Reviewerly/exHarmony huggingface dataset:
Sharing Augmented Reality Experience Between HMD and Non-HMD User

See-through type augmented reality (AR) head mounted displays (HMDs) allow for a highly immersive experience and are currently becoming widely used in e-commerce domains. Current AR systems mainly focus on improving the experience for the user who is wearing the HMD (HMD user), resulting in the exclusion of bystander without HMD (non-HMD user) from AR experience. We propose sharing the AR experience between the HMD user and non-HMD user by visualizing and synchronizing the virtual objects to a smartphone, thereby enabling non-HMD user to see and interact with the virtual objects on their smartphones directly. We also proposed a pre-experience system which provides users with a mobile AR based virtual product experience for enhancing online shopping. We share the experience between HMD user and non-HMD user to illustrate the concept of our approach, where the HMD user can interact with a virtual product when shopping online to get a better sense of the real product, and where the non-HMD user can have a similar AR experience through smartphones.
Targeted Kernel Networks: Faster Convolutions with Attentive Regularization

We propose Attentive Regularization (AR), a method to constrain the activation maps of kernels in Convolutional Neural Networks (CNNs) to specific regions of interest (ROIs). Each kernel learns a location of specialization along with its weights through standard backpropagation. A differentiable attention mechanism requiring no additional supervision is used to optimize the ROIs. Traditional CNNs of different types and structures can be modified with this idea into equivalent Targeted Kernel Networks (TKNs), while keeping the network size nearly identical. By restricting kernel ROIs, we reduce the number of sliding convolutional operations performed throughout the network in its forward pass, speeding up both training and inference. We evaluate our proposed architecture on both synthetic and natural tasks across multiple domains. TKNs obtain significant improvements over baselines, requiring less computation (around an order of magnitude) while achieving superior performance.
HOW DO TEACHERS INTEGRATE DIGITAL MEDIA INTO PEDAGOGICAL ACTION AND PROFESSIONAL SELF-IMAGE?

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 6369-6376ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.1523Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Neuronal Environmental Pattern Recognizer: Optical-by-Distance LSTM Model for Recognition of Navigation Patterns in Unknown Environments

The level of autonomy and intelligence of a robot is a fundamental characteristic of tasks related to service robotics, particularly due to its interaction with human environments. These environments tend to change constantly over time (dynamic environments) so a mapping turns out to be inefficient in the long run. A service robot must be able to perform basic actions (displacements in the environment) autonomously with the lowest computational requirement. As for indoor navigation, it is ideal for the robot to be able to identify certain characteristics in the environment that allow it to plan its movement. This paper proposes a direct data-driven path planner for small robots capable of producing control actions directly from sensor readings. The motion decision is based on the similarity of the captured data with respect to models previously stored on the robot. These models were developed for different types of obstacles (obstacles in different positions and free space) using deep regression. The proposed scheme was successfully evaluated on a real prototype in the laboratory.
Are My EHRs Private Enough? Event-Level Privacy Protection

Privacy is a major concern in sharing human subject data to researchers for secondary analyses. A simple binary consent (opt-in or not) may significantly reduce the amount of sharable data, since many patients might only be concerned about a few sensitive medical conditions rather than the entire medical records. We propose event-level privacy protection, and develop a feature ablation method to protect event-level privacy in electronic medical records. Using a list of 13 sensitive diagnoses, we evaluate the feasibility and the efficacy of the proposed method. As feature ablation progresses, the identifiability of a sensitive medical condition decreases with varying speeds on different diseases. We find that these sensitive diagnoses can be divided into three categories: (1) five diseases have fast declining identifiability (AUC below 0.6 with less than 400 features excluded); (2) seven diseases with progressively declining identifiability (AUC below 0.7 with between 200 and 700 features excluded); and (3) one disease with slowly declining identifiability (AUC above 0.7 with 1,000 features excluded). The fact that the majority (12 out of 13) of the sensitive diseases fall into the first two categories suggests the potential of the proposed feature ablation method as a solution for event-level record privacy protection.
Conf-Net: Predicting Depth Completion Error-Map For High-Confidence Dense 3D Point-Cloud.

This work proposes a new method for depth completion of sparse LiDAR datausing a convolutional neural network which learns to generate almost full 3Dpoint-clouds with significantly lower root mean squared error (RMSE) overstate-of-the-art methods. An almost dense high-confidence/low-variancepoint-cloud is more valuable for safety-critical applications specificallyreal-world autonomous driving than a dense point-cloud with high error rate andhigh variance. We examine the error of the standard depth completion methodsand demonstrate that the error exhibits a long tail distribution which can besignificantly reduced if a small portion of the generated depth points can beidentified and removed. We add a purging step to our neural network and presenta novel end-to-end algorithm that learns to predict a high-quality error-map ofits prediction. Using our predicted error map, we demonstrate that byup-filling a LiDAR point cloud from 18,000 points to 285,000 points, versus300,000 points for full depth, we can reduce the RMSE error from 1004 to 399.This error is approximately 60% less than the state-of-the-art and 50% lessthan the state-of-the-art with RGB guidance. We only need to remove 0.3% of thepredicted points to get comparable results with the state-of-the-art which hasRGB guidance. Our post-processing step takes the output of a standardencoder-decoder network, to generate high resolution 360 degrees densepoint-cloud. In addition to analyzing our results on Kitti depth completiondataset, we demonstrate the real-world performance of our algorithm using datagathered with a Velodyne VLP-32C LiDAR mounted on our vehicle to verify theeffectiveness and real-time performance of our algorithm for autonomousdriving. Codes and demo videos are available athttp://github.com/hekmak/Conf-net.
Developing a Prototype to Translate Pakistan Sign Language into Text and Speech While Using Convolutional Neural Networking

The purpose of the study is to provide a literature review of the work done on sign language in Pakistan and the world.This study also provides a framework of an already developed prototype to translate Pakistani sign language into speech and text while using convolutional neural networking (CNN) to facilitate unimpaired teachers to bridge the communication gap among the deaf learners and unimpaired teachers.Due to the lack of sign language teaching, unimpaired teachers face difficulty in communicating with impaired learners.This communication gap can be filled with the help of this translation tool.Research indicates that a prototype has been evolved that can translate the English textual content into sign language and highlighted that there is a need for translation tool which can translate the signs into English text.The current study will provide an architectural framework of the Pakistani sign language to English text translation tool that how different components of technology like deep learning, convolutional neural networking, python, tensor Flow, and NumPy, InceptionV3 and transfer learning, eSpeak text to speech help in the development of a translation tool prototype.
Level sets and drift estimation for reflected Brownian motion with drift

We consider the estimation of the drift and the level sets of the stationary distri- bution of a Brownian motion with drift, reflected in the boundary of a compact set $S\subset R^d$ , departing from the observation of a trajectory of this process. We obtain the uniform consistency and rates of convergence for the proposed kernel based estimators. This problem has relevant applications in ecology, in estimating the home-range and the core-area of an animal based on tracking data. Recently, the problem of estimat- ing the domain of a reflected Brownian motion was considered in Cholaquidis, et al. (2016), when the stationary distribution is uniform and the estimation of the core-area, defined as a level set of the stationary distribution, is meaningless. As a by-product of our results, we obtain an estimation of the drift function. In order to prove our re- sults, some new theoretical properties of the reflected Brownian motion with drift are obtained, under fairly general assumptions. These properties allow us to perform the estimation for flexible regions close to reality. The theoretical findings are illustrated on simulated and real data examples.
CROSS-CULTURAL TRAINING PROGRAMS INTEGRATION WITHIN THE MODEL OF MULTICULTURAL PERSONALITY DEVELOPMENT

CROSS-CULTURAL TRAINING PROGRAMS INTEGRATION WITHIN THE MODEL OF MULTICULTURAL PERSONALITY DEVELOPMENT
Binomial exponential sums

We obtain new bounds of exponential sums modulo a prime $p$ with binomials $ax^k + bx^n$. In particular, for $k=1$, we improve the bound of Karatsuba (1967) from $O(n^{1/4} p^{3/4})$ to $O\left(p^{3/4} + n^{1/3}p^{2/3}\right)$ for any $n$, and then use it to improve the bound of Akulinichev (1965) from $O(p^{5/6})$ to $O(p^{4/5})$ for $n | (p-1)$. The result is based on a new bound on the number of solutions and of degrees of irreducible components of certain equations over finite fields.
Linear complexity of Ding-Helleseth generalized cyclotomic sequences of order eight

During the last two decades, many kinds of periodic sequences with good pseudorandom properties have been constructed from classical and generalized cyclotomic classes, and used as keystreams for stream ciphers and secure communications. Among them are a family DH-GCSd of generalized cyclotomic sequences on the basis of Ding and Helleseth’s generalized cyclotomy, of length pq and order $d={\gcd }(p-1,q-1)$ for distinct odd primes p and q. The linear complexity (or linear span), as a valuable measure of unpredictability, is precisely determined for DH-GCS8 in this paper. Our approach is based on Edemskiy and Antonova’s computation method with the help of explicit expressions of Gaussian classical cyclotomic numbers of order 8. Our result for d = 8 is compatible with Yan’s low bound (pq − 1)/2 on the linear complexity for any order d, which is high enough to resist attacks of the Berlekamp–Massey algorithm. Finally, we include SageMath codes to illustrate the validity of our result by examples.
A New Three Party Authenticated Key Agreement Protocol which is Defiant towards Password Guessing Attack

In order to develop a ‘common session secret key’ though the insecure channel, cryptographic Key Agreement Protocol plays a major role. Many researchers’ cryptographic protocol uses smart card as a medium to store transaction secret values. The tampered resistance property of smart card is unable to defend the secret values from side channel attacks. It means a lost smart card is an easy target for any attacker. Though password authentication helps the protocol to give secrecy but on-line as well as off-line password guessing attack can make the protocol vulnerable. The concerned paper manifested key agreement protocol based on three party authenticated key agreement protocol to defend all password related attacks. The security analysis of our paper has proven that the accurate guess of the password of a legitimate user will not help the adversary to generate a common session key.
A Study on the Relationship between NCS Education-Based Job Basic Capability and Progress Level of Students at Beauty Specialization High School.

본 연구의 목적은 미용특성화고등학교 학생의 직업기초능력과 진로결정수준과의 관계에 대하여 알아보는 것이다. 연구를 위하여 전국의 미용특성화교육 고등학교에 재학 중인 고등학생들 중 전라북도, 충청북도에 거주하는 학생 321명 대상으로 2017년 4월 9일부터 2017년 4월 22일까지 실시하였다. 연구의 결과로는 미용특성화고등학교 학생의 직업기초능력은 대인관계능력이 가장 높았다. 직업기초능력의 모든 요인사이에 유의미한 정의 상관관계가 나타났고, 그 중 문제해결능력과 자기개발능력이 가장 높은 상관관계가 나타났다. 또한 직업기초능력과 진로결정 수준과의 상관관계와 영향을 살펴본 결과에서는 직업기초능력의 모든 요인과 진로확신수준에 의미 있는 상관관계가 나타났다. 특히 직업기초능력 중 자기개발능력이 높을수록 진로결정 수준이 매우 높은 것으로 나타났다. 그러므로 본 연구는 지식기반 미용분야산업에 대한 진로결정능력을 향상시키기 위하여 직업기초능력의 자기개발능력 향상을 위한 교육과정의 프로그램 개발 및 적용이 필요하다는 것을 시사한다.The purpose of this study is to investigate the correlation between job basic capability and career-setting levels of students at beauty-specialized high schools. 321 of beauty-specialized high schools students who are resident in North Jeolla and North Chungcheong provinces were participated from April 9, 2017 to April 22, 2017. According to the results of the study, beauty-specialized high school students showed high rank in interpersonal capacity among the job basic capability factor. Additionally, all the factors of job basic capacity showed remarkable correlation and significant strength in problem solving and self-development capability. Accordingly, correlation and influence between the job basic capability and the level of career decision showed that there was a meaningful correlation between all factors of job basic capability and the level of career assurance. In particular, students with the higher self-development capacity among job basic capability showed the higher the level of career decision-making. Therefore, this study suggests that education courses for self-development of job basic capability need to be well developed and applicable in order to perform the professional duties of knowledge-based beauty industries.
New Results on the Zero-Visibility Cops and Robber Game

We study the zero-visibility cops and robber game, a variant of the cops and robber game in which the robber is invisible. We give a method for proving lower bounds on the zero-visibility cop number. Using this method, we investigate graph joins, lexicographic products of graphs, complete multipartite graphs and split graphs. Lower bounds and upper bounds, along with rigorous proofs, are given for the zero-visibility cop number of each of these types of graphs.
Product recommendation system based user purchase criteria and product reviews

&lt;span lang="EN-US"&gt;In this paper, we propose a system that provides customized product recommendation information after crawling product review data of internet shopping mall with unstructured &lt;/span&gt;&lt;span lang="EN-US"&gt;data&lt;/span&gt;&lt;span lang="EN-US"&gt;, morphological analysis using Python. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product.&lt;/span&gt;&lt;span lang="EN-US"&gt; And extracts and analyzes only the review including the purchase criterion selected by the user among the product reviews left by other users. The positive and negative evaluations contained in the extracted product review data are quantified and using the average value, we extract the top 10 products with good product evaluation, sort and recommend to users. And provides user-customized information that reflects the user's preference by arranging and providing a center around the criteria that the user occupies the largest portion of the product purchase. This allows users to reduce the time it takes to purchase a product and make more efficient purchasing decisions.&lt;/span&gt;
Graph and String Parameters: Connections Between Pathwidth, Cutwidth and the Locality Number

We investigate the locality number, a recently introduced structural parameter for strings (with applications in pattern matching with variables), and its connection to two important graph-parameters, cutwidth and pathwidth. These connections allow us to show that computing the locality number is NP-hard but fixed-parameter tractable (when the locality number or the alphabet size is treated as a parameter), and can be approximated with ratio O(sqrt{log{opt}} log n). As a by-product, we also relate cutwidth via the locality number to pathwidth, which is of independent interest, since it improves the best currently known approximation algorithm for cutwidth. In addition to these main results, we also consider the possibility of greedy-based approximation algorithms for the locality number.
Multiple Function Approximation — A New Approach Using Asymmetric Complex Fuzzy Inference System

This paper proposes an asymmetric complex fuzzy inference system (ACFIS) that improves a conventional fuzzy inference system (FIS) in two ways. First, the proposed model uses the novel neural-net-like aim–object parts, making the model flexible, in terms of model size of parameters and terse asymmetric structure. Second, the enhanced complex fuzzy sets (ECFSs) are used to expand membership degree from a single real-valued state to complex-valued vector state. Hence, the ACFIS can have the ability to predict multiple targets simultaneously. In addition, a hybrid learning algorithm, combining the particle swarm optimization (PSO) and the recursive least-square estimator (RLSE), is utilized to optimize the proposed model. To test the proposed approach, we did experimentation on four-function approximation using one single model only with 10 repeated trails. Based on the experimental results, the ACFIS has shown excellent performance.
Collaborative Recommendation for Scenic Spots Based on Distance

In the collaborative filtering recommendation algorithm, the similarity calculation plays an important role in the recom-mendation quality. For the traditional collaborative filtering recommendation algorithm, the similarity calculation is performed by a single user score, and the user's demand for the item cannot be accurately reflected. In order to solve this problem, the research proposes a distance-based scenic recommendation algorithm. The algorithm introduces the distance between the user and the item when performing the similarity calculation, then calculating the user's score on target scenic spots for recommendation. The experimental results show that, compared with the traditional collaborative filtering recommendation algorithm based on user score, the result of the distance-based scenic spot recommendation algorithm have some improvement in root-mean-square error, mean-absolute error, coverage, precision and f-measure.
Spatial-aware global contrast representation for saliency detection

Deep learning networks have been demonstrated to be helpful when used in salient object detection and achieved superior performance than the methods that are based on low-level hand-crafted features. In this paper, we propose a novel spatial-aware contrast cube-based convolution neural network (CNN) which can further improve the detection performance. From this cube data structure, the contrast of the superpixel is extracted. Meanwhile, the spatial information is preserved during the transformation. The proposed method has two advantages compared to the existing deep learning-based saliency methods. First, instead of feeding the deep learning networks with raw image patches or pixels, we explore the spatial-aware contrast cubes of superpixels as training samples of CNN. Our method is superior because the saliency of a region is more dependent on its contrast with the other regions than its appearance. Second, to adapt to the diversity of a real scene, both the color and textural cues are considered. Two CNNs, color CNN and textural CNN, are constructed to extract corresponding features. The saliency maps generated by two cues are concatenated in a dynamic way to achieve optimum results. The proposed method achieves the maximum precision of 0.9856, 0.9250, and 0.8949 on three benchmark datasets, MSRA1000, ECSSD, and PASCAL-S, respectively, which shows an improvement of performance in comparison to the state-of-the-art saliency detection methods.
Comparison of the change in interpretative stances of lifelog photos versus manually captured photos over time

Purpose The purpose of this paper is to explore how people differently create meaning from photos taken by either a lifelogging camera (LC) (i.e. automatic capture) or a mobile phone camera (MC) (i.e. manual capture). Moreover, the paper investigates the different changes in the interpretative stance of lifelog photos and manually captured photos over time to figure out how the LC application could support the users’ iconological interpretation of their past. Design/methodology/approach A 200-day longitudinal study was conducted with two different user groups that took and reviewed photos taken by either a LC or a MC. The study was structured in two phases: a photo collection phase, which lasted for five days (Day 1‒Day 5), and a three-part semi-structured interview phase, which was conducted on Days 8, 50 and 200. Findings Results revealed that the interpretative stance of the LC group changed greatly compared to the MC group that kept a relatively consistent interpretative stance over time. A significant difference between the two groups was revealed on Day 200 when the lifelog photos provoked a more iconological and less pre-iconographical interpretative stance. This stance allowed the viewers of lifelog photos to systemically interpret the photos and look back upon their past with different viewpoints that were not recognized before. Originality/value This paper contributes to further understand the dynamic change in interpretative stance of lifelog photos compared to manually captured photos through a longitudinal study. The results of this study can support the design guidelines for a LC application that could give opportunities for users to create rich interpretations from lifelog photos.
Learning Units-of-Measure from Scientific Code

CamFort is our multi-purpose tool for lightweight analysis and verification of scientific Fortran code. One core feature provides units-of-measure verification (dimensional analysis) of programs, where users partially annotate programs with units-of-measure from which our tool checks consistency and infers any missing specifications. However, many users find it onerous to provide units-of-measure information for existing code, even in part. We have noted however that there are often many common patterns and clues about the intended units-of-measure contained within variable names, comments, and surrounding code context. In this work-in-progress paper, we describe how we are adapting our approach, leveraging machine learning techniques to reconstruct units-of-measure information automatically thus saving programmer effort and increasing the likelihood of adoption.