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HP3C 2022 - 2022 6th international conference on High Performance Compilation, computing and Communications

HP3C 2022 - 2022 6th International Conference on High Perfor...

引用

6th international conference on High Performance Compilation, computing and Communications, HP3C 2022

ISBN: (纸本)9781450396295

the proceedings contain 34 papers. the topics discussed include: large-scale parallel exact diagonalization algorithm of the Hubbard model on Tianhe-2 supercomputer;reinforcement learning enabled throughput optimization for interconnection networks of interposer-based system;a node selection scheme for data repair using erasure code in distributed storage system;research on recognition method of textual implication;research on used car valuation problem based on model fusion;an efficient parallel architecture for convolutional neural networks accelerator on FPGAs;performance optimization of sparse deep neural networks based on GPU;a quantum group signature based on quantum walk in d dimensions;parallel performance and optimization of the lattice Boltzmann method software Palabos using CUDA;secure mechanism of intelligent urban railway cloud platform based on zero-trust security;and a digital signature virtual platform based on hardware-software co-design.

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international Public Opinion Analysis in COVID-19 Pandemic from Mainstream and Social Media 6

International Public Opinion Analysis in COVID-19 Pandemic f...

引用

6th international conference on Data Science and Information Technology, DSIT 2023

作者： Yu, Xingyu Zhang, Yu Wang, Chunling Zhang, Yijia Dalian Maritime University School of Information Science and Technology Liaoning Dalian116024 China Dalian University of Technology School of Marxism Liaoning Dalian China

ISBN: (纸本)9798350304442

COVID-19 has become a global pandemic, and it is crucial to study and assess international public opinion during this period. In this study, we have chosen China as a typical case to analyze international public opinion during COVID-19. Our research aims to address the following questions: (1) What is the correlation between the results of public opinion analysis and factors such as polarity, the density of active words, and the density of pronouns? (2) What aspects of China do foreign media focus on during the COVID-19 period, and what are their public opinion analysis results in these areas? (3) Does the severity of the epidemic affect the degree of concern of foreign media on China? To conduct our analysis, we collected 735,566 pieces of data from GDELT and millions of pieces of data from Twitter as the object of our analysis. We combined adversarial learning for zero-shot sentiment analysis and PLS (Partial least squares regression) to propose a new method for public opinion analysis. Our model analysis revealed that the results of public opinion analysis were related to several factors, including polarity, active words, and the proportion of pronouns. Among these factors, polarity had the most significant influence. © 2023 IEEE.

关键词： COVID-19

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Emotions in Video Games: An Investigation of Game Mechanic Influences 6

Emotions in Video Games: An Investigation of Game Mechanic I...

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6th international conference on applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Ali, Nurwathiqah Tajuddin, Sharul Bramantoro, Arif Universiti Teknologi Brunei School of Computing and Informatics Jalan Tungku Link Gadong BE1410 Brunei

ISBN: (纸本)9798350317428

An increase in various video game development has contributed significantly to the growth of eSports, reflected in millions of active users on game distribution platforms. the foundation of most video games is game mechanics, potentially capable of keeping players interested. Although it helps in the development of video games, there is also a notable research gap in understanding the potential negative consequences game mechanics could have on the emotional state of players, and could result in serious mental health problems, such as addiction, despair, and anxiety. this paper aims to investigate how game mechanics affect emotional state, providing an insight into the game design influences. To address the problem and provide a comprehensive understanding of the effects of video games on players' emotional states, this study proposed to identify the effect of different mechanics from a review of existing video games on emotional states employed through a mixed-method approach, combining qualitative and quantitative methods. To ensure players are familiar with game mechanics and in-game terms, participants were chosen from the experienced 'League of Legend' players who are at least level 10 in the game. A questionnaire was used based on the five-point scale rating system and the Positive and Negative Affect Schedule (PANAS) scale. the findings revealed that combat (core), collectible (feedback), and multiplayer (social) game mechanics, have a significant positive correlation to the positive emotions of the PANAS scale, thus presenting a more nuanced and balanced perspective on the impact of game mechanics on the emotional state of players. © 2023 IEEE.

关键词： Software design

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Managerial Budget Improvements Using Data Analytics 6

Managerial Budget Improvements Using Data Analytics

引用

6th international conference on computing, Electronics and Communications Engineering, iCCECE 2023

作者： Pushkarna, Shubhi Walcott, Terry H. University of Wales Trinity Saint David School of Applied Computing Swansea United Kingdom

ISBN: (纸本)9798350340921

Covid-19 has created a series of unknowns that have afforded organisations, large and small, with an opportunity to appraise their spending and general budgeting practices. In so doing, the average consumer is now impacted in such a manner that improvements in terms of addressing budget shortcomings are now paramount to any form of success. Henceforth, the budget management learning curve does require organisations generally to manage finances and staffing demands equally. Owing to this the speed of engagement is heightened and a measured approach for arriving at solid results are necessitated. Henceforth, this study will demonstrate the need to comprehend budgets and align each to the core business processes of a given organisation. Having done so, a schema of approaches underpinned by data analytics can be harnessed for addressing the deficiencies faced in businesses generally. To ensure that the needs of most businesses can be met, this paper will also demonstrate fundamental business drivers for collating business expenses and business projections from historical records. In so doing, results garnered have indicated that contemporary methodologies associated to data analytics;overlooked budgeting areas are highlighted;business growth opportunities are initiated;and evidential suggestions and budget optimization situations are considered by all businesses and consumers. © 2023 IEEE.

关键词： Budget control

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A New Feature Fusion Method Based on Pre-Training Model for Sequence Labeling 6

A New Feature Fusion Method Based on Pre-Training Model for ...

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6th international conference on Data Storage and Data Engineering, DSDE 2023

作者： Yu, Shichuan Yang, Yan School of Computer Science and Technology Heilongjiang University Harbin China Heilongjiang University Heilongjiang Key Laboratory of Database and Parallel Computing Harbin China

ISBN: (纸本)9798350333145

To fuse vocabulary features into the pre-training model is the mainstream data feature processing method for sequence labelling tasks. In general, the feature fusion methods that have been proposed at present are direct fusion outside the pre-training model or fusion of lexical features using attention mechanism. However, the study found that this way of vocabulary enhancement does not conform to the word formation rules of modern Chinese. In the Chinese language, it is easy to fuse irrelevant or even incorrect lexical features into the sequence using the above feature processing methods, which is bad for the experimental results of the Chinese sequence labelling task. To solve these problems, we propose to use Cosine Similarity Adapter to process lexical features in Chinese sequence labelling tasks. CSBERT is a hybrid model using this structure based on BERT, which conforms to the word formation rules of modern Chinese to a certain extent. It can fuse the features of the word into the character or eliminate the features of the word in the character according to the cosine similarity between the character vector and a word vector. the experimental results show that CSBERT has better ability to label Chinese sequences than the benchmark model. CSBERT has achieved the best experimental results such as F1-Score on 7 open datasets and the best ability of multi-label classification, which proves that the model has good practical value. © 2023 IEEE.

关键词： Classification (of information)

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ALSP: Adaptive Long Short-term Preference Modeling with Temporal Graph Neural Networks 6

ALSP: Adaptive Long Short-term Preference Modeling with Temp...

引用

6th international conference on Data Science and Information Technology, DSIT 2023

作者： Chi, Huixuan Hao, Qinfen SKLP Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350304442

Modeling users' long-term and short-term (LS-term) preferences is crucial for accurate recommendation. Recently, continuous-time sequential recommendation tasks succeed in modeling the evolution of user preferences. However, existing works have not yet addressed three main challenges. Firstly, LS-term preferences reflect quite different aspects of user preferences. Secondly, user behaviors in their rich historical records often include implicit and noisy preference signals. Lastly, the importance of two kinds of preferences varies in different user-item interactions. In this paper, to address the challenges, we propose a novel framework Adaptive Long Short-term Preference (ALSP) Modeling with temporal graph neural networks (TGNN). Specifically, we first introduce two TGNNs with collaborative signals to capture users' LS-term preferences over different time scales. then we minimize the distance of the respective representations of LS-term preferences and maximize the distance between LS-term preferences by the contrastive module. Finally, we design a fusion network that aggregates the LS-term preferences adaptively. We conduct experiments on four real-world datasets for continuous-time sequential recommendation tasks. the experimental results demonstrate a significant performance improvement of the ALSP compared to seven state-of-the-art (SOTA) methods. © 2023 IEEE.

关键词： Behavioral research

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parallel implementation for dynamic mesh optimization on distributed computer system 22

Parallel implementation for dynamic mesh optimization on dis...

引用

6th High Performance computing and Cluster Technologies conference, HPCCT 2022

作者： Tang, Jing Zhang, Jian Wu, Xiao-Jun Zhang, Yao-Bin Zhou, Nai-Chun Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang621000 China

ISBN: (纸本)9781450396646

the large scale computer system provides a high performance platform for engineering applications. Mesh generation is the basis for numerical simulation for computing science, which heavily relies on user' experience and may not match the demand of application itself. Dynamic mesh optimization according to the features of application is an automatic and smart way during the parallel simulation. the shared-memory parallelism on a standalone computer is the main strategy for mesh generation which is regarded as a pre-processing of simulation. However, it is not suitable for dynamic mesh optimization which is involved in parallel simulation commonly on distributed computer system. In this paper, the parallel implementation on distributed system is established. A two-stages method was proposed for mesh parallel editing to make new mesh objects parallel consistent. the parallel repartition-migration method was devised to recover the load balance for numerical simulation after mesh editing. parallel performance test shows that parallel mesh editing is a communication dense procedure and so the parallel efficiency drops as the computing cores increase. © 2022 ACM.

关键词： Mesh generation

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Analysis to Improve Classifier Accuracy in Crime Data Prediction 6

Analysis to Improve Classifier Accuracy in Crime Data Predic...

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6th international conference on computing Methodologies and Communication, ICCMC 2022

作者： Menaka, M. Booba, B. Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Chennai India

ISBN: (纸本)9781665410281

Crime is a global issue that has the potential to harm a country's social and economic well-being. Controlling crime is an unavoidable step that is necessary for a nation's welfare and long-term development. Exposing crimes and the vulnerable conditions which are constantly impacted by their unlawful activity in this digital world is a herculean task. the difficulty of analysing a huge volume of data about crimes as well criminals is a main task of law enforcing officers. Data mining provides us with a number of practical and convenient methods for evaluating large and varied sets of data. It assists organisations and users in uncovering hidden data from a massive database of crime records in order to investigate, control and prevent crime. By applying a blend of computer science and criminal justice, a data mining process can be created which can help in solving crimes more rapidly. the primary goal of this paper is to discuss various classification algorithms, namely decision tree, random forest, and multilayer perceptron and their role in classifying crime data. the work examines crime characteristics of communities and crime data set taken from UCI machine learning repository. To improve accuracy, pre-processing techniques like missing value replacement and manual feature selection based on human expertise are applied to the crime data set. the results of experiments for the classifier models include accuracy, precision, sensitivity, and F-Measure. According to the results, random forest outperforms all other classifiers in predicting criminal activities. © 2022 IEEE.

关键词： Decision trees

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Prediction of Chronic Diseases Using parallel 1D-CNN Feature Extraction and SVM Classification Based on SMOTE 4th

Prediction of Chronic Diseases Using Parallel 1D-CNN Feature...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Mesbah, Fatma Zohra Tassadit Ait Abidine, M'hamed Bilal Fergani, Belkacem Univ Sci & Technol Houari Boumediene USTHB Lab Ingn Syst Intelligents & Commun LISIC Fac Elect Engn BP 32 El Alia Algiers 16111 Algeria

ISBN: (纸本)9783031821523;9783031821530

Due to their severity and prevalence, the prediction of chronic diseases (CDs) has become an important area of research, particularly with advances in deep learning. In this paper, we propose a new automated technique for detecting these diseases. A deep network architecture using a parallel unidimensional convolutional neural network (1D-PCNN) is employed to extract deep features. Subsequently, the Support Vector Machine (SVM) technique is applied for CD classification. the uniqueness of our framework lies in the design of the 1D-PCNN, which can learn the deep features of the input layer through parallel convolutional layers. As a result, the deep features of each parallel branch are simultaneously extracted before being combined in the fusion layer. Furthermore, in order to improve the efficiency of the proposed model, the Synthetic Minority Oversampling Technique (SMOTE) is used. this strategy manages class imbalance in CD databases. the suggested model is analysed against standard 1D-CNN, 1D-CNN model combined with conventional machine learning methods and other existing state-of-the-art models. the effectiveness of the suggested method was tested using two known databases the Pima Indian Diabetes Database (PIDD) and the Cleveland Heart Disease Database (CHDD). Results, from these databases indicate that the proposed approach yielded an accuracy rate of 83% and 88% an F score of 73% and 90% and an AUC of 80% and 87% correspondingly. Finally, after applying the SMOTE method, accuracy was improved to 86% and 92%, Fscore to 86% and 93%, and AUC to 86% and 92%, respectively, and outperforms other methods.

关键词： Chronic diseases Data classification SMOTE parallel unidimensional convolutional neural network (1D-PCNN) SVM

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Physical design of fusion target with edge computing 6

Physical design of fusion target with edge computing

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2022 6th international conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2022

作者： Gaoyangliu Peng, Dongfang Xiong, Wenbin Gao, Qiang Qu, Fanyu Tang, Zhangchun Liu, Pan Guangzhou EMU Depot of China Railway Guangzhou Co. Ltd. Guangzhou511442 China Nuclear and Radiation Safety Center MEE. Beijing102445 China China National Nuclear Power Co. Ltd. Beijing100045 China University of Electronic Science and Technology of China Sichuan Chengdu610000 China Tianfu Innovation Energy Establishment Chengdu610000 China

the physical design of the fusion target is an important part of controlled thermonuclear fusion, and the geometric model and material selection of the target is also critical to achieving fusion ignition. We have modularised the target and introduced digital modeling, edge computing, and deep learning technologies to build a data-driven hybrid computing framework. We construct physical models and integrate them into a unified digital model of a multi-domain system;simulate and iteratively modify the physical model, and use edge computing technologies for information modeling. Edge computing is well applied to the calculation of each module of the target. Each module is both correlated and independent, and the values of the fusion ignition temperature and density achieved in the target are obtained, and the neutron products in the ignition and main fuel regions are 1016 - 1017 and 1019 respectively. this will be an important reference value for the design of actual fusion targets. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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