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25th International Arab Conference on Information Technology, ACIT 2024

作者： Moubayed, Abdallah Injadat, MohammadNoor Kanan, Mohammad King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Intelligent Secure Systems Computer Engineering Department Dhahran Saudi Arabia Zarqa University Data Science & Artificial Intelligence Department Zarqa Jordan University of Business and Technology Department of Industrial Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

The eye-mind hypothesis suggests that people tend to look at what they're actively thinking about, forming the basis of eye and gaze tracking. This concept is gaining attention in deep learning due to its broad applications. The use of AI alongside webcams to monitor eye movements is increasingly popular and expected to grow further. This is further emphasized by recent data showing a growing use of eye gaze estimation techniques, especially in marketing research, e-commerce, and educational tools. Accordingly, multiple previous research works have developed various eye and gaze estimation and tracking models. However, one main limitation is that many models use their own datasets for performance evaluation as well as having different underlying computing resources that are used during training. Consequently, it becomes harder to compare the effectiveness and efficiency of these models. To that end, this work aims at providing a comprehensive comparative study of three well-established eye gaze estimation models, namely OpenGaze, GazeRefineNet, ODABE, and FAZE models using a unified evaluation framework. Experimental results conducted using GazeCapture dataset illustrate that OpenGaze model achieves a mean error of 2.27 cm mean error, GazeRefineNet model achieves 1.91 cm, ODABE model achieves 3.46 cm, and FAZE model achieves 2.9 cm. This indicates that GazeRefineNet outperforms the other models in terms of accuracy while having comparable computational complexity. © 2024 IEEE.

关键词： Market research

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ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations 25

ZuantuSet: A Collection of Historical Chinese Visualizations...

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2025 CHI Conference on Human Factors in Computing systems, CHI 2025

作者： Mei, Xiyao Zhang, Yu Yang, Chaofan Shi, Rui Yuan, Xiaoru Peking University Beijing China University of Oxford Oxford United Kingdom National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Department of Computer Science University of Oxford United Kingdom Center for Research on Ancient Chinese History Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China PKU-WUHAN Institute for Artificial Intelligence China

ISBN: (纸本)9798400713941

Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Flow visualization

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Enhancing Pneumonia Diagnosis Using a Ensemble Transformer-Based Attention Network for Chest X-Ray Image Analysis 2

Enhancing Pneumonia Diagnosis Using a Ensemble Transformer-B...

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2nd International Conference on Big data and Privacy Computing, BDPC 2024

作者： Song, Yangyi Ren, Hao Jing, Fengshi He, Chaocheng Xie, Yewei Zhou, Jiandong City University of Macau Faculty of Data Science China Institute for Healthcare Artificial Intelligence Application Guangdong Second Provincial General Hospital Guangzhou China School of Information Management Wuhan University Wuhan China Duke-NUS Medical School Singapore Programme in Health Services & Systems Research Singapore Warwick Medical School University of Warwick Coventry United Kingdom

ISBN: (纸本)9798350307597

The intersection of artificial intelligence (AI) and medical imaging has significantly advanced the diagnosis of pneumonia, a leading cause of mortality worldwide. This paper presents a novel ensemble transformer-based attention network architecture that enhances the detection of pneumonia in chest X-ray images, leveraging the comprehensive Chest X-ray14 dataset. Our model integrates the strengths of GoogleNet, DenseNet, and MobileNet, refined by a sophisticated attention mechanism, yielding a robust framework for medical diagnosis. In a comparative study against established models such as GoogleNet, ResNet50, DenseNet, p-ResNet, and MobileNet, our approach demonstrated superior diagnostic performance, particularly for pneumonia, with an AVC of 91.3. The proposed model also showed exceptional capabilities in identifying other thoracic pathologies like Cardiomegaly, Edema, and Emphysema, with competitive performance in Effusion and Mass detection. These results not only set a new benchmark in the field but also suggest the transformative potential of AI-enhanced diagnostic systems for timely and accurate pneumonia detection, paving the way for their integration into clinical workflows and the advancement of precision medicine. © 2024 IEEE.

关键词： Medical imaging

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Federated privacy computing based on oblivious transfer in financial applications: A case study in credit risk management 3

Federated privacy computing based on oblivious transfer in f...

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3rd International Symposium on Computer applications and Information systems, ISCAIS 2024

作者： Liang, Jian Quan, Fan Yang, Suyi Tao, Ping Ge, Fei Li, Hualin School of Software and Artificial Intelligence Chongqing Institute of Engineering Chongqing China Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center Chongqing China Shu Yi Xin Credit Management Co LTD Chongqing China School of Information and Communication Studies University College Dublin Dublin Ireland Department of Statistics and Data Science University of California Los Angeles United States

ISBN: (纸本)9781510681316

Invoice data is critical and highly sensitive for enterprises, yet it stands as one of the most extensively used data categories in the financial credit sector. Participants in the credit market, engaged in corporate lending activities, face the dual challenge of ensuring the security of enterprise invoice data and safeguarding the integrity of institutional rules. Traditional data computation and encryption present a dilemma, as two encrypted datasets cannot directly engage in calculations, and two datasets participating in calculations must be in plaintext. This necessitates the convergence of enterprise data and institutional rules for computation. Consequently, two sets of data must be exposed to one of the parties or a mutually trusted third party. In practice, identifying a genuinely trusted third party proves to be a formidable task. This paper introduces a federated privacy computing method based on an oblivious transfer algorithm in the context of risk control admission rules within the credit risk management field. This approach enables both parties holding data to determine admission rules without disclosing their own data. © 2024 SPIE.

关键词： Risk management

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A Scalable Reinforcement Learning Approach for Attack Allocation in Swarm to Swarm Engagement Problems

arXiv

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arXiv 2022年

作者： Demir, Umut Ure, Nazim Kemal Istanbul Technical University Artificial Intelligence and Data Science Application and Research Center Istanbul Turkey

In this work we propose a reinforcement learning (RL) framework that controls the density of a large-scale swarm for engaging with adversarial swarm attacks. Although there is a significant amount of existing work in applying artificial intelligence methods to swarm control, analysis of interactions between two adversarial swarms is a rather understudied area. Most of the existing work in this subject develop strategies by making hard assumptions regarding the strategy and dynamics of the adversarial swarm. Our main contribution is the formulation of the swarm to swarm engagement problem as a Markov Decision Process and development of RL algorithms that can compute engagement strategies without the knowledge of strategy/dynamics of the adversarial swarm. Simulation results show that the developed framework can handle a wide array of large-scale engagement scenarios in an efficient manner. © 2022, CC BY.

关键词： Markov processes

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Scalable Planning and Learning Framework Development for Swarm-to-Swarm Engagement Problems

arXiv

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arXiv 2022年

作者： Demir, Umut Satir, A. Sadik Sever, Gulay Goktas Yikilmaz, Cansu Ure, N. Kemal Istanbul Technical University Artificial Intelligence and Data Science Application and Research Center Istanbul Turkey

Development of guidance, navigation and control frameworks/algorithms for swarms attracted significant attention in recent years. That being said, algorithms for planning swarm allocations/trajectories for engaging with enemy swarms is largely an understudied problem. Although small-scale scenarios can be addressed with tools from differential game theory, existing approaches fail to scale for large-scale multi-agent pursuit evasion (PE) scenarios. In this work, we propose a reinforcement learning (RL) based framework to decompose to large-scale swarm engagement problems into a number of independent multi-agent pursuit-evasion games. We simulate a variety of multi-agent PE scenarios, where finite time capture is guaranteed under certain conditions. The calculated PE statistics are provided as a reward signal to the high level allocation layer, which uses an RL algorithm to allocate controlled swarm units to eliminate enemy swarm units with maximum efficiency. We verify our approach in large-scale swarm-to-swarm engagement simulations. © 2022, CC BY.

关键词： Reinforcement learning

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Optimizing One-Stage Object Detection for URLLC: A Two-Stage Pipeline with Oculi SPU 25

Optimizing One-Stage Object Detection for URLLC: A Two-Stage...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Moghlebey, Adam Bou Moubayed, Abdallah Injadat, MohammadNoor Lebanese American University Electrical & Computer Engineering Department Byblos Lebanon King Fahd University of Petroleum & Minerals Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia Zarqa University Data Science & Artificial Intelligence Department Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Pedestrian-related accidents account for an estimated 20-25% of the approximately 1.19 million road fatalities occurring annually, highlighting the urgent need for enhanced detection systems in surveillance and smart transportation to mitigate such incidents. The escalating demand for Ultra-Reliable Low-Latency Communications (URLLC) in modern surveillance and transportation systems has become a solution to that problem but highlighted significant challenges in reducing inference times of machine learning models, particularly on constrained hardware. The maximum permissible latency for most vehicular applications ranges from 6 to 20 milliseconds based on the application. When tested on an 8GB RAM setup using the NTU, LLVIP, and VIRAT datasets, YOLOv8n achieved a minimum inference time of approximately 200 milliseconds per frame, making it unsuitable for URLLC applications despite 8GB RAM being an adequate hardware setup for edge devices. This research introduces a new two-stage pipeline that integrates Oculi's Sensing and Processing Unit (SPU) simulation with the YOLOv8n one-stage object detection model to enhance pedestrian detection, thereby addressing these challenges. By leveraging Regions of Interest (ROIs) generated by Oculi's SPU, our approach optimizes the input data for the YOLO model, substantially reducing computational overhead and thus making it suitable for URLLC applications. This integration can lead to a reduction in inference time by up to 93% in the tested frames when being compared to YOLOv8n pre-trained model. © 2024 IEEE.

关键词： Digital storage

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Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms

Self-Improving Safety Performance of Reinforcement Learning ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Resul Dagdanov Halil Durmus Nazim Kemal Ure Department of Aeronautical Engineering ITU Artificial Intelligence and Data Science Research Center Istanbul Technical University Turkey Department of Electronics and Communication Engineering Eatron Technologies Istanbul Technical University Turkey Department of Computer Engineering ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Turkey

In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods. RL algorithms have become popular in AD applications in recent years. However, the performance of existing RL algorithms heavily depends on the diversity of training scenarios. A lack of safety-critical scenarios during the training phase could result in poor generalization performance in real-world driving applications. We propose a novel framework in which the weaknesses of the training set are explored through black-box verification methods. After discovering AD failure scenarios, the RL agent's training is re-initiated via transfer learning to improve the performance of previously unsafe scenarios. Simulation results demonstrate that our approach efficiently discovers safety failures of action decisions in RL-based adaptive cruise control (ACC) applications and significantly reduces the number of vehicle collisions through iterative applications of our method. The source code is publicly available at https://***/data-and-decision-lab/self-improving-RL.

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Towards understanding structure–property relations in materials with interpretable deep learning

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npj Computational Materials 2023年第1期9卷 157-168页

作者： Tien-Sinh Vu Minh-Quyet Ha Duong-Nguyen Nguyen Viet-Cuong Nguyen Yukihiro Abe Truyen Tran Huan Tran Hiori Kino Takashi Miyake Koji Tsuda Hieu-Chi Dam Japan Advanced Institute of Science and Technology NomiIshikawaJapan HPC SYSTEMS Inc. MinatoTokyoJapan Applied Artificial Intelligence Institute Deakin UniversityGeelongVICAustralia School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGAUSA Research and Services Division of Materials Data and Integrated System National Institute for Materials ScienceTsukubaIbarakiJapan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and TechnologyTsukubaIbarakiJapan Department of Computational Biology and Medical Sciences Graduate School of Frontier SciencesUniversity of TokyoKashiwa-shiChiba-kenJapan International Center for Synchrotron Radiation Innovation Smart(SRIS) Tohoku UniversityAoba-kuSendaiJapan

Deep learning(DL)models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material *** address these limitations,we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property *** proposed architecture is evaluated using two well-known datasets(the QM9 and the Materials Project datasets),and three in-house-developed computational materials ***–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities,which are comparable to those of current state-of-the-art ***,comparative validations,based on first-principles calculations,indicate that the degree of attention of the atoms’local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical *** properties encompass molecular orbital energies and the formation energies of *** proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

关键词： properties property structure

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An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control With Limited Pilot Demonstration data

SSRN

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SSRN 2023年

作者： Sever, Gulay Goktas Demir, Umut Satir, Abdullah Sadik Sahin, Mustafa Cagatay Ure, Nazım Kemal Istanbul Technical University Artificial Intelligence and Data Science Application and Research Center Istanbul34467 Turkey Turkish Aerospace Industries Inc. Ankara06980 Turkey

In this paper, we present a methodology for constructing data-driven maneuver generation models for agile aircraft that can generalize across a wide range of trim conditions and aircraft model parameters. Maneuver generation models play a crucial role in the testing and evaluation of aircraft prototypes, providing insights into the maneuverability and agility of the aircraft. However, constructing the models typically requires extensive amounts of real pilot data, which can be time-consuming and costly to obtain. Moreover, models built with limited data often struggle to generalize beyond the specific flight conditions covered in the original dataset. To address these challenges, we propose a hybrid architecture that leverages a simulation model, referred to as the source model. This open-source agile aircraft simulator shares similar dynamics with the target aircraft and allows us to generate unlimited data for building a proxy maneuver generation model. We then fine-tune this model to the target aircraft using a limited amount of real pilot data. Our approach combines techniques from imitation learning, transfer learning, and reinforcement learning to achieve this objective. To validate our methodology, we utilize real agile pilot data provided by Turkish Aerospace Industries (TAI). By employing the F-16 as the source model, we demonstrate that it is possible to construct a maneuver generation model that generalizes across various trim conditions and aircraft parameters without requiring any additional real pilot data. Our results showcase the effectiveness of our approach in developing robust and adaptable models for agile aircraft. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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