Reviewerly/exHarmony huggingface dataset:
Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network

In this paper, we propose a new few-shot text classification method.Compared with supervised learning methods which require a large corpus of labeled documents, our method aims to make it possible to classify unlabeled text with few labeled data.To achieve this goal, we take advantage of advanced pre-trained language model to extract the semantic features of each document.Furthermore, we utilize an edge-labeling graph neural network to implicitly models the intra-cluster similarity and the inter-cluster dissimilarity of the documents.Finally, we take the results of the graph neural network as the input of a prototypical network to classify the unlabeled texts.We verify the effectiveness of our method on a sentiment analysis dataset and a relation classification dataset and achieve the state-of-the-art performance on both tasks.
Human Behavior Recognition System Based on Infrared Array Sensors

With the increase in the aging population,the demand to identify and monitor the daily activities of the elderly is growing.A monitoring system can effectively prevent accidents of elderly people.The traditional activity monitoring system based on the use of camera or wearable sensors has issues,such as privacy violations and inconvenience of use.Therefore,this study designs a human behavior recognition system based on infrared array sensors.The system recognizes activities on different temperature distributions in the environment.There is no need for the sensor to be worn by the elderly.The sensor is small in size,easy to install indoors,and can work in the dark.In addition,the data acquired by the sensor have a low resolution;therefore,there is no privacy violation.The designed system has significant advantages over the traditional systems.The features are extracted from the obtained temperature data,and the K-nearest neighbors algorithm is used to identify the three poses of“walking,”“sitting,”and“falling.”The experimental results show that the average accuracy can reach 95%,of which the accuracies for falling,walking,and sitting are 97.5%,100%,and 92.5%,respectively.
Augmented Reality Technology, and It’s Effect in Improving the Acceptance to Use It among 7&amp;lt;sup&amp;gt;th&amp;lt;/sup&amp;gt; Graders in Medical Technology Unit

The current study aimed at revealing the effect of augmented reality technology in improving the applicability of the seventh grade students in Medical Technology Unit.The study followed the experimental method according to the design of a pre-post type for the same case.The study sample consisted of 20 students among seventh grade of Beat Dagan school in east Gaza, acceptance scale was used to answer the main question of the study, as the results showed that there were statistically significant differences in the results of the scale of applicability between the pre and post applications in the favor of the post-application, also there is an effect of using the augmented reality technology in increasing students' acceptance to learn.The study recommended to using the augmented reality technology in teaching in order to overcome the problems and obstacles that face students and teachers in presenting scientific knowledge.
Label Distribution Learning-Based Semantic Retrieval Model on Knowledge Graph

The traditional retrieval technology is difficult to grip users’ intention accurately and apace in massive and fragmented data. To organize fragmented knowledge in reason and retrieve useful knowledge to recommend for users in a big data environment, this paper proposes a label distribution learning-based semantic retrieval model on the knowledge graph. The model firstly constructs the knowledge graph based on the topic map model. And then it clusters knowledge by using K-means label distribution learning (KM-LDL) and reduces retrieval scope. To address the problem that the space vector converted from the input text is sparse, this paper improves K-means label distribution learning according to the idea of probability and constructs a semantic retrieval model on a knowledge graph based on Bayes-K-means label distribution learning (Bayes-KM-LDL). The experimental results show that the proposed model can organize fragmented knowledge more reasonably than the traditional retrieval approach and its accuracy and recall rate have good performance.
Traffic Sign Detection and Classification based on Combination of MSER Features and Multi-language OCR

Road signs are so important because they help preserve safe driving conditions; they also influence the safety of drivers and pedestrians. Without these signs, no one would know the driving speed limit, on which direction to drive down a road, any upcoming hazard, or whether they are approaching a merge. It would be chaotic to drive in such situations. Moreover, these signs help new drivers to find their way in the absence of navigators. Therefore, traffic sign recognition takes a critical place in computer vision applications to develop an effective algorithm. In order to tackle this challenge, we proposed the use of Multi-language Traffic Sign Detection and Classification. One of our contributions in this work is that, instead of using the standard grayscale image, we used the RGB colored image. This image is converted into the 2D highest-level grayscale image using the largest values of each pixel in the RGB channels. The novel generated image has the strongest features of the RGB image that make the features distinct and more informative in the classification step. Consider that, in general, the traffic sign has two colors only, the foreground (text location) and background (non-text location). The Maximally Stable Extremal Regions (MSER) used to extract features from the 2D image where the locations of interest are well-identified exclusively by an extremal property of the intensity function in the location and on its outer boundary. The geometrical properties and thinning operations were used to remove the non-text locations. A multi-language OCR was used to understand multi-language. This proposed method has been tested using 240 images which were collected from the Internet and two datasets. The experimental results demonstrated the performance of the proposed method where the traffic sign detected in 92% of the tested images with a very high percentage of localization.
NON-FORMAL EDUCATION OF EDUCATORS IN MEDIA CENTERS OF LEADING EUROPEAN COUNTRIES: EDUCATIONAL AND METHODOLOGICAL ASPECT

Media literacy is recognized as a basic, vital skill for European citizens. It must be formed during life, at all stages of personality development. That is why we have chosen the leading European countries such as Finland, Sweden, France, Germany, the United Kingdom and Spain to analyze the current state of media education. Europe should be at the forefront of media literacy, as historically this region of the world has become the cradle of media civilization, a center for coordinating discussions of philosophical, cultural and technical development of the media. The article deals with the analysis of educational and methodical support of the European system of educators’ media education. The importance of media education of educators as the greatest agents of direct educational influence on the younger generation, whose media education is a requirement of the time, is revealed. The need for the development of non-formal and informal media education of educators is identified, as the media component can take place in any lesson and in extracurricular activities. Common components of the organization system of this process (participants, content, forms, methods, tools) are identified and educational and methodological resources are described, which are publicly available and can be useful to educators regardless of country of residence, citizenship or language.As a result of the research, we made the following conclusions: specialized centers offer a wide range of short-term training courses for teachers of various topics; forms of professional development in media education of teachers are different (distance courses, trainings, seminars, workshops, conferences, educational films, etc.); modern scientific, educational, methodical literature for teachers is available (monographs, professional journals, lesson plans, information packages); non-formal and informal preparation of teachers for media education activities is diverse in content, forms, means and methods, but remains fragmentary, not systematic, which does not allow to solve our issue comprehensively, and therefore it needs further development and critical analysis.
Resources and Tools for Automated Speech Segmentation of the African Language Naija (Nigerian Pidgin)

The development of HLT tools inevitably involves the need for language resources. However, only a handful number of languages possesses such resources. This paper presents the development of HLT tools for the African language Naija (Nigerian Pidgin), spoken in Nigeria. Particularly, this paper is focusing on developing language resources for a tokenizer, an automatic speech system for predicting the pronunciation of the words and their segmentation. The newly created resources are integrated into SPPAS software tool and distributed under the terms of public licenses.
Science in mobile apps for education: tackling online learning within pandemic outbreak

In adapting the issues of pandemic, educational sectors in Malaysia from the lower school level till university levels are encouraged to forecast any specific tools that may help in cater the needs in utilizing online learning. It is by right the issues that been face by global outbreak for everyone on earth as Covid19 that embarked big challenges in face to face and normal daily classes and learning method. Therefore this paper is written to give a general overview, uncover the possible models in used and technologies that may overcome the needs of online education specifically focused for improving the usage of online educational tools especially with the use of mobile ap-plication for learning itself. The concept of manual for pen and paper reporting can makes the reporting very slow especially in terms of communication and sharing of information. Plus, within the outbreak patterns may will provide inaccurate information and may also late results to be achieved. This paper is discussing model in plan and the implementation proposed with overcome results of the post results questionnaire conducted to get the major feedback on usage of the current proposed app.
Intra-/Inter-Interaction Network with Latent Interaction Modeling for Multi-turn Response Selection

Multi-turn response selection has been extensively studied and applied to many real-world applications in recent years.However, current methods typically model the interactions between multi-turn utterances and candidate responses with iterative approaches, which is not practical as the turns of conversations vary.Besides, some latent features, such as user intent and conversation topic, are under-discovered in existing works.In this work, we propose Intra-/Inter-Interaction Network (I 3 ) with latent interaction modeling to comprehensively model multi-level interactions between the utterance context and the response.In specific, we first encode the intra-and inter-utterance interaction with the given response from both individual utterance and the overall utterance context.Then we develop a latent multi-view subspace clustering module to model the latent interaction between the utterance and response.Experimental results show that the proposed method substantially and consistently outperforms existing state-of-the-art methods on three multi-turn response selection benchmark datasets.
A presentation method of the thermophysical properties of matter in the form of spreadsheets

Abstract The research of methods to reduce CO 2 emissions into the atmosphere has led to formation of new thermodynamic cycles in which oxygen is separated from the air before combustion. Fuel, pure oxygen and some recirculating substances, from which it is easy to separate CO 2 formed during the combustion, are fed into the combustion chamber. Usually, CO 2 , H 2 O or a mixture of thereof are used in the form of recirculated flue gas. The parameters in such cycles are chosen at different points in the cycle, where the working fluid can be in liquid, gaseous or supercritical states. The computational study of such cycles requires a convenient presentation of the thermophysical properties of different substances that can be part of the working fluid in a wide range of parameters. The aim of this work is to develop a data array and a computational module (spreadsheet) considering the dependence of the basic thermophysical properties of various substances. A conversion method of variables that allowed the formation of a compact interpolation grid with minimal loss of accuracy during subsequent interpolation was proposed, where the use of integers for the nodal values of the independent variables saved computational resources during interpolation significantly.
Examining the Effect of Chatbot Gender and Gender Congruence between a Chatbot and a Customer in a Banking Context

Chatbots, automated text-based conversational programs, are a popular technology enabled by artificial intelligence (AI). Chatbots have been adopted in diverse service areas to enhance business performance by facilitating interactions between humans and machines. This study tests the effect of chatbot gender, and the gender congruence between chatbot and customer, on customer responses in a banking context. Data was collected using a scenario-based online survey with a 2 (chatbot gender: male vs. female) x 2 (congruence of chatbot gender and respondent gender: congruent vs. incongruent) between-subject factorial design. Across two chatbot gender and respondent gender contexts, the results show that a male banking chatbot increases customer satisfaction and brand conceptual fluency significantly more than a female banking chatbot. Study results also show the interaction between chatbot gender and the congruence of chatbot gender and respondent gender influences perceived intimacy and information richness. Our findings are relevant to banks, fintech companies, and other businesses that are rapidly adopting chatbots to engage consumers and enhance branding.
Fast Low-Space Algorithms for Subset Sum

We consider the canonical Subset Sum problem: given a list of positive integers $a_1,\ldots,a_n$ and a target integer $t$ with $t > a_i$ for all $i$, determine if there is an $S \subseteq [n]$ such that $\sum_{i \in S} a_i = t$. The well-known pseudopolynomial-time dynamic programming algorithm [Bellman, 1957] solves Subset Sum in $O(nt)$ time, while requiring $\Omega(t)$ space. In this paper we present algorithms for Subset Sum with $\tilde O(nt)$ running time and much lower space requirements than Bellman's algorithm, as well as that of prior work. We show that Subset Sum can be solved in $\tilde O(nt)$ time and $O(\log(nt))$ space with access to $O(\log n \log \log n+\log t)$ random bits. This significantly improves upon the $\tilde O(n t^{1+\varepsilon})$-time, $\tilde O(n\log t)$-space algorithm of Bringmann (SODA 2017). We also give an $\tilde O(n^{1+\varepsilon}t)$-time, $O(\log(nt))$-space randomized algorithm, improving upon previous $(nt)^{O(1)}$-time $O(\log(nt))$-space algorithms by Elberfeld, Jakoby, and Tantau (FOCS 2010), and Kane (2010). In addition, we also give a $\mathrm{poly} \log(nt)$-space, $\tilde O(n^2 t)$-time deterministic algorithm. We also study time-space trade-offs for Subset Sum. For parameter $1\le k\le \min\{n,t\}$, we present a randomized algorithm running in $\tilde O((n+t)\cdot k)$ time and $O((t/k) \mathrm{polylog} (nt))$ space. As an application of our results, we give an $\tilde{O}(\min\{n^2/\varepsilon, n/\varepsilon^2\})$-time and $\mathrm{polylog}(nt)$-space algorithm for "weak" $\varepsilon$-approximations of Subset Sum.
Sentiment Analysis Of Movie Reviews Based On Improved Word2vec And Ensemble Learning

Abstract Taking the field of movie review as an example, this paper proposes a sentiment analysis method based on improved word2vec and ensemble learning. The basic design idea is: firstly build the corresponding corpus through new word discovery and use the TF-IDF algorithm to exponentially weight the word2vec word vector, which is used to integrate the semantic relationship between words and the importance of vocabulary information into the model; secondly, to avoid the cumbersome problems of data labeling, the existing algorithms of automatic labeling reviews are improved to increase the adoption rate of data; finally, Stacking algorithm is used to train and classify the emotional data. The proposed model can simplify the domain text representation and improve the classification performance of the model. The experimental results show that compared with existing methods, the accuracy, precision and recall rate of the algorithm proposed in this paper have been improved on film review data.
Statistical Parsing of Tree Wrapping Grammars

We describe an approach to statistical parsing with Tree-Wrapping Grammars (TWG).TWG is a tree-rewriting formalism which includes the tree-combination operations of substitution, sisteradjunction and tree-wrapping substitution.TWGs can be extracted from constituency treebanks and aim at representing long distance dependencies (LDDs) in a linguistically adequate way.We present a parsing algorithm for TWGs based on neural supertagging and A * parsing.We extract a TWG for English from the treebanks for Role and Reference Grammar and discuss first parsing results with this grammar.
Narrative Incoherence Detection

We propose the task of narrative incoherence detection as a new arena for inter-sentential semantic understanding: Given a multi-sentence narrative, decide whether there exist any semantic discrepancies in the narrative flow. Specifically, we focus on the missing sentence and discordant sentence detection. Despite its simple setup, this task is challenging as the model needs to understand and analyze a multi-sentence narrative, and predict incoherence at the sentence level. As an initial step towards this task, we implement several baselines either directly analyzing the raw text (\textit{token-level}) or analyzing learned sentence representations (\textit{sentence-level}). We observe that while token-level modeling has better performance when the input contains fewer sentences, sentence-level modeling performs better on longer narratives and possesses an advantage in efficiency and flexibility. Pre-training on large-scale data and auxiliary sentence prediction training objective further boost the detection performance of the sentence-level model.
Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN

Due to the limitations of sensors, the transmission medium and the intrinsic properties of ultrasound, the quality of ultrasound imaging is always not ideal, especially its low spatial resolution. To remedy this situation, deep learning networks have been recently developed for ultrasound image super-resolution (SR) because of the powerful approximation capability. However, most current supervised SR methods are not suitable for ultrasound medical images because the medical image samples are always rare, and usually, there are no low-resolution (LR) and high-resolution (HR) training pairs in reality. In this work, based on self-supervision and cycle generative adversarial network (CycleGAN), we propose a new perception consistency ultrasound image super-resolution (SR) method, which only requires the LR ultrasound data and can ensure the re-degenerated image of the generated SR one to be consistent with the original LR image, and vice versa. We first generate the HR fathers and the LR sons of the test ultrasound LR image through image enhancement, and then make full use of the cycle loss of LR-SR-LR and HR-LR-SR and the adversarial characteristics of the discriminator to promote the generator to produce better perceptually consistent SR results. The evaluation of PSNR/IFC/SSIM, inference efficiency and visual effects under the benchmark CCA-US and CCA-US datasets illustrate our proposed approach is effective and superior to other state-of-the-art methods.
Aplikasi Penyewaan Lapangan Futsal Berbasis Web

In Indonesia, there is one well-known sport, namely Futsal. Therefore, there are many businesses such as futsal court rental places. Basically, the futsal field rental is still manual, that is, customers come to the futsal location and make field rental transactions. This manual futsal court rental has a problem, namely that prospective customers cannot immediately find out the available empty schedule, this causes when they are already at the location but there is no desired schedule they are just wasting time. The purpose of this research is to find a solution in order to solve the problem in the manual futsal court rental method. With the creation of a web-based futsal court rental application that is expected to be able to solve this problem. This application was created using the waterfall development method so that the process of making this application is recorded in order and neat. This study produces a web-based futsal court rental application that has been tested for its feasibility using the black box testing method. This application provides a field rental feature for potential customers, as well as facilitates the processing of field rental data and financial report data for field owners.
Robustness Testing of Language Understanding in Dialog Systems.

Most language understanding models in dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution. However, these models can lead to system failure or undesirable outputs when being exposed to natural perturbation in practice. In this paper, we conduct comprehensive evaluation and analysis with respect to the robustness of natural language understanding models, and introduce three important aspects related to language understanding in real-world dialog systems, namely, language variety, speech characteristics, and noise perturbation. We propose a model-agnostic toolkit LAUG to approximate natural perturbation for testing the robustness issues in dialog systems. Four data augmentation approaches covering the three aspects are assembled in LAUG, which reveals critical robustness issues in state-of-the-art models. The augmented dataset through LAUG can be used to facilitate future research on the robustness testing of language understanding in dialog systems.
CSECU_KDE_MA at SemEval-2020 Task 8: A Neural Attention Model for Memotion Analysis

A meme is a pictorial representation of an idea or theme.In the age of emerging volume of social media platforms, memes are spreading rapidly from person to person and becoming a trending ways of opinion expression.However, due to the multimodal characteristics of meme contents, detecting and analyzing the underlying emotion of a meme is a formidable task.In this paper, we present our approach for detecting the emotion of a meme defined in the SemEval-2020 Task 8. Our team CSECU KDE MA employs an attention-based neural network model to tackle the problem.Upon extracting the text contents from a meme using an optical character reader (OCR), we represent it using the distributed representation of words.Next, we perform the convolution based on multiple kernel sizes to obtain the higher-level feature sequences.The feature sequences are then fed into the attentive time-distributed bidirectional LSTM model to learn the long-term dependencies effectively.Experimental results show that our proposed neural model obtained competitive performance among the participants' systems.