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Density estimation-based method to determine sample size for random sample partition of big data

Frontiers of computer Science 2024年第5期18卷 57-70页

作者： Yulin HE Jiaqi CHEN Jiaxing SHEN Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing and Decision Sciences Lingnan UniversityHong Kong 999077China

Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation *** research and practical applications have confirmed that RSP is an efficient solution for big data processing and ***,a challenge for implementing RSP is determining an appropriate sample size for RSP data *** a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RSP data *** address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RSP data ***,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)***,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RSP data blocks for an increasing sample ***,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of *** results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of *** demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation.

关键词： random sample partition big data sample size Dvoretzky-Kiefer-Wolfowitz inequality kerneldensity estimator probability density function

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CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for Large Language Models

CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for ...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Zhang, Qin Cai, Sihan Zhao, Jiaxu Pechenizkiy, Mykola Fang, Meng College of Computer Science and Software Engineering Shenzhen University China Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9798891761681

Ambiguity is an inherent feature of language, whose management is crucial for effective communication and collaboration. This is particularly true for Chinese, a language with extensive lexical-morphemic ambiguity. Despite the wide use of large language models (LLMs) in numerous domains and their growing proficiency in Chinese, there is a notable lack of datasets to thoroughly evaluate LLMs' ability to handle ambiguity in Chinese. To bridge this gap, we introduce the CHAmbi dataset, a specialized Chinese multi-label disambiguation dataset formatted in Natural Language Inference. It comprises 4,991 pairs of premises and hypotheses, including 824 examples featuring a wide range of ambiguities. In addition to the dataset, we develop a series of tests and conduct an extensive evaluation of pre-trained LLMs' proficiency in identifying and resolving ambiguity in the Chinese language. Our findings reveal that GPT-4 consistently delivers commendable performance across various evaluative measures, albeit with limitations in robustness. The performances of other LLMs, however, demonstrate variability in handling ambiguity-related tasks, underscoring the complexity of such tasks in the context of Chinese. The overall results highlight the challenge of ambiguity handling for current LLMs and underscore the imperative need for further enhancement in LLM capabilities for effective ambiguity resolution in the Chinese language. © 2024 Association for Computational Linguistics.

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AutoMESC: Automatic Framework for Mining and Classifying Ethereum Smart Contract Vulnerabilities and Their Fixes 49

AutoMESC: Automatic Framework for Mining and Classifying Eth...

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49th Euromicro Conference on software engineering and Advanced Applications, SEAA 2023

作者： Soud, Majd Qasse, Ilham Liebel, Grischa Hamdaqa, Mohammad Reykjavik University Department of Computer Science Reykjavik Iceland Polytechnique Montreal Department of Computer and Software Engineering Montreal Canada

ISBN: (纸本)9798350342352

Due to the risks associated with vulnerabilities in smart contracts, their security has gained significant attention in recent years. However, there is a lack of open datasets on smart contract vulnerabilities and their fixes that allows for data-driven research. Towards this end, we propose an automated framework for mining and classifying Ethereum's smart contract vulnerabilities and their corresponding fixes from GitHub and from the Common Vulnerabilities and Exposures (CVE) records in the National Vulnerability Database. We implemented the proposed method in a fully automated framework, which we call AutoMESC. AutoMESC uses seven of the most well-known smart contract security tools to classify and label the collected vulnerabilities based on vulnerability types. Furthermore, it collects metadata that can be used in data-intensive smart contract security research (e.g., vulnerability detection, vulnerability classification, severity prediction, and automated repair). We used AutoMESC to construct a sample dataset and made it publicly available. Currently, the dataset contains 6.7K smart contract vulnerability-fix pairs written in Solidity. We assess the quality of the constructed dataset in terms of accuracy, provenance, and relevance, and compare it with existing datasets. AutoMESC is designed to collect data continuously and keep the corresponding dataset up-to-date with newly discovered smart contract vulnerabilities and their fixes from GitHub and CVE records. © 2023 IEEE.

关键词： Smart contract

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models 16

VisioSLR: A Vision Data-Driven Framework for Sign Language V...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Ismail, Leila Shahin, Nada Tesfaye, Henok Hennebelle, Alain Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Transfer learning

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EF-IMR: Embedded Flash with Interlaced Magnetic Recording Technology

EF-IMR: Embedded Flash with Interlaced Magnetic Recording Te...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Ma, Chenlin Zheng, Xiaochuan Sun, Kaoyi Wang, Tianyu Wang, Yi College of Computer Science and Software Engineering Shenzhen University China State Key Laboratory of Radio Frequency Heterogeneous Integration China

ISBN: (纸本)9783982674100

Interlaced Magnetic Recording (IMR), a technology that improves storage density through track overlap, introduces significant latency due to Read-Modify-Write (RMW) operations. Writing to overlapped tracks affects underlying tracks, requiring additional I/O operations to read, back up, and rewrite them, resulting in significant head movement latency. We propose EF-IMR, a new architecture that ensures crash consistency in IMR while minimizing RMW latency and head movement. EF-IMR reduces head movement during RMW operations and decreases redundant RMW operations. Evaluations under real-world, intensive I/O workloads show that EF-IMR reduces RMW latency by 20.11 % and head movement latency by 89.37% compared to existing methods. © 2025 EDAA.

关键词： Magnetic tape storage

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Enhancing Cloud Resource Management through Predictive Neural Networks and Reinforcement Learning 15

Enhancing Cloud Resource Management through Predictive Neura...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Lakshya Khan, Mohammad Ilham Hasan Bajarange, Anish P. Delhi Technological University Software Engineering Delhi India Jamia Millia Islamia Electrical Engineering Delhi India Parul University Computer Science and Engineering Gujarat India

ISBN: (纸本)9798350370249

Prior strategies for scaling microservices encompassed various techniques, including diverse processing approaches and mathematical models. However, these methodologies often exhibited limitations in predictive accuracy and management complexity. This study employs Reinforcement Learning as an innovative alternative, particularly the Double Deep Q-Networks Algorithm. This approach autonomously monitors and evaluates essential scaling parameters, promptly triggering on-site actions when warranted. It adeptly balances the dual aspects of environment exploitation and exploration, optimizing computational resource usage while enhancing cost-effectiveness. Empirical evaluations substantiate the superiority of this approach in dynamically scaling cloud services with precision and resource optimization. By integrating LSTM and RNN predictive capabilities with Reinforcement Learning, this research contributes to efficient cloud resource management, a vital facet of contemporary cloud computing paradigms. © 2024 IEEE.

关键词： autoscaling Average Memory Utilization SLA double DQN reinforcement learning

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IsoTGAN: Spatial and Geometrical Constraints at GAN and Transformer for 3D Contour Generation 17th

IsoTGAN: Spatial and Geometrical Constraints at GAN and Tr...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Phuong, Thao Nguyen Duy, Vinh Nguyen Sakaino, Hidetomo FPT Consulting Japan FPT Software Minato Japan Department of Computer Science and Communications Engineering Waseda University Shinjuku Japan

ISBN: (纸本)9789819626434

Image generation in 2D and 3D has become an active research topic in Deep Learning. Single or multiple input images with non-orthogonal views are used for another shape and texture with different viewing angles. On the other hand, computer-Aided Design (CAD) relies on handling-based 3D generation, i.e., isometric view images, from three orthographic view line drawings in 2D. However, since unique viewing pairs of such 2D and 3D images are required to train, SOTA models are insufficient to generate desirable images. More spatial and geometrical constraints remain undone due to less corresponding image features between images. This paper proposes IsoTGAN with a GAN-based generator with the Transformer in its generator, where three images of an object’s front, side, and top view are input. The encoder is trained by the spatial and geometrical relation among three view images. A novel Gaussian Enhanced Euclidean attention mechanism and a geometry-constrained loss function are also proposed for further local image feature enhancement. Extensive experiments on the SPARE3D dataset demonstrate that the proposed IsoTGAN outperforms State-of-the-art (SOTA) models, i.e., DINO, regarding local and global image feature accuracy. This helps generate 3D isometric view images in auto-CAD system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D generation attention CAD GAN Isometric view Transformer

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Efficient High-Precision Classification Algorithm for Radar Deceptive Jamming via Array Detection

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Progress in Electromagnetics Research C 2025年 155卷 29-35页

作者： Liu, Wenjie Qin, Wenfang Shen, Mingwei Han, Guodong College of Information Science and Engineering Hohai University Jiangsu Changzhou213200 China College of Computer Science and Software Engineering Hohai University Jiangsu Nanjing211106 China The 54th Research Institute of CETC Hebei Shijiazhuang050081 China

In complex electromagnetic scenarios where multiple deceptive jamming signals are simultaneously aliased in the time frequency domain, conventional single-channel electronic detection systems struggle to effectively separate and classify overlapping jamming sources. To address this limitation, this paper investigates an array detection-based classification scheme for multi-source jamming. First, due to the spatial degrees of freedom offered by array systems, the Direction-of-Arrival (DOA) of each jamming source is precisely estimated using the MUSIC algorithm. Then, the adaptive digital beamforming (ADBF) filters are designed based on the estimated DOAparameters, enabling spatial-domain extraction of individual jamming signals. Finally, the DOA information is integrated into the Pulse Description Word (PDW) of each separated jamming measurement, which can facilitate adaptive K-Means clustering with enhanced class separability. Simulation results demonstrate that the proposed method achieves a 22.4% improvement in classification accuracy compared to existing single-channel detection approaches, while maintaining computational efficiency. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Frequency response

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Image Representations of Numerical Simulations for Training Neural Networks

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computer Modeling in engineering & Sciences 2023年第2期134卷 821-833页

作者： Yiming Zhang Zhiran Gao Xueya Wang Qi Liu School of Civil and Transportation Engineering Hebei University of TechnologyTianjin300401China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China

A large amount of data can partly assure good fitting quality for the trained neural *** the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality *** the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation ***,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and *** this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by *** temporal and spatial information is kept and the data are greatly *** addition,the results are readable for not only computers but also human *** examples are given,indicating the effectiveness of the proposed strategy.

关键词： Numerical simulations neural network pre-/post-processing data compression

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Optimization of Heliostat Field Arrangement Model Based on Geometric Relationship and Particle Swarm Algorithm 4

Optimization of Heliostat Field Arrangement Model Based on G...

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4th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Liu, Zhishuai Wei, Zhengyang Li, Jiangnan School of Mathematics Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9798350359589

With the continuous progress of photovoltaic (PV) technology and the steady reduction of related costs, solar energy, as an important renewable energy source, shows an increasingly strong competitiveness in energy market competition. In the tower solar thermal power station, the arrangement of the heliostat field directly affects the power generation efficiency of the tower power generation system as well as the working cost. Therefore, this paper presents a model based on geometric relationship and particle swarm algorithm for the optimization of the heliostat field arrangement, mathematical modeling and calculating cosine efficiency, truncation efficiency, etc., and effectively improves the output thermal power as well as the optical efficiency of the heliostat field. Geometric planning is utilized to determine the location of the absorption tower, the coordinates of the heliostat arrangement and other layout parameters to optimize the layout of the tower solar system. Drawing on the idea of clustering, the model analyzes the characteristics of the heliostat mirrors in the same region, uses same or similar parameters to minimize the cost of computation, improving the overall optical efficiency of the mirror field. The particle swarm algorithm is utilized to solve the parameters such as mirror length and mirror width of the heliostat to get the suitable size of the heliostat for the heliostat mirror field. After completing all the calculation and optimization steps, the final solution of the model is carried out in this paper. The layout scheme of the heliostat field optimized by the implementation of the model gets a significant performance improvement. Specifically, the average annual thermal power output of the heliostat field is improved by 33.2309 MW, while the average annual optical efficiency is also improved by 43.2%. These improvements effectively enhance the power generation efficiency of the whole system, confirming the effectiveness of the optimization

关键词： Campo arrangement method Geometric relationship Mirror field optimization arrangement Particle swarm algorithm Spatial transformation

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