Reviewerly/exHarmony huggingface dataset:
Solving General Elliptical Mixture Models through an Approximate Wasserstein Manifold

We address the estimation problem for general finite mixture models, with a particular focus on the elliptical mixture models (EMMs). Compared to the widely adopted Kullback–Leibler divergence, we show that the Wasserstein distance provides a more desirable optimisation space. We thus provide a stable solution to the EMMs that is both robust to initialisations and reaches a superior optimum by adaptively optimising along a manifold of an approximate Wasserstein distance. To this end, we first provide a unifying account of computable and identifiable EMMs, which serves as a basis to rigorously address the underpinning optimisation problem. Due to a probability constraint, solving this problem is extremely cumbersome and unstable, especially under the Wasserstein distance. To relieve this issue, we introduce an efficient optimisation method on a statistical manifold defined under an approximate Wasserstein distance, which allows for explicit metrics and computable operations, thus significantly stabilising and improving the EMM estimation. We further propose an adaptive method to accelerate the convergence. Experimental results demonstrate the excellent performance of the proposed EMM solver.
Artificial Creativity: Emergent Works and the Void in Current IP Doctrine

Artificial intelligence (AI) is on everyone’s lips and is in everyday use. Yet discussion on what this means for our present and future — particularly in terms of the revolutions that AI might bring to the legal sphere — has only just begun. One topic that warrants, but has yet to receive, in-depth attention is the relevance of AI for innovative and creative activity and production. Legal analyses thus far have focused on humans and their role as innovators, authors, or creators. Left in the dark, however, is the question of how to regulate AI when it “innovates” or “creates” autonomously — without human direction or intervention. Examples of such artificial creativity abound. Robots and computers have recently come to paint works of art, compose symphonies, and write news articles, poetry, and novels. All of these “works” would doubtlessly be protected by copyright if created by a human being. But we are hopelessly naïve when confronted with whether and how copyright law and neighboring areas of intellectual property protection should respond to the rise of AI. Indeed, current law is devoid of rules and doctrines for artificial creativity — with the result that AI-generated works are left unprotected. The consequences of such neglect are yet to be discussed. This article provides an overview of the status quo of artificial creativity — i.e., creative production by AI — and its regulation (or, rather, non-regulation) in different jurisdictions, as well as an analysis of relevant doctrinal debate and economic foundations. It then offers suggestions for a reconceptualization of current doctrine, outlining a road map and overarching framework for legislative action and practical adjudication.
Development of Web-Based Application in Population Administration System Using Scrum Framework

There are two methods of collecting data of population in Indonesia, they are: population census which conducted by the Central Statistics Agency using data sampling techniques, and the population data record which is conducted by the Population and Civil Registry Office during registering the ID number and family card.Differences in data collection method will potentially cause different result between two institutions; this will be a serious problem regarding to the validity and accuracy of population data.The population data of e two institutions are commonly used as a basis for making policies regarding beneficiaries for the poor, recipients of educational assistance, determining voting rights in elections and etc.The solution is to produce a website-based computer application platform to record population data by a bottom up method, which can be used by both institutions.The web application makes population data into a single data and eliminates data redundancy; it is also can be updated fast and real time.The web application is developed by implementing the Scrum framework, and only takes 30 days to complete the project.SUS examined the usability of this web application and the result is 83.5%.
Benchmarking large-scale multi-view 3D reconstruction using realistic synthetic images

Benchmarking multi-view 3D reconstruction using synthetic images is a supplement for the traditional ways. The ground-truth data of synthetic benchmarks can be easily recorded in simulation and have a high accuracy. However, synthetic images from artificial scenes are usually lack of geometry and texture details. We propose a framework to build realistic synthetic benchmarks for real-world scenes. Real photos are also taken around the scenes. The key step of our approach is to transfer the image properties of the real photos to corresponding synthetic images, followed by a validation step to verify their similarity. Based on the proposed framework, we construct the Yungang Grotto dataset for large-scale multi-view 3D reconstruction, in which the largest scene is up to four thousand images, and the benchmarking results of a prevalent reconstruction pipeline on our dataset are also presented.
Analysis and research on lexical errors in machine translation in Chinese and Korean translation

In the current Korean language major learning, there are not a few students who rely on machine translation to complete the learning, Especially for beginner and intermediate students. Due to their lack of Korean proficiency, they cannot make a correct judgment on the quality of the translation, and the current machine translation quality has not reached a high level. Therefore, it is particularly important to analyze the errors in machine translation to improve the quality of the output text of machine translation and to make appropriate post-editing.
KYC as a Service (KASE)—A Blockchain Approach

KYC or know-your-customer is an integral part of the on-boarding process of a customer for a company. This process requires independent and tedious verification of a customer’s identity documents by the businesses leading to wastage of resources. In this paper, we propose a solution where the submission and verification of a customer are done only once, and the results are shared with the businesses which require the information. The proposed system uses blockchain to record and manage the KYC requests and ensure transparency. The KYC data is verified using machine learning processes to ensure further efficiency in the process by reducing a significant amount of time spent on verifying the customers.
The Parallel CMAC Synthetic Initialization Vector Algorithm Implementation on FPGA

The need for high-speed communication has led the research towards designing parallel architectural algorithms for data security. The utilization of the Advanced Encryption Standard in Counter mode (AES-CTR) in cipher-based Authenticated Encryption (AE) algorithms has realized the importance of a unique Initialization Vector (IV) for data security. The Synthetic Initialization Vector (SIV) is an improvement of a generic IV used in the AES-CTR algorithm for nonce misuse and key wrapping attacks. In this work, we have proposed a new AE algorithm with parallel architecture named as Parallel Cipher-based Message Authentication Code with SIV Algorithm (PCMAC-SIV). The PCMAC-SIV AE algorithm is consists of an AES-CTR algorithm and parallel implementation of the Cipher-based Message Authentication Code (CMAC) algorithm with SIV algorithm for avoiding nonce misuse. The proposed algorithm is implemented on FPGA for showing its utility for high throughput applications. In this work, we compared the proposed algorithm implementation with the AES-GCM -SIV algorithm. The experimental results show that the throughput of the proposed algorithm show higher throughput of 1.629 Gbps for single plaintext and 13.06 Gbps for eight plaintexts.
Features of Using «Flipped Learning» Technology in Higher Education Institutions

The article describes the use of the «flipped learning» technology in the practice of higher education institutions. The benefit of the study is that the authors have proved that the technology of «flipped learning» is based on the ideas of active learning, involving students in joint activities and combined teaching system. This technology helps to optimize studying at higher educational institutions. The essence of this technology is that students learn the new material which is based on video lectures, presentations, or other media technologies on their own at home and the next classroom work involves discussing problem issues, consolidating theoretical knowledge and developing practical skills, assessing the absorption of new material. Moreover, this allows students to determine their own learning pace and develop individual teaching strategies. Another advantage of «flipped learning» technology is the ability to use more class time for group activities, where students can discuss the content of the lecture, check their knowledge and interact with each other in an interactive mode that promotes the development of their practical skills. It is proved that as any educational technology «inverted learning» technology is characterized by algorithmicity and a certain order of educational actions of teachers and students. For effective implementation of «flipped learning» technology, the certain cycles (phases) are used, such as a phase of viewing the educational video, a phase of interactive work; a phase of the result awareness; a feedback phase and more. Each stage requires not only student`s but also teacher`s activity, who have to develop additional training or control materials for classes. The authors have analyzed the advantages and difficulties of applying «flipped learning» technology in higher education institutions. The article outlines the features of «flipped learning» in high school (the expediency of its use in senior courses, low awareness of this technology, lack of time and the need for some adaptation to its application, low technical readiness of teachers to «flipped learning» technology) and describes the experience of using this technology in the practice of teaching «High School Pedagogy» subject for students of the Pereiaslav-Khmelnytskyi Hryhorii Skovoroda State Pedagogical University.
Haar Cascade Vs Facial Landmarks Technique for Face Recognition Uniqueness

This paper presents Haar Cascade vs Facial LandmarksTechnique for Face Recognition Uniqueness that developed the Face detection module using ROI and Selective Features of Distances and angles between facial feature's centroid using Haar Cascade Technique and distance contour using Facial Landmarks technique.The feature's data was tested by the Neural Network module for verification purpose.When the Haar Cascade Technique is implemented, the angle is unreliable to be used as a feature.The System has achieved accuracy of 60% Haar cascade, 85% Face Landmark using the distance features.
Augmented Reality Plant &amp; Animal Cells: Design and Evaluation of an Educational Augmented Reality Application

In this paper, we present a usability evaluation of a custom AR educational tool designed to improve students’ understanding of the similarities and differences between plant and animal cells. We argue that the design of the Augmented Reality Plant &amp; Animal Cells (ARPAC) tool promotes a self-driven approach to learning by presenting textbook content as interactive, 3D models that can be uncovered by exploring sections of their school textbooks with our app. Furthermore, the design of ARPAC can be mapped easily onto other subjects, providing teachers with additional pedagogical tools to utilize in the classroom. Preliminary results of a usability study support our initial design of a textbook-driven AR application to support learning in science subjects.
A Collaborative Effort to Improve Lossy Compression Methods for Climate Data

Climate model simulations produce large volumes of data, and reducing the storage burden with data compression is increasingly of interest to climate scientists. A key concern to the climate community, though, is ensuring that any data loss due to compression does not in any way affect their scientific analysis. For this reason, the climate community is taking a cautious approach to adopting lossy compression by carefully investigating the potential existence of artifacts due to compression in a wide variety of analysis settings. Spatio-temporal statistical analysis in particular can highlight compression-induced features that would go unnoticed by the standard metrics common to the data compression community. Communicating such findings to the algorithm developers in the context of a collaborative improvement cycle is one - in our view productive - way to foster trust within the climate community and pave the way for eventual adoption of lossy compression. In this work, we report on the initial results of a successful and mutually beneficial collaboration between the two communities that led to improvements in a well regarded compression algorithm and more effective compression of climate simulation data.
Cybercrime, its Adherent Negative Effects on Nigerian Youths and the Society at Large: Possible Solutions

Every country has its fair share of security challenges, but when the state of insecurity increases by the day, and then there is a need for urgent action.In Nigeria, the introduction of these three major inventions; the Computers, the Internet and the Mobile telephones gave rise to a huge outbreak of cybercrimes.It could be very evident that criminals and fraudsters leverage the anonymity supplied by means of the Internet to defraud unsuspecting victims.Unfortunately, the current social, economic and political trends have left a sour taste in the mouth of the youths; particularly, those from poor social and economic backgrounds.Notably, cybercrimes are of different categories in Nigeria ranging from; Internet frauds, software piracy, hacking, online scam, ATM or Credit card fraud, virus dissemination, phishing, cyber-stalking, and cyber-defamation.The occurrence of a high rate of unemployment, harsh economic conditions and bad educational systems also contributes immensely to the proliferation of cybercrimes in Nigeria.This paper analyses the adherent effects on the average Nigerian youth and the possible solutions.With Nigeria venturing into the cashless society, there is an urgent need to reduce the cybercrime menace ravaging our dear country.
Real-Time Hand Model Estimation from Depth Images for Wearable Augmented Reality Glasses

This work presents a hand model estimation method designed specifically with augmented reality (AR) glasses and 3D AR interface in mind. The proposed work is capable of estimating the 3D positions of all ten finger from a single depth image. By leveraging a low-dimensional hand model and exploiting hand geometries from an ego-centric view, we build a lightweight algorithm that is accurate, environment agnostic, and runs in real time on mobile hardware. One major consideration in our design for AR is that the user's hand is likely to interact with planar surfaces since they serve as ideal "touchscreens". As a result, our method will not fail to detect the hand even when the hand is in physical contact with a surface such as a table, wall, or even another palm. Our experiment shows using the CVAR database that the accuracy with clear background at 98% and with cluttered background at around 85%.
Space Breakdown Method A new approach for density-based clustering

Overlapping clusters and different density clusters are recurrent phenomena of neuronal datasets, because of how neurons fire. We propose a clustering method that is able to identify clusters of arbitrary shapes, having different densities, and potentially overlapped. The Space Breakdown Method (SBM) divides the space into chunks of equal sizes. Based on the number of points inside the chunk, cluster centers are found and expanded. Even if we consider the particularities of neuronal data in designing the algorithm - not all data points need to be clustered, and the data space has a relatively low dimensionality - it can be applied successfully to other domains involving overlapping and different density clusters as well. The experiments performed on benchmark synthetic data show that the proposed approach has similar or better results than two well-known clustering algorithms.
Intelligent Analysis System of Learning Situation in Class Based on Behavior Recognition

In the context of educational informationization, various artificial intelligence and information technologies are widely used in educational reform and teaching practice. Among them, the intelligent identification and big data analysis systems based on student behaviors in class are very meaningful. This paper establishes a learning situation analysis and quality assessment system on the basis of target detection and behavior recognition under class scenarios, and the quality indicators of the degree of positivity and the degree of negativity are defined to achieve a systematic and quantitative assessment of the participatory status of students in class. The application of this system can provide necessary technical supports for individualized learning of students, effective organization and efficient management of teaching.
PENGARUH MEDIA AUDIO VISUAL TERHADAP HASIL BELAJAR SISWA KELAS V SEKOLAH DASAR

This research is motivated by problems in the field of recognizing low student learning outcomes, lack of utilization of facilities, infrastructure, and learning media, especially audio-visual media. The purpose of this study was to determine the effect of the use of audio visual media on learning outcomes of fifth grade students of SDN 066 Halimun. The method used in this study is a quantitative method of experimentation with a quasi experimental design type nonequivalent group design. Based on the results of research the data obtained from the implementation of research learning outcomes is proven by the average initial test pretest experimental class 60.65 increased in the number of posttest tests of 85.48. While the average control class without using audio visual media or treatment got an average of learning outcomes of pretest 49.52 and posttest 58.55. The results of the statistical data analysis also proved that Ha was received from the t test obtained 𝑡ℎ𝑖𝑡𝑢𝑛𝑔3,214&gt; 𝑡𝑡𝑎𝑏𝑒𝑙 2,045. It can be seen from the data above that the experimental class given action or audio-visual media has a positive effect on student learning outcomes in class V.
Comparative analysis of Kotlin and Java languages used to create applications for the Android system

This publication presents the results of a comparative analysis of Java and Kotlin programming languages used to create mobile applications for system Android. The analysis covers performance aspects such as CPU load, RAM load, as well as the compilation and execution times. Aspects such as code structure, availability of libraries, supported databases, popularity and community support were taken under consolidation.
An Image Cues Coding Approach for 3D Human Pose Estimation

Although Deep Convolutional Neural Networks (DCNNs) facilitate the evolution of 3D human pose estimation, ambiguity remains the most challenging problem in such tasks. Inspired by the Human Perception Mechanism (HPM), we propose an image-to-pose coding method to fill the gap between image cues and 3D poses, thereby alleviating the ambiguity of 3D human pose estimation. First, in 3D pose space, we divide the whole 3D pose space into multiple subregions named pose codes , turning a disambiguation problem into a classification problem. The proposed coding mechanism covers multiple camera views and provides a complete description for 3D pose space. Second, it is noteworthy that the articulated structure of the human body lies on a sophisticated product manifold and the error accumulation in the chain structure will undoubtedly affect the coding performance. Therefore, in image space, we extract the image cues from independent local image patches rather than the whole image. The mapping relationship between image cues and 3D pose codes is established by a set of DCNNs. The image-to-pose coding method transforms the implicit image cues into explicit constraints. Finally, the image-to-pose coding method is integrated into a linear matching mechanism to construct a 3D pose estimation method that effectively alleviates the ambiguity. We conduct extensive experiments on widely used public benchmarks. The experimental results show that our method effectively alleviates the ambiguity in 3D pose recovery and is robust to the variations of view.
Character-Level Attention Convolutional Neural Networks for Short-Text Classification

This paper proposes a character-level attention convolutional neural networks model (ACNN) for short-text classification task. The model is implemented on the deep learning framework which named tensorflow. The model can achieve better short-text classification result. Experimental datasets are from three different categories and scales. ACNN model are compared with traditional model such as LSTM and CNN. The experimental results show that ACNN model significantly improves the short-text classification results.
Methodology of Scenario Clustering for Predictive Safety Functions

Data clustering is recently a common technique to group similar data with certain features. It enables ﬁnding the representative in each cluster as well. However, the clustering analysis comprises several challenging tasks, e.g., feature selection, choice among different clustering algorithms, deﬁning the optimal cluster number, clustering with the use of a distance measure dealing with various levels of measurement, cluster validation, and interpretation of results in the end. The objective of this paper is the conceptual design of a scenario catalog including extracted representative near-crash and crash scenarios. Two clustering algorithms based on kcovers and k-medoids are applied to data in a naturalistic driving study under consideration of aforementioned aspects. Afterwards, the comparison of two clustering algorithms is conducted based on the cluster representativeness, purity, and average silhouette width. Moreover, the clusters are visualized in a two dimensional scenario space by t-Distributed Stochastic Neighbor Embedding (t-SNE). The derived scenario catalog covers the selected database at best possible rate and enables a cost-efﬁcient development of predictive safety functions.
Importance of Power Digraph in Computer Science

The understanding of a computer based software or self-designed tool is extremely difficult to analyze without some appropriate mathematics. In any algorithm, it is always possible to visualize its core steps with the help of a flow chart or a graph. Thus, drawing graphs and digraphs of any structure based algorithm or computer oriented outputs is much more helpful in achieving a better understanding. In discrete mathematics, digraphs based on modular arithmetic are becoming a core interest of many computer scientists and number theorist. As these digraphs are easy to label using the residues of any given integer. Thus, algorithms based on integral mathematics are easy to evaluate with the help of a power digraph. A power digraph can be constructed from the congruence equation a <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ≡ b(mod n), where a, b, ∈ Zn. In this note, we highlight some fields of interest where power digraphs can be utilized in a more friendly way to entertain a layman without knowing much about its mathematics.