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Investigation of operation and degradation mechanisms in ZnTeSe blue quantum-dot light-emitting diodes by identifying recombination zone

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Nano research 2024年第7期17卷 6527-6533页

作者： Oul Cho Sujin Park Hogeun Chang Jiwhan Kim Jaekwon Kim Sungwoo Kim Taehyung Kim Jeonghun Kwak Department of Electrical and Computer Engineering Inter-university Semiconductor Research Centerand Soft Foundry InstituteSeoul National UniversitySeoul 08826Republic of Korea Material Research Center Samsung Advanced Institute of TechnologySuwonGyeonggi-do 16419Republic of Korea

ZnTeSe quantum dots(QDs),recognized as promising eco-friendly blue electroluminescent emitters,remain under-explored in light-emitting diode(LED)***,to elucidate the operation and degradation mechanisms of ZnTeSe blue QD-LEDs,stacked ZnTeSe QD layers with discernable luminescence are designed by varying Te doping concentrations,and the recombination zones(RZs)of the blue QD-LEDs are *** RZs are identified near the hole-transport layer(HTL),confirmed by angular-dependent electroluminescence measurements and optical *** addition,in order to investigate carrier dynamics in the process of recombination,the transient electroluminescence(tr-EL)signals of the dichromatic QD-LEDs are *** a result,it is inferred that the RZ initially formed near the electron-transport layer(ETL)due to the high injection barriers of ***,due to the fast electron mobility,the RZ shifts toward the HTL as the operating current *** the device lifetime tests,the RZ remains stationary while the photoluminescence(PL)corresponding to the RZ undergoes a substantial decrease,indicating that the degradation is accelerated by the concentrated *** this study contributes to a deeper understanding of the operational mechanisms of ZnTeSe blue QD-LEDs.

关键词： quantum dots light-emitting diodes degradation recombination zones carrier dynamics

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A Facial Expression Recognition Method Integrating Uncertainty Estimation and Active Learning

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computers, Materials & Continua 2024年第10期81卷 533-548页

作者： Yujian Wang Jianxun Zhang Renhao Sun School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Data Space Research Institute of Hefei Comprehensive National Science Center Hefei230088China

The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression ***,labeling large datasets demands significant human,time,and financial *** active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition *** issue arises because the initial labeled data often fails to represent the full spectrum of facial expression *** paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale *** method is divided into two primary ***,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction ***,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition *** the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled *** features are then weighted through a self-attention mechanism with rank ***,data from the low-weighted set is relabeled to further refine the model’s feature extraction *** pre-trained model is then utilized in active learning to select and label information-rich samples more *** results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.

关键词： Expression recognition active learning self-supervised learning uncertainty estimation

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Minimal Context-Switching Data Race Detection with Dataflow Tracking

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Journal of computer Science & technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Frontiers of computer Science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN 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 TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer Science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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Deep learning applications in the Internet of Things: a review, tools, and future directions

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Evolutionary Intelligence 2024年第5-6期17卷 3621-3654页

作者： Raoufi, Parisa Hemmati, Atefeh Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin64002 Taiwan

The emergence of the Internet of Things (IoT) has enabled the proliferation of interconnected devices and sensors, generating vast amounts of often complex and unstructured data. Deep learning (DL), a subfield of machine learning (ML), has shown great promise in addressing the challenges of processing and analyzing such data. Considering the increasing importance of DL and data analysis, we decided to review the articles of the last few years in this field to pave the way for researchers. In this article, we used the systematic literature review (SLR) method, and in line with that, we selected and analyzed 56 articles published from 2019 to April 2024. We first discuss the DL models used in the IoT field and clarify their specific use cases. Secondly, we outline an analysis of research areas in DL-based IoT. In addition, our research extends to the tools and simulators used to evaluate studies in the DL-based IoT domain. We also examine the DL-based IoT research datasets. Finally, our review identifies future directions and open issues in DL-based IoT. We aim to contribute to an accurate understanding of the current state, challenges, and potential breakthroughs at the intersection of DL and the IoT. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Internet of things

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Anti-saturation fault-tolerant adaptive torsional vibration control with fixed-time prescribed performance for rolling mill main drive system

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Journal of Iron and Steel research International 2024年第3期31卷 660-669页

作者： Shuang Liu Chen Du Cong Zhang Zhen-hua Bai Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao 066004HebeiChina National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Yanshan UniversityQinhuangdao 066004HebeiChina

An anti-saturation fault-tolerant adaptive torsional vibration control method with fixed-time prescribed performance for the rolling mill main drive system(RMMDS)was investigated,which is affected by control input saturation,actuator faults,sensor measurement errors,and parameter ***,we gave a continuously differentiable saturation function to approximate the control input saturation characteristic of the RMMDS,translating the saturation characteristic into the matched uncertainty and unknown time-varying gain in the ***,an RMMDS mathematical model with unmatched uncertainty and unknown time-varying gain was developed,taking into account the presence of control input saturation,actuator faults,sensor measurement errors,and parameter *** on the established mathematical model,an error transformation model of the roll speed tracking was constructed by the equivalent error transformation *** to the error transformation model,a barrier Lyapunov function and a novel adaptive controller were studied to ensure that the roll speed tracking error always evolves inside a fixed-time asymmetric ***,numerical simulations were performed in Matlab/Simulink to verify the effectiveness and superiority of the proposed control method in suppressing the RMMDS torsional vibration.

关键词： Fixed-time prescribed performance Control input saturation Actuator fault Unknown time-varying gain Adaptive control Asymmetric constraint

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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

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Frontiers of computer Science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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Automatic text summarization using deep reinforced model coupling contextualized word representation and attention mechanism

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Multimedia Tools and Applications 2024年第1期83卷 733-762页

作者： Aliakbarpour, Hassan Manzuri, Mohammad Taghi Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Sharif University of Technology Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

With the rapid and unprecedented growth of textual data in recent years, there is a remarkable need for automatic text summarization models to retrieve useful information from these large numbers of textual documents without human intervention within a reasonable time. Text summarization is commonly performed based on extractive and abstractive paradigms. Although different machine learning and deep learning based methods have been proposed for the task of text summarization during the last decades, they are still in their early steps of development and their potential has yet to be fully explored. Accordingly, a new summarization model is proposed in this paper which takes advantage of both extractive and abstractive text summarization models as a single unified model based on the strategy gradient of reinforcement learning. The proposed model also employs the combination of convolutional neural network and gated recurrent unit in both extraction and abstraction modules besides attention mechanism. Moreover, language models, namely Word2Vec and BERT, are used as the backbone of the proposed model to better express sentence semantics as a word vector. We conducted our experiments on widely-studied text summarization datasets (CNN\Daily Mail and DUC-2004) and according to the empirical results, not only the proposed model achieved higher accuracy compared to both extractive and abstractive summarization models in terms of ROUGE metric but also its generated summaries presented higher saliency and readability based on human evaluation. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Reinforcement learning

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An Energy-Aware Resource Management Strategy Based on Spark and YARN in Heterogeneous Environments

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期8卷 635-644页

作者： Shabestari, Fatemeh Jafari Navimipour, Nima Islamic Azad University Sofian Branch Department of Computer Engineering Sofian Iran Kadir Has University Department of Computer Engineering Istanbul34083 Turkey National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan

Apache Spark is a popular framework for processing big data. Running Spark on Hadoop YARN allows it to schedule Spark workloads alongside other data-processing frameworks on Hadoop. When an application is deployed in a YARN cluster, its resources are given without considering energy efficiency. Furthermore, there is no way to enforce any user-specified deadline constraints. To address these issues, we propose a new deadline-aware resource management system and a scheduling algorithm to minimize the total energy consumption in Spark on YARN for heterogeneous clusters. First, a deadline-aware energy-efficient model for the considered problem is proposed. Then, using a locality-aware method, executors are assigned to applications. This algorithm sorts the nodes based on the performance per watt (PPW) metric, the number of application data blocks on nodes, and the rack locality. It also offers three ways to choose executors from different machines: greedy, random, and Pareto-based. Finally, the proposed heuristic task scheduler schedules tasks on executors to minimize total energy and tardiness. We evaluated the performance of the suggested algorithm regarding energy efficiency and satisfying the Service Level Agreement (SLA). The results showed that the method outperforms the popular algorithms regarding energy consumption and meeting deadlines. © 2017 IEEE.

关键词： Energy utilization

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