Computer Science and Information Systems最新文献_第7页

Guest editorial - Management of digital ecosystems 客座社论-数字生态系统的管理

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis230100viib

D. Benslimane, Z. Maamar, Ladjel Bellatreche

引用次数: 0

A novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition 一种新的多层堆叠的SqueezeNet手写体汉字识别模型

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis221210030d

Yuankun Du, F. Liu, Zhilong Liu

{"title":"A novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition","authors":"Yuankun Du, F. Liu, Zhilong Liu","doi":"10.2298/csis221210030d","DOIUrl":"https://doi.org/10.2298/csis221210030d","url":null,"abstract":"To solve the problems of large number of similar Chinese characters, difficult feature extraction and inaccurate recognition, we propose a novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition. First, we design a deep convolutional neural network model for feature grouping extraction and fusion. The multilevel stacked feature group extraction module is used to extract the deep abstract feature information of the image and carry out the fusion between the different feature information modules. Secondly, we use the designed down-sampling and channel amplification modules to reduce the feature dimension while preserving the important information of the image. The feature information is refined and condensed to solve the overlapping and redundant problem of feature information. Thirdly, inter-layer feature fusion algorithm and Softmax classification function constrained by L2 norm are used. We further compress the parameter clipping to avoid the loss of too much accuracy due to the clipping of important parameters. The dynamic network surgery algorithm is used to ensure that the important parameters of the error deletion are reassembled. Experimental results on public data show that the designed recognition model in this paper can effectively improve the recognition rate of handwritten Chinese characters.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"1771-1795"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FSASA: Sequential recommendation based on fusing session-aware models and self-attention networks FSASA:基于会话感知模型和自关注网络融合的顺序推荐

4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis230522067g

Shangzhi Guo, Xiaofeng Liao, Fei Meng, Qing Zhao, Yuling Tang, Hui Li, Qinqin Zong

{"title":"FSASA: Sequential recommendation based on fusing session-aware models and self-attention networks","authors":"Shangzhi Guo, Xiaofeng Liao, Fei Meng, Qing Zhao, Yuling Tang, Hui Li, Qinqin Zong","doi":"10.2298/csis230522067g","DOIUrl":"https://doi.org/10.2298/csis230522067g","url":null,"abstract":"The recommendation system can alleviate the problem of ?information overload?, tap the potential value of data, push personalized information to users in need, and improve information utilization. Sequence recommendation has become a hot research direction because of its practicality and high precision. Deep Neural Networks (DNN) have the natural advantage of capturing comprehensive relations among different entities, thus almost occupying a dominant position in sequence recommendation in the past few years. However, as Deep Learning (DL)-based methods are widely used to model local preferences under user behavior sequences, the global preference modeling of users is often underestimated, and usually, only some simple and crude user latent representations are introduced. Therefore, this paper proposes a sequential recommendation based on Fusing Session-Aware models and Self-Attention networks (FSASA). Specifically, we use the Self-Attentive Sequential Recommendation (SASRec) model as a global representation learning module to capture long-term preferences under user behavior sequences and further propose an improved session-aware sequential recommendation model as a local learning representation module from user model the user?s dynamic preferences in the historical behavior, and finally use the Gated Recurrent Unit (GRU) module to calculate their weights. Experiments on three widely used recommendation atasets show that FSASA outperforms state-of-the-art baselines on two commonly used metrics.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135446194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic voltage stabilization system for substation using deep learning 基于深度学习的变电站自动稳压系统

4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220509050m

Jiyong Moon, Minyeong Son, Byeongchan Oh, Jeongpil Jin, Younsoon Shin

{"title":"Automatic voltage stabilization system for substation using deep learning","authors":"Jiyong Moon, Minyeong Son, Byeongchan Oh, Jeongpil Jin, Younsoon Shin","doi":"10.2298/csis220509050m","DOIUrl":"https://doi.org/10.2298/csis220509050m","url":null,"abstract":"The operating voltage in the substation must be maintained at its rated voltage within the specified standard because a voltage outside the specified range may cause a malfunction of the power facility and interfere with the stable power supply. Therefore, the voltage regulation process to maintain the rated voltage of the substation is essential for the stability of the power system. However, the voltage regulation process is currently performed manually by resident staff. Voltage regulation based on human judgment increases the uncertainty of voltage stabilization and makes efficient operation in consideration of the economic feasibility of power facilities difficult. Therefore, this paper proposes an automatic voltage stabilization system that can automatically perform voltage regulation. Instead of predicting the electrical load or overvoltage conditions studied so far, we focus on more direct, scalable input capacity prediction for an automatic voltage stabilization system. First, the proposed system predicts the input capacity required for a given situation through a trained stacked LSTM model. Second, an optimal regulation plan is derived through an optimization process that considers the economic feasibility of power facility operation. Additionally, the development of the user interface makes it possible to visualize the operation of algorithms and effectively communicate the models? predictions to the user. Experimental results based on real substation data show that the proposed system can effectively automate the voltage regulation process.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"364 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The effective skyline quantify-utility patterns mining algorithm with pruning strategies 基于剪枝策略的有效天际线量化效用模式挖掘算法

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220615040w

J. Wu, Ranran Li, Pi-Chung Hsu, Mu-En Wu

{"title":"The effective skyline quantify-utility patterns mining algorithm with pruning strategies","authors":"J. Wu, Ranran Li, Pi-Chung Hsu, Mu-En Wu","doi":"10.2298/csis220615040w","DOIUrl":"https://doi.org/10.2298/csis220615040w","url":null,"abstract":"Frequent itemsetmining and high-utility itemsetmining have been widely applied to the extraction of useful information from databases. However, with the proliferation of the Internet of Things, smart devices are generating vast amounts of data daily, and studies focusing on individual dimensions are increasingly unable to support decision-making. Hence, the concept of a skyline query considering frequency and utility (which returns a set of points that are not dominated by other points) was introduced. However, in most cases, firms are concerned about not only the frequency of purchases but also quantities. The skyline quantity-utility pattern (SQUP) considers both the quantity and utility of items. This paper proposes two algorithms, FSKYQUP-Miner and FSKYQUP, to efficiently mine SQUPs. The algorithms are based on the utility-quantity list structure and include an effective pruning strategy which calculates the minimum utility of SQUPs after one scan of the database and prunes undesired items in advance, which greatly reduces the number of concatenation operations. Furthermore, this paper proposes an array structure superior to utilmax for storing the maximum utility of quantities, which further improves the efficiency of pruning. Extensive comparison experiments on different datasets show that the proposed algorithms find all SQUPs accurately and efficiently.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"14 1","pages":"1085-1108"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82491348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing data locality by executor allocation in spark computing environment spark计算环境下通过执行器分配优化数据局部性

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220131065f

Zhongming Fu, Mengsi He, Zhuo Tang, Yang Zhang

{"title":"Optimizing data locality by executor allocation in spark computing environment","authors":"Zhongming Fu, Mengsi He, Zhuo Tang, Yang Zhang","doi":"10.2298/csis220131065f","DOIUrl":"https://doi.org/10.2298/csis220131065f","url":null,"abstract":"Data locality is an important concept in big data processing. Most of the existing research optimized data locality from the aspect of task scheduling. However, as the execution container of tasks, the executors started on which nodes can directly affect the locality level achieved by the tasks. This paper tries to improve the data locality by executor allocation for reduce stage in Spark computing environment. Firstly, we calculate the network distance matrix of executors and formulate an optimal executor allocation problem to minimize the total communication distance. Then, when the network distance between executors satisfies the triangular inequality, an approximate algorithm is proposed; and when the network distance between executors does not satisfy the triangular inequality, a greedy algorithm is proposed. Finally, we evaluate the performance of our algorithms in a practical Spark cluster by using several representative micro-benchmarks (Sort and Join) and macro-benchmarks (PageRank and LDA). Experimental results show that the proposed algorithms can decrease the execution time of tasks for lower data communication.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"3 1","pages":"491-512"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75509879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Content-only attention network for social recommendation 只关注内容的社交推荐网络

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220705012w

Bin Wu, Zhang Tao, Yeh-Cheng Chen

引用次数: 0

Internet of things and agent-based system to improve water use efficiency in collective irrigation 利用物联网和代理系统提高集体灌溉用水效率

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220227062i

Abdelouafi Ikidid, A. E. Fazziki, Mohamed Sadgal

{"title":"Internet of things and agent-based system to improve water use efficiency in collective irrigation","authors":"Abdelouafi Ikidid, A. E. Fazziki, Mohamed Sadgal","doi":"10.2298/csis220227062i","DOIUrl":"https://doi.org/10.2298/csis220227062i","url":null,"abstract":"The efficient management of water resources is a major issue in the field of sustainable development. Several models of solving this problem can be found in the literature, especially in the agricultural sector which represents the main consumer through irrigation. Therefore, Irrigation management is an important and innovative field that has been the subject of several types of research and studies to deal with the different activities, behaviors, and conflicts between the different users. This article introduces an intelligent irrigation system based on smart sensors that can be used moderately and economically to monitor farms by integrating some connected electronic devices and other advantageous instruments widely used in the field of IoT, it determines the water requirement of each farm according to the water loss due to the process of evapotranspiration. The water requirement is calculated from data collected from a series of sensors installed in the plantation farm. This project focuses on smart irrigation based on IoT and agent technology, it can be used by farmer associations whose endowments and irrigation planning are defined according to the need and quantity of water available in the rural municipality. The system includes a microcontroller with the integration of sensors, actuators, and valve modules where each node serves as an IoT device. Environmental parameters are monitored directly through a multi-agent system that facilitates the control of each node and the configuration of irrigation parameters. The amount of water calculated for irrigation is based on the Penman model for calculating the daily evapotranspiration baseline. Compared to the conventional irrigation method, it is expected that the proposed irrigation model would contribute to saving water use and distributing it impartially without compromising its production.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"35 1","pages":"405-421"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75098809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A hierarchical federated learning model with adaptive model parameter aggregation 具有自适应模型参数聚合的分层联邦学习模型

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220930026c

Zhuo Chen, Chuan Zhou, Yang Zhou

{"title":"A hierarchical federated learning model with adaptive model parameter aggregation","authors":"Zhuo Chen, Chuan Zhou, Yang Zhou","doi":"10.2298/csis220930026c","DOIUrl":"https://doi.org/10.2298/csis220930026c","url":null,"abstract":"With the proposed Federated Learning (FL) paradigm based on the idea of ?data available but invisible?, participating nodes which create or hold data can perform local model training in a distributed manner, then a global model can be trained only by continuously aggregating model parameters or inter mediate results from different nodes, thereby achieving a balance between data privacy protection and data sharing. However, there are some challenges when deploying a FL model. First, there may be hierarchical associations between participating nodes, so that the datasets held by each node are no longer independent of each other. Secondly, due to the possible abnormal delay of data transmission, it can seriously influence the aggregation of model parameters. In response to the above challenges, this paper proposes a newly designed FL framework for the participating nodes with hierarchical associations. In this framework, we design an adaptive model parameter aggregation algorithm, which can dynamically decide the aggregation strategy according to the state of network connection between nodes in different layers. Additionally, we conduct a theoretical analysis of the convergence of the proposed FL frame work based on a non-convex objective function. Finally, the experimental results show that the proposed framework can be well applied to applications in different network connections, and can achieve faster model convergence efficiency while ensuring the accuracy of the model prediction.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"1 1","pages":"1037-1060"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89977483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Large-scale image classification with multi-perspective deep transfer learning 基于多视角深度迁移学习的大规模图像分类

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220714015w

Bin Wu, Tao Zhang, Mao Li

引用次数: 0