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49th Euromicro Conference on software engineering and Advanced applications, SEAA 2023

作者： Soud, Majd Qasse, Ilham Liebel, Grischa Hamdaqa, Mohammad Reykjavik University Department of Computer Science Reykjavik Iceland Polytechnique Montreal Department of Computer and Software Engineering Montreal Canada

ISBN: (纸本)9798350342352

Due to the risks associated with vulnerabilities in smart contracts, their security has gained significant attention in recent years. However, there is a lack of open datasets on smart contract vulnerabilities and their fixes that allows for data-driven research. Towards this end, we propose an automated framework for mining and classifying Ethereum's smart contract vulnerabilities and their corresponding fixes from GitHub and from the Common Vulnerabilities and Exposures (CVE) records in the National Vulnerability Database. We implemented the proposed method in a fully automated framework, which we call AutoMESC. AutoMESC uses seven of the most well-known smart contract security tools to classify and label the collected vulnerabilities based on vulnerability types. Furthermore, it collects metadata that can be used in data-intensive smart contract security research (e.g., vulnerability detection, vulnerability classification, severity prediction, and automated repair). We used AutoMESC to construct a sample dataset and made it publicly available. Currently, the dataset contains 6.7K smart contract vulnerability-fix pairs written in Solidity. We assess the quality of the constructed dataset in terms of accuracy, provenance, and relevance, and compare it with existing datasets. AutoMESC is designed to collect data continuously and keep the corresponding dataset up-to-date with newly discovered smart contract vulnerabilities and their fixes from GitHub and CVE records. © 2023 IEEE.

关键词： Smart contract

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On Evaluation of Bangla Word Analogies

On Evaluation of Bangla Word Analogies

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2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Akter, Mousumi Sarkar, Souvika Karmaker, Shubhra Kanti Lab Department of Computer Science & Software Engineering Auburn University AL United States

ISBN: (纸本)9798891760608

This paper presents a benchmark dataset of Bangla word analogies for evaluating the quality of existing Bangla word embeddings. Despite being the 7th largest spoken language in the world, Bangla is still a low-resource language and popular NLP models often struggle to perform well on Bangla data sets. Therefore, developing a robust evaluation set is crucial for benchmarking and guiding future research on improving Bangla word embeddings, which is currently missing. To address this issue, we introduce a new evaluation set of 16,678 unique word analogies in Bangla as well as a translated and curated version of the original Mikolov dataset (10,594 samples) in Bangla. Our experiments with different state-of-the-art embedding models reveal that current Bangla word embeddings struggle to achieve high accuracy on both data sets, demonstrating a significant gap in multilingual NLP research. ©2023 Association for Computational Linguistics.

关键词： Embeddings

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Study on the Role of Adaptive Power Control in Jamming Mitigation for Remote Keyless Entry Systems 29

Study on the Role of Adaptive Power Control in Jamming Mitig...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Alkofahi, Hamza Altarawneh, Amani Jordan University of Science and Technology Department of Software Engineering Irbid Jordan Tennessee Tech University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9798331517816

Remote keyless entry (RKE) systems have become a standard feature in modern vehicles, offering the convenience of wireless control. However, this convenience is accompanied by significant security vulnerabilities, particularly susceptibility to jamming attacks. These attacks disrupt communication between the key fob and the vehicle, potentially leading to unauthorized access and vehicle theft. This study investigates the effectiveness of adaptive power control as a countermeasure to jamming attacks on RKE systems and examines the influence of jammer placement on data transmission success rates. We conducted experiments using a custom-built jammer, transmitter, and receiver to simulate real-world scenarios. Our findings demonstrate that jammer proximity significantly affects transmission success, and while increasing key fob power can initially mitigate jamming, its effectiveness diminishes beyond a certain threshold. © 2024 IEEE.

关键词： Jamming

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BIFT: A federated learning System for Connected and Autonomous Vehicles Based on Blockchain 3

BIFT: A federated learning System for Connected and Autonomo...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Yadav, Aarti Kaur, Amandeep Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

Algorithms for machine learning(ML) are essential for autonomous driving. The vast majority of history's automated and connected cars send a significant quantity of movement data collected from many automobiles to a central server for regular training. Nonetheless, the info process has made the safety and confidentiality of information essential. Nowadays, the development of federated learning (FL) for data security has the potential to potential to enhance distributed machine learning's data privacy. The malicious attackers might still target the training process, though. FL is extremely vulnerable because of its total dependency on the central server. We suggest BIFT as a solution to the aforementioned issue, which consists of two parts: 1) a completely decentralized 2) A blockchain-based machine learning system for CAVs that guarantees privacy. With the use of their own driving data, BIFT's scattered CAVs may train local machine learning models, which they can subsequently upload to improve the global model. In order to fend off potential attackers, BIFT offers a consensus mechanism called Proof of Federated Learning. We assess BIFT's performance and demonstrate that it is scalable, reliable, and capable of fending off harmful assaults. © 2023 IEEE.

关键词： Autonomous vehicles

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Smart Cities Paradigm with AI-Enabled Effective Requirements engineering 18

Smart Cities Paradigm with AI-Enabled Effective Requirements...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Bashir, Asima Latif, Niha Khan, Arisha Masood Sohail, Muhammad Mcs National University of Science and Technology Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350328172

Effective requirements gathering and management tools are key factors for sustainable smart cities. These tools enable stakeholders including government officials, private sector partners, and citizens to identify and prioritize requirements, manage changes, and make sure that the product is as per their expectations. In the context of smart cities, these tools must be able to capture the requirements for a wide range of systems, including transport, energy sector, citizens safety, and environment maintenance. Overall, effective requirements gathering and management tools are critical to the success of smart cities, enabling them to be more efficient, sustainable and responsive. With the advent of new technologies, there is a growing need for advanced tools to support the RE process for smart cities. This paper investigates different artificial in-telligence (AI) based requirements gathering and management tools. Amongst those techniques dependent on natural language processing (NLP) and machine learning (ML) in recommendation systems for requirements gathering and ReQtest, ReqSuite, Jama Connect, Visure, Xabrio, and IBM DOORS as management tools have been proposed. Their summarized performance revealed different scores across parameters highlighting their impact as effective tools for smart city paradigm. The study concludes that AI-based techniques and tools can be employed in smart cities, with selection based on project requirements and constraints. © 2023 IEEE.

关键词： Recurrent neural networks

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Comparative study of IoT-and AI-based computing disease detection approaches

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Data science and Management 2025年第1期8卷 94-106页

作者： Wasiur Rhmann Jalaluddin Khan Ghufran Ahmad Khan Zubair Ashraf Babita Pandey Mohammad Ahmar Khan Ashraf Ali Amaan Ishrat Abdulrahman Abdullah Alghamdi Bilal Ahamad Mohammad Khaja Shaik Department of Computer Application Lovely Professional UniversityPhagwaraPunjab144411India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationGunturAndhra Pradesh522502India School of Computing Science and Engineering VIT Bhopal UniversityKothrikalanSehore466114India Babasaheb Bhimrao Ambedkar University Lucknow226025India College of Commerce and Business Administration Dhofar UniversitySalalah211Oman Faculty of Computer Studies Arab Open University18211Bahrain Department of Computer Application Shri Ramswaroop Memorial UniversityLucknow225003India College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia

The emergence of different computing methods such as cloud-,fog-,and edge-based Internet of Things(IoT)systems has provided the opportunity to develop intelligent systems for disease *** to other machine learning models,deep learning models have gained more attention from the research community,as they have shown better results with a large volume of data compared to shallow ***,no comprehensive survey has been conducted on integrated IoT-and computing-based systems that deploy deep learning for disease *** study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection,using the most recent papers on IoT-based disease detection systems that include computing approaches,such as cloud,edge,and *** analysis focused on an IoT deep learning architecture suitable for disease *** also recognizes the different factors that require the attention of researchers to develop better IoT disease detection *** study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms.

关键词： Deep learning Internet of Things(IoT) Cloud computing Fog computing Edge computing

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Next Generation HR: Transforming Talent Management with AI and ML Innovations 2

Next Generation HR: Transforming Talent Management with AI a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Niharika Singh, Pooja Chakraborty, Sudeshna Gl Bajaj Institute of Technology & Management Dept. of Master of Computer Application U.P. Greater Noida India School of Computing Science & Engineering Galgotias University Department of Cse Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Application Greater Noida India

ISBN: (纸本)9798350371055

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-depth analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success. © 2024 IEEE.

关键词： Artificial intelligence human resource management machine learning Natural Language Processing

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A Multi-Objective Decision Assistance System for Selecting Security Controls Based On Simulation 3

A Multi-Objective Decision Assistance System for Selecting S...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Shree, Ritu Sandhu, Komal Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

In this work, we present the results of our continuing investigations into simulation-based risk analysis for information security and multi-objective investment optimization for security controls. We provide a methodological approach that takes into account the organization's features, information architecture, assets that must be safeguarded, unique threat sources encountered, and decision-makers' risk preferences. This framework includes dynamic attack graph generation techniques, ontological modelling of security knowledge, probabilistic reenactment of assaults by objective driven danger specialists, Identification of compelling arrangement of data security controls utilizing meta-heuristics, and intuitive choice help. These components make it possible for cutting-edge techniques to generate attack graphs and infer potential attack routes based on the motivations, goals, skills, and available entry points of attackers, Then, using this inferred information, simulate assaults based on the infrastructure a model of an organisation. The strategy aids decision-makers weigh financial and non-financial factors related to risks, costs, and benefits when assessing possible security control initiatives. In order to assess the strategy through application case studies, we are now constructing a prototype implementation of the framework. © 2023 IEEE.

关键词： Decision support systems

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Genetic Algorithm-Based Data Allocation in Multi Media Using Cloud Computing 3

Genetic Algorithm-Based Data Allocation in Multi Media Using...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Yadav, Surendra Kaur, Manpreet Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

The recent growth of Internet-of-Things (IoT) applications using cloud computing has been amazing. One of the advancements is heterogeneous cloud computing, which has made it possible to use the cloud for a range of infrastructure solutions, including multimedia big data. The optimizations of on-premise heterogeneous memory have been the subject of several past studies. However, the performance and financial limits brought on by hardware distributions and manipulative techniques are placing restrictions on the heterogeneous cloud memory. It is an NP-hard combinatorial issue to distribute data jobs across dispersed memory with different capacities. In order to provide high performance cloud-based heterogeneous memory service offerings, this study focuses on this problem and suggests a unique solution called Cost-Aware Heterogeneous Cloud Memory Model. It allocates data to the cloud-based memory via genetic programming. In our suggested method, we take into account a number of important elements that have a significant influence on how well Communication expenses, data transfer operating costs, energy performance, and time constraints all play a role in how cloud memories operate. Finally, we put our suggested paradigm to the test via experimental assessments. The trial findings have demonstrated the viability and scalability of our technique as a cost-conscious cloud-based solution. © 2023 IEEE.

关键词： Genetic algorithms

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MedNetV3: A Deep Learning Model for MRI Brain Tumor Detection with MobileNetV3 and Advanced Data Augmentation 21

MedNetV3: A Deep Learning Model for MRI Brain Tumor Detectio...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Tariq, Hussain Ahmed Fengli, Zhang Salim Shakib, Muhamud Md Javed, Iram School of Information and Software Engineering University of Electronic Science and Technology of China China University of Agriculture Department of Computer Science Faisalabad Pakistan

ISBN: (纸本)9798331519254

In this paper, we present a novel deep learning model medical network (MedNetV3) developed for brain tumor detection. It incorporates advanced data augmentation techniques based on the MobileNetV3 architecture. MedNetV3 is developed for higher classification accuracy at the cost of slightly lower computational efficiency and is therefore suitable for clinical use. The proposed model overcomes the class imbalance problem and can make the model robust to the variations of MRI scans included in the extensive data augmentation methods, such as random rotations, flips. The proposed model trained and evaluated using a publicly available MRI dataset. It achieved a high Area Under the Curve (AUC) value of 0.99 for both the Precision-Recall and Receiver Operating Characteristic (ROC) curves. The results show that MedNetV3 is effective in correctly distinguishing brain tumors and therefore has the potential to be used in medical image classification. © 2024 IEEE.

关键词： Deep learning

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