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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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Investigating the Role of Metaphors in Human-AI Conversation and Engagement 24

Investigating the Role of Metaphors in Human-AI Conversation...

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24th ieee international conference on data Mining Workshops, ICDMW 2024

作者： Maheshwary, Pragati Liu, Qiawen Mao, Rui Carnegie Mellon University Human-Computer Interaction Institute United States University of Wisconsin-Madison Department of Psychology United States Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798331530631

Metaphorical expressions are widely used in daily communication between humans to improve the understanding of both complex and abstract concepts. However, it is unclear whether the usage of metaphorical language is helpful for enhancing the conversational engagement between humans and AI. In this work, we leverage a state-of-the-art computational metaphor processing tool to gain insights from human and ChatGPT conversations. Our quantitative analysis finds that although metaphors may enhance the quality of human-AI interactions, they do not directly lead to higher levels of conversational engagement, which is measured by the duration of the conversation. However, regression analysis shows a notable relationship between user and ChatGPT metaphor usage, suggesting that ChatGPT is adept at reflecting the linguistic style of users, especially in terms of metaphorical language. Additional topic modeling and concept mapping analyses further explored the patterns of metaphorical language across various engagement levels and topics in user and ChatGPT messages. © 2024 ieee.

关键词： Modeling languages

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Task-based methodology to characterise immersive user experience with multivariate data 31

Task-based methodology to characterise immersive user experi...

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ieee conference on Virtual Reality and 3D User Interfaces (VR)

作者： Robert, Florent Wu, Hui-Yin Sassatelli, Lucile Winckler, Marco Univ Cote Azur CNRS Inria I3S Nice France Univ Cote Azur Inria Nice France Inst Univ France Paris France

ISBN: (纸本)9798350374025;9798350374032

Virtual Reality (VR) technologies enable strong emotions compared to traditional media, stimulating the brain in ways comparable to real-life interactions. This makes VR systems promising for research and applications in training or rehabilitation, to imitate realistic situations. Nonetheless, the evaluation of the user experience in immersive environments is daunting, the richness of the media presents challenges to synchronise context with behavioural metrics in order to provide fine-grained personalised feedback or performance evaluation. The variety of scenarios and interaction modalities multiplies this difficulty of user understanding in face of lifelike training scenarios, complex interactions, and rich context. We propose a task-based methodology that provides fine-grained descriptions and analyses of the experiential user experience (UX) in VR that (1) aligns low-level tasks (i.e. take an object, go somewhere) with multivariate behaviour metrics: gaze, motion, skin conductance, (2) defines performance components (i.e., attention, decision, and efficiency) with baseline values to evaluate task performance, and (3) characterises task performance with multivariate user behaviour data. To illustrate our approach, we apply the task-based methodology to an existing dataset from a road crossing study in VR. We find that the task-based methodology allows us to better observe the experiential UX by highlighting fine-grained relations between behaviour profiles and task performance, opening pathways to personalised feedback and experiences in future VR applications.

关键词： virtual reality user experience multivariate data task modeling behaviour characterisation Human-centered computing-HCI theory,concepts and models Human-centered computing-User models computing methodologies-Computer graphics-Graphics systems and interfaces-Virtual reality

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Combined Cloud and Deep Learning Models for Enhanced Decision Support 4

Combined Cloud and Deep Learning Models for Enhanced Decisio...

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4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Vyshnavi, A. Borade, Monali Ravindra Kannimuthu, S. Singla, Abhishek Raajini, X. Mercilin Rampal, Sourav School of Mangement - Ug Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam College of Engineering Department of Information Technology Coimbatore641032 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798331518981

Cloud computing and deep learning are changing decision-support systems. This combination was driven by cloud computing and deep learning models' complimentary data processing capabilities. Traditional decision-support systems typically fail due to the complexity and variety of current information, particularly unstructured and semi-structured data. Innovative methods are required to overcome this restriction and exploit big data's immense potential. Cloud computing is essential for handling and storing huge amounts of data because to its design, scalability, and storage capacity. Company data is securely accessed, processed, and stored while being cost-effective and flexible. Deep learning models - a subset of AI - offer a great toolbox of neural networks and algorithms for analyzing complex data patterns. These models are versatile, train well, and can find patterns in unstructured data. However, ideal outcomes demand a lot of processing power. Deep learning and cloud computing operate together better than traditional decision-support systems. Instead of static, rule-based models, the decision-support system may now react to complicated, ever-changing data. Modern cloud-based algorithms enable organizations to make accurate, personalized choices in real time. These changes will affect healthcare, finance, manufacturing, and autonomous automobiles. Convergence causes actual concerns. data security, privacy, and equality are key concerns. Cloud systems are difficult to balance between usability and safety. Since deep learning algorithms may be prejudiced and provide unfair outcomes, we must continue to promote fairness and transparency. Additional challenges to the effective implementation of these converged systems include controlling scalability, ensuring compliance with laws, and addressing interoperability. © 2024 ieee.

关键词： Artificial intelligence Big data Cloud computing data analysis data privacy data processing Decision support Deep learning Machine learning Scalability

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VQGG: Generating Adaptive Graphs for Traffic Forecasting via a Vector-Quantized Graph Generator

VQGG: Generating Adaptive Graphs for Traffic Forecasting via...

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international Joint conference on Neural Networks (IJCNN)

作者： Ke, Songyu Wu, Chenyu Guo, Jinjin Zhang, Junbo Zheng, Yu Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China JD Intelligent Cities Res & JD iCity JD Technol Beijing 100176 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Traffic forecasting is crucial in the realm of intelligent urban planning, playing an essential role in route optimization, arrival time estimation, and the prevention of congestion-related incidents. Deep learning has catalyzed the development of advanced spatio-temporal graph neural networks (STGNNs) for traffic predictions. A key advantage of STGNNs is their capability to capture complex spatial correlations, thereby improving prediction accuracy. Nevertheless, the existing methodologies, including dynamical graph-based networks and attention-based architectures, tend to overlook regular traffic patterns, leading to overly complex neural networks with less interpretability and more computational costs, which could be a bottleneck for practical applications. To address the challenge, we propose a lightweight and user-friendly dynamic adaptive graph generator, termed the Vector-Quantized Graph Generator (VQGG), which can autonomously identify prevalent spatial patterns and construct the corresponding adaptive graphs efficiently for enhanced traffic forecasting. We have extensively tested the VQGG with two benchmark datasets by integrating it as a substitute for the adaptive graphs in baseline models with minor modifications. Extensive experimental results demonstrate that VQGG can reduce forecasting errors by approximately 2.59%, indicating a significant improvement in predictive performance.

关键词： Urban computing Spatio-Temporal data Traffic Forecasting

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Enhanced Anomaly Detection in IoT: A Transformer Based Approach for Multivariate Time Series data

Enhanced Anomaly Detection in IoT: A Transformer Based Appro...

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2024 international conference on Engineering and computing, ICECT 2024

作者： Zia, Saher Bibi, Nargis Fatima Jinnah Women University Department of Computer Science Rawalpindi Pakistan

ISBN: (纸本)9798350349719

In IoT (Internet of Things) systems, the identification of anomalies plays a crucial role in ensuring the security and reliability of data. The rapid progression of digitization has led to the widespread deployment of sensor networks across various domains, supplying vital information for organizations to achieve complete autonomy. These sensor networks generate copious amounts of data, often in the form of multivariate time series, capturing both typical operational patterns and unusual occurrences. In the realm of anomaly detection for multivariate time series within IoT networks, our research introduces a transformer-based approach. The chosen model employs a transformer encoder-decoder architecture, leveraging its robust capabilities to analyze and identify aberrant patterns within intricately connected and complex data streams. To enhance the accuracy of anomaly detection, the study addresses the challenges posed by multivariate time series data and proposes a distinctive transformer-based methodology. Through experimentation and evaluation of authentic IoT dataset, this research illustrates the potential of the proposed model in delivering improved anomaly detection performance, consequently contributing to the development of more resilient and efficient IoT systems. © 2024 ieee.

关键词： Anomaly detection

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An Algebraic Structure for computing Big data in Livestream 13

An Algebraic Structure for Computing Big Data in Livestream

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13th international conference on Control, Automation and Information Sciences, ICCAIS 2024

作者： Van Pham, Dang Cong-Vinh, Phan Trong Tran, Toan Nguyen, Bao Khang Graduate University of Science and Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Institute of Applied Mechanics and Informatics Vietnam Academy of Science and Technology Ho Chi Minh City Viet Nam Nguyen Tat Thanh University Faculty of Information Technology Ho Chi Minh City Viet Nam British International School Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331542047

Nowadays, big data in livestream (BDL) is becoming increasingly prominent in computing systems (CS). Algebraic structures apply algebraic concepts to BDL in CS, aiding in the analysis and resolution of BDL-related problems. This research focuses on algebraic structures related to BDL, such as monoids, to analyze and categorize their properties. These algebraic monoids are essential in formal methods for verifying and analyzing applications of BDL, as they model program behavior and ensure program correctness. This paper examines the algebraic structure of BDL in CS, specifically detailing BDL and formalizing the properties of BDL monoids. Generally, algebraic structures are crucial for computing BDL, offering fundamental frameworks for comprehending and addressing complex issues of BDL in CS. © 2024 ieee.

关键词： Big data

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Evaluating Rule-Based Automated Reasoning for complex data 15

Evaluating Rule-Based Automated Reasoning for Complex Data

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Khushboo Nithya, M.S. Alam, Intekhab Amudha, A. Aravind, A.R. Tiwaskar, Shweta A. Department of Electrical Engineering Vivekananda Global University Jaipur India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350370249

— Rule-primarily based automated reasoning (RBAR) is a computational venture that may be used to efficiently system complex statistics sets. RBAR is primarily based on programming regulations implemented for a given information collection, and the resulting shape of the facts is then evaluated in opposition to the shared assumptions and constraints. This reasoning has been carried out on large, complicated datasets with medical points, economic transactions, and consumer data for decision-making functions. In this paper, we discover the efficacy of RBAR for complex point processing and compare how well it plays for properly described enter-output issues. We speak of critical factors and challenges, inclusive of the selection of suitable reasoning rules and the evaluation of the ensuing output. We additionally present a case look wherein the impact of rule-primarily based reasoning is evaluated for an e-commerce dataset. Results show that RBAR can offer vast performance improvements to complex statistics processing over traditional processes. ©2024 ieee.

关键词： Case based reasoning

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Towards a Generalized SDK for a Programmable Drones-as-a-Service 31

Towards a Generalized SDK for a Programmable Drones-as-a-Ser...

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31st ieee international conference on High Performance computing, data, and Analytics Workshops, HiPCW 2024

作者： Raj, Suman Singh, Rajdeep Astu, Kautuk Simmhan, Yogesh Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India

ISBN: (纸本)9798331509118

The proliferation of Unmanned Aerial Vehicles (UAVs) in spatio-temporal applications has given rise to the need for Drone-as-a-Service (DaaS) platforms that support diverse tasks, ranging from urban air mobility to precision agriculture. However, existing DaaS platforms are often tightly-coupled to hardware-specific frameworks that lack programmability and flexibility for custom applications. For instance, using UAVs to help a Visually Impaired Person (VIP) navigate requires a suite of applications, such as obstacle avoidance, route planning, and real-time tracking. In this poster, we propose Ocularone SDK (O-SDK) that integrates on our prior Ocularone stack for VIP applications with Aerostack2, which generalizes the hardware abstraction. O-SDK offers an analytics layer as a novel contribution to enable developers to easily integrate Deep Neural Network (DNN)-based models, and interface swarms of UAVs for complex tasks like navigating the visually impaired. Our evaluations show that the minimal overheads of using O-SDK for real-time experiments using Tello drones and Jetson Orin Nano. This opens the potential to develop a domain-neutral, programmable and customizable DaaS Software Development Kit (SDK) in future. © 2024 ieee

关键词： Drones

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Blockchain and Machine Learning for data-Driven Insights in Consumer Behavior Analytics 13

Blockchain and Machine Learning for Data-Driven Insights in ...

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13th ieee international conference on Communication Systems and Network Technologies, CSNT 2024

作者： Rawat, Bhupender Singh Thapliyal, Nitin Pal, Sunil Kumar Mishra, Nikhil Verma, Rohan College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Axis Colleges Department of Computer Science & Application Kanpur India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305463

Comparing with neural network method to more known methods, this article assesses their simplicity and effectiveness. Our approach uses new network pruning, interpretability-based regularization, and gradient-based sensitivity analysis. Numerous dataset trials show that the suggested method is more accurate, faster, and more durable than others. Also easier to understand. It turns out that small neural networks may simplify models without compromising accuracy. This comparative study shows how neural networks can handle complex data and make good predictions. These networks will be increasingly accessible in daily life. © 2024 ieee.

关键词： Sensitivity analysis

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Design and Optimization of Smart Contracts for Cross-Domain Sharing of Sensitive data 30

Design and Optimization of Smart Contracts for Cross-Domain ...

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30th ieee international conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhu, Manqing Wang, Lingxiao Li, Xiaohong Tianjin University College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798331515966

While studying the scenarios of sensitive data cross-domain sharing, we found there was no security assessment of smart contracts within existing data sharing schemes, which might lead to data leakage or unauthorized data access. To address this issue, we proposed a comprehensive security assessment of the smart contracts involved in existing data sharing solutions. In which, we extended the current smart contract tools to include detection capabilities for 7 additional types of vulnerabilities and implemented automatic repair functions for smart contracts. We also designed 3 types of smart contracts for the Hyperledger Fabric platform to facilitate sensitive data cross-domain sharing, specifically for data upload, data access, and data management functions to make sure that complex vulnerabilities could be automatically repaired. In our designation, the smart contracts for data upload and data access utilize the CP-ABE method and the data management smart contract includes various functions for secure management and tracing of data on the blockchain, which collectively ensure the security and comprehensive functionality of sensitive data cross-domain sharing scheme. © 2024 ieee.

关键词： Information leakage

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