Proceedings of the 2021 10th International Conference on Software and Computer Applications最新文献_第4页

Untyped lambda calculus with functionally referable environments 具有功能可引用环境的无类型lambda演算

Proceedings of the 2021 10th International Conference on Software and Computer Applications Pub Date : 2021-02-23 DOI: 10.1145/3457784.3457798

S. Nishizaki, Ryotaro Kasuga

{"title":"Untyped lambda calculus with functionally referable environments","authors":"S. Nishizaki, Ryotaro Kasuga","doi":"10.1145/3457784.3457798","DOIUrl":"https://doi.org/10.1145/3457784.3457798","url":null,"abstract":"The environment is the relationship between variables and their bound values during program execution and is a notion in program semantics. A first-class environment is a mechanism that allows the environment to be treated like data, such as integer values or Boolean values, and can be passed to a function as an argument or received as a return value. The environment calculus is a formal computational system proposed by Nishizaki and is a lambda calculus that extends the first-class environment mechanism. The formulation of the environment was based on explicit substitution by Curien et al., who viewed the environment as a substitution. The operational semantics of the environmental calculus, or the reduction, is based on the reduction of the lambda-sigma calculus. In the calculus, there are two constructs for first-class environments: one is the identity environment to reify the current environment, that is, to transfer a meta-level environment to object-level data; the other is the environment composition to reflect the object-level environment data, that is, to transfer object-level environment data back to a meta-level environment. In this paper, instead of the environment composition, we propose a new interface with a first-class environment, a functionally referable environment. If object-level environment data is given as an argument for a function application, the functional reflection brings the environment back to the meta-level and makes the lambda term evaluable under that environment. Using the functionally referable environment, one can unify the environment composition with the function application. We define the untyped lambda calculus with functionally referable environments: we give the syntax of the calculus and its reduction. Then we provide the semantics for the reduction using a translation of the environment calculus into the record calculus. We prove the soundness of the translation semantics. Finally, we discuss the evaluation strategy, especially the call-by-value reduction.","PeriodicalId":373716,"journal":{"name":"Proceedings of the 2021 10th International Conference on Software and Computer Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121290006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The role of industrial digitization in modularized supply chain management 工业数字化在模块化供应链管理中的作用

Proceedings of the 2021 10th International Conference on Software and Computer Applications Pub Date : 2021-02-23 DOI: 10.1145/3457784.3457818

Lan Wang, Ping Wang, Xiang-kun Zeng

引用次数: 0

Towards a Service-Driven Model for Industry-4.0 迈向服务驱动的工业4.0模式

Proceedings of the 2021 10th International Conference on Software and Computer Applications Pub Date : 2021-02-23 DOI: 10.1145/3457784.3457787

Zakaria Benzadri, Takieddine Bouheroum, Youcef Ouassim Cheloufi, F. Belala, M. Hassani

{"title":"Towards a Service-Driven Model for Industry-4.0","authors":"Zakaria Benzadri, Takieddine Bouheroum, Youcef Ouassim Cheloufi, F. Belala, M. Hassani","doi":"10.1145/3457784.3457787","DOIUrl":"https://doi.org/10.1145/3457784.3457787","url":null,"abstract":"In recent years, the digital transformation, characterizing the fourth generation of the industry, has emerged as a promising technological framework for supporting manufacturing processes at different levels. In the fourth-generation industry, the Smart Factory model covers several forms of future industrial systems. In particular, it refers to an interconnected system that links machines, management methods and products. In practice, this interconnection is only achievable if we adopt an architectural design that reduces the complexity of such a system. In this thought, Cyber-Physical Systems (CPSs) are recent complex systems, subject to distributed control, cooperation, influence, cascading effects and emerging behaviors. However, in the context of Industry 4.0, few research attempts are interested in integrating CPSs to study, design and implement more intelligent manufacturing systems. The main contribution of this paper is to propose a service-driven approach, based on CPS, to model intelligent production systems. We specify static and behavioral aspects of these complex and distributed systems, in terms of services, interfaces and choreography. Then, an executable model is deduced to implement and validate our proposal through a realistic case study, using IBM Rational Software Architect Designer (RSAD) tool. This allows us to study in depth the manufacturing process of a grader in an Algerian company (SOMATEL-ENMTP) and identify a set of findings to move this factory towards industry 4.0, while developing a digital plan.","PeriodicalId":373716,"journal":{"name":"Proceedings of the 2021 10th International Conference on Software and Computer Applications","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121080547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Convolutional Matrix Completion via Relation Reconstruction 通过关系重建的图卷积矩阵补全

Proceedings of the 2021 10th International Conference on Software and Computer Applications Pub Date : 2021-02-23 DOI: 10.1145/3457784.3457792

Chang Su, Min Chen, Xianzhong Xie

{"title":"Graph Convolutional Matrix Completion via Relation Reconstruction","authors":"Chang Su, Min Chen, Xianzhong Xie","doi":"10.1145/3457784.3457792","DOIUrl":"https://doi.org/10.1145/3457784.3457792","url":null,"abstract":"To alleviate sparsity and improve recommender systems performance, it is necessary to go beyond modeling user-item interactions and take auxiliary information into account. Besides user-item interactions, auxiliary information can be used to build relation graphs. Recently, Graph Convolution Networks (GCNs), which can integrate content information and structural information of nodes, have been demonstrated to be powerful in learning on graph data and applied in recommendation systems. However, existing approaches do not consider multiple types of relations between nodes and high-order structural information. In this paper, we propose a new model called Graph Convolutional Matrix Completion via relation reconstruction (RE-GCMC) to capture structural information and relations between nodes in the graph. We construct user-user, item-item, and user-item relation graphs by evaluating the feature similarity of the nodes. Then, we introduce the Graph Convolutional Networks (GCNs) and self-attention mechanism to be applied in the graphs to refine feature embeddings. We apply the proposed model to four datasets and experimental results demonstrate that our approach outperforms state-of-the-art recommender baselines.","PeriodicalId":373716,"journal":{"name":"Proceedings of the 2021 10th International Conference on Software and Computer Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116930015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Cost Modeling and Analysis of the Consumer Price Index in the Philippines 菲律宾消费者物价指数的成本模型与分析

Proceedings of the 2021 10th International Conference on Software and Computer Applications Pub Date : 2021-02-23 DOI: 10.1145/3457784.3457836

Jefferson A. Costales

{"title":"Cost Modeling and Analysis of the Consumer Price Index in the Philippines","authors":"Jefferson A. Costales","doi":"10.1145/3457784.3457836","DOIUrl":"https://doi.org/10.1145/3457784.3457836","url":null,"abstract":"Rising expenses and uncontrolled market movements necessitate technology solutions to arm individual's adequate information for better decisions. These technologies supported processes can help people to strategize to budget their everyday expenses. One of the indicators to determine the prices of the commodity in the markets is called the Consumer Price Index (CPI). According to the Philippine Statistical Authority-formerly National Statistics Office, CPI is an indicator of the change in the average retail prices of a fixed basket of goods and services commonly purchased by households relative to a base year. It is a major statistical series used for economic analysis and as a monitoring indicator of government economic policy. Moreover, CPI is most widely used in the calculation of the inflation rate and purchasing power of the peso. The researchers utilized the data came from the official website of the Philippine Statistical Authority from 2000 to 2015. The researchers also adopted the modern forecasting techniques called the Box-jenkins time series analysis. This is a method to form a mathematical model designed to forecast a time series. The researchers sought to find the best model using the said forecasting method from January 2015 to December 2017. The results show that the plot of the Consumer Price Index of the Philippines. It can be seen that CPI is increasing with some fluctuation. In addition, the average CPI from 2000 to 2015 is 108.20 while the highest CPI in that span of time is 142.6 that happened December 2015 and the lowest CPI is 75.3 during January 2000. The analysis also includes the testing of the stationarity of the data using augmented dickey fuller, error diagnostics and model adequacy. Finally, the researchers arrived the best mathematical model that can predict the future values of the CPI based on the AIC as criteria in selecting the best model is SARIMA(1,1,0)(1,0,0){12} with drift.","PeriodicalId":373716,"journal":{"name":"Proceedings of the 2021 10th International Conference on Software and Computer Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114907663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Local Mean k-General Nearest Neighbor Classifier 局部均值-一般最近邻分类器

Proceedings of the 2021 10th International Conference on Software and Computer Applications Pub Date : 2021-02-23 DOI: 10.1145/3457784.3457828

N. Mukahar, B. A. Rosdi

{"title":"Local Mean k-General Nearest Neighbor Classifier","authors":"N. Mukahar, B. A. Rosdi","doi":"10.1145/3457784.3457828","DOIUrl":"https://doi.org/10.1145/3457784.3457828","url":null,"abstract":"The well-known k-Nearest Neighbor classifier is a simple and flexible algorithm that has sparked wide interest in pattern classification. In spite of its straightforward implementation, the kNN is sensitive to the presence of noisy training samples and variance of the distribution. Local mean based k-nearest neighbor rule has been developed to overcome the negative effect of the noisy training sample. In this article, the local mean rule is implemented with the general nearest neighbors that are selected in a more generalized way. A new local mean based nearest neighbor classifier is proposed termed Local Mean k-General Nearest Neighbor (LMkGNN). The proposed LMkGNN classifier finds the local mean vector from general nearest neighbors of each class and classifies the test sample based on the distances between the test sample and local mean vectors. Fifteen real-world datasets from the UCI machine learning repository are used to assess and evaluate the classification performance of the proposed classifier. The performance comparison is also made with five benchmark classifiers (kNN, PNN, LMkNN, LMPNN and kGNN) in terms of the classification accuracy. Experimental results demonstrate that the proposed LMkGNN classifier performs significantly well and obtain the best classification accuracy com-pared to the five competing classifiers.","PeriodicalId":373716,"journal":{"name":"Proceedings of the 2021 10th International Conference on Software and Computer Applications","volume":"19 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128826672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Improved Security System in Smart Wheelchairs for Detecting Stair Descent using Image Analysis 基于图像分析的智能轮椅楼梯下降检测系统改进

Proceedings of the 2021 10th International Conference on Software and Computer Applications Pub Date : 2021-02-23 DOI: 10.1145/3457784.3457808

Fitri Utaminingrum, Ahmad Wali Satria Bahari Johan, Y. A. Sari, I. K. Somawirata, Abass A. Olaode

{"title":"The Improved Security System in Smart Wheelchairs for Detecting Stair Descent using Image Analysis","authors":"Fitri Utaminingrum, Ahmad Wali Satria Bahari Johan, Y. A. Sari, I. K. Somawirata, Abass A. Olaode","doi":"10.1145/3457784.3457808","DOIUrl":"https://doi.org/10.1145/3457784.3457808","url":null,"abstract":"A smart wheelchair requires a security system for its users to feel safe and comfortable. The process of observing road conditions is one of the solutions to maintaining user safety, which one of these hurdles can be a sudden transition of the situation in surface road height level for example, such as a descending staircase. Integration system for safety in smart wheelchairs consists of three main parts, namely input (Camera), output (Driver motor Left and Right), and main processing (Mini PC). The proposed research will be carried out stair decent detection using a Gray Level Co-occurrence matrix (GLCM) algorithm method as an extraction feature algorithm. The usage of GLCM methods can be applied to images that have textures. While if we look at the descent of the stairs also has a different texture when compared to the usual floor. Support Vector Machine (SVM) is used for the classification of stairs descent and floor. SVM algorithms have advantageous in it is effortless and strong consistency of implementation in classification. In this research propose combination methods between the texture features using GLCM and classification method using SVM to obtain effective detection stairs descent and floor.The proposed method by setting the GLCM parameter with a value of d = 1 and θ = 135o, and SVM classification using the Radial Basis Function Kernel (RBF Kernel) has an accuracy of 87 for detecting the stair descent with relatively fast computation time equal to 0.007 second.","PeriodicalId":373716,"journal":{"name":"Proceedings of the 2021 10th International Conference on Software and Computer Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131757529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Identification of Business Intelligence in Big Data Maintenance of Government Sector in Putrajaya 布城政府部门大数据维护中的商业智能识别

Proceedings of the 2021 10th International Conference on Software and Computer Applications Pub Date : 2021-02-23 DOI: 10.1145/3457784.3457816

Ain Farhana Jamaludin, M. N. Razali, Rohaya jalil, S. H. Othman, Y. Adnan

引用次数: 1

Predicting Movie Box-Office Success and The Main Determinants of Movie Box Office Sales in Malaysia using Machine Learning Approach 使用机器学习方法预测马来西亚电影票房成功和电影票房销售的主要决定因素

Proceedings of the 2021 10th International Conference on Software and Computer Applications Pub Date : 2021-02-23 DOI: 10.1145/3457784.3457793

Y. Cheang, Chye Cheah Tan

{"title":"Predicting Movie Box-Office Success and The Main Determinants of Movie Box Office Sales in Malaysia using Machine Learning Approach","authors":"Y. Cheang, Chye Cheah Tan","doi":"10.1145/3457784.3457793","DOIUrl":"https://doi.org/10.1145/3457784.3457793","url":null,"abstract":"A movie's box office is the revenue generated by the movie via ticket sales. Predicting the success of a movie in the box office is never easy. There are many factors that could potentially affect movie box office, such as its reviews and ratings, star power, genre, seasonality, and et cetera. This study aims to explore the most vital factors that influence the Malaysian box office, and to build an accurate predictive model that is tailored to this market using knowledge discovery in databases (KDD) methodology. Movie data will be obtained from FINAS [1], Box Office Mojo [2] and IMDb [3], so that it can be cleaned and processed. The cleaned dataset will be used to build support vector machine (SVM), neural networks (NN) and multilayer perceptron (MLP) models. This paper analyses the efficiency of the three models to predict the box-office success of movies, while analysing the influence of variables. At the end of the study, the most suitable model will be selected. The analysis shows that the most important factors that influence movie box office are movie budget and movie review scores for both local and international movies. In addition, the multilayer perceptron (MLP) model with its accuracy of 0.7529 is the best fit model to predict Malaysia box office for local movies. On the other hand, for predicting the Malaysia box office performance of international movies, neural network (NN) is the best fit model with an accuracy of 0.6171.","PeriodicalId":373716,"journal":{"name":"Proceedings of the 2021 10th International Conference on Software and Computer Applications","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126587890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0