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arXiv 2023年

作者： Zhu, Lipeng Ma, Wenyan Ning, Boyu Zhang, Rui The Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore Chengdu611731 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Movable antenna (MA) is a promising technology to improve wireless communication performance by varying the antenna position in a given finite area at the transceivers to create more favorable channel conditions. In this paper, we investigate the MA-enhanced multiple-access channel (MAC) for the uplink transmission from multiple users each equipped with a single MA to a base station (BS) with a fixed-position antenna (FPA) array. A field-response based channel model is used to characterize the multi-path channel between the antenna array of the BS and each user's MA with a flexible position. To evaluate the MAC performance gain provided by MAs, we formulate an optimization problem for minimizing the total transmit power of users, subject to a minimum-achievable-rate requirement for each user, where the positions of MAs and the transmit powers of users, as well as the receive combining matrix of the BS are jointly optimized. To solve this non-convex optimization problem involving intricately coupled variables, we develop two algorithms based on zero-forcing (ZF) and minimum mean square error (MMSE) combining methods, respectively. Specifically, for each algorithm, the combining matrix of the BS and the total transmit power of users are expressed as a function of the MAs' position vectors, which are then optimized by using the proposed multi-directional descent (MDD) framework. It is shown that the proposed ZF-based and MMSE-based MDD algorithms can converge to high-quality suboptimal solutions with low computational complexities. Simulation results demonstrate that the proposed solutions for MA-enhanced multiple access systems can significantly decrease the total transmit power of users as compared to conventional FPA systems employing antenna selection under both perfect and imperfect field-response information. Copyright © 2023, The Authors. All rights reserved.

关键词： Radio transceivers

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Large Models for Aerial Edges: An Edge-Cloud Model Evolution and Communication Paradigm

arXiv

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arXiv 2024年

作者： Zhang, Shuhang Liu, Qingyu Chen, Ke Di, Boya Zhang, Hongliang Yang, Wenhan Niyato, Dusit Han, Zhu Poor, H. Vincent The Pengcheng Laboratory Shenzhen518055 China The School of Electronic and Computer Engineering Peking University Shenzhen518055 China The Department of Electronics Peking University Beijing100871 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

The future sixth-generation (6G) of wireless networks is expected to surpass its predecessors by offering ubiquitous coverage through integrated air-ground facility deployments in both communication and computing domains. In this network, aerial facilities, such as unmanned aerial vehicles (UAVs), conduct artificial intelligence (AI) computations based on multi-modal data to support diverse applications including surveillance and environment construction. However, these multi-domain inference and content generation tasks require large AI models, demanding powerful computing capabilities and finely tuned inference models trained on rich datasets, thus posing significant challenges for UAVs. To tackle this problem, we propose an integrated air-ground edge-cloud model evolution framework, where UAVs serve as edge nodes for data collection and small model computation. Through wireless channels, UAVs collaborate with ground cloud servers, providing large model computation and model updating for edge UAVs. With limited wireless communication bandwidth, the proposed framework faces the challenge of information exchange scheduling between the edge UAVs and the cloud server. To tackle this, we present joint task allocation, transmission resource allocation, transmission data quantization design, and edge model update design to enhance the inference accuracy of the integrated air-ground edge-cloud model evolution framework by mean average precision (mAP) maximization. A closed-form lower bound on the mAP of the proposed framework is derived based on the mAP of the edge model and mAP of the cloud model, and the solution to the mAP maximization problem is optimized accordingly. Simulations, based on results from vision-based classification experiments, consistently demonstrate that the mAP of the proposed integrated air-ground edge-cloud model evolution framework outperforms both a centralized cloud model framework and a distributed edge model framework across various communica

关键词： Bandwidth

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Percept U-Net: Percept Attention-based Convolutional Neural Network for Atrial Fibrillation Episode Localization

Percept U-Net: Percept Attention-based Convolutional Neural ...

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International Conference on data science and Advanced Analytics (DSAA)

作者： Ying An Lebing Pan Lin Guo Xianlai Chen Yu Shen Big Data Institute Central South University Changsha China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665473316

Early detection of paroxysmal atrial fibrillation (AF) is valuable in determining treatment options and diagnosing complications, and identifying the number and duration of AF episodes in the dynamic electrocardiogram (ECG) may help advance the study of pathological AF mechanisms. Current computer-aided ECG signal diagnosis methods can be divided into two categories: (a) methods based on every single heartbeat split from ECG records, which cannot use information from adjacent heartbeats, and (b) methods based on a segment of ECG records that contain multiple heartbeats, which can only give a classification of the whole segment but cannot identify the categories of each heartbeat in the record at a fine granularity. Few methods can exploit the relationship between heartbeats as well as identify the onset and end of disease episodes. To fill this gap, we designed an improved U-Net model named Percept U-Net to detect AF episodes from the ECG records. In this model, we propose the percept attention to replace skip connections in U-Net to integrate low-level features with high-level features, thereby enhancing the ability to detect and discriminate between normal and abnormal heartbeats. Experimental results show that Percept U-Net achieves higher classification and localization accuracy with less computational overhead and paraments than other comparative methods.

关键词： Location awareness Pathology Heart beat Computational modeling Atrial fibrillation Medical services Electrocardiography

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Optimal Energy Storage Operation under Demand Uncertainty: A Prospect Theory Analysis

Optimal Energy Storage Operation under Demand Uncertainty: A...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Qisheng Huang Jin Xu Peng Sun Bo Liu Costas Courcoubetis School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China School of Management Huazhong University of Science and Technology Wuhan China College of Computer Science and Electronic Engineering Hunan University Changsha China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

In this paper, we study the consumer’s optimal energy storage operation problem under demand uncertainty. Each consumer can purchase energy storage service from an independent energy storage aggregator to shift demand from peak periods to off-peak periods under time-of-use (ToU) pricing. Previous studies on energy storage operation and investment mainly focused on the expected cost-minimization problem of a risk-neutral consumer based on the expected utility theory (EUT). We propose a prospect theory (PT) model from behavioral economics to understand the consumer’s realistic energy storage operation behaviors considering their risk preferences. Although it is challenging to solve the PT-based non-convex optimization problem, we are able to characterize the optimal solution by exploiting the unimodal structure of the objective function. Theoretical and numerical results show that the consumer’s risk preferences have a significant impact on his decisions: 1) a PT-consumer with a low reference point is more willing to use energy storage to reduce risk compared with the EUT benchmark; and 2) a PT consumer is more willing to use the energy storage when the probability of high demand is small, due to the probability distortion.

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Vader protocol based sentiment analysis technique using LSTM for Stock Trend prediction

Vader protocol based sentiment analysis technique using LSTM...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Amrita Ticku Niva Tripathy Pankaj Kumar Mishra Anurag Sinha Shivangi Jadon Aditya Raj Harsh Kulshrestha Vipin Rai Rejuwan Shamim Bharti Vidyapeeth’s College of Engineering India Driems Autonomous Engineering College Cuttack India Department of Electronics and Telecommunication Engg. Rungta College of Engg. & TechnologyR1 Bhilai CG India School of Computing and Information Science IGNOU New Delhi India Dept. of Computer Science and Engineering Galgotias University Noida India Department of Computer Science and Engineering Galgotias University Noida India Dept. of Computer Science and Engineering With Data Science Maharishi University of Information Technology Noida India

Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& amp; P 500 dataset reveal that it outperforms traditional stock prediction approaches.

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Keeping Pace with Ever-Increasing data: Towards Continual Learning of Code Intelligence Models

Keeping Pace with Ever-Increasing Data: Towards Continual Le...

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International Conference on Software engineering (ICSE)

作者： Shuzheng Gao Hongyu Zhang Cuiyun Gao Chaozheng Wang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory and Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Big Data and Software Engineering Chongqing University China

Previous research on code intelligence usually trains a deep learning model on a fixed dataset in an offline manner. However, in real-world scenarios, new code repositories emerge incessantly, and the carried new knowledge is beneficial for providing up-to-date code intelligence services to developers. In this paper, we aim at the following problem: How to enable code intelligence models to continually learn from ever-increasing data? One major challenge here is catastrophic forgetting, meaning that the model can easily forget knowledge learned from previous datasets when learning from the new dataset. To tackle this challenge, we propose REPEAT, a novel method for continual learning of code intelligence models. Specifically, REPEAT addresses the catastrophic forgetting problem with representative exemplars replay and adaptive parameter regularization. The representative exemplars replay component selects informative and diverse exemplars in each dataset and uses them to re-train model periodically. The adaptive parameter regularization component recognizes important parameters in the model and adaptively penalizes their changes to preserve the knowledge learned before. We evaluate the proposed approach on three code intelligence tasks including code summarization, software vulnerability detection, and code clone detection. Extensive experiments demonstrate that REPEAT consistently outperforms baseline methods on all tasks. For example, REPEAT improves the conventional fine-tuning method by 1.22, 5.61, and 1.72 on code summarization, vulnerability detection and clone detection, respectively.

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Twin Auto-Encoder Model for Learning Separable Representation in Cyberattack Detection

arXiv

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arXiv 2024年

作者： Phai, Vu Dinh Uy, Quang Nguyen Hoang, Dinh Thai Nguyen, Diep N. Son, Pham Bao Dutkiewicz, Eryk The School of Electrical and Data Engineering The University of Technology Sydney SydneyNSW2007 Australia The Computer Science Department Faculty of Information Technology Le Quy Don Technical University Hanoi Viet Nam The University of Engineering and Technology Vietnam National University Hanoi Viet Nam

Representation Learning (RL) plays a pivotal role in the success of many problems including cyberattack detection. Most of the RL methods for cyberattack detection are based on the latent vector of Auto-Encoder (AE) models. An AE transforms raw data into a new latent representation that better exposes the underlying characteristics of the input data. Thus, it is very useful for identifying cyberattacks. However, due to the heterogeneity and sophistication of cyberattacks, the representation of AEs is often entangled/mixed resulting in the difficulty for downstream attack detection models. To tackle this problem, we propose a novel model called Twin Auto-Encoder (TAE). TAE deterministically transforms the latent representation into a more distinguishable representation namely the separable representation and then reconstruct the separable representation at the output. The output of TAE called the reconstruction representation is input to downstream models to detect cyberattacks. We extensively evaluate the effectiveness of TAE using a wide range of benchmarking datasets. Experiment results show the superior accuracy of TAE over state-of-the-art RL models and well-known machine learning algorithms. Moreover, TAE also outperforms state-of-the-art models on some sophisticated and challenging attacks. We then investigate various characteristics of TAE to further demonstrate its superiority. © 2024, CC BY.

关键词： Learning algorithms

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Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis

arXiv

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arXiv 2022年

作者： Zhang, Kai Zhang, Kun Zhang, Mengdi Zhao, Hongke Liu, Qi Wu, Wei Chen, Enhong School of Data Science University of Science and Technology of China China School of Computer Science and Information Engineering Hefei University of Technology China Meituan China College of Management and Economics Tianjin University China

Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence. While pre-trained language models such as BERT have achieved great success, incorporating dynamic semantic changes into ABSA remains challenging. To this end, in this paper, we propose to address this problem by Dynamic Re-weighting BERT (DR-BERT), a novel method designed to learn dynamic aspect-oriented semantics for ABSA. Specifically, we first take the Stack-BERT layers as a primary encoder to grasp the overall semantic of the sentence and then fine-tune it by incorporating a lightweight Dynamic Re-weighting Adapter (DRA). Note that the DRA can pay close attention to a small region of the sentences at each step and re-weigh the vitally important words for better aspect-aware sentiment understanding. Finally, experimental results on three benchmark datasets demonstrate the effectiveness and the rationality of our proposed model and provide good interpretable insights for future semantic modeling. Copyright © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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Diabetes Mellitus Type 2 data Sharing System Based on Blockchain and Attribute-Encryption

Diabetes Mellitus Type 2 Data Sharing System Based on Blockc...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weiqi Dai Zhenhui Lu Xia Xie Duoqiang Wang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Hainan University Haikou China

Medical data sharing can improve the quality of medical care and promote progress in the field of public health. However, medical data has high confidentiality and complexity. Taking the clinical diagnosis and treatment data of Diabetes Mellitus Type 2 (T2DM) in medical treatment as an example, T2DM data often has the phenomenon of "multi-disease coexistence" and is insecure and inaccurate in the process of sharing. Based on Blockchain and Attribute- Encryption (ABE), we propose a T2DM data sharing system to solve the above problems. An Attribute-Encryption algorithm T2DM-CP-ABE is proposed to solve the problem of excessive sharing granularity. T2DM-CP-ABE, combined with the medical data contribution module, takes the contribution factor as an important consideration, to stimulate the power of T2DM data sharing and promote the development of national diabetes prevention. Based on the Blockchain, a medical data attribute authorization model is proposed, which provides a credible solution for multiple hospitals to realize medical data attribute authorization, and realizes the clinical diagnosis and treatment data sharing of T2DM among doctors. Based on Blockchain and ABE technology, a data sharing system for T2DM has been implemented on the Fabric platform. The result shows that the average time for the system to complete a single sharing is less than 1 second.

关键词： Authorization Hospitals Fabrics Blockchains Diabetes Encryption Security

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Low-Complexity Pareto-Optimal 3D Beamforming for the Full-Dimensional Multi-User Massive MIMO Downlink

arXiv

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arXiv 2023年

作者： Zhu, W. Tuan, H.D. Dutkiewicz, E. Fang, Y. Hanzo, L. School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Electrical and Data Engineering University of Technology Sydney BroadwayNSW2007 Australia School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Full-dimensional (FD) multi-user massive multiple-input multiple output (m-MIMO) systems employ large two-dimensional (2D) rectangular antenna arrays to control both the azimuth and elevation angles of signal transmission. We introduce the sum of two outer products of the azimuth and elevation beamforming vectors having moderate dimensions as a new class of FD beamforming. We show that this low-complexity class is capable of outperforming 2D beamforming relying on the single outer product of the azimuth and elevation beamforming vectors. It is also capable of performing close to its FD counterpart of massive dimensions in terms of either the users’ minimum rate or their geometric mean rate (GM-rate), or sum rate (SR). Furthermore, we also show that even FD beamforming may be outperformed by our outer product-based improper Gaussian signaling solution. Explicitly, our design is based on low-complexity algorithms relying on convex problems of moderate dimensions for max-min rate optimization or on closed-form expressions for GM-rate and SR maximization. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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