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2nd World Symposium on Communication Engineering, WSCE 2019

作者： Memos, Vasileios A. Psannis, Kostas E. Minopoulos, Georgios Kokkonis, George Ishibashi, Yutaka University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9781728153292

Internet of Things (IoT) is a new technological revolution which brings many advantages to its users. However, IoT devices and sensor nodes in Wireless Sensor Networks (WSNs) have limited resources, such as energy, memory, computational speed, and communication bandwidth. Thus, better utilization of energy is necessary to avoid problems like high energy consumption and low network lifetime. This paper presents the most well-known and current methods for energy efficiency in both WSNs and the expandable IoT. In addition, a novel low-energy approach for maximizing energy efficiency in IoT, called Energy Efficient Scheme for IoT (EES4IoT), is proposed. This scheme merges efficiently two other schemes for energy efficiency, network lifetime enhancement, maintenance cost reduction and shortest path finding for data transmission. The new proposed scheme demonstrates energy savings in the sensor nodes and extending lifetime of WSNs. © 2019 IEEE.

关键词： Energy efficiency

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LETHE: A Digital Intervention for Cognitive Decline*

LETHE: A Digital Intervention for Cognitive Decline*

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Vasileios S. Loukas Thomas Kassiotis Ignacio Lamata Martinez Lefteris Koumakis Jeroen Bruinsma Roberto Pasciuti Monica Balatresi Ville Tenhunen Adam Fiakkas Lelia Ataliani Georgia S. Karanasiou Manolis Tsiknakis Hannes Hilberger Markus Bödenler Bianca Schnalzer Simone Huber Mattia Pirani Matteo Colombo Sten Hanke Dimitrios I. Fotiadis Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Biomedical Research Institute FORTH-BRI Ioannina Greece Computational BioMedicine Laboratory Institute of Computer Science FORTH-ICS Heraklion Greece EGI Foundation Amsterdam The Netherlands Dept. of Health Promotion Care and Public Health Research Institute Maastricht University Maastricht The Netherlands ExtraRed Innovations Pisa Italy Infotrend Innovations Co. Ltd Nicosia Cyprus eHealth Institute FH Joanneum University of Applied Sciences Graz Austria Innovation2Grow I2G Bologna Italy

Dementia is the main cause of disability in elderly populations. It has been shown that the risk factors of dementia are a mixture of pathological, lifestyle and heritable factors, with some of those being provably modifiable. Early diagnosis of dementia and approaches to slow down its evolution are currently the most prominent management methodologies due to lack of a cure. For that reason, a plethora of home-based assistive technologies for dementia management do exist, with most of them focusing on the improvement of memory and thinking. The main objective of LETHE is prevention in the whole spectrum of cognitive decline in the elderly population at risk reaching from asymptomatic to subjective or mild cognitive impairment to prodromal Dementia. LETHE will provide a Big Data collection platform and analysis system, that will allow prevention, personalized risk detection and intervention on cognitive decline. Through the subsequent 2-year clinical trial, the LETHE system, as well as the respective knowledge gained will be evaluated and validated. The scope of the current paper is to introduce the LETHE study and its respective novel platform as a holistic approach to multidomain lifestyle intervention trial studies. The present work depicts the architectural perspective and extends beyond state-of-the-art guidelines and approaches to health management systems and cloud platform *** Relevance — Patient Management Systems as well as lifestyle management platforms have significant clinical relevance as they allow for remote and continuous monitoring of patients' health status. LETHE aims to improve patient outcomes by providing predictive models for cognitive decline and patient adherence to the multimodal lifestyle intervention, enabling prompt and appropriate medical decisions.

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Detecting Worker Attention Lapses in Human-Robot Interaction: An Eye Tracking and Multimodal Sensing Study

arXiv

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arXiv 2023年

作者： Dai, Zhuangzhuang Park, Jinha Kaszowska, Aleksandra Li, Chen Dept. of Computer Science Engineering and Physical Science School of Engineering and Applied Science Aston University BirminghamB4 7ET United Kingdom SDU Robotics The Maersk Mc Kinney Moller Institute University of Southern Denmark Campusvej 55 Odense5230 Denmark the Department of Electronic Systems Aalborg University Fredrik Bajers Vej 7 B5-215 Aalborg9220 Denmark Department of Materials and Production Aalborg University Fibigerstræde 16 Aalborg9220 Denmark

The advent of industrial robotics and autonomous systems endow human-robot collaboration in a massive scale. However, current industrial robots are restrained in co-working with human in close proximity due to inability of interpreting human agents’ attention. Human attention study is non-trivial since it involves multiple aspects of the mind: perception, memory, problem solving, and consciousness. Human attention lapses are particularly problematic and potentially catastrophic in industrial workplace, from assembling electronics to operating machines. Attention is indeed complex and cannot be easily measured with single-modality sensors. Eye state, head pose, posture, and manifold environment stimulus could all play a part in attention lapses. To this end, we propose a pipeline to annotate multimodal dataset of human attention tracking, including eye tracking, fixation detection, third-person surveillance camera, and sound. We produce a pilot dataset containing two fully annotated phone assembly sequences in a realistic manufacturing environment. We evaluate existing fatigue and drowsiness prediction methods for attention lapse detection. Experimental results show that human attention lapses in production scenarios are more subtle and imperceptible than well-studied fatigue and drowsiness. © 2023, CC BY.

关键词： Eye tracking

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Solving Facility Location Problems for Disaster Response Using Simheuristics and Survival Analysis: A Hybrid Modeling Approach

Solving Facility Location Problems for Disaster Response Usi...

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Simulation Winter Conference

作者： Bhakti Stephan Onggo Xabier Martin Javier Panadero Canan Gunes Corlu Angel A. Juan CORMSIS - Southampton Business School University of Southampton Southampton UK Department of Computer Science Universitat Oberta de Catalunya Barcelona SPAIN Metropolitan College Boston University Boston MA USA Dept. of Applied Statistics and Operations Research Universitat Politècnica de València Alcoy SPAIN

ISBN: (纸本)9781665476621

One of the important decisions for mitigating the risk from a sudden onset disaster is to determine the optimal location of relevant facilities (e.g., warehouses), because this affects the subsequent humanitarian operations. Researchers have proposed several methods to solve the facility location problem (FLP) in disaster management. This paper considers a stochastic FLP where the goal is to minimize the expected time required to provide service to all affected regions when travel times are stochastic due to uncertain road conditions. The number of facilities to open is constrained by a certain maximum budget. To solve this stochastic optimization problem, we propose a hybrid simulation optimization model that combines a simheuristic algorithm with a survival analysis method to evaluate the probability of meeting the demand of all affected areas within a time target. An experiment using a benchmark set shows our model outperforms deterministic solutions by about 8.9%.

关键词： Analytical models Roads Stochastic processes Disaster management Benchmark testing Optimization

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

arXiv

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arXiv 2025年

作者： Quijano, Andrew Halkidis, Spyros T. Gallagher, Kevin Akkaya, Kemal Samaras, Nikolaos Dept. of Computer Science and Engineering New York University New YorkNY United States Dept. of Applied Informatics University of Macedonia Thessaloniki Greece NOVA LINCS NOVA School of Science and Technology Lisbon Portugal Advanced Wireless and Security Lab Florida International University MiamiFL United States

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user’s input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Side channel attack

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A convolutional LSTM based residual network for deepfake video detection

arXiv

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arXiv 2020年

作者： Tariq, Shahroz Lee, Sangyup Woo, Simon S. Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Applied Data Science Sungkyunkwan University Suwon Korea Republic of

In recent years, deep learning-based video manipulation methods have become widely accessible to masses. With little to no effort, people can easily learn how to generate deepfake videos with only a few victims or target images. This creates a significant social problem for everyone whose photos are publicly available on the Internet, especially on social media websites. Several deep learning-based detection methods have been developed to identify these deepfakes. However, these methods lack generalizability, because they perform well only for a specific type of deepfake method. Therefore, those methods are not transferable to detect other deepfake methods. Also, they do not take advantage of the temporal information of the video. In this paper, we addressed these limitations. We developed a Convolutional LSTM based Residual Network (CLRNet), which takes a sequence of consecutive images as an input from a video to learn the temporal information that helps in detecting unnatural looking artifacts that are present between frames of deepfake videos. We also propose a transfer learning-based approach to generalize different deepfake methods. Through rigorous experimentations using the FaceForensics++ dataset, we showed that our method outperforms five of the previously proposed state-of-the-art deepfake detection methods by better generalizing at detecting different deepfake methods using the same model. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Raja Hashim Ali Muhammad Zeeshan Shabbir Zain Ul Abideen Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Muhammad Abu Bakar Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models.

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Asymptotically Optimal Blind Calibration of Acoustic Vector Sensor Uniform Linear Arrays

Asymptotically Optimal Blind Calibration of Acoustic Vector ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Amir Weiss Boaz Nadler Arie Yeredor Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science School of Electrical Engineering Tel-Aviv University

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

We study the blind calibration problem of uniform linear arrays of acoustic vector sensors for narrowband Gaussian signals, and propose an improved, asymptotically optimal blind calibration scheme. Following recent work by Ramamohan et al., we exploit the special (block-Toeplitz) structure of the underlying signals' spatial covariance matrix. However, we offer a substantial improvement over their ordinary Least Squares (LS)-based approach: Using asymptotic approximations we obtain Optimally-Weighted LS estimates of the sensors' gains and phases offsets. We show via simulations that our estimates exhibit near-optimal performance, with improvements reaching more than an order of magnitude in the mean squared estimation errors of the calibration parameters, as well as in directions of-arrival estimation.

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Experience Report on Key Success Factors for Promoting Students’ Engagement in Software Development Group Projects

Experience Report on Key Success Factors for Promoting Stude...

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IEEE World Engineering Education Conference (EDUNINE)

作者： Ruth Schorr Dept. of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany

ISBN: (数字)9781728166384

ISBN: (纸本)9781728166391

To teach a software development group project course can be a challenging task. Several large-scale surveys on students' engagement indicate that computer science rates lower than average in many of the major benchmarks. This paper presents a framework that consist of a variety of factors for setting up software development project courses for computer science bachelor students at Frankfurt University of applied sciences in their final year. Groups of 3 to 6 students have to develop an executable system for an existing realistic problem by going through all phases of the Software Development Life Cycle (SDLC). The paper presents the advantages and disadvantages as well as the dependencies between the various factors. Special emphasis is paid on the impact of these factors on students' work and learning motivation. The paper is concluded with a short review of the proposed teaching strategy and the results received.

关键词： Software computer science Buildings Task analysis Games Poles and towers Education

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Generating "who wants to be a millionaire?" Questions sets automatically from wikidata 15

Generating "who wants to be a millionaire?" Questions sets a...

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15th International Conference on Semantic Systems, SEMPDS 2019

作者： Wohlan, Markus Schröder, Yannik Höppner, Frank Ostfalia University of Applied Sciences Dept. of Computer Science WolfenbüttelD-38302 Germany

Quiz shows and apps have enjoyed great popularity in recent years, which increases the demand for fresh question sets. We investigate how to derive such sets automatically from the Wikidata knowledge graph. Utilizing the graph, node connectivity and diversity, we propose measures to identify appealing wrong answers and rate the difficulty of a question, which is a prerequisite to compose a full question set. First results align quite well with human perception of the questions. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Knowledge graph

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