DOI: 10.3897/jucs.161202

Authors: Yelda Fırat, Yılmaz Kılıçaslan, Hüseyin Ali Sarıkaya, Murat Kaan Yılmaz

Abstract: Autism Spectrum Disorder is a neurodevelopmental disorder with onset in early childhood and its diagnosis often requires clinical processes based on long, subjective observations. Although early diagnosis and intervention can significantly improve developmental outcomes, existing methods are limited in terms of scalability and objectivity. The aim of this study is to develop a hybrid deep learning model that detects Autism Spectrum Disorder with high accuracy by analyzing motor behaviors from videos of children recorded in their natural home environment. In this study, joint coordinates were extracted using the MediaPipe Pose model and spatial, temporal, frequency and coordination-based features were calculated from these data. The features were processed with a hybrid architecture integrating CNN, BiLSTM and attention mechanism. CNN captured spatial patterns, BiLSTM learned the dynamics over time, and the attention mechanism focused on critical movement segments. The model achieves over 97% accuracy on closed datasets and over 83% on public videos such as YouTube and TikTok. These results show that the method performs robustly under both controlled and real-world conditions. The study provides a scalable, objective and clinically applicable screening tool that overcomes the problems of artificial environments and limited data.

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DOI: 10.3897/jucs.161636

Authors: Wicus J. van der Linden, Trienko L. Grobler, Lynette van Zijl

Abstract: The effect of imbalanced data on the optical character recognition of Braille text is investigated by applying two techniques to a set of convolutional neural network image classification models. A multilabel classification framework is applied to identify the combination of Braille dots present in a character sample. This approach is compared to the multiclass classification framework prevalent in the literature, which directly identifies each sample as one of 64 possible Braille characters. Furthermore, data resampling methods are applied to investigate the impact of class imbalance on the multilabel and multiclass modelling approaches, respectively. The multilabel models are shown to achieve statistically significantly better performance than multiclass models, across different data resampling strategies. This includes better generalisation to out of distribution testing data from different Braille language codes, as well as robust performance under experimental image augmentation conditions. Furthermore, while multiclass models achieve better performance when trained on resampled data compared to training without resampling, this performance increase fails to rival the performance of the multilabel classification models across metrics and resampling strategies.

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DOI: 10.3897/jucs.114357

Authors: Samit Bhanja, Banani Ghose, Abhishek Das

Abstract: Recently time series forecasting has become one of the prime application areas of climatology, economics and industries. Many research works are conducted to forecast the time series more accurately. But few of them are concentrated on predicting the time series over an extended future horizon, and there is also a scope to improve their forecasting accuracy. This work proposes a multi-step-ahead foresting method to produce a stable and accurate forecasting result for the extended future horizon. Firstly, a deep learning-based forecasting model is proposed to predict the time series. Secondly, a fuzzy time series-based error correction model is implemented to enhance the prediction performance of the deep learning model. Here to optimize all the fuzzy time series (FTS) parameters in an integrated way, an integrated butterfly optimization (FTS-IBO) algorithm is proposed. In this study, two different types of real-world multivariate time series datasets are used to analyze the forecasting performance of the proposed model. The performance of the proposed FTS-IBO algorithm is compared with the traditional butterfly optimization (FTS-BO) algorithm. The experimental results show that the FTS-IBO technique is superior to the FTS-BO technique. The forecasting performance of the proposed model has also compared the other state-of-the-art models, and the simulation results exhibit that the proposed model produces a more accurate prediction performance for multi-step-ahead time series forecasting problems compared to other models.

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DOI: 10.3897/jucs.119234

Authors: Panji Bintoro, Zulkifli Zulkifli, Yaya Heryadi, Fitriana Fitriana, Nopi Anggista Putri, Dwi Yana Ayu Andini

Abstract: Mpox is a disease like smallpox caused by the Mpox virus (MPXV), which belongs to the Orthopoxvirus (OPXV) group in the Poxviridae family. The virus is transmitted through direct contact with infected individuals, animals, or contaminated materials. Transmission can occur through direct body contact, animal bites, respiratory droplets, or mucous membranes in the eyes, nose, or mouth. However, since the recent outbreak in May 2022, the disease has spread to various countries, posing a threat to develop into a global pandemic. Several image processing and deep learning models, including Convolutional Neural Network (CNN), have been employed for Mpox disease prediction. The default CNN algorithm performs poorly on image orientations such as tilting, rotation, zooming, or other abnormal images. Therefore, we propose a new framework adopted from deep learning by combining Generative Adversarial Network (GAN), PyramidalNet, and Long Short-Term Memory (LSTM). This new method is referred to as GPLNet. The research results indicate that the GPLNet algorithm model can surpass the accuracy achieved by CNN and CNN-LSTM, reaching 99%. The performance of the GPLNet algorithm model is also evaluated using various measurement metrics, yielding an accuracy of 98%, precision of 99%, recall of 98%, sensitivity of 98%, specificity of 98%, f1-score of 98%, and ROC of 99%.

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DOI: 10.3897/jucs.107081

Authors: Alaor Cervati Neto, Alexandre L. M. Levada

Abstract: Dimensionality reduction based unsupervised metric learning consists in finding meaningful compact data representations previously to clustering and classification problems. One of the major aspects of these algorithms is the approximation of the underlying manifold by a weighted graph. A limitation with most manifold learning algorithms is that edge weights in the proximity graph rely heavily on the Euclidean distance, which is known to be quite sensitive to the presence of outliers. In this paper, we propose to improve the Locality Preserving Projections (LPP) algorithm by incorporating a recently proposed graph inference method called Probabilistic Nearest Neighbors (PNN), an extension of the Clustering with Adaptive Neighbors (CAN) approach, used with success in graph-based semi-supervised learning. The proposed PNN-LPP algorithm is able to achieve better classification results than regular LPP, showing competitive performance against state-of-the-art approaches for dimensionality reduction, such as the UMAP algorithm, especially in datasets with a limited number of samples.

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DOI: 10.3897/jucs.112513

Authors: Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan, Clement Pang

Abstract: Diagnosing IT issues is a challenging problem for large-scale distributed cloud environments due to complex and non-deterministic interrelations between the system components. Modern monitoring tools rely on AI-empowered data analytics for detection, root cause analysis, and rapid resolution of performance degradation. However, the successful adoption of AI solutions is anchored on trust. System administrators will not unthinkingly follow the recommendations without sufficient interpretability of solutions. Explainable AI is gaining popularity by enabling improved confidence and trust in intelligent solutions. For many industrial applications, explainable models with moderate accuracy are preferable to highly precise black-box ones. This paper shows the benefits of rule-induction classification methods, particularly RIPPER, for the root cause analysis of performance degradations. RIPPER reveals the causes of problems in a set of rules system administrators can use in remediation processes. Native cloud applications are based on the microservices architecture to consume the benefits of distributed computing. Monitoring such applications can be accomplished via distributed tracing, which inspects the passage of requests through different microservices. We discuss the application of rule-learning approaches to trace traffic passing through a malfunctioning microservice for the explanations of the problem. Experiments performed on datasets from cloud environments proved the applicability of such approaches and unveiled the benefits.

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DOI: 10.3897/jucs.2020.035

Authors: Indraja Germanaitė, Kętutis Zaleckis, Rimantas Butleris, Kristina Jarmalavičienė

Abstract: In this case study the authors created and tested a configurable and expandable spatial patterns (SP) description, identification, and application methodology (SPDIAM) and an SP identification algorithm. SPDIAM allows urban planning and design (UPD) practitioners to describe SP in a computerized manner, identify SP automatically and then apply them in the UPD domain. SPDIAM is based on the space syntax (SS) method and normalized spatial and non-spatial measures and can be used with the statistical social, economic, and environmental indicators, which are related to the urban sustainability and spatial capital. The goal of the case study experiment was to proof a concept of SPDIAM and to identify the rules and the values of the measures used for the SP identification. For this City Layout SP was identified in the vector data of 12 European, North American, and African cities. The experiment results confirmed that SPDIAM is appropriate to describe SP and identify them automatically. The use of the normalized measures enables the comparison of different SP and reduces the degree of the subjectivity of the UPD solutions. SPDIAM no longer relies on statistical information but forms SP based on the probabilistic complex modelling of a city, which lets SPDIAM indicate possible directions of SP future transformation. SPDIAM uses the newly offered measures CENTER and URBAN COMPACTNESS INDEX to identify SP automatically and can add quantitative and qualitative improvement to the spatial network analysis tools in Geographic Information Systems.

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DOI: 10.3897/jucs.2020.025

Authors: Kamil Maliński, Krzysztof Okarma

Abstract: Rapid growth of availability of modern electronic and robotic solutions, also for home and amateur use, related to the progress in home automation and popularity of the IoT systems, makes it possible to develop some unique hardware solutions, also by independent researchers and engineers, often with the help of the 3D printing technology. Although in many industrial applications high speed pick and place machines are used for assembling small surface-mount devices (SMD), especially in mass production of electronic parts, there are still some applications, where the traditional through-hole technology used in Printed Circuit Boards (PCB) is utilised, particularly considering some mechanical, thermal or power conditions, preventing the use of the SMD technology. One of the possibilities of supporting such types of production and prototyping, in some cases supported by relatively less sophisticated robotic solutions, may be the application of vision systems, making it possible to classify and recognize some electronics parts with the use of shape analysis of their packages as well as further optical recognition of markings. Another application of such methods may be related to the automatic vision based verification of the assembling quality and correctness of the placement of electronic parts after completing the production. In the paper some experimental results, obtained using various shape descriptors for the classification of electronic packages, are presented. The initial experiments, obtained for a prepared dedicated database of synthetic images, have been verified and confirmed also for some natural images, leading to promising results.

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DOI: 10.3897/jucs.2020.009

Authors: Patrick Juola

Abstract: The computational analysis of documents to learn about their authorship (also known as authorship attribution and/or authorship profiling) is an increasingly important area of research and application of technology. This paper discusses the technology, focusing on its application to social media in a variety of disciplines. It includes a brief survey of the history as well as three tutorial case studies, and discusses several significant applications and societal benefits that authorship analysis has brought about. It further argues, though, that while the benefits of this technology have been great, it has created serious risks to society that have not been sufficiently considered, addressed, or mitigated.

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DOI: 10.3217/jucs-025-06-0667

Authors: Sonia Contreras, Miguel Manzanedo, Álvaro Herrero

Abstract: Workplace accident rates are always a constant source of concern in different scenarios. On the other hand, different economic cycles modify employment indicators, thereby affecting both the health and the wellbeing of workers. In this study, a Hybrid Neural System is proposed to support the analysis of both workplace accidents and different macroeconomic variables. The application of exploratory projection and nonlinear autoregressive models allow us to recognize patterns and subsequently study the interplay between the two fields, providing valuable information, so that directors can take more informed management decisions.

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DOI: 10.3217/jucs-025-06-0611

Authors: Dominik Pieczyński, Marek Kraft, Michał Fularz

Abstract: In this paper, a method for improving the quality of person re-identification results is presented. The method is based on the assumption, that including segmentation information into re-identi_cation pipeline discards the automated detections that are of poor quality due to occlusions, misplaced regions of interest (ROI), multiple persons found within a single ROI, etc. using a simple segment number, bounding box fill rate and aspect ratio check. Assuming that a joint detector-segmented approach is used, the additional cost associated with the use of the proposed approach is very low.

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DOI: 10.3217/jucs-025-04-0334

Authors: Rogerio C. P. Fragoso, Roberto H. W. Pinheiro, George Cavalcanti

Abstract: Dimensionality reduction is a crucial task in text classification. The most adopted strategy is feature selection using filter methods. This approach presents a difficulty in determining the best size for the final feature vector. At Least One FeaTure (ALOFT), Maximum f Features per Document (MFD), Maximum f Features per Document-Reduced (MFDR) and Class-dependent Maximum f Features per Document-Reduced (cMFDR) are feature selection methods that define automatically the number of features per Corpus. However, MFD, MFDR, and cMFDR require a parameter that defines the number of features to be selected per document. Automatic Feature Subsets Analyzer (AFSA) is an auxiliary method that automates such configuration. In this paper, we evaluate dimensionality reduction, classification performance and execution time of this family of methods: ALOFT, MFD, MFDR, cMFDR and AFSA. The experiments are conducted using three feature evaluation functions and twenty databases. MFD obtained the best results among the feature selection methods. In addition, the experiments showed that the use of AFSA does not significantly affect the classification performances or the dimensionality reduction rates of the feature selection methods, but considerably reduces their execution times.

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DOI: 10.3217/jucs-024-09-1271

Authors: Yuyong Jeon, Ilkyeun Ra, Youngjin Park, Sangmin Lee

Abstract: In this paper, a fast and effective method of parameter optimization for noise estimation is proposed for various types of noise. The proposed method is based on gradient descent, which is one of the optimization methods used in machine learning. The learning rate of gradient descent was set to a negative value for optimizing parameters for a speech quality improvement problem. The speech quality was evaluated using a suite of measures. After parameter optimization by gradient descent, the values were re-checked using a wider range to prevent convergence to a local minimum. To optimize the problem's five parameters, the overall number of operations using the proposed method was 99.99958% smaller than that using the conventional method. The extracted optimal values increased the speech quality by 1.1307%, 3.097%, 3.742%, and 3.861% on average for signal-to-noise ratios of 0, 5, 10, and 15 dB, respectively.

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DOI: 10.3217/jucs-024-04-0475

Authors: Ugnius Malūkas, Rytis Maskeliūnas, Robertas Damaševičius, Marcin Woźniak

Abstract: The paper presents a computer vision based system, which performs real time path finding for visually impaired or blind people. The semantic segmentation of camera images is performed using deep convolutional neural network (CNN), which able to recognize patterns across image feature space. Out of three different CNN architectures (AlexNet, GoogLeNet and VGG) analysed, the fully connected VGG16 neural network is shown to perform best in the semantic segmentation task. The algorithm for extracting and finding paths, obstacles and path boundaries is presented. The experiments performed using own dataset (300 images extracted from two hours of video recording walking in outdoors environment) show that the developed system is able to find paths, path objects and path boundaries with an accuracy of 96.1 ± 2.6%.

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DOI: 10.3217/jucs-023-03-0304

Authors: Sonia Ortiz-Ángeles, Yenny Villuendas-Rey, Itzamá López-Yáñez, Oscar Camacho-Nieto, Cornelio Yáñez-Márquez

Abstract: The contemporary world has witnessed technological advances, such as Online Social Networks (OSN), whose influence in almost every action of the human being is remarkable. Among the human activities most significantly impacted by OSNs are: entertainment, human relationships, education, and political activities, including those related to electoral campaigns and electoral preferences prediction. The research contribution of the current paper regards the usefulness of OSNs users generated data to predict the political context. More specifically, 25 Computational Intelligence (CI) algorithms are used to predict voting intentions on the United States primary presidential elections for 2016, taking as input the data sets generated by 1200 users of the YouGov OSN, as well as the answers they gave to an online study run by the American National Election Studies (ANES). The application of the 25 supervised classification algorithms is done over the Waikato Environment for Knowledge Analysis (WEKA), using a stratified 5-fold cross validation scheme. Also, the experimental results obtained were validated in order to identify significant differences in performance by mean of a non-parametric statistical test (the Friedman test), and a post-hoc test (the Holm test). The hypothesis testing analysis of the experimental results indicates that predicting voting intentions in favour of a democrat or republican candidate is simpler than predicting the particular candidate, given that the prediction performances for a democrat or republican candidate (best performances of 80% and 78%, respectively) are better than those given when predicting a specific candidate (70% for democrat candidates and 56% for republican candidates).

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DOI: 10.3217/jucs-022-04-0589

Authors: Gunupudi Kumar, Nimmala Mangathayaru, Gugulothu Narsimha

Abstract: Software Security and Intrusion Detection need to be dealt at three levels Network, Host level and Application level. In this paper the major objective is to design and analyze the suitability of Gaussian similarity measure for intrusion detection. The objective is to use this as a distance measure to find the distance between any two data samples of training set such as DARPA Data Set, KDD Data Set. This major objective is to use this measure as a distance metric when applying k-means algorithm. The novelty of this approach is making use of the proposed distance function as part of k-means algorithm so as to obtain disjoint clusters. This is followed by a case study, which demonstrates the process of Intrusion Detection. The proposed similarity has fixed upper and lower bounds.

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DOI: 10.3217/jucs-021-11-1454

Authors: Ahmed Al-Zahrani, Hassan Mathkour, Hassan Abdalla

Abstract: Feature-based approaches play an important role and are widely applied in extractive summarization. In this paper, we use particle swarm optimization (PSO) to evaluate the effectiveness of different state-of-the-art features used to summarize Arabic text. The PSO is trained on the Essex Arabic summaries corpus data to determine the best particle that represents the most appropriate simple/combination of eight informative/structure features used regularly by Arab summarizers. Based on the elected features and their relevant weights in each PSO iteration, the input text sentences are scored and ranked to extract the top ranking sentences in the form of an output summary. The output summary is then compared with a reference summary using the cosine similarity function as the fitness function. The experimental results illustrate that Arabs summarize texts simply, focusing on the first sentence of each paragraph.

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DOI: 10.3217/jucs-019-15-2224

Authors: Wedad Hussein, Rasha Ismail, Tarek Gharib, Mostafa G. M. Mostafa

Abstract: Recommender systems are means for web personalization and tailoring the browsing experience to the users' specific needs. There are two categories of recommender systems; memory-based and model-based systems. In this paper we propose a personalized recommender system for the next page prediction that is based on a hybrid model from both categories. The generalized patterns generated by a model based techniques are tailored to specific users by integrating user profiles generated from the traditional memory-based system's user-item matrix. The suggested system offered a significant improvement in prediction speed over traditional model-based usage mining systems, while also offering an average improvement in the system accuracy and system precision by 0.27% and 2.35%, respectively.

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DOI: 10.3217/jucs-019-09-1242

Authors: Jan Havlik, Lenka Lhotska, Jakub Parak, Jan Dvorak, Zdenek Horcik, Matous Pokorny

Abstract: Remote patient monitoring is gradually attracting more attention as the population in developed countries ages, and as chronic diseases appear more frequently in the population. Miniaturization in electronics and mobile technologies has led to rapid development of various wearable systems for remote monitoring of vital signs, supervision systems in home care, assistive technologies and similar systems. There is a significant demand for developing the necessary devices very rapidly, especially for shortening the way from an idea to a first function sample. This paper presents a solution for rapidly developing devices for telemedical applications, remote monitoring and assistive technologies. The approach used here is to design and realize a modular system consisting of input modules for signal acquisition, a control unit for signal pre-processing, handshaking of data communication, controlling the system and providing the user interface and communication modules for data transmission to a superordinate system. A description of specific applications developed on the basis of the system is also presented in the paper.

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DOI: 10.3217/jucs-018-20-2832

Authors: Daewon Kim, Doosung Hwang

Abstract: As the augmented reality technology has become more pervasive and applicable, it is easily seen in our daily lives regardless of fields and scopes. Existing camera vision based augmented reality techniques depend on marker based approaches rather than real world information. The augmented reality technology using marker recognition has limitations in its applicability and provision of proper environment to guarantee user's immersiveness to relevant service application programs. This study aims to implement a smart mobile terminal based augmented reality technology by using a camera built in a terminal device and image and video processing technology without any markers so that users can recognize multimedia objects from real world images and build an augmented reality service, where 3D content connected to objects and relevant information are added to the real world image. Object recognition from a real world image is involved in a process of comparison against preregistered reference information, where operation to measure similarity is reduced for faster running of the application, considering the characteristics of smart mobile devices. Furthermore, the design allows users to interact through touch events on the smart device after 3D content is output onto the terminal screen. Afterward, users can browse object related information on the web. The augmented reality technology appropriate for the smart mobile environment is proposed and tested through several experiments and showed reliable performances in the results.

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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-018-04-0507

Authors: Onur Can Sert, Kayhan Dursun, Tansel Özyer, Jamal Jida, Reda Alhajj

Abstract: Multi objective clustering is one focused area of multi objective optimization. Multi objective optimization attracted many researchers in several areas over a decade. Utilizing multi objective clustering mainly considers multiple objectives simultaneously and results with several natural clustering solutions. Obtained result set suggests different point of views for solving the clustering problem. This paper assumes all potential solutions belong to different experts and in overall; ensemble of solutions finally has been utilized for finding the final natural clustering. We have tested on categorical datasets and compared them against single objective clustering result in terms of purity and distance measure of k-modes clustering. Our clustering results have been assessed to find the most natural clustering. Our results get hold of existing classes decided by human experts.

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DOI: 10.3217/jucs-017-01-0081

Authors: Rakesh Rampalli, Angarai Ramakrishnan

Abstract: This work describes an online handwritten character recognition system working in combination with an offline recognition system. The online input data is also converted into an offline image, and in parallel recognized by both online and offline strategies. Features are proposed for offline recognition and a disambiguation step is employed in the offline system for the samples for which the confidence level of the classier is low. The outputs are then combined probabilistically resulting in a classier out-performing both individual systems. Experiments are performed for Kannada, a South Indian Language, over a database of 295 classes. The accuracy of the online recognizer improves by 11% when the combination with offline system is used.

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DOI: 10.3217/jucs-016-21-3245

Authors: João Luis Garcia Rosa, Juan Adan-Coello

Abstract: In Natural Language Processing (NLP) symbolic systems, several linguistic phenomena, for instance, the thematic role relationships between sentence constituents, such as AGENT, PATIENT, and LOCATION, can be accounted for by the employment of a rule-based grammar. Another approach to NLP concerns the use of the connectionist model, which has the benefits of learning, generalization and fault tolerance, among others. A third option merges the two previous approaches into a hybrid one: a symbolic thematic theory is used to supply the connectionist network with initial knowledge. Inspired on neuroscience, it is proposed a symbolic-connectionist hybrid system called BIOθPRED (BIOlogically plausible thematic (θ) symbolic-connectionist PREdictor), designed to reveal the thematic grid assigned to a sentence. Its connectionist architecture comprises, as input, a featural representation of the words (based on the verb/noun WordNet classification and on the classical semantic microfeature representation), and, as output, the thematic grid assigned to the sentence. BIOθPRED is designed to "predict" thematic (semantic) roles assigned to words in a sentence context, employing biologically inspired training algorithm and architecture, and adopting a psycholinguistic view of thematic theory.

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DOI: 10.3217/jucs-015-13-2528

Authors: Yifei Chen, Feng Liu, Bram Vanschoenwinkel, Bernard Manderick

Abstract: This paper focuses on the use of support vector machines on a typical context-dependent classification task, splice site prediction. For this type of problems, it has been shown that a context-based approach should be preferred over a transformation approach because the former approach can easily incorporate statistical measures or directly plug sensitivity information into distance functions. In this paper, we designed three types of context-sensitive kernel functions: polynomial-based, radial basis function-based and negative distance-based kernels. From the experimental results it becomes clear that the radial basis function-based kernel with information gain weighting gets the best accuracies and can always outperform their simple non-sensitive counterparts both in accuracy and in model complexity. And with well designed features and carefully chosen context sizes, our system can predict splice sites with fairly high accuracy, which can achieve the F P 95% rate, 3.94 for donor sites and 5.98 for acceptor sites, an approximate state of the art performance for the moment.

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DOI: 10.3217/jucs-013-03-0363

Authors: Javier Díaz, Eduardo Ros, Rafael Rodriguez-Gomez, Begoña Pino

Abstract: Motion estimation from image sequences is a complex problem which requires high computing resources and is highly affected by changes in the illumination conditions in most of the existing approaches. In this contribution we present a high performance system that deals with this limitation. Robustness to varying illumination conditions is achieved by a novel technique that combines a gradient-based optical flow method with a non-parametric image transformation based on the Rank transform. The paper describes this method and quantitatively evaluates its robustness to different illumination changing patterns. This technique has been successfully implemented in a real-time system using reconfigurable hardware. Our contribution presents the computing architecture, including the resources consumption and the obtained performance. The final system is a real-time device capable to computing motion sequences in real-time even in conditions with significant illumination changes. The robustness of the proposed system facilitates its use in multiple potential application fields.

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DOI: 10.3217/jucs-012-10-1439

Authors: Rajasvaran Logeswaran

Abstract: When dealing with remote systems, it is desirable that these systems are capable of operation within acceptable levels with minimal control and maintenance. In terms or transmission of telemetry information, a prediction-based compression scheme has been introduced. This paper studies the influence of some typical transmission and network errors on the encoded residue stream produced by a number of predictors used in the scheme, with the intention of identifying the more fault tolerant architecture that may be preferred as predictors. Classical linear predictors such as FIR and lattice filters, as well as a variety of feedforward and recurrent neural networks are studied. The residue streams produced by these predictors are subjected to two types of commonly occurring transmission noise, namely gaussian and burst. The noisy signal is decoded at the receiver and the magnitude of error, in terms or MSE and MAE are compared. Hardware failures in the input receptor and multiplier are also simulated and the performance of various predictors is compared. Overall, it is found that even small low-complexity neural networks are more resilient to faults due to the characteristics of their parallel architecture and distributed storage/processing characteristics.

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DOI: 10.3217/jucs-012-10-1426

Authors: Limin Liu, Tian-Shyr Dai

Abstract: Fingerprint image enhancement is a common and critical step in fingerprint recognition systems. To enhance the images, most of the existing enhancement algorithms use filtering techniques that can be categorized into isotropic and anisotropic according to the filter kernel. Isotropic filtering can properly preserve features on the input images but can hardly improve the quality of the images. On the other hand, anisotropic filtering can effectively remove noise from the image but only when a reliable orientation is provided. In this paper, we propose a ridge orientation estimation and verification algorithm which can not only generate an orientation of ridge flows, but also verify its reliability. Experimental results show that, on average, over 51 percent of an image in the NIST-4 database has reliable orientations. Based on this algorithm, a hybrid fingerprint enhancement algorithm is developed which applies isotropic filtering on regions without reliable orientations and anisotropic filtering on regions with reliable orientations. Experimental results show the proposed algorithm can combine advantages of both isotropic and anisotropic filtering techniques and generally improve the quality of fingerprint images.

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DOI: 10.3217/jucs-008-11-1016

Authors: Lutz Prechelt, Guido Malpohl, Michael Philippsen

Abstract: JPlag is a web service that finds pairs of similar programs among a given set of programs. It has successfully been used in practice for detecting plagiarisms among student Java program submissions. Support for the languages C, C++ and Scheme is also available. We describe JPlag's architecture and its comparsion algorithm, which is based on a known one called Greedy String Tiling. Then, the contribution of this paper is threefold: First, an evaluation of JPlag's performance on several rather different sets of Java programs shows that JPlag is very hard to deceive. More than 90 percent of the 77 plagiarisms within our various benchmark program sets are reliably detected and a majority of the others at least raise suspicion. The run time is just a few seconds for submissions of 100 programs of several hundred lines each. Second, a parameter study shows that the approach is fairly robust with respect to its configuration parameters. Third, we study the kinds of attempts used for disguising plagiarisms, their frequency, and their success.

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