Reviewerly/exHarmony huggingface dataset:
Employment of baby sign language in interactive learning for Hearing-impaired people in Malaysia

&lt;span lang="EN-GB"&gt;Communication is very important for people including adults and babies to exchange their thoughts, views and information. They can express their thoughts verbally and non-verbally. Baby sign language is non-verbal type that consists of a set of standard and organised form of hand gestures motion to represent different word. This language allows the children who have hearing impairment problem to communicate with others. In addition, it can also be taught to normal babies to convey their need before they have the ability to speak. It is very challenging for adults to understand babies and children with hearing impaired problem. Thus, a real-time hand gesture recognition system has been developed for adult to interactively learn the baby sign language. Firstly, the face region was detected in order to use the skin colour model of the face to detect the hand. After that, the hand shape was determined by finding the palm centre and convex hull. Convexity defects which representing gaps between the fingers were used to determine the number of fingertips whereas the movement of the hand was detected based on motion analysis. By setting the background colour to blue and adding extra light in front of the laptop, the system successfully obtained an accuracy higher than 85% rate for sign “mom”, “eat” and “milk” when tested on Malay, Chinese and Indian races in Malaysia.&lt;/span&gt;
Detecting Drug Non-Compliance in Internet Fora Using Information Retrieval and Machine Learning Approaches.

Non-compliance situations happen when patients do not follow their prescriptions and take actions that lead to potentially harmful situations. Although such situations are dangerous, patients usually do not report them to their physicians. Hence, it is necessary to study other sources of information. We propose to study online health fora. The purpose of our work is to explore online health fora with supervised classification and information retrieval methods in order to identify messages that contain drug non-compliance. The supervised classification method permits detection of non-compliance with up to 0.824 F-measure, while the information retrieval method permits detection non-compliance with up to 0.529 F-measure. For some fine-grained categories and new data, it shows up to 0.65-0.70 Precision.
SemEval-2015 Task 3: Answer Selection in Community Question Answering

Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts. In this context, we organized SemEval-2015 Task 3 on Answer Selection in cQA, which included two subtasks: (a) classifying answers as good, bad, or potentially relevant with respect to the question, and (b) answering a YES/NO question with yes, no, or unsure, based on the list of all answers. We set subtask A for Arabic and English on two relatively different cQA domains, i.e., the Qatar Living website for English, and a Quran-related website for Arabic. We used crowdsourcing on Amazon Mechanical Turk to label a large English training dataset, which we released to the research community. Thirteen teams participated in the challenge with a total of 61 submissions: 24 primary and 37 contrastive. The best systems achieved an official score (macro-averaged F1) of 57.19 and 63.7 for the English subtasks A and B, and 78.55 for the Arabic subtask A.
Some features of training teachers for design educational programs

The article clarifies the essential characteristics of the concept of "the teacher's readiness to educational programs designing", it examines the conditions for preparing teachers for designing activity and identifies the specifics of the process of forming the teacher's readiness for educational programs designing as a factor of raising the level of individual professional competence.
Isogeny formulas for Jacobi intersection and twisted hessian curves

The security of public-key systems is based on the difficulty of solving certain mathematical problems. With the possible emergence of large-scale quantum computers several of these problems, such as factoring and computing discrete logarithms, would be efficiently solved. Research on quantum-resistant public-key cryptography, also called post-quantum cryptography (PQC), has been productive in recent years. Public-key cryptosystems based on the problem of computing isogenies between supersingular elliptic curves appear to be good candidates for the next generation of public-key cryptography standards in the PQC scenario. In this work, motivated by a previous work by D. Moody and D. Shumow [17], we derived maps for elliptic curves represented in Jacobi Intersection and Twisted Hessian models. Our derivation follows a multiplicative strategy that contrasts with the additive idea presented in the Vélu formula. Finally, we present a comparison of computational cost to generate maps for isogenies of degree $ l $, where $ l = 2k + 1 $. In affine coordinates, our formulas require $ 46.8 \% $ less computation than the Huff model and $ 48\% $ less computation than the formulas given for the Extended Jacobi Quartic model when computing isogenies of degree $ 3 $. Considering higher degree isogenies as $ 101 $, our formulas require $ 23.4\% $ less computation than the Huff model and $ 24.7 \% $ less computation than the formula for the Extended Jacobi Quartic model.
CONTEXTUAL TEACHING AND LEARNING BASED ON LEARNING MODULE FOR SDN 09 AIRPURA SELATAN COASTAL

This study aims to develop an Indonesian Language Learning Module with a Contextual Teaching and Learning Approach on child rhyming material for Grade IV students of SD Negeri 09 Airpura Pesisir Selatan.The theory used as a reference is the opinion of Hasan [1] about learning, Bowers opinion [2] about the module and Kurniati [3] opinion about the Contextual Teaching and Learning approach..This research was conducted in fourth grade students even semester 2018/2019 Academic Year.Data collection was carried out with validation and trial modules developed.The module design that was designed was validated by two experts then tested in class IV A SDN 09 Airpura to find out practicality.The results showed that the Indonesian language learning module with the Contextual Teaching and Learning Approach on child rhyming material for fourth grade students at SD 09 Airpura was in a very practical category with an average percentage of practicality 91.31 by students.The results of the validation by two experts obtained a value of 96.87 which is included in the category of very valid and the practicality score of the teacher of 100 which is included in the category of very practical.
Specific features of introducing the components of inclusive education in higher education institutions when training professional communication in English

This paper is devoted to the problems of inclusive education in terms of training professional communication in English. The objective of this paper is to study and analyze specific features of inclusion from a methodological perspective. The authors sum up various approaches to the definition of inclusion and reveal that the main requirements to the teaching staff include professional attributes, competence and some general criteria. All this can significantly affect the process of teaching handicapped students. Language training syllabus components are also taken into consideration, the described teaching strategy represents an antipode of traditional educational form called “flipped classroom” pedagogical technique. The authors study the technique application from different angles showing advantages and disadvantages that are intrinsically linked with the issues of teaching quality improvement. All the above mentioned proves the fact that foreign language teaching within the framework of inclusive education has to represent the form of componential complimentarity interconnected with the professional language theory towards individual needs and the system of values of the handicapped students.
Spatio-Temporal Change Detection of North Antelope Rochelle and Black Thunder Coal Fields of US Using Multi-temporal Remote Sensing Satellite Data

Opencast mining activities including mineral exploration, extraction, waste dumping and stockpiling affect the nearby environment and land cover. To minimize the negative impacts of mining on the environment, it is highly recommended to adopt sustainable mining practices along with proper and regular monitoring of mining activities. In this research study, 32 years of Landsat satellite data from 1984 to 2016 was used to map the spatiotemporal changes in coal mining activities of the World’s largest North Antelope Rochelle and Black Thunder coal fields of the United States of America. Digital image processing of satellite images was done and the Maximum Likelihood Classification algorithm was applied to quantify the land cover changes in the region. The most appropriate band selection was done for the identification of coal seams through satellite images. The results showed that the Shortwave Infrared-2, Near Infrared and Green bands of Landsat satellite, highlighted as Red, Green, and Blue respectively are proven to be very effective in the identification of areas of coal mining. The classification results showed that since 1984, coal mining has been expanded by 74,000 ha with an increase of more than 87% in thirty-two years. Whereas almost 94% decrease in the vegetation cover of the region was also observed during the same period of time. The methodology adopted in this research study is efficient, time-saving and inexpensive to monitor the detailed surface mining activities over a large period of time, and is applicable to other mining regions, as well.
Improving Document Classification with Multi-Sense Embeddings

Efficient representation of text documents is an important building block in many NLP tasks. Research on long text categorization has shown that simple weighted averaging of word vectors for sentence representation often outperforms more sophisticated neural models. Recently proposed Sparse Composite Document Vector (SCDV) (Mekala et. al, 2017) extends this approach from sentences to documents using soft clustering over word vectors. However, SCDV disregards the multi-sense nature of words, and it also suffers from the curse of higher dimensionality. In this work, we address these shortcomings and propose SCDV-MS. SCDV-MS utilizes multi-sense word embeddings and learns a lower dimensional manifold. Through extensive experiments on multiple real-world datasets, we show that SCDV-MS embeddings outperform previous state-of-the-art embeddings on multi-class and multi-label text categorization tasks. Furthermore, SCDV-MS embeddings are more efficient than SCDV in terms of time and space complexity on textual classification tasks.
한국어 보편 의존 구문 분석 (Universal Dependencies) 방법론 연구

The purpose of this paper is to find a methodology for parsing Korean by the method of ''Universal Dependencies''. Universal Dependencies expresses the annotation marking system that can be applied to the formal patterns and syntactic relationships of various types of languages. The UD project presents a framework for consistently annotated multi-lingual treebank corpus for various languages around the world through the ''CoNLL shared task'' and is currently applied to more than 50 languages. This flow has a great influence on research related to not only the UD project itself but also the syntactic annotation for individual languages, and efforts are being made to apply the universally dependent syntax analysis marking system in the UD according to the characteristic of the individual language. When annotation labels cannot be applied due to the nature of the Korean language in consideration of compatibility, they need to be excluded. Among DEPREL labels, several labels are difficult to apply in Korean when looking at the specific items. In this paper,, DEPREL overall annotation system of UD was translated and a representative category of elements that needed to be transformed was selected to discuss the actuality of linguistic convergence and its application.
Adversarial Inverse Reinforcement Learning for Decision Making in Autonomous Driving.

To make human-like decisions under complex driving environment is a challenging task for autonomous agents. Imitation Learning or learning-from-demonstration methods have seen great potential for achieving such a goal. Some state-of-the-art studies apply Generative Adversarial Imitation Learning (GAIL) to learn sequential decision-making and control policies. While GAIL can directly learn a policy, it lacks the ability to recover a reward function, which is considered robust and adaptable to environments changes. Adversarial Inverse Reinforcement Learning (AIRL) is another learning-from-demonstration method that can achieve similar benefits as GAIL but also learns the reward function with the policy simultaneously. In the original work of AIRL, it has been demonstrated in single-agent environments such as maze navigation and ant running tasks in OpenAI Gyms. In this paper, we augment AIRL by concatenating semantic reward terms in the learning framework to improve and stabilize its performance, and then extend it to a more practical but challenging situation, i.e. decision-making scenario in highly interactive driving environment. Four performance evaluation metrics are proposed and compared with some Imitation Learning based methods and Reinforcement Learning based methods. Simulation results show that the augmented AIRL outperforms all the other methods, and the trained vehicle agent can perform decision-making behaviors comparable with that of the experts.
Emerging woman leader in technical computing award winner announced

In recognition of her innovative and transformative deployment of machine learning for knowledge discovery in molecular dynamic simulations and in situ analytics, SIGHPC is pleased to announce Dr. Trilce Estrada (University of New Mexico) as the winner of the 2019 Emerging Woman Leader in Technical Computing (EWLTC) Award.
Analysing Russian Trolls via NLP tools

The fifty-eighth American presidential election in 2016 still arouse fierce controversyat present. A portion of politicians as well as medium and voters believe that theRussian government interfered with the election of 2016 by controlling malicioussocial media accounts on twitter, such as trolls and bots accounts. Both of them willbroadcast fake news, derail the conversations about election, and mislead people.Therefore, this paper will focus on analysing some of the twitter dataset about theelection of 2016 by using NLP methods and looking for some interesting patterns ofwhether the Russian government interfered with the election or not. We apply topicmodel on the given twitter dataset to extract some interesting topics and analysethe meaning, then we implement supervised topic model to retrieve the relationshipbetween topics to category which is left troll or right troll, and analyse the pattern.Additionally, we will do sentiment analysis to analyse the attitude of the tweet. Afterextracting typical tweets from interesting topic, sentiment analysis offers the ability toknow whether the tweet supports this topic or not. Based on comprehensive analysisand evaluation, we find interesting patterns of the dataset as well as some meaningfultopics.
Dont Even Look Once: Synthesizing Features for Zero-Shot Detection

Zero-shot detection, namely, localizing both seen and unseen objects, increasingly gains importance for large-scale applications, with large number of object classes, since, collecting sufficient annotated data with ground truth bounding boxes is simply not scalable. While vanilla deep neural networks deliver high performance for objects available during training, unseen object detection degrades significantly. At a fundamental level, while vanilla detectors are capable of proposing bounding boxes, which include unseen objects, they are often incapable of assigning high-confidence to unseen objects, due to the inherent precision/recall tradeoffs that requires rejecting background objects. We propose a novel detection algorithm Dont Even Look Once (DELO), that synthesizes visual features for unseen objects and augments existing training algorithms to incorporate unseen object detection. Our proposed scheme is evaluated on Pascal VOC and MSCOCO, and we demonstrate significant improvements in test accuracy over vanilla and other state-of-art zero-shot detectors
AttKGCN: Attribute Knowledge Graph Convolutional Network for Person Re-identification

Discriminative feature representation of person image is important for person re-identification (Re-ID) task. Recently, attributes have been demonstrated beneficially in guiding for learning more discriminative feature representations for Re-ID. As attributes normally co-occur in person images, it is desirable to model the attribute dependencies to improve the attribute prediction and thus Re-ID results. In this paper, we propose to model these attribute dependencies via a novel attribute knowledge graph (AttKG), and propose a novel Attribute Knowledge Graph Convolutional Network (AttKGCN) to solve Re-ID problem. AttKGCN integrates both attribute prediction and Re-ID learning together in a unified end-to-end framework which can boost their performances, respectively. AttKGCN first builds a directed attribute KG whose nodes denote attributes and edges encode the co-occurrence relationships of different attributes. Then, AttKGCN learns a set of inter-dependent attribute classifiers which are combined with person visual descriptors for attribute prediction. Finally, AttKGCN integrates attribute description and deeply visual representation together to construct a more discriminative feature representation for Re-ID task. Extensive experiments on several benchmark datasets demonstrate the effectiveness of AttKGCN on attribute prediction and Re-ID tasks.
The Representation of Facts about Old Russian Charters on the Semantic Web: Attempto Controlled English (ACE)

This paper discusses some promising approaches to the representation of meta-information (facts) about medieval Russian charters on the Semantic Web in order to provide researchers with appropriate tools for the capture, aggregation, and semantic linkage of facts related to the charters and automatic logical inference. The authors demonstrate the actual and potential possibilities of a controlled natural language, ACE (Attempto Controlled English), in the representation of the meta-information about Old Russian charters. Recent research conducted by the authors confirms that the prospects for using ACE as a tool for representation of the facts about the charters on the Semantic Web are closely connected with the possibilities to make logic inferences based on ACE texts. Therefore, special attention is devoted to ACE tools, especially to Ace Rules and ACE Reasoner (RACE).
Texture Hallucination for Large-Factor Painting Super-Resolution

We aim to super-resolve digital paintings, synthesizing realistic details from high-resolution reference painting materials for very large scaling factors (e.g., 8X, 16X). However, previous single image super-resolution (SISR) methods would either lose textural details or introduce unpleasing artifacts. On the other hand, reference-based SR (Ref-SR) methods can transfer textures to some extent, but is still impractical to handle very large factors and keep fidelity with original input. To solve these problems, we propose an efficient high-resolution hallucination network for very large scaling factors with an efficient network structure and feature transferring. To transfer more detailed textures, we design a wavelet texture loss, which helps to enhance more high-frequency components. At the same time, to reduce the smoothing effect brought by the image reconstruction loss, we further relax the reconstruction constraint with a degradation loss which ensures the consistency between downscaled super-resolution results and low-resolution inputs. We also collected a high-resolution (e.g., 4K resolution) painting dataset PaintHD by considering both physical size and image resolution. We demonstrate the effectiveness of our method with extensive experiments on PaintHD by comparing with SISR and Ref-SR state-of-the-art methods.
The use of ICT by science teachers in middle secondary science education in the Himalayan Kingdom of Bhutan

This research investigated Bhutanese middle secondary science teachers’ ICT knowledge, skills, perceptions and attitudes, patterns of ICT use, and associated factors linked to effective ICT implementation in their classrooms. The study was based on the hypothesis that the use of ICT in secondary science education in Bhutan was affected by the teachers’ attitudes and perceptions towards the use of ICT; their knowledge and skills related to ICT; their TPACK and the barriers associated with effective implementation of ICT.The research was primarily an exploratory study accommodating a post-positivist approach employing mixed design of both quantitative and qualitative approaches. A questionnaire survey on a sample of middle secondary science teachers was the quantitative study. Case studies of eight schools consisting of their historical background and performance records, semi-structured interviews with eight science teachers from these schools and focus group of students from three of these schools formed the case studies. The survey questionnaire targeted 189 middle secondary science teachers from a total of 63 middle secondary schools and secured a response rate of 85.7%. The survey questions covered patterns of ICT use in daily life, interest in ICT, confidence in using ICT and application of ICT in science teaching. A set of Technological Pedagogical and Content Knowledge (TPACK) questions (Mishra & Kohler, 2009) were also included to test the level of ICT integration achieved by these teachers.The findings showed that overall, the teachers possessed moderate levels of ICT knowledge and skills; very few ICT elements were used in the teachers’ daily life, and still less were employed in science teaching due to lack of confidence. Although teachers had positive perceptions and attitudes regarding use of ICT in teaching science, many could not fully utilise ICT due to personal and institutional constraints. Teachers were found to use various ICT tools dependent upon their personal competence and confidence. ICT trained teachers used more tools and engaged with more innovation in the classroom, whilst teachers with low ICT competency and confidence exhibited little integration. ICT trained teachers also helped the school management in developing administrative facilities that engaged ICT, and they also involved students in using ICT presentations. Specialised ICT teachers facilitated innovative uses of ICT such as: interactive student-centred teaching; enhanced collaborative student work; improved problem-based and project-based learning. These teachers also shared their ICT knowledge and skills with other colleagues. Schools with a poor ICT resources often provided less time for professional engagement and therefore, inhibited the implementation ICT in classrooms.Based on these findings, the research recommended the government to increase funding for ICT in schools to provide: more computer laboratories and extend the professional development opportunities for both pre-service and in-service science teacher…