Reviewerly/exHarmony huggingface dataset:
The Future of Speech-to-Speech Translation

Since English is very different from Japanese in terms of grammar and vocabulary, it is not an easy task ( http://www.effectivelanguagelearning.com/language-guide/language-difficulty ) for a native English speaker to learn Japanese, and vice versa. Only a small number of native Japanese speakers can speak English, and the other way around is less likely. The part of the world where the Japanese language is used is basically limited to Japan. English, known as the “world language,” is not commonly used in Japan and neither are other foreign languages. There is a clear communication barrier between the Japanese and non-natives, and the large need to overcome this became the motivation for researchers in Japan to initiate research on automatic speech-to-speech translation. We have so far briefly looked back on how the state-of-the-art technology was developed, but the following sections will explore some of the near future possibilities of the technology and how it will likely to be utilized in society before and after the year 2020.
A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators

Thermal analysis using resonating micro-electromechanical systems shows great promise in characterizing materials in the early stages of research. Through thermal cycles and actuation using a piezoelectric speaker, the resonant behaviour of a model drug, theophylline monohydrate, is measured across the surface whilst using a laser-Doppler vibrometer for readout. Acquired is a sequence of spectra that are strongly correlated in time, temperature and spatial location of the readout. Traditionally, each spectrum is analyzed individually to locate the resonance peak. We propose a Bayesian model using a warped Gaussian process prior taking the correlations into account and demonstrate on both synthetic and experimental data, that it yields better estimates of both location and amplitude of the resonance peak. Thus, the proposed model can give a more precise characterization of drugs, which is important in drug discovery and development.
Current Efficiency Prediction of Aluminum Reduction Production Based on TCGWO-KELM

Because the method of the current efficiency has not been properly determined in the process of aluminum reduction so far, it is difficult to obtain real-time effective current efficiency data to guide aluminum reduction production. In this paper, a current efficiency prediction model based on kernel extreme learning machine (KELM) is established, which is improved by the tent chaotic grey wolf optimization algorithm. Firstly, aluminum reduction process is analyzed and obtained the parameters affecting the current efficiency of the aluminum production. In order to avoid the adverse effect of data redundancy on the prediction model, the principal component analysis method is used to reduce the dimension of the data. Secondly, the model of current efficiency based on KELM is established. In order to improve the training precision and increase the robustness of the algorithm, the improved grey wolf optimization algorithm is proposed to optimize the key parameters of the model. Finally, the current efficiency prediction model was trained and tested using the on-site production data. The results demonstrate the effectiveness of the prediction model and can provide decision-making reference for aluminum reduction production.
Critical Thinking in the Digital Age: Strategies of Human Resource Management

The research introduces a strategy of utilising critical thinking (CT) as an instrument of developing professional skills set in the digital age. The research aims to evaluate the potential of CT in human resources management (HRM). It has been established that the strategy of CT offers a way of intellectual improvement within the framework of professional communication. Therefore, a systematic fostering of CT allows for successful integration of affective and cognitive aspects of communication in the context of HRM.&#x0D; Conversely, this research has identified the potential of the offered strategy in change- and risk management. The CT strategy has proved itself to be an effective way of professional decision-making in key cognitive aspects as well as in effective ones (intellectual courage, perseverance, confident reasoning, independent thinking in tandem with overcoming egocentrism and sociocentrism). Based on the results of the empirical research within the modern digital environment, the long-term efficiency of the offered strategy has been substantiated; the researchers have outlined the potential ways of implementing the results of the study, as well as strategy’s growth point.
Strategies of research competence development in language teacher education

The article presents a model of researcher training of students in university-level language teacher education programmes. Based on the Russian Federal Educational Standard of Higher Education and the Educational Standard of Lomonosov Moscow State University, the authors of the article describe the main components of the language teacher’s research competences. The content of researcher training is outlined for the second, third and fourth year of the BA-programmes and the two years of the MA-programmes. The article also suggests research competences assessment procedures and tools. Combining the approaches of a classical and a pedagogical university, federal and university-developed standards, the article can serve as a foundation of researcher training programmes development, design of courses aimed at the development of the research competences and to develop specifications of researcher competences assessment for trainee language teachers on different levels of higher education.
Application of a text analytics model for the automatic discovery and classification of research trends in e-learning (the UOC case)

The review of scientific literature is a common task among researchers who wish to detect trends that are influencing levers of change in the evolution of a specific discipline or field of research. Obtaining such knowledge requires heavy and repetitive tasks of extraction, analysis, synthesis and classification of hundreds, and in some cases thousands, of bibliographic references. These early phases require a great effort by the team of analysts to collect and order results of the scientific evidence on which to base their conclusions, dedication that can be increased depending on the zoom of each search. In its ability to observe the "world map" of knowledge to detect and reflect on the trends and events that are transforming online higher education in the world, the eLC integrates such review and prospective documentary tasks into its activity. With the intention of facilitating the path towards the discovery and analysis of the relevant objective information of each study, the center has piloted a project consisting of automating part of this review process based on the use of analytical and text mining technologies.
k-Maximum Subarrays for Small k: Divide-and-Conquer Made Simpler

Given an array A of n real numbers, the maximum subarray problem is to find a contiguous subarray which has the largest sum. The k-maximum subarrays problem is to find k such subarrays with the largest sums. For the 1−maximum subarray the well known divide-and-conquer algorithm, presented in most textbooks, although suboptimal, is easy to implement and can be made optimal with a simple change that speeds up the combine phase. On the other hand, the only known divide-and-conquer algorithm for $$k > 1$$, that is efficient for small values of k, is difficult to implement, due to the intricacies of the combine phase. In this paper, we show how to simplify the combine phase considerably while preserving the overall running time. In the process of designing the combine phase of the algorithm we provide a simple, sublinear, $$O(\sqrt{k} \log ^{3} k)$$ time algorithm, for finding the k largest sums of $$X + Y$$, where X and Y are sorted arrays of size n and $$k \le n^2$$. The k largest sums are implicitly represented and can be enumerated with an additional O(k) time. Our solution relies on simple operations such as merging sorted arrays, binary search and selecting the $$k^{th}$$ smallest number in an array. We have implemented our algorithm and report excellent performance as compared to previous results.
Performance of New Global Appearance Description Methods in Localization of Mobile Robots

Autonomous robots should be able to perform localization and map creation robustly. In order to solve these problems many studies and techniques have been evaluated over the past few years. This work focuses on the use of an omnidirectional vision sensor and global appearance techniques to describe each image. Global-appearance techniques consist in obtaining a unique vector that describes globally the panoramic image. Once the images have been described the mobile robot can use these descriptors both to create a map of the environment or to estimate its position and orientation in the environment. The main objective of this work is to propose and test new alternatives to describe scenes globally. The results will be used to propose new robust methods to estimate the position and orientation of the robot, from the combination of several measurements of similitude of visual information. Therefore, the present work is an initial study towards a new localization method. In this initial study a comparative the previous and the new methods is performed. The experiments will be carried out with real images that have been taken in an heterogeneous scenario where simultaneously humans and robots work together. For this reason, variations of the lighting conditions, people who occlude the scene and changes on the furniture may appear.
CNN based Multi-View Object Detection and Association

We present a system capable of detecting and associating traffic participants in camera images with different points of view to a street scene. Our system is based on a multitask CNN architecture and detection as well as association are performed within the network. The association between different images is estimated without explicit knowledge of scene geometry and camera calibration information. One of the main applications of our system are currently test areas for autonomous vehicles. For this use case ground truth information for testing the environment perception as well as external environment information sent to autonomous vehicles via car-to-infrastructure is of great importance. This is particularly interesting in complex scenarios like big intersections. Our system shows promising results on the association task of big intersections taken from 8 different points of view.
Design and implementation of elderly assistant &amp; custody system based on Kinect and Gizwits

Abstract Since most of the traditional elderly assistant system cannot achieve remote control and send immediate notifications to their families when falling of the elderly occurs, a somatosensory interactive and remote controllable elderly assistant &amp; custody system is designed and realized based on Kinect and Gizwits in this paper. Old people’s falling can be detected via the depth images and skeleton information captured by Kinect sensor and notified to their family members through web service. Remote control of the simulated smart home appliance through the mobile APP can be achieved via Data Points set in Gizwits cloud platform. Because of the remote control of the domestic appliance and accurate identification of falling-down action, the presented system can provide the elderly people with great convenience and ensure their living safety.
Super Resolution for Multi Frames with 3D Feature Extraction and RNN Prediction

Super-resolution research has received extensive attention in recent years. With the rise and maturity of deep learning technology, learning-based superresolution (SR) method has gradually become the mainstream. Its main idea is to directly learn the mapping between low-resolution images and highresolution images by constructing a corresponding neural network. Different from the SR method only for a single image, the study of video SR is developing at the same time. For videos, in addition to considering the spatial characteristics of each frame, the variation of between frames in the time dimension is also a vital element affecting the video SR performance. However, the use of inter-frame motion characteristics remains a problem. Due to the characteristic limitations of the convolutional neural networks (CNN), it is not an efficient way to model the dependency in time dimension. There are also some SR methods using the optical flow to help with the modeling, but they still struggle with a problem of coping with complex large-scale motions. In order to deal with the above problems, we propose a network utilizing 3D convolution and recurrent neural network (RNN). In the proposed method, we use 3D convolution to extract the intra-frame and inter-frame features, then combine it with our original RNN to model the long-term dependence and short-term dependence in the video sequence. Furthermore, for reducing the huge amount of computational cost caused by the 3D convolution, we used the structure of the Laplacian pyramid to balance the calculation of the network. Finally, we conducted an experimental evaluation of our model and confirmed its effectiveness.
State Predictor of Classification Cognitive Engine Applied to Channel Fading

This study presents the application of machine learning (ML) to a space-to-ground communication link, showing how ML can be used to detect the presence of detrimental channel fading. Using this channel state information, the communication link can be used more efficiently by reducing the amount of lost data during fading. The motivation for this work is based on channel fading observed during on-orbit operations with NASA's Space Communication and Navigation (SCaN) testbed on the International Space Station (ISS). This paper presents the process to extract a target concept (fading and not-fading) from the raw data. The preprocessing and data exploration effort is explained in detail, with a list of assumptions made for parsing and labelling the dataset. The model selection process is explained, specifically emphasizing the benefits of using an ensemble of algorithms with majority voting for binary classification of the channel state. Experimental results are shown, highlighting how an end-to-end communication system can utilize knowledge of the channel fading status to identity fading and take appropriate action. With a laboratory testbed to emulate channel fading, the overall performance is compared to standard adaptive methods without fading knowledge, such as adaptive coding and modulation.
Deep learning for climate patterns

Deep learning provides a unique nonlinear tool for calibration, simulation, and prediction in diagnosis and mining of big climate data. It is used to not only discover evolving patterns of climate change, but also predict climate change impacts. The main advantage is that deep learning makes full use of some unknown information hidden in big climate data, although they cannot be extracted directly. The most important deep learning method for climate patterns is the neural network, which employs a massive interconnection of simple computing cells called neurons (or processing units). These neurons have a natural propensity for storing observation knowledge and making it available for use. A neural network resembles the brain in the following two respects: (1) the neural network acquires knowledge from the observation of its environment through a learning process (or learning algorithm); (2) the interneuron connection strengths, which are called synaptic weights, are used to store the acquired knowledge.
A fast moving object recognition method for video surveillance system

Prevailing object detection algorithms such as RCNN, YOLO and SSD usually are not suitable for high definition surveillance systems because of the fixed size of network input and masses of object candidate regions in the select search process. This paper proposes a fast moving object detection and recognition method for video surveillance system, which applies background extraction and frame difference to fulfill select search process, followed by a pretrained CNN model inference to complete object recognition. Proposed method was proved to be fast and effective in our experimental results which is more suitable for moving object detection for video surveillance system, compared to current other object detection algorithms.
WORKSHOP FOR INDONESIAN LANGUAGE TEACHERS OF JUNIOR HIGH SCHOOL LEVEL IN THE DISTRICT OF JALAN CAGAK KAB. SUBANG ON THE USE OF GOOD AND APPROPRIATE INDONESIAN LANGUAGE TO IMPROVE THE ETHICS OF KARIMAH

Language is something that is very important because by using language a person can communicate and adapt, and based on good and correct speech, he/she becomes an individual who has the morality of karimah. A society can be classified as having good characters if they use polite and appropriate language in daily life or various opportunities. In line with the goals and objectives of national development, national education is directed at efforts to improve the quality of Indonesian people. Humans with faith and devoted to God Almighty are those with virtuous character, personality, disciplined, hardworking, tough, responsible, independent, intelligent and skilled, and healthy physical and physical. In this connection, teachers, as the spearhead, are good role model in the eyes of their students. The teachers are those who are followed and imitated by students. Even thousands of teachers teach in various schools or formal and informal institutions, teachers are scattered in rural and urban areas throughout Indonesia. The teacher is one of the centers for the development of mental mentality in societies with different sociocultural stratification. Teachers can produce sociocultural and civilizational stratification in the sense of forming good and noble character of moral character, thus there must be coaching in every aspect in order to create a synergy between good and right language with moral character to the high school teachers in the district of Kec. Jalan Cagak Kab. Subang.
Reducing the Computational and Communication Complexity of a Distributed Optimization for Regularized Logistic Regression

In this paper, we propose a new distributed optimization method that computes a Lasso estimator for logistic regression in the case when two parties have explanatory variables corresponding to distinct attributes. An existing protocol using the alternating direction method of multipliers (ADMM) for linear regression can be applied to logistic regression. However, this protocol needs an underlying iterative method such as the gradient method. We show that the proposed protocol using the generalized Bregman ADMM, which removes the necessity to use the underlying iterative method, requires lower computational and communication complexity.
Rapid, Progressive Sub-Graph Explorations for Interactive Visual Analytics over Large-Scale Graph Datasets

Exploring a voluminous graph dataset visually is a challenging task due to the sheer amount of data and the lack of structure to rely on during the navigation. Indra, our framework for large-scale graph data, provides responsive visual analytics over large-scale graph datasets. In this study, we propose a novel graph indexing scheme that pivots the view of the graph to a hierarchical structure while preserving the semantic importance of vertices within the user's analysis scenario. Indra allows users to compare and track multiple aspects of sub-graphs by supporting linked multi-views and multi-resolution operations such as drill-in and roll-ups. We have performed a set of empirical benchmarks profiling Indra and these demonstrate that several operations are executed with sub-second latency to effectively support interactive visual analytics.
Proposal of a simple recommendation system for small and medium enterprises for decision making based on unsupervised learning

Recommendation systems are generally complicated, due they search to increase their reach and robustness, they combine different artificial intelligence approaches mainly of supervised learning. A disadvantage of this type of systems is that they must have a prior classification to be able to train a system and after they can be able to make decisions in a simmilar way that a human would do it; however, the task of classification is often expensive because is needed to consult with experts the possible classification (also known as label) that should be given to a specific data; although this method can be profitable for large companies, it is not for small and medium companies. This is the reason which the present work shows a proposal of a simple system that does not need to have a previous classification, allowing it to be profitable for small and medium enterprises in decision making.
Intestinal Polyps Recognition Based on Annular Spatial Pyramid Matching with Locality-Constrained Linear Coding for Gastroscopy Diagnosis

A novel automatic polyp recognition scheme called Annular Spatial Pyramid Matching (ASPM) with Locality-Constrained Linear Coding (LLC) is proposed by considering the annular structure of the intestinal images at multilevel. Firstly, detailed texture features extracted from the samples including normal and polyp images are calculated and then LLC method is employed on these features to obtain a sparse representation. Secondly, a strategy of annular region segmentation based on Spatial Pyramid Matching is proposed to improve the effectiveness of processing for intestinal images. Then, the final representation for each image is obtained by max-pooling the codes of features. Finally, SVM classifier is developed to carry out polyp images classification tasks. The experimental results indicate that the proposed algorithm outperforms the analysed state-of-the-art methods on the polyps recognition.
Space-time adaptive precision imaging

On the design methods and traditional micro system structures in multiple configurations, there are some certain limitations. In order to resolve the problems of the conventional multiple structures present in the system, new sensors and components are applied in the development of the design method and system configuration. Micro lens, as a kind of typical components, are developing the methodology in research advanced multiple configuration systems. The effect of system evaluated criterion that Modulation Transfer Function (Modulation Transfer Function, the MTF), Energy concentration (Radial Energy Analysis, REA), diffusion (Spot Diagram, RMS) can support our issue qualitatively. Results showed that can not only change systems weight, size and structural stability, but also increase freedom in these kinds design. While improving the implementation of existing multiple path configuration from traditional complex.