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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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BClean: A Bayesian data Cleaning System 40

BClean: A Bayesian Data Cleaning System

引用

40th ieee international conference on data Engineering, ICDE 2024

作者： Qin, Jianbin Huang, Sifan Wang, Yaoshu Zhu, Jing Zhang, Yifan Miao, Yukai Mao, Rui Onizuka, Makoto Xiao, Chuan Shenzhen University China Shenzhen Institute of Computing Science China Zhongguancun Laboratory China Osaka Univeristy Japan Nagoya University Japan

ISBN: (纸本)9798350317152

There is a considerable body of work on data cleaning which employs various principles to rectify erroneous data and transform a dirty dataset into a cleaner one. One of prevalent approaches is probabilistic methods, including Bayesian methods. However, existing probabilistic methods often assume a simplistic distribution (e.g., Gaussian distribution), which is frequently under-fitted in practice, or they necessitate experts to provide a complex prior distribution (e.g., via a programming language). This requirement is both labor-intensive and costly, rendering these methods less suitable for real-world applications. In this paper, we propose BClean, a Bayesian Cleaning system that features automatic Bayesian network construction and user interaction. We recast the data cleaning problem as a Bayesian inference that fully exploits the relationships between attributes in the observed dataset and any prior information provided by users. To this end, we present an automatic Bayesian network construction method that extends a structure learning-based functional dependency discovery method with similarity functions to capture the relationships between attributes. Furthermore, our system allows users to modify the generated Bayesian network in order to specify prior information or correct inaccuracies identified by the automatic generation process. We also design an effective scoring model (called the compensative scoring model) necessary for the Bayesian inference. To enhance the efficiency of data cleaning, we propose several approximation strategies for the Bayesian inference, including graph partitioning, domain pruning, and pre-detection. By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0.9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%. © 2024 ieee.

关键词： Bayesian networks

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Temperature and Energy Optimization in GPUs: An Illustration 10

Temperature and Energy Optimization in GPUs: An Illustration

引用

10th Indian Control conference, ICC 2024

作者： Das, Rajarshi Rao, Sachit Qualcomm India Pvt Ltd Bangalore560 066 India The International Institute of Information Technology Bangalore Bangalore560 100 India

ISBN: (纸本)9798331517212

Optimizing energy consumption of Graphics Processing Units (GPUs) has received considerable interest. Unlike Central Processing Units (CPUs), where energy consumption can be reduced by scaling the core clock frequency dynamically, for a class of workloads, denoted as compute-bound workloads, experimental data indicate that GPUs (the variants considered in this paper) consume the lowest energy at the highest core frequency, thus also leading to the lowest workload runtimes. Operating at the highest frequency, however, leads to a significant increase in the GPU temperature - thus, minimizing both temperature and energy become conflicting objectives. In this work, the simultaneous optimization of energy consumption and GPU temperature is posed as a Multi-Objective Optimization Problem (MOOP) and is solved using the GICOV kernel, borrowed from the medical imaging domain, as an illustration. Experimental data reveal that the speed of the on-board GPU fan and the clock frequency can be used as control variables to solve the MOOP. Pareto-optimal solutions are presented using classical and evolutionary approaches;both yield the same solutions. These solutions show that lower GPU temperatures can be obtained in comparison with default GPU settings. Two additional results are presented: the implementation of a dynamic frequency governor i. as a feedback controller to control temperature alone and ii. as the solution to the Brachistochrone problem to minimize runtime. © 2024 ieee.

关键词： Graphics processing unit

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Research on Power Security Control Technology Based on Multimode data Fusion 4

Research on Power Security Control Technology Based on Multi...

引用

4th ieee international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Shi, Ruida Zhao, Hua Luo, Huafeng Wang, Liuwang Fan, Weidong Nari Group Corporation/State Grid Electric Power Research Institute Nanjing211106 China Nanjing Nari Information & Communication Technology Co. Ltd. Nanjing211106 China State Grid Zhejiang Electric Power Co. Ltd. Hangzhou310011 China

ISBN: (纸本)9798350363586

Power grid operation, equipment maintenance, construction and other power grid field operations, the task is heavy, many points and wide. There are many dangerous factors in the process of power grid operation, including human factors, environmental factors, equipment factors, management factors and other influencing factors. Aiming at the problems of single mode and complex factor, a multi-mode fusion method is proposed. Based on the operation ticket mechanism in the "two votes and three systems"of the power system, under the supervision of the original traditional IP layout and control ball, various influencing factors on the site are collected as comprehensively as possible combined with sensors such as blood oxygen, gas concentration, tension, pressure and inclination Angle, and the information characteristics feedback from these factors are utilized across modes. Multi-level fusion method is adopted to fuse the text features extracted by BERT and the image features extracted by Faster R-CNN at an early stage, which improves the normalization of the operation process. By making decision fusion of image features and sensor features extracted by LSTM, the alarm of potential hazards is advanced. Under the multi-mode integration, the average level of overall risk prevention and control on the job site has been improved. © 2024 ieee.

关键词： Power grids

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WGS_YOLO_dual: A Detection Model for Strip Steel Surface Defects Based on Attention Mechanism and Spatial Pyramid Pooling Structure

WGS_YOLO_dual: A Detection Model for Strip Steel Surface Def...

引用

cognitive computing and complex data (ICCD), international conference on the

作者： Qi Liu Yong Zhong Wenin Yang Ziliang Liu Jinli Ruan Zhongrong Zeng Foshan University Foshan China Guangdong Strong Metal Technology Co. Ltd Foshan China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Surface defect detection in strip steel is crucial for metal manufacturing and processing. Yet, current detection methods frequently lack precision. This paper introduces the WGS_YOLO_dual model, which leverages attention mechanisms and spatial pyramid pooling to enhance detection. The model incorporates a GAM module to fuse channel and spatial data, boosting interaction between dimensions. It also swaps out SPPELAN for SPPFCSPC in its backbone to manage varying defect scales more effectively. Additionally, it employs the WIOU loss function in place of CIOU to enhance accuracy. Tested on the NEU-DET dataset, WGS_YOLO_dual showed a 1.9% boost in mAP50, a 2.3% rise in mAP50-95, and an 0.8% increase in recall.

关键词： Strips Accuracy Pedestrians Computational modeling Refining data models Steel Surface treatment Defect detection Context modeling

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Improved YOLOv8n Helmet Detection Algorithm for Missed and False Detection

Improved YOLOv8n Helmet Detection Algorithm for Missed and F...

引用

cognitive computing and complex data (ICCD), international conference on the

作者： Jing Li Chengjun Qiu Yuangan Wang Song Wang College of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China Faculty of Electronics and Information Technology Beibu Gulf University Qinzhou China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Aiming at the missed and false detection of helmet-wearing detection caused by a small target size or target being occluded in complex working scenarios. An improved helmet-wearing detection algorithm based on YOLOv8n is proposed. First, utilizing the BiFPN enhances the performance in detecting small targets and enhances the capability for generalization and feature fusion. Second, the incorporation of the CA attention mechanism into the backbone network aims to minimize the chances of misclassification, and enhance the precision of detection, thus optimizing the model performance. Finally, Introducing the SioU loss function improves the robustness of the detection. It is concluded from the experiments that the average accuracy of helmet-wearing detection improved from 93.0% to 94.9%, and the average accuracy of non-helmet-wearing detection improved from 91.3% to 92.4%, which is 1.9% and 1.1% respectively. The results show that the improved YOLOv8n algorithm can effectively improve the detection accuracy when the target size is small or the occlusion is missing.

关键词： Head Accuracy Attention mechanisms Feature extraction Robustness Safety Detection algorithms cognitive systems

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EEG Brainwave data Classification of a Confused Student Using Moving Average Feature 3

EEG Brainwave Data Classification of a Confused Student Usin...

引用

3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Mehta, Jay N. Lakhani, Hairya Dave, Harsh Degadwala, Sheshang Vyas, Dhairya University of Cincinnati College of Medicine OH United States Smt.B.K.Shah Medical Institute & Research Centre Gujarat India Sigma University Department of Computer Engineering Gujarat Vadodara India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (纸本)9798350322842

The measurement of electrical activity in the brain, known as Electroencephalogram (EEG), is a common non-invasive diagnostic method used to detect neurological disorders and investigate cognitive processes such as memory, attention, and learning. Nonetheless, classifying and interpreting EEG data can be challenging due to the signals' complex and noisy nature. This research study examines the classification of EEG data from a student whose brainwave patterns were irregular during academic challenges. The proposed study has first processed the data by smoothing the signals using a moving average feature, and then a variety of deep and machine learning methods are used to categorize the data. Our results demonstrate that the student's EEG data was unique and did not fit within established categories. Our analysis also revealed that the technology used to collect the data may have contributed to the irregular patterns. However, the data can be accurately classified by utilizing a deep learning approach. In addition to highlighting the value of properly processing the data to remove noise and artefacts, this research study demonstrates the potential of both deep learning and machine learning techniques in the interpretation of EEG data. Additionally, the research findings suggest that EEG data classification should consider individual brain activity differences rather than solely relying on existing categories. © 2023 ieee.

关键词： Electroencephalography

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A Deep Learning-Based Multi-Layer Dynamic Multivariate Prediction Model for Power System Time Series data Prediction Model 6

A Deep Learning-Based Multi-Layer Dynamic Multivariate Predi...

引用

6th international conference on Industrial Artificial Intelligence, IAI 2024

作者： Du, Hangyuan Dong, Hui Ma, Dazhong Hu, Xuguang School of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University Shenyang China

ISBN: (纸本)9798350356618

Multivariate time series forecasting is an important but challenging problem in future power system. Existing fore-casting models struggle to handle the increasing amount of time series data due to the continuous upgrading of system structures, leading to a decrease in model performance and making it difficult to meet the accurate prediction and response speed requirements of the system. To address this issue, this paper proposes a multilayer dynamic multi-type prediction model based on deep learning, designed to adapt to the complex and diverse dynamic changes brought about by the high uncertainty of renewable energy sources in future power systems. Firstly, the model introduces a dynamic variable convolutional structure to address the different application scenarios of multiple regions and roles in real power systems. Secondly, a three-task prediction framework is proposed to adapt to the multi-scale features of temporal information in power systems. Then, an imbalanced prediction structure is designed to concentrate computing re-sources on the main tasks. Finally, to meet the fast response requirements of the prediction system, a combination of linear and nonlinear structures is proposed to construct a dynamic multivariate forecasting model, simplifying the network structure design and accelerating training speed while ensuring prediction performance. Experiments on three real datasets demonstrate the strong generalization performancestrong generalization performance and effectiveness of this model in complex multivariate time series forecasting. © 2024 ieee.

关键词： Prediction models

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Software Testing Combining the Fusion of Surrogate Model and Evolutionary Algorithm

Software Testing Combining the Fusion of Surrogate Model and...

引用

2023 international conference on cognitive computing and complex data, ICCD 2023

作者： Dang, Xiangying Bao, Wenzheng Qu, Qian Li, Dan Xuzhou University of Technology School of Information Engineering School of Big Data Xuzhou China Yancheng Institute of Technology School of Information Engineering Yancheng China

ISBN: (纸本)9798350306293

The primary challenge is the extensive number of iterations required to converge on suitable solutions in using Genetic Algorithms (GA) for test data generation. Each iteration involves executing candidate solutions and evaluating their fitness values, resulting in prolonged time consumption. This limitation can impede the scalability and efficiency of the evolutionary algorithm, particularly in large-scale or time-critical software testing scenarios. Addressing this issue is crucial for enhancing test data generation efficiency. The method we proposed to mitigate this problem is by fusing surrogate models that approximate the fitness values of candidate solutions instead of executing them on the actual software system. The proposed method typically begins with initial candidate solutions (test data), which along with their fitness values, can be used as a training set to develop the surrogate model. Additional samples may be collected during the evolutionary process to enhance the model's accuracy. Subsequently, the surrogate model is employed to evaluate the fitness values of new candidate solutions. Solutions with high predicted fitness values are then selected as the initial population to execute the program and undergo the genetic operations. The preliminary experimental results show that by utilizing surrogate models, the need for numerous evaluations on the actual system can be significantly reduced, enabling faster convergence and improved efficiency in test data generation. © 2023 ieee.

关键词： Software testing

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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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Evaluation of cognitive computing and Algorithms in Engineering

Evaluation of Cognitive Computing and Algorithms in Engineer...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Pavel A. Panilov Tatiana Y. Tsybizova Maxim A. Kocheshkov Georgy A. Voskresensky Kirill P Grishin Bauman Moscow State Technical University Moscow Russian Federation

Modern engineering practice includes complex decision-making processes that require analyzing huge data and taking into account many factors. cognitive computing is a way of analyzing information that can enhance the objectivity and accuracy of decision-making. The article discusses the use of cognitive computing in data analysis, revealing hidden patterns, forecasting and prediction, as well as determining optimal solutions in engineering practice. Particular attention is paid to cognitive decision-making algorithms that take into account many factors and adapt to a changing environment, contributing to the objectivity of decisions. To visualize and evaluate the contribution of cognitive computing to improving the efficiency of engineering projects, the article presents graphs and diagrams demonstrating various aspects and advantages of their application. Special attention is paid to the analysis of the role of cognitive computing in forecasting and prediction, which contributes to the optimization of project cycles and improvement of results.

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