Reviewerly/exHarmony huggingface dataset:
Boosted Random Forest Learning Based Convolution Neural Network Model for Face Recognition System

Convolution Neural Network (CNN) accommodates high dimension features and large amount of data with high computation. In this paper, a CNN model for face recognition system that is based upon random forest learning approach is presented. It extracts the convolution neural network based linear and non-linear features of images. Random forest learns the linear and nonlinear features with different number of trees. The random forest learning is used with adaptive boosting algorithm for enhancing the recognition accuracy. It selects effective tree by boosting approach using adaptive threshold at testing time. For performance evaluation, the proposed boosted random forest based CNN model is compared with the existing model of soft-max learner based CNN model. The YALE dataset is used that contains the images of 38 persons, having 64 images for each person. The proposed approach achieves significant accuracy of 99.7%.
REPRESENTASI PENGETAHUAN SECARA VISUAL: KAJIAN TERHADAP ORANG TUNANETRA DAN PEMECAHAN MASALAH MATEMATIKA SOAL CERITA

Examining visual representations of knowledge, generally speaking of imagery or mental imagery. In recent years, mental imagery through visual representation in mathematical problem solving is still an interesting study to be explored. However, studies on the implications of visual representation of blind people have received much less investigative attention. In this paper, we will investigate three factors that address the goal of this review and to present some of the prominent questions for future research: 1) format of visual representation; 2) mental imagery on the congenital blindness; and 3) visualization on mathematical word problem-solving. Results indicate that the visual representation of knowledge is very substantially used to communicate various types of knowledge that describe relationships or patterns. Mental visualization between congenitally blind and sighted people qualitatively similar, but this results only on visuospatial skills. The results also indicate that visual representation can be used as a tool to solve mathematical word problems. For further study, this result proposes how the mental imagery of blind people can solve the mathematical problems (e.g. geometry, statistics) through visualization.
Bibliometric analysis about Economics approach to Bitcoins

The objective of this article is to identify the main characteristics of scientific research regarding the approach economic about bitcoins. This study is based on bibliometric research and network analysis, with the use of descriptive statistics in 84 articles found on the Web of Science database. The analysis used the programs VOSviewer and Excel. The main results show that Bouri, Bouoiyour, Dyhrberg and Selmi are the authors who published most articles and that the Université de Pau (France) and the University College Dublin (Ireland) are the institutions with most publications. The journal with most publications is Plos One, which presented a 3.057 impact factor. There are only three co-authoring networks with greater expressiveness, however, there are no links between them and the others. Despite the growing number of publications on the subject, the researchs is beginning, showing a wide field of exploration.
Deep Spatial-Temporal Field for Human Head Orientation Estimation

We present a human-head-orientation estimation approach which enables effective estimation of head orientations of multiple individuals appeared within the same scene. Our approach bases deep representation in order to obtain adequate visual features of head orientations. To boost the estimation performance, we propose a conditional random field which fuses shallow feature, deep feature and spatial-temporal contextual cues, where the fusing parameters are learned from data via structured support vector machine. We demonstrate that the three components of fusion are complementary to each other in terms of head orientation estimation. Meanwhile, the proposed spatial-temporal field outperforms the state-of-the-art significantly on public dataset.
Robust Pose Estimation and Calibration of Catadioptric Cameras With Spherical Mirrors

Catadioptric cameras broaden the field of view and reveal otherwise occluded object parts. They differ geometrically from central-perspective cameras because of light reflection from the mirror surface. To handle these effects, we present new pose-estimation and reconstruction models for imaging through spherical mirrors. We derive a closed-form equivalent to the collinearity principle via which three methods are established to estimate the system parameters: a resection-based one, a trilateration-based one that introduces novel constraints that enhance accuracy, and a direct and linear transform-based one. The estimated system parameters exhibit improved accuracy compared to the state of the art, and analysis shows intrinsic robustness to the presence of a high fraction of outliers. We then show that 3D point reconstruction can be performed at accurate levels. Thus, we provide an in-depth look into the geometrical modeling of spherical catadioptric systems and practical enhancements of accuracies and requirements to reach them.
Hepatic vessel segmentation using a reduced filter 3D U-Net in ultrasound imaging

Accurate hepatic vessel segmentation on ultrasound (US) images can be an important tool in the planning and execution of surgery, however proves to be a challenging task due to noise and speckle. Our method comprises a reduced filter 3D U-Net implementation to automatically detect hepatic vasculature in 3D US volumes. A comparison is made between volumes acquired with a 3D probe and stacked 2D US images based on electromagnetic tracking. Experiments are conducted on 67 scans, where 45 are used in training, 12 in validation and 10 in testing. This network architecture yields Dice scores of 0.740 and 0.781 for 3D and stacked 2D volumes respectively, comparing promising to literature and inter-observer performance (Dice = 0.879).
Uma Proposta de Processo de Avaliação de Usabilidade para DSLs

Several studies indicate the lack of well-deﬁned methods to evaluate usability criteria in Domain-Speciﬁc Languages (DSL). Therefore, software en-gineers evaluate experimentally, even subjectively, whether a DSL is easy to use and easy to understand. In this context, this paper presents the proposal of an evaluation process based on the Usa-DSL framework, which was developed to evaluate the usability of DSLs. The purpose of the Usa-DSL process is to help DSL designers and developers to develop, execute, analyze and disseminate the results of assessing those languages, making this task easier and more intuitive
SISTEM KONVERSI UCAPAN KATA KE TEKS MENGGUNAKAN SUPPORT VECTOR MACHINE

Artificial intelligence technology is developing very rapidly. Various fields have applied this technology to help human work. Speech recognition system is one of the artificial intelligence technologies that are widely applied in various fields. However, some research showed that it was still necessary to develop a method for a good speech recognition system. In addition, the development of speech recognition systems that can provide benefits needs to be developed, such as text recording. Based on this, the research focuses on developing a speech recognition system, in the form of spoken words and convert to text form. Speech words that have been recorded are then extracted features using linear predictive coding method. After that, the characteristic features of each sound are trained and tested using the Support Vector Machine (SVM) method for the process of recognition and convert it into text. Based on the evaluation results show that this system is able to recognize words with an accuracy rate of 71.875%. These percentages indicate that the system is able to recognize spoken words and transform them into text form properly.
Augmented Reality Application to Enhance Training of Lumbar Puncture Procedure: Preliminary Results

With the recent rapid development of Augmented Reality (AR) headsets, new possi- bilities emerge for applications of AR technologies. Today, publicly available AR headsets provide novel storytelling platforms, expand the vision of doctors and engineers, remove boundaries of educational processes and assist humanity in multiple endeavors. Our research encompasses various levels of the learning process by examining design principals and developing reliable software for multiple educational applications. This paper focuses on the software development process for an AR program that teaches the medical procedure known as the Lumbar Puncture. Our team utilized the Meta 2 headset by Metavision to create a software application to enhance the student experience and extend training effectiveness. We will discuss the requirements and specification for the future application, describe the development process and issues encountered for the current version of the application, as well as present preliminary results of testing and evaluation.
Automated Security Testing Framework for Validating Content Rights On Video Streaming Devices

Proliferation of high speed internet services and mass adoption of smart phones and internet enabled smart devices has led to a meteoritic rise in streaming video services around the globe. As streaming audio-video content takes center-stage in this digital era, “Cut-the-Cord” phenomenon has gained significant momentum all over the globe. New streaming models like Over-the-Top (OTT) services have disrupted this industry globally. OTT platforms like Netflix, Amazon Prime, YouTube, Sony Crackle, BBC iPlayer have gained lot of market share in the recent years. In India alone, there are more than 40 video streaming services [1] like Hotstar, AirtelTV, Voot, Sony Liv, JioTV and the list is growing by the day. However, streaming video applications, face constant challenges with respect to security of content rights across the streaming value chain. A leading global research firm KPMG, in a recent report suggests that, global streaming players could suffer losses of over 50 billion dollars (USD) due to pirated content in the next few years. Similarly a recent report released by FICCI highlights the fact that, upto 30% of overall film sector revenue in India has been lost due to piracy alone. As millions of internet videos get streamed on various consumer devices, risks of security breaches, online piracy and video hacking, pose considerable challenge for the entire video streaming industry. Therefore, it becomes absolutely essential for the video content creators, aggregators and distributors to adopt best-in-class secure development methodologies along with cutting edge security testing techniques to protect digital video assets. This requires special focus beyond traditional quality assurance (QA) and conventional security testing techniques like Pen Testing, Vulnerability Assessment etc. In this paper we propose a unique automated security testing framework focusing on critical aspects of the video streaming domain. In-order to provide a holistic model for enhancing cyber security aspects of streaming video systems, we expanded our earlier research work [3], [4] to cover latest content security concepts like Digital Rights Management (DRM), High-bandwidth Digital Content Protection (HDCP). Our proposed security testing methodology uses latest automation techniques and proven statistical methods to yield more than 90% better efficiencies with accuracy as high as 97%.
STRATEGIES FOR ACHIEVING UNDER SCHOOL (ATS) THROUGH SMART INDONESIA PROGRAMS (PIP)

This study aims to: 1) Data Collection of Non-School Children (ATS), 2) Factors that cause ATS and return to school, 3) Attempts and motivation of ATS back to school. Using qualitative and quantitative methods with purposive sampling. The ATS data collection is constrained by synchronization of data from the district / city starting from the village and kelurahan level to facilitate verification of PIP data on children who have not yet received access to education in Surabaya City and Padang City according to the target because HR / data is less than the location and difficult geographical conditions, while the data collection time is limited. For this reason, a one-door mechanism is needed for data synchronization. Whereas the regions which were well recorded even exceeded the target achievement due to the area being the ATS enclave was Bogor Regency and Gorontalo Regency. The most common factors causing ATS are economic, geographical, social environment influences, and lack of self motivation in students. Efforts are being made so that ATS can return to education, among others; 1). The government and regional government guarantee that registered ATS will receive financial assistance and can continue schooling again; 2). Provide a learning place for ATS of at least one village, one study group with adequate facilities; 3). To provide guidance or assistance to ATS to motivate the ATS to get education services again; 4). Bring a psychologist or motivator to encourage ATS to come to learning; 5). Persuasive approach both to ATS and to parents; 6). Bringing ATS alumni who are successful and successful as an example / figure that is expected to generate enthusiasm and motivation for ATS to succeed as its alumni.
ACTIVITIES OF HAEORUM ALLIANCE NUCLEAR INNOVATION CENTER FOR IMPROVED PUBLIC ACCEPTANCE AND RESEARCH INFRASTRUCTURES

Nuclear power plants in Republic of Korea are concentrated at East Sea, especially for Ulsan, Gyeongju, and Pohang, those are affiliated to Haeorum alliance. There have been public acceptance issues on nuclear power plant due to high concentration of nuclear power plant in Haeroum region. The issue is being intensified through the present government`s policies to phase out NPPs, despite absence of alternative energy and blackout risk. To resolve the public acceptance issue and reinforce the safety of NPPs under operation, Haeroum Alliance Nuclear Innovation Center was established at April 2018, funded by Korea hydro & Nuclear Power Co., Ltd. (KHNP), Ulsan, Gyeongju, and Pohang local governments. The center consists of three subcommittees; innovation research committee, scientific technology-society fusion committee, and education committee, participated by six local universities and institutes (UNIST, University of Ulsan, POSTECH, Handong Global University, Dongguk University, Uiduk University). Innovation research committee conducts R&D programs, related to the forth industrial revolution technologies (big data processing, artificial intelligence, 3D printing, drone, virtual reality, and so on) and improvement of nuclear safety, to advance operation technology of NPPs. Scientific technology-society fusion committee carries out activities such as forum, seminar, meeting, small businesses bridge service, to resolve the public acceptance issue. Education committee manages several education programs, training the undergraduates, high school students, and citizens, for the securing of skilled resources and reconsidering of recognition on NPPs. This paper introduces the various activities conducted as R&D programs of Haeorum Alliance Nuclear Innovation Center, and results of the activities in terms public acceptance, scientific technology associated with nuclear safety and fourth industrial revolution, and education.
Using Models of Cognitive Maps for Wireless Sensor Network Monitoring

Microminiaturization elements of communication systems contributed to development Internet of Things, which led the expansion of various railway transport services. One of the Internet of Things component's is the wireless sensor network (WSN). Feature of WSN is a dynamically changing spatial and information network environment. It complicates the adoption of promptly reliable management decisions based on assessments of the states of network elements. One of the directions of making a reliable decision in a complex network environment on the WSN is using cognitive maps in the WSN control nodes. In this article discusses various models of cognitive maps for assessing the states of network elements and network processes.
Customer Relationship Management, Service Delivery and Responsiveness: A supply chain Perspective for Customer’s Retention

This study aims to analyze the supply chain perspective in terms of customer relationship management, delivery of services, and responsiveness with their impact on customer’s retention. For the analysis purpose, this study has collected primary data through questionnaire approach from a sample of 341 customers. Both descriptive and regression techniques are applied, and findings are presented with some significant discussion. It is observed that customer’s retention is positively influenced by the customer relationship management factors like passionate and friendly behavior of sales personnel, their courtesy and offering the comprehensive product’s introduction to the customers. Whereas, delivery of service in terms of high satisfaction to customers, quality based are positively influencing on customer’s retention. However, the effect of reliable, timely and flexible service delivery is negatively influencing on customer’s retention. In addition, more responsiveness in terms of time to inquiries and orders, and being timeliness to execute the order have shown their direct impact on customer’s retention.
Offline Object Detection and Image based Offline Data Mining

Internet is a major source of information for many people all around the world where the data is just a few clicks away. With today’s technology, image recognition is possible where a user can upload an image onto the server and get the results. But when there is no internet there is no way to do image recognition and get any information about the object in the image. The purpose of this project is to tackle this problem of being limited to do image recognition and search for information only with the help of internet. In this project with the help of Machine learning and mobile application, the user can point the camera to any object and detect the object without internet and also display information about the object. The scope of this project is only some food categories and be able to successfully show the proof the concept. It provides a useful tool which can be expanded to detect any category of objects in the image without using internet. The results obtained were validated and the application had an accuracy of 95%.
Live Signal Recording and Segmenting Solution Based on Cloud Architecture

Combined with the construction of integrated media practice teaching platform and the operation of Communication University of China Network Television Station, a live signal recording and segmenting solution based on cloud architecture is proposed. The program converts the video and audio live signal into RTMP stream and pushes it to the cloud, which can perform real-time video and audio live recording and segmenting. The realization of the solution changes the working mode of the traditional manual collection of live signals, improves the production and distribution efficiency of the live program content, and implements the accurate collection and rapid segmenting processing of the live program signal in the media convergence environment.
The Semantic Analysis and Representation of “Hai-NP-Ne” Construction with NP Quoted from Context

This paper mainly analyzes the semantics of three types of “hai-NP-ne” construction with NP quoted from context, formalizes their semantics with the scale structure, and compares similarities and differences among these three types of construction semantics. It is found that all these three types of “hai-NP-ne” are at the lower point of entailing scales. Meanwhile, three different types of entailing scales, including likelihood scale, felicity scale, and truth value scale, are activated by the various types of verbs that are omitted in the “hai-NP-ne” construction. As a result, various meanings are derived.
WASH your data off: navigating statistical uncertainty in compositional data analysis

International monitoring of access to drinking water, sanitation and hygiene (WASH) is essential to inform policy planning, implementation and delivery of services. The Joint Monitoring Programme for Water Supply and Sanitation (JMP) is the recognized mechanism for tracking access and progress, and it is based on household surveys and linear regression modelling over time. However, the methods employed have two substantial limitations: they do not address the compositional nature of the data, nor its statistical uncertainty (Ezbakhe & P´erez-Foguet 2018). While the first issue has been tackled previously in the literature (P´erez-Foguet et al. 2017), the effect of non-uniform sampling errors on the regressions remains ignored. This article aims to address these shortcomings in order to produce a more truthful interpretation of JMP data. The main challenge we try to overcome is how to translate the sampling errors provided in household surveys to the space of compositional data. A Normal distribution is commonly assumed for estimates in household surveys, with a mean and its standard deviation. However, when working with binary data on households – the proportions of households that have access to WASH services – the errors cannot follow normal distributions due to the domain restrictions of proportions, limited to the range 0 to 1. Thus, the Beta distributions seems a better option to characterize the uncertainty around mean access coverage. Yet, as the Beta distribution is defined on the [0,1] interval, the zero values must be dealt with in order to employ the isometric log-ratio (ilr) transformation designed for compositional data. In this article, we investigate the use of two probability distributions (Pearson Type I and Truncated Normal) and Monte Carlo simulations to reinterpret the error in the JMP data so that compositional data analysis is possible. With a specific focus on the WASH sector, our article shows that the importance of including the survey errors of the data – and its compositional nature – when using this information to support evidence-based policy-making. Indeed, given the current levels of statistical uncertainty in WASH, data may lead to misleading results if errors are not acknowledged (or minimized).
A Depth-Assisted Deblurring Flow Using Dual Cameras with Different Exposure Times

Motion blur is often observed when taking pictures of moving objects under long exposure settings. To reduce motion blur, a well-known temporal-domain approach is to use dual cameras with different exposure times, and the deblurred image can be obtained by subtracting one view from another after image warping. In this paper, we propose to modify the abovementioned flow by including depth information, which can greatly improve the qualities of edges in the deblurred images. The proposed method is very effective, especially for scenes with multiple objects moving in different directions at different depths.
Depth Attention Net

Depth estimation has been achieved much attention in recent five years for visual SLAM, AR, VR, autonomous vehicle, 3D object understanding and so on. Similar to stereo matching analysis, stereo image based depth estimation methods using deep learning surprisingly obtain great improvement in performance but still cannot understand why learning depth can estimate depth in a new image and also require accuracy improvement. Therefore, we apply attention mechanism into depth learning. Attention mechanism achieve great improvement in various applications such as visual identification of objects, speech recognition, reasoning, image captioning, summarization, segmentation, machine translation(or NLP) and image classification etc. but yet depth estimation. This is because depth estimation is considered as geometric area. We apply attention mechanism in the PSMNet [1] method. Attention mechanism usually apply to channel attention. In our experiments, we apply our method into two benchmark dataset: KITTI [2] and SceneFlow [3]. In our experiments, we found the attention net can improve the quality of depth.