Reviewerly/exHarmony huggingface dataset:
Augmented Reality Books: A User Experience Evaluation

The rise of information society and the profound mediatization phenomenon led to a book use decrease. On the other hand, the attempt to promote book use and widen the capacities of the traditional book led to the application of computational resources, one of these resources is Augmented Reality (AR). This study is about AR published Books and their User Experience (UX). We want to know if publishing AR books shows sufficient UX. We chose three AR books published in Portugal and applied a quantitative empirical study to evaluate their UX and answer this research question. Sixty users read the AR books and then answered the User Experience Questionnaire (UEQ) used as the research instrument of this study. We also compare the results with a benchmark. The results show a positive evaluation in almost all the means of the scales of the three AR books. Only in one book do we find a neutral evaluation in the scales of Stimulation and Novelty. Regarding the benchmark, the scales Attractiveness, Novelty and Stimulation achieved Excellent/Good category levels. On the other hand, the pragmatic quality aspects like Perspicuity, Dependably, and Efficiency revels a below “Good category” level, emphasizing the urgent need to improve the UX of AR books.
Default Factors in Motorcycle Sales in Developing Countries

A basic concept of loan is reconsidered all over the world by the new BIS regulations. However, many people in Latin America still have a vague way of thinking about loans. It is due to the global recession. As a result, companies have not been able to recover their manufacturing costs. However, a large potential market has been formed in Latin America. Therefore, the challenge for companies is how to formulate product strategies that can meet the needs of the market. Therefore, in this study, we create a classification model of whether customers will default or not. In addition, we explore the characteristics of the default customers. Propose a sales strategy for the product based on these characteristics. This would help companies to improve their financing problems and secure profits. In this study, we compare the accuracy of Logistic Regression, Random Forest and XGBoost. Since the data handled in this study were unbalanced data, data expansion by Synthetic Minority Over-sampling Technique (SMOTE) was effective. Finally, we analyze analyzes what variables contribute to the model by using SHapley Additive exPlanations (SHAP). From this analysis result, we will explore the characteristics of what kind of person is the loan unpaid customers. The variables with the highest contribution were the type of vehicle purchased, the area where the customer lives, and credit information. We propose sales strategy by focusing on the variables that are significant to the model.
Augmented-Reality-Based Real-Time Patient Information for Nursing

While the usage of digital systems in the medical sector has increased, nursing activities are still mostly performed without any form of digital assistance. Considering the complex and demanding procedures the medical personnel is confronted with, a high task load is expected which is prone to human errors. Solutions, however, need to match staff requirements and ideally involve them in the development process to ensure acceptance and usage. Based on desired application scenarios, we introduce a concept of an augmented reality (AR)-based patient data application that provides context-relevant information for nursing staff and doctors. Developed for the Hololens 2, the application allows the retrieval and synchronization of the patient data from the host network of the respective hospital information system. For this purpose, a system infrastructure consisting of several software components was developed to simulate the exchange between the AR device and the independent hospital environment. The paper outlines the conceptual approach based on requirements collected from nurses, related work, the technical implementation and discusses limitations and future developments.
Innovation in Shared Virtual Spaces for Mental Health Therapy

Rates of mental health disorders among adolescents and younger adults are on the rise [19] with the lack of widespread access remaining a critical issue [9]. It has been shown that teletherapy, defined as therapy delivered remotely with the use of a phone or computer system [20], may be a viable option to replace in-person therapy in situations where in-person therapy is not possible [21]. This research seeks to address the need for mental health therapy in situations where patients do not have access to traditional in-person care. This research investigates the use of Augmented Reality (AR) technology to provide a similar experience to in-person care while remaining remote. AR is an innovative technology that allows for virtual “holograms” to appear in the real world, blending the real and virtual worlds together. Exploratory research was focused on creating a minimum viable product (MVP) that could facilitate the basic level interaction between two or more users in a virtual space. By doing so, this paper served as a formative step toward developing AR teletherapy prototypes for larger scale studies. A user study was also conducted at a mid-size university (N = 48) to solicit opinions on AR technology as it relates to mental health therapy.
Ph: A Predictor of Interaction Networks Among Soil Organo-Mineral Fractions

Mineral-associated organic matter (MAOM) acts as the largest and longest-turnover carbon pool in soils, and understanding its dynamics and feedback with respect to climate change is of great significance for future climate change mitigation. MAOM is expected to have relatively little sensitivity to climate change due to mineral protection, but its persistence involves several organo-mineral fractions. The uncertainty in the response of specific organo-mineral fractions to climate change hampers the reliability of predictions of MAOM preservation in the future. Here, we applied a sequential chemical fractionation method integrated with network analysis to investigate MAOM stabilization mechanisms across five alpine ecosystems: alpine desert, alpine steppe, alpine meadow, alpine wetland, and alpine forest. The following results were obtained. Seven extractable organic matter (OM) fractions in MAOM were grouped into three OM clusters: a weak linkages cluster consisting of the water-soluble organic matter (WSOM) and weakly adsorbed fractions (7.5-21.8% TOC); an Fe and Al complex-strong linkages cluster comprising Fe and Al complexes and a massive fraction with strong linkages (9.1-21.2% TOC); and a Ca complex-mineral cluster composed of Ca complexes, carbonates and Fe oxyhydroxides (5.7-19.3% TOC). The relative percentages of OM in the three OM clusters exhibited distinct pH dependence patterns: with increasing pH, the weak linkages cluster, Fe and Al complex-strong linkages cluster and Ca complex-mineral cluster showed U-shaped, hump-shaped and monotonically increasing patterns, respectively. Moreover, the contribution of metal cations to MAOM stabilization was closely related to soil pH; that is, Ca contributed the most in alkaline soils, and Fe and Al contributed the most in acidic soils. Organo-mineral fractions and metal cations in MAOM constructed a complex network with pH as the central node. Precipitation not only altered vegetation type and microbial biomass but also regulated soil pH, which is balanced by specific metal cations, thus resulting in particular pH preferences of specific OM clusters. These findings demonstrate that soil pH plays a central role in modulating MAOM dynamics and that its climatic feedback can thus serve as a good predictor of soil organo-mineral fractions across ecosystems.
Animated Backgrounds on the Web Reduce Reading Speed: Some Empirical Evidence from a Remote Experiment

It is generally considered a bad practice to place animations as backgrounds to text. There are many convincing arguments against using animated backgrounds, yet there are few empirical studies that have assessed effects of animated backgrounds in the context of the web. This study therefore set out to collect empirical evidence to support the recommendation of avoiding animated backgrounds. A remote web-based controlled reading experiment was conducted. The results showed that an animated background led to a significant slower reading speed and lower preference scores. Hence, the empirical evidence supports the established practices.
Lyndon Interpolation for Modal Logic Via Type Elimination Sequences

This note describes a method for constructing Lyndon interpolants based on quasi-models and type elimination sequences. The same method was employed in [Benedikt, ten Cate and Vanden Boom, 2015] to compute optimal-size Lyndon interpolants for formulas in the guarded-fragment and the guarded-negation fragment. This note serves to showcase the method in a simpler setting, namely that of the basic modal language. To provide context, I also briefly survey some other general approaches that have been used to prove interpolation for modal logic in the past.I finish with a list of questions.
Are You There? A Study on Measuring Presence in Immersive Virtual Reality

With the recent development of virtual reality (VR) technologies, we can create a virtual environment that exceeds the constraints of the real environment. A concept called “presence” is one of the most important elements of a VR experience. To provide more interesting VR experiences, we need to measure how much the users feel their presence in the VR experiences is important. However, when we measure this presence by a questionnaire inside or outside the VR, the subjects’ sense of presence in virtual reality environments is disrupted by the transition from the VR experience to the questionnaire. Therefore, in this study, we propose a method to integrate questionnaires into VR experiences. We conduct two comparative experiments between the proposed method and the existing method to validate the effectiveness of our proposed method. From the results of the two experiments, although we cannot determine the optimal design for the proposed method and cannot say our proposed method can measure presence more accurately, we confirmed the partial effectiveness of the proposed method.
A Survey on Automated Machine Learning: Problems, Methods and Frameworks

Automated Machine Learning (AutoML) is a research field that automates machine learning processes and optimizes their costs. As machine learning begins to be widely used, many users in industry and academia are paying attention to AutoML. However, to satisfy the different demands in their various fields, the AutoML systems have been defined and studied differently according to several requirements. This paper classifies various characteristics of the AutoML systems into Hyperparameter Optimization (HPO), Combined Algorithm Search and Hyperparameter Optimization (CASH), and Machine Learning Pipeline Creation (MLPC) problems, and formulates their problem statements in a similar fashion. Moreover, we review the methods and frameworks widely used in the AutoML field in order to improve the practical understanding of the future direction of AutoML.
A Model for Sustainable Design Curatorial Approach in the Post-epidemic Era

In the post pandemic era, both design and design curation pay considerable attention on UN sustainable goals due to the social and environmental crisis. Based on the analyzing on nearly hundred exhibitions before and after the pandemic, this article concluded trends, methodologies and models for curatorial actives responding to emergencies and massive challenges of this era. Firstly, the author proposes the layer-cake model as the foundation of the curation. Secondly, the trends of the exhibition theme were concluded according to the analyze of typical sixty-six design exhibitions. Thirdly, the author innovates seven models of curatorial approaches dealing with uncertainty of the crisis age. The aim of this article is to draw attention of the necessity to bring up innovative and dynamic methods in face of the special era in both design and curatorial dialogue.
A Novel GBT-Based Approach for Cross-Channel Fraud Detection on Real-World Banking Transactions

The most recent research on hundreds of financial institutions uncovered that only $$26\%$$ of them have a team assigned to detect cross-channel fraud. Due to the developing technologies, various fraud techniques have emerged and increased in digital environments. Fraud directly affects customer satisfaction. For instance, only in the UK, the total loss of fraud transactions was £1.26 billion in 2020. In this paper, we come up with a Gradient Boosting Tree (GBT)-based approach to efficiently detect cross-channel frauds. As part of our proposed approach, we also figured out a solution to generate training sets from imbalanced data, which also suffers from concept drift problems due to changing customer behaviors. We boost the performance of our GBT model by integrating additional demographic, economic, and behavioral features as a part of feature engineering. We evaluate the performance of our cross-channel fraud detection method on a real banking dataset which is highly imbalanced in terms of frauds which is another challenge in the fraud detection problem. We use our trained model to score real-time cross-channel transactions by a leading private bank in Turkey. As a result, our approach can catch almost $$75\%$$ of total fraud loss in a month with a low false-positive rate.
Statistical Inference Using the Three-Parameter Generalized von Mises Distribution and Outlier Detection Method for Asymmetrically Distributed Circular Data

It is well known that circular data are rarely symmetrically distributed; therefore, symmetric distributions such as von Mises and wrapped Cauchy distributions are often not suitable in practice. Furthermore, studies of most existing approaches in outlier detection have focused on symmetrically distributed circular data and the identification of a single outlier. The three-parameter generalized von Mises distribution [12] was shown to be mathematically tractable and flexible enough to model asymmetrically distributed circular data. This paper provides an asymptotic simultaneous inference of the three parameters and a simulation study for the identification of multiple outliers in asymmetrically distributed circular data using a data depth approach. We illustrate the methods in this paper using 59 diherdral angles in 3CTJ protein.
A Pilot Experiment on Attention Prompt Method for Car Driving Based on VR Technology

Lack of attention is one of the main causes of traffic accidents at present. The use of virtual reality technology can simulate the driver’s driving environment. Because the driver undertakes a lot of cognitive load when driving in real time, such as searching, identifying and identifying traffic signals, traffic lights, pedestrians and other objects, the method of attracting the driver’s attention to a specific position of his field of vision becomes very important. In this study, three attention prompt methods are proposed and compared with traditional ones, and their feasibility is preliminarily tested. In order to carry out the experiment, the authors developed a virtual driving simulation system driven by the Unity3D creation engine. There are five kinds of attention prompt methods: “Point”, “Arrow”, “Night Vision”, “Blur” and “Dusk”. Among them, “Point” and “Arrow” are traditional ones, and the latter three are the methods proposed by the authors. The results show that compared with the traditional prompt methods, the “Blur” and “Dusk” have a certain effect on improving thes driver’s attention.
Extracting Domain Terms from Data Model Elements

In the compliance domain of tax laws, a barrier to term extraction from documents written in natural language is getting a sizable training set of documents to train a well-grounded term-extraction model. To alleviate term-extraction silence, i.e. the outcome of the automated process missing legitimate term candidates, we extract terms from a string datatype that is written in a quasi-natural language. Domain software applications rely on structured content in XML documents for their processes. One type of XML data is the element; elements have names typically written in a variant of a natural language. Term extraction restores the element names to the detected natural language. These extracted expressions are either novel terms for our terminology or are flagged as synonymous expressions to existing terms. Increasing term coverage improves semantic parsing, query understanding and explanation generation. For a subset of XML documents of one tax-domain software application, we augment the existing terminology by 49%.
A Study of Bilingual Oscillatory Display: Effect of Interference Language and Comparison with Language Switching Display

Multilingual display methods on digital signage can be broadly classified into simultaneous multilingual display and language switching display (LSD). However, these methods have problems such as reduced readability due to smaller font size and time spent waiting for the desired language to be displayed. To solve these problems, the authors have proposed bilingual oscillatory display (BOD), a method of simultaneous display of two languages using animation, to display both language without diminishing their font size in the same size of screen, either without waiting time for both native speakers of two languages. The objectives of this study were to examine the effect of interfering language difference in BOD, and to compare BOD and conventional LSD. An experiment was conducted to measure reading time of the target Japanese sentence displayed in BOD of Japanese and Korean BOD (JP-KR BOD), BOD of Japanese and Japanese (JP-JP BOD) and Japanese monolingual display (Single JP). As a result, reading time of JP-KR BOD was significantly shorter than JP-JP BOD and expected reading time of LSD calculated by single JP. Results suggested that readability of BOD was improved if the interference sentence was written in different language with the target sentence. It was also suggested that BOD could provide more information in a unit of time.
Future Experience Boundary and Interaction Design Integration Path

Our lives are still not back to normal because of the novel coronavirus epidemic, and people around the world are looking for ways to compensate for the limited experience. The global buzz around Metaverse can also be seen as a concern to upgrade the experience, and the trend toward virtual world interaction is unstoppable, in the future, the boundary between virtual world and real world will be a direction of further exploration in the field of interaction design. If the meta-universe can be developed into a future society, it should not be viewed as a mere technical question. Whether this future is worthy of human pursuit requires us to think more. So based on the future doomed to borderline ambiguous social forms, the establishment of a new order first needs to clarify the virtual world and the real world and the relationship between the difference. This paper critically interprets the concept of meta-cosmic society, and discusses the thinking of the boundary definition in the future context, it is hoped that this discussion will provide a valuable perspective for interaction design to be integrated into the new experience pattern in the future.
Cross-modal Representation Learning for Understanding Manufacturing Procedure

Assembling, biochemical experiments, and cooking are representatives that create a new value from multiple materials through multiple processes. If a machine can computationally understand such manufacturing tasks, we will have various options of human-machine collaboration on those tasks, from video scene retrieval to robots that act for on behalf of humans. As one form of such understanding, this paper introduces a series of our studies that aim to associate visual observation of the processes and the procedural texts that instruct such processes. In those studies, captioning is the key task, where input is image sequence or video clips and our methods are still state-of-the-arts. Through the explanation of such techniques, we overview machine learning technologies that deal with the contextual information of manufacturing tasks.
Do We Speak the Same Language? The Effect of Emojis on Learners in an Online Learning Environment

Emojis are quickly spreading in text-based communication. Accordingly, most students and teachers in an online learning environment integrate their online text communication with emojis to explain or support their perspective. However, because of the variation in students and teachers’ age, gender and culture, it was essential to ensure that students and teachers have the same understanding of used emojis. Consequently, we ran our study to ask students and teachers about their interpretations of the most common emojis. The results show moderate agreement between students and teachers. For example, was interpreted as ‘perfect’ by students and ‘punishment’ by teachers. This variation in interpretation must be resolved to ensure acceptable online communication between students and teachers.
A Learning Support System of Acquiring Four Skills to Succeed a Fundamental Trick of Kendama Named as “Around Japan”

The kendama is a traditional Japanese skill toy. It has been played for a long time in Japan. In addition, it is also well-known to foreigners, e.g. people in United States, Denmark, and Taiwan. Playing kendama might be an ice breaker between players and non-players and induce communication among them. In this study, we propose a learning support system of acquiring four skills to succeed a fundamental trick of kendama named as “around Japan”. The system measures the acquisition degree of each skill of users. When users do wrong movement during measurement, the system shows it by a voice message. In addition, after the measurement, the system shows graphs which express the acquisition degrees of each skill of users and shows text messages about the evaluation of each skill. As a result of experiment, it was not able to demonstrate that the system raised the success rate of “around Japan” significantly. However, the system made users keeping their motivation to acquire “knee bending and stretching skill” and “keeping hole insight skill”.
An Augmented Reality Update of a Classic Game: “Where in the World is Carmen Sandiego?”, Case Study

37 years after its original launch, it is proposed a study comparing the Augmented Reality (AR) prototype of the game “Where in the World is Carmen Sandiego?” with this classic edutainment game. The main goal of the experiment is to determine if there is a significant difference between the 13th mission (last level) of the game, and it was done with a sample of 20 individuals aged between 5 and 7 years. The results could be a promising future for both, gaming and education areas. The author used 3 investigation tools to reach the answer: Perceived Understanding, Perceived Usability and a version of the PSSUQ (Post-Study System Usability Questionnaire). A big constraint for this study was the COVID-19 pandemic that the world is living at the time it was completed, however, the use of technology played a big role to make it possible.
Revisiting the Past to Reinvent the Future: Topic Modeling with Single Mode Factorization

This paper proposes reexamining ancestors of modern topic modeling technique that seem to have been forgotten. We present an experiment where results obtained using six contemporary techniques are compared with a factorization technique developed in the early sixties and a contemporary adaptation of it based on non-negative matrix factorization. Results on internal and external coherence as well as topic diversity suggest that extracting topics by applying factorization methods on a word-by-word correlation matrix computed on documents segmented into smaller contextual windows produces topics that are clearly more coherent and show higher diversity than other topic modeling techniques using term-document matrices.