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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Gupta, Riddhi Kumar, Puru Rani, Sonia Gupta, Aastha Kommuru, Anusha Ramkumar Dronacharya Group of Institutions Department of Computer Science &engineering Greater Noida India Galgotias University Department of Computer Science &engineering Greater Noida India

ISBN: (纸本)9798350342338

Malware is a severe danger to everyone from home users to huge corporations. As a result, it's a popular research topic. Malware fingerprints and activity patterns are analyzed both statically and dynamically to detect it. Analysis of malware on a daily basis by humans is a tedious task and still we are not ready for zero-day attack malware. Many machine learning methods are currently being utilized to find and analyze malware. The detection and classification accuracy varies based on algorithm used and the dataset taken to perform the testing. New and improved dataset will provide us with more accurate data and then new algorithm can be constructed based on it. The result will be more malware analysis data which can be used for malware detection by antivirus software. Given that Android smartphones are currently the target of the majority of attacks, the field of android malware analysis is expanding. Finding the best method to employ for a generic dataset is the research's ultimate objective, and the tested algorithm should surpass all prior tests on that dataset. © 2023 IEEE.

关键词： Android Malware Analysis Deep Learning Machine Learning Malware Analysis Static and Dynamic Analysis

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Fog Computing: An Emerging Paradigm for Edge Intelligence and Efficient Data Processing

Fog Computing: An Emerging Paradigm for Edge Intelligence an...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Singh, Kushagra Singh, Nikita Panchal, Mukul Gupta, Prince KIET Group of Institutions Department of Computer Science and Information Technology U.P. Ghaziabad India

ISBN: (纸本)9798350394412

Fog computing is a cloud computing extension that processes data locally on a device rather than uploading it to the cloud, which would speed up processing and lessen the strain on the Internet. We discuss fog computing applications, infrastructure, and technology in this paper. In particular, we showcase the most recent advancements in fog networking, platforms, security, privacy, cloud computing at the edge, and quality of experience. Lastly, we go over the difficulties that still need to be resolved, as well as the state of the field's open research in fog computing. © 2024 IEEE.

关键词： Cloud platforms

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Advancing Service Composition Platforms: A Context-Aware and Blockchain EnhancedFramework for Trusted Service Process Creation and Recommendation

Advancing Service Composition Platforms: A Context-Aware and...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Bhardwaj, Mukesh Kumar Singh, Mahesh Kumar Baghel, Deepak Pal, Aditya Kumar, Angad Department of Computer Science and Engineering Dronacharya Group of Institutions Uttar Pradesh Greater Noida India Department of Computer Science and Information Technology Dronacharya Group of Institutions Uttar Pradesh Greater Noida India Department of Information Technology Dronacharya Group of Institutions Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350355338

In service computing, platforms like IFTTT, Zapier, and Omelette enable users to create customized workflows. However, this flexibility brings challenges like security, trust, and efficiency. Recommending components and workflows is also difficult due to concerns about data integrity, service reliability, and the limitations of traditional recommendation *** this groundbreaking study, we unveil the ContextAware Responsible Service Process Creation and Recommendation (SPCR-CA) framework, a revolutionary approach leveraging blockchain forrobust service provisioning and harnessing the power of Recurrent Neural Network (RNN) and Skip-Gram models for intelligent service recommendations. SPCR-CA is dedicated to elevating trust andfostering reusability within service processes through its blend of secure, transparent, and efficient composition techniques. Through meticulous experimentation with ProgrammableWeb datasets, SPCR-CA emerges triumphant, showcasing unparalleled performance in recommending service components, promising to revolutionize efficiency, security, and user satisfaction across composition platforms. This endeavour not only confronts present-day obstacles head-on but also sets the stage for pioneering exploration in the realm of service computing. © 2024 IEEE.

关键词： Efficiency

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A Case Study on Contextual Machine Translation in a Professional Scenario of Subtitling 25

A Case Study on Contextual Machine Translation in a Professi...

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25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Vincent, Sebastian Prescott, Charlotte Bayliss, Chris Oakley, Chris Scarton, Carolina Department of Computer Science University of Sheffield United Kingdom ZOO Digital Group PLC United Kingdom

ISBN: (纸本)9781068690709

Incorporating extra-textual context such as film metadata into the machine translation (MT) pipeline can enhance translation quality, as indicated by automatic evaluation in recent work. However, the positive impact of such systems in industry remains unproven. We report on an industrial case study carried out to investigate the benefit of MT in a professional scenario of translating TV subtitles with a focus on how leveraging extra-textual context impacts post-editing. We found that post-editors marked significantly fewer context-related errors when correcting the outputs of MTCUE, the context-aware model, as opposed to non-contextual models. We also present the results of a survey of the employed post-editors, which highlights contextual inadequacy as a significant gap consistently observed in MT. Our findings strengthen the motivation for further work within fully contextual MT. © 2024 The authors, © 2024 European Association for Machine Translation.

关键词： Machine translation

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An Effective Intrusion Detection in Mobile Ad-hoc Network Using Deep Belief Networks and Long Short-Term Memory

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 123-135页

作者： Hanafi, Abdulfatai Shola Saheed, Yakub Kayode Arowolo, Micheal Olaolu Department of Computer Science Al-Hikmah University Ilorin Nigeria Department of Computer Science School of IT & Computing American University of Nigeria Yola Nigeria Kaptain A. I & Innovation Research Group Landmark University Omu-Aran Nigeria Omu-Aran Nigeria

A Mobile Ad-hoc Network (MANET) is a self-organizing collection of mobile devices communicating in a distributed fashion across numerous hops. MANETs are an appealing technology for many applications, including rescue operations, environmental monitoring, tactical operations, and so on, because they let people communicate without the usage of permanent infrastructure. This flexibility, however, creates additional security vulnerabilities. Because of its benefits and expanding demand, MANETs have attracted a lot of interest from the scientific community. They do, however, seem to be more vulnerable to numerous attacks that wreak havoc on their performance than any network. Traditional cryptography techniques cannot entirely defend MANETs in terms of fresh attacks and vulnerabilities due to the distributed architecture of MANETs;however, these issues can be overcome by using machine learning approaches-based intrusion detection systems (IDS). IDS, typically screening system processes and identifying intrusions, are commonly employed to supplement existing security methods because preventative techniques are never enough. Because MANETs are continually evolving, their highly limited nodes, and the lack of central observation stations, intrusion detection is a complex and tough process. Conventional IDSs are difficult to apply to them. Existing methodologies must be updated for MANETs or new approaches must be created. This paper aims to present a novel concept founded on deep belief networks (DBN) and long shortterm memory (LSTM) for MANET attack detection. The experimental analysis was performed on the probe, root to local, user to root, and denial of service (DoS) attacks. In the first phase of this paper, particle swarm optimization was used for feature selection, and subsequently, the DBN and LSTM were used for the classification of attacks in the MANET. The experimental results gave an accuracy reaching 99.46%, a sensitivity of 99.52%, and a recall of 99.52% for D

关键词： Intrusion detection

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Prediction of Listed Company Innovation Ability Based on Attention Mechanism

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Journal of the Operations Research Society of China 2023年第1期11卷 177-187页

作者： Huan-Yu Ma Yan Xu Yu-Long Liu Department of Information and Computer Science University of Science and Technology BeijingBeijing100083China No.15 Research Institute China Electronics Technology Group CorporationBeijing100083China

Innovation is the center of the enterprise development, which is the main driving force of enterprises’ competitiveness. To evaluate the enterprises’ innovation ability, we firstly establish an index system which aims at analyzing the enterprise innovation ability. We also retrieve the data of the listed companies in Wind database from 2015 to 2019 and label them using factor analysis method. Then, a new deep learning classificational framework with attention mechanism and LSTM is established. The results show that when attention mechanism and LSTM are added into the convolutional neural network(CNN), the model’s prediction performance is better improved, and the accuracy, recall, precision and F-score are 0.914, 0.914, 0.916 and 0.915, respectively. This indicates the strong generalization ability of our new model. Finally, we also find that patents and R &D expenditures are the most important factor affecting the corporate innovation ability through SHapley Additive exPlanations(SHAP). Companies with more patents and R &D expenditures are generally considered to be more innovative.

关键词： Corporate innovation ability convolutional neural network Attention mechanism long short-term memory

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Investigating the positive effects of purposeful-mixed-approach pedagogy for computer Systems Administration and Support Course 40

Investigating the positive effects of purposeful-mixed-appro...

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40th Information Systems Education Conference, ISECON 2024

作者： Olade, Ilesanmi Oluwaleye, Eyitayo McLaughlin, Brendan McFarland, James Oyelade, Olaide Onwuyali, Nweike Department of Computer Science Queen’s University Belfast United Kingdom Department of Computer Science Chichester University Sussex United Kingdom Innovative Learning Group Ziphii Limited Cambridge United Kingdom

In today’s information technology era, education must adapt to a rapidly changing world. Teachers should transition from mere information providers to active learning facilitators, ensuring knowledge is practical, purposeful, and retainable, impacting learners’ long-term development. This study investigated whether incorporating socially and environmentally beneficial activities, such as recycling electronic waste, into traditional laboratory practicals could enhance academic performance and knowledge retention, while promoting sustainability. The research addressed two questions: (1) What strategies enhance a well-established course while incorporating active learning techniques? and (2) Will learning outcomes be more impactful, enjoyable, and less abstract if applied immediately to achieve tangible results and community accomplishment, such as combating electronic waste and promoting sustainability?. The study, conducted by the computer science department at Queen’s University Belfast over two academic years starting in 2021/2022, defined the scope and connection to repurposing computer systems slated to be disposed as e-waste. The student cohort freely explored solutions and innovations while completing tasks, ensuring practical learning and testing the limits of their knowledge. Academic performance was tracked via grades from the learning management system, and survey-based self-reports assessed students’ perceptions of the project. The analysis concludes with an evaluation of the methodology’s effectiveness and a presentation of the results and outcomes. © 2024 ISECON. All Rights Reserved.

关键词： Students

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Understanding and Minimising Outlier Features in Transformer Training 38

Understanding and Minimising Outlier Features in Transformer...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： He, Bobby Noci, Lorenzo Paliotta, Daniele Schlag, Imanol Hofmann, Thomas Department of Computer Science ETH Zürich Switzerland Machine Learning Group University of Geneva Switzerland

Outlier Features (OFs) are neurons whose activation magnitudes significantly exceed the average over a neural network's (NN) width. They are well known to emerge during standard transformer training and have the undesirable effect of hindering quantisation in afflicted models. Despite their practical importance, little is known behind why OFs emerge during training, nor how one can minimise them. Our work focuses on the above questions, first identifying several quantitative metrics, such as the kurtosis over neuron activation norms, to measure OFs. With these metrics, we study how architectural and optimisation choices influence OFs, and provide practical insights to minimise OFs during training. As highlights, we introduce a novel unnormalised transformer block, the Outlier Protected block, and present a previously unknown benefit of non-diagonal preconditioning optimisers, finding both approaches to significantly reduce OFs and improve quantisation without compromising convergence speed, at scales of up to 7B parameters. Notably, our combination of OP block and non-diagonal preconditioner (SOAP) achieves 14.87 weight-and-activation int8 perplexity (from 14.71 in standard precision), compared to 63.4 int8 perplexity (from 16.00) with a default OF-prone combination of Pre-Norm model and Adam, when quantising OPT-125m models post-training. © 2024 Neural information processing systems foundation. All rights reserved.

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WifiGO: Assessment of PUV Viability for Opportunistic Coverage Mapping of WiFi Networks

WifiGO: Assessment of PUV Viability for Opportunistic Covera...

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2024 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2024

作者： Pecson, Jeff Nereo G. Tan, Wilson M. Festin, Cedric Angelo M. Dulva, Philip Angelo Y. Networks and Distributed Systems Group Department of Computer Science University of the Philippines - Diliman Metro Manila Philippines

ISBN: (纸本)9798331521219

Current methods in mapping the availability of WiFi networks, such as crowdsourcing platforms (e.g. Project BASS and CoverageMap) and dedicated wardriving, face limitations in terms of data recency, volume, and cost-effectiveness. Due to the downsides of both these methods, opportunistic wardriving is proposed which utilizes public utility vehicles (PUVs). This study investigates the feasibility of PUVs as potential wardriving vehicles for opportunistic coverage mapping. This method is cost-efficient and removes the problem of data recency because of its continuous daily trips. The findings suggest that opportunistic wardriving with PUVs is viable for wardriving with its capability to detect a high volume of access points (APs). Multiple runs within the University of the Philippines Diliman campus showed the PUVs’ effectiveness in detecting a significant number of APs, with higher success rates at closer distances. Comparatively, warwalking detected fewer APs, but there was a significant overlap between the methods. The study also highlighted significant differences in detected AP types, with wardriving finding more mobile hotspots and miscellaneous devices than warwalking. Overall, the results underline the effectiveness of PUVs in providing extensive WiFi coverage data and further enhancements to the detection system could optimize the approach. ©2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Reference-less Analysis of Context Specificity in Translation with Personalised Language Models 30

Reference-less Analysis of Context Specificity in Translatio...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Vincent, Sebastian Dowek, Alice Sumner, Rowanne Prescott, Charlotte Preston, Emily Bayliss, Chris Oakley, Chris Scarton, Carolina Department of Computer Science University of Sheffield United Kingdom ZOO Digital Group PLC United Kingdom

ISBN: (纸本)9782493814104

Sensitising language models (LMs) to external context helps them to more effectively capture the speaking patterns of individuals with specific characteristics or in particular environments. This work investigates to what extent detailed character and film annotations can be leveraged to personalise LMs in a scalable manner. We then explore the use of such models in evaluating context specificity in machine translation. We build LMs which leverage rich contextual information to reduce perplexity by up to 6.5% compared to a non-contextual model, and generalise well to a scenario with no speaker-specific data, relying on combinations of demographic characteristics expressed via metadata. Our findings are consistent across two corpora, one of which (CORNELL-RICH) is also a contribution of this paper. We then use our personalised LMs to measure the co-occurrence of extra-textual context and translation hypotheses in a machine translation setting. Our results suggest that the degree to which professional translations in our domain are context-specific can be preserved to a better extent by a contextual machine translation model than a non-contextual model, which is also reflected in the contextual model's superior reference-based scores. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Machine translation

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