Information, Volume 11, Issue 10 (October 2020) – 36 articles

The recognition and translation of organization names (ONs) is challenging due to the complex structures and high variability involved. ONs consist not only of common generic words but also names, rare words, abbreviations and business and industry jargon. ONs are a sub-class of named entity (NE) phrases, which convey key information in text. As such, the correct translation of ONs is critical for machine translation and cross-lingual information retrieval. The existing Chinese–Uyghur neural machine translation systems have performed poorly when applied to ON translation tasks. As there are no publicly available Chinese–Uyghur ON translation corpora, an ON translation corpus is developed here, which includes 191,641 ON translation pairs. A word segmentation approach involving characterization, tagged characterization, byte pair encoding (BPE) and syllabification is proposed here for ON translation tasks. A recurrent neural network (RNN) attention framework and transformer are adapted here for ON translation tasks with different sequence granularities. The experimental results indicate that the transformer model not only outperforms the RNN attention model but also benefits from the proposed word segmentation approach. In addition, a Chinese–Uyghur ON translation system is developed here to automatically generate new translation pairs. This work significantly improves Chinese–Uyghur ON translation and can be applied to improve Chinese–Uyghur machine translation and cross-lingual information retrieval. It can also easily be extended to other agglutinative languages. Full article

Technologies, such as real-time magnetic resonance (RT-MRI), can provide valuable information to evolve our understanding of the static and dynamic aspects of speech by contributing to the determination of which articulators are essential (critical) in producing specific sounds and how (gestures). While a visual analysis and comparison of imaging data or vocal tract profiles can already provide relevant findings, the sheer amount of available data demands and can strongly profit from unsupervised data-driven approaches. Recent work, in this regard, has asserted the possibility of determining critical articulators from RT-MRI data by considering a representation of vocal tract configurations based on landmarks placed on the tongue, lips, and velum, yielding meaningful results for European Portuguese (EP). Advancing this previous work to obtain a characterization of EP sounds grounded on Articulatory Phonology, important to explore critical gestures and advance, for example, articulatory speech synthesis, entails the consideration of a novel set of tract variables. To this end, this article explores critical variable determination considering a vocal tract representation aligned with Articulatory Phonology and the Task Dynamics framework. The overall results, obtained considering data for three EP speakers, show the applicability of this approach and are consistent with existing descriptions of EP sounds. Full article

Organizational reliance on virtual teams (VTs) is increasing tremendously due to the significant benefits they offer, such as efficiently reaching objectives and increasing organizational performance. However, VTs face a lot of challenges that, if overlooked, will prevent them from yielding the required benefits. One of the major issues that hinders the effectiveness of VTs is the decision-making process. There is a lack of scientific research that attempts to understand the factors affecting decision making processes in VTs. Studies in this area have only been done in the United States and Europe. However, such research has not been conducted in the Middle East, where specific scientific solutions are still required to improve the performance of VTs. Therefore, this study is conducted in the Middle East, namely in the United Arab Emirates, to gain scientific knowledge on this region’s specificity. An online questionnaire (Google forms) was used to obtain the necessary data. Hypotheses were developed to test the influence of ICT (Information and communications technologies), language, information sharing, and trust on the decision-making processes, and the effect of decision making on team performance. Structural equational model (SEM) methodology was used to test our proposed model. The results showed that factors such as trust, ICT, and information sharing have a direct effect on decision-making processes, while language has no effect on decision making, and decision-making processes have a direct effect on the performance of the VTs. Full article

In this paper, we incorporate a GARCH model into an artificial neural network (ANN) for financial volatility modeling and estimate the parameters in Tensorflow. Our goal was to better predict stock volatility. We evaluate the performance of the models using the mean absolute errors of powers of the out-of-sample returns between 2 March 2018 and 28 February 2020. Our results show that our modeling procedure with an ANN can outperform the standard GARCH(1,1) model with standardized Student’s t distribution. Our variable importance analysis shows that Net Debt/EBITA is among the six most important predictor variables in all of the neural network models we have examined. The main contribution of this paper is that we propose a Long Short-Term Memory (LSTM) model with a GARCH framework because LSTM can systematically take into consideration potential nonlinearity in volatility structure at different time points. One of the advantages of our research is that the proposed models are easy to implement because our proposed models can be run in Tensorflow, a Python package that enables fast and automatic optimization. Another advantage is that the proposed models enable variable importance analysis. Full article

At present, range-free localization algorithm is the mainstream of node localization method, which has made tremendous achievements. However, there are few algorithms that can be used in concave regions, and the existing algorithms have defects such as hop distance error, excessive time complexity and so on. To solve these problems, this paper proposes a two-stage PSO (Particle Swarm Optimization) algorithm for wireless sensor nodes localization in “concave regions”. In the first stage, it proposes a method of distance measuring based on similar path search and intersection ratio, and completes the initial localization of unknown nodes based on maximum likelihood estimation. In the second stage, the improved PSO algorithm is used to optimize the initial localization results in the previous stage. The experimental result shows that the localization error of this algorithm is always within 10% and the execution time is maintained at about 20 s when the communication radius and beacon node ratio is changing. Therefore, the algorithm can obtain high localization accuracy in wireless sensor network with “concave regions”, requiring low computing power for nodes, and energy consumption. Given this, it can greatly extend the service life of sensor nodes. Full article

Today, the Internet of Things (IoT) is an emerging topic in research and industry. Famous examples of IoT applications are smart homes, smart cities, and smart factories. Through highly interconnected devices, equipped with sensors and actuators, context-aware approaches can be developed to enable, e.g., monitoring and self-organization. To achieve context-awareness, a large amount of environment models have been developed for the IoT that contain information about the devices of an environment, their attached sensors and actuators, as well as their interconnection. However, these models highly differ in their content, the format being used, for example ontologies or relational models, and the domain to which they are applied. In this article, we present a comparative survey of models for IoT environments. By doing so, we describe and compare the selected models based on a deep literature research. The result is a comparative overview of existing state-of-the-art IoT environment models. Full article

The development of multimodal media compensates for the lack of information expression in a single modality and thus gradually becomes the main carrier of sentiment. In this situation, automatic assessment for sentiment information in multimodal contents is of increasing importance for many applications. To achieve this, we propose a joint sentiment part topic regression model (JSP) based on latent Dirichlet allocation (LDA), with a sentiment part, which effectively utilizes the complementary information between the modalities and strengthens the relationship between the sentiment layer and multimodal content. Specifically, a linear regression module is developed to share implicit variables between image–text pairs, so that one modality can predict the other. Moreover, a sentiment label layer is added to model the relationship between sentiment distribution parameters and multimodal contents. Experimental results on several datasets verify the feasibility of our proposed approach for multimodal sentiment analysis. Full article

The human mood has a temporary effect on the face shape due to the movement of its muscles. Happiness, sadness, fear, anger, and other emotional conditions may affect the face biometric system’s reliability. Most of the current studies on facial expressions are concerned about the accuracy of classifying the subjects based on their expressions. This study investigated the effect of facial expressions on the reliability of a face biometric system to find out which facial expression puts the biometric system at greater risk. Moreover, it identified a set of facial features that have the lowest facial deformation caused by facial expressions to be generalized during the recognition process, regardless of which facial expression is presented. In order to achieve the goal of this study, an analysis of 22 facial features between the normal face and six universal facial expressions is obtained. The results show that the face biometric systems are affected by facial expressions where the disgust expression achieved the most dissimilar score, while the sad expression achieved the lowest dissimilar score. Additionally, the study identified the five and top ten facial features that have the lowest facial deformations on the face shape in all facial expressions. Besides that, the relativity score showed less variances between the sample using the top facial features. The obtained results of this study minimized the false rejection rate in the face biometric system and subsequently the ability to raise the system’s acceptance threshold to maximize the intrusion detection rate without affecting the user convenience. Full article

Two sentences can be related in many different ways. Distinct tasks in natural language processing aim to identify different semantic relations between sentences. We developed several models for natural language inference and semantic textual similarity for the Portuguese language. We took advantage of pre-trained models (BERT); additionally, we studied the roles of lexical features. We tested our models in several datasets—ASSIN, SICK-BR and ASSIN2—and the best results were usually achieved with ptBERT-Large, trained in a Brazilian corpus and tuned in the latter datasets. Besides obtaining state-of-the-art results, this is, to the best of our knowledge, the most all-inclusive study about natural language inference and semantic textual similarity for the Portuguese language. Full article

Higher education is changing, and a new normal is coming. Students are even more demanding, and professors need to follow the evolution of technology and try to increase student engagement in the classrooms (presential or virtual). Higher education students recognise that the introduction of new tools and learning methods can improve the teaching quality and increase the motivation to learn. Regarding a question about which type of classes students preferred, ninety-one point ninety-nine per cent (91.99%) of the students wanted interactive classes over traditional. Having this concern in mind over the past years, a professor explored a set of methods, strategies and tools and designed a new and innovative paradigm using gamification. This approach is denominated TechTeach and explores a set of trending concepts and interactive tools to teach computer science subjects. It was designed to run in a B-learning environment. The paradigm uses flipped classrooms, bring your own device (BYOD), gamification, training of soft-skills and quizzes and surveys to increase the student’s engagement and provide the best learning environment to students. Currently, COVID-19 is bringing about new challenges, and TechTeach was improved in order to be more suitable for this new way of teaching (from 0% to 100% online classes). This article details this method and shows how it can be applied in a real environment. A case study was used to prove the functionality and relevance of this approach, and the achieved results are motivating. During the semester, more than a hundred students experienced this new way of teaching and assessment. In the end, more than eighty-one per cent (81%) of the students gave a positive grade to the approach, and more than ninety-five per cent (95.65%) of the students approved the use of the concept of BYOD in the classroom. With TechTeach, the classroom is not a boring place anymore; it is a place to learn and enjoy regardless of being physical or not. Full article

This study examines the impact of word of mouth (WOM) communication through social media and how it affects satisfaction with the Summer Universiade in Taipei. This study hopes to understand the usage characteristics of social media among university students and the implementation of social media and their effectiveness as a marketing strategy for sport organization. The hypotheses were verified using a survey of 572 university students from four universities that hosted competitions for the Summer Universiade Games. Data were analyzed using t test, Pearson’s correlation analysis and two-way ANOVA analysis. The results indicated that WOM has impacted satisfaction via social media, and the level of understanding of sporting events was significantly affected by WOM communication and overall satisfaction. Moreover, gender showed no significant differences in WOM communication and overall satisfaction with sporting events. However, male participants had significantly higher value in WOM dissemination than female respondents. In addition, the spectators’ understanding of the sporting event on WOM communication and overall satisfaction was not affected by the continued use of social media. Suggestions are provided, including sufficient sports marketing and service quality from the organizers, in order to maintain good sports events and enhance spectators’ feelings. Full article

The interactive mechanism among platform operators, content producers, and information receivers is increasingly complex in human–computer symbiosis. The purpose of this study is to identify the interactive value among platform operators, content producers, and information receivers with regard to information through the health and fitness apps by adopting an advanced Analytic Hierarchy Process (AHP) method derived from professional perspectives of app users and operators, key opinion leaders, scholars, and officers. The AHP method was allocated weightings to the evaluation criteria from the twelve panelists from three groups of platform operators, content producers, and information receivers. After focus group interviews were conducted, four dimensions and twelve sub-dimensions of the initial health and fitness apps were obtained as follows: Content category: Monitoring, exercise, journaling, and sleeping; (2) User reviews: Fuctionality, interactivity, and criticism; (3) Content updates: New feature, correctness, and new language; (4) Platform terms: Privacy, accuracy, ownership, and right of use. The study integrated the panelists’ opinions toward health and fitness apps and analyzed the weight of each indicator according to their importance by Power Choice V2.5. The results revealed that the weights of dimensions of health and fitness apps were sorted by content category, user review, platform terms, and content update, as well as that the weights of the top six sub-dimensions were followed: monitoring, exercise, functionality, interactivity, privacy, and accuracy. Content producers suggested increasing the popularity of their products by adding new features, whereas information receivers preferred to correct problems. Content producers and information receivers graded platform terms as less essential, whereas platform operators rated platform terms higher. This study can contribute to assisting the health and fitness industry and the overall strategic operative process by identifying how the effectiveness in the procedures, estimative process, and cost-down can enhance competitiveness to further improve users experience and satisfaction. Full article

Multi-Resolution Modeling (MRM) is a modeling technology that creates a model that expresses the same phenomenon at more than two different resolutions. Since the advent of distributed simulation systems, the MRM study began in the military field, where the modeling and simulation (M&S) was most actively developed and was recognized as an essential area in the integrated system of live, virtual and constructive (LVC) simulations. Models of the various resolutions had already been built based on the characteristics and training purposes of each weapon system, and the interoperability of these models was a primary task in the M&S community. In this study, we report the results from a systematic review of the MRM to address two questions: (1) What research has been done towards the MRM for integrating LVC system? (2) What are the research and technology challenges for the MRM implementation in the future? In total, 22 papers have been identified and studied in this review by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The structures of the significant 20 MRM implementation experiments in those papers are analyzed based on the relationship between the MRM and integrating the LVC system being implemented in the military. We explored the various issues related to the MRM. Then, we discussed the direction in which the MRM should move forward, comparing civilian modeling techniques with those being used in the military. Full article

We propose a novel end-to-end image colorization framework which integrates attention mechanism and a learnable adaptive normalization function. In contrast to previous colorization methods that directly generate the whole image, we believe that the color of the significant area determines the quality of the colorized image. The attention mechanism uses the attention map which is obtained by the auxiliary classifier to guide our framework to produce more subtle content and visually pleasing color in salient visual regions. Furthermore, we apply Adaptive Group Instance Normalization (AGIN) function to promote our framework to generate vivid colorized images flexibly, under the circumstance that we consider colorization as a particular style transfer task. Experiments show that our model is superior to previous the state-of-the-art models in coloring foreground objects. Full article

The study focuses on the canvas of online information transmission that has expanded exponentially. Especially due to social media networks, consumers have been exposed to significant amounts of disinformation, misinformation and actual information. Electronic word-of-mouth (eWOM) on social media networks has been facilitating swift information spread. Henceforth, it has become increasingly problematic for consumers to adopt authentic information and differentiate between marketers-generated content and user-generated content. The study aims to unfold the factors that lead to the information adoption that consequently motivates consumers to purchase products and services. The research study provides a comprehensive framework to re-configure factors that lead to consumers’ purchase intentions in the digital economy. Respondents of the study were those individuals who have been buying products online. The theoretically knitted causal relationships were estimated using a structural equation modelling (SEM) technique. The results indicate that trust inclination and information adoption sequentially mediate relationships between information quality, information usefulness, perceived risk and argument quality with purchase intentions. Full article

High-quality power demand side information is necessary for scientific decision-making of power grid construction projects. Literature research shows that the current demand side management (DSM) information quality theories and methods need to be improved, and the information quality indicators and evaluation work are essential. In this paper, based on the grounded theory, about 250 copies of relevant literatures and interview records are reviewed. Through open coding, spindle coding, and selective coding, 105 initial concepts are finally extracted to 35 categories and 10 main categories. On this basis, four information dimensions including load extraction, monitoring, management, and government planning are summarized. An index system containing 34 indicators for DSM information quality evaluation on the power demand side is constructed. Finally, using matter-element extension evaluation method, a case study in China is performed to verify the feasibility and scientificity of the indexes. The results show that DSM information quality evaluation indexes are effective, and the evaluation method is also applicable. The establishment of DSM information quality indicators and the evaluation methods in this paper can provide a reference for similar information quality evaluation work in power systems. Full article

Recently, the information analysis technology of underwater has developed rapidly, which is beneficial to underwater resource exploration, underwater aquaculture, etc. Dangerous and laborious manual work is replaced by deep learning-based computer vision technology, which has gradually become the mainstream. The binocular cameras based visual analysis method can not only collect seabed images but also construct the 3D scene information. The parallax of the binocular image was used to calculate the depth information of the underwater object. A binocular camera based refined analysis method for underwater creature body length estimation was constructed. A fully convolutional network (FCN) was used to segment the corresponding underwater object in the image to obtain the object position. A fish’s body direction estimation algorithm is proposed according to the segmentation image. The semi-global block matching (SGBM) algorithm was used to calculate the depth of the object region and estimate the object body length according to the left and right views of the object. The algorithm has certain advantages in time and accuracy for interest object analysis by the combination of FCN and SGBM. Experiment results show that this method effectively reduces unnecessary information, improves efficiency and accuracy compared to the original SGBM algorithm. Full article

Due to changes in illumination, adverse weather conditions, and interference from signs similar to real traffic signs, the false detection of traffic signs is possible. Nevertheless, in order to improve the detection effect of small targets, baseline SSD (single shot multibox detector) adopts a multi-scale feature detection method to improve the detection effect to some extent. The detection effect of small targets is improved, but the number of calculations needed for the baseline SSD network is large. To this end, we propose a lightweight SSD network algorithm. This method uses some 1 × 1 convolution kernels to replace some of the 3 × 3 convolution kernels in the baseline network and deletes some convolutional layers to reduce the calculation load of the baseline SSD network. Then the color detection algorithm based on the phase difference method and the connected component calculation are used to further filter the detection results, and finally, the data enhancement strategy based on the image appearance transformation is used to improve the balance of the dataset. The experimental results show that the proposed method is 3% more accurate than the baseline SSD network, and more importantly, the detection speed is also increased by 1.2 times. Full article

Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is continuously running, using the statistic personal biographical information and sequential behavior data with VLE. To achieve this goal, a novel recurrent neural network (RNN)-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, where the data completion mechanism is also adopted to fill the missing stream data. To incorporate the sequential relationship of learning data, three kinds of time-series deep neural network algorithms: simple RNN, GRU, and LSTM are first taken into consideration as baseline models. Their performances are compared in identifying at-risk students. Experimental results on Open University Learning Analytics Dataset (OULAD) show that simple methods like GRU and simple RNN have better results than the relatively complex LSTM model. The results also reveal that different models have different peak performance time, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester. Full article

Nowadays, the use of smartphones has become essential for daily activities that have either a personal or professional purpose. A large number of resources is necessary for both the production and the use of these devices, which means that solutions in terms of sustainability are needed. The purpose of this research is to highlight the concept of sustainability when talking about smartphones, as well to underline the possibilities that exist for the consumers. This study examines the habits of young consumers in Romania, the reasons behind a mobile phone replacement, and the factors that influence the purchase decision. The methodology section follows quantitative and qualitative market research. An analysis was performed in order to have a deep understanding of trends in terms of mobile phone ownership and preferred brands. This study also provides a general view on the neglectfulness of the young population of Romania regarding the dangers to which the environment is exposed because of the purchasing habits that go against sustainability. For accomplishing this purpose, important results have been discovered through the analysis of the data obtained from self-administered questionnaires and interviews. The results show that people are usually using only one mobile phone at a time and they change it once every two years for sustainability and financial reasons. The same applies when it comes to choosing a certain brand. The reasons behind the purchase of a new phone and the decisions regarding an old one are based on healthy principles of the circular economy and sustainability. The preferences in terms of technology and design, and the decision process are correlated with incomes. Full article

Effectiveness is a key feature of good governance, as the public sector must make the best use of resources to comply with the needs of the population. Several indicators can be analyzed to evaluate the effectiveness of a service. This study analyzes theoretical references and presents a systematic research of indicators to assess the effectiveness of digital public services in the perspective of the user. First, a literature review was carried out to identify the most common indicators employed to evaluate effectiveness in the public sector; then, the perception of academics and professionals regarding digital government was assessed to analyze the relevance of these indicators. As a result, two groups of indicators were found: technical factors based on service quality and usefulness of the service. This work contributes to enrich the discussion on how to create an effective model to evaluate the effectiveness of public services to guarantee quality standards and comply with the expectations of users. Full article

This research investigated the current maturity levels of cybersafety in South African schools. The maturity level indicates if schools are prepared to assist relevant role players (teachers and learners) in establishing a cybersafety culture within the school environment. The research study measured the maturity levels of cybersafety in 24 South African schools by evaluating the four main elements that are needed to improve cybersafety within schools. These elements are (1) leadership and policies, (2) infrastructure, (3) education, and (4) standards and inspection. The study used a UK-approved measurement tool (360safe) to measure the cybersafety maturity of schools within South Africa, using five levels of compliance (Level 1: full compliance, to Level 5: no compliance). The data analysis clearly indicated that all the schools that participated in the study had a significantly low level of cybersafety maturity and compliance. Schools are starting to adopt technology as part of their educational and social approach to prepare learners for the future, but there is a clear lack of supporting cybersafety awareness, policies, practices and procedures within South African schools. The research proposed a step-by-step approach involving a ten-phase cybersafety plan to empower schools to create and grow their own cybersafety culture. Full article

In order to develop computer tools for speech therapy that reliably classify speech productions, there is a need for speech production corpora that characterize the target population in terms of age, gender, and native language. Apart from including correct speech productions, in order to characterize the target population, the corpora should also include samples from people with speech sound disorders. In addition, the annotation of the data should include information on the correctness of the speech productions. Following these criteria, we collected a corpus that can be used to develop computer tools for speech and language therapy of Portuguese children with sigmatism. The proposed corpus contains European Portuguese children’s word productions in which the words have sibilant consonants. The corpus has productions from 356 children from 5 to 9 years of age. Some important characteristics of this corpus, that are relevant to speech and language therapy and computer science research, are that (1) the corpus includes data from children with speech sound disorders; and (2) the productions were annotated according to the criteria of speech and language pathologists, and have information about the speech production errors. These are relevant features for the development and assessment of speech processing tools for speech therapy of Portuguese children. In addition, as an illustration on how to use the corpus, we present three speech therapy games that use a convolutional neural network sibilants classifier trained with data from this corpus and a word recognition module trained on additional children data and calibrated and evaluated with the collected corpus. Full article

Multiple resolution modeling (MRM) is the future of distributed simulation. This article describes different definitions and notions related to MRM. MRM is a relatively new research area, and there is a demand for simulator integration from a modeling complexity point of view. This article also analyzes a taxonomy based on the experience of the researchers in detail. Finally, an example that uses the high-level architecture (HLA) is explained to illustrate the above definitions and, in particular, to look at the problems that are common to these distributed simulation configurations. The steps required to build an MRM distributed simulation system are introduced. The conclusions describe the lessons learned for this unique form of distributed simulation. Full article

Nowadays, with smartphones, people can easily take photos, post photos to any social networks, and use the photos for various purposes. This leads to a social problem that unintended appearance in photos may threaten the facial privacy of photographed people. Some solutions to protect facial privacy in photos have already been proposed. However, most of them rely on different techniques to de-identify photos which can be done only by photographers, giving no choice to photographed person. To deal with that, we propose an approach that allows a photographed person to proactively detect whether someone is intentionally/unintentionally trying to take pictures of him. Thereby, he can have appropriate reaction to protect the facial privacy. In this approach, we assume that the photographed person uses a wearable camera to record the surrounding environment in real-time. The skeleton information of likely photographers who are captured in the monitoring video is then extracted and put into the calculation of dynamic programming score which is eventually compared with a threshold for recognition of photo-taking behavior. Experimental results demonstrate that by using the proposed approach, the photo-taking behavior is precisely recognized with high accuracy of 92.5%. Full article

In this research study, we investigate the ability of deep learning neural networks to provide a mapping between features of a parallel distributed discrete-event simulation (PDDES) system (software and hardware) to a time synchronization scheme to optimize speedup performance. We use deep belief networks (DBNs). DBNs, which due to their multiple layers with feature detectors at the lower layers and a supervised scheme at the higher layers, can provide nonlinear mappings. The mapping mechanism works by considering simulation constructs, hardware, and software intricacies such as simulation objects, concurrency, iterations, routines, and messaging rates with a particular importance level based on a cognitive approach. The result of the mapping is a synchronization scheme such as breathing time buckets, breathing time warp, and time warp to optimize speedup. The simulation-optimization technique outlined in this research study is unique. This new methodology could be realized within the current parallel and distributed simulation modeling systems to enhance performance. Full article

A maturity model is a widely used tool in software engineering and has mostly been extended to domains such as education, health, energy, finance, government, and general use. It is valuable for evaluations and continuous improvement of business processes or certain aspects of organizations, as it represents a more organized and systematic way of doing business. In this paper, we only focus on college higher education. For this reason, we present a novel approach that allows detecting some gaps in the existing maturity models for universities, as they are not models that address the dimensions in their entirety. To identify these models and their validities, as well as a classification of models that were identified in universities, we carried out a systematic literature review on 27,289 articles retrieved with respect to maturity models and published in peer-reviewed journals between 2007 and 2020. We found 23 articles that find maturity models applied in universities, through exclusion and inclusion criteria. We then grouped these items into nine categories with specific purposes. We concluded that maturity models used in Universities move towards agility, which is supported by the semantic web. Full article

Over the past decade, wireless communication technologies have developed significantly for intelligent applications in road transport. This paper provides an overview of telecommunications-based intelligent transport systems with a focus on ensuring system safety and resilience. In vehicle-to-everything, these problems are extremely acute due to the specifics of the operation of transport networks, which requires the use of special protection mechanisms. In this regard, it was decided to use blockchain as a system platform to support the needs of transport systems for secure information exchange. This paper describes the technological aspects of implementing blockchain technology in vehicle-to-network; the features of such technology are presented, as well as the features of their interaction. The authors considered various network characteristics and identified the parameters that have a primary impact on the operation of the vehicle-to-network (V2N) network when implementing the blockchain. In the paper, an experiment was carried out that showed the numerical characteristics for the allocation of resources on devices involved in organizing V2N communication and conclusions were drawn from the results of the study. Full article

In emergency scenarios, such as a terrorist attack or a building on fire, it is desirable to track first responders in order to coordinate the operation. Pedestrian tracking methods solely based on inertial measurement units in indoor environments are candidates for such operations since they do not depend on pre-installed infrastructure. A very powerful indoor navigation method represents collaborative simultaneous localization and mapping (collaborative SLAM), where the learned maps of several users can be combined in order to help indoor positioning. In this paper, maps are estimated from several similar trajectories (multiple users) or one user wearing multiple sensors. They are combined successively in order to obtain a precise map and positioning. For reducing complexity, the trajectories are divided into small portions (sliding window technique) and are partly successively applied to the collaborative SLAM algorithm. We investigate successive combinations of the map portions of several pedestrians and analyze the resulting position accuracy. The results depend on several parameters, e.g., the number of users or sensors, the sensor drifts, the amount of revisited area, the number of iterations, and the windows size. We provide a discussion about the choice of the parameters. The results show that the mean position error can be reduced to ≈0.5 m when applying partly successive collaborative SLAM. Full article

This paper aims to identify financial measures that are related to Corporate Social Responsibility (CSR) involvement activities. The study concerns the food industry, in which clients, as well as stakeholders, increasingly appreciate socially responsible companies, which could be a crucial factor for future growth strategy. An analysis was made on a sample of 448 food companies from 50 countries in 2009–2020. As a financial measure for CSR assessment, we used profitability ratios, dividend payout ratio, price-to-earnings ratio and market capitalization. The results confirmed that CSR reporting was a crucial division that differentiated companies from the perspective of profitability, OE, market capitalization, and share price. The CSR practices that are realized and published in reports become an important signal for investors that the company has a good financial situation and is able to invest in CSR without reducing its performance. Full article