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Towards a Visual Perception-Based analysis of Clustering Quality Metrics

Towards a Visual Perception-Based Analysis of Clustering Qua...

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ieee visualization in data Science (VDS)

作者： Graziano Blasilli Daniel Kerrigan Enrico Bertini Giuseppe Santucci Sapienza Università di Roma Italy Northeastern University USA

ISBN: (数字)9798331528423

ISBN: (纸本)9798331528430

Clustering is an essential technique across various domains, such as data science, machine learning, and explainable artificial intelligence. Information visualization and visual analytics techniques have been proven to effectively support human involvement in the visual exploration of clustered data to enhance the understanding and refinement of cluster assignments. To support the human involvement, several perceptual studies and visual quality metrics have already been proposed. However, the visual perception of clustering quality metrics, also known as Cluster Validity Indexes (CVIs), still remains to be further explored. This paper presents the first attempt of a deep and exhaustive evaluation of the perceptive aspects of clustering quality metrics, focusing on the Davies-Bouldin Index, Dunn Index, Calinski-Harabasz Index, and Silhouette Score. Our research is centered around two main objectives: a) assessing the human perception of common CVIs in 2D scatterplots and b) exploring the potential of large Multimodal Models, in particular GPT-4o, to emulate the assessed human perception. To this end, we conducted two systematic data studies and a user study covering a broad collection of datasets. By discussing the obtained results, highlighting limitations, and areas for further exploration, this paper aims to propose a foundation for future research activities.

关键词： Uniform resource locators Systematics Visual analytics Focusing data visualization data science Market research Indexes Replicability Visual perception

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AMuSE: Adaptive Multimodal analysis for Speaker Emotion Recognition in Group Conversations 9

AMuSE: Adaptive Multimodal Analysis for Speaker Emotion Reco...

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9th ieee International Conference on Multimedia Big data, BigMM 2023

作者： Anand, Sidharth Devulapally, Naresh Kumar Bhattacharjee, Sreyasee Das Yuan, Junsong Chang, Yu-Ping The State University of New York Buffalo United States

ISBN: (纸本)9798350360004

Analyzing individual emotions during group conversation is crucial in developing intelligent agents capable of natural human-machine interaction. While reliable emotion recognition techniques depend on different modalities (text, audio, video), the inherent heterogeneity between these modalities and the dynamic cross-modal interactions influenced by an individual's unique behavioral patterns make the task of emotion recognition very challenging. This difficulty is compounded in group settings, where the emotion and its temporal evolution are not only influenced by the individual but also by external contexts like audience reaction and context of the ongoing conversation. To meet this challenge, we propose a Multimodal Attention Network (MAN) that captures cross-modal interactions at various levels of spatial abstraction by jointly learning its interactive bunch of mode-specific Peripheral and Central networks. The proposed MAN "injects"cross-modal attention via its Peripheral keyvalue pairs within each layer of a mode-specific Central query network. The resulting cross-attended mode-specific descriptors are then combined using an Adaptive Fusion (AF) technique that enables the model to integrate the discriminative and complementary mode-specific data patterns within an instance-specific multimodal descriptor. Given a dialogue represented by a sequence of utterances, the proposed AMuSE (Adaptive Multimodal analysis for Speaker Emotion) model condenses both spatial (within-mode and within-utterance) and temporal (across-mode and across-utterances in the sequence) features into two dense descriptors: speaker-level and utterance-level. This helps not only in delivering better classification performance (3-5% improvement in Weighted-F1 and 5-7% improvement in Accuracy) in large-scale public datasets (MELD and IEMOCAP) but also helps the users in understanding the reasoning behind each emotion prediction made by the model via its Multimodal Explainability visualization mo

关键词： Emotion Recognition

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Key Technologies of Media Big data in-Depth analysis System Based on 5G Platform 5

Key Technologies of Media Big Data in-Depth Analysis System ...

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2022 5th International symposium on Big data and Applied Statistics, ISBDAS 2022

作者： Lin, Qiang Zhang, Xilin School of Maritime Economics and Management Dalian Maritime University Dalian116026 China Dalian University of Science and Technology Dalian116052 China

To meet the needs of large-scale users for personalized streaming media services with high speed, low delay, and high quality in a 5G mobile network environment, this paper studies the resource allocation mechanism of streaming media based on a 5G network from the perspective of user demand prediction, which can alleviate the pressure of mobile network, improve the utilization rate of streaming media resources and the quality of user service experience. The augmented reality visualization of large-scale social media data must rely on the computing power of distributed clusters. This paper constructs a distributed parallel processing framework in a high-performance cluster environment, which adopts a loosely coupled organizational structure. Each module can be combined, called, and expanded arbitrarily under the condition of following a unified interface. In this paper, the algebraic method of parallel computing algorithm is innovatively proposed to describe parallel processing tasks and organize and call large-scale data-parallel processing operators, which effectively supports the business requirements of large-scale parallel processing of large-scale spatial social media data and solves the bottleneck of large-scale spatial social media data-parallel processing. © Published under licence by IOP Publishing Ltd.

关键词： Wireless networks

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Applying Sentiment analysis on Children's Stories 4

Applying Sentiment Analysis on Children's Stories

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4th International Informatics and Software Engineering Conference, IISEC 2023

作者： Bilal, Azhar Ahmed Erdem, O. Ayhan Toklu, Sinan Gazi University Graduate School of Natural and Applied Sciences Department of Computer Engineering Ankara Turkey Gazi University Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350318036

Today in the world, the rapid and widespread adoption of modern technologies has changed the way that we can access information from news, conversations, and exchange of views about any topics that could attract people's attention. Current communication on the Internet allows people in the community to present and share their ideas in publications and interact with others on different subjects. These data contain a large amount of common knowledge that can be used in scientific and humanitarian research to generalize the benefit to all people everywhere. With the advancement of artificial intelligence and its branches and by using these data, we can solve the various problems that face us in life. Natural language processing is one of the most important issues that have arisen in parallel with the developments of artificial intelligence. Sentiment analysis is regarded as a computational technique that aids in determining and evaluating people's emotions through written documents. Therefore, in this study, we will focus on the sentiment analysis by applying Random Forest and Decision Tree algorithms on the dataset containing children's stories. The results showed that the accuracy rate attained by Random Forest was 98.32% while Decision Tree gave 97.74%. © 2023 ieee.

关键词： Sentiment analysis

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End-to-End Model-Based Gait Recognition with Matching Module Based on Graph Neural Networks 6

End-to-End Model-Based Gait Recognition with Matching Module...

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6th International symposium on Autonomous Systems, ISAS 2023

作者： Xu, Yixin Wang, Zhihao Nanjing University of Science and Technology School of Automation Nanjing China Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing China

ISBN: (纸本)9798350316155

Existing gait recognition systems have achieved success by extracting robust gait features from silhouette images, but gait can be sensitive to appearance features such as clothing and carried items. As a more promising alternative, model-based gait recognition is robust against some variations, such as clothing and baggage carried. With the recent development of human pose estimation, the difficulty of implementing model-based methods has been mitigated. This paper introduces an end-to-end model-based gait recognition method designed for large-scale and uncontrolled datasets. The proposed method takes 3D data as input and utilizes the ST-GCN as the embedding module. We have replaced the simple nearest neighbor algorithm for better performance. Specifically, the 3D skeletons are embedded into the Graph Neural Network to represent similarities. The proposed method is evaluated on the GREW dataset, showing state-of-the-art (SOTA) results in model-based gait recognition. © 2023 ieee.

关键词： Gait analysis

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RS-AGENT: large Language Models Guided Agent System for Remote Sensing Image Generation

RS-AGENT: Large Language Models Guided Agent System for Remo...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Lingxuan Zhu Jiaji Wu Biao Wang Guoshuai Zhang Jiarong Wang Shaohong Chen Mingzhou Tan National Key Laboratory of Scattering and Radiation Shanghai Xidian University

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Remote Sensing Image Generation (RSIG) offers a viable solution to the high data collection costs by facilitating the generation of large datasets. However, it is often hindered by the complexity of tasks and the diverse requirements of generated images. This paper presents the RS-Agent system, a novel approach that harnesses the capabilities of large Language Models (LLMs) within an innovative agent solution, effectively addressing these issues. The system comprises a Task Agent, a Prompt Agent, and a LoRA Agent, each performing crucial roles in task decomposition, prompt generation, and scene-specific fine-tuning, respectively. Anchored by Diffusion models and employing natural language dialogues for interaction, the system aligns closely with user intent and produces high-quality results. Experimental results demonstrate the efficacy of the RS-Agent system in managing diverse RSIG tasks, adapting to various input forms, and generating high-quality results.

关键词： Productivity Adaptation models visualization Image synthesis large language models Natural languages Feature extraction

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DeFraudify4ALL: Prototyping and Validation of a System for Fraud Detection with Big data and Cloud Technology

DeFraudify4ALL: Prototyping and Validation of a System for F...

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International symposium for Design and Technology of Electronics Packages (SIITME)

作者： Eduard-Cristian Popovici Cosmina Stalidi Dan-Alexandru Teodoras George Suciu Ioan Cosma Telecommunication Department POLITEHNICA University of Bucharest ETTI Faculty Bucharest Romania R&D Department Beia Consult International Bucharest Romania

ISBN: (数字)9798331539511

ISBN: (纸本)9798331539528

This paper presents the prototyping and validation of a Financial data Management and analysis System designed to enhance fraud detection using advanced Big data and Cloud technologies. The system incorporates a Java-based worker for data retrieval and analysis from various sources, including external APIs. The processed data is efficiently stored in a data Lake utilizing Dremio, Apache Arrow, and Apache Parquet technologies. By leveraging cloud platforms such as AWS, the system ensures scalability, security, and flexibility. Additionally, the integration of sophisticated data visualization tools and AI algorithms, particularly Random Forest, enhances the system’s ability to detect fraudulent activities.

关键词： Solid modeling Java Scalability Microservice architectures Prototypes Real-time systems data models Fraud Security Random forests

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visualization and analysis of technological systems experimental operating results 16

Visualization and analysis of technological systems experime...

引用

16th ieee International Conference on Computer Science and Information Technologies, CSIT 2021

作者： Aksonov, Yevhen Kombarov, Volodymyr Tsegelnyk, Yevgen Plankovskyy, Sergiy Fojtu, Olena Piddubna, Lidiia O.M. Beketov National University of Urban Economy in Kharkiv Department of Automation and Computer Integrated Technologies Kharkiv Ukraine Czech Technical University in Prague Research Center of Manufacturing Technology Prague Czech Republic

ISBN: (纸本)9781665442572

Improving the accuracy, reliability and performance of high-speed technological systems is one of the most pressing challenges in modern industry. These technologies are implemented at high speeds of tool movement and require a clear interaction of a large number of the technological system or robotic complex elements. Experimental investigations of such systems functioning lead to the receipt of a large amount of data, the display, and analysis of which with the help of universal software means certain difficulties. The paper proposes the creation of specialized software for solving problems of visualization and analysis of experimental data with the ability to simultaneously display several hundred graphs. The proposed algorithm for layer-by-layer graph formation, which provides the task of increasing the speed of data conversion for display and increasing the speed of rendering an image. The proposed algorithm is implemented in specialized software for the visualization and analysis of experimental data obtained in the CNC system. © 2021 ieee.

关键词： data visualization

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Robustness analysis of power cyber-physical system considering mixed information attack risk 9

Robustness analysis of power cyber-physical system consideri...

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9th International symposium on Advances in Electrical, Electronics, and Computer Engineering, ISAEECE 2024

作者： Zhou, Yutian Yao, Ji Nie, Yu Gao, Wenmao Xin, Ran Chen, Siyu State Grid Liaoning Electric Power Co. Ltd. Dandong power supply company Liaoning Dandong118000 China

ISBN: (纸本)9781510683303

With the integration of information networks and traditional power grids, the power information physical integration system (CPPS) has become the foundation of modern smart grids. However, the establishment of information networks has also brought new challenges to the safe and stable operation of smart grids. In CPPS, false data injection attacks (FDIA) and denial of service (DoS) attacks are two common attack methods. FDIA attacks may interfere with the evaluation of power grid status, mislead the control center to issue unnecessary or erroneous instructions;while DoS attacks occupy communication resources by sending a large number of fake data packets to the communication network channel, thereby affecting the control center’s judgment and control of the power grid operation status. Both types of attacks pose a threat to the safe and stable operation of CPPS. Given that a singular attack may not effectively accomplish the desired outcome of the attacker and that mixed attacks are increasingly preferred, this study examines the impact of a mixed attack model on the resilience of CPPS systems, specifically from the perspective of the attacker. By incorporating the attributes of DoS attacks into the FDIA model, simulation experiments are conducted to evaluate the effectiveness of the mixed attack model. Based on the simulation results of ieee standard data sets, compared with FDIA, the hybrid attack model can achieve similar attack objectives with less attack cost. On the one hand, the hybrid attack model expands the application scope of information attack and unifies FDIA and DOS models. On the other hand, The findings of this study will help CPPS system operators better understand the security risks of CPPS systems and provide new research ideas for improving CPPS system detection algorithms and mechanisms. © 2024 SPIE.

关键词： Cyber attacks

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An Effective Buffer Overflow Detection With Super data-Flow Graphs 20

An Effective Buffer Overflow Detection With Super Data-Flow ...

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20th ieee Int symposium on Parallel and Distributed Processing with Applicat / 15th ieee Int Conf on Social Comp and Networking / 12th ieee Int Conf on Big data and Cloud Comp / 12th ieee Int Conf on Sustainable Comp and Commun

作者： Zhang, Yuantong Chen, Liwei Nie, Xiaofan Shi, Gang Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781665464970

Buffer overflow vulnerabilities have been a severe threat to computer systems in the past few decades. Therefore, several static approaches have been proposed for automatic vulnerability detection and fixing. Static approaches are efficient but require large memory space to maintain all necessary information at different program points for precision. Existing works are usually tradeoffs between throughput capacity and precision. In this paper, we propose a novel buffer overflow detection approach by performing the progressive data-flow evaluation on programs with their super data-flow graphs, which are expected to cover all real data-flow paths. For this purpose, we realized the super data-flow graph generation based on classic reaching-definition analysis, as well as the progressive data-flow evaluation approach. With progressive evaluation, we transform all necessary dataflow paths into SMT formulas and use an SMT solver to check whether these formulas satisfy certain vulnerable conditions. Finally, we evaluated our approach with Juliet Test Suites v1.3 and it detected all vulnerabilities with a very low false positive rate. Meanwhile, it also detected 8 known CVE vulnerabilities.

关键词： buffer overflow detection data-flow analysis satisfiability-modulo theory

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