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arXiv 2024年

作者： Liu, Ruizhe Luo, Qian Yang, Yanchao HKU Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong School of Electronics Engineering and Computer Science Peking University China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

We focus on the self-supervised discovery of manipulation concepts that can be adapted and reassembled to address various robotic tasks. We propose that the decision to conceptualize a physical procedure should not depend on how we name it (semantics) but rather on the significance of the informativeness in its representation regarding the low-level physical state and state changes. We model manipulation concepts - discrete symbols - as generative and discriminative goals and derive metrics that can autonomously link them to meaningful sub-trajectories from noisy, unlabeled demonstrations. Specifically, we employ a trainable codebook containing encodings (concepts) capable of synthesizing the end-state of a sub-trajectory given the current state - generative informativeness. Moreover, the encoding corresponding to a particular sub-trajectory should differentiate the state within and outside it and confidently predict the subsequent action based on the gradient of its discriminative score - discriminative informativeness. These metrics, which do not rely on human annotation, can be seamlessly integrated into a VQ-VAE framework, enabling the partitioning of demonstrations into semantically consistent sub-trajectories, fulfilling the purpose of discovering manipulation concepts and the corresponding sub-goal (key) states. We evaluate the effectiveness of the learned concepts by training policies that utilize them as guidance, demonstrating superior performance compared to other baselines. Additionally, our discovered manipulation concepts compare favorably to human-annotated ones while saving much manual effort. Our code is available at: https://***/InfoCon/. © 2024, CC BY.

关键词： Trajectories

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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN computer science 2024年第5期5卷 546页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering data aggregation Gateway Internet of things LEACH Lifetime

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Self-Renewal Machine Learning Approach for Fast Wireless Network Optimization 20

Self-Renewal Machine Learning Approach for Fast Wireless Net...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ajayi, Oluwaseun T. Cao, Xianghui Shan, Hangguan Cheng, Yu Illinois Institute of Technology Department of Electrical and Computer Engineering Chicago60616 United States Southeast University School of Automation Nanjing210096 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

ISBN: (纸本)9798350324334

The throughput maximization in multi-hop wireless networks is largely limited by interference due to the reuse of the channel resources. Although machine learning (ML) can accelerate the optimization of wireless network capacity, the existing system can become limited because of insufficient knowledge from available data. We propose a self-renewal ML (SRML) method that incrementally improves the throughput maximization of future optimization instances through the design of a data selection algorithm for scheduling structure classification and application identification model retraining. With one round of implementation, the SRML method outperforms the fixed ML (FML) method, random (RAND) method and Greedy Heuristic method in the multi-commodity flow deployment setting with an average achievable throughput of 100% for small flows and at least 79% for large flows, relative to the delayed column generation (DCG) benchmark algorithm, while reducing the computational complexity and achieving a high solution efficiency. By leveraging the transfer learning of parameters during self-renewal, the computational cost of model training is reduced by at least 71.09%. © 2023 IEEE.

关键词： Heuristic methods

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Consent Management System Based on User data Security and Privacy Using Hyperledger Fabric Blockchain 6

Consent Management System Based on User Data Security and Pr...

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6th International Conference on Information Technologies and Electrical engineering, ICITEE 2023

作者： Mehmood, Faisal Khalid, Umara Karim, Shahid School of Electronic and Computer Engineering Peking University ShenZhen Graduate School Shenzhen518055 China School of Information Science and Technology Northwest University Xi'an710069 China Research & Development Institute of Northwestern Polytechnical University Shenzhen518057 China

ISBN: (纸本)9798400708299

The challenge of safeguarding user data privacy becomes pronounced when private data is outsourced to cloud services, potentially exposing it to unauthorized access. The challenge of centralized storage of user data introduces heightened security risks and a dependence on a single authority, posing difficulties in safeguarding against internal breaches. This study is dedicated to advancing user privacy and data security, especially in scenarios involving the sharing of sensitive information within or across organizations, including small enterprises functioning in a distributed environment. The research introduces a consent management framework built on Blockchain technology, with a particular focus on user consent management. This framework is designed to prioritize the principles of privacy, security, scalability, and data integrity. It utilizes Hyperledger Fabric which is a permissioned distributed ledger solution, and incorporates Hyperledger Composer to establish and maintain a secure record of user data. To enhance the security of stored data, the framework incorporates the Interplanetary File System (IPFS) and employs a unique cryptographic public key encryption algorithm for data encryption. The overarching aim of this research is to establish a robust security solutions foundation against cyber threats by harnessing the inherent capabilities of blockchain technology, ultimately strengthening the security landscape for sharing user information. © 2023 ACM.

关键词： Blockchain

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Application of Gromov-Wasserstein Matching for Vehicular Social Network Security Profiling

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IEEE Transactions on Vehicular Technology 2025年

作者： Chang, Shih Yu He, Ji Nallanathan, Arumugam San Jose State University Department of Applied Data Science San Jose United States Xidian University School of Computer Science and Technology Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

The Vehicular Social Network (VSN) seamlessly combines social networks with the Internet of Vehicles (IoVs), fostering social connections not only among the drivers but also among the vehicles themselves. VSN security profiling helps in comprehending and ranking VSN security risks according to a range of criteria. The existing approaches to VSN security profiling and risk quantification have notable limitations. Firstly, they often overlook the intensity of interactions between VSN devices. Secondly, these methods require re-evaluation of security scores and the likelihood of attacks whenever VSN configurations change. Additionally, most current risk quantification methods cannot concurrently assess risks across multiple networks, further limiting their utility. Given these challenges, this work focuses on VSN security profiling quantitatively based on users accounts because various types of accounts are used for security control mechanisms. To address these issues, we first propose Distributed Accounts Association Algorithms (D-AAA) for matching service accounts with malicious accounts based on Gromov-Wasserstein (GW) metric, and apply accounts matching results to sort service accounts by vulnerability and compute the average risk for multiple service VSN networks. To verify the proposed D-AAA, the performance of the proposed D-AAA is evaluated through numerical experiments, including an assessment of computational complexities, association correctness, and risk evaluations. Additionally, the D-AAA are compared with other graph matching methods in terms of association correctness and runtime with real VSN datasets. © 1967-2012 IEEE.

关键词： Risk assessment

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Feature Analysis and Selection for BGP Anomaly Detection

Feature Analysis and Selection for BGP Anomaly Detection

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Rifqy Hakimi Martin J. Reed School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

Automated incidence reporting requires accurate and timely anomaly detection. This paper considers this in the context of the Border Gateway Protocol (BGP). BGP is crucial for Internet routing but is vulnerable to attacks due to a lack of widespread authentication. While rare, BGP disruptions can be highly detrimental to Internet performance and security. Detecting BGP anomalies helps network operators protect their networks and improve Internet reliability. In this work, we investigate and develop anomaly detection for BGP using Machine-Learning. We extract relevant features of BGP control plane messages from RIPE RIS and RouteViews public datasets. After extracting features, we employ various feature selection algorithms to extract the most relevant features and explore balancing the datasets. Finally, we explore detection latency, an important operating parameter for automated anomaly detection. The results show that BGP anomalies can be detected in the order of seven minutes when using real attack data and that the observation point for the BGP data has a significant effect on anomaly detection.

关键词： Technological innovation Soft sensors Machine learning Feature extraction Routing Border Gateway Protocol Security Reliability Anomaly detection Faces

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Integrating I4.0 Knowledge Graphs with Large Language Models Beyond SPARQL Endpoints 1

Integrating I4.0 Knowledge Graphs with Large Language Models...

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Joint of the 1st Software Lifecycle Management for Knowledge Graphs Workshop and the 3rd International Workshop on Semantic Industrial Information Modelling, SOFLIM2KG-SEMIIM 2024

作者： Wahid, Abdul Yahya, Muhammad Zaman, Farooq Zhou, Baifan Breslin, John G. Intizar, Muhammad Ali Kharlamov, Evgeny Data Science Institute University of Galway IDA Business Park Lower Dangan GalwayH91 AEX4 Ireland Valeo Vision Systems Tuam Galways Ireland Information Technology University Lahore Pakistan Department of Computer Science Oslo Metropolitan University Oslo Norway School of Electronic Engineering Dublin City University Dublin Ireland Bosch Center for AI Renningen Germany

Industry 4.0 (I4.0) knowledge graphs are a common way to represent industrial information models. Conventional SPARQL querying systems require the users to be familiar with the data schema and SPARQL syntax. However, this is often very difficult for many users in industrial production, who have mostly an engineering background, instead of a semantic web. Recent developments in large language models (LLMs) make it possible for non-semantic experts to use natural language to query knowledge graphs (KG). In this work, we present a framework and preliminary results of integrating Industry 4.0 KGs with LLMs to improve how data is represented, reasoned, and processed in manufacturing contexts, facilitating user interaction with KGs and contributing to operational efficiency. Our technique enhances Language Models (LLMs) by utilising the semantic complexity and interdependence of Knowledge Graphs (KGs). This allows us to incorporate domain-specific knowledge. We used the FAISS library and LLaMA2 to optimise the storage and retrieval of vectors, which improved the system’s performance and scalability. This integration allows for advanced fault detection, proactive maintenance, and process optimisation, resulting in decreased periods of inactivity and improved productivity. We introduce the framework’s architecture, implementation strategy, and possible advantages while also discussing the difficulties associated with data integration and scalability. The results of our study show that the integration of KG-LLM surpasses traditional approaches in terms of operational efficiency, as evidenced by enhanced fault detection, proactive maintenance, and process optimisation, thereby opening up possibilities for the advancement of more intelligent and resilient production systems. © 2022 Copyright for this paper by its authors.

关键词： Knowledge graph

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Privacy-Preserving Short-Term Travel Demand Forecasting Based on Federated Learning

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IEEE Transactions on Vehicular Technology 2025年

作者： Du, Runmeng He, Daojing Song, Yan Ding, Zikang Chan, Sammy Li, Xuru Xi'an Polytechnic University School of Computer Science Xi'an710048 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China East China Normal University School of Software Engineering Shanghai200062 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan611731 China City University of Hong Kong Department of Electrical Engineering Hong Kong Shanghai Maritime University College of Information Engineering Shanghai201306 China

Travel demand forecasting (TDF) is a crucial task in route planning, navigation, and scheduling. However, developing models for such tasks requires significant amount of order data, which can pose privacy concerns for users. Federated learning (FL) has addressed privacy concerns related to TDF. However, previous works only consider the data of a single factor to train the model, resulting in a less precise assessment of the demand for on-demand ride services. Moreover, non-independent and identically distributed ( non-IID) data across clients also brings a challenge in personalizing models for TDF tasks when applying FL directly. In this work, a privacy-preserving short-term TDF scheme for non-IID data is proposed to deal with these problems. Our approach analyzes the correlation between various potential influencing factors and travel demand and summarizes the input variables of the FL model to achieve accurate TDF prediction with privacy preservation. Additionally, we provide each client with a personalized model trained on independent and identically distributed (IID) data to compensate for local model weight divergence. Finally, experiments are conducted on the 2022 Shanghai data of the Didi platform as a use case, and the results demonstrate that our scheme effectively improves the model's performance. © 1967-2012 IEEE.

关键词： Differential privacy

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ECIL-MU: EMBEDDING BASED CLASS INCREMENTAL LEARNING AND MACHINE UNLEARNING

arXiv

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arXiv 2024年

作者： Zuo, Zhiwei Tang, Zhuo Wang, Bin Li, Kenli Datta, Anwitaman College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

New categories may be introduced over time, or existing categories may need to be reclassified. Class incremental learning (CIL) is employed for the gradual acquisition of knowledge about new categories while preserving information about previously learned ones in such dynamic environments. It might also be necessary to also eliminate the influence of related categories on the model to adapt to reclassification. We thus introduce class-level machine unlearning (MU) within CIL. Typically, MU methods tend to be time-consuming and can potentially harm the model's performance. A continuous stream of unlearning requests could lead to catastrophic forgetting. To address these issues, we propose a non-destructive eCIL-MU framework based on embedding techniques to map data into vectors and then be stored in vector databases. Our approach exploits the overlap between CIL and MU tasks for acceleration. Experiments demonstrate the capability of achieving unlearning effectiveness and orders of magnitude (upto ∼ 278×) of acceleration. © 2024, CC BY.

关键词： Embeddings

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Enhancing Speech Emotion Recognition With Conditional Emotion Feature Diffusion and Progressive Interleaved Learning Strategy

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1787-1800页

作者： Yang Liu Xin Chen Zhichao Peng Yongwei Li Xingfeng Li Peng Song Masashi Unoki Zhen Zhao School of Information Science and Technology Qingdao University of Science and Technology Qingdao China School of Information Hunan University of Humanities Science and Technology Loudi China CAS Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Beijing China Faculty of Data Science City University of Macau Macau China School of Computer and Control Engineering Yantai University Yantai China School of Information Science Japan Advanced Institute of Science and Technology Nomi Japan

Speech emotion recognition (SER) aims to identify the speaker's emotional states in specific utterances accurately. However, existing methods still face feature confusion when attempting to recognize certain emotions because traditional acoustic feature extraction methods fail to capture dynamic emotional changes, blurring emotional boundaries. Additionally, existing classification networks (CNs) are constrained by fixed learning strategies, hindering their ability to capture subtle emotional nuances and resulting in label confusion. To address these two issues, we introduce 3D multiresolution modulation filtered cochleogram (MMCG) features by computing the deltas and delta-deltas of MMCG features to enhance the dynamic emotional changes and produce distinct emotional boundaries. We then customize a conditional emotion feature diffusion (CEFD) module, which progressively diffuses features based on emotional context to retain emotional nuances effectively and reduce reliance on conditioned information. In addition, a confidence filtering module is used to filter diffused features based on confidence-based posterior probabilities to ensure enhanced feature discrimination. We design a flexible training strategy named the progressive interleaved learning strategy (PILS) to learn further complex emotional nuances, which consists of two alternating stages: fine-tuning the CN parameters and supervising the CEFD output. Testing on the IEMOCAP, CASIA, and EMODB corpora demonstrates significant performance improvements in SER.

关键词： Feature extraction Emotion recognition Training Three-dimensional displays Diffusion models Speech enhancement electronic mail Speech recognition Semantics Face recognition

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