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A novel approach for calculating single-source shortest paths of weighted digraphs based on rough sets theory

Mathematical Biosciences and Engineering 2024年第2期21卷 2626-2645页

作者： Hua, Mingfeng Xu, Taihua Yang, Xibei Chen, Jianjun Yang, Jie School of Computer Jiangsu University of Science and Technology Zhenjiang212100 China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China School of Physics and Electronic Science Zunyi Normal University Zunyi563002 China

Calculating single-source shortest paths (SSSPs) rapidly and precisely from weighted digraphs is a crucial problem in graph theory. As a mathematical model of processing uncertain tasks, rough sets theory (RST) has been proven to possess the ability of investigating graph theory problems. Recently, some efficient RST approaches for discovering different subgraphs (e.g. strongly connected components) have been presented. This work was devoted to discovering SSSPs of weighted digraphs by aid of RST. First, SSSPs problem was probed by RST, which aimed at supporting the fundamental theory for taking RST approach to calculate SSSPs from weighted digraphs. Second, a heuristic search strategy was designed. The weights of edges can be served as heuristic information to optimize the search way of k-step R-related set, which is an RST operator. By using heuristic search strategy, some invalid searches can be avoided, thereby the efficiency of discovering SSSPs was promoted. Finally, the W3SP@R algorithm based on RST was presented to calculate SSSPs of weighted digraphs. Related experiments were implemented to verify the W3SP@R algorithm. The result exhibited that W3SP@R can precisely calculate SSSPs with competitive efficiency. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Heuristic algorithms

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Public Awareness of Phishing in Indonesia: System Literature Review

Public Awareness of Phishing in Indonesia: System Literature...

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2023 International Conference on data science and Its Applications, ICoDSA 2023

作者： Chandra, Anthony William Chandiarta, Zefanya Setiyawan, Lutfi Gunawan, Alexander Agung Santoso Edbert, Ivan Sebastian Bina Nusantara University Cyber Security Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350305197

In an era where technology is advancing at an unprecedented rate, the internet has become an indispensable tool for everyday life, offering both convenience and efficiency. While the internet has simplified tasks ranging from communication to information retrieval, it also poses significant challenges in the realm of cyber security. Users are not just potential victims but can also become unwitting perpetrators of cybercrimes. This highlights the urgent need for cyber security awareness among the general populace. Focusing on Indonesia, a nation where cyber security education is still in its infancy, this study aims to assess the level of public awareness regarding the specific threat of phishing. Employing the Systematic Literature Review (SLR) methodology, this research analyzes a comprehensive range of literature encompassing subjects such as phishing, data leakage, broader cyber security threats, and the current state of public awareness about phishing. The objective is to provide a detailed overview of existing knowledge and identify areas where educational efforts should be concentrated. The results indicate a considerable gap in the understanding of phishing among Indonesians. Many studies reviewed did not offer a clear definition or explanation of phishing, creating an environment where individuals are more susceptible to these cyber-attacks. This research underscores the urgent need for targeted educational initiatives aimed at increasing public awareness and understanding of phishing and other cyber threats. © 2023 IEEE.

关键词： Cybersecurity

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CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network

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IEEE Internet of Things Journal 2025年第12期12卷 20138-20155页

作者： Zhao, Zijian Chen, Tingwei Cai, Zhijie Li, Xiaoyang Li, Hang Chen, Qimei Zhu, Guangxu Shenzhen Research Institute of Big Data Shenzhen518115 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Shenzhen Research Institute of Big Data Shenzhen518115 China Wuhan University School of Electronic Information Wuhan430072 China

In recent years, Wi-Fi sensing has garnered significant attention due to its numerous benefits, such as privacy protection, low cost, and penetration ability. Extensive research has been conducted in this field, focusing on areas such as gesture recognition, people identification, and fall detection. However, many data-driven methods encounter challenges related to domain shift, where the model fails to perform well in environments different from the training data. One major factor contributing to this issue is the limited availability of Wi-Fi sensing datasets, which makes models learn excessive irrelevant information and over-fit to the training set. Unfortunately, collecting large-scale Wi-Fi sensing datasets across diverse scenarios is a challenging task. To address this problem, we propose CrossFi, a siamese network-based approach that excels in both in-domain scenario and cross-domain scenario, including few-shot, zero-shot scenarios, and even works in few-shot new-class scenario where testing set contains new categories. The core component of CrossFi is a sample-similarity calculation network called CSi-Net, which improves the structure of the siamese network by using an attention mechanism to capture similarity information, instead of simply calculating the distance or cosine similarity. Based on it, we develop an extra Weight-Net that can generate a template for each class, so that our CrossFi can work in different scenarios. Experimental results demonstrate that our CrossFi achieves state-of-the-art performance across various scenarios. In gesture recognition task, our CrossFi achieves an accuracy of 98.17% in in-domain scenario, 91.72% in one-shot cross-domain scenario, 64.81% in zero-shot cross-domain scenario, and 84.75% in one-shot new-class scenario. The code for our model is publicly available at https://***/RS2002/CrossFi. © 2014 IEEE.

关键词： Gesture recognition

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Cloud datacenter Selection Using Service Broker Policies:A Survey

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computer Modeling in Engineering & sciences 2024年第4期139卷 1-41页

作者： Salam Al-E’mari Yousef Sanjalawe Ahmad Al-Daraiseh Mohammad Bany Taha Mohammad Aladaileh Department of Information Security Faculty of ITUniversity of PetraAmman11196Jordan Department of Cybersecurity School of ITAmerican University of Madaba(AUM)Amman11821Jordan Department of Computer Science School of ITAmerican University of Madaba(AUM)Amman11821Jordan Department of Data Science and Artificial Intelligence School of ITAmerican University of Madaba(AUM)Amman11821Jordan

Amid the landscape of Cloud Computing(CC),the Cloud datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC *** to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service *** tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)*** numerous CSB policies,their implementation grapples with challenges like costs and *** article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current *** foremost objective is to pinpoint research gaps and remedies to invigorate future policy ***,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers *** synthetic analysis,the article systematically assesses and compares myriad DC selection *** analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their *** summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC *** emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.

关键词： Cloud computing cloud service broker datacenter selection quality-of-service user request

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Building a Personalized Model for Social Media Textual Content Censorship

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Proceedings of the ACM on Human-computer Interaction 2022年第2 CSCW期6卷 1-31页

作者： Liu, Baoxi Zhang, Peng Shu, Yubo Guan, Zhengqing Lu, Tun Gu, Hansu Gu, Ning School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University Shanghai China Independent Seattle United States

Social media users often suffer from the problem of content over-disclosure. Most existing studies attempt to solve this problem by recommending proper audiences for users when sharing content. However, the audience management strategy cannot filter out sensitive information from the post and narrow the scope of content permeation. On the contrary, this paper conducts research from the content perspective and aims to design a content censorship model to help users evaluate the publicity of a post and find the sensitive information from it. The user can revise the content accordingly to achieve goals of sensitive information protection and broader content permeation. For this intention, we first built a dataset to explore the factors related to the public level of a post and the sensitive information. Based on the findings, a novel personalized multi-task content censorship model was built using several state-of-the-art deep learning techniques such as Seq2Seq and Co-training. We also implemented a prototype, i.e. a Browser plugin-based content censorship tool, by utilizing Weibo as a research site. Our model and its prototype were evaluated through automatic and human evaluations. The automatic evaluation suggests that our model outperforms the baseline methods on several metrics including precision, recall, and F1-score. The human evaluation also reveals that our model and prototype play an important role in helping users identify sensitive information. Based on these results, we proposed several insights for the future design of the social media content censorship system. © 2022 ACM.

关键词： Social networking (online)

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DSAFF-Net:A Backbone Network Based on Mask R-CNN for Small Object Detection

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computers, Materials & Continua 2023年第2期74卷 3405-3419页

作者： Jian Peng Yifang Zhao Dengyong Zhang Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India

Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature extraction strategy in this paper,which name is *** this strategy,we design:1)a sandwich attention feature fusion module(SAFF module).Its purpose is to enhance the semantic information of shallow features and resolution of deep features,which is beneficial to small object detection after feature fusion.2)to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive *** method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network(RPN).In the experimental phase,we use the new strategy to extract *** experiment takes the public dataset of Microsoft Common Objects in Context(MS COCO)object detection and the dataset of Corona Virus Disease 2019(COVID-19)image classification as the experimental object *** results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%,and small object detection in object detection tasks is improved by 4.0%.

关键词： Small object detection classification RPN MS COCO COVID-19

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Improving Entity Linking in Chinese Domain by Sense Embedding Based on Graph Clustering

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Journal of computer science & Technology 2023年第1期38卷 196-210页

作者：张照博钟芷漫袁平鹏金海 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity *** is of great significance to some NLP(natural language processing)tasks,such as question *** English entity linking,Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words,which is more evident in certain *** Chinese domains,such as industry,the generated candidate entities are usually composed of long strings and are heavily *** addition,the meanings of the words that make up industrial entities are sometimes *** semantic space is a subspace of the general word embedding space,and thus each entity word needs to get its exact ***,we propose two schemes to achieve better Chinese entity ***,we implement an ngram based candidate entity generation method to increase the recall rate and reduce the nesting ***,we enhance the corresponding candidate entity ranking mechanism by introducing sense *** the contradiction between the ambiguity of word vectors and the single sense of the industrial domain,we design a sense embedding model based on graph clustering,which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with *** test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general *** confirm that our method can better learn candidate entities’fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.

关键词： natural language processing(NLP) domain entity linking computational linguistics word sense disambiguation knowledge graph

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Knowledge Leaks in data-Driven Business Models? Exploring Different Types of Knowledge Risks and Protection Measures

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Schmalenbach Journal of Business Research 2024年第3期76卷 357-396页

作者： Fruhwirth, Michael Pammer-Schindler, Viktoria Thalmann, Stefan Know-Center GmbH Institute for Interactive Systems and Data Science Faculty of Computer Science and Biomedical Engineering Graz University of Technology Silicon Austria Labs GmbH Business Analytics and Data Science-Center (BANDAS-Center) School of Business Economics and Social Sciences University of Graz

data-driven business models imply the inter-organisational exchange of data or similar value objects. data science methods enable organisations to discover patterns and eventually knowledge from data. Further, by training machine learning models, knowledge is materialised in those models. Thus, organisations might risk the exposure of competitive knowledge by sharing data-related value objects, such as data, models or predictions. Although knowledge risks have been studied in traditional business models, little research has been conducted in the direction of data-driven business models. In this explorative qualitative study, we conducted 28 expert interviews in three rounds (two exploratory and one evaluatory) and identified five types of risks along the three basic types of value objects: data, models and predictions. These risks depend on the context, i.e., when competitive knowledge could be discovered from shared value objects. We found that those risks can be mitigated by technology, contractual regulations, trusted relationships, and adjusting the business model design. In this study, we show that the risk of knowledge leakage is a relevant risk factor in data-driven business models. Overall, knowledge risks should be considered already during business model design, and their management requires an interdisciplinary approach via a balanced assessment. The level of knowledge protection from a technology perspective highly depends on computer science innovations and thus is a moving target. As an outlook, we suggest that knowledge risk will become even more relevant with the extensive usage of machine learning and artificial intelligence in data-driven business models. © The Author(s) 2024.

关键词： Business model innovation data analytics data-driven business models Knowledge risks Risk management Value objects

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Testing Most Distant Neighbor (MDN) Variants for Semi-Factual Explanations in XAI 32

Testing Most Distant Neighbor (MDN) Variants for Semi-Factua...

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Workshops at the 32nd International Conference on Case-Based Reasoning, ICCBR-WS 2024

作者： Aryal, Saugat Keane, Mark T. School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics Dublin Ireland

Recently, Semi-factual explanations have gained popularity in the eXplainable AI (XAI) community. They provide "even if" justifications to indicate what key input features could change without changing the outcome. Although several methods have now been proposed to compute semi-factuals, the instance-based, Most Distant Neighbors (MDN) method has emerged in recent comprehensive tests to be quite competitive, even though was originally proposed as a naive benchmark. However, on some metrics MDN comes bottom of the class (e.g., sparsity). In this paper, we explore nine variants of the MDN method to determine whether its performance on some metrics can be improved relative to older methods by performing comprehensive tests on key metrics. The results show that there are MDN variants that perform better on some key metrics, but that some of the historical methods still do better. © 2024 Copyright for this paper by its authors.

关键词： Most Distant Neighbor Semi-Factual XAI XCBR

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SeP2CNN: A Simple and Efficient Privacy-Preserving CNN for AIoT Applications

SeP2CNN: A Simple and Efficient Privacy-Preserving CNN for A...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Xiao, Tao Zhang, Zhenyong Shao, Kuan Li, Hao School of Computer Science and Engineering Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798331507886

As convolutional neural networks (CNNs) have shown excellent performance in various inference tasks, it has become increasingly critical to enable Artificial Intelligence of Things (A IoT) systems to run CNN-based applications. However, using CNN-based applications in many AIoT scenarios raises privacy concerns. Protecting the inference phase can preserve the user's privacy, which is an urgent issue according to worldwide rules and policies. In this paper, we propose SeP2CNN, a simple and efficient fully homomorphic encryption (FHE) method to realize the privacy-preserving CNN inference in pervasive AIoT applications. By carefully combining cryptography and deep learning, we compute ReLU with a composite polynomial as the activation function over ciphertexts. To improve the efficiency, we transform the three-dimensional convolution into a two-dimensional convolution to simplify the calculation. Finally, through extensive experiments on multiple datasets, we validate and evaluate the performance of SeP2CNN. © 2024 IEEE.

关键词： Differential privacy

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