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From Rule-Based Models to Deep Learning Transformers Architectures for Natural Language Processing and Sign Language Translation systems: Survey, Taxonomy and Performance Evaluation

arXiv

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arXiv 2024年

作者： Shahin, Nada Ismail, Leila Intelligent Distributed Computing and Systems Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University Abu Dhabi United Arab Emirates

With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translation system, from sign to gloss to text and vice-versa. There has been a wealth of research on machine translations and related reviews. However, there are few works on sign language machine translation considering the particularity of the language being continuous and dynamic. This paper aims to address this void, providing a retrospective analysis of the temporal evolution of sign language machine translation algorithms and a taxonomy of the Transformers architectures, the most used approach in language translation. We also present the requirements of a real-time Quality-of-Service sign language machine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation *** Codes I.2, I.2.7, I.4, I.4.9 © 2024, CC BY.

关键词： Neural machine translation

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AutoDebloater: Automated Android App Debloating

AutoDebloater: Automated Android App Debloating

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IEEE International Conference on Automated software engineering (ASE)

作者： Jiakun Liu Xing Hu Ferdian Thung Shahar Maoz Eran Toch Debin Gao David Lo School of Computing and Information Systems Singapore Management University Singapore School of Software Technology Zhejiang University China School of Computer Science Tel Aviv University Israel Department of Industrial Engineering Tel Aviv University Israel

Android applications are getting bigger with an increasing number of features. However, not all the features are needed by a specific user. The unnecessary features can increase the attack surface and cost additional resources (e.g., storage and memory). Therefore, it is important to remove unnecessary features from Android applications. However, it is difficult for the end users to fully explore the apps to identify the unnecessary features, and there is no off-the-shelf tool available to assist users to debloat the apps by themselves. In this work, we propose AutoDebloater to debloat Android applications automatically for end users. AutoDebloater is a web application that can be accessed by end-users through a web browser. In particular, AutoDebloater can automatically explore an app and identify the transitions between activities. Then, AutoDebloater will present the Activity Transition Graph to users and ask them to select the activities they do not want to keep. Finally, AutoDebloater will remove the activities that are selected by users from the app. We conducted a user study on five Android apps downloaded from three categories (i.e., Finance, Tools, and Navigation) in Google Play and F-Droid. The results show that users are satisfied with AutoDebloater in terms of the stability of the debloated apps and the ability of AutoDebloater to identify features that are never noticed before. The tool is available at http://***. The code is available at https://***/jiakun-liu/autodebloater/ and the demonstration video can be found at https://***/Gmz0-p2n9D4.

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Bayesian Formal Synthesis of Unknown systems via Robust Simulation Relations

arXiv

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arXiv 2023年

作者： Schön, Oliver van Huijgevoort, Birgit Haesaert, Sofie Soudjani, Sadegh The School of Computing Newcastle University NewcastleNE4 5TG United Kingdom The Electrical Engineering Department TU Eindhoven Netherlands The Max Planck Institute for Software Systems KaiserslauternD-67663 Germany

This paper addresses the problem of data-driven computation of controllers that are correct by design for safety-critical systems and can provably satisfy (complex) functional requirements. With a focus on continuous-space stochastic systems with parametric uncertainty, we propose a two-stage approach that decomposes the problem into a learning stage and a robust formal controller synthesis stage. The first stage utilizes available Bayesian regression results to compute robust credible sets for the true parameters of the system. For the second stage, we introduce methods for systems subject to both stochastic and parametric uncertainties. We provide simulation relations for enabling correct-by-design control refinement that are founded on coupling uncertainties of stochastic systems via sub-probability measures. The presented relations are essential for constructing abstract models that are related to not only one model but to a set of parameterized models. The results are demonstrated on three case studies, including a nonlinear and a high-dimensional system. © 2023, CC BY-NC-ND.

关键词： Stochastic systems

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Manjula R Jagadeesan S Venkadeshan Ramalingam Vetriselvi T Johnpeter T Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Information Technology Department University of Technology and Applied Sciences Sultanate of Oman Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user.

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Transparent AI for Adaptive Fraud Detection

Transparent AI for Adaptive Fraud Detection

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Okechukwu Clement Agomuo Agomuo Kingsley Uzoma Zohaib Khan Agomuo Ijeoma Otuomasirichi Junaid Hussain Muzamal Department of Operations and Information Systems University of Utah Salt Lake City USA Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria dept. of Software Engineering FAST - NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Fast School of computing Fast - NUCES Lahore Pakistan

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The rapid evolution of digital financial services has significantly increased the vulnerability of financial systems to fraudulent activities. Traditional machine learning models have offered substantial progress in detecting fraudulent transactions; however, they often lack adaptability and transparency, critical for dynamic financial environments and regulatory compliance. This paper introduces a novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature of DRL to continuously learn and update its fraud detection strategies based on evolving transaction patterns. Concurrently, the integration of XAI provides clear, interpretable insights into the decision-making process, thus ensuring transparency and building trust. The proposed model demonstrates superior performance with an achieved accuracy of 99.5% and an Fl-score of 99.0%, significantly outperforming traditional fraud detection systems. These results not only underscore the efficacy of our model in improving the accuracy of fraud detection but also highlight its capability in offering significant improvements in understanding the underlying reasons for each decision, thereby aiding compliance with financial regulations. The practical implications of this research highlight its potential to provide a robust, scalable, and transparent solution for realtime fraud detection, which is essential for safeguarding the integrity of modern financial systems.

关键词： Adaptation models Accuracy Explainable AI Scalability Decision making Predictive models Regulation Fraud Security Standards

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Data-Driven Decision Management: Multi-Modal Analytics in a Health Case Study

Data-Driven Decision Management: Multi-Modal Analytics in a ...

引用

International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Okechukwu Clement Agomuo Zohaib Khan Agomuo Ijeoma Otuomasirichi Agomuo Kingsley Uzoma Junaid Hussain Muzamal Department of Operations and Information Systems University of Utah Salt Lake City USA dept. of Software Engineering FAST - NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Fast - NUCES Fast School of computing Lahore Pakistan

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

In an era where data is abundant yet underutilized, organizations across various sectors face challenges in extracting actionable insights from complex, multi-modal datasets. To address these challenges, this study proposes a versatile data analytics framework designed to enhance predictive accuracy and support informed decision-making. The framework integrates structured and unstructured data, leveraging advanced Transformer architectures to capture temporal dynamics and complex interactions within the data. In a healthcare use case, the framework demonstrated its effectiveness by achieving an Accuracy of 0.854, an F1-Score of 0.895, and an AUC-ROC of 0.930, significantly outperforming traditional models. By incorporating SHAP for interpretability, the framework ensures that its predictions are both accurate and transparent, making it a valuable tool for decision-makers. While healthcare is one application, the framework's flexibility allows it to be adapted to a wide range of industries. Future work will focus on validating the framework across different sectors and integrating it into operational workflows to optimize decision-making and outcomes.

关键词： Accuracy Decision making Mortality Medical services Organizations Predictive models Transformers Data models Information management X-ray imaging

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Optimizing User Data Privacy and Confidentiality in Cloud Storage systems Through Advanced Obfuscrypt Encryption Methods for Enhanced Security and Efficient Data Protection

Optimizing User Data Privacy and Confidentiality in Cloud St...

引用

Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： P.S. Ramesh I. Sudha J. Jagannathan Rajkumar Pandiarajan A. Ponmalar Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India R.M.K. Engineering College Chennai Tamilnadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

With a steady increase in the adoption of cloud storage systems, the client’s data are at risk of being leaked. There are several methods of encrypting data which are quite useful but they lack some of the features of today’s sophisticated threats. The methodology used in this research propose an encryption framework known as Obfuscrypt to improve the issues of data privacy as well as confidentiality of data stored in cloud storage systems. Obfuscrypt uses conventional encryption strategies incorporated with obfuscation and post-quantum cryptographic algorithms to provide a complex security plan immune to classic and quantum approaches. Some of the proposed other innovations include the decentralised key management with multi-factor authentication and RBAC-ABE as the fine-grained access control techniques. These features make it possible for only the authorized personnel to be in a position to be in a position of decrypting and retrieving the stored data thereby minimizing the possibility of the data being accessed by unauthorized people. The work also considers the issue of performance efficiency of the cloud storage systems. Similar to other symmetric encryption programs, Obfuscrypt uses light encryption algorithms and brings in to play the hardware acceleration so that it may infuse lesser computational overhead in the process of encryption and decryption. As can be clearly seen from the results, the actual work proves that the proposed framework not only offers a higher level of security but does not affect the system performance to a great extent. Also, the study addresses the post-quantum cryptographic algorithm for making the system resistant to an attack that can be posed by quantum computing systems. With the help of these improved algorithms, Obfuscrypt guarantees the permanent data security in the world of constantly emerging cyber threats.

关键词： Resistance Cloud computing Technological innovation Multi-factor authentication Quantum computing System performance Memory management software performance Encryption Protection

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Securing Multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure Multiparty Computation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： A. Ponmalar Rajkumar Pandiarajan I. Sudha P.S. Ramesh J. Jagannathan R.M.K. Engineering College Chennai Tamilnadu India Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The aim of this work is to examine the application of FHE-SMPC as a viable security model for protecting the tenant isolation in multi-tenant cloud model. In multi-tenant environments, where various customers use the same cloud system, controlling the data leakage and protecting privacy is a major issue. Actual safeguards fail to meet the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes must happen. This research work focuses on the use of FHE technique and SMPC such that data can undergo processing while the plaintext remains unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system-level architecture, and cryptographic algorithms using open-source libraries with cloud service providers. The results of such implementation indicate that despite the fact that the FHE-SMPC system has a considerable computational overhead in comparison with classical schemes it is very secure and protected against different types of cryptographic attacks. The problem of scalability is still an open issue, but the complex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in multitenant environment and will go along way in creating fertile ground for research and innovation in secure clouding.

关键词： Resistance Technological innovation Scalability Data protection Libraries Service-oriented architecture Encryption Cryptography Time complexity Secure storage

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Quality of Service (QoS)-driven Edge computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions

arXiv

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arXiv 2023年

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Australia School of Computer and Information Sciences The University of Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in healthcare and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage and monitor the hospital systems in real-time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, CC BY.

关键词： Internet of things

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Advanced Machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

Advanced Machine Learning Algorithms for 1Predictive Analyti...

引用

Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： Rajkumar Pandiarajan J. Jagannathan P.S. Ramesh A. Ponmalar Sudha Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India R.M.K. Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image information, and genomic data the work proposes and assesses multiple machine learning models including deep learning, ensemble learning, and reinforcement learning. The main findings shown very high performance in disease prediction, in chronic illness and in the application of efficiency improvements in hospitals as a whole. Overall, such threats as data quality, data privacy, and model interpretability have been discussed in the research and emphasize the importance of the machine learning approach in healthcare. It is highlighted that further development and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and machine learning models can be urgent to increase the acceptance among the healthcare staff. This paper enhances the literature on applying machine learning in healthcare systems adding to the repertoire of knowledge for improved patient care.

关键词： Data privacy Machine learning algorithms Hospitals Data integrity Machine learning Reinforcement learning Predictive models Data models Predictive analytics Diseases

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