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A Meta-model for Key Performance Indicators in Higher Education 高等教育关键绩效指标的元模型
IF 0.4 Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.09
G. Savić, M. Segedinac, Milan Čeliković, I. Luković
We propose a software solution for representing diverse sets of key performance indicators in higher education. Our solution addresses both the heterogeneity and the common structure of key performance indicators. To tackle the issue of heterogeneity, we employ metamodeling and propose a meta-model that is expressive and generic enough to represent any set of key performance indicators in higher education. The proposed meta-model is more abstract than any specific key performance indicators set, and the sets are considered as models, which are instances of the proposed metamodel. We address the heterogeneity in calculating the key performance indicators' values by representing them with mathematical formulas and utilizing an expression language that allows for their dynamic evaluation. We verified the solution by representing typical key performance indicator sets and developing a software application prototype that enables the creation, monitoring, and further development of key performance indicator sets. The verification confirms the wide applicability of our proposed solution.
我们提出了一个软件解决方案来表示高等教育中不同的关键绩效指标。我们的解决方案解决了关键绩效指标的异质性和共同结构。为了解决异质性的问题,我们采用元模型并提出了一个元模型,该模型具有足够的表现力和通用性,可以表示高等教育中的任何一组关键绩效指标。所建议的元模型比任何特定的关键性能指标集更抽象,并且这些集合被视为模型,它们是所建议的元模型的实例。我们通过用数学公式表示关键绩效指标值并使用允许动态评估的表达语言来解决计算关键绩效指标值的异质性。我们通过表示典型的关键性能指标集和开发一个软件应用程序原型来验证该解决方案,该原型支持关键性能指标集的创建、监视和进一步开发。验证证实了我们提出的解决方案的广泛适用性。
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引用次数: 0
An analysis of using binary JSON versus native JSON on the example of Oracle DBMS 以Oracle DBMS为例,分析使用二进制JSON与本机JSON的对比
IF 0.4 Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.10
Srđa Bjeladinović, Milica Škembarević, Olga Jejic, Marko Asanović,
JSON is a popular and proven standard for specifying self-describing text files with a flexible structure. To maintain its position in the market, Oracle introduced support for JSON data in the 12c R1 version of its DBMS. This version has introduced functions for storing and managing JSON data in native form but also showed some limitations. Each new version introduced new or updated JSON functions. The 21c can store JSON data in binary form, provides more straightforward syntax and even supports JSON as a predefined data type. The paper aims to compare the performance when the underlying storage of JSON is native or binary. A data model and seven use cases were designed to demonstrate earlier and new functionalities. Additionally, experiments showed the impact of JSON data stored in native (19c and 21c) and binary form (21c) on the average execution time and costs of SQL statements.
JSON是一种流行且经过验证的标准,用于指定具有灵活结构的自描述文本文件。为了保持其在市场上的地位,Oracle在其DBMS的12c R1版本中引入了对JSON数据的支持。这个版本引入了以本机形式存储和管理JSON数据的函数,但也有一些限制。每个新版本都引入了新的或更新的JSON函数。21c可以以二进制形式存储JSON数据,提供更直接的语法,甚至支持JSON作为预定义的数据类型。本文旨在比较JSON的底层存储是本机存储还是二进制存储时的性能。设计了一个数据模型和七个用例来演示早期的和新的功能。此外,实验还显示了以原生(19c和21c)和二进制(21c)形式存储的JSON数据对SQL语句的平均执行时间和成本的影响。
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引用次数: 0
LVRF: A Latent Variable Based Approach for Exploring Geographic Datasets LVRF:一种基于潜变量的地理数据集挖掘方法
IF 0.4 Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.02
Liangdong Deng, Arpan Mahara, N. Rishe, Malek Adjouadi
Geographic datasets are usually accompanied by spatial non-stationarity – a phenomenon that the relationship between features varies across space. Naturally, nonstationarity can be interpreted as the underlying rule that decides how data are generated and alters over space. Therefore, traditional machine learning algorithms are not suitable for handling non-stationary geographic datasets, as they only render a single global model. To solve this problem, researchers often adopt the multiple-local-model approach, which uses different models to account for different sub-regions of space. This approach has been proven efficient but not optimal, as it is inherently difficult to decide the size of subregions. Additionally, the fact that local models are only trained on a subset of data also limits their potential. This paper proposes an entirely different strategy that interprets nonstationarity as a lack of data and addresses it by introducing latent variables to the original dataset. Backpropagation is then used to find the best values for these latent variables. Experiments show that this method is at least as efficient as multiple-local-model-based approaches and has even greater potential.
地理数据集通常伴随着空间非平稳性——一种特征之间的关系在空间上变化的现象。当然,非平稳性可以解释为决定数据如何生成和随空间变化的基本规则。因此,传统的机器学习算法不适合处理非平稳地理数据集,因为它们只呈现单一的全局模型。为了解决这一问题,研究人员通常采用多局部模型方法,即使用不同的模型来考虑空间的不同子区域。这种方法已被证明是有效的,但不是最佳的,因为确定分区域的大小本身就很困难。此外,局部模型只在数据子集上训练的事实也限制了它们的潜力。本文提出了一种完全不同的策略,将非平稳性解释为缺乏数据,并通过向原始数据集引入潜在变量来解决这个问题。然后使用反向传播来找到这些潜在变量的最佳值。实验表明,该方法至少与基于多局部模型的方法一样有效,并且具有更大的潜力。
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引用次数: 1
Diameter-2-critical graphs with at most 13 nodes 直径为2的临界图,最多有13个节点
IF 0.4 Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.11
Jovan G. Radosavljević
Diameter-2-critical graphs (abbr. D2C) are diameter 2 graphs whose diameter increases by removing any edge. The procedure used to obtain the list of D2C graphs of the order at most 13 is described. This is achieved by incorporating the diameter 2 test and the criticality test into geng, the program from the package nauty that generates the list of all non-isomorphic connected graphs. Experiments with the two heuristics in diameter 2 test, which is intensively used during the search, show that it is slightly more efficient to start the test with the largest degree node using BFS algorithm. As an application of the obtained list, the three conjectures concerning the maximum number of edges in D2C graphs were checked for graphs of the order at most 13 and one counterexample was found. Index Terms: diameter-2-critical graphs, graph diameter, primitive graph.
直径-2临界图(缩写D2C)是直径为2的图,其直径通过去除任何边而增加。描述了用于获取最多13阶D2C图的列表的程序。这是通过将直径2测试和临界性测试合并到耿中来实现的,耿是来自软件包的程序,它生成所有非同构连接图的列表。对搜索过程中频繁使用的直径2测试中的两种启发式方法进行实验,结果表明,使用BFS算法从最大度节点开始测试的效率略高。作为得到的列表的应用,对D2C图中最大边数的3个猜想进行了检验,并找到了一个反例。索引项:直径-2临界图,图直径,原始图。
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引用次数: 0
A Decision Support System for Internal Migration Policy-Making 国内移民决策的决策支持系统
IF 0.4 Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.07
Boris Delibasic, S. Radovanović, S. Vukanovic
This paper proposes a decision support system for internal migration policy in the Republic of Serbia, which uses machine learning and knowledge extraction methods to analyze data and identify key features for policy decision-making. Internal migration is an issue that creates uneven development and sustainability challenges in countries. More specifically, internal migrations are putting a big pressure on cities and urban areas, while leaving vast less-urbanized areas depopulated and unsustainable to future generations. This paper includes two machine learning models with an accuracy of 70% for predicting internal migration intensity in local selfgovernments (LSGs), as well as the proposed decision-support tool that achieves an accuracy of 66%. The proposed system maintains desirable properties of decision support systems such as correctness, completeness, consistency, comprehensibility, and convenience and allows the what-if analysis to evaluate appropriate policies for each LSG. The identified key features can be used to influence migration levels in LSGs and promote balanced development in Serbia.
本文提出了塞尔维亚共和国内部移民政策的决策支持系统,该系统使用机器学习和知识提取方法来分析数据并识别政策决策的关键特征。国内移徙是一个造成各国发展不平衡和可持续性挑战的问题。更具体地说,国内移徙给城市和城市地区带来了巨大压力,同时给后代留下了大量人口稀少和不可持续的城市化程度较低的地区。本文包括两个机器学习模型,用于预测地方自治政府(LSGs)的内部迁移强度,准确率为70%,以及提出的决策支持工具,准确率为66%。所建议的系统保持了决策支持系统所需的属性,如正确性、完整性、一致性、可理解性和便利性,并允许假设分析为每个LSG评估适当的策略。确定的关键特征可用于影响低人口群体的移民水平,促进塞尔维亚的平衡发展。
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引用次数: 0
Spatiotemporal Model of Real Estate Valuation Trend 房地产估价趋势的时空模型
IF 0.4 Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.05
N. Rishe, Dan Tamir, Malek Adjouadi
resented here is a model objectivizing real estate prices so that prices across time could be compared to understand historical price trends and also to assist in a property evaluation or appraisal, as well as for the analysis of comparables in estimating a reasonable offer for a property on the market. Given a timespan of interest, a locale (e.g., a particular zipcode, a city, a county, a state), a category of properties of interest (e.g., condos), an objective historical trend in values can be computed by first evaluating the ratios between the transactions’ realized prices and objective governmental assessment of the properties at some fixed point of time; then, for each period (a month) averaging the ratios of all transaction in that period; then, comparing said averages (or medians) between different periods.
这里要介绍的是一个将房地产价格客观化的模型,这样就可以对不同时期的价格进行比较,以了解历史价格趋势,并有助于对房地产进行评估或评估,以及分析可比性,以估计市场上房地产的合理报价。给定感兴趣的时间范围、地点(例如特定的邮政编码、城市、县、州)、感兴趣的财产类别(例如公寓),可以通过首先评估交易的实际价格与某一固定时间点对财产的客观政府评估之间的比率来计算价值的客观历史趋势;然后,对每个时期(一个月)计算该时期所有交易的平均比率;然后,比较不同时期的平均值(或中位数)。
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引用次数: 0
An Organizational Perspective of Human Resource Modeling 人力资源建模的组织视角
IF 0.4 Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.08
Dajana Antanasijević, Marko Vještica, Vladimir Dimitrieski, L. Grubić-Nešić, S. Ristić, M. Pisarić
Although Industry 4.0 improved humanmachine relationship in technical aspects, it failed to put human needs at the focus of the production process. Industry 5.0 is complementing the Industry 4.0 focusing on the workers’ skills, knowledge, and abilities to cooperate with machines and robots. In our previous research, we proposed a framework for the formal description and automatic execution of production processes within Industry 4.0. As a result, a Domain-Specific Modeling Language (DSML) named Multi-Level Production Process Modeling Language (MultiProLan) was created aimed at modeling production processes at different levels of abstraction. The importance given to the workers within Industry 5.0 motivated us to investigate two different roles of a human worker: as an employee within an organization and as a human production resource. We propose a DSML named HResModLan aimed at human resource modeling from two different perspectives: organizational and production. The part of HResModLan language representing the organizational perspective is presented in this paper. The main goal of its creation is to enable the easier and more effective requiring, selection, hiring and development of employees within an organization. The paper presents an analysis of the human resource domain, abstract and concrete syntaxes of the HResModLan language, and a model of a furniture factory and its employees expressed using theconcepts of the HResModLan language.
虽然工业4.0在技术方面改善了人机关系,但它没有把人的需求放在生产过程的中心。工业5.0是对工业4.0的补充,工业4.0侧重于工人的技能、知识以及与机器和机器人合作的能力。在我们之前的研究中,我们提出了一个框架,用于工业4.0中生产过程的正式描述和自动执行。因此,一种名为多级生产过程建模语言(MultiProLan)的领域特定建模语言(DSML)被创建,旨在对不同抽象层次的生产过程进行建模。工业5.0对工人的重视促使我们研究人类工人的两种不同角色:作为组织内的员工和作为人力生产资源。我们提出了一个名为HResModLan的DSML,旨在从组织和生产两个不同的角度对人力资源进行建模。本文介绍了HResModLan语言中代表组织视角的部分。其创建的主要目标是使组织内的员工更容易、更有效地要求、选择、雇用和发展。本文分析了人力资源领域、HResModLan语言的抽象语法和具体语法,并用HResModLan语言的概念表达了一个家具厂及其员工的模型。
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引用次数: 0
"Detecting and Removing Clouds Affected Regions from Satellite Images Using Deep Learning" “利用深度学习从卫星图像中检测和去除云影响区域”
IF 0.4 Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.03
Lawrence Egharevba, Sanjoy Kumar, N. Rishe, Hadi Amini, Malek Adjouadi
Deep Learning is becoming a very popular tool for generating and reconstructing images. Research has shown that deep learning algorithms can perform cutting-edge restoration tasks for various types of images. The performance of these algorithms can be achieved by training Deep Convolutional Neural Networks (DCNNs) with data from a large sample size. The processing of high-resolution satellite imagery becomes difficult when there are only a few images in a dataset. An approach based on the intrinsic properties of Deep Convolutional Neural Networks (DCNNs) is presented in this paper for the detection and removal of clouds from remote sensing images without any prior training. Our results demonstrated that the algorithm we used performed well when compared to trained algorithms.
深度学习正在成为一种非常流行的生成和重建图像的工具。研究表明,深度学习算法可以对各种类型的图像执行尖端的恢复任务。这些算法的性能可以通过使用大样本量的数据训练深度卷积神经网络(DCNNs)来实现。当数据集中只有少量图像时,高分辨率卫星图像的处理变得困难。本文提出了一种基于深度卷积神经网络(Deep Convolutional Neural Networks, DCNNs)固有特性的方法,用于在不经过任何预先训练的情况下从遥感图像中检测和去除云。我们的结果表明,与经过训练的算法相比,我们使用的算法表现良好。
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引用次数: 0
"Towards Real-time House Detection in Aerial Imagery Using Faster Region-based Convolutional Neural Network" 利用更快的基于区域的卷积神经网络实现航拍图像中的实时房屋检测
IF 0.4 Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.06
Khandaker Mamun Ahmed, Farid Ghareh Mohammadi, M. Matus, Farzan Shenavarmasouleh, Luiz Manella Pereira, Zisis Ioannis, M. Amini
In the past few years, automatic building detection in aerial images has become an emerging field in computer vision. Detecting the specific types of houses will provide information in urbanization, change detection, and urban monitoring that play increasingly important roles in modern city planning and natural hazard preparedness. In this paper, we demonstrate the effectiveness of detecting various types of houses in aerial imagery using Faster Region-based Convolutional Neural Network (Faster-RCNN). After formulating the dataset and extracting bounding-box information, pre-trained ResNet50 is used to get the feature maps. The fully convolutional Region Proposal Network (RPN) first predicts the bounds and objectness score of objects (in this case house) from the feature maps. Then, the Region of Interest (RoI) pooling layer extracts interested regions to detect objects that are present in the images. To the best of our knowledge, this is the first attempt at detecting houses using Faster R-CNN that has achieved satisfactory results. This experiment opens a new path to conduct and extent the works not only for civil and environmental domain but also other applied science disciplines.
近年来,航拍图像中的建筑物自动检测已成为计算机视觉中的一个新兴领域。检测特定类型的房屋将为城市化、变化检测和城市监测提供信息,在现代城市规划和自然灾害防范中发挥越来越重要的作用。在本文中,我们证明了使用Faster- rcnn快速区域卷积神经网络(Faster- rcnn)检测航空图像中各种类型房屋的有效性。在建立数据集并提取边界框信息后,使用预训练的ResNet50得到特征映射。全卷积区域建议网络(RPN)首先从特征映射中预测对象(在本例中为房屋)的边界和对象得分。然后,感兴趣区域(RoI)池化层提取感兴趣的区域来检测图像中存在的物体。据我们所知,这是第一次尝试使用更快的R-CNN来检测房屋,并取得了令人满意的结果。这一实验为民用和环境领域以及其他应用科学领域的研究开辟了一条新的道路。
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引用次数: 0
"Integrating Location Information as Geohash Codes in Convolutional Neural Network-Based Satellite Image Classification" 基于卷积神经网络的卫星图像分类中位置信息Geohash码的集成
IF 0.4 Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.04
Arpan Mahara, N. Rishe
In the past few years, there have been many research studies conducted in the field of Satellite Image Classification. The purposes of these studies included flood identification, forest fire monitoring, greenery land identification, and land-usage identification. In this field, finding suitable data is often considered problematic, and some research has also been done to identify and extract suitable datasets for classification. Although satellite data can be challenging to deal with, Convolutional Neural Networks (CNNs), which consist of multiple interconnected neurons, have shown promising results when applied to satellite imagery data. In the present work, first we have manually downloaded satellite images of four different classes in Florida locations using the TerraFly Mapping System, developed and managed by the High Performance Database Research Center at Florida International University. We then develop a CNN architecture suitable for extracting features and capable of multi-class classification in our dataset. We discuss the shortcomings in the classification due to the limited size of the dataset. To address this issue, we first employ data augmentation and then utilize transfer learning methodology for feature extraction with VGG16 and ResNet50 pretrained models. We use these features to classify satellite imagery of Florida. We analyze the misclassification in our model and, to address this issue, we introduce a location-based CNN model. We convert coordinates to geohash codes, use these codes as an additional feature vector and feed them into the CNN model. We believe that the new CNN model combined with geohash codes as location features provides a better accuracy for our dataset.
近年来,在卫星图像分类领域开展了大量的研究工作。这些研究的目的包括洪水识别、森林火灾监测、绿地识别和土地利用识别。在该领域中,寻找合适的数据往往被认为是一个问题,并且已经做了一些研究来识别和提取合适的数据集进行分类。尽管处理卫星数据具有挑战性,但由多个相互连接的神经元组成的卷积神经网络(cnn)在应用于卫星图像数据时显示出了令人鼓舞的结果。在目前的工作中,首先,我们使用佛罗里达国际大学高性能数据库研究中心开发和管理的TerraFly测绘系统,手动下载了佛罗里达州四个不同班级的卫星图像。然后,我们开发了一个适合提取特征并能够在我们的数据集中进行多类分类的CNN架构。我们讨论了由于数据集大小有限而导致的分类缺陷。为了解决这个问题,我们首先采用数据增强,然后利用迁移学习方法对VGG16和ResNet50预训练模型进行特征提取。我们使用这些特征对佛罗里达州的卫星图像进行分类。我们分析了模型中的错误分类,并引入了基于位置的CNN模型来解决这个问题。我们将坐标转换为geohash代码,使用这些代码作为附加的特征向量,并将它们输入CNN模型。我们认为,新的CNN模型结合geohash码作为位置特征,为我们的数据集提供了更好的准确性。
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引用次数: 1
期刊
IPSI BgD Transactions on Internet Research
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