Reviewerly/exHarmony huggingface dataset:
Hand Gesture Based Touch-Free User Interface for Elevator/Lift

The user interfaces of control panels used for electronic automation have been limited to devices such as remote control, key pad and touch screen for a long time. These interfaces are not natural and nor flexible for users. Consumers are expecting natural efficient user interfaces. Recent improvements in sensor technology are now opening the path for developers to add gesture control to their products. This Project presents a switch control panel for Touch-free user interface using simple hand gestures technique to control operations of Elevator/Lift. This gesture-based switch control panel is not just limited to Elevator/Lift, but can also be implemented in other areas such as Vending machines, ATMs and many more with little changes in software &amp; hardware if required. To use this interactive system based on hand gestures, one does not require any prerequisite knowledge as in the case of touch-screens, keyboard etc.
Playtesting: What is Beyond Personas

Playtesting is an essential step in the game design process. Game designers use the feedback from playtests to refine their designs. Game designers may employ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">procedural personas</i> to automate the playtesting process. In this article, we present two approaches to improve automated playtesting. First, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">developing persona</i> , which allows a persona to progress to different goals. In contrast, the procedural persona is fixed to a single goal. Second, a human playtester knows which paths she has tested before, and during the consequent tests, she may test different paths. However, reinforcement learning (RL) agents disregard these previous paths. We propose a novel methodology that we refer to as alternative path finder (APF). We train APF with previous paths and employ APF during the training of an RL agent. APF modulates the reward structure of the environment, while preserving the agent’s goal. When evaluated, the agent generates a different trajectory that achieves the same goal. We use the general video game artificial intelligence and VizDoom frameworks to test our proposed methodologies. We use proximal policy optimization RL agent during experiments. First, we compare the playtest data generated by developing and procedural persona. Our experiments show that developing persona provides better insight into the game and how different players would play. Second, we present the alternative paths found using APF and argue why traditional RL agents cannot learn those paths.
Risky Profession: How U.S. Librarians Fight for Their Safety

In society, the concept of public library is traditionally associated with silence, peace and safety. However, modern realities indicate that the library profession is associated with many risks. As the experience of the United States shows, employees of public libraries are increasingly faced with problems that disrupt the normal functioning of library and information services and negatively affect the emotional and psychological state of staff and patrons. Ensuring safety of the library is a common concern, equally affecting both employees and patrons of the library. A threat to the safety of library is a potentially or actually possible event, action, process or phenomenon that can disrupt the stability of its operation or lead to shutdown of its functioning. The article considers the subjective threats, i. e. caused by the actions and behaviour of people. These can be mentally unbalanced, substance abusers or armed visitors, computer hackers, etc. Many of them pose a real danger to life and health of both librarians and library patrons. Based on the analysis of the U.S. periodical press, the author provides facts that clearly demonstrate the vulnerability of public library staff to the threats of the modern world: the risk of being killed, the risk of drug overdose of visitors, the risk of hacker attacks on library computers, the risk of developing post-traumatic stress disorder, etc. Library rules and regulations are created to protect the rights, interests and safety of users and employees. It is necessary to keep them up-to-date taking into account the socio-cultural changes in modern society and transformation of human behaviour. The article shows what measures are taken by U.S. public libraries to ensure the safety of employees and patrons.
Predictive Analytics Based on Open Source Technologies for Acute Respiratory Distress Syndrome

The continuous growth of high volumes of biomedical data in healthcare generates significant challenges for their efficient management. This data requires efficient management and analysis in order to derive meaningful and actionable information. Especially in the current situation of the COVID-19 pandemic, complications that might occur after the onset of this disease are important. Such a complication is Acute Respiratory Distress Syndrome (ARDS), which is a serious respiratory condition with high mortality and associated morbidity. A large number of basic and clinical studies demonstrated that early diagnosis and intervention are keys to improve the survival rate of patients with ARDS. Therefore, there is a pressing need for the development and clinical testing of predictive models for ARDS events, which might improve the clinical diagnosis or the management of ARDS. In this paper, we focus on two distinct objectives; namely a) to design a scalable data science platform, built on open source technologies able to streamline the development of such models, and b) to exploit the platform using publicly available big datasets to develop such models. To this direction, we employ random forests and logistic regression algorithmic models for the early prediction and diagnosis of ARDS. Our approach achieves better results in all metrics, when compared to relevant published efforts using the MIMIC III dataset.
The "art" of teaching study skills: a teacher or a student as a homo hubris in media space?

Dynamic cultural, economical, or scientific and technical transitions enforce transformation in educational processes. Currently, teaching study skills cannot be considered as simple field and certainly not a pleasant one. Nowadays, learning process does not consist in executing an educational programme rigidly, but in responding to emerging problems and in ability to create the right conditions to resolve them.
English Education Model Based on Artificial Intelligence

The development of artificial intelligence will further promote the intelligent revolution of the whole society. This research mainly discusses the design of English education model based on artificial intelligence. Taking constructivism as the basic guiding theory, and at the same time relying on the idea of 4C/ID model. Emphasize the creation of a real learning situation for learning spoken language, use the technological advantages of robots to stimulate students’ interest in learning, promote students’ learning motivation, and help teachers optimize learning strategies. The educational robot can monitor the student’s learning process when the student communicates individually, and establish a personal information database and a feature database for the student to provide personalized learning services. In this learning method, robots help teachers complete repetitive tools and provide more intelligent auxiliary functions, while teachers monitor the teaching process, check for deficiencies, and ensure the orderly progress of teaching activities. In terms of students’ language and cognitive ability, the post-test has an average improvement of 1.07 compared with the pre-test. This research contributes to the realization of intelligent classroom teaching methods.
Exact Physical Design of Quantum Circuits for Ion-Trap-based Quantum Architectures

Quantum computers exploit quantum effects in a controlled manner in order to efficiently solve problems that are very hard to address on classical computers. The ion-trap-based technology is a particularly advanced concept of realizing quantum computers with advantages with respect to physical realization and fault-tolerance. Accordingly, several physical design methods aiming at realizing quantum circuits to corresponding architectures have been proposed. However, all these methods are heuristic and cannot guarantee minimality. In this work, we propose a solution which can generate exact physical designs, i.e., solutions which require a minimal number of time steps. To this end, satisfiability solvers are utilized. Experimental evaluations confirm that, despite the underlying computational complexity of the problem, this allows to generate minimal physical designs for several quantum circuits for the first time.
Usage of a Graph Neural Network for Large-Scale Network Performance Evaluation

To quickly and accurately perform network evaluation, we propose a graph convolutional network-based performance evaluation method for ultralarge-scale networks. The learning results show that our method outperforms the fully connected network and convolutional neural network in the prediction error of the end-to-end latency and network throughput. In addition, we show that our method is significantly less time-consuming than traditional methods.
E-Voting Information System for the General Election of the Head of the Community with Black Box Testing and Dummy Variable Regression Analysis

General elections are basically still carried out conventionally, namely by using paper media in voting. This will lead to weaknesses in the calculation of votes such as a waste of money and time. This study aims to develop a website-based e-voting information system in the general election for the chairman of the RW which is expected to minimize the weaknesses of conventional elections. This system was developed to make the voting process more efficient and easy to use. This application is designed using the PHP programming language and MySQL database, using the waterfall method. By doing black box testing, it is hoped that the e-voting system can run well functionally without any error, and dummy variable testing to determine the influence of voters in selecting candidates based on graduation rate
Using Head-Mounted Displays for Virtual Reality: Investigating Subjective Reactions to Eye-Tracking Scenarios

Virtual reality head-mounted displays (HMDs) have recently incorporated eye-tracking into hardware design. For the present study, the Varjo VR-1 and HTC Vive Pro Eye HMDs were used for three eye-tracking scenarios; and immersion, simulator sickness, and visual discomfort questionnaires measured subjective reactions. A lack of research exists comparing HMDs equipped with eye-tracking capabilities, in terms of the subjective measures employed. In this study, the HMDs were investigated using between-subjects and within-subjects designs. For the between-subjects design, the independent variable was the HMD (i.e., Varjo VR-1 or HTC Vive Pro Eye) and the dependent variables were the immersion, simulator sickness, and visual discomfort measurements. For the within-subjects design, simulator sickness, and visual discomfort were evaluated across scenarios. Forty participants were asked to detect and confirm the same target stimulus in each of the three eye-tracking scenarios of increasing ecological complexity. For the results, non-parametric statistics were used, given non-normal data. Since between-groups differences were not found for immersion, simulator sickness, and visual discomfort, recommendations are provided for updating the survey measures. Within-groups differences for both simulator sickness and visual discomfort are explained for plausible cause and reduction methods. Correlation results indicate overlap between visual discomfort and simulator sickness; immersion lacked significant correlations with the other measures. Overall, survey updates and individual differences (e.g., ocular aspects) should be considered for future research and development.
Non-Functional Requirement Extraction by using Conceptual Graphs

In software engineering process, the representation of non-functional requirements (NFR) is one of major problems during analysis and design phase. Since NFR is difficult to be understood and may be interfered, conflicted, or contradicted with other NFRs. In addition, many of NFRs relate to the same quality attribute which leads to be the requirement misunderstanding problem. This paper presents a knowledge representation method for extracting the NFRs using the conceptual graphs. The proposed method enhances the understanding of NFR. The case study of billing system in telecommunication company is discussed for a feasibility and an effectiveness of the proposed method.
Vascular Intervention Training System Based on Electromagnetic Tracking Technology

We designed a virtual reality system which can assist training of vascular interventional surgery. The hardware including electromagnetic navigation, performs real-time guide wire position tracking and display. Through k-dimensional tree data structure we store and calculate the distance between guide wire tip and vascular centerline for security warning, avoiding guide wire tip penetration through vascular wall. The neural network model aims to predict the next operation of guide wire status in real time. The operation information will be displayed by virtual reality devices.
A Term Weighting Scheme Using Fuzzy Logic for Enhancing Candidate Screening Task

Habous, Amine Nfaoui, El HabibThe candidate screening is an essential task in the recruitment process. It is about choosing a suitable candidate that satisfies the recruiter requirements for a given job position. The evolution of information technologies leads to an increase in the use of the recruitment web portals by the candidates that apply for the job positions published in the job boards. Thus the candidate screening process automation becomes necessary to handle the enormous volume of CVs applying for the job positions. In Information Technology (IT) domain, the technology skills are the key competencies to identify the job profile; Consequently, they have priority to the candidate screening task. In this paper, we enhance the candidate screening task in the IT field. For this purpose, we propose a fuzzy-based weighting scheme using domain ontology for Information Retrieval (IR). Experimental results on a recruiter company data show the effective results of our proposed solution.
Smoothing Neural Network for Non‐Lipschitz Optimization with Linear Inequality Constraints

This paper presents a smoothing neural network to solve a class of non-Lipschitz optimization problem with linear inequality constraints. The proposed neural network is modelled with a differential inclusion equation, which introduces the smoothing approximate techniques. Under certain conditions, we prove that the trajectory of neural network reaches the feasible region in finite time and stays there thereafter, and that any accumulation point of the solution is a stationary point of the original optimization problem. Furthermore, if all stationary points of the optimization problem are isolated, then the trajectory converges to a stationary point of the optimization problem. Two typical numerical examples are given to verify the effectiveness of the proposed neural network.
One Stroke Alphanumeric Input Method by Sliding-in and Sliding-out on the Smartwatch Screen

We have developed a character input method for smartwatches with circular screen, that allows you to enter one alphanumeric character with a single stroke. With this method, characters are selected by the combination of the start and end positions of the stroke. On the standby screen, a square inscribed inside the circular display is assigned to the text area. The left, top, and right of the remaining area is split in two, with each segment used as a key. As a result, the key length is longer than the fingertip size of 10 mm. The screen occupancy of the keyboard is 36%. Each key is selected by slide-in, which is the operation of moving the fingertip that touches the outside of the screen inward. The slide-in ensures that it crosses the edge of the screen, so that passing of the fingertip can be detect with very thin keys. As the fingertip passes the edge of the screen, the screen is separated to 12 areas. Each area is assigned one alphanumeric character or symbol. By moving the fingertip to one area then releasing from the screen, the character assigned to that position is entered. The key assignment is based on the telephone keyboard. The two keys on the telephone keyboard are combined into one key in our method. As the slide-in passes through a key, the two numbers assigned to the key and the 6 or 7 alphabets associated with those numbers are displayed in each split area.
The Potential of Augmented Reality for Remote Support

This paper addresses the question whether an AR enabled remote service based on standard hardware offers benefits for remote support or not. To answer this question a lab experiment has been conducted with 66 participants to examine the current potential of AR for field service tasks. The authors compare the productivity of a pure video-based remote support system with the productivity of an AR enabled remote support system. The AR system offers real-time video sharing and additionally virtual objects that can be used by the remote experts as “cues” to guide the field engineer. Thirdly a system consisting of a head mounted display with cues has been tested. The results of the lab experiment show that the guided engineers could clearly benefit from AR based remote support. Nevertheless, the influence of both the engineer’s prior skillset as well as the remote experts’ ability to instruct is bigger than of the deployed tool.
Distributed filtering stochastic parameter learning algorithm for discrete-time random nonlinear systems with delay

Abstract Distributed filtering algorithm for a discrete time random nonlinear stochastic systems associated with state delay for the distributed wireless communication sensors are discussed in this paper. Stochastic Parameter Learning Algorithm (SPLA) is tend to aim at obtaining a collection of stochastic filter parameters in a finite limited time horizon, that minimize the traces of the upper limits which is permitted to reduce the error variance matrices of the concerned stochastic filter system’s states and delay measurements. Filter gain values of the filter derived by the determination of Riccati type difference equations, estimates systems states with delay. Two different filter rules are taken into account for the SPLA discrete time random nonlinear systems with steady state space equations model. Zero mean distinct covariance matrix along with the constructive state values and constant time delay are focused in compatible dimensions. The variance of the projected systems predicted noise and the actual estimated noise are validated through numerical examples.
Content Analysis of Twitter Data of College Students on Alcohol Consumption

Background: Alcohol use has become a serious threat for college students who show the highest rates of binge drinking, alcohol abuse, and alcohol dependence in the United States. Globally, alcohol misuse is a prominent concern for college students, which could have negative impacts on these students physically and psychologically including poorer academic and memory performance and more memory blackouts, higher unemployment rates, more sexual risk behaviours, and higher tendency of mental illness. However, little research has included the impacts from the most basic needs aspect – safety. And few researchers have considered using social media app as the research instrument with the prevalence of social media usage.Objectives: This study aims to determine the frequency of Tweets on news related to college students’ alcohol use, the typical effects of alcohol consumption on college students expressed in news, and the general attitude towards college students’ alcohol use.Methods: A manual content analysis was used to explore the mentioned effects and the general attitude of tweets of news on this topic collected by key word search method.Results: The study found that people shared news of the topic approximately twice a month, indicating a low awareness and concern level of public on college students’ alcohol consumption. Among seven types of effects mentioned in tweets, sexual risk was referred to most often. Moreover, people expressed a negative attitude towards college students’ alcohol use generally except a few who did not give any opinion on this topic.Conclusion: This study was among the first attempts to employ Twitter as the analysis instrument in the area of college students’ alcohol consumption. Practically, this study may provide college administrators with functional materials to deconstruct the negative impact of alcohol on campus.
Spheroids as Playful Audiovisual Interface on Tabletop Display

This paper presents a playful interactive system in which players manipulate solids of revolution called spheroids on tabletop display to create audiovisual expressions and make corresponding performance. Spheroids allow four basic movements based on spinning and rolling, whose geometric and physical parameters such as the contact area, its centroid, the (angular) velocity, and the curvature of locus are recognized from the data of the sensor sheet at the display based on electromagnetic induction. These parameters are effectively used for generating audiovisual expressions in real-time that match the movements. We developed an audiovisual content that integrates these functionalities and enables players to create their own expressions by manipulating a set of spheroids.
Layer Zero: An Approach for Deepening Self-reflection on Instagram Shares

Self-reflection is an essential part of personal well-being and development. One of the strategies to support reflective thinking is to revisit personal recordings. Existing and emerging social media platforms are powerful mediums for self-reflection. However, additional mechanisms may be necessary to support deepening reflective thinking through social media use. We explore the concept; Layer Zero, a tool that helps users record reflective logs when they share on Instagram through a question set, and present them later for reflection. This paper focuses on the data collection phase of Layer Zero. We conducted 2 user studies through Instagram with a total of 29 participants to understand the potential of Instagram shares for reflection and measure the effectiveness of questions on self-reflection. We found that our approach can help users engage in deeper reflective thinking about their motivations, actions and thought processes. The results indicate various design insights for future social media reflection tools.
On the Benefits of Two Dimensional Metric Learning

In this paper, we study two dimensional metric learning (2DML) for matrix data from both theoretical and algorithmic perspectives. We first investigate the generalization bounds of 2DML based on the notion of Rademacher complexity, which theoretically justifies the benefits of learning from matrices directly. Furthermore, we present a novel boosting-based algorithm that scales well with the feature dimension. Finally, we introduce an efficient rank-one correction algorithm, which is tailored to our boosting learning procedure to produce a low-rank solution to 2DML. As our algorithm works directly on the data in matrix representation, it scales well with the feature dimension, keeps the structure and dependence in the data, and has a more compact structure and much fewer parameters to optimize. Extensive evaluations on several benchmark data sets also empirically verify the effectiveness and efficiency of our algorithm.