Reviewerly/exHarmony huggingface dataset:
Quantitative Based Mechanism for Resolving Goals Conflicts in Goal Oriented Requirement Engineering

Software requirements elicitation is the base of the software development process and involves multiple stakeholders from the same organization, each having its own requirements and priorities, which may be conflicting with the requirements and priorities of the other stakeholders for the same system. Resolving these conflicts in requirements is necessary to ensure the development of a successful software system. In this paper we will review different models and techniques available in academia to resolve these conflicts in stakeholder goals. We will also discuss strengths and weaknesses of each of the solution. Finally, we will present an optimized solution for resolving these conflicts at requirements level.
Emotional evaluation for pictures displayed with small FOV telescope environment in virtual reality headset

We carried out an experiment to examine emotional effect associated with images displayed in small field of view (FOV) telescope like virtual screen environment with VR-HMDs. We manipulate FOV of a virtual camera in VR to change the view of contents. Decreasing FOV makes view of angle narrow, and therefore, the view in VR looks like telescope, in which environment the view greatly moves with even small HMD movement. With smaller value of FOV, the view in VR is more sensitive to HMD movement and requires subjects to keep concentrating not to move their heads to watch target contents properly. Stronger emotional responses were observed with smaller value of FOV in the experiment. The result indicates the possibility of controlling effectiveness of the same contents with the same VR-HMD. This result is expected to improve a wide range of medical usage of VR contents.
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions

The objective of goodness-of-fit testing is to assess whether a dataset of observations is likely to have been drawn from a candidate probability distribution. This paper presents a rank-based family of goodness-of-fit tests that is specialized to discrete distributions on high-dimensional domains. The test is readily implemented using a simulation-based, linear-time procedure. The testing procedure can be customized by the practitioner using knowledge of the underlying data domain. Unlike most existing test statistics, the proposed test statistic is distribution-free and its exact (non-asymptotic) sampling distribution is known in closed form. We establish consistency of the test against all alternatives by showing that the test statistic is distributed as a discrete uniform if and only if the samples were drawn from the candidate distribution. We illustrate its efficacy for assessing the sample quality of approximate sampling algorithms over combinatorially large spaces with intractable probabilities, including random partitions in Dirichlet process mixture models and random lattices in Ising models.
An investigation of benchmark image collections: how different from digital libraries?

Purpose This paper aims to introduce the construction methods, image organization, collection use and access of benchmark image collections to the digital library (DL) community. It aims to connect two distinct communities: the DL community and image processing researchers so that future image collections could be better constructed, organized and managed for both human and computer use. Design/methodology/approach Image collections are first identified through an extensive literature review of published journal articles and a web search. Then, a coding scheme focusing on image collections’ creation, organization, access and use is developed. Next, three major benchmark image collections are analysed based on the proposed coding scheme. Finally, the characteristics of benchmark image collections are summarized and compared to DLs. Findings Although most of the image collections in DLs are carefully curated and organized using various metadata schema based on an image’s external features to facilitate human use, the benchmark image collections created for promoting image processing algorithms are annotated on an image’s content to the pixel level, which makes each image collection a more fine-grained, organized database appropriate for developing automatic techniques on classification summarization, visualization and content-based retrieval. Research limitations/implications This paper overviews image collections by their application fields. The three most representative natural image collections in general areas are analysed in detail based on a homemade coding scheme, which could be further extended. Also, domain-specific image collections, such as medical image collections or collections for scientific purposes, are not covered. Practical implications This paper helps DLs with image collections to understand how benchmark image collections used by current image processing research are created, organized and managed. It informs multiple parties pertinent to image collections to collaborate on building, sustaining, enriching and providing access to image collections. Originality/value This paper is the first attempt to review and summarize benchmark image collections for DL managers and developers. The collection creation process and image organization used in these benchmark image collections open a new perspective to digital librarians for their future DL collection development.
Usage of Information Technology on Employee Commitment: Airport &amp; Aviation Industry in Sri Lanka (AASL)

Aims: The objective of the study was to examine how Information Technology (IT) affects the commitment of employees in Airport &amp; Aviation Industry in Sri Lanka (AASL). &#x0D; Study Design: This study is primarily a survey research, relying mainly on the collection and analysis of primary data collected through a questionnaire.&#x0D; Methodology: Based on the review of theoretical background and the literature and exploratory interviews with the participants, three determinant on commitment were identify in this study, including usage of IT, IT infrastructure, IT knowledge management. The population of study constituted of 4000 employees in 19 departments in AASL as a population of interest. The study employed purposive sampling method to select six department from 19 in the population and then random sampling method was used to select 250. Structured questionnaires were used in collecting data. The results of Cronbach’s alpha values are greater than 0.7 for all variables. To analysis data, descriptive statistics, correlation analysis, ANOVA and multiple regression analysis were used.&#x0D; Results: Four hypotheses were tested and all were accepted. Results indicate that employee background on IT, IT infrastructure and IT knowledge management are affecting by 50.4 percent to employee commitment. In addition, results showed that there is a positive and moderate relationship between employee background on IT and IT knowledge management with employee commitment. Moreover, there is a positive and weak relationship between IT infrastructure and employee commitment. The study concluded that Employee background on IT, IT infrastructure and IT knowledge management factors take part in a critical role in influencing the level of employee commitment is a conclusion that was made.
Vehicle Activity Recognition using Mapped QTC Trajectories

The automated analysis of interacting objects or vehicles has many uses, including autonomous driving andsecurity surveillance. In this paper we present a novel method for vehicle activity recognition using DeepConvolutional Neural Network (DCNN). We use Qualitative Trajectory Calculus (QTC) to represent the relativemotion between pair of vehicles, and encode their interactions as a trajectory of QTC states. We thenuse one-hot vectors to map the trajectory into 2D matrix which conserves the essential position informationof each QTC state in the sequence. Specifically, we project QTC sequences into a two dimensional imagetexture, and subsequently our method adapt layers trained on the ImageNet dataset and transfer this knowledgeto the activity recognition task. We have evaluated our method using two different datasets, and shownthat it out-performs state-of-the-art methods, achieving an error rate of no more than 1.16%. Our motivationoriginates from an interest in automated analysis of vehicle movement for the collision avoidance application,and we present a dataset of vehicle-obstacle interaction, collected from simulator-based experiments.
FORMATION AND STRUCTURE OF Y. V. BUTOROVA’S ARCHIVAL FUND IN SYZRAN BRANCH OF THE CENTRAL STATE ARCHIVES OF SAMARA REGION

The article is devoted to studying the formation and structure of the archival fund of Y. V. Butorova, a participant of the First World War. The author describes the fund’s history, considers the stages of its formation, analyses the typology of documents. Relying on archeographic methods the researcher proposes headlines for the fund documents, characterizes the peculiarities of their arrangement. Special attention is paid to Y. V. Butorova’s diaries, their origin and value as a historical source. The paper identifies the basic trends of studying Y. V. Butorova’s fund documents in domestic scientific and popular scientific literature.
Sentiment Analysis of Student’s Facebook Posts

Students are the major part of the colleges/universities and a beneficial of the institutions. Students reviews and opinions are important to improve the institutional problem, matters, and issues. The success of any college/university is to increase the students’ satisfaction level and it’s good for increasing the ranking of the institution. Its paper target the students’ sentiments post on Facebook colleges/university groups to express their behaviors, opinions, and views related curriculum and extra curriculum activities. Developing an automated system, use the students’ post of the Facebook group to implement the Novel approach. This paper uses the dataset that is based on the issues of the National College of Business Administration & Economics. This research work provides the automated system to detect the students’ post of related issues is positive, negative and neutral. Developing tool provides the best outcomes to improve the related issues, matters and institution’s policies.
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation

Nowadays, the majority of state of the art monocular depth estimation techniques are based on supervised deep learning models. However, collecting RGB images with associated depth maps is a very time consuming procedure. Therefore, recent works have proposed deep architectures for addressing the monocular depth prediction task as a reconstruction problem, thus avoiding the need of collecting ground-truth depth. Following these works, we propose a novel self-supervised deep model for estimating depth maps. Our framework exploits two main strategies: refinement via cycle-inconsistency and distillation. Specifically, first a \emph{student} network is trained to predict a disparity map such as to recover from a frame in a camera view the associated image in the opposite view. Then, a backward cycle network is applied to the generated image to re-synthesize back the input image, estimating the opposite disparity. A third network exploits the inconsistency between the original and the reconstructed input frame in order to output a refined depth map. Finally, knowledge distillation is exploited, such as to transfer information from the refinement network to the student. Our extensive experimental evaluation demonstrate the effectiveness of the proposed framework which outperforms state of the art unsupervised methods on the KITTI benchmark.
Design of Mobile Control system for wheeled Mobile Robot based on embedded ARM Technology

The mobile control of the wheeled mobile robot is affected by local disturbance error, which leads to low control precision. A mobile control model of wheeled mobile robot based on RTK-GPS (real-time kinematic GPS) is proposed. The mobile control system of wheeled mobile robot is designed with embedded ARM technology as the core processor of controller. Firstly, the mobile control algorithm of wheeled mobile robot is designed. The attitude determination parameter of wheeled mobile robot is taken as the control constraint parameter, and the attitude determination control of the wheeled mobile robot and the path map traversal of attitude parameter output are processed. According to the coordinate position of the locus map, the robot's moving location is carried out, and the trajectory following adjustment is realized by RTK-GPS technology, and the control law is optimized. Then the hardware of the control system is developed under the embedded ARM technology integrated information processing environment. The robot attitude adjusting system, the central integrated control module and the man-machine interaction module are designed and described in detail. The system test results show that the proposed method has better output performance, lower attitude determination error and better control convergence of wheeled mobile robot.
CapMat

We present CapMat, a smart foot mat that enables user identification, supporting applications such as multi-layer authentication. CapMat leverages a large form factor capacitive sensor to capture shoe sole images. These images vary based on shoe form factors, the individual wear, and the user's weight. In a preliminary evaluation, we distinguished 15 users with an accuracy of up to 100%.
PENINGKATAN DISIPLIN SISWA DALAM MENAATI TATA TERTIB DENGAN MENGGUNAKAN TEKNIK MODELLING MELALUI LAYANAN PENGUASAAN KONTEN DI SMPN 49 JAKARTA PADA SISWA KELAS 8 TAHUN AJARAN 2018-2019

&#x0D; &#x0D; &#x0D; Discipline can be said as an activity or business that is made because it is considered important and done in order to achieve certain positive things. Discipline is part of the attitude and behavior that characterizes compliance with a rule or provision. Discipline means a demand for the implementation of a regular, the same and orderly life which is made an absolute price so that progress can be made and changes to things that are more positive and good. The research aims to obtain the stages of student discipline. When content is categorized as medium with a percentage of 66.6% after obtaining content mastery service, content mastery services increase to 77.6%, through the t test 2.04. The results obtained prove that content mastery services with modeling techniques can improve discipline in students. The study was a class 8 student at SMPN 49 Jakarta with a total of 32 respondents. The research conclusions were obtained tt 10.67 &gt; t table-n using experiments. Population in this research using modeling techniques through mastery services&#x0D; Keyword: discipline, code of conduct, telkmile modeling, content improvement&#x0D; &#x0D; &#x0D;
Kernelized Spectral Clustering based Conditional MapReduce function with big data

Clustering is the significant data mining technique for big data analysis, where large volume data are grouped. The resulting of clustering is to minimize the dimensionality while accessing large volume of data. The several data mining techniques have been developed for clustering the data. But the problem of clustering becomes increasing rapidly in recent years since the existing clustering algorithm failed to minimize the clustering time and majority of techniques require huge memory to perform clustering task. In order to improve clustering accuracy and minimize the dimensionality, a Kernelized Spectral Clustering based Conditional Maximum Entropy MapReduce (KSC-CMEMR) technique is introduced. The number of data is collected from big dataset. The KSC-CMEMR technique partitions the data into different clusters using Kernelized Spectral Clustering Process based on the spectrum of similarity matrix and to perform dimensionality reduction. Based on the similarity, the Kernelized Spectral Clustering is carried out with higher clustering accuracy. After that, Conditional Maximum Entropy MapReduce model eliminates the irrelevant data present in the cluster. The designed model predicts the maximum probabilities of data become a member of the cluster and remove the irrelevant data from the cluster. This helps to reduce the false positive and space complexity. Experimental evaluation is carried out with certain parameters such as clustering accuracy, clustering time, false positive rate, and space complexity with respect to the number of data. The experimental results reported that the proposed KSC-CMEMR technique obtains high clustering accuracy with minimum time as well as space complexity.
Introducing Cray OpenSHMEMX - A Modular Multi-communication Layer OpenSHMEM Implementation

SHMEM has a long history as a parallel programming model. It is extensively used since 1993, starting from Cray T3D systems. For the past two decades SHMEM library implementation in Cray systems evolved through different generations. The current generation of the SHMEM implementation for Cray XC and XK systems is called Cray SHMEM. It is a proprietary SHMEM implementation from Cray Inc. In this work, we provide an in-depth analysis of need for a new SHMEM implementation and then introduce the next evolution of Cray SHMEM implementation for current and future generation Cray systems. We call this new implementation Cray OpenSHMEMX. We provide brief design overview, along with a review of functional and performance differences in Cray OpenSHMEMX comparing against the existing Cray SHMEM implementation.
Augmented Reality Using High Resolution Adaptive Headlights

Implementing augmented reality in a vehicle is able to increase safety and comfort of the driver. Integrating an active matrix liquid crystal display in an adaptive headlight enables an intuitive use of augmented reality and provides additional communication with other road users.
Computation Efficient Coded Linear Transform

In large-scale distributed linear transform problems, coded computation plays an important role to reduce the delay caused by slow machines. However, existing coded schemes could end up destroying the significant sparsity that exists in large-scale machine learning problems, and in turn increase the computational delay. In this paper, we propose a coded computation strategy, referred to as diagonal code, that achieves the optimum recovery threshold and the optimum computation load. Furthermore, by leveraging the ideas from random proposal graph theory, we design a random code that achieves a constant computation load, which significantly outperforms the existing best known result. We apply our schemes to the distributed gradient descent problem and demonstrate the advantage of the approach over current fastest coded schemes.
An Extensible Deep Architecture for Action Recognition Problem

Human action Recognition has been extensively addressed by deep learning. However, the problem is still open and many deep learning architectures show some limits, such as extracting redundant spatio-temporal informations, using hand-crafted features, and instability of proposed networks on different datasets. In this paper, we present a general method of deep learning for the human action recognition. This model fits on any type of database and we apply it on CAD-120 which is a complex dataset. Our model thus clearly improves in two aspects. The first aspect is on the redundant informations and the second one is the generality and the multi-functionality application of our deep architecture. Our model uses only raw data for human action recognition and the approach achieves state-of-the-art action classification performance. Figure 1: Pipline of human action recognition using deep learning method extracted from (TEIVAS, 2017). The upper box shows the training phase and the lower one the testing phase.
Next Viewpoint Recommendation by Pose Ambiguity Minimization for Accurate Object Pose Estimation

3D object pose estimation by using a depth sensor is one of the important tasks in activities by robots. To reduce the pose ambiguity of an estimated object pose, several methods for multiple viewpoint pose estimation have been proposed. However, these methods need to select the viewpoints carefully to obtain better results. If the pose of the target object is ambiguous from the current observation, we could not decide where we should move the sensor to set as the next viewpoint. In this paper, we propose a best next viewpoint recommendation method by minimizing the pose ambiguity of the object by making use of the current pose estimation result as a latent variable. We evaluated viewpoints recommended by the proposed method and confirmed that it helps us to gain better pose estimation results than several comparative methods on a synthetic dataset.
Generating RDFS Based Knowledge Graph from SBVR

This work investigates how to generate RDFS based knowledge graph from SBVR. Semantic web is developed the idea of Resource description Framework (RDF). Knowledge graph is collection of interconnected entities. We required RDF knowledge graph because we want to show the date, which represented in graph. RDF vocabulary is used to provide description of instances. RDF can be constructed by three components subject, object, and predicate in which subject and object are resources while predicate is the property which describe the relationship between these resources. In this paper we represented that how we Generate an RDFS based knowledge graph from SBVR Semantic of Business Vocabulary and Rules (SBVR) in a way that it is easy to machine process. We used SBVR Tool and described that with the help of that tool SBVR to RDF transformation is possible. In this method SBVR is our input, SBVR is a textual representation, we used SBVR rules and create triple (subject object predicate) and then generate RDF and RDFS.
A Robust and Efficient System to Detect Human Faces Based on Facial Features

Face detection is considered as a one of the most important issues in the identification and authentication systems which use biometric features. Face detection is not straightforward as long as it has lots of dissimilarity of image appearance. Some challenges are required to be resolved to improve the detection process. These challenges include environmental constraints, device specific constraints and the facial feature constraints. Here in our paper we present a modified method to enrich face detection by using combination of Haar cascade files using skin detection, eye detection and nose detection. Our proposed system has been evaluated using Wild database. The experimental results have shown that the proposed system can achieve accuracy of detection up to 96%. Also, here we compared the proposed system with the other face detection systems and the success rate of the proposed system is better than the considered systems.