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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： computer vision

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Effects of an automated programming assessment system on the learning performances of experienced and novice learners

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Interactive Learning Environments 2023年第8期31卷 5347-5363页

作者： Cheng, Li-Chen Li, Wei Tseng, Judy C. R. Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan STEM Education Research Center Wenzhou University Zhejiang Province Wenzhou China Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan

Programming ability is the core ability of this era and can be obtained and improved through practice. In this paper, an Automated Programming Assessment system based on Mastery learning and Peer competition (APAMP) was proposed and developed. APAMP allows students to practice repeatedly by providing immediate feedback after their programs are submitted. It also presents an analytical dashboard as a competition mechanism for students to visualize their learning performance and compare their performance with peers. By incorporating APAMP into programming courses, students can master programming skills through repeated practice, and their enthusiasm for learning programming can also be encouraged by peer competition. To evaluate the effects of APAMP on the learning performance of students with programming language learning and novice students, a quasi-experiment was conducted in a high school. The experimental results showed that the learning achievement of the two groups of students improved significantly. Moreover, the learning attitude of students in the experienced group improved significantly, with the experienced students benefiting more from the system than the novice students. In fact, the novice students showed a significant decline in learning attitude and learning motivation, which was contrary to our intuition. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： automated programming assessment systems learning performance mastery learning peer competition Programming instruction

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PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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ChestBox: Enabling Fast State Sharing for Stateful Serverless Computing with State Functions

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第03期10卷 491-502页

作者： Zhang, Xinmin Wu, Song Gu, Lin He, Qiang Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan Hubei430074 China

This paper presents ChestBox, a novel approach that utilizes state functions to facilitate low-latency state sharing for stateful serverless computing. When an application function needs to share a state, the state function creates a memory space with Linux's shared memory object to store the state. Other application functions can then read the state directly from the shared memory. ChestBox enables fast state sharing that avoids excessive memory overhead without compromising on-demand resource allocation compared to existing solutions. This effectively reduces the energy consumption of serverless computing and promotes sustainable computing. The implementation of ChestBox on Apache OpenWhisk unearths two major implementation challenges, which we address with respective optimization techniques, i.e., state function channel and state swapping. The evaluation of ChestBox with four real-world applications shows that compared with the state-of-The-Art approach, it can reduce state-sharing latency by up to 99.71%, while reducing execution costs by 24.59% and storage costs by 99.76%. © 2016 IEEE.

关键词： Storage allocation (computer)

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Processor Vulnerability Discovery 23

Processor Vulnerability Discovery

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Yongqiang Lyu Rihui Sun Gang Qu National Research Center for Information Science and Technology Tsinghua University Beijing China Department of Computer scienece and technology Harbin Institue of Technology Harbin China Department of Electrical and Computer Engineering University of Maryland College Park Maryland USA

ISBN: (纸本)9798350323481

Processor security vulnerability discovery has drawn increasing attention since the disclosure of Meltdown, Spectre and other vulnerabilities. This paper presents a concise roadmap of this emerging research direction from the simple manual discovery to automated discovery methodologies, as well as the major challenges along the roadmap.

关键词： processor vulnerability

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VMINer: Versatile Multi-view Inverse Rendering with Near-and Far-field Light Sources

VMINer: Versatile Multi-view Inverse Rendering with Near-and...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Fan Fei Jiajun Tang Ping Tan Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University Light Illusions Hong Kong University of Science and Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper introduces a versatile multi-view inverse rendering framework with near-and far-field light sources. Tackling the fundamental challenge of inherent ambiguity in inverse rendering, our framework adopts a lightweight yet inclusive lighting model for different near-and far-field lights, thus is able to make use of input images under varied lighting conditions available during capture. It leverages observations under each lighting to disentangle the intrinsic geometry and material from the external lighting, using both neural radiance field rendering and physically-based surface rendering on the 3D implicit fields. After training, the reconstructed scene is extracted to a textured triangle mesh for seamless integration into industrial rendering soft-ware for various applications. Quantitatively and qualitatively tested on synthetic and real-world scenes, our method shows superiority to state-of-the-art multi-view inverse rendering methods in both speed and quality.

关键词： Geometry Training Surface reconstruction Three-dimensional displays Lighting Rendering (computer graphics) Neural radiance field

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AegonKV: a high bandwidth, low tail latency, and low storage cost KV-separated LSM store with SmartSSD-based GC offloading 25

AegonKV: a high bandwidth, low tail latency, and low storage...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Zhuohui Duan Hao Feng Haikun Liu Xiaofei Liao Hai Jin Bangyu Li National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133458

The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of Value region, especially for garbage collection (GC) operation that is used to reduce the redundant space occupation. In response, many efforts have been made to optimize the GC mechanism for KV separation. However, our analysis indicates that such solution based on trade-offs between CPU and I/O overheads cannot simultaneously satisfy the three requirements of KV separated systems in terms of throughput, tail latency, and space usage. This limitation hinders their real-world *** this paper, we introduce AegonKV, a "three-birds-one-stone" solution that comprehensively enhances the throughput, tail latency, and space usage of KV separated systems. AegonKV first proposes a SmartSSD-based GC offloading mechanism to enable asynchronous GC operations without competing with LSM read/write for bandwidth or CPU. AegonKV leverages offload-friendly data structures and hardware/ software execution logic to address the challenges of GC offloading. Experiments demonstrate that AegonKV achieves the largest throughput improvement of 1.28-3.3 times, a significant reduction of 37%-66% in tail latency, and 15%-85% in space overhead compared to existing KV separated systems.

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Uncertainty analysis of turbulence model in capturing flows involving laminarization and retransition

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Chinese Journal of Aeronautics 2022年第10期35卷 148-164页

作者： Hongkang LIU Shishang ZHANG Yong ZOU Wu YUAN Tanghong LIU Yatian ZHAO Key Laboratory of Traffic Safety on Track of Ministry of Education School of Traffic&Transportation EngineeringCentral South UniversityChangsha 410075China Joint International Research Laboratory of Key Technology for Rail Traffic Safety Central South UniversityChangsha 410075China National&Local Joint Engineering Research Center of Safety Technology for Rail Vehicle Central South UniversityChangsha 410075China Computer Network Information Center Chinese Academy of SciencesBeijing 100190China School of Aeronautics and Astronautics Central South UniversityChangsha 410083China

Flows experiencing laminarization and retransition are universal and crucial in many engineering *** objective of this study is to conduct an uncertainty quantification and sensitivity analysis of turbulence model closure coefficients in capturing laminarization and retransition for a rapidly contracting channel ***,two commonly used turbulence models are considered:the Spalart-Allmaras(SA)one-equation model and the Menter Shear Stress Transport(SST)two-equation ***,a series of steady Reynolds Averaged Navier-Stokes(RANS)predictions of aero-engine intake acceleration scenarios are carried out with the purposely designed turbulence model closure *** a result,both SA and SST models fail to capture the retransition phenomenon though they achieve pretty good performance in *** the non-intrusive polynomial chaos method,solution uncertainties in velocity,pressure,and surface friction are quantified and analyzed,which reveals that the SST model possesses much great uncertainty in the non-laminar regime,especially for the logarithmic law ***,a sensitivity analysis is performed to identify the critical contributors to the solution uncertainty,and then the correlations between the closure coefficients and the deviations of the outputs of interest are obtained via the linear regression *** results indicate that the diffusion-related constants are the dominant uncertainty contributors for both SA and SST ***,the remarkably strong correlation between the critical closure coefficients and the outputs might be a good guide to recalibrate and even optimize the commonly used turbulence models.

关键词： Laminarization Retransition Reynolds-averaged NavierStokes simulation Turbulence modeling Uncertainty analysis

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Blockchain integration in big data: Review, vision, and opportunities

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Security and Privacy 2024年第5期7卷

作者： Bakhtiary, Vahid Rahmani, Amir Masoud Department of Computer Engineering Faculty of Engineering Islamic Azad University South Tehran Branch Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan

Big data has attracted extensive attention from industries and universities during the past few years. Big data is crucial in many fields, such as business analytics, healthcare, the Internet of Things (IoT), smart home, supply chain, transportation, and fraud detection. Nevertheless, some challenges must be addressed, such as decentralization, integration, data sharing, privacy, and security. On the other hand, blockchain has numerous potential for improving big data services and applications because of its decentralized, verifiable, and anti-tamper features. We apply the Systematic Literature Review (SLR) approach in this study to investigate blockchain integrity in big data and understand the different topics and significant areas already presented. This paper aims to analyze studies on blockchain integration in big data published between 2017 and 2022. A technical taxonomy is presented for blockchain integration into big data, including data storage, security, and applications based on the field of articles selected by the SLR method. Finally, the achievements and shortcomings of each study are discussed, and future research challenges and open issues related to blockchain integration in big data are highlighted. © 2024 John Wiley & Sons Ltd.

关键词： Internet of things

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LuminAIRe: illumination-aware conditional image repainting for lighting-realistic generation 23

LuminAIRe: illumination-aware conditional image repainting f...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jiajun Tang Haofeng Zhong Shuchen Weng Boxin Shi National Key Laboratory for Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University

We present the ilLumination-Aware conditional Image Repainting (LuminAIRe) task to address the unrealistic lighting effects in recent conditional image repainting (CIR) methods. The environment lighting and 3D geometry conditions are explicitly estimated from given background images and parsing masks using a parametric lighting representation and learning-based priors. These 3D conditions are then converted into illumination images through the proposed physically-based illumination rendering and illumination attention module. With the injection of illumination images, physically-correct lighting information is fed into the lighting-realistic generation process and repainted images with harmonized lighting effects in both foreground and background regions can be acquired, whose superiority over the results of state-of-the-art methods is confirmed through extensive experiments. For facilitating and validating the LuminAIRe task, a new dataset CAR-LUMINAIRE with lighting annotations and rich appearance variants is collected.

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