Reviewerly/exHarmony huggingface dataset:
Information-theoretic Vocabularization via Optimal Transport for Machine Translation

It is well accepted that the choice of token vocabulary largely affects the performance of machine translation. One dominant approach to construct a good vocabulary is the Byte Pair Encoding method (BPE). However, due to expensive trial costs, most previous studies only conduct simple trials with commonly used vocabulary sizes. This paper finds an exciting relation between an information-theoretic feature and BLEU scores with a given vocabulary. With this observation, we formulate the quest of vocabularization -- finding the best token dictionary with a proper size -- as an optimal transport problem. We then propose Info-VOT, a simple and efficient solution without the full and costly trial training. We evaluate our approach on multiple machine translation tasks, including WMT-14 English-German translation, TED bilingual translation, and TED multilingual translation. Empirical results show that Info-VOT can generate well-performing vocabularies on diverse scenarios. Also, one advantage of the proposed approach lies in its low consumption of computation resources. On TED bilingual translation, Info-VOT only spends a few CPU hours generating vocabularies, while the traditional BPE-Search solution takes hundreds of GPU hours.
Characterization of the impact of visual odometry drift on the control of an autonomous vehicle.

Autonomous vehicle navigation requires the desired trajectory and the current localization to be able to calculate the command that must be sent to the actuators. The localization of the vehicle (usually defined by a position vector and an orientation vector), can be provided by external systems. GPS localization is the most accurate solution but when it is no longer available or precise, an on-board localization estimation based on proprioceptive and exteroceptive sensors is needed. Visual odometry is a well-known approach to estimate the vehicle motion from a camera. Unfortunately, visual localization is subject to errors that increase over time (drift). In this paper, we provide a study of the impact of localization errors on the control of an autonomous vehicle. In order to validate visual odometry algorithms in simulation, a drift model is proposed. Real navigation experiments with errors on the localization are presented to characterize the drift model and the propagation of localization errors in the controller module and the associated command signal.
Development of ecological values of future teachers of natural sciences by changing the educational content

The thesis that environmental problems are the most dangerous ones is widely recognized nowadays. In modern educational conditions, a teacher of natural sciences can be a promoter of ecological values. This makes relevant the need to develop new approaches to the training of teachers of this profile.&#x0D; This study deals with the formation of eco-values of future natural sciences teachers and proposes a number of changes to the educational content of their training by involving the author’s educational and methodological developments – subject “Axiology”, special course “Axiological foundations of training future natural sciences teachers”, and axio-pedagogical training. The material presents an empirical part of the study that reflects the definition and analysis of the dynamics of formation and assimilation of environmental values due to the changes in the educational content.&#x0D; The research used the following elements of the diagnostic system: method of diagnosing the intensity of subjective attitude to nature “Nature Lover” by V. Yasvin, S. Deryaba and determination of the peculiarities of the formation of ecological consciousness components, methodology of the questionnaire “Ecological concepts”, questionnaire “Determination of the level of assimilation of ecological concepts” by T. Yevdokimova, technique “Incomplete sentences” by O. Hrybanova, and technique “Test of the development level of the ecological consciousness components of the individual” by O. Asafova.&#x0D; The empirical research established the psychological and pedagogical features of the ecological values development among students.
AR Interactive Game of Monopoly Based on New Eight Scenes of Macau

Pillars of Macau's economy today are mainly gaming and tourism of which tourism takes larger part. At present, Macau's cultural and tourism services have insufficient creativity. A single display method does not play a role in the spread and inheritance of culture. As a part of tourism, cultural and creative services continue to improve to meet people's needs. The form of cultural and creative services has gradually evolved from focusing on formal functions to emotions and experiences. Redesign and interpretation of culture are unique and important. How to combine the current Macao local culture, innovative technology interaction technology and regional special attractions is also critical. Monopoly, a well-known board game, uses picture cards and rules which provide an experience of travel. This study considered the perspective of the game experience with AR technology to build a virtual scene of the new eight scenes of Macau in the Monopoly game. Interactive operation gives users an intuitive experience. Technology, culture, tourism and games are integrated to bring tourists a fun and immersive virtual tour, so as to promote Macao's culture, and achieve cultural dissemination and inheritance.
역번역을 활용한 한국 시조 이해 교육 연구 - 영어권 한국어 고급 학습자를 대상으로 -

This study aimed to present the availability and implications of back-translation learning methods in Korean sijo education as a foreign language. Results were based on the understanding of Korean sijo by advanced English-speaking Korean learners using back translation. In this study, the significance of back translation as a method of learning sijo is summarized as follows: First, learner-centered activities that encourage learners to participate exist; second, reading native texts can help obtain “advanced knowledge;” third, results are an exploratory process involving learners’ independent thinking; and fourth, awareness of diversity in interpretation encourages freedom to express views on texts. This study selected Hwang Jin-i’s sijo “A Long Night of Winter Solstice” as an experimental text and categorized the understanding of English-speaking advanced learners of the Korean language using back translation into “accepting the meaning of text through reading translations,” “interpretation of the meaning of text through back translations,” and “deeper understanding of the text by comparing back translations with source texts.” Advanced English-speaking Korean learners showed attitudes related to actively organizing the meaning of sijo while performing back translation; they continued to interpret the text by mobilizing their own experience and knowledge through the back translation process. The different order of words in the mother tongue and Korean also led to difficulty in switching to the target language. The following implications were derived based on the understanding of advanced English-speaking Korean learners using back translation. First, the teacher should effectively adjust the primary response the learner sees after reading the translation text. Second, proper educational measures from teachers are needed when learners spend a considerable amount of time on a single poem. Third, the cultural context of learners should be extended after comparing the back translated and source texts.(Seoul National University)
A Reversible Data Hiding Scheme in Encrypted Images for Medical Applications

Storage and exchange of data of the patient images are common in medical applications. To protect the information of the patient and to avoid miss handling of the patient information data hiding scheme is very much essential. Reversible Data Hiding (RDH) scheme is one such scheme paid more attention to hide the data in encrypted images, since it maintains the excellent property that the original cover can be lossless recovered after embedded data is extracted while protecting the image content’s confidentiality. In this paper initially space is reserved from the encrypted images, which may be used to embed the information later stage. Histogram shifting based Reversible Data Hiding scheme used to reserve the room before encryption process. The proposed method can achieve real reversibility, that is, data extraction and image recovery are free of any error. Experiments show that this novel method and achieves better perceptual quality.
Assisting law enforcement tasks with thermal camera drones

Drones have by now become an indispensable tool in disaster situations and law enforcement. The 2020 coronavirus epidemic has put these tools in a new role. They were deployed, for example, to monitor compliance with quarantine and curfews, to disinfect the streets, and to pass on important information as an announcer. In the course of our research, we aimed to assemble a drone camera system suitable for recognising objects and measuring their temperature. With the available tools, we examined combinations of hardware and software that may be suitable for law enforcement tasks.
Bounded Myopic Adversaries for Deep Reinforcement Learning Agents

Adversarial attacks against deep neural networks have been widely studied. Adversarial examples for deep reinforcement learning (DeepRL) have significant security implications, due to the deployment of these algorithms in many application domains. In this work we formalize an optimal myopic adversary for deep reinforcement learning agents. Our adversary attempts to find a bounded perturbation of the state which minimizes the value of the action taken by the agent. We show with experiments in various games in the Atari environment that our attack formulation achieves significantly larger impact as compared to the current state-of-the-art. Furthermore, this enables us to lower the bounds by several orders of magnitude on the perturbation needed to efficiently achieve significant impacts on DeepRL agents.
Extraction and analysis of massive skeletal information from video data of crowded urban locations for understanding implicit gestures of road users.

This work explains the possible inferable information from a long-term video acquisition with cameras installed in close proximity to pedestrian movements with an unobstructed view of the entire intersection. The main goal is detecting implicit and explicit gestures and understanding communication and interactions between different types of road users. After explaining the designs of different gesture classification approaches, we relate the qualitative approach with our classification scheme for the extracted skeletons. To this end, a sequence with selected moving entities is selected and compared with the manually annotated video sequence. Results show the limitations of the automated approach and indicate a level of subjectivity in the manual annotation procedure. Subsequently, we discuss possibilities and restrictions of our approach and reflect on the importance of the specific conditions of video acquisitions. Depending on the field of view and distance between installed video cameras and moving vulnerable road users (VRUs), we are able to define the restrictions of our approach. As a result, we are able to define a selection of suitable applications for our approach.
A Truly Constant-time Distribution-aware Negative Sampling

Softmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval. The calculation of full-softmax is very expensive from the computational and energy perspective. There have been a variety of sampling approaches to overcome this challenge, popularly known as negative sampling (NS). Ideally, NS should sample negative classes from a distribution that is dependent on the input data, the current parameters, and the correct positive class. Unfortunately, due to the dynamically updated parameters and data samples, there does not exist any sampling scheme that is truly adaptive and also samples the negative classes in constant time every iteration. Therefore, alternative heuristics like random sampling, static frequency-based sampling, or learning-based biased sampling; which primarily trade either the sampling cost or the adaptivity of samples per iteration, are adopted. In this paper, we show a class of distribution where the sampling scheme is truly adaptive and provably generates negative samples in constant time. We demonstrate a negative sampling implementation that is significantly faster, in terms of wall clock time, compared to the most optimized TensorFlow implementations of standard softmax or other sampling approaches on the best available GPUs (V100s).
Adversarial Convolutional Neural Network for Text Classification

Text classification is a fundamental task of natural language processing. The convolutional neural network (CNN) has been employed popularly and achieved excellent results on text classification. Nevertheless, the training parameters of CNN is prone to overfitting in the training process, which limits the performance of the convolutional neural network. Adversarial training is an effective regularization method to restrain overfitting to make the model robust against the worst perturbation. In this article, adversarial training is proposed in the convolutional neural network for text classification. In our model, the perturbation is applied to the word embedding layer, not the original input. We implement training and verification on five benchmark datasets. Our experiments indicate that the classifier with adversarial training performs better resisting small perturbation and effectively controls the overfitting. We also analyzed the influence of norm constraint parameters on the classifier and make the model find the appropriate parameters.
RELATIONSHIP OF STUDENTS 'READING INTEREST AND LEARNING READINESS WITH IPS LEARNING OUTCOMES

The background of this study is the ranking of Indonesian education in the world in 2019 showed that the quality of learning in Indonesia is on 108th ranks. While in the ASEAN region, education in Indonesia takes fifth ranks out of nine countries. The data showed the low quality of education in Indonesia. One of the evidence is the problem occurred on the fourth grade students in elementary school Cluster Diponegoro Semarang. The result of student learning of social studies on fourth grade are still relatively low. This study aims to examine the correlation between: 1) reading interest and social studies learning outcomes; 2) readiness to study and social studies learning outcomes; 3) reading interest, simultaneous learning readiness, and the learning outcomes of social studies. This study used a correlation research design with a quantitative approach. The research samples of 103 students were taken using nonprobability sampling technique by random sampling. The data collection used questionnaire, interview, and documentation. The instrument trials were carried out by validity and reliability tests. The data analysis techniques used the prerequisite test for data analysis, descriptive statistical analysis, correlation analysis and regression analysis. The results showed that: 1) there was a positive correlation between reading interest and social studies learning outcomes the proved by its correlation showed a strong contribution of 48%; 2) there was a positive correlation between learning readiness and social studies learning outcomes the proved by its correlation showed a strong contribution of 49.6%; 3) there was a positive correlation between reading interest and learning readiness simultaneously with social studies learning outcomes the proved by its correlation showed a strong contribution of 58.9%. It can be concluded that there was a positive correlation between reading interest, learning readiness, and social studies learning outcomes of fourth grade students in elementary school Cluster Diponegoro Semarang.
Soft Graph Matching: Submodular Relaxation and Lovasz Extension

Graph matching aims to align a pair of graphs by minimizing their edge disagreements. As the problem is NP–hard in the worst-case, various methods have been proposed for approximately solving the problem. One popular approach is to relax the combinatorial problem to a continuous formulation, whose solution represents a soft correspondence between the vertex-sets of the graphs. Previous work has primarily motivated such soft matching formulations as an intermediate step towards obtaining hard correspondences. In this paper, we depart from this viewpoint and provide an alternate motivation for soft matching as a means of identifying classes of topologically-invariant subgraphs, which cannot be revealed by hard correspondences. Drawing on this observation, we consider the family of doubly-stochastic relaxations for graph matching and propose a new convex relaxation for the problem. We establish that the objective function of our formulation can be interpreted as the tightest convex relaxation of the combinatorial quadratic graph matching objective function in a certain sense, and describe an efficient first-order algorithm for computing its solution. Through experiments conducted on real-world data, we demonstrate the empirical effectiveness of the algorithm relative to the prevailing relaxations for graph matching.
A Brief Analysis and Comparison of DCT- and DWT-Based Image Compression Techniques

Image compression is a method to get rid of the redundant data from the image. Just the solely essential data is to be kept, and one must cut back the storage size by eliminating unimportant data, transmission information measure and coordinated universal time. This significant data is obtained by numerous transform techniques which imply reconstruction while not losing quality and important data of the image. The authors have undertaken the task of conducting a study of the performance distinction of the two very significant methods for the same, namely discrete cosine transform (DCT) and discrete wavelet transform (DWT). These techniques are mainly deployed for JPEG image compression. This paper highlights the implementation techniques adopted for these two methods. Also, the pros and cons of each is mentioned after an in-depth analysis.
Artificial neural networks in panel method for solving aerodynamics problem

Abstract The authors proposed an experimental panel method combined with an artificial neural network to solve the problems of aerodynamics with minimum computational power consumption and high accuracy. A software solution based on the proposed method has been created. A comparison with experiments and other existing solutions has been made. The proposed method is intended for solving aerodynamic problems in systems of multi-criteria optimization, as well as in related problems. For example, solutions to the problem of strength and aerodynamics in the associated wing of a civil aircraft. The proposed method and software solution based on it can open new horizons in aerospace industry optimization calculations.
RECOGNIZING GIFTED IN CHILDREN THROUGH LANGUAGE DEVELOPMENT AND CHARACTER EDUCATION AS A MEANS OF MAXIMIZING ABILITY

This study aims to describe and explain how to recognize gifted children through language development and steps to maximize abilities. This study used a qualitative method with a descriptive analysis approach without using statistical formulas or formulas or SPSS assistance. The data in this study were obtained from the analysis of conversation or dialogue as well as the development of the Early Dini Suryani language of the third-grade students of SDN 27 / III Lolo Kecil with their peers, interacting in class with the teacher and while in the family environment. Researchers found that early language development was different from normal children who tended to have the use of well-ordered and well-structured language. Dini also uses a variety of vocabulary that is appropriate for certain contexts. The use of intonation and facial expression in interacting was also applied by Dini in communicating. Even so, Dini has a mental disorder that is high egoistic like when she wants everything she will do even though she forces her will. To maximize early abilities character education is the right support step or method. Thus, Dini can be a gifted child with a good personality.
Geochemistry and petrogenesis of Ghezelhesar Quaternary alkali olivine basalt flows (NE Hamedan, Iran)

Ghezelhesar alkali olivine basalt flows like a dark horizontal flows with about 20 m thickness are placed on Quaternary deposits, 2 km southwest of Ghezelhesar village, 60 km northeast of Hamedan city and in the Sanandaj-Sirjan geological and tectonic zone. Their phenocrysts consists of olivine and clinopyroxene often as euhedral crystals. The matrix of the rocks is formed from volcanic glass, leucite, nepheline and microliths of pyroxene. The Porphyritic, vitrophyritic and glomerophyric textures are common in these rocks. The Ghezelhesar basalts are silica-under saturated, have an alkaline nature. Normative minerals of the Ghezelhesar basalts consist of Diopside, Hematite, Anorthite, Albite, Orthoclace, ± Hypersthene, ± Olivine, ± Nepheline and ± Quartz. REE normalized patterns show enrichment in the LREEs rather than the HREEs and fractionation between the LREE and HREE (LaN/YbN = 37.05–43.43) respectively is high. A negative anomaly in the High Field Strength Elements such as Nb, Ta, Zr, Ti, P and Rb and positive anomaly in the Large Ion Lithophile Elements such as Ba, Pb and some other elements such as Th, La, Ce, Nd and Sm present in these rocks. The geochemical properties of Ghezelhesar volcanic flow and parameters of their trace elements such as the ∆Nb show that the magma of these rocks is derived from the low-grade degree partial melting of an enriched garnet-mantle, and the fractional crystallization has affected their chemical properties. These rocks have the properties of the continental within plate alkaline basalts.
Philosophical foundations and interpretation of technical heritage in museum space

The article is devoted to the issues of preservation of technology in the museum space and its conceptual understanding on the example of interpretation. The purpose of the article is to identify possible methods of interpreting technology through the study of concepts of philosophy of technology and interdisciplinary museum projects. The article provides examples of interdisciplinary and inter-museum projects, during the implementation of which the possibilities for interpreting both the printed instrument and the legacy of the industrial era were identified. As a result, the illuminated approaches to the consideration of technology can be used to create museum projects. Technical museums can generate new meanings, initiate intercultural dialogue, attract specialists of various profiles and museum visitors themselves to preserve technology through updating and interpretation.
A Semi-Supervised Feature Fusion Method for Sentence Similarity Matching

Ahstract-The semantic textual similarity plays an important role in the NLP fields and it aims to find whether the semantic between two sentences is similar or not. The methods for processing the features would have a significant impact on the performance of the model. However, most of the previous work was focused on modeling with a complex model and some handcrafted feature like syntax tree and positional information is ignored, and the mathematical feature coding model is lack of flexibility. In this paper, a semi-supervised feature fusion method is proposed to utilized handcrafted features. A method for generating embedded representations of dependency trees is designed to utilized the syntactic features. The syntactic with the positional feature are fused by a set of auto-encoder operations. It enables the feature representation more effective. Experiments are performed on the STS tasks datasets and demonstrated the effectiveness of our approach. It is found that our proposed approach get the best performance in 5 datasets compared to previous work.