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Generalized autocorrelation function in the family of deterministic and stochastic anomalous diffusion processes

Physical Review Research 2024年第3期6卷 033169-033169页

作者： Muhammad Tayyab School of Computing Sciences Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Mang Haripur 22621 Pakistan Faculty of Engineering Sciences Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Swabi Topi 23640 Pakistan

We investigate the observables of the one-dimensional model for anomalous transport in semiconductor devices where diffusion arises from scattering at dislocations at fixed random positions, known as the Lévy-Lorentz gas. To gain insight into the microscopic properties of such a stochastically complex system, deterministic dynamics known as the slicer map and fly-and-die dynamics are used. We analytically derive the generalized position autocorrelation function of these dynamics and study the special case, the 3-point position correlation function. For this, we derive single-parameter-dependent scaling and compare it with the numerically estimated 3-point position autocorrelation of the Lévy-Lorentz gas, for which the analytical expression is still an open question. Here we obtained a remarkable agreement between them, irrespective of any functional relationship with time. Moreover, we demonstrate that the position moments and the position autocorrelations of these systems scale in the same fashion, provided the times are large enough and far enough apart. Other observables, such as velocity moments and correlations, are reported to distinguish the systems.

关键词： Ballistic transport Classical transport Diffusion Random walks Stochastic processes 1-dimensional systems

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A Taxonomy for Requirements engineering and Software Test Alignment

arXiv

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arXiv 2023年

作者： Unterkalmsteiner, Michael Feldt, Robert Gorschek, Tony Software Engineering Research Lab School of Computing Blekinge Institute of Technology Sweden Department of Computer Science and Engineering Chalmers University of Technology Sweden

Requirements engineering and Software Testing are mature areas and have seen a lot of research. Nevertheless, their interactions have been sparsely explored beyond the concept of traceability. To fill this gap we propose a definition of requirements engineering and software test (REST) alignment, a taxonomy that characterizes the methods linking the respective areas, and a process to assess alignment. The taxonomy can support researchers to identify new opportunities for investigation, as well as practitioners to compare alignment methods and evaluate alignment, or lack thereof. We constructed the REST taxonomy by analyzing alignment methods published in literature, iteratively validating the emerging dimensions. The resulting concept of an information dyad characterizes the exchange of information required for any alignment to take place. We demonstrate use of the taxonomy by applying it on five in-depth cases and illustrate angles of analysis on a set of thirteen alignment methods. In addition we developed an assessment framework (REST-bench), applied it in an industrial assessment, and showed that it, with a low effort, can identify opportunities to improve REST alignment. Although we expect that the taxonomy can be further refined, we believe that the information dyad is a valid and useful construct to understand alignment. Categories and Subject Descriptors: D.2.1 [Software engineering]: Requirements/Specifications;D.2.4 [Software engineering]: Software/Program Verification;D.2.9 [Software engineering]: Management—Software quality assurance © 2023, CC BY-NC-SA.

关键词： Requirements engineering

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Multi layer segmentation method for ophthalmic OCT images based on Gabor filtering 23

Multi layer segmentation method for ophthalmic OCT images ba...

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2023 International Conference on Computer, Vision and Intelligent technology, ICCVIT 2023

作者： Liu, Jiayike Li, Qingbo Liu, Ailin Liu, Dong Liu, Yang Zhang, Tianqiao School of Information and Communication Guilin University of Electronic Technology Guilin541000 China School of Computer and Artificial Intelligence Xiangnan University Chenzhou423000 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Chenzhou423000 China School of Life and Environmental Sciences Guilin University of Electronic Technology Guilin541000 China

ISBN: (纸本)9798400708701

Optical coherence tomography is a non-destructive optical signal acquisition and processing method for imaging surface tissue structure. OCT has become an important and innovative corneal measurement and diagnostic technology. Propose a multi-layer detection method for ophthalmic OCT retinal images. Firstly, perform 2D Gabor filtering on the OCT image, then binarize the filtered image, and finally perform eight neighborhood contour localization. To provide reference for the quantitative analysis of the thickness of different physiological layers in the anterior segment in the clinical diagnosis and lesion assessment of ophthalmic diseases. © 2023 ACM.

关键词： Optical tomography

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Optimized Learning Model for Brain-Computer Interface Using Electroencephalogram (EEG) for Neuroprosthetics Robotic Arm Design for Society 5.0

Optimized Learning Model for Brain-Computer Interface Using ...

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computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), International Conference on

作者： Arun Kumar S Anand L Anil Kannur Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Tamil Nadu India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Potheri SRM Nagar Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Dr. P.G. Halakatti College of Engineering and Technology Vijayapura Karnataka India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

The brain activity can be represented by EEG, which does it by measuring voltage fluctuations in the brain. in this paper, the work intends to develop a Brain-computer Interface-based system that can identify and forecast the class of motor action corresponding to the left or right direction. There are two datasets used in this study, relating to motor movements - left/right-hand movement and eye open/close. The collected data is pre-processed by applying a Convolution Neuron Network to obtain a frequency domain matrix. The result computed by applying CNN provides a condensed representation of the data. The second dataset captured brain impulses corresponding to two states of the eyes opened and eyes closed, over a period of two minutes. This system is designed to effectively separate and utilize signals, with the adoption of AI techniques in these devices emerging as the most viable option for Society. The overall efficiency of this system ranges from 80% to 87%.

关键词： Training Accuracy Voltage measurement Neuroprostheses Predictive models Brain modeling Motors Electroencephalography Brain-computer interfaces Data models

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An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

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Computer Systems Science & engineering 2023年第9期46卷 3993-4006页

作者： ShahidMohammad Ganie Pijush Kanti Dutta Pramanik Majid BashirMalik Anand Nayyar Kyung Sup Kwak School of Business Woxsen UniversityHyderabadTelangana502345India School of Computing Science&Engineering Galgotias UniversityGreater NoidaUP 203201India Department of Computer Sciences Baba Ghulam Shah Badshah UniversityRajouri185234India Graduate School Faculty of Information TechnologyDuy Tan UniversityDa Nang50000Vietnam Department of Information and Communication Engineering Inha University22212Korea

Cardiovascular disease is among the top five fatal diseases that affect lives ***,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier *** learning(ML)techniques are used to assist healthcare providers in better diagnosing heart *** study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart *** dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML *** data analysis is performed to find the characteristics of data samples about descriptive and inferential ***,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation *** were recorded before and after the data preprocessing techniques were *** of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed *** proposed model yielded better results with gradient boosting in terms of precision,recall,and *** attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.

关键词： Heart disease prediction machine learning classifiers ensemble approach XGBoost AdaBoost gradient boost

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PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification

PM2: A New Prompting Multi-modal Model Paradigm for Few-shot...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhenwei Wang Qiule Sun Bingbing Zhang Weijian Su Pengfei Wang Jianxin Zhang Qiang Zhang Key Laboratory of Social Computing and Cognitive Intelligence (Ministry of Education) Dalian University of Technology China School of Computer Science and Technology Dalian University of Technology China School of Computer Science and Engineering Dalian Minzu University China School of Information and Communication Engineering Dalian University of Technology China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Few-shot learning has become a key technical solution for addressing the challenges of limited data and difficult annotation acquisition in medical image classification. However, relying solely on a single image modality proves inadequate for capture conceptual categories. This paper proposes a novel medical image classification paradigm based on a multi-modal foundation model, called PM 2 . In addition to the image modality, PM 2 introduces supplementary text input (prompt) to further describe images or conceptual categories and facilitate cross-modal few-shot learning. We empirically studied five different prompting schemes under this new paradigm. Furthermore, linear probing in multi-modal models only takes class token as input, ignoring the rich statistical data contained in high-level visual tokens. Therefore, we alternately perform linear classification on the feature distributions of visual tokens and class token. To effectively extract statistical information, we use global covariance pool with efficient matrix power normalization to aggregate the visual tokens. We then combine two classification heads: one for handling image class token and prompt representations encoded by the text encoder, and the other for classifying the feature distributions of visual tokens. Experiments on two medical datasets demonstrate that regardless of the prompting scheme, our method PM 2 outperforms its counterparts, achieving state-of-the-art performance.

关键词： Visualization Head Foundation models Feature extraction Data models Data mining Few shot learning Covariance matrices Medical diagnostic imaging Image classification

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IDeploySmart: A Machine Learning-Based Approach for Predicting the Deployment Type of Identity Servers

IDeploySmart: A Machine Learning-Based Approach for Predicti...

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International Conference on Information technology Research (ICITR)

作者： Mamoru D. L. S. Amjadh Ifthikar Computer Science and Engineering University of Westminster London United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

Identity servers are integral to modern Information technology (IT) infrastructures, managing secure access to critical applications through authentication and authorization processes. However, determining the optimal deployment strategy for these servers in complex environments remains challenging, especially when considering factors like performance, scalability, and security. This paper introduces IDeploySmart, a machine learning-based system designed to predict the most efficient deployment type of identity servers. By employing Gradient Boosting Regressor, IDeploySmart achieves a prediction accuracy of 92.5%, surpassing industry benchmarks. The system was tested across multiple deployment scenarios, showing significant improvements in system performance, scalability, and security. This paper details the system's design, data preprocessing techniques, model evaluations, and real-world applications, providing insights into how machine learning can enhance identity server deployment strategies in modern IT infrastructure.

关键词： Industries Scalability System performance Data preprocessing Predictive models Boosting Data models Servers Security Information technology

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A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural Networks

A New Method for Predefined-Time Synchronization Control of ...

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Memristive computing and Applications (ICMCA), IEEE International Conference on

作者： Aidi Liu Wenyue Yu Lili Huang Jian Guo Tengfei Lei Hui Zhao School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan China

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory.

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Abnormal Clustering and Cross Slicing Transformer for Insect Fine-Grained Image Classification

Abnormal Clustering and Cross Slicing Transformer for Insect...

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Algorithm, Image Processing and Machine Vision (AIPMV), International Conference on

作者： Aokun Mei Hua Huo Big Data Analysis Laboratory of Henan Medical Big Data and Computing Intelligence Engineering Technology Research Center School of Information Engineering Henan University of Science and Technology Luoyang China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Insect fine-grained image classification is an application scenario in fine-grained image classification. It not only has the characteristics of small inter-class differences and large intra-class differences, but also has the difficulty that some categories have multiple life-stage forms, which makes the general fine-grained image classification model difficult to play a role in insect scenes. To this end, based on the Vision Transformer, we design a fine-grained classification network for insect images based on abnormal clustering and cross slicing, called ACCS-Trans. In the first stage of the model, we use segmentation and clustering operations to distinguish the special morphology of insects in those few-shot life stages. The model can avoid the interference of few-sample abnormal morphology on the class feature extraction of the current class during training. The second stage is the cross slicing module, which uses the anchor box of the image sample segmentation region in the first stage to cut the original sample image to form the main target image, which is used as the information supplement area of the original image. Finally, the image is divided into doubling patch groups by two vertical patch operations. In the third stage, we concatenate the multiplication patch group and input it into the Vision Transformer network for class feature extraction. We fully experiment with ACCS-Trans on two insect image datasets. Compared with the current mainstream fine-grained image classification models, ACCS- Trans achieves state-of-the-art effects on both datasets, and we do ablation experiments for each module. The effect of each module on our ACCS- Trans is analyzed. The excellent performance of our ACCS- Trans in insect scenes is verified in these experiments, these provide new ideas for the task of fine-grained classification of insect images.

关键词： Training Image segmentation Computer vision Visualization Insects Image edge detection Morphology Transformers Feature extraction Image classification

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An AoI-Guaranteed Sensor Data Collection Strategy for RIS-assisted UAV Communication System

An AoI-Guaranteed Sensor Data Collection Strategy for RIS-as...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Yang, Bowen Yu, Yao Li, Jianqi Xu, Tongze Deng, Der-Jiunn Chin, Hui-Hsin Li, Yonghui Northeastern University School of Computer Science and Engineering Shenyang China National Changhua University of Education Department of Computer Science and Information Engineering China Overseas Chinese University Department of Information Technology China University of Sydney School of Electrical and Information Engineering Sydney Australia Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9798350345384

The recently proposed concept of Age of Information (AoI) measures the freshness of the sensor data sampled by remote Internet of Things (IoT) devices, which is an important indicator for the smart city. Unmanned Aerial Vehicles (UAV) communication can significantly reduce the sensor data's AoI due to its high maneuverability. However, the UAV's high energy consumption remains a challenging issue. In this paper, we propose a Reconfigurable Intelligent Surface (RIS) assisted UAV data collection framework to minimize UAV energy consumption. By adjusting the phase of the RIS, the UAV can collect more sensor data simultaneously. A RIS phase alignment method is introduced to reduce the complexity of RIS phase solving. The IoT devices are clustered to discretize UAV trajectory. The optimal clustering scheme to maximize the AoI suppression efficiency is found by adjusting the RIS phase alignment points continuously. Therefore, the AoI requirement can be achieved with a shorter UAV flight distance. Finally, the UAV trajectory is solved by the Deep Q network (DQN) algorithm to guarantee the long-term AoI requirement. Simulation results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Maneuverability

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