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arXiv 2024年

作者： Zhang, Yedi Song, Fu Chen, Taolue Wu, Xuzhi Department of Computing Science National University of Singapore Singapore State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom College of Computer Science and Software Engineering Shenzhen University China

Reasoning about strategic abilities is key to AI systems comprising multiple agents, which provide a unified framework for formalizing various problems in game theory, social choice theory, etc. In this work, we propose a probabilistic extension of the alternating-time μ-calculus (AMC), named PAMC, for reasoning about the strategic abilities of agents in stochastic multi-agent systems. We show that PAMC subsumes two existing logics AMC and PμTL (a probabilistic extension of the modal μ-calculus), but is incomparable with the probabilistic alternating-time temporal logic (PATL). We study the problems of model checking and satisfiability checking for PAMC. We first give a model checking algorithm by leveraging algorithms for solving normal-form games and AMC model checking. We establish that the model checking problem of PAMC remains in UP∩co-UP, the same complexity class as the model checking problem for AMC and PμTL. We also provide a new reduction from the satisfiability problem of PAMC to solving parity games, by which we obtain an EXPTIME decision procedure, as well as the small model property which allows to construct a model for each satisfiable PAMC formula. Satisfiability in PAMC has the same complexity as in the modal μ-calculus, unlike PCTL and PATL whose satisfiability checking problems remain open. We have implemented both the model checking and satisfiability checking algorithms as open-source tools. Experimental results are reported, showcasing the practical applications and effectiveness of our approaches. © 2024, CC BY.

关键词： Stochastic systems

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Exploiting the sequential nature of genomic data for improved analysis and identification

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Computers in Biology and Medicine 2024年 183卷 109307-109307页

作者： Nawaz, M. Saqib Nawaz, M. Zohaib Junyi, Zhang Fournier-Viger, Philippe Qu, Jun-Feng College of Computer Science and Software Engineering Shenzhen University China Faculty of Computing and Information Technology Department of Computer Science University of Sargodha Pakistan School of Computer Engineering Hubei University of Arts and Science Hubei Xiangyang China

Genomic data is growing exponentially, posing new challenges for sequence analysis and classification, particularly for managing and understanding harmful new viruses that may later cause pandemics. Recent genome sequence classification models yield promising performance. However, the majority of them do not consider the sequential arrangement of nucleotides and amino acids, a critical aspect for uncovering their inherent structure and function. To overcome this, we introduce GenoAnaCla, a novel approach for analyzing and classifying genome sequences, based on sequential pattern mining (SPM). The proposed approach first constructs and preprocesses datasets comprising RNA virus genome sequences in three formats: nucleotide, coding region, and protein. Then, to capture sequential features for the analysis and classification of viruses, GenoAnaCla extracts frequent sequential patterns and rules in three forms and in codons. Eight classifiers are utilized, and their effectiveness is assessed by employing a variety of evaluation metrics. A performance comparison demonstrates that the suggested approach surpasses the current state-of-the-art genome sequence classification and detection techniques with a 3.18% performance increase in accuracy on average. © 2024 Elsevier Ltd

关键词： Genome

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Developing Trustworthy Reinforcement Learning Applications for Next-Generation Open Radio Access Networks

Developing Trustworthy Reinforcement Learning Applications f...

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： Ahmad M. Nagib Hatem Abou-zeid Hossam S. Hassanein School of Computing Queen’s University Canada Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Electrical and Software Engineering University of Calgary Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Artificial intelligence is envisioned to transform the design and operation of 6G networks. Reinforcement learning (RL), in particular, has emerged as a fundamental approach toward this goal with strong support from the industry and the Open Radio Access Network (O-RAN) Alliance. While research efforts have demonstrated the potential of RL, the lack of trustworthiness of RL algorithms remains a challenge to its adoption in real-world networks. In this paper, we propose a trustworthy RL framework that addresses the core challenges experienced by RL-based radio resource management applications deployed in O-RANs. We then demonstrate a case study on O-RAN slicing incorporating several modules of the proposed framework. The experimental results show improvements in the average RL convergence rate, initial reward value, percentage of converged scenarios, and reward variance. Hence, the RL-based algorithms exhibit fast convergence and enhanced generalizability, safety, and robustness.

关键词： Industries Open RAN Reinforcement learning Transforms Robustness Safety Resource management

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Triple Key Security Algorithm Against Single Key Attack on Multiple Rounds

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Computers, Materials & Continua 2022年第9期72卷 6061-6077页

作者： Muhammad Akram Muhammad Waseem Iqbal Syed Ashraf Ali Muhammad Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Department of Computer Science Superior University Lahore54000Pakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science The Institute of Management Sciences(PAK-AIMS)Lahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia

In cipher algorithms,the encryption and decryption are based on the same *** are some limitations in cipher algorithms,for example in polyalphabetic substitution cipher the key size must be equal to plaintext otherwise it will be repeated and if the key is known then encryption becomes *** paper aims to improve the said limitations by designing of Triple key security algorithm(TKS)in which the key is modified on polyalphabetic substitution cipher to maintain the size of the key and *** plaintext character is substituted by an alternative *** mode of substitution is transformed cyclically which depends on the current position of the modified *** keys are used in the encryption and decryption process on 8 or 16 rounds with the Exclusively-OR(XOR)of the 1st *** study also identifies a single-key attack on multiple rounds block cipher in mobile communications and applied the proposed technique to prevent the *** utilization of the TKS algorithm,the decryption is illustrated,and security is analyzed in detail with mathematical examples.

关键词： Encryption decryption cipher symmetric key cryptography single key attack

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Explainable AI for Healthcare: An Approach Towards Interpretable Healthcare Models

Explainable AI for Healthcare: An Approach Towards Interpret...

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Information Technology (ACIT)

作者： Kanwal Zahoor Narmeen Zakaria Bawany Usman Ghani Department of Computer Science and Software Engineering Center for Computing Research Jinnah University for Women Institute of Business Administration Karachi Pakistan

Artificial intelligence (AI) along with deep learning techniques has become an integral part of almost all aspects of life. One of the domains significantly impacted by this technological revolution is healthcare. Deep learning-based AI systems assist clinicians and medical professionals in disease diagnosis, personalized treatment, and monitoring through wearables, among other applications. Despite its expedient integration in healthcare, the trustworthiness of deep learning models remains a concern, primarily due to a lack of understanding of their underlying processes. However, Explainable AI (XAI) offers explanations through various methods, including Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive explanation (SHAP), and GRAD-CAM. XAI is utilized to enhance transparency, allowing users to understand and trust AI decisions. In this study, we present deep learning models for the classification of pneumonia disease in Chest X-ray Images followed by their explanations. Convolutional Neural Networks (CNNs) and other pre-trained models, including VGG16, MobileNetV3, and ResNet50, were used for classification of images as ‘normal’ or ‘pneumonia’. The VGG16 model, known for its exceptional image understanding capabilities, achieved the highest accuracy, with an impressive 93% score. Further, we used XAI techniques including SHAP, LIME, and Grad-CAM for explanation of models. LIME and Grad-CAM provided more accurate results than SHAP in our experiments. This approach was taken to evaluate the fairness and transparency of the model. The insights gained from XAI can be used to refine and improve machine learning models by identifying areas of weakness or misinterpretation which increases overall model robustness.

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An Efficient High-Dimensional Gene Selection Approach based on Binary Horse Herd Optimization Algorithm for Biological Data Classification

arXiv

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arXiv 2023年

作者： Mehrabi, Niloufar Boroujeni, Sayed Pedram Haeri Pashaei, Elnaz School of Computing Clemson University ClemsonSC United States Department of Software Engineering Istanbul Aydin University Istanbul Turkey

The Horse Herd Optimization Algorithm (HOA) is a new meta-heuristic algorithm based on the behaviors of horses at different ages. The HOA was introduced recently to solve complex and high-dimensional problems. This paper proposes a binary version of the Horse Herd Optimization Algorithm (BHOA) in order to solve discrete problems and select prominent feature subsets. Moreover, this study provides a novel hybrid feature selection framework based on the BHOA and a minimum Redundancy Maximum Relevance (MRMR) filter method. This hybrid feature selection, which is more computationally efficient, produces a beneficial subset of relevant and informative features. Since feature selection is a binary problem, we have applied a new Transfer Function (TF), called X-shape TF, which transforms continuous problems into binary search spaces. Furthermore, the Support Vector Machine (SVM) is utilized to examine the efficiency of the proposed method on ten microarray datasets, namely Lymphoma, Prostate, Brain-1, DLBCL, SRBCT, Leukemia, Ovarian, Colon, Lung, and MLL. In comparison to other state-of-the-art, such as the Gray Wolf (GW), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), the proposed hybrid method (MRMR-BHOA) demonstrates superior performance in terms of accuracy and minimum selected features. Also, experimental results prove that the X-Shaped BHOA approach outperforms others methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Support vector machines

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Empowering Donors: How Blockchain Technology Can Help Ensure Their Contributions Reach the Right Recipients 2

Empowering Donors: How Blockchain Technology Can Help Ensure...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ajmal, Sahar Muzammil, Muteeb Bin Shoaib, Muhammad Mehmood, Muhammad Hassaan National University of Computer and Emmerging Sciences Department of Software Engineering Faisalabad Pakistan Riphah International University College of Computing Faisalabad Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential. © 2023 IEEE.

关键词： Transparency

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Privacy-Aware Web APIs Recommendation for Consumer Mashup Creation Based on Iterative Quantification

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Zhang, Ruowei Qi, Lianyong Yan, Chao Chen, Zige Gong, Wenwen Xu, Yanwei Rafique, Wajid School of Computer Science Qufu Normal University China China Tsinghua University China College of Intelligence and Computing Tianjin University China Department of Electrical and Software Engineering University of Calgary Canada

With the emergence of the "unmanned" field, unmanned supermarket software has entered consumers’ lives in line with the pace of development of the times. Nowadays, developers of unmanned supermarket software often tend to create feature-rich consumer mashup applications by invoking a variety of Web APIs (Application Programming Interfaces) in order to save time and costs. The number of APIs available on edge servers has significantly increased with the rise of mobile edge computing. To cope with the continuous growth in API volume and the increasing diversity of API functions in API sharing communities (e.g., ***), lightweight recommendation techniques have been employed to assist consumer mashup developers in finding their desired Web APIs from a vast pool of candidates. However, during the data processing in edge computing, traditional Web API recommendation approaches often prioritize the accuracy of recommended APIs, neglecting the potential privacy risks associated with disclosure. This significantly reduces the willingness and incentive of consumer mashup developers to share API information, such as historical edge mashup-API invocation records. To address this issue, we propose a privacy-preserving Web API recommendation approach, WARecITQ, based on Iterative Quantification (ITQ). Specifically, we convert the sensitive mashup-API invocation records into less-sensitive mashup indices using Iterative Quantization hash coding. We then utilize these less-sensitive mashup indices as the primary decision-making criteria for Web API recommendations, thereby achieving the goal of completing top-k API recommendations. Lastly, we crawled a real-world dataset of mashup-API invocation records from *** and conducted a series of experiments on the dataset to evaluate performance. The results of the experiments demonstrate the superiority of our WARecITQ approach in multiple performance metrics compared to other related approaches, while ensu

关键词： Iterative methods

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A Plugin for Kotlin based Android Apps to Detect Security Breaches through Dataflow

A Plugin for Kotlin based Android Apps to Detect Security Br...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Md Arabin Islam Talukder Sumaiya Farzana Mishu Hossain Shahriar ABM Kamrul Islam Riad Fan Wu Akond Rahman College of Computing and Software Engineering Kennesaw State University Marietta GA USA Department of Computer Science Tuskegee University Tuskegee Alabama USA Department of Computer Science and Software Engineering Auburn University Auburn Alabama USA

Android developers have already adopted Kotlin as their preferred language. Kotlin is more well-accepted than Java because of its simplicity, readability, and new features like scope functions, extension functions, null safety, etc. Google has introduced several new libraries such as RoomDb, Jetpack-Compose, as well as dependency injection frameworks like Dagger-Hilt to enhance Android Development. The question is how secure these libraries are? Are there tools available to analyze new Libraries? DroidPatrol [1] is our existing static analysis plugin that works on Android Apps written in Java. To support Kotlin code analysis we were continuously working on upgrading our plugin. Finally, we released DroidPatrol 2.0 [2] which is compatible to perform static analysis of Android apps developed in Kotlin. It also works on Java-based Android app developed in Java. In this latest edition, we restructured the architecture of the plugin to optimize its efficiency. We also found a vulnerability in RoomDb. The latest version is developed in Kotlin, and we used IntelliJ Idea. Lastly, version 2.0 is independent of Android Studio editions meaning that any version of Android Studio is compatible.

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Optimal Multidimensional Differentially Private Mechanisms in the Large-Composition Regime

Optimal Multidimensional Differentially Private Mechanisms i...

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IEEE International Symposium on Information Theory

作者： Wael Alghamdi Shahab Asoodeh Flavio P. Calmon Juan Felipe Gomez Oliver Kosut Lalitha Sankar School of Engineering and Applied Sciences Harvard University Department of Computing and Software McMaster University School of Electrical Computer and Energy Engineering Arizona State University

We construct vector differentially-private (DP) mechanisms that are asymptotically optimal in the limit of the number of compositions growing without bound. First, we derive via the central limit theorem a reduction from DP to a KL-divergence minimization problem. Second, we formulate the general theory of spherically-symmetric DP mechanisms in the large-composition regime. Specifically, we show that additive, continuous, spherically-symmetric DP mechanisms are optimal if one considers a spherically-symmetric cost (e.g., bounded noise variance) and an ℓ 2 sensitivity metric. We then formulate a finite-dimensional problem that produces noise distributions that can get arbitrarily close to optimal among monotone mechanisms. Finally, we demonstrate numerically that our proposed mechanism achieves better DP parameters than the vector Gaussian mechanism for the same variance constraint.

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