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12th International Symposium on Communication systems. Networks and Digital Signal Processing, CSNDSP 2020

作者： Devika Menon, M.K. Rodrigues, Joseph Gudino, Lucy J. Dept. of Electrical and Electronics Engineering Bangalore India Bits Pilani Dept. of Computer Science and Information Systems Pilani India

ISBN: (纸本)9781728167435

In this paper, a filter based pulse shaper is proposed for efficient Ultra-wideband (UWB) pulse transmission through human tissues. A bandpass Finite Impulse Response (FIR) filter is synthesized and its closed form expression for the impulse response coefficients is obtained. The filter shapes the basic UWB pulse, to closely fit the desired Federal Communication Commission (FCC) mask specifications, to achieve high spectral utilization efficiency. In this approach, the effects due to Gibb's phenomenon are minimized thereby resulting in lower dominant sidelobe of the resultant UWB pulse. The interference between adjacent pulses of the UWB data stream is minimized thus it allows shorter duration UWB pulses to be synthesized leading to higher data rate transmission compared to some techniques in literature. © 2020 IEEE.

关键词： FIR filters

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Adopting Heterogeneous Computing Modules: Experiences from a ToUCH Summer Workshop

Adopting Heterogeneous Computing Modules: Experiences from a...

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Workshop on Education for High Performance Computing (EduHPC)

作者： David P. Bunde Kishwar Ahmed Sridevi Ayloo Tisha Brown-Gaines Joel Fuentes Vishwesh Jatala Ruth Kurniawati Işıl Öz Apan Qasem Philip J. Schielke Mary C. Tedeschi Thomas Y. Yeh Dept. Computer Science Knox College Galesburg IL USA Dept. Electrical Engineering and Computer Science The University of Toledo Toledo OH USA Dept. Computer Systems Technology CityTech City University of New York Brooklyn NY USA Computer Science Dept. Belmont University Nashville TN USA Computer Science Dept. Universidad del Bí o-Bío Chillán Chile Dept. of EECS Indian Institute of Technology Bhilai India Computer and Information Science Dept. Westfield State University Westfield MA USA Computer Engineering Dept. Izmir Institute of Technology Izmir Turkey Dept. Computer Science Texas State University San Marcos TX USA Dept. Computer Science Concordia University Texas Austin TX USA Dept. Computer Science St. John's University New York NY USA Dept. Information Systems and Statistics Baruch College New York NY USA Computer Science Dept. Pomona College Claremont CA USA

ISBN: (纸本)9781665473675

We present efforts to encourage the adoption of modules for teaching heterogeneous parallel computing through a faculty development workshop. The workshop was held remotely using a novel format to exploit the advantages of a virtual format and mitigate its disadvantages. Adoption at a wide variety of institutions showed module effectiveness and also gathered feedback leading to several module improvements. We also report on the adoptions themselves, which show the importance of supporting adaptation of the modules for diverse settings.

关键词： Technological innovation Conferences High performance computing Education Parallel processing Heterogeneous networks

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Streaming process discovery method for semi-structured business processes 3

Streaming process discovery method for semi-structured busin...

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3rd IEEE International Conference on Data Stream Mining and Processing, DSMP 2020

作者： Batyuk, Anatoliy Voityshyn, Volodymyr Lviv Polytechnic National University Institute of Computer Science and Information Technologies Dept. of Automated Control Systems Lviv Ukraine

ISBN: (纸本)9781728132143

A streaming process discovery method for semi-structured business processes is proposed. The method is further development of the Fuzzy Miner which originally was intended for stationary event data sets. The streaming Fuzzy Miner supports concept drift of a business process model so that recent events are considered more important as the earlier ones. The method was used to represent business process models in the real-time business process monitoring information technology. © 2020 IEEE.

关键词： Miners

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Using Ensemble Inference to Improve Recall of Clone Detection

Using Ensemble Inference to Improve Recall of Clone Detectio...

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International Workshop on Software Clones (IWSC)

作者： Gul Aftab Ahmed James Vincent Patten Yuanhua Han Guoxian Lu David Gregg Jim Buckley Muslim Chochlov Lero Research Centre Trinity College Dublin Dublin Ireland Dept. of Computer Science and Information Systems University of Limerick Limerick Ireland WN Digital IPD and Trustworthiness Enabling Huawei Technologies Co. Ltd. Xi'an Shaanxi China

Large-scale source-code clone detection is a challenging task. In our previous work, we proposed an approach (SSCD) that leverages artificial neural networks and approximates nearest neighbour search to effectively and efficiently locate clones in large-scale bodies of code, in a time-efficient manner. However, our literature review suggests that the relative efficacy of differing neural network models has not been assessed in the context of large-scale clone detection approaches. In this work, we aim to assess several such models individually, in terms of their potential to maximize recall, while preserving a high level of precision during clone detection. We investigate if ensemble inference (in this case, using the results of more than one of these neural network models in combination) can further assist in this task. To assess this, we employed four state-of-the-art neural network models and evaluated them individually/in combination. The results, on an illustrative dataset of approximately 500K lines of C/C++ code, suggest that ensemble inference outperforms individual models in all trialled cases, when recall is concerned. Of individual models, the ADA model (belonging to the ChatGPT family of models) has the best performance. However commercial companies may not be prepared to hand their proprietary source code over to the cloud, as required by that approach. Consequently, they may be more interested in an ensemblecombination of CodeBERT-based and CodeT5 models, resulting in similar (if slightly lesser) recall and precision results.

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Using Ensemble Inference to Improve Recall of Clone Detection

arXiv

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arXiv 2024年

作者： Ahmed, Gul Aftab Patten, James Vincent Han, Yuanhua Lu, Guoxian Gregg, David Buckley, Jim Chochlov, Muslim Lero Research Centre Trinity College Dublin Dublin Ireland Dept. of Computer Science and Information Systems University of Limerick Limerick Ireland WN Digital IPD and Trustworthiness Enabling Huawei Technologies Co. Ltd. Shaanxi Xi’an China WN Digital IPD and Trustworthiness Enabling Huawei Technologies Co. Ltd. Shanghai China

Large-scale source-code clone detection is a challenging task. In our previous work, we proposed an approach (SSCD) that leverages artificial neural networks and approximates nearest neighbour search to effectively and efficiently locate clones in large-scale bodies of code, in a time-efficient manner. However, our literature review suggests that the relative efficacy of differing neural network models has not been assessed in the context of large-scale clone detection approaches. In this work, we aim to assess several such models individually, in terms of their potential to maximize recall, while preserving a high level of precision during clone detection. We investigate if ensemble inference (in this case, using the results of more than one of these neural network models in combination) can further assist in this task. To assess this, we employed four state-of-the-art neural network models and evaluated them individually/in combination. The results, on an illustrative dataset of approximately 500K lines of C/C++ code, suggest that ensemble inference outperforms individual models in all trialled cases, when recall is concerned. Of individual models, the ADA model (belonging to the ChatGPT family of models) has the best performance. However commercial companies may not be prepared to hand their proprietary source code over to the cloud, as required by that approach. Consequently, they may be more interested in an ensemble-combination of CodeBERT-based and CodeT5 models, resulting in similar (if slightly lesser) recall and precision results. Copyright © 2024, The Authors. All rights reserved.

关键词： Neural network models

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The Architecture of Intelligent Agent-based Educational System for Training Students in a Technical University 5

The Architecture of Intelligent Agent-based Educational Syst...

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5th International Conference on information technologies in engineering Education, Inforino 2020

作者： Shvetsov, Anatoly Rzheutskaya, Svetlana Sergushicheva, Anna Sukonschikov, Alexey Andrianov, Igor Kochkin, Dmitry Vologda State University Dept. of Information Systems and Technologies Vologda Russia Vologda State University Dept. of Automation and Computer Engineering Vologda Russia

ISBN: (纸本)9781728148106

The article presents the development, implementation and approbation of a multi-level architecture of the intelligent agent-based educational system for training of technical specialists. The proposed architecture combines openness, flexibility and good scalability. This article shows how these quality indicators were achieved through a combination of multi-agent and service-oriented approaches. It describes the purpose and components of each level, presents some key aspects of the implementation and shows the results of pedagogical experiments. © 2020 IEEE.

关键词： Intelligent agents

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Konstantina-Helen Tsarapatsani Vasilios D. Tsakanikas Antonis Sakellarios Hans J. Trampisch Efterpi Karapintzou Henrik Rudolf George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greeece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina and Biomedical Research Institute-FORTH Ioannina Greece

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction.

关键词： Accuracy Sensitivity Explainable AI Biological system modeling Peptides Receivers Predictive models Indexes Cardiovascular diseases Random forests

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Decoding the Obfuscated: Advanced NER Techniques on Commercial Sex Advertisement Data

Decoding the Obfuscated: Advanced NER Techniques on Commerci...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Alejandro Rodriguez Perez Pablo Rivas Javier Turek Korn Sooksatra Ernesto Quevedo Gisela Bichler Tomas Cerny Laurie Giddens Stacie Petter Dept. of Computer Science School of Eng. & Computer Science Baylor University Human-Robot Collaboration Lab Intelligent Systems Research Intel Labs School of Criminology & Criminal Justice California State University San Bernardino Dept. of Systems and Industrial Eng. College of Engineering The University of Arizona Information Tech. & Decisions Sci. Dept. G. Brint Ryan College of Business University of North Texas School of Business Wake Forest University

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Detecting potential human trafficking activity within online Commercial Sex Advertisements (CSAs) presents unique challenges for Named Entity Recognition (NER) due to the complex and often obfuscated textual content. This paper thoroughly evaluates state-of-the-art language models and tokenization techniques, focusing on their efficacy in the NER task within the context of CSAs. Our results indicate that the Longformer model, equipped with byte-level BPE tokenization, outperforms other models regarding precision, recall, and the $F$ 1 score. The study also uncovers specific areas for improvement, offering avenues for future research. Our findings have significant implications for the automated analysis and monitoring of CSAs for suspected human trafficking activity, which contributes to developing more robust and transparent online ecosystems.

关键词： Adaptation models Biological system modeling Focusing Named entity recognition Tokenization Data models Web sites

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Mitigating Epilepsy by Stabilizing Linear Fractional-Order systems

Mitigating Epilepsy by Stabilizing Linear Fractional-Order S...

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American Control Conference (ACC)

作者： Emily A. Reed Guilherme Ramos Paul Bogdan Sérgio Pequito Ming Hsieh Electrical and Computer Engineering Department University of Southern California USA Dept. of Computer Science and Engineering Instituto Superior Técnico University of Lisbon Portugal and Instituto de Telecomunicações Lisbon Portugal Department of Information Technology Division of Systems and Control Uppsala University Uppsala Sweden

Epilepsy affects approximately 50 million people worldwide. Despite its prevalence, the recurrence of seizures can be mitigated only 70% of the time through medication. Furthermore, surgery success rates range from 30% - 70% because of our limited understanding of how a seizure starts. However, one leading hypothesis suggests that a seizure starts because of a critical transition due to an instability. Unfortunately, we lack a meaningful way to quantify this notion that would allow physicians to not only better predict seizures but also to mitigate them. Hence, in this paper, we develop a method to not only characterize the instability of seizures but also to leverage these conditions to stabilize the system underlying these seizures. Remarkably, evidence suggests that such critical transitions are associated with long-term memory dynamics, which can be captured by considering linear fractional-order systems. Subsequently, we provide for the first time tractable necessary and sufficient conditions for the global asymptotic stability of discrete-time linear fractional-order systems. Next, we propose a method to obtain a stabilizing control strategy for these systems.using linear matrix inequalities. Finally, we apply our methodology to a real-world epileptic patient dataset to provide insight into mitigating epilepsy and designing future cyber-neural systems.

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A Review Study on ML-based Methods for Defect-Pattern Recognition in Wafer Maps 4

A Review Study on ML-based Methods for Defect-Pattern Recogn...

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4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Theodosiou, T. Rapti, A. Papageorgiou, K. Tziolas, T. Papageorgiou, E. Dimitriou, N. Margetis, G. Tzovaras, D. University of Thessalyirst Affiliation Dept. of Energy Systems Gaiopolis Larissa41500 Greece Centre for Research and Technology Hellas Information Technologies Institute 6th Km Charilaou-Thermi Road Thermi-Thessaloniki57001 Greece Foundation for Research and Technology Hellas Institute of Computer Science N. Plastira 100 Vassilika Vouton HeraklionGR-700 13 Greece

The identification of defects plays a key role in the semiconductor industry as it can reduce production risks, minimize the effects of unexpected downtimes and optimize the production process. A literature review protocol is implemented and latest advances are reported in defect detection considering wafer maps towards quality control. In particular, the most recent works are outlined to demonstrate the use of AI-technologies in wafer maps defect detection. The popularity of these technologies is then presented in the form of visualizing graphs. This enables the identification of the most popular and most prominent ML-methods that can be exploited for the purposes of Industry 4.0. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Industry 4.0

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