Reviewerly/exHarmony huggingface dataset:
Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement

Owing to refraction, absorption, and scattering of light by suspended particles in water, raw underwater images suffer from low contrast, blurred details, and color distortion. These characteristics can significantly interfere with the visibility of underwater images and the result of visual tasks, such as segmentation and tracking. To address this problem, we propose a new robust adversarial learning framework via physics model based feedback control and domain adaptation mechanism for enhancing underwater images to get realistic results. A new method for simulating underwater-like training dataset from RGB-D data by underwater image formation model is proposed. Upon the synthetic dataset, a novel enhancement framework, which introduces a domain adaptive mechanism as well as a physics model constraint feedback control, is trained to enhance the underwater scenes. Final enhanced results on synthetic and real underwater images demonstrate the superiority of the proposed method, which outperforms nondeep and deep learning methods in both qualitative and quantitative evaluations. Furthermore, we perform an ablation study to show the contributions of each component we proposed.
Event Detection with Relation-Aware Graph Convolutional Neural Networks

Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks. However, these methods usually ignore dependency label information, which conveys rich and useful linguistic knowledge for ED. In this paper, we propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN), which simultaneously exploits syntactic structure and typed dependency label information to perform ED. Specifically, an edge-aware node update module is designed to generate expressive word representations by aggregating syntactically-connected words through specific dependency types. Furthermore, to fully explore clues hidden in dependency edges, a node-aware edge update module is introduced, which refines the relation representations with contextual information. These two modules are complementary to each other and work in a mutual promotion way. We conduct experiments on the widely used ACE2005 dataset and the results show significant improvement over competitive baseline methods.
Classification of Transition Human Activities in IoT Environments via Memory-Based Neural Networks

Human activity recognition is a crucial task in several modern applications based on the Internet of Things (IoT) paradigm, from the design of intelligent video surveillance systems to the development of elderly robot assistants. Recently, machine learning algorithms have been strongly investigated to improve the recognition task of human activities. Though, in spite of these research activities, there are not so many studies focusing on the efficient recognition of complex human activities, namely transitional activities, and there is no research aimed at evaluating the effects of noise in data used to train algorithms. In this paper, we bridge this gap by introducing an innovative activity recognition system based on a neural classifier endowed with memory, able to optimize the performance of the classification of both transitional and non-transitional human activities. The system recognizes human activities from unobtrusive IoT devices (such as the accelerometer and gyroscope) integrated in commonly used smartphones. The main peculiarity provided by the proposed system is related to the exploitation of a neural network extended with short-term memory information about the previous activities’ features. The experimental study proves the reliability of the proposed system in terms of accuracy with respect to state-of-the-art classifiers and the robustness of the proposed framework with respect to noise in data.
Hybrid ARIMA and Neural Network Modelling Applied to Telecommunications in Urban Environments in the Amazon Region

This study explores the use of a hybrid Autoregressive Integrated Moving Average (ARIMA) and Neural Network modelling for estimates of the electric field along vertical paths (buildings) close to Digital Television (DTV) transmitters. The work was carried out in Belém city, one of the most urbanized cities in the Brazilian Amazon and includes a case study of the application of this modelling within the subscenarios found in Belém. Its results were compared with the ITU recommendations P. 1546-5 and proved to be better in every subscenario analysed. In the worst case, the estimate of the model was approximately 65% better than that of the ITU. We also compared this modelling with a classic modelling technique: the Least Squares (LS) method. In most situations, the hybrid model achieved better results than the LS.
A copula-based time series model for global horizontal irradiation

The increasing importance of solar power for electricity generation leads to increasing demand for probabilistic forecasting of local and aggregated photovoltaic (PV) yields. Based on publicly available irradiation data, this paper uses an indirect modeling approach for hourly medium to long-term local PV yields. We suggest a time series model for global horizontal irradiation that allows for multivariate probabilistic forecasts for arbitrary time horizons. It features several important stylized facts. Sharp time-dependent lower and upper bounds of global horizontal irradiations are estimated. The parameters of the beta distributed marginals of the transformed data are allowed to be time-dependent. A copula-based time series model is introduced for the hourly and daily dependence structure based on simple vine copulas with so-called tail dependence. Evaluation methods based on scoring rules are used to compare the model’s power for multivariate probabilistic forecasting with other models used in the literature showing that our model outperforms other models in many respects.
Bayesian Network Model for a Zimbabwean Cybersecurity System

The purpose of this research was to develop a structure for a network intrusion detection and prevention system based on the Bayesian Network for use in Cybersecurity. The phenomenal growth in the use of internet-based technologies has resulted in complexities in cybersecurity subjecting organizations to cyberattacks. What is required is a network intrusion detection and prevention system based on the Bayesian Network structure for use in Cybersecurity. Bayesian Networks (BNs) are defined as graphical probabilistic models for multivariate analysis and are directed acyclic graphs that have an associated probability distribution function. The research determined the cybersecurity framework appropriate for a developing nation; evaluated network detection and prevention systems that use Artificial Intelligence paradigms such as finite automata, neural networks, genetic algorithms, fuzzy logic, support-vector machines or diverse data-mining-based approaches; analysed Bayesian Networks that can be represented as graphical models and are directional to represent cause-effect relationships; and developed a Bayesian Network model that can handle complexity in cybersecurity. The theoretical framework on Bayesian Networks was largely informed by the NIST Cybersecurity Framework, General deterrence theory, Game theory, Complexity theory and data mining techniques. The Pragmatism paradigm used in this research, as a philosophy is intricately related to the Mixed Method Research (MMR). A mixed method approach was used in this research, which is largely quantitative with the research design being a survey and an experiment, but supported by qualitative approaches where Focus Group discussions were held. The performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms was discussed. Alternative improved solutions discussed include the use of machine learning algorithms specifically Artificial Neural Networks (ANN), Decision Tree C4.5, Random Forests and Support Vector Machines (SVM).
Exocentric Distance Judgment and Accuracy of Head-Mounted and Stereoscopic Widescreen Displays in Frontal Planes

An experiment was done to explore the effects of two virtual display systems on the accuracy of exocentric distance judgment and position. Sixteen participants viewed animated virtual targets using either a head-mounted display (HMD) or a stereoscopic widescreen display (SWD). The virtual targets have been shown, one at a time, at three depth levels and with two corresponding exocentric distances and three target sizes at each target distance and, afterward, via pointing by holding a stick to estimate the exocentric distance and position of each target. The position data were collected using an OptiTrack motion capture system. The results showed that the accuracy of exocentric distance judgment was higher with the head-mounted displays than with the stereoscopic widescreen displays. In addition, higher position accuracy in the X-direction was obtained from the stereoscopic widescreen displays, whereas no significant difference was observed in position accuracy in the Y-direction. However, it is possible that the HMD could give better accuracy in both exocentric distance and position judgments in the frontal plane, if the HMD had been perfectly mounted and flawlessly fit the participant’s eyes. The result also revealed that exocentric distance judgment was significantly higher at the farthest target distances than at the nearest distance. Similarly, the position accuracy significantly increased as exocentric distance decreased. Moreover, engineers may allude to the findings as the evidence from the study suggests that the intermediate target distances might be fitting or ideal distances to design and structure 3D applications.
Implementing Quantum Fourier Transform in a Digital Quantum Coprocessor

In this paper, the digital quantum coprocessor has been checked for the possibility of quantum Fourier transform, which is the main quantum operation of the Shor's algorithm.To do this, the model of the 4-qubit coprocessor has been created, its work has been simulated and it has been implemented in FPGA.
Credibility of Growth and Development Measures in Rentier Economies: The Case of GCC

Credibility of Growth and Development Measures in Rentier Economies: The Case of GCC Abbas A. Al-Mejren Ph.D. Abstract The six countries of the Gulf Cooperation Council (GCC) rely heavily on a depleting, and unstable source of income, namely crude oil and natural gas. The paper explores various imbalances resulting from such over-reliance and disputes the validity of widely used conventional measures of growth and developments for measuring growth and development in such oil based economies. The paper argues that constraints facing the GCC countries require the use of distinctive non-conventional indicators that can assess their performance towards development. Thus, it suggests some relevant indicators of development performance that are specifically practical in the case of GCC countries. It also applied a dispersion index to assess the GCC economic diversification efforts during the past four and half decades. The UAE and Saudi Arabia have achieved the best pace of success in transforming their domestic economy into more diversified ones. The paper also proposes measures associated with international indices that were introduced by world organizations and institutions. In this context, the paper presented three of these indices, the good governance index, democracy index and corruption perceptions index. Full Text: PDF DOI: 10.15640/jeds.v7n4a8
Introduction to Spark and Spark MLlib

Spark is a unified big data processing framework for processing and analyzing large datasets. Spark provides high-level APIs in Scala, Python, Java, and R with powerful libraries including MLlib for machine learning, Spark SQL for SQL support, Spark Streaming for real-time streaming, and GraphX for graph processing.2 Spark was founded by Matei Zaharia at the University of California, Berkeley’s AMPLab and was later donated to the Apache Software Foundation, becoming a top-level project on February 24, 2014.3 The first version was released on May 30, 2017.4
Method for Time Minimization of Api Requests Service From Cyber-Physical System to Cloud Database Management System

have analyzed the current state of cloud calculations and discovered the necessity of Application Programming Interface (API) usage in some of the cyberphysical systems (CPhS).The expediency of creating tools to reduce service time of Application Programming Interface requests and effective synchronization of the local and cloud database has been established.The authors clarified one of the main features of data replication.The expediency of using counter generations in the replication process instead of using system timer has been justified.The authors proposed the asynchronous method of accelerated service time for API requests to cloud database management systems using a synchronization accumulative table and registering changes in database with two-step sets of generations.Library that provides implementation of asynchronous API requests for Salesforce cloud management systems has been developed.Any Ruby on Rails application can use this library.Authors have evaluated and carried out benefits for the proposed solutions in test cases.Results of the test cases confirm service time minimization of API requests to the cloud database management system based on the proposed asynchronous method.
Progressive Learning and Disentanglement of Hierarchical Representations

Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE). However, by extracting high-level abstractions in the bottom-up inference process, the goal of preserving all factors of variations for top-down generation is compromised. Motivated by the concept of "starting small", we present a strategy to progressively learn independent hierarchical representations from high- to low-levels of abstractions. The model starts with learning the most abstract representation, and then progressively grow the network architecture to introduce new representations at different levels of abstraction. We quantitatively demonstrate the ability of the presented model to improve disentanglement in comparison to existing works on two benchmark data sets using three disentanglement metrics, including a new metric we proposed to complement the previously-presented metric of mutual information gap. We further present both qualitative and quantitative evidence on how the progression of learning improves disentangling of hierarchical representations. By drawing on the respective advantage of hierarchical representation learning and progressive learning, this is to our knowledge the first attempt to improve disentanglement by progressively growing the capacity of VAE to learn hierarchical representations.
English Edugame Application for Childhood base on Android

apply arithmetic with play compared to without playing. Play and child is an inseparable unity. Play activities carried out children and children’s activities always indicate play activities. In childhood the comprehension is very high, more activities a child does, the ability to remember will also be higher. There are several factors make it difficult for children to recite and remember the object, color around them, it is less interested in the media used. Childhood always follows the rhyme of its development. at an early age, language development is very important, because at this age it is a sensitive period for children. The English language is the International language. English language for children starts from introducing vocabulary that is closest to the child, the goals to make it easier for children to remember. One way to maximal comprehension by giving learning fun involves learning with the game. The research resulted in an English game application based on android. In this application, there are education and game features. The education features an exciting good and gets the sound from the lesson that is available. Game features available are guessed pictures, guess sounds, and puzzles. The purpose of English edugame application to teach children vocabulary of English and make children not bored to learn.
Handwritten Devanagari Character Generation Using Deep Convolutional Generative Adversarial Network

As deep learning became popular, the need for huge amounts of data has risen. The major problem faced in deep learning is the data scarcity. Many researchers have done research in areas such as image processing, pattern recognition, artificial intelligence, and cognitive science to solve handwritten character recognition problem but the data availability remains the problem particularly in Indian languages. The main motive of this paper is to generate the handwritten character of Devanagari, for which DCGANs are used which help us to generate training data images from the vector representation. Here, we use three-layer CNN having a stride value of two for feature extraction of the handwritten character. The characters generated look like the character in the original dataset.
Compactly Representing Uniform Interpolants for EUF using (conditional) DAGS.

The concept of a uniform interpolant for a quantifier-free formula from a given formula with a list of symbols, while well-known in the logic literature, has been unknown to the formal methods and automated reasoning community. This concept is precisely defined. Two algorithms for computing the uniform interpolant of a quantifier-free formula in EUF endowed with a list of symbols to be eliminated are proposed. The first algorithm is non-deterministic and generates a uniform interpolant expressed as a disjunction of conjunction of literals, whereas the second algorithm gives a compact representation of a uniform interpolant as a conjunction of Horn clauses. Both algorithms exploit efficient dedicated DAG representation of terms. Correctness and completeness proofs are supplied, using arguments combining rewrite techniques with model-theoretic tools.
PENGARUH PERHATIAN ORANG TUA DAN KEMANDIRIAN BELAJAR TERHADAP PRESTASI BELAJAR IPS SISWA DI SDN 130 KARAMBUA DESA RINJANI KECAMATAN WOTU KABUPATEN LUWU TIMUR

ABSTRACK&#x0D; This study aims to determine (i) The Effect of Parents' Attention on Social Studies Student Achievement at SDN 130 Karambua. (ii) The Effect of Learning Independence on Social Studies Learning Achievement of Students at SDN 130 Karambua. (iii) Effects of Parental Attention and Learning Independence together on Social Studies Achievement of Students at SDN 130 Karambua, Rinjani Village, Wotu District.&#x0D; This type of research is expost facto. Data analysis used was a simple regression analysis technique for the first and second hypotheses and multiple regression analysis for the third hypothesis. Before analyzing the data, an analysis of the requirements for testing is carried out including: normality, homogeneity, independent t test and linearity test The results of this study are: (i) There is a positive and significant influence of Parents' Attention on Social Studies Student Achievement at SDN 130 Karambua, with rx1y = 0.516; r2x1y = 0.266; and tcount is greater than ttable, namely: 3.85&gt; 1.701. (ii) There is a positive and significant influence of Learning Independence on Social Studies Achievement of Students in SDN 130 Karambua, with rx2y = 0.372; r2x2y = 0.138; and tcount is greater than ttable, namely: 2.119&gt; 1.701. (iii) There is a positive and significant influence of Parental Attention and Learning Independence together on Social Studies Achievement of Students in SDN 130 Karambua, with Ry (1,2) = 0.631; R2y (1,2) = 0.398; and Fcount is greater than Ftable namely: 5.542&gt; 3.35 at a significance level of 5%. The regression line equation Y = 0.615X1 + -0.252X2 + 51.198.
“Preparedness for Practice” for Dental Graduates Is a Multifaceted Concept That Extends Beyond Academic and Clinical Skills

Conceptual framework explaining "preparedness for practice" of dental graduates: a systematic review. Mohan M, Ravindran TKS. J Dent Educ 2018;82(11):1194-1202.None declared.Systematic review.
SFLNet: Direct Sports Field Localization via CNN-Based Regression

In this paper we propose a novel approach to build a single shot regressor, called SFLNet, that directly predicts a parameter set relating a sports field seen in an input frame to its metric model. This problem is challenging due to the huge intra-class variance of sports fields and the large number of free parameters to be predicted. To address these issues, we propose to train our regressor in combination with semantic segmentation in a multi-task learning framework. We also introduce an additional module to exploit the spacial consistency of sports fields, which boosts both regression and segmentation performances. SFLNet can be learned with a training dataset that can be semi-automatically built from human annotated point-to-point correspondences. To our knowledge, this work is the first attempt to solve this sports field localization problem relying only on an end-to-end deep learning framework. Experiments on our new dataset based on basketball games validate our approach over baseline methods.
Adversarial behavioral cloning

Imitation learning has been widely applied for autonomous robotics control. A popular IL approach is apprenticeship learning (AL) which alternates RL and inverse reinforcement learning (IRL). AL fundamentally requires a large number of environment interactions and thus takes a long time for training. We believe that IL algorithms would be more applicable to real-world problems if the number of interactions could be reduced as close to zero as possible. In this paper, we propose an IL algorithm which we call Adversarial Behavioral Cloning (ABC). Experimental results on MuJoCo physics simulator show that our algorithm achieves competitive results with a state-of-the-art AL algorithm, namely generative adversarial imitation learning (GAIL), even without any environment interactions.
String Diagrams for Optics

Optics are a data representation for compositional data access, with lenses as a popular special case. Hedges has presented a diagrammatic calculus for lenses, but in a way that does not generalize to other classes of optic. We present a calculus that works for all optics, not just lenses; this is done by embedding optics into their presheaf category, which naturally features string diagrams. We apply our calculus to the common case of lenses, extend it to effectful lenses, and explore how the laws of optics manifest in this setting.
Marathi To English Neural Machine Translation With Near Perfect Corpus And Transformers

There have been very few attempts to benchmark performances of state-of-the-art algorithms for Neural Machine Translation task on Indian Languages. Google, Bing, Facebook and Yandex are some of the very few companies which have built translation systems for few of the Indian Languages. Among them, translation results from Google are supposed to be better, based on general inspection. Bing-Translator do not even support Marathi language which has around 95 million speakers and ranks 15th in the world in terms of combined primary and secondary speakers. In this exercise, we trained and compared variety of Neural Machine Marathi to English Translators trained with BERT-tokenizer by huggingface and various Transformer based architectures using Facebook's Fairseq platform with limited but almost correct parallel corpus to achieve better BLEU scores than Google on Tatoeba and Wikimedia open datasets.