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High-performance self-powered ultraviolet to near-infrared photodetector based on WS_(2)/InSe van der Waals heterostructure

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Nano Research 2023年第5期16卷 7851-7857页

作者： Jinping Chen Zhen Zhang Yi Ma Jiying Feng Xiaoyu Xie Xiaoxuan Wang Aoqun Jian Yuanzheng Li Zhuxin Li Heng Guo Yizhi Zhu Qiannan Cui Zengliang Shi Chunxiang Xu State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China Micro Nano System Research Center Key Laboratory of Advanced Transducers and Intelligent Control System of the Ministry of Education&College of Information Engineering and ComputerTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of UV-Emitting Materials and Technology of Ministry of Education Northeast Normal UniversityChangchun 130024China

van der Waals heterostructures(vdWHs)based on two-dimensional(2D)materials without the crystal lattice matching constraint have great potential for high-performance optoelectronic ***,a WS_(2)/InSe vdWH photodiode is proposed and fabricated by precisely stacking InSe and WS_(2)flakes through an all-dry transfer *** WS_(2)/InSe vdWH forms an n–n heterojunction with strong built-in electric field due to their intrinsic n-type semiconductor characteristics and energy-band alignments with a large Fermi level offset between WS_(2)and *** a result,the device displays excellent photovoltaic behavior with a large open voltage of 0.47 V and a short-circuit current of 11.7 nA under 520 nm light ***,a fast rising/decay time of 63/76μs,a large light on/off ratio of 105,a responsivity of 61 mA/W,a high detectivity of 2.5×10^(11) Jones,and a broadband photoresponse ranging from ultraviolet to near-infrared(325–980 nm)are achieved at zero *** study provides a strategy for developing high-performance self-powered broadband photodetectors based on 2D materials.

关键词： two-dimensional materials van der Waals heterostructure self-powered photodetector photocurrent

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection 24

Machine Learning is All You Need: A Simple Token-Based Appro...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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Computer Vision in Smart City Application: A Mapping Review 6

Computer Vision in Smart City Application: A Mapping Review

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6th International Conference on Applied Computational Intelligence in information systems, ACIIS 2023

作者： Syahidi, Aulia Akhrian Kiyokawa, Kiyoshi Okura, Fumio Politeknik Negeri Banjarmasin Smart City Information System Department of Electrical Engineering Banjarmasin Indonesia Nara Institute of Science and Technology Division of Information Science Nara Ikoma Japan Osaka University Graduate School of Information Science and Technology Osaka Suita Japan

ISBN: (纸本)9798350317428

Research interest in the application of Computer Vision (CV) in the context of smart cities has grown significantly in recent years. However, finding relevant studies in databases can be challenging due to the use of non-explicit or varying terms. To address this issue, this research aims to conduct a literature review by collecting papers that discuss the application of CV for smart city needs. The method used is a mapping review, with the following stages: 1) Establishing research questions;2) Selecting relevant reference sources based on relevant databases;3) Identifying appropriate keywords;4) Conducting screening based on criteria;and 5) Extracting and synthesizing relevant data. For this research, we selected a total of 35 papers as the primary review. Our findings show that research trends and interests vary widely, with the most prevalent being in smart mobility components, which on average come from the IEEE Xplore Digital Library database. YOLO and CNN algorithms are also widely used to solve problems. Furthermore, we conclude that there are many benefits to be gained from the application of this technology to support the sustainable development of smart cities, including ensuring the quality of life of their inhabitants. © 2023 IEEE.

关键词： Computer vision

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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A Real- Time Multi-Camera Auto-Adjustment Framework for Infrastructure Inspections

A Real- Time Multi-Camera Auto-Adjustment Framework for Infr...

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2024 IEEE/SICE International Symposium on system Integration, SII 2024

作者： Phuoc Le, Chuong Ellison, Charles Bunkley, Steven La, Hung Netchaev, Anton Lab Department of Computer Science and Engineering RenoNV89557 United States Information Technology Lab VicksburgMS39180 United States

ISBN: (纸本)9798350312072

In this paper, we proposed a novel multi-camera auto-adjustment framework for infrastructure inspections, specifically 3D point cloud mapping via lidar-camera fusion. Our method uses a single controller for consensus target brightness and adaptive lighting. In addition, a vignette correction filter was created to apply to images outputted from cameras to avoid dark strips on the point cloud data. This approach is designed to be a versatile, real-time friendly, and platform agnostic software stack. © 2024 IEEE.

关键词： Cameras

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Acronym Identification Based on SpanBERT-CRF 3

Acronym Identification Based on SpanBERT-CRF

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3rd International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Ou, Lizhen Luo, Xueshan Li, Xinmeng Chen, Peipei Science and Technology on Information System Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781510657281

Acronym Identification means to recognizing acronyms and their definitions in a given paragraph by an algorithm. Existing studies have shown that the pre-training model has significant performance advantages in acronyms recognition. However, the vanilla Bert measures randomly masking tokens in the pre-training, which may also abbreviate the token of acronyms in sentence. The continuous masking method will be more helpful to the recognition and interpretation of acronyms. In this paper, we propose to use the SpanBERT model for acronym recognition and obtaining the transfer relationship between indicators with the help of a conditional random field (CRF). Through experimental comparison, the results demonstrate that our method increases the F1 value compared with the Bert and BERT-CRF models. © 2022 SPIE.

关键词： Random processes

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Analysis of Cross-Domain Combat Capability Based on Kill Chain 10th

Analysis of Cross-Domain Combat Capability Based on Kill Cha...

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10th China Conference on Command and Control, C2 2022

作者： Hu, Tao Liu, Junxian Liu, Junjie Wu, Saisai Luo, Xueshan Science and Technology on Information System Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789811960512

With the progress of science and technology and the change of combat environment, the single combat mode is no longer suitable for the present, and multi domain combat has gradually become the mainstream. However, compared with the effectiveness analysis of single domain operation, the effectiveness research of multi domain operation has just begun. In order to solve the deficiency of capability analysis method of cross domain combat system, this paper proposes a cross domain combat effectiveness analysis method based on kill chain analysis. From the perspective of the kill chain, the cross-domain kill chain and the single-domain kill chain are defined, and extended to the battle network. It realizes the overall effectiveness of the cross-domain combat network and the preliminary analysis of the effectiveness of each domain in the combat process. Therefore, it provides a new combat analysis model for China’s subsequent national defense and combat system construction. © 2022, Chinese Institute of Command and Control.

关键词： Command and control systems

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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Computer Modeling in engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Layered Structure Aware Dependent Microservice Placement Toward Cost Efficient Edge Clouds 42

Layered Structure Aware Dependent Microservice Placement Tow...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Zeng, Deze Geng, Hongmin Gu, Lin Li, Zhexiong China University of Geosciences School of Computer Science Wuhan China 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 China

ISBN: (纸本)9798350334142

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies. © 2023 IEEE.

关键词： Containers

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