Reviewerly/exHarmony huggingface dataset:
Impact of Co-creation Footwear Workshops on Older Women in Elderly Centers in Hong Kong

Background: The use of appropriate footwear could reduce the risk of falls among the geriatric population. However, the elderly are generally reluctant to accept new footwear designs particularly with the incorporation of fabrication materials and functional design features that are perceived to be less comfortable. Co-creation activities that encourage user involvement during the product design process and development can therefore address this issue and provide unique benefits and better value for users, increase acceptance, and even lead to positive perception as well as positive psychological impacts, thereby increasing the practical use of newly designed footwear.&#x0D; Objective: This study aims to investigate the impact of hands-on footwear workshops on perceived functionality of geriatric footwear for older women.&#x0D; Method: Footwear co-design workshops for older women were carried out in two elderly centers in Hong Kong. Twenty-one healthy females between 64 and 80 years old (mean: 71; SD: 4.2), were recruited. Subjects were invited to decorate the prescribed geriatric footwear prototype and discuss their ideas on footwear design in a group of three. They were surveyed after completing the footwear decoration.&#x0D; Main outcome measures: The perceptions of the subjects on the geriatric footwear and related design features were analysed.&#x0D; Results: The questionnaire survey shows positive results in the psychological value of the workshops for older women. The participants express high levels of satisfaction with the co-design process and a strong sense of achievement with the newly designed geriatric footwear. The acceptance and awareness of the functions of the footwear are greatly enhanced.&#x0D; Conclusions: The co-design approach has positive psychological impacts on the elderly and their acceptance of the final product. This approach also better meets the aesthetic needs of the users.
Opinion mining framework using proposed RB-bayes model for text classication

&lt;p&gt;&lt;span lang="EN-US"&gt;Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.&lt;/span&gt;&lt;/p&gt;
Cloud-Based Global Online Marketplaces Review on Trust and Security

Customer disposition to data, nature of the information on site, protection&lt;strong&gt; &lt;/strong&gt;concerns, trust, security concerns, and the notoriety of organization efficaciously affect the trust of Internet shoppers in the site. Two noteworthy and basic issues for e-commerce sites and consumers are trust as well as security. A belief that someone is good and honest and will not harm you, or something is safe and reliable is called trust; while security is an attempt to safeguard the data from unauthorized access. Information security is a vital management as well as technical requirement over the internet for effective and secure payment transaction activities. The safety of e-commerce resources from use, destruction, unauthorized access and alteration is known as E-commerce security so there is an urgent need to study its dimensions such as authenticity, integrity, availability, privacy, confidentiality and non-repudiation. This paper reports a review of four popular online marketplaces which are Alibaba, Amazon, eBay andTaoBao as case study on two main criteria namely building trust among users and ensuring security on the platform. Furthermore, we discuss the methods being used by each online marketplace to build trust and their unique way ofimproving the security. Finally, different ways of building trust and technique to ensure the security is presented in a tabular form for each online marketplace.
DESIGNING INNOVATIVE LEARNING EXPERIENCES. AN EXPERIMENTAL PROJECT TO SUPPORT EDUCATION FACILITIES

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 6620-6629ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1610Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Improving Students Character Using Fairy Tales

In the 2003 National Education System Law, education is a conscious and planned effort to realize a learning atmosphere and learning process so that students actively develop their potential to have spiritual strength, self-control, personality, intelligence, noble character, and skills. it needs itself, society, nation, and country. Educational goals in Indonesia shape the noble character of students. To realize that, fairytale learning is one of them that can be used. It examines the values contained in fairy tales, so research is conducted. The form of this research is qualitative, and the research method is a description. Data collection techniques study documents. Fairy tales that are used as data sources accessed on the internet and storybooks. The sociology of literature approach is used to analyze data. Fairy tale oral literature has values that can be used to instill positive character in students. The values that are reflected in the fairy tales studied are the values of caring for the environment, thinking wisely before acting, the greedy nature of bringing disaster. The activity of appreciating fairy tales is closely related to training to sharpen feelings, reasoning, and imagination, as well as sensitivity to society, culture and the environment. Students are expected to be able to enjoy, appreciate, understand, and utilize literary works to develop their personality, broaden their horizons, and improve their knowledge and language skills. With a lot of reading literary works will help students become cultured humans who are responsive to noble values in social life. Students who are cultured are expected to be great human beings but still simple, free but self-controlled, strong but full of tenderness.
READING COMPREHENSION LEVEL AND STUDY SKILLS COMPETENCE OF THE ALTERNATIVE LEARNING SYSTEM (ALS) CLIENTELE

This study aimed to assess the reading comprehension level and study skills competencies of the Alternative Learning System-Accreditation and Equivalency (ALS-A&amp;E) clientele. It utilized the descriptive-quantitative research design. Data were obtained from various adopted reading passages. Of the four reading dimensions, the clientele registered a good performance in the literal level but poorly performed in the inferential, evaluative, and creative dimensions. As to their study skills competencies, outlining and semantic mapping were their top concerns but they were nevertheless good in locating thesis statement and very good in book parts familiarization. Considering these findings, it is the imperative for the ALS implementers to revisit their pedagogic practices relative to the teaching of reading as a macro language skill especially on the areas were their clientele need instructional related interventions.
Spatially Adaptive Losses for Video Super-resolution with GANs

Deep Learning techniques and more specifically Generative Adversarial Networks (GANs) have recently been used for solving the video super-resolution (VSR) problem. In some of the published works, feature-based perceptual losses have also been used, resulting in promising results. While there has been work in the literature incorporating temporal information into the loss function, studies which make use of the spatial activity to improve GAN models are still lacking. Towards this end, this paper aims to train a GAN guided by a spatially adaptive loss function. Experimental results demonstrate that the learned model achieves improved results with sharper images, fewer artifacts and less noise.
A Threat Computation Model using a Markov Chain and Common Vulnerability Scoring System and its Application to Cloud Security

Securing cyber infrastructures has become critical because they are increasingly exposed to attackers while accommodating a huge number of IoT devices and supporting numerous sophisticated emerging applications. Security metrics are essential for assessing the security risks and making effective decisions concerning system security. Many security metrics rely on mathematical models, but are mainly based on empirical data, qualitative methods, or compliance checking, and this renders the outcome far from satisfactory. Computing the probability of an attack, or more precisely a threat that materialises into an attack, forms an essential basis for a quantitative security metric. This paper proposes a novel approach to compute the probability distribution of cloud security threats based on a Markov chain and Common Vulnerability Scoring System. Moreover, the paper introduces the method to estimate the probability of security attacks. The use of the new security threat model and its computation is demonstrated through their application to estimating the probabilities of cloud threats and types of attacks.
Achievements and Usage of Learning Materials in Computer Science Hybrid Courses

The term gamification is used to describe the implementation of computer game elements into non-game-based systems.We have expended our previous research and in this article we've introduced new concept model for comprehensive gamified approach based on literature findings.Next, we empirically tested two hypotheses regarding the learning achievement in specific topics of online university courses "3D Modeling" and "Programming II".These hypotheses were: H1-an online course designed with the use of computer game elements enables better learning achievement in comparison to traditional non-gamified e-learning and H2an online course designed with the use of computer game elements positively influences the frequency of use of the learning material.Both hypotheses were confirmed, based on which final conclusions were drawn regarding the use of gamification in computer science hybrid courses.
Block-chain Reaction: Why Development of Blockchain is at the Heart of the Legal Technology of Tomorrow

Blockchain, arguably the most discussed and promising of FinTech trends, has incredible potential to transform legal technology. Infamous as the technology behind cryptocurrencies, distributed ledger technology (DLT) has developed far beyond these origins and may prove far more valuable than the currency it supports. As a form of DLT, blockchain allows for secure decentralisation of peer-to-peer, irreversible exchanges, providing a transparent and irrevocable record as these occur. The technology is already used across the legal industry and a plethora of opportunities to extend its application bubble beneath the surface, under development, waiting to break into the current market.
A Novel Neighborhood Calculation Method by Assessing Users' Varying Preferences in Collaborative Filtering

To recommend an item to a target user, Collaborative Filtering (CF) considers the preferences of other similar users or neighbors. The accuracy of the recommendation depends on the effectiveness of assessing the neighbors. But over the time, the mutual likings of two individuals change; hence, the neighbors of the target user also should change. However, this shifting of preferences is not considered by traditional methods of calculating neighborhood in CF. As a result, the calculated set of neighbors does not always reflect the optimal neighborhood at any given point of time. In this paper, we argue for considering the continuous change in likings of the previous similar users and calculating the neighbor- hood of a target user based on different time periods. We propose a method that assesses the similarity between users in the different time period by using K-means clustering. This approach significantly improves the accuracy in the personalized recommendation. The performance of the proposed algorithm is tested on the MovieLens datasets (ml-100k and ml-1m) using different performance metrics viz. MAE, RMSE, Precision, Recall, F-score, and accuracy.
Enhancement of Chest Radiograph in Emergency Intensive Care Unit by Means of Reverse Anisotropic Diffusion-Based Unsharp Masking Model

In intensive care units (ICUs), supporting devices play an important role, and the placement of these devices must be accurate, such as catheters and tubes. Taking portable chest radiograph (CXRs) for patients in ICU is a standard procedure. However, non-optimized exposure settings and misaligned body positions usually mean that portable CXRs are not in acceptable working condition. The purpose of this study was to enhance ICU CXRs to assist radiologists in the positioning of endotracheal, feeding, and nasogastric tubes in ICU patients. The unsharp masking model (USM) was a classical image enhancement technique. Because of the isotropic diffusion filter applied in this model, USM enhanced the edge information and noise simultaneously. In this paper, we proposed a reverse anisotropic diffusion (RAD)-based USM technique for enhancement of line structures in ICU CXRs. First, a RAD algorithm was applied to replace the Gaussian filter in the classical USM. The RAD algorithm only produced a smoothed image, in which edge information was smoothed while the noise was preserved. Then, the smoothed image was subtracted from the original image to produce the unsharp mask whereby only the edges were retained. Consequently, only edge information was enhanced in the final enhanced image by using the RAD-based USM model. The proposed method was tested for 87 ICU CXRs and the findings indicate that this approach can enhance image edges efficiently while suppressing noise.
Electric Acoustic

'Energy' is an abstract concept, invisible except through its effects, yet with vast geopolitical and environmental consequences-while driving many everyday practices. It is a curious 'material' to work with for designers, with experiential properties which are underexplored. In Electric Acoustic, we are exploring both sonification and vibration (cymatic displays) as media for experiencing energy, specifically electricity use. These materializations potentially enable deeper engagement with the invisible systems and infrastructures of everyday life. This short paper reports on our preliminary experiments and some of the issues and considerations arising during this initial exploration.
Octennial Review (2010-2018) of Literature on M-Learning for Promoting Distributed-Based Medical Education in Sub-Saharan Africa

Medical education in Africa is in desperate need of reforms, evident in widespread diseases, and an inability to mobilise and train the required medical workforce to deal with these health issues. However, the exponential rise in the use of mobile technologies due to the spread of the Internet and increased telecommunication networks offer an opportunity for the transformation of medical education and practice through the deployment of mobile devices as a medium for learning and conveying health care services to the remote and resource-constrained locations of Sub-Saharan Africa (SSA). This paper reviewed articles on the affordances of m-learning for distributed medical education in SSA published between 2010-2018. Results from 18 articles identified in the review revealed a slow-paced ascendancy of practice and research in the field; it further exposed competing priorities, infrastructural deficit, and chronic workforce shortages as the bane of m-learning implementation in the subregion. This paper makes recommendations that will enhance the growth of mobile-based distance medical education and practice in SSA.
A novel multi-core algorithm for frequent itemsets mining in data streams

Data streams are modern data sources that are gaining attention as a consequence of their many practical applications (they can be found in data transmission, eCommerce, and intrusion detection system among others). Nevertheless, the efforts to obtain insights from data streams are limited due to their massive information volume and the time needed to process them. In this paper, a new approach for Frequent Itemsets Mining on data streams based on prefix trees which takes advantage of multi-core systems is proposed. This approach uses the Gearman framework as the interface for multi-core processing, and it allows to exploit their scalability efficiently. Experimental results show that the proposed method obtains the same patterns compared with similar approaches reported in the state-of-the-art and outperforms them concerning the processing time required. Also, it is proved that the proposed method is insensitive to variations in the support threshold value, and its efficiency depends on the size of the transactions and not on the size of the alphabet, which is a significant issue in other Frequent Itemsets Mining algorithms.
Socio-demographic and professional characteristics of academic staff at a regional university

УДК 378+37
An inkjet printed pressure sensor for applications in Active Ageing monitoring

In this paper a novel approach is presented for the monitoring of sitting posture. This work addresses the application context of assistive technology for the elderly, as part of a PC Interreg Italy-Malta project, called NATIFLife. The system developed consists in a flexible sensor matrix realized by low-cost inkjet printing. Main advantages of the solution proposed are related to the adopted direct printing technology, which allows for the rapid prototyping of customized solution shaped as a function of users and/or the application context. The idea behind the application, its implementation and the first experimental results on sensor characterization are presented.
Incremental Multi-view Support Vector Machine

Multi-view classification has received considerable attention in recent years. We observed that the existing multi-view classification methods learn a consensus result by collecting all views and thus have two critical limitations. First, it is not scalable. Second, in many applications views of data are available over time; it is in-feasible to apply the existing multi-view learning methods to such streaming views. To address the two limitations, in this paper we propose a novel incremental multi-view SVM method, i.e., instead of processing all views simultaneously, we integrate them one by one in an incremental way. We first learn an initial model from the first view; next when a new view is available, we update the model and then apply it to learn a new consensus result. This incremental method is scalable and applicable to streaming views. We present a block coordinate descent algorithm whose convergence is theoretically guaranteed to optimize the induced objective function. Experimental results on several benchmark data sets further demonstrate the effectiveness of our method.
Measurement Bias Estimation in the Problem of Target Tracking

In the problem of target tracking, different types of biases can enter into the measurement collected by sensors due to various reasons. In order to accurately track the target, it is essential to estimate and correct the measurement bias. Considering practical backgrounds, the bias is assumed to be locally stationary Gaussian distributed and an iterative estimation algorithm is proposed. Firstly, a mechanism is established to detect whether the bias switches between different Gaussian distributions. Secondly, the expectation maximization algorithm with the assistance of extended Kalman filtering and smoothing is proposed to iteratively estimate the bias and target state in an offline manner. Simulations show the proposed algorithm can suppress the impact of the measurement bias on target tracking.
Reversible Data Hiding in Binary Images by Symmetrical Flipping Degree Histogram Modification

In recent years, reversible data hiding in binary images has become more and more important in the field of data hiding. For the binary property, hiding information in binary images can only be achieved by flipping pixels. But flipping the pixels randomly in a binary image will cause remarkable distortion. The existing methods are capable of reversible data hiding in binary images but the visual quality is not good. In this paper, we propose a novel reversible data hiding method based on symmetrical flipping degree (SFD) histogram modification. First, we define the SFD of the block, which is a visual score measured the distance between the pixels in the block. The smaller SFD is, the better the visual quality the image has. Then the histogram is generated from the SFD, and it utilizes the zero or the minimum point and modifies the pixels with high SFD for its pre-processing. Finally, the appropriate corresponding pixels are selected adaptively to flip for information embedding. Experimental results demonstrate the feasibility of our proposed method and the visual quality is satisfactory.
Risk Averse Reinforcement Learning for Mixed Multi-agent Environments

Most real world applications of multi-agent systems, need to keep a balance between maximizing the rewards and minimizing the risks. In this work we consider a popular risk measure, variance of return (VOR), as a constraint in the agent's policy learning algorithm in the mixed cooperative and competitive environments. We present a multi-timescale actor critic method for risk sensitive Markov games where the risk is modeled as a VOR constraint. We also show that the risk-averse policies satisfy the desired risk constraint without compromising much on the overall reward for a popular task.
Virtual Reality in Self-regulated Learning: Example in Art Domain

In recent decades, learning devices using virtual reality (VR) environments have evolved rapidly. The potential positive impact of VR has been attributed to two characteristics: immersion, and of interaction with objects in the environment. However, results from the literature have not always shown the presumed benefits and few of them have assessed the effects on self-regulation. This study aims to assess the impact of immersion and on motivation, self-regulation, and performance. Participants had to acquire knowledge about sculptures by visiting a 3D virtual museum and then recall this knowledge. The participants were divided into four independent groups. They were: #1 In strong immersion (with VR headset) and active (control of interaction); #2 In strong (VR) and passive (non-interaction control) conditions; #3 In low immersion (tablet) and active conditions; #4 In low and passive immersion conditions. Intrinsic motivation and emotion were evaluated by a questionnaire, self-regulation was identified by behavioral indicators and performance was evaluated through a gap-fill exercise. Results showed that the control feature had a positive impact on performance, unlike immersion. Also, neither immersion nor had an impact on motivation. However, immersion and had a partial impact on self regulation. Educational implications will be discussed.