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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xin Wang Wordh Ul Hasan Juan Li Rasha Hendawi Yang Du Bo Xie Cui Tao Department of Computer Science North Dakota State University Fargo USA Department of Computer Systems and Software Engineering Valley City State University Valley City USA School of Nursing The University of Texas at Austin Austin USA School of Biomedical Informatics University of Texas Health Science Center at Houston Houston USA

The proliferation of digital health technologies has led to an abundance of personal health data. However, querying and retrieving specific health-related information from disparate sources can be challenging and inconvenient, particularly for older individuals unfamiliar with technology. While ChatGPT offers a conversational interface, it lacks domain-specific knowledge, including personalized health information. To address this limitation, we present a novel approach that combines a knowledge graph and GPT to enable personalized health queries. Our solution utilizes a personal knowledge graph as a comprehensive knowledge source and fine-tunes GPT to provide accurate responses. We have implemented a voice assistant mobile app incorporating this knowledge graph-assisted GPT model and conducted initial feasibility testing.

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Deep Reinforcement Learning for Sleep Control in 5G and Beyond Radio Access Networks: An Overview

Deep Reinforcement Learning for Sleep Control in 5G and Beyo...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Nessrine Trabelsi Rihab Maaloul Lamia Chaari Fourati Wael Jaafar Laboratory of Signals systeMs aRtificial intelligence and neTworkS – Digital Research Center of Sfax Sfax Tunisia Higher Institute of Computer Science of Mahdia (ISIMa) University of Monastir Tunisia Higher Institute of Computer Science and Multimedia in Sfax (ISIMS) University of Sfax Tunisia Department of Software and IT Engineering École de technologies supérieure (ÉTS) Montreal Canada

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The advent of 5G and beyond networks is envisioned to support lower latency, higher data rates, and wider connectivity than previous cellular network generations. However, given the denser deployment of base stations (BSs) to accommodate such improvements, this results inevitably in a significant and unsustainable increase in the network’s energy consumption. Sleep Control (SC), which allows switching off some BS hardware components during light-traffic time, is considered a viable solution for greener and more energy-efficient Radio Access Networks (RAN). However, the optimization of SC is a highly challenging large-scale network combinatorial problem that depends on dynamic wireless channel conditions and varying traffic demands with stringent Quality-of-Service (QoS) requirements. Driven by the benefits and efficiency of Deep Reinforcement Learning (DRL), which has been successfully applied to multiple wireless network optimization problems, this paper investigates DRL approaches addressing sleep control in 5G and beyond RAN. To this end, we propose a taxonomy to classify the related literature. Then, we provide an overview of the different components of the Markov Decision Process (MDP) modeling the sequential decision-making of sleep control and the applied DRL algorithms. Finally, we highlight the main challenges in existing works and suggest novel strategies to address them.

关键词： 5G mobile communication Wireless networks Taxonomy Decision making Quality of service Switches Deep reinforcement learning

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on computer Aided systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications

arXiv

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arXiv 2023年

作者： Chu, Nam H. Van Huynh, Nguyen Nguyen, Diep N. Hoang, Dinh Thai Gong, Shimin Shu, Tao Dutkiewicz, Eryk Phan, Khoa T. School of Electrical and Data Engineering University of Technology Sydney Australia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology La Trobe University Melbourne Australia

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts. The first part, i.e., the active-transmit message, is transmitted by the transmitter using conventional RF signals. Simultaneously, the second part, i.e., the backscatter message, is transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Backscattering

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Develop a Lightweight MEC Platform Simulator

Develop a Lightweight MEC Platform Simulator

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International Russian Automation Conference (RusAutoCon)

作者： Ivan Filianin Vladimir Bogatyrev Matvey Lesonen Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia Department of Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia

This paper reviews the use of Minikube in multi-node mode to work with the ETSI MEC Sandbox as a lightweight multi-access edge computing platform simulator (LWMECPS). The work included a comparative analysis of MEC platforms in terms of functionality, cost of deployment, and complexity of adoption. An experiment was also conducted to test LWMECPS as a MEC platform using a Python3 test application. The experiment confirmed the feasibility of using LWMECPS as a MEC platform by deploying a Kubernetes deployment based on the number of users on the simulated 4g-macro network service zones from the ETSI MEC Sandbox. Particular attention was paid to the analysis of existing scientific literature on the use of multi-access edge computing platforms in various use cases.

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Data-Driven Building Occupancy Profile Identification and Application for Ventilation Operating Schedule Optimization

SSRN

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SSRN 2023年

作者： Vassiljeva, Kristina Matson, Margarita Ferrantelli, Andrea Petlenkov, Eduard Thalfeldt, Martin Belikov, Juri Tallinn University of Technology Tallinn19086 Estonia Department of Computer Systems Tallinn University of Technology Tallinn12618 Estonia Department of Software Science Tallinn University of Technology Tallinn12618 Estonia Department of Civil Engineering and Architecture Tallinn University of Technology Tallinn19086 Estonia Department of Civil Engineering Aalto University Aalto00076 Finland

Facing the current sustainability challenges requires reduction of the building stock energy consumption towards the European Green Deal targets by 2050. This can be accomplished by adopting techniques such as fault detection and diagnosis, and efficiency optimization. An occupancy-based algorithm for scheduling ventilation operations in buildings is here developed, aimed at optimizing the system’s operation according to occupancy profiles while maintaining a comfortable indoor climate. By relying only on electricity meters without using carbon dioxide or occupancy sensors, we use historical data of an Estonian school to develop a DBSCAN-based clustering algorithm that generates consumption profiles. A novel occupancy estimation algorithm, based on threshold and time series methods, then creates 12 occupancy schedules that are either based on classical detection with on-off method or on occupancy estimation for demand-controlled ventilation (DCV). We find that, compared to standard on-off scheduling, DCV-based occupancy estimation induces systematic savings in terms of both energy and costs from 18.1% up to 62.6%, still complying with current national regulations of indoor air quality. Remarkably, as our method relies only on occupancy schedules, thus eliminating reliance on weather and other external factors, it can immediately be extended to other countries. © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Designing a Broadband MIMO Antenna for Next Generation Wireless Communication systems

Designing a Broadband MIMO Antenna for Next Generation Wirel...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Sarosh Ahmad Wael Jaafar Halim Yanikomeroglu department of Signal Theory Communications of Universidad Carlos III de Madrid Spain department of Software Information Technology Engineering of École de Technologie Supéerieure QC Canada department of Systems Computer Engineering of Carleton University ON Canada

ISBN: (纸本)9781665435413

Within the last few years, the multiple-input multiple-output (MIMO) technology has been developing to cope with the stringent 5G and beyond requirements, in terms of high throughput, energy efficiency, and coverage. With the rapid development and miniaturization of Internet-of-Things (IoT) devices, there is an urgent need to design compact-size, low-cost and efficient MIMO antennas for IoT. In this context, we propose here the design of a novel four-element wideband MIMO antenna, supplied using the coplanar waveguide (CPW) technology. The design covers a wide spectrum band between 1.5 and 5.5 GHz, with a total size of 80 × 80 × 1.6 mm 3 , which is suitable for small IoT devices. The designed MIMO antenna achieves a diversity gain above 9.9 and an envelope correlation coefficient lower than 0.016. Its mutual coupling among any pair of elements is lower than -15 dB, while the group delay demonstrates stability for the targeted band of operation. Hence, the proposed MIMO antenna has a great potential for future wireless communication systems, especially for the sub-6 GHz portable and IoT devices.

关键词： Wireless communication Correlation coefficient Mutual coupling Diversity methods Coplanar waveguides Internet of Things Broadband antennas

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Simulating the Social Influence in Transport Mode Choices

Simulating the Social Influence in Transport Mode Choices

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Simulation Winter Conference

作者： Kathleen Salazar-Serna Lynnette Hui Xian Ng Kathleen Carley Lorena Cadavid Carlos Jaime Franco Department of Civil and Industrial Engineering Pontificia Universidad Javeriana Cali Valle del Cauca Colombia Software and Societal Systems Carnegie Mellon University Pittsburgh PA USA Department of Computer and Decision Sciences Universidad Nacional de Colombia Medellín Antioquia Colombia

Agent-based simulations have been used in modeling transportation systems for traffic management and passenger flows. In this work, we hope to shed light on the complex factors that influence transportation mode decisions within developing countries, using Colombia as a case study. We model an ecosystem of human agents that decide at each time step on the mode of transportation they would take to work. Their decision is based on a combination of their personal satisfaction with the journey they had just taken, which is evaluated across a personal vector of needs, the information they crowdsource from their prevailing social network, and their personal uncertainty about the experience of trying a new transport solution. We simulate different network structures to analyze the social influence for different decision-makers. We find that in low/medium connected groups inquisitive people actively change modes cyclically over the years while imitators cluster rapidly and change less frequently.

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EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration

arXiv

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arXiv 2022年

作者： Sipper, Moshe Halperin, Tomer Tzruia, Itai Elyasaf, Achiya Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under the BSD 3-Clause License, and compatible with scikit-learn. Designed with modern software engineering and machine learning integration in mind, EC-KitY can support all popular EC paradigms, including genetic algorithms, genetic programming, coevolution, evolutionary multi-objective optimization, and more. This paper provides an overview of the package, including the ease of setting up an EC experiment, the architecture, the main features, and a comparison with other libraries. © 2022, CC BY.

关键词： Machine learning

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Information Technology for Contact Tracking in the COVID-19 Pandemic 8

Information Technology for Contact Tracking in the COVID-19 ...

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8th IEEE International Conference on Problems of Infocommunications, Science and Technology, PIC S and T 2021

作者： Kuznetsov, Alexandr Gorbenko, Yuriy Kiian, Anastasiia Kuznetsova, Tetiana Ulianovska, Yuliia Rudyanova, Tatyana V. N. Karazin Kharkiv National University Department of Information Systems And Technologies Security Kharkiv Ukraine Jsc 'Institute of Information Technologies' Kharkiv Ukraine University of Customs And Finance Department of Computer Science And Software Engineering Dnipro Ukraine

ISBN: (纸本)9781665406826

The development and implementation of the latest information technologies for contact tracking in the context of the COVID-19 pandemic is an extremely important and urgent task, which is directly related to the possible adjustment and mitigation of quarantine restrictions without increasing the risks and threats of infection. In this article, we analyze, research, and substantiate various information technologies (such as Bluetooth, Wi-Fi, GPS, etc.) in contact tracking tools to prevent the spread of infectious diseases. We explore various aspects such as centralization;the need to use a central server (data warehouse);used cryptographic protection algorithms;acceptable actions when establishing contact, etc. The research results make it possible to formulate practical recommendations on the choice of information technologies for building global contact tracing systems. © 2021 IEEE.

关键词： Data warehouses

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