Reviewerly/exHarmony huggingface dataset:
Dialect-Aware Modeling for End-to-End Japanese Dialect Speech Recognition

In this paper, we present a novel model for building end-to-end Japanese-dialect automatic speech recognition (ASR) system. It is known that ASR systems modeling for the standard Japanese language is not suitable for recognizing Japanese dialects, which include accents and vocabulary different from standard Japanese. Therefore, we aim to produce dialect-specific end-to-end ASR systems for Japanese. Since it is difficult to collect a massive amount of speech-to-text paired data for each Japanese dialect, we utilize both dialect data and standard Japanese language data for constructing the dialect-specific end-to-end ASR systems. One primitive approach is a multi-condition modeling that simply merges the dialect data with the standardlanguage data. However, this simple multi-condition modeling causes inadequate dialect-specific characteristics to be captured because of a mismatch between the dialects and standard language. Thus, to produce reliable dialect-specific end-to-end ASR systems, we propose the dialect-aware modeling that utilizes dialect labels as auxiliary features. The main strength of the proposed method is that it effectively utilizes both dialect and standard-language data while capturing adequate dialect-specific characteristics. In our experiments using a home-made database of Japanese dialects, the proposed dialect-aware modeling out-performedthe simple multi-condition modeling and achieved an error reduction of 19.2%.
Pengolahan Data Infrastruktur Suku Dinas Pemuda Dan Olahraga Jakarta Barat Berbasis Web

Public service is one of the most important things to ensure the goal of creating good government in a work arrangement between all components. One of the most important parts in an institution is often measured by the community through the success or failure of an event that is held and every event or event held must have infrastructure or infrastructure and facilities that are sufficiently supportive or qualified, currently at the West Jakarta Youth and Sports Office. still managing infrastructure and facilities data manually, this is what results in infrastructure management being less effective and efficient, for that by making the design of the Infrastructure and Facilities Management system is an effective way of managing infrastructure to be stored in the database This system is designed using the prototype method. This system can search for data on complaints about existing problems in infrastructure to be faster and can present data to be effective and efficient because it is website-based.&#x0D;
How University Professors Assess Their Teaching Experience at Schools

The problem of interaction between general education schools and higher education institutions is one of the most relevant for interdisciplinary research. For this reason, the purpose of the article is not only to evaluate the experience of University professors in modern schools, but also to consider one of the possible mechanisms for improving the intellectual culture of schoolchildren. Universities’ request for the intellectualism of their students cannot be dissociated from the mission of school. Therefore, the article analyzes some of the possibilities of professors working both at Universities and at schools at least five years, to improve the continuity between schools and universities. The professors’ experience of working as school teachers is considered both as a subjective factor (“the desire to feel young”) and as an objective one, aimed at working with gifted young people, with students who are ready to continue their studies at Universities in the future. The article used the method of online survey with the participation of 50 professors of the Russian classical universities. The main requirement for respondents was to combine the positions of school teacher and University professor for at least 5 years. 232 professors had such experience, but not all of them were able to take part in the study. The results of the study can be summarized in the following main provisions: 1) the professors evaluate their teaching experience as a positive not only from the perspective of one’s personal development, but also as a contribution to the development of educational strategies ensuring continuity between schools and universities or their constructive cooperation; 2) the participation of professors in school life significantly affects the quality of schooling, and contributes to the formation of the academic principle in the school environment; 3) the professors’ teaching experience is also important for universities (e.g., students).
Graduated Assignment Graph Matching for Realtime Matching of Image Wireframes

We present an algorithm for the realtime matching of wireframe extractions in pairs of images. Here we treat extracted wireframes as graphs and propose a simplified Graduated Assignment algorithm to use with this problem. Using this algorithm we achieve a 30% accuracy improvement over the baseline method. We show that, for this problem, the simplified Graduated Assignment algorithm can achieve realtime performance without a significant drop in accuracy as compared to the standard Graduated Assignment algorithm. We further demonstrate a method of utilizing this simplified Graduated Assignment algorithm for achieving a similar real-time improvement in the matching quality of standard features without wireframe detection.
Self-Attention Network for Human Pose Estimation

Estimating the positions of human joints from monocular single RGB images has been a challenging task in recent years. Despite great progress in human pose estimation with convolutional neural networks (CNNs), a central problem still exists: the relationships and constraints, such as symmetric relations of human structures, are not well exploited in previous CNN-based methods. Considering the effectiveness of combining local and nonlocal consistencies, we propose an end-to-end self-attention network (SAN) to alleviate this issue. In SANs, attention-driven and long-range dependency modeling are adopted between joints to compensate for local content and mine details from all feature locations. To enable an SAN for both 2D and 3D pose estimations, we also design a compatible, effective and general joint learning framework to mix up the usage of different dimension data. We evaluate the proposed network on challenging benchmark datasets. The experimental results show that our method has significantly achieved competitive results on Human3.6M, MPII and COCO datasets.
New application of blockchain: digital currency model design in the context of Winter Olympics

With the further expansion of blockchain application ecology, various applications and infrastructure around digital currency are constantly enriched and improved, and the whole market is booming along the healthy track. The Chinese government also attaches great importance to the development and application of block chain technology. President Xi Jinping stressed the importance of the block chain as an important breakthrough in the core technology independent innovation in the eighteenth collective learning of the Central Political Bureau. In October 2020, the pilot project of digital RMB will be launched in Shenzhen. Under the blockchain empowerment, the design, issuance and landing preparation of China's digital RMB have been in the forefront of the central bank's digital currency. Although the technical feature of blockchain is decentralization, the central bank can effectively integrate the distributed operation by using blockchain, so as to better realize the centralized management and control of digital currency. Taking the Beijing Olympic Games as an example, this paper analyzes the problems that may occur in the use of digital RMB in the Winter Olympics, puts forward an assumption of using digital currency based on partial decentralization, and puts forward the overall architecture design, and analyzes its feasibility and practicability.
Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering

Object Detection (OD) is an important task in Computer Vision with many practical applications. For some use cases, OD must be done on videos, where the object of interest has a periodic motion. In this paper, we formalize the problem of periodic OD, which consists in improving the performance of an OD model in the specific case where the object of interest is repeating similar spatio-temporal trajectories with respect to the video frames. The proposed approach is based on training a Gaussian Process to model the periodic motion, and use it to filter out the erroneous predictions of the OD model. By simulating various OD models and periodic trajectories, we demonstrate that this filtering approach, which is entirely data-driven, improves the detection performance by a large margin.
Semi-Supervised Contrastive Learning with Generalized Contrastive Loss and Its Application to Speaker Recognition

This paper introduces a semi-supervised contrastive learning framework and its application to text-independent speaker verification. The proposed framework employs generalized contrastive loss (GCL). GCL unifies losses from two different learning frameworks, supervised metric learning and unsupervised contrastive learning, and thus it naturally determines the loss for semi-supervised learning. In experiments, we applied the proposed framework to text-independent speaker verification on the VoxCeleb dataset. We demonstrate that GCL enables the learning of speaker embeddings in three manners, supervised learning, semi-supervised learning, and unsupervised learning, without any changes in the definition of the loss function.
Spoken Dialog Training System for Customer Service Improvement

In the hospitality industry, including convenience stores and airport service counters, operational staff must be trained to serve customers satisfactorily and to avoid problems with them. This study investigates a spoken dialog training system for improving customer service by operational staff. In a conventional spoken dialog system, a system user uses the dialog system as a customer, and the dialog agent assists the system user in fulfilling his or her requirements. In our system, in contrast, the dialog agent plays the role of the customer. Consequently, the behaviors of the human and the customer are opposite to those in a traditional dialog system, and there has thus far been no research on such systems. This paper introduces a prototype of such a system that we have developed and describes a simple experiment. The results of the experiment confirm the usefulness of our system for hospitality training.
Depth Trajectory-Aware Stereoscopic Video Retargeting

The image/video retargeting (resolution adaptation) problem-adaption of source data (image/video) to different screen resolution-has garnered wide attention due to compelling applications in intelligent adaptive display. Most of the current image/video displaying application achieves the adaption through scaling source uniformly. Compared with traditional image/video retargeting, stereoscopic video retargeting poses new challenges that should preserve shape and depth of salient objects with temporal coherence. In this paper, we propose a depth trajectory-aware stereoscopic video retargeting method by imposing constraints to optimize the coordinates and depths simultaneously. To reduce visual discomfort induced by fast depth motion, our method uses a temporal depth distortion energy to optimize the depth trajectory in temporal direction. As a result, our solution can preserve shape, depth and temporal fidelity of salient objects simultaneously with comfortable visual experience.
Human motion recognition using three-dimensional skeleton model based on RGBD vision system

Abstract Human action recognition is one of the most important topics in computer vision. It has a lot of applications in intelligent monitoring, human-computer interaction, virtual reality, motion analysis and video annotation, etc. However, the effects of recognition are usually affected by human occlusion, self-occlusion, ambiguity and so on. In view of the above problems, this article describes an algorithm to recognize human motion using three-dimensional skeleton model based on RGBD vision system. Firstly, the human body three-dimensional skeleton model is established with the Kinect. Next, the normalized relative positions of skeleton joints and joint angles are adopted as features, and the action database containing the color image, the depth image, the skeletal image and the coordinates of the joints is built. Then, the features are trained and classified by multi-class support vector machine classifier. Finally, human motion recognitions are carried out. Experimental results indicate that the algorithm can recognize 12 kinds of actions behaved by different humans effectively.
Formation of Educational Motivation and Thinking in Students by Means of Virtual Reality

The work is aimed at studying the effectiveness of the influence of virtual reality training programs on the formation of educational motivation and thinking among students in the study of higher mathematics. The materials of an empirical study obtained on a sample of 1st and 4th year students of the Russian Technological University MIREA are presented. The study included 3 groups: one experimental group and two control groups. In group 1 (experimental), participants were offered to work in the VR program “Second-order Surfaces”, in group 2 — to read a textbook on this topic, group 3 was offered to relax between the two stages of the experiment. The study (N=90) involved respondents aged 17 to 21 years, of which 23.33% were female. The method of group alignment is applied. The study included 2 measurements of knowledge of the theory of linear algebra on the topic “Second-order surfaces” (before and after working in the program\ textbook reading\rest) and 2 measurements of the current state according to the SAN methodology. The results obtained suggest that working in a virtual reality program for the formation of thinking is as effective as reading a textbook. At the same time, there was an improvement in the overall condition of respondents after working in the VR program: the effectiveness of students increased. A further survey of students using the methodology for diagnosing students ‘ learning motivation (A. A. Rean and V. A. Yakunin, modifications by N. TS Badmaeva) showed an increase in educational and cognitive motivation among students working in the VR program.
Visual Security Evaluation of Learnable Image Encryption Methods against Ciphertext-only Attacks

Various methods for protecting visual information have been proposed for privacy-preserving deep neural networks (DNNs). In contrast, methods of attack against such protection methods have been simultaneously developed. In this paper, we evaluate state-of-the-art visual-protection methods for privacy-preserving DNNs in terms of visual security against ciphertext-only attacks (COAs). We focus on the brute-force attack, feature reconstruction attack (FR-Attack), inverse transformation attack (ITN-Attack), and GAN-based attack (GAN-Attack), which have been proposed to reconstruct the visual information of plain images from visually protected images. The details of the various attacks are first summarized, and the visual security of the protection methods is then evaluated. Experimental results demonstrate that most of the protection methods, including pixelwise encryption, are not robust enough against GAN-Attack, while a few are robust enough against it.
Activation-level uncertainty in deep neural networks

Current approaches for uncertainty estimation in deep learning often produce too confident results. Bayesian Neural Networks (BNNs) model uncertainty in the space of weights, which is usually high-dimensional and limits the quality of variational approximations. The more recent functional BNNs (fBNNs) address this only partially because, although the prior is specified in the space of functions, the posterior approximation is still defined in terms of stochastic weights. In this work we propose to move uncertainty from the weights (which are deterministic) to the activation function. Specifically, the activations are modelled with simple 1D Gaussian Processes (GP), for which a triangular kernel inspired by the ReLu non-linearity is explored. Our experiments show that activation-level stochasticity provides more reliable uncertainty estimates than BNN and fBNN, whereas it performs competitively in standard prediction tasks. We also study the connection with deep GPs, both theoretically and empirically. More precisely, we show that activation-level uncertainty requires fewer inducing points and is better suited for deep architectures.
Meta Attention Networks: Meta-Learning Attention to Modulate Information Between Recurrent Independent Mechanisms

Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning agent interacting with the environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of knowledge is particularly relevant for being able to generalize in a systematic way to out of distribution changes. To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs, as well as its reward function are stationary and can be re-used across tasks. The attention mechanisms dynamically select which modules should be adapted, and the parameters of the \textit{selected} modules are changed quickly as a learner is confronted with variations in what it experiences, while the parameters of the attention mechanisms act as slowly changing meta-parameters. We focus on pieces of knowledge captured by an ensemble of modules sparsely communicating with each other via a bottleneck of attention. We find that meta-learning the modular aspects of the proposed system greatly help in achieving faster learning, in experiments with a reinforcement learning setup involving navigation in a partially observed grid world with image-level input. We also find that reversing the role of parameters and meta-parameters does not work nearly as well, suggesting a particular role for fast adaptation of the dynamically selected modules.
PENGARUH TEKNIK PEMBELAJARAN THINK TALK WRITE (TTW) BERBANTUAN MEDIA GAMBAR BERSERI TERHADAP KETERAMPILAN MENULIS TEKS FABEL

ABSTRACT The purpose of this writing is to research the influence of TTW’s learning by using picture in series as the media towards class VII of SMP Negeri 2 2X11 Enam Lingkung’s students’ skill on writing fable. The data of this research are the students’ scores of writing fables. It is collected by doing the test of writing fable toward the sample before and after using TTW’s technique by using picture in series. The result of this technique is using T-test formula. The result of this research showing 1) the writing skill of fable before and after using TTW’s technique of picture in series are in “Lebih dari Cukup” (LdC) qualification; 2) the writing skill of fable before and after using TTW’s technique of picture in series are in the “Baik Sekali” (BS) qualification and 3) as the result of using T-test formula showing that the writing skill of fable before and after using TTW’s technique of picture in series giving very good influence towards the students’ skill of writing fable text in class VII of SMP Negeri 2 2X11 Enam Lingkung. Kata kunci: Pengaruh, TTW Berbantuan Media Gambar Berseri, Keterampilan Menulis Teks Fabel
Non-parametric identification of homogeneous dynamical systems

The aim of this study is to design a non-parametric identifier for homogeneous systems based on a class of artificial neural networks with continuous dynamics. The identification algorithm is developed for input-affine systems with uncertain gains and diverse degrees of homogeneity. One of the main contributions of this study is the extension of the universal approximation property of neural networks for continuous homogeneous systems. Another contribution is the development of a differential non-parametric identifier based on the novel concept of homogeneous neural networks. The adjustment laws for the weights are obtained from a Lyapunov stability analysis taking homogeneity properties of the system into account. The ultimate boundedness of the origin for the identification error is demonstrated using the persistent excitation condition. The effectiveness of the proposed identifier is verified by the simulation of the three-tank homogeneous model. In this example, the proposed identification scheme is compared with a classical ANN identifier, and we present a statistical analysis of such comparison. It is shown in simulations that the identification error of the proposed homogeneous algorithm has faster convergence and less oscillations.
Recent Issues with Machine Vision Applications for Deep Network Architectures

In this chapter, we put an effort to focus on the processing and storage issues that can be carried out from the updates of unstructured data in the unconstraint environment. In the technological aspects, we discuss the existing intelligent deep network architectures along with traditional machine learning approaches. The outcomes of the analytical discussion presented in this chapter enable to picturize a generalized intelligent neural network for processing highly complex visual data with graphs and manifold structures. The essential issues with updating the hidden layers and some fast optimization techniques are also introduced. Finally, this can be concluded that the presented work reflects the sounding challenges to process and extract qualitative information from the densely unstructured tremendous amount of training data. In case of video processing, we need to frame out various deep learning aspects that can lead the research work to highly resourceful scope for deep data analytics and many problems that require high-performance computing in visual media.
Efficient Searching With MCTS and Imitation Learning: A Case Study in Pommerman

Pommerman is a popular reinforcement learning environment because it imposes several challenges such as sparse and deceptive rewards and delayed action effects. In this paper, we propose an efficient reinforcement learning approach that uses a more efficient Monte Carlo tree search combined with action pruning and flexible imitation learning to accelerate the search performance, allowing the agent to avoid meaningless explorations and find some high-level strategies. Under the Pommerman benchmark, we evaluate the agent driven by the proposed approach against the heuristic and pure reinforcement learning baselines, and the results show that our method can yield a relatively high-level agent performance during combat, which demonstrates the efficiency of our method in this specific domain and its potential ability.