1 GUEST EDITORIAL

The constant growth of technology leads to the development of expert systems that serve to support critical decision-making and have applications in many areas, such as healthcare, business, chemistry, financial decision-making, and engineering. These systems are computer programs derived from a computer science research branch called Artificial Intelligence (AI) and use human knowledge intensively in problem-solving. These programs combine expert knowledge and use the knowledge necessary to solve problems (Kidd, 2012). In this special issue, we present a range of papers covering some of the subareas of expert systems such as intelligent and decision support systems, ethics, computers, and security, health informatics, simulations, and big-data analytics.

This special issue comprises six research papers. All manuscripts are extended versions of selected papers from WorldCIST'19 - 7th World Conference on Information Systems and Technologies, held in at La Toja Island, Galicia, Spain, April 2019. The WorldCIST conference have become a global forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences, and concerns in the several perspectives of Information Systems and Technologies, as well as computer science in general. The six selected papers in this special section include a Virtual Programming Lab (VPL), a model's predictions, a novel information systems architecture for the agri-food sector, various approaches for detection of malware, an intelligent system to assess, in real-time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment, as well as an expert system comprising a self-aware framework for resource-efficient and accurate data transmission within a low-power lossy sensor network (LLN) deployed for indoor monitoring.

Cardoso et al. (2020) present the VPL, a Moodle plugin that allows students to submit their code and get prompt feedback without the teacher's intervention. To test this concept, an experiment was performed with several classes of beginner programming students, in two editions of Algorithms and Programming course unit of the degree in Informatics Engineering lectured at the Informatics Engineering Department at the School of Engineering, Polytechnic Institute of Porto. The students were challenged to test their assignments in VPL with a set of test values previously defined by the teachers. After the experiments, the authors used surveys to gather the involved students' and teachers' opinion, and more than 70% of the students answered that they considered the VPL an added value for the teaching–learning process. The dynamics verified in the classes, the general opinion of the teachers, and the acceptance and participation of the students allow to classify the experience as positive.

Almomani et al. (2020) have analysed the decision-making process underlying choice behaviour. First, neural and gaze activity were recorded experimentally from different subjects performing a choice task in a Web Interface. Second, choice models and ensembles were fitted using rational, emotional, and attentional features. The model's predictions were evaluated in terms of their accuracy and rankings were made for each user. The results show that (a) the attentional models are the best in terms of its average performance across all users, (b) each subject shows a different best model, and (c) ensembles may perform better than single choice models but an optimal building method must be found.

Branco et al. (2020) have shown the information systems and technologies grow in usage in the agri-food industry, the same has happened to the relevance of Information Systems (IS) that allow for a parallel control, monitoring and management of the organizations' activities and business processes. As the literature proves, the benefits of implementing adequate and interoperable IS are very numerous and tend to represent a significant determinant regarding the organizations' overall success. In this paper the authors present a novel information systems architecture for the agri-food sector. The artefact is composed by 12 integrated main components and a set of subcomponents aimed at supporting all the monitoring, control, and management activities. To validate the proposed architecture a case study was implemented at a mushroom production organization. This allowed to perceive the ability of the artefact to serve as the basis for the development of IS that address all the organization's business and environmental needs.

Jan et al. (2020) have shown that malware analysis and detection over the Android have been the focus of considerable research, during recent years, as customer adoption of Android attracted a corresponding number of malware writers. Antivirus companies commonly rely on signatures and are error-prone. Traditional machine learning techniques are based on static, dynamic, and hybrid analysis; however, for large scale Android malware analysis, these approaches are not feasible. Deep neural architectures can analyze large scale static details of the applications, but static analysis techniques can ignore many malicious behaviours of applications. The study contributes to the documentation of various approaches for detection of malware, traditional and state-of-the-art models, developed for analysis that facilitates the provision of basic insights for researchers working in malware analysis, and the study also provides a dynamic approach that employs deep neural network models for detection of malware. Moreover, the study uses Android permissions as a parameter to measure the dynamic behaviour of around 16,900 benign and intruded applications. A dataset is created which encompasses a large set of permissions-based dynamic behaviour pertaining applications, with an aim to train deep learning models for prediction of behaviour. The proposed architecture extracts representations from input sequence data with no human intervention. The state-of-the-art Deep Convolutional Generative Adversarial Network extracted deep features and accomplished a general validation accuracy of 97.08% with an F1-score of 0.973 in correctly classifying input. Furthermore, the concept of blockchain is utilized to preserve the integrity of the dataset and the results of the analysis.

Oliveira et al. (2020) presents an intelligent system to assess, in real-time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment and to account the individuals' variability. The system is based on wearable technology and intelligent processing of the patients' biometric information to assess some quality-of-life related parameters. A longitudinal study was conducted among 16 lymphoma patients using this intelligent system. Mixed-effect regression models were performed to investigate predictors for and time effects on HRQoL. There were no significant changes in all HRQoL domains over time. Some quality-of-life domains revealed similar time trends as HRV indices. These HRV indices also have a significant effect on the domains of quality of life.

Habib et al. (2020) have developed an expert system comprising a self-aware framework for resource-efficient and accurate data transmission within a low-power LLN deployed for indoor monitoring. We derived both individual and group awareness, which could ensure the awareness of each sensor regarding its resources, neighbours and network environment. The proposed expert system facilitates decision-making under dynamic environmental conditions and employs a multi-criteria decision-making (MCDM) model to determine the selection of the best path towards the sink node with awareness of the existing network environment. The proposed system is validated by constructing a 6LoWPAN network in the Contiki Cooja simulator. MCDM is applied to generate an adaptive objective function for the IPv6 routing protocol for the LLN (RPL) and to aid in ranking the nodes to select the best available neighbouring node, while the data accuracy is ensured by the cluster head through data correlation among its associated members. The network performance is assessed by analyzing the packet delivery rate, throughput and energy consumption against varying sensors and by comparing our proposed MCDM-RPL with a standard RPL and a fuzzy-based RPL, where the results show that our framework is found to be better with gains of 13%, 25% and 13%, respectively.

中文翻译：

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WorldCist'19第七届世界信息系统和技术会议知识工程专刊

1位来宾社论

技术的不断发展导致专家系统的发展，这些系统可用于支持关键决策，并在医疗，商业，化学，财务决策和工程等许多领域得到应用。这些系统是从计算机科学研究部门（称为人工智能（AI））派生的计算机程序，并在解决问题中大量使用了人类的知识。这些程序结合了专家知识，并使用了解决问题所必需的知识（Kidd，  2012年）。在本期特刊中，我们提出了一系列涵盖专家系统某些子领域的论文，例如智能和决策支持系统，道德，计算机和安全性，健康信息学，模拟和大数据分析。

本期特刊包括六篇研究论文。所有手稿均为WorldCIST 19-第七届世界信息系统和技术会议的精选论文的扩展版本，该会议于2019年4月在西班牙加利西亚的La Toja岛举行.WorldCIST会议已成为研究人员和从业人员的全球论坛并从信息系统和技术以及整个计算机科学的多个角度讨论最新的创新，趋势，结果，经验和关注点。在此特殊部分中选择的六篇论文包括虚拟编程实验室（VPL），模型的预测，用于农业食品领域的新颖信息系统体系结构，各种检测恶意软件的方法，用于实时评估的智能系统，潜在的HRV指数，

Cardoso等。（2020年）展示了VPL，这是一个Moodle插件，可让学生提交代码并获得及时反馈，而无需老师干预。为了验证这一概念，在波尔图理工学院工程学院信息工程系的信息学工程学位和算法与编程课程单元的两个版本中，对几类初学者进行了实验。要求学生使用教师预先定义的一组测试值在VPL中测试他们的作业。实验之后，作者通过调查收集了相关学生和老师的意见，超过70％的学生回答说他们认为VPL对于教学过程具有附加价值。在课程中验证的动态，

Almomani等。（2020）分析了选择行为的决策过程。首先，实验性记录了在Web界面中执行选择任务的不同受试者的神经和凝视活动。其次，使用理性，情感和注意力特征来拟合选择模型和合奏。根据模型的预测准确性和每个用户的排名对其进行评估。结果表明，（a）注意模型在所有用户中的平均表现方面是最佳的；（b）每个主题都显示出不同的最佳模型；（c）集合的效果可能比单选模型更好，但构建效果最佳必须找到方法。

布兰科等。（2020年）表明了信息系统和技术在农业食品行业中的使用正在增长，信息系统（IS）的相关性也发生了同样的变化，该信息系统允许对组织的活动和业务流程进行并行控制，监视和管理。正如文献所证明的那样，实施适当且可互操作的IS的好处非常多，并且往往代表着组织整体成功的重要决定因素。在本文中，作者提出了一种用于农业食品领域的新型信息系统架构。该工件由12个集成的主要组件和一组子组件组成，旨在支持所有监视，控制和管理活动。为了验证所提出的体系结构，在蘑菇生产组织中进行了案例研究。

Jan等。（2020年）表明，近年来，随着Android的客户采用吸引了相应数量的恶意软件编写者，对Android进行恶意软件分析和检测一直是相当多的研究重点。防病毒公司通常依靠签名并且容易出错。传统的机器学习技术基于静态，动态和混合分析。但是，对于大规模Android恶意软件分析，这些方法不可行。深度神经架构可以分析应用程序的大规模静态细节，但是静态分析技术可以忽略应用程序的许多恶意行为。这项研究有助于记录各种检测恶意软件的方法，传统模型和最新模型，该软件专为分析而开发，有助于为从事恶意软件分析的研究人员提供基本见识，并且该研究还提供了一种动态方法，该方法采用深度神经网络模型来检测恶意软件。此外，该研究使用Android权限作为参数来衡量大约16,900个良性和入侵应用程序的动态行为。创建了一个数据集，其中包含与应用程序相关的大量基于权限的动态行为，目的是训练深度学习模型以预测行为。提出的体系结构无需人工干预即可从输入序列数据中提取表示形式。最先进的深度卷积生成对抗网络提取了深层特征，并以0的F1得分实现了97.08％的总体验证精度。973在正确分类输入。此外，利用区块链的概念来保​​持数据集和分析结果的完整性。

Oliveira等。（2020年）提出了一个智能系统，以实时评估潜在的HRV指数，该指数可以预测整个化疗过程中淋巴瘤患者的HRQoL并说明个体的变异性。该系统基于可穿戴技术和对患者生物特征信息的智能处理，以评估一些生活质量相关参数。使用该智能系统在16名淋巴瘤患者中进行了纵向研究。进行了混合效应回归模型以研究HRQoL的预测因素和时间影响。随着时间的推移，所有HRQoL域都没有显着变化。一些生活质量域显示出与HRV指数相似的时间趋势。这些HRV指数也对生活质量领域产生重大影响。

哈比卜（Habib）等人。（2020年）开发了一个专家系统，该系统包括一个自我感知的框架，用于在部署用于室内监控的低功率LLN中实现资源高效和准确的数据传输。我们获得了个人和团体的感知，这可以确保每个传感器对其资源，邻居和网络环境的感知。所提出的专家系统有助于在动态环境条件下进行决策，并采用多标准决策（MCDM）模型来确定通向汇聚节点的最佳路径的选择，同时要了解现有的网络环境。通过在Contiki Cooja模拟器中构建6LoWPAN网络来验证所提出的系统。MCDM用于为LLN（RPL）的IPv6路由协议生成自适应目标函数，并有助于对节点进行排名以选择最佳可用的相邻节点，而簇头通过其之间的数据相关性来确保数据准确性。相关成员。通过针对不同的传感器分析数据包的传输速率，吞吐量和能耗，并通过将我们建议的MCDM-RPL与标准RPL和基于模糊的RPL进行比较，来评估网络性能，结果表明我们的框架更好收益分别为13％，25％和13％。