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Background Design patterns are supposed to improve various quality attributes of software systems. However, there is controversial quantitative evidence of this impact. Especially for younger paradigms such as service- and Microservice-based systems, there is a lack of empirical studies. Objective In this study, we focused on the effect of four service-based patterns—namely Process Abstraction, Service Façade, Decomposed Capability, and Event-Driven Messaging—on the evolvability of a system from the viewpoint of inexperienced developers. Method We conducted a controlled experiment with Bachelor students (N = 69). Two functionally equivalent versions of a service-based web shop—one with patterns (treatment group), one without (control group)—had to be changed and extended in three tasks. We measured evolvability by the effectiveness and efficiency of the participants in these tasks. Additionally, we compared both system versions with nine structural maintainability metrics for size, granularity, complexity, cohesion, and coupling. Results Both experiment groups were able to complete a similar number of tasks within the allowed 90 min. Median effectiveness was 1/3. Mean efficiency was 12% higher in the treatment group, but this difference was not statistically significant. Only for the third task, we found statistical support for accepting the alternative hypothesis that the pattern version led to higher efficiency. In the metric analysis, the pattern version had worse measurements for size and granularity while simultaneously having slightly better values for coupling metrics. Complexity and cohesion were not impacted. Interpretation For the experiment, our analysis suggests that the difference in efficiency is stronger with more experienced participants and increased from task to task. With respect to the metrics, the patterns introduce additional volume in the system, but also seem to decrease coupling in some areas. Conclusions Overall, there was no clear evidence for a decisive positive effect of using service-based patterns, neither for the student experiment nor for the metric analysis. This effect might only be visible in an experiment setting with higher initial effort to understand the system or with more experienced developers.

The proposed research motivates the 6G cellular networking for the Internet of Everything’s (IoE) usage empowerment that is currently not compatible with 5G. For 6G, more innovative technological resources are required to be handled by Mobile Edge Computing (MEC). Although the demand for change in service from different sectors, the increase in IoE, the limitation of available computing resources of MEC, and intelligent resource solutions are getting much more significant. This research used IScaler, an effective model for intelligent service placement solutions and resource scaling. IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. The research also highlights several challenges geared by architectonics that submerge an Intelligent Scaling and Placement module.

Background Microservices are an architectural approach of growing use, and the optimal granularity of a microservice directly affects the application’s quality attributes and usage of computational resources. Determining microservice granularity is an open research topic. Methodology We conducted a systematic literature review to analyze literature that addresses the definition of microservice granularity. We searched in IEEE Xplore, ACM Digital Library and Scopus. The research questions were: Which approaches have been proposed to define microservice granularity and determine the microservices’ size? Which metrics are used to evaluate microservice granularity? Which quality attributes are addressed when researching microservice granularity? Results We found 326 papers and selected 29 after applying inclusion and exclusion criteria. The quality attributes most often addressed are runtime properties (e.g., scalability and performance), not development properties (e.g., maintainability). Most proposed metrics were about the product, both static (coupling, cohesion, complexity, source code) and runtime (performance, and usage of computational resources), and a few were about the development team and process. The most used techniques for defining microservices granularity were machine learning (clustering), semantic similarity, genetic programming, and domain engineering. Most papers were concerned with migration from monoliths to microservices; and a few addressed green-field development, but none address improvement of granularity in existing microservice-based systems. Conclusions Methodologically speaking, microservice granularity research is at a Wild West stage: no standard definition, no clear development—operation trade-offs, and scarce conceptual reuse (e.g., few methods seem applicable or replicable in projects other than their initial proposal). These gaps in granularity research offer clear options to investigate on continuous improvement of the development and operation of microservice-based systems.

One of the disciplines behind the science of science is the study of scientific networks. This work focuses on scientific networks as a social network having different nodes and connections. Nodes can be represented by authors, articles or journals while connections by citation, co-citation or co-authorship. One of the challenges in creating scientific networks is the lack of publicly available comprehensive data set. It limits the variety of analyses on the same set of nodes of different scientific networks. To supplement such analyses we have worked on publicly available citation metadata from Crossref and OpenCitatons. Using this data a workflow is developed to create scientific networks. Analysis of these networks gives insights into academic research and scholarship. Different techniques of social network analysis have been applied in the literature to study these networks. It includes centrality analysis, community detection, and clustering coefficient. We have used metadata of Scientometrics journal, as a case study, to present our workflow. We did a sample run of the proposed workflow to identify prominent authors using centrality analysis. This work is not a bibliometric study of any field rather it presents replicable Python scripts to perform network analysis. With an increase in the popularity of open access and open metadata, we hypothesise that this workflow shall provide an avenue for understanding scientific scholarship in multiple dimensions.

Sentiment analysis plays a key role in companies, especially stores, and increasing the accuracy in determining customers’ opinions about products assists to maintain their competitive conditions. We intend to analyze the users’ opinions on the website of the most immense online store in Iran; Digikala. However, the Persian language is unstructured which makes the pre-processing stage very difficult and it is the main problem of sentiment analysis in Persian. What exacerbates this problem is the lack of available libraries for Persian pre-processing, while most libraries focus on English. To tackle this, approximately 3 million reviews were gathered in Persian from the Digikala website using web-mining techniques, and the fastText method was used to create a word embedding. It was assumed that this would dramatically cut down on the need for text pre-processing through the skip-gram method considering the position of the words in the sentence and the words’ relations to each other. Another word embedding has been created using the TF-IDF in parallel with fastText to compare their performance. In addition, the results of the Convolutional Neural Network (CNN), BiLSTM, Logistic Regression, and Naïve Bayes models have been compared. As a significant result, we obtained 0.996 AUC and 0.956 F-score using fastText and CNN. In this article, not only has it been demonstrated to what extent it is possible to be independent of pre-processing but also the accuracy obtained is better than other researches done in Persian. Avoiding complex text preprocessing is also important for other languages since most text preprocessing algorithms have been developed for English and cannot be used for other languages. The created word embedding due to its high accuracy and independence of pre-processing has other applications in Persian besides sentiment analysis.

Background Parkinson’s disease (PD) is a neurodegenerative condition of the central nervous system that causes motor and non-motor dysfunctions. The disease affects 1% of the world population over 60 years and remains cureless. Knowledge and monitoring of PD are essential to provide better living conditions for patients. Thus, diagnostic exams and monitoring of the disease can generate a large amount of data from a given patient. This study proposes the development and usability evaluation of an integrated system, which can be used in clinical and research settings to manage biomedical data collected from PD patients. Methods A system, so-called Sistema Integrado de Dados Biomédicos (SIDABI) (Integrated Biomedical Data System), was designed following the model-view-controller (MVC) standard. A modularized architecture was created in which all the other modules are connected to a central security module. Thirty-six examiners evaluated the system usability through the System Usability Scale (SUS). The agreement between examiners was measured by Kendall’s coefficient with a significance level of 1%. Results The free and open-source web-based system was implemented using modularized and responsive methods to adapt the system features on multiple platforms. The mean SUS score was 82.99 ± 13.97 points. The overall agreement was 70.2%, as measured by Kendall’s coefficient (p < 0.001). Conclusion According to the SUS scores, the developed system has good usability. The system proposed here can help researchers to organize and share information, avoiding data loss and fragmentation. Furthermore, it can help in the follow-up of PD patients, in the training of professionals involved in the treatment of the disorder, and in studies that aim to find hidden correlations in data.

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profit or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, Spam Review Detection problem (SRD) has gained much attention from researchers, but still there is a need to identify review spammers who often work collaboratively to promote or demote targeted products. It can severely harm the review system. This work presents the Spammer Group Detection (SGD) method which identifies suspicious spammer groups based on the similarity of all reviewer’s activities considering their review time and review ratings. After removing these identified spammer groups and spam reviews, the resulting non-spam reviews are displayed using diversification technique. For the diversification, this study proposed Diversified Set of Reviews (DSR) method which selects diversified set of top-k reviews having positive, negative, and neutral reviews/feedback covering all possible product features. Experimental evaluations are conducted on Roman Urdu and English real-world review datasets. The results show that the proposed methods outperformed the existing approaches when compared in terms of accuracy.

Science across all disciplines has become increasingly data-driven, leading to additional needs with respect to software for collecting, processing and analysing data. Thus, transparency about software used as part of the scientific process is crucial to understand provenance of individual research data and insights, is a prerequisite for reproducibility and can enable macro-analysis of the evolution of scientific methods over time. However, missing rigor in software citation practices renders the automated detection and disambiguation of software mentions a challenging problem. In this work, we provide a large-scale analysis of software usage and citation practices facilitated through an unprecedented knowledge graph of software mentions and affiliated metadata generated through supervised information extraction models trained on a unique gold standard corpus and applied to more than 3 million scientific articles. Our information extraction approach distinguishes different types of software and mentions, disambiguates mentions and outperforms the state-of-the-art significantly, leading to the most comprehensive corpus of 11.8 M software mentions that are described through a knowledge graph consisting of more than 300 M triples. Our analysis provides insights into the evolution of software usage and citation patterns across various fields, ranks of journals, and impact of publications. Whereas, to the best of our knowledge, this is the most comprehensive analysis of software use and citation at the time, all data and models are shared publicly to facilitate further research into scientific use and citation of software.

Integration of heterogeneous data sources in a single representation is an active field with many different tools and techniques. In the case of text-based approaches—those that base the definition of the mappings and the integration on a DSL—there is a lack of usability studies. In this work we have conducted a usability experiment (n = 17) on three different languages: ShExML (our own language), YARRRML and SPARQL-Generate. Results show that ShExML users tend to perform better than those of YARRRML and SPARQL-Generate. This study sheds light on usability aspects of these languages design and remarks some aspects of improvement.

Framing is a process of emphasizing a certain aspect of an issue over the others, nudging readers or listeners towards different positions on the issue even without making a biased argument. {Here, we propose FrameAxis, a method for characterizing documents by identifying the most relevant semantic axes ("microframes") that are overrepresented in the text using word embedding. Our unsupervised approach can be readily applied to large datasets because it does not require manual annotations. It can also provide nuanced insights by considering a rich set of semantic axes. FrameAxis is designed to quantitatively tease out two important dimensions of how microframes are used in the text. \textit{Microframe bias} captures how biased the text is on a certain microframe, and \textit{microframe intensity} shows how actively a certain microframe is used. Together, they offer a detailed characterization of the text. We demonstrate that microframes with the highest bias and intensity well align with sentiment, topic, and partisan spectrum by applying FrameAxis to multiple datasets from restaurant reviews to political news.} The existing domain knowledge can be incorporated into FrameAxis {by using custom microframes and by using FrameAxis as an iterative exploratory analysis instrument.} Additionally, we propose methods for explaining the results of FrameAxis at the level of individual words and documents. Our method may accelerate scalable and sophisticated computational analyses of framing across disciplines., Comment: 24 pages; published in PeerJ CS

Digital fraud has immensely affected ordinary consumers and the finance industry. Our dependence on internet banking has made digital fraud a substantial problem. Financial institutions across the globe are trying to improve their digital fraud detection and deterrence capabilities. Fraud detection is a reactive process, and it usually incurs a cost to save the system from an ongoing malicious activity. Fraud deterrence is the capability of a system to withstand any fraudulent attempts. Fraud deterrence is a challenging task and researchers across the globe are proposing new solutions to improve deterrence capabilities. In this work, we focus on the very important problem of fraud deterrence. Our proposed work uses an Intimation Rule Based (IRB) alert generation algorithm. These IRB alerts are classified based on severity levels. Our proposed solution uses a richer domain knowledge base and rule-based reasoning. In this work, we propose an ontology-based financial fraud detection and deterrence model.

The recent proliferation of multimedia information on the web enhances user information need from simple textual lookup to multi-modal exploration activities. The current search engines act as major gateways to access the immense amount of multimedia data. However, access to the multimedia content is provided by aggregating disjoint multimedia search verticals. The aggregation of the multimedia search results cannot consider relationships in them and are partially blended. Additionally, the search results’ presentation is via linear lists, which cannot support the users’ non-linear navigation patterns to explore the multimedia search results. Contrarily, users’ are demanding more services from search engines. It includes adequate access to navigate, explore, and discover multimedia information. Our discovery approach allow users to explore and discover multimedia information by semantically aggregating disjoint verticals using sentence embeddings and transforming snippets into conceptually similar multimedia document groups. The proposed aggregation approach retains the relationship in the retrieved multimedia search results. A non-linear graph is instantiated to augment the users’ non-linear information navigation and exploration patterns, which leads to discovering new and interesting search results at various aggregated granularity levels. Our method’s empirical evaluation results achieve 99% accuracy in the aggregation of disjoint search results at different aggregated search granularity levels. Our approach provides a standard baseline for the exploration of multimedia aggregation search results.

Several higher education institutions have harnessed e-learning tools to empower the application of different learning models that enrich the educational process. Nevertheless, the reliance on commercial or open-source platforms, in some cases, to deliver e-learning could impact system acceptability, usability, and capability. Therefore, this study suggests design methods to develop effective learning management capabilities such as attendance, coordination, course folder, course section homepage, learning materials, syllabus, emails, and student tracking within a university portal named MyGJU. In particular, mechanisms to facilitate system setup, data integrity, information security, e-learning data reuse, version control automation, and multi-user collaboration have been applied to enable the e-learning modules in MyGJU to overcome some of the drawbacks of their counterparts in Moodle. Such system improvements are required to motivate both educators and students to engage in online learning. Besides, features comparisons between MyGJU with Moodle and in-house systems have been conducted for reference. Also, the system deployment outcomes and user survey results confirm the wide acceptance among instructors and students to use MyGJU as a first point of contact, as opposed to Moodle, for basic e-learning tasks. Further, the results illustrate that the in-house e-learning modules in MyGJU are engaging, easy to use, useful, and interactive.

While the publication of Linked Data has become increasingly common, the process tends to be a relatively complicated and heavy-weight one. Linked Data is typically published by centralized entities in the form of larger dataset releases, which has the downside that there is a central bottleneck in the form of the organization or individual responsible for the releases. Moreover, certain kinds of data entries, in particular those with subjective or original content, currently do not fit into any existing dataset and are therefore more difficult to publish. To address these problems, we present here an approach to use nanopublications and a decentralized network of services to allow users to directly publish small Linked Data statements through a simple and user-friendly interface, called Nanobench, powered by semantic templates that are themselves published as nanopublications. The published nanopublications are cryptographically verifiable and can be queried through a redundant and decentralized network of services, based on the grlc API generator and a new quad extension of Triple Pattern Fragments. We show here that these two kinds of services are complementary and together allow us to query nanopublications in a reliable and efficient manner. We also show that Nanobench makes it indeed very easy for users to publish Linked Data statements, even for those who have no prior experience in Linked Data publishing.

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

The rapid growth of the worldwide web and accompanied opportunities of web applications in various aspects of life have attracted the attention of organizations, governments, and individuals. Consequently, web applications have increasingly become the target of cyberattacks. Notably, cross-site scripting (XSS) attacks on web applications are increasing and have become the critical focus of information security experts’ reports. Machine learning (ML) technique has significantly advanced and shown impressive results in the area of cybersecurity. However, XSS training datasets are often limited and significantly unbalanced, which does not meet well-developed ML algorithms’ requirements and potentially limits the detection system efficiency. Furthermore, XSS attacks have multiple payload vectors that execute in different ways, resulting in many real threats passing through the detection system undetected. In this study, we propose a conditional Wasserstein generative adversarial network with a gradient penalty to enhance the XSS detection system in a low-resource data environment. The proposed method integrates a conditional generative adversarial network and Wasserstein generative adversarial network with a gradient penalty to obtain necessary data from directivity, which improves the strength of the security system over unbalance data. The proposed method generates synthetic samples of minority class that have identical distribution as real XSS attack scenarios. The augmented data were used to train a new boosting model and subsequently evaluated the model using a real test dataset. Experiments on two unbalanced XSS attack datasets demonstrate that the proposed model generates valid and reliable samples. Furthermore, the samples were indistinguishable from real XSS data and significantly enhanced the detection of XSS attacks compared with state-of-the-art methods.

It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence network and use its embedding for further generalizing author attributes. Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as LP problem and prediction of future graph structure. We evaluate our survey on the dataset containing temporal information on National Research University Higher School of Economics over 25 years of research articles indexed in Russian Science Citation Index and Scopus. Our model of network representation shows better performance for stated binary classification tasks on several co-authorship networks.

In Human-Computer Interaction research, the positive effect of aesthetics on users’ subjective impressions and reactions is well-accepted. However, results regarding the influence of interface aesthetics on a user’s individual performance as an objective outcome are very mixed, yet of urgent interest due to the proceeding of digitalization. In this web-based experiment (N = 331), the effect of interface aesthetics on individual performance considering three different types of tasks (search, creative, and transfer tasks) is investigated. The tasks were presented on an either aesthetic or unaesthetic website, which differed significantly in subjective aesthetics. Goal orientation (learning versus performance goals) was included as a possible moderator variable, which was manipulated by using different task instructions. Both aesthetics and goal orientation were a between-subject factor, leading to a 2 × 2 between subject design. Manipulation checks were highly significant. Yet the results show neither significant main effects of aesthetics and goal orientation on performance regarding both accuracy and response times in each of the three tasks, nor significant interaction effects. Nevertheless, from a practical perspective aesthetics still should be considered due to its positive effects on subjective perceptions of users, even as no substantial effects on user performance occurred in the present experiment.

Despite most Spatial Data Infrastructures are offering service-based visualization of geospatial data, requirements are often at a very basic level leading to poor quality of maps. This is a general observation for any geospatial architecture as soon as open standards as those of the Open Geospatial Consortium (OGC) shall be applied. To improve the situation, this paper does focus on improvements at the portrayal interoperability side by considering standardization aspects. We propose two major redesign recommendations. First to consolidate the cartographic theory at the core of the OGC Symbology Encoding standard. Secondly to build the standard in a modular way so as to be ready to be extended with upcoming future cartographic requirements. Thus, we start by defining portrayal interoperability by means of typical use cases that frame the concept of sharing cartography. Then we bring to light the strengths and limits of the relevant open standards to consider in this context. Finally we propose a set of recommendations to overcome the limits so as to make these use cases a true reality. Even if the definition of a cartographic-oriented standard is not able to act as a complete cartographic design framework by itself, we argue that pushing forward the standardization work dedicated to cartography is a way to share and disseminate good practices and finally to improve the quality of the visualizations.

We describe best practices for providing convenient, high-speed, secure access to large data via research data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance data enclaves and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research data portals.