Information, Volume 14, Issue 6 (June 2023) – 51 articles

Video games have become a ubiquitous form of entertainment and have been enjoyed by people of all ages around the world. The gaming industry has evolved rapidly, with new games being released every year that push the boundaries of technology and creativity. To ensure that video games are not just technically advanced, but also enjoyable and engaging, measuring the gaming experience is essential because it helps game designers understand how players interact with the game and identify areas for its improvement. The objective of this paper is to examine an interplay of gaming experience dimensions in the context of platform video games and to determine the extent to which they contribute to players’ behavioral intentions. To fulfil this objective, an empirical study was undertaken, involving participants with diverse gaming backgrounds. They were requested to engage in the gameplay of the Stranded Away platformer game and subsequently respond to a post-use questionnaire. The psychometric features of the introduced conceptual model were evaluated with the partial least squares structural equation modeling (PLS-SEM) method. The reported findings demonstrate the importance of evaluating different facets of the gaming experience in video games and showcase the potential of the proposed model and measuring instrument as tools for game designers to enhance the overall quality of their products. Full article

The untraceable part of the Deep Web, also known as the Dark Web, is one of the most used “secretive spaces” to execute all sorts of illegal and criminal activities by terrorists, cybercriminals, spies, and offenders. Identifying actions, products, and offenders on the Dark Web is challenging due to its size, intractability, and anonymity. Therefore, it is crucial to intelligently enforce tools and techniques capable of identifying the activities of the Dark Web to assist law enforcement agencies as a support system. Therefore, this study proposes four deep learning architectures (RNN, CNN, LSTM, and Transformer)-based classification models using the pre-trained word embedding representations to identify illicit activities related to cybercrimes on Dark Web forums. We used the Agora dataset derived from the DarkNet market archive, which lists 109 activities by category. The listings in the dataset are vaguely described, and several data points are untagged, which rules out the automatic labeling of category items as target classes. Hence, to overcome this constraint, we applied a meticulously designed human annotation scheme to annotate the data, taking into account all the attributes to infer the context. In this research, we conducted comprehensive evaluations to assess the performance of our proposed approach. Our proposed BERT-based classification model achieved an accuracy score of 96%. Given the unbalancedness of the experimental data, our results indicate the advantage of our tailored data preprocessing strategies and validate our annotation scheme. Thus, in real-world scenarios, our work can be used to analyze Dark Web forums and identify cybercrimes by law enforcement agencies and can pave the path to develop sophisticated systems as per the requirements. Full article

Agent-based computing remains an active field of research with the goal of building (semi-)autonomous software for dynamic ecosystems. Today, this task should be realized using dedicated, specialized frameworks. Over almost 40 years, multiple agent platforms have been developed. While many of them have been “abandoned”, others remain active, and new ones are constantly being released. This contribution presents a historical perspective on the domain and an up-to-date review of the existing agent platforms. It aims to serve as a reference point for anyone interested in developing agent systems. Therefore, the main characteristics of the included agent platforms are summarized, and selected links to projects where they have been used are provided. Furthermore, the described platforms are divided into general-purpose platforms and those targeting specific application domains. The focus of the contribution is on platforms that can be judged as being under active development. Information about “historical platforms” and platforms with an unclear status is included in a dedicated website accompanying this work. Full article

The popularity of blockchain technology stems largely from its association with cryptocurrencies, but its potential applications extend beyond this. Fungible tokens, which are interchangeable, can facilitate value transactions, while smart contracts using non-fungible tokens enable the exchange of digital assets. Utilizing blockchain technology, tokenized platforms can create virtual markets that operate without the need for a central authority. In principle, blockchain technology provides these markets with a high degree of security, trustworthiness, and dependability. This article surveys recent developments in these areas, including examples of architectures, designs, challenges, and best practices (case studies) for the design and implementation of tokenized platforms for exchanging digital assets. Full article

The COVID-19 pandemic spurred older adults to use information and communication technology (ICT) for maintaining connections and engagement during social distancing. This trend raises concerns about privacy and data safety for older individuals with limited technical knowledge who have adopted ICT reluctantly and may be distinct in their susceptibility to scams, fraud, and identity theft. This paper highlights the gap in the literature regarding the increased privacy and data security risks for older adults adopting technology due to isolation during the pandemic (referred to here as quarantine technology initiates (QTIs)). A literature search informed by healthcare experts explored the intersection of older adults, data privacy, online activity, and COVID-19. A thin and geographically diverse literature was found to consider the risk profile of QTIs with the same lens as for older adults who adopted ICT before or independent of COVID-19 quarantines. The mentioned strategies to mitigate privacy risks were broad, including education, transaction monitoring, and the application of international regulatory models, but were undistinguished from those for non-QTI older adults. Future research should pursue the hypothesis that the risk profile of QTIs may differ in character from that of other older adults, referencing by analogy the nuanced distinctions quantified in credit risk scoring. Such studies would examine the primary data on privacy and data safety implications of hesitant ICT adoption by older adults, using COVID-19 as a natural experiment to identify and evaluate this vulnerable group. Full article

The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms. Full article

In this paper, we proposed a new method for image-based grammatical inference of deterministic, context-free L-systems (D0L systems) from a single sequence. This approach is characterized by first parsing an input image into a sequence of symbols and then, using a genetic algorithm, attempting to infer a grammar that can generate this sequence. This technique has been tested using our test suite and compared to similar algorithms, showing promising results, including solving the problem for systems with more rules than in existing approaches. The tests show that it performs better than similar heuristic methods and can handle the same cases as arithmetic algorithms. Full article

The present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL training is conducted under realistic conditions using a distributed hardware infrastructure. The suggested pipeline and FL system’s robustness are tested against challenges related to tool deployment, data heterogeneity, and privacy attacks for multiple tasks and data types. A representative set of AI-based tools and related datasets have been selected to cover several validation cases and distributed to each edge device to closely reflect real-world scenarios. The study presents significant outcomes of the experiments and analyzes the models’ performance under different realistic FL conditions, while highlighting potential limitations and issues that occurred during the FL process. Full article

Zebrafish is a well-established model organism for cardiovascular disease studies in which one of the most popular tasks is to assess cardiac functions from the heart beating echo-videos. However, current techniques are often time-consuming and error-prone, making them unsuitable for large-scale analysis. To address this problem, we designed a method to automatically evaluate the ejection fraction of zebrafish from heart echo-videos using a deep-learning model architecture. Our model achieved a validation Dice coefficient of 0.967 and an IoU score of 0.937 which attest to its high accuracy. Our test findings revealed an error rate ranging from 0.11% to 37.05%, with an average error rate of 9.83%. This method is widely applicable in any laboratory setting and can be combined with binary recordings to optimize the efficacy and consistency of large-scale video analysis. By facilitating the precise quantification and monitoring of cardiac function in zebrafish, our approach outperforms traditional methods, substantially reducing the time and effort required for data analysis. The advantages of our method make it a promising tool for cardiovascular research using zebrafish. Full article

Virtual environments have been widely adopted for design and training tasks in the industrial domain. Low-cost automation (LCA) is a technology that automatizes some activities using mostly standard automation mechanisms available off the shelf. However, LCA systems should adapt to existing standard production lines and workstations. Thus, workers must customize standard LCA templates and perform adaptation and customization steps. This activity can be very time consuming with physical LCA systems, and in case of errors, it may be necessary to rebuild many parts from scratch. Thus, LCA systems would greatly benefit from a design and prototyping step experienced in a virtual simulation environment. An immersive virtual reality (IVR) application for rapid and easy prototyping of LCA solutions has been investigated in previous work; the assessment of the system usability proved that the users highly appreciated the proposed solutions. This research explores further improvements to exploit the existing IVR application as a training tool for LCA prototyping trainees. The proposed application now provides users with two different interaction paradigms based on the VIVE controllers and the Manus Prime II data gloves. The application’s interface has been revised to allow a proper comparison of the two interaction models. The two interfaces have been compared, involving 12 participants in an LCA building task. The System Usability Scale (SUS) and the NASA Task Load Index (TLX) questionnaires have been used to assess the usability and workload of the two solutions. Full article

The rapid growth in greenhouse gases (GHGs), the lack of electricity production, and an ever-increasing demand for electrical energy requires an optimal reduction in coal-fired thermal generating units (CFTGU) with the aim of minimizing fuel costs and emissions. Previous approaches have been unable to deal with such problems due to the non-convexity of realistic scenarios and confined optimum convergence. Instead, meta-heuristic techniques have gained more attention in order to deal with such constrained static/dynamic economic emission load dispatch (ELD/DEELD) problems, due to their flexibility and derivative-free structures. Hence, in this work, the elephant herd optimization (EHO) technique is proposed in order to solve constrained non-convex static and dynamic ELD problems in the power system. The proposed EHO algorithm is a nature-inspired technique that utilizes a new separation method and elitism strategy in order to retain the diversity of the population and to ensure that the fittest individuals are retained in the next generation. The current approach can be implemented to minimize both the fuel and emission cost functions of the CFTGUs subject to power balance constraints, active power generation limits, and ramp rate limits in the system. Three test systems involving 6, 10, and 40 units were utilized to demonstrate the effectiveness and practical feasibility of the proposed algorithm. Numerical results indicate that the proposed EHO algorithm exhibits better performance in most of the test cases as compared to recent existing algorithms when applied to the static and dynamic ELD issue, demonstrating its superiority and practicability. Full article

Canonical ensembles of walks in a finite connected graph assign the properly normalized probability distributions to all nodes, subgraphs, and nodal subsets of the graph at all time and connectivity scales of the diffusion process. The probabilistic description of graphs allows for introducing the quantitative measures of navigability through the graph, walkability of individual paths, and mutual perspicacity of the different modes of the (diffusion) processes. The application of information theory methods to problems about graphs, in contrast to geometric, combinatoric, algorithmic, and algebraic approaches, can be called information graph theory. As it involves evaluating communication efficiency between individual systems’ units at different time and connectivity scales, information graph theory is in demand for a wide range of applications, such as designing network-on-chip architecture and engineering urban morphology within the concept of the smart city. Full article

The purpose of the paper is to present a model of factors affecting the successful project implementation by introducing agility and artificial intelligence to increase the company’s competitiveness. In the model, the multidimensional constructs describing the implementation of an agile work environment and artificial intelligence technologies and tools were developed. These multidimensional constructs are agile work environment, agile leadership, agile team skills and capabilities, improving the work of the leader in the project, adopting AI technologies in the project, and using AI solutions in a project. Their impact on successful project implementation and on the company competitiveness was tested. The fundamental reason for conducting this research and developing the model is to enhance the understanding of factors that contribute to the successful implementation of projects and to increase a company’s competitiveness. Our developed model encompasses multidimensional constructs that describe the agile work environment and the utilization of AI technologies. By examining the impact of these constructs on both successful project implementation and company competitiveness, we aimed to establish a comprehensive framework that captures the relationship between agility, AI, and successful project implementation. This model serves as a valuable tool for companies seeking to improve their project implementation processes and gain a competitive edge in the market. The research was based on a sample of 473 managers/owners in medium-sized and large companies. Structural equation modeling was used to test the hypotheses. In today’s turbulent environment, the results will help develop guidelines for a successful combination of agile business practices and artificial intelligence to achieve successful project implementation, increasing a company’s competitiveness. Full article

Knowledge graphs are graph-based data models which can represent real-time data that is constantly growing with the addition of new information. The question-answering systems over knowledge graphs (KGQA) retrieve answers to a natural language question from the knowledge graph. Most existing KGQA systems use static knowledge bases for offline training. After deployment, they fail to learn from unseen new entities added to the graph. There is a need for dynamic algorithms which can adapt to the evolving graphs and give interpretable results. In this research work, we propose using new auction algorithms for question answering over knowledge graphs. These algorithms can adapt to changing environments in real-time, making them suitable for offline and online training. An auction algorithm computes paths connecting an origin node to one or more destination nodes in a directed graph and uses node prices to guide the search for the path. The prices are initially assigned arbitrarily and updated dynamically based on defined rules. The algorithm navigates the graph from the high-price to the low-price nodes. When new nodes and edges are dynamically added or removed in an evolving knowledge graph, the algorithm can adapt by reusing the prices of existing nodes and assigning arbitrary prices to the new nodes. For subsequent related searches, the “learned” prices provide the means to “transfer knowledge” and act as a “guide”: to steer it toward the lower-priced nodes. Our approach reduces the search computational effort by 60% in our experiments, thus making the algorithm computationally efficient. The resulting path given by the algorithm can be mapped to the attributes of entities and relations in knowledge graphs to provide an explainable answer to the query. We discuss some applications for which our method can be used. Full article

This article proposes an algorithm for recognizing road signs based on a determination of their color and shape. It first searches for the edge segment of the road sign. The boundary curve of the road sign is defined by the boundary of the edge segment. Approximating the boundaries of a road sign reveals its shape. The hierarchical road sign recognition system forms classes in the form of a sign. Six classes are at the first level. Two classes contain only one road sign. Signs are classified by the color of the edge segment at the second level of the hierarchy. The image inside the edge segment is cut at the third level of the hierarchy. The sign is then identified based on a comparison of the pattern. A computer experiment was carried out on two collections of road signs. The proposed algorithm has a high operating speed and a low percentage of errors. Full article

(1) Background: The prevalence of uncontrolled hypertension is rising all across the world, making it a concern for public health. The usage of mobile health applications has resulted in a number of positive outcomes for the management and control of hypertension. (2) Objective: The study’s primary goal is to explain the steps to create a hypertension application (app) that considers cultural and social standards in Saudi Arabia, motivational features, and the needs of male and female Saudi citizens. (3) Methods: This study reports the emerged features and content needed to be adapted or developed in health apps for hypertension patients during an interactive qualitative analysis focus group activity with (n = 5) experts from the Saudi Ministry of Health. A gap analysis was conducted to develop an app based on a deep understanding of user needs with a patient-centred approach. (4) Results: Based on the participant’s reviews in this study, the app was easy to use and can help Saudi patients to control their hypertension, the design was interactive, motivational features are user-friendly, and there is a need to consider other platforms such as Android and Blackberry in a future version. (5) Conclusions: Mobile health apps can help Saudis change their unhealthy lifestyles. Target users, usability, motivational features, and social and cultural standards must be considered to meet the app’s aim. Full article

The semantic segmentation of 3D medical image stacks enables accurate volumetric reconstructions, computer-aided diagnostics and follow-up treatment planning. In this work, we present a novel variant of the Unet model, called the NUMSnet, that transmits pixel neighborhood features across scans through nested layers to achieve accurate multi-class semantic segmentation with minimal training data. We analyzed the semantic segmentation performance of the NUMSnet model in comparison with several Unet model variants in the segmentation of 3–7 regions of interest using only 5–10% of images for training per Lung-CT and Heart-CT volumetric image stack. The proposed NUMSnet model achieves up to 20% improvement in segmentation recall, with 2–9% improvement in $Dice$ scores for Lung-CT stacks and 2.5–16% improvement in $Dice$ scores for Heart-CT stacks when compared to the Unet++ model. The NUMSnet model needs to be trained with ordered images around the central scan of each volumetric stack. The propagation of image feature information from the six nested layers of the Unet++ model are found to have better computation and segmentation performance than the propagation of fewer hidden layers or all ten up-sampling layers in a Unet++ model. The NUMSnet model achieves comparable segmentation performance to previous works while being trained on as few as 5–10% of the images from 3D stacks. In addition, transfer learning allows faster convergence of the NUMSnet model for multi-class semantic segmentation from pathology in Lung-CT images to cardiac segmentation in Heart-CT stacks. Thus, the proposed model can standardize multi-class semantic segmentation for a variety of volumetric image stacks with a minimal training dataset. This can significantly reduce the cost, time and inter-observer variability associated with computer-aided detection and treatment. Full article

Community detection in dynamic networks is a challenging research problem. One of the main obstacles is the stability issues that arise during the evolution of communities. In dynamic networks, new communities may emerge and existing communities may disappear, grow, or shrink. As a result, a community can evolve into a completely different one, making it difficult to track its evolution (this is known as the drifting/identity problem). In this paper, we focused on the evolution of a single community. Our aim was to identify the community that contains a particularly important node, called the anchor, and to track its evolution over time. In this way, we circumvented the identity problem by allowing the anchor to define the core of the relevant community. We proposed a framework that tracks the evolution of the community defined by the anchor and verified its efficiency and effectiveness through experimental evaluation. Full article

Demand fluctuations and uncertainty bring challenges to inventory management, and intermittent demand patterns increase the risk of inventory backlogs and raise inventory holding costs. In previous studies on inventory routing problems, different variants have been proposed to cope with complicated industrial scenarios. However, there are few studies on inventory routing problems with intermittent demand patterns. To solve this problem, we introduce a lateral transshipment strategy and build a single-product multi-period inventory routing mixed integer programming model to reduce customers’ inventory backlogs, balance regional inventory, reduce inventory holding costs, and improve inventory management efficiency. Furthermore, we design an adaptive large-neighborhood search algorithm with new operators to improve the solving efficiency. The experimental results show that an appropriate transshipment price can reduce the share of distribution costs. Another finding is that higher-capacity vehicles lead to higher revenue. Our findings not only expand the scope of the IRP domain but also provide actionable management insights for business practitioners. Full article

On 24 February 2022, the invasion of Ukraine by Russian troops began, starting a dramatic conflict. As in all modern conflicts, the battlefield is both real and virtual. Social networks have had peaks in use and many scholars have seen a strong risk of disinformation. In this study, through an unsupervised topic tracking system implemented with Natural Language Processing and graph-based techniques framed within a biological metaphor, the Italian social context is analyzed, in particular, by processing data from Twitter (texts and metadata) captured during the first month of the war. The system, improved if compared to previous versions, has proved to be effective in highlighting the emerging topics, all the main events and any links between them. Full article

Electric induction motors are one of the most important and widely used classes of machines in modern industry. Large motors, which are commonly process-critical, will usually have built-in condition-monitoring systems to facilitate preventive maintenance and fault detection. Such capabilities are usually not cost-effective for small (under ten horsepower) motors, as they are inexpensive to replace. However, large industrial sites may use hundreds of these small motors, often to drive cooling fans or lubrication pumps for larger machines. Multiple small motors may further be assigned to a single electrical circuit, meaning a failure in one could damage other motors on that circuit. There is thus a need for condition monitoring of aggregations of small motors. We report on an ongoing project to develop a machine-learning-based solution for fault detection in multiple small electric motors. Shallow and deep learning approaches to this problem are investigated and compared, with a hybrid deep/shallow system ultimately being the most effective. Full article

Video question answering (QA) is a cross-modal task that requires understanding the video content to answer questions. Current techniques address this challenge by employing stacked modules, such as attention mechanisms and graph convolutional networks. These methods reason about the semantics of video features and their interaction with text-based questions, yielding excellent results. However, these approaches often learn and fuse features representing different aspects of the video separately, neglecting the intra-interaction and overlooking the latent complex correlations between the extracted features. Additionally, the stacking of modules introduces a large number of parameters, making model training more challenging. To address these issues, we propose a novel multimodal knowledge distillation method that leverages the strengths of knowledge distillation for model compression and feature enhancement. Specifically, the fused features in the larger teacher model are distilled into knowledge, which guides the learning of appearance and motion features in the smaller student model. By incorporating cross-modal information in the early stages, the appearance and motion features can discover their related and complementary potential relationships, thus improving the overall model performance. Despite its simplicity, our extensive experiments on the widely used video QA datasets, MSVD-QA and MSRVTT-QA, demonstrate clear performance improvements over prior methods. These results validate the effectiveness of the proposed knowledge distillation approach. Full article

Metrics are key elements that can give us valuable information about the effectiveness of agile software development processes, particularly considering the Scrum environment. This study aims to learn about the metrics adopted to assess agile development processes and explore the impact of how the role performed by each member in Scrum contributed to increasing/reducing the perception of the importance of these metrics. The impact of years of experience in Scrum on this perception was also explored. To this end, a quantitative study was conducted with 191 Scrum professionals in companies based in Portugal. The results show that the Scrum role is not a determining factor, while individuals with more years of experience have a higher perception of the importance of metrics related to team performance. The same conclusion is observed for the business value metric of the product backlog and the percentage of test automation in the testing phase. The findings allow for extending the knowledge about Scrum project management processes and their teams, in addition to offering important insights into the implementation of metrics for software engineering companies that adopt Scrum. Full article

Decentralized Autonomous Organizations (DAOs) have gained widespread attention in academia and industry as potential future models for decentralized governance and organization. In order to understand the trends and future potential of this rapidly growing technology, it is crucial to conduct research in the field. This research aims at a data-driven approach for the objective content analysis of big data related to DAOs, using text mining and Latent Dirichlet Allocation (LDA)-based topic modeling. The study analyzed tweets with the hashtag #DAO and all Reddit data with “DAO”. The results were from the identification of the top 100 frequently appearing keywords, as well as the top 20 keywords with high network centrality, and key topics related to finance, gaming, and fundraising, from both Twitter and Reddit. The analysis revealed twelve topics from Twitter and eight topics from Reddit, with the term “community” frequently appearing across many of these topics. The findings provide valuable insights into the current trend and future potential of DAOs, and should be used by researchers to guide further research in the field and by decision makers to explore innovative ways to govern the organizations. Full article

This research studied people’s responses to requests that ask for accessing their personal information when using augmented reality (AR) technology. AR is a new technology that superimposes digital information onto the real world, creating a unique user experience. As such, AR is often associated with the collection and use of personal information, which may lead to significant privacy concerns. To investigate these potential concerns, we adopted an experimental approach and examined people’s actual responses to real-world requests for various types of personal information while using a designated AR application on their personal smartphones. Our results indicate that the majority (57%) of people are willing to share sensitive personal information with an unknown third party without any compensation other than using the application. Moreover, there is variability in the individuals’ willingness to allow access to various kinds of personal information. For example, while 75% of participants were open to granting access to their microphone, only 35% of participants agreed to allow access to their contacts. Lastly, monetary compensation is linked with an increased willingness to share personal information. When no compensation was offered, only $35\%$ of the participants agreed to grant access to their contacts, but when a low compensation was offered, $57.5\%$ of the participants agreed. These findings combine to suggest several practical implications for the development and distribution of AR technologies. Full article

As the digital era advances, many industries continue to expand their use of digital technologies to support company operations, notably at the customer interface, bringing new commercial opportunities and increased efficiencies. However, there are new sets of responsibilities associated with the deployment of these technologies, encompassed within the emerging concept of corporate digital responsibility (CDR), which to date has received little attention in the academic literature. This exploratory paper thus looks to make a small contribution to addressing this gap in the literature. The paper adopts a qualitative, inductive research method, employing an initial scoping literature review followed by two case studies. Based on the research findings, a simple model of CDR parameters is put forward. The article includes a discussion of a number of emergent issues—fair and equitable access, personal and social well-being, environmental implications, and cross-supply chain complexities—and a conclusion that summarises the main findings and suggests possible directions for future research. Full article

Spread spectrum techniques, such as the Chirp Spread Spectrum (CSS) used by LoRa technology, are important for machine-to-machine communication in the context of the Internet of Things. They offer high processing gain, reliable communication over long ranges, robustness to interference and noise in harsh environments, etc. However, these features are compromised by their poor spectral efficiency, resulting in a very low data transmission rate. This paper deals with a spectrally efficient variant of CSS. The system uses M-ary phase keying to modulate the data and exploits CSS’s properties to transmit the modulated symbols as overlapping chirps. The overlapping of chirp signals may affect the system performance due to inter-symbol interference. Therefore, we analyse the relationship between the number of overlaps and the effect of inter-symbol interference (ISI), and we also determine the BER expression as a function of the number of overlaps. Finally, we derive the optimal number of overlapping symbols that corresponds to the minimum error probability. Full article

Agile software requirements engineering processes enable quick responses to reflect changes in the client’s software requirements. However, there are challenges associated with agile requirements engineering processes, which hinder fast, sustainable software development. Research addressing the challenges with available solutions is patchy, diverse and inclusive. In this study, we use a systematic literature review coupled with thematic classification and gap mapping analysis to examine extant solutions against challenges; the typologies/classifications of challenges faced with agile software development in general and specifically in requirements engineering and how the solutions address the challenges. Our study covers the period from 2009 to 2023. Scopus—the largest database for credible academic publications was searched. Using the exclusion criteria to filter the articles, a total of 78 valid papers were selected and reviewed. Following our investigation, we develop a framework that takes a three-dimensional view of agile requirements engineering solutions and suggest an orchestrated approach balancing the focus between the business context, project management and agile techniques. This study contributes to the theoretical frontier of agile software requirement engineering approaches and guidelines for practice. Full article

Email classification systems are essential for handling and organizing the massive flow of communication, especially in a business context. Although many solutions exist, the lack of standardized classification categories limits their applicability. Furthermore, the lack of Romanian language business-oriented public datasets makes the development of such solutions difficult. To this end, we introduce a versatile automated email classification system based on a novel public dataset of 1447 manually annotated Romanian business-oriented emails. Our corpus is annotated with 5 token-related labels, as well as 5 sequence-related classes. We establish a strong baseline using pre-trained Transformer models for token classification and multi-task classification, achieving an F1-score of 0.752 and 0.764, respectively. We publicly release our code together with the dataset of labeled emails. Full article