep learning, and data analytics. This creates significant challenges for decision-making and the potential for transformation in areas such as the economy, government, and industry. Artificial Intelligence tools, techniques, and technologies, in conjunction with Big Data, improve the predictive power of the systems created and allow the government, public, and private sectors to discover new patterns and trends, as well as improve public values such as accountability, safety, security, and transparency to enable better decision-making, policies, and governance. They also have a wide range of capabilities to perform complex tasks that humans cannot. They could be used to collect, organize, and analyze large, diverse data sets to discover patterns and trends that address a variety of problems related to the development of the economy, such as identifying new sources of revenue, expanding the customer base for business, product reviews, and promotion, disease prediction and prevention, climatic variation prediction, and the provision of energy solutions. The wide variety of subject areas discussed at the 5th International Conference on Intelligent Computing is reflected in the seven accepted papers presented in the following section.

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DOI: 10.3897/jucs.67953

Authors: Julio Cesar Vale Neves, Luiz Enrique Zarate, Mark Alan Junho Song

Abstract: Due to the high complexity of real problems, a considerable amount of research that deals with high volumes of information has emerged. The literature has considered new applications of data analysis for high dimensional environments in order to manage the difficulty in extracting knowledge from a database, especially with the increase in social and professional networks. Tri- adic Concept Analysis (TCA) is a technique used in the applied mathematical area of data analysis. Its main purpose is to enable knowledge extraction from a context that contains objects, attributes, and conditions in a hierarchical and systematized representation. There are several algorithms that can extract concepts, but they are inefficient when applied to large datasets because the compu- tational costs are exponential. The objective of this paper is to add a new data structure, binary decision diagrams (BDD), in the TRIAS algorithm and retrieve triadic concepts for high dimen- sional contexts. BDD was used to characterize formal contexts, objects, attributes, and conditions. Moreover, to reduce the computational resources needed to manipulate a high-volume of data, the usage of BDD was implemented to simplify and represent data. The results show that this method has a considerably better speedup when compared to the original algorithm. Also, our approach discovered concepts that were previously unachievable when addressing high dimensional contexts.

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DOI: 10.3897/jucs.74230

Authors: Luca Calderoni, Dario Maio, Paolo Palmieri

Abstract: In this paper we compare two probabilistic data structures for association queries derived from the well-known Bloom filter: the shifting Bloom filter (ShBF), and the spatial Bloom filter (SBF). With respect to the original data structure, both variants add the ability to store multiple subsets in the same filter, using different strategies. We analyse the performance of the two data structures with respect to false positive probability, and the inter-set error probability (the probability for an element in the set of being recognised as belonging to the wrong subset). As part of our analysis, we extended the functionality of the shifting Bloom filter, optimising the filter for any non-trivial number of subsets. We propose a new generalised ShBF definition with applications outside of our specific domain, and present new probability formulas. Results of the comparison show that the ShBF provides better space efficiency, but at a significantly higher computational cost than the SBF.

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DOI: 10.3897/jucs.76822

Authors: Ashot Harutyunyan, Gregor Schiele

Abstract: Based on a successful funded collaboration between the American University of Armenia, the University of Duisburg-Essen and the University of Chile, in previous years a network was built, and in September 2020 a group of researchers gathered (although virtually) for the 2nd CODASSCA workshop on “Collaborative Technologies and Data Science in Smart City Applications”. This event has attracted 25 paper submissions which deal with the problems and challenges mentioned above. The studies are in specialized areas and disclose novel solutions and approaches based on existing theories suitably applied.The authors of the best papers published in the conference proceedings on Collaborative Technologies and Data Science in Artificial Intelligence Applications by Logos edition Berlin were invited to submit significantly extended and improved versions of their contributions to be considered for a journal special issue of J.UCS. There was also a J.UCS open call so that any author could submit papers on the highlighted subject. For this volume, we selected those devoted mainly to human-computer interaction problematics, which were rigorously reviewed in three rounds and 6 papers nominated to be published.

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DOI: 10.3897/jucs.76602

Authors: Amit Kumar, Sonali Agarwal

Abstract: Social capital is an asset earned by people through their social connections. One of the motivations among developers to contribute to open source development and maintenance tasks is to earn social capital. Recent studies suggest that the social capital of the project has an impact on the sustained participation of the developers in open source software (OSS). One way to improve the social capital of the project is to help the developers in connecting with their peers. However, to the best of our knowledge, there is no prior research which attempts to predict future collaborations among developers and establish the significance of these collaborations on improving the social capital at the project level. To address this research gap, in this paper, we model the past collaborations among developers on version control system (VCS) and issue tracking system (ITS) as homogeneous and heterogeneous developer social network (DSN). Along with the novel path count based features, defined on proposed heterogeneous DSN, multifaceted proximity features are used to generate a feature set for machine learning classifiers. Our experiments performed on 5 popular open source projects (Spark, Kafka, Flink, WildFly, Hibernate) indicate that the proposed approach can predict the future collaborations among developers on both the platforms i.e. VCS as well as ITS with a significant accuracy (AUROC up to 0.85 and 0.9 for VCS and ITS respectively). A generic metric- recall of gain in social capital is proposed to investigate the efficacy of these predicted collaborations in improving the social capital of the project. We also concretised this metric on various measures of social capital and found that collaborations predicted by our approach have significant potential to improve the social capital at project level (e.g. Recall of gain in cohesion index up to 0.98 and Recall of gain in average godfather index up to 0.99 for VCS). We also showed that structure of collaboration network has an impact on the accuracy and usefulness of predicted collaborations. Since the past research suggests that many newcomers abandon the open source project due to social barriers which they face after joining the project, our research outcomes can be used to build the recommendation systems which might help to retain such developers by improving their social ties based on similar skills/interests.

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DOI: 10.3897/jucs.76605

Authors: Mariam Haroutunian

Abstract: One of the problems of information - theoretic security concerns secure communication over a wiretap channel. The aim in the general wiretap channel model is to maximize the rate of the reliable communication from the source to the legitimate receiver, while keeping the confidential information as secret as possible from the wiretapper (eavesdropper).We introduce and investigate the E - capacity - equivocation region and the E - secrecy capacity function for the wiretap channel, which are, correspondingly, the generalizations of the capacity - equivocation region and secrecy - capacity studied by Csiszár and Körner (1978). The E - capacity equivocation region is the closure of the set of all achievable rate - reliability and equivocation pairs, where the rate - reliability function represents the optimal dependence of rate on the error probability exponent (reliability). By analogy with the notion of E - capacity, we consider the E - secrecy capacity function that for the given E is the maximum rate at which the message can be transmitted being kept perfectly secret from the wiretapper.

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DOI: 10.3897/jucs.76608

Authors: Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan, Nicholas Kushmerick

Abstract: Effective root cause analysis (RCA) of performance issues in modern cloud environ- ments remains a hard problem. Traditional RCA tracks complex issues by their signatures known as problem incidents. Common approaches to incident discovery rely mainly on expertise of users who define environment-specific set of alerts and >target detection of problems through their occurrence in the monitoring system. Adequately modeling of all possible problem patterns for nowadays extremely sophisticated data center applications is a very complex task. It may result in alert/event storms including large numbers of non-indicative precautions. Thus, the crucial task for the incident-based RCA is reduction of redundant recommendations by prioritizing those events subject to importance/impact criteria or by deriving their meaningful groupings into separable situations. In this paper, we consider automation of incident discovery based on rule induction algorithms that retrieve conditions directly from monitoring datasets without consuming the sys- tem events. Rule-learning algorithms are very flexible and powerful for many regression and classification problems, with high-level explainability. Since annotated or labeled data sets are mostly unavailable in this area of technology, we discuss data self-labelling principles which allow transforming originally unsupervised learning tasks into classification problems with further application of rule induction methods to incident detection.

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DOI: 10.3897/jucs.76554

Authors: Nelson Baloian, José Pino

Abstract: Modern technologies and various domains of human activities increasingly rely on data science to develop smarter and autonomous systems. This trend has already changed the whole landscape of the global economy becoming more AI-driven. Massive production of data by humans and machines, its availability for feasible processing with advent of deep learning infrastructures, combined with advancements in reliable information transfer capacities, open unbounded horizons for societal progress in close future. Quite naturally, this brings also new challenges for science and industry.In that context, Internet of things (IoT) is an enormously huge factory of monitoring and data generation. It enables countless devices to act as sensors which record and manipulate data, while requiring efficient algorithms to derive actionable knowledge. Billions of end-users equipped with smart mobile phones are also producing immensely large volumes of data, being it about user interaction or indirect telemetry such as location coordinates. Social networks represent another kind of data-intensive sources, with both structured and unstructured components, containing valuable information about world’s connectivity, dynamism, and more. Last but not least, to help businesses run smoothly, today’s cloud computing infrastructures and applications are also serviced and managed through measuring huge amounts of data to leverage in various predictive and automation tasks for healthy performance and permanent availability. Therefore, all these technology areas, experts and practitioners, are facing innovation challenges on building novel methodologies, accurate models, and systems for respective data-driven solutions which are effective and efficient. In view of the complexity of contemporary neural network architectures and models with millions of parameters they derive, one of such challenges is related to the concept of explainability of the machine learning models. It refers to the ability of the model to give information which can be interpreted by humans about the reasons for the decision made or recommendation released. These challenges can only be met with a mix of basic research, process modeling and simulation under uncertainty using qualitative and quantitative methods from the involved sciences, and taking into account international standards and adequate evaluation methods.Based on a successful funded collaboration between the American University of Armenia, the University of Duisburg-Essen and the University of Chile, in previous years a network was built, and in September 2020 a group of researchers gathered (although virtually) for the 2nd CODASSCA workshop on “Collaborative Technologies and Data Science in Smart City Applications”. This event has attracted 25 paper submissions which deal with the problems and challenges mentioned above. The studies are in specialized areas and disclose novel solutions and approaches based on existing theories suitably applied.The authors of the best papers published in the conference proceedings on Collaborative Technologies and Data Science in Artificial Intelligence Applications by Logos edition Berlin were invited to submit significantly extended and improved versions of their contributions to be considered for a journal special issue of J.UCS. There was also a J.UCS open call so that any author could submit papers on the highlighted subject. For this volume, we selected those dealing with more theoretical issues which were rigorously reviewed in three rounds and 6 papers nominated to be published.The editors would like to express their gratitude to J.UCS foundation for accepting the special issues in their journal, to the German Research Foundation (DFG), the German Academic Exchange Service (DAAD) and the universities and sponsors involved for funding the common activities and thank the editors of the CODASSCA2020 proceedings for their ongoing encouragement and support, the authors for their contributions, and the anonymous reviewers for their invaluable support.The paper “Incident Management for Explainable and Automated Root Cause Analysis in Cloud Data Centers” by Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan, and Nicholas Kushmerick addresses an increasingly important problem towards autonomous or self-X systems, intelligent management of modern cloud environments with an emphasis on explainable AI. It demonstrates techniques and methods that greatly help in automated discovery of explicit conditions leading to data center incidents.The paper “Temporal Accelerators: Unleashing the Potential of Embedded FPGAs” by Christopher Cichiwskyj and Gregor Schiele presents an approach for executing computational tasks that can be split into sequential sub-tasks. It divides accelerators into multiple, smaller parts and uses the reconfiguration capabilities of the FPGA to execute the parts according to a task graph. That improves the energy consumption and the cost of using FPGAs in IoT devices.The paper “On Recurrent Neural Network based Theorem Prover for First Order Minimal Logic” by Ashot Baghdasaryan and Hovhannes Bolibekyan investigates using recurrent neural networks to determine the order of proof search in a sequent calculus for first-order minimal logic with a history mechanism. It demonstrates reduced durations in automated theorem proving systems. The paper “Incremental Autoencoders for Text Streams Clustering in Social Networks” by Amal Rekik and Salma Jamoussi proposes a deep learning method to identify trending topics in a social network. It is built on detecting changes in streams of tweets. The method is experimentally validated to outperform relevant data stream algorithms in identifying “hot” topics.The paper “E-Capacity–Equivocation Region of Wiretap Channel” by Mariam Haroutunian studies a secure communication problem over the wiretap channel, where information transfer from the source to a legitimate receiver needs to be realized maximally secretly for an eavesdropper. This is an information-theoretic research which generalizes the capacity-equivocation region and secrecy-capacity function of the wiretap channel subject to error exponent criterion, thus deriving new and extended fundamental limits in reliable and secure communication in presence of a wiretapper.The paper “Leveraging Multifaceted Proximity Measures among Developers in Predicting Future Collaborations to Improve the Social Capital of Software Projects” by Amit Kumar and Sonali Agarwal targets improving the social capital of individual software developers and projects using machine learning. Authors’ approach applies network proximity and developer activity features to build a classifier for predicting the future collaborations among developers and generating relevant recommendations.

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DOI: 10.3897/jucs.70330

Authors: Sara Qassimi, El Hassan Abdelwahed

Abstract: Research on digital cultural heritage has raised the importance of providing visitors with relevant assistance before and during their visits. With the advent of the social web, the cultural heritage area is affected by the problem of information overload. Indeed, a large number of available resources have emerged coming from the social information systems (SocIS). Therefore, visitors are swamped with enormous choices in their visited cities. SocIS platforms use the features of collaborative tagging, named folksonomy, to commonly contribute to the management of the shared resources. However, collaborative tagging uses uncontrolled vocabulary which semanti- cally weakens the description of resources, consequently decreases their classification, clustering, thereby their recommendation. Therefore, the shared resources have to be pertinently described to ameliorate their recommendations. In this paper, we aim to enhance the cultural heritage visits by suggesting semantically related places that are most likely to interest a visitor. Our proposed approach represents a semantic graph-based recommender system of cultural heritage places through two steps; (1) constructing an emergent semantic description that semantically augments the place and (2) effectively modeling the emerging graphs representing the semantic relatedness of similar cultural heritage places and their related tags. The experimental evaluation shows relevant results attesting the efficiency of the proposed approach.

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DOI: 10.3897/jucs.66375

Authors: Alessia D'Andrea, Maria Chiara Caschera, Fernando Ferri, Patrizia Grifoni

Abstract: The paper aims to provide a method to analyse and observe the characteristics that distinguish the individual communication style such as the voice intonation, the size and slant used in handwriting and the trait, pressure and dimension used for sketching. These features are referred to as Communication Extensional Features. Observing from the Communication Extensional Features, the user’s behavioural features, such as the communicative intention, the social style and personality traits can be extracted. These behavioural features are referred to as Communication Intentional Features. For the extraction of Communication Intentional Features, a method based on Hidden Markov Models is provided in the paper. The Communication Intentional Features have been extracted at the modal and multimodal level; this represents an important novelty provided by the paper. The accuracy of the method was tested both at modal and multimodal levels. The evaluation process results indicate an accuracy of 93.3% for the Modal layer (handwriting layer) and 95.3% for the Multimodal layer.

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DOI: 10.3897/jucs.2020.073

Authors: Izabela Rojek, Dariusz Mikołajewski, Piotr Kotlarz, Alžbeta Sapietová

Abstract: This article presents the evolution of databases from classical relational databases to distributed databases and data warehouses to fuzzy databases used in a production enterprise. This paper discusses characteristics of this kind of enterprise. The authors precisely define centralized and distributed databases, data warehouses and fuzzy databases. In the modern global world, many companies change their management strategy from the one based on a centralized database to an approach based on distributed database systems. Growing expectations regarding business intelligence encourage companies to deploy data warehouses. New solutions are sought as the demand for engineers' expertise continues to rise. The requested knowledge can be certain or uncertain. Certain knowledge does not any problems and is easy to obtain. However, uncertain knowledge requires new ways of obtaining, including the use of fuzzy logic. It is from where the fuzzy database approach takes its beginning. The above-mentioned strategies of a production enterprise were described herein as a case of special interest.

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DOI: 10.3897/jucs.2020.007

Authors: Anastasija Nikiforova, Janis Bicevskis, Zane Bicevska, Ivo Oditis

Abstract: The paper proposes a new data object-driven approach to data quality evaluation. It consists of three main components: (1) a data object, (2) data quality requirements, and (3) data quality evaluation process. As data quality is of relative nature, the data object and quality requirements are (a) use-case dependent and (b) defined by the user in accordance with his needs. All three components of the presented data quality model are described using graphical Domain Specific Languages (DSLs). In accordance with Model-Driven Architecture (MDA), the data quality model is built in two steps: (1) creating a platform-independent model (PIM), and (2) converting the created PIM into a platform-specific model (PSM). The PIM comprises informal specifications of data quality. The PSM describes the implementation of a data quality model, thus making it executable, enabling data object scanning and detecting data quality defects and anomalies. The proposed approach was applied to open data sets, analysing their quality. At least 3 advantages were highlighted: (1) a graphical data quality model allows the definition of data quality by non-IT and non-data quality experts as the presented diagrams are easy to read, create and modify, (2) the data quality model allows an analysis of "third-party" data without deeper knowledge on how the data were accrued and processed, (3) the quality of the data can be described at least at two levels of abstraction - informally using natural language or formally by including executable artefacts such as SQL statements.

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DOI: 10.3217/jucs-025-10-1323

Authors: Adi Darliansyah, M. Naeem, Farhaan Mirza, Russel Pears

Abstract: Personality distinctively characterises an individual and profoundly influences behaviours. Social media offer the virtual community an unprecedented opportunity to generate content and share aspects of their life which often reflect their personalities. The interest in using deep learning to infer traits from digital footprints has grown recently; however, very limited work has been presented which explores the sentiment information conveyed. The present study, therefore, used a computational approach to classify personality from social media by gauging public perceptions underlying factors encompassing traits. In the research reported in this paper, a Sentiment-based Personality Detection system was developed to infer trait from short texts based on the 'Big Five' personality dimensions. We exploited the spirit of Neural Network Language Model (NNLM) by using a uni ed model that combines a Recurrent Neural Network named Long Short-Term Memory (LSTM) with a Convolutional Neural Network (CNN). We performed sentiment classi cation by grouping short messages harvested online into three categories, namely positive, negative, and nonpartisan. This is followed by employing Global Vectors (GloVe) to build vectorial word representations. As such, this step aims to add external knowledge to short texts. Finally, we trained each variant of the models to compute prediction scores across the ve traits. Experimental study indicated the e ectiveness of our system. As part of our investigation, a case study was carried out to investigate the existing correlation of personality traits and opinion polarities which employed the proposed system. The results support the prior ndings of the tendency of persons with the same traits to express sentiments in similar ways.

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DOI: 10.3217/jucs-025-09-1199

Authors: François Bouchaud, Gilles Grimaud, Thomas Vantroys, Pierrick Buret

Abstract: The Internet of Things (IoT) is everywhere around us. Smart communicating objects are offering the digitalization of lives. They create new opportunities within criminal investigations. In recent years, the scientific community sought to develop a common digital framework and methodology adapted to IoT-based infrastructure. However, the difficulty in exploiting the IoT lies in the heterogeneous nature of the devices, the lack of standards and the complex architecture. Although digital forensics are considered and adopted in IoT investigations, this work only focuses on the collection. The identification phase is quite unexplored. It addresses the challenges of locating hidden devices and finding the best evidence to be collected. The matter of facts is the traditional method of digital forensics does not fully fit the IoT environment. Furthermore, the investigator can no longer consider a connected object as a single device, but as an interconnected whole one, anchored in a cross-disciplinary environment. This paper presents the methodology for identifying and classifying connected objects in search of the best evidence to be collected. It offers techniques for detecting and locating the appropriate equipment. Based on frequency mapping and interactions, it transfers the concept of "fingerprinting" into the field of crime scene. It focuses on the technical and data criteria to successfully select the relevant IoT devices. It gives a general classiffication as well as the limits of such an approach. It shows the collection of digital evidence by focusing on pertinent information from the Internet of Things.

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DOI: 10.3217/jucs-025-09-1131

Authors: Hui Tian, Meilun Huang, Chin-Chen Chang, Yongfeng Huang, Jing Lu, Yongqian Du

Abstract: Steganography is a promising technique for covert communications. However, illegal United States of Americage of this technique would facilitate cybercrime activities and thereby pose a great threat to information security. Therefore, it is crucial to study its countermeasure, namely, steganalysis. In this paper, we aim to present an efficient steganalysis method for detecting adaptive-codebook based steganography in adaptive multi-rate (AMR) speech streams. To achieve this goal, we first design a new low-dimensional feature set for steganalysis, including an improved calibrated Markov transition probability matrix for the second-order difference of pitch delay values (IC-MSDPD) and the probability distribution of the odevity for pitch delay values (PDOEPD). The dimension of the proposed feature set is 14, far smaller than the feature set in the state-of-the-art steganalysis method. Employing the new feature set, we further present a steganalysis scheme for AMR speech based on support vector machines. The presented scheme is evaluated with a large number of AMR-encoded speech samples, and compared with the state-of-the-art one. The experimental results show that the proposed method is effective, and outperforms the state-of-the-art one in both detection accuracy and computational overhead.

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DOI: 10.3217/jucs-025-08-0925

Authors: Ashot Harutyunyan, Arnak Poghosyan, Naira Grigoryan, Narek Hovhannisyan, Nicholas Kushmerick

Abstract: We address several problems in intelligent log management of distributed cloud computing applications and their machine learning solutions. Those problems concern various tasks on characterizing data center states from logs, as well as from related or other quantitative metrics (time series), such as anomaly and change detection, identification of baseline models, impact quantification of abnormalities, and classification of incidents. These are highly required jobs to be performed by today's enterprise-grade cloud management solutions. We describe several approaches and algorithms that are validated to be effective in an automated log analytics combined with analytics from time series perspectives. The paper introduces novel concepts, approaches, and algorithms for feasible log-plus-metric-based management of data center applications in the context of integration of relevant technology products in the market.

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DOI: 10.3217/jucs-025-08-0904

Authors: Yanling Chen, O. Koyluoglu, A. J. Han Vinck

Abstract: Communication networks have had a transformative impact on our society as they have revolutionized almost all aspects of human interaction. The explosive growth of data traffic has led to an increased demand on improving the reliability, efficiency and security aspects of the systems. In this paper, we focus on the multiple access channel, a communication model where several transmitters communicate to a common receiver (e.g., a cellular telephone network) in the presence of an external eavesdropper. The goal is to explore the competitive yet cooperative relationship between the transmitters in order to obtain an efficient communication under a certain reliability and security guarantee. Moreover, we take a special look into the inner and outer bounds on the secrecy capacity regions over the 2-transmitter DM-MAC with a degraded eavesdropper (assuming that both transmitters are cooperative). We notice that the inner and outer bounds differentiate themselves in the permissible sets of input distributions and thus not tight in general. This leaves the problem of secrecy capacity regions still open.

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DOI: 10.3217/jucs-025-08-0887

Authors: Mariam Haroutunian, Karen Mkhitaryan, Josiane Mothe

Abstract: Community detection is a research area from network science dealing with the investigation of complex networks such as social or biological networks, aiming to identify subgroups (communities) of entities (nodes) that are more closely related to each other inside the community than with the remaining entities in the network. Various community detection algorithms have been developed and used in the literature however evaluating community structures that have been automatically detected is a challenging task due to varying results in different scenarios. Current evaluation measures that compare extracted community structures with the reference structure or ground truth suffer from various drawbacks; some of them having been point out in the literature. Information theoretic measures form a fundamental class in this domain and have recently received increasing interest. However even the well employed measures (NVI and NID) also share some limitations, particularly they are biased toward the number of communities in the network. The main contribution of this paper is to introduce a new measure that overcomes this limitation while holding the important properties of measures.We review the mathematical properties of our measure based on x2 divergence inspired from f-divergence measures in information theory. Theoretical properties as well as experimental results in various scenarios show the superiority of the proposed measure to evaluate community detection over the ones from the literature.

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DOI: 10.3217/jucs-025-08-0868

Authors: Gurgen Khachatrian, Sergey Abrahamyan

Abstract: In many applications implemented in mistrusted environments all operations with the secret key during an encryption operation are "obfuscated" in a way that while an attacker has access to all routines of operations, it will nevertheless be hard for him to determine the value of the secret key used during these operations. This kind of execution of encryption operation is called a white-Box implementation. The design of a secure cryptosystem robust in the context of white-box attack is a difficult task which has been addressed by many researchers in the last two decade. The existing implementations of white-box algorithms have been mainly based on AES block cipher, however, all the known systems have been broken. In this paper a design and security analysis of a novel white-box encryption based on SAFER+ block cipher algorithm is presented which is shown to be secure against major attacks successfully applied to AES-based cryptosystems, such as the so-called BGE attack and others.

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DOI: 10.3217/jucs-025-03-0294

Authors: Bagus Santoso, Chunhua Su

Abstract: Recently, in order to guarantee security against quantum adversaries, several identification (ID) schemes based on computational problems which are supposed to be hard even for quantum computers have been proposed. However, their security are only proven against non-quantum adversaries. In this paper, we proposed a novel four-pass code-based identification scheme. By using quantum random oracle model, we provide a security proof for our scheme against quantum adversaries which aim to impersonate the prover under concurrent active attacks, based on the hardness assumption of syndrome decoding (SD) problem. Our security proof is interesting in its own right, since it only requires a non-programmable quantum random oracle, in contrast to existing security proofs of digital signatures generated from ID scheme via Fiat-Shamir transform which require programmable quantum random oracles.

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DOI: 10.3217/jucs-025-03-0270

Authors: Xiuze Dong, Yunchuan Guo, Fenghua Li, Liju Dong, Arshad Khan

Abstract: Measuring security is a core step for guaranteeing security of network and information systems. Due to massiveness and heterogeneity of measurement data, it is difficult to classify and combine them on demand. In this paper, considering implication relationship of metrics, we propose a combination model and combination policy for security measurement. Several examples demonstrate the effectiveness of our model.

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DOI: 10.3217/jucs-025-03-0245

Authors: Meijuan Huang, Bo Yang, Yi Zhao, Kaitai Liang, Liang Xue, Xiaoyi Yang

Abstract: Deterministic public-key encryption, encrypting a plaintext into a unique ciphertext without involving any randomness, was introduced by Bellare, Boldyreva, and O'Neill (CRYPTO 2007) as a realistic alternative to some inherent drawbacks in randomized public-key encryption. Bellare, Kiltz, Peikert and Waters (EUROCRYPT 2012) bring deterministic public-key encryption to the identity-based setting, and propose deterministic identity-based encryption scheme (DIBE). Although the construc- tions of chosen plaintext attack (CPA) secure DIBE scheme have been studied intensively, the construction of chosen ciphertext attack (CCA) secure DIBE scheme is still challenging problems. In this paper, we introduce the notion of identity-based all-but-one trapdoor functions (IB-ABO-TDF), which is an extension version of all-but-one lossy trapdoor function in the public-key setting. We give a instantiation of IB-ABO-TDF under decisional linear assumption. Based on an identity-based lossy trapdoor function and our IB-ABO-TDF, we present a generic construction of CCA-secure DIBE scheme.

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DOI: 10.3217/jucs-025-03-0222

Authors: Yunling Wang, Jianfeng Wang, Shi-Feng Sun, Joseph Liu, Willy Susilo, Joonsang Baek, Ilsun You, Xiaofeng Chen

Abstract: Searchable encryption enables the data owner to outsource their data to the cloud server while retaining the search ability. Recently, some researchers proposed a variant of searchable encryption, named single-writer/multi-reader searchable encryption (SMSE), in which any authorized data user can perform a search query. That is, each document identifier is encrypted using attribute-based encryption (ABE), such that an arbitrary authorized user whose attributes match the corresponding access policy can access the document. However, the cloud server cannot determine whether the user has the ability to decrypt the matched data. Thus, it has to response all the search results to the data user, which causes a heavy communication and computation cost. To cope with this problem, we present a novel SMSE scheme based on server-side match technique, where the cloud can filter the documents that cannot be decrypted by the user and only return the matched ones. In addition, the decryption is also efficient, independent with the access policy structure. Security and efficiency evaluation show that our proposed scheme can achieve the desired security goals, while dramatically reducing the communication and computation overhead.

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DOI: 10.3217/jucs-025-03-0182

Authors: Yi-Fan Tseng, Chun-I Fan, Chih-Wen Lin

Abstract: Ciphertext-policy attribute-based encryption (CP-ABE) is an access control mechanism where a data provider encrypts a secret message and then sends the ciphertext to the receivers according to the access policy which she/he decides. If the attributes of the receivers match the access policy, then they can decrypt the ciphertext. This paper shows a relation between CP-ABE and identity-based encryption (IBE), and presents a bi-directional conversion between an access structure and identities. By the proposed conversion, the CP-ABE scheme constructed from an IBE scheme will inherit the features, such as constant-size ciphertexts and anonymity, from the IBE scheme, and vice versa. It turns out that the proposed conversion also gives the first CP-ABE achieving access structures with wildcard and constant-size ciphertexts/private keys. Finally, we prove the CCA security for confidentiality and anonymity.

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DOI: 10.3217/jucs-025-03-0158

Authors: Yuan Chen, Qingkuan Dong, Yannan Li, Qiqi Lai, Zhedong Wang

Abstract: The existence of natural public-key encryption (PKE) schemes satisfying secretly detectable replayable CCA (sd-RCCA) security is left as open. By introducing probabilistic message authentication codes (MACs) into popular KEM plus DEM paradigms, several instances of such schemes are presented in this paper. It is known that the encrypt-then-authenticate paradigm gives an RCCA secure DEM when the underlying MAC is regular (but not strong) secure, where forgeries for old messages might be possible. By further requiring that the validity of such forgeries can be verified only secretly, sd-RCCA secure DEMs is obtained. Combining such DEMs with CCA secure KEMs gives sd-RCCA secure hybrid PKEs. We first formalize the related notions and this paradigm, and also other variants of KEM plus DEM hybrid paradigm since MACs are commonly used in them. Then we show natural examples of desired probabilistic MACs under the standard DDH assumption, and find appropriate KEMs to match the message space for those MACs and then obtain natural instances of sd-RCCA secure hybrid PKEs.

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DOI: 10.3217/jucs-024-09-1258

Authors: Nipon Theera-Umpon, Kun-Hee Han, Woo-Sik Bae, Sanghyuk Lee, Van Pham

Abstract: The evolving Internet of Things (IoT) technology has driven the advancement of communication technology for implantable devices and relevant services. Still, concerns are raised over implantable medical devices (IMDs), because the wireless transmission section between patients and devices is liable to intrusions on privacy attributable to hacking attacks and resultant leakage of patients' personal information. Also, manipulating and altering patients' medical information may lead to serious leakage of personal information and thus adverse medical incidents. To address the foregoing challenges, the present paper proposes a security protocol that copes with a range of vulnerabilities in communication between IMDs and other devices. In addition, the proposed protocol encrypts the communication process and data to eliminate the likelihood of personal information being leaked. The verification highlights the safety and security of the proposed protocol in wireless communication.

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DOI: 10.3217/jucs-024-09-1153

Authors: Ahyoung Choi, Hangsik Shin

Abstract: Recently, many studies have been conducted on how to manage and analyze various types of health data such as clinical data, genomic data, and wirelessly collected multiple sensory data. In this paper, we propose a web-based healthcare data integration and management platform that collects heterogeneous types of health-related medical record as well as real-time lifelogging data. This platform provides flexible architecture to different types of data exchanges. The platform manages real-time data such as heart rate, blood pressure, and activity information extracted from various healthcare devices and provides functions to transmit them to the server. Then it analyses the risk based on a domain knowledge and individual differences by applying machine learning tools, then visualizes the result to the patient and doctor dynamically based on information simplification method. It also controls the data access authority concerning the level of expertise and role. For evaluation of integrated data analysis, we apply open database and evaluate the proposed risk analyser result. The proposed platform could be utilized for future healthcare service to share accumulated healthcare data in various situations.

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DOI: 10.3217/jucs-024-07-0892

Authors: Andrea Huszti, Zita Kovács

Abstract: A reductionist proof for sender anonymity of an asymmetric bilinear pairing based mixnet (BILMIX) is presented. We give an experiment-based definition for anonymity and show that BILMIX possesses anonymity in the semi-honest model against static adversaries assuming that the co-Bilinear Diffie-Hellman Problem, the Matching Find-Guess Problem and the Matching Diffie-Hellman Problem are hard. A new problem called Divisible Decisional Factorized Diffie-Hellman Problem (DDF- DHP) is introduced and showed that finding connection between data stored by the Registration Authority and the receiver is at least as hard as breaking DDF-DHP, with the assumption that secret keys of the Registration Authority and the special bulletin board are kept secret.

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DOI: 10.3217/jucs-024-04-0488

Authors: Zbigniew Marszałek

Abstract: The use of encryption methods such as secure multiparty computation is an important issue in applications. Applications that use encryption of information require special algorithms of sorting data in order to preserve the secrecy of the information. This proposition is composed for parallel architectures. Presented algorithm works with a number of logical processors. Operations are flexibly distributed among them. Therefore sorting of data sets takes less time. Results of the experimental tests confirm the effectiveness of the proposed flexible division of tasks between logical processors and show that this proposition is a valuable method that can find many practical applications in high performance computing.

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DOI: 10.3217/jucs-024-04-0341

Authors: Alawi Al-Saggaf

Abstract: Cryptography and biometrics are widely used in providing information security. Cryptographic systems rely on keys for secure information. Unlike biometric systems, the keys in cryptographic systems must be exactly reproducible and not strongly linked to the user identity. Each of the two systems has some issue associated with it. Combining cryptography and biometrics in a secure way can mitigate these issues. This paper presents a secure cryptographic authentication method based on the discrete logarithm problem. Through the security analysis of the proposed scheme, we prove that the security characteristics of the proposed scheme relative to the properties of the discrete logarithm problem. Based on the proposed scheme, a biometric cryptosystem is constructed. The proposed system evaluated using CASIA iris database from 70 different eyes, with 7 samples of each eye. Experimental results demonstrate that the proposed system is more effective, promising in terms of cope up to 21.41% percent of error bits within an iris code, and can generate up to 98 bits of the cryptographic key from the iris codes.

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DOI: 10.3217/jucs-024-01-0025

Authors: Flavio De Mello, José A. M. Xexéo

Abstract: This paper analyzes the use of machine learning techniques for the identification of encryption algorithms, from ciphertexts only. The experiment involved corpora of plain texts in seven different languages; seven encryption algorithms, each one in ECB and CBC modes; and six data mining algorithms for classification. The plain text files were encrypted with each cryptographic algorithm under both cipher modes. After that, the ciphertexts were processed to produce metadata, which were then used by the classification algorithms. The overall experiment involved not only a high quantity of ciphertexts, but also time consuming procedures for metadata creation as well as for identification. Therefore, a high performance computer and customized memory management were employed. As expected, the results for ECB mode encryption algorithm identification were significantly high, and also reached full recognition. On the other hand, algorithm identification under CBC is supposed to be marginal, but successful identification was up to six times higher than the probabilistic bid.

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DOI: 10.3217/jucs-023-11-1070

Authors: Andreas Heberle, Welf Löwe, Anders Gustafsson, Örjan Vorrei

Abstract: Nowadays, many companies are running digitalization initiatives or are planning to do so. There exist various models to evaluate the digitalization potential of a company and to define the maturity level of a company in exploiting digitalization technologies summarized under buzzwords such as Big Data, Artificial Intelligence (AI), Deep Learning, and the Industrial Internet of Things (IIoT). While platforms, protocols, patterns, technical implementations, and standards are in place to adopt these technologies, small- to medium-sized enterprises (SME) still struggle with digitalization. This is because it is hard to identify the most beneficial projects with manageable cost, limited resources and restricted know-how. In the present paper, we describe a real-life project where digitalization use cases have been identified, evaluated, and prioritized with respect to benefits and costs. This effort led to a portfolio of projects, some with quick and easy wins and some others with mid- to long-term benefits. From our experiences, we extracted a general approach that could be useful for other SMEs to identify concrete digitalization activities and to define projects implementing their digital transformation. The results are summarized in a Digitalization Canvas.

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DOI: 10.3217/jucs-023-11-1057

Authors: Giangiacomo Bravo, Mike Farjam

Abstract: Computational social sciences (CSS) refer to computer-enabled investigations of human behaviour and social interaction. They include three main components - (i) computational modelling and social simulation, (ii) the analysis of digital traces of online interactions, (iii) virtual labs and online experiments - and allow researchers to perform studies that were even hard to imagine a few decades ago. Moreover, CSS favour a more systematic test of theories and increase the possibility of study replication, two factors holding the potential to help social sciences reach a higher scientific status. Despite the huge potential of CSS, we follow previous works in identifying several impediments to a larger adoption of computational methods in social sciences. Most of them are linked with the humanistic attitude and a lack of technical skills of many social scientist. Significant changes in the basic training of social scientist and in the relation patterns with other disciplines and departments are needed before the potential of CSS can be fully exploited.

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DOI: 10.3217/jucs-023-11-1038

Authors: Mikko Laitinen, Jonas Lundberg, Magnus Levin, Alexander Lakaw

Abstract: This paper presents the Nordic Tweet Stream, a cross-disciplinary digital humanities project that downloads Twitter messages from Denmark, Finland, Iceland, Norway and Sweden. The paper first introduces some of the technical aspects in creating a real-time monitor corpus that grows every day, and then two case studies illustrate how the corpus could be used as empirical evidence in studies focusing on the global spread of English. Our approach in the case studies is sociolinguistic, and we are interested in how widespread multilingualism which involves English is in the region, and what happens to ongoing grammatical change in digital environments. The results are based on 6.6 million tweets collected during the first four months of data streaming. They show that English was the most frequently used language, accounting for almost a third. This indicates that Nordic Twitter users choose English as a means of reaching wider audiences. The preference for English is the strongest in Denmark and the weakest in Finland. Tweeting mostly occurs late in the evening, and high-profile media events such as the Eurovision Song Contest produce considerable peaks in Twitter activity. The prevalent use of informal features such as univerbated verb forms (e.g., gotta for (HAVE) got to) supports previous findings of the speech-like nature of written Twitter data, but the results indicate that tweeters are pushing the limits even further.

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DOI: 10.3217/jucs-023-11-1035

Authors: Giangiacomo Bravo, Mikko Laitinen, Magnus Levin, Welf Löwe, Göran Petersson

Abstract: The ubiquity of sensor, computing, communication, and storage technologies provides us with access to previously unknown amounts of data - Big Data. Big Data has revolutionized research communities and their scientific methodologies. It has, for instance, innovated the approaches to knowledge and theory building, validation, and exploitation taken in the engineering sciences. The humanities and social sciences even face a paradigm shift away from data-scarce, static, coarse-grained and simple studies towards data-rich, dynamic, high resolution, and complex observations and simulations. The present focused topic presents investigations from different research fields in which the focus is either on utilizing Big Data or charting the benefits of using such evidence in basic research.

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DOI: 10.3217/jucs-022-11-1437

Authors: Vladimir Ciric, Aleksandar Cvetkovic, Ivan Milentijevic, Oliver Vojinovic

Abstract: Motivated by the large number of vertices that future technologies will put in the front of path-search algorithms, and inspired by highly regular 2D mesh structures that exist in the domain applications, in this paper we propose a new allpairs shortest paths algorithm, for any given regular 2D mesh topology, with complexity Ο(|V|2), where |V| is the number of vertices in the graph. The proposed algorithm can achieve better runtime than other known algorithms at the cost of narrowing the scope of the graphs that it can process to the graphs with regular 2D topology. The algorithm is developed into formalism by algebraic transformations in tropical algebra of the well-known Floyd-Warshall's algorithm. First we prove the equivalency of the Floyd-Warshall's algorithm and its tropical algebraic representation, and put the transformations of the algorithm into the algebraic domain. Secondly, having in mind the structure of the target class of graphs, we transform the original algorithm in the algebraic domain and develop a simple, low-complexity iterative algorithm for all-pairs shortest paths calculation. Decreasing of computational complexity can contribute to better exploitation of the algorithm in the wide range of applications from hardware design in new emerging technologies to big data problems in information technologies.

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DOI: 10.3217/jucs-022-06-0760

Authors: Saptarsi Goswami, Amlan Chakrabarti, Basabi Chakraborty

Abstract: Feature selection is an important prerequisite of any pattern recognition, machine learning or data mining problem. A lot of algorithms for feature subset selection have been developed so far for reduction of dimensionality of the data set in order to achieve high recognition accuracy with low computational cost. However, some methods or algorithms work well for some of the data sets and perform poorly on others. For any particular data set, it is difficult to find out the most suitable algorithm without some random trial and error process. It seems that the characteristics of the data set might have some effect on the algorithm for feature selection. In this work, the data set characteristics is studied for recommendation of appropriate feature selection algorithm to be used for a particular data set. A new proposal in terms of intra attribute relationship and a measure MVS (multivariate score) has been introduced to quantify and group different data sets on the basis of the data set correlation structure into several categories. The measure is used to group 63 publicly available bench mark data set according to their characteristics. The performance of different feature selection algorithms on different groups of data are then studied by simulation experiments to verify the relationship o f data set characteristics and the feature selection algorithm. The effect of some other data set characteristics has also been studied. Finally a framework of recommendation regarding the choice of proper feature selection algorithm has been indicated.

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DOI: 10.3217/jucs-022-02-0228

Authors: Luca Carafoli, Federica Mandreoli, Riccardo Martoglia, Wilma Penzo

Abstract: A major societal challenge to be tackled in megacities is sustainable urban transportation. Intelligent Transportation Systems (ITSs) are actually data-centric applications that need to store and query real-time as well as historical/static data from various data sources and have to provide timely responses to users' transportation needs. In this paper we introduce a data management middleware that offers the robustness of a common framework to support the development of smart applications having the above needs. It supports the efficient storage and access to real-time and historical/static data and provides both one-time and continuous query capabilities. While the middleware has been designed to be general and versatile to support data management for any kind of application, in this paper we explore its suitability to ITS smart services also by means of an experimental evaluation conducted on a variety of traffic scenarios.

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DOI: 10.3217/jucs-021-12-1536

Authors: Neil Buckley, Atulya Nagar, Subramanian Arumugam

Abstract: Visual cryptography (VC) encodes an image into noise-like shares, which can be stacked to reveal a reduced quality version of the original. The problem with encrypting colour images is that they must undergo heavy pre-processing to reduce them to binary, entailing significant quality loss. This paper proposes VC that works directly on intermediate grayscale values per colour channel and demonstrates real-valued basis matrices for this purpose. The resulting stacked shares produce a clearer reconstruction than in binary VC, and to the best of the authors' knowledge, is the first method posing no restrictions on colour values while maintaining the ability to decrypt with human vision. Grayscale and colour images of differing entropies are encrypted using fuzzy OR and XOR, and their PSNR and structural similarities are compared with binary VC to demonstrate improved quality. It is compared with previous research and its advantages highlighted, notably in high quality reconstructions with minimal processing.

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DOI: 10.3217/jucs-021-09-1136

Authors: Peter Fenwick

Abstract: A recent paper described a variable-length integer code based on the Goldbach conjecture where every codeword had exactly 2 1-bits but with an extremely irregular structure. A later, unpublished, work produced a much more regular code, again with a Hamming weight of 2. This paper extends that later work to weight-3 and weight-4 codes, which are shown to be competitive with more-usual codes over a useful range of values.

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DOI: 10.3217/jucs-021-06-0757

Authors: Ana Torre-Bastida, Esther Villar-Rodriguez, Sergio Gil-Lopez, Javier Ser

Abstract: The amount of open information available on-line from heterogeneous sources anddomains is growing at an extremely fast pace, and constitutes an important knowledge base for the consideration of industries and companies. In this context, two relevant data providers can behighlighted: the "Linked Open Data" (LOD) and "Social Media" (SM) paradigms. The fusion of these data sources - structured the former, and raw data the latter -, along with the informationcontained in structured corporate databases within the organizations themselves, may unveil significant business opportunities and competitive advantage to those who are able to understand andleverage their value. In this paper, we present two complementary use cases, illustrating the potential of using the open data in the business domain. The first represents the creation of an existingand potential customer knowledge base, exploiting social and linked open data based on which any given organization might infer valuable information as a support for decision making. Thesecond focuses on the classification of organizations and enterprises aiming at detecting potential competitors and/or allies via the analysis of the conceptual similarity between their participatedprojects. To this end, a solution based on the synergy of Big Data and semantic technologies will be designed and developed. The first will be used to implement the tasks of collection, data fusionand classification supported by natural language processing (NLP) techniques, whereas the latter will deal with semantic aggregation, persistence, reasoning and information retrieval, as well aswith the triggering of alerts based on the semantized information.

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DOI: 10.3217/jucs-021-03-0483

Authors: Guoyan Zhang, Jing Qin, Shams Qazi

Abstract: Access control can selectively restrict access to sensitive information stored by third-party sites on the Internet. Attribute-based encryption (ABE) schemes can strengthen the effective combination of flexibility and operability of access control. They allow one sender to encrypt a message for more than one recipient, and to specify who should be able to decrypt, using attributes alone. Since 2005, many powerful ABE schemes have been presented, but there are two types of problem that haven't be efficiently resolved so far. On the one hand, as practical extension of identity-based encryption (IBE) schemes, ABE schemes are also confronted with key escrow problem. On the other hand, attribute set belonging to one user is usually monitored by different authorities in this era of collaboration. Multi-authority ABE (MA-ABE) schemes can simultaneously resolve these problems, but now they have not been thoroughly investigated yet. More precisely, MA-ABE schemes against quantum attack are the main barrier of the development of ABE schemes in a `post-quantum' world. In this paper, we firstly present a MA-ABE scheme from lattices, in which identities of users are authenticated by a central authority, which improves the efficiency of authentication. Furthermore, different attribute private keys are still distributed by different authorities, and the central authority cannot obtain any secret information of other attribute authorities, which resolves key escrow problem to some extent. In MAABE, attribute private keys belonging to one user are generated by different authorities, and how to ensure correct decryption is one of the crux of schemes. Our scheme gives a simple solution, and each user's attribute private keys are combined using sharing of common public information to automatically realize correct decryption. To our best knowledge, this is the first MA-ABE scheme from lattices, and it is more efficient than the MA-ABE presented by Melissa Chase. Finally, we present a multi-authority large universe ABE scheme, in which the sizes of the public key and the ciphertext are only relative to the number of the attribute authorities, and a user will be able to decrypt a ciphertext if and only if he has at least tK attributes from each authority K.

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DOI: 10.3217/jucs-021-03-0419

Authors: Lucjan Hanzlik, Mirosław Kutyłowski

Abstract: In this paper we consider restricted identification (RI) protocols which enable strongauthentication and privacy protection for access control in an unlimited number of domains. A single secret key per user is used to authenticate and derive his identity within any domain,while the number of domains is unlimited and the scheme guarantees unlinkability between identities of the same user in different domains. RI can be understood as an universal solution thatmay replace unreliable login and password mechanisms. It has to secure against adversaries that gather personal data by working on a global scale, e.g. by breaking into one service for gettingpasswords that a user frequently re-uses at different places. We consider security of an extended version of the Chip Authentication Restricted Identification(ChARI) protocol presented at the 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2012). We preserve the features of ChARI (avoidingthe critical security problems of group keys in the RI solution deployed in the German personal identity cards), but provide security proof in the well-studied Canetti-Krawczyk model (sucha proof has not been provided for ChARI). Our extension has similar computational complexity as the original ChARI protocol in terms of the number of modular exponentiations.

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DOI: 10.3217/jucs-021-01-0066

Authors: Sean Goggins, Wanli Xing, Xin Chen, Bodong Chen, Bob Wadholm

Abstract: This study proposes a process-oriented, automatic, formative assessment model for small group learning based on complex systems theory using a small dataset from a technology-mediated, synchronous mathematics learning environment. We first conceptualize small group learning as a complex system and explain how group dynamics and interaction can be modeled via theoretically grounded, simple rules. These rules are then operationalized to build temporally-embodied measures, where varying weights are assigned to the same measures according to their significance during different time stages based on the golden ratio concept. This theory-based measure construction method in combination with a correlation-based feature subset selection algorithm reduces data dimensionality, making a complex system more understandable for people. Further, because the discipline of education often generates small datasets, a Tree-Augmented Naïve Bayes classifier was coded to develop an assessment model, which achieves the highest accuracy (95.8%) as compared to baseline models. Finally, we describe a web-based tool that visualizes time-series activities, assesses small group learning automatically, and also offers actionable intelligence for teachers to provide real-time support and intervention to students. The fundamental contribution of this paper is that it makes complex, small group behavior visible to teachers in a learning context quickly. Theoretical and methodological implications for technology mediated small group learning and learning analytics as a whole are then discussed.

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DOI: 10.3217/jucs-020-15-2068

Authors: Basit Shahzad, Esam Alwagait

Abstract: Twitter has gained phenomenal popularity over time, especially in Saudi Arabia, where it enjoys unmatched usage and is considered highly attractive. Tweets are now an important source of trend-setting and marketing along with their conventional use for information exchange. Recently, Saudi Arabia changed its working days so that weekends are Friday and Saturday instead of Thursday and Friday. To identify the impact of such a change on the usage of social networks, five detailed experiments are conducted. Time-stamped data for selected individuals are retrieved from Twitter and analyzed accordingly to observe usage behavior. Moreover, the results of these experiments are compared with results obtained last year, before the weekend was changed.

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DOI: 10.3217/jucs-020-11-1583

Authors: Gia Vo, Richard Lai

Abstract: In recent years, there has been a tremendous growth in e-Government services due to advances in Information Communication Technology and the number of citizens engaging in e-Government transactions. In government administration, it is very time consuming to process different types of documents and there are many data input problems. There is also a need to satisfy citizens’ requests to retrieve government information and to link these requests to build an online document without asking the citizen to input the data more than once. To provide an e-Government service which is easy to access, fast and secure, the e-Document plays an important role in the management and interoperability of e-Government Systems. To meet these challenges, this paper presents a Secure Multilayer e-Application (SMeA) method for improving e-Government processes. This method involves five steps: namely (i) identifying an e-Template; (ii) building a SMeA; (iii) mapping the data; (iv) processing the e-Application; and (v) approving the e-Application. The first step involves requirements analysis and the last four involve data analysis for building a SMeA. To demonstrate its usefulness, we applied SMeA to a case study of an application for a licence to set up a new business in Vietnam.

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DOI: 10.3217/jucs-020-10-1511

Authors: Esam Alwagait, Basit Shahzad

Abstract: The increase in Twitter's popularity has been phenomenal over time. Tweets are now not only a means of status update and one-on-one communication, but they are also widely used for trend setting and marketing. The probability that the user will see a Tweet when he was offline at the time it was tweeted is very low. In order to increase the Tweet impact, it is important to determine the number of individuals online so that maximum number of users see the Tweets. This research focuses on identifying the individual users from Saudi Arabia based on the parameters already set for the conduct of this study. The time-stamped data for 1000 selected individuals is retrieved from Twitter and is analyzed accordingly. The number of online users is observed by recording the "last seen" status. The retrieval of data is based on a number of experiments that was run at same time on all days of the week to reduce the inconsistent patterns. The data is then analyzed to see the time slots where the online user percentage is higher as compared to other time slots. The results of the study are focused to identify and recommend the timings when the Tweets are better valued and the impact is considerable.

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DOI: 10.3217/jucs-020-10-1433

Authors: Rolando Quintero, Giovanni Guzman, Miguel Torres, Rolando Menchaca-Mendez, Marco Moreno-Ibarra, Felix Mata

Abstract: Nowadays, there are many applications such as disaster mitigation, survey-ing or geology-support, and others where Digital Elevation Models (DEM) are useful in the field. DEM typically requires a huge amount of data, making the tasks of DEMtransmission over a wireless network or storing and displaying it in a mobile device very complex. These tasks are important challenges in computer science research. Up-to-date, the compression techniques are used to compress DEMs with a high compression coefficient, nevertheless, whether the user requires to access the file or to obtain certaininformation about the raster data, it is necessary to decompress the entire DEM file. In consequence, these approaches are not well suited for applications focused on deviceswith limited hardware resources. In this paper, a novel compression/decompression technique is presented. This approach is capable of obtaining the specific parameterssuch as altitudes and contour lines of a sub-region of the DEM without using a full decompression stage. A detailed analysis of the properties and complexity of our approachis presented.

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DOI: 10.3217/jucs-019-17-2559

Authors: Heather Mcdonald, Chris Nugent, Dewar Finlay, George Moore, William Burns, Josef Hallberg

Abstract: The lack of a standard format to store data generated within the smart environments research domain is limiting the opportunity for researchers to share and reuse datasets. The opportunity to exchange datasets is further hampered due to the lack of an online resource to facilitate this. In our current work we have attempted to resolve these issues through the development of homeML, a proposed format to support the storage and exchange of data generated within a smart environment and the homeML suite, an online tool to support data exchange and reuse. A usability and functionality study performed by 8 unbiased members of the research community is presented and discussed. All participants in the study agreed that the homeML format could address the need for a standard format within this domain. Participants also agreed that the homeML suite would be a useful tool to be available to researchers as they perform experiments in the area of smart environments.

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DOI: 10.3217/jucs-019-17-2490

Authors: Juan Álvaro Muñoz Naranjo, Pablo Orduña, Aitor Gómez-Goiri, Diego López-De-Ipiña, Leocadio Casado

Abstract: This work introduces a novel access control solution for wireless network services in Internet of Things scenarios. We focus on a minimal use of computation, energy and storage resources at wireless sensors so as to address constrained devices: the proposed methods for key distribution and access control rely on extremely fast key derivation functions and, for the same reason, memory usage is reduced since keys are computed on the fly when needed. Our solution achieves privacy, authentication, semantic security, low energy, low computational demand and impacts mitigation of compromised devices on a simple manner. The access control provided is based on user identity and time intervals. We discuss these properties, compare our proposal to previous related work and provide experimental results that confirm its viability.

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DOI: 10.3217/jucs-019-16-2453

Authors: Ting Cui, Chenhui Jin, Guoshuang Zhang

Abstract: Impossible differential cryptanalysis is an important tool for evaluating the security level of a block cipher, and the key step of this cryptanalysis is to find the longest impossible differential. This paper focuses on retrieving impossible differentials for m-cell Skipjack-like structure with SP/SPS round function (named SkipjackSP and SkipjackSPS resp.). Up to now, known longest impossible differentials in m-cell Skipjack-like structures is m2 rounds. In this paper, we provide some new m2 rounds impossible differentials for these two structures. Further, we prove that if P layer is chosen from binary matrices, we can always retrieve m2 + 1 rounds impossible differentials for these two structures, and m2 + 2 rounds impossible differentials for SkipjackSP. Moreover, if P layer satisfies some satiable conditions, we may further obtain m2 + 2 rounds impossible differential for SkipjackSPS. Our results show that we should choose P layer carefully when employing these two structures.

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DOI: 10.3217/jucs-019-16-2420

Authors: Bo Liu, Baokang Zhao, Bo Liu, Chunqing Wu

Abstract: Quantum Key Distribution (QKD) technology, based on the laws of physics, can create unconditional security keys between communication parties. In recent years, researchers draw more and more attention to the QKD technology. Privacy amplification is a very significant procedure in QKD system. In this paper, we propose the real-time privacy amplification (RTPA) scheme which converts the weak secret string to a uniform key that is fully secret from Eve. Our detailed proofs show the security of our RTPA scheme. In order to prevent the potential man-in-middle attacks, we employ an authentication procedure to RTPA scheme (ARTPA) with the "-XOR almost universal hash functions. We implement our ARTPA scheme based on CLIP system, which is connected to the quantum communication system. Considering the privacy amplification and authentication overhead and the finite size effect on the security of final keys, we set the secret key length be 256k before privacy amplification and the authentication tag length be 60. Our experimental results show the efficiency of the proposed ARTPA scheme.

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DOI: 10.3217/jucs-019-16-2404

Authors: Tran Ho, Ho-Seok Kang, Sung-Ryul Kim

Abstract: In the modern life, SMS (Short Message Service) is one of the most necessary services on mobile devices. Because of its popularity, many companies use SMS as an effective marketing and advertising tool. Also, the popularity gives hackers chances to abuse SMS to cheat mobile users and steal personal information in their mobile phones, for example. In this paper, we propose a method to detect spam SMS on mobile devices and smart phones. Our approach is based on improving a graph-based algorithm and utilizing the KNN Algorithm - one of the simplest and most effective classification algorithms. The experimentation is carried out on SMS message collections and the results ensures the efficiency of the proposed method, with high accuracy and small processing time enough for detecting spam messages directly on mobile phones in real time.

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DOI: 10.3217/jucs-019-16-2368

Authors: Leandro Marin, Antonio Jara, Antonio Skarmeta

Abstract: Cryptographic primitives are the key component in the security protocols to support the authentication, key management and secure communication establishment. For that reason, this work presents the optimization of the Elliptic Curve Cryptography through the usage of Shifting Primes for constrained devices. Specifically, this presents the optimization for the chipsets JN51XX from NXP/Jennic, which are based on OpenRISC architecture and offer a class-2 constrained device. In details, Shifting Primes features have allowed to optimize the multiplication and squaring through a double accumulator and shifting reduction. This work is ancillary to the previous works about optimization of Shifting Primes for class-1 constrained devices. The optimization of the Elliptic Curve Cryptography for the class-2 constrained devices brings several opportunities for realistic scenarios, where the security interoperability between a gateway (class-2 device) and end-nodes (class 1 devices) is a major requirement.

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DOI: 10.3217/jucs-019-16-2349

Authors: Xingxing Xie, Hua Ma, Jin Li, Xiaofeng Chen

Abstract: Attribute-Based Encryption (ABE) is one of the new visions for finegrained access control in cloud computing. Plenty of research work has been done in both academic and industrial communities. However, before ABE can be deployed in data outsourcing systems, efficient enforcement of authorization policies and policy updates are the main obstacles. Therefore, in order to solve this problem, efficient and secure attribute and user revocation should be proposed in original ABE scheme, which is still a challenge in existing work. In this paper, we propose a new ciphertext-policy ABE (CP-ABE) construction with efficient attribute and user revocation, which largely eliminates the overhead computation at data service manager and data owner. Besides, we present an efficient access control mechanism based on the CP-ABE construction with one outsourcing computation service provider.

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DOI: 10.3217/jucs-019-04-0521

Authors: Chun-Wei Lin, Tzung-Pei Hong, Yi-Fan Chen, Tsung-Ching Lin, Shing-Tai Pan

Abstract: In the past, many algorithms have been proposed for mining association rules from binary databases. Transactions with quantitative values are, however, also commonly seen in real-world applications. Each transaction in a quantitative database consists of items with their purchased quantities. The multiple fuzzy frequent pattern tree (MFFP-tree) algorithm was thus designed to handle a quantitative database for efficiently mining complete fuzzy frequent itemsets. It however, only processes a database for mining the desired rules. In this paper, we propose an integrated MFFP (called iMFFP)-tree algorithm for merging several individual MFFP trees into an integrated one. The proposed iMFFP-tree algorithm firstly handles the fuzzy regions for providing linguistic knowledge for human beings. The integration mechanism of the proposed algorithm thus efficiently and completely moves a branch from one sub-tree to the integrated tree. The proposed approach can derive both global and local fuzzy rules from distributed databases, thus allowing managers to make more significant and flexible decisions. Experimental results also showed the performance of the proposed approach.

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DOI: 10.3217/jucs-018-12-1741

Authors: Lin Ding, Jie Guan, Wen-long Sun

Abstract: The stream cipher TPypy has been designed by Biham and Seberry in January 2007 as the strongest member of the Py-family of stream ciphers. At Indocrypt 2007, Sekar, Paul and Preneel showed related-key weaknesses in the Py-family of stream ciphers including the strongest member TPypy. Furthermore, they modified the stream ciphers TPypy and TPy to generate two fast ciphers, namely RCR-32 and RCR-64, in an attempt to rule out all the attacks against the Py-family of stream ciphers. So far there exists no attack on RCR-32 and RCR-64. In this paper, we show that the related-key weaknesses can be still used to construct related-key distinguishing attacks on all Py-family of stream ciphers including the modified versions RCR-32 and RCR-64. Under related keys, we show distinguishing attacks on RCR-32 and RCR-64 with data complexity 2139.3 and advantage greater than 0.5. We also show that the data complexity of the distinguishing attacks on Py-family of stream ciphers proposed by Sekar et al. can be reduced fromto. These results constitute the best attacks on the strongest members of the Py-family of stream ciphers Tpypy, RCR-32 and RCR-64. By modifying the key setup algorithm, we propose two new stream ciphers TRCR-32 and TRCR-64 which are derived from RCR-32 and RCR-64 respectively. Based on our security analysis, we conjecture that no attacks lower than brute force are possible on TRCR-32 and TRCR-64 stream ciphers.

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DOI: 10.3217/jucs-018-09-1177

Authors: Xingang Liu, Jiantan Liu, Kook-Yeol Yoo, Haengrae Cho

Abstract: For the Wireless Multimedia Sensor Network (WMSN), the intraframe video coding is widely used for the robust transmission and computation complexity. Though the intraframe algorithm requires much smaller computational complexity than the interframe coding, the amount of computation of the intraframe should be reduced to use WMSN application. In this paper, we propose an intra mode decision algorithm to reduce the computation complexity of intraframe H.264/AVC encoders. The proposed algorithm determines the candidate modes and skips the remaining modes based on the smoothness and directional similarity of MB. The simulation results show that the proposed algorithm achieves 18% to 70% reduction in the computational complexity, compared with the various conventional methods.

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DOI: 10.3217/jucs-018-05-0676

Authors: Ahmed Nait-Sidi-Moh, Mohamed Bakhouya, Wafaa Ait-Cheik-Bihi, Jaafar Gaber

Abstract: Distributed systems become ubiquitous by allowing users access to a wide range of services at any time, anywhere, and from a variety of devices. In these open environments where there are many opportunities for both fraudulent services and misbehaving clients, service discovery systems are subject to security challenges. Controlling services' access is one of the fundamental issues that must be faced in the context of service discovery in distributed and open environments. Therefore, secure accesses and utilization of available services must be ensured for users. In our previous work, a contract-based approach for controlling the service access in a distributed computing context was presented. In this paper, we address the purpose and the usage of digital signature on negotiated electronic queries between a server and clients in service discovery systems and web service composition. The paper discusses the combined use of Timed Event Graphs and (max, +)- algebra to model, evaluate and optimize the performance of the signature process and client requests validation by a service provider (server). Based on an optimization resource allocation algorithm, an improvement study of the quality of service offered to the clients, in terms of waiting times and validation of their requests, is proposed. The results are reported and show the efficiency of the use of the proposed formal tools for performance analysis, evaluation and tuning of the considered process.

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DOI: 10.3217/jucs-018-04-0577

Authors: Anna Zygmunt, Piotr Bródka, Przemysław Kazienko, Jarosław Koźlak

Abstract: Identifying key persons active in social groups in the blogosphere is performed by means of social network analysis. Two main independent approaches are considered in the paper: (i) discovery of the most important individuals in persistent social communities and (ii) regular centrality measures applied either to social groups or the entire network. A new method for separating of groups stable over time, fulfilling given conditions of activity level of their members is proposed. Furthermore, a new concept for extracting user roles and key persons in such groups is also presented. This new approach was compared to the typical clustering method and the structural node position measure applied to rank users. The experimental studies have been carried out on real two-year blogosphere data.

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DOI: 10.3217/jucs-017-11-1623

Authors: Kyung-Ah Shim

Abstract: In this paper, we show that a verifier-based password authentication scheme and two remote user authentication schemes are insecure against several active attacks. These results demonstrate that no more password authentication schemes should be constructed with such ad-hoc methods, i.e, the formal design methodology using provable security should be employed.

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DOI: 10.3217/jucs-017-08-1183

Authors: Hui Ma, Klaus-Dieter Schewe, Bernhard Thalheim, Qing Wang

Abstract: Data warehouses integrate and aggregate data from various sources to support decision making within an enterprise. Usually, it is assumed that data are extracted from operational databases used by the enterprise. Cloud warehousing relaxes this view permitting data sources to be located anywhere on the world-wide web in a so-called "cloud", which is understood as a registry of services. Thus, we need a model of dataintensive web services, for which we adopt the view of the recently introduced model of abstract state services (AS2s). An AS2 combines a hidden database layer with an operation-equipped view layer, and thus provides an abstraction of web services that can be made available for use by other systems. In this paper we extend this model to an abstract model of clouds by means of an ontology for service description. The ontology can be specified using description logics, where the ABox contains the set of services, and the TBox can be queried to find suitable services. Consequently, AS2 composition can be used for cloud warehousing.

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DOI: 10.3217/jucs-017-03-0426

Authors: Wynand Van Staden, Martin Olivier

Abstract: This paper puts forward a verification method for compound purposes and compound reasons to be used during purpose limitation. When it is absolutely necessary to collect privacy related information, it is essential that privacy enhancing technologies (PETs) protect access to data - in general accomplished by using the concept of purposes bound to data. Compound purposes and reasons are an enhancement of purposes used during purpose limitation and binding and are more expressive than purposes in their general form. Data users specify their access needs by making use of compound reasons which are defined in terms of (compound) purposes. Purposes are organised in a lattice with purposes near the greatest lower bound (GLB) considered weak (less specific) and purposes near the least upper bound (LUB) considered strong (most specific). Access is granted based on the verification of the statement of intent (from the data user) against the compound purpose bound to the data; however, because purposes are in a lattice, the data user is not limited to a statement of intent that matches the purposes bound to the data exactly - the statement can be a true reflection of their intent with the data. Hence, the verification of compound reasons against compound purposes cannot be accomplished by current published verification algorithms. Before presenting the verification method, compound purposes and reasons, as well as the structures used to represent them, and the operators that are used to define compounds is presented. Finally, some thoughts on implementation are provided.

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DOI: 10.3217/jucs-016-21-3164

Authors: Raju Halder, Shantanu Pal, Agostino Cortesi

Abstract: Digital watermarking for relational databases emerged as a candidate solution to provide copyright protection, tamper detection, traitor tracing, maintaining integrity of relational data. Many watermarking techniques have been proposed in the literature to address these purposes. In this paper, we survey the current state-of-theart and we classify them according to their intent, the way they express the watermark, the cover type, the granularity level, and their verifiability.

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DOI: 10.3217/jucs-016-20-3043

Authors: Klaus-Dieter Schewe, Qing Wang

Abstract: Database transformations provide a unifying umbrella for queries and updates. In general, they can be characterised by five postulates, which constitute the database analogue of Gurevich's sequential ASM thesis. Among these postulates the background postulate supposedly captures the particularities of data models and schemata. For the characterisation of XML database transformations the natural first step is therefore to define the appropriate tree-based backgrounds, which draw on hereditarily finite trees, tree algebra operations, and extended document type definitions. This defines a computational model for XML database transformation using a variant of Abstract State Machines. Then the incorporation of weak monadic second-order logic provides an alternative computational model called XML machines. The main result is that these two computational models for XML database transformations are equivalent.

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