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14th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Senthilkumar, B. Sujithra, M. Mani Barathi, S.P.S. Department of Mechanical Engineering Kumaraguru College of Technology India Department of Computing – Data Science Coimbatore Institute of Technology India

Number of Smartphone users are increasing day by day and mobiles have become an integral part of the society. This is because of the rich variety of mobile devices and essential applications provided by its manufacturers. The increasing number of mobile devices invite skilled developers and hackers to develop malware that invades personal and business information in a very efficient manner. Therefore, mobile devices are an ideal target for various security issues and data privacy threats in a mobile ecosystem. Threats posed by malwares include leaking of private information, financial loss to users, system damage. For better protection on computers, researchers and manufacturers making great efforts to produce anti-malware systems with effective detection methods why the most targeted platform for the malware developers is Android and why and how and when malwares propagate into the mobile system with how they are detected, and protection mechanism is discussed in this paper. © Grenze Scientific Society, 2023.

关键词： Personal computing

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Research on Information System Cloud computing Scheduling Method Based on Swarm Intelligence Algorithm 3

Research on Information System Cloud Computing Scheduling Me...

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3rd International Signal Processing, Communications and Engineering Management Conference, ISPCEM 2023

作者： Xiang, Wang Yancheng, Wang School of Computing and Data Science Xiamen University Malaysia Jalan Sunsuria Bandar Sunria Selangor Sepang43900 Malaysia

ISBN: (纸本)9798350307023

With the advancement of cloud computing technology, research on cloud computing task scheduling has become a hot topic. This paper mainly studies the cloud computing scheduling method of information systems based on swarm intelligence algorithm, including cloud computing scheduling model based on particle swarm optimization(PSO), improved Invasive Tumor Growth Optimization(ITGO) model structure, and multi-objective cloud computing task scheduling based on improved artificial bee colony algorithm. This paper concludes that by introducing multi-objective optimization terms, replacing multi-objective optimization terms with fitness functions, initializing tumor cell set functions, and improving search strategies and selection strategies, the efficiency and quality of cloud computing task scheduling can be improved. In order to realize the optimal utilization of cloud computing resources and meet the needs of users. © 2023 IEEE.

关键词： Multiobjective optimization

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FVCap: An Approach to Understand Scanned Floor Plan Images Using Deep Learning and its Applications

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SN Computer science 2024年第4期5卷 383页

作者： Goyal, Shreya Chattopadhyay, Chiranjoy Bhatnagar, Gaurav AI Labs American Express Bengaluru India School of Computing and Data Science FLAME University Pune India Department of Mathematics Indian Institute of Technology Jodhpur Jodhpur India

Understanding the layouts and features of indoor scenes necessitates graphic recognition in scanned floor plan images. Scanning floor plans usually results in obvious and unanticipated interruptions. Accurately identifying symbols in such a scenario has long been a contentious issue in the document image analysis field. The task was performed with respectable efficiency using classic machine learning approaches such as hand-crafted features and deep neural networks. The strengths of the previously proposed Capsule network and VGG19 are combined in this research to create a hybrid network. The proposed network FVCap employs the VGG19 network for feature encoding and the Capsule network for classification and decoding from floor plan region images. The proposed approach can recognise and identify region elements in scanned floor plan images, as well as perform end-to-end room labeling with significantly more accuracy than other hybrid networks and conventional machine learning models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Application Capsule Floor plans Graphic recognition Room labelling

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LSGraph: A Locality-centric High-performance Streaming Graph Engine 24

LSGraph: A Locality-centric High-performance Streaming Graph...

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19th European Conference on Computer Systems, EuroSys 2024

作者： Qi, Hao Wu, Yiyang He, Ligang Zhang, Yu Luo, Kang Cai, Minzhi Jin, Hai Zhang, Zhan Zhao, Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom Zhejiang Lab China

ISBN: (纸本)9798400704376

Streaming graph has been broadly employed across various application domains. It involves updating edges to the graph and then performing analytics on the updated graph. However, existing solutions either suffer from poor data locality and high computation complexity for streaming graph analytics, or need high overhead to search and move graph data to ensure ordered neighbors during streaming graph update. This paper presents a novel locality-centric streaming graph engine, called LSGraph, to enable efficient both graph analytics and graph update. The main novelty of this engine is a differentiated hierarchical indexed streaming graph representation approach to achieve efficient data search and movement for graph update and also maintain data locality and ordered neighbors for efficient graph analytics simultaneously. Besides, a locality-aware streaming graph data update mechanism is also proposed to efficiently regulate the distance of data movement, minimizing the overhead of memory access during graph update. We have implemented LSGraph and conducted a systematic evaluation on both real-world and synthetic datasets. Compared with three cutting-edge streaming graph engines, i.e., Terrace, Aspen, and PaC-tree, LSGraph achieves 2.98×-81.08×, 1.46×-12.56×, and 1.26×-10.31× speedups during graph update, while obtaining 1.02×-4.28×, 1.58×-3.55×, and 1.20×-2.72× speedups during graph analytics, respectively. © 2024 Owner/Author.

关键词： Engines

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Towards Impactful Applications of AI4EO in the Global South

Towards Impactful Applications of AI4EO in the Global South

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2023 Joint Urban Remote Sensing Event, JURSE 2023

作者： Tingzon, Isabelle Miraflor, James Matthew Xiang Zhu, Xiao Kochupillai, Mrinalini Technical University of Munich Data Science in Earth Observation Germany University of the Philippines Diliman Scientific Computing Lab Philippines

ISBN: (纸本)9781665493734

Advances in artificial intelligence (AI) and earth observation (EO) have led to promising new opportunities for promoting sustainable development in the Global South. However, adapting AI for EO (AI4EO) solutions to real-world problems in these contexts are not without risks and challenges. In this paper, we discuss the barriers to successful adoption of AI4EO in developing countries and propose a roadmap for more impactful applications of future AI4EO research. The recommendations emerge from three case studies that illustrate the need to create AI4EO systems that take into account technical, social, and ethical considerations specific to the Global South such as data and infrastructural limitations, gaps in technological literacy, geodiversity issues in remote sensing (RS) datasets, and the need for stronger stakeholder engagement, among others. Finally, we propose a framework based on more inclusive, human-centered methodologies rooted in the needs, perspectives, and lived experiences of domain experts, decision-makers, and local communities in the Global South. © 2023 IEEE.

关键词： Artificial intelligence

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SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding 40

SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Cheng, Feng Xiao, Jiang Liu, Cunyang Zhang, Shijie Zhou, Yifan Li, Bo Li, Baochun Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab China Hong Kong University of Science and Technology Hong Kong University of Toronto Canada

ISBN: (纸本)9798350317152

Directed Acyclic Graph (DAG)-based blockchain (a.k.a distributed ledger) has become prevalent for supporting highly concurrent applications. Its inherent parallel data structure accelerates block generation significantly, shifting the bottleneck from performance to storage scalability. An intuitive solution is to apply state sharding that divides the entire ledger (i.e., transactions and states) into multiple shards. While each node only stores proportional transactions, it suffers from the challenges of storing and ensuring the processing consistency of cross-shard transactions. In this paper, we propose SharDAG, a new mechanism that leverages adaptive sharding for DAG-based blockchains to achieve high performance and strong consistency. The key idea of SharDAG is to exploit unique characteristics -silent assets -and design a lightweight processing mechanism based on avatar account caching. Furthermore, we design a Byzantine resilient cross-shard verification mechanism with a theoretically optimal number of participating nodes, which guarantees the consistency and security of avatar account aggregation. Our comprehensive evaluations on real-world workloads demonstrate that SharDAG presents up to 3.8 x throughput improvement compared to the state-of-the-art and reduces the storage overhead of cross-shard transactions. © 2024 IEEE.

关键词： Blockchain

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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An Analysis of Human Factors in Malware Attacks

An Analysis of Human Factors in Malware Attacks

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Alotaibi, Jawza Al Ja'am, Jihad M. Hoda, Mohammed Oryx Universal College in Partnership with Liverpool John Moores University Dept. of Computing and Data Science Doha Qatar Kuwait College of Science and Technology Dept. of computer Science and Engineering Doha Kuwait

ISBN: (纸本)9798350367560

This research study investigates human responses to malware attacks, focusing on attacker motivations, individual reactions, and key factors influencing user vulnerability and resilience. Using a structured survey questionnaire, the study gathered data from 104 participants with diverse demographics across various occupations. The data was analyzed using SPSS statistical software and Python-based machine learning algorithms. The survey included demographic information and responses to cybersecurity-related questions about human behavior and experience. Machine learning techniques identified ten critical factors as significant predictors of vulnerability to malware attacks. The prediction model indicated that 55 out of 104 participants were vulnerable, with higher vulnerability observed among female participants and those in non-IT professions. This study contributes valuable insights into the human factors influencing cybersecurity resilience, providing a foundation for developing effective strategies to mitigate malware attack vulnerability. © 2024 IEEE.

关键词： Cyber attacks

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XGBD: Explanation-Guided Graph Backdoor Detection 26

XGBD: Explanation-Guided Graph Backdoor Detection

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Guan, Zihan Du, Mengnan Liu, Ninghao School of Computing University of Georgia United States Department of Data Science New Jersey Institute of Technology United States

ISBN: (纸本)9781643684369

Backdoor attacks pose a significant security risk to graph learning models. Backdoors can be embedded into the target model by inserting backdoor triggers into the training dataset, causing the model to make incorrect predictions when the trigger is present. To counter backdoor attacks, backdoor detection has been proposed. An emerging detection strategy in the vision and NLP domains is based on an intriguing phenomenon: when training models on a mixture of backdoor and clean samples, the loss on backdoor samples drops significantly faster than on clean samples, allowing backdoor samples to be easily detected by selecting samples with the lowest loss values. However, the ignorance of topological feature information on graph data limits its detection effectiveness when applied directly to the graph domain. To this end, we propose an explanation-guided backdoor detection method to take advantage of the topological information. Specifically, we train a helper model on the graph dataset, feed graph samples into the model, and then adopt explanation methods to attribute model prediction to an important subgraph. We observe that backdoor samples have distinct attribution distribution than clean samples, so the explanatory subgraph could serve as more discriminative features for detecting backdoor samples. Comprehensive experiments on multiple popular datasets and attack methods demonstrate the effectiveness and explainability of our method. Our code is available: https://***/GuanZihan/GNN-backdoor-detection. © 2023 The Authors.

关键词： Topology

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Sentiment Analysis on Climate Change for Sustainable Investment 24

Sentiment Analysis on Climate Change for Sustainable Investm...

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24th IEEE International Conference on data Mining Workshops, ICDMW 2024

作者： Ong, Keane Mao, Rui Varshney, Deeksha Satapathy, Ranjan Cambria, Erik Mengaldo, Gianmarco National University of Singapore Asian Institute of Digital Finance Singapore Nanyang Technological University College of Computing and Data Science Singapore National University of Singapore College of Design & Engineering Singapore Agency for Science Technology and Research Institute of High Performance Computing Singapore

ISBN: (纸本)9798331530631

This research involves examining whether sentiment on climate change can be accounted as a systematic risk factor within sustainable finance. Tweets related to climate change from 2014-2022 are collected via the Twitter API. RoBERTa is then deployed to label the collected tweets according to sentiment polarity. Thereafter, DistilBERT is utilised to classify tweets according to their different climate change topics Aggregated, Impact, Politics &icy, Mitigation, Rootcause. Using these sentiment and topic labels, monthly sentiment scores are aggregated from the frequency of sentiment polarity (number of positive, and negative tweets), and segmented according to the topics that the tweets are classified under. On the other hand, we collect the monthly returns of various green ETFs, alongside ETFs that contain stocks from other sectors (technology, oil and gas), as well as 2 baskets of stocks (sustainable and heavy-polluting). We run Fama-French regressions by conducting time series regression of monthly returns on the traditional 3 factors, with specific addition of sentiment scores. © 2024 IEEE.

关键词： Tweets

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