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arXiv 2024年

作者： Wang, Xuebin Liu, Chunxiuzi Zhao, Meng Zhang, Ke Di, Zengru Liu, He Department of Systems Science Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China International Academic Center of Complex Systems Beijing Normal University Zhuhai519087 China School of Systems Science Beijing Normal University Beijing100875 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

This study introduces an artificial neural network (ANN) for image classification task, inspired by the aversive olfactory learning circuits of the nematode Caenorhabditis elegans (C. elegans). Despite the remarkable performance of ANNs in a variety of tasks, they face challenges such as excessive parameterization, high training costs and limited generalization capabilities. C. elegans, with its simple nervous system comprising only 302 neurons, serves as a paradigm in neurobiological research and is capable of complex behaviors including learning. This research identifies key neural circuits associated with aversive olfactory learning in C. elegans through behavioral experiments and high-throughput gene sequencing, translating them into an image classification ANN architecture. Additionally, two other image classification ANNs with distinct architectures were constructed for comparative performance analysis to highlight the advantages of bio-inspired design. The results indicate that the ANN inspired by the aversive olfactory learning circuits of C. elegans achieves higher accuracy, better consistency and faster convergence rates in image classification task, especially when tackling more complex classification challenges. This study not only showcases the potential of bio-inspired design in enhancing ANN capabilities but also provides a novel perspective and methodology for future ANN design. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Joint Message Detection, Channel, and User Position Estimation for Unsourced Random Access in Cell-Free Networks

Joint Message Detection, Channel, and User Position Estimati...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Eleni Gkiouzepi Burak Çakmak Manfred Opper Giuseppe Caire Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany Institute of Mathematics University of Potsdam Germany Centre for Systems Modeling and Quantitative Biomedicine University of Birmingham United Kingdom

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

We consider unsourced random access (uRA) in user-centric cell-free (CF) wireless networks, where random access users send codewords from a common codebook during specifically dedicated random access channel (RACH) slots. The system is conceptually similar to the so-called 2-step RACH currently discussed in 3GPP standardization. In order to cope with the distributed and CF nature of the network, we propose to partition the network coverage area into zones (referred to as "locations") and assign an uRA codebook to each location, such that users in a certain location make use of the associated codebook. The centralized uRA decoder makes use of the multisource AMP algorithm recently proposed by the authors. This yields at once the list of active uRA codewords, an estimate of the corresponding channel vectors, and an estimate of the active users’ position. We show excellent performance of this approach and perfect agreement with the rigorous theoretical "state evolution" analysis. We also show that the proposed "location-based" partitioned codebook approach significantly outperforms a baseline system with a single non-partitioned uRA codebook.

关键词： Wireless networks Message passing Channel estimation Signal processing algorithms Estimation Standardization Signal processing Vectors Partitioning algorithms Decoding

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Research on Distributed Machine Learning Model for Predicting Users’ Interest by Acquired Web Contents Similarity

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SN computer science 2023年第6期4卷 1-10页

作者： Tsuchiya, Takeshi Misawa, Rika Mochizuki, Ryuichi Hirose, Hiroo Yamada, Tetsuyasu Yamamoto, Yoshito Ichikawa, Hiroshi Minh, Quang Tran Tokyo International University Tokyo Japan Suwa University of Science Nagano Japan Tokyo University of Science Tokyo Japan Otsuma Women’s University Tokyo Japan Department of Information Systems Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology (HCMUT) Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City (VNU-HCM) Ho Chi Minh City Viet Nam

This paper discusses and proposes a method for predicting and analyzing the current user interests based on their characteristics including their own recently acquired web content, and it is used. The traditional methods of using cookies on browsers and capturing users’ web activities cannot be used in the future for privacy reasons. It is difficult for anyone other than the users themselves to obtain accurate web activities. Thus, all methods based on the identification of individual users, such as user similarity comparisons between users, are no longer available. This paper focuses on the web content information obtained by users of each web service. Assuming that the web content acquired by each user contains his or her current interests at that point in time, a combinable machine learning model named the fog model is acquired from web content that is similar to these features. The fog models are then combined in the same manner along the most recent web activities. The combined fog models include the features of each web service, which can be considered to be equivalent to the features obtained by accumulating and analyzing the entire content. As an evaluation of the proposed method, the performance of the proposed method is compared to that of the conventional method using cookies on the data from targeted advertisements. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Combinations of content characteristics Distributed machine learning Fog computing Users’ interest

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Predictive Modeling of Portuguese Student Performance: Comparative Machine Learning Analysis

Predictive Modeling of Portuguese Student Performance: Compa...

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Telecommunications Conference (ITC-Egypt), International

作者： Faris H. Rizk Mahmoud Elshabrawy Mohamed Basant Sameh Ahmed Mohamed Zaki Marwa M. Eid El-Sayed M. El-kenawy Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectations of learners' results for education system administrations and respective policymakers. The current study makes use of the “Student Performance in Portuguese Secondary education” dataset and employs machine learning algorithms, namely MLPRegressor, XGBoost, DecisionTreeRegressor, CatBoost, and KNeighborsRe-gressor, to the corpus. Performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc., are used to judge every model's performance. The conclusion that can be drawn from the data is that the MLPRegressor model leads among the competitors, having an MSE equivalent of 0.0103, which is superior to others. The findings of this study are of great significance for educational institutions and policymakers as they work to make appropriate contact with students' performance prediction.

关键词： Measurement Machine learning algorithms Education Machine learning Predictive models Market research Data models

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Analog, Programmable Switched Capacitor FIR Filter Based on Rotator Architecture Implemented in CMOS Technology

Analog, Programmable Switched Capacitor FIR Filter Based on ...

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Paweł Pawłowski Rafał Długosz Marek Radkowski Mateusz Wątły Adam Dąbrowski Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Signal Processing and Electronic Systems Poznań University of Technology Poznań Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents project and its verification of a prototype integrated circuit containing an analog, programmable finite impulse response (FIR) filter, implemented in CMOS 350 nm technology. The structure of the filter is based on the switched capacitor technique. In circuits of this type, one of main challenges is an efficient implementation of filter coefficients, which result from several factors described in this work. When implementing such filters as programmable circuits, the values of their coefficients have to be limited to a selected range, i.e. a given resolution in bits. In the implemented prototype filter, the filter coefficients are represented by 6 bits in sign-magnitude notation, so they can take 63 different values only. In such filters, it is not possible to directly implement any frequency response of the filter. Each time, it is necessary to properly round the theoretical values of the coefficients so that they fit into the available range of discrete values resulting from the implementation. The authors of the work designed an algorithm that allows such matching. The paper also presents results of measurements of the prototype chip.

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Joint Message Detection, Channel, and User Position Estimation for Unsourced Random Access in Cell-Free Networks

arXiv

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arXiv 2024年

作者： Gkiouzepi, Eleni Çakmak, Burak Opper, Manfred Caire, Giuseppe Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany Institute of Mathematics University of Potsdam Germany Centre for Systems Modeling and Quantitative Biomedicine University of Birmingham United Kingdom

We consider unsourced random access (uRA) in usercentric cell-free (CF) wireless networks, where random access users send codewords from a common codebook during specifically dedicated random access channel (RACH) slots. The system is conceptually similar to the so-called 2-step RACH currently discussed in 3GPP standardization. In order to cope with the distributed and CF nature of the network, we propose to partition the network coverage area into zones (referred to as "locations") and assign an uRA codebook to each location, such that users in a certain location make use of the associated codebook. The centralized uRA decoder makes use of the multisource AMP algorithm recently proposed by the authors. This yields at once the list of active uRA codewords, an estimate of the corresponding channel vectors, and an estimate of the active users' position. We show excellent performance of this approach and perfect agreement with the rigorous theoretical "state evolution" analysis. We also show that the proposed "location-based" partitioned codebook approach significantly outperforms a baseline system with a single non-partitioned uRA codebook. © 2024, CC BY.

关键词： Network coding

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Research on Conceptual Model of the Remote Sensing Big Data Workflow 5

Research on Conceptual Model of the Remote Sensing Big Data ...

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5th International Conference on E-Business, Information Management and computer science, EBIMCS 2022

作者： Du, Ying Zhang, Shuo Kaabar, Mohammed K.A. Yue, Xiao-Guang School of Computer and Information Engineering Henan University Kaifeng475004 China Institute of Mathematical Sciences Faculty of Science University Malaya Kuala Lumpur50603 Malaysia Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus Department of Information and Knowledge Management School of Consumer Intelligence and Information Systems University of Johannesburg Apk Campus Johannesburg2006 South Africa

ISBN: (纸本)9781450397834

Remote sensing big data workflows are formed by combining workflow and the remote sensing data processing system, and have the capabilities of remote sensing data processing, analysis, and visualization. There are many issues such as numerous sensors, sophisticated processing algorithms, and lacking of a unified process management about the operation control of the remote sensing data processing system. Therefore, the concept and characteristics of remote sensing big data workflows is reanalysed and the metamodel of remote sensing big data workflows from different aspects is constructed in order to establish a conceptual framework of them, and thus establish concept framework of Remote Sensing Big Data Workflow. © 2022 ACM.

关键词： Remote sensing

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Perception Gaps Between Hashtag Stakeholders on X (Twitter) - Application on the Australian National Disability Insurance Scheme (#NDIS)

Perception Gaps Between Hashtag Stakeholders on X (Twitter) ...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Yu Zhang Hang Thanh Bui Michael Zipperle Fiona Buick Satish Chand Omar Hussain Min Wang Huadong Mo Wenjie Zhang School of Business UNSW Canberra Canberra Australia Faculty of Science and Technology University of Canberra Canberra Australia School of Systems & Computing UNSW Canberra Canberra Australia School of Computer Science and Engineering UNSW Sydney Sydney Australia

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

A hashtag on X (Twitter) can attract a range of stakeholders with diverse perceptions on the hashtag. Under-standing and considering the differences between these stake-holders' perceptions on specific hashtags can be highly valuable for policy making in both public and private sectors. This paper proposes a framework to analyse the depth and width of the perception gap between stakeholders of a hashtag using sentiment analysis and zero-shot learning on large scale Twitter data. The framework was applied to the hashtag “#NDIS”, the National Disability Insurance Scheme, Australia's pilot personalised disability service which provides funding to support people with disability. Over 155,000 tweets were collected and analysed to identify and assess the perception gap between the two most significant stakeholders (NDIS participants and service providers) on six main topics during the implementation of the NDIS. The study verified the effectiveness of the proposed framework, revealing a widespread perception gap between the two NDIS stakeholders, as well as differences in their online behaviours using Twitter. The results suggest that the NDIS policymakers and service providers should pay attention to the utilisation of social media, including hearing voice from the broader public and creating channels for transparent engagement with other NDIS stakeholders.

关键词： Crowdsourcing Sentiment analysis Social networking (online) Large language models Blogs Zero shot learning Insurance Oral communication People with disabilities Stakeholders

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Exploring the Potential of LLM-based Chatbots for Task Scheduling in Robot Operations

Exploring the Potential of LLM-based Chatbots for Task Sched...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Catarina Rema Armando Sousa Heber Sobreira Pedro Costa Manuel F. Silva CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

The rise of Industry 4.0 has revolutionized manufacturing by integrating real-time data analysis, artificial intelligence (AI), automation, and interconnected systems, enabling adaptive and resilient smart factories. Autonomous Mobile Robots (AMRs), with their advanced mobility and navigation capabilities, are a pillar of this transformation. However, their deployment in job shop environments adds complexity to the already challenging Job Shop Scheduling Problem (JSSP), expanding it to include task allocation, robot scheduling, and travel time optimization, creating a multi-faceted, non-deterministic polynomial-time hardness (NP-hard) problem. Traditional approaches such as heuristics, meta-heuristics, and mixed integer linear programming (MILP) are commonly used. Recent AI advancements, particularly large language models (LLM), have shown potential in addressing these scheduling challenges due to significant improvements in reasoning and decision-making from textual data. This paper examines the application of LLM to tackle scheduling complexities in smart job shops with mobile robots. Guided by tailored prompts inserted manually, LLM are employed to generate scheduling solutions, being these compared to an heuristic-based method. The results indicate that LLM currently have limitations in solving complex combinatorial problems, such as task scheduling with mobile robots. Due to issues with consistency and repeatability, they are not yet reliable enough for practical implementation in industrial environments. However, they offer a promising foundation for augmenting traditional approaches in the future.

关键词： Job shop scheduling Service robots Large language models Chatbots Real-time systems Mobile robots Resource management Reliability Autonomous robots Smart manufacturing

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Impact of COVID-19 on Predicting 2020 US Presidential Elections on Social Media 16th

Impact of COVID-19 on Predicting 2020 US Presidential Electi...

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16th International Conference on Information Technology and Applications, ICITA 2022

作者： Khan, Asif Zhang, Huaping Boudjellal, Nada Hayat, Bashir Dai, Lin Ahmad, Arshad Al-Hamed, Ahmed School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Faculty of New Information and Communication Technologies University Abdelhamid Mehri Constantine 2 Constantine25000 Algeria Institute of Management Sciences Peshawar Peshawar Pakistan Institute of Software Systems Engineering Johannes Kepler University Linz4040 Austria Department of IT and Computer Science Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Khanpur Road Haripur22620 Pakistan

ISBN: (纸本)9789811993305

By the beginning of 2020, the world woke up to a global pandemic that changed people’s everyday lives and restrained their physical contact. During those times Social Media Platforms (SMPs) were almost the only mean of individual-to-individual and government-to-individuals communications. Therefore, people’s opinions were more expressed on SM. On the other hand, election candidates used SM to promote themselves and engage with voters. In this study, we investigate how COVID-19 affected voters’ opinions through the months of the US presidential campaign and eventually predict the 2020 US Presidential Election results using Twitter’s data. Mainly two types of experiments were conducted and compared;(i) transformer-based, and (ii) rule-based sentiment analysis (SA). In addition, vote shares for the presidential candidates using both approaches were predicted. The results show that the rule-based approach nearly predicts the right winner, Joe Biden with MAE 2.1, outperforming the predicted results from CNBC, Economist/YouGov, and transformer-based (BERTweet) approach, except for RCP (MAE 1.55). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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