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Proceedings of the 41st International Conference on Machine Learning

作者： Wenjie Wang Biwei Huang Feng Liu Xinge You Tongliang Liu Kun Zhang Mingming Gong School of Mathematics and Statistics The University of Melbourne Australia and Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Halicioğlu Data Science Institute (HDSI) University of California San Diego School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates and Department of Philosophy Carnegie Mellon University

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods.

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Evaluating Security of MQTT Protocol in Internet of Things

Evaluating Security of MQTT Protocol in Internet of Things

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Ayman Al-Ani Wong Kang Shen Ahmed K. Al-Ani Shams A. Laghari Omar E. Elejla Cybersecurity Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah Malaysia School of Computing and Data Science Xiamen University Malaysia Sepang Selangor Malaysia School of Engineering Technology and Applied Science Centennial College Toronto Ontario Canada National Advanced IPv6 Centre (NAv6) Universiti Sains Malaysia (USM) Gelugor Penang Malaysia Department of Computer Science Al-Aqsa University Gaza Palestine

The Internet of Things (IoT) has revolutionized the way people interact, communicate, and perform daily activities in various domains ranging from households to industries and cities. MQTT is one of the commonly adopted protocols for implementing IoT. However, IoT systems that are connected through MQTT are susceptible to security breaches as MQTT was not originally designed with security as a priority. The credentials and messages transmitted in plaintext by default, thereby compromising data confidentiality and integrity. This study presents a comprehensive analysis of the MQTT protocol, including experimentation on an MQTT system using various cryptographic implementations, such as AES-CBC, RSA, and ECC AES Hybrid Scheme, to assess the processing time and message size. The findings indicate that payload encryption increases processing time and message bytes. Among the cryptographic implementations, RSA incurs the highest processing time, followed by ECC AES Hybrid Scheme and AES- CBC. Furthermore, the study demonstrates the effectiveness of attack prevention between standard MQTT and secured MQTT implementations by simulating various IoT attacks, such as black-box penetration attack, identity spoofing, DoS attack, and MITM attack. The results and subsequent discussion provide insights that answer the research question, revealing the cryptographic algorithms that result in the most overhead on the standard MQTT implementation and their capacity to resist common attacks.

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ZETA: ZEro-Trust Attack Framework with Split Learning for Autonomous Vehicles in 6G Networks

ZETA: ZEro-Trust Attack Framework with Split Learning for Au...

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IEEE Conference on Wireless Communications and Networking

作者： Sunder Ali Khowaja Parus Khuwaja Kapal Dev Keshav Singh Lewis Nkenyereye Dan Kilper Faculty of Computing Digital and Data Technological University Dublin Ireland Institute of Business Administration University of Sindh Pakistan Department of Computer Science CONNECT Centre Munster Technological University Ireland Institute of Communications Engineering (ICE) National Sun Yat-sen University Taiwan Department of Computer and Information Security Sejong University South Korea CONNECT Centre School of Engineering Trinity College Dublin Ireland

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In past, due to data and model security concerns, modern communication systems mainly focus on the use of edge computing devices for enabling immersive applications and services. Federated learning is one of the preferred solutions but it stresses the computation capability of the edge devices for immersive applications. Much research is now focusing on split learning as an alternative due to its ability of performing joint training with limited computing resources. However, split learning is also vulnerable to data reconstruction, feature space hijacking, and model inversion attacks, which are quite common concerning immersive applications such as Metaverse. In this regard, we propose a ZEro-Trust Attack (ZETA) framework for data reconstruction and model inversion attacks for autonomous vehicles opting for split learning strategies. We propose the joint training of client, server, and shadow models for both the reconstruction and main task to fool existing methods. Our experimental results demonstrate that the proposed method is capable of reconstructing client's data with an error of 0.0032. This study is proposed as a basis to design more sophisticated defense mechanisms for autonomous vehicles to protect user services in 5G/6G networks.

关键词： Training Performance evaluation Sensitivity analysis Computational modeling data models Zero Trust Servers

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A Novel Mode Selection-Based Fast Intra Prediction Algorithm for Spatial SHVC

A Novel Mode Selection-Based Fast Intra Prediction Algorithm...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yu Sun Weisheng Li Lele Xie Xin Lu Frederic Dufaux Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Department of Computer Science University of Central Arkansas Computing Chongqing Key Laboratory of Image Cognition School of Information and Communication Engineering Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University CNRS CentraleSupélec Laboratoire des Signaux et systèmes Université Paris-Saclay School of Information and Communication Engineering University of Electronic Science and Technology of China

Due to multi-layer encoding and Inter-layer prediction, Spatial Scalable High-Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SSHVC applications. In this paper, we have proposed a novel Mode Selection-Based Fast Intra Prediction algorithm for SSHVC. We reveal the RD costs of Inter-layer Reference (ILR) mode and Intra mode have a significant difference, and the RD costs of these two modes follow Gaussian distribution. Based on this observation, we propose to apply the classic Gaussian Mixture Model and Expectation Maximization in machine learning to determine whether ILR is the best mode so as to skip the Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve the coding speed with negligible coding efficiency loss.

关键词： Machine learning algorithms Costs Signal processing algorithms Machine learning Gaussian distribution Prediction algorithms Encoding

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Detecting Anomalous LAN Activities under Differential Privacy

arXiv

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arXiv 2022年

作者： Rattanavipanon, Norrathep Ponnoprat, Donlapark Ochiai, Hideya Tantayakul, Kuljaree Angchuan, Touchai Kamolphiwong, Sinchai College of Computing Prince of Songkla University Phuket83120 Thailand Data Science Research Center Department of Statistics Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Graduate School of Information Science and Technology The University of Tokyo Tokyo113-8656 Japan Faculty of Engineering Prince of Songkla University Songkhla90110 Thailand

Anomaly detection has emerged as a popular technique for detecting malicious activities in local area networks (LANs). Various aspects of LAN anomaly detection have been widely studied. Nonetheless, the privacy concern about individual users or their relationship in LAN has not been thoroughly explored in the prior work. In some realistic cases, the anomaly detection analysis needs to be carried out by an external party, located outside the LAN. Thus, it is important for the LAN admin to release LAN data to this party in a private way in order to protect privacy of LAN users;at the same time, the released data must also preserve the utility of being able to detect anomalies. This paper investigates the possibility of privately releasing ARP data that can later be used to identify anomalies in LAN. We present four approaches, namely naïve, histogram-based, naïveδ and histogram-based-δ, and show that they satisfy different levels of differential privacy – a rigorous and provable notion for quantifying privacy loss in a system. Our real-world experimental results confirm practical feasibility of our approaches. With a proper privacy budget, all of our approaches preserve more than 75% utility of detecting anomalies in the released data. © 2022, CC BY.

关键词： Local area networks

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Topic-based Multi-layer Knowledge Filtering for Emotion Recognition in Conversation

Topic-based Multi-layer Knowledge Filtering for Emotion Reco...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiaoyang Li Zhenyu Yang Yiwen Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Emotion recognition in conversation (ERC) is a prominent research topic in natural language processing, widely applicable in various scenarios. However, accessing external commonsense knowledge and related dialogue topics, along with the complexity of fusing utterances, presents significant challenges. In this paper, we introduce a Topic-based Multilayer Knowledge Filtering (TMKF) model to enhance dialog emotion recognition accuracy. TMKF acquires commonsense knowledge for each utterance and employs two Variational Autoencoder (VAE) modules to extract global and speaker-level dialogue topics. We utilize Global Knowledge Filtering (GKF) and Local Knowledge Filtering (LKF) to obtain topic-specific knowledge representations after filtering commonsense information through global and local topic hierarchies. Subsequently, we leverage relational graphs to convolve commonsense knowledge with utterances, resulting in knowledge-constrained utterance representations for classification. Our proposed model is evaluated through extensive experiments on four widely used benchmark datasets for conversational emotion recognition. The results underscore the effectiveness of TMKF, which significantly outperforms other methods in standard metric evaluations.

关键词： Measurement Emotion recognition Filtering Convolution Oral communication Benchmark testing Nonhomogeneous media

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CAFI: News Recommendation with Candidate Perception of Fine-Grained Interaction Information

CAFI: News Recommendation with Candidate Perception of Fine-...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Xiaoyang Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The most critical task in personalized news recommendation is to perform exact matching between candidate news and users’ interests. Existing news recommendation methods usually model users’ interests from historical clicked news items without considering candidate news, which makes it difficult to precisely match candidate news with users’ interests. Moreover, it is challenging to distinguish similar news when forming a vector representation. Simply adding candidate news to the user interest modeling process without considering word-level information does not provide sufficient discrimination for recommending appropriate news. In this paper, we propose a news recommendation model with candidate-aware fine-grained interaction information (CAFI). In our approach, we propose a fine-grained interaction module that matches candidate news items and text fragments of each historical news item at each semantic granularity to achieve interaction between the two at the word level and help similar historical news form more specific and accurate representations. In addition, our proposed gated self-attention mechanism utilizes the candidate news features as channel moderation gates to implement the process of filtering out information that is irrelevant to the candidate news for learning user interest representations, thereby better matching the candidate news to specific user interests. Experiments conducted on the large real-world Microsoft News dataset (MIND) show that our model significantly outperforms the previously developed models in terms of all metrics. Our code is published at the following URL: https://***/liyiweneven/CAFI.

关键词： Uniform resource locators Measurement Codes Filtering Federated learning Semantics Focusing

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HFM++: An Enhanced Holographic Factorization Machine for Recommendation 19th

HFM++: An Enhanced Holographic Factorization Machine for Rec...

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19th Australasian Conference on data Mining, AusDM 2021

作者： Fang, Zhengxin Qu, Mingcheng Zhang, Shuai Zhang, Jingyu Yuan, Yi Yao, Lina Chen, Shiping Faculty of Computing Harbin Institute of Technology Harbin China ETH Zurich Zürich Switzerland School of Computer Science and Engineering University of New South Wales Kensington Australia School of Engineering University of Sydney Sydney Australia CSIRO Data61 Sydney Australia

ISBN: (纸本)9789811685309

Recommender systems are essential and are playing a more and more important role in our daily life, ranging from entertainment to online shopping. They have great commercial value, not only can improve the user experience by saving users time to locate related items, but also increase the exposure rate of long-tail items. Factorization machines (FMs) have been serving as an effective go-to recommender algorithm for years. Yet, it is not without disadvantages, such as mediocre performances. In this paper, we propose an enhanced factorization model, namely, enhanced holographic factorization machines (or HFM++). Our model integrates a Restricted Boltzmann Machine (RBM) and a generalized matrix factorization (GMF) into holograph FM, resulting in a more powerful recommendation model that inherits the benefits of all these three models. To evaluate the effectiveness and accuracy of HFM++, we conduct experiments with several well-known public benchmarking datasets and compare them against a number of baseline models. The experimental results show that HFM++ obtains the best performances across all metrics, outperforming recently published baselines. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Collaborative filtering

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An Early Detection Model of Prostate Cancer Based on MRI

An Early Detection Model of Prostate Cancer Based on MRI

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IEEE International Conference on data science in Cyberspace (DSC)

作者： Yuankun Liu Lumin Xing Di Wang Xin Nie Xueyan Zhang Di Wu Wenjian Liu Faculty of Data Science City University of Macau China Shandong Provincial Qianfoshan Hospital The First Affiliated Hospital of Shandong First Medical University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Chongqing Jianzhu College Chongqing China School of Information Work Office Shandong Youth University of Political Science Jinan China School of Mathematics Physics and Computing University of Southern Queensland Queensland Australia

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively.

关键词： Computer vision Visualization Accuracy Magnetic resonance imaging Mortality Transformers Feature extraction Reliability Prostate cancer Medical diagnostic imaging

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TSFE: Research on Transformer and Feature Enhancement Methods for Image-Text Retrieval

TSFE: Research on Transformer and Feature Enhancement Method...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jinxiao Yang Xingang Wang Qingao Wang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In recent years, cross-modal retrieval has a wide range of applications in fields such as information retrieval and multimedia processing, where the key is to find relevant data in another modality through one modal query. In cross-modal graphic retrieval, mutual attention mechanisms are commonly used to interact image and text features. However, this interaction mechanism makes it difficult to extract separate visual and textual features for subsequent indexing steps, especially in large-scale retrieval tasks. Furthermore, in image representation, non-semantic information in image features can lead to inaccuracies in the alignment of image and text features. Therefore this paper proposes a Transformer and Feature Enhancement Method (TSFE) for Image-Text Retrieval. The method better fuses the underlying features and the higher level features by using a hierarchical Transformer coding structure on two separate branches, image and text, to proofread and localise the final output features. The Feature Enhanced Attention (FEA) module is designed in order to infer non-semantic information in the image and enhance the features of salient regions. Channel-space attention and max pooling is introduced on top of Transformer encoder self-attention to filter redundant and interfering information from channel and spatial dimensions. Experiments on Flickr30k dataset and MS-COCO dataset in this paper show improved performance in cross-modal retrieval, thus validating the effectiveness and sophistication of the proposed method.

关键词： Visualization Semantics Pipelines Noise Image representation Transformers Feature extraction

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