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arXiv 2022年

作者： Minin, Oleg V. Zhou, Song Cao, Yinghui Baranov, Pavel Minin, Igor V. Tomsk Polytechnic University 30 Lenin Ave. Tomsk634050 Russia Jiangsu Key Laboratory of Advanced Manufacturing Technology Faculty of Mechanical and Material Engineering Huaiyin Institute of Technology Huaian223003 China College of Computer Science and Technology Jilin University 2699 Qianjin Street Changchun130021 China

Some new unusual physical phenomena and effects associated with dielectric mesoscale particles with Mie size parameter near 10 were studied and have been discovered during the last decade. In this paper, we propose nanoholes structured wavelength-scaled dielectric cubic particle with refractive index near two, where the array of nanoholes can act as a plurality of near-field probes to simultaneously illuminate the sample surface and it has the potential of surpassing the performance of most existing nearfield imaging approaches. We also offer the concept of the single nano-structuring of a dielectric cylinder or sphere made from conventional optical materials. The choice of the diameter of the nanohole in the particle "transfers" it into the resonance mode, when the characteristics of the field localized in the shadow part of the particle are determined not by the wavelength, but by the size of the nanohole. Thus, the diameter of the focused spot at the exit from the particle can be much smaller than the solid immersion diffraction *** Codes 65Z05 © 2022, CC BY.

关键词： Dielectric materials

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AFF-Dehazing: Attention-based feature fusion network for low-light image Dehazing

AFF-Dehazing: Attention-based feature fusion network for low...

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作者： Zhou, Yu Chen, Zhihua Sheng, Bin Li, Ping Kim, Jinman Wu, Enhua Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong School of Information Technologies The University of Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

Images captured in haze conditions, especially at nighttime with low light, often suffer from degraded visibility, contrasts, and vividness, which makes it difficult to carry out the following vision tasks. In this article, we propose an attention-based feature fusion network (AFF-Dehazing) for low-light image dehazing. Our method decomposes the low-light image dehazing into two task-independent streams containing four modules: image dehazing module, low-light feature extractor module, feature fusion module, and image restoration module. The basic block of these modules is the proposed attention-based residual dense block. Since the dual-branch are used, AFF-Dehazing can avoid learning the mixed degradation all-in-one and enhance the details of low-light haze images. Extensive experiments show that our method surpasses previous state-of-the-art image dehazing methods and low-light enhancement methods by a very large margin both quantitatively and qualitatively. © 2021 John Wiley & Sons, Ltd.

关键词： Image reconstruction

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Parallel Monte Carlo Integration Algorithm Based on GPU 14

Parallel Monte Carlo Integration Algorithm Based on GPU

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14th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2019

作者： Zong, Hui Hua, Renfeng Zhao, Jianyang Cao, Zining Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China

ISBN: (纸本)9781728123486

Big data comes with the increasing maturity of computer network technology and its application popularity. How to process big data and mine its potential value is a hot issue today. Artificial intelligence is one of the main sources of algorithms for dealing with big data, while parallel computation is the only choice to improve computing performance. Monte Carlo method plays an important role in the application of artificial intelligence, such as computer game AlphaGo, and it requires a lot of random data to calculate, so we use it, in this paper, to explore the general method of parallel processing big data. According to Monte Carlo method, the calculation time of the algorithm will increase exponentially with the increase of the calculation accuracy. As the calculation accuracy will increase accordingly when the number of points increases. In order to improve the computing performance, a Monte Carlo parallel algorithm based on GPU-CUDA programming model is designed. The quality of the random number generator is the key factor affecting the accuracy of Monte Carlo calculation, so the higher quality Mersenne Twister random number algorithm is chosen. Taking \pi and e as examples, the experimental results show that the GPU-based Monte Carlo parallel algorithm is effective. This method improves both the calculation performance and the calculation accuracy. © 2019 IEEE.

关键词： Monte Carlo methods

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An IoT-Based System Architecture for Environmental Monitoring 5th

An IoT-Based System Architecture for Environmental Monitorin...

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5th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2020

作者： Pattanayak, Binod Kumar Nohur, Deojeet Cowlessur, Sanjeev K. Mohanty, Rajani Kanta Department of Computer Science and Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar Odisha India Department of Software Engineering Faculty of Information and Communication Technology Université Des Mascareignes Beau Bassin-Rose Hill Mauritius Department of CSE PSIT Kanpur Uttar Pradesh India

ISBN: (纸本)9789813342989

Internet of Things (IoT) has grown in popularity in all spheres of life. Environmental issues in the modern era present a major concern for researchers, scientists and government. In combating the environmental issues, monitoring, identifying and quantifying different contaminating parameters in the environment are necessary, and for this purpose, IoT technology can be extremely useful. In this paper, we propose an IoT-based environmental monitoring system that operates on a four-layer architecture wherein environmental data are captured by a sensor network, aggregated and then communicated to a web server via a cloud service. Then, these data are analyzed by various analytical IoT specific tools and then delivered to the applications for further processing. This system can be cost effective and easy to deploy considering that the underlying architecture is simpler than other existing similar systems. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensor networks

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GPT-Based Automated Induction: Vulnerability Detection in Medical software

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Deng, Liangjun Lei, Hang Khan, Fazlullah Srivastava, Gautam Chen, Jingxue Haque, Mainul University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China University of Nottingham Ningbo China School of Computer Science and the Next-Generation Internet of Everything Laboratory Faculty of Science and Engineering Zhejiang Ningbo315104 China Lovely Professional University Division of Research and Development Phagwara India Chandigarh University University Centre of Research and Development Mohali India Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India University of Nottingham Ningbo China Department of Mathematics Faculty of Science and Engineering Zhejiang Ningbo315104 China

Integrating Natural Language Processing (NLP) with Generative Pre-trained Transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the well-being of the patient. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, symbolic execution often faces problems such as path explosion, which seriously affects efficiency. Although there have been several studies to reduce the number of computational paths in symbolic execution, this problem remains a major obstacle. Therefore, more efficient solutions are urgently needed to ensure the software security. This paper proposes a large-scale language model(LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software. © 2025 IEEE. All rights reserved,

关键词： Scalability

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Discrimination of Quantum States Under Locality Constraints in the Many-Copy Setting

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Communications in Mathematical Physics 2023年第1期404卷 151-183页

作者： Cheng, Hao-Chung Winter, Andreas Yu, Nengkun Department of Electrical Engineering and Graduate Institute of Communication Engineering National Taiwan University Tapei 106 Taiwan Department of Mathematics National Taiwan University Tapei 106 Taiwan Center for Quantum Science and Engineering National Taiwan University Tapei 106 Taiwan Physics Division National Center for Theoretical Sciences Taipei 10617 Taiwan Hon Hai (Foxconn) Quantum Computing Center New Taipei City 236 Taiwan ICREA-Institució Catalana de Recerca i Estudis Avançats Barcelona 08010 Spain Departament de Física Física Teòrica: Informaciò i Fenòmens Quàntics Universitat Autònoma de Barcelona Barcelona 08193 Spain Computer Science Department Stony Brook University New York NY United States Centre for Quantum Software and Information and Faculty of Engineering and Information Technology University of Technology Sydney Ultimo 2007 NSW Australia

We study quantum hypothesis testing between orthogonal states under restricted local measurements in the many-copy scenario. For testing arbitrary multipartite entangled pure state against its orthogonal complement state via the local operation and classical communication (LOCC) operation, we prove that the optimal average error probability always decays exponentially in the number of copies. Second, we provide a sufficient condition for the LOCC operations to achieve the same performance as the positive-partial-transpose (PPT) operations. We further show that testing a maximally entangled state against its orthogonal complement and testing extremal Werner states both fulfill the above-mentioned condition. Hence, we determine the explicit expressions for the optimal average error probability, the optimal trade-off between the type-I and type-II errors, and the associated Chernoff, Stein, Hoeffding, and strong converse exponents. Then, we show an infinite asymptotic separation between the separable (SEP) and PPT operations by providing a pair of states constructed from an unextendible product basis (UPB). The quantum states can be distinguished perfectly by PPT operations, while the optimal error probability, with SEP operations, admits an exponential lower bound. On the technical side, we prove this result by providing a quantitative version of the well-known statement that the tensor product of UPBs is a UPB. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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Self-Adaptive Teaching-Learning-Based Optimizer with Improved Rbf and Sparse Autoencoder for High-Dimensional Problems

SSRN

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SSRN 2022年

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Zhou, MengChu School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Computer Science The Lyle School of Engineering Southern Methodist University DallasTX75205 United States Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Evolutionary algorithms are commonly used to solve many complex optimization problems in different fields. Yet, their performance is limited when dealing with high-dimensional complex problems because they often require enormous computational resources to yield desired solutions, and they may easily trap into local optima. To solve this problem, this work proposes a Selfadaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA). In STORA, a Self-adaptive Teaching-Learning-Based Optimizer (STLBO) is designed to dynamically adjust parameters for balancing exploration and exploitation abilities. Then, a sparse autoencoder (SAE) is adopted as a dimension reduction method to compress search space into lower-dimensional one for more efficiently guiding population to converge towards global optima. Besides, an Improved Radial Basis Function model (IRBF) is designed as a surrogate model to balance training time and prediction accuracy. It is adopted to save computational resources for improving overall performance. In addition, a dynamic population allocation strategy is adopted to well integrate SAE and IRBF in STORA. We evaluate STORA by comparing it with several state-of-the-art algorithms through eight benchmark functions. We further test its actual performance by applying it to solve a real-world computation offloading problem. © 2022, The Authors. All rights reserved.

关键词： Radial basis function networks

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Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing

arXiv

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arXiv 2024年

作者： Stewart, Kenneth Neumeier, Michael Shrestha, Sumit Bam Orchard, Garrick Neftci, Emre Department of Computer Science UC Irvine IrvineCA United States Department of Neuromorphic Computing Fortiss-Research Institute of the Free State of Bavaria Munich Germany Intel Labs Intel Corporation Santa ClaraCA United States Peter Grünberg Institute - Neuromorphic Software Ecosystems Forschungszentrum Jülich Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen Germany

Achieving personalized intelligence at the edge with real-time learning capabilities holds enormous promise in enhancing our daily experiences and helping decision making, planning, and sensing. However, efficient and reliable edge learning remains difficult with current technology due to the lack of personalized data, insufficient hardware capabilities, and inherent challenges posed by online learning. Over time and across multiple developmental stages, the brain has evolved to efficiently incorporate new knowledge by gradually building on previous knowledge. In this work, we emulate the multiple stages of learning with digital neuromorphic technology that simulates the neural and synaptic processes of the brain using two stages of learning. First, a meta-training stage trains the hyperparameters of synaptic plasticity for one-shot learning using a differentiable simulation of the neuromorphic hardware. This meta-training process refines a hardware local three-factor synaptic plasticity rule and its associated hyperparameters to align with the trained task domain. In a subsequent deployment stage, these optimized hyperparameters enable fast, data-efficient, and accurate learning of new classes. We demonstrate our approach using event-driven vision sensor data and the Intel Loihi neuromorphic processor with its plasticity dynamics, achieving real-time one-shot learning of new classes that is vastly improved over transfer learning. Our methodology can be deployed with arbitrary plasticity models and can be applied to situations demanding quick learning and adaptation at the edge, such as navigating unfamiliar environments or learning unexpected categories of data through user engagement. © 2024, CC BY.

关键词： Zero-shot learning

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Innovative Poverty Estimation through Machine Learning Approaches

Innovative Poverty Estimation through Machine Learning Appro...

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International Conference on Emerging Technologies, ICET

作者： Ahmad Mashhood Zain ul Abideen Usama Arshad Raja Hashim Ali Ammad Aslam Khan Bahram Khan Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Dept. of Biological Sciences M.A. Road Satellite Town Virtual University of Pakistan Rawalpindi Pakistan

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. In analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges.

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Smart Harvesting based on Image Processing

Smart Harvesting based on Image Processing

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2020 International computer Symposium, ICS 2020

作者： Joseph, S.P Wijerathna, L.L.M.C. Epa, K.G.R.D. Egalla, E.K.W.A.P.K. Abeygunawardhana, P.W.K. De Silva, R Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Faculty of Computing Malabe10115 Sri Lanka Sri Lanka Institute of Information Technology Dep. of Software Engineering Faculty of Computing Malabe10115 Sri Lanka

ISBN: (纸本)9781728192550

Vision device is a critical component of fruit harvesting mobile robot which is designed for recognizing and harvesting of tomato fruits. The system of vision device of the moving robot, for a dedicated path identification with the use of colour detection algorithms and contour detection algorithms could be stated as an additional usage of the image-based technology. When the robot operates in real life, varying environmental parameters would not affect its activity, since the robot will be functioning mainly indoors, specifically in a greenhouse. Robust fruit segmentation algorithms for the visual system and the fruit plucking mechanism using a readymade robot arm with a soft gripper where it operates with basic hardware components like motors, controlled by a microcontroller with the intervention of kinematic theories, is being used with the goal of collecting fruit objects efficiently in the natural world. Plucking only the ripe and healthy object fruit without damaging both the fruit and the tree is the intended task of the system. The aim is to reduce human effort by promoting automation concepts in agriculture in developing countries like Sri Lanka with great potential, where agriculture plays a vital role in the development of the economy while indoor plantation and harvesting mechanism are also being developing to cater to the economy of the country as well. The way that this system is implemented will be discussed in this paper. © 2020 IEEE.

关键词： Fruits

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