Reviewerly/exHarmony huggingface dataset:
Decision Making using Data Fusion for Wheelchair Navigation

This paper presents a subsystem of a semi-autonomous wheelchair controlled by eye gaze. The control decision is based on video and laser scanning acquisition. Data fusion between the information provided by the two systems was used. The obtained data are processed, adapted and provided to the Eye Gaze Control system. Various configurations for the proposed system were studied. Experiments conducted using RGB-D images and on points cloud provided by the LIDAR system demonstrated the reliability and the robustness of the proposed method. The next step will involve obstacles detecting, the objects that can block a specific given path. The two systems complement each other eliminating the disadvantages of each system. In case of removing one of them, the obtained system suffers from the lack of redundancy and safety in the event of a fault (essential condition for a semi-autonomous wheelchair).
Distributed Data Summarization in Well-Connected Networks

We study distributed algorithms for some fundamental problems in data summarization. Given a communication graph $G$ of $n$ nodes each of which may hold a value initially, we focus on computing $\sum_{i=1}^N g(f_i)$, where $f_i$ is the number of occurrences of value $i$ and $g$ is some fixed function. This includes important statistics such as the number of distinct elements, frequency moments, and the empirical entropy of the data. In the CONGEST model, a simple adaptation from streaming lower bounds shows that it requires $\tilde{\Omega}(D+ n)$ rounds, where $D$ is the diameter of the graph, to compute some of these statistics exactly. However, these lower bounds do not hold for graphs that are well-connected. We give an algorithm that computes $\sum_{i=1}^{N} g(f_i)$ exactly in $\tau_G \cdot 2^{O(\sqrt{\log n})}$ rounds where $\tau_G$ is the mixing time of $G$. This also has applications in computing the top $k$ most frequent elements. We demonstrate that there is a high similarity between the GOSSIP model and the CONGEST model in well-connected graphs. In particular, we show that each round of the GOSSIP model can be simulated almost-perfectly in $\tilde{O}(\tau_G $ rounds of the CONGEST model. To this end, we develop a new algorithm for the GOSSIP model that $1\pm \epsilon$ approximates the $p$-th frequency moment $F_p = \sum_{i=1}^N f_i^p$ in $\tilde{O}(\epsilon^{-2} n^{1-k/p})$ rounds, for $p \geq2$, when the number of distinct elements $F_0$ is at most $O\left(n^{1/(k-1)}\right)$. This result can be translated back to the CONGEST model with a factor $\tilde{O}(\tau_G)$ blow-up in the number of rounds.
UCSY-SC1: A Myanmar speech corpus for automatic speech recognition

This paper introduces a speech corpus which is developed for Myanmar Automatic Speech Recognition (ASR) research. Automatic Speech Recognition (ASR) research has been conducted by the researchers around the world to improve their language technologies. Speech corpora are important in developing the ASR and the creation of the corpora is necessary especially for low-resourced languages. Myanmar language can be regarded as a low-resourced language because of lack of pre-created resources for speech processing research. In this work, a speech corpus named UCSY-SC1 (University of Computer Studies Yangon - Speech Corpus1) is created for Myanmar ASR research. The corpus consists of two types of domain: news and daily conversations. The total size of the speech corpus is over 42 hrs. There are 25 hrs of web news and 17 hrs of conversational recorded data.&lt;br /&gt;The corpus was collected from 177 females and 84 males for the news data and 42 females and 4 males for conversational domain. This corpus was used as training data for developing Myanmar ASR. Three different types of acoustic models such as Gaussian Mixture Model (GMM) - Hidden Markov Model (HMM), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) models were built and compared their results. Experiments were conducted on different data sizes and evaluation is done by two test sets: TestSet1, web news and TestSet2, recorded conversational data. It showed that the performance of Myanmar ASRs using this corpus gave satisfiable results on both test sets. The Myanmar ASR using this corpus leading to word error rates of 15.61% on TestSet1 and 24.43% on TestSet2.&lt;br /&gt;&lt;br /&gt;
Determine Types of Indonesian Hospital by Criteria-based Proses Model, K-means Cluster, and Hierarchical Average Linkage

Hospital is an institution that provides health services to all communities. Hospitals in Indonesia have class type divisions, namely class A, class B, class C, and class D. Several criteria determine each classification. These criteria are human resources, such as the availability of doctors and pharmacists and time criteria. However, the values of those criteria are usually obtained by observation directly. The aim of this study is defining hospital values for each criterion through model process hospital directly. Firstly, this experiment forms a log of processes in the hospital instantly by using a workflow system. Then, a log is processes based on the hospital criteria. The final values of those criteria are processed by K-means Cluster and Hierarchical Average Linkage to obtain the hospital class. The final results of the experiment show that the K-means Cluster method is better with the Hierachical Average Linkage method in terms of accuracy, precision, and recall.
Many-to-Many Collaborator Recommendation Based on Matching Markets Theory

As the academic data developing rapidly, it is crucially important to obtain more scientific achievements by helping scholars to make the right choices of potential collaborators among the big data. The conventional methods are to provide each scholar with a Top N recommendation list of candidates, in which some scholars are recommended to overmany collaborators and only guarantee the optimal results of them. To bridge this gap, in this work-in-progress, we present an academic collaborator recommendation method based on matching theory from the perspectives of maximizing preference and minimizing cost. The method adopts the multiple indicators extracted from the papers published by scholars to integrate the preference matrix among scholars. which applies matching theory to achieve a stable many-to-many matching of recommendation. It aims that each scholar can choose the collaborators they are keen on. After conducting abundant experiments on Microsoft Academic Graph, we demonstrate that this approach is definitely effective in improving recommendation accuracy and coverage.
MVP Development Process for Software Startups

This paper presents a proposal of a Minimal Viable Development Process for Software Startups that can be used during the MVP development process. Defining a methodology is a major challenge for startups because they are creative, flexible, and reluctant to include bureaucratic measures in their day-to-day procedures that may disrupt their natural attributes. Thus, to make the process simple, we defined it in 3 main phases: requirements gathering, software development, and market validation.
My Name is Sonny, How May I help You Searching for Information?

In this paper, we describe the set up and procedure followed in an initial study where we observe a small group of primary school children interact with a vocal assistant or a standard GUI interface, while looking for online resources that can help them answer a set of predefined questions. We examine log files, observations, and feedback provided by the young users via an elementary interview at the end of each search section. Results from our analysis prompt us to consider how to evaluate the search process, specially one initiated with a vocal assistant, while considering both correctness of results, children preferences, and context of the search.
State of e-Learning Marketing: Results and Recommendations from a Nationwide Survey

To better understand how librarians across the country market e-learning resources to faculty, a survey was distributed to academic librarians in the United States to gauge what marketing strategies were being used, how the marketing responsibilities were being divided, and what strategies were deemed successful. The survey’s results suggested that active, tailored marketing strategies which utilize the traditional liaison model tend to be the most successful. This study emphasizes the need for communication among librarians and includes recommendations for increasing the awareness of e-learning resources on your campus.
Uses of Augmented Reality in Pre-Primary Education

This chapter is focused on possible educational uses of augmented reality as a support for digital literacy development in primary school. Development of digital literacy, a competence necessary for children to achieve learning goals by themselves, should be a natural part of education practice. Our goals were, through qualitative methodology, to identify impacts and benefits of augmented reality usage on the development of children’s digital literacy in kindergarten. In this action research conducted in a natural didactic context, we used direct and indirect observation of children in primary school preparation class as they used augmented reality in educational activities, as well as a focus group with the teachers in the learning group. For the interview data analysis, we used the open encoding method. We identified teachers’ views/preconceptions about augmented reality usage in their didactic practice. We relied on a strategy of validity and reliability, in which the collection of research data and its analysis must go together, in the form of mutual interaction between what is already known and what yet needs to be known. We used the constant comparison method to analyse the classroom observation data.
Quantitative Evaluation and Attribute of Overall Brightness in a High Dynamic Range World

Brightness is an attribute of visual perception used to describe the intensity of the light entering the eye. Since human perception is not linearly related to light intensity, characterizing brightness is a challenging task. In standard dynamic range (SDR) imagery, brightness is often quantified using the average picture level (APL), which is the average of all pixels’ code values normalized by the maximum allowed signal code value. APL provides a simple and commonly used brightness metric for SDR; however, its validity for high dynamic range (HDR) content has never been assessed. Due to the higher luminance range that HDR supports, HDR content is encoded using a different transfer function than SDR. Thus, a different distribution of pixel code values is to be expected between HDR and SDR content. In this work, we evaluate the efficiency of the APL metric to quantify the brightness of HDR content. We describe, using patches and professionally graded images, pixel distributions where the APL fails to distinguish relative brightness between pairs of images. To overcome APL shortcomings, we propose a brightness metric based on the geometric mean (GM) and variance of an image’s luma code values. We then conduct two subjective experiments to compare the efficiency of APL and our metric. Results show that the proposed metric predicts more accurately the relative brightness between two frames. However, our results also suggest that a simple statistical model, although useful as a general guideline, cannot be considered accurate enough for HDR content. Therefore, we hypothesize that work on a spatial model might provide a still better fit in characterizing brightness in HDR.
A Kind of Personalized Advertising Recommendation Method Based on User-Interest-Behavior Model

With the rapid development of internet and mobile internet, delivering ads on the internet has become the main channel for major advertisers. However, the accurate recommendation of online ads is a major problem which plagued advertisers and agencies. By analyzing the unstructured characteristics of online ads and search engine user behavior data, proposed a kind of personalized ads recommendation method based on User-Interest-Behavior model, which can extract the user’s interest preferences by the topic model and generate the recommended list of ads based on the nearest neighbor and user behavior. The experimental results demonstrate that the personalized ads recommendation method based on nearest neighbor and user behavior can recommend personalized ads and have a better performance than the content-based recommendation method.
CUDA Virtualization and Remoting for GPGPU Based Acceleration Offloading at the Edge

In the last decade, GPGPU virtualization and remoting have been among the most important research topics in the field of computer science and engineering due to the rising of cloud computing technologies. Public, private, and hybrid infrastructures need such virtualization tools in order to multiplex and better organize the computing resources. With the advent of novel technologies and paradigms, such as edge computing, code offloading in mobile clouds, deep learning techniques, etc., the need for computing power, especially of specialized hardware such as GPUs, has skyrocketed. Although many GPGPU virtualization tools are available nowadays, in this paper we focus on improving GVirtuS, our solution for GPU virtualization. The contributions in this work focus on the CUDA plug-in, in order to provide updated performance enabling the next generation of GPGPU code offloading applications. Moreover, we present a new GVirtuS implementation characterized by a highly modular approach with a full multithread support. We evaluate and discuss the benchmarks of the new implementation comparing and contrasting the results with the pure CUDA and with the previous version of GVirtuS. The new GVirtuS yielded better results when compared with its previous implementation, closing the gap with the pure CUDA performance and trailblazing the path for the next future improvements.
Rapid Visual Screening (RVS) for School Buildings after Earthquake in Lombok, West Nusa Tenggara, Indonesia

Abstract This study discusses the results of the rapid assessment of school buildings in Lombok after an earthquake occurs. The earthquake in July 2018 with a high enough intensity caused a lot of damage to the building. The earthquake including the school building affected the entire area of Lombok Island. This study assessed 15 school buildings on Lombok Island. School samples were taken randomly from several districts that were rocked by the earthquake. The results showed that school buildings in all locations were part of a Non-Engineered Building where there were no results of analysis of earthquake resistant buildings. Overall, the condition of the building is categorized as being moderately damaged with non-structural components such as walls not having good connections with columns and beams. In addition, the structure and attributes of the roof are also damaged by a high percentage. In some buildings, it still does not follow good structural rules so that it is very dangerous for building users in the event of a large earthquake. Improvements using the retrofitting method are one of the most recommended ways to improve the performance of buildings that have mild and moderate damage.
VMIGuard: Detecting and Preventing Service Integrity Violations by Malicious Insiders Using Virtual Machine Introspection

Organizations often focus their IT security strategy on protecting the perimeter from outside attacks, but internal attacks can often cause the greatest damage. Version control systems are frequently used in software development, including processes for automated build and deployment. Malicious insider manipulations in a version control system can, for example, lead to a clandestine distribution of software with implanted vulnerabilities, backdoors, or other malicious functionality. In this paper, we present VMIGuard, a solution that leverages virtual machine introspection (VMI) to detect integrity violations and prevent the propagation of unauthorized changes to a version control system caused by an insider attack. VMIGuard logs metadata about all authenticated modifications, and for each retrieval of version control system content, it verifies on-the-fly if the retrieved content matches the expected state. VMIGuard prevents the delivery of manipulated version control system content and notifies the user about integrity violations. We evaluate VMIGuard based on the open-source version control system git with several scenarios, in which it increases the response time in the worst case of the version control system server by a maximum of only 10%.
Augmented Reality and Future Mathematics Teachers

The chapter is devoted to the application of augmented reality (AR) in teacher-training programmes at universities. The first part describes the opportunities of future mathematics teachers at the Faculty of Mathematics, Physics and Informatics of Comenius University in Bratislava to work with digital technologies. The second, main part, describes the research of the AR application: Augmented Polyhedrons – Mirage 2.2. We conducted two studies with 40 future teachers. Results support the suitability of the selected application and AR in general. However, the implementation has to be precise, with carefully chosen and formulated tasks to solve. The last part of the chapter summarizes useful AR applications for secondary schools.
Augmented input estimation in multiple maneuvering target tracking

This paper presents augmented input estimation (AIE) for multiple maneuvering target tracking.Multi-target tracking (MTT) is based on two main parts, data association and estimation.In data association (DA), the best observations are assigned to the considered tracks.In real conditions, the number of observations is more than targets and also locations of observations are often so scattered that the association between targets and observations cannot be done simply.In this case, for general MTT problems with unknown numbers of targets, we present a Markov chain Monte-Carlo DA (MCMCDA) algorithm that approximates the optimal Bayesian filter with low complexity in computations.After DA, estimation and tracking should be done.Since in general cases, many targets can have maneuvering motions, then AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and the maneuver detection procedure is eliminated.This model with an input estimation (IE) approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state vector.Some comparisons based on the Monte-Carlo simulations are also made to evaluate the performances of the proposed method and other older methods in MTT.
Deep stair walking detection using wearable inertial sensor via long short-term memory network

This paper proposes a stair walking detection via Long-short Term Memory (LSTM) network to prevent stair fall event happen by alerting caregiver for assistance as soon as possible. The tri-axial accelerometer and gyroscope data of five activities of daily living (ADLs) including stair walking is collected from 20 subjects with wearable inertial sensors on the left heel, right heel, chest, left wrist and right wrist. Several parameters which are window size, sensor deployment, number of hidden cell unit and LSTM architecture were varied in finding an optimized LSTM model for stair walking detection. As the result, the best model in detecting stair walking event that achieve 95.6% testing accuracy is double layered LSTM with 250 hidden cell units that is fed with data from all sensor locations with window size of 2 seconds. The result also shows that with similar detection model but fed with single sensor data, the model can achieve very good performance which is above 83.2%. It should be possible, therefore, to integrate the proposed detection model for fall prevention especially among patients or elderly in helping to alert the caregiver when stair walking event occur.
Principles of reforming an agile-compliant performance appraisal

This paper presents principles of reforming an Agile-compliant performance appraisal. In this study, several semi-structured interviews have been carried out and discovered eight principles for reforming an Agile-compliant performance appraisal for Agile teams. Performance appraisal for software engineers in an Agile software development environment is complex and different from the traditional software development. Performance appraisal should be aligned to Agile values, principles, and practices, which advocate interactions, collaborations, teamwork, and knowledge transfer among Agile team members. Therefore, a transition to Agile Software Development requires the implementation of Agile-compliant performance appraisal. These principles embark the proper practices and guidance to support management and Agile teams in deriving and implementing an Agile-compliant performance appraisal. Therefore, the emerged principles can be a baseline in generating an Agile-compliant performance appraisal to assess Agile team members in a fair and consistent manner. This indirectly increases motivation amongst team members and tends to produce capable workforce to perform at a higher level.
Exploring digital-age literacy among prospective science teachers in West Kalimantan, Indonesia

Abstract Digital-age literacy is one of the main skills of the 21st century to be considered in the future education program for prospective science teachers. This study aims to explore the digital-age literacy among prospective science teachers in West Kalimantan (Indonesia) based on the results of self-reflection and self-requirements and to identify the relationship between both variables in digital-age literacy. A sample of 180 prospective science teachers from three educational institutions for prospective science teacher programs in West Kalimantan (Indonesia) completed the survey using two forms of the questionnaires. Findings indicated that the self-requirements (M = 4.40; SD = 0.16) of prospective science teachers thinking on digital-age literacy are higher than the results of self-reflection (M = 2.65; SD = 0.29) on their abilities in digital-age literacy. This result also shows the relationship between digital-age literacy based on self-reflection and self-requirements to be at the medium level, where the correlation coefficient for both variables is 0.560 and value of the correlation is positive and significant. This is explain that if digital-age literacy based on self-requirements increases, then digital-age literacy based on self-reflection also increases.
Sentiment Analysis for Movies Prediction Using Machine Leaning Techniques

The main intention of this paper is to determine the sentimental analysis for movies prediction using machine learning techniques. A novel methodology along with necessary algorithms is presented here. The input dataset obtained from IMDb is chosen for prediction of box office collection. Firstly preprocessing is performed to remove the redundant features such as stop words, punctuations, numbers, whitespace, lowercase conversion, word stemming, sparse term removal etc.. Preprocessing technique involves two steps namely tokenization and stop word removal. The preprocessed text is sent for transformation process. This formation involves calculating the values of TF and IDF. These are performed for estimating the sentiment analysis. Afterwards, fuzzy clustering is performed that helps in getting both positive and negative outcomes on basis of different movies datasets. In this way, a classification model is made utilizing SVM classifier for foreseeing the pattern of the box office incomes from the reviews of movies.