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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Chniter, Malak Abid, Abir Kallel, Ilhem Kanoun, Slim Mir@cl Lab: Multimedia InfoRmation Systems and Advanced Computing Laboratory University of Sfax ReGIM-Lab: Research Groups in Intelligent Machines Tunisia

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The task of assessing, grouping and arranging data into meaningful groups or clusters based on their similarities/dissimilarities measures known as cluster analysis. Thereby, there are numerous clustering algorithms: hierarchical and partitional. In the last decade, clustering using bio-inspired algorithms received more attention, specifically the ant clustering algorithms. Regardless, they have required a lot of processing power due to the massive amount of data that has been generated during the last years. As a consequence, determining the computational cost of these algorithms is one of the most interesting tasks in the quest for optimal clustering solutions in a real-time system. This study presents a research guide for the researchers working in the same field. A series of experiments are elaborated to investigate the computational complexity of the most promising algorithms applied to students grouping problem. The results indicate two challenges that arise when using ant clustering algorithms: the difficulty in adjusting parameters and extended computation time. © 2022 IEEE.

关键词： Computational complexity

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Adaptive Time Slot Resource Allocation in SWIPT IoT Networks

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2787-2813页

作者： Yunong Yang Yuexia Zhang Zhihai Zhuo Key Laboratory of Information and Communication Systems Ministry of Information IndustryBeijing Information Science and Technology UniversityBeijing100101China Key Laboratory of ModernMeasurement&Control Technology Ministry of EducationBeijing Information Science&Technology UniversityBeijing100101China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100101China

The rapid advancement of Internet of Things(IoT)technology has brought convenience to people’s lives;however further development of IoT faces serious challenges,such as limited energy and shortage of network spectrum *** address the above challenges,this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation(SIATS)***,an adaptive time slot consisting of periods for sensing,information transmission,and energy harvesting is designed to ensure that the minimum energy harvesting requirement ismet while the maximumuplink and downlink throughputs are ***,the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods,and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is *** results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4%compared with that of the fixed time slot allocation(FTS)*** the case of a large noise variance,the SIATS algorithm has good noise immunity,and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7%compared with that obtained using the FTS algorithm.

关键词： Internet of Things simultaneous wireless information and power transfer adaptive time slot resource allocation

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Bridging the Gap using Contrastive Learning and Semantic Consistency for Unsupervised Neural Machine Translation 5

Bridging the Gap using Contrastive Learning and Semantic Con...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Zhang, Chuancai Qu, Dan Du, Liming Yang, Kaiyuan School of Information Systems Engineering University of Information Engineering Zhengzhou China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou China School of Cyberspace Security Zhengzhou University Zhengzhou China

ISBN: (纸本)9798331528911

In Unsupervised Neural Machine Translation (UNMT) tasks, the lack of extensive parallel corpora makes it challenging for the model to directly optimize the correspondence between the source and target languages. UNMT models primarily rely on learning from monolingual data, making it difficult to fully capture cross-linguistic semantic alignment and consistency during training. Therefore, effectively enhancing semantic alignment between the source and target languages under unsupervised conditions has become a key challenge in the field of UNMT. To address this issue, this study proposes a semantic alignment and consistency enhancement mechanism based on contrastive learning, aiming to improve the model's ability to capture cross-lingual semantic differences by constructing an optimization objective focused on semantic consistency. The method utilizes a pretrained cross-lingual model to generate semantic embeddings for both the source and target languages, then designs a contrastive learning-based loss function that minimizes the semantic distance between correct translations and source sentences while increasing the semantic distance between incorrect translations and source sentences, thereby enhancing the model's performance in semantic consistency. The experiments validate the effectiveness and broad applicability of the semantic consistency optimization method based on contrastive learning, demonstrating great potential and advantages, especially in multilingual tasks of UNMT. The model achieved significant improvements in BLEU scores across various language pairs. © 2024 IEEE.

关键词： Contrastive Learning

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Electromagnetic metamaterial agent

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Light(Science & Applications) 2025年第1期14卷 118-130页

作者： Shengguo Hu Mingyi Li Jiawen Xu Hongrui Zhang Shanghang Zhang Tie Jun Cui Philipp del Hougne Lianlin Li State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing100871China National Key Laboratory for Multimedia Information Processing School of Computer SciencePeking UniversityBeijing100871China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Pazhou Laboratory(Huangpu) GuangzhouGuangdong510555China Univ Rennes CNRSIETR-UMR 6164F-35000RennesFrance

Metamaterials have revolutionized wave control;in the last two decades,they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified *** deep-learning techniques play an increasingly important role in metamaterial inverse design,measurement post-processing and end-to-end optimization,their role is ultimately still limited to approximating specific mathematical relations;the metamaterial is still limited to serving as proxy of a human operator,realizing a predefined ***,we propose and experimentally prototype a paradigm shift toward a metamaterial agent(coined metaAgent)endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks,including electromagnetic(EM)field manipulations and interactions with robots and *** recently released foundation models,metaAgent reasons in high-level natural language,acting upon diverse prompts from an evolving complex ***,metaAgent's cerebrum performs high-level task planning in natural language via a multi-agent discussion mechanism,where agents are domain experts in sensing,planning,grounding,and *** response to live environmental feedback within a real-world setting emulating an ambient-assisted living context(including human requests in natural language),our metaAgent prototype self-organizes a hierarchy of EM manipulation tasks in conjunction with commanding a *** masters foundational EM manipulation skills related to wireless communications and sensing,and it memorizes and learns from past experience based on human feedback.

关键词： agent prompt reasoning

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Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

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Computers, Materials & Continua 2022年第8期72卷 3247-3267页

作者： Xinghai Ju Jicang Lu Xiangyang Luo Gang Zhou Shiyu Wang Shunhang Li Yang Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China School of Computing and Information Systems Singapore Management University188065Singapore College of Computer Science and Big Data Fuzhou UniversityFuzhou350116China

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method *** address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short *** on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each ***,the PoI similarities between long and short windows are calculated for training and ***,series of experiments is conducted based on real Internet forum *** results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed *** the length of long window is small,traditional methods perform better,and SVR is the *** the contrary,the deep learning methods show superiority,and LSTM performs *** results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

关键词： Point of interest(PoI)analysis long and short windows sequential analysis deep learning

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Improving Speech Emotion Recognition Using Data Augmentation and Balancing Techniques

Improving Speech Emotion Recognition Using Data Augmentation...

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International Conference on Cyberworlds

作者： Chawki Barhoumi Yassine Ben Ayed National School of Electronics and Telecommunications of Sfax Stax Tunisia Multimedia InfoRmation systems and Advanced Computing Laboratory Sfax Tunisia

Speech Emotion Recognition (SER) is a challenging task due to the complexity and variability of human emotions. In this paper, we propose an innovative approach to improve SER performance on the EMODB dataset. Our approach employs data augmentation techniques, such as noise addition and spectrogram shift, as well as balancing techniques, including random oversampling. We also extract five different features from the dataset samples: MFCC, Chroma, Mel Spectrogram, ZCR, and RMS. We compare the performance of four different classifiers - MLP, SVM, KNN, and CNN - with and without the use of our proposed approach. Our results demonstrate that the proposed approach significantly enhances the accuracy of speech emotion recognition compared to the approach without data augmentation and balancing techniques. Our experiments reveal that the proposed approach achieves higher accuracy and F1-score compared to other approaches, with MLP and CNN models achieving 100% accuracy. These findings highlight the effectiveness of data augmentation and balancing techniques in improving the performance of speech emotion recognition. Moreover, our approach holds great potential for application in various real-life scenarios, including mental health monitoring, human-robot interaction, and speech-based virtual assistants.

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Federated Graph Learning for Cross-Domain Recommendation 38

Federated Graph Learning for Cross-Domain Recommendation

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Yang, Ziqi Peng, Zhaopeng Wang, Zihui Qi, Jianzhong Chen, Chaochao Pan, Weike Wen, Chenglu Wang, Cheng Fan, Xiaoliang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computing and Information Systems The University of Melbourne Australia College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods. We open source the code at https://***/LafinHana/FedGCDR. © 2024 Neural information processing systems foundation. All rights reserved.

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Evaluation of acoustic features for early diagnosis of Alzheimer disease 19th

Evaluation of acoustic features for early diagnosis of Alzhe...

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19th International Conference on Intelligent systems Design and Applications, ISDA 2019

作者： Ammar, Randa Ben Ayed, Yassine Ben Multimedia Information System and Advanced Computing Laboratory Sfax University Sfax Tunisia

ISBN: (纸本)9783030493417

Automatic diagnosis and monitoring of Alzheimer’s Disease (AD) can have a significant impact on society as well as the well-being of patients. It is known that Alzheimer’s disease (AD) influences the language abilities of affected peoples. Hence, the diagnosis of Alzheimer’s disease using speech-based features is gaining growing attention. The purpose of this article is extracting a set of acoustic features from Dementia Bank conversations from subjects with and without Alzheimer’s disease. Extracted features will be trained with Machine Learning (ML) algorithms to testing the detection accuracy. Obtained results indicate that the proposed features extracted from the speech samples can be used to distinguish between the patients with Alzheimer’s disease and the healthy control group. Classification accuracy over 90% was obtained with Support Vector Machine (SVM) classifier. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Diagnosis

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Circuit level implementation of the Reduced Quantum Genetic Algorithm using Qiskit 16

Circuit level implementation of the Reduced Quantum Genetic ...

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16th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2022

作者： Ardelean, Sebastian Mihai Udrescu, Mihai Politehnica University of Timisoara Advanced Computing Systems and Architectures Laboratory Department of Computer and Information Technology Timisoara300223 Romania

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

Genetic Algorithm (GA) are common probabilistic optimization methods inspired by the process of natural selection. Quantum computers promise substantial speedups over conventional machines, and libraries allow the emulation of circuits on a quantum computer in different highly configurable noise models and even run on quantum computers. Therefore, we need to analyze this class of heuristic methods in the quantum context. We propose a circuit level implementation of the Reduced Quantum Genetic Algorithm (RQGA) using Python and Qiskit. Our main goal is to analyze the circuit complexity from the perspectives of the number of qubits required and the number of quantum gates used. To achieve our goal, we instantiate the framework for solving the knapsack problem, examine the results in a simulated environment, and analyze the circuit's complexity. © 2022 IEEE.

关键词： Heuristic methods

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Performance Analysis of Parkinson Detection Techniques

Performance Analysis of Parkinson Detection Techniques

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International Conference on advanced Technologies for Signal and Image Processing (ATSIP)

作者： Donia Ammous Hajer Bouzayen Yassine Ben Ayed National School of Engineers of Sfax Laboratory of Electronics and Information Technology (LETI) Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACLE) Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Parkinson’s Disease (PD) presents significant challenges in timely diagnosis and management, driving the need for advanced automatic recognition systems. This comparative study explores various methodologies for PD detection, leveraging machine learning and signal processing techniques across diverse modalities. Key phases, including data collection, preprocessing, feature extraction, selection, and classification, are scrutinized. Through comprehensive analysis of methodologies, datasets, and performance metrics, the study reveals promising accuracies in PD classification. However, challenges such as dataset availability and class imbalance persist. Future research avenues include multi-modal fusion, real-time monitoring, and telemedicine integration, aiming to enhance diagnostic accuracy and accessibility. Embracing these perspectives can accelerate the translation of research findings into clinical practice, improving outcomes for individuals with Parkinson’s disease.

关键词： Measurement Accuracy Telemedicine Machine learning Signal processing Real-time systems Performance analysis

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