Reviewerly/exHarmony huggingface dataset:
Short Text Categorization using World Knowledge

The content of the World Wide Web is drastically multiplying, and thus the amount of available online text data is increasing every day. Today, many users contribute to this massive global network via online platforms by sharing information in the form of a short text. Such an immense amount of data covers subjects from all the existing domains (e.g., Sports, Economy, Biology, etc.). Further, manually processing such data is beyond human capabilities. As a result, Natural Language Processing (NLP) tasks, which aim to automatically analyze and process natural language documents have gained significant attention. Among these tasks, due to its application in various domains, text categorization has become one of the most fundamental and crucial tasks. However, the standard text categorization models face major challenges while performing short text categorization, due to the unique characteristics of short texts, i.e., insufficient text length, sparsity, ambiguity, etc. In other words, the conventional approaches provide substandard performance, when they are directly applied to the short text categorization task. Furthermore, in the case of short text, the standard feature extraction techniques such as bag-of-words suffer from limited contextual information. Hence, it is essential to enhance the text representations with an external knowledge source. Moreover, the traditional models require a significant amount of manually labeled data and obtaining labeled data is a costly and time-consuming task. Therefore, although recently proposed supervised methods, especially, deep neural network approaches have demonstrated notable performance, the requirement of the labeled data remains the main bottleneck of these approaches.In this thesis, we investigate the main research question of how to perform \textit{short text categorization} effectively \textit{without requiring any labeled data} using knowledge bases as an external source. In this regard, novel short text categorization models, namely, Knowledge-Based Short Text Categorization (KBSTC) and Weakly Supervised Short Text Categorization using World Knowledge (WESSTEC) have been introduced and evaluated in this thesis. The models do not require any hand-labeled data to perform short text categorization, instead, they leverage the semantic similarity between the short texts and the predefined categories. To quantify such semantic similarity, the low dimensional representation of entities and categories have been learned by exploiting a large knowledge base. To achieve that a novel entity and category embedding model has also been proposed in this thesis. The extensive experiments have been conducted to assess the performance of the proposed short text categorization models and the embedding model on several standard benchmark datasets.
New techniques, new questions in visual development

In the last two decades, the range of research on visual development has been expanded by new methodologies, some represented in this symposium, which provide richer data and more direct insights into the visual brain mechanisms underlying development. This talk provides a brief overview of other advances which have started to answer some key questions in visual development: (i) application of eye tracking to automated visual assessment; (ii) head-mounted eye tracking yielding data on how infants sample their natural visual environment; (iii) frequency-tagging to refine the specificity of information yielded by EEG; (iv) MRI approaches to the connectivity and structure of the developing visual brain, including individual differences in development; (v) broader studies of the impact of visual deprivation on human visual development. As well as applying new methods, developmental research, in common with vision research more generally, has also extended its scope into the interfaces of vision with attention, action systems, decision processes, and other aspects of cognition. All these advances open the prospects of a wider and deeper understanding of the role of vision in the development of brain systems in infancy and childhood. However, there remain challenges in understanding the origins of individual differences across children in visuospatial, visuomotor, and visuosocial cognition.
Content-based image retrieval using combined features of color descriptors, GLCM-SFTA, and Hu’s moments

Due to the information technology which is rapidly developing, digital content is becoming increasingly difficult to handle. This includes images that are kept on digital cameras, CCTV, and medical scanners. Areas such as medical and forensic science are using these databases to do critical tasks which include diagnosing diseases or identification of criminal suspects. However, managing and search the similar images from these databases is not an easy task. Content-Based Image Retrieval (CBIR) is one of the techniques used to manage and search similar images from a database. The performance of CBIR depends on the low level (Color, Texture, and Shape) features. In this paper, a new feature vector to represent the image in terms of low-level features and to improve the performance of CBIR is proposed. The proposed CBIR system was developed based on the combined features of Color, Texture, and Shape Features. Color features are extracted by using Color moments, HSV Color Autocorrelogram Feature (CAF), and Color Layout Descriptor (CLD). Texture features are extracted by Gray Level Co-Occurrence Matrix (GLCM) and Segmentation-based Fractal Texture Analysis (SFTA). Shape features are extracted by Hu Moments. The combined features which are made up of 165 Color Descriptor features, 109 GLCM-SFTA texture features, and 8 shape Hu's moment values are extracted to both query and database images. The extracted feature vector of the query image is compared with extracted feature vectors of the database images to obtain similar images.
HASIL BELAJAR KOGNITIF MENGGUNAKAN MODEL PEMBELAJARAN READING CONCEPT MAP COOPERATIVE SCRIPT (REMAP-CS) DAN READING CONCEPT MAP TIMED PAIR SHARE (REMAP-TMPS)

This study determined the comparison of student learning outcomes between the application of the Reading Concept Map Cooperative Script (ReMap-CS) learning model and the Reading Concept Map Timed Pair Share (ReMap-TmPS). This type of study was quasi-experimental using the pretest-posttest nonequivalent control group design. The research population is all students of class XI IPA SMAN 2 Gowa as many as 323 students. The research sample consisted of 24 students of class XI IPA 6 and 24 students of class XI IPA 7 selected by the purposive sampling technique. The learning outcomes research instruments were learning outcomes tests in the form of questions and assignments regarding the excretory system material used to measure student competency achievement. The data analysis technique used was descriptive data analysis and inferential data analysis. The results showed that the average student learning outcomes using the ReMap-CS learning model were 83,33 while the ReMap-TmPS learning model was 78,33. The t-test results show that the sig (2-tailed) value is 0,008. Results indicate that the sig. (2-tailed) value <0.05, which means a significant difference between learning outcomes in both the experimental class 1 and the experimental class 2, where experimental class 1 using Remap-CS is better than experiment 2 using ReMap-TmPs
Apple's iOS 13: Here are the latest features

Even if you don’t plan on upgrading to a new Apple phone, expect a notification for iOS 13.
Developing Digital Platform of The Askin Application Using House of Quality

Healthcare is the need of many people to fulfill their lifestyles. The desire to achieve maximum appearance encourages everyone to the healthcare of skin. The relatively high cost and the low number of aesthetic doctors in certain areas have led some people to do facial treatments independently using skincare products. Askin's business idea in the form of a facial care application is one of the solutions to help users with facial skincare. This research aims to analyze the problem factors of skincare users in carrying out facial skincare, analyze the right solution, and design a Minimum Viable Product that suits the Askin application. This research was conducted from December 2020 to January 2021 using the House of Quality research method to identify the best features. This research also used female research respondents aged 19-34 years using skincare products and domiciled in the City of Bukit Tinggi as a sample of this study. The results of this research contained six problems from skincare users, which is not knowing the type/condition of the skin in detail, not knowing the right skin care product to use, experiencing an incompatibility when using skincare products, difficulty obtaining skincare products, having no place to ask and consult, and not using skin care regularly. In conclusion, the Askin Application has ten application features formulated based on consumer needs and desires. These features are implemented in the minimum viable product of the Askin application. Keywords: business development, digital platform, facial care, house of quality &nbsp
The Use of Educative Videos to Develop Critical Competence

Abstract: The technology society and the increasing number of communication tools have given a rise to the development of new forms of teaching. Nowadays it is practically impossible to teach using traditional approaches. The media world requires implementation of new teaching-learning methodologies that enable students to acquire skills for life in society. These skills help them prepare for the world of work where they will have to face the challenge of intercommunication and understanding. Therefore, applying a literature review, this research focuses on analyzing the educational potential of videos and their application in the education in the 21st century. When implementing video activities into classes, students become co-designers of their own knowledge. To transmit the acquired knowledge students need to develop critical thinking skills as these skills enable students to act autonomously and responsibly. The main objectives of the paper are: learning and constructive learning; a construction that is carried out in an orderly manner, supervised by subject specialist teachers. The study aims to analyze the research that has been conducted in the field and prove that there is a need to change the traditional approach to teaching. Finally, the study presents the current situation and the future direction for further development.
DESIGNING E-KING (English speaKING) MASTERY AS LEARNING MEDIA FOR X GRADE STUDENTS

The background for the development of E-KING (English speaKING) Mastery media is to see the condition of learning media in general. In senior high school or Madrasah Aliyah Sayyid Abdurrahman learning still uses the usual method without media. Learning media is needed to facilitate teachers in transferring knowledge and making it easier for students to understand English material. Therefore, the purpose of this study is to develop E-KING (English speaKING) Mastery learning media to X grade students at Senior High School. The research method used is Research and Development, this development used ADDIE learning model which consists of five steps, that : 1) Analyze, 2) Design, 3) Development, 4) Implementation, and 5) Evaluation. The results of this assessment indicate that the E-KING (English speaKING) Mastery media has “very good” feasibility, according to media expert getting an average of 4,07 and material expert 4,13. Subject of this research was students  of X Grade students at Madrasah Aliyah Sayyid Abdurrahman Pagerwojo Perak Jombang. The students responses of try out result 81,41 which means this Learning Media is good category. It can be concluded that E-KING (English speaKING) Mastery feasible to use.
Improving Speed and Efficiency of Dynamic Programming Methods through Chaos

A prominent weakness of dynamic programming methods is that they perform operations throughout the entire set of states in a Markov decision process in every updating phase. This paper proposes a novel chaos-based method to solve the problem. For this purpose, a chaotic system is first initialized, and the resultant numbers are mapped onto the environment states through initial processing. In each traverse of the policy iteration method, policy evaluation is performed only once, and only a few states are updated. These states are proposed by the chaos system. In this method, the policy evaluation and improvement cycle lasts until an optimal policy is formulated in the environment. The same procedure is performed in the value iteration method, and only the values of a few states proposed by the chaos are updated in each traverse, whereas the values of other states are left unchanged. Unlike the conventional methods, an optimal solution can be obtained in the proposed method by only updating a limited number of states which are properly distributed all over the environment by chaos. The test results indicate the improved speed and efficiency of chaotic dynamic programming methods in obtaining the optimal solution in different grid environments.
Application Of An Artificial Intelligence Algorithm For Computer-Assisted Cancer Detection

The perceptron algorithm has been developed as a tool for detecting cancer. Currently, virtually all techniques for identifying illness are based on micro calcifications (MC's). MC's are calcium reserves that appear as a magnificent spot on a mammography. There are three types of AI calculations: directed learning, unassisted learning, and support learning. The perceptron algorithm is a mammography grouping method that uses an administered arrangement approach to group the MC's current cell and MC's missing cells. In the realm of clinical image preparation, CAD (Computer Aided Detection) for mammography is a research area. The goal of this project is to use the Perceptron method to find the MC's cells in a mammogram.
Hybrid Computational Model to Assist in the Location of Victims Buried in the Tragedy of Brumadinho

This paper presents a hybrid computational model based on regression techniques, machine learning and physicomathematical algorithms developed for assistance in locating victims in the Brumadinho tragedy in 2019. The physicomathematical model, which provided results to help search teams, is based on integral and vector calculus, and fluid mechanics concepts. In addition, from data provided by the physicomathematical algorithm, two hybrid model were developed. One of them uses regression statistical and the other one uses support vector regression which is a type of machine learning. With good prospects of the advances in research, it is expected in future work, a more accurate model that can be used in other possible situations of dam-break. Moreover the model can be applied to situations involving computational fluid dynamics in general
A Brief Study on Institutions, Investment and Economic Growth

A renewed interest in the role of institutions in achieving economic growth was noted from the 1990s. Indeed, a healthy institutional environment is capable of stimulating domestic and international investment and thus helping to achieve economic growth. Exactly, within this framework that fits the objective of this chapter which aims to survey the empirical literature on the matter to then proceed to two econometric investigations in order to test the effects of institutions on the economic growth of a group of countries. The results of these various investigations stipulate a positive effect, direct and indirect, of institutional factors on investment and economic growth. Thus, the work carried out within the framework of this chapter confirms the basic idea, according to which a healthy institutional framework can only play a crucial role in the achievement of good economic performances and essentially economic growth.
Internet of Things Enabled Healthcare Kit

The Internet of Things (IoT) is a network of intelligent heterogeneous objects that can communicate and share the data. Patients are required to attend the doctor on a daily basis in traditional models of healthcare. Both the patient and the medical personnel can find these traditional procedures inconvenient. The aim of this project is to create a smart IoT-based health monitoring system which have non-invasive sensors to read various health parameters and displays them on an LCD module in real time. The user can also transfer the data to the cloud to be safely stored and can share this data with doctor for treatment. Data can be live viewed from any location on the planet. Since the battery capacity of each system used in this project is minimal, it would reduce the power usage in order to prolong the life of the healthcare kit. The IoT-based hospital healthcare kit developed using an Arduino Uno, ESP8266, pulse rate sensors, blood oxygen sensor, ECG sensor, blood pressure sensor, and temperature sensor is described in this paper. As a result, IoT-enabled systems improve treatment delivery while also lowering costs by continuously collecting and analysing data.
An Improved Method for Multisensor High Conflict Data Fusion

Dempster-Shafer evidence theory can effectively process imperfect information and is widely used in a data fusion system. However, classical Dempster-Shafer evidence theory involves counter-intuitive behaviors with the data of multisensor high conflict in target identification system. In order to solve this problem, an improved evidence combination method is proposed in this paper. By calculating the support degree and the belief entropy of each sensor, the proposed method combines conflict evidences. A new method is used to calculate support degree in this paper. At the same time, inspired by Deng entropy, the modified belief entropy is proposed by considering the scale of the frame of discernment (FOD) and the relative scale of the intersection between evidences with respect to FOD. Because of these two modifications, the effect has been improved in conflict data fusion. Several methods are compared and analyzed through examples. And the result suggests the proposed method can not only obtain reasonable and correct results but also have the highest fusion reliability in solving the problem of high conflict data fusion.
Teaching Writing Recount Text by Using Powtoon Video

Powtoon. video and finding the advantages of using it. This research was a qualitative descriptive method. It was done in class IX of SMPN 6 Madiun. The sample of this research is class IX H. The data were collected by doing observation, interview, and documentation. Then, it was analyzed through three techniques. They involve data reduction. Displaying data, conclusion and verification. The result of reserach shows that using Powtoon. Powtoon video in teaching writing recount text done in three-phase technique. It involves pre activities, main activities and post activities. The process of writing done based on Linses’ theory. Those are pre writing, writing, revising, editing and publishing. Powtoon video was played twice. First, it was played in pre activities to stimulate the students' understanding of the material. Second, it was used in main activities specifically in revising step to make sure the students about their works. The advantages of using Powtoon video in writing class are; (1) creating a joyful class; (2) raising the students’ motivation and enthusiasm in writing; (3) realize the students to be independent learners; and (4) develop the teachers’ creativity.
Terminological subsystems of modern Russian school textbooks: A study based on Word2Vec and neural networks

The article reports the results of the study that explored the inventory and functioning of scientific terms and special lexemes in textbooks for Russia’s secondary schools. The toolset included modern methods of natural language processing and deep learning. The number of terms from different fields of knowledge that a secondary school student should learn has never been evaluated. According to the preliminary evaluations based on the Model Basic Curriculum for General and Secondary Education 2015, a secondary school leaver is supposed to be able to understand, recognise and use about 1,000 terms and terminological combinations in the subject Russian Language alone. Thus, taking into account the number of school subjects, the total number of special vocabulary studied in general education schools is measured in thousands. At the same time, the comparative characteristics of the inventory and functioning of terms in textbooks for different school subjects are under-scrutinized and remain unknown. Besides, it is unclear how the terminological density of school textbooks for different subjects correlates with the place occupied by these subjects in the curriculum. The traditional way of compiling lists of special terms is simply to glean them from special texts and write down manually. This method is reliable to gain insights into the best selection practices, however, it cannot be applied to large data sets and does not reflect the term frequency, the specificity of their syntagmatic connections, or the systemic relationship between them. Our project is aimed at filling this gap through: 1) creating a full-text corpus of school textbooks approved by the Ministry of Education for grades 5–11, 2) automatic extraction, stratification, and mapping of terms with the help of distribution semantics algorithms, 3) creation and training of a deep neural network capable of predicting the subject, level of education and educational topic given a group of vector theoretical development of terminology science. They may also find practical application, e. g., in the development of different types of educational literature.
Facial similarity judgements are well predicted by image-computable DNNs and a statistical face distribution model

Despite the importance of face perception in human and computer vision, no quantitative model of perceived facial similarity exists. We designed a novel behavioural task to efficiently collect similarity and same/different identity judgements, and used it to test diverse candidate models. We rendered two sets of 232 pairs of realistic faces from the Basel Face Model (BFM), a generative statistical model based on principal components analysis of 3D face scans. The two sets had identical relative geometries in BFM but different face exemplars. Participants (N=26) arranged face pairs on a large touch-screen according to how similar they appeared, relative to anchoring pairs at the top and bottom of the screen and to other adjusted pairs. Participants also placed a horizontal bar on each trial indicating the threshold between pairs that appeared to depict “the same person” vs different individuals. We compared perceived dissimilarities with diverse distance metrics derived from the BFM, image properties, or deep neural networks (DNNs) trained either on objects or faces. A face-recognition trained DNN (especially late intermediate layers) predicted human judgements best and explained unique variance over simpler image-based models. However, distances within the BFM performed almost as well, despite capturing only the distance between faces in principal-components space, with no image information. The performance was improved by taking a sigmoidal function of BFM distances. Judgements were similar for both stimulus sets (different face exemplars but same relative geometries). Faces with clearly perceptible differences were tolerated as belonging to the same identity, and the identity threshold aligned approximately with the statistically expected distance between random individuals in the BFM. Human facial similarity judgements appear tuned to the distribution of facial features, and can be well predicted by both statistical face models and image-computable DNNs.