Computer Science and Information Systems最新文献_第5页

Read between the interactions: Understanding non-interacted items for accurate multimedia recommendation 在交互之间阅读:了解非交互项目，以便进行准确的多媒体推荐

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis221031041k

Jiyeon Kim, Taeri Kim, Sang-Wook Kim

{"title":"Read between the interactions: Understanding non-interacted items for accurate multimedia recommendation","authors":"Jiyeon Kim, Taeri Kim, Sang-Wook Kim","doi":"10.2298/csis221031041k","DOIUrl":"https://doi.org/10.2298/csis221031041k","url":null,"abstract":"This paper addresses the problem of multimedia recommendation that additionally utilizes multimedia data, such as visual and textual modalities of items along with the user-item interaction information. Existing multimedia recommender systems assume that all the non-interacted items of a user have the same degree of negativity, thus regarding them as candidates for negative samples when training the model. However, this paper claims that a user?s non-interacted items do not have the same degree of negativity. We classify these non-interacted items of a user into two kinds of items with different characteristics: unknown and uninteresting items. Then, we propose a novel negative sampling technique that only considers the uninteresting items (i.e., rather than the unknown items) as candidates for negative samples. In addition, we show that using the multiple Bayesian personalized ranking (BPR) losses with both unknown and uninteresting items (i.e., all the non20 interacted items) in existing multimedia recommendation methods is very effective in improving recommendation accuracy. By conducting extensive experiments with three real-world datasets, we show the superiority of our ideas. Our ideas can be easily and orthogonally applied to any multimedia recommender systems.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"39 1","pages":"933-948"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77857088","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

Adaptive multiscale sparse unmixing for hyperspectral remote sensing image 高光谱遥感图像的自适应多尺度稀疏解混

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220828009l

Yalan Li, Qian Du, Yixuan Li, Wenwu Xie, Jing Yuan, Lin Li, Chen Qi

{"title":"Adaptive multiscale sparse unmixing for hyperspectral remote sensing image","authors":"Yalan Li, Qian Du, Yixuan Li, Wenwu Xie, Jing Yuan, Lin Li, Chen Qi","doi":"10.2298/csis220828009l","DOIUrl":"https://doi.org/10.2298/csis220828009l","url":null,"abstract":"Sparse unmixing of hyperspectral images aims to separate the endmembers and estimate the abundances of mixed pixels. This approach is the essential step for many applications involving hyperspectral images. The multi scale spatial sparse hyperspectral unmixing algorithm (MUA) could achieve higher accuracy than many state-of-the-art algorithms. The regularization parameters, whose combinations markedly influence the unmixing accuracy, are determined by manually searching in the broad parameter space, leading to time consuming. To settle this issue, the adaptive multi-scale spatial sparse hyperspectral unmixing algorithm (AMUA) is proposed. Firstly, the MUA model is converted into a new version by using of a maximum a posteriori (MAP) system. Secondly, the theories indicating that andnorms are equivalent to Laplacian and multivariate Gaussian functions, respectively, are applied to explore the strong connections among the regularization parameters, estimated abundances and estimated noise variances. Finally, the connections are applied to update the regularization parameters adaptively in the optimization process of unmixing. Experimental results on both simulated data and real hyperspectral images show that the AMUA can substantially improve the unmixing efficiency at the cost of negligible accuracy. And a series of sensitive experiments were undertook to verify the robustness of the AMUA algorithm.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"15 1","pages":"551-572"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80463118","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

Data-centric UML profile for agroecology applications: Agricultural autonomous robots monitoring case study 农业生态学应用的以数据为中心的UML概要文件:农业自主机器人监测案例研究

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220301064b

S. Bimonte, Hassan Badir, Pietro Battistoni, Houssam Bazza, Amina Belhassena, C. Cariou, G. Chalhoub, Juan F. Corrales, Adrian Couvent, J. Laneurit, Rim Moussa, Julián Eduardo Plazas, M. Sebillo, N. Tricot

{"title":"Data-centric UML profile for agroecology applications: Agricultural autonomous robots monitoring case study","authors":"S. Bimonte, Hassan Badir, Pietro Battistoni, Houssam Bazza, Amina Belhassena, C. Cariou, G. Chalhoub, Juan F. Corrales, Adrian Couvent, J. Laneurit, Rim Moussa, Julián Eduardo Plazas, M. Sebillo, N. Tricot","doi":"10.2298/csis220301064b","DOIUrl":"https://doi.org/10.2298/csis220301064b","url":null,"abstract":"The conceptual design of information systems is mandatory in several application domains. The advent of the Internet of Things (IoT) technologies pushes conceptual design tools and methodologies to consider the complexity of IoT data, architectures, and communication networks. In agroecology applications, the usage of IoT is quite promising, but it raises several methodological and technical issues. These issues are related to the complexity and heterogeneity of data (social, economic, environmental, and agricultural) needed by agroecology practices. Motivated by the lack of a conceptual model for IoT data, in this work, we present a UML profile taking into account different kinds of data (e.g., sensors, stream, or transactional) and non-functional Requirements. We show how the UML profile integrates with classical UML diagrams to support the design of complex systems. Moreover, We prove the feasibility of our conceptual framework through a theoretical quality assessment and its implementation in the agroecology case study concerning the monitoring of autonomous agricultural robots.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"19 1","pages":"459-489"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89845205","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

Efficient neural network accelerators with optical computing and communication 具有光计算和通信功能的高效神经网络加速器

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220131066x

Chengpeng Xia, Yawen Chen, Haibo Zhang, Hao Zhang, Fei Dai, Jigang Wu

{"title":"Efficient neural network accelerators with optical computing and communication","authors":"Chengpeng Xia, Yawen Chen, Haibo Zhang, Hao Zhang, Fei Dai, Jigang Wu","doi":"10.2298/csis220131066x","DOIUrl":"https://doi.org/10.2298/csis220131066x","url":null,"abstract":"Conventional electronic Artificial Neural Networks (ANNs) accelerators focus on architecture design and numerical computation optimization to improve the training efficiency. However, these approaches have recently encountered bottlenecks in terms of energy efficiency and computing performance, which leads to an increase interest in photonic accelerator. Photonic architectures with low energy consumption, high transmission speed and high bandwidth have been considered as an important role for generation of computing architectures. In this paper, to provide a better understanding of optical technology used in ANN acceleration, we present a comprehensive review for the efficient photonic computing and communication in ANN accelerators. The related photonic devices are investigated in terms of the application in ANNs acceleration, and a classification of existing solutions is proposed that are categorized into optical computing acceleration and optical communication acceleration according to photonic effects and photonic architectures. Moreover, we discuss the challenges for these photonic neural network acceleration approaches to highlight the most promising future research opportunities in this field.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"94 1","pages":"513-535"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81747468","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

Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things 将深度学习应用于物联网条件下制造业印刷电路板自动检测系统

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220718020z

Kaiwen Zhang

{"title":"Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things","authors":"Kaiwen Zhang","doi":"10.2298/csis220718020z","DOIUrl":"https://doi.org/10.2298/csis220718020z","url":null,"abstract":"Industry 4.0 is currently the goal of many factories, promoting manufacturing factories and sustainable operation. Automated Optical Inspection (AOI) is a part of automation. Products in the production line are usually inspected visually by operators. Due to human fatigue and inconsistent standards, product inspections still have defects. In this study, the sample component assembly printed circuit board (PCB), PCB provided by the company was tested for surface components. The types of defects on the surface of the PCB include missing parts, multiple parts, and wrong parts. At present, the company is still using visual inspection by operators, the PCB surface components are more complex. In order to reduce labor costs and save the development time required for different printed circuit boards. In the proposed method, we use digital image processing, positioning correction algorithm, and deep learning YOLO for identification, and use 450 images and 10500 components of the PCB samples. The result and contribution of this paper shows the total image recognition rate is 92% and the total component recognition rate reaches 99%, and they are effective. It could use on PCB for different light, different color backplanes, and different material numbers, and the detection compatibility reaches 98%.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"18 1","pages":"723-741"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88056569","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

Sustainability-oriented route generation for ridesharing services 面向可持续性的路线生成，用于拼车服务

4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis221209053l

Mengya Liu, Vahid Yazdanpanah, Sebastian Stein, Enrico Gerding

{"title":"Sustainability-oriented route generation for ridesharing services","authors":"Mengya Liu, Vahid Yazdanpanah, Sebastian Stein, Enrico Gerding","doi":"10.2298/csis221209053l","DOIUrl":"https://doi.org/10.2298/csis221209053l","url":null,"abstract":"Sustainability is the ability to maintain and preserve natural and man made systems for the benefit of current and future generations. The three pillars of sustainability are social, economic, and environmental. These pillars are interdependent and interconnected, meaning that progress in one area can have positive or negative impacts on the others. This calls for smart methods to balance such benefits and find solutions that are optimal with respect to all the three pillars of sustainability. By using AI methods, in particular, genetic algorithms for multiobjective optimisation, we can better understand and manage complex systems in order to achieve sustainability. In the context of sustainability-oriented ridesharing, genetic algorithms can be used to optimise route finding in order to lower the cost of trans portation and reduce emissions. This work contributes to this domain by using AI, specifically genetic algorithms for multiobjective optimisation, to improve the efficiency and sustainability of transportation systems. By using this approach, we can make progress towards achieving the goals of the three pillars of sustainability.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"136 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":"136209122","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

Analyzing feature importance for a predictive undergraduate student dropout model 预测大学生辍学模型的特征重要性分析

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis211110050j

Alberto Jiménez-Macías, Pedro Manuel Moreno-Marcos, P. Muñoz-Merino, Margarita Ortiz-Rojas, C. D. Kloos

{"title":"Analyzing feature importance for a predictive undergraduate student dropout model","authors":"Alberto Jiménez-Macías, Pedro Manuel Moreno-Marcos, P. Muñoz-Merino, Margarita Ortiz-Rojas, C. D. Kloos","doi":"10.2298/csis211110050j","DOIUrl":"https://doi.org/10.2298/csis211110050j","url":null,"abstract":"Worldwide, one of the main concerns of universities is to reduce the dropout rate. Several initiatives have been taken to avoid this problem; however, it is essential to recognize at-risk students as early as possible. This article is an extension of a previous study that proposed a predictive model to identify students at risk of dropout from the beginning of their university degree. The new contribution is the analysis of the feature importance for dropout segmented by faculty, degree program, and semester in the different predictive models. In addition, we propose a dropout model based on faculty characteristics to try to infer the dropout based on faculty features. We used data of 30,576 students enrolled in a Higher Education Institution ranging from years 2000 to 2020. The findings indicate that the variables related to Grade Point Average(GPA), socioeconomic factor, and a pass rate of courses taken have a more significant impact on the model, regardless of the semester, faculty, or program. Additionally, we found a significant difference in the predictive power between Science, Technology, Engineering, and Mathematics (STEM) and humanistic programs.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"6 1","pages":"175-194"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80524241","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

Multi-perspective approach for curating and exploring the history of climate change in Latin America within digital newspapers 在数字报纸中策划和探索拉丁美洲气候变化历史的多视角方法

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220110008v

Genoveva Vargas-Solar, J. Zechinelli-Martini, Javier-Alfonso Espinosa-Oviedo, Luis Manuel Vilches Blázquez

{"title":"Multi-perspective approach for curating and exploring the history of climate change in Latin America within digital newspapers","authors":"Genoveva Vargas-Solar, J. Zechinelli-Martini, Javier-Alfonso Espinosa-Oviedo, Luis Manuel Vilches Blázquez","doi":"10.2298/csis220110008v","DOIUrl":"https://doi.org/10.2298/csis220110008v","url":null,"abstract":"This paper introduces a multi-perspective approach to deal with curation and exploration issues in historical newspapers. It has been implemented in the platform LACLICHEV (Latin American Climate Change Evolution platform). Exploring the history of climate change through digitalized newspapers published around two centuries ago introduces four challenges: (1) curating content for tracking entries describing meteorological events; (2) processing (digging into) colloquial language (and its geographic variations5) for extracting meteorological events; (3) analyzing newspapers to discover meteorological patterns possibly associated with climate change; (4) designing tools for exploring the extracted content. LACLICHEV provides tools for curating, exploring, and analyzing historical news paper articles, their description and location, and the vocabularies used for referring to meteorological events. This platform makes it possible to understand and identify possible patterns and models that can build an empirical and social view of the history of climate change in the Latin American region.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"11 1","pages":"1179-1205"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81007825","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

Intrusion detection model of internet of things based on deep learning 基于深度学习的物联网入侵检测模型

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis230418058w

Yan Wang, Dezhi Han, Mingming Cui

{"title":"Intrusion detection model of internet of things based on deep learning","authors":"Yan Wang, Dezhi Han, Mingming Cui","doi":"10.2298/csis230418058w","DOIUrl":"https://doi.org/10.2298/csis230418058w","url":null,"abstract":"The proliferation of Internet of Things (IoTs) technology is being seriously impeded by insecure networks and data. An effective intrusion detection model is essential for safeguarding the network and data security of IoTs. In this pa per, a hybrid parallel intrusion detection model based on deep learning (DL) called HPIDM features a three-layer parallel neural network structure. Combining stacked Long short-term memory (LSTM) neural networks with convolutional neural net work (CNN) and SK Net self-attentive mechanism in the model allows HPIDM to learn temporal and spatial features of traffic data effectively. HPIDM fuses the acquired temporal and spatial feature data and then feeds it into the CosMargin classifier for classification detection to reduce the impact of data imbalance on the 23 performance of the Intrusion Detection System (IDS). Finally, HPIDM was experimentally compared with classical intrusion detection models and the two comparative models designed in this paper, and the experimental results show that HPIDM achieves 99.87% accuracy on the ISCX-IDS 2012 dataset and 99.94% accuracy on the CICIDS 2017 dataset. In addition, it outperforms other comparable models in terms of recall, precision, false alarm rate (FAR), and F1 score, showing its feasibility and superiority.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"1519-1540"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464346","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

Ensemble of top3 prediction with image pixel interval method using deep learning 基于深度学习的图像像素间隔方法集成top3预测

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis230223056a

Abdulaziz Anorboev, Javokhir Musaev, Sarvinoz Anorboeva, Jeongkyu Hong, Yeong-Seok Seo, N. Nguyen, D. Hwang

{"title":"Ensemble of top3 prediction with image pixel interval method using deep learning","authors":"Abdulaziz Anorboev, Javokhir Musaev, Sarvinoz Anorboeva, Jeongkyu Hong, Yeong-Seok Seo, N. Nguyen, D. Hwang","doi":"10.2298/csis230223056a","DOIUrl":"https://doi.org/10.2298/csis230223056a","url":null,"abstract":"Computer vision (CV) has been successfully used in picture categorization applications in various fields, including medicine, production quality control, and transportation systems. CV models use an excessive number of photos to train potential models. Considering that image acquisition is typically expensive and time-consuming, in this study, we provide a multistep strategy to improve image categorization accuracy with less data. In the first stage, we constructed numerous datasets from a single dataset. Given that an image has pixels with values ranging from 0 to 255, the images were separated into pixel intervals based on the type of dataset. The pixel interval was split into two portions when the dataset was grayscale and five portions when it was composed of RGB images. Next, we trained the model using both the original and newly constructed datasets. Each image in the training process showed a non-identical prediction space, and we suggested using the top three prediction probability ensemble technique. The top three predictions for the newly created images were combined with the corresponding probability for the original image. The results showed that learning patterns from each interval of pixels and ensembling the top three predictions significantly improve the performance and accuracy, and this strategy can be used with any model.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"1503-1517"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464386","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