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检索条件"任意字段=Conference on Visual Data Exploration and Analysis II"

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Using Business Intelligence to Analyze Road Traffic Accidents 22

Using Business Intelligence to Analyze Road Traffic Accident...

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conference on Central and Eastern European eDem / eGov Days on Hate Speech and Fake News - Fate or Issue to Tackle(CEEE Gov Days)

作者： Awad, Mohammed Al Redhaei, Aneesa Fraihat, Salam Amer Univ Ras Al Khaimah Rak U Arab Emirates Ajman Univ AIRC Coll Engn & Informat Technol Ajman U Arab Emirates

ISBN: (纸本)9781450397667

Road Traffic Accidents constitute a significant concern around the world. Understanding the primary and contributing factors may combat traffic accidents and mitigate their impact. Based on an actual traffic accidents dataset of the United Arab Emirates (UAE) between 2012-2019, we investigate the importance of data science and Business Intelligence (BI) in visualizing traffic accidents in a descriptive format. The proposed BI solution provides visual data exploration for authorities to analyze and make informed decisions. This paper provides an example of how open data can save lives and resources. The design and implementation of the BI solution and its features are also presented in this paper.

关键词： Business intelligence data analytics Road traffic accidents Traffic management E-government Descriptive analysis

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Emotional analysis through EEG on In-Store Journey Pilot Methodology for Evocation of Emotions Through Video Stimuli to Measure Performance Metrics Using EEG Emotiv EPOC plus on In-Store Experiences 10th

Emotional Analysis through EEG on In-Store Journey Pilot Met...

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10h International conference on HCI in Business, Government and Organizations (HCIBGO) Part of the 24th International conference on Human-Computer Interaction (HCI)

作者： Osornio Garcia, Fernando U. Fragoso Gonzalez, Gilberto A. Martinez Perez, Mayte, V Baez Martinez, Fernando Salas Barraza, Mario H. Gonzalez, Victor M. Sperientia Studio Lab Cholula 72810 Puebla Mexico

ISBN: (纸本)9783031360480;9783031360497

This work presents the results of a pilot study for emotion evocation to explore the customer journey in a departmental store to detect possible friction points using biosignals and a retrospective verbalization. Our proposed methodology relies on the assumption that states how episodic memory elicits recollection of past events, thus, the previous emotional state of that specific event, providing a more efficient and vivid approach to measure their experience in a controlled environment. The present methodology is based on video-recording the participant's purchase journeys at the store, subsequently using the mentioned recording as visual stimuli to measure and report the emotional journey (as-is) based on quantitative data acquired by the use of an electroencephalograph (EEG). This data was complemented by retrospective interviews in order to build a solid comprehension of the user's journeys and their interactions with the services. The results obtained through these tests (n = 3) offer measurable genuine reactions towards their personal journey through the store, areas of friction, and key interactions during critical stages of their journey such as the payment system failures, crucial interactions, and the presence of the store employees, along with the imperative store's infrastructure to offer a homogeneous in-store experience. We present these results despite the limitations of the study (sample size) due to the potential of the methodology and expect that more researchers can expand our findings.

关键词： UX Research EEG Emotional Journey Customer Journey Episodic Memory Human computer interaction (HCI) Bioinformatics Laboratory experiments

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Unveiling Sentiments: Social Media Analytics on Indonesian Startup and Fintech Discourse

Unveiling Sentiments: Social Media Analytics on Indonesian S...

引用

2024 ASU International conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Putra, Merios Gusan Mustafid Nugroho, Budi University of Diponegoro Program of Information System Semarang Indonesia Res. Center for Data and Information Sci. National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798350372229

This paper unveils the sentiments embedded in the dynamic discourse surrounding Indonesian startups and financial technology (Fintech) through a comprehensive Social Media Analytics (SMA) framework. Leveraging the power of Sentiment analysis (SA) facilitated by the Brand24 tool, we delve into the extensive dataset sourced from Twitter and Instagram accounts of the national library, focusing on a one-month timeframe. Our analysis navigates the diverse landscape of Indonesian startup and Fintech discourse, revealing distinctive topics such as Fintech Support and Gaming Vouchers Trend. The multifaceted dynamics within this digital realm unfold as startups demonstrate a higher total mention count, positive sentiments, social media reach, and Average Value Equivalent (AVE), signifying a robust online presence and favorable reception. Fintech, in turn, asserts its influence with notable user-generated content and competitive non-social media reach. This paper not only presents a snapshot of sentiment within the examined timeframe but also paves the way for future research endeavors. The intricate interplay between sentiment, social media, and the vibrant entrepreneurial and financial technology ecosystems in Indonesia invites ongoing exploration, providing a foundation for strategic decision-making in these rapidly evolving sectors. © 2024 IEEE.

关键词： Financial Inclusion Fintech Indonesia Influencer Marketing Startups

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Unveiling the Impact of Image Transformations on Deepfake Detection: An Experimental analysis 22nd

Unveiling the Impact of Image Transformations on Deepfake De...

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22nd International conference on Image analysis and Processing (ICIAP)

作者： Cocchi, Federico Baraldi, Lorenzo Poppi, Samuele Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy Univ Pisa Pisa Italy IIT CNR Pisa Italy

ISBN: (纸本)9783031431524;9783031431531

With the recent explosion of interest in visual Generative AI, the field of deepfake detection has gained a lot of attention. In fact, deepfake detection might be the only measure to counter the potential proliferation of generated media in support of fake news and its consequences. While many of the available works limit the detection to a pure and direct classification of fake versus real, this does not translate well to a real-world scenario. Indeed, malevolent users can easily apply post-processing techniques to generated content, changing the underlying distribution of fake data. In this work, we provide an in-depth analysis of the robustness of a deepfake detection pipeline, considering different image augmentations, transformations, and other pre-processing steps. These transformations are only applied in the evaluation phase, thus simulating a practical situation in which the detector is not trained on all the possible augmentations that can be used by the attacker. In particular, we analyze the performance of a k-NN and a linear probe detector on the COCOFake dataset, using image features extracted from pre-trained models, like CLIP and DINO. Our results demonstrate that while the CLIP visual backbone outperforms DINO in deepfake detection with no augmentation, its performance varies significantly in presence of any transformation, favoring the robustness of DINO.

关键词： Deepfake Detection Self-Supervised Vision Transformers

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JobViewer: Graph-based visualization for Monitoring High-Performance Computing System 9

JobViewer: Graph-based Visualization for Monitoring High-Per...

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IEEE/ACM 15th International conference on Utility and Cloud Computing (UCC) / 9th International conference on Big data Computing, Applications and Technologies (BDCAT)

作者： Dang, Tommy Nguyen, Ngan V. T. Li, Jie Sill, Alan Hass, Jon Chen, Yong Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Texas Tech Univ Ctr High Performance Comp Lubbock TX 79409 USA Dell Inc Austin TX USA

ISBN: (纸本)9781665460903

visualization aims to strengthen data exploration and analysis, especially for complex and high-dimensional data. High-performance computing (HPC) systems are typically large and complicated instruments that generate massive performance and operation time series. Monitoring HPC systems' performance is a daunting task for HPC admins and researchers due to their dynamic natures. This work proposes a visual design using the bi-partite graph's idea to visualize HPC clusters' structure, metrics, and job scheduling data. We built a web-based prototype, called JobViewer, that integrates advanced methods in visualization and human-computer interaction (HCI) to demonstrate the benefits of visualization in real-time monitoring HPC centers. We also showed real use cases and a user study to validate the efficiency and highlight the current approach's drawbacks.

关键词： High-Performance Computing Center Job Scheduling Radar Charts Time-Series data analysis Multidimensional data visualization

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A Comparative analysis of Rust-Based SGX Frameworks: Implications for Building SGX Applications 26th

A Comparative Analysis of Rust-Based SGX Frameworks: Implica...

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26th International conference on Information Security and Cryptology (ICISC)

作者： Shin, Heekyung Ock, Jiwon No, Hyeon Kim, Seongmin Sungshin Womens Univ Seoul South Korea Sungshin Womens Univ Dept Future Convergence Technol Engn Seoul South Korea

ISBN: (纸本)9789819712373;9789819712380

The widespread adoption of Intel Software Guard Extensions (SGX) technology has garnered significant attention, primarily owing to its robust hardware-based data-in-use protection. To alleviate the complexities of SGX application development, an approach involving the incorporation of a Library Operating System (LibOS) within an enclave has gained prominence. This strategy enables SGX utilization without necessitating extensive modifications to legacy code. However, this approach increases the potential attack surface and may be susceptible to memory corruption vulnerabilities. To address this challenge, the trend of leveraging Rust programming language offering memory safety guarantees for implementing system components has prompted the development of Rust-based SGX frameworks. But still, a gap exists in providing guidelines or systematic analyses to aid developers in selecting a suitable Rust-based SGX framework, considering factors like implementation cost and runtime overhead. This study undertakes a comprehensive comparative analysis of three representative SGX frameworks implemented with Rust: Rust SGX SDK, Occlum, and Fortanix EDP. Our analysis encompasses an exploration of their internal implementations, focusing on their impact on both performance and security. Additionally, we quantify the engineering effort required for migrating legacy Rust applications and evaluate the supplementary overhead incurred when subjecting these frameworks to CPU and memory-intensive workloads. By conducting this analysis, we aim to provide valuable guidance to developers seeking to choose a Rust-based SGX framework that aligns with their application's specific purpose and workload characteristics.

关键词： Trusted Execution Environment Intel SGX Rust

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How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational Behavior 22

How do you Converse with an Analytical Chatbot? Revisiting G...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Setlur, Vidya Tory, Melanie Tableau Res Palo Alto CA 94306 USA Northeastern Univ Roux Inst Portland ME USA

ISBN: (纸本)9781450391573

Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interaction. We also draw inspiration from natural language interfaces for data exploration to support ambiguity and intent handling. We ran Wizard of Oz studies with 30 participants to evaluate user expectations for text and voice chatbot design variants. Results identified preferences for intent interpretation and revealed variations in user expectations based on the interface affordances. We subsequently conducted an exploratory analysis of three analytical chatbot systems (text + chart, voice + chart, voice-only) that implement these preferred design variants. Empirical evidence from a second 30-participant study informs implications specific to data-driven conversation such as interpreting intent, data orientation, and establishing trust through appropriate system responses.

关键词： chatbots intent visual analysis ambiguity repair refinement

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exploration Versus Exploitation in Model-Based Reinforcement Learning: An Empirical Study 1

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11th Brazilian conference on Intelligent Systems (BRACIS)

作者： Lovatto, Angelo Gregorio de Barros, Leliane Nunes Maua, Denis D. Univ Sao Paulo Inst Matemat & Estat Sao Paulo Brazil

ISBN: (数字)9783031216893

ISBN: (纸本)9783031216886;9783031216893

Model-based Reinforcement Learning (MBRL) agents use data collected by exploration of the environment to produce a model of the dynamics, which is then used to select a policy that maximizes the objective function. Stochastic Value Gradient (SVG) methods perform the latter step by optimizing some estimate of the value function gradient. Despite showing promising empirical results, many implementations of SVG methods lack rigorous theoretical or empirical justification;this casts doubts as to whether good performance are in large part due to the benchmark-overfitting. To better understand the advantages and shortcomings of existing SVG methods, in this work we carry out a fine-grained empirical analysis of three core components of SVG-based agents: (i) the gradient estimator formula, (ii) the model learning and (iii) the value function approximation. To this end, we extend previous work that proposes using Linear Quadratic Gaussian (LQG) regulator problems to benchmark SVG methods. LQG problems are heavily studied in optimal control literature and deliver challenging learning settings while still allowing comparison with ground-truth values. We use such problems to investigate the contribution of each core component of SVG methods to the overall performance. We focus our analysis on the model learning component, which was neglected from previous work, and we show that overfitting to on-policy data can lead to accurate state predictions but inaccurate gradients, highlighting the importance of exploration also in model-based methods.

关键词： Reinforcement learning Model-based reinforcement learning Optimal control Gradient optimization methods

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Interactive, visual-Learning based Tool for Hearing Impaired Children to Improve Language and Cognitive Skills 11

Interactive, Visual-Learning based Tool for Hearing Impaired...

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11th International conference on Information Technology, ICIT 2023

作者： Udbhasa, Saluka Lelkada, Parami Priyanka, Modeesha Senarathna, Esala Vidhanaarachchi, Samitha Wickramarathne, Jagath Wijekoon, Janaka L. Sri Lanka Institute of Information Technology Colombo Sri Lanka Military Technological College Muscat Oman

ISBN: (纸本)9798350320060

Hearing impairment is a common condition that affects millions of people worldwide, with approximately 1 in 1000 newborns experiencing some degree of hearing loss. This condition can significantly impact a person's quality of life, causing communication difficulties and social isolation. Early childhood hearing impairment can present significant challenges to a child's cognitive development, making it difficult for them to learn in a traditional educational setting. Consequently, there is an urge for effective learning tools that can assist hearing-impaired children in learning their first language. This paper introduces visual-based and interactive learning tools as a promising approach to enhancing the learning experience and engagement of hearing-impaired children. The proposed system utilizes machine learning to assess a child's initial status and subsequently generates adaptive content while continuously monitoring progress. Moreover, the system incorporates an object exploration feature that enables children to learn from their surroundings. Additionally, it employs natural language learning processes to present contextually similar content, supported by audio and lip movement analysis features that guide correct pronunciation. In response to the ML models utilized, object detection was accomplished through YOLO5, while lip movements were analyzed using LipNet. Regression models were deployed for assessing a child's initial status and subsequent progress. The data collection process involved comprehensive sources that covered a wide array of visual, auditory, and linguistic elements, with the dataset being rigorously tested and verified. Testing implementations were conducted in various real-world settings, encompassing diverse environmental factors and learning conditions. The YOLO model demonstrated an accuracy of 90.3%, LipNet achieved 92.2% accuracy in lip movement analysis, and the multivariate regression models showed a prediction accuracy of 92.8% in ev

关键词： Object detection

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EuroVis 2022 - 24th Eurographics conference on visualization 2022, Short Papers

EuroVis 2022 - 24th Eurographics Conference on Visualization...

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24th Eurographics conference on visualization, EuroVis 2022

ISBN: (纸本)9783038681847

The proceedings contain 21 papers. The topics discussed include: evaluating countable texture elements to represent bathymetric uncertainty;how effective are uni- and multivariate typographic encodings? studying the usage of fontweight, oblique angle, and spacing;inferential tasks as an evaluation technique for visualization;face-based glyphs revisited;the effect of graph layout on the perception of graph density: an empirical study;metaphoric maps for dynamic vertex-weighted graphs;augmented intelligence with interactive Voronoi treemap for scalable grouping: a usage scenario with wearable data;blocks: creating rich tables with drag-and-drop interaction;towards multimodal exploratory data analysis: SoniScope as a prototypical implementation;data+Shift: supporting visual investigation of data distribution shifts by data scientists;explaining black box with visual exploration of latent space;DASH: visual analytics for debiasing image classification via user-driven synthetic data augmentation;and a design study of visualizing historical book movement.

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