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2nd International Conference on Data Analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Xiong, Zetong Du, Zihao Rong, Xi Yang, Yuting Zhang, Xiaoyi Zhongnan University of Economic and Law School of Information and Safety Engineering Wuhan430073 China McMaster University Department of Computing and Software HamiltonL8S 4L8 Canada Tufts University School of Engineering MedfordMA02155 United States The Ohio State University Department of Computer Science and Engineering Columbus43212 United States University of California Department of Mathematics San Diego921222 United States

ISBN: (纸本)9798350339765

As adverse drug reactions (ADRs) can cause serious consequences to medication treatment, identifying and predicting adverse drug reactions (ADRs) play a crucial role in drug effects and drug use safety. In this paper, we reviewed existing machine learning methods in ADR prediction and proposed a neural collaborative filtering model (NCF) for the prediction of monopharmacy ADRs. NCF is based on dimension reduction by matrix factorization (MF) and combined with an MLP model to enhance prediction performance by handling complex non-linear relationships using MLN. Our NCF model optimizes the MF model by replacing dot product in the MF model with MLP, integrating drug and ADR embeddings from monopharmacy ADR benchmark data and drug-related chemical, physical and biological descriptors in a two-layer deep neural network. The model was tested by using 10-fold cross-validation. On 10-fold cross-validation, the resulting AUC, AUPR, and running time indicated a better performance and higher efficiency on drug-ADR prediction achieved by applying our NCF model. © 2023 IEEE.

关键词： Collaborative filtering

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Practical and Efficient Model Extraction of Sentiment Analysis APIs

Practical and Efficient Model Extraction of Sentiment Analys...

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International Conference on software engineering (ICSE)

作者： Weibin Wu Jianping Zhang Victor Junqiu Wei Xixian Chen Zibin Zheng Irwin King Michael R. Lyu School of Software Engineering Sun Yat-sen University Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Computing The Hong Kong Polytechnic University Tencent

Despite their stunning performance, developing deep learning models from scratch is a formidable task. Therefore, it popularizes Machine-Learning-as-a-Service (MLaaS), where general users can access the trained models of MLaaS providers via Application Programming Interfaces (APIs) on a pay-per-query basis. Unfortunately, the success of MLaaS is under threat from model extraction attacks, where attackers intend to extract a local model of equivalent functionality to the target MLaaS model. However, existing studies on model extraction of text analytics APIs frequently assume adversaries have strong knowledge about the victim model, like its architecture and parameters, which hardly holds in practice. Besides, since the attacker's and the victim's training data can be considerably discrepant, it is non-trivial to perform efficient model extraction. In this paper, to advance the understanding of such attacks, we propose a framework, PEEP, for practical and efficient model extraction of sentiment analysis APIs with only query access. Specifically, PEEP features a learning-based scheme, which employs out-of-domain public corpora and a novel query strategy to construct proxy training data for model extraction. Besides, PEEP introduces a greedy search algorithm to settle an appropriate architecture for the extracted model. We conducted extensive experiments with two victim models across three datasets and two real-life commercial sentiment analysis APIs. Experimental results corroborate that PEEP can consistently outperform the state-of-the-art baselines in terms of effectiveness and efficiency.

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Comparison on Blockchain-based Intrusion Detection Systems for Internet of Things

Comparison on Blockchain-based Intrusion Detection Systems f...

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2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Mago Vistro, Daniel Kamran Abid, Muhammad Ahmad, Mughees Ali, Mubashir Hassan, Jawad Younas, Akhlaq School of Computing Asia Pacific University of Technology and Innovation Kuala lumpur Malaysia Nfc Institute of Engineering and Technology Department of Computer Science Multan Pakistan Department of Computer Science Zhob Pakistan Lahore Garrison University Department of Software Engineering Lahore Pakistan Air University Multan Campus Department of Computer Science and Engineering Multan Pakistan

ISBN: (纸本)9781665456371

The Internet of Things (IoT) is a cutting-edge concept that unites the Internet with actual physical objects from a variety of industries, such as home automation, manufacturing, human health, and environmental monitoring. Users are now able to use gadgets with Internet access for routine tasks. Along with many advantages, IoT also presents a number of security-related difficulties. The security of the IoT network has grown in importance as the popularity of the Internet of Things increases daily. An IoT network is more secure thanks to encryption and authentication, yet it can be challenging to defend IoT devices from online threats. Cyberattacks on the Internet of Things system not only result in data loss, but they also have the potential to bring the entire system to a standstill. The performance of the network can be negatively impacted by hostile activities, which are easily detected by intrusion detection systems (IDS). To safeguard IoT systems, an efficient intrusion detection system (IDS) is required. Another cutting-edge technology that helps security systems counter the most recent threats is blockchain. In this post, we analysed state-of-the-art blockchain-based intrusion detection methods for Internet of Things devices. New advancements to alleviate security threats are also listed in a table. Finally, we list the major questions brought on by the current restrictions. © 2022 IEEE.

关键词： Internet of things

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ETHACA - A Container-Based Cyber Range for Research and Education 29

ETHACA - A Container-Based Cyber Range for Research and Educ...

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29th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Chouliaras, Nestoras Kantzavelou, Ioanna Pantziou, Grammati Maglaras, Leandros Chouliara-Sidera, Panagiota Adamakos, Antonis Chalatsakos, Panagiotis University of West Attica Department of Informatics and Computer Engineering Athens Greece School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom University of Thessaly Department of Computer Science and Biomedical Infromatics Lamia Greece Senior Software Engineer at *** Athens Greece Open University of Cyprus Faculty of Pure and Applied Sciences Nicosia Cyprus

ISBN: (纸本)9798350377644

The advent of Industry 4.0 and the Industrial Internet of Things (IIoT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies such as IoT devices and AI. However, this increased digitization has also led to heightened cybersecurity risks, with a notable rise in the frequency and complexity of cyber-attacks. Cyber Ranges emerges as a critical solution in addressing these challenges, offering a controlled environment for simulating cyber-attack scenarios, training cybersecurity teams, and developing robust defense strategies. The escalating demand for cybersecurity professionals has highlighted the crucial need for effective training solutions. This article presents an open-source Cyber Range constructed on container-based architecture, meticulously engineered for efficient operation with minimal resource consumption. In response to the scarcity of cybersecurity experts, this platform prioritizes extensive scalability and robust isolation between exercises, ensuring secure learning environments, and also integrates a complex developed exercise series aimed at strengthening cybersecurity learning experiences. These exercises encompass a variety of scenarios and challenges, catering to professionals at different skill levels. Each exercise replicates real-world cyber threats, offering practical, hands-on learning opportunities. Additionally, a comprehensive questionnaire accompanies these exercises, designed to gather feedback on participants' experiences. © 2024 IEEE.

关键词： Cyber attacks

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BeSafe: Air Quality Monitoring and Control with IoT

BeSafe: Air Quality Monitoring and Control with IoT

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2023 International Conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Arun, K.C. Azam, Sadia Siddique, Ayesha Ali, Mubashir Hussain, Aamir Asia Pacific University School of Computing Kuala Lumpur Malaysia Universita di Verona Dipartimento di Informatica San Francesco Italy University of Agriculture Department of Computer Science Faisalabad Pakistan Lahore Garrison University Department of Software Engineering Lahore Pakistan Muhammad Nawaz Shareef University of Agriculture Department of Computer Science Multan Pakistan

ISBN: (纸本)9798350313413

Air pollution is a common matter nowadays. It's defined as contamination of physical, biological, or chemical alteration in the air which results in harmful effects on living objects and makes it difficult for them to survive. The prominent cause of air pollution is the discharge of injurious gases from industrial waste and the use of fossil fuels. The effects of air pollution are very severe climate change, soil erosion, different diseases, and many more thus it's very important to take proper note of air superiority as long-term effects of air pollution can result in heart diseases, lung cancer, etc. We have devised a system to measure the contamination level of air through sensors base hardware which sends the data directly to the associated server making it easier to read even on mobile 'through a software application. The sensors would be plotted in different routes and locations i.e., industrial region, traffic zone, etc.' to investigate the pollution stages of the air making it easier for the user to take a safer route. One of the important usages of the system is to give information about the low contaminated areas near roads and industrial areas so that they can be safeguarded through certain measures. © 2023 IEEE.

关键词： Air Quality Environmental Pollution Internet of Things Quality Monitoring Sensors

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Object on Use Case Description: Sequence Diagram Conformance based on Step Performed using Text Pre-Processing on Sipranta Application SRS

Object on Use Case Description: Sequence Diagram Conformance...

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AI IoT Congress (AIIoT), World

作者： Muhammad Alfath Abibi Yudi Priyadi Ati Suci Dian Martha Department of Informatics Telkom University Bandung Indonesia Department of Software Engineering Telkom University Bandung Indonesia School Of Computing Telkom University Bandung Indonesia

In the Use Case Diagram, a Step Performed in Tabulation contains an object's explanation flow. A sequence diagram attempts to depict in detail the interaction of objects in a system, the messages or commands sent, and the implementation time. In other words, the Sequence Diagram, like the Use Case Diagram, encapsulates an object's explanation flow in a graphical diagram. The Step Performed and the Sequence Diagram should be related, yet the Step Performed and Sequence Diagram diverge while creating the Sipranta Application SRS. By adopting Text Pre-processing, this research intends to quantify the value of conformance between the Sequence Diagram and the Step Performed in Use Case Description. This study identifies the Step Performed and the Sequence Diagram using 22 documents, 11 of which are Step Performed and 11 of which are Sequence Diagrams. In addition, the Text Analysis process produces two documents with the highest conformity: d1 with d12 of 0.933461. Cohen Cappa Score produces a validation value of 0.29195, which is included in the "Fair Agreement" category. While the reliability results from 2 groups of experts, which is 0.8275, so it is included in the "Substantial agreement" category. The results differ between the two methods, which is 0.536.

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Enabling Collaborative and Green Generative AI Inference in Edge Networks

Enabling Collaborative and Green Generative AI Inference in ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Meng Tian Zhicheng Liu Chao Qiu Xiaofei Wang Dusit Niyato Victor C. M. Leung College of Intelligence and Computing Tianjin University Tianjin China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm’s ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content.

关键词： Energy consumption Adaptation models Generative AI Computational modeling Image edge detection Green products Collaboration Inference algorithms Energy efficiency Internet of Things

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C++ and Interoperability between Libraries: The GraphBLAS C++ Specification

C++ and Interoperability between Libraries: The GraphBLAS C+...

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2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023

作者： Brock, Benjamin McMillan, Scott Buluc, Aydin Mattson, Timothy G. Moreira, Jose E. Parallel Computing Lab Intel United States Software Engineering Institute Carnegie Mellon University United States Lawrence Berkeley National Laboratory Computational Research Department United States University of California EECS Department Berkeley United States IBM Thomas J. Watson Research Center United States

ISBN: (纸本)9798350311990

Interoperability between libraries is often hindered by incompatible data formats, which can necessitate creating new copies of data when transferring data back and forth between different libraries. This additional data movement incurs additional runtime costs, particularly for sparse applications, where the costs of data movement often dwarf compute costs. In this paper, we investigate interoperability in the context of the C++ GraphBLAS Specification, where C++ concepts allow GraphBLAS algorithms to accept any matrix type as long as it follows the matrix interface defined in the GraphBLAS matrix concept. We first develop non-owning, lazily evaluated adapted views for a number of external data structures, including two categories of graphs defined in the Northwest Graph Library (NWGraph) and traditional pointer-based CSR data structures. These adapted views fulfill the C++ GraphBLAS matrix concept, allowing them to be used inside GraphBLAS algorithms. We then evaluate the performance of these adapted views across two kernels, matrix reduction and sparse times dense matrix multiplication (SpMM), where the performance achieved using a single generic implementation with these views largely matches the performance achieved operating directly on the original data structures, with a slight performance loss in one case. We then propose a mechanism for automatically discovering the availability of these views, allowing algorithms to directly accept external data structures. We also discuss potential extensions to the C++ GraphBLAS specification that might eliminate the small performance dip observed for one of the views. © 2023 IEEE.

关键词： Data structures

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Evaluating the Impact of Microservices Centric Computations In Internet of Connected Autonomous Vehicles

SSRN

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SSRN 2023年

作者： Din, Muhammad Salah Ud Rehman, Muhammad Atif Ur Imran, Muhammad Kim, Byung Seo Department of Software and Communications Engineering Hongik University Sejong City30016 Korea Republic of Department of Computing & Mathematics Manchester Metropolitan University United Kingdom

This paper presents the design and implementation of a hierarchical and multilayered microservices based in-network computing framework, specifically tailored for connected autonomous vehicular networks, aiming to provide proximate computation for latency-sensitive and compute-intensive vehicular applications while minimizing network resource utilization. The proposed system architecture comprises three layers: an autonomous vehicular network layer, an Edge computing layer, and a centralized Cloud. Modifications were made to the vanilla Named Data Networking (NDN) codebase to facilitate communication between the vehicular network layer and the Edge computing layer, utilizing the C++ Boost Asio library. A microservices-based driver assistance application, consisting of various heterogeneous microservices, was emulated in .NET Core and deployed on different Edge computing terminals and the Cloud. Additionally, RESTful APIs have been developed to enable the vehicular network layer and the physical Edge layer to offload microservice-centric computation requests and retrieve the corresponding computational outcomes. The Entity Framework is employed to ensure proper tracking and management of these requests. An HTML-based user interface has also been developed for a visual representation of the request pattern. Extensive testbed experiments reveal that the proposed framework significantly optimizes bandwidth consumption, reduces latency, and increases the computed results delivery ratio when compared to conventional monolithic systems. © 2023, The Authors. All rights reserved.

关键词： Edge computing

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Application of Deep Learning Algorithm for Prediction of Flood Severity

Application of Deep Learning Algorithm for Prediction of Flo...

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Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： C Jamunadevi N Naveen M Manimaran M Janani Department of Computer Technology PG Kongu Engineering College Erode Department of IT Excel Engineering College Namakkal School of Computing Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India M.Sc (Software Systems) Kongu Engineering College Erode

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Natural disasters like floods have caused immense harm to lives and resources, underscoring the need for accurate prediction methods to mitigate their impact. Meteorological flood indices such as the standardized precipitation index help identify flood occurrences and severity levels, enabling proactive measures. While past research primarily used statistical methods like Least Square-Support Vector Machine (LS-SVM), these often lack accuracy for long-term forecasts. Advancements in machine learning, particularly artificial neural networks and deep neural networks, offer more accurate predictions over extended periods. Long Short-Term Memory (LSTM) models and Convolutional Neural Networks (CNN) are used to predict flood indices, handling real-time nonlinear data effectively. Benchmarking trials have shown that an LSTM and CNN configuration produced the best predictions, with an accuracy, prediction, MSE, and RMSE of 97.93,0.89, 0.01, and 0.10 respectively.

关键词： Accuracy Weather forecasting Predictive models Surfaces Data models Vectors Floods

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