Reviewerly/exHarmony huggingface dataset:
EFL LEARNERS’ PHONOLOGICAL INTERFERENCE OF ENGLISH ARTICULATION

This research investigated the mispronunciation of Putera Batam University EFL learners by adapting the standard of Received Pronunciation (RP) and the factors of English vowels and consonants mispronunciation. This descriptive qualitative research applied observational method with participatory technique in collecting data, and articulatory identity method in analyzing the data. The English mispronunciation was found in the EFL learners’ English pronunciation. The consonant sounds /ð/, /θ/, /th/, /z/, /r/, /ʃ/, /ʧ/, /ʤ/, /nj/, and consonant-closed syllable sound of /k/ were pronounced incorrectly. The consonant sound /ð/ was pronounced as /d/, /θ/ as /t/, /th/ as /t/, /z/as /ɉ/, /r/ as /ɾ/, /ʃ/ as /s/, /ʧ/ as /s/, /ʤ/ as /d/, and /nj/ is pronounced as /ɲ/. Consonant-closed syllable sound of /k/ is articulated as /Ɂ/. The vowel sounds /ə/ and /æ/ were pronounced incorrectly as /e/ and the diphthong sound /eɪ/ were pronounced incorrectly as /e/. These mispronunciation phenomena were caused by some factors based on their background. The factors were; the mother tongue interference (native language), the differences between Indonesian and English sound systems (phonetic ability), the educational background, and the environmental background (amount of exposure).
AI4TV 2019

Technological developments in comprehensive video understanding - detecting and identifying visual elements of a scene, combined with audio understanding (music, speech), as well as aligned with textual information such as captions, subtitles, etc. and background knowledge - have been undergoing a significant revolution during recent years. The workshop brings together experts from academia and industry in order to discuss the latest progress in artificial intelligence research in topics related to multimodal information analysis, and in particular, semantic analysis of video, audio, and textual information for smart digital TV content production, access and delivery.
Breast cancer Data classification Using Machine Learning Mechanisms

During their life, among 8% of women is diagnosed with Breast cancer (BC), after lung cancer, BC is the second popular cause of death in both developed and undeveloped countries. BC is characterized by the mutation of genes, constant pain, changes in the size, color (redness), skin texture of breasts. Classification of breast cancer leads pathologists to find a systematic and objective prognostic, generally the most frequent classification is binary (benign cancer/malign cancer). Today, Machine Learning (ML) techniques are being broadly used in the breast cancer classification problem. They provide high classification accuracy and effective diagnostic capabilities. In this paper, we apply five different machine learning algorithms for classifying BC data, and results are visually represented.
Smartphones: An Effective Aid in Teaching-Learning of English Language

This research paper covers the utilization of smart phones effectively in the teaching-learning process of English at a variety of levels.In this world of technology use of technological innovations and electronic gadgets have become the inseparable parts of our life.These technological innovations have brought enormous change in everybody's life.Smart phone is, being the handy electronic gadget, one of the outstanding inventions of technology.The concept of ICT based education has changed entire teaching-learning process.Teachers as well as students are techno-savvy and believe in learning through the electronic gadgets.As the students have knowhow of the electronic gadgets, the teachers also need to employ such devices in their teaching process.Day by day the sacred field of education is being developed and modified with the help of technological progress all over the world.It also saves time of both the teachers and the students.This paper specifically covers the ways of smartphones implementation in the teaching-learning process.Thus, the present paper is a modest attempt and the result of the continual research and use of smartphones in teaching-learning process of English language.
Cultural-Dialogic Interdeterminist Metaperspective for Mental Health and Well-Being

The phenomenology of mental health and well-being itself is not homogeneous and static, but heterogeneous and dynamic.In most areas of study now exists a plethora of theories, each with its own concepts, language, and underlying assumptions, but there is a lack of critical comparison, integration, and synthesis in this body of theory, resulting in conceptual clouding and ambiguity.Reflecting the current state of psychological knowledge, we can state the presence in it of an infinite number of local theories and empirical findings, leading to the drowning in the unlimited of disparate fragments, not accompanied by holistic understanding of the psychological phenomenology particularity as the integral reality of human existence that determines its functioning in it.In the framework of the proposed metatheoretical approach, an author's attempt to solve this fundamental problem, which is relevant for psychological knowledge development.Initially stated pluralistic character of modern psychological knowledge and its philosophical and epistemological grounds.Existing crisis debates in the psychological science (replicability, disintegration, empiricism, etc.) is considered in the context of deterministic and indeterministic directions of its development.I propose an interdeterministic alternative in psychological development and its innovative potential.The authors attempt to integrate the variety of existing psychological knowledge in the form of positioning in the heterogeneousmultidimensional-multiparadigm spaces of psychological knowledge in the form of three interconnected four-dimensional continua, allowing to integrate all the existing traditions and approaches
Energy processes with natural quantization

The paper shows that the quantum-mechanical approach is applicable to most macro processes occurring in nature include the power industry. The mathematical apparatus of the isomorphic Heisenberg algebra is proposed. A non-commutative ring is constructed within which the commutation relations are given. The transition from quantum to classical theory is shown.
The Effect of Multipass Strategy and Studentsâ€™ Attitude Toward Their Reading Comprehension at the Tenth Graders of SMAN 2 Lubuk Alung

This study aims to determine the effect of multipass strategies and student attitudes on students' reading comprehension in historical recount texts. The design of this research is Quasi Experiment Research with a 2x2 factorial design. The population of this research is the students of class X SMAN 2 Lubuk Alung Academic Year 2018/2019. Reading tests and student attitude questionnaires were used as research instruments. Then, the research data were analyzed using the T-test formula and Two-Way ANOVA. The results of this study indicate that (1) students taught with multipass strategies have better reading scores than students taught with ordinary strategies, (2) students taught with multipass strategies with positive attitudes have better reading values than students taught with ordinary strategies, (3) students taught with multipass strategies with negative attitudes have better reading scores than students taught with common strategies, and (4) there is no interaction between the two teaching strategies (multipass strategies and ordinary strategies) and students 'attitudes towards students' reading comprehension.
SELF-DEVELOPMENT OF DIGITAL COMPETENCE OF THE STUDENTS AT COLLEGE

Globalization processes of society and WorldSkills Russia have a significant impact on the Russian system of education. Nowadays the Pedagogy develops rapidly, therefore, it is very important to use the actual results and methods. Today we can see the changes in normative documents and requirements to the results of professional education. These documents focus on preparing graduates to work in a competitive environment, to develop digital competence, to live in digital communities. Russia takes only the 43rd place in the development of digital technologies in international rankings. It is significantly inferior to European countries. Besides, the analysis of data from the Regional Public Organization's Internet Technology Center project shows that the Index of digital literacy for the population in 2018 decreased by 15. 6 % compared to the previous period. This is due to the following contradiction: on the one hand, we observe the modernization of Russian education system that causes the increase of requirements to graduates that should possess competencies in the field of information technology on the global average level, and, on the other hand, we see a low level of graduates’ digital competence. The study aims to determine the prerequisites of appearance and development of the problem of digital competence self-development. To solve the problem, we use the following methods: analysis of the literature and regulatory documentation; observation, comparison analysis; deduction and induction; qualitative and quantitative analysis. The paper describes the structure and content of concepts “selfdevelopment”, “digital competence”. The analysis made shows the aggravated contradictions and the need to solve the problem stated.
Matching People Across Surveillance Cameras

This work addresses the person re-identification problem, which consists on matching images of individuals captured by multiple and non-overlapping surveillance cameras. Works from literature tackle this problem proposing robust feature descriptors and matching functions, where the latter is responsible to assign the correct identity for individuals and is the focus of this work. Specifically, we propose two matching methods: the Kernel MBPLS and the Kernel X-CRC. The Kernel MBPLS is a nonlinear regression model that is scalable with respect to the number of cameras and allows the inclusion of additional labelled information (e.g., attributes). Differently, the Kernel X-CRC is a nonlinear and multitask matching function that can be used jointly with subspace learning approaches to boost the matching rates. We present an extensive experimental evaluation of both approaches in four datasets (VIPeR, PRID450S, WARD and Market-1501). Experimental results demonstrate that the Kernel MBPLS and the Kernel X-CRC outperforms approaches from literature. Furthermore, we show that the Kernel X-CRC can be successfuly applied in large-scale and multiple cameras datasets.
Initial Flight Simulation Testing of a Gesture-Based Interface for Flight Control

As our knowledge in aeronautics has improved, so have aircraft.Modern flight control systems can use electronic systems to greatly enhance the controllability, efficiency and safety of even aerodynamically unstable aircraft, however the man-machine interface method has remained the same since the early days; sticks and pedals.This paper depicts the very first simulated test flights on a prototype flight control interface for manned aircraft under development, this interface uses the pilot's eye focus direction (known as "gaze") and hand movements to control the aircraft in flight by just looking into a desired direction, or by having the aircraft to "imitate" her or his hand like one would do to illustrate an aircraft flight maneuver: an open hand with thumb and pinky finger extended like "wings" to imitate an aircraft motion in flight, except that this time is the other way around, with the aircraft following the hand rolls and climbs as actual commands.Although Gaze-guided and Hand gesture-guided flight control has been proven feasible using this system, only the initial operative experience is covered, with a general overview on the systems functionality and of data collected, this exploratory research does not intends to result in a immediately-applicable system, rather as proof of concept of the interface method of the proposed system and as foundation for further development.
An Enhanced Multi-Population Ensemble Differential Evolution

MPEDE integrates multiple effective strategies to solve optimization problems. However, there is still some room to improve the optimization performance of it. In this work, we introduce an enhanced multi-population ensemble DE (eMPEDE). In the proposed algorithm, an improved mutation strategy "rand-to-mpbest/1" replaces "rand/1" in MPEDE to balance the exploration and exploitation, which utilizes multiple best solutions to guide searching. Moreover, an improved parameter adaptation method is employed to alleviate premature convergence by using success-history based adaptation. The experiments on CEC2005 benchmark problems are executed, including a comparison with other peer competitors. The experimental results reveal the capability of eMPEDE to generate more competitive results compared to MPEDE and other peer competitors.
Ensemble Mixed Breed Deep Clustering Algorithm for Complex Datasets

Clustering is process of having similar items at one place. Many datasets are available for the research in clustering. Machine learning (ML) and deep learning (DL) are two latest domains that to improve the clustering techniques. From past decade so many applications are developed in clustering for pattern recognition, speech and other prediction type of algorithms. According to the latest research, deep clustering algorithms can be used to learn better representations of the data. In this paper, the Ensemble Mixed Breed Deep Clustering Algorithm (EMBDCA) which is adopted various deep learning algorithms for improving the performance. For the training, Information Maximizing Self-Augmented Training (IMSAT) is utilized. This will improve the accuracy especially for the datasets such as mushroom and MIST dataset. The parameters sensitivity, specificity and quality of clusters are also improved.
DMU-ABSE: Dynamic Multi-user Attribute-Based Searchable Encryption with File Deletion and User Revocation

Searchable encryption (SE) is a new cryptographic technique that allows data users searching for the files of their interests over huge amounts of encrypted files on the cloud. When it comes to multi-user setting, more issues should be addressed comparing to single-user setting, including key distribution, search privilege control and access control. In this paper, we propose DMU-ABSE, a dynamic multi-user ciphertext-policy attribute-based searchable encryption scheme with file deletion and user revocation. We manipulate an attribute-based encryption to achieve fine-grained search privilege control and hidden policy in multi-user setting while searching time of the proposed scheme is constant (O(1)). With the help of proxy re-encryption, we build one searchable index matrix by different owners in order to improve the searching efficiency. Furthermore, our scheme implements access control by embedding decryption keys into the index matrix. The proposed scheme is proved IND-CKA and IND-CPA semantically secure and experimental results shows that our scheme is efficient.
Exploring Writing Anxiety of the EFL University Students in Pontianak Indonesia

The purpose of the descriptive study is to explore the English as a foreign language second-semester students’ writing anxiety. The students were from a private university in Pontianak, West Kalimantan, Indonesia. A total of 92 students who were selected randomly filled the writing anxiety questionnaire. The writing anxiety questionnaire reports that 53.26 % (n= 49) of the students experienced low writing anxiety and 46.74% (n= 43) of the students experienced high writing anxiety. In terms of the components of writing anxiety, the students experienced anxiety in the component of evaluation (mean score of 19.86), product (mean score of 19.25), and stress (mean score of 16.52). This study also has shown that writing anxiety caused by stress can be considered less frequent anxiety component that has been experienced by the students. It is suggested that students should learn to manage their problems that are related to writing. To do so, the lecturer should guide the students to help them to reduce their writing anxiety and help improve the students’ writing skills.
Index for task scheduling in phased array radar

In the task scheduling problem of phased array radar, the objective function is usually based on the time shifting rate (TSR). However, the TSR cannot achieve expected scheduling performance in non-linear filtering systems. To further improve the scheduling performance, a novel index named shifting improve rate (SIR) is proposed by analysing the influence of time shifting and its formula in Kalman filtering and expand Kalman filtering is derived. Based on SIR, a new objective function for scheduling of phased array radar tasks was designed and used the genetic algorithm to solve it. Simulated experiments are done to validate the proposed index and the new objective function. The results demonstrate that the SIR achieves better scheduling performance and tracking accuracy than TSR in non-linear filtering systems while keeping similar performance in linear filtering systems.
MOOC-technology as a basis for the solution of actual problems of the transition to mass customized physics education: problems and their proven solutions

Various aspects of the use of MOOC TECHNOLOGY in modern full-time and part-time education are considered on the basis of the accumulated experience of creating, publishing and supporting five online courses in physics for students of different age and target groups. The actual variants of development of the system of online training in physics and mathematics courses are analyzed. Specific ways of solving problems caused by differences in Internet education and fundamental education are proposed. New approaches to MOOC-learning as a basis for solving the problem of mass individual education are suggested on the base of the performed analysis. As a possible ways of complex solutions of the formulated actual problems&nbsp; the following steps are proposed: 1) creation of fundamental educational resources with basic knowledge, protected from unskilled interpretations, 2) new balanced approaches to the development of methods of automation of electronic educational content development, 3) introduction of electronic educational testing with &nbsp;dialog imitation on the basis of automated analysis of students responses and adaptation of the electronic system to the level of individual users. Specific versions of software solutions that carry out these actions are proposed, as well as examples of their practical use in the educational process.
Gaze Detection and Prediction Using Data from Infrared Cameras

Knowing the point of gaze on a screen can benefit a variety of applications and improve user experiences. Some electronic devices with infrared cameras can generate 3D point cloud for user identification. We propose a paradigm to use 3D point cloud and eye images for gaze detection and prediction. Our method fuses 3D point cloud with eye images by image registration methods. We develop a cost function to detect saggital plane from point cloud data, and reconstruct a symmetric face by saggital plane. Symmetric face data increase the accuracy of gaze detection. We use long-short term memory models to track head and eye movement, and predict next point of gaze. Our method utilizes the existing hardware setup and provides options to improve user experiences.
Application Model for Travel Recommendations Based on Android

Abstract—Traveling is a fun activity. Everyone has a different preference for the type of tour they like. Some like nature tourism, culinary tours, religious tourism, and other tourism. Problems encountered by tourists when planning tourism activities, usually do not know complete information related to tourist destinations that will be visited, and can not make weather forecasts from the destination tourist destination. This research aims to provide a model travel recommendation that is implemented into the Android mobile application so that tourists can be given recommendations on which tourism destinations are most suitable based on the criteria that are the assessment criteria, namely the price of tourist accommodation, weather and rating of tourist attractions. Mobile technology used is using GPS sensors, Google Maps API, Open WeatherMap API, and Facebook API. Based on the results of testing, the application in this research can help in recommending tourist attractions that meet the criteria desired by tourists.
An integrative approach to teaching mathematical disciplines to students of pedagogical directions in the undergraduate system

conclusions were made: it is advisable to carry out the design of the process of teaching the mathematical disciplines to undergraduate students of pedagogical directions on the basis of competence and integrative approaches; effective formation of students' professional competence is possible by means of educational content of mathematical courses using ICT, electronic information educational environment, the project method, specially developed methodological support and the case-study system using psychological and sociological diagnostics to ensure an effective teamwork.
On the Training Algorithms for Restricted Boltzmann Machines

Deep learning techniques have been studied extensively in the last years due to their good results related to essential tasks on a large range of applications, such as speech and face recognition, as well as object classification. Restrict Boltzmann Machines (RBMs) are among the most employed techniques, which are energy-based stochastic neural networks composed of two layers of neurons whose objective is to estimate the connection weights between them. Recently, the scientific community spent much effort on sampling methods since the effectiveness of RBMs is directly related to the success of such a process. Thereby, this work contributes to studies concerning different training algorithms for RBMs, as well as its variants Deep Belief Networks and Deep Boltzmann Machines. Further, the work covers the application of meta-heuristic methods concerning a proper fine-tune of these techniques. Moreover, the validation of the model is presented in the context of image reconstruction and unsupervised feature learning. In general, we present different approaches to training these techniques, as well as the evaluation of meta-heuristic methods for fine-tuning parameters, and its main contributions are: (i) temperature parameter introduction in DBM formulation, (ii) DBM using adaptive temperature, (iii) DBM meta-parameter optimization through meta-heuristic techniques, and (iv) infinity Restricted Boltzmann Machine (iRBM) meta-parameters optimization through meta-heuristic techniques.
An assault detection system based on human Pose Tracking for video surveillance

The development of new technologies for video surveillance and automatic violence detection can bring more security to our daily lives. Solutions previously published in the state-of-the-art had presented techniques to detect violence at movie scenes, sports matches, or crowds. In this work, we propose a novel system architecture based on human Pose Track for detecting evidence of assaults in real-world videos from closed-circuit television (CCTV) of Brazilian lottery agencies. The results showed that our method can identify individuals with hands up and lying down with accuracy rates up to 85%. We believe that the detection of potentially risky situations in real-time is a crucial tool in the fighting against crime.