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2nd IEEE International Conference on Artificial Intelligence in Everything, aiE 2022

作者： Vubangsi, Mercel Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin 10 Nicosia Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin 10 Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we design and implement a conference attendance monitoring system based on blockchain and Artificial Intelligence (ai) technologies. This system is built with *** for front-end and makes use of Google's real-time database for storage. The system is designed to auto-track the users device location and screen behavior during a time slot allocated to a conference or conference session for which the user has subscribed. ai powered analytics of the data using custom mathematical models and JavaScript Machine Learning Libraries permits decision making on the data, facilitating the task on the conference organizers with regards to rating attendee performances. © 2022 IEEE.

关键词： Smart contract

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ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce NAS Space for Heterogeneous Pipeline of CNNs 29

ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Duan, Huijuan Wang, Danghui Zhao, Xingting Mei, Kuizhi Wang, Jihe Polytechnical University School of Computer Science Northwestern Xi'an China Northwestern Polytechnical University Engineering Research Center of Embedded System Integration Ministry of Education Xi'an China Xi'An Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an China Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9798350330717

Efficiently deploying neural network models on heterogeneous edge systems faces unique challenges. Two major issues are underutilized pipelines caused by hardware heterogeneity and explosive growth of architecture search space. To address these challenges, we propose ExpoNAS, a framework enabling fast deployment-oriented neural architecture search on heterogeneous platforms. ExpoNAS evaluates model partitioning and deployability before search to meet accuracy and latency constraints. However, repeated partitioning leads to exponential growth in architecture search space. We first observe a strong correlation between operational exposure and accuracy improvement, and then we propose an exposure-based reduction technique that excludes the operations of low exposure. By removing these non-critical operations, the exposure-based pruning successfully reduces architecture search time. Our experiments on popular datasets show that the ExpoNAS pipelined design improves execution efficiency by a factor of 2 compared to traditional batch processing. Without compromising accuracy, ExpoNAS is able to reduce the architecture search time by 47.5% for discovering high-accuracy networks, compared to the SOTA NAS strategies. © 2023 IEEE.

关键词： heterogeneous systems NAS pipeline mapping search space reduction

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Self-supervised Reflective Learning through Self-distillation and Online Clustering for Speaker Representation Learning

arXiv

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arXiv 2024年

作者： Cai, Danwei Cai, Zexin Li, Ming Department of Electrical and Computer Engineering Duke University DurhamNC27705 United States Data Science Research Center Duke Kunshan University Kunshan China

Speaker representation learning is critical for modern voice recognition systems. While supervised learning techniques require extensive labeled data, unsupervised methodologies can leverage vast unlabeled corpora, offering a scalable solution. This paper introduces self-supervised reflective learning (SSRL), a novel paradigm that streamlines existing iterative unsupervised frameworks. SSRL integrates self-supervised knowledge distillation with online clustering to refine pseudo labels and train the model without iterative bottlenecks. Specifically, a teacher model continually refines pseudo labels through online clustering, providing dynamic supervision signals to train the student model. The student model undergoes noisy student training with input and model noise to boost its modeling capacity. The teacher model is updated via an exponential moving average of the student, acting as an ensemble of past iterations. Further, a pseudo label queue retains historical labels for consistency, and noisy label modeling directs learning towards clean samples. Experiments on VoxCeleb show SSRL's superiority over current iterative approaches, surpassing the performance of a 5-round method in just a single training round. Ablation studies validate the contributions of key components like noisy label modeling and pseudo label queues. Moreover, consistent improvements in pseudo labeling and the convergence of cluster counts demonstrate SSRL's effectiveness in deciphering unlabeled data. This work marks an important advancement in efficient and accurate speaker representation learning through the novel reflective learning paradigm. © 2024, CC BY.

关键词： Self-supervised learning

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Visual Lifelog Retrieval through Captioning-Enhanced Interpretation

Visual Lifelog Retrieval through Captioning-Enhanced Interpr...

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IEEE International Conference on Big data

作者： Yu-Fei Shih An-Zi Yen Hen-Hsen Huang Hsin-Hsi Chen Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science and Information Engineering AI Research Center (AINTU) National Taiwan University Taipei Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

People often struggle to remember specific details of past experiences, which can lead to the need to revisit these memories. Consequently, lifelog retrieval has emerged as a crucial application. Various studies have explored methods to facilitate rapid access to personal lifelogs for memory recall assistance. In this paper, we propose a Captioning-Integrated Visual Lifelog (CIVIL) Retrieval System for extracting specific images from a user’s visual lifelog based on textual queries. Unlike traditional embedding-based methods, our system first generates captions for visual lifelogs and then utilizes a text embedding model to project both the captions and user queries into a shared vector space. Visual lifelogs, captured through wearable cameras, provide a first-person viewpoint, necessitating the interpretation of the activities of the individual behind the camera rather than merely describing the scene. To address this, we introduce three distinct approaches: the single caption method, the collective caption method, and the merged caption method, each designed to interpret the life experiences of lifeloggers. Experimental results show that our method effectively describes first-person visual images, enhancing the outcomes of lifelog retrieval. Furthermore, we construct a textual dataset that converts visual lifelogs into captions, thereby reconstructing personal life experiences.

关键词： Visualization Big data Cameras Vectors Image reconstruction

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End-to-end Online Speaker Diarization with Target Speaker Tracking

arXiv

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arXiv 2023年

作者： Wang, Weiqing Li, Ming The Department of Electrical and Computer Engineering Duke University United States Data Science Research Center Duke Kunshan University China

This paper proposes an online target speaker voice activity detection system for speaker diarization tasks, which does not require a priori knowledge from the clustering-based diarization system to obtain the target speaker embeddings. By adapting the conventional target speaker voice activity detection for real-time operation, this framework can identify speaker activities using self-generated embeddings, resulting in consistent performance without permutation inconsistencies in the inference phase. During the inference process, we employ a front-end model to extract the frame-level speaker embeddings for each coming block of a signal. Next, we predict the detection state of each speaker based on these frame-level speaker embeddings and the previously estimated target speaker embedding. Then, the target speaker embeddings are updated by aggregating these frame-level speaker embeddings according to the predictions in the current block. Our model predicts the results for each block and updates the target speakers’ embeddings until reaching the end of the signal. Experimental results show that the proposed method outperforms the offline clustering-based diarization system on the DIHARD III and AliMeeting datasets. The proposed method is further extended to multi-channel data, which achieves similar performance with the state-of-the-art offline diarization systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

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computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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Space-air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges

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China Communications 2022年第2期19卷 90-108页

作者： Huanxi Cui Jun Zhang Yuhui Geng Zhenyu Xiao Tao Sun Ning Zhang Jiajia Liu Qihui Wu Xianbin Cao School of Electronic and Information Engineering Beijing Key Laboratory for Network-Based Cooperative Air application ManagementBeihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China China Mobile Research Institute Beijing 100053China Department of Electrical and Computer Engineering University of WindsorONCanadaN9B 3P4 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China College of Electronics and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210007China

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(ai),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.

关键词： 6G ai DT SAGIN NTN Network architecture

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P2TC: A Lightweight Pyramid Pooling Transformer-CNN Network for Accurate 3D Whole Heart Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4279-4289页

作者： Hengfei Cui Yifan Wang Fan Zheng Yan Li Yanning Zhang Yong Xia National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China Chongqing Innovation Center Northwestern Polytechnical University Chongqing China

Cardiovascular disease is a leading global cause of death, requiring accurate heart segmentation for diagnosis and surgical planning. Deep learning methods have been demonstrated to achieve superior performances in cardiac structures segmentation. However, there are still limitations in 3D whole heart segmentation, such as inadequate spatial context modeling, difficulty in capturing long-distance dependencies, high computational complexity, and limited representation of local high-level semantic information. To tackle the above problems, we propose a lightweight Pyramid Pooling Transformer-CNN (P2TC) network for accurate 3D whole heart segmentation. The proposed architecture comprises a dual encoder-decoder structure with a 3D pyramid pooling Transformer for multi-scale information fusion and a lightweight large-kernel Convolutional Neural Network (CNN) for local feature extraction. The decoder has two branches for precise segmentation and contextual residual handling. The first branch is used to generate segmentation masks for pixel-level classification based on the features extracted by the encoder to achieve accurate segmentation of cardiac structures. The second branch highlights contextual residuals across slices, enabling the network to better handle variations and boundaries. Extensive experimental results on the Multi-Modality Whole Heart Segmentation (MM-WHS) 2017 challenge dataset demonstrate that P2TC outperforms the most advanced methods, achieving the Dice scores of 92.6% and 88.1% in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities respectively, which surpasses the baseline model by 1.5% and 1.7%, and achieves state-of-the-art segmentation results.

关键词： Transformers Image segmentation Three-dimensional displays Heart Accuracy Feature extraction Convolutional neural networks Context modeling Computational modeling Solid modeling

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A Study and Recognition of Alzheimer's Disease Using Machine Learning 8

A Study and Recognition of Alzheimer's Disease Using Machine...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Basha, S.K. Masthan Ashalatha, G. Vara Prasad, Srisailapu D Al-Hameed, Mazin R. Zearah, Sajad Ali Saikumar, K. Vignan's Institute of Management and Technology for Women ECE Department Hyderabad India VNR Vignana Jyothi Institute of Engineering and Technology Department of Computer Science-CS-DS&AI&DS Telangana Hyderabad India GITAM Deemed to Be University Computer Science and Engineering School of Technology Telangana Hyderabad India National University of Science and Technology College of Engineering Technology Department of Medical Device Industry Engineering Dhi Qar Iraq Al-Ayen University Scientific Research Center Thi-Qar Iraq Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (纸本)9798350334012

Nowadays, Alzheimer's disease is one of the most severe threats to people of all ages and socioeconomic backgrounds. Their everyday activities may measure this disorder, and how they speak about it shows how concerned they are about it. The way we live today is different from how it was in the past because of our working environment and the lack of opportunities to interact with people. WHO estimates that about 10 billion individuals perish each year due to Alzheimer's disease since not been appropriately diagnosed and treated. An advanced Machine Learning-based recognition system design performs in this research work. The proposed model has been designed based on the human being EEG dataset(testing) and the Kaggle Alzheimer dataset (training). The developed intelligent system will be updated with data collection and pre-processing through the Random Forest machine model to locate our health condition and recognize daily routine changes. Only by developing a machine-based approach will we be able to discover Alzheimer's patients who previously had no idea that they had the illness and are now unable to reverse the progression of their condition. The designed application has attained a sensitivity of 93.01%, an accuracy of 91.32%, a recall of 97.28%, and a detection rate of 98.91%. The implemented application is beneficial to investigators and surgeons for easy treatment. The proposed RFO Machine learning design competes with present technology and outperforms methodology. © 2023 IEEE.

关键词： Intelligent systems

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Systematic Biases in LLM Simulations of Debates

Systematic Biases in LLM Simulations of Debates

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Taubenfeld, Amir Dover, Yaniv Reichart, Roi Goldstein, Ariel The Hebrew University of Jerusalem School of Computer Science and Engineering Israel Google Research United States The Hebrew University Business School Jerusalem Israel Federmann Center for the Study of Rationality Hebrew University Jerusalem Israel Faculty of Data and Decision Sciences Technion Israel Department of Cognitive and Brain Sciences Hebrew University Jerusalem Israel

ISBN: (纸本)9798891761643

The emergence of Large Language Models (LLMs), has opened exciting possibilities for constructing computational simulations designed to replicate human behavior accurately. Current research suggests that LLM-based agents become increasingly human-like in their performance, sparking interest in using these ai agents as substitutes for human participants in behavioral studies. However, LLMs are complex statistical learners without straightforward deductive rules, making them prone to unexpected behaviors. Hence, it is crucial to study and pinpoint the key behavioral distinctions between humans and LLM-based agents. In this study, we highlight the limitations of LLMs in simulating human interactions, particularly focusing on LLMs' ability to simulate political debates on topics that are important aspects of people's day-to-day lives and decision-making processes. Our findings indicate a tendency for LLM agents to conform to the model's inherent social biases despite being directed to debate from certain political perspectives. This tendency results in behavioral patterns that seem to deviate from well-established social dynamics among humans. We reinforce these observations using an automatic self-fine-tuning method, which enables us to manipulate the biases within the LLM and demonstrate that agents subsequently align with the altered biases. These results underscore the need for further research to develop methods that help agents overcome these biases, a critical step toward creating more realistic simulations. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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