Information, Volume 13, Issue 11 (November 2022) – 44 articles

EEG-based emotion recognition has become an important part of human–computer interaction. To solve the problem that single-modal features are not complete enough, in this paper, we propose a multimodal emotion recognition method based on the attention recurrent graph convolutional neural network, which is represented by Mul-AT-RGCN. The method explores the relationship between multiple-modal feature channels of EEG and peripheral physiological signals, converts one-dimensional sequence features into two-dimensional map features for modeling, and then extracts spatiotemporal and frequency–space features from the obtained multimodal features. These two types of features are input into a recurrent graph convolutional network with a convolutional block attention module for deep semantic feature extraction and sentiment classification. To reduce the differences between subjects, a domain adaptation module is also introduced to the cross-subject experimental verification. This proposed method performs feature learning in three dimensions of time, space, and frequency by excavating the complementary relationship of different modal data so that the learned deep emotion-related features are more discriminative. The proposed method was tested on the DEAP, a multimodal dataset, and the average classification accuracies of valence and arousal within subjects reached 93.19% and 91.82%, respectively, which were improved by 5.1% and 4.69%, respectively, compared with the only EEG modality and were also superior to the most-current methods. The cross-subject experiment also obtained better classification accuracies, which verifies the effectiveness of the proposed method in multimodal EEG emotion recognition. Full article

Over the last two decades, auctions have become an integral part of e-commerce and a promising field for applying artificial intelligence technologies. The use of signals has been studied extensively in the existing auction literature. Specifically, it has been shown that when an external strategic entity (such as an information broker) is present, it can be beneficial to use signaling as a preliminary step before offering to sell information. However, these results apply only in cases where all auction participants are completely rational agents. However, in many real-life scenarios some of the participants are humans, and hence are easily affected by external factors, i.e., their rationality is bounded. In this paper, we offer a thorough investigation of a case in which the prospective information buyer is a human auctioneer. Using a set of MTurk-based experiments with people, we tested 10,000 independent auctions with diverse characteristics, and were able to identify a varied set of practical insights regarding human behavior. Real-life strategic information brokers could potentially use these insights to achieve a better understanding of how humans operate, paving the way for optimizing the benefit obtainable from the information they own. Full article

Real-time detection and identification of orchard pests is related to the economy of the orchard industry. Using lab picture collections and pictures from web crawling, a dataset of common pests in orchards has been created. It contains 24,748 color images and covers seven types of orchard pests. Based on this dataset, this paper combines YOLOv5 and GhostNet and explains the benefits of this method using feature maps, heatmaps and loss curve. The results show that the mAP of the proposed method increases by 1.5% compared to the original YOLOv5, with 2$\times$ or 3$\times$ fewer parameters, less GFLOPs and the same or less detection time. Considering the fewer parameters of the Ghost convolution, our new method can reach a higher mAP with the same epochs. Smaller neural networks are more feasible to deploy on FPGAs and other embedding devices which have limited memory. This research provides a method to deploy the algorithm on embedding devices. Full article

Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive data exploration and visualization. However, web-based solutions for visual analytics remain scattered and relatively problem-specific. This leads to per-case re-implementations of common components, system architectures, and user interfaces, rather than focusing on innovation and building sophisticated applications for visual analytics. In this paper, we present the Statistics Online Computational Resource Analytical Toolbox (SOCRAT), a dynamic, flexible, and extensible web-based visual analytics framework. The SOCRAT platform is designed and implemented using multi-level modularity and declarative specifications. This enables easy integration of a number of components for data management, analysis, and visualization. SOCRAT benefits from the diverse landscape of existing in-browser solutions by combining them with flexible template modules into a unique, powerful, and feature-rich visual analytics toolbox. The platform integrates a number of independently developed tools for data import, display, storage, interactive visualization, statistical analysis, and machine learning. Various use cases demonstrate the unique features of SOCRAT for visual and statistical analysis of heterogeneous types of data. Full article

Automated citation analysis is becoming increasingly important in assessing the scientific quality of publications and identifying patterns of collaboration among researchers. However, little attention has been paid to analyzing the scientific content of the citation context. This study presents an unsupervised citation detection method that uses semantic similarities between citations and candidate sentences to identify implicit citations, determine their functions, and analyze their sentiments. We propose different document vector models based on TF-IDF weights and word vectors and compare them empirically to calculate their semantic similarity. To validate this model for identifying implicit citations, we used deep neural networks and LDA topic modeling on two citation datasets. The experimental results show that the F1 values for the implicit citation classification are 88.60% and 86.60% when the articles are presented in abstract and full-text form, respectively. Based on the citation function, the results show that implicit citations provide background information and a technical basis, while explicit citations emphasize research motivation and comparative results. Based on the citation sentiment, the results showed that implicit citations tended to describe the content objectively and were generally neutral, while explicit citations tended to describe the content positively. This study highlights the importance of identifying implicit citations for research evaluation and illustrates the difficulties researchers face when analyzing the citation context. Full article

Using a qualitative research-based approach, this study aimed to understand (i) the way home-based teleworkers in France perceive and organize their professional activities and workspaces, (ii) their teleworking conditions, (iii) the way they characterize the modalities and the nature of their interactions with their professional circle, and more broadly (iv) their quality of life ‘at work’. We performed a lexical and morphosyntactic analysis of interviews conducted with 28 teleworkers (working part-time or full-time from home) before the COVID-19 crisis and the associated establishment of emergency telework. Our results confirm and complement findings in the literature. Participant discourses underlined the beneficial effects of teleworking in terms of professional autonomy, flexibility, concentration, efficiency, performance, productivity, and being able to balance their professional and private lives. Nevertheless, they also highlighted the deleterious effects of teleworking on temporal workload, setting boundaries for work, work-based relationships and socio-professional integration. Despite the study limitations, our findings highlight the need for specific research-based and practical strategies to support the implementation of a sustainable telework organization in the post-COVID-19 pandemic era. Full article

This paper introduces a computational strategic game model capable of mitigating the adversarial impact of node misbehaviour in large-scale Internet of Things (IoT) deployments. This security model’s central concept is to preclude the participation of misbehaving nodes during the routing process within the ad hoc environment of mobile IoT nodes. The core of the design is a simplified mathematical algorithm that can strategically compute payoff embrace moves to maximise gain. At the same time, a unique role is given to a node for restoring resources during communication or security operations. Adopting an analytical research methodology, the proposed model uses public and private cloud systems for integrating quality service delivery with secure agreements using a Global Trust Controller and core node selection controller to select an intermediate node for data propagation. The initiation of the game model is carried out by identifying mobile node role followed by choosing an optimal payoff for a normal IoT node. Finally, the model leads to an increment of gain for selecting the regular IoT node for routing. The findings of the evaluation indicate that the proposed scheme offers 36% greater accuracy, 25% less energy, 11% faster response time, and 27% lower cost than the prevalent game-based models currently used to solve security issues. The value added by the proposed study is the simplified game model which balances both security demands and communication demands. Full article

Since the start of the COVID-19 pandemic, government authorities have responded by issuing new public health policies, many of which were intended to contain its spread but ended up limiting economic and social activities. The citizen responses to these policies are diverse, ranging from goodwill to fear and anger. It is challenging to determine whether or not these public health policies achieved the intended impact. This requires systematic data collection and scientific studies, which can be very time-consuming. To overcome such challenges, in this paper, we provide an alternative approach to continuously monitor and dynamically make sense of how public health policies impact citizens. Our approach is to continuously collect Twitter posts related to COVID-19 policies and to analyze the public reactions. We have developed a web-based system that collects tweets daily and generates timelines and geographical displays of citizens’ “concern levels”. Tracking the public reactions towards different policies can help government officials assess the policy impacts in a more dynamic and real-time manner. For this paper, we collected and analyzed over 16 million tweets related to ten policies over a 10-month period. We obtained several findings; for example, the “COVID-19 (General)” and ”Ventilators” policies engendered the highest concern levels, while the “Face Coverings” policy caused the lowest. Nine out of ten policies exhibited significant changes in concern levels during the observation period. Full article

Understanding the network usage patterns of university users is very important today. This paper focuses on the research of DNS request behaviors of university users in Shanghai, China. Based on the DNS logs of a large number of university users recorded by CERNET, we conduct a general analysis of the behavior of network browsing from two perspectives: the characteristics of university users’ behavior and the market share of CDN service providers. We also undertake experiments on DNS requests patterns for CDN service providers using different prediction models. Firstly, in order to understand the university users’ Internet access patterns, we select the top seven universities with the most DNS requests and reveal the characteristics of different university users. Subsequently, to obtain the market share of different CDN service providers, we analyze the overall situation of the traffic distribution among different CDN service providers and its dynamic evolution trend. We find that Tencent Cloud and Alibaba Cloud are leading in both IPv4 and IPv6 traffic. Baidu Cloud has close to 15% in IPv4 traffic, but almost no fraction in IPv6 traffic. Finally, for the characteristics of different CDN service providers, we adopt statistical models, traditional machine learning models, and deep learning models to construct tools that can accurately predict the change in request volume of DNS requests. The conclusions obtained in this paper are beneficial for Internet service providers, CDN service providers, and users. Full article

In multigranulation environments, variable precision multigranulation rough set (VPMGRS) is a useful framework that has a tolerance for errors. Approximations are basic concepts for knowledge acquisition and attribute reductions. Accelerating update of approximations can enhance the efficiency of acquiring decision rules by utilizing previously saved information. In this study, we focus on exploiting update mechanisms of approximations in VPMGRS with the addition of granular structures. By analyzing the basic changing trends of approximations in VPMGRS, we develop accelerating update mechanisms for acquiring approximations. In addition, an incremental algorithm to update variable precision multigranulation approximations is proposed when adding multiple granular structures. Finally, extensive comparisons elaborate the efficiency of the incremental algorithm. Full article

Some researchers suggest that information is a form of matter, calling it the fifth state of matter or the fifth element. Recent results from the general theory of information (GTI) contradict this. This paper aims to explain and prove that the claims of adherents of the physical nature of information are inaccurate due to the confusion between the definitions of information, the matter that represents information, and the matter that is a carrier of information. Our explanations and proofs are based on the GTI because it gives the most comprehensive definition of information, encompassing and clarifying many of the writings in the literature about information. GTI relates information, knowledge, matter, and energy, and unifies the theories of material and mental worlds using the world of structures. According to GTI, information is not physical by itself, although it can have physical and/or mental representations. Consequently, a bit of information does not have mass, but the physical structure that represents the bit indeed has mass. Moreover, the same bit can have multiple representations in the form of a physical substance (e.g., a symbol on a paper or a state of a flip-flop circuit, or an electrical voltage or current pulse.) Naturally, these different physical representations can have different masses, although the information is the same. Thus, our arguments are not against Landauer’s principle or the empirical results of Vopson and other adherents of the physical nature of the information. These arguments are aimed at the clarification of the theoretical and empirical interpretations of these results. As the references in this paper show, recently many publications in which it is claimed that information is a physical essence appeared. That is why it is so important to elucidate the true nature of information and its relation to the physical world eliminating the existing misconceptions in information studies. Full article

We live in an era where time is a scarce resource and people enjoy the benefits of technological innovations to ensure prompt and smooth access to information required for our daily activities. In this context, conversational agents start to play a remarkable role by mediating the interaction between humans and computers in specific contexts. However, they turn out to be laborious for cross-domain use cases or when they are expected to automatically adapt throughout user dialogues. This paper introduces a method to plug in multiple domains of knowledge for a conversational agent localized in Romanian in order to facilitate the extension of the agent’s area of expertise. Furthermore, the agent is intended to become more domain-aware and learn new information dynamically from user conversations by means of a knowledge graph acting as a network of facts and information. We ensure high capabilities for natural language understanding by proposing a novel architecture that takes into account RoBERT-contextualized embeddings alongside syntactic features. Our approach leads to improved intent classification performance (F1 score = 82.6) when compared with a basic pipeline relying only on features extracted from the agent’s training data. Moreover, the proposed RDF knowledge representation is confirmed to provide flexibility in storing and retrieving natural language entities, values, and factoid relations between them in the context of each microworld. Full article

Vehicles equipped with so-called partially automated driving functions are becoming more and more common nowadays. The special feature of this automation level is that the driver is relieved of the execution of the lateral and longitudinal driving task, although they must still monitor the driving environment and the automated system. The method presented in this paper should enable the assessment of the usability and safety of such systems in a standardized manner. It is designed to capture a driver’s interaction with a system via the human–machine interface in specific scenarios in user studies. It evaluates several observable aspects of this interaction in real time and codes inadequate behavior in the categories “system operation”, “driving behavior” and “monitoring behavior”. A generic rating regarding the overall handling of the scenario is derived from these criteria. The method can be used with the assistance of a tablet application called the S.A.D.E. app (Standardized Application for Automated Driving Evaluation). Initial studies using driving simulators show promising results regarding its ability to detect problems related to a system or HMI, with some future challenges remaining open. Full article

Advances in network technology have enhanced the concern for network security issues. In order to address the problem that hopping graph are vulnerable to external attacks (e.g., the changing rules of fixed graphs are more easily grasped by attackers) and the challenge of achieving both interactivity and randomness in a network environment, this paper proposed a scheme for a dynamic graph based on chaos and cryptographic random mapping. The scheme allows hopping nodes to compute and obtain dynamically random and uncorrelated graph of other nodes independently of each other without additional interaction after the computational process of synchronous mirroring. We first iterate through the chaos algorithm to generate random seed parameters, which are used as input parameters for the encryption algorithm; secondly, we execute the encryption algorithm to generate a ciphertext of a specified length, which is converted into a fixed point number; and finally, the fixed point number is mapped to the network parameters corresponding to each node. The hopping nodes are independently updated with the same hopping map at each hopping period, and the configuration of their own network parameters is updated, so that the updated graph can effectively prevent external attacks. Finally, we have carried out simulation experiments and related tests on the proposed scheme and demonstrated that the performance requirements of the random graphs can be satisfied in both general and extreme cases. Full article

With the increasing use of machine learning across various fields to address several aims and goals, the complexity of the ML and Deep Learning (DL) approaches used to provide solutions has also increased. In the last few years, Explainable AI (XAI) methods to further justify and interpret deep learning models have been introduced across several domains and fields. While most papers have applied XAI to English and other Latin-based languages, this paper aims to explain attention-based long short-term memory (LSTM) results across Arabic Sentiment Analysis (ASA), which is considered an uncharted area in previous research. With the use of Local Interpretable Model-agnostic Explanation (LIME), we intend to further justify and demonstrate how the LSTM leads to the prediction of sentiment polarity within ASA in domain-specific Arabic texts regarding medical insights on LASIK surgery across Twitter users. In our research, the LSTM reached an accuracy of 79.1% on the proposed data set. Throughout the representation of sentiments using LIME, it demonstrated accurate results regarding how specific words contributed to the overall sentiment polarity classification. Furthermore, we compared the word count with the probability weights given across the examples, in order to further validate the LIME results in the context of ASA. Full article

A consortium system can leverage information to improve workflows, accountability, and transparency through setting up a backbone for these cross-company and cross-discipline solutions, which make it become a hot spot of market application. Users of a consortium system may register and log in different target domains to get the access authentications, so how to access resources in different domains efficiently to avoid the trust-island problem is a big challenge. Cross-domain authentication is a kind of technology that breaks trust islands and enables users to access resources and services in different domains with the same credentials, which reduces service costs for all parties. Aiming at the problems of traditional cross-domain authentication, such as complex certificate management, low authentication efficiency, and being unable to prevent the attack users’ accounts, a cross-domain authentication protocol based on face recognition is proposed in this paper. The protocol makes use of the decentralized and distributed characteristics of the consortium chain to ensure the reliable transmission of data between participants without trust relationships, and achieves biometric authentication to further solve the problem of account attack by applying a deep-learning face-recognition model. An asymmetric encryption algorithm is used to encrypt and store the face feature codes on the chain to ensure the privacy of the user’s face features. Finally, through security analysis, it is proved that the proposed protocol can effectively prevent a man-in-the-middle attack, a replay attack, an account attack, an internal attack, and other attacks, and mutual security authentication between different domains can be realized with the protocol. Full article

The mixture Rasch model is a popular mixture model for analyzing multivariate binary data. The drawback of this model is that the number of estimated parameters substantially increases with an increasing number of latent classes, which, in turn, hinders the interpretability of model parameters. This article proposes regularized estimation of the mixture Rasch model that imposes some sparsity structure on class-specific item difficulties. We illustrate the feasibility of the proposed modeling approach by means of one simulation study and two simulated case studies. Full article

Public organizations lack adequate models and methods to efficiently support and manage processes related to information security and IT investments. The objective is to optimize the management of strategic projects planned to improve the information security of a public organization and make efficient use of its available resources. The deductive method and exploratory research were used to review and analyze the available information. A mathematical model resulted that optimizes two objectives: (1) minimizing the costs of the strategic projects to be executed, and (2) maximizing the percentage of improvement in the organization’s information security. According to the result of the simulation, a subset of planned strategic projects was obtained that allows improving the information security of a public organization from 84.64% to 92.20%, considering the budgetary limitations of the organization. It was concluded that the proposed model is efficient, practical and can be a support tool for the IT management of a public organization. Full article

With the increasing value of various kinds of data in the era of big data, the demand of different subjects for data transactions has become more and more urgent. In this paper, a blockchain-based data transaction protection scheme is proposed to realize the secure transaction sharing among data. This paper carries out the following work: by analyzing the existing data transaction models, we find the data security and transaction protection problems, establish a third-party-free data transaction platform using blockchain, protect users’ data security by combining AES and improved homomorphic encryption technology, and upload the encrypted data to the Interplanetary File System (IPFS) for distributed storage. Finally, we use the powerful functions of the IPFS, combined with inadvertent transmission protocol, two-way authentication, zero-knowledge proof, and other security verification for data transactions. The security analysis proves that this scheme has higher security despite the time overhead, and we will continue to optimize the scheme to improve efficiency in the future. Full article

An adaptive optics scanning laser ophthalmoscope (AOSLO) has the characteristics of a high resolution and a small field of view (FOV), which are greatly affected by eye motion. Continual eye motion will cause distortions both within the frame (intra-frame) and between frames (inter-frame). Overcoming eye motion and achieving image stabilization is the first step and is of great importance in image analysis. Although cross-correlation-based methods enable image registration to be achieved, the manual identification and distinguishing of images with saccades is required; manual registration has a high accuracy, but it is time-consuming and complicated. Some imaging systems are able to compensate for eye motion during the imaging process, but special hardware devices need to be integrated into the system. In this paper, we proposed a deep-learning-based algorithm for automatic image stabilization. The algorithm used the VGG-16 network to extract convolution features and a correlation filter to detect the position of reference in the next frame, and finally, it compensated for displacement to achieve registration. According to the results, the mean difference in the vertical and horizontal displacement between the algorithm and manual registration was 0.07 pixels and 0.16 pixels, respectively, with a 95% confidence interval of (−3.26 px, 3.40 px) and (−4.99 px, 5.30 px). The Pearson correlation coefficients for the vertical and horizontal displacements between these two methods were 0.99 and 0.99, respectively. Compared with cross-correlation-based methods, the algorithm had a higher accuracy, automatically removed images with blinks, and corrected images with saccades. Compared with manual registration, the algorithm enabled manual registration accuracy to be achieved without manual intervention. Full article

With the rapid development of internet technology, the amount of collected or generated data has increased exponentially. The sheer volume, complexity, and unbalanced nature of this data pose a challenge to the scientific community to extract meaningful information from this data within a reasonable time. In this paper, we implemented a scalable design of an artificial bee colony for big data classification using Apache Spark. In addition, a new fitness function is proposed to handle unbalanced data. Two experiments were performed using the real unbalanced datasets to assess the performance and scalability of our proposed algorithm. The performance results reveal that our proposed fitness function can efficiently deal with unbalanced datasets and statistically outperforms the existing fitness function in terms of G-mean and $F_1$-Score. In additon, the scalability results demonstrate that our proposed Spark-based design obtained outstanding speedup and scaleup results that are very close to optimal. In addition, our Spark-based design scales efficiently with increasing data size. Full article

Various methods are used to prevent the deterioration of the biotechnological properties of brewer’s yeast during storage. This paper studied the use of artificial neural networks for the mathematical modeling of correcting the biosynthetic activity of brewer’s seed yeast of the C34 race during storage with natural minerals. The input parameters for the artificial neural networks were the suspending medium (water, beer wort, or young beer); the type of the zeolite-containing tuff from Siberian deposits; the tuff content (0.5–4% of the total volume of the suspension); and the duration of storage (3 days). The output parameters were the number of yeast cells with glycogen, budding cells, and dead cells. In the yeast stored with tuffs, the number of budding cells increased by 1.2–2.5 times, and the number of cells with glycogen increased by 9–190% compared to the control sample (without tuff). The presence of kholinskiy zeolite and shivyrtuin tuffs resulted in a significant effect. The artificial neural networks were required for solving the regression tasks and predicting the output parameters based on the input parameters. Four networks were created: ANN1 (mean relative error = 4.869%) modeled the values of all the output parameters; ANN2 (MRE = 1.8381%) modeled the number of cells with glycogen; ANN3 (MRE = 6.2905%) modeled the number of budding cells; and ANN4 (MRE = 4.2191%) modeled the number of dead cells. The optimal parameters for yeast storage were then determined. As a result, the possibility of using ANNs for mathematical modeling of undesired deviations in the physiological parameters of brewer’s seed yeast during storage with natural minerals was proven. Full article

In embedded electronic system applications being developed today, complex datasets are required to be obtained, processed, and communicated. These can be from various sources such as environmental sensors, still image cameras, and video cameras. Once obtained and stored in electronic memory, the data is accessed and processed using suitable mathematical algorithms. How the data are stored, accessed, processed, and communicated will impact on the cost to process the data. Such algorithms are traditionally implemented in software programs that run on a suitable processor. However, different approaches can be considered to create the digital system architecture that would consist of the memory, processing, and communications operations. When considering the mathematics at the centre of the design making processes, this leads to system architectures that can be optimized for the required algorithm or algorithms to realize. Mathematics of Arrays (MoA) is a class of operations that supports n-dimensional array computations using array shapes and indexing of values held within the array. In this article, the concept of MoA is considered for realization in software and hardware using Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) technologies. The realization of MoA algorithms will be developed along with the design choices that would be required to map a MoA algorithm to hardware, software or hardware-software co-designs. Full article

The ubiquitous access and exponential growth of information available on social media networks have facilitated the spread of fake news, complicating the task of distinguishing between this and real news. Fake news is a significant social barrier that has a profoundly negative impact on society. Despite the large number of studies on fake news detection, they have not yet been combined to offer coherent insight on trends and advancements in this domain. Hence, the primary objective of this study was to fill this knowledge gap. The method for selecting the pertinent articles for extraction was created using the preferred reporting items for systematic reviews and meta-analyses (PRISMA). This study reviewed deep learning, machine learning, and ensemble-based fake news detection methods by a meta-analysis of 125 studies to aggregate their results quantitatively. The meta-analysis primarily focused on statistics and the quantitative analysis of data from numerous separate primary investigations to identify overall trends. The results of the meta-analysis were reported by the spatial distribution, the approaches adopted, the sample size, and the performance of methods in terms of accuracy. According to the statistics of between-study variance high heterogeneity was found with τ² = 3.441; the ratio of true heterogeneity to total observed variation was I² = 75.27% with the heterogeneity chi-square (Q) = 501.34, the degree of freedom = 124, and p ≤ 0.001. A p-value of 0.912 from the Egger statistical test confirmed the absence of a publication bias. The findings of the meta-analysis demonstrated satisfaction with the effectiveness of the recommended approaches from the primary studies on fake news detection that were included. Furthermore, the findings can inform researchers about various approaches they can use to detect online fake news. Full article

Knowledge graphs gained popularity in recent years and have been useful for concept visualization and contextual information retrieval in various applications. However, constructing a knowledge graph by scraping long and complex unstructured texts for a new domain in the absence of a well-defined ontology or an existing labeled entity-relation dataset is difficult. Domains such as cybersecurity education can harness knowledge graphs to create a student-focused interactive and learning environment to teach cybersecurity. Learning cybersecurity involves gaining the knowledge of different attack and defense techniques, system setup and solving multi-facet complex real-world challenges that demand adaptive learning strategies and cognitive engagement. However, there are no standard datasets for the cybersecurity education domain. In this research work, we present a bottom-up approach to curate entity-relation pairs and construct knowledge graphs and question-answering models for cybersecurity education. To evaluate the impact of our new learning paradigm, we conducted surveys and interviews with students after each project to find the usefulness of bot and the knowledge graphs. Our results show that students found these tools informative for learning the core concepts and they used knowledge graphs as a visual reference to cross check the progress that helped them complete the project tasks. Full article

Higher educational institutions in Western Balkan countries strive for continuous development of their teaching and learning processes. One of the priorities is employing state-of-the-art technology to facilitate experience-based learning, and virtual and augmented reality are two of the most effective solutions to providing the opportunity to practice the acquired theoretical knowledge. This report presents (apart from the theoretical introduction to the issue) an overall picture of the knowledge of AR and VR technology in education in Western Balkan universities. It is based on a semi-structured online questionnaire whose recipients were academic staff and students from universities in Albania, Kosovo, and North Macedonia. The questionnaire differed for each target group; the version for academics comprised 11 questions for 710 respondents, and the version for students comprised 10 questions for 2217 respondents. This paper presents and discusses the results for each question with the aim to illustrate Western Balkan countries’ current state of VR and AR application in education. Full article

When analyzing a financial asset, it is essential to study the trend of its time series. It is also necessary to examine its evolution and activity over time to statistically analyze its possible future behavior. Both retail and institutional investors base their trading strategies on these analyses. One of the most used techniques to study financial time series is to analyze its dynamic structure using auto-regressive models, simple moving average models (SMA), and mixed auto-regressive moving average models (ARMA). These techniques, unfortunately, do not always provide appreciable results both at a statistical level and as the Risk-Reward Ratio (RRR); above all, each system has its pros and cons. In this paper, we present CryptoNet; this system is based on the time series extraction exploiting the vast potential of artificial intelligence (AI) and machine learning (ML). Specifically, we focused on time series trends extraction by developing an artificial neural network, trained and tested on two famous crypto-currencies: Bitcoinand Ether. CryptoNet learning algorithm improved the classic linear regression model up to 31% of MAE (mean absolute error). Results from this work should encourage machine learning techniques in sectors classically reluctant to adopt non-standard approaches. Full article

In many parts of the world, long before social media, trust in media and journalism was fragile and shaky. Today, however, with an unprecedented information abundance, the situation has worsened because, in the high-speed information free-for-all of social media platforms and the internet, anyone can consume and produce. As a result, citizens find it difficult to discern what is real and what is fake. In this context, the aim of the study is to explore how information and fake news consumption affects the perception of media in terms of trust. The methodology applied for this purpose was a mixed method using both quantitative and qualitative data in order to provide not only descriptive data but more thorough results. For the quantitative analysis, a sample of 849 university students participated: from these, a smaller sample of 100 participated in the qualitative phase. Conclusions indicate that the distribution of fake news is worryingly associated with the media and, consequently, a concerning distrust of media is shown among participants who express feeling insecure, vulnerable, confused, and distrusting of media. Full article

Teachers often need to adapt their teaching methodologies in order to overcome possible limitations and ensure that education does not lose quality in the face of different scenarios that may arise in the educational environment, which are not always the most desirable. Techniques such as the Educational Escape Room (ERE) in higher education, are taking a great increase due to its popularity among young people as a leisure activity. This study shows an educational research based on the application of a Digital Educational Escape Room (DEER) to respond to the limitations of hybrid teaching with students divided between the classroom and their homes. Through the analysis of a control group, with a traditional lecture class, and an experimental group with the use of a pretest and a posttest, with the addition of studying the different learning styles of the students in each group, interesting results and conclusions have been obtained that offer a replicability of this technique for other fields and educational modalities. Full article

With the development of ultrahigh-voltage transmission lines in China, the reradiation interference caused by the steel tower used in ultrahigh-voltage transmission becomes increasingly aggravating for nearby radio stations. In this paper, using the multilevel fast multipole algorithm, the reradiation interference of an ultrahigh-voltage transmission steel tower is investigated. Additionally, the reradiation interference characteristics of a transmission steel tower were investigated with various frequencies and azimuth angles of incidence wave. The results show that the frequency and the azimuth angle of incident wave are the impact factors for the reradiation interference of steel towers. It is better to use a detailed angle steel model for the truss structure of steel towers at high frequency, while a simplified structural model can be used at low frequency. Additionally, the diffraction at the edge of the angle steel has a great influence on the reradiation interference, particularly at high frequencies. Full article