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Smart World Congress (SWC), IEEE

作者： Yao Xiao Xin Liu Yurong Qian Bo Huang Panrui Tang Zuping Zhang School of Software Xinjiang University Urumqi China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The current research focus on Major Depressive Disorder (MDD) is a reflection of its significant adverse impact on individuals and society. Structural magnetic resonance imaging (SMRI) is a valuable tool for clinicians in diagnosing and identifying MDD. sMRI employs a uniform imaging protocol, and images of gray matter slices in the same plane across different sMRIs demonstrate high similarity, revealing repetitive anatomical structures between patients. Given the consistency of anatomical structures, the same regional evidence in normal images presents similar physiological structural information. Lesions, meanwhile, cause structural changes that manifest as abnormal patterns. In this paper, we apply the SQUID model, which is capable of detecting abnormal structural information, to the MDD classification problem. The study employed gray matter slice data generated by SMRI from 1025 individuals with MDD and 1205 Healthy Controls(HC). The objective was to undertake an abnormality detection classification task in MDD and HC, and to further verify the pivotal role of the hippocampus in the diagnosis of MDD through masked experiment. The findings contribute novel methods and evidence for the clinical diagnosis of MDD.

关键词： Dictionaries Protocols Magnetic resonance imaging Grey matter Depression Physiology Clinical diagnosis SQUIDs Hippocampus Anomaly detection

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A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning 41

A Profit-Maximizing Model Marketplace with Differentially Pr...

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41st IEEE Conference on computer Communications, INFOCOM 2022

作者： Sun, Peng Chen, Xu Liao, Guocheng Huang, Jianwei The Chinese University of Hong Kong School of Science and Engineering Shenzhen China Shenzhen China Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Software Engineering Zhuhai China

ISBN: (纸本)9781665458221

Existing machine learning (ML) model marketplaces generally require data owners to share their raw data, leading to serious privacy concerns. Federated learning (FL) can partially alleviate this issue by enabling model training without raw data exchange. However, data owners are still susceptible to privacy leakage from gradient exposure in FL, which discourages their participation. In this work, we advocate a novel differentially private FL (DPFL)-based ML model marketplace. We focus on the broker-centric design. Specifically, the broker first incentivizes data owners to participate in model training via DPFL by offering privacy protection as per their privacy budgets and explicitly accounting for their privacy costs. Then, it conducts optimal model versioning and pricing to sell the obtained model versions to model buyers. In particular, we focus on the broker's profit maximization, which is challenging due to the significant difficulties in the revenue characterization of model trading and the cost estimation of DPFL model training. We propose a two-layer optimization framework to address it, i.e., revenue maximization and cost minimization under model quality constraints. The latter is still challenging due to its non-convexity and integer constraints. We hence propose efficient algorithms, and their performances are both theoretically guaranteed and empirically validated. © 2022 IEEE.

关键词： Electronic data interchange

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DAGC: Data-Aware Adaptive Gradient Compression 42

DAGC: Data-Aware Adaptive Gradient Compression

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Lu, Rongwei Song, Jiajun Chen, Bin Cui, Laizhong Wang, Zhi Tsinghua Shenzhen International Graduate School Tsinghua-Berkeley Shenzhen Institute China Dalian University of Technology School of Software Technology China Harbin Institute of Technology Shenzhen China Shenzhen University College of Computer Science and Software Engineering China China Peng Cheng Laboratory China

ISBN: (纸本)9798350334142

Gradient compression algorithms are widely used to alleviate the communication bottleneck in distributed ML. However, existing gradient compression algorithms suffer from accuracy degradation in Non-IID scenarios, because a uniform compression scheme is used to compress gradients at workers with different data distributions and volumes, since workers with larger volumes of data are forced to adapt to the same aggressive compression ratios as others. Assigning different compression ratios to workers with different data distributions and volumes is thus a promising solution. In this study, we first derive a function from capturing the correlation between the number of training iterations for a model to converge to the same accuracy, and the compression ratios at different workers;This function particularly shows that workers with larger data volumes should be assigned with higher compression ratios1 to guarantee better accuracy. Then, we formulate the assignment of compression ratios to the workers as an n-variables chi-square nonlinear optimization problem under fixed and limited total communication constrain. We propose an adaptive gradient compression strategy called DAGC, which assigns each worker a different compression ratio according to their data volumes. Our experiments confirm that DAGC can achieve better performance facing highly imbalanced data volume distribution and restricted communication. © 2023 IEEE.

关键词： Nonlinear programming

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Semantic Preserving Learning for Task-Oriented Point Cloud Downsampling 48

Semantic Preserving Learning for Task-Oriented Point Cloud D...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xiong, Jianyu Dai, Tao Zha, Yaohua Wang, Xin Xia, Shu-Tao Shenzhen University College of Computer Science and Software Engineering China Tsinghua University Tsinghua Shenzhen International Graduate School China Peng Cheng Laboratory Research Center of Artificial Intelligence China

ISBN: (纸本)9781728163277

Recent years have witnessed a tremendous growth in the scale and resolution of point clouds. To facilitate the applications of point cloud in downsampling tasks (e.g., point cloud classification), several task-oriented downsampling works have been developed by training with the task-specific loss with one-hot encoded label. However, these methods still suffer from performance degradation at high downsampling scales. In this paper, we propose a general semantic-preserved downsampling framework (SPDF) for point clouds by exploiting the rich knowledge inherent in the task network. Specifically, we firstly refine the previous pipeline to generate richer semantic supervised information. Then, the semantic feature learning is subdivided into label-level and feature-level to guide the training of downsampling network, which can better limit the semantic loss during downsampling. Extensive experiments on the benchmark dataset show that SPDF outperforms state-of-the-art downsampling methods. © 2023 IEEE.

关键词： Downsampling Point cloud Semantic preserving

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract) 39

A Survey on Modern Deep Neural Network for Traffic Predictio...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Tedjopurnomo, David Alexander Bao, Zhifeng Zheng, Baihua Choudhury, Farhana Murtaza Qin, A.K. Rmit University School of Computing Technologies Australia Singapore Management University School of Computing and Information Systems Singapore The University of Melbourne School of Computing and Information Systems Australia Swinburne University of Technology Department of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350322279

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare;making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field. © 2023 IEEE.

关键词： Deep neural networks

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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A Node Confidence Determination Based Moving Target Defense Strategy for SDN 15

A Node Confidence Determination Based Moving Target Defense ...

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15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

作者： Sun, Shimin Xu, Guowei Han, Li Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong Tianjin China Tianjin Univ. of Technol. Natl. Eng. Laboratory for Computer Virus Prevention and Control Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350308693

With the rapid development of Internet technology, network assets have become an indispensable part of modern society. Especially in the face of high-value network assets, the attacker is faced with paying a greater cost to study the network structure. Due to the unknown attacker and attack means, the attacker has an advantage in the attack and defense situation. The moving target defense technique can flexibly change the static characteristics of the network attack surface and increase the difficulty for the attacker to detect the network, thus achieving the purpose of enhancing network security. Based on this technique, this paper proposes a defense strategy based on node confidence. It puts forward stricter constraints on the differential selection of address/port hopping paths, dynamically selects the hopping objects according to the node's confidence, and implements the defense strategy of differential complexity. This further increases the network's uncertainty and the difficulty for the attacker to detect the network structure. The experimental results show that in the path differentiation comparison, the maximum improvement is about 18% compared to the Dynamic Random Route Hopping strategy. In the CPU load experiment, the CPU load rate is reduced by about 4.625% on average compared to the Double Address Hopping strategy. Moreover, for larger hopping period, the proposed strategy also performs well in terms of CPU load rate. The proposed strategy also exhibits good performance in terms of CPU load ratio for large hopping cycles. © 2023 IEEE.

关键词： Network security

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A computer-aided speech analytics approach for pronunciation feedback using deep feature clustering

A computer-aided speech analytics approach for pronunciation...

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作者： Nazir, Faria Majeed, Muhammad Nadeem Ghazanfar, Mustansar Ali Maqsood, Muazzam Department of Software Engineering University of Engineering and Technology Taxila Taxila Pakistan Department of Data Science University of the Punjab Lahore Pakistan School of Architecture Computing and Engineering University of East London London United Kingdom Department of Computer Science COMSATS University Islamabad Attock Campus Pakistan

Nowadays, the demand for language learning is increasing because people need to communicate with other people belonging to different regions for their business deals, study, etc. During language learning, a lot of pronunciation mistakes occur due to unfamiliarity with a new language and differences in accent. In this paper, we perform speech mistakes analysis using deep feature-based clustering. We proposed two novel methods for speech analysis, one to deal with phonemic errors (confusing phonemes) and the other to deal with the prosodic errors (partially changed pronunciation variation of phones). For accurate and efficient language learning, it is important to learn both phonemic as well as prosodic error corrections. In our first method, we perform speech analysis by combining deep CNN features and clustering algorithm to detect the phonemic errors. We classify the phonemes using K-nearest neighbor, Naïve Bayes, and support vector machine (SVM). We perform experiments on the six most frequently mispronounced confusing pairs of Arabic to handle phonemic errors and achieve an accuracy of 94%. In our second method, we proposed the unsupervised phone variation model (PVM) to detect prosodic errors. In PVM, each phone is extended to represent the different types of pronunciation variation of that phone with different proficiency levels. We use an Arabic dataset of 28 individual phones for speech analysis and provide feedback based on the variation of each phone and achieves an accuracy of 97%. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Error correction

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Design and Implementation of an Internet of Things-Based Real-Time Five-Layer Security Surveillance System

Design and Implementation of an Internet of Things-Based Rea...

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International Conference on Artificial Intelligence and Communication Technology, ICAICT 2023

作者： Narwani, Kamlesh Liaquat, Fahad Laghari, Asif Ali Jumani, Awais Khan Jamshed, Junaid Ibrar, Muhammad School of Electronic Information and Communication Huazhong University of Science Technology Wuhan China Computer Science Department DCK Campus DHA SUFFA University Sindh Karachi Pakistan Computer Science ILMA University Sindh Karachi Pakistan Software College Shenyang Normal University Shenyang China School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou China Shanghai University Baoshan China

ISBN: (纸本)9789819966400

The Internet of Things (IoT) is one the major part of the fourth Industrial Revolution;it’s the process to enable things to gather data and get controlled remotely from anywhere in the world through the Internet. IoT is changing the world so that impossibilities are converting into possibilities, e.g., self-driving cars, automated machine payments without human involvement, and remote surgeries. It’s changing almost every domain. The phenomenon of IoT has been observed to have a pervasive impact across various domains. Despite its potential benefits in enabling remote control of devices, security has emerged as a persistent challenge for both businesses and households. This issue has been observed to be particularly pronounced in the context of goldsmith shops, where the implementation of IoT systems has been found to be associated with relatively lower levels of security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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A Novel Association Rule Mining Method for Streaming Temporal Data

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Annals of Data science 2022年第4期9卷 863-883页

作者： Zheng, Hui Li, Peng He, Jing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Nanjing China School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia

Streaming temporal data contains time stamps and values, challenging to quantify relationships of time stamps and corresponding values. Moreover, the characteristics and relationships of streaming temporal data are not invariable. Thus, it is impossible to analyse all data by a trained model at the beginning of data streams. Practically, the trained model to analyse streaming temporal data should change according to the increasing volume of data. Association rule mining, on the other hand, can find potential relationships from given data. This paper proposes an association rule mining method for streaming temporal data to discover potential relationships from streaming temporal data. Our experiments verify our proposed method. A public data set is applied to compare the performance of the proposed method and its counterpart. A small data set is also applied for two case studies to further illustrate our proposed method mine association rules with streaming temporal data with time stamps and corresponding values. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Association rule mining Potential relationships Streaming data

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