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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： King, Irwin Long, Guodong Xu, Zenglin Yu, Han Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Faculty of Engineering and IT University of Technology Sydney Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400701726

In the evolving landscape of machine learning research, two significant developments have emerged to prominence: foundation models and federated learning. The FL@FM-TheWebConf’24 workshop provides an exciting forum for researchers and practitioners to discuss promising works and chart potential trajectories forward for this emerging field. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Frequency modulation

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Anonymous Crime Reporting using Blockchain and Smart Contract

Anonymous Crime Reporting using Blockchain and Smart Contrac...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： Prince Kumar Chaurasia Darshnilsinh Rana Rajaram V S. Srividhya Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

The traditional crime reporting systems face challenges such as inefficiencies, security vulnerabilities, and witness reluctance. Despite recent advancements like mobile applications and web platforms, challenges persist in ensuring data security, transparency, and user anonymity. These include maintaining confidentiality, data integrity, and preventing unauthorized access. To address these issues, this paper proposes leveraging blockchain technology and smart contracts. The primary objective is to enhance the security, efficiency, and anonymity of crime reporting processes. By utilizing the decentralized and immutable nature of blockchain, the system aims to ensure data integrity, transparency, and decentralization. Smart contracts will automate functions such as complaint validation and evidence verification, streamlining the reporting process. By addressing challenges in maintaining confidentiality, data integrity, and access control, the proposed solution seeks to revolutionize crime reporting, promoting trust, transparency, and accountability.

关键词： Pervasive computing Technological innovation Law enforcement Social networking (online) data integrity Smart contracts Robustness

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Uniform-in-N log-Sobolev inequality for the mean-field Langevin dynamics with convex energy

arXiv

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arXiv 2024年

作者： Chewi, Sinho Nitanda, Atsushi Zhang, Matthew S. Department of Statistics and Data Science Yale University United States College of Computing and Data Science Nanyang Technological University Singapore Department of Computer Science University of Toronto Vector Institute Canada

We establish a log-Sobolev inequality for the stationary distribution of mean-field Langevin dynamics with a constant that is independent of the number of particles N. Our proof proceeds by establishing the existence of a Lipschitz transport map from the standard Gaussian measure via the reverse heat flow of Kim and Milman. © 2024, CC BY.

关键词： Heat transfer

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Pose-Transformation and Radial Distance Clustering for Unsupervised Person Re-identification ov

arXiv

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arXiv 2024年

作者： Seth, Siddharth Sonth, Akash Chakraborty, Anirban Visual Computing Lab Department of Computational and Data Sciences Indian Institute of Science Bangalore India

Person re-identification (re-ID) aims to tackle the problem of matching identities across non-overlapping cameras. Supervised approaches require identity information that may be difficult to obtain and are inherently biased towards the dataset they are trained on, making them unscalable across domains. To overcome these challenges, we propose an unsupervised approach to the person re-ID setup. Having zero knowledge of true labels, our proposed method enhances the discriminating ability of the learned features via a novel two-stage training strategy. The first stage involves training a deep network on an expertly designed pose-transformed dataset obtained by generating multiple perturbations for each original image in the pose space. Next, the network learns to map similar features closer in the feature space using the proposed discriminative clustering algorithm. We introduce a novel radial distance loss, that attends to the fundamental aspects of feature learning - compact clusters with low intra-cluster and high inter-cluster variation. Extensive experiments on several large-scale re-ID datasets demonstrate the superiority of our method compared to state-of-the-art approaches. © 2024, CC BY.

关键词： Clustering algorithms

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Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

Genre Classification of Bangla Poem Using Machine Learning a...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Pasha, Syed Tangim Islam, Ashraful Rahman, Mohammed Masudur Ahmed, Eshtiak Ahmed Foysal, Md. Ferdouse Zahangir Alam, Md Daffodil International University Department of Computing and Information System Bangladesh Independent University Center for Computational and Data Sciences Bangladesh Independent University Department of Computer Science and Engineering Bangladesh International Standard University Department of Computer Science and Engineering Bangladesh Daffodil International University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350337617

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%. © 2023 IEEE.

关键词： Semantics

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1,2-Norm and CUR Decomposition based Sparse Online Active Learning for data Streams with Streaming Features

1,2-Norm and CUR Decomposition based Sparse Online Active Le...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Chen, Zhong He, Yi Wu, Di Zuo, Liudong Li, Keren Zhang, Wenbin Deng, Zhiqiang Southern Illinois University School of Computing United States William & Mary School of Data Science United States Southwest University College of Computer and Information Science United States California State University Dominguez Hills Computer Science Department United States University of Alabama at Birmingham Department of Mathematics United States Florida International University School of Computing and Information Sciences United States Louisiana State University Department of Civil and Environmental Engineering United States

ISBN: (纸本)9798350362480

Aiming at learning from a sequence of data instances over time, online learning has attracted increasing attention in the big data era. As two important variants, sparse online learning has been extensively explored by facilitating sparse constraints for online models such as truncated gradient, 1-norm regularization, 1-ball projection, and regularized dual averaging;while online active learning aims to build an online prediction model with a limited number of labeled instances, deploying the so called query strategies to select informative instances over time. However, most existing studies consider sparse online learning or online active learning with fixed feature spaces, whereby in real practice the features may be dynamically evolved over time. To the end, we propose a novel unified one-pass online learning framework named OASF for simultaneously online active learning and sparse online learning tailored for data streams described by open feature spaces, where new features can emerge constantly, and old features may be vanished over various time spans. Specifically, we technically develop an effective online CUR matrix decomposition based on the 1,2 mixed norm constraint for simultaneously selecting important up-to-date samples in a sliding window and facilitating stable and meaningful features in open feature spaces over time. If the loss function is simultaneously Lipschitz and convex, a sub-linear regret bound of our proposed algorithm is guaranteed with. Extensive experiments that are conducted with multiple streaming datasets have demonstrated the effectiveness of the proposed OASF compared with state-of-the-art online active learning and sparse online learning methods. © 2024 IEEE.

关键词： Active learning

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PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing

arXiv

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arXiv 2024年

作者： Chen, Xinyi Wu, Jibin Ma, Chenxiang Yan, Yinsong Wu, Yujie Tan, Kay Chen Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

Spiking Neural Networks (SNNs) hold great potential to realize brain-inspired, energy-efficient computational systems. However, current SNNs still fall short in terms of multi-scale temporal processing compared to their biological counterparts. This limitation has resulted in poor performance in many pattern recognition tasks with information that varies across different timescales. To address this issue, we put forward a novel spiking neuron model called Parallel Multi-compartment Spiking Neuron (PMSN). The PMSN emulates biological neurons by incorporating multiple interacting substructures and allows for flexible adjustment of the substructure counts to effectively represent temporal information across diverse timescales. Additionally, to address the computational burden associated with the increased complexity of the proposed model, we introduce two parallelization techniques that decouple the temporal dependencies of neuronal updates, enabling parallelized training across different time steps. Our experimental results on a wide range of pattern recognition tasks demonstrate the superiority of PMSN. It outperforms other state-of-the-art spiking neuron models in terms of its temporal processing capacity, training speed, and computation cost. Specifically, compared with the commonly used Leaky Integrate-and-Fire neuron, PMSN offers a simulation acceleration of over 10× times and a 30% improvement in accuracy on Sequential CIFAR10 dataset, while maintaining comparable computational cost. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Bridging Linguistics and Machine Learning: A new Human-Machine Communication

Bridging Linguistics and Machine Learning: A new Human-Machi...

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2024 International Conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kaur, Kawaljit Chahar, Suman Manwal, Manika Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh University Institute of Computing Chandigarh University Mohali India Department of Computer Science and Engineering Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (纸本)9798350355338

This research paper explores the evolution of Natural Language Processing (NLP) and Computational Linguistics, tracing the transition from grammar-based approaches to the adoption of machine learning methods. Initially rooted in linguistic theories, early NLP systems relied on rule-based methods for language parsing and processing, albeit with limitations in handling natural language complexities. The advent of machine learning brought about a paradigm shift, empowering NLP systems to learn patterns directly from data, leading to more adaptable and robust language processing capabilities. Hybrid approaches, blending rule-based techniques with machine learning, have emerged as a synergistic solution, combining the interpretability of rules with the learning capacity of algorithms. Recent advancements in NLP, including transformer models and multimodal techniques, have further accelerated progress, enabling state-of-the-art systems capable of human-level performance across diverse applications. Looking forward, future directions in NLP encompass neural symbolic approaches, self-supervised learning, multimodal NLP, continual learning, and ethical considerations, promising further innovation in the field. Through interdisciplinary collaboration and a commitment to responsible AI, the evolution of NLP continues to bridge the gap between human language and machine understanding, shaping a future where intelligent communication between humans and machines is seamless and ubiquitous. © 2024 IEEE.

关键词： Self-supervised learning

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Incentivizing Crowdsensing for DT-Enabled Metaverse 20th

Incentivizing Crowdsensing for DT-Enabled Metaverse

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20th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2024

作者： Ye, Dongdong Huang, Xumin Wu, Yuan Kang, Jiawen Zhong, Weifeng Niyato, Dusit School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China Department of Computer and Information Science University of Macau Taipa China Collge of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Digital Twin (DT) has been proposed as a key enabling technology to produce a digital clone of the physical world and facilitate the convergence of the physical and virtual worlds for building a real-world Metaverse. For physical-virtual synchronization, crowdsensing is applied to employ Internet of Things (IoT) devices to sense physical objects and collect status information for timely updating the associated DTs. However, an incentive mechanism is necessitated to stimulate the devices to perform sensing tasks while meeting task requirements such as Age of Information (AoI) requirements. Thus, we adopt a contract theoretic approach to design an incentive mechanism for the devices that are classified into several types and the type parameters are unknown to others. The contract consists of a set of contract items with each contract item representing a device type, which specifies the required data updating frequency and monetary reward. We formulate an optimal contract design problem subject to feasible constraints including the average AoI requirement for each device type. Furthermore, we solve the problem by transforming it into a convex optimization problem that can be directly tackled by the existing methods. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of our scheme. © IFIP International Federation for Information Processing 2025.

关键词： Convex optimization

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