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2nd International Conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2021

作者： Zheng, Menghua Xu, Jiayu Shen, Yinjie Tian, Chunwei Li, Jian Fei, Lunke Zong, Ming Liu, Xiaoyang School of Software Northwestern Polytechnic University Xi'an China Yangtze River Delta Research Institute of NPU Taicang China Shenzhen Research Institute Guangdong Databeyond Technology Co. Ltd Guangdong Shenzhen China Software Technology Co. Ltd. China School of Computers Guangdong University of Technology Guangzhou510006 China School of Electronics Engineering and Computer Science Peking University Beijing China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

Deep learning techniques as well as CNNs can learn power context information, they have been widely applied in image recognition. However, deep CNNs may reply on large width and large depth, which may increase computational costs. Attention mechanism fused into CNNs can address this problem. In this paper, we summary an attention mechanism acts a CNN for image classification. Firstly, the survey shows the development of CNNs for image classification. Then, we illustrate basis of CNNs and attention mechanisms for image classification. Next, we give the main architecture of CNNs with attentions, public and our collected datasets, experimental results in image classification. Finally, we point out potential research points, challenges attention-based for image classification and summary the whole paper. © 2022 institute of Physics Publishing. All rights reserved.

关键词： Image classification

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Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation

arXiv

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arXiv 2023年

作者： Carrazco, Julio Ivan Davila Morerio, Pietro Del Bue, Alessio Murino, Vittorio Italian Institute of Technology Genoa Italy Department of Computer Science University of Verona Verona Italy Department of Computer Science and Technology Bioengineering Robotics and Systems Engineering University of Genova Genoa Italy Department of Marine Electrical Electronic and Telecommunications Engineering Robotics and Systems Engineering University of Genova Genoa Italy

This paper presents a classification framework based on learnable data augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA) problem. OS-UDA is the most challenging setting in Domain Adaptation, as only one single unlabeled target sample is assumed to be available for model adaptation. Driven by such single sample, our method LearnAug-UDA learns how to augment source data, making it perceptually similar to the target. As a result, a classifier trained on such augmented data will generalize well for the target domain. To achieve this, we designed an encoder-decoder architecture that exploits a perceptual loss and style transfer strategies to augment the source data. Our method achieves state-of-the-art performance on two well-known Domain Adaptation benchmarks, DomainNet and VisDA. The project code is available at https://***/IIT-PAVIS/LearnAug-UDA. © 2023, CC BY.

关键词： Benchmarking

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Communication-Efficient Distributed Stochastic Gradient Descent with Pooling Operator

SSRN

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SSRN 2023年

作者： Cai, Zhengao Chen, Aiguo Luo, Yi Li, Jiahao Shenzhen Institute of Advanced Study University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Information and Software Engineering University of Electronic Science and Technology of China China

Training deep neural networks on large datasets can be accelerated by distributing the computations across multiple worker nodes. The distributed stochastic gradient descent (D-SGD) algorithm combined with gradient sparsification is a typical algorithm for this distributed training model, which guarantees better convergence and can significantly reduce communication bandwidth. However, we find that the existing gradient sparsification algorithms ignore the local correlation between gradient values under certain conditions, which can incur a loss of accuracy since the update region of the global parameter will stay in a particular area for a long epoch. Based on this observation, we introduced the pooling operator to solve this problem and combined it with the error-feedback method to design a new communication-efficient distributed gradient descent algorithm. We prove our algorithm converges at the same rate as vanilla SGD when equipped with error feedback. Experiments on CNN, an LSTM, and a ResNet model demonstrated that our algorithm could compress the number of uploaded gradient bits by two or three orders of magnitude while guaranteeing the high accuracy of the model. © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Experimental Evaluation of Internet of Things Assisted Industrial Automation System with Smart Emergency Alert Mechanism

Experimental Evaluation of Internet of Things Assisted Indus...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Yamini, B. Sreeraj, S. Supriya, K. Vinithra Banu, T. Ragini, P.Y. Kurada, Phaneendra Srm Institute of Science and Technology School of Computing Department of Networking and Communications Tamil Nadu Chennai India Sri Krishna College of Engineering and Technology Department of M. Tech Cse Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350308167

Environmental sustainability, energy and raw material availability, and globalization are just a few of the major societal concerns that are generating new demands on a wide range of societal players. Efficiency, adaptability, sustainability, and competitiveness are some of the new criteria. These have been around for a while, and a variety of systems have been tuned locally in terms of a few of these requirements, but recent developments with regard to communication and computational characteristics have made them much more useful to guarantee new possibilities for combining various parts of society, allowing for optimum performance on a scale that is far greater. The impact of IoT on several sectors of the economy has been substantial. The Internet of Things (IoT) is a technology that allows us to manage physical things over the internet in order to save time and energy. There are several factories in India because of the country's rapid economic growth. However, industrial accidents were a severe problem in many sectors. However, industrial accidents were a big problem because they resulted in casualties and financial losses for businesses. This initiative aims to reduce the devastating effects of fires in high-risk industries including the oil, chemical and petrochemical sectors, where many lives and fortunes have been lost. It is crucial to have a system in place that can ensure the safety of the factory grounds and promptly alert the proper authorities should an emergency arise. In the past, a single bottle at a time would be loaded onto a conveyor and filled. This strategy is time-consuming and costly. Internet of Things (IoT) in Industries has been implemented to monitor and notify the individual in charge to take suitable measures in order to reduce manual overhead and therefore partially meet our needs. The manufacturing sector might benefit from AI-based management and oversight. 'Ecosystem having Manufacturing Relevant Implication' better describes the I

关键词： Accident prevention

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A 3d convolutional neural network for bacterial image classification 3rd

A 3d convolutional neural network for bacterial image classi...

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3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019

作者： Mhathesh, T.S.R. Andrew, J. Martin Sagayam, K. Henesey, Lawrence Department of Bioinformatics Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Systems and Software Engineering School of Engineering Blekinge Institute of Technology Karlskrona Sweden

ISBN: (纸本)9789811552847

Identification and analysis of biological microscopy images need high focus and years of experience to master the art. The rise of deep neural networks enables analyst to achieve the desired results with reduced time and cost. Light sheet fluorescence microscopies are one of the types of 3D microcopy images. Processing microscopy images is tedious process as it consists of low-level features. It is necessary to use proper image processing techniques to extract the low-level features of the biological microscopy images. Deep neural networks (DNN) are efficient in extracting the features of images and able to classify with high accuracy. Convolutional neural networks (CNN) are one of the types of neural networks that can provide promising results with less error rates. The ability of CNN to extract the low-level features of images makes it popular for image classification. In this paper, a CNN-based 3D bacterial image classification is proposed. 3D images contain more in-depth features than 2D images. The proposed CNN model is trained on 3D light sheet fluorescence microscopy images of larval zebrafish. The proposed CNN model classifies the bacterial and non-bacterial images effectively. Intense experimental analyses are carried out to find the optimal complexity and to get better classification accuracy. The proposed model provides better results than human comprehension and other traditional machine learning approaches like random forest, support vector classifier, etc. The details of network architecture, regularization, and hyperparameter optimization techniques are also presented. © Springer Nature Singapore Pte Ltd 2021.

关键词： Image classification

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Artificial intelligence to link environmental endocrine disruptors(EEDs)with bone diseases

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第3期13卷 187-206页

作者： Khaled A.Al-Utaibi M.Idreest Ayesha Sohail Fatima Arift Alessandro Nutini Sadiq M.Sait Computer Science and Software Engineering Department University of Ha'il Ha ilSaudi Arabia Department of Mathematics Comsats University Islamabad Lahore 54000Pakistan Center for Study in Motor Science 94 via di Tiglio loc.Arancio55100LuccaItaly Center for Communications and IT Research Research Institute King Fahd University of Petroleum Minerals Dhahran 31261Saudi Anabia

Our endocrine system is not only complex,but is also enormously sensitive to the imbalances caused by the environmental stressors,extreme weather situation,and other geographical *** endocrine disruptions are associated with the bone *** is a bone disorder that occurs when bone mineral density and bone mass *** affects women and men of all races and ethnic groups,causing bone weakness and the risk of *** stresses are referred to physical,chemical,and biological factors that can impact species *** research aims to examine the impact of environmental stresses on bone diseases like osteoporosis and low bone mass(LBM)in the United States(US).For this purpose,we use an artificial neural network model to evaluate the correlation between the data.A multilayer neural network model is constructed using the Levenberg–Marquardt training algorithm,and its performance is evaluated by mean absolute error and coefficient of *** data of osteoporosis and LBM cases in the US are divided into three groups,including gender group,age group,and race/ethnicity *** group shows a positive correlation with environmental stresses and thus the endocrinology.

关键词： Endocrine disruptors osteoporosis LBM environmental stresses spatial ANNs

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Understanding Research Trends in Android Malware Research Using Information Modelling Techniques

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computers, Materials & Continua 2021年第3期66卷 2655-2670页

作者： Jaiteg Singh Tanya Gera Farman Ali Deepak Thakur Karamjeet Singh Kyung-sup Kwak Chitkara University Institute of Engineering&Technology Chitkara UniversityPunjab140401India Department of Software Sejong UniversitySeoul05006South Korea Department of Computer Science and Engineering Thapar UniversityPatiala147001India Department of Information&Communication Engineering Inha UniversityIncheon22212South Korea

Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8%of the market *** popularity of Android has drawn the attention of cybercriminals and malware *** malicious applications can steal sensitive information like contacts,read personal messages,record calls,send messages to premium-rate numbers,cause financial loss,gain access to the gallery and can access the user’s geographic *** surveys on Android security have primarily focused on types of malware attack,their propagation,and techniques to mitigate *** the best of our knowledge,Android malware literature has never been explored using information modelling ***,promulgation of contemporary research trends in Android malware research has never been done from semantic point of *** paper intends to identify intellectual core from Android malware literature using Latent Semantic Analysis(LSA).An extensive corpus of 843 articles on Android malware and security,published during 2009–2019,were processed using ***,the truncated singular Value Decomposition(SVD)technique was used for dimensionality ***,machine learning methods were deployed to effectively segregate prominent topic solutions with minimal *** to observed term and document loading matrix values,this five core research areas and twenty research trends were ***,potential future research directions have been detailed to offer a quick reference for information *** study concludes to the fact that Android security is crucial for pervasive Android *** analysis is the most widely investigated core area within Android security research and is expected to remain in trend in near *** trends indicate the need for a faster yet effective model to detect Android applications causing obfuscation,financial attacks and stealing user information.

关键词： Android security research trends latent semantic analysis vulnerabilities malware machine learning clustering

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Beyond the network-based multi-omics data integration in COPD: a pathway-centric analysis

Beyond the network-based multi-omics data integration in COP...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Pasquale Sibilio Federica Conte Paola Paci Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Institute for Systems Analysis and Computer Science “Antonio Ruberti” National Research Council Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Chronic obstructive pulmonary disease (COPD) is a lung disease causing hundred thousand of death each year worldwide and defined as a respiratory and airflow impairment majorly due to large and small airways dysfunctions. COPD could be considered as a syndrome that includes disease axes of variable pathological and clinical conditions. Despite recent advances, the comprehension of the molecular mechanisms responsible for COPD disease spectrum is far to be reached. Moreover, we should also consider continuous smoking exposure, which could lead to variability in the disease mechanisms and progression. Network-based multi-omics data integration can help to study the association between molecular determinants on diverse biomolecular layers in a disease context. In a previous study, we leveraged lung RNA-seq and DNA-methylation data of a COPD-control cohort to build a correlation-based integrated network (called coupled network), that helped to unveil genes involved in immune and inflammatory modulation of COPD. Therefore, in our previous study we highlighted the most important genes of the coupled network and inspected their single contribution to disease-related pathways. In this study we aim to overcome this limitation by performing a pathway activity analysis by considering the expression and DNA methylation profiles of coupled network genes. Moreover, we exploit this analysis to study the possible contribution of coupled network genes to the differential disease progression between current and former smokers patients.

关键词： Pathology Lungs Data integration Modulation DNA Chronic obstructive pulmonary disease Biology Bioinformatics Immune system

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Towards Hierarchical Categorical Named Entity Recognition via Injective Multi-task Learning Strategy 8

Towards Hierarchical Categorical Named Entity Recognition vi...

引用

8th International Conference on Big Data and Information Analytics, BigDIA 2022

作者： Xiaoguang, Yuan Chaofan, Dai Youchen, Wang Yaokai, Cheng Zengwenpro, Yu Tianfeng, Du National University of Defense Technology Changsha China Beijing Institute of Computer Technology and Application Beijing China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China School of Computer Science and Technology Xidian University Xi'an China School of Management and Engineering Captital University of Economics and Business Beijing China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

The Named Entity Recognition (NER) task aims to locate meaningful entities in a text and classify them into pre-defined categories. NER is a fundamental task in the field of natural language processing. In practice, entities often have multiple types at different levels of granularity, and it is essential to identify various levels of categories of entities simultaneously. A deep learning-based approach treats NER as a sequence annotation problem by giving each character a unique entity label, which significantly improves the accuracy of NER. Deep learning-based NER performs well on single-category and coarse-grained NER but does not perform well on hierarchical category NER problems. This paper proposes a multi-task learning-based hierarchical category named entity recognition model to solve this problem. The model uses a multi-task learning mechanism to simultaneously learn multiple levels of information of named entity recognition tasks. Named entity recognition results at higher levels are directly constructed from lower-level recognition structures. Experiments show that both systems can significantly improve the recognition performance at each level compared to the basic multi-tasking model. The model is also compared with each level's traditional named entity recognition method. The hierarchical category named entity recognition model can sig-nificantly improve the performance of named entity recognition tasks at each level. © 2022 IEEE.

关键词： Learning systems

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CoChain: High Concurrency Blockchain Sharding via Consensus on Consensus

CoChain: High Concurrency Blockchain Sharding via Consensus ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Mingzhe Li You Lin Jin Zhang Wei Wang Computer Science and Engineering Department Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Computer Science and Engineering Department Hong Kong University of Science and Technology Technology and Research Institute of High Performance Computing and Agency for Science Singapore

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes (<2/3) while securing the system, thus reducing the shard size safely. We implement CoChain based on Harmony and conduct extensive experiments. Compared with Harmony, CoChain achieves 35x throughput gain with 6,000+ nodes.

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