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MERNet: Multimodal Emotion Recognition in Conversation Network via Cross-Modal Knowledge Distillation and Contrastive Learning

MERNet: Multimodal Emotion Recognition in Conversation Netwo...

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international conference on Big Data and Smart computing (BIGCOMP)

作者： Sooyeon Hwang Gahgene Gweon Eunseong Kwon Interdisciplinary Program in Artificial Intelligence Seoul National University South Korea Graduate School of Convergence Science and Technology Seoul National University South Korea Dept. of Computer Science and Engineering POSTECH South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Multimodal Emotion Recognition in Conversation (ERC) is a task of predicting the emotion of each utterance in a conversation by utilizing both verbal and non-verbal modalities. However, existing approaches often struggle to bridge cross-modal gaps, resulting in misaligned features and frequent misclassification of minority emotions into semantically similar majority emotions. To address these challenges, we propose MERNet, a framework that employs cross-modal knowledge distillation and contrastive learning to align multimodal features and effectively distinguish subtle emotions in conversations. Our framework consists of two stages: 1) guiding non-verbal modalities with the text modality to transfer knowledge and align their features, and 2) applying contrastive learning with emotion labels as anchors to distinguish subtle differences between similar emotions and address the class imbalance problem. Experiments conducted on two benchmark datasets, IEMOCAP and MELD, demonstrate that our MERNet outperforms existing state-of-the-art models.

关键词： Knowledge engineering Bridges Emotion recognition Visualization Computational modeling Contrastive learning Benchmark testing Linguistics Big Data

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Source Channel Model Selection for Downlink CSI Feedback Estimation using Transfer Learning in Massive MIMO System

Source Channel Model Selection for Downlink CSI Feedback Est...

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international conference on computing, Networking and Communications (ICNC)

作者： Liu Jingyu Mondher Bouazizi Tomoaki Ohtsuki Graduate School of Science and Technology Keio Univerity Yokohama Japan Department of Information and Computer Science Faculty of Science and Technology Keio University Yokohama Japan

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Transfer learning-based methods for Channel State Information (CSI) feedback can achieve a good CSI reconstruction performance with small amounts of computation time and cost. However, for higher efficacy, such methods need to fine-tune a properly selected source channel model each time. This is because fine-tuning a model trained on drastically different data will suffer from slow convergence and non-optimal performance, which defies the purpose of transfer-learning. In this paper, we use a one-stage algorithm utilizing k-nearest neighbors (k-NN) and Deep Neural Network (DNN) methods to classify the downlink Clustered Delay Line (CDL) channels into one of 5 channel models with the help of a selected set of features. Based on the classification results, the appropriate source channel model for fine-tuning is selected, and the model is fine-tuned on the received data. To demonstrate the usefulness of our method, we compare its results to those of existing methods. Through simulation, we show that the overall classification accuracy for 5 CDL channel models using the one-step algorithm is 80.73%. Upon classification, and by selecting the proper source model, our method can reach a better normalized mean square error (NMSE) of CSI feedback performance in a shorter time and with fewer samples.

关键词： Computational modeling Transfer learning Estimation Channel estimation Artificial neural networks Nearest neighbor methods Downlink Data models Classification algorithms Channel models

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Framework for Effective Information Security Investments: A Data-Driven Model Recommendation for Information Security Investment Decision-Making

Framework for Effective Information Security Investments: A ...

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international conference on Big Data and Smart computing (BIGCOMP)

作者： Byeongjo Park Tae-Sung Kim Dept. of Convergence Security Chungbuk National University Chungbuk South Korea Dept. of Management Information System Chungbuk National University Chungbuk South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

The rapid advancement of information and communication technology has increased work efficiency but also introduced complex security challenges for all stakeholders. While companies have become more aware of the importance of information security, they face difficulties in making effective security investment decisions due to budget constraints and a shortage of skilled security personnel. This study proposes a data-driven framework to support information security investment decision-making. The proposed framework employs quantitative models such as Genetic Algorithm, and Game Theory to select the optimal model based on specific input parameters. The framework systematically categorizes key input parameters necessary for risk assessment and countermeasure selection, guiding users through the optimization process by choosing the most suitable decision-making model. This structured approach enables organizations to select cost-effective security measures within a given budget. Additionally, the framework provides actionable insights by presenting results in visualizations and detailed reports, enhancing investment efficiency. Furthermore, it can be used as a tool by security managers to justify budget requests and assists executives in making data-driven information security decisions. This framework ultimately aims to streamline the information security investment process, enabling organizations to mitigate risks effectively within limited resources.

关键词： Optimization models Information security Companies Data models Information and communication technology Stakeholders Risk management Personnel Optimization Investment

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A Case Study: Leveraging SASE technology for Zero Trust Implementation in Cloud Environments

A Case Study: Leveraging SASE Technology for Zero Trust Impl...

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international conference on Information Networking

作者： Joo Hyun Lee Jungsoo Park Department of ICT Convergence Engineering Kangnam University Yong-in South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Recently, the changes in the cybersecurity environment and the proliferation of cloud-based services have highlighted the limitations of traditional security models. Zero Trust (ZT) is a security concept based on the principle of trusting no one. Zero Trust Architecture (ZTA) is a security framework that operates without trusting any device or user, instead requiring authentication and authorization for each request. This concept shares similarities with Secure Access Service Edge (SASE), a framework that integrates multiple network and security functions at the cloud edge, reducing costs and maintenance for organizations implementing their network strategies. Zero Trust is a response to modern enterprise network trends, including remote users, bring your own device (BYOD), and cloud-based assets that exist outside traditional enterprise network boundaries. In this paper, we will analyze SASE within the context of Zero Trust Architecture and recommend an adoption strategy.

关键词： Firewalls (computing) Focusing Organizations Market research Remote working Zero Trust Safety Maintenance Next generation networking Faces

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Optimized Cascaded Deep Capsule Cell Attention Network for Efficient Music Genre Classification

Optimized Cascaded Deep Capsule Cell Attention Network for E...

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international conference on ent computing and Control Systems (ICICCS)

作者： Chennaiah Kate R. Sangeethapriya M Revathy J. Mahil A. Anto Spiritus Kingsly S. Rukmani Devi Department of Computer Science and Engineering St. Peter's Engineering College Hyderabad Telangana India Department of Information Technology Sona College of Technology Salem Tamil Nadu India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Electrical and Electronics Engineering K. Ramakrishnan College of Technology Trichy Tamil Nadu India Department of Electronics and Communication Engineering Oasys Institute of Technology Trichy Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This research work develops a new approach towards music genre prediction by fusing high-end signal processing and machine learning methodologies that will improve prediction quality and resilience. The system commences by signal preprocessing comprising Discrete Wavelet Transformation (DWT) in combination with Pre-Gaussian removal of noise. Feature extraction is done with the Mellin Transform that extracts the important features, which immune to changes in pitch and variations in the general characteristics of the different genre of a particular artist or singer. Hierarchical classification is realized by the proposed Cascaded Capsule Cell Attention Network (CCCAN), which utilizes the capsule networks’ advantage of dynamic routing and the spatial hierarchy to capture and identify the intricate structures of audio patterns. The parameter of the network is optimized with Boosted Sooty Tern Optimization so that convergence speed and classification accuracy is above par. Experimental results presented in this study reveal that the proposed model provides around 99.5% of accuracy, precision, recall, and F1-score improved in comparison to the previous method which provides reliable classification of music genres.

关键词： Accuracy Noise Transforms Feature extraction Routing Multiple signal classification Discrete wavelet transforms Reliability Optimization Resilience

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Adaptive exponential reaching laws and high-gain observers for enhanced PMSM sliding mode control systems

Adaptive exponential reaching laws and high-gain observers f...

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international conference on Energy, Power, Environment, Control and computing (ICEPECC 2025)

作者： Muhammad Zafran Zenab Javaid Ameen Ullah Safeer Ullah Mian Rizwan Shafiq Ahmad College of Electrical Engineering Southeast University Nanjing 210096 People's Republic of China College of Integrated Circuit Science and Engineering Southeast University Nanjing 210096 People's Republic of China College of Mecatronics and Control Engineering Shenzhen University Shenzhen 518060 People's Republic of China Department of Engineering Quaid-e-Azam College of Engineering and Technology Sahiwal 57000 Pakistan Electrical Engineering Department University of Gujrat Punjab Pakistan Industrial Engineering Department College of Engineering King Saud University Riyadh 11421 Saudi Arabia

ISBN: (数字)9781837243150

This research presents a novel Adaptive Hybrid Exponential Reaching Law (AHERL) designed to enhance sliding mode control (SMC) systems for Permanent Magnet Synchronous Motors (PMSMs). The method incorporates an adaptive exponential component to facilitate rapid convergence with minimal overshoot, complemented by a high-gain disturbance observer for effective disturbance compensation. The simulation results reveal that AHERL delivers exceptional performance, including a 52% decrease in the integral square error (ISE), which quantifies the cumulative squared errors over time, and a 35% reduction in the integral time-weighted absolute error (ITAE), which evaluates time-weighted control errors. Moreover, AHERL achieved the target speed regulation within 0.1014 seconds, substantially outperforming the Conventional Exponential Reaching Law (CERL) at 0.335 seconds and other approaches, thereby demonstrating superior robustness and accuracy. These results underscore AHERL’s potential to significantly enhance speed control in PMSM applications.

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Optimizing Shortest Paths in Big Data Using the Floyd-Warshall Algorithm

Optimizing Shortest Paths in Big Data Using the Floyd-Warsha...

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Intelligent Control, computing and Communications (IC3), international conference on

作者： D.P. Sangeetha Satheeshkumar Sekar Palaniraj Rajidurai Parvathy Soundarya. R T. R. GaneshBabu M Muthulekshmi Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Mphasis Corporation Chandler Arizona USA Department of Computer Science and Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College(Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Improving the efficacy and precision of route computations in massive datasets is the goal of optimizing big data shortest routes using the Floyd- Warshall algorithm. The algorithm's ability to determine the shortest paths between all pairs of vertices in a graph allows for optimal route design, network optimization, and urban transportation solutions. Creating a solid foundation that can analyze large amounts of data and provide trustworthy outcomes for decision-making is the main objective. In addressing these challenges, concerns related to scalability, slow convergence rates, and potential errors in route calculations need resolution. The objective is to improve the efficiency and decision-making abilities of intricate systems by adjusting the Floyd- Warshall algorithm for extensive data environments. This will lead to a scalable and efficient solution that can be used in transportation, network routing, and urban planning, among other areas. In one example, the intermediate matrix displays the nodes from A to D, while in the other, the results from the road network show the initial adjacency matrix, where the distances between points A and D vary from 10 to 35 kilometers. In the final matrix, for the nodes A-D, the distance in kilometers varies from 10–35 for the shortest distance traveled; in the same data set, this range is 10–40.

关键词： Scalability Roads Decision making Urban planning Transportation Big Data Solids Routing Optimization convergence

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Enhancing Resource Scheduling Efficiency in Cloud Data Centers Through Hybrid Optimization Techniques

Enhancing Resource Scheduling Efficiency in Cloud Data Cente...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Abdul Sajid Mohammed Nikhila Sundarraj Rajkumar Abdul Khaleeq Mohammed Anuteja Reddy Neravetla Ketan Gupta N. Yuvaraj Dept of School of Computer and Information Sciences University of the Cumberlands Kentucky USA University of North Texas Denton Texas USA MS - Computer Science Governors State University IL USA Department of Information Technology University of the Cumberlands Kentucky USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of CSE Sri Shakthi Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

This research addresses the critical issue of ineffective resource scheduling in cloud data centers, which frequently leads to higher consumption of energy, more expensive operations, and worse system performance. Given the pivotal role of cloud data centers in facilitating scalable and efficient computing resources, optimal resource scheduling is critical for performance maximization and operational cost reduction. Integrating advanced met heuristic techniques like Genetic Algorithms (GA) and particle Swarm Optimization (PSO) with machine learning models, a hybrid optimization framework is proposed to allocate resources adaptively. The hybrid model effectively manages exploration and exploitation by leveraging the global searching capabilities of GAs and the fast convergence behavior of PSO, hence overcoming multi-faceted complexities and heterogeneity prevalent in cloud infrastructures. Of note, the inclusion of machine learning elements further augments the predictive ability of the model, facilitating dynamic updates in response to changes in load and diverse user requirements. Those experimental results show that significant improvements in scheduling efficiency are achieved to minimize both the job completion times and the energy consumption without violating the SLAs.

关键词： Cloud computing Data centers Adaptation models Processor scheduling Computational modeling Predictive models Dynamic scheduling Real-time systems Particle swarm optimization Genetic algorithms

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Magnetic Levitation Control Using Deep Reinforcement Learning and Prioritized Experience Replay with Action Noise Variations

Magnetic Levitation Control Using Deep Reinforcement Learnin...

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Power, Control and computing Technologies (ICPC2T), conference on

作者： Gopala Pare Manas Shil G.N. Pillai Arnab Dey Department of Electrical Engineering Indian Institute of Technology Roorkee Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

In recent years, Reinforcement Learning (RL) has emerged as a promising approach for addressing control problems in complex, nonlinear environments. The ability of RL algorithms to autonomously learn optimal control policies without requiring an explicit system model makes them particularly suited for real-time applications in fields like robotics and industrial automation. This paper investigates the application of RL algorithms for controlling a magnetic levitation (maglev) system, which presents significant challenges due to its nonlinear dynamics. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and the integration of Prioritized Experience Replay (PER) are employed to enhance learning efficiency. The impact of varying action noise (random perturbations added to actions) for exploration and system performance is explored; results demonstrate that TD3 with PER achieves superior stability, faster convergence, and reduced rise and settling times without overshoot. These findings emphasize the importance of tailored exploration strategies in optimizing control policies for complex environments, highlighting the potential of advanced RL techniques for real-time control applications.

关键词： Service robots Heuristic algorithms System performance Noise Refining Power control Real-time systems Stability analysis Standards Magnetic levitation

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Variable Step Size ZALMS with Optimized PI for Power Quality in Wind Integrated PMSG System

Variable Step Size ZALMS with Optimized PI for Power Quality...

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Power, Control and computing Technologies (ICPC2T), conference on

作者： Chirisavada Jagadeesh Sabha Raj Arya Ashutosh k. Giri Papia Ray Dept. of Electrical Engg. Sardar Vallabhbhai National Institute of Technology Surat India Dept. of Electrical Engg Government Engineering College Bharuch India Dept. of Electrical Engg Veer Surendra Sai University of Technology Burla Sambalpur India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Wind energy generation system will play the best role in supplying power to rural areas without any interruptions, low running cost and fast fault recoveries. Permanent magnet synchronous generator (PMSG) involvement in the system makes it operate at high speeds with low maintenance and it has higher efficiency than other induction generators. Wind integrated PMSG with DSTATCOM device will make an economic power generation system to small rural areas or to make micro-grid. A variable step size least mean square error is selected to improve the convergence of the converter control and to achieve high tracking accuracy in dynamic systems like wind energy power generation systems. The zero attracting concept will attract the higher order components in the system to zero. The variable step size zero attractive least mean square (VSSZALMS) controlled standalone wind integrated power generation system has developed to maintain harmonic free voltage and current at the PMSG terminals under all situations, including imbalanced load currents, load harmonics, and load alterations. Battery energy storage system (BESS) integrated DSTATCOM starts injecting active power into the system to supply required load demand. The PI controller is included in VSSZALMS to maintain terminal voltage at its reference value. Local minimum free, exploration and exploitation involved well optimized African vulture optimization algorithm (AVOA) is selected to tune the PI controller parameters.

关键词： Wind energy generation PI control Accuracy Battery energy storage system Power quality Wind power generation Power system harmonics Harmonic analysis Voltage control convergence

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