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Building a Personalized Model for Social Media Textual Content Censorship

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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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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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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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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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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Optimization-Inspired Graph Neural Network for Cellular Network Optimization

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6446-6459页

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China Tongji University School of Electronic and Information Engineering Shanghai China

The rapid development of wireless communications have driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model's generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model's applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. © 2002-2012 IEEE.

关键词： Graph neural networks

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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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Grasping by Parallel Shape Matching

Grasping by Parallel Shape Matching

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2024 Australasian Conference on Robotics and Automation, ACRA 2024

作者： Zhang, Wenzheng Maken, Fahira Afzal Lai, Tin Ramos, Fabio School of Computer Science The University of Sydney Australia Data61 CSIRO Australia NVIDIA United States

Grasping is essential in robotic manipulation, yet challenging due to object and gripper diversity and real-world complexities. Traditional analytic approaches often have long optimization times, while data-driven methods struggle with unseen objects. This paper formulates the problem as a rigid shape matching between gripper and object, which optimizes with Annealed Stein Iterative Closest Point (AS-ICP) and leverages GPU-based parallelization. By incorporating the gripper’s tool centre point and the object’s centre of mass into the cost function and using a signed distance field of the gripper for collision checking, our method achieves robust grasps with low computational time. Experiments with the Kinova KG3 gripper show an 87.3% success rate and 0.926s computation time across various objects and settings, highlighting its potential for real-world applications. © 2024 Australasian Robotics and Automation Association. All rights reserved.

关键词： Cost functions

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Learning Heuristics via Genetic Programming for Multi-Mode Resource-Constrained Project Scheduling 13

Learning Heuristics via Genetic Programming for Multi-Mode R...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Tian, Yuan Mei, Yi Zhang, Mengjie Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand

ISBN: (纸本)9798350308365

The multi-mode resource-constrained project scheduling problem (MRCPSP) is a challenging problem for researchers and practitioners in operations research and project management. MRCPSP involves both selecting the execution mode for each activity and sequencing the activities in the schedule. Thus, activity prioritisation and mode selection are the two main decisions in building a project schedule. A rule-based heuristic approach is commonly used for solving this problem in practical complex scenarios. However, designing effective project scheduling rules highly relies on the expertise of professionals and domain knowledge. To address the above issue, this paper proposes a genetic programming-based hyper-heuristic (GPHH) to design heuristic rules automatically. Various decision strategies based on decision orders are proposed and their impact on the capacity of GPHH to learn effective scheduling rules is investigated. The experiment results demonstrate the evolved rules generated by GPHH outperform the existing manual heuristic rules and making two decisions simultaneously is identified as the most effective strategy. © 2024 IEEE.

关键词： Genetic programming

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