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Computational Intelligence and Neuroscience 2023年第1期2023卷

作者： Mohammad Jafari Sadr Seyedeh Leili Mirtaheri Sergio Greco Keivan Borna Department of Computer Science Faculty of Mathematical Sciences and Computer Kharazmi University Tehran Irantums.ac.ir Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Irankhu.ac.ir Department of Informatics Modeling Electronics and System Engineering University of Calabria Calabria Italyunical.it

Although “a picture is worth a thousand words,” this may not be enough to get your post seen on social media. This study’s main objective was to determine the best ways to characterize a photo in terms of viral marketing and public appealing. We have to obtain this dataset for this reason from the social media site such as Instagram. A total of 1.4 million hashtags were used in the 570,000 photos that we crawled. Prior to training the text generation module to produce such popular hashtags, we had to determine the components and features of the photo. We trained a multilabel image classification module using a ResNet neural network model for the first section. In order to create hashtags pertaining to their popularity, we trained a cutting-edge GPT-2 language model for the second portion. This work differs from others in that, and it initially offered a cutting-edge GPT-2 model for hashtag generation using a combination of the multilabel image classification module. The popularity issues and ways to make an Instagram post popular are also highlighted in our essay. Social science and marketing research can both be conducted on this subject. Which content can be considered popular from the perspective of consumers can be researched in the social science setting. As a marketing strategy, end users can help by offering such well-liked hashtags for social media accounts. This essay adds to the body of knowledge by demonstrating the two possible uses of popularity. Compared to the base model, our popular hashtag generating algorithm creates 11% more relevant, acceptable, and trending hashtags, according to the evaluation that was carried out.

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MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers

MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-...

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Supercomputing Conference

作者： Kiran Ranganath Joshua D. Suetterlein Joseph B. Manzano Shuaiwen Leon Song Daniel Wong Department of Electrical and Computer Engineering University of California Riverside CA USA High-Performance Computing Group Pacific Northwest National Lab WA USA Future System Architecture Lab School of Computer Science University of Sydney Sydney Australia

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

Multi-accelerator servers are increasingly being deployed in shared multi-tenant environments (such as in cloud data centers) in order to meet the demands of large-scale compute-intensive workloads. In addition, these accelerators are increasingly being inter-connected in complex topologies and workloads are exhibiting a wider variety of inter-accelerator communication patterns. However, existing allocation policies are ill-suited for these emerging use-cases. Specifically, this work identifies that multi-accelerator workloads are commonly fragmented leading to reduced bandwidth and increased latency for inter-accelerator communication. We propose Multi-Accelerator Pattern Allocation (MAPA), a graph pattern mining approach towards providing generalized allocation support for allocating multi-accelerator workloads on multi-accelerator servers. We demonstrate that MAPA is able to improve the execution time of multi-accelerator workloads and that MAPA is able to provide generalized benefits across various accelerator topologies. Finally, we demonstrate a speedup of 12.4% for 75th percentile of jobs with the worst case execution time reduced by up to 35% against baseline policy using MAPA.

关键词： Network topology Processor scheduling High performance computing Graphics processing units Throughput Hardware Topology

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Survey on Deep Face Restoration: From Non-blind to Blind and Beyond

arXiv

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arXiv 2023年

作者： Li, Wenjie Wang, Mei Zhang, Kai Li, Juncheng Li, Xiaoming Zhang, Yuhang Gao, Guangwei Deng, Weihong Lin, Chia-Wen The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China The Computer Vision Lab ETH Zürich Zürich Switzerland The School of Communication and Information Engineering Shanghai University Shanghai China The Nanyang Technological University Singapore The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan

Face restoration (FR) is a specialized field within image restoration that aims to recover low-quality (LQ) face images into high-quality (HQ) face images. Recent advances in deep learning technology have led to significant progress in FR methods. In this paper, we begin by examining the prevalent factors responsible for real-world LQ images and introduce degradation techniques used to synthesize LQ images. We also discuss notable benchmarks commonly utilized in the field. Next, we categorize FR methods based on different tasks and explain their evolution over time. Furthermore, we explore the various facial priors commonly utilized in the restoration process and discuss strategies to enhance their effectiveness. In the experimental section, we thoroughly evaluate the performance of state-of-the-art FR methods across various tasks using a unified benchmark. We analyze their performance from different perspectives. Finally, we discuss the challenges faced in the field of FR and propose potential directions for future advancements. The open-source repository corresponding to this work can be found at https://***/24wenjie-li/Awesome-Face-Restoration. Copyright © 2023, The Authors. All rights reserved.

关键词： Restoration

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A systematic Review of Multi-Sensor Information Fusion for Equipment Fault Diagnosis

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Lin, Tantao Ren, Zhijun Zhu, Linbo Zhu, Yongsheng Feng, Ke Ding, Weiping Yan, Ke Beer, Michael Xi'an Jiaotong University Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System Xi'an710049 China Xi'an Jiaotong University Frontier Institute of Science and Technology Xi'an710049 China Xi'an Jiaotong University School of School of Chemical Engineering and Technology Xi'an710049 China Xi'an Jiaotong University School of Mechanical Engineering Xi'an710049 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science Macau999078 China Leibniz University Institute for Risk and Reliability Hannover30167 Germany University of Liverpool Department of Civil and Environmental Engineering LiverpoolL69 3BX United Kingdom Tongji University International Joint Research Center for Resilient Infrastructure International Joint Research Center for Engineering Reliability and Stochastic Mechanics Shanghai200092 China

In contrast to fault diagnosis relying solely on a single sensor, the method of multi-sensor information fusion for fault diagnosis (MSIFFD) broadens the spectrum of available information sources. It is renowned for its high accuracy and reliability, traits that have attracted growing attention within the research community and led to the generation of a substantial body of publications. However, there is currently a lack of a comprehensive and systematic review in this domain. Thereby, this review aims to thoroughly explore all research achievements in the field of MSIFFD. At the outset, an analysis is undertaken to delineate the fusion level of multi-sensor information, with a specific focus on its location and types of inputs and outputs within the information flow. Subsequently, an examination of the six primary fundamental operations for amalgamating multi-sensor information is undertaken to clarify the motivations and processes involved in information fusion across various operations. Following this, factors influencing fusion diagnostics and strategies aimed at improving their performance are examined to explore approaches for enhancing fusion accuracy. The subsequent sections delve into the analysis of sensor types and application scenarios, providing a reference guide for practical applications. Finally, this review outlines potential future challenges in the field of MSIFFD and provides a range of recommendations and possible solutions for consideration. © 1963-2012 IEEE.

关键词： Information fusion

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An Sgx-Based Key Protection Solution for Ethereum Blockchain

SSRN

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SSRN 2023年

作者： Ren, Zhengwei He, Guangxin Yu, Rongwei Tong, Yan Xu, Shiwei Tang, Jinshan School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Health Informatics George Mason University FairfaxVA United States

The security of Ethereum digital assets mainly depends on users’ private keys. Cryptocurrency wallets are usually used to provide key management and protection for blockchain systems. However, the security of most existing software wallets is weak and hardware wallets are susceptible to man-in-the-middle attacks, exacerbating their security risks. In this paper, we propose an SGX-based key protection solution for the Ethereum blockchain. Operations related to private keys are enforced in Enclaves. Private keys that are not in service are sealed and stored locally. The corresponding sealed private key will be loaded into an Enclave only when a transaction needs to be signed, ensuring that private keys can be used securely and continuously. By employing this approach, we can significantly enhance the security of Ethereum key generation, storage, and usage without disrupting the standard functionalities of Ethereum. Furthermore, an efficient batch scheme is also proposed, designed to handle situations where multiple transactions from the same user need to be processed within a short time frame. We have implemented the prototype system and verified the functionalities and performances through rigorous testing. The experimental results show that the proposed solution can work normally in Ethereum with a reasonable performance overhead, validating its feasibility. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Multi-Object Tracking Based on Anchor-Free Detection and Message Passing Association

Multi-Object Tracking Based on Anchor-Free Detection and Mes...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhou Fang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665409858

Due to the current multi-target tracking work, in the scene of dense and staggered targets, the tracked targets are prone to ID switch, which greatly affects the tracking effect. Aiming at this problem, this paper proposed a new multi-target tracking algorithm: using an improved anchor-free detection method and extracting features from multiple aspects, combined with the message passing graph neural network to participate in the data association process to further improve the exchange of feature information, so as to improve the algorithm's ability to associate target information data between frames and improve the tracking accuracy. After conducting quantitative analysis and comparison experiments on datasets such as MOT17 and MOT20, the experimental results show that the algorithm in this paper can effectively improve the problem of target ID switching and provide better tracking accuracy and performance.

关键词： Industrial electronics Target tracking Statistical analysis Message passing Switches Feature extraction Real-time systems

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Microcomb based silicon photonics data link with 100Gbps/Lane PAM4 transmission

Microcomb based silicon photonics data link with 100Gbps/Lan...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Haowen Shu Bitao Shen Lin Chang Yuansheng Tao Weiqiang Xie Ming Jin Andrew Netherton Xuguang Zhang Jun Qin Ruixuan Chen Shaohua Yu Xingjun Wang John. E. Bowers State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara USA Peng Cheng Laboratory Shenzhen China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China

We achieve 100 Gbps/Lane PAM4 transmission by employing an AlGaAsOI microcomb source and silicon modulators. With 20 parallel wavelength channels utilized at C-band, an aggregate data rate of 2 Tbit/s is achieved.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： C-band Modulation Lasers and electrooptics Electro-optic modulators Data aggregation Silicon photonics

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Prevalence of Surgical Flow Disruptions Across Intra-operative High- and Low-Workload Phases in Cardiac Surgery

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Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2021年第1期10卷 263 - 266页

作者： Lauren R. Kennedy-Metz Roger D. Dias Annette M. Phillips Alexander Shapeton Suzana Zorca Kay B. Leissner Marco A. Zenati Medical Robotics and Computer Assisted Surgery Lab Veterans Affairs Boston Healthcare System Harvard Medical School Boston MA USA Human Factors & Cognitive Engineering Lab Brigham & Women’s Hospital Harvard Medical School Boston MA USA Veterans Affairs Boston Healthcare System Boston MA USA Veterans Affairs Boston Healthcare System Boston MA USA

While the influence of surgical flow disruptions (SFDs) on surgical performance has been observed in live cardiovascular surgery and their influence on cognitive workload has been evaluated in high-fidelity simulations, the relationship between SFDs and cognitive workload of surgical providers in the live cardiac surgery operating room has yet to be explored. Recent evidence suggests that preventable surgical errors often have a cognitive basis, warranting investigation into the association between SFDs and workload. This study aimed to characterize SFDs according to auditory and cognitive domains and further to compare the frequency and nature of SFDs within periods of high team cognitive workload and low team cognitive workload. Overall, the presence of cognitive distractions was associated more with high team cognitive workload states, while the frequency of auditory distractions was significantly higher during periods of low team cognitive workload states. Future work should consider the types, frequency, and sources of SFDs as well as their impact on surgical procedures and outcomes in order to devise appropriate methods to mitigate or manage potential disruptions to surgical workflow.

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Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans (vol 13, 3291, 2023)

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SCIENTIFIC REPORTS 2023年第1期13卷 1-29页

作者： Qureshi, Shahzad Ahmad Hussain, Lal Ibrar, Usama Alabdulkreem, Eatedal Nour, Mohamed K. Alqahtani, Mohammed S. Nafie, Faisal Mohammed Mohamed, Abdullah Mohammed, Gouse Pasha Duong, Tim Q. Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Pakistan Department of Computer Science and IT Neelum Campus The University of Azad Jammu and Kashmir Muzaffarabad Azad Kashmir Pakistan Department of Computer Science and IT King Abdullah Campus The University of Azad Jammu and Kashmir Muzaffarabad Azad Kashmir Pakistan Department of Radiology Albert Einstein College of Medicine and Montefiore Medical Center 111 East 210th Street Bronx NY 10467 USA Farooq Hospital Lahore Pakistan Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Sciences College of Computing and Information System Umm Al-Qura University Mecca Saudi Arabia Radiological Sciences Department College of Applied Medical Sciences King Khalid University Abha 61421 Saudi Arabia Department of Computer Science College of Science and Humanities at Alghat Majmaah University Al-Majmaah 11952 Saudi Arabia Research Centre Future University in Egypt New Cairo 11845 Egypt Department of Computer and Self Development Preparatory Year Deanship Prince Sattam Bin Abdulaziz University AlKharj Saudi Arabia

Accurate radiogenomic classification of brain tumors is important to improve the standard of diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we propose a novel two-stage MGMT Promoter Methylation Prediction (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Deep Learning Radiomic Feature Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and the size of tumorous structure through radiomic features: (GLCM, HOG, and LBP). The application of the novice rejection algorithm has been found significantly effective in selecting and isolating the negative training instances out of the original dataset. The fused feature vectors are then used for training and testing byk-NN and SVM classifiers. The 2021 RSNA Brain Tumor challenge dataset (BraTS-2021) consists of four structural mpMRIs, viz. fluid-attenuated inversion-recovery, T1-weighted, T1-weighted contrast enhancement, and T2-weighted. We evaluated the classification performance, for the very first time in published form, in terms of measures like accuracy, F1-score, and Matthews correlation coefficient. The Jackknife tenfold cross-validation was used for training and testing BraTS-2021 dataset validation. The highest classification performance is (96.84 ± 0.09)%, (96.08 ± 0.10)%, and (97.44 ± 0.14)% as accuracy, sensitivity, and specificity respectively to detect MGMT methylation status for patients suffering from glioblastoma. Deep learning feature extraction with radiogenomic features, fusing imaging phenotypes and molecular structure, using rejection algorithm has been found to perform outclass capable of detecting MGMT methylation status of glioblastoma patients. The approach relates the genomic variation with radiomic features forming a bridge between two areas of

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Development of a UAV-based system for Technical Diagnostics of Overhead Power Lines

Development of a UAV-based System for Technical Diagnostics ...

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International Conference on Computational Problems of Electrical engineering (CPEE)

作者： Oleksandr Kozlovskyi Dmitro Trushakov Serhiy Rendzinyak Vasyl Korud Department of Electrical Systems and Energy Management Central Ukrainian National Technical University Kropyvnytskyi Ukraine Department of Automation and Control in Technical Systems Central Ukrainian National Technical University Kropyvnytskyi Ukraine Faculty of Electronics and Computer Technologies Ivan Franko National University of Lviv Lviv Ukraine Institute of Power Engineering and Control System Lviv Polytechnic National University Lviv Ukraine

The problems associated with the operation of overhead power lines and ways of improving control over their condition with the help of UAVs are considered. A structural diagram of the system of technical diagnostics of overhead lines based on UAVs was developed, for which the necessary diagnostic parameters were selected according to informative criteria. An analysis of types of UAVs was carried out in order to determine their suitability and efficiency of use for diagnosing the condition of overhead lines.

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