Reviewerly/exHarmony huggingface dataset:
Usability and Playability Heuristics for Augmented Reality Video Games in Smartphones

Nowadays, Augmented reality technology is being used in many applications of the industry. This technology generates new user experiences by mixing elements of the real world with virtual objects. Its use in video games is one of the most prominent, which expands the ways in which players interact with the game and its characters. This article presents a proposal of usability and playability heuristics for the evaluation of augmented reality video games on smartphones. The proposal was validated with surveys and a case study, with the help of end users and usability experts.
Online Advertising with Verifiable Fairness

Online advertising is a popular business model where advertisers can deliver promotional marketing messages to their potential consumers via Ad brokers. However, as the proxy between advertisers and customers, a malicious Ad broker could arbitrarily fabricate the advertising rates to overcharge advertisers, which causes unnecessary financial loss. To deal with this issue, we propose a publicly verifiable and fair online advertising scheme. Specifically, a proof-of-downloading (PoD) protocol is first designed based on the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK), to help the customer generate a unique acknowledgment for downloading the Ad; the acknowledgment will then be published to both the advertiser and the Ad broker such that anyone can verify the acknowledgment to guarantee the fairness and transparency of online advertising. Moreover, as long as the customer's private key is not leaked, our scheme can resist the collusion attack, i.e., the Ad broker and the customer collude with each other to deceive the advertiser, which has not been addressed in previous works. Finally, we evaluate the performance of the proposed scheme to demonstrate its computational efficiency.
Primary School Students’ Science Inquiry Learning and Behavior Patterns While Exploring Augmented Reality Science Learning

Scientific inquiry has been recognized as an effective instructions ability for learners in science education. The present study aims to explore the behavioral patterns of the science inquiry learning process and the behavior patterns of conception construction in augmented reality (AR) science learning activities for primary school students. An AR-based inquiry learning activity of “Exploring the External Conditions of Seed Germination” was supported, which is an attempt to integrate virtual objects with real situations. Learners utilize the AR based learning materials to accumulate experiences and identify problems while observing and perceiving science concepts. They carry out scientific inquiry based on problems. The scientific inquiry learning can be implemented the following eight-step teaching strategies: constructing environment with AR, revealing misconception under experience, correcting mistakes through AR concepts, obtaining new knowledge by reflection and exploration, establishing the experimental scheme with AR, bringing the scheme to completion by modifying, solving scientific questions through AR, making progress under self-reflection. Students’ learning behaviors were analyzed by adopting behavioral sequence analysis. The results suggest that the eight-step teaching strategies may support learners’ inquiry learning processes to achieve a positive outcome for their self-efficacy in science inquiry learning.
Inquiring Evaluation Aspects of Universal Design and Natural Interaction in Socioenactive Scenarios

New technologies and ubiquitous systems present new forms and modalities of interaction. Evaluating such systems, particularly in the novel socioenactive scenario, poses a difficult issue, as existing instruments do not capture all aspects intrinsic to such scenario. One of the key aspects is the wide range of characteristics and needs of both users and technology involved. In this paper, we are concerned with aspects of both Universal Design (UD) and Natural User Interfaces (NUIs). We present a case study where we applied, within a socioenactive scenario, evaluation instruments relying on principles and heuristics from these areas. The scenario involved six children from a hospital that treats craniofacial deformities, playing in a rich interactive environment with displays and plush animals that respond to hugs. Our results based on the analysis of the evaluation conducted in the case study suggest informed recommendations of how to use the evaluation instruments in the context of socioenactive systems and their limitations.
Methodology for using games as an educational tool

To motivate today's students that have access to various learning online platforms and use computers and different mobile devices every day, as a teacher I am forced to constantly change and use a variety of teaching methods. This paper will describe the methodology for the following types of games: guessing games, conclusion games, competition games, memory games, knowledge and validating resources games, games played in pairs. These games are intended primarily for teaching computer science subjects. Activities listed in this paper can help create an active learning environment and encourage teachers to look further for the better solutions of their application.
Superhuman sports --- a testing ground for augmenting our senses

This article reviews recent developments in superhuman sports with regard to extending the human body and augmenting the playing field, and gives an outlook on future research directions.
Consideration of a Bayesian Hierarchical Model for Assessment and Adaptive Instructions

People appear to practice what they do know rather than what they do not know [1], suggesting a necessity of an improved assessment of multi-level complex skill components. An understanding of the changing knowledge states is also important in that such an assessment can support instructions. The changing knowledge states can be generally visualized through learning curves. These curves would be useful to identify and predict the learner’s changing knowledge states in multi-domains, and to understand the features of task/subtask learning. Here, we provide a framework based on a Bayesian hierarchical model that can be used to investigate learning and performance in the learner and domain model context—particularly a framework to estimate learning functions separately in a psychomotor task. We also take an approach of a production rule system (e.g., ACT-R) to analyze the learner’s knowledge and skill in tasks and subtasks. We extend the current understanding of cognitive modeling to better support adaptive instructions, which helps to model the learner in multi-domains (i.e., beyond the desktop) and provide a summary of estimating a probability that the learner has learned each of a production rule. We find the framework being useful to model the learner’s changing knowledge and skill states by supporting an estimate of probability that the learner has learned from a knowledge component, and by comparing learning curves with varying slopes and intercepts.
Convolutional Neural Networks Towards Diagnosis of Dermatosis

In the field of machine learning and image recognition, deep convolutional neural networks have become a powerful tool for solving practical problems. Based on convolutional neural network's fast speed and high accuracy, we applied it to the diagnosis of pigmented skin in the medical field. By training the models on the dataset of ISIC, we eventually achieved up to 93.1% accuracy rate. If applied as an actual auxiliary tool, the diagnostic accuracy of related illness will be greatly improved.
Hybrid Genetic Algorithm: Traveling Salesman Problem

A genetic algorithm has three main operators namely selection, crossover and mutation. Each operator has various sub operators. Selection of sub operator that can be applied on particular problem is difficult task. Thus this paper proposes a hybrid genetic algorithm (HGA). HGA algorithm finds the sub operators that can be applied on traveling salesman problem. After that it finds the threshold value. Based on threshold value it switches from one sub operator to other sub operator. The HGA algorithm score over existing genetic algorithm on traveling salesman problem on large number of cities.
Perceptual Loss Based Super-Resolution Reconstruction from Single Magnetic Resonance Imaging

Magnetic Resonance Imaging (MRI) can provide anatomical images of internal organs to facilitate early diagnosis of the disease. But the inherent defects of medical imaging system make the acquisition of HR medical images face many problems. One way to solve these problems is to use super-resolution reconstruction technique. We design a feed-forward full connection convolution neural network, which includes five convolution layers and five residual blocks. In addition, loss function based on perception is also used to solve the problem caused by mean square error loss function which cannot meet the human visual sense very well. This method realizes build-in 4 times magnification reconstruction and avoids the checkerboard artifacts, which are often occurred when using deconvolution layers to up-sample images in convolution neural networks (CNN). The effectiveness of the method is verified by experiments, and both the visual and numerical results are improved.
Conversational AIS as the Cornerstone of Hybrid Tutors

This paper describes the benefits of artificially intelligent conversational exchanges as they apply to multi-level adaptivity in learning technology. Adaptive instructional systems (AISs) encompass a great breadth of pedagogical techniques and approaches, often targeting the same domain. This suggests the utility of combining individual systems that share concepts and content but not form or presentation. Integration of multiple approaches within a unified system presents unique opportunities and accompanying challenges, notably, the need for a new level of adaptivity. Conventional AISs may adapt to learners within problems or between problems, but the hybrid system requires recommendations at the level of constituent systems as well. I describe the creation of a hybrid tutor, called ElectronixTutor, with a conversational AIS as its cornerstone learning resource. Conversational exchanges, when properly constructed and delivered, offer substantial diagnostic power by probing depth, breadth, and fluency of learner understanding, while mapping explicitly onto knowledge components that standardize learner modeling across resources. Open-ended interactions can also reveal psychological characteristics that have bearing on learning, such as verbal fluency and grit.
Management of Physiological Data Streams within Brain Disorder Rehabilitation

This paper developes a novel telemedicine solution for rehabilitation of balance disorders based on analysis and visualization of patient physiological data gathered by a set of sensors during the rehabilitation process. The training and physiological data sensing are based on two home-grown systems called Homebalance and Scope. The kernel functionality is implemented by the data stream management technology provided by the Esper system. Moreover, it supports training at homes integrated together and to specialized rehabilitation centers by the Dew/Cloud technology. A small core of early adopters is currently successfully conducting balance disorder rehabilitation according to methodology relying on the proposed approach.
Creating Enhanced User Experience Through Persona and Interactive Design: A Case of Designing a Motion Sensing Game

Motion sensing games are operated with body movements that are captured dynamically by somatosensory devices so that more authentic experiences can be gained for users compared with the traditional operations with keyboards or joysticks. The motion sensing game design involving interactive design, user cognition, information layout of product, interface, influences nearly every important aspect of user experience. To our knowledge, the main problems exist in the motion sensing game design are as follows: (1) motion sensing games, as new type of video games, lack guiding theories and methods for reference. (2) To enhance user experiences for players, it is necessary to increase the research on user perception of user l behaviors in early stages of the design. Therefore, inefficiency of game design arises due to lack of effective research methods. In addition, (3) it takes a very long time to develop the project and it also costs a lot. Only by determine users’ needs in early stages can these problems be resolved. Inspired by current user research methods within human-computer interaction, product design, and architecture, this paper argue that a persona that is created as a design tool can be used in the process of motion sensing game design. This paper ground the persona method through a design project, Ball Game, based on Kinect. This approach opens new avenues for the analysis and practice of user-centered interaction design of motion sensing games and improves the game experience significantly.
Improve Cutting Skill According to Skill and Difficulty Level

In this paper, we aim to measure the cutting skill for creative paper cutting and increase practice effect. The practice effect changes according to the difficulty level of the cutting pattern and the skill level of the user. The cutting pattern of the picture consists of a straight line and a curved line, and we generalized the index of difficulty (ID) based on Steering law. One of cutting skills that shows the difference between novices and experts is time to cut. Besides, we developed a system consisting of a drawing display and a stylus with a knife to measure the cutting movement times (MT). The system measures MT according to the ID of the cutting pattern. We confirmed skill improvements by measuring changes in MT with various patterns from three experiments. In test 1, we confirmed the degree of conformity between the straight and curve pattern based on the steering law. In test 2, we measured skill improvement in repeatedly cutting patterns. In test 3, we compared the change in skill improvement of novices and artists. In these tests, we measured the reduction rate of MT to investigate the effectiveness of practice with various ID. As a result, we confirmed the difference in practice efficacy according to each ID.
Lexicon-Mono-Seq, DOM Text Based Async MSA Viewer

In Bioinformatics, one of the most common problems is depiction of long amino-acid / DNA sequences and their comparison (Li et al., 2018).Routinely, these sequences are aligned to a reference and the data is stored in common formats such as clustal (Larkin et al., 2007).These alignments are called Multiple Sequence Alignments (MSA) and can be viewed with a variety of different tools.
A No-Pairing Proxy Re-Encryption Scheme for Data Sharing in Untrusted Cloud

To protect data sharing from leakage in untrusted cloud, proxy re-encryption is commonly exploited for access control. However, most proposed schemes require bilinear pair to attain secure data sharing, which gives too heavy burden on the data owner. In this paper, we propose a novel lightweight proxy re-encryption scheme without pairing. Our scheme builds the pre-encryption algorithm based on computational Diffie–Hellman problem, and designs a certificateless protocol based on a semi-trusted Key Generation Center. Theoretical analysis proves that our scheme is Chosen-Ciphertext-Attack secure. The performance is also shown to achieve less computation burden for the data owner compared with the state-of-the-art.
An Imperceptible Secure Transfer of Medical Images for Telemedicine Applications

Medical image transfer is an important task to transfer the patient’s medical observations to the doctors at remote areas to know patient condition through the Internet/open network for expert opinion. But information transmission through unsecured channel raises confidentiality problems for any image data. This paper addresses the issue of confidentiality for medical images. The proposed technique uses a hybrid combination of Stationary Wavelet Transformation (SWT) and Singular Value Decomposition of the original medical image for the embedding of Patient watermark into it. The robustness is improved by verifying the peak signal to noise ratio to avoid wrong diagnosis of medical conditions. The robust performance of the proposed scheme is evaluated for known common processing attacks such as Salt and pepper, Gaussian, Mean and Median Filters, Histogram Equalization etc. The method is robust for embedded watermark at the acceptable visual quality of the watermarked image.
Measuring Eye Movements in Learning Research: A Useful Window to Cognition

Analyzing the eye movements made while inspecting a visual environment can be of valuable help to get insight in the involved cognitive processes. With the help of state-of-the-art eye-tracking systems and using various paradigms, researchers are able to measure and interpret different eye movement parameters such as fixations, saccades, pupil size and scan paths when evaluating visual surfaces (e.g. in consumer research or user interface design). These analyses allow to widen the knowledge on complex processes, such as decision-making, or to investigate e.g. learning strategies. The current article aims at giving an overview of how eye-tracking and the analysis of eye movements, respectively, might be useful in the emerging and changing field of Learning Research: Firstly, the physiological and anatomical basics of eye movements are reflected, then the concept of eye-tracking and different technological systems for that purpose are introduced. Moreover, we will summarize eye movement parameters and finally end with a reflection of eye-tracking in Learning Research.
Acquisition and Preprocessing of Face Recognition Data

Face has always been the research object in the fields of biometric recognition, computer vision, computer graphics and animation generation. In face recognition research, the acquisition of three-dimensional face data is the first step, and the quality of the acquired data directly affects the follow-up research. With the further development of computer vision technology, there are some tools and new methods for data acquisition.
ONLINE COURSES: REVOLUTION IN EDUCATION OR SUCCESSFUL PR CAMPAIGN?

The article aims to describe the “online education” phenomenon, which has become popular in our country nowadays due to the development of distant and electronic learning systems. The paper summarizes the experience of the West European and American education systems, which influences directly the development of the Russian online education. The analysis of the advantages and shortcomings of online education leads the authors to the conclusion about the necessity to develop the so called “hybrid education” system, which is synthesis of traditional and electronic formats of teaching.
Running and Cycling Induced Fatigue on Wrapper vs. BLR Feature Selection for IBk Classification

Abstract Running and cycling fatigue causes muscle pains, cramps and accidental injuries. Previous studies had considered the importance of tri-axial accelerometer to detect fatigue motion in stability, balance and postural deviation aspects. While tri-axial accelerometer is important, the capability to predict running and cycling fatigue from the biomechanical attributes were unclear. Therefore, the study aims to (i) compare the featured attributes selected from wrapper approach and Binary Logistic Regression (BLR) on running and cycling datasets and (ii) perform IBk classification accuracy comparison on the feature selection attributes. Public running, experimental running and cycling induced fatigue datasets were employed to test the analysis. The most significant attributes identified in the public running was RMS_ML, followed by Range_ML and the cycle frequency in experimental running and cycling respectively. On 10 folds cross-validation classification test using the IBk algorithm in WEKA, accuracies for experimental running and cycling datasets were 93.1% and 90.5% from wrapper method, 65.6% and 76.2% from BLR respectively. Wrapper method performs better than BLR in data overfitting phenomenon. Findings reveal that the mediolateral variation at body trunk motion plays a major impact to predict fatigue running but fatigue cycling shows cycling frequency as the main attribute in fatigue cycling prediction.
Face Recognition Based on Local Binary Pattern Auto-correlogram

Face recognition mainly includes face feature extraction and recognition. Color is an important visual feature. Color correlogram (CC) algorithm is commonly used in the color-based image retrieval as a feature descriptor, but most of the existing methods based on CC have problems of high computational complexity and low retrieval accuracy. Aiming at this problem, this paper proposes an image retrieval algorithm based on color auto-correlogram. The new color feature vector which describes the global and spatial distribution relation among different colors is obtained in the CC feature matrix, thus reducing the computational complexity. Inter-feature normalization is applied in color auto-correlogram (CAC) to enhance the retrieval accuracy. The experimental result shows that this integrated method can reduce the computational complexity and improves real-time response speed and retrieval accuracy.
Optimization System for Dynamic Flight Planning for Groups of Drones using Cooperation with Mobile Recharge Bases by Means of Multiagent System and Recursive Auctions

This work presents a proposal for a cooperation system aimed to optimize flights of unmanned aerial vehicle like a quadricopter, applied to precision agriculture. The system uses technologies that allow the opening, which is the property of inserting and removing system elements at any time, and dynamicity, allowing the system to recover itself from adverse events or failures. It is also proposed a distributed optimization algorithm, that optimizes the number of points visited by the quadricopter, considering the limitation of it's autonomy. This work starts by presenting the techniques used to define the research problem, such as Problem Solving, Stakeholder Diagram, Evaluation Frame, Value Pie and Building Blocks of Culture. Next, it presents the Systematic Mapping and Systematic Review. These studies allowed to define the research problem, and propose a system to solve it, as to define the technologies used, such as Multiagent Systems, cognitive agents considering mental states, as beliefs, desires, and intentions, the negotiation among agents using FIPA Contract-Net protocol, and optimization using the proposed recursive auction algorithm. Finally, tests were developed to evaluate the proposed Multiagent System and the algorithm used to perform the recursive auctions. The Multiagent System guaranteed the opening of the system in tests with inclusion and exclusion of elements, the cognitive agents considering mental states allows the dynamicity of the system. The optimization using recursive auctions was tested in scenarios with 4, 9 and 16 points, and in all of these the optimal result was found. To minimize the processing time, as the number of message exchanges among the agents, two heuristics were proposed. After applying the heuristics, a reduction of up to 99% was achieved in the number of messages exchanged between agents in complex scenarios, like the one with 16 points.