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2nd Music Recommender Systems Workshop, MuRS 2024

作者： Porcaro, Lorenzo Gómez, Emilia Catarci, Tiziana Department of Computer Control and Management Engineering Sapienza University of Rome Italy Joint Research Centre European Commission Spain

The evolution of online platforms over the past decades has radically transformed the way people discover music, and thanks to social media and music streaming services nowadays listeners have access to an ever-increasing amount of tracks and artists. Within these platforms, one of the goals of recommender systems is to help users discover music without making them feel overwhelmed while exploring the huge catalogues available. However, these systems have come under scrutiny from the scientific community, policy-makers, and civil society due to their potential negative societal impact, notably with regard to issues of fairness, non-discrimination, inclusion and diversity. Algorithmic auditing has emerged as a tool to analyse the problematic behaviours exhibited by recommenders, and to offer remedies that can limit their negative impact. In this position paper, we advocate for the involvement of end-users in the auditing process, which can contribute to the recognition, analysis, and mitigation of problematic behaviours which may arise while discovering music. Highlighting how recommenders, by influencing the discoverability of music, may impact listeners’ exposure to culturally diverse content, we seek to address the challenges posed by music recommender systems problematic behaviours, ultimately with the goal of fostering a more inclusive and diverse environment for music discovery within the digital landscape. © 2024 Copyright for this paper by its authors.

关键词： Recommender systems

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Modeling of Trust in Social Relationships for Secure Communication in Social Internet of Things 4

Modeling of Trust in Social Relationships for Secure Communi...

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4th International Conference on Blockchain Technology and Information Security, ICBCTIS 2024

作者： Khan, Salabat Amin, Farhan Khan, Mansoor School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan China School of Computer Science and Engineering Yeungnam University Korea Republic of School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Shandong Jinan China

ISBN: (纸本)9798331528324

Herein, a social trust model is presented for investigating social relationships and social networks in the real world. Our proposal addresses the design of conceptual concepts to easily implement and develop complex applications that require direct interaction among objects. The secure social relationship paradigm guarantees network navigability, even if there are more objects on the traditional Internet of Things (IoT). This paradigm shift is very interesting, but very limited researchers have worked in this direction. Therefore, the core objective of our article is to study and classify these objects and emphasize the importance and the characteristics of trust in social objects and their relationships. Herein, five types of secure social relationships were identified and explained with real scenarios. Finding new social relationships and understanding different features and characteristics helps the researcher understand their structure and, thus, build trust between smart objects. Based on these social relationships we propose secure social relationship modeling. The achieved experimental result shows that our proposed model outperforms as compared to the state-of-the-art model. The efficiency is measured based on processing time and precision. We present a critical alternative vision based on the most recent literature. We discuss social relations along with strengths and weaknesses. Our critical analysis guides the reader to understand this paradigm in-depth and its impact on future technologies, i.e., artificial social intelligence. We also present the major ongoing research activities and highlight the most important technical and critical challenges associated with these objects, such as inter-object relations, support and definition of new communication primitives, and security and privacy concerns. © 2024 IEEE.

关键词： Secure relationships Secure social network analysis Security Social Network Analytics Social networks Trust

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A Novel Visual Transformer for Long-Distance Pipeline Pattern Recognition in Complex Environment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2933-2945页

作者： Zhu, Chengyuan Pu, Yanyun Yang, Kaixiang Yang, Qinmin Chen, C.L. Philip Zhejiang University College of Control Science and Engineering Zhejiang Hangzhou310007 China Huzhou Institute of Zhejiang University Zhejiang Huzhou313000 China South China University of Technology College of Computer Science and Engineering Guangdong Guangzhou510006 China

The effective warning of dangerous events along long-distance pipelines is critical to ensure the safety of oil and gas transportation. Distributed optical fiber sensing (DOFS) technology can assist operators to identify threat vibration signals. However, due to the complex and changeable environmental background noise along long-distance pipeline, most of the existing methods only extract one-dimensional features of the signal, making it difficult to distinguish various types of environmental noise, strong interference, and dangerous events. Besides, the samples of different classes in the actual scene are unbalanced. The sample size of dangerous events is often smaller than others. To address these problems, we use image encoding to transform the time series signals collected by the DOFS system into image data, and fully extract the time dependence and the correlation between different elements in the signal. Moreover, a visual transformer model PipelineADWinT is proposed. The self-attention mechanism of diagonal-axial window designed in this model can perfectly combine image encoding features, and obtain local to global multiscale features through hierarchical structure. By optimizing the loss function, the model's ability to handle the class imbalance problem is enhanced. The experimental results show that PipelineADWinT has more comprehensive classification performance and fewer false alarms than all the baseline models, which proves the effectiveness and superiority of the model. © 2020 IEEE.

关键词： Pattern recognition

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Fault Tolerant control Based on GA-DRE for a Manipulator with Actuator Fault 43

Fault Tolerant Control Based on GA-DRE for a Manipulator wit...

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43rd Chinese control Conference, CCC 2024

作者： Qiu, Jiongzhi Fang, Yiming Zhao, Donliang Liu, Le Shao, Nuan Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Hebei University of Environmental Engineering Department of Environmental Engineering Qinhuangdao China

ISBN: (纸本)9789887581581

To solve the trajectory tracking control problem of a rigid manipulator system in the case of unexpected actuator failure, considering the uncertainty of system parameters and bounded disturbance, an adaptive fault-tolerant tracking control strategy is proposed in this paper. Firstly, an adaptive strategy is designed to estimate and compensate the effects of actuator failure and system uncertainty, and the dynamic regression matrix is extended and mixed to improve the accuracy of parameter estimation without persistency of excitation (PE), which will enhance the trajectory tracking performance. Then, in the framework of sliding mode fault-tolerant control, combined with standard Gradient Adaptive and Dynamic Regressor Extension (GA-DRE) parameter estimation technology, a composite adaptive fault-tolerant controller is designed. Finally, theoretical analysis and numerical simulation results show that the proposed method can perform accurate parameter estimation in a finite time under the premise of matching the new conditions based on DRE additional terms, and the trajectory tracking errors of the expected positions and velocities of each joint of the manipulator can asymptotically converge to zero. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Sliding mode control

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Reversible Data Hiding Scheme Based on Prediction Error Bit-plane Compression 21

Reversible Data Hiding Scheme Based on Prediction Error Bit-...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Yuan, Guo Heng, Chao Ping, Zhai Ziqi, Liu Haibo, Li Wenpeng, Li Heilongjiang University College of Computer Science and Technology Harbin China Qiqihar University College of Computer and Control Engineering Qiqihar China

ISBN: (纸本)9798350329223

How to maximize embedding capacity is one of the current challenges in the field of reversible data hiding. A reversible data hiding scheme is proposed based on the rearrangement and compression of prediction error bit-planes in this paper. The image holder first predicts the pixels to obtain the error map, then decomposes them into bit-planes, rearranges and compresses them to maximize the embedding space, and embeds the auxiliary information and secret information into the freed space according to certain rules to generate a cryptographic image. After acquiring the cryptographic image, the image receiver extracts the embedded secret information and recovers the original image using the auxiliary information. In this paper, a rearrangement strategy based on full pixel correlation was built to maximize compression efficiency and further compress the auxiliary information to free up more embeddable space and improve embedding capacity. A large number of test has revealed that the embedding rate of this method is 30% higher on average than the current state-of-the-art algorithm. © 2023 IEEE.

关键词： Pixels

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End-to-End Audio Pattern Recognition Network for Overcoming Feature Limitations in Human-Machine Interaction

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computers, Materials & Continua 2025年第5期83卷 3187-3210页

作者： Zijian Sun Yaqian Li Haoran Liu Haibin Li Wenming Zhang School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan UniversityQinhuangdao066004China School of Electrical Engineering Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao066004China

In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and *** methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation *** address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification *** proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic ***,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio ***,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced *** guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification *** results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,*** results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing *** addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems.

关键词： Audio pattern recognition raw audio end-to-end network feature fusion

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A dynamic bidirectional heuristic trust path search algorithm

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CAAI Transactions on Intelligence Technology 2022年第3期7卷 340-353页

作者： Jiaying Che Xiangrong Tong Lei Yu School of Computer and Control Engineering Yantai UniversityYantaiShandongChina Department of Computer Science State University of New York at BinghamtonBinghamtonNew YorkUSA

Online social networks greatly promote peoples'online interaction,where trust plays a crucial *** prediction with trust path search is widely used to help users find the trusted friends and obtain valid ***,the shortcomings of accuracy and time still exist in some famous ***,the dynamic bidirectional heuristic search(DBHS)algorithm is proposed in this paper to find the reliable trust path by studying the heuristic ***,the trust value and path length are comprehensively considered to find the most trusted ***,it constrains the traversal depth based on the‘small world’theory and obtains the acceptable path set by using the relaxation coefficientλto relax the depth of the shortest *** this way,some longer path with the higher trust can be considered to improve the precision of ***,an adjustment factor is designed based on the meet in the middle(MM)algorithm to assign search weights to two directions based on the size of the search tree expanded,so as to improve the problem of no priori when fixed parameters are ***,the complexity of unidirectional trust path search can also be reduced by searching from two directions,which can reduce the depth and improve the efficiency of ***,the predictive trust degree is outputted by the trust propagation *** public datasets are used to generate experimental results,which show that DBHS can quickly search and form reliable trust relationship,and it partly improves other algorithms.

关键词： directions algorithm dynamic

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RLVNA: a Platform for Experimenting with Virtual Networks Adaptations over Public Testbeds 3

RLVNA: a Platform for Experimenting with Virtual Networks Ad...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Angi, Antonino Sacco, Alessio Alberti, Enrico Marchetto, Guido Esposito, Flavio Politecnico di Torino Department of Control and Computer Engineering Italy Saint Louis University Computer Science Department United States

ISBN: (纸本)9798350333732

Network emulators and simulation environments traditionally support computer networking and distributed system research. The continued use of multiple approaches highlights both the value and inadequacy of each approach. To this end, several large-scale virtual networks testbeds, such as GENI and CloudLab, have emerged, allowing testing of a networked system in controlled yet realistic environments, focusing in particular on facilitating the test of network management schema in Software-Defined Network (SDN) scenarios. Nevertheless, setting up those experiments first and integrating machine learning models later in these deployments is challenging. In this paper, we propose designing and implementing a web-based platform that integrates Reinforcement Learning (RL)-based models with a virtual network experiment using resources acquired within a real-world testbed, e.g., GENI. Users are able to reserve the network resources (links, switches, and hosts) and configure them through our intuitive interface with little effort. The RL algorithm is then launched to learn how to steer traffic dynamically and according to diverse traffic network conditions. Such a model can be easily customized by the user, while our architecture enables fast reprogramming of the Open Virtual Switches via the SDN controller instantiated. We experimented with trace-based traffic to validate this user-friendly platform and evaluated how centralized and decentralized RL algorithms can effectively lead to self-driving networks. While in this paper, the system focuses on the deployment of experiments for virtual network adaptation, the platform can be easily extended to other network management mechanisms and machine learning algorithms. © 2023 IEEE.

关键词： Network management

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A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 462-473页

作者： Bingyun Li Qinmin Yang Innocent Kamwa IEEE the College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Huzhou Institute of Zhejiang University China the Department of Electrical and Computer Engineering Laval UniversityQuebec CityQCG1V 0A6Canada

Demand response(DR)using shared energy storage systems(ESSs)is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU)price.A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this *** this scheme,the interactions between selfish users and an operator are characterized as a Stackelberg *** holds a large-scale ESS that is shared among users in the form of energy *** sells energy to users and sets the selling price *** maximizes its profit through optimal pricing and ESS *** purchase some energy from operator for the reduction of their demand charges after operator's selling price is *** game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization *** upper-level optimization maximizes operator's profit and the lower-level optimization minimizes users'*** bi-level model is transformed and linearized into a mixed-integer linear programming(MILP)model using the mathematical programming with equilibrium constraints(MPEC)method and model linearizing *** studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.

关键词： Bi-level optimization demand charge energy storage system(ESS)sharing energy transaction mathematical program with equilibrium constraints(MPEC) stackelberg game

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Automated and controlled patch generation for enhanced fixing of communication software vulnerabilities

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Intelligent and Converged Networks 2024年第3期5卷 222-236页

作者： Shuo Feng Shuai Yuan Zhitao Guan Xiaojiang Du School of Control and Computer Engineering North China Electric Power UniversityBeijing 100029China Department of Finance Operationsand Information SystemsBrock UniversitySt.Catharines L2S3A1Canada Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Software is a crucial component in the communication systems,and its security is of paramount ***,it is susceptible to different types of attacks due to potential ***,significant time and effort is required to fix such *** propose an automated program repair method based on controlled text generation ***,we utilize a fine-tuned language model for patch generation and introduce a discriminator to evaluate the generation process,selecting results that contribute most to vulnerability ***,we perform static syntax analysis to expedite the patch verification *** effectiveness of the proposed approach is validated using QuixBugs and Defects4J datasets,demonstrating significant improvements in generating correct patches compared to other existing methods.

关键词： automatic program repair controlled text generation communication software security program language model

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