Reviewerly/exHarmony huggingface dataset:
Indoor 3D Reconstruction from a Single Image

3D indoor reconstruction has been an important research area in the field of computer vision and photogrammetry. While the initial techniques developed for this purpose use sensor devices and multiple images for data acquisition and extracting 3D information and representation of the scene, with the advent of deep learning techniques, there has been good progress in extracting 3D information of an indoor scene reconstruction using a single image. This has potential in minimizing user efforts and cost for data acquisition. The current state-of-the-art method involves two main components, the global depth map and plane instances. After investigating the current state-of-the-art methods, it is observed that there is inconsistency in reconstructed surface boundaries and depth estimation over the curvature and edges of the objects present in the scene, despite having good 3D representation in the surrounding regions. We devise a loss function for optimizing depth estimation during supervision of the neural network by providing geometric awareness to the pixels at local level based on its neighborhood properties defined by spatial compatibility and color similarity. A similar function is used during 3D reconstruction for orientation consistency in the point cloud. Based on the quantitative and qualitative analysis, it is observed that the proposed approach helps in improving the 3D reconstruction from a single image in an indoor environment.
ПЕДАГОГИЧЕСКАЯ СИНЕРГЕТИКА КАК СОСТАВЛЯЮЩАЯ ЛИЧНОСТНО ОРИЕНТИРОВАННОГО ОБУЧЕНИЯ В СОВРЕМЕННОЙ ШКОЛЕ

The prospects of the development of a modern school are mainly determined by the peculiarities of educational technologies available to teachers. Orientation towards the development of a child’s personality requires of schools the transition to the technologies based on the integration of traditional and innovative methods of teaching. The relevance of the study in the sphere of pedagogical synergy carried out by the author is determined by the unconditional need of society in the formation of an open education system and the willingness of teaching communities to work in the format relevant to the challenges of modern society. The paper considers the issue of formation of new approaches to the person-centered education, proves the reasonability of integration of traditional educational technologies and methods of social simulation through playing, focuses on the synergy of the interaction of diverse methods, which ensures high results of the educational process and opens for practicing teachers the unique possibilities of self-fulfillment and self-improvement. The author carried out the study in several directions: a systems analysis of the concepts of a personality-oriented approach to teaching and upbringing; defining the role and place of innovative educational technologies within the system of modern education; identifying the essence of a synergistic approach to pedagogical activity, and identifying the features of modeling and designing of pedagogical activity based on the idea of self-organization. The author carried out sociological research aimed at identifying the attitude of the participants of the educational process to the application of new teaching methods. The study revealed a positive attitude of the educational process participants to the idea of integration of teaching methods of different classes.
Intra-day Variability Quantification from Ground-based Measurements of Global Solar Irradiance

Worldwide electricity production has switched from fossil fuel combustion to renewable energy sources and solar power generation has increased significantly in recent years, particularly in the form of photovoltaic (PV) power. Solar radiation is mainly influenced by the process of sunlight interaction with clouds. Being able to quantify the variability of solar irradiance due to clouds is crucial for better integration of the energy generated in the power grid by reducing the uncertainties in solar irradiance forecasts. Our objective is the characterization of a given solar site by quantifying the variability of solar irradiance caused by clouds. To do so, we provide a classification scheme of clouds conditions classes based on the daily clear sky index K c , and the hourly intraday variability I´(K c ) defined by the standard deviation of the variations of the clear sky index. As a result of irradiance classification, we obtained nine classes identified as the overcast, mixed, and clear sky conditions and subdivided into three categories: low, medium, and high variability. We used the solar irradiance data set measured at the high precision meteorological station installed in Benguerir, Morocco, for the period from 01/01/2018 to 31/12/2018.
A Big Data Software Paradigm for Heterogeneous Cloud Deployments

E2Data is an EU-funded research project aiming to provide a new Big Data software paradigm for achieving maximum resource utilization for heterogeneous cloud deployments without requiring developers to change their code. It essentially addresses the fundamental question of how modern Big Data frameworks can dynamically and transparently exploit heterogeneous hardware accelerators. The E2Data solution takes a cross-layer approach by allowing vertical communication between the four key layers of Big Data deployments: application, Big Data software, scheduler/cloud provider, and execution run time. For this purpose, four dierent use-case scenarios have been selected to prove the applicability of the project's approach: health analytics, natural language processing, green buildings and biometric security. To achieve those goals, Apache Flink and the Tornado VM are used in the project as the building blocks for enabling heterogeneous execution for Big Data frameworks.
Business English Preparation Course: Levels B1 and B2

This book is intended to support students in learning business vocabulary development, grammar, and the skills of listening, speaking, reading, and writing.
Kendon Model-Based Gesture Recognition Using Hidden Markov Model and Learning Vector Quantization.

The paper presents a dynamic gesture recognizer, that assumes that the gesture can be described by Kendon Gesture model. The gesture recognizer has four modules. The first module performs the feature extaction, using the skeleton representation of the body person provided by NITE library of Kinect. The second module, formed by Learning Vector Quantization, has the task of individuating the initial and the final handposes of the gesture, i.e., when the gesture starts and terminates. The third unit performs the dimensionality reduction. The last module, formed by a discrete Hidden Markov, perfoms the gesture classification. The proposed recognizer compares favourably, in terms of accuracy, most of existing dynamic gesture recognizers.
SymFields: An Open Source Symbolic Fields Analysis Tool for General Curvilinear Coordinates in Python

An open source symbolic tool for general vector fields analysis “SymFields” is developed in pure Python. The SymFields module is constructed upon Python symbolic module sympy, which could only conduct field analysis under Cartesian coordinate. Using the mathematics of metric tensor, the analytic expression of vector fields operators in general curvilinear coordinates are obtained. Where the operators are normalized to real physical components value with unity direction vectors. With SymFields, the vector analysis for general curvilinear coordinates can be conducted regardless whether it is orthogonal or not. This could greatly free the physicists from the tedious calculation due to the complicity of coordinates.
Neuromorphic Reservoir Computing System Using a Semiconductor Nanolaser with Double Phase Conjugate Feedbacks

A neuromorphic reservoir computing (RC) system using a semiconductor nanolaser (SNL) with double phase conjugate feedbacks (PCF) is proposed for the first time and demonstrated numerically, and enhanced performance can be obtained.
Penerapan Particle Swarm Optimization (PSO) dalam Pemilihan Atribut untuk Meningkatkan Akurasi Prediksi Diagnosis Penyakit Hepatitis dengan Metode Naïve Bayes

Abstract- Hepatitis is a major cause of death among all infectious diseases, and become a public health problem in developing countries in the world,  including Indonesia which ranks second among the WHO South East Asian Region member countries  with the number 2 person with Hepatitis B after Myanmar (WHO, 2017). Hepatitis includes chronic diseases, when the patient is infected, the condition is still healthy and has not shown typical symptoms and signs, but the transmission continues. Then there needs to be early treatment to reduce cases of hepatitis, one of them is by making an accurate hepatitis diagnosis system. In this research, naive bayes optimization was carried out using particle swarm optimization which was applied to patient data with 2 classes, namely positive hepatitis and negative hepatitis in Al-Mulk Hospital Sukabumi City. After testing, the accuracy value for the Naive Bayes algorithm model is 86.52% with AUC value 0.860 and the accuracy value of the Naive Bayes model with attribute selection using Particle Swarm Optimization is 91.67% with AUC 0.895. Based on this value, the accuracy difference was 5.15% and the difference in AUC was 0.035. Keyword: Hepatitis, Naive Bayes , Particle Swarm Optimazation . Abstrak- Hepatitis adalah penyebab utama kematian di antara semua penyakit menular, dan menjadi masalah kesehatan masyarakat di negara-negara berkembang di dunia, termasuk Indonesia yang menempati peringkat kedua di antara negara-negara anggota WHO Kawasan Asia Tenggara dengan orang nomor 2 dengan Hepatitis B setelah Myanmar ( WHO, 2017). Hepatitis termasuk penyakit kronis, ketika pasien terinfeksi, kondisinya masih sehat dan belum menunjukkan gejala dan tanda yang khas, tetapi penularannya berlanjut. Maka perlu ada pendeteksian lebih dini untuk mengurangi kasus hepatitis, salah satunya adalah dengan membuat sistem diagnosis hepatitis yang akurat. Dalam penelitian ini, optimasi naive bayes dilakukan dengan menggunakan optimasi partikel swarm yang diterapkan pada data pasien dengan 2 kelas, yaitu hepatitis positif dan hepatitis negatif di Rumah Sakit Al-Mulk Kota Sukabumi Kota. Setelah pengujian, nilai akurasi untuk model algoritma Naive Bayes adalah 86,52% dengan nilai AUC 0,860 dan nilai akurasi model Naive Bayes dengan pemilihan atribut menggunakan Particle Swarm Optimization adalah 91,67% dengan AUC 0,895. Berdasarkan nilai ini, perbedaan akurasi adalah 5,15% dan perbedaan dalam AUC adalah 0,035. Kata Kunci : Hepatitis, Naive Bayes, Particle Swarm Optimazation.
PERKEMBANGAN UPT. PERPUSTAKAAN UNIVERSITAS SYIAH KUALA, 1970-2016

study, entitled The Development Unit. Library of the University of Syiah Kuala, 1970-2016 , this raises issues about how the development of UPT. Library of the University of Syiah Kuala in terms of facilities and infrastructure, a collection of books, visitors and the factors that influence its development. This study used a qualitative approach and using the historical method, while data collection technique using the documentation, observation and interviews. Primary sources used include document libraries and related informant interviews. Based on research that has been done, the results are as follows: (1) UPT. Library of the University of Syiah Kuala established in 1970, continue to increase in the development of a collection of books as well as facilities and infrastructure is also progressing in the needs of its visitors. number of visitors has increased and a dec reased in a visit to the library (2) Availability of the budget is the most important factor in influencing the development of facilities and infrastructure, a collection of books and visitors in UPT. Library of the University of Syiah Kuala. Keywords: Development, UPT. Syiah Kuala University Library.
When heritage speaks t-emoticons: emotional experience design in heritage tourism

Heritage tourism makes up a substantial share of cultural tourism. In order to be competitive, new avenues for deeper engage with visitors are sought for. Experience design offers psychological and emotional visitor satisfaction and use of the senses in experience design is effective in emotional engagement of tourists. Specific visual and acoustic cues may have influence on the tourist experience, as may also different smells and gustative experiences impact their memory. Physical participation allowing tourists to experience heritage by the sense of touch may further their mental or emotional involvement. This chapter deals with specific sensory cues termed heritage t-emoticons which act as experience enhancers. Systematic overview of the existing knowledge of these cues may be used as a guideline for emotional heritage experience design. Relationship of experience design with heritage interpretation theory is also studied while pointing out to the challenges and avenues of future research.
Students’ Attitudes towards the Use of Smartphone for Language Learning Purposes

Utilization of smartphone technology and English language, emerging English language and smartphone technology in non-academic and academic activities for learning purposes, student’s perception towards the usage of smartphone apps for English learning purposes, and smartphone apps for enhancing English proficiency are the area where this study is focused, gained from Indonesian High School students. Two hundred participants from 10th-12th grader of private high schools in Surabaya - Indonesia participated by completing an anonymous questionnaire and 16 participants of each grade were randomly selected and interviewed to gain further understanding towards the findings. The findings indicated smartphone technologies are mostly used by the participants for communication and social media purposes. On the other hand, smartphone is also used incontrovertibly by participants for the certain educational purposes. In addition, learning English via smartphone mobile apps, and diverse types of teenager’s online activities were discovered, as well as area of their English skill improvement. The findings suggest that: the students are considerably enthusiastic for using apps to support learning and they also view that smartphones bring substantial potentials and should be optimized for learning through systematic and purposeful activities properly designed for school learning-related activities
Penggunaan Virtual Machine untuk Mengoptimalkan Server pada Cloud Gaming dengan GamingAnywhere

The development of the game industry is increasingly advanced until the emergence of cloud gaming network technology. Cloud gaming allows low-spec clients to play high-spec games. An open-source cloud gaming platform is GamingAnywhere. In this study, we will implement a cloud gaming server using GamingAnywhere and combine it with a virtual machine. The virtual machines that will be used are VirtualBox and VMware. This research is aimed at providing information about resource usage on servers and clients as well as Quality of Service (QoS) and Frames Per Second (FPS) from GamingAnywhere running on virtual machines. From the results of server measurements it only takes 12-21% CPU usage, 5-7% GPU usage, and 75-77% memory usage for VirtualBox and 17-26% CPU usage, 26-35% GPU usage, and 64-65% memory usage for VMware. From the FPS measurement results obtained on the client, it has an average of more than 59 fps for the three test games when GamingAnywhere is running on VirtualBox, VMware, and without using a virtual machine. From the measurement results, to get optimal QoS in accessing games with GamingAnywhere, a minimum bandwidth of 5 Mbps is needed and the distance between the client and the router is a maximum of 7 meters. If the bandwidth is less than 5 Mbps, the system experiences a delay of ± 0.003 seconds and the packet loss is more than 10%.
Android Arcade-Style Racing Game “CarsTime” With Antigravitation Theme

Abstract CarsTime is an arcade-style racing game with antigravitation theme. This game is made using Unity3D game engine with C# programming language. This game is an Android game. In this game, player will race against bots on track that is not bound by gravity so each car can move even on upside down track. There are three cars that can be used by players, namely C.car, Sonic and Magnum. There are three stages that player can choose to play, namely Map Aspal which doesn’t have any additional difficulties, Map Pasir which makes player’s car move slower and Map Salju which makes player’s car slippery. There are three items that player can collect or interact while racing, namely coin which player can collect for buying car, boost can which player can collect to fill boost meter which can be used when full to increase player’s speed for a certain time, and boost pad which will increase player’s and bots speed for certain time when touched. This game is tested by using three methods which are blackbox testing, alpha testing and beta testing. Based on the testing result, it can be concluded that “CarsTime” has an interesting racing gameplay with hard difficutly on Map Salju and easy difficulty on Map Aspal and Map Pasir.
Using ILP to Detect Anomalies in Pipelines

Simulated data generated from an accurate modelling toolcan demonstrate real-life events. This approach mimics pipeline opera-tion without the need for maintaining the original anomaly record that isalready scarce in the pipeline industry. This synthetic data carries precisesignatures and the shape of the curve depicts the type of alarm. Learningrules can be inferred from this parametric data and the examples are con-strued from the threshold levels. The issue is addressed by consideringthe method that lessens data handling and the associated complexity ofthe problem. Probabilistic ILPs can be the most appropriate candidatefor classifying anomalies of this nature. A logic program addresses thisissue by learning the parameters of a program given the structure (therules), using the ability to incorporate probability in logic programming,interpreting the examples for target predicate, and refining backgroundknowledge for the dissemination of discoveries. This synthetic data alsodevelops a direct link with the ILP parameter learning for competinghypotheses. The modelling tool will receive feedback, check and tune thehypothesis until exclusivity is evolved. This will fall in the domain ofclosed-loop active learning
Becoming a Borderland Human

The article discusses categories of becoming in the philosophical and pedagogical context. Becoming is a feature of a borderland man living in a multicultural world. The borderland was defined and the features of a person living on this borderland have been indicated. The borderland unit is dynamic, it crosses both territorial and axiological boundaries, it just becomes.
Design of Art Appreciation Course Based on Multimedia Network

In the context of the rapid development of science and technology, people's life has experienced profound changes under the development of science and technology. Multimedia information technology has been applied in many fields of human society. In the field of education, multimedia technology also began to get in-depth application, whether it is compulsory education stage, higher education, or the popularity of multimedia technology in the field of education is more and more high. With the deepening of education reform, the education of the masses concept also began to constantly innovation, the traditional teaching mode has been difficult to adapt to the modern fine arts appreciation lesson teaching requirements, therefore, innovation of teaching mode, has become the key of art appreciation teaching content
Research on Vehicle Detection Based on Visual Convolution Network Optimization

Aiming at the problem that the vehicle detection algorithm based on convolutional neural network is too deep in the network layer, resulting in low training efficiency, this paper proposes a visualization method to adjust the structure of convolutional neural network, so as to improve training efficiency and detection effect. Firstly, the existing convolutional neural network model for image classification is visualized using the intermediate layer visualization method of convolutional neural network. Then, the layers of the convolutional neural network model are analyzed to select the layer with the best visualization effect for network reconstruction, so as to obtain a relatively simplified network model. The experimental results show that the similar multi-target detection method proposed in this paper has obvious improvement in training efficiency and accuracy.
Secure QAM Transmission using Private Synchronous Chaotic Phase Scrambling/Descrambling

We propose a physical-layer secure optical communication scheme by applying private synchronous chaotic phase scrambling/descrambling into QAM messages, and numerically demonstrate an exemplary 40Gbit/s 16QAM secure transmission over 80km for the feasibility confirmation.
Vocabulary-constrained Question Generation with Rare Word Masking and Dual Attention

Question generation is the task of generating questions from a text passage that can be answered using information available in the passage. Known models for question generation are trained to predict words from a large, predefined vocabulary. However, a large vocabulary increases memory usage, training and inference times and a predefined vocabulary may not include context-specific words from the input passage. In this paper, we propose a neural question generation framework that generates semantically accurate and context-specific questions using a small-size vocabulary. We break the question generation task into two subtasks namely, generating the skeletal structure of a question using common words from the vocabulary and pointing to rare words from the input passage to complete the question.
LibGuides: LSC 504:Baby Story Time: Tools for Reading to the Wee Ones.: Equipment

Reading and providing quality programming to your youngest patrons is quite the task: however, with the proper tools, you will be able to provide your wee patrons with many wonderful story time experiences and a lifetime love of reading. Various Equipment Used for Children's Programming
VR Educator: A Virtual Reality based Educational Application

Virtual realty is quickly becoming a vastly used tools not only for entertainment but majorly for educational purpose. The main concept behind VR is to facilitate a fully immersive virtual environment that emulates a real world. The use of virtual realty (VR) presents a unique virtual experience, indulging the students in a virtual world to tach different topics visually as if they are experiencing them physically. This paper presents the use of VR to educate students of internal process and working mechanism of computer. VR Educator creates a new diversion in educational technology. It is basically a general idea which is based on e-learning through VR and it can be applied to almost every educational field i.e. Chemistry, Biology, Physics, Astronomy etc. In our system students learn the internal workings of a CPU, involves the user into the process which is initiated whenever a key is pressed on keyboard. The user experiences the VR game in which he/she performs the tasks of busses i.e. carrying data bits to different parts of a computer.
Fake COVID-19 testing kits and lockdown puppy scams: how to protect yourself from fraud in a pandemic

Fraudsters are ruthless and will use any means necessary to gain financial advantage. Earlier this year, as Australians were battling the devastating bushfires, fraudsters were tailoring their approaches to exploit the good intentions of citizens wanting to help victims. And come March and the declaration of COVID-19 as a global pandemic, offenders have seamlessly shifted their approaches to take advantage of yet another crisis.