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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sundaram, G. Meenakshi Eatesh, Nalluru Mourya Sai Karande, Manjiri Ulhas Patel, Warish Kannadasan, B. Patil, Harshal Aditya College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Surampalem533 437 India Computer Science and Business Systems R.M.K Engineering College Tamil Nadu Chennai601 206 India Padm. Dr. V. B. Kolte College of Engineering Department of Computer Science and Engineering Maharashtra Malkapur443 101 India Parul Institute of Engineering and Technology Parul University Post Limda Department of Computer Science and Engineering Gujarat Waghodia391760 India Arch. Engineering and Planning Kannadasan Balakrishnan Research Foundation Kalieswari Nagar Department of Civil Tamil Nadu Peraiyur625703 India Maharashtra Pune412 115 India

ISBN: (纸本)9798350343632

In the domain of autonomous vehicle systems, the accurate detection and tracking of moving objects, particularly vehicles, are of paramount importance for ensuring safe and efficient operations. Conventional methods based solely on image information face substantial challenges when confronted with complex and dynamic environments. To address these challenges, this research utilizes deep learning (DL) techniques for vehicle detection and tracking. The study draws upon data from the UA-DETRAC dataset, a valuable resource providing real-world scenarios for experimentation. For vehicle detection, DL models, including Faster Region Convolutional Neural Network (Faster R-CNN), You Only Look Once (YOLO), and Single Shot MultiBox Detector (SSD), are employed. These models are assessed using essential metrics like mAP (mean Average Precision), IoU (Intersection over Union), Precision, and Recall. When it comes to object tracking, the study delves into the capabilities of the SORT and DeepSORT algorithms, which represent advanced tracking solutions. Evaluation metrics like MOTA (Multiple Object Tracking Accuracy), IDF1 (Identification F1 score), MT (Mostly Tracked), and ML (Mostly Lost), offer a comprehensive view of tracking performance. The outcomes of this research indicate that YOLO stands out as the top performer in vehicle detection, while DeepSORT excels in the critical task of vehicle tracking. This study's findings contribute significantly to the advancement of AI techniques, enhancing the capabilities of autonomous systems for object detection and tracking in complex and dynamic real-world environments. © 2023 IEEE.

关键词： Deep Learning DeepSORT Object Detection Tracking UA-DETRAC dataset

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Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics

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IEEE Transactions on communications 2025年

作者： Yang, Zonghui Gao, Shijian Cheng, Xiang Yang, Liuqing Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Internet of Things Thrust Guangzhou511400 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Integrated sensing and communication (ISAC) technology is vital for vehicular networks, yet the time-varying communication channels and rapid movement of targets present significant challenges for real-time precoding design. Traditional optimization-based methods are computationally complex and strongly depend on perfect prior information, which is often unavailable in double-dynamic scenarios. In this paper, we propose a synesthesia of machine (SoM)-enhanced precoding paradigm that leverages modalities such as positioning and initial channel information to adapt to these dynamics. Utilizing a deep reinforcement learning (DRL) framework, our approach pushes ISAC performance boundaries. We also introduce a parameter-shared actor-critic architecture to accelerate training in complex state and action spaces. Extensive experiments validate the superiority of our method over existing approaches. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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torchosr — a PyTorch extension package for Open Set Recognition models evaluation in Python

arXiv

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arXiv 2023年

作者： Komorniczak, Joanna Ksieniewicz, Pawel Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

The article presents the torchosr package – a Python package compatible with PyTorch library – offering tools and methods dedicated to Open Set Recognition in Deep Neural Networks. The package offers two state-of-the-art methods in the field, a set of functions for handling base sets and generation of derived sets for the Open Set Recognition task (where some classes are considered unknown and used only in the testing process) and additional tools to handle datasets and methods. The main goal of the package proposal is to simplify and promote the correct experimental evaluation, where experiments are carried out on a large number of derivative sets with various Openness and class-to-category assignments. The authors hope that state-of-the-art methods available in the package will become a source of a correct and open-source implementation of the relevant solutions in the domain. © 2023, CC BY-NC-SA.

关键词： Python

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Automatic Detection of Learning Styles on OpenEdx Using Trace Analysis

Automatic Detection of Learning Styles on OpenEdx Using Trac...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Youness Chaabi Khadija Lekdioui Yahya Al-Ashmoery Center of Computer Studies Information and Communication Systems (CEISIC) Royal Institute of Amazigh Culture (IRCAM) Rabat Morocco SETIME Laboratory Faculty of Science ENSC Ibn Tofail University Kenitra Morocco Department of Information Technology Al-Razi University Department of Mathematics & Computer Faculty of Science Sana’a University Sana’a Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

In a learning management system (LMS), the tutor does not interact with the students in person to interpret their reactions and grimaces or to determine the level of course assimilation. As a result, analyzing learners' behavior and traces in an e-learning environment has become necessary for tutors or teachers to assist their students. Many researchers argue that learners' learning styles are an important factor to consider in LMS. These learning styles enable students to tailor their learning to their specific environment. Every learner has a unique learning style and way of perceiving, processing, retaining, and comprehending new information. This approach, which is based on the analysis of learners' interaction traces in LMS, gives teachers a sense of their students' behavior and identifies their learning styles. Our method has demonstrated an accuracy rate of 80% in detecting learning styles, significantly enhancing engagement and motivation. By tailoring content, activities, and assessments to match each student's preferences, we can adapt interventions dynamically.

关键词： Training Learning management systems Adaptation models Electronic learning Accuracy Computational modeling

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A Storage Optimization Scheme for Blockchain Transaction Databases

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computer systems science & Engineering 2021年第3期36卷 521-535页

作者： Jingyu Zhang Siqi Zhong Jin Wang Xiaofeng Yu Osama Alfarraj School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Systems Engineering National University of Defense TechnologyChangsha410000China School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China School of Business Nanjing UniversityNanjing210093China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia

As the typical peer-to-peer distributed networks, blockchain systemsrequire each node to copy a complete transaction database, so as to ensure newtransactions can by verified independently. In a blockchain system (e.g., bitcoinsystem), the node does not rely on any central organization, and every node keepsan entire copy of the transaction database. However, this feature determines thatthe size of blockchain transaction database is growing rapidly. Therefore, with thecontinuous system operations, the node memory also needs to be expanded tosupport the system running. Especially in the big data era, the increasing networktraffic will lead to faster transaction growth rate. This paper analyzes blockchaintransaction databases and proposes a storage optimization scheme. The proposedscheme divides blockchain transaction database into cold zone and hot zone usingexpiration recognition method based on Least Recently Used (LRU) algorithm. Itcan achieve storage optimization by moving unspent transaction outputs outsidethe in-memory transaction databases. We present the theoretical analysis on theoptimization method to validate the effectiveness. Extensive experiments showour proposed method outperforms the current mechanism for the blockchaintransaction databases.

关键词： Blockchain distributed systems transaction databases

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Stores are Liable for Their Robots!? An Empirical Study on Liability in HRI with an Anthropomorphic Frontline Service Robot

Stores are Liable for Their Robots!? An Empirical Study on L...

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IEEE International Workshop on Robot and Human communication (ROMAN)

作者： Philip Busch Jérôme Kirchhoff Judith S. Heinisch Klaus David Oskar von Stryk Janine Wendt Department of Law and Economics Civil and Company Law Group Technical University of Darmstadt Germany Department of Computer Science Simulation Systems Optimization and Robotics Group Technical University of Darmstadt Germany Department of Electrical Engineering and Computer Science Chair for Communication Technology University of Kassel Germany

Everyday life scenarios where non-expert users (e.g., customers) are confronted with frontline service robots will become more and more likely. In particular, misunderstandings and incidents may occur during these interactions because of wrong expectations of the robot’s capabilities. Current applicable laws are based on technological assumptions from prior decades unsuitable to modern robotics and AI. The new AI Act as a part of the solution to this is still in development. In addition to the pure legal view, a technological viewpoint may be beneficial for establishing a fitting, trustful, and, thus, acceptable technology liability law. This work contributes to this by empirically evaluating the service robot non-expert user’s liability expectations, the use of robots, and well-being. The results in a DIY store environment significantly show that the store deploying the robot should be liable if an incident happens. Further, we examined that even a minor simulated incident affected the participants’ emotions and moods. Consequently, this influences their perception of liability while not mitigating their acceptance of frontline service robots.

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation 7

Multi-Layer Perceptron Regression Model for Material Charact...

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7th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Hanima Kannan, C.H. Gupta, Deepa Mathur, Parul Kurup, Dhanesh G. Chaudhari, Tilesh Sanjay Mathur, Priyanka Amrita School of Engineering Amrita Vishwa Vidyapeetham Rf and Wireless Systems Laboratory Department of Electronics and Communication Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Indian Institute of Technology Creative and Advanced Research Based on Nanomaterials Laboratory Department of Chemical Engineering Hyderabad India School of Engineering and Applied Science Bennett University U.P. Greater Noida India

ISBN: (纸本)9798331531836

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an R2 of 99.991% and MSE of 6.65 × 10-5, with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions. © 2025 IEEE.

关键词： Linear regression

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Blind Asynchronous Goal-Oriented Detection for Massive Connectivity

Blind Asynchronous Goal-Oriented Detection for Massive Conne...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Sajad Daei Saeed Razavikia Marios Kountouris Mikael Skoglund Gabor Fodor Carlo Fischione School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Communication Systems Department EURECOM Sophia Antipolis France Ericsson Research Sweden

Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this paper, we present a novel random access scheme that addresses one of the most severe barriers of current strategies to achieve massive connectivity and ultra reliable and low latency communications for 6G. The proposed scheme utilizes wireless channels' angular continuous group-sparsity feature to provide low latency, high reliability, and massive access features in the face of limited time-bandwidth resources, asynchronous transmissions, and preamble errors. Specifically, a reconstruction-free goal oriented optimization problem is proposed which preserves the angular information of active devices and is then complemented by a clustering algorithm to assign active users to specific groups. This allows to identify active stationary devices according to their line of sight angles. Additionally, for mobile devices, an alternating minimization algorithm is proposed to recover their preamble, data, and channel gains simultaneously, enabling the identification of active mobile users. Simulation results show that the proposed algorithm provides excellent performance and supports a massive number of devices. Moreover, the performance of the proposed scheme is independent of the total number of devices, distinguishing it from other random access schemes. The proposed method provides a unified solution to meet the requirements of machine-type communications and ultra reliable and low latency communications, making it an important contribution to the emerging 6G networks.

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Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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IEEE Transactions on Fuzzy systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big Data Monitoring Production Real-time systems

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AfriBit: A People-Oriented Wallet Fostering Financial Inclusion in Kibera

AfriBit: A People-Oriented Wallet Fostering Financial Inclus...

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Blockchain, Robotics and AI for Networking Security Conference (BRAINS)

作者： Marco Leder Daria Schumm Bruno Rodrigues Burkhard Stiller Department of Informatics IfI Communication Systems Group CSG University of Zürich UZH Zürich Switzerland Institute of Computer Science in Vorarlberg ICV University St. Gallen HSG Dornbirn Austria

ISBN: (数字)9798350367843

ISBN: (纸本)9798350367850

For many Western countries, access to a functioning banking system is taken for granted. However, many people in developing countries are still excluded from having a bank account for savings and worldwide money transfers. In the region of Kenya, MPesa has established itself as a mobile financial money service. This service provides a possible solution to mitigate some of the issues of financial exclusion and offers the possibility of accessing mobile banking services. This paper presents AfriBit, a wallet that addresses financial exclusion in Kibera, improves the user experience (UX), facilitates cryptocurrencies adaptation, and provides a feasible alternative to MPesa. AfriBit is evaluated based on a 20-person user study, with a group from Kibera and a group from Switzerland. The study involves real-world scenarios to gather qualitative and quantitative data on the new design changes. Our results have shown that the AfriBit wallet makes it easier to understand the application and solve tasks with less effort.

关键词： Banking Developing countries User experience Cryptocurrency

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