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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Song, Ao Lei, Zhou Chen, Shengbo Shanghai University College of Computer Engineering and Science Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (纸本)9798350316520

With the rapid development of the Internet, the problem of information overload is becoming more and more serious, which makes it difficult for users to choose the content they are interested in from a large number of information. With the emergence of recommendation systems, the historical interaction information between users and information and items is used to provide users with topics of interest, which not only alleviates the problem of information overload, but also brings great satisfaction to users. However, training a model that can make accurate recommendations is not an easy task. In addition to an appropriate training strategy, it also requires a large amount of data support. Getting data, especially labeled data, takes a lot of time and money. Therefore, this paper proposes a new active learning algorithm. Firstly, the recommendation problem was transformed into a PU learning problem, and then the method was introduced in the positive sample learning process to achieve the purpose of increasing positive sample data, thereby reducing the cost of data labeling. Through experiments, the proposed method reduces the cost of data labeling in the recommendation task and further verifies the rationality of transforming the recommendation problem into a PU learning problem. © 2024 IEEE.

关键词： Recommender systems

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Deepfake Detection: Emerging Techniques and Evolving Challenges 12

Deepfake Detection: Emerging Techniques and Evolving Challen...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Natarajan, Krishnaraj Mathur, Abeer Bhargava, Kshitiz Singh, Manvendra Tejpal, Moulik Department of Database Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350387315

Several newly developed techniques and tools for manipulating images, audio, and videos have been introduced as an outcome of the recent and rapid breakthroughs in AI, machine learning, and deep learning. While most applications for these techniques or tools are in the fields of entertainment and education, some individuals with unlawful intent have also been benefited from them. These individuals use such techniques for various purposes, including the spread of misleading information and unnecessary propaganda, the incitement of political instability, hate and unrest, as well as for purposes of torture and blackmail. These high-quality and convincing manipulated images, audio, or videos are commonly referred to as ‘Deepfakes’. Since then, various solutions to the problems raised by Deepfakes have been proposed in academic studies. This literature review contains relevant publications that offered a variety of approaches to give an updated summary of the research activities in different types of Deepfake attacks, their detection, and countermeasures. It also assesses the effectiveness of the detection capabilities of different techniques with various datasets and algorithms applied in Deepfake detection, while also outlining the various benefits and drawbacks of various methodologies. © 2024 IEEE.

关键词： Deep learning

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Adaptive Selection of BERT Layer for Multi-Label Text Classification

Adaptive Selection of BERT Layer for Multi-Label Text Classi...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Chen, Zhichao Liu, Han Zhang, Qin College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350303780

Multi-label text classification is a special type of natural language processing tasks, which is more complex than traditional single-label classification. Moreover, due to the key strength of pre-trained language models such as BERT in feature embedding at both token and sentence levels, transfer learning driven text classification has become increasingly popular. In this context, it is a commonly used strategy to take the output of the last layer of BERT as sentence embedding for text classification. However, the above-mentioned way of obtaining sentence embedding may not always be suitable for multi-label text classification, i.e., the best layer that leads to the optimal performance of classification may be varied for different labels, especially when a multi-label classification task is transformed into several binary ones. In order to address the aforementioned issue, we set a validation data driven way in this paper to explore the performance of sentence embedding obtained from each layer and adaptively select the best layer to output the sentence vector for each specific label. The experimental results show that using a sentence vector obtained from an intermediate layer may lead to better performance in comparison with simply taking the output of the last layer as the adopted sentence embedding. The results also indicate the effectiveness of the above-mentioned adaptive selection of the best layer in improving the performance of multi-label text classification. © 2023 IEEE.

关键词： Embeddings

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De-Biasing Methods in Neural Networks: A Survey

De-Biasing Methods in Neural Networks: A Survey

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Wang, Yixin Liu, Han College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350303780

Bias is a common problem in both human cognition and machine learning tasks. However, machines struggle more than humans with bias reduction, mainly because most algorithms rely on the assumption that the training data represent a full population in the real world. This assumption does not hold in most cases, i.e., collecting unbiased or representative data is challenging due to various factors such as sampling errors, human prejudices, and cultural differences. Therefore, bias mitigation has become an essential task in artifical intelligence. In recent years, significant progress has been made in improving the effectivess of debiasing, especially in computer vision. This paper provides a comprehensive review of de-biasing methods for neural networks trained on image data. Firstly, we present a formal defination of the bias mitigation problem and discuss some relevant topics. Moreover, we categorize the existing methods into three main types, namely, data-level methods that aim to balance or augment the training data, model-level methods that aim to modify or regularize the learning algorithms, and adversarial and ensembling approaches that use additional models to capture the biases present in data and to guide the training of the primary model. Finally, we conclude this survey and suggest potential research directions for the future. © 2023 IEEE.

关键词： Learning algorithms

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Motivating Users to Atend to Privacy: A Theory-Driven Design Study

Motivating Users to Atend to Privacy: A Theory-Driven Design...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Shiri, Varun Xiong, Maggie Cheng, Jinghui Guo, Jin L.C. Department of Computer and Software Engineering Polytechnique Montreal MontrealQC Canada School of Computer Science McGill University MontrealQC Canada

ISBN: (纸本)9798400705830

In modern technology environments, raising users’ privacy awareness is crucial. Existing eforts largely focused on privacy policy presentation and failed to systematically address a radical challenge of user motivation for initiating privacy awareness. Leveraging the Protection Motivation Theory (PMT), we proposed design ideas and categories dedicated to motivating users to engage with privacy-related information. Using these design ideas, we created a conceptual prototype, enhancing the current App Store product page. Results from an online experiment and follow-up interviews showed that our design efectively motivated participants to attend to privacy issues, raising both the threat appraisal and coping appraisal, two main factors in PMT. Our work indicated that efective design should consider combining PMT components, calibrating information content, and integrating other design elements, such as visual cues and user familiarity. Overall, our study contributes valuable design considerations driven by the PMT to amplify the motivational aspect of privacy communication. © 2024 Copyright held by the owner/author(s).

关键词： Motivation

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Research advances on blockchain-as-a-service:architectures,applications and challenges

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Digital Communications and Networks 2022年第4期8卷 466-475页

作者： Jie Song Pengyi Zhang Mohammed Alkubati Yubin Bao Ge Yu Software College Northeastern University Shenyang110819China School of Computer Science and Engineering Northeastern UniversityShenyang110819China

Due to the complexity of blockchain technology,it usually costs too much effort to build,maintain and monitor a blockchain system that supports a targeted *** this end,the emerging“Blockchain as a Service”(BaaS)makes the blockchain and distributed ledgers more accessible,particularly for businesses,by reducing costs and *** combines the high computing power of cloud computing,the pervasiveness of IoT and the decentralization of blockchain,allowing people to build their own applications while ensuring the transparency and openness of the *** paper surveys the research outputs of both academia and ***,it introduces the representative architectures of BaaS systems and then summarizes the research contributions of BaaS from the technologies for service provision,roles,container and virtualization,interfaces,customization and *** typical applications of BaaS in both academic and practical domains are also *** present,the research on the blockchain is abundant,but research on BaaS is still in its *** challenges of BaaS are concluded in this paper for further study directions.

关键词： Blockchain as a service Blockchain Service Internet of things Cloud computing

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SFPL: Improving CRS via Prompt Learning based Semantic Fusion Module 4

SFPL: Improving CRS via Prompt Learning based Semantic Fusio...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

ISBN: (纸本)9798350363586

With the popularity of the Internet and the massive increase of content production, users are faced with massive information and content, and it is often difficult to accurately and efficiently find the content that meets their needs. This information overload motivates the emergence of recommendation systems that help users filter and recommend relevant content. Dialogue recommendation systems (CRS) aim to dynamically recommend high-quality items to users while eliciting user preferences by means of dialogue. CRS mainly consists of two parts, namely, the dialogue module and the recommendation module. An efficient CRS is to solve the problems existing in the two sub-tasks of dialogue and recommendation, namely, generating appropriate responses in dialogue and predicting user favorite items in recommendation. In addition, it is necessary to adopt a suitable method to integrate these two modules to ensure the accuracy of CRS recommendation. This paper proposes an efficient way of semantic fusion module to solve the integration problem of two modules, and proves the effectiveness of our method through experiments © 2024 IEEE.

关键词： Adversarial machine learning

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Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 849-862页

作者： Ye‐Qun Wang Jian‐Yu Li Chun‐Hua Chen Jun Zhang Zhi‐Hui Zhan School of Computer Science and Engineering South China University of TechnologyGuangzhouChina School of Software Engineering South China University of TechnologyGuangzhouChina Hanyang University AnsanSouth Korea

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial ***,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally *** tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)***,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best ***,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate ***,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational *** SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

关键词： deep learning evolutionary computation hyperparameter and architecture optimisation neural networks particle swarm optimisation scale‐adaptive fitness evaluation

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Combatting the TrickBot Threat: Analysis, Impact, and Defensive Strategies in Cybersecurity 17

Combatting the TrickBot Threat: Analysis, Impact, and Defens...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Cao, Jintao Null, Allie Stewart, Marissa Bhunia, Suman Salman, Mohammad Miami University Department of Computer Science and Software Engineering OxfordOH United States University of Anbar College of Computer Anbar Iraq

ISBN: (纸本)9798350351637

The TrickBot Botnet, emerging in late 2016, has been a significant cybersecurity threat, leveraging sophisticated attack vectors such as phishing emails, network vulnerabilities, and secondary payloads. This paper provides a comprehensive analysis of TrickBot's evolution, attack methodologies, and widespread impact on individual users, businesses, and government entities. Despite some disruptions to its infrastructure, TrickBot has demonstrated remarkable resilience, continually adapting its tactics to evade detection and enhance its malicious capabilities. Our research offers detailed insights into the operational mechanisms of TrickBot, supported by extensive data on its propagation and impact. Furthermore, we evaluate various defense strategies, including advanced technical measures and human-centric approaches, to mitigate the ongoing threat posed by TrickBot and similar botnets. The findings underscore the critical need for continuous vigilance and innovation in cybersecurity practices to effectively counter such persistent threats. © 2024 IEEE.

关键词： Bot (Internet)

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Countering Deepfakes using an Improved Advanced CNN and its Ensemble with Pretrained Models 3

Countering Deepfakes using an Improved Advanced CNN and its ...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Mathur, Abeer Tejpal, Moulik Bhargava, Kshitiz Natarajan, Krishnaraj Singh, Manvendra Vellore Institute of Technology School of Computer Science & Engineering Department of Software Systems Vellore India Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India

ISBN: (纸本)9798350369083

The extensive spread of DeepFake images on the internet has emerged as a significant challenge, with applications ranging from harmless entertainment to harmful acts like blackmail, misinformation, and spreading false propaganda. To tackle this issue, this paper introduces a sophisticated DeepFake detection model designed to identify and mitigate the increase of these deceptive images. The model architecture integrates an ensemble approach, combining the strengths of two pre-trained Convolutional Neural Network (CNN) models - MobileNet and Xception - with a novel CNN architecture, the Advanced CNN (ACNN). This rigorous validation process enabled the model to achieve a high accuracy rate of 97.89% in detecting DeepFakes. The successful implementation of this ensemble CNN approach demonstrates its effectiveness in distinguishing between real and fabricated imagery with high precision. This research makes a substantial contribution to the field of digital image forensics, offering a reliable tool for stakeholders across various sectors to identify and counteract the spread of DeepFake images online. © 2024 IEEE.

关键词： Generative adversarial networks

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