Information, Volume 13, Issue 7 (July 2022) – 46 articles

Urban mobility is one of the main issues in the pursuit of sustainability. The United Nations 2030 Agenda assigns mobility and transport central roles in sustainable development and its components: economic, social, and environment. In this context, the emerging concept of Mobility as a Service (MaaS) offers an alternative to unsustainable mobility, often based on private car use. From the point of view of sustainable mobility, the MaaS paradigm implies greater insights into the transport system and its components (supply, demand, and reciprocal interactions). This paper proposes an approach to the transport system aimed at overcoming the current barriers to the implementation of the paradigm. The focus is on the implications for the transport supply subsystem. The investigation method is based on the analysis of the main components of such subsystem (governance, immaterial, material, equipment) and its role in the entire transport system. Starting with the first experiences of Finnish cities, the paper investigates some real case studies, which are experimenting with MaaS, to find common and uncommon elements. From the analyses, it emerges that the scientific literature and real experiences mainly focus on the immaterial components alone. To address the challenges related to sustainable mobility, this paper underlines the need to consider all components within a transport system approach. The findings of the paper are useful in several contexts. In the context of research, the paper offers an analysis of the transport supply system from the point of view of the MaaS paradigm. In the real context, the paper offers further useful insights for operators and decision-makers who intend to increase the knowledge and skills necessary to face challenges related to the introduction of MaaS. Full article

Because of the splendid front of sustainable energy reassets in a DC Microgrid, it is profoundly willing to variances in energy age. A hybrid energy storage system (HESS) which includes a battery and a supercapacitor (SC) is used to decrease in-built fluctuations. The two different characteristics of the battery and supercapacitor make it a great match for HESS applications. The HESS is connected to the DC Microgrid through a bidirectional converter, which allows energy to be exchanged between the battery and supercapacitor. This paper discusses a converter presenting an approach for a double-input bidirectional converter. Related to this, a regulator was designed for use as a voltage regulation in a DC Microgrid. The designed controllers accelerated PV generation and load disturbance DC link voltage restoration, in addition to effective power balancing among the battery and the SC. The conventional PI, proposed PI, and predictive PI control techniques are effectively validated using MATLAB Simulink. Experimental findings with low power have been used to validate the operation of the predictive PI control technique. The DC grid voltage profile showed substantial improvement while using the predictive PI control in comparison with the proposed and conventional PI control techniques in terms of setting time and maximum peak overshoot. Full article

People’s acceptance of technological changes has escalated with time. However, the acceptance and adoption of fintech services hiked after the outbreak of the virulent coronavirus. With this breakout, the adoption of mobile fintech services (MFS) increased among general citizens and business sectors around the world, including in developed, emerging, and developing economies. This study aimed to identify the factors that impact the adoption intention of consumers to embrace and enhance the use of mobile fintech services in an emerging market, Bangladesh. A research model was developed to strengthen the objective of this paper. A total of 218 respondents responded to the questionnaire. The study utilized structural equation modeling to analyze the results in SmartPLS software. The results showed significant positive effects of social influence, trust, perceived benefit, and facilitating conditions on the adoption intention towards MFS. Mobile fintech service providers must keep their users’ needs and literacy rates in mind when designing the user interface (UI). Moreover, they should also cater more efficient services to the users and work based on the feedback received. The customers’ satisfaction will ultimately lead to customers conducting more digital transactions and will contribute to the escalation of fintech transactions, resulting in more financial inclusion. Full article

Mobile edge computing (MEC) sinks the functions and services of cloud computing to the edge of the network to provide users with storage and computing resources. For workflow tasks, the interdependency and the sequence constraint being among the tasks make the offloading strategy more complicated. To obtain the optimal offloading and scheduling scheme for workflow tasks to minimize the total energy consumption of the system, a workflow task offloading and scheduling scheme based on an improved genetic algorithm is proposed in an MEC network with multiple users and multiple virtual machines (VMs). Firstly, the system model of the offloading and scheduling of workflow tasks in a multi-user and multi-VMs MEC network is built. Then, the problem of how to determine the optimal offloading and scheduling scheme of workflow to minimize the total energy consumption of the system while meeting the deadline constraint is formulated. To solve this problem, the improved genetic algorithm is adopted to obtain the optimal offloading strategy and scheduling. Finally, the simulation results show that the proposed scheme can achieve a lower energy consumption than other benchmark schemes. Full article

Maximum distance separable (MDS) codes have the maximum branch number in cryptography, and they are generally used in diffusion layers of symmetric ciphers. The diffusion layer of the Advanced Encryption Standard (AES) uses the circulant MDS matrix with the row element of $\left\{2;3;1;1\right\}$ in ${\mathbb{F}}_{2^8}$. It is the simplest MDS matrix in ${\mathbb{F}}_{2^n}^4$, recorded as $\mathit{A}=Circ\left(2;3;1;1\right)$. In this paper, we study the more extensive MDS constructions of $\mathit{A}$ in ${\mathbb{F}}_{2^n}^4$. By transforming the element multiplication operation in the finite field into the bit-level operation, we propose a multivariable operation definition based on simple operations, such as cyclic shift, shift, and XOR. We apply this multivariable operation to more lightweight MDS constructions of $\mathit{A}$ and discuss the classification of the MDS clusters. We also give an example of the MDS cluster of $\mathit{A}$. Without changing the structure, elements, and the implementation cost of the known MDS matrix, the number of existing MDS transformations is expanded to $n^2/2$ times that of its original. The constructions in this paper provide rich component materials for the design of lightweight cryptographic algorithms. Full article

Passenger mobility plays an important role in today’s society and optimized transport services are a priority. In recent years, MaaS (Mobility as a Service) has been studied and tested as new integrated services for users. In this paper, MaaS is studied considering the sustainability objectives and goals to be achieved with particular reference to the consolidated methodologies adopted in the transport systems engineering for design, management, and monitoring of transport services; it is defined as Sustainable MaaS (S-MaaS). This paper considers the technological and communication platform essential and assumed to be a given considering that it has been proposed in many papers and it has been tested in some areas together with MaaS. Starting from the MaaS platform, the additional components and models necessary for the implementation of an S-MaaS are analyses in relation to: a Decision Support System (DSS) that supports MaaS public administrations and MaaS companies for the design of the service and demand management; a system for the evaluation of intervention policies; and also considers smart planning for a priori and a posteriori evaluation of sustainability objectives and targets. Full article

Storytelling content is where the facts are conveyed by emotion and that make people more engaged and want to take action or change their surroundings. Stories fascinate people and can easily be remembered compared to the facts alone. The much-hyped feature “stories” of Instagram, a trendy social media platform, has become a game-changer for influencer marketing. The present study extends reactance theory in the context of Instagram’s millennial users. Previous researchers have tested the effectiveness of the stories feature of this particular social media platform. Therefore, in line with the earlier studies, we propose a sequential mediation model that investigates the effect of storytelling content (made by Instagram Influencers) on audience engagement using two sequential mediation mechanisms of relatability and trust. Data were obtained using a cross-sectional study design from 273 millennial users of Instagram. Our results justify the direct and indirect hypothesized relationship through Process Macros. We found that relatability and trust play a significant role in building a strong relationship between storytelling content and audience engagement. Ultimately, the research findings suggest that professionals should be more creative while making the content on Instagram to engage the millennial market. Moreover, this research has tried to fill the gap in the literature on Instagram “stories” as an advertising platform. Full article

In the context of e-commerce, online travel agencies often derive useful information from online reviews to improve transactions. Based on the dispute on the usefulness of different types of reviews and social exchange theory, this study investigates how the characteristics of pictures and text influence review reading and review posting behaviors and thus influencing the efficiency of online review systems. By analyzing crawled data of online hotels and conducting experiments, we first find that picture reviews are more useful than text reviews, and high-quality pictures in reviews have a significant impact on review usefulness. Second, posting pictures requires review posters to pay more perceived costs. Third, negative review posters have higher perceived costs, so they are more unwilling to post pictures, especially high-quality pictures. Our results indicate that review platforms need to add incentives to encourage consumers to post high-quality picture reviews and design workable interfaces to reduce the burden of negative reviewers to speed up the purchase decision process for review readers. This study provides theoretical implications by demonstrating how the adoption of the picture in review systems influences both review readers’ and review posters’ behaviors. Additionally, our findings also provide useful managerial insights for online travel suppliers in terms of building an effective review system to promote sales. Full article

Guidance systems for visually impaired persons have become a popular topic in recent years. Existing guidance systems on the market typically utilize auxiliary tools and methods such as GPS, UWB, or a simple white cane that exploits the user’s single tactile or auditory sense. These guidance methodologies can be inadequate in a complex indoor environment. This paper proposes a multi-sensory guidance system for the visually impaired that can provide tactile and auditory advice using ORB-SLAM and YOLO techniques. Based on an RGB-D camera, the local obstacle avoidance system is realized at the tactile level through point cloud filtering that can inform the user via a vibrating motor. Our proposed method can generate a dense navigation map to implement global obstacle avoidance and path planning for the user through the coordinate transformation. Real-time target detection and a voice-prompt system based on YOLO are also incorporated at the auditory level. We implemented the proposed system as a smart cane. Experiments are performed using four different test scenarios. Experimental results demonstrate that the impediments in the walking path can be reliably located and classified in real-time. Our proposed system can function as a capable auxiliary to help visually impaired people navigate securely by integrating YOLO with ORB-SLAM. Full article

This work studies a general distributed coded computing system based on the MapReduce-type framework, where distributed computing nodes within a half-duplex network wish to compute multiple output functions. We first introduce a definition of communication delay to characterize the time cost during the date shuffle phase, and then propose a novel coding strategy that enables parallel transmission among the computation nodes by delicately designing the data placement, message symbols encoding, data shuffling, and decoding. Compared to the coded distributed computing (CDC) scheme proposed by Li et al., the proposed scheme significantly reduces the communication delay, in particular when the computation load is relatively smaller than the number of computing nodes K. Moreover, the communication delay of CDC is a monotonically increasing function of K, while the communication delay of our scheme decreases as K increases, indicating that the proposed scheme can make better use of the computing resources. Full article

Vertical integration, also known as make-or-buy, defines whether activities are conducted by company or provided by external parties. There are different models to support decision making for vertical integration in the literature. However, they ignore the uncertainty aspect of vertical integration. As a strategic decision, vertical integration is multidimensional and less frequent. This study contributes a new data-driven model that includes all these characteristics of vertical integration decisions. In this study, a methodology is suggested that benefits from the models in the literature and assesses the results with data obtained from real IT cases. Different methodologies were followed to reach a model that accurately predicts make-or-buy decisions in IT projects at a retail company. Firstly, three different knowledge-based generic models derived from the literature were applied to predict decisions for twenty-one different make-or-buy cases in IT. The highest accuracy rate reached among these knowledge-based models was 76%. Secondly, the same cases were also analyzed with Naïve Bayes using factors originally introduced by these generic models. The Naïve Bayes algorithm can represent the uncertainty inherent in the decision model. The highest accuracy rate obtained was 67%. Thirdly, a new data-driven model based on Naïve Bayes using IT-related factors was proposed for the decision problem of vertical integration. The data-driven model correctly classified 86% of the decisions. Full article

Modern technology and the digitisation era accelerated the pace of data generation and collection for various purposes. The orchestration of such data is a daily challenge faced by even experienced professional users in the context of Internet of Things (IoT)-enabled environments, especially when it comes to cybersecurity and privacy risks. This article presents the application of a user-centric process for the visualisation of automated decision making security interventions. The user interface (UI) development was guided by iterative feedback collection from user studies on the visualisation of a dynamic risk assessment (DRA)-based security solution for regular lay users. The methodology we applied starts with the definition of the methodological process to map possible technical actions to related usable actions. The definition and refinement of the user interface (UI) was controlled by the survey feedback loop from end user studies on their general technological knowledge, experience with smart homes, cybersecurity awareness and privacy preservation needs. We continuously improved the visualisation interfaces for configuring a cybersecurity solution and adjusting usable transparency of the control and monitoring of the dynamic risk assessment (DRA). For this purpose, we have designed, developed and validated a decision tree workflow and showed the evolution of the interfaces through various stages of the real-life trials executed under European H2020 project GHOST. Full article

The use of information technology in the field of cultural heritage makes it possible to involve more and more people in the promotion of cultural heritage, fostering social, cultural, economic and community growth. This work stems from the interest in using Virtual Reality (VR) in the field of cultural heritage, creating a tour of the city of Bari that tells its evolution over the years. To this end, a low-cost VR360 application has been developed which, by means of a cardboard, allows the user to experience a virtual journey through time. It tells the story of the city, focusing on its urban expansion and the evolution of its architectural styles, influenced by various dominations over the centuries, up to the current state. The virtual environment was created from spherical images of the city, captured through 360° cameras and enriched with various types of information content. The user experience was assessed by means of a questionnaire derived from previous work that was generalised and adapted to the considered scenario: the results showed a very good level of satisfaction, usability, engagement, immersion and sense of presence; the highest score was obtained for the visual quality of the images of the virtual environment. Full article

Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are aware of their body-rocking behavior. For this task, similarities of body rocking to other non-related repetitive activities may cause false detections which prevent continuous engagement, leading to alarm fatigue. We present a pipeline using Bayesian Neural Networks with uncertainty quantification for jointly reducing false positives and providing accurate detection. We show that increasing model capacity does not consistently yield higher performance by itself, while pairing it with the Bayesian approach does yield significant improvements. Disparities in uncertainty quantification are better quantified by calibrating them using deep neural networks. We show that the calibrated probabilities are effective quality indicators of reliable predictions. Altogether, we show that our approach provides additional insights on the role of Bayesian techniques in deep learning as well as aids in accurate body-rocking detection, improving our prior work on this subject. Full article

Normalization as a layer within neural networks has over the years demonstrated its effectiveness in neural network optimization across a wide range of different tasks, with one of the most successful approaches being that of batch normalization. The consensus is that better estimates of the BatchNorm normalization statistics ($\mu$ and ${\sigma }^2$) in each mini-batch result in better optimization. In this work, we challenge this belief and experiment with a new variant of BatchNorm known as GhostNorm that, despite independently normalizing batches within the mini-batches, i.e., $\mu$ and ${\sigma }^2$ are independently computed and applied to groups of samples in each mini-batch, outperforms BatchNorm consistently. Next, we introduce sequential normalization (SeqNorm), the sequential application of the above type of normalization across two dimensions of the input, and find that models trained with SeqNorm consistently outperform models trained with BatchNorm or GhostNorm on multiple image classification data sets. Our contributions are as follows: (i) we uncover a source of regularization that is unique to GhostNorm, and not simply an extension from BatchNorm, and illustrate its effects on the loss landscape, (ii) we introduce sequential normalization (SeqNorm) a new normalization layer that improves the regularization effects of GhostNorm, (iii) we compare both GhostNorm and SeqNorm against BatchNorm alone as well as with other regularization techniques, (iv) for both GhostNorm and SeqNorm models, we train models whose performance is consistently better than our baselines, including ones with BatchNorm, on the standard image classification data sets of CIFAR–10, CIFAR-100, and ImageNet (($+0.2\%$, $+0.7\%$, $+0.4\%$), and ($+0.3\%$, $+1.7\%$, $+1.1\%$) for GhostNorm and SeqNorm, respectively). Full article

Augmented Reality (AR) could provide key benefits in education and create a richer user experience by increasing the motivation and engagement of the students. To this end, the current paper presents a system with three AR applications for teaching physics in the fifth and sixth grades of primary school and in the first grade of secondary school, and the ultimate goal is the development of a unified platform that covers the subject of physics in all classes of K-12 education. The platform provides a useful tool to familiarize both teachers and pupils with AR technologies, aiming to improve the learning and teaching experience and to enhance their skills. The developed system is evaluated in terms of usability, gamification and willingness of the teachers to incorporate this technology into the teaching process. A total of 314 users participated in the research, where they were divided into three user groups: (i) teachers (N = 15), (ii) pupils (N = 189) and (iii) computer science students (N = 110). The outcomes were satisfactory, revealing that the gamified AR applications are easy to use, and teachers are interested in using these AR applications in their classrooms. Full article

Recent reports indicate that over 85% of data breaches are still caused by a human element, of which healthcare is one of the organizations that cyber criminals target. As healthcare IT infrastructure is characterized by a human element, this study comprehensively examined the effect of psycho-socio-cultural and work factors on security behavior in a typical hospital. A quantitative approach was adopted where we collected responses from 212 healthcare staff through an online questionnaire survey. A broad range of constructs was selected from psychological, social, cultural perception, and work factors based on earlier review work. These were related with some security practices to assess the information security (IS) knowledge, attitude and behavior gaps among healthcare staff in a comprehensive way. The study revealed that work emergency (WE) has a positive correlation with IS conscious care behavior (ISCCB) risk. Conscientiousness also had a positive correlation with ISCCB risk, but agreeableness was negatively correlated with information security knowledge (ISK) risk and information security attitude (ISA) risk. Based on these findings, intrinsic and extrinsic motivation methods combined with cutting-edge technologies can be explored to discourage IS risks behaviors while enhancing conscious care security practice. Full article

In recent years, the concept of “open data” has received increasing attention among data providers and publishers. For some data portals in public sectors, such as data.gov, the openness enables public oversight of governmental proceedings. For many other data portals, especially those in academia, open data has shown its potential for driving new scientific discoveries and creating opportunities for multidisciplinary collaboration. While the number of open data portals and the volume of shared data have increased significantly, most open data portals still use keywords and faceted models as their primary methods for data search and discovery. There should be opportunities to incorporate more intelligent functions to facilitate the data flow between data portals and end-users. To find more theoretical and empirical evidence for that proposition, in this paper, we conduct a systematic literature review of open data, social trust, and recommender systems to explain the fundamental concepts and illustrate the potential of using trust-based recommender systems for open data portals. We hope this literature review can benefit practitioners in the field of open data and facilitate the discussion of future work. Full article

This study explores how drivers of an automated vehicle distribute their attention as a function of environmental events and driving task instructions. Twenty participants were asked to monitor pre-recorded videos of a simulated driving trip while their eye movements were recorded using an eye-tracker. The results showed that eye movements are strongly situation-dependent, with areas of interest (windshield, mirrors, and dashboard) attracting attention when events (e.g., passing vehicles) occurred in those areas. Furthermore, the task instructions provided to participants (i.e., speed monitoring or hazard monitoring) affected their attention distribution in an interpretable manner. It is concluded that eye movements while supervising an automated vehicle are strongly ‘top-down’, i.e., based on an expected value. The results are discussed in the context of the development of driver availability monitoring systems. Full article

Uyghur text detection and recognition in images with simple backgrounds is still a challenging task for Uyghur image content analysis. In this paper, we propose a new effective Uyghur text detection method based on channel-enhanced MSERs and the CNN classification model. In order to extract more complete text components, a new text candidate region extraction algorithm is put forward, which is based on the channel-enhanced MSERs according to the characteristics of Uyghur text. In order to effectively prune the non-text regions, we design a CNN classification network according to the LeNet-5, which gains the description characteristics automatically and avoids the tedious and low efficiency artificial characteristic extraction work. For Uyghur text recognition in images, we improved the traditional CRNN network, and to verify its effectiveness, the networks trained on a synthetic dataset and evaluated on the text recognition datasets. The experimental results indicated that the Uyghur text detection method in this paper is robust and applicable, and the recognition result by improvedCRNN was better than the original CRNN network. Full article

Speech coding is an essential technology for digital cellular communications, voice over IP, and video conferencing systems. For more than 25 years, the main approach to speech coding for these applications has been block-based analysis-by-synthesis linear predictive coding. An alternative approach that has been less successful is sample-by-sample tree coding of speech. We reformulate this latter approach as a multistage reinforcement learning problem with L step lookahead that incorporates exploration and exploitation to adapt model parameters and to control the speech analysis/synthesis process on a sample-by-sample basis. The minimization of the spectrally shaped reconstruction error to finite depth manages complexity and serves as an effective stand in for the overall subjective evaluation of reconstructed speech quality and intelligibility. Different control policies that attempt to persistently excite the system states and that encourage exploration are studied and evaluated. The resulting methods produce reconstructed speech quality competitive with the most popular speech codec utilized today. This new reinforcement learning formulation provides new insights and opens up new directions for system design and performance improvement. Full article

The World Health Organization labelled the new COVID-19 breakout a public health crisis of worldwide concern on 30 January 2020, and it was named the new global pandemic in March 2020. It has had catastrophic consequences on the world economy and well-being of people and has put a tremendous strain on already-scarce healthcare systems globally, particularly in underdeveloped countries. Over 11 billion vaccine doses have already been administered worldwide, and the benefits of these vaccinations will take some time to appear. Today, the only practical approach to diagnosing COVID-19 is through the RT-PCR and RAT tests, which have sometimes been known to give unreliable results. Timely diagnosis and implementation of precautionary measures will likely improve the survival outcome and decrease the fatality rates. In this study, we propose an innovative way to predict COVID-19 with the help of alternative non-clinical methods such as supervised machine learning models to identify the patients at risk based on their characteristic parameters and underlying comorbidities. Medical records of patients from Mexico admitted between 23 January 2020 and 26 March 2022, were chosen for this purpose. Among several supervised machine learning approaches tested, the XGBoost model achieved the best results with an accuracy of 92%. It is an easy, non-invasive, inexpensive, instant and accurate way of forecasting those at risk of contracting the virus. However, it is pretty early to deduce that this method can be used as an alternative in the clinical diagnosis of coronavirus cases. Full article

The rapid development of effective vaccines against COVID-19 is an extraordinary achievement. However, no medical product can ever be considered risk-free. Several countries have a pharmacovigilance system that detects, assesses, understands, and prevents possible adverse effects of a drug. To benefit from such huge data sources, specialists and researchers need advanced big data analysis tools able to extract value and find valuable insights. This paper defines a general framework for a pharmaceutical data analysis application that provides a predefined (but extensible) set of functions for each data processing step (i.e., data collection, filtering, enriching, analysis, and visualization). As a case study, we present here an analysis of the potential side effects observed following the administration of the COVID-19 vaccines. The experimental evaluation shows that: (i) most adverse events can be classified as non-serious and concern muscle/joint pain, chills and nausea, headache, and fatigue; (ii) the notification rate is higher in the age group 20–39 years and decreases in older age groups and in very young people. Full article

Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks. Full article

Mutation testing is an effective, yet costly, testing approach, as it requires generating and running large numbers of faulty programs, called mutants. Mutation testing also suffers from a fundamental problem, which is having a large percentage of equivalent mutants. These are mutants that produce the same output as the original program, and therefore, cannot be detected. Higher-order mutation is a promising approach that can produce hard-to-detect faulty programs called subtle mutants, with a low percentage of equivalent mutants. Subtle higher-order mutants contribute a small set of the large space of mutants which grows even larger as the order of mutation becomes higher. In this paper, we developed a genetic algorithm for finding subtle higher-order mutants. The proposed approach uses a new mechanism in the crossover phase and uses five selection techniques to select mutants that go to the next generation in the genetic algorithm. We implemented a tool, called GaSubtle that automates the process of creating subtle mutants. We evaluated the proposed approach by using 10 subject programs. Our evaluation shows that the proposed crossover generates more subtle mutants than the technique used in a previous genetic algorithm with less execution time. Results vary on the selection strategies, suggesting a dependency relation with the tested code. Full article

Physiological and kinematic signals from humans are often used for monitoring health. Several processes of interest (e.g., cardiac and respiratory processes, and locomotion) demonstrate periodicity. Training models for inference on these signals (e.g., detection of anomalies, and extraction of biomarkers) require large amounts of data to capture their variability, which are not readily available. This hinders the performance of complex inference models. In this work, we introduce a methodology for improving inference on such signals by incorporating phase-based interpretability and other inference tasks into a multi-task framework applied to a generative model. For this purpose, we utilize phase information as a regularization term and as an input to the model and introduce an interpretable unit in a neural network, which imposes an interpretable structure on the model. This imposition helps us in the smooth generation of periodic signals that can aid in data augmentation tasks. We demonstrate the impact of our framework on improving the overall inference performance on ECG signals and inertial signals from gait locomotion. Full article

Security cameras have been proven to be particularly useful in preventing and combating crime through identification tasks. Here, two areas can be mainly distinguished: person and vehicle identification. Automatic license plate readers are the most widely used tool for vehicle identification. Although these systems are very effective, they are not reliable enough in certain circumstances. For example, due to traffic jams, vehicle position or weather conditions, the sensors cannot capture an image of the entire license plate. However, there is still a lot of additional information in the image which may also be of interest, and that needs to be analysed quickly and accurately. The correct use of the processing mechanisms can significantly reduce analysis time, increasing the efficiency of video cameras significantly. To solve this problem, we have designed a solution based on two technologies: license plate recognition and vehicle re-identification. For its development and testing, we have also created several datasets recreating a real environment. In addition, during this article, it is also possible to read about some of the main artificial intelligence techniques for these technologies, as they have served as the starting point for this research. Full article

Search on the web, specifically fetching of the relevant content, has been paid attention to since the advent of the web and particularly in recent years due to the tremendous growth in the volume of data and web pages. This paper categorizes the search services from the early days of the web to the present into keyword search engines, semantic search engines, question answering systems, dialogue systems and chatbots. As the first generation of search engines, keyword search engines have adopted keyword-based techniques to find the web pages containing the query keywords and ranking search results. In contrast, semantic search engines try to find meaningful and accurate results on the meaning and relations of things. Question-answering systems aim to find precise answers to natural language questions rather than returning a ranked list of relevant sources. As a subset of question answering systems, dialogue systems target to interact with human users through a dialog expressed in natural language. As a subset of dialogue systems, chatbots try to simulate human-like conversations. The paper provides an overview of the typical aspects of the studied search services, including process models, data preparation and presentation, common methodologies and categories. Full article

Businesses affected by the pandemic have realized the importance of incorporating digital transformation into their operations. However, as a result of the market lockdown, they realized that they needed to digitalize their firms immediately and make greater attempts to enhance their economic situation by integrating a greater number of technological components. While there have been numerous studies conducted on the adoption of digital transformation in small–medium enterprises, there has been no research carried out on the implementation of digital transformation in the specific industry of driving schools. This paper investigates the significance of digital transformation, as well as the potential for its application in this industry’s business setting and the ways in which it can be utilized to improve innovation capabilities and performance. The data for this study came from 300 driving instructors in Greece and Cyprus. Multivariate regression analysis was used to analyze the data. The outcomes suggest that driving schools have a generally positive reaction to and acknowledgement of the increasing speed of digital transformation. The results also give driving school owners useful information that helps them show how important digital transformation is to their businesses. Using the findings of this study, driving schools will be able to improve their operational capabilities and accelerate their development in the post-COVID era. Full article

The integration of communication networks and the internet of industrial control in Industrial Control System (ICS) increases their vulnerability to cyber attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDS) largely rely on predefined models and are trained mostly on specific cyber attacks, which means the traditional IDS cannot cope with unknown attacks. Additionally, most IDS do not consider the imbalanced nature of ICS datasets, thus suffering from low accuracy and high False Positive Rates when being put to use. In this paper, we propose the NCO–double-layer DIFF_RF–OPFYTHON intrusion detection method for ICS, which consists of NCO modules, double-layer DIFF_RF modules, and OPFYTHON modules. Detected traffic will be divided into three categories by the double-layer DIFF_RF module: known attacks, unknown attacks, and normal traffic. Then, the known attacks will be classified into specific attacks by the OPFYTHON module according to the feature of attack traffic. Finally, we use the NCO module to improve the model input and enhance the accuracy of the model. The results show that the proposed method outperforms traditional intrusion detection methods, such as XGboost and SVM. The detection of unknown attacks is also considerable. The accuracy of the dataset used in this paper reaches 98.13%. The detection rates for unknown attacks and known attacks reach 98.21% and 95.1%, respectively. Moreover, the method we proposed has achieved suitable results on other public datasets. Full article