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Topic categorization of Tamil News Articles using PreTrained Word2Vec Embeddings with Convolutional Neural Network 1

Topic categorization of Tamil News Articles using PreTrained...

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1st IEEE International Conference on Computational Intelligence for Smart Power System and Sustainable Energy, CISPSSE 2020

作者： Ramraj S Arthi R Murugan, Solai Julie Srm Institute of Science and Technology Department of Software Engineering Chennai India Sri Sai Ram Engineering College Department of Information Technology Chennai India Data Analytics C-DAC Chennai India

ISBN: (纸本)9781728172743

Almost all the problems in NLP are solved using various techniques from machine learning to Deep Learning. Still, there is mystery in language localization. NLP problems are unclear for languages other than English. The problems may be named as Entity Extraction, OCR or classification and prediction in sequence modelling. The amount of people using local language (Tamil, Telegu, Hindi etc) in the social media is increasing, so it is important to automate the process of classifying those contents. Here, the aim is to classify the Tamil news articles to its related topics (Sports, Cinema, Politics). In the existing work they have approached traditional machine learning methods with TFIDF of words as features. In this work we have compared the existing TFIDF feature learning along with Pre-Trained embeddings given to Convolutional Neural Networks (CNN). We found that CNN with pretrained embeddings gave better F1 score compare to TFIDF feature learned with Support Vector Machine (SVM), Naive Bayes (NB) algorithm. © 2020 IEEE.

关键词： Convolutional neural networks

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Systematically Evaluation of Challenge Obfuscated APUFs

arXiv

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arXiv 2022年

作者： Gao, Yansong Yao, Jianrong Pang, Lihui Zhang, Zhi Fu, Anmin Xiong, Naixue Kim, Hyoungshick School of Computer Science and Engineering Nanjing University of Science and Technology China School of Electrical Engineering University of South China China Department of Software Sungkyunkwan University Korea Republic of Data61 CSIRO Sydney Australia National Engineering Research Center for E-Learning Central China Normal University Wuhan430079 China

As a well-known physical unclonable function that can provide huge number of challenge response pairs (CRP) with a compact design and fully compatibility with current electronic fabrication process, the arbiter PUF (APUF) has attracted great attention. To improve its resilience against modeling attacks, many APUF variants have been proposed so far. Though the modeling resilience of response obfuscated APUF variants such as XOR-APUF and lightweight secure APUF (LSPUF) have been well studied, the challenge obfuscated APUFs (CO-APUFs) such as feed-forward APUF (FF-APUF), and XOR-FF-APUF are less elucidated, especially, with the deep learning (DL) methods. This work systematically evaluates five CO-APUFs including three influential designs of FF-APUF, XOR-FF-APUF, iPUF, one very recently design (dubbed as MnS1,S2,S3-APUF) and our newly optimized design (dubbed as OAX-FF-APUF), in terms of their reliability, uniformity (related to uniqueness), and modeling resilience. Three DL techniques of GRU, TCN and MLP are employed to examine these CO-APUFs' modeling resilience-the first two are newly explored. With computation resource of a common personal computer, we show that all five CO-APUFs with relatively large scale can be successfully modeled-attacking accuracy higher or close to its reliability. The hyper-parameter tuning of DL technique is crucial for implementing efficient attacks. Increasing the scale of the CO-APUF is validated to be able to improve the resilience but should be done with minimizing the reliability degradation. As the powerful capability of DL technique affirmed by us, we recommend the DL, specifically the MLP technique always demonstrating best efficacy, to be always considered for examining the modeling resilience when newly composited APUFs are devised or to a large extent, other strong PUFs are constructed. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Heterogeneous and plaintext checkable signcryption for integrating IoT in healthcare system

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Journal of Systems Architecture 2024年 154卷

作者： Hadabi, Abdalla Qu, Zheng Yeh, Kuo-Hui Chen, Chien-Ming Kumari, Saru Xiong, Hu Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of Information Management National Dong Hwa University Hualien974 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Department of Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India

Preserving the confidentiality and integrity of data transmission is paramount in the Internet of Things (IoT)-based healthcare systems. Current encryption techniques that allow plaintext checks primarily serve a specific cryptosystem, lacking the adaptability to work with a diverse system incorporating various cryptographic methods. To address this, we present a unique online/offline heterogeneous signcryption scheme with plaintext checkable encryption (HOOSC-PCE). This approach enables the transition of signcrypted messages from an identity-based cryptosystem (IBC) to a public key infrastructure (PKI) system, improving information interoperability. A key aspect of our scheme is its capacity to allow cloud servers to perform plaintext queries, facilitating efficient data searches using plaintext keywords over encrypted data. Furthermore, the signcryption process is divided into online and offline phases. The online phase handles tasks that require fewer resources, while the offline phase carries out more resource-intensive preparatory tasks. We rigorously tested the HOOSC-PCE scheme's security by proving it secure under the Random Oracle Model (ROM). Meanwhile, compared to similar work, it effectively reduces computation costs by 46.39%, 19.45%, 18.73%, and 13.25% across offline encryption, online encryption, decryption, and search algorithms. The results indicate that the HOOSC-PCE is secure and efficient, confirming its feasibility for IoT-based healthcare systems. © 2024 Elsevier B.V.

关键词： Internet of things

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An Intelligent Resource Scheduling Method with Edge Channel Deployment for BPM

An Intelligent Resource Scheduling Method with Edge Channel ...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Bowen Liu Wanchun Dou Xiaokang Zhou Xuyun Zhang Lianyong Qi Fei Dai Chaochao Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Data Science Shiga University Hikone Japan Department of Computing Macquarie University Sydney Australia College of Computer and Software China University of Petroleum (East China) Qingdao China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Computer Science and Technology Zhejiang University Hangzhou China

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users’ status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method.

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Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI

arXiv

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arXiv 2023年

作者： Xing, Hong Zhu, Guangxu Liu, Dongzhu Wen, Haifeng Huang, Kaibin Wu, Kaishun Guangzhou511453 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Shenzhen Research Institute of Big Data Shenzhen518172 China The School of Computing Science University of Glasgow GlasgowG12 8RZ United Kingdom The University of Hong Kong Hong Kong Guangzhou511453 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518066 China

With the advent of emerging IoT applications such as autonomous driving, digital-twin and metaverse etc. featuring massive data sensing, analyzing and inference as well critical latency in beyond 5G (B5G) networks, edge artificial intelligence (AI) has been proposed to provide high-performance computation of a conventional cloud down to the network edge. Recently, convergence of wireless sensing, computation and communication (SC2) for specific edge AI tasks, has aroused paradigm shift by enabling (partial) sharing of the radio-frequency (RF) transceivers and information processing pipelines among these three fundamental functionalities of IoT. However, most existing design frameworks separate these designs incurring unnecessary signaling overhead and waste of energy, and it is therefore of paramount importance to advance fully integrated sensing, computation and communication (ISCC) to achieve ultra-reliable and low-latency edge intelligence acquisition. In this article, we provide an overview of principles of enabling ISCC technologies followed by two concrete use cases of edge AI tasks demonstrating the advantage of task-oriented ISCC, and pointed out some practical challenges in edge AI design with advanced ISCC solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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DP-GAN+B: A lightweight generative adversarial network based on depthwise separable convolutions for generating CT volumes

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Computers in Biology and Medicine 2024年 174卷 108393-108393页

作者： Xing, Xinlong Li, Xiaosen Wei, Chaoyi Zhang, Zhantian Liu, Ou Xie, Senmiao Chen, Haoman Quan, Shichao Wang, Cong Yang, Xin Jiang, Xiaoming Shuai, Jianwei Postgraduate Training Base Alliance of Wenzhou Medical University Zhejiang Wenzhou325000 China Wenzhou Institute University of Chinese Academy of Sciences Zhejiang Wenzhou325000 China School of Artificial Intelligence Guangxi Minzu University Nanning530006 China Department of Radiology The First Affiliated Hospital of Wenzhou Medical University Wenzhou325000 China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University China Department of Mathematics and Statistics Carleton College 300 N College St NorthfieldMN55057 United States School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

X-rays, commonly used in clinical settings, offer advantages such as low radiation and cost-efficiency. However, their limitation lies in the inability to distinctly visualize overlapping organs. In contrast, Computed Tomography (CT) scans provide a three-dimensional view, overcoming this drawback but at the expense of higher radiation doses and increased costs. Hence, from both the patient's and hospital's standpoints, there is substantial medical and practical value in attempting the reconstruction from two-dimensional X-ray images to three-dimensional CT images. In this paper, we introduce DP-GAN+B as a pioneering approach for transforming two-dimensional frontal and lateral lung X-rays into three-dimensional lung CT volumes. Our method innovatively employs depthwise separable convolutions instead of traditional convolutions and introduces vector and fusion loss for superior performance. Compared to prior models, DP-GAN+B significantly reduces the generator network parameters by 21.104 M and the discriminator network parameters by 10.82 M, resulting in a total reduction of 31.924 M (44.17%). Experimental results demonstrate that our network can effectively generate clinically relevant, high-quality CT images from X-ray data, presenting a promising solution for enhancing diagnostic imaging while mitigating cost and radiation concerns. © 2024 Elsevier Ltd

关键词： Convolution

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Interpretation Area-Guided Detection of Adversarial Samples 20

Interpretation Area-Guided Detection of Adversarial Samples

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20th IEEE International Conference on software Quality, Reliability, and Security, QRS 2020

作者： Wei, JiaLi Fan, Ming Xu, Xi Jia, Ang Xu, Zhou Xue, Lei School of Cyber Science and Engineering Xi'an Jiaotong University China School of Big Data and Software Engineering Chongqing University China The Hong Kong Polytechnic University Department of Computing China

ISBN: (纸本)9781728189154

Deep learning systems are known to be vulnerable to adversarial samples, which are implemented to change the prediction results by adding small perturbations to benign samples. It is significant to defend against an adversarial attack in critical fields such as automatic drive. In this paper, we propose an interpretation area-guided detection method of adversarial samples, which can improve the performance of the typical feature squeezing method by combining the generated interpretation results. Specifically, we divide the input image into two main parts, the interpretation part, and the non-interpretation part. Then we only squeeze the non-interpretation part, which can reduce the side-effect for benign samples. We evaluate our approach on two widely used datasets, and the results demonstrate that our approach outperforms the original feature squeezing method. © 2020 IEEE.

关键词： Learning systems

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Novel Truncated-rank Graph-structured and Tree-guided Sparse Linear Mixed Models for Variable Selection on Genome-wide Association Studies

Novel Truncated-rank Graph-structured and Tree-guided Sparse...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Xiang Liu, Xueling Diao, Jing Zheng, Mengyao Li, Jihe Xie, Yongyi Lai, Kang Geng, Xiao Song, Yijun Jiang, Linshan National University of Singapore School of Computing Singapore National University Health System Group Technology Office Singapore Peking University School Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Department of Preventive Dentistry China Harvard University Harvard T.H. Chan School of Public Health United States Peking University School of Software and Microelectronics China Tongji University Shanghai Skin Disease Hospital Institute of Dermatology School of Medicine China Nanyang Technological University School of Mechanical & Aerospace Engineering Singapore Peking University Third Hospital Engineering Research Center of Bone and Joint Precision Medicine Ministry of Education Orthopaedic Department China Zhejiang University of Finance & Economics Dongfang College China National University of Singapore Institute of Data Science Singapore

ISBN: (纸本)9798350386226

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nalively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential. © 2024 IEEE.

关键词： Confounding factors genome-wide association study mixed model variable selection

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Multi-Dimensional Research software Categorization

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Computing in science and engineering 2025年

作者： Hasselbring, Wilhelm Druskat, Stephan Bernoth, Jan Betker, Philine Felderer, Michael Ferenz, Stephan Hermann, Ben Lamprecht, Anna-Lena Linxweiler, Jan Prat, Arnau Rumpe, Bernhard Schoening-Stierand, Katrin Yang, Shinhyung Kiel University Kiel24098 Germany Berlin12489 Germany University of Potsdam Potsdam14476 Germany Helmholtz Centre for Infection Research Department for Epidemiology Brunswick Germany University of Cologne Cologne51147 Germany Carl von Ossietzky Universität Oldenburg Department of Computer Science Oldenburg26129 Germany Secure Software Engineering Group TU Dortmund Dortmund44227 Germany TU Braunschweig Braunschweig38106 Germany Braunschweig38108 Germany RWTH Aachen University Germany University of Hamburg Hub of Computing and Data Science Hamburg22761 Germany

Research software has been categorized in different contexts to serve different goals. We start with a look at what research software is, before we discuss the purpose of research software categories. We propose a multi-dimensional categorization of research software. We present a template for characterizing such categories. As selected dimensions, we present our proposed role-based, readiness-based, developer-based, and dissemination-based categories. Since our work has been inspired by various previous efforts to categorize research software, we discuss them as related works. We characterize all these categories via the previously introduced template, to enable a systematic comparison. We report on the multi-dimensional categorization of selected research software examples. © 1999-2011 IEEE.

关键词： Aerospace engineering Analytical models data collection data models data visualization Monitoring Numerical models software software development management software engineering

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Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching

Attention-Weighted Federated Deep Reinforcement Learning for...

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作者： Wang, Xiaofei Li, Ruibin Wang, Chenyang Li, Xiuhua Taleb, Tarik Leung, Victor C. M. College of Intelligence and Computing Tianjin University Tianjin China Ministry of Education China Department of Communications and Networking School of Electrical Engineering Aalto University Espoo Finland College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Information Technology and Electrical Engineering Oulu University Oulu90570 Finland Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic. © 1983-2012 IEEE.

关键词： Reinforcement learning

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