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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Wenjing Zhang Yining Wang Mingzhe Chen Tao Luo Dusit Niyato Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and ComputingUniversity of Miami Coral Gables FL USA School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665435413

In this paper, a semantic communication framework for image transmission is investigated. In the framework, a server transmits image data to a set of users utilizing semantic communication techniques, which enable the server to transmit only the semantic information that accurately captures the meaning of an image. To evaluate the performance of the studied semantic communication system, we propose a multimodal metric called image-to-graph semantic similarity (ISS). The significance of this new metric is that it can measure the correlation of the meaning between semantic information and the original image. To meet the ISS requirement of each user, the server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem whose goal is to minimize the average transmission latency while reaching the ISS requirement. To solve this problem, we propose a model-based actor critic deep reinforcement learning (DRL) algorithm. Compared to traditional actor critic DRL, in the proposed algorithm, we design a novel value function to improve the action exploration thus improving the probability of finding an optimal solution. Simulation results show that the proposed method can reduce the transmission delay by 16.4% and improves the convergence speed by up to 50% compared to the traditional actor critic DRL.

关键词： Measurement Wireless communication Image communication Simulation Semantics Reinforcement learning Servers

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Real Time Emotion Support System in Text Mining [RTESTM]

Real Time Emotion Support System in Text Mining [RTESTM]

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2021 International Conference on computer Communication and Informatics, ICCCI 2021

作者： Ramya, R.S. Sandhya, K. Venugopal, K.R. Iyengar, S.S. Patnaik, L.M. Rns Institute of Technology Bengaluru India University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bengaluru India Bangalore University Bangalore India School of Computing and Information Sciences Florida International University United States National Institute of Advanced Studies Bangalore India

ISBN: (纸本)9781728158754

Mining opinions from online reviews is an essential step in obtaining the overall sentiment of a product. Deep learning procedure is applied over various fields. User ratings are huge for recommender structures since they consolidate various kinds of energetic information that may influence the exactness of the suggestion. In this work, a deep learning model is utilized to process the user remarks and to create a potential user rating for user comments is proposed. To start with, the system uses sentiments to create a feature vector as the input nodes. Further, the framework tools reduce the noise in the dataset to recover the classification of information mining. To finish, Deep Belief Network (DBN) and sentiment analysis reaches data learning for the approvals. © 2021 IEEE.

关键词： Sentiment analysis

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Optimal Geospatial Query Placement in Cloud 1st

Optimal Geospatial Query Placement in Cloud

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1st International Conference on Intelligent and Cloud computing, ICICC 2019

作者： Das, Jaydeep Addya, Sourav Kanti Ghosh, Soumya K. Buyya, Rajkumar Advanced Technology Development Centre Indian Institute of Technology Kharagpur KharagpurWest Bengal721302 India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur KharagpurWest Bengal721302 India School of Computing and Information Systems University of Melbourne MelbourneVIC3010 Australia

ISBN: (纸本)9789811559709

computing resources requirements are increasing with the massive generation of geospatial queries. These queries extract information from a large volume of spatial data. Placement of geospatial queries in virtual machines with minimum resource and energy wastage is a big challenge. Getting query results from mobile locations within a specific time duration is also a major concern. In this work, a bi-objective optimization problem has been formulated to minimize the energy consumption of cloud servers and service processing time. To solve the problem, a crow search based bio-inspired heuristic has been proposed. The proposed algorithm has been compared with traditional First Fit and Best Fit algorithms through simulation, and the obtained results are significantly better than the traditional techniques. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Energy efficiency

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Mathematical capabilities of ChatGPT 23

Mathematical capabilities of ChatGPT

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Simon Frieder Luca Pinchetti Alexis Chevalier Ryan-Rhys Griffiths Tommaso Salvatori Thomas Lukasiewicz Philipp Petersen Julius Berner Department of Computer Science University of Oxford Oxford UK School of Mathematics Institute for Advanced Study Princeton Department of Physics University of Cambridge Cambridge UK VERSES AI Research Lab Los Angeles and Institute of Logic and Computation Vienna University of Technology Vienna Austria Institute of Logic and Computation Vienna University of Technology Vienna Austria and Department of Computer Science University of Oxford Oxford UK Faculty of Mathematics University of Vienna Vienna Austria and Research Network Data Science University of Vienna Vienna Austria Department of Computing and Mathematical Sciences Caltech Pasadena

We investigate the mathematical capabilities of two versions of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel evaluation scheme. In contrast to formal mathematics, where large databases of formal proofs are available (e.g., mathlib, the Lean Mathematical Library), current datasets of natural-language mathematics used to benchmark language models either cover only elementary mathematics or are very small. We address this by publicly releasing two new datasets: GHOSTS and miniGHOSTS. These are the first natural-language datasets curated by working researchers in mathematics that (1) aim to cover graduate-level mathematics, (2) provide a holistic overview of the mathematical capabilities of language models, and (3) distinguish multiple dimensions of mathematical reasoning. These datasets test, by using 1636 human expert evaluations, whether ChatGPT and GPT-4 can be helpful assistants to professional mathematicians by emulating use cases that arise in the daily professional activities of mathematicians. We benchmark the models on a range of fine-grained performance metrics. For advanced mathematics, this is the most detailed evaluation effort to date. We find that ChatGPT and GPT-4 can be used most successfully as mathematical assistants for querying facts, acting as mathematical search engines and knowledge base interfaces. GPT-4 can additionally be used for undergraduate-level mathematics but fails on graduate-level difficulty. Contrary to many positive reports in the media about GPT-4 and ChatGPT's exam-solving abilities (a potential case of selection bias), their overall mathematical performance is well below the level of a graduate student. Hence, if you aim to use ChatGPT to pass a graduate-level math exam, you would be better off copying from your average peer!

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Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos

arXiv

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arXiv 2023年

作者： Lin, Junhao Dai, Qian Zhu, Lei Fu, Huazhu Wang, Qiong Li, Weibin Rao, Wenhao Huang, Xiaoyang Wang, Liansheng School of Informatics Xiamen University Xiamen China Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Medicine Xiamen University Xiamen China

Breast lesion segmentation in ultrasound (US) videos is essential for diagnosing and treating axillary lymph node metastasis. However, the lack of a well-established and large-scale ultrasound video dataset with high-quality annotations has posed a persistent challenge for the research community. To overcome this issue, we meticulously curated a US video breast lesion segmentation dataset comprising 572 videos and 34,300 annotated frames, covering a wide range of realistic clinical scenarios. Furthermore, we propose a novel frequency and localization feature aggregation network (FLA-Net) that learns temporal features from the frequency domain and predicts additional lesion location positions to assist with breast lesion segmentation. We also devise a localization-based contrastive loss to reduce the lesion location distance between neighboring video frames within the same video and enlarge the location distances between frames from different ultrasound videos. Our experiments on our annotated dataset and two public video polyp segmentation datasets demonstrate that our proposed FLA-Net achieves state-of-the-art performance in breast lesion segmentation in US videos and video polyp segmentation while significantly reducing time and space complexity. Our model and dataset are available at https://***/jhlDet/FLA-Net. Copyright © 2023, The Authors. All rights reserved.

关键词： Location

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Learning latent structures in network games via data-dependent gated-prior graph variational autoencoders 24

Learning latent structures in network games via data-depende...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xue Yu Muchen Li Yan Leng Renjie Liao Center for Applied Statistics School of Statistics Renmin University of China Beijing China and Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Department of Computer Science University of British Columbia Vancouver BC Canada McCombs School of Business The University of Texas at Austin Austin TX Vector Institute for AI and Canada CIFAR AI Chair and Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

In network games, individuals interact strategically within network environments to maximize their utilities. However, obtaining network structures is challenging. In this work, we propose an unsupervised learning model, called data-dependent gated-prior graph variational autoencoder (GPGVAE), that infers the underlying latent interaction type (strategic complement vs. substitute) among individuals and the latent network structure based on their observed actions. Specially, we propose a spectral graph neural network (GNN) based encoder to predict the interaction type and a data-dependent gated prior that models network structures conditioned on the interaction type. We further propose a Transformer based mixture of Bernoulli encoder of network structures and a GNN based decoder of game actions. We systematically study the Monte Carlo gradient estimation methods and effectively train our model in a stage-wise fashion. Extensive experiments across various synthetic and real-world network games demonstrate that our model achieves state-of-the-art performances in inferring network structures and well captures interaction types.

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MMCo-Clus – An Evolutionary Co-clustering Algorithm for Gene Selection (Extended abstract)

MMCo-Clus – An Evolutionary Co-clustering Algorithm for Gen...

引用

International Conference on Data Engineering

作者： Laizhong Cui Sudipta Acharya Sumit Mishra Yi Pan Joshua Zhexue Huang College of Computer Science and Software Engineering Shenzhen University Shenzhen P.R. China School of Computing Queen’s University Ontario Canada Department of CSE Indian Institute of Information Technology Guwahati Assam India Shenzhen Institute of Advanced Technology Chinese Academy of Sciences P.R. China

Dimensionality reduction through feature selection becomes inevitable to overcome the problem of the Curse of dimensionality. In this article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through a Multi-objective optimization-based Multi-view Co-Clustering algorithm (named MMCo-Clus). A thorough comparative analysis with existing feature selection algorithms using external/internal evaluation metrics supports our proposed method’s potency.

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Trust Online Over-the-air Computation for Wireless Federated Learning

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IEEE Transactions on Mobile computing 2025年

作者： Sun, Mingjie Zheng, Jie Du, Hongyang Zhang, Haijun Niyato, Dusit Kang, Jiawen Wang, Jiacheng Ren, Jie Gao, Ling Wang, Zheng Northwest University State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Shaanxi Xian710127 China University of Hong Kong department of electrical and electronic engineering Hong Kong University of Science and Technology Beijing Institute of Artificial Intelligence Beijing100083 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Guangdong University of Technology Automation of School Guangzhou510006 China Shaanxi Normal University School of Computer Science Xi'an710062 China University of Leeds School of Computing LS2 9JT United Kingdom

Using the wireless waveform superposition property, over-the-air computation (OAC) enables federated learning (FL) to achieve fast model aggregation. However, this computing paradigm is vulnerable to poisoning attacks due to the openness of a wireless channel over time, where malicious mobile devices can introduce cumulative errors for the global FL model in a time-varying wireless environment for each communication round. This article presents a trust online OAC (TO-OAC) scheme to minimize impacts on the global model introduced by malicious devices adjusting to dynamic attack and wireless channel fluctuations over time. TO-OAC achieves this by utilizing trustworthy security quantification of OAC for each FL training round. To optimize the cumulative training loss at the aggregation node with the long-term power and trust constraints of mobile devices, we propose a joint trust, power, and channel-aware algorithm to flexibly update local and global models in response to the dynamic changes in the wireless and secure environment. We analyze the performance limits for the aggregation of trust models, considering metrics for computation and communication through time. We then propose another trust online regularization over-the-air computation (TOR-OAC) as an improved version of the TO-OAC scheme to decrease convergence time while ensuring long-term trust and power limitation. Experimental results performed on real-life datasets show that the two proposed schemes (TO-OAC and TOR-OAC) outperform prior works, especially in noisy, time-varying wireless channels and malicious attacks. 2002-2012 IEEE.

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