Reviewerly/exHarmony huggingface dataset:
UTILIZING AUDIO VISUAL AIDS TO IMPROVE ENGLISH SPEAKING SKILL FOR THE EIGHT GRADE STUDENTS OF SMP NEGERI 3 BANAWA

This study aims to improve the speaking skill of the eighth grade students of SMP Negeri 3 Banawa in the use of audio visual aids. This research was conducted through action research consisting of two cycles. The data were obtained from observation during the implementation of the action; interview with Teacher of English and the eighth grade students from class VIII A; and discussion with Teacher of English. Data in the form of field notes, interview transcripts, and photographs. The validity of the data is obtained by applying utilization Audio Visual Aids, results and processes. The results show that there was an increase in students' speaking skills through the use of audio visual aids. The audio-visual tool used in this study was video and slide. Videos were taken from youtube.com in the form of dialogue in certain expressions. These videos can attract students' attention and increase their learning motivation. Students can have a better understanding of the use of English in real situations when they learn appropriate Englishlanguage models through audio-visual aids (video and slide). Students make improvements in several aspects of speaking skills, such as fluency and comprehensibility. They are more confident in speaking English. They actively participate during the teaching and learning process. In addition, they did not hesitate to ask the teacher when they found difficulties. The research findings are also supported by the results of students' speaking achievement which increased from 57.78 in cycle I to 72.67 in cycle II.
Discriminative Cross-Domain Feature Learning for Partial Domain Adaptation

Partial domain adaptation aims to adapt knowledge from a larger and more diverse source domain to a smaller target domain with less number of classes, which has attracted appealing attention. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better fight off the cross-domain distribution divergences. However, it is essential to align target data with only a small set of source data. In this paper, we develop a novel Discriminative Cross-Domain Feature Learning (DCDF) framework to iteratively optimize target labels with a cross-domain graph in a weighted scheme. Specifically, a weighted cross-domain center loss and weighted cross-domain graph propagation are proposed to couple unlabeled target data to related source samples for discriminative cross-domain feature learning, where irrelevant source centers will be ignored, to alleviate the marginal and conditional disparities simultaneously. Experimental evaluations on several popular benchmarks demonstrate the effectiveness of our proposed approach on facilitating the recognition for the unlabeled target domain, through comparing it to the state-of-the-art partial domain adaptation approaches.
THEORY OF PLANNED BEHAVIOR: PERSONAL ATTITUDE AND PERCEIVED BEHAVIORAL CONTROL AS KEY DETERMINANTS IN CREATION OF ENTREPRENEURIAL SOCIETIES AND SOCIAL INCLUSION OF YOUNG PEOPLE

This paper examines the basic variables from the Theory of Planned Behavior in order to explain entrepreneurial intentions of 317 young people (students of economics and business) in a transitional economy, the Republic of North Macedonia (hereafter N. Macedonia).Confirmatory factor analysis for model fit and multiple regression analysis are used to test the hypotheses.The findings indicate that the young people"s personal attitude and perceived behavioral control are two variables that have significant positive association with their entrepreneurial intent (the intent to start their own business in future).However, even when young people have high positive perceptions and strong perceived behavioral control (self-confidence in their own capabilities), their intent is not very clear (high).In order to contribute to the development of entrepreneurial societies and increase youth social inclusion through self-employment, policymakers and the scientific community should search for further answers for the underlying factors that hinder the entrepreneurial intention in transitional economies.
Space Complexity of Streaming Algorithms on Universal Quantum Computers

Universal quantum computers are the only general purpose quantum computers known that can be implemented as of today. These computers consist of a classical memory component which controls the quantum memory. In this paper, the space complexity of some data stream problems, such as PartialMOD and Equality, is investigated on universal quantum computers. The quantum algorithms for these problems are believed to outperform their classical counterparts. Universal quantum computers, however, need classical bits for controlling quantum gates in addition to qubits. Our analysis shows that the number of classical bits used in quantum algorithms is equal to or even larger than that of classical bits used in corresponding classical algorithms. These results suggest that there is no advantage of implementing certain data stream problems on universal quantum computers instead of classical computers when space complexity is considered.
KONTRIBUSI KONSEP DIRI DAN PERHATIAN ORANG TUA TERHADAP HASIL BELAJAR MATEMATIKA SISWA KELAS X SMA DI KECAMATAN WERA BIMA

The purpose of this study is (1) Want to know how much the contribution of self-concept, and the attention of parents, to the results of mathematics learning in class X SMA in Wera Bima District; (2) Want to know how much the positive contribution of self-concept to the results of mathematics learning in class X SMA in Wera Bima District; and (3) Want to know how much the positive contribution of parents' attention to the results of mathematics learning in class X SMA in Wera Bima District. The type of this study was ex-post facto, the population in this study were Class X students of State High Schools in Wera Bima Subdistrict in the year 2017/2018 which were taken using Proportional stratified random sampling. Instruments used: (1) self-concept scale, (2) the scale of parental attention, (3) tests of mathematics learning outcomes. Data were analyzed by descriptive statistics and path analysis. The results of the study illustrate that; (1) Positive contribution to self-concept, and parents' attention together (simultaneous) to the mathematics learning outcomes of class X high school students in Wera Bima District, for R2square = 11.10%. (2) The positive contribution of self-concept towards the mathematics learning outcomes of class X high school students in Wera Bima Subdistrict, amounting to 15.70%. and (3) The positive contribution of parents' attention to the mathematics learning outcomes of class X high school students in Wera Bima District, amounting to 19.80%.
REPETITION TECHNIQUE IN AN EFL SPEAKING CLASS IN ISLAMIC HIGHER EDUCATION IN INDONESIA

This research aims at analysing the development of studentsâ€™ speaking skill in Islamic higher education in Indonesia through repetition technique. This is a CAR (Classroom Action Research) implemented in cyclical process, covering planning, acting, observing, and reflecting. The subject of research was the fourth semester students of EFL speaking class which consist of 25 students. The data were obtained from observation checklist, field notes, questionnaire, and test. They were analysed qualitatively and quantitatively. Quantitative data dealt with teaching-learning process, i.e. studentsâ€™ participation in the classroom activities. They were gained from studentsâ€™ achievement tests. The findings revealed that in cycle 1 there were twelve students who achieved minimum criteria of achievement. It means that the classical achievement was 48%. In cycle 2, it showed that there were twenty of students who achieved the minimum criteria of achievement. It means that the classical achievement was 80%. Both classical and individual achievements have met the criteria of success. It can be concluded that the repetition technique is proved to be useful to develop studentsâ€™ speaking skill.
A comprehensive review of the principal component analysis applied to the demodulation of phase-shifting interferograms

The use of the Principal Components Analysis (PCA) for recovering the modulating phase, given a sequence of Phase-Shifted Interferograms (PSI), is a very important contribution to the field. However, its verbatim translation from statistics to PSI has limited the view to consider only constant background illuminations. Here, we show that the Singular Value Decomposition (SVD), used in PCA, actually separates the background illumination (constant or not) and the phase modulation terms. We show that the modulating phase can be correctly recovered if the phase-shifts sample full periods uniformly, independently of the spatial distribution of the number of fringes.
Analytical Projects As An Innovative Means In Training Of Programmers

The article discusses an innovative approach to the organization of future programmers training based on analytical projects at college level. The technician-programmer performs such actions as analysis, formalization, algorithmization of tasks, development, control, optimization, and thorough debugging of program code. All these functions are performed in conditions that correspond to those of project work. A programmer works within time and resource limitations, any software product has the property of uniqueness. The authors make a conclusion on the adequacy of analytical projects to the conditions of real professional activity of programmers. The article presents the authors’ definitions of project work and analytical activities, as well as readiness for project work and analytical activities of future technicians-programmers. The detailed component structure and the levels of readiness of students to design and analyze the project as future technicians and programmers have been developed, justified and tested. The essence of the analytical project, the specifics of its organization for the conditions of the college providing training in this area is substantiated. The authors propose a phased description of the implementation of innovative analytical projects in the future technicians-programmers training, as well as criteria for evaluating the results of project implementation. The organizational and pedagogical conditions for their successful implementation are substantiated. The most important condition is the training of teachers for the use of analytical projects in their own educational activity. Cases of interdisciplinary and intra-disciplinary projects using mathematical knowledge are given. The positive results of implemented analytical projects in college are presented.
Multiview Translation Learning for Knowledge Graph Embedding

Recently, knowledge graph embedding methods have attracted numerous researchers’ interest due to their outstanding effectiveness and robustness in knowledge representation. However, there are still some limitations in the existing methods. On the one hand, translation-based representation models focus on conceiving translation principles to represent knowledge from a global perspective, while they fail to learn various types of relational facts discriminatively. It is prone to make the entity congestion of complex relational facts in the embedding space reducing the precision of representation vectors associating with entities. On the other hand, parallel subgraphs extracted from the original graph are used to learn local relational facts discriminatively. However, it probably causes the relational fact damage of the original knowledge graph to some degree during the subgraph extraction. Thus, previous methods are unable to learn local and global knowledge representation uniformly. To that end, we propose a multiview translation learning model, named MvTransE, which learns relational facts from global-view and local-view perspectives, respectively. Specifically, we first construct multiple parallel subgraphs from an original knowledge graph by considering entity semantic and structural features simultaneously. Then, we embed the original graph and construct subgraphs into the corresponding global and local feature spaces. Finally, we propose a multiview fusion strategy to integrate multiview representations of relational facts. Extensive experiments on four public datasets demonstrate the superiority of our model in knowledge graph representation tasks compared to state-of-the-art methods.
Gender classification of full-body biological motion of aperiodic actions using machine learning

Gender classification of dynamic motion using machine learning has been an active research area using mostly periodic human motion action analysis. This work proposes gender classification for aperiodic dynamic overarm throwing motion actions in an unrestricted environment. Skeletal data from motion capture recordings of throwing actions are represented using posture matrices, and pre-selected kinematic variables are extracted from the skeletal sequences to create biomechanical matrices for each throw action. The posture and biomechanical time-series matrices are converted to a feature vector describing the dynamic characteristics of each sequence. Gender classification is performed on the feature vectors using multiple machine learning models, including support vector machine, k-nearest neighbor, and decision tree with and without AdaBoost. The proposed machine learning classifier models demonstrate high level of gender recognition using aperiodic overarm throwing motion measured with motion capture data. Gender classification accuracy is shown to be further improved with the novel application of biomechanical features.
Restrictions on shareability of classical correlations for random multipartite quantum states

Unlike quantum correlations, the shareability of classical correlations (CCs) between two parties of a multipartite state is assumed to be free since there exist states for which CCs for each of the reduced states can simultaneously reach their algebraic maximum value. However, when one randomly picks out states from the state space, we find that the probability of obtaining those states possessing the algebraic maximum value is vanishingly small. Therefore, the possibility of a nontrivial upper bound on the distribution of CCs that is less than the algebraic maxima emerges. We explore this possibility by Haar uniformly generating random multipartite states and computing the frequency distribution for various CC measures, conventional classical correlators, and two axiomatic measures of classical correlations, namely, the classical part of quantum discord and local work of work-deficit. We find that the distributions are typically Gaussian-like and their standard deviations decrease with the increase in number of parties. It also reveals that, among the multiqubit random states, most of the reduced density matrices possess a low amount of CCs which can also be confirmed by the mean of the distributions, thereby showing a kind of restrictions on the shareability of classical correlations for random states. Furthermore, we also notice that the maximal value for random states is much lower than the algebraic maxima obtained for a set of states, and the gap between the two increases further for states with a higher number of parties. We report that, for a higher number of parties, the classical part of quantum discord and local work can follow a monogamy-based upper bound on shareability while classical correlators have a different upper bound. The trends of shareability for classical correlation measures in random states clearly demarcate between the axiomatic definition of classical correlations and the conventional ones.
HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs

Network visualisation techniques are important tools for the exploratory analysis of complex systems. While these methods are regularly applied to visualise data on complex networks, we increasingly have access to time series data that can be modelled as temporal networks or dynamic graphs. In dynamic graphs, the temporal ordering of time-stamped edges determines the causal topology of a system, i.e., which nodes can, directly and indirectly, influence each other via a so-called causal path. This causal topology is crucial to understand dynamical processes, assess the role of nodes, or detect clusters. However, we lack graph drawing techniques that incorporate this information into static visualisations. Addressing this gap, we present a novel dynamic graph visualisation algorithm that utilises higher-order graphical models of causal paths in time series data to compute time-aware static graph visualisations. These visualisations combine the simplicity and interpretability of static graphs with a time-aware layout algorithm that highlights patterns in the causal topology that result from the temporal dynamics of edges.
Dyadic Obligations over Complex Actions as Deontic Constraints in the Situation Calculus

With the advent of artificial agents in everyday life, it is important that these agents are guided by social norms and moral guidelines. Notions of obligation, permission, and the like have traditionally been studied in the field of Deontic Logic, where deontic assertions generally refer to what an agent should or should not do; that is they refer to actions. In Artificial Intelligence, the Situation Calculus is (arguably) the best known and most studied formalism for reasoning about action and change. In this paper, we integrate these two areas by incorporating deontic notions into Situation Calculus theories. We do this by considering deontic assertions as constraints, expressed as a set of conditionals, which apply to complex actions expressed as GOLOG programs. These constraints induce a ranking of "ideality" over possible future situations. This ranking in turn is used to guide an agent in its planning deliberation, towards a course of action that adheres best to the deontic constraints. We present a formalization that includes a wide class of (dyadic) deontic assertions, lets us distinguish prima facie from all-things-considered obligations, and particularly addresses contrary-to-duty scenarios. We furthermore present results on compiling the deontic constraints directly into the Situation Calculus action theory, so as to obtain an agent that respects the given norms, but works solely based on the standard reasoning and planning techniques.
Multi-Face: Self-supervised Multiview Adaptation for Robust Face Clustering in Videos

Robust face clustering is a key step towards computational understanding of visual character portrayals in media. Face clustering for long-form content such as movies is challenging because of variations in appearance and lack of large-scale labeled video resources. However, local face tracking in videos can be used to mine samples belonging to same/different persons by examining the faces co-occurring in a video frame. In this work, we use this idea of self-supervision to harvest large amounts of weakly labeled face tracks in movies. We propose a nearest-neighbor search in the embedding space to mine hard examples from the face tracks followed by domain adaptation using multiview shared subspace learning. Our benchmarking on movie datasets demonstrate the robustness of multiview adaptation for face verification and clustering. We hope that the large-scale data resources developed in this work can further advance automatic character labeling in videos.
Wigner and Wishart Ensembles for graphical models

Vinberg cones and the ambient vector spaces are important in modern statistics of sparse models and of graphical models. The aim of this paper is to study eigenvalue distributions of Gaussian, Wigner and covariance matrices related to growing Vinberg matrices, corresponding to growing daisy graphs. For Gaussian or Wigner ensembles, we give an explicit formula for the limiting distribution. For Wishart ensembles defined naturally on Vinberg cones, their limiting Stieltjes transforms, support and atom at 0 are described explicitly in terms of the Lambert-Tsallis functions, which are defined by using the Tsallis $q$-exponential functions.
Efficient Passive Sensing Monocular Relative Depth Estimation

We propose a method to perform monocular relative depth perception using a passive visual sensor. Specifically, the proposed method makes depth estimation with a superpixel based regression model based on features extracted by a deep convolutional neural network. We have established and conducted an analysis of the key components required to create a high-efficiency pipeline to solve the depth estimation problem with superpixel-level regression and deep learning. The key contributions of our method compared to prior works are as follows. First, we have drastically simplified the depth estimation model while attaining near state-of-the-art prediction performance, through two important optimizations: the idea of the depth estimation model is completely based on superpixels that very effectively reduces the dimensionality; additionally, we exploited the scale invariant mean squared error loss function which incorporates a pairwise term with linear time complexity. Additionally, we have developed optimizations of the superpixel feature extraction, that leverage GPU computing to achieve real-time performance (over 50fps during training) Furthermore, this model does not perform up-sampling, which avoids many issues and difficulties that one would otherwise have to deal with. To perpetuate future research in this area we have created a synchronized multiple-view depth estimation training dataset that is available to the public.
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition

In this paper, we explore the neural architecture search (NAS) for automatic speech recognition (ASR) systems. With reference to the previous works in the computer vision field, the transferability of the searched architecture is the main focus of our work. The architecture search is conducted on the small proxy dataset, and then the evaluation network, constructed with the searched architecture, is evaluated on the large dataset. Especially, we propose a revised search space for speech recognition tasks which theoretically facilitates the search algorithm to explore the architectures with low complexity. Extensive experiments show that: (i) the architecture searched on the small proxy dataset can be transferred to the large dataset for the speech recognition tasks. (ii) the architecture learned in the revised search space can greatly reduce the computational overhead and GPU memory usage with mild performance degradation. (iii) the searched architecture can achieve more than 20% and 15% (average on the four test sets) relative improvements respectively on the AISHELL-2 dataset and the large (10k hours) dataset, compared with our best hand-designed DFSMN-SAN architecture. To the best of our knowledge, this is the first report of NAS results with large scale dataset (up to 10K hours), indicating the promising application of NAS to industrial ASR systems.