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TechRxiv 2021年

作者： Yang, Xinghao Gong, Yongshun Liu, Weifeng Bailey, James Zhu, Tianqing Tao, Dacheng Liu, Wei The School of Computer Science University of Technology Sydney 15 Broadway UltimoNSW2007 Australia The School of Software Shandong University Jinan China Qingdao266580 China The Department of Computing and Information Systems University of Melbourne Australia The JD Explore Academy China The Digital Science Institute Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

Deep learning models are known immensely brittle to adversarial image examples, yet their vulnerability in text classification is insufficiently explored. Existing text adversarial attack strategies can be roughly divided into three categories, i.e., character-level attack, word-level attack, and sentence-level attack. Despite the success brought by recent text attack methods, how to induce misclassification with the minimal text modifications while keeping the lexical correctness, syntactic soundness, and semantic consistency simultaneously is still a challenge. To examine the vulnerability of deep models, we devise a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) approach which attacks text documents not only at a unigram word level but also at a bigram level to avoid generating meaningless sentences. We also present a hybrid attack strategy that collects substitution words from both synonyms and sememe candidates, to enrich the potential candidate set. Besides, a Semantic Preservation Optimization (SPO) method is devised to determine the word substitution priority and reduce the perturbation cost. Furthermore, we constraint the SPO with a semantic Filter (dubbed SPOF) to improve the semantic similarity between the input text and the adversarial example. To estimate the effectiveness of our proposed methods, BU-SPO and BU-SPOF, we attack four victim deep learning models trained on three real-world text datasets. Experimental results demonstrate that our approaches accomplish the highest semantics consistency and attack success rates by making the minimal word modifications compared with competitive methods. © 2021, CC BY.

关键词： Semantics

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Branch-based Link Planning for Time-varying Space-air Integrated networks

Branch-based Link Planning for Time-varying Space-air Integr...

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IEEE International Conference on Communications (ICC)

作者： Feng Wang Dingde Jiang Houbing Song Lei Shi School of Astronautics and Aeronautic University of Electronic Science and Technology of China Chengdu China Department of Electrical Computer Software and Systems Engineering ERAU Daytona Beach FL USA Department of Computing Carlow Institute of Technology Carlow Ireland

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

The space-air integrated networks (SAIN) has been a valuable architecture due to its characteristics of wide coverage and high survey accuracy. However, it is not easy to design routing strategy in SAIN, considering complex relative motion of low-earth-orbit (LEO) satellites and unmanned aerial vehicles (UAV). Specifically, the main problem is how to find optimal links to realize stable and efficient UAV data transmission in time-varying SAIN. To address the problem above, this paper first analyzes the motion characteristics of satellites and UAVs to find the optimal accessing control satellites (ACSs) for the UAV. Then, different from traditional routing, a branch-based link planning strategy (BLPS) is proposed to realize efficient and stable inter-satellite link (ISL) deployment between ACSs, which can guarantee timely transmission of UAV data. Simulation results show that the proposed BLPS strategy is feasible and effective.

关键词： Satellites Gold Attenuation Low earth orbit satellites Data communication Unmanned aerial vehicles Routing

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Secure and Privacy-Preserving Automated Machine Learning Operations into End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction

arXiv

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arXiv 2022年

作者： Hennebelle, Alain Ismail, Leila Materwala, Huned Kaabi, Juma Al Ranjan, Priya Janardhanan, Rajiv School of Computing and Information Systems The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates College of Health Sciences Department of Internal Medicine United Arab Emirates University United Arab Emirates Mediclinic Al Ain Abu Dhabi United Arab Emirates Bhubaneswar Institute of Technology India Faculty of Medical & Health Sciences SRM Institute of Science & Technology India

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to take precautionary measures to avoid/predict the occurrence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by the blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and to ensure security and privacy of the user’s data. Furthermore, we provide a comparative analysis of different medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out between our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system using RF predicts diabetes with 4.57% more accuracy on average compared to LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show significant improvement. The performance is improved by 1.14% and 0.02% after feature selection for PIMA Indian and Sylhet datasets respectively, while it reduces by 0.89% for MIMIC III. © 2022, CC BY.

关键词： Logistic regression

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Timed data plane update in data center network 25

Timed data plane update in data center network

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25th IEEE International Conference on Parallel and Distributed systems, ICPADS 2019

作者： Liu, Yuxiang Gao, Xiaofeng Zheng, Jiaqi Chen, Guihai Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781728125831

software-Defined Networks make it more principled and formal to operate and verify networks, which decouple the control plane and the data plane and introduce flexibility and programmability into the network. The SDN control plane needs to frequently update the data plane. However, the decoupled control and data planes face new challenges. The network is still an asynchronous distributed system in rule updates. If the update operations are not carefully scheduled, it may result in blackhole, congestions, and violate the consistency properties. Motivated by the tightly synchronized SDNs, we proposed TIDERS Algorithm for consistent network updates in 'timed SDNs'-where one node update can be scheduled at specific time. In TIDERS, we use synchronized time technique to achieve fast network updates while guaranteeing consistency properties. We focus on congestion-free in scheduling the update operations. To achieve this, we first set up a flow-based dependency graph and transform it into switch-operation level dependency graph. Based on operation dependency graph, we generate an update schedule and gradually optimize it to finally get a fast update schedule. After that, we conduct a series of simulations comparing our approach with several other methods to evaluate it. © 2019 IEEE.

关键词： Flow graphs

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Intelligent Management Frameworks for Global Cooperation

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International Journal of Intelligent systems 2025年第1期2025卷

作者： Ana M. Gonzalez de Miguel Antonio Sarasa-Cabezuelo Computer Science Faculty Department of Software Engineering and Artificial Intelligence Complutense University of Madrid 9 Prof. J. Garcia Santesmases Ciudad Universitaria Madrid 28040 Spain ucm.es Computer Science Faculty Department of Information Systems and Computing Complutense University of Madrid 9 Prof. J. Garcia Santesmases Ciudad Universitaria Madrid 28040 Spain ucm.es

This paper presents the definition, use, and evaluation of intelligent management frameworks for global cooperation. The research work brings new concepts and ideas to design new management models and artificial intelligence solutions in sustainable environments. An intelligent management framework is a flexible and efficient vertical association of models, architectures, and processes. It is a mixed (architectural and methodological) association of services and procedures across IT departments of global organizations. The paper presents a general top-down approach to design these frameworks for global, cooperative models of intelligence. The approach includes five levels of abstraction and three refinement techniques. These elements are used to design an evaluation case study with global services and process-oriented cooperation for current sustainable targets in education. In our future work, we will implement these management solutions for government organizations currently involved with digital transformations.

关键词： artificial intelligence global cooperation intelligence sustainability management frameworks

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Synergy of edge intelligence and blockchain: a comprehensive survey

TechRxiv

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TechRxiv 2021年

作者： Wang, Xiaofei Ren, Xiaoxu Qiu, Chao Xiong, Zehui Yao, Haipeng Leung, Victor C.M. College of Intelligence and Computing Tianjin University Tianjin300350 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100088 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Edge intelligence (EI), as an emerging technology, has been attracting significant attention. It pushes the frontier of artificial intelligence (AI) from the cloud to the network edge, aiming to embrace and support the next-generation communications while unleashing AI services. However, it faces challenges in its decentralized management and security, which limit its capabilities to support services with numerous requirements. On the other side, blockchain (BC), as a promising decentralized technology, is beneficial to tackle the above issues. However, there exist some technical challenges for BC, such as transaction capacity, scalability, and fault tolerance. Motivated by significant current interest around EI and BC, this survey examines whether the synergy of EI and BC can make a powerful network with combined functionalities of both cutting-edge technologies. Accordingly, we develop EI-chain and Chain-intelligence to realize reliable computing-power management, data administration, and model optimization at the edges, while improving the functions of BC by leveraging complementary characteristics of EI and BC, further making up for their own limitations. In this survey, a wide spectrum of literature is carefully reviewed to enable the EI-chain and Chain-intelligence. Moreover, we cover the technological aspects of EI-chain and Chain-intelligence: overview, motivations, and frameworks. Finally, some challenges and future directions are explored. We believe this survey will provide developers and researchers a comprehensive view on the synergy of EI and BC, while accelerating the design of a powerful integrated network. © 2021, CC BY-NC-SA.

关键词： Blockchain

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Comparative analysis of time series model and machine testing systems for crime forecasting

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Neural computing and Applications 2020年第17期33卷 10621-10636页

作者： Jha, Sudan Yang, Eunmok Almagrabi, Alaa Omran Bashir, Ali Kashif Joshi, Gyanendra Prasad School of Computer Science and Engineering Lovely Professional University Phagwara India School of Software Soongsil University Seoul South Korea Department of Information Systems King Abdul Aziz University Jeddah Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan University Manchester UK School of Electrical Engineering and Computer Science NUST Islamabad Pakistan Department of Computer Science and Engineering Sejong University Seoul South Korea

Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.

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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

arXiv

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arXiv 2024年

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Yu, Zihao Guan, Fengbin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos V. Timofte, Radu Lu, Yiting Li, Xin Wang, Xinrui Guan, Fengbin Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Fang, Xi Wang, Weigang Yan, Jun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Chen, Baoying Zeng, Jishen Xie, Haiyi Wang, Chengwei Yang, Jianquan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie Shanghai Jiao Tong University China Nanyang Technological University Singapore ETH Zürich Switzerland University of Würzburg Germany Korea Electronics Technology Institute Korea Republic of ByteDance China Hangzhou Dianzi University China Sankuai China Beijing University of Posts and Telecommunications China Beijing Xiaomi Mobile Software Co. Ltd. China University of Science and Technology of China China High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran University of Texas Austin United States University of Massachusetts Amher United States Department of Informatics Systems and Communication Xiangjiang Laboratory Hunan University of Technology and Business China Jinan University China Sun Yat-sen University China Alibaba Group China Cisco Systems Inc. United States DP Technology Ltd. Shopee Pte. Ltd Tongji University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China University of Würzburg Germany Kuaishou Technology China Peking University China Zhejiang University China Bytedance Inc China Xidian University China University of British Columbia Canada University of Toronto Canada

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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RETRACTED ARTICLE: An Intelligent and Secure Air Quality Monitoring System Using Neural Network Algorithm and Blockchain

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IETE Journal of Research 2023年第11期69卷 CVII-CXX页

作者： Abu Buker Siddique Rafaqat Kazmi Habib Ullah Khan Sikandar Ali Ali Samad Gulraiz Javaid 1 Department of Software Engineering The Islamia University of Bahawalpur Bahawalpur63100 Pakistan 2 Department of Accounting and Information Systems College of Business and Economics Qatar University Doha 2713 Qatar 3 Department of Information Technology The University of Haripur Haripur22620 Khyber Pakhtunkhwa Pakistan 4 Faculty of Computing Department of Computer Sciences The Islamia University of Bahawalpur Bahawalpur 63100 Pakistan

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain systems and Industrial Internet of Things”:Abu Buker Siddique, Rafaqat Kazmi, Habib Ullah Khan, Sikandar Ali, Ali Samad & Gulraiz Javaid (2022) An Intelligent and Secure Air Quality Monitoring System Using Neural Network Algorithm and Blockchain, IETE Journal of Research, DOI:10.1080/03772063.2022.2052984Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal’s peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as “Retracted”.

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Green IoT using UAVs in B5G networks: A review of applications and strategies

arXiv

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arXiv 2021年

作者： Alsamhi, Saeed H. Afghah, Fatemeh Sahal, Radhya Hawbani, Ammar Al-Qaness, A.A. Lee, B. Guizani, Mohsen Software Research Institute Athlone Institute of Technology Athlone Ireland IBB University Ibb Yemen School of Informatics Computing and Cyber Systems Northern Arizona University United States Hudaidah University Hudaida Yemen School of Computer Science and Technology University of Science and Technology of China Hefei China State Key Laboratory for Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Department of Computer Science and Engineering Qatar University Qatar

Recently, Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented. © 2021, CC BY.

关键词： Energy efficiency

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