Reviewerly/exHarmony huggingface dataset:
BERT for Monolingual and Cross-Lingual Reverse Dictionary

Reverse dictionary is the task to find the proper target word given the word description. In this paper, we tried to incorporate BERT into this task. However, since BERT is based on the byte-pair-encoding (BPE) subword encoding, it is nontrivial to make BERT generate a word given the description. We propose a simple but effective method to make BERT generate the target word for this specific task. Besides, the cross-lingual reverse dictionary is the task to find the proper target word described in another language. Previous models have to keep two different word embeddings and learn to align these embeddings. Nevertheless, by using the Multilingual BERT (mBERT), we can efficiently conduct the cross-lingual reverse dictionary with one subword embedding, and the alignment between languages is not necessary. More importantly, mBERT can achieve remarkable cross-lingual reverse dictionary performance even without the parallel corpus, which means it can conduct the cross-lingual reverse dictionary with only corresponding monolingual data. Code is publicly available at https://github.com/yhcc/BertForRD.git.
Upaya Meningkatkan Aktifitas dan Hasil Belajar Bahasa Indonesia Pokok Bahasan Teks Cerita (Novel) Sejarah dengan Menggunakan Model Pembelajaran STAD Siswa Kelas XII IPS 1 SMAN 3 Kotabumi Tahun Pelajaran 2018-2019

This study aims to increase the activities and outcomes of Indonesian language learning by using the STAD learning model in historical text material (novels) of class XII IPS 1 students of SMA Negeri 3 Kotabumi in the 2018-2019 academic year. This classroom action research used students of XII IPS 1 SMA Negeri 3 Kotabumi as research subjects. In practice, it was carried out to test the effect of learning historical story text material (novel) by practicing the STAD type cooperative learning model on student learning outcomes. The research was carried out in 2 cycles, namely for the material structure, content, value and language of historical story texts (novels) and elements, topics and frameworks of historical story texts (novels) where all cycles began with planning activities followed by implementation and observation and ended with reflection. . Data collection techniques using cognitive tests and student attitude questionnaires obtained from each cycle, the results were analyzed whether there was an increase in each cycle. After conducting research and data analysis, the results obtained were an increase in student learning outcomes and activities from cycle I to cycle II. For indicators of student learning outcomes there was an increase of 58% in cycle II when compared to cycle I. Meanwhile, for student activities in disciplinary indicators there was an average increase of 0.7, for indicators of cooperation the average increase was 1.05, indicators of courtesy. The average score was 0.56 and for the tolerance indicator the average increase was 1.16 and overall for student learning activities increased by 20.47. The conclusion of this study is that the application of the STAD learning model to historical text material (novels) can increase the activities and learning outcomes of Indonesian students in class XII IPS 1 at SMAN 3 Kotabumi in the 2018-2019 academic year.
Dynamic and Interactive Scientific Posters: Visualising 3D Models and Simulation Data Using AR

&amp;lt;p&amp;gt;Researchers often have to carefully select data for figures to best show their results for a static 2D format such as a conference poster or outreach handout. This can result in the scientific message being harder to understand or only part of the story being visualised. Augmented reality can help in improving the clarity of temporal data as well as the understanding of 3D structures which may be challenging to otherwise visualise.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;A series of software packages may be used in order to take video files (MP4, AVI etc&amp;amp;#8230;) and 3D model files (OBJ, STL, PLY etc&amp;amp;#8230;) and pair them with a target image, detectable by a mobile app for Android or iOS. The Vuforia engine plug-in for Unity allows for target images to be imported for use with AR and paired with a 3D model or video in Unity. Manipulation of the AR element is achieved using the Lean-Touch asset in Unity, allowing for scaling, rotation and movement.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The incorporation of AR in science communication at a professional and public level creates a memorable interaction which is also enriched by greater&amp;lt;span&amp;gt;&amp;amp;#160; &amp;lt;/span&amp;gt;scientific clarity. The interactive element of AR, especially using Lean-Touch, makes it an appealing tool for the public and children which results in greater engagement with science. The ability to show more data such as full simulations or experiment time lapses rather than a select series of still images also makes this an appealing tool for researchers in a variety of fields including modellers, experimentalists and anyone using digital data.&amp;lt;/p&amp;gt;
A Robust and Oblivious Watermarking Method Using Maximum Wavelet Coefficient Modulation and Genetic Algorithm

An image watermarking method using Discrete Wavelet Transform (DWT) and Genetic Algorithm (GA) is presented for applications like content authentication and copyright protection. This method is robust to various image attacks. For watermark detection/extraction, the cover image is not essential. Gray scale images of size 512 × 512 as cover image and binary images of size 64 × 64 as watermark are used in the simulation of the proposed method. Watermark embedding is done in the DWT domain. 3rd and 2nd level detail sub-band coefficients are selected for further processing. Selected coefficients are arranged in different blocks. The size of the block and the number blocks depends on the size of the watermark. One watermark bit is embedded in each block. Then, inverse DWT operation is performed to get the required watermarked image. This watermarked image is used for transmission and distribution purposes. In case of any dispute over the ownership, the hidden watermark is decoded to solve the problem. Threshold-based method is used for watermark extraction. Control parameters are identified and optimized based on GA for targeted performance in terms of PSNR and NCC. Performance comparison is done with the existing works and substantial improvement is witnessed.
Best school innovations within the system of public education in 2019, implemented with state grant support

This article is dedicated to the results of implementation of educational innovations within the framework of state grant support of 2019. The subject of this research is the best school innovations of 2019 within the system of public education aimed at formation of technological, digital, and preprofessional competences of students based in creation of modern teaching models, environment in, development of functional thinking, and socialization of school students by means of engaging into personal and socially beneficial activity. The results of innovation activity of the grantees are presented by the latest educational and methodological, practice-oriented inventions, creation of the modern models of training, upbringing and development of the students, as well as interest in innovation activity among the pedagogues. As a result of the conducted research, the author highlighted the best and most socially beneficial projects with regards to the regions on implementation of digital technologies in the educational process; integration of public education and extended activities; development of the effective mechanisms for formation of functional literacy among students; elaboration of the models for preventing delinquent behavior among students; as well as model solutions on realization of most recent practice-oriented inventions aimed at improvement of the quality of education. The conclusion is made on the social significance of state support with regards to implementation of innovative projects of educational organizations, which would create prerequisites for the development of educational system of the country.
Revisiting Cloud Security Attacks: Credential Attack

Security has a ubiquitous influence on cloud services. With technology advancement, the superiority of attacks is continuously scaling in volume. In response, the security experts have increased the defensive perimeters, enhanced the malware protection and upgraded the countermeasures to detect malicious activity. However, weak identity and access management system and destabilized privilege system has created loopholes for cloud security, resulting in abnormal development using unsecured credentialing challenges and vulnerable applications. Organizations, which do not focus on the importance of secure credentials, are at greater risk. Credentials (password) are considered as easiest and cheaper measures for security but are more susceptible to be stolen. The organizations internally might use technologies to protect credentials by applying new policies using an exclusive username and password credentials, which may protect their cloud accounts. In spite of this fact, the valuable cloud information at data stores accessed remotely by virtual logins to an organization leads to attack issues and challenges. Thus, a credential attack is the major root cause of other attacks occurrence. The objective of the research study is to revisit the concept of credential attack and its emerging root causes. The focal point is to represent a broad overview of credential attack, intensifying as a vital security aspect in the cloud. The paper purposes to reconsider the prior literature and highlighting on conclusive findings for prospective research in the interrelated sphere of influence based on the published reports and industry/organization work.
Uncertainty Estimation For Community Standards Violation In Online Social Networks

Online Social Networks (OSNs) provide a platform for users to share their thoughts and opinions with their community of friends or to the general public. In order to keep the platform safe for all users, as well as to keep it compliant with local laws, OSNs typically create a set of community standards organized into policy groups, and use Machine Learning (ML) models to identify and remove content that violates any of the policies. However, out of the billions of content that is uploaded on a daily basis only a small fraction is so unambiguously violating that it can be removed by the automated models. Prevalence estimation is the task of estimating the fraction of violating content in the residual items by sending a small sample of these items to human labelers to get ground truth labels. This task is exceedingly hard because even though we can easily get the ML scores or features for all of the billions of items we can only get ground truth labels on a few thousands of these items due to practical considerations. Indeed the prevalence can be so low that even after a judicious choice of items to be labeled there can be many days in which not even a single item is labeled violating. A pragmatic choice for such low prevalence, $10^{-4}$ to $10^{-5}$, regimes is to report the upper bound, or $97.5\%$ confidence interval, prevalence (UBP) that takes the uncertainties of the sampling and labeling processes into account and gives a smoothed estimate. In this work we present two novel techniques Bucketed-Beta-Binomial and a Bucketed-Gaussian Process for this UBP task and demonstrate on real and simulated data that it has much better coverage than the commonly used bootstrapping technique.
Slice sampling for general completely random measures

Completely random measures provide a principled approach to creating flexible unsupervised models, where the number of latent features is infinite and the number of features that influence the data grows with the size of the data set. Due to the infinity the latent features, posterior inference requires either marginalization---resulting in dependence structures that prevent efficient computation via parallelization and conjugacy---or finite truncation, which arbitrarily limits the flexibility of the model. In this paper we present a novel Markov chain Monte Carlo algorithm for posterior inference that adaptively sets the truncation level using auxiliary slice variables, enabling efficient, parallelized computation without sacrificing flexibility. In contrast to past work that achieved this on a model-by-model basis, we provide a general recipe that is applicable to the broad class of completely random measure-based priors. The efficacy of the proposed algorithm is evaluated on several popular nonparametric models, demonstrating a higher effective sample size per second compared to algorithms using marginalization as well as a higher predictive performance compared to models employing fixed truncations.
APPLYING TRANSCRIPT 1.23 IN IMPROVING THE STUDENTS’ WEAK FORMS AND CONNECTED SPEECH

This true experimental research is mainly aimed at investigating the effectiveness of using Computer-assisted Pronunciation Teaching (CAPT) application software Transcript 1.23 in teaching English pronunciation of weak forms and connected speech. The data used to measure the effectiveness of the application of CAPT software in the treatment is collected from participants’ oral performance in the form of voice records. The research involved the Government Islamic Senior High School 2 Pontianak. The pre-test and pos-test data from the participants is analysed and compared to measure the effect of the given treatment. The result of the research demonstrates a strong effect of the application of the software to improve the quality of the participants’ pronunciation of weak forms and connected speech. The effectiveness the application of the software is categorised as strong according to the criteria developed by Cohen.
ORGANIZATION OF STUDENTS’ PRODUCTIVE EDUCATIONAL AND COGNITIVE ACTIVITY DURING THE WORK WITH DIDACTIC MATERIALS OF THE TEXTBOOK ON PHYSICS FOR THE 9TH GRADE PUPILS BY MEANS OF FUNCTIONALLY SELECTED ELECTRONIC MANUALS AS A METHODOLOGICAL ISSUE

The article reveals the essence of the organization of productive educational and cognitive pupils’ activity in the process of occupation with didactic materials of a textbooks on physics by means of functional selected e-manuals. In this context, the aim of the article is to explain theoretically the realization of the productive educational and cognitive pupils’ activity in the process of occupation with didactic materials of a textbook on physics by means of functional selected e-textbooks.The authors isolate peculiarities of structural elements of school textbook on physics and specific features of the realization of its didactic function in organization of productive educational and cognitive pupils’ activities.The article gives the examples of methodological recommendations for realization of productive educational and cognitive pupils’ activity during the work with the textbook on physics and selected didactic materials by means of author’s e-manual.The usage of such approach to organization of pupils work with didactic materials of school textbook on physics reveals other possibilities for introduction of information and communicative technologies in traditional form of training, and makes it possible to combine and to ensemble established didactics and contemporary information and methodological systems as a whole.Current research proves that organization of productive educational and cognitive pupils’ activities in the process of occupation with didactic materials of a textbook on physics by means of functional selected e-textbook makes it possible to expand methodical opportunity of educational process.
THE EFFECTIVENESS OF USING WORD MAP TECHNIQUE IN TEACHING VOCABULARY AT THIRD GRADE STUDENTS OF IKIP MATARAM IN ACADEMIC YEAR 2015/2016

This research aims to find out the effectiveness of using “Word Map” Technique in teaching vocabulary at third grades students of IKIP Mataram in academic year 2015/2016?”. This was a quasi experimental research. The sample of this research was two classes, they were A class and B class in which consist of 39 students. It takes by using cluster random sampling. The sample of the research divided into two groups namely experimental group and control group. Experimental group was taught by Word Map Technique and control group was taught by Three Phase Technique. The design of the research as follow: Pre-test, Treatment, and Posttest.The instrument was multiple choices test consist of 25 items for right answers was 4 for each item. Then, for all right answer ranged 100 for the highest and for the lowest was 0. Based on the data analysis it was found that t-test score was 7.743 and t-table was 1.668. The result showed that the value of t-test was highest then the value of t-table. So, it conclude that there was significant effect of Word Map Technique in teaching vocabulary at third grades students of IKIP Mataram in academic year 2015/2016.
TinyGAN: Distilling BigGAN for Conditional Image Generation

Generative Adversarial Networks (GANs) have become a powerful approach for generative image modeling. However, GANs are notorious for their training instability, especially on large-scale, complex datasets. While the recent work of BigGAN has significantly improved the quality of image generation on ImageNet, it requires a huge model, making it hard to deploy on resource-constrained devices. To reduce the model size, we propose a black-box knowledge distillation framework for compressing GANs, which highlights a stable and efficient training process. Given BigGAN as the teacher network, we manage to train a much smaller student network to mimic its functionality, achieving competitive performance on Inception and FID scores with the generator having $16\times$ fewer parameters.
Application of the Pixel Velocity Clustering Model in the Tasks of Preprocessing images of Earth Remote Sensing

Purpose of research is to apply the modified Ward method in high-speed processing of full-size images of Earth remote sensing. Methods . The classical Ward method is modified by dividing the computational process into three successive stages. At the first stage, a rough hierarchy of approximations is built. At the second stage, an intermediate improvement of the quality of the given partition is performed for a fixed number of colours. At the third stage, the obtained superpixels are clustered using the classical Ward method. The software-algorithmic toolkit consists of four operations on pixel clusters and image segments: merging a pair of clusters into one, dividing a cluster into two original ones, singling out a subset of pixels into a separate cluster and reclassifying some pixels by excluding them from one cluster and assigning them to another. The quality is assessed by the total squared error. Improving the image decomposition quality is ensured by iterative execution of a combination of merging and deviding pixel clusters, image segments, in particular. One of the clusters (segments) is devided in two and a couple of others non-coincident with it are combined into one according to the criterion of minimum increment of the total squared error. Results . The proposed modified Ward method is applied in the processing of full-size images of Earth remote sensing taken from the database of the USC Signal and Image Processing Institute. The results of processing in the modes of pure segmentation and clustering are compared. Conclusion . The proposed pixel clustering model is suitable for high-speed processing of full-size images. Pixel clustering in comparison with image segmentation makes it possible to define in more detail both the contours of objects of interest and their internal structure.
Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning

We present a target-driven navigation system to improve mapless visual navigation in indoor scenes. Our method takes a multi-view observation of a robot and a target as inputs at each time step to provide a sequence of actions that move the robot to the target without relying on odometry or GPS at runtime. The system is learned by optimizing a combinational objective encompassing three key designs. First, we propose that an agent conceives the next observation before making an action decision. This is achieved by learning a variational generative module from expert demonstrations. We then propose predicting static collision in advance, as an auxiliary task to improve safety during navigation. Moreover, to alleviate the training data imbalance problem of termination action prediction, we also introduce a target checking module to differentiate from augmenting navigation policy with a termination action. The three proposed designs all contribute to the improved training data efficiency, static collision avoidance, and navigation generalization performance, resulting in a novel target-driven mapless navigation system. Through experiments on a TurtleBot, we provide evidence that our model can be integrated into a robotic system and navigate in the real world. Videos and models can be found in the supplementary material.
Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy

The development of rigorous quality assessment model relies on the collection of reliable subjective data, where the perceived quality of visual multimedia is rated by the human observers. Different subjective assessment protocols can be used according to the objectives, which determine the discriminability and accuracy of the subjective data. Single stimulus methodology, e.g., the Absolute Category Rating (ACR) has been widely adopted due to its simplicity and efficiency. However, Pair Comparison (PC) is of significant advantage over ACR in terms of discriminability. In addition, PC avoids the influence of observers' bias regarding their understanding of the quality scale. Nevertheless, full pair comparison is much more time-consuming. In this study, we therefore 1) employ a generic model to bridge the pair comparison data and ACR data, where the variance term could be recovered and the obtained information is more complete; 2) propose a fusion strategy to boost pair comparisons by utilizing the ACR results as initialization information; 3) develop a novel active batch sampling strategy based on Minimum Spanning Tree (MST) for PC. In such a way, the proposed methodology could achieve the same accuracy of pair comparison but with the compelxity as low as ACR. Extensive experimental results demonstrate the efficiency and accuracy of the proposed approach, which outperforms the state of the art approaches.
INTERPERSONAL DIALOGUE TO BUILD STUDENTS’ ABILITY IN USING ENGLISH

This study aims to see the implementation of interpersonal dialogue in learning English to enhance students' ability in using the English language. This study used a qualitative method. The data collection technique uses observation techniques to see the process of the implementation of learning to use interpersonal dialogue, and interview techniques to determine the impact of implementing interpersonal dialogue. Respondents in this study were lecturer and students. The results showed that during the learning process, the interpersonal dialogue was created between students and students and lecturer. Then, there is an increase in learning activities, student participation, students get additional knowledge in the form of vocabulary, grammar, pronunciation, and self-confidence. In conclusion, the implementation of interpersonal dialogue can build communicative learning, because of the interactive communication between students and lecturers, as well as students and students during the learning process. As well as increasing student ability and student confidence in using English.