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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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An Automatic Question Answering Method for Small-Scale Corpus

An Automatic Question Answering Method for Small-Scale Corpu...

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2020 International Conference on computer science and Communication technology, ICCSCT 2020

作者： Shao, Yikang Xu, Guangde Xu, Mingxue Dong, Lanfang School of Computer Science and Technology University of Science and Technology of China Hefei China Guochuang Software Co. Ltd. Hefei China

It is a growing trend for automatic question answering system to be prominent in the development process of society. There are many methods trying to address this problem, but with deficiencies-relatively developed methods based on template matching need a lot of manual work writing templates, and machine learning based methods need plenty of work collecting a large number of corpus, bring huge burden on small-scale scenery. Facing these problems, we propose an automatic question answering methods meeting the needs of small-scale corpus. This method consists of combining an improved text similarity calculation algorithm and an intention recognition method based on slot filling. We conduct experiments on the problem sets of related fields, and it shows a good performance of the proposed automatic question answering method. Our methods make small scene applications for dialog systems more practical. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Template matching

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The Design and Implementation of Secure Distributed Image Classification Reasoning System for Heterogeneous Edge Computing

The Design and Implementation of Secure Distributed Image Cl...

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

作者： Cong Cheng Lingzhi Li Jin Wang Fei Gu School of Computer Science and Technology Soochow University Collaborative Innovation Center of Novel Software Technology and Industrialization

ISBN: (纸本)9781665416597

Nowadays, the combination of edge computing and artificial intelligence has become a mainstream trend. Based on edge computing and image classification technologies, we design and implement a secure distributed image classification reasoning system for heterogeneous edge computing. The functions of the system consists of two parts: model distributed deployment and image classification reasoning. Firstly, we have designed three distributed deployment schemes for the model deployment on edge devices: random, static and dynamic deployment schemes. Secondly, we have designed three secure distributed image classification reasoning schemes: uncoded, 2-replication and MDS coding reasoning schemes. These reasoning schemes can protect the security of image data in the process of image reasoning and meet the weak security standard. Our system uses edge devices as computing devices, so it has the advantages of low computing cost and saving bandwidth. The experimental results show that our system can protect the security of image data, also has favorable stability and efficiency under the environment of heterogeneous edge computing.

关键词： Privacy Image coding Image edge detection Computational modeling Market research Cognition Stability analysis

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A novel sleep stage classification using CNN generated by an efficient neural architecture search with a new data processing trick

arXiv

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arXiv 2021年

作者： Xue, Yu Yuan, Ziming Slowik, Adam School of Computer and Software Nanjing University of Information Science and Technology China Koszalin University of Technology Poland

With the development of automatic sleep stage classification (ASSC) techniques, many classical methods such as k-means, decision tree, and SVM have been used in automatic sleep stage classification. However, few methods explore deep learning on ASSC. Meanwhile, most deep learning methods require extensive expertise and suffer from a mass of handcrafted steps which are time-consuming especially when dealing with multi-classification tasks. In this paper, we propose an efficient five-sleep-stage classification method using convolutional neural networks (CNNs) with a novel data processing trick and we design neural architecture search (NAS) technique based on genetic algorithm (GA), NAS-G, to search for the best CNN architecture. Firstly, we attach each kernel with an adaptive coefficient to enhance the signal processing of the inputs. This can enhance the propagation of informative features and suppress the propagation of useless features in the early stage of the network. Then, we make full use of GA’s heuristic search and the advantage of no need for the gradient to search for the best architecture of CNN. This can achieve a CNN with better performance than a handcrafted one in a large search space at the minimum cost. We verify the convergence of our data processing trick and compare the performance of traditional CNNs before and after using our trick. Meanwhile, we compare the performance between the CNN generated through NAS-G and the traditional CNNs with our trick. The experiments demonstrate that the convergence of CNNs with data processing trick is faster than without data processing trick and the CNN with data processing trick generated by NAS-G outperforms the handcrafted counterparts that use the data processing trick too. Our research suggests that deep learning has a great potential on the electroencephalogram signal processing especially with the intensification of NAS. Meanwhile, NAS can exert greater power in practical engineering applications. © 2021

关键词： Network architecture

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Reliable Central Nervous System Tumor Differentiation on MRI Images with Deep Neural Networks and Conformal Prediction 1

Reliable Central Nervous System Tumor Differentiation on MRI...

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1st Workshop on Explainable Artificial Intelligence for the Medical Domain, EXPLIMED 2024

作者： Balderas, Luis de Castro, María Moreno Lastra, Miguel Martínez, Jose P. Pérez, Francisco J. Laínez, Antonio Arauzo-Azofra, Antonio Benítez, José M. School of Sciences Technology and Engineering’s Doctorates Department of Computer Science and Artificial Intelligence DiCITS DaSCI iMUDS University of Granada Granada18071 Spain Department of Software Engineering DiCITS DaSCI iMUDS University of Granada Granada18071 Spain Radiodiagnosis Service Hospital Universitario "Virgen de las Nieves" Granada Spain ibs.Granada: Instituto Biosanitario de Granada Granada Spain Rural Engineering Department DiCITS Lab University of Córdoba Córdoba14005 Spain

Central nervous system tumors, particularly gliomas, rank among the top 10 causes of cancer-related deaths worldwide. Thus, precise differentiation of these tumors is crucial for effective treatment, which can reduce patient suffering and lower mortality rates. We propose a deep learning-based technique for glioma differentiation using MRI imaging, which incorporates two novel approaches. First, we represent perfusion sequences as time series, which serve as inputs for a Deep Neural Network (DNN) classifier. This classifier is trained to distinguish the sequences between low-grade glioma (LGG) and high-grade glioma (HGG). Second, we employ Conformal Prediction to calibrate the results, ensuring they include the true category with a 90% probability. This approach has been rigorously tested to evaluate its performance. Our experimental results demonstrate that our deep neural network not only provides accurate but also ensures trustworthy predictions. © 2024 Copyright © for this paper by its authors.

关键词： Lung cancer

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An Intelligent Topology Optimization Strategy Toward the Robust Onion-like Structure 4

An Intelligent Topology Optimization Strategy Toward the Rob...

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4th IEEE International Conference on Smart Internet of Things, SmartIoT 2020

作者： Sun, Qianzhen Qiu, Tie Guo, He School of Software Dalian University of Technology Dalian China School of Computer Science and Technology College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9781728165141

ISBN: (纸本)9781728165141

The Internet of Things (IoT) is susceptible to threats from natural disasters and human damage, thereby affecting the communication efficiency and security of the network. The scale-free IoT topology can resist the impact of random attacks, but is particularly vulnerable to malicious attacks against critical nodes. To tackle this problem, this paper proposes an intelligent topology optimization strategy combined with convolutional neural network, which evolves the initial IoT topology into a robust onion-like structure against malicious attacks. In addition, we introduce a method to convert topology information into a sequence model suitable for feature extraction. The experimental results show that this strategy can efficiently optimize the network topology into an onion-like structure. © 2020 IEEE.

关键词： Internet of things

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Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma

arXiv

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arXiv 2024年

作者： Bhattacharjya, Abanti Islam, Md Manowarul Uddin, Md Ashraf Talukder, Md. Alamin Azad, A.K.M. Aryal, Sunil Paul, Bikash Kumar Tasnim, Wahia Abdulllah Almoyad, Muhammad Ali Moni, Mohammad Ali School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Riyadh13318 Saudi Arabia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Bangladesh Department of Basic Medical Sciences College of Applied Medical Sciences King Khalid University Saudi Arabia Artificial Intelligence & Data Science Faculty of Health and Behavioural Sciences The University of Queensland Australia

With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously. © 2024, CC BY.

关键词： Topology

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A survey on natural language video localization

arXiv

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arXiv 2021年

作者： Liu, Xinfang Nie, Xiushan Tan, Zhifang Guo, Jie Yin, Yilong School of software Shandong University China School of Computer Science and Technology Shandong Jianzhu University China

Natural language video localization (NLVL), which aims to locate a target moment from a video that semantically corresponds to a text query, is a novel and challenging task. Toward this end, in this paper, we present a comprehensive survey of the NLVL algorithms, where we first propose the pipeline of NLVL, and then categorize them into supervised and weakly-supervised methods, following by the analysis of the strengths and weaknesses of each kind of methods. Subsequently, we present the dataset, evaluation protocols and the general performance analysis. Finally, the possible perspectives are obtained by summarizing the existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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SPA: a graph spectral alignment perspective for domain adaptation 23

SPA: a graph spectral alignment perspective for domain adapt...

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

作者： Zhiqing Xiao Haobo Wang Ying Jin Lei Feng Gang Chen Fei Huang Junbo Zhao College of Computer Science and Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University CUHK-SenseTime Joint Lab The Chinese University of Hong Kong School of Computer Science and Engineering Nanyang Technological University Alibaba Group

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a finegrained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA.

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Drowsy Driving Detection System Using Face Detection

Drowsy Driving Detection System Using Face Detection

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Ismoiljon Mirabdullayev Mohamed Uvaze Ahamed Ayoobkhan A.M. Jasmine Hashana Liayakath Ali Khan Subair Ali Software Engineering New Uzbekistan University Tashkent Uzbekistan Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India School of Information Technology Digital Media and Mass Communication TMC Institute in Tashkent Tashkent Uzbekistan

Drowsy driving poses a major threat to road safety, demanding effective solutions. To address this issue, this paper introduces a cutting-edge drowsy driving detection system that leverages advanced face detection algorithms to analyze real-time facial features. By closely monitoring eye movements and mouth patterns, the system is capable of accurately identifying early signs of fatigue. In response, timely warnings are promptly issued to the driver through various means, including audible alarms, visual cues, or even physical interventions. Although the system holds great potential in enhancing road safety, there are still challenges to overcome, particularly regarding accuracy and adaptability. However, ongoing research endeavors aim to refine the algorithms and integrate additional physiological and behavioral measures, with the goal of further improving the system's capabilities. By preventing accidents caused by drowsiness, this innovative system demonstrates a promising pathway towards bolstering road safety. By addressing the critical issue of drowsy driving, it contributes significantly to the overall safety of drivers and passengers alike, making our roads a safer place for everyone.

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