Information, Volume 11, Issue 8 (August 2020) – 30 articles

Like in the real world, perceptions of risk can influence the behavior and decisions that people make in online platforms. Users of Social Network Sites (SNSs) like Facebook make continuous decisions about their privacy since these are spaces designed to share private information with large and diverse audiences. In particular, deciding whether or not to disclose such information will depend largely on each individual’s ability to assess the corresponding privacy risks. However, SNSs often lack awareness instruments that inform users about the consequences of unrestrained self-disclosure practices. Such an absence of risk information can lead to poor assessments and, consequently, undermine users’ privacy behavior. This work elaborates on the use of risk scenarios as a strategy for promoting safer privacy decisions in SNSs. In particular, we investigate, through an online survey, the effects of communicating those risks associated with online self-disclosure. Furthermore, we analyze the users’ perceived severity of privacy threats and its importance for the definition of personalized risk awareness mechanisms. Based on our findings, we introduce the design of preventative nudges as an approach for providing individual privacy support and guidance in SNSs. Full article

The use of information age technology favors pervasive communication exchanges and complex phenomenologies, which affect the production of knowledge and the permanent transformation of personalities and contexts, not always with a view to prosocial empowerment of differences. From the analysis of the liquid socio–psycho–educational frames explored in the research activated at the University of Salerno, the permanent need for a widespread media education emerges, to be rooted in a lifelong learning vision to achieve systemic inclusiveness. This is the basis of the epistemology of the existential design model Flipped Inclusion, promoted and tested at the University of Salerno, whose complex idiomatic phrase constitutes the integrated and complex synthesis of the multi-perspective and multimodal approach pursued by the model. In the exploratory–descriptive–transformative research underway since 2014, through blended learning, complex blended learning and with formal, non-formal and informal contexts, the design–organizational, algorithmic–computational architecture of flipped inclusion is experimented upon. The trend of data since 2014 confirms the educational value of the model, due to the positive impact relating to inclusiveness on personal styles and social contexts, hence the intention to continue research on larger samples. Full article

One of the aims of the Secondary Education Certificate (SEC) Physics syllabus is “to provide the basis for further study of the subject”. This research determined the extent to which the syllabus is fulfilling this aim. In this study, seven post-secondary Physics teachers participated in semi-structured interviews and 200 students provided feedback to a questionnaire. Areas in which the SEC Physics syllabus is not preparing students well enough to further their studies in the subject were identified and suggestions were given to help improve the situation. This study confirmed that there is an academic disparity between SEC and Advanced Matriculation (AM) Physics. This disparity is highlighted in the problem-solving skills necessary for success at both levels, mathematical physics, language and in concepts which are highly abstract. The study also confirmed that there is a large amount of rote learning involved in SEC level Physics. As a result, students learn superficially and struggle to grasp the complex concepts taught in A-level Physics. In order to prepare students better for post-secondary education, SEC Physics students should be asked to answer questions which involve higher levels of thinking and to solve more complex mathematical problems. Furthermore, more frequent practical sessions, a greater degree of student involvement and a greater emphasis on the link between theoretical ideas and practical work is also recommended. A shift of emphasis is required from teaching content to teaching higher order thinking skills. Full article

Technological development at the turn of the 20th and 21st centuries determined the possibilities of communication. Internet access has resulted in the rapid development of social media, bringing together users from around the world. Social media affect all aspects of human life, including leisure and tourism. The article focuses on the element of this influence, namely the selection of tourist destinations made by Generation Y. It presents the influence of social media on consumer choices in tourism and the specificity of tourist products. The main purpose of the study was to indicate the most commonly used social media in the process of selecting a tourist destination and implementing the journey by Generation Y. The analysis of research results shows the important place of social media in the life of Generation Y. They mostly trust materials shared in social media, although they are aware of it coloring reality, and sharing impressions from tourist destinations is per se the purpose of the trip. Facebook, YouTube, and Instagram are the most used social media for Generation Y. It was also important to define the purpose of activity in social media (SM). Studies have shown the emergence of goals not yet declared among young SM users. One of the main goals of a trip is to report and share travel content in social media. The survey method and the analysis of the literature and available reports were used. A diagnostic survey was conducted among Polish SM users, who are considered representatives of Generation Y. The survey was conducted using the CAWI (Computer-Assisted Web Interviews) method. The subject requires further empirical research. Full article

In many nations, limited power from providers and an increase in demand for electricity have created new opportunities that can be used by home energy management systems (HEMSs) systems to enforce proper use of energy. This paper presents a virtual intelligent home with demand response (DR) model home appliances that have an inverter air conditioner, water pump, washing machine, and inverter refrigerator. A binary backtracking search algorithm (BBSA) is proposed to introduce the optimal schedule controller. With the proposed BBSA schedule controller, the highest energy consumption during DR can be reduced by 33.84% during the weekends and by 30.4% daily during the weekdays. The results indicate the effectiveness of the proposed HEMS. Additionally, the model can control the appliances and maintain total residential energy consumption below the defined demand limit. Full article

With the rapid development of cloud computing, massive data are transferred to cloud servers for storage and management savings. For privacy concerns, data should be encrypted before being uploaded. In the encrypted-domain (ED), however, many data computing methods working in the plain-domain are no longer applicable. Data retrieval has become a significant obstacle to cloud storage services. To break through this limitation, we propose a multi-keyword searchable encryption scheme by introducing probability trapdoors. Firstly, a keywords probability trapdoor is established to ensure that the scheme can resist indistinguishable attacks. Based on the keywords trapdoor, we present the keywords vector to make the scheme realize multi-keyword search in the process of data retrieval in the ED. Both security and performance analysis confirm the advantages of the proposed scheme in terms of search functionality and complexity. Full article

Cluster headache (CH) belongs to the group III of The International Classification of Headaches. It is characterized by attacks of severe pain in the ocular/periocular area accompanied by cranial autonomic signs, including parasympathetic activation and sympathetic hypofunction on the symptomatic side. Iris pigmentation occurs in the neonatal period and depends on the sympathetic tone in each eye. We hypothesized that the presence of visible or subtle color iris changes in both eyes could be used as a quantitative biomarker for screening and early detection of CH. This work scrutinizes the scope of an automatic diagnosis-support system for early detection of CH, by using as indicator the error rate provided by a statistical classifier designed to identify the eye (left vs. right) from iris pixels in color images. Systematic tests were performed on a database with images of 11 subjects (four with CH, four with other ophthalmic diseases affecting the iris pigmentation, and three control subjects). Several aspects were addressed to design the classifier, including: (a) the most convenient color space for the statistical classifier; (b) whether the use of features associated to several color spaces is convenient; (c) the robustness of the classifier to iris spatial subregions; (d) the contribution of the pixels neighborhood. Our results showed that a reduced value for the error rate (lower than 0.25) can be used as CH marker, whereas structural regions of the iris image need to be taken into account. The iris color feature analysis using statistical classification is a potentially useful technique to investigate disorders affecting the autonomous nervous system in CH. Full article

Facial beauty prediction (FBP) is a burgeoning issue for attractiveness evaluation, which aims to make assessment consistent with human opinion. Since FBP is a regression problem, to handle this issue, there are data-driven methods for finding the relations between facial features and beauty assessment. Recently, deep learning methods have shown its amazing capacity for feature representation and analysis. Convolutional neural networks (CNNs) have shown tremendous performance on facial recognition and comprehension, which are proved as an effective method for facial feature exploration. Lately, there are well-designed networks with efficient structures investigated for better representation performance. However, these designs concentrate on the effective block but do not build an efficient information transmission pathway, which led to a sub-optimal capacity for feature representation. Furthermore, these works cannot find the inherent correlations of feature maps, which also limits the performance. In this paper, an elaborate network design for FBP issue is proposed for better performance. A residual-in-residual (RIR) structure is introduced to the network for passing the gradient flow deeper, and building a better pathway for information transmission. By applying the RIR structure, a deeper network can be established for better feature representation. Besides the RIR network design, an attention mechanism is introduced to exploit the inner correlations among features. We investigate a joint spatial-wise and channel-wise attention (SCA) block to distribute the importance among features, which finds a better representation for facial information. Experimental results show our proposed network can predict facial beauty closer to a human’s assessment than state-of-the-arts. Full article

This study investigated how driver discomfort was influenced by different types of automated vehicle (AV) controllers, compared to manual driving, and whether this response changed in different road environments, using heart-rate variability (HRV) and electrodermal activity (EDA). A total of 24 drivers were subjected to manual driving and four AV controllers: two modelled to depict “human-like” driving behaviour, one conventional lane-keeping assist controller, and a replay of their own manual drive. Each drive lasted for ~15 min and consisted of rural and urban environments, which differed in terms of average speed, road geometry and road-based furniture. Drivers showed higher skin conductance response (SCR) and lower HRV during manual driving, compared to the automated drives. There were no significant differences in discomfort between the AV controllers. SCRs and subjective discomfort ratings showed significantly higher discomfort in the faster rural environments, when compared to the urban environments. Our results suggest that SCR values are more sensitive than HRV-based measures to continuously evolving situations that induce discomfort. Further research may be warranted in investigating the value of this metric in assessing real-time driver discomfort levels, which may help improve acceptance of AV controllers. Full article

The focus on sustainability represents the key to the tourism industry today. Green marketing was constantly on the agenda of local authorities and started to be a subject of interest for Academia too, but it was very little approached in the Eastern European countries, especially in the tourism studies. This article describes the presence of green marketing in Romanian ecotourism and its impact on tourist’s perception. Due to its natural landscapes and tourist attractions, Romanian ecotourism can take advantage of green marketing practices and promote tourism products based on the new preference of the tourists for sustainable activities. However, there is a lack of legislative framework regarding green labeling that can effect in the long term the destination brand. The main objective of the present article is to give an overview image of the online presence of the Romanian ecotourist accommodation units that applied for the Eco-label provided by the ECO-Romania Association. The study describes how these establishments use green marketing practices and if their guests perceive the green image too. The methodology of the study consists of a qualitative analysis of the primary data obtained by the authors from the websites of ECO Romania Association, TripAdvisor platform and accommodation units. The results show a high interest of the accommodation units from the ecotourism field for green marketing practices which is also perceived by tourists. The tendency of tourism stakeholders to apply for an accreditation system provided by non-governmental organization instead of choosing a European accreditation shows the impact of such a national initiative and emphasizes the need to develop a legal framework for green ecotourism practices. Full article

With the rapid increase in the popularity of big data and internet technology, sequential recommendation has become an important method to help people find items they are potentially interested in. Traditional recommendation methods use only recurrent neural networks (RNNs) to process sequential data. Although effective, the results may be unable to capture both the semantic-based preference and the complex transitions between items adequately. In this paper, we model separated session sequences into session graphs and capture complex transitions using graph neural networks (GNNs). We further link items in interaction sequences with existing external knowledge base (KB) entities and integrate the GNN-based recommender with key-value memory networks (KV-MNs) to incorporate KB knowledge. Specifically, we set a key matrix to many relation embeddings that learned from KB, corresponding to many entity attributes, and set up a set of value matrices storing the semantic-based preferences of different users for the corresponding attribute. By using a hybrid of a GNN and KV-MN, each session is represented as the combination of the current interest (i.e., sequential preference) and the global preference (i.e., semantic-based preference) of that session. Extensive experiments on three public real-world datasets show that our method performs better than baseline algorithms consistently. Full article

The consumption awareness of people in recent years has increased, with food safety becoming more and more important. While non-toxic products can be achieved by avoiding using too much antibiotics to control growth factors in a water environment, the measurement tools for dissolved oxygen on the market are very expensive and a great economic burden to fishermen. Thus, the purpose of this study is to design more economical measurement modules and algorithms for monitoring ponds. The research collected pond data through Oxidation-Reduction Potential (ORP), pH and temperature sensors, used regression analysis to infer Dissolved Oxygen (DO) by ORP and pH, and employed a real-time pond monitoring data map to figure out pond conditions. Compared with traditional equipment, findings show our approach reduces costs by about 20%, and increases production capacity and output value. Full article

Acute kidney injury (AKI) is a common complication in hospitalized patients and can result in increased hospital stay, health-related costs, mortality and morbidity. A number of recent studies have shown that AKI is predictable and avoidable if early risk factors can be identified by analyzing Electronic Health Records (EHRs). In this study, we employ machine learning techniques to identify older patients who have a risk of readmission with AKI to the hospital or emergency department within 90 days after discharge. One million patients’ records are included in this study who visited the hospital or emergency department in Ontario between 2014 and 2016. The predictor variables include patient demographics, comorbid conditions, medications and diagnosis codes. We developed 31 prediction models based on different combinations of two sampling techniques, three ensemble methods, and eight classifiers. These models were evaluated through 10-fold cross-validation and compared based on the AUROC metric. The performances of these models were consistent, and the AUROC ranged between 0.61 and 0.88 for predicting AKI among 31 prediction models. In general, the performances of ensemble-based methods were higher than the cost-sensitive logistic regression. We also validated features that are most relevant in predicting AKI with a healthcare expert to improve the performance and reliability of the models. This study predicts the risk of AKI for a patient after being discharged, which provides healthcare providers enough time to intervene before the onset of AKI. Full article

Today, crowd computing is successfully applied for many information processing problems in a variety of domains. One of the most acute issues with crowd-powered systems is the quality of results (as humans can make errors). Therefore, a number of methods have been proposed to process the results obtained from the crowd in order to compensate human errors. Most of the existing methods of processing contributions are constructed based on a (natural) assumption that the only information available is unreliable data obtained from the crowd. However, in some cases, additional information is available, and it can be utilized in order to improve the overall quality of the result. The paper describes a crowd computing application for community tagging of running race photos. It presents a utility analysis to identify situations in which community photo tagging is a reasonable choice. It also proposes a data fusion model making use of runners’ location information recorded in their Global Positioning System (GPS) tracks. Field experiments with the applications show that community-based tagging can collect enough contributors to process photosets from medium-sized running events. Simulation results confirm, that the use of data fusion in processing the results of crowd computing is a promising technique, and the use of probabilistic graphical models (e.g., Bayesian networks) for data fusion allows one to smoothly increase the quality of the results with an increase of the available information. Full article

The purpose of this study is to examine the effect of chain restaurant employees’ perception of abusive supervision on employee satisfaction and customer satisfaction. The sample for the survey was collected from 228 customers and 93 employees in a chain restaurant. The results showed a negative relationship between abusive supervision and employee satisfaction. However, abusive supervision did not have a significant, direct effect on customer satisfaction, but showed an indirect effect via employee satisfaction. In addition, employee satisfaction was positively associated with customer satisfaction. Full article

The onset of COVID-19 has re-emphasized the importance of FinTech especially in developing countries as the major powers of the world are already enjoying the advantages that come with the adoption of FinTech. Handling of physical cash has been established as a means of transmitting the novel corona virus. Again, research has established that, been unbanked raises the potential of sinking one into abject poverty. Over the years, developing countries have been piloting the various forms of FinTech, but the very one that has come to stay is the Mobile Money Transactions (MMT). As mobile money transactions attempt to gain a foothold, it faces several problems, the most important of them is mobile money fraud. This paper seeks to provide a solution to this problem by looking at machine learning algorithms based on support vector machines (kernel-based), gradient boosted decision tree (tree-based) and Naïve Bayes (probabilistic based) algorithms, taking into consideration the imbalanced nature of the dataset. Our experiments showed that the use of gradient boosted decision tree holds a great potential in combating the problem of mobile money fraud as it was able to produce near perfect results. Full article

Cloud has become one of the most widely used technologies to store data due to its availability, flexibility, and low cost. At the same time, the security, integrity, and privacy of data that needs to be stored on the cloud is the primary threat for cloud deployment. However, the increase in cloud utilization often results in the creation of a multi-user cloud environment, which requires its owners to manage and monitor the data more effectively. The security of information faces an additional threat, which is related to the increasing number of users and owners who deal with the data stored on the cloud. Many researchers have developed several frameworks and algorithms to address the security issues of the cloud environment. In the present work, a novel algorithm is proposed with the integration of Ciphertext Policy-Identity Attribute-based Encryption (CP-IDABE) and the Rivest–Shamir–Adelman (RSA) algorithm for securing the cloud. Both the owners and users are provided with the public and distinct secret keys that are generated by the Automated Certificate Authority (ACA). The attribute policy differentiates between the user and owner for accessing the cloud data. The proposed RSA-CP-IDABE algorithm also prevents the Man in the Middle (MITM) attack effectively. The performance of the proposed algorithm is evaluated for its time used for encryption, decryption, and execution for varying sizes of data. The obtained results are compared with the existing framework to show its effectiveness. The proposed algorithm can be enhanced with the revocation of privileges in the future. Full article

Metacognitive training reflects knowledge, consideration and control over decision-making and task performance evident in any social and learning context. Interest in understanding the best account of effective (win-win) negotiation emerges in different social and cultural interactions worldwide. The research presented in this paper explores an extended study of metacognitive training system during negotiation using an embodied conversational agent. It elaborates on the findings from the usability evaluation employing 40 adult learners pre- and postinteraction with the system, reporting on the usability and metacognitive, individual- and community-level related attributes. Empirical evidence indicates (a) higher levels of self-efficacy, individual readiness to change and civic action after user-system experience, (b) significant and positive direct associations between self-efficacy, self-regulation, interpersonal and problem-solving skills, individual readiness to change, mastery goal orientation and civic action pre- and postinteraction and (c) gender differences in the perceptions of system usability performance according to country of origin. Theoretical and practical implications in tandem with future research avenues are discussed in light of embodied conversational agent metacognitive training in negotiation. Full article

A micro-expression is defined as an uncontrollable muscular movement shown on the face of humans when one is trying to conceal or repress his true emotions. Many researchers have applied the deep learning framework to micro-expression recognition in recent years. However, few have introduced the human visual attention mechanism to micro-expression recognition. In this study, we propose a three-dimensional (3D) spatiotemporal convolutional neural network with the convolutional block attention module (CBAM) for micro-expression recognition. First image sequences were input to a medium-sized convolutional neural network (CNN) to extract visual features. Afterwards, it learned to allocate the feature weights in an adaptive manner with the help of a convolutional block attention module. The method was testified in spontaneous micro-expression databases (Chinese Academy of Sciences Micro-expression II (CASME II), Spontaneous Micro-expression Database (SMIC)). The experimental results show that the 3D CNN with convolutional block attention module outperformed other algorithms in micro-expression recognition. Full article

In this study, information overload is viewed through the lenses of Library & Information Science and Communication Theory in order to offer recommended solutions for individuals experiencing overload. The purpose of this research was to apply LIS and COMM theories to the pathologies and symptoms of information overload as experienced by individuals in an increasingly digital world. Extant survey work was reviewed and updated with literature collected through limited keyword searches. The authors framed active responses to information overload through dimensions selected from the European Commission’s Digital Competence Framework as applied to Al-Shboul & Abrizah’s (2016) Modes of Information Seeking. Further study should focus on international perspectives and addressing disparities in access to information. Full article

Laser machining has been widely used for materials processing, while the inherent complex physical process is rather difficult to be modeled and computed with analytical formulations. Through attending a workshop on discovering the value of laser machining data, we are profoundly motivated by the recent work by Tani et al., who proposed in situ monitoring of laser processing assisted by neural networks. In this paper, we propose an application of deep learning in extracting representative features from laser processing images with a multi-task loss that consists of cross-entropy loss and logarithmic smooth $L^1$ loss. In the experiment, AlexNet with multi-task learning proves to be better than deeper models. This framework of deep feature extraction also has tremendous potential to solve more laser machining problems in the future. Full article

In recent years, intermodal transport has become an increasingly attractive alternative to freight shippers. However, the current intermodal freight transport is not as efficient as it could be. Oftentimes an empty container needs to be transported from the empty container depot to the shipper, and conversely, an empty container needs to be transported from the receiver to the empty container depot. These empty container movements decrease the freight carrier’s profit, as well as increase traffic congestion, decrease roadway safety, and add unnecessary emissions to the environment. To this end, our study evaluates a potential collaboration strategy to be used by carriers for domestic intermodal freight transport based on an optimization approach to reduce the number of empty container trips. A binary integer-linear programming model is developed to determine each freight carrier’s optimal schedule while minimizing its operating cost. The model ensures that the cost for each carrier with collaboration is less than or equal to its cost without collaboration. It also ensures that average savings from the collaboration are shared equally among all participating carriers. Additionally, two stochastic models are provided to account for uncertainty in truck travel times. The proposed collaboration strategy is tested using empirical data and is demonstrated to be effective in meeting all of the shipment constraints. Full article

In the event of a natural disaster, geo-tagged Tweets are an immediate source of information for locating casualties and damages, and for supporting disaster management. Topic modeling can help in detecting disaster-related Tweets in the noisy Twitter stream in an unsupervised manner. However, the results of topic models are difficult to interpret and require manual identification of one or more “disaster topics”. Immediate disaster response would benefit from a fully automated process for interpreting the modeled topics and extracting disaster relevant information. Initializing the topic model with a set of seed words already allows to directly identify the corresponding disaster topic. In order to enable an automated end-to-end process, we automatically generate seed words using older Tweets from the same geographic area. The results of two past events (Napa Valley earthquake 2014 and hurricane Harvey 2017) show that the geospatial distribution of Tweets identified as disaster related conforms with the officially released disaster footprints. The suggested approach is applicable when there is a single topic of interest and comparative data available. Full article

A crucial area in which information overload is experienced is news consumption. Ever increasing sources and formats are becoming available through a combination of traditional and new (digital) media, including social media. In such an information and media rich environment, understanding how people access and manage news during a global health epidemic like COVID-19 becomes even more important. The designation of the current situation as an infodemic has raised concerns about the quality, accuracy and impact of information. Instances of misinformation are commonplace due, in part, to the speed and pervasive nature of social media and messaging applications in particular. This paper reports on data collected using media diaries from 15 university students in the United Arab Emirates documenting their news consumption in April 2020. Faced with a potentially infinite amount of information and news, participants demonstrate how they are managing news overload (MNO) using a number of complementary strategies. Results show that while consumption patterns vary, all diaries indicate that users’ ability to navigate the news landscape in a way that fulfils their needs is influenced by news sources; platform reliability and verification; sharing activity; and engagement with news. Full article

Machine Learning has a significant impact on different aspects of science and technology including that of medical researches and life sciences. Diabetes Mellitus, more commonly known as diabetes, is a chronic disease that involves abnormally high levels of glucose sugar in blood cells and the usage of insulin in the human body. This article has focused on analyzing diabetes patients as well as detection of diabetes using different Machine Learning techniques to build up a model with a few dependencies based on the PIMA dataset. The model has been tested on an unseen portion of PIMA and also on the dataset collected from Kurmitola General Hospital, Dhaka, Bangladesh. The research is conducted to demonstrate the performance of several classifiers trained on a particular country’s diabetes dataset and tested on patients from a different country. We have evaluated decision tree, K-nearest neighbor, random forest, and Naïve Bayes in this research and the results show that both random forest and Naïve Bayes classifier performed well on both datasets. Full article

This article aims to show the significance of branding in achieving set marketing goals and ensuring the sustainable development of a selected education and research library as a non-profit organization. The research is based on available data from foreign research studies concerning the image of a brand and the branding of non-profit organizations, and it expands on them by utilizing other methods of brand image measurement. A survey involving a sample of 220 respondents was conducted at the particular site, taking into account the library’s target segments. An analysis of the awareness and favorability of its brand was also utilized to evaluate the library’s image. The library has recently undergone a rebranding process and is applying a new visual style. The new visual style of the library resulting from the rebranding was met with a positive response in 69% of cases. Branding and appropriate marketing communication that reflects the latest trends can have a positive influence on the sustainability of libraries. The research results have contributed to the adoption of corrective measures in planning the strategy of the selected library, and the case study results can be applied across the board to other contributory organizations in the Czech Republic. Full article

Cloud computing technology has revolutionized the field of data management as it has enhanced the barriers of storage restrictions and high-cost establishment for its users. The benefits of the cloud have paved the way for its extensive implementation in large enterprises. However, the data in the cloud have succumbed to various security threats, and its privacy issues remain one of the biggest and topmost concerns for the data owners. Several techniques, such as Attribute-based Encryption (ABE), have been proposed by several researchers to preserve the privacy of the data. However, the issue of security still looms largely over the cloud. In the present work, we introduce the novel encryption model called “Advanced Encryption Standard–Cipher-text-Identity and Attribute-based Encryption” (AES–CP–IDABE) to preserve data privacy along with its access control. In the proposed scheme, the data have been double encrypted initially through the ABE, along with the attributes and the identity of the user. Secondly, the Advanced Encryption Standard (AES) is used to encrypt the encrypted data and provide it to the authorized users. The user access control is established using the digital signature with the help of user ID and security keys. Additionally, the set up includes Denial-of-Service (DoS) detection through IP address monitoring and control. The proposed scheme has also been evaluated for its performance in the communication between the user and the data owner, along with the user’s execution time. From the outcome, it is evident that the proposed scheme was more effective than the existing scheme of ABE over execution, encryption, and decryption time. Additionally, the performance over DoS detection and impact of attribute numbers for the proposed scheme was also studied to prove its effectiveness. Full article

Business gamification has been gaining in popularity in Poland in recent years and is indeed appearing in companies, especially large ones. However, the implementation of game-based solutions is still not sufficiently described. The technology allows the use of solutions such as AI or Machine Learning, but gamification is not only an IT project. The aim of the article is to determine the stages of implementation of business gamification according to various models, describe the existing differences and confront the results with business practice in Poland. To this end, a scoping review on the subject was carried out in terms of the existing methodologies for the implementation of gamification solutions. In the next stage, a scenario was created to conduct individual in-depth interviews (IDI) with companies implementing gamification projects in business. As a result of the research, the practice of implementing business gamification in Poland was described against the background of the methodologies proposed in the literature. This has led to the identification of several significant differences in implementation stages both between theory and practice and among the implementations proposed by the companies participating in the interviews. An attempt was made to explain these differences by taking the type of IT solution as a criterion. Full article

This study aimed to explore the effects of type and specifications of bus stop on bicycle speed and cycle track capacity. This paper investigates the traffic flow operations of tracks at basic sections, curbside stops, and bus bays by video recording. T-test and comparative study were used to analyze the influences of stop types on bicycle speed and capacity of track. The relationships between stop specifications and speed and capacity of track are analyzed with correlation analysis. The main results are as follows: (1) Without passengers crossing, bus bays have significant impact on bicycle speed, while it is not for curbside stops; (2) except platform length, there are strong negative relationships between bicycle speed and density of platform access, total width of platform accesses (TWPA), total width of platform accesses-to-platform length ratio (TWPA-to-PL ratio), total width of platform accesses-to-track width ratio (TWPA-to-TW ratio); (3) curbside stop and bus bay reduce track capacities by 32% and 13.5% on average, respectively; and (4) in contrast to bus bays, curbside stops have more significant impact on capacity of track, which also presents in the influence of the setting parameters of stops. Based the results above, some suggestions on stop specifications are finally proposed. Full article