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46th International Symposium on Mathematical Foundations of Computer Science, MFCS 2021

作者： Glos, Adam Kokainis, Martins Mori, Ryuhei Vihrovs, Jevgenijs Institute of Theoretical and Applied Informatics Polish Academy of Sciences Warsaw Poland Centre for Quantum Computer Science University of Latvia Riga Latvia Tokyo Institute of Technology School of Computing Japan

ISBN: (纸本)9783959772013

Motivated by the quantum speedup for dynamic programming on the Boolean hypercube by Ambainis et al. (2019), we investigate which graphs admit a similar quantum advantage. In this paper, we examine a generalization of the Boolean hypercube graph, the n-dimensional lattice graph Q(D, n) with vertices in {0, 1, . . . ,D}n. We study the complexity of the following problem: given a subgraph G of Q(D, n) via query access to the edges, determine whether there is a path from 0n to Dn. While the classical query complexity is eΘ((D+1)n), we show a quantum algorithm with complexity eO(TnD ), where TD © Adam Glos, Martins Kokainis, Ryuhei Mori, and Jevg¯enijs Vihrovs;licensed under Creative Commons License CC-BY 4.0 46th International Symposium on Mathematical Foundations of Computer Science (MFCS 2021).

关键词： Dynamic programming

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Implementation of a web-based data archival management system

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Test Engineering and Management 2020年 83卷 11513-11523页

作者： Mkpojiogu, Emmanuel O.C. Akusu, Gerard Efe Hussain, Azham Hashim, Wahidah School of Computing Universiti Utara Malaysia Sintok06010 UUM Malaysia Department of Computer and Information Technology Veritas University Abuja Nigeria Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia

This study reports on the implementation of a web-based data archival management system. Informed and vital decisions are made on the basis of the information that is available to either individuals or organizations. Since information is important, it is needful to achieve and manage them properly for ease of their retrieval when needed. Nonetheless, the sorting, storing, searching, retrieving and moving of files has hitherto been done manually and with papers which is inefficient, tedious, and time-consuming. This research implements a data archival and management system in Veritas University, Abuja which solves the challenges and complexities in the manual system of archiving and managing of information in the institution. © 2020 Mattingley Publishing. All rights reserved.

关键词： Information management

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Comparing Different Membership Inference Attacks with a Comprehensive Benchmark

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IEEE Transactions on Information Forensics and Security 2025年

作者： Niu, Jun Zhu, Xiaoyan Zeng, Moxuan Zhang, Ge Zhao, Qingyang Huang, Chunhui Zhang, Yangming An, Suyu Wang, Yangzhong Yue, Xinghui He, Zhipeng Guo, Weihao Shen, Kuo Liu, Peng Zhang, Lan Ma, Jianfeng Zhang, Yuqing Xidian University School of Cyber Engineering School of Computer Science and Technology Xi'an710071 China Xidian University School of Telecommunications Engineering Xi'an710071 China Xidian University School of Hangzhou Institute of Technology Hangzhou311231 China Pennsylvania State University Information Sciences and Technology 16802 United States Northern Arizona University The School of Informatics Computing & Cyber Systems FlagstaffAZ86005 United States Xidian University School of Cyber Engineering Xi'an710071 China Yanshan University The College of Information Science and Engineering Qinhuangdao066004 China University of Chinese Academy of Sciences The School of Computer Science and Technology Beijing101408 China The School of Cyberspace Security Hainan University Haikou570228 China

Membership inference (MI) attacks pose a significant threat to user privacy in machine learning. While numerous attacks have been proposed, the lack of standardized evaluation parameters and metrics has led to inconsistent and even conflicting comparison results. To address this issue and facilitate a systematic analysis of these disparate findings, we introduce MIBench, a comprehensive benchmark that includes a suite of carefully designed evaluation scenarios (ESs) and evaluation metrics to provide a consistent framework for assessing various MI techniques. The ESs are crafted to encompass four critical factors: intra-dataset distance distribution, inter-sample distance within the target dataset, differential distance analysis, and inference withholding ratio. MIBench includes ten typical evaluation metrics and 84 distinct ESs per dataset. Using MIBench, we conducted a thorough comparative analysis of 15 state-of-the-art MI attacks across 588 ESs, seven widely adopted datasets, and seven representative model architectures. Our analysis revealed 83 instances of Conflicting Comparison Results (CCR), providing substantial evidence for the CCR Phenomenon. We identified two CCR types: Type 1 (single-factor) and Type 2 (dual-factor). The CCR distribution across four factors was: inter-sample distance (40.96%), differential distance (37.35%), inference withholding ratio (19.28%), and intra-dataset distance (2.41%). All MIBench codes and evaluations are available at link. © 2025 IEEE.

关键词： Differential privacy

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Blockchain-based Data Sharing Framework for Malaysia Government Aid Management System

Blockchain-based Data Sharing Framework for Malaysia Governm...

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Digital Applications, Transformation & Economy (ICDATE), International Conference on

作者： Azman Azmi Farashazillah Yahya Ervin Gubin Moung Hasimi Sallehudin Rio Guntur Utomo Nur Afrina Azman Advanced Machine Intelligence Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Data Technologies and Application Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Centre for Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia School of Computing Telkom University Bandung Indonesia Foundation Study and Language Institute University of Reading Malaysia Iskandar Puteri Johor Malaysia

The Covid-19 pandemic has impacted the world economy, and Malaysia is no exception. Due to the closure of businesses, the rate of unemployment has been recorded at an all-time high. The Malaysian Government through its agencies is assisting citizens through an online platform for incentives and an aid-giving platform. The current architecture of data sharing is using centralized, organization-oriented authentication and is not immutable for audit capability. In addition, the blockchain network is still a proof of concept in the digital government ecosystem. This paper studies the current aid data-sharing ecosystem and analyzes the potential of having a state government consortium blockchain framework that will be used in the Government Network Infrastructure for decentralized, transparent data sharing with audit capability. Several works involving Blockchain adoption based on domain, Blockchain consortium architecture and Government applications using blockchain are being presented. The need for a framework in implementing decentralized data sharing using consortium blockchain architecture for multi-agencies data sharing focusing on Malaysia Government Aid Management is discussed in this paper to provide the opportunity for the research domain. The findings in this paper will be used for future work on the requirement of a proposed government multi-layer consortium blockchain framework for data sharing in the Malaysian Government specifically for the Johore State Government ecosystem.

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Eliciting and modeling the requirements for an online data archival management system

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Test Engineering and Management 2020年 83卷 11503-11512页

Information is an integral part of any person's or organisation's life as important decisions are made based on them. As such there is a need to properly store/achieve, manage and easily retrieve important and sensitive information when necessary. However, the use of papers and manually sorting, storing, searching, retrieving and moving files is tedious, time-consuming and not very efficient. The purpose of this study is to elicit and model requirements for a data archival management system to be used in Veritas University, Abuja. The system when implemented will solve a good number of the complications involved in manually archiving and managing files in the institution. The requirements elicitation was carried out via interviews while the requirements modelling were done using UML. © 2020 Mattingley Publishing. All rights reserved.

关键词： Information management

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Classification Based on Texture Analysis for Synthetic Hazy Image from Mount Kelud Haze Image Density

Classification Based on Texture Analysis for Synthetic Hazy ...

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International Conference on Electrical, Electronics and Information Engineering (ICEEIE)

作者： Fresy Nugroho Puspa Miladin Safitri A. Basid Yunifa Miftachul Arif Norizan Mat Diah Firma Sahrul Bahtiar Hani Nurhayati Fachrul Kurniawan Nurizal Dwi Priandani Dodik Arwin Dermawan Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia School of Computing Sciences College of Computing Informatics and Media Universiti Teknologi MARA Shah Alam Malaysia Faculty of Science and Technology Library and Information Science Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia Manajemen Informatika Program Vokasi Universitas Negeri Surabaya Surabaya Indonesia

This paper classifies the simulations of homogeneous synthetic images, heterogeneous synthetic hazy images, and original hazy images taken from CCTV (Close Circuit Television) of Mt. Kelud crater using the GLCM (Gray Level Co-Occurrence Matrix) method. The average feature values obtained using the GLCM (Gray Level Co-Occurrence Matrix) method are used to compare the similarity of gray feature values of the three and then classify thin, medium, and thick images. The results for classifying thin haze, medium haze, and thick haze on the homogeneous synthetic hazy image test data obtained an accuracy value of 50%, a precision value of 46%, and a sensitivity value of 65%. As for the classification of thin, medium, and thick fog on heterogeneous synthetic hazy images, test data obtained an accuracy value of 42%, a precision value of 32%, and a sensitivity value of 48%.

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EMERGING PRACTICES FOR LARGE MULTIMODAL MODEL (LMM) ASSISTANCE FOR PEOPLE WITH VISUAL IMPAIRMENTS: IMPLICATIONS FOR DESIGN

arXiv

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arXiv 2024年

作者： Xie, Jingyi Yu, Rui Zhang, He Lee, Sooyeon Billah, Syed Masum Carroll, John M. College of Information Sciences and Technology Pennsylvania State University United States Department of Computer Science and Engineering J.B. Speed School of Engineering University of Louisville United States Department of Informatics Ying Wu College of Computing New Jersey Institute of Technology United States

People with visual impairments perceive their environment non-visually and often use AI-powered assistive tools to obtain textual descriptions of visual information. Recent large vision-language model-based AI-powered tools like Be My AI are more capable of understanding users’ inquiries in natural language and describing the scene in audible text;however, the extent to which these tools are useful to visually impaired users is currently understudied. This paper aims to fill this gap. Our study with 14 visually impaired users reveals that they are adapting these tools organically – not only can these tools facilitate complex interactions in household, spatial, and social contexts, but they also act as an extension of users’ cognition, as if the cognition were distributed in the visual information. We also found that although the tools are currently not goal-oriented, users accommodate this limitation and embrace the tools’ capabilities for broader use. These findings enable us to envision design implications for creating more goal-oriented, real-time processing, and reliable AI-powered assistive technology. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Predictive Classification of Covid-19: Assessing the Impact of Digital Technologies

Predictive Classification of Covid-19: Assessing the Impact ...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Bonda Kiran Kumar Anurima Majumdar Saidatul Akmar Ismail Rohit R Dixit Hairani Wahab Md Hafizi Ahsan KL School of Architecture KL Deemed to be University Koneru Lakshmaiah Educational Foundation vaddeswaram Andhra Pradesh Department of Electronics and Communication Engineering Guru Nanak Institute of Technology School of Information Science College of Computing Informatics & Mathematics Universiti Teknologi MARA Shah Alam Malaysia Siemens Healthineers School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Labuan Faculty of International Finance Universiti Malaysia Sabah

The recent severe outbreak of the disease has been linked to the COVID-19 virus. It is essential for healthcare professionals and individuals with the capacity to provide intensive care to offer clear explanations of this situation. To address this issue, the World Health Organization (WHO) has implemented several measures. The National Emergency Telecritical Care Network (NETCCN) has the capability to assess these actions using digital technologies. This section provides a comprehensive explanation of these developments, and NETCCN has put forth a recommendation. Version 2.0 of this digital health data will be made available; it will significantly enhance potential crises in the future. Artificial Intelligence (AI) is used in the suggested digital prediction approach to gather past data on the infection. Telemetry, sophisticated maintenance robotics, and monitoring intelligence may all be found with this technology. These may be created to incorporate data relevant to various infections. Health experts will coordinate all of these. To maintain sustained preparedness for emergency catastrophe protection, frequent testing should be conducted.

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Hybrid deep learning-based strategy for hepatocellular carcinoma cancer grade classification

arXiv

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arXiv 2024年

作者： Deshpande, Ajinkya Gupta, Deep Bhurane, Ankit Meshram, Nisha Singh, Sneha Radeva, Petia Department of Electronics and Communication Engineering Visvesvaraya National Institute of Technology MH Nagpur India Department of Pathology AIIMS MH Nagpur India School of Computing & Electrical Engineering The Indian Institute of Technology Mandi India Department of Mathematics and Informatics Universitat de Barcelona Barcelona08007 Spain

Hepatocellular carcinoma (HCC) is a common type of liver cancer whose early-stage diagnosis is a challenge. This is mainly done by a manual assessment of hematoxylin and eosin-stained whole slide images, which is a time-consuming process with high variability in the decision-making. In order to achieve accurate detection of HCC, we propose a hybrid deep learning architecture that leverages the advantages of fine-tuning selective layers of convolutional neural network (CNN) alongside a deep classifier comprising a sequence of fully connected layers, i.e., Artificial Neural Network (ANN). In order to validate the method, this study uses a publicly available The Cancer Genome Atlas Hepatocellular Carcinoma (TCGA-LIHC) [1] database (n=491) for model development and proprietary database of Kasturba Gandhi Medical College (KMC), India [2] for validation. The proposed hybrid model with ResNet50-based feature extractor provided the sensitivity, specificity, F1-score, accuracy, and AUC of 100.00%, 100.00%, 100.00%, 100.00%, and 1.00, respectively on the TCGA database. It was contrasted with eight different state-of-the-art models. On the KMC database, EfficientNetb3 resulted in the optimal choice of the feature extractor giving sensitivity, specificity, F1-score, accuracy, and AUC of 96.97±0.83, 98.85±0.24, 96.71±0.66, 96.71±0.66, and 0.99±0.01, respectively. The proposed hybrid models showed improvement in accuracy of 2% and 4% over the pre-trained models in TCGA-LIHC and KMC databases. © 2024, CC BY.

关键词： Convolutional neural networks

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A Brief Analysis on Efficient Machine Learning Techniques for Intrusion Detection Model to Provide Network Security

A Brief Analysis on Efficient Machine Learning Techniques fo...

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Sustainable computing and Data Communication Systems (ICSCDS), International Conference on

作者： Aswadhati. Sirisha Bulla Premamayudu Department of IT and computer Applications school of computing and informatics Vignan’s Foundation for science Technology & Research Deemed to be university vadlamudi Guntur Andhra pradesh India Department of IT Vignan’s Institute of Information And Technology (Autonomous) Vishakapatnam Andhra Pradesh

An Intrusion Detection System (IDS) is a software used to monitor a single computer or a network of computers to combat network attacks, block access to certain websites, or otherwise the attack on network protocols. The dynamic and complicated nature of cyber attacks on computer systems is beyond the capabilities of the majority of current IDS solutions. By combating intrusion attempts, network security professionals ensure that the services can be accessed at all times. One of the tools at disposal to detect and categorize suspicious behaviour is IDS. For almost 40 years, researchers in both academia and industry have been proposing strategies for detecting and avoiding such security breaches, and even constructing systems to do so. Intrusion detection systems that rely on attack signatures examine network traffic for suspicious activity. Anomaly-based intrusion detection systems, on the other hand, build a model to tell good users apart from bad ones, allowing them to spot previously unseen attacks. Machine learning (ML) techniques are increasingly being employed as a means of categorizing normal and abnormal activities. Many other ML-based intrusion detection systems have been proposed in the past few years. The purpose of this work is to provide a critical overview of the state of the art in machine learning (ML)-based intrusion detection methods. Researchers focusing on ML-based systems that detect intrusions could use this survey as a supplement to previous generic surveys on intrusion detection and a source to recent work done in the area.

关键词： Industries Analytical models Computer hacking Computational modeling Intrusion detection Machine learning Telecommunication traffic

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