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Smart Devices (ICSD), International Conference on

作者： Ramnarayan Nagendar Yamsani Girija Shankar Joshi Jasjit Singh Kshitij Nautiyal Gaurav Kumar CSE Department UIT Uttaranchal University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun Uttarakhand Masters in Mechanical Engineering Concordia University Canada Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

Technologies are the patronage for the sustainable development and growth in the field of hospitality industry. Today’s hotel visitors are accustomed to using remote keyless entry to open and lock their vehicles, placing food orders directly from a smartphone app, and using the technology to switch on the needful thing, add something to their shopping list, or to on the Television and music also. Many of these visitors also anticipate having the same frictionless experience while staying at hotels. Prior studies tended to be preoccupied with technological service innovation, leaving human-related service innovation relatively unexplored. TBSI (Technology Based Service and Innovation) can be examine in this study to develop the sustainable application and system can have the stronger role for the customer satisfaction and delight. For the hospitality business to flourish and grow quickly in the future, we now also need to know what the customers are saying. In this study, we focus on technology and how it may help the hotel sector grow based on client input.

关键词： Industries Technological innovation TV Customer satisfaction Switches Sustainable development Smart devices Business

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Fusing Dynamics Equation: A Social Opinions Prediction Algorithm with LLM-based Agents

arXiv

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arXiv 2024年

作者： Yao, Junchi Zhang, Hongjie Ou, Jie Zuo, Dingyi Yang, Zheng Dong, Zhicheng College of Computer Science Sichuan Normal University Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou China College of Information Science Technology Tibet University Lhasa China

In the context where social media is increasingly becoming a significant platform for social movements and the formation of public opinion, accurately simulating and predicting the dynamics of user opinions is of great importance for understanding social phenomena, policy making, and guiding public opinion. However, existing simulation methods face challenges in capturing the complexity and dynamics of user behavior. Addressing this issue, this paper proposes an innovative simulation method for the dynamics of social media user opinions, the FDE-LLM algorithm, which incorporates opinion dynamics and epidemic model. This effectively constrains the actions and opinion evolution process of large language models (LLM), making them more aligned with the real cyber world. In particular, the FDE-LLM categorizes users into opinion leaders and followers. Opinion leaders are based on LLM role-playing and are constrained by the CA model, while opinion followers are integrated into a dynamic system that combines the CA model with the SIR model. This innovative design significantly improves the accuracy and efficiency of the simulation. Experiments were conducted on four real Weibo datasets and validated using the open-source model ChatGLM. The results show that, compared to traditional agent-based modeling (ABM) opinion dynamics algorithms and LLM-based opinion diffusion algorithms, our FDE-LLM algorithm demonstrates higher accuracy and interpretability. Copyright © 2024, The Authors. All rights reserved.

关键词： Chatbots

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Stochastic Dominant Cognitive Experience Guided Particle Swarm Optimization

Stochastic Dominant Cognitive Experience Guided Particle Swa...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Pan, Han-Yang Yang, Qiang Li, Ming Zhang, En Ma, Yuan-Yuan Li, Tao Liu, Dong Zhang, Jun College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Zhejiang Normal University Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Jinhua China Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9798350337020

This paper proposes a stochastic dominant cognitive experience-guided learning framework for particle swarm optimization (SDCEGPSO) to enhance its search ability in complex environment. Specifically, different from classical PSOs, SDCEGPSO randomly selects dominant cognitive experiences to guide the learning of particles. To this end, the cognitive experiences of all particles, namely their personal best positions, are sorted from the best to the worst. Then, each particle randomly chooses a personal best position better than its own to learn. For the cognitive experience selection, this paper designs three selection methods, namely the random selection, the roulette wheel selection, and the tournament selection. With this learning framework, particles have diverse guiding exemplars to learn from and thus high search diversity is expectedly maintained. Experiments conducted on the 50-D and 100-D CEC2014 problem suite have verified the effectiveness of SDCEGPSO. Compared with the classical global PSO (GPSO) and local PSO (LPSO), SDCEGPSO with the three selection schemes achieve significantly better performance. Besides, among the three selection schemes, the binary tournament selection is the most effective one to help SDCEGPSO solve optimization problems. © 2023 IEEE.

关键词： Evolutionary Algorithms Global Optimization Particle Swarm Optimization Stochastic Dominant Cognitive Experience Guided Learning

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Boosting LLMS with Ontology-Aware Prompt for Ner Data Augmentation

Boosting LLMS with Ontology-Aware Prompt for Ner Data Augmen...

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

作者： Zhizhao Luo Youchen Wang Wenjun Ke Rui Qi Yikai Guo Peng Wang Beijing Institute of Computer Technology and Application Beijing China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Ministry of Education Nanjing China China Life Property & Casualty Insurance Company Limited Changsha China

Named Entity Recognition (NER) data augmentation (DA) aims to improve the performance and generalization capabilities of NER models by generating scalable training data. The key challenge lies in ensuring the generated samples maintain contextual diversity while preserving label consistency. However, existing dominant methods fail to simultaneously satisfy both criteria. Inspired by the extensive generative capabilities of large language models (LLMs), we propose ANGEL, a frAmework integrating the oNtoloGy structure and instructivE prompting within LLMs. Specifically, the hierarchical ontology structure guides prompt ranking, while instructive prompting enhances LLMs’ mastery of domain knowledge, empowering synthetic sample generation and annotation. Experiments show ANGEL surpasses state-of-the-art (SOTA) baselines, conferring absolute F1 increases of 2.86% and 0.93% on two benchmark datasets, respectively.

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Cluster-Driven CA Selection Space Optimization in Blockchain with POA for Trust Calculation

Cluster-Driven CA Selection Space Optimization in Blockchain...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Amrutanshu Panigrahi Abhilash Pati Bibhuprasad Sahu Bibhuti Bhusan Dash Subrata Chowdhury Sudhansu Shekhar Patra Dept of CSE Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar India Dept of Information Technology Vardhaman College of Engineering (Autonomous) Hydrabad Telengana India School of Computer Application Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar Odisha India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Blockchain technology has gained prominence because of its features and characteristics, such as transparency, immutability, and decentralization. However, choosing an appropriate CA for authenticating these transactions is one of the major issues, especially in a larger network. Mechanisms like PoW and PoS suffer from very high computational costs, significant energy wastage, and scalability problems. While simple, centralized CA systems contradict the decentralized principles of blockchain and present with single points of failure. In large networks, this selection process is very long when working with such a large number of nodes, thereby increasing the transaction processing times and the overall inefficiency of the systems. To address the above challenges, the current research aims to implement the K-Means++ clustering algorithm with the Elbow and Silhouette methods as internal validation methods to define the number of clusters in the network. In addition, the current work also aims to define a new trust calculation mechanism using a Proof of Authority (PoA) consensus mechanism, depending on which node will become the CA. Gas utilization becomes an effective measure of efficacy.

关键词： Scalability Current measurement Clustering algorithms Process control Consensus protocol Computational efficiency Time factors Intelligent systems Elbow Optimization

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Sliding-Time-Window and Event-Trigger Based Data Collection Strategy for Non-intrusive Load Monitoring 6

Sliding-Time-Window and Event-Trigger Based Data Collection ...

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6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022

作者： Yang, Jian Hua, Wenjia Xu, Mengfei Ouyang, Zhiyou Zhang, Han Nanjing University of Posts and Telecommunications School of Computer NUPT-JSTZ Carbon Peaking & Carbon Neutrality Application Technology R&D Institute Nanjing China Nanjing University of Posts and Telecommunications Institute of Advanced Technology for Carbon Neutrality Nanjing China Southeast University and ZTE Corporation School of Cyber Science and Engineering Nanjing China

ISBN: (纸本)9798350347159

Non-intrusive load monitoring (NILM) technology can effectively identify the type and operation state of the electrical equipments in the load, so as to help the better analyze the power consumption state and power consumption of users, and achieve better energy saving and emission reduction and load optimization control. However, the large amount of data collected by non-invasive load monitoring terminals, especially high-frequency data, brings challenges to data acquisition, transmission and training of non-intrusive load monitoring models. Hence, in this paper, a sliding-time-window and event trigger based non-intrusive load monitoring data acquisition strategy is proposed, which can select power equipment of state change events through the event trigger mechanism, and fix the length of collect data samples and ensure not lose equipment state change process of context information by using sliding-time-window. Experiments by using widely used non-intrusive load monitoring algorithm evaluation toolkit that named NLIMTK, and widely used public dataset which named REDD, show that the proposed data collection strategy can not only make non-intrusive load monitoring data collected by the size of the largest decreased by 97.07%, and the prediction performance has improved significantly, which fully demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Electric power utilization

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DGLNet: A Cross-Modal Global-Local Collaborative Network for Chest Radiology Report Generation

SSRN

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SSRN 2022年

作者： Liu, Lijun Wang, Lizhen Liu, Li Peng, Wei Fu, Xiaodong Huang, Qingsong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Department of Computer Science and Engineering School of Information Science and Engineering Yunnan University Kunming650228 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

Automatic chest radiology report generation is crucial for improving diagnostic efficiency, enhancing diagnostic quality, and reducing misdiagnoses or missed diagnoses. Existing approaches either resize the high-resolution radiology image into smaller sizes or segment it into multiple patches for separate processing, which may lose fine local details or global contextual information, and the accuracy of report generation will be limited. In addition, the direct fusion of visual features with sentence features reduces the accuracy of aligning fine-grained visual features with text features. In this paper, we focus on designing a cross-modal global-local collaborative network to capture more local fine details and global contextual information, which comprises a global-to-local network and a local-to-global network that takes the resized entire image and segmented image patches as respective inputs. The former handles the extraction of global visual features, the fusion of global and local visual features, and the fusion of visual features into report sentences. The latter implements the extraction of local visual features, the fusion of local to global visual features, and the fusion of visual features to report words. Experimental results on two publicly available datasets, IU X-Ray and MIMIC-CXR, show that our proposed network outperforms state-of-the-art image captioning-based methods in most evaluation metrics. © 2022, The Authors. All rights reserved.

关键词： Extraction

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Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

arXiv

引用

arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Design and Development of a Smart Antenna System for Electromagnetic Compatibility

Design and Development of a Smart Antenna System for Electro...

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Artificial Intelligence and Machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Manish Kumar Goyal H Malathi Binod Kumar Choudhary Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Electrical & Electronics Engineering ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

This technical abstract introduces the design and development of a smart Antenna gadget for Electromagnetic Compatibility (EMC). The clever antenna device pursuits to lessen unwanted interference in wireless verbal exchange systems, and also to locate time Electromagnetic interference (EMI) alerts and optimize running conditions. This antenna gadget is built on both software and hardware systems. Its design relies on the improvement of antenna arrays, which make use of adaptive sign-processing techniques with the intention of reaping the desired EMC necessities. The antenna array, with its signal-processing architecture, gives the possibility of at the same time optimizing radiation samples and electrical parameters by way of electronically scanning the antenna in more than one direction and taking into consideration disturbances in the environment. The layout additionally incorporates an optimized weight matrix that is used to optimize the antenna’s signal-processing structure. Subsequently, the advanced system is simulated and evaluated for its overall performance, and the results are compared with theoretical predictions.

关键词： Wireless communication Performance evaluation Layout Electromagnetic interference Adaptive arrays Medical services Machine learning

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Two Step Recognition of Raags in Hindustani Classical Music Using Supervised Deep Learning

Two Step Recognition of Raags in Hindustani Classical Music ...

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Data, Decision and Systems (ICDDS), IEEE International Conference on

作者： Shobhan Banerjee Geetanjali Hota Ribhu Sanyal Manas Kumar Rath Department of Engineering Technology BITS Pilani Rajasthan India PGT Computer Science DA V Public School - Pokhariput Bhubaneswar India School of Computer Application Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9781665491631

In the Indian Classical system of music, we have various raags which differ from one another based on the notes being used in them, their frequency bandwidth, the subtle intricacies of the progression of notes, etc. In [1], we have worked upon the classification of a sample of music into a thaat which is an upper-level classification simply based on the frequencies present in it. In this paper, we take the work one step ahead to recognize the raag after successful classification of the thaat. In this way, it forms a two-step process where we first identify the thaat under which the music falls, followed by which we recognize the raag which corresponds to the music in consideration.

关键词： Industries Deep learning Plagiarism Detectors Bandwidth

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