Reviewerly/exHarmony huggingface dataset:
Children’s Password-Related Books: Efficacious, Vexatious and Incongruous

Abstract Software is developed specifically for children and this often requires them to authenticate themselves, usually by entering a password. Password hygiene is important for children, because the principles they learn in early life will often endure across their life span. Children learn from their parents, siblings, teachers, and peers. They also learn from educational resources, such as children’s books. We carried out a content analysis of a range of children’s books that aims to educate children about passwords. We used directional coding, as informed by a systematic literature review of methods, such as those used in other content analysis-based studies of children’s books. We examined the principles the books taught, and whether these were correct. We also analysed how the books portrayed the genders of characters, in various roles. We found that principle coverage was variable, with books sometimes teaching outdated principles. Genders were evenly represented in the books. Finally, our analysis revealed conflation of the terms “safety” and “security” in the cyber domain. We conclude the paper by justifying the adjectives we use in the title.
Research on Cross-cultural Participatory Design by Design Teams Based on Chinese Cultural Background

With the acceleration of globalization, the acculturation issue of design is becoming an emerging challenge. At the same time, as one of the several design patterns in service design, which is becoming a mainstream in design society, has been adopted by more and more designers in design practice. Based on the output of a design workshop on acculturation issue of design, we presented the process of and learnings from participatory design aiming at helping foreigners living in China to use WeChat. To that end, we described the practice, problems and achievements, lessons learned, and outlook into the future for design practice using participatory design to address the acculturation issue.
Virtual Reality as a Stress Reduction Measure – Chilling Out on the Beach in My Living Room

Digitization has many advantages, but it also creates an ever-increasing burden on people due to constant availability and a flood of information, which then results in an increased level of stress. Therefore, the study tries to use the advantages of digitalization by using Virtual Reality (VR) to create calming virtual environments, in which users can take a break from their everyday life to relax. In this special case, a 360° VR video of the city beach in Würzburg was recorded and the subjects watched it through a Head Mounted Display (HMD) while they were relaxing in a sunbed. In addition, skin conductivity was measured as an indicator of stress to find out whether the VR session had a positive influence on the stress level of the subjects. In addition, before and after the VR session the subjects should indicate their subjectively perceived stress level by means of a color scale. Although, there was a decrease in the values of electrodermal activity (EDA) during the VR session, it was not significant. On the other hand, the subjective perceived stress level before and after the VR session showed a clear difference and this difference was highly significant. Subjects felt less stressed after a VR session on the city beach than before.
Modeling of digital dental articulator and its accuracy verification using optical measurement

Temporomandibular joint has been considered one of the most complex joints in human body. Dental articulation hinged upon temporomandibular joint is essential and fundamentally important for dental restoration design and prosthetic/orthodontic occlusion analysis. As digital dentistry rapidly grows, a complete digital work flow requires the use of a digital articulator for occlusion analysis. However, commercial CAD/CAM systems do not provide any method to verify the modeling accuracy of a digital articulator. There is also a lack of detail and generalized mathematical modeling of the digital articulator for simulating the jaw movement. This paper presents the development of a digital articulator by mathematically modeling a general dental articulator which simulates the relative jaw motion between the maxilla and mandible. As the digital articulator moves, the digital upper teeth move relatively to the digital lower teeth, thus simulating the occlusal path with teeth collision detection function. To verify the accuracy of our modeled digital articulator, an improved optical tracking method is proposed to measure the pose of a mechanical articulator with 6 degrees of freedom and compare that with the digital articulator. The digital articulator system proposed in this paper achieves the following functions: 1. Digitalize the dental articulator with verified precision. Combined with dental design software, restorations can be designed with more efficiency and accuracy. 2. Provide an improved optical tracking method which can compare the movement error between the mechanical articulator and digital articulator. Thus the accuracy of the digital articulation can be verified. The result shows the error of our system is controlled under sub-millimeter which provides sufficient accuracy for the design of restoration under static and dynamic occlusion conditions. We develop a general digital articulator which can simulate jaw movement between opposing teeth and an improved optical tracking method to verify the accuracy of the digital articulator. The modeling and accuracy verification of the digital articulator shows that there is a systematic and reliable way to replace traditional mechanical articulator and can close the gap for digital restoration fabrication.
Security-Centric Investigation of Social Networks and Preventative Behavioral Analysis of Online Activity by the Kuleshov effect

The Internet beside providing many advantages has some disadvantages. The full unfamiliarity with cyberspace can bring harms and inconvenience to families. This study attempted to introduce the disadvantages and threats on the Internet and present some countermeasures to decrease such threats and harms. Social networks are currently so epidemic that even many companies and organizations formally supply the internal communication system among their members and employees through this social networks and the number of the users of such networks is increasing daily. The term “information security” covers a broad spectrum of activities in an organization. Information security means preventing the unauthorized access to information and products or apply some changes or delete such information using a series of processes. Such actions can be considered as a protection package against current resources in different situations (e.g., a hacker attacks which occurs a lot) by those in charge of information security. One of the most important issues which is considered as a security challenge in these networks is the security and retention of the data and information shared by users from unauthorized access and unwanted disclosure. In this article the Kuleshov effect from cinema has been deployed to explain and launch a behavioral analysis of online activities.
Proposal of Quality in Use in Software Quality

Both product quality and quality in use which are positioned in software quality model deal with "usability" for users. The former, usability is positioned as one of product quality. The latter, usability is dealt with outcome of use by using product, system and service as quality. Especially, it is necessary for quality in use to consider influence on not only users when interact directly but also wider stakeholders. For this, it is important to prepare a model to realize making a product which usable for users and which effective for almost stakeholders by using the product. In this paper, we cluster four target user groups, they are, "operator of system and/or software", "organization which has responsibility for system and/or software management", "customer using system and/or software" and "Society which exists system and/or software". According to these clustering, we propose four models about quality in use correspond to target users.
Context Relevancy Assessment in Tensor Factorization-based Recommender Systems

Recommender systems (RSs) are modern data filtering tools aiming to generate personalized recommendations using different approaches. The data sparsity problem raised due to unavailability of sufficient rating information is observed as a major challenge in front of RSs as they are highly dependent on the explicit rating data. Factorization based dimensionality reduction approach provides a favorable solution to this sparsity problem as it solves the missing data problem using matrix completion. Context aware recommender systems are advanced RSs, which take into consideration various contexts e.g. age, location, gender etc. while generating recommendations. The data sparsity problem in context aware RSs is handled through Tensor Factorization (TF) based techniques, which is the generalization of Matrix Factorization approach. In this work, the importance of incorporating adequate number of relevant contexts into Tensor factorization based RSs is highlighted. Too less contexts lack in adding the true flavor of context awareness while too many degrades the performance of Context aware Recommender System (CARS) by adding noise. We propose a fusion based techniques built on Information Gain and correlation based feature selection technique to derive the list of relevant contexts. We further determine the optimum number of relevant contexts to maintain the trade-off between accuracy and computational complexity. The experiments are performed on two context aware datasets from different domains to generalize the findings.
Adaptive Learning in Computing Education: A Systematic Mapping Study

Abstract Adaptive learning has become popular among researches in the recent decade. It is believed in giving good benefits which promotes effective ways or methods to improve youth performance in education either for computing field or any non-computing fields. This study focuses on investigating the contribution of adaptive learning in computing education including the adaptation environment involved. This systematic mapping study was undertaken to analyse all relevant and related studies. A set of three research questions were defined in which 68 primary studies, dated from 2012-2020, were analysed and evaluated. The mapping shows the trends and the taxonomy of the contribution adaptive learning in computing education. However, the result also can be applied for non-computing education field. Most of the studied areas are on providing adaptive learning environment and investigating the suitability of learning contents for the youth in teaching and learning. This study summarized the existing research of adaptive learning over past seven years and there is a potential for more research in this area.
Recommendation Systems and Machine Learning: Mapping the User Experience

Human-algorithm interaction emerges as the new frontier of studies involving interaction design and interface ergonomics. This paper aims to discuss the effectiveness and communicability of streaming media recommendation systems, based on machine learning, from users' mental model point of view. We examined the content consumption practices on the Netflix platform, identifying some sensitive aspects of the interaction with recommendation algorithms. One-on-one semi-structured interviews were applied to a sample of students from three different universities in Rio de Janeiro, Brazil. We realised that interviewees had not correctly understood how the system works and have not formed an adequate mental model about tracked data and how it is processed to create personalised lists. Another issue concerns data privacy: Users have revealed a suspicion concerning algorithms and what might happen to usage data, not only in the Netflix platform but also in other services that use algorithm-based recommendation. Interviewees' responses suggested that there may be communication failures, and UX designers should strive to make it visible how the system tracks and processes user interaction data.
Embedding Ethics in Human Factors Design and Evaluation Methodologies

Methodologies are required to enable the active translation of ethical issues pertaining to the human and social dimensions of new technologies, in a manner that considers the diversity of practices across research and innovation and commercial research projects. This paper presents a new methodology for embedding ethics assessment in human machine interaction (HMI)/human factors (HF) design and evaluation activities.
Comparative Analysis of Software Components Reusability Level using GFS and ANFIS Soft-Computing Techniques

The following topics are dealt with: power grids; photovoltaic power systems; learning (artificial intelligence); power generation economics; cellular radio; optimisation; error statistics; power engineering computing; educational institutions; mobile computing.
How to Assess the Exhaustiveness of Longitudinal Web Archives

Longitudinal web archives can be a foundation for investigating structural and content-based research questions. One prerequisite is that they contain a faithful representation of the relevant subset of the web. Therefore, an assessment of the authority of a given dataset with respect to a research question should precede the actual investigation. Next to proper creation and curation, this requires measures for estimating the potential of a longitudinal web archive to yield information about the central objects the research question aims to investigate. In particular, content-based research questions often lack the ab-initio confidence about the integrity of the data. In this paper we focus on one specifically important aspect, namely the exhaustiveness of the dataset with respect to the central objects. Therefore, we investigate the recall coverage of researcher names in a longitudinal academic web crawl over a seven year period and the influence of our crawl method on the dataset integrity. Additionally, we propose a method to estimate the amount of missing information as a means to describe the exhaustiveness of the crawl and motivate a use case for the presented corpus.
A Fault Diagnosis Method of Transmission Network Based on Bayesian Network and Fault Decision Table

In view of the complexity and low accuracy of existing fault diagnosis methods for transmission network, this paper proposes a novel transmission network fault diagnosis method combining simplified Bayesian network and fault decision table. First, using the information from Supervisory Control and Data Acquisition (SCADA) system, there develop a fault area identification method, which utilizes circuit breaker (CB) information to isolate the components to build a fault area. In fault area, there creates the simplified Bayesian network to associate components with CBs. Further, the paper proposes a calculation method to calculate the fault probability of components in fault area, which is able to determine the suspicious component set. According to the sequence of relay actions, a fault decision table for the suspicious component set can be established. Finally, the fault condition of components, CBs and protection devices are diagnosed by comparing the local fault decision table. The above method greatly simplifies the complexity of Bayesian network, and the test results show that the speed and accuracy of its fault diagnosis have been significantly improved.
Visual Data Storytelling: A Case Study of Turning Big Data into Chinese Painting

Visual data storytelling is a relatively new terminology that has emerged over the last decade. The use of information technology in data visualizing is becoming more common and accessible to users. The artist apply a variety of multimedia, symbols and metaphors to independently create a visual form that expresses data and communicates with real life. The visual data are the media that provide powerful and essential means of communication. If “visual data storytelling” is viewed as a process of communication, it follows naturally that how the artist’s performances are conceived, developed, delivered and received, and how the audience is attracted, accurately understanding the data and affected by the data visualization are worth exploring. Therefore, this study is intended to propose a framework focusing on how the conception of the artist affects the creation process and how the creation process is understood by the audience. The artist’s creation activities were analyzed through the framework of four steps using a case study of turning “big data” into “Chinese painting.” The results showed that the approach could be applied to understanding data visualization and provides artists with an idea of how to concentrate their efforts at the creation stage, the easier to communicate with their audience. In addition, the research framework seems to provide a better way to explore the understanding of how data transforms into art forms, which is clearly worthy of further study.
Multi-method Approach Measuring Trust, Distrust, and Suspicion in Information Technology

In two studies, we examined the measurement of complex state variables with length of response, construct word counts, Likert-type responding, self-reports of past behaviors, and implicit associations. In the first study, participants were primed to write in a control condition and a suspicion condition, which were also used as referents for self-reports, past behaviors, and priming for implicit associations. In the second study, participants were primed for trust and distrust. Results indicated length of response, construct word counts, Likert-type responding, and self-reports of behavior were all affected by the manipulations, indicating they measure the state constructs adequately. However, length of response was also influenced by which condition participants received first, indicating a possible exhaustion effect. Implicit associations indicated no change due to the manipulations.
Scaled unscented transformation method and adaptive Monte Carlo method used for effects of fault parameters estimation on green function matrix in slip distribution inversion

The elements of Green function matrix are the nonlinear functions of fault parameters estimation, the randomness of fault parameters estimation causes that the slip distribution inversion turns to be the parameter estimation problem of total least squares. Second-order approaching function method, scaled unscented transformation (SUT) method and adaptive Monte Carlo method are designed for biases of displacements in rectangular dislocation model. They are used to analyze effects of the length, width, depth and dip of fault with different variances on the corresponding displacements of unit strike slip dislocation fault, unit dip slip dislocation fault and unit tensile dislocation fault. Results of the simulated fault show that compared with second-order approaching function method and adaptive Monte Carlo method, SUT method has better computational efficiency. The second-order term has dominant effects on nonlinear relationship between displacements and the fault parameter in the rectangular dislocation model. The main biases of displacements are near to fault. The corresponding displacements of unit tensile dislocation are mostly susceptible to fault parameters estimation, followed by the unit dip slip dislocation and unit strike slip dislocation. In addition, the vertical displacement is more sensitive to fault parameters estimation than horizontal displacements.
A Passenger Context Model for Adaptive Passenger Information in Public Transport

Passengers in public transport expect passenger information to be exact, timely and appropriate to their situation. Therefore, future passenger information systems should adapt to the passenger's context as precisely as possible. In this paper, we present a context model and describe our architecture for an adaptive, multi-device passenger information system. We will also present adaptation scenarios that show the application of our context model.
Transformation matrices for 3D shape sensing with polyimide-coated multicore optical fiber

This paper presents a three-dimensional shape-reconstructing algorithm applied on a shape sensing system based on a polyimide-coated 7-core optical fiber. Fiber optic-based shape sensing consists in numerically evaluating the strains applied to the fiber in order to identify useful spatial parameters such as the curvature radii defining its shape. Multicore fibers are preferable since they can also provide information regarding the direction of bending. The proposed method relies on the homogeneous transformation matrices normally used in robotics, which can express the position of a point in different frames, i.e. from local to global coordinates. The results are shown for both a two-dimensional and a three-dimensional configuration, achieving an average relative error of 0.23% and 0.19% of the total length respectively. The small computation time (0.0023 s) makes this algorithm suitable for real-time shape reconstructions in a variety of engineering fields.
Towards Automated Testing and Robustification by Semantic Adversarial Data Generation

Widespread application of computer vision systems in real world tasks is currently hindered by their unexpected behavior on unseen examples. This occurs due to limitations of empirical testing on finite test sets and lack of systematic methods to identify the breaking points of a trained model. In this work we propose semantic adversarial editing, a method to synthesize plausible but difficult data points on which our target model breaks down. We achieve this with a differentiable object synthesizer which can change an object's appearance while retaining its pose. Constrained adversarial optimization of object appearance through this synthesizer produces rare/difficult versions of an object which fool the target object detector. Experiments show that our approach effectively synthesizes difficult test data, dropping the performance of YoloV3 detector by more than 20 mAP points by changing the appearance of a single object and discovering failure modes of the model. The generated semantic adversarial data can also be used to robustify the detector through data augmentation, consistently improving its performance in both standard and out-of-dataset-distribution test sets, across three different datasets.
A Method for Quickly Establishing Personas

The use of personas can help teams better understand the characteristics of users, which leads to more accurately discovery the problems and real pain points that users face. At present, there are two main ways to establish personas. One is to generate personas qualitatively or quantitatively through interviews, questionnaires, etc. These processes are related to the experiences of analysts and the statistical methods used, usually resulting in different conclusions and spending much time. The other is that the technical teams directly obtain the users' operation data on the products and use algorithm models to automatically generate personas. But this method is only suitable for mature products or existing functions, while the questionnaire method has nothing to do with mature products and functions. In this paper, we present persona segmentation through K-Means and PAM clustering algorithms in machine learning for questionnaire data, including mixed data, as an objective, quick, low-cost method for establishing personas. The method consists of four steps: first, design questionnaire. Second, transform the multi variables caused by multiple choice questions into a single variable. K-Means clustering algorithm is used for the continuous data of multi variables. The rule-based clustering method is used for the classified data. Then, cluster the processed data by PAM. The fourth step is to create personas, which are labeled in this paper. In the end, we demonstrate that the method is appropriate to create useful personas by machine evaluation and expert evaluation.