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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data science and systems, HPCC/SmartCity/DSS 2019

作者： Hu, Qiqi Chen, Jianxi Feng, Dan Yang, Qing Liu, Bo Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan China Hikstor Technology Co. LTD. Zhejiang China

ISBN: (纸本)9781728120584

Since the number of NAND flash write cycles reaches the upper limit, data cannot be reliably stored. Therefore, the lifespan of Solid State Disks (SSDs) is limited, making it impossible to become a reliable storage device in a write-intensive data center. Deduplication can effectively reduce write traffic and increase the lifespan of SSDs. However, the fingerprint calculation overhead in the traditional data deduplication scheme is too large, which will reduce the write performance of the SSDs. In this paper, we propose a new deduplication scheme D-pblk based on Open-Channel SSDs(OCSSDs), which can reduce fingerprint computing overhead with modern high performance CPUs. In D-pblk, we use the light-weight hash CRC32 to filter most of the non-duplicated data, and When CRC32 is first hit, we read the data from Open-Channel SSDs and calculate the heavy-weight hash SHA-1 for accurate comparison, which reducing fingerprint computing overhead and hardly affects the deduplication rate of workloads. And we use multi-thread to calculate fingerprints in parallel, further reducing fingerprint computing overhead. In addition, we use double ring buffers to solve the problem that the data size after deduplication may not match the size of a flash page. We use FEMU to simulate Open-Channel SSDs for evaluation. The experimental results show that D-pblk can achieve the deduplication rate ranging from 4.61% to 31.63% and the write latency is improved by a factor of up to 29%. © 2019 IEEE.

关键词： Program processors

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Passenger BIBO detection with IoT support and machine learning techniques for intelligent transport systems 24

Passenger BIBO detection with IoT support and machine learni...

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24th KES International Conference on Knowledge-Based and Intelligent information and engineering systems, KES 2020

作者： Mastalerz, Marcin W. Malinowski, Aleksander Kwiatkowski, Slawomir sniegula, Anna Wieczorek, Bartosz University of Szczecin Department of Computer Science in Management Cukrowa 8 Szczecin71-004 Poland Bradley University Department of Electrical and Computer Engineering 1501 W Bradley Ave PeoriaIL61625 United States Asseco Data Systems S.A. St. Podolska 21 Gdynia81-321 Poland University of Lodz Department of Computer Science in Economics St. POW 3/5 Lódz90-255 Poland Lodz University of Technology Institute of Information Technology St. Wólczanska 215 Lodz90-924 Poland

The present article discusses the issue of automation of the CICO (Check-In/Check-Out) process for public transport fare collection systems, using modern tools forming part of the Internet of Things, such as Beacon and Smartphone. It describes the concept of an integrated passenger identification model applying machine learning technology in order to reduce or eliminate the risks associated with the incorrect classification of a smartphone user as a vehicle passenger. This will allow for the construction of an intelligent fare collection system, operating in the BIBO (Be-In/Be-Out) model, implementing the "hands-free" and "pay-as-you-go" approach. The article describes the architecture of the research environment, and the implementation of the elaborated model in the ***4 proprietary solution. We also presented the complete process of concept verification under real-life conditions. Research results were described and supplemented with commentary. © 2020 The Authors. Published by Elsevier B.V.

关键词： Smartphones

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Improving the performance of AgCdF 3 -based perovskite solar cells using machine learning-driven adjustment of active layer and charge transport materials with SCAPS-1D

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Inorganic Chemistry Communications 2025年 179卷

作者： Avijit Ghosh Al Shahriar Zishan Mahbuba Moumita Yedluri Anil Kumar Anup Kumar Roy Siful Islam Shahan Ahmed Hasanur Zaman Anonto Asma Tabassum Happy Md Shameem Ahsan H.A. Alrafai Abeer A. Hassan Department of Electrical and Electronic Engineering Begum Rokeya University Rangpur 5400 Bangladesh Department of Electrical and Electronics Engineering American International University – Bangladesh 408/1 Kuratoli Road Kuril Dhaka 1229 Bangladesh Department of Mathematics Lamar University Beaumont TX 77705 USA Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 602105 Tamil Nadu India Phillip M. Drayer Department of Electrical and Computer Engineering Lamar University TX USA Master of Science in Management Information Systems College of Business Lamar University TX USA Master of Arts in Social Research & Data Analysis Montclair State University NJ USA Department of Industrial and Systems Engineering Lamar University Beaumont TX 77710 USA Department of Chemistry Faculty of Science King Khalid University PO Box 9004 Abha 61413 Saudi Arabia

This study investigates the possibility of combining inorganic substances—which act as absorbers, HTLs) in order to produce stable, effective PSCs. A ZnSe ETL, HTLs (MoS 2 , MoTe 2 , and CuI), and an AgCdF 3 perovskite absorber, comprise the suggested architecture for this study. This study examines the impact of metal contacts, doping density, interface defect densities, absorber thickness, HTL and ETL layers, and defect density on the output of a solar device using the SCAPS-1D model. The investigation of how temperature impacts the performance of the proposed PSC is another important aspect of the study. By integrating the MoS 2 /MoTe 2 /CuI layers as an HTL with the studied structure of Al/FTO/ZnSe/AgCdF 3 /HTL/Ni, the V OC climbed to 0.834/0.910/0.969 V, the J SC to 42.122/42.862/42.885 mA cm −2 , the PCE to 30.31/34.05/36.06 %, and the FF to 86.21/87.23/86.69 %. A machine learning model was created to forecast the solar cells’ performance metrics. The model precisely evaluates the importance of each parameter by utilizing SHAP values, providing important insights into their contributions. With an accuracy rate of around 83.75 %, machine learning predicted the performance matrix of the optimal solar cell under investigation. An affordable AgCdF 3 solar cell may be developed as a result of this study’s significant findings and practical design.

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DeepKeyGen: A deep learning-based stream cipher generator for medical image encryption and decryption

arXiv

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arXiv 2020年

作者： Ding, Yi Tan, Fuyuan Qin, Zhen Cao, Mingsheng Choo, Kim-Kwang Raymond Qin, Zhiguang Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China ChengduSichuan610054 China Institute of Electronic and Information Engineering of UESTC in Guangdong Guangdong523808 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China ChengduSichuan610054 China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249-0631 United States

The need for medical image encryption is increasingly pronounced, for example to safeguard the privacy of the patients’ medical imaging data. In this paper, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepKeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the "style" of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepKeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepKeyGen using three datasets, namely: the Montgomery County chest X-ray dataset, the Ultrasonic Brachial Plexus dataset, and the BraTS18 dataset. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Robot Hand Interaction Using Plastic Deformation Control with Inner Position Loop

Robot Hand Interaction Using Plastic Deformation Control wit...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Kenichi Murakami Koki Ishimoto Taku Senoo Masatoshi Ishikawa Data Science Research Division Information Technology Center The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Department of Information Physics and Computing The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Electrical Systems and Control Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima City Hiroshima Japan

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

In this study, the inner position loop addition for plastic deformation control of the error robustness of the model and actual parameters is proposed. The inner position loop is a type of position feedback control based on the estimation of model parameters and dynamics of robots. This position control system with proportional and derivative gains modifies the trajectory of the end effector close to the desired trajectory. The robustness of the proposed control scheme is evaluated by comparing it with plastic deformation control without an inner position loop in both simulation and actual environments. Finally, the stable reaction with the proposed control is demonstrated for the case where a human pushes the robot finger.

关键词： Strain Plastics Force Deformable models Robots Friction Trajectory

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Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross Correlation for Fast and Accurate Stereo Matching on Embedded GPU

Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross C...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Qiong Chang Aolong Zha Weimin Wang Xin Liu Masaki Onishi Tsutomu Maruyama Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory Tokyo Japan Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9781728197104

ISBN: (纸本)9781728197111

Mobile stereo matching systems are becoming more important in many applications such as auto-driving and autonomous robots. However, to maintain its low power consumption, mobile platforms have only limited hardware resources. Accurate stereo matching methods require a high computational complexity, and it is difficult to maintain both acceptable accuracy and processing speed on the mobile platforms. To solve this trade-off, in this paper, we propose a novel acceleration approach for a well-known matching algorithm Zero-means Normalized Cross Correlation (ZNCC), and show its effectiveness on a Jetson TX2 embedded GPU. By combining our new approach, Z 2 - ZNCC, with the Semi-Global Matching (SGM) algorithm, our system achieves a low error rate of 7.76% while keeping 28 fps for 1242×375 pixels images with the maximum disparity of 128 on the KITTI 2015 dataset. This performance is higher than previous state-of-the-art system on the same hardware platform.

关键词： Correlation Power demand Graphics processing units Hardware System-on-chip Acceleration Field programmable gate arrays

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Detecting fraud in adversarial environments: A reinforcement learning approach

Detecting fraud in adversarial environments: A reinforcement...

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2018 systems and information engineering Design Symposium, SIEDS 2018

作者： Mead, Adrian Lewris, Tyler Prasanth, Sai Adams, Stephen Alonzi, Peter Beling, Peter Data Science Institute University of Virginia United States Systems and Information Engineering University of Virginia United States Research Data Services University of Virginia United States Department of Systems and Information Engineering University of Virginia United States

ISBN: (纸本)9781538663431

Credit card fraud is a costly problem for banks and a major frustration for consumers. As such, static models to detect fraud that rely on supervised training are exposed to the risk of being learned and circumvented. Previous adversarial learning work in fraud prevention showed increased effectiveness over static models that did not account for changing fraudster behavior. We extend this work by utilizing Reinforcement Learning and framing the fraudster and card issuer interaction as a Markov Decision Process (MDP) and performing prediction and control. Our MDP takes on the perspective of an agent (in this case the fraudster with a stolen credit card) who interacts with an environment (merchants and a fraud classifier), by taking actions (transactions), and receiving rewards (relating to whether the transactions were successful/declined). This approach allows us to simulate fraudulent episodes in such a way that techniques like model-free policy iteration can identify an optimal policy for the fraudster. The episode ends when the card is terminated by the credit card company for fraud. We found that, compared to a static classifier, making small changes to our fraud classifier on a regular basis led to a significant decrease in the ability of a fraud agent to learn an optimal policy. © 2018 IEEE.

关键词： Reinforcement learning

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Multivariate multi-horizon time-series forecasting for real-time patient monitoring based on cascaded fine tuning of attention-based models

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Computers in Biology and Medicine 2025年 194卷 110406页

作者： Saleh, Hager El-Sappagh, Shaker McCann, Michael Alsamhi, Saeed Hamood Breslin, John G. Insight Research Ireland Centre for Data Analytics School of Engineering University of Galway University Road GalwayH91 TK33 Ireland Atlantic Technological University Letterkenny Donegal Ireland Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt Department of Computing Atlantic Technological University Letterkenny Donegal Ireland Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Department of Electronics Engineering IBB University Ibb Ibb Yemen

The real-time forecasting of critical physiological indicators in intensive care units (ICUs) is essential for early intervention and clinical decision support. This study introduces a novel framework, StreamHealth Multi-Horizon AI, which has been designed to perform multivariate, multi-horizon time-series forecasting for vital signs, specifically for a person's blood oxygen saturation level (SpO2) and respiratory rate (RR). The framework leverages advanced attention-based models, with a particular emphasis on the Temporal Fusion Transformer (TFT) and Temporal Convolutional Network (TCN), and we benchmark its performance against classical deep learning architectures, including LSTM, GRU, Bi-LSTM, Bi-GRU, CNN, and Sequence-to-Sequence (Seq2Seq) models with and without attention mechanisms. Both univariate and multivariate forecasting tasks are explored across multiple prediction horizons (i.e., 7, 15 and 25 min), using physiological time-series data from the MIMIC-III database. The proposed system incorporates a cascaded fine-tuning strategy, wherein the TFT model is sequentially fine-tuned on individual patients’ data, significantly enhancing the model's generalizability to unseen patient profiles. Empirical results demonstrate that the TFT model consistently outperforms baseline models across all forecasting settings, achieving lower RMSE and MAE values, and exhibiting superior capacity for capturing long-sequence dependencies and temporal feature dynamics. To validate its applicability in real-time clinical environments, the framework integrates a simulated streaming infrastructure using Apache Kafka and Apache Flink, enabling continuous data ingestion, forecasting, and visualization of vital signs. This end-to-end deployment underscores the system's potential for ICU monitoring, allowing clinicians to anticipate patient deterioration proactively. In summary, we introduce a comprehensive framework that uniquely integrates TFT with cascaded fine-tuning for multivar

关键词： Electrotherapeutics

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DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping

DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NL...

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IEEE International Symposium on Innovations in Intelligent systems and Applications (INISTA)

作者： Kareem Amin Stelios Kapetanakis Nikolaos Polatidis Klaus-Dieter Althoff Andreas Dengel Smart Data and Knowledge Services German Research Center for Artificial Intelligence Technische Universität Kaiserslautern Kaiserslautern Germany School of Computing Engineering and Mathematics University of Brighton Brighton United Kingdom Intelligent Information Systems Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.

关键词： Machine learning Training Neural networks Natural language processing Cognition Buildings Task analysis

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