Reviewerly/exHarmony huggingface dataset:
Dyslexia and Password Usage: Accessibility in Authentication Design

Governments and businesses are moving online with alacrity, driven by potential cost savings, changing consumer and citizen expectations, and the momentum towards general digital provision. Services are legally required to be inclusive and accessible. Now consider that almost every online service, where people have to identify themselves, requires a password. Passwords seem to be accessible, until one considers specific disabilities, one of which can lead to many challenges: dyslexia being a case in point. Dyslexia is associated with word processing and retention difficulties, and passwords are essentially words, phrases or alphanumeric combinations. We report on a literature review conducted to identify extant research into the impact of dyslexia on password usage, as well as any ameliorations that have been proposed. We discovered a relatively neglected field. We conclude with recommendations for future research into the needs of a large population of dyslexics who seem to struggle with passwords, in a world where avoiding passwords has become almost impossible. The main contribution of this paper is to highlight the difficulties dyslexics face with passwords, and to suggest some avenues for future research in this area.
The concept of transformation and inverted electronic textbooks

Problem and goal. Despite a significant amount of work on the creation of electronic textbooks, interest in them does not wane. Currently, when developing them, it becomes necessary to take into account the features of the Z generation. The purpose of the study is a didactic analysis and description of the concept of transformer books and an inverted textbook. Methodology. The idea of creating such intellectual teaching aids is associated with the use of mental schemes of the subject area, hidden psychoanalysis of the perception type of the learner and the question-task form of training. The textbooks being designed provide for a change in the learning strategy (from the paradigm accumulating knowledge to solve problems to the paradigm solving problems by mastering the necessary knowledge). In this case, the educational model of the white box is used, which allows visualizing the dynamics of the acquisition by student the knowledge of the subject area. Results. A procedure for developing inverted textbooks and transformer books is proposed. Prototypes of such electronic tools when used in a real educational process have shown their high effectiveness in self-educational activities of students. Conclusion. The use of transformational and inverted electronic textbooks allows not only to automate the learning process without real contact with the teacher, but also contributes to a significant increase in the motivation of modern students to self-study. The materials of the article are of practical value for developers of e-learning tools, as well as teachers who prefer to use innovative teaching methods.
Designing Multi-Agent System Organisations for Flexible Runtime Behaviour

We address the challenge of multi-agent system (MAS) design for organisations of agents acting in dynamic and uncertain environments where runtime flexibility is required to enable improvisation through sharing knowledge and adapting behaviour. We identify behavioural features that correspond to runtime improvisation by agents in a MAS organisation and from this analysis describe the OJAzzIC meta-model and an associated design method. We present results from simulation scenarios, varying both problem complexity and the level of organisational support provided in the design, to show that increasing design time guidance in the organisation specification can enable runtime flexibility afforded to agents and improve performance. Hence the results demonstrate the usefulness of the constructs captured in the OJAzzIC meta-model.
Implementation of Modern Distance Learning Platforms in the Educational Process of HEI and their Effectiveness

The growing role of distance learning platforms at higher educational institutions in developing countries, and the inadequate study of their effectiveness have necessitated the elimination of this imbalance. An additional problem in studying the issue of platforms’ effectiveness is the limitation of studies, which is based on qualitative methods of assessing the effectiveness. The quantitative assessment of the effectiveness and level of satisfaction with the implementation of distance learning platforms at higher educational institutions has been conducted in this academic paper. The assessment has been conducted using the System Usability Scale (SUS) to assess the Usability of the Moodle remote platform in Ukraine and the User Satisfaction Questionnaire (USQ) to assess students’ satisfaction. The article proves the connection between the Usability of the distance learning platform and the level of satisfaction with its use. This provides an opportunity to improve the problem areas of the Usability platform in order to increase the efficiency of its use. The following effects of application have been revealed, namely: increase of internal motivation, involvement in the learning process, level of satisfaction from courses and training programs (curricula), recognition of homework’s importance, increased interest in subjects, and students’ self-efficacy. Effective communication and quick response, an automatic control system are factors that contribute to the introduction of modern distance learning platforms in the educational process of HEI. The important elements of the effectiveness of distance learning platforms in the educational process of HEI are interactivity, simplicity, convenience, the speed of student-teacher interaction, platform flexibility, and quality control.
Blockchain Based Information Sharing Mechanism for Cyber Threat Intelligence

In recent years, networked computers are extensively used in every aspect of our daily lives. Besides, the anonymous structure of the Internet results in an increase in the number of attacks not only for individual users but also for local area networks. Current attacks are more sophisticated, and they are developed by experienced intruders with the use of automated malware production methods. These organized intrusions can go over the defense lines of the systems due to the weakness of the detection/prevention mechanisms or carelessness of individual users. After sneaking into the system, these attacks can work until they are detected, and they can access many critical resources of the company. Earlier detection of these attacks is very trivial issue for the security admins. This can be accomplished by acquiring the signature (critical information) of the newest attacks as early as possible. One suggested solution is the use of a Threat Information Sharing system, which is set up between security firms and authorities. This approach enables the distribution of the marks of the recent (zero-day) attacks and the development of some proactive prevention mechanisms for them. The use of both peer to peer and centralized sharing mechanisms have some inherited deficiencies. Therefore, in this paper, a pure decentralized cyber security information sharing system is proposed with the use of blockchain technology. A controlled decision-making mechanism, authorization termination, and rule-sets maintenance are proposed to make distributed decisions within the system. For making a decision, two smart contracts should be used in the blockchain. One holds the positive votes while the other holds the negative ones. Members of the system are able to access cyber threat data by using company-related queries. The system can facilitate the integration of many data sources into cyber security management system. Additionally, it enables to collect in a single repository that can be accessed for implementing real-time cyber security applications.
The Promise of Clinical Decision Support Systems Targetting Low-Resource Settings

Low-resource clinical settings are plagued by low physician-to-patient ratios and a shortage of highquality medical expertise and infrastructure.Together, these phenomena lead to over-burdened healthcare systems that under-serve the needs of the community.Alleviating this burden can be undertaken by the introduction of clinical decision support systems (CDSSs); systems that support stakeholders (ranging from physicians to patients) within the clinical setting in their day-to-day activities.Such systems, which have proven to be effective in the developed world, remain to be under-explored in low-resource settings.This review attempts to summarize the research focused on clinical decision support systems that either target stakeholders within low-resource clinical settings or diseases commonly found in such environments.When categorizing our findings according to disease applications, we find that CDSSs are predominantly focused on dealing with bacterial infections and maternal care, do not leverage deep learning, and have not been evaluated prospectively.Together, these highlight the need for increased research in this domain in order to impact a diverse set of medical conditions and ultimately improve patient outcomes.
"Sticky Baby Dust" and Emoji: Social Support on Instagram during In Vitro Fertilization

Searching the Internet for health information is now routine; recommending and receiving medical expertise on social media platforms such as Instagram (IG) during medical treatment remains understudied. After analyzing more than 200 images on IG related to (in)fertility treatment, we employed a directed content analysis. In this manuscript, we investigate self- disclosure related to in vitro fertilization treatment on IG and the types of linguistic (e.g., written affirmations, hashtags) and paralinguistic (e.g., emoji) feedback given in response. We found users on IG received emotional (e.g., expressions of care), informational (e.g., medical and treatment advice), tangible (e.g., gifted medicine and care packages), and belonging (e.g., #ttcsisters) functions of social support in response to self-disclosure (Uchino, 2004). By concluding that social media platforms allow for unique social support exchanges, we offer theoretical and practical implications for scholars, practitioners, and patients interested in social support, supportive communication, and emoji on social media platforms.
Generalized adversarial networks for stress field recovering processes from photoelasticity images

For overcoming conventional photoelasticity limitations when evaluating the stress field in loaded bodies, this paper proposes a Generative Adversarial Network (GAN) while maintaining performance, gaining experimental stability, and shorting time response. Due to the absence of public photoelasticity data, a synthetic dataset was generated by using analytic stress maps and crops from them. In this case, more than 100000 pair of images relating fringe colors to their respective stress surfaces were used for learning to unwrap the stress information contained into the fringes. Main results of the model indicate its capability of recovering the stress field achieving an averaged performance of 0.93&plusmn;0.18 according to the structural similarity index (SSIM). These results represent a great opportunity for exploring GAN models in real time stress evaluations.
Camera-based Hand Tracking using a Mirror-based Multi-view Setup

Current clinical practice of measuring hand joint range of motion relies on a goniometer as it is inexpensive, portable, and easy to use, but it can only measure the static angle of a single joint at a time. To measure dynamic hand motion, a camera-based system that can perform markerless hand pose estimation is attractive, as the system is ubiquitous, low-cost, and non-contact. However, camera-based systems require line-of-sight, and tracking accuracy degrades when the joint is occluded from the camera view. Thus, we propose a multi-view setup using a readily available color camera from a single mobile phone, and plane mirrors to create multiple views of the hand. This setup eliminates the complexity of synchronizing multiple cameras and reduce the issue of occlusion. Experimental results show that the multi-view setup could help to reduce the error in measuring the flexion angle of finger joints. Dynamic hand pose estimation with object interaction is also demonstrated.
Framing Outcomes and Programming Assessment for Digital Scholarship Services: A Logic Model Approach

Assessing digital scholarship services offered either through academic libraries or elsewhere on campuses is important for both program development and service refinement. Digital scholarship support is influenced by fluid campus priorities and limited resources, including staffing, service models, infrastructure, and partnership opportunities available at a university. Digital scholarship support is built upon deep, ongoing relationships, and there is an intrinsic need to balance these time-intensive collaborations with scalable service offerings. Therefore, typical library assessment methods do not adequately capture the sustained engagement and impacts to research support and collaboration that come from digital scholarship services. This article discusses the creation of a logic model as one approach to frame assessment of digital scholarship services in the university environment.
Learning through Watching: Using Animation Movie to Improve Students’ Writing Ability

&lt;p&gt;&lt;em&gt;Writing is often considered as a complicated skill to master. In order to be able to write effectively, there are several aspects to consider, which often make students face some problems in composing a written text. Thus, the use of the appropriate instructional media is necessary to help students overcome those problems, and enhance their writing ability. This classroom action research aims to investigate the improvement of students’ writ&lt;/em&gt;&lt;em&gt;ing&lt;/em&gt;&lt;em&gt; skill&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;through the use of animation movie as the instructional media. The participants of this research were two observers and &lt;/em&gt;&lt;em&gt;38&lt;/em&gt;&lt;em&gt; second grade senior high school&lt;/em&gt;&lt;em&gt; students&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;This research used observation as the technique of collecting the qualitative data about the teaching and learning process, which then analyzed by using &lt;/em&gt;&lt;em&gt;thematic analysis. Meanwhile, writing tests were used to obtain the quantitative data about the students’ scores &lt;/em&gt;&lt;em&gt;o&lt;/em&gt;&lt;em&gt;n the test given at the end of the cycles. Descriptive statistics are used to analyse the quantitative data obtained in this study. The research result showed that their average scores increased from the pre-test (75.68), to the post test in cycle 1 (78.71) and the post- test in cycle 2 (83.55). It indicated that their writing skill was also increased. Therefore, the use of animation movie in English class can improve the second grade senior high school students’ writing skill&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;/p&gt;
Quantifying the preferential direction of the model gradient in adversarial training with projected gradient descent

Adversarial training, especially projected gradient descent (PGD), has proven to be a successful approach for improving robustness against adversarial attacks. After adversarial training, gradients of models with respect to their inputs have a preferential direction. However, the direction of alignment is not mathematically well established, making it difficult to evaluate quantitatively. We propose a novel definition of this direction as the direction of the vector pointing toward the closest point of the support of the closest inaccurate class in decision space. To evaluate the alignment with this direction after adversarial training, we apply a metric that uses generative adversarial networks to produce the smallest residual needed to change the class present in the image. We show that PGD-trained models have a higher alignment than the baseline according to our definition, that our metric presents higher alignment values than a competing metric formulation, and that enforcing this alignment increases the robustness of models.
Data mining of corporate financial fraud based on neural network model

Under the active market economy, more and more listed companies emerge. Because of the various interest relationships faced by listed companies, some enterprises which are not well managed or want to enhance company’s value will choose to forge financial reports by improper means. In order to find out the false financial reports as accurately as possible, this paper briefly introduced the relevant indicators for judging the fraudulence of financial reports of listed companies and the recognition model of financial reports based on back propagation (BP) neural network. Then the selection of the input relevant indexes was improved. The improved BP neural network was simulated and analyzed in MATLAB software and compared with the traditional BP neural network and support vector machine (SVM). The results showed that the importance of total assets net profit, earnings per share, cash reinvestment rate, operating gross profit and pre-tax ratio of profit to debt was the top 5 among 20 judgment indexes. In the identification of testing samples of financial report, the accuracy, precision, recall rate and F value all showed that the performance of the improved BP neural network was better than that of the traditional BP network and SVM.
Collaborative Filtering Recommendation Algorithm with Item Label Features

The traditional item-based recommendation algorithm only considers the user's score when calculating, but the actual user's score has a malicious evaluation, which seriously affects the accuracy of using the score for prediction. At the same time, the user's evaluation is small, and the amount of usage will also affect the accuracy of the recommendation. Aiming at the above two problems, this paper proposes a collaborative filtering recommendation algorithm with the characteristics of the item label. Firstly, the item label feature and the user's behavior data are comprehensively considered. Secondly, the user's feature data on the label selection is calculated, and finally the item is calculated in combination with the feature data. Similarity, thus making recommendations for users. Experiments show that the algorithm can solve the cold start problem of data well, and the interpretation of the recommended results is also convincing.
Artificial Intelligence Helps Protect Smart Homes against Thieves

Interaction with the environments in which humans live is increasing more and more, and Artificial Intelligence (AI) offers significant contributions to this.Although the topic of smart homes has attracted a great deal of attention from researchers, the AI-based application in this area is still in its infancy.In this study, a home security automation system, which is quite simple, but smart and AI-based, is proposed.When the home-dwellers were not at home, the home lighting system tried to be managed with AI at night, as if life was still there.The AIbased smart home physical design was done using Arduino equipment and was tried to be adapted to the real-life environment with software support.As if there was someone at home, a special dataset, which was consisted of nine inputs, one output vector and about 5500 samples was created to turn on/off the home lights in a manner suitable for night life.The home lighting system was successfully managed using an AI-based system that learns nightlife lighting habits.The proposed system performance was tested in support of commonly used machine learning classification algorithms such as Multi-layer perceptron (MLP), Linear support vector machine (L-SVM), Gaussian Naive Bayes (NB), and linear discriminant analysis (LDA).The accuracy values of MLP, L-SVM and NB algorithms were 96.69%, 94.98% and 91.23%, respectively.Our results show that a home with AI could be safer and more secure against theft.
Information informing design: <scp>Information Science</scp> research with implications for the design of digital information environments

Abstract This debut curated “virtual special issue” of JASIST is on the theme of “information informing design.” It comprises several excellent scholarly research articles previously published in JASIST with important implications for the design of digital information environments. It covers articles that motivate the need for Information Science research to inform design and those that have empirically examined information‐related concepts such as information behavior, practices, interaction, and experience and, based on their findings, proposed recommendations or posed questions for design. This article argues that as JASIST exists at the intersection between information, systems, and users, it is natural to want to understand how people engage with information to inform design and, by doing so, Information Science research can build bridges between Information Science and computing disciplines and make contributions that transcend its discipline boundaries. It argues that Information Science research not only has the potential but also the duty to inform the design of future digital information environments.
Local-Global Fusion Network for Video Super-Resolution

The goal of video super-resolution technique is to address the problem of effectively restoring high-resolution (HR) videos from low-resolution (LR) ones. Previous methods commonly used optical flow to perform frame alignment and designed a framework from the perspective of space and time. However, inaccurate optical flow estimation may occur easily which leads to inferior restoration effects. In addition, how to effectively fuse the features of various video frames remains a challenging problem. In this paper, we propose a Local-Global Fusion Network (LGFN) to solve the above issues from a novel viewpoint. As an alternative to optical flow, deformable convolutions (DCs) with decreased multi-dilation convolution units (DMDCUs) are applied for efficient implicit alignment. Moreover, a structure with two branches, consisting of a Local Fusion Module (LFM) and a Global Fusion Module (GFM), is proposed to combine information from two different aspects. Specifically, LFM focuses on the relationship between adjacent frames and maintains the temporal consistency while GFM attempts to take advantage of all related features globally with a video shuffle strategy. Benefiting from our advanced network, experimental results on several datasets demonstrate that our LGFN can not only achieve comparative performance with state-of-the-art methods but also possess reliable ability on restoring a variety of video frames. The results on benchmark datasets of our LGFN are presented on https://github.com/BIOINSu/LGFN and the source code will be released as soon as the paper is accepted.