DOI: 10.3897/jucs.150393

Authors: Alexandru Pintea

Abstract: Smart homes, live streaming IoT devices, and smart sensors can all be optimised to enhance energy efficiency. In order to offer a cheap alternative to the traditional real-time monitoring systems, this study proposes a sensor-based occupancy system. The evaluation in real time of the number of occupants in buildings/ rooms /houses is reflected in the energy usage. Sensor data can provide insight into many characteristics of a considered environment. The sensor dataset considered was collected with the aim of determining how many people are present in a given space/room. The sensor data does not portray the people present in the room, but rather their impact on it (e.g. CO2/ noise/ light level changes). The dataset was cleaned and preprocessed to optimise model performance. The results obtained by training several classifiers yielded accuracies that reach 98%-99%. The research provides an end-to-end solution for the considered problem, through data preprocessing/feature selection/outlier removal and model training/evaluation. Hyperparameters were tuned for more than twenty models. All chosen models and features were ranked based on performance and robustness. A novel solution for optimising sensor placement has also been proposed by this study, to further improve sensor-based monitoring systems.

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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: 10.3217/jucs-015-04-0705

Authors: Jeen-Shing Wang, Jen-Chieh Chiang

Abstract: This paper presents an efficient data preprocessing procedure for the of support vector clustering (SVC) to reduce the size of a training dataset. Solving the optimization problem and labeling the data points with cluster labels are time-consuming in the SVC training procedure. This makes using SVC to process large datasets inefficient. We proposed a data preprocessing procedure to solve the problem. The procedure contains a shared nearest neighbor (SNN) algorithm, and utilizes the concept of unit vectors for eliminating insignificant data points from the dataset. Computer simulations have been conducted on artificial and benchmark datasets to demonstrate the effectiveness of the proposed method.

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DOI: 10.3217/jucs-014-14-2263

Authors: Damian Dudek

Abstract: Adaptive behavior and learning are required of software agents in many application domains. At the same time agents are often supposed to be resource-bounded systems, which do not consume much CPU time, memory or disk space. In attempt to satisfy both requirements, we propose a novel framework, called APS (standing for Analysis of Past States), which provides agent with learning capabilities with respect to saving system resources. The new solution is based on incremental association rule mining and maintenance. The APS process runs periodically in a cycle, in which phases of agent's normal performance intertwine with learning phases. During the former ones an agent stores observations in a history. After a learning phase has been triggered, the history facts are analyzed to yield new association rules, which are added to the knowledge base by the maintenance algorithm. Then the old observations are removed from the history, so that in the next learning runs only recent facts are processed in search of new association rules. Keeping the history small can save both processing time and disk space as compared to batch learning approaches.

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DOI: 10.3217/jucs-012-01-0080

Authors: Adel Taweel, Alan Rector, Jeremy Rogers

Abstract: The convergence of need between improved clinical care and post genomics research presents a unique challenge to restructuring information flow so that it benefits both without compromising patient safety or confidentiality. The CLEF project aims to link-up heath care with bioinformatics to build a collaborative research platform that enables a more effective biomedical research. In that, it addresses various barriers and issues, including privacy both by policy and by technical means, towards establishing its eventual system. It makes extensive use of language technology for information extraction and presentation, and its shared repository is based around coherent "chronicles" of patients' histories that go beyond traditional health record structure. It makes use of a collaborative research workbench that encompasses several technologies and uses many tools providing a rich platform for clinical researcher.

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