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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Sahlu, Ameneshewa Abush Yohannes, Gobena Mulugeta Dawit, Bekalu Nigus Teshome, Molla Woretaw School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331519254

Mental stress poses significant health risks, manifesting in various psychological and physical issues such as depression, anxiety, and cardiovascular complications. Establishing a reliable method for swiftly and accurately classifying mental stress levels is crucial due to its widespread impact. Existing classification approaches predominantly rely on either data-intensive traditional machine learning algorithms or opaque deep learning models, hindering real-world use and interpretability. This study addresses these limitations by proposing a novel approach for stress level classification using the WESAD dataset. Our method combines an attention-based ResN et with CBAM. This innovative approach synergistically integrates shallow and deep features, harnessing both low-level details and high-level semantic information to effectively classify stress levels into four distinct states: baseline, stress, amusement, and meditation, or three states excluding the meditation state. The experimental result shows 92.74% accuracy and 91.19% F1 score for the 4-class setup, and 97.58% accuracy and 97.51 % F1 score for the 3-class setup. Our approach offers a more robust and interpretable framework for mental stress classification, with potential applications in health care and stress management. © 2024 IEEE.

关键词： Deep learning

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AI-Driven Virtual Mock Interview Development 13

AI-Driven Virtual Mock Interview Development

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mishra, Prabhat Kumar Arulappan, Arun Kumar Ra, In-Ho Thanga Mariappan, L. Gina Rose, G. Lee, Young-Seok School of Computer Science Engineering and Information Systems Vit University Vellore India School of Computer and Software Kunsan National University Gunsan Korea Republic of Anna University Department of Computer Technology Chennai India

ISBN: (纸本)9798350373332

The integration of Artificial Intelligence (AI) into educational technologies marks a significant shift in learning methodologies and operational dynamics within educational institutions. At the forefront is an AI-driven virtual mock interview platform designed to address the high Customer Acquisition Costs (CAC) in the edtech sector, especially for interview preparation services. This initiative harnesses a blend of AI technologies, including ADA 2 for creating context-aware embeddings and Machine Learning (ML), to transform the traditional mock interview process into a dynamic, cost-effective system. Central to the platform is its use of advanced Natural Language Processing (NLP) techniques and GPT-4 Large Language Model (LLM), automating the process of mock interviews and providing personalized feedback, ensuring a preparation journey that meets specific candidate needs and mirrors real interview scenarios. A key evaluation among 100 students from a cohort of 1800 demonstrated a 90% cost reduction for three mock interviews, reducing expenses from 3000 to just 300 per candidate. This cost efficiency significantly enhances access to quality interview preparation, improving student satisfaction and accessibility. Moreover, the platform provides valuable insights into student performance, setting a new standard in educational technology by offering an effective, personalized interview preparation experience. This project reflects a holistic approach to student development and the critical role of technology in addressing the evolving needs of learners. © 2024 IEEE.

关键词： Cost reduction

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Adaptive IoT Decision Making in Uncertain Environments 7

Adaptive IoT Decision Making in Uncertain Environments

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7th IEEE International Conference on Smart Internet of Things, SmartIoT 2023

作者： Wang, Yan Zheng, Liwei He, Jingjing Cui, Zhanqi Computer School Software Engineering Research Center Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350316575

The rapid growth of the Internet of Things (IoT) has led to widespread deployment of IoT systems in domains such as smart homes, healthcare, and transportation. However, IoT systems often operate under uncertainty, making it difficult to predict and control their behavior. In this paper, we propose an adaptive decision making approach for IoT systems in uncertain, dynamic environments. We present a framework with perception, decision and execution layers to handle uncertainty in IoT systems. The perception layer senses the environment and system state. The decision layer employs an optimized deep Q-network algorithm (Ad-DQN) specifically designed to handle uncertain environments, enabling it to make informed decisions based on learned experiences. The execution layer implements the actions. We demonstrate the framework on an intelligent air conditioning system as a case study of an IoT system operating under uncertainty. The Ad-DQN based decision layer adapts the air conditioning control policy to maximize comfort while minimizing energy usage. Experiments show our method outperforms traditional DQN method in uncertain environments. © 2023 IEEE.

关键词： Reinforcement learning

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Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images 41

Tabular Insights, Visual Impacts: Transferring Expertise fro...

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41st International Conference on Machine Learning, ICML 2024

作者： Jiang, Jun-Peng Ye, Han-Jia Wang, Leye Yang, Yang Jiang, Yuan Zhan, De-Chuan School of Artificial Intelligence Nanjing University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Ministry of Education & School of Computer Science Peking University Beijing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Transferring knowledge across diverse data modalities is receiving increasing attention in machine learning. This paper tackles the task of leveraging expert-derived, yet expensive, tabular data to enhance image-based predictions when tabular data is unavailable during inference. The primary challenges stem from the inherent complexity of accurately mapping diverse tabular data to visual contexts, coupled with the necessity to devise distinct strategies for numerical and categorical tabular attributes. We propose CHannel tAbulaR alignment with optiMal tranSport (CHARMS), which establishes an alignment between image channels and tabular attributes, enabling selective knowledge transfer that is pertinent to visual features. Specifically, CHARMS measures similarity distributions across modalities to effectively differentiate and transfer relevant tabular features, with a focus on morphological characteristics, enhancing the capabilities of visual classifiers. By maximizing the mutual information between image channels and tabular features, knowledge from both numerical and categorical tabular attributes are extracted. Experimental results demonstrate that CHARMS not only enhances the performance of image classifiers but also improves their interpretability by effectively utilizing tabular knowledge. Copyright 2024 by the author(s)

关键词： Knowledge transfer

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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SeeMe: An intelligent edge server selection method for location-aware business task computing over IIoT

SeeMe: An intelligent edge server selection method for locat...

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作者： Dou, Wanchun Liu, Bowen Duan, Jirun Dai, Fei Qi, Lianyong Xu, Xiaolong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China School of Computer Science Qufu Normal University Qufu China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

In the past few years, latency-sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self-driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across-domain business task computing on distributed edge servers. With this observation, a trade-off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low-latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low-latency edge server. The experiments and comparison analysis verify the availability of our method. © 2022 John Wiley & Sons Ltd.

关键词： Economic and social effects

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A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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WiHunter: Enabling Real-time Small Object Detection via Wireless Sensing 31

WiHunter: Enabling Real-time Small Object Detection via Wire...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zou, Xiang Liu, Jianwei Han, Jinsong Xi, Wei Wang, Zhi School of Computer Science and Technology Xi'an Jiaotong University China School of Cyber Science and Technology Zhejiang University China School of Software Engineering Xi'an Jiaotong University China

ISBN: (纸本)9798350399738

Rodent infestation is a great danger to human society, continuously threatening food safety and inducing disease spread. Existing methods to deal with rodent infestation are mainly based on passive bait traps and poisoning. These methods lack timeliness and effectiveness due to the missing of real-time detection. In this paper, we develop WiHunter, a new wireless sensing system to discover small objects (e.g., rat). Our idea is to exploit reflection signal effect of wireless channels induced by the movement of small objects around the receiver antenna. However, existing wireless sensing works usually employ customized or costly device-dependency Network Interface Cards(NIC), which are impractical to be densely deployed in reality. We implement WiHunter with several CSI-enabled standalone IoT nodes. We show how such devices enable moving small object detection via WiFi signal. The rationale behind this is 1) thanks to the widespread deployments of WiFi infrastructures and IoT devices, the WiFi signal covers almost every location of the corner, 2) the signal amplitude of each device is related to the small object near the receiver antenna. This ability gives us the opportunity to sense object as small as a rat. We implement WiHunter with ESP32 microcontroller on Espressif IoT Development Framework (both of them are cheap commodity off-the-shelf (COTS) devices) and design a practical small object intrusion detection system. Comprehensive and real-world experiments demonstrate that our system is effective in detecting the presence of small objects with an average accuracy of 92.1%. © 2023 IEEE.

关键词： Rats

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Momentum Posterior Regularization for Multi-hop Dense Retrieval 31

Momentum Posterior Regularization for Multi-hop Dense Retrie...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xia, Zehua Wu, Yuyang Xia, Yiyun Nguyen, Cam-Tu State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science Nanjing University China School of Artificial Intelligence Nanjing University China

ISBN: (纸本)9798891761964

Multi-hop question answering (QA) often requires sequential retrieval (multi-hop retrieval), where each hop retrieves missing knowledge based on information from previous hops. To facilitate more effective retrieval, we aim to distill knowledge from a posterior retrieval, which has access to posterior information like an answer, into a prior retrieval used during inference when such information is unavailable. Unfortunately, current methods for knowledge distillation in one-time retrieval are ineffective for multi-hop QA due to two issues: 1) Posterior information is often defined as the response (i.e. the answer), which may not clearly connect to the query without intermediate retrieval;and 2) The large knowledge gap between prior and posterior retrievals makes existing distillation methods unstable, even resulting in performance loss. As such, we propose MoPo (Momentum Posterior Regularization) with two key innovations: 1) Posterior information of one hop is defined as a query-focus summary from the golden knowledge of the previous and current hops;2) We develop an effective training strategy where the posterior retrieval is updated along with the prior retrieval via momentum moving average method, allowing smoother and effective distillation. Experiments on HotpotQA and StrategyQA demonstrate that MoPo outperforms existing baselines in both retrieval and downstream QA tasks. © 2025 Association for Computational Linguistics.

关键词： Question answering

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A Novel Deep Learning Model Architecture Design that Improves Skin Lesion Diagnosis 24

A Novel Deep Learning Model Architecture Design that Improve...

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8th International Conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Lei, Gaoming Zhao, Jian Li, Ning Mei, Fengcheng Qin, Wei Liang, Lixin College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen China School of Computer Science and Software Shenzhen Institute of Information Technology Guangdong Shenzhen China

ISBN: (纸本)9798400710094

Mainstream deep learning models do not perform well in skin lesion image classification for skin disease diagnosis due to multiple factors, such as noise and illumination intensity variations of skin lesion images, similarity of lesion features, morphological diversity, small volume of datasets and unbalanced image samples. In order to improve the diagnosis performance, in this paper, we first preprocess skin lesion images through a new color constant algorithm to mitigate their illumination intensity variations. Then, we apply a novel deep learning model, which combines multi-scale convolution and attention mechanisms. In the model, stacked multi-scale convolution are used to obtain local and global features of lesion images. Both local and global features are fused as input of the flatten transformer shift window. This approach expands local receptive field of the shift window, focuses more on local and global lesion feature areas of a skin lesion and enhances relationship among local and global features. Extensive experiments demonstrate the efficacy of our method. Compared with other models, our proposed model pays more attention to features in the skin lesion areas. The overall accuracy rate, specificity, and sensitivity have increased by 2.91%, 0.58%, and 4.02% respectively. The overall F1-Score has reached the state-of-the-art value of 0.8841. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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