Reviewerly/exHarmony huggingface dataset:
Research on gender prediction based on short texts on Chinese social platforms

With the rapid development of Internet technology, various social networking platforms are becoming more and more popular. When using social platforms, users will generate a large amount of data containing rich information and value. Gender is one of the basic attributes of users. The correct gender attribute has an important influence on the research of personalized recommendation, precision marketing and user behavior. However, there are some problems in the short text of social platforms, such as scarcity of data and high noise interference, which will have a significant impact on the accuracy of prediction. This paper takes users of Sina Weibo as the research object, analyzes the emotional expressions of users of different genders, and proposes a de-noising algorithm based on emotional dictionaries to enhance the gender characteristics of users in the text and reduce the interference of noise. Experiments show that the short text can accurately predict the user's gender and improve its influence in the main evaluation indicators.
Divide-and-conquer for holistic and expressive 3D human body reconstruction from a single RGB image

Despite that 3D human body reconstruction from a single image has obtained rapid progress in recent years, most methods aim at the body without the hands and face. However, hand gestures and facial expressions are also important for delivering human intentions or emotions. This paper proposes a method for holistic 3D reconstruction of the human body from a single RGB image, including hands, body, and face. Our approach is based on the SMPL eXpressive (SMPL-X), a unified 3D parametric human body model of body, hands, and face. Since it is difficult to exactly regress the model's parameters of different body parts by a single framework, we use a divide-and-conquer strategy for the whole human body reconstruction. We exploit different deep neural networks to predict the hand, body, and head model's parameters, then integrate them into an entire 3D model to realize a holistic and expressive 3D human body reconstruction. Simulation results demonstrate that our method has obtained state-of-the-art performance with better facial expression.
Textlytic: Automatic Project Report Summarization Using NLP Techniques

Academic project reports can be very verbose and lengthy since they include comprehensive descriptions, diagrams, tables, charts, graphs, and illustrations. Such reports tend to be too long and detailed for quick assessment or perusal. The proposed system aims to generate a concise extractive summary of technical project reports. As each section of the report contains important details and contributes to a sequence, it must be summarized separately. To achieve this objective, the system accepts a multi-page document as input and performs section-wise segregation before processing the contents. It summarizes each section, retaining the topic structure of the original document in the resulting output. Additionally, the proposed system implements figure and caption extraction for respective sections and also generates a downloadable summary output file. The resultant summary was evaluated using the BLEU metric on an open-source dataset. An average score of 38.996% was obtained.
Short and Distort Manipulations in the Cryptocurrency Market: Case Study, Patterns and Detection

Recently, with the development of blockchain, cryptocurrencies such as bitcoin are gaining popularity. However, at the same time, there are some problems such as lack of laws and supervision, leading to an endless stream of frauds and market manipulations, which seriously impacts the interests of investors and the development of the economic society. This paper studies Short and Distort, a new manipulation as opposed to Pump and Dump in the cryptocurrency market for the first time. By using the method of case study, Short and Distort is explained. Further, we mine three important patterns with ordinary least squares regression. That is, cryptocurrencies always show price reversal, abnormal trading volume, and widening of bid-ask spreads. Moreover, a model is constructed to detect Short and Distort. Thorough experiments show that our model is superior to the other four methods.
ASSESSMENT OF THE VIETNAMESE STUDENTS' EXPERIMENTAL CAPACITY WHEN USING EXPERIMENTS IN TEACHING HIGH SCHOOLS CHEMISTRY

Vietnam's new general education curriculum was just released in 2018, focusing on developing students' qualities and capabilities. Chemistry is an experimental science, thus developing and assessing students' experimental competence in teaching chemistry is a very important task. Based on the experimental competence scale, the article introduced and illustrated the assessment tools for this competence including student worksheets, observation sheets, and special tests. To pilot evaluation tools, we use specifically pre-test, post-test, and worksheets affecting 63 students in grade 12 of 2 high schools in Ben Tre Province, Vietnam to collect evidence to assess students' experimental competence. Thereby assessing the feasibility and effectiveness of constructed tools.
Scale Linking for the Testlet Item Response Theory Model

In their 2005 paper, Li and her colleagues proposed a test response function (TRF) linking method for a two-parameter testlet model and used a genetic algorithm to find minimization solutions for the linking coefficients. In the present paper the linking task for a three-parameter testlet model is formulated from the perspective of bi-factor modeling, and three linking methods for the model are presented: the TRF, mean/least squares (MLS), and item response function (IRF) methods. Simulations are conducted to compare the TRF method using a genetic algorithm with the TRF and IRF methods using a quasi-Newton algorithm and the MLS method. The results indicate that the IRF, MLS, and TRF methods perform very well, well, and poorly, respectively, in estimating the linking coefficients associated with testlet effects, that the use of genetic algorithms offers little improvement to the TRF method, and that the minimization function for the TRF method is not as well-structured as that for the IRF method.
Assessing Generalizability of CodeBERT

The International Conference on Software Maintenance and Evolution is the premier international forum for researchers and practitioners from academia, industry, and government to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution.
Learning to Reason on Tree Structures for Knowledge-Based Visual Question Answering

Collaborative reasoning for knowledge-based visual question answering is challenging but vital and efficient in understanding the features of the images and questions. While previous methods jointly fuse all kinds of features by attention mechanism or use handcrafted rules to generate a layout for performing compositional reasoning, which lacks the process of visual reasoning and introduces a large number of parameters for predicting the correct answer. For conducting visual reasoning on all kinds of image-question pairs, in this paper, we propose a novel reasoning model of a question-guided tree structure with a knowledge base (QGTSKB) for addressing these problems. In addition, our model consists of four neural module networks: the attention model that locates attended regions based on the image features and question embeddings by attention mechanism, the gated reasoning model that forgets and updates the fused features, the fusion reasoning model that mines high-level semantics of the attended visual features and knowledge base and knowledge-based fact model that makes up for the lack of visual and textual information with external knowledge. Therefore, our model performs visual analysis and reasoning based on tree structures, knowledge base and four neural module networks. Experimental results show that our model achieves superior performance over existing methods on the VQA v2.0 and CLVER dataset, and visual reasoning experiments prove the interpretability of the model.
A CUDA-QUDA architecture, Hyperscale-Data Quantum GPUs

Abstract:QUDA is an architecture similar to CUDA for HPC applications of Quantum GPU architectures to be used in conjunction with GPU and MCU based processing. There is no QUDA pipeline similar to a stream processing architecture or an out of order instruction pipeline. True ILP is achieved with a QUDA architecture, which is better than quasi-parallelism in a CUDA or scalar/vector architecture.Keywords: QUDA architecture, HPC, Quantum Cloud, Qiskit, spin waves, spintronic, hyper-data, quantum operating system.What:Quantum Unified Compute Architecture or QUDA architectures is the use of Q and Qiskit for a reconfigurable quantum array architecture on spintronic units with a reconfigurable scheduler for a quantum operating system.
Design of Modified Dual-Coupled Linear Congruential Generator Method Architecture for Pseudorandom Bit Generation

In various cryptography applications, security during data transmission and reception and its storage plays a key role. Digital signatures and authentication are a very important application of public-key cryptography. Regular pattern bits can be easily detected and hacked by the third party. To avoid this problem Pseudo Random Bit Generator is used. Linear Feedback Shift Register, Linear Congruential Generator (LCG), Couple LCG (CLCG) and Dual-Coupled LCG (Dual-CLCG) are most popular existing PRBG methods. However, uniform clock rate is finally achieved using Dual CLCG method, but it fails to attainmaximum sequence length and some parameters like high initial clock latency, excessive memory usage are drawbacks. To overcome these drawbacks, dual CLCG method is modified in this paper. The modified method generates Pseudo Random Bit by achieving uniform clock rate with one initial clock delay and reduced hardware complexity. The existing and proposed architectures are designed for 8-bit using Microsemi Libero Tool.
Free Theory

This monograph takes stock of the situation in higher spin gauge theories for the first time. Besides a thorough recapitulation of the field's history, it reviews the progress that has been made and offers a pedagogical introduction to the subject. Abstract approaches to the theory are offered to facilitate a conceptual rethinking of the main problems and to help see patterns hidden by heavy formalism.
Public policies and inclusion facing the COVID-19 pandemic in Mexico : Case: The Autonomous University of Nayarit facing the COVID-19

Along with the new challenges that universities must face, technology keeps advancing, seek to guarantee interaction with the rest of the world in real-time, encouraging the development of skills to produce, share and use knowledge. Public policies seek to fight against discrimination, not only limiting themselves to guaranteeing people's rights but also extending them towards decisions and social plans, with the aim of closing gaps and general well-being.
Peer review declaration

All papers published in this volume of IOP Conference Series: Earth and Environmental Science have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Double-blind • Conference submission management system : Iconf submission system • Number of submissions received : 27 papers • Number of submissions sent for review : 27 papers • Number of submissions accepted : 13 papers • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100) : 48.1% • Average number of reviews per paper : 3 papers • Total number of reviewers involved : 20 reviewers • Any additional info on review process : 1. Preliminary Review When we received authors’ paper (abstract and full paper), we submitted the paper to auditing department for checking, the auditing department reviewed the content, theme, format and grammar. 2. The full paper passed the first review would be reviewed again by conference technical committees from the following aspects: Originality, Innovation, Technical Soundness, Applicability, Clarity of presentation and Relevance. 3. After paper passed the first review and the second review, we sent the notification. 4. Paper can only be arranged for publication if it was modified according to comments and template. Contact person for queries : Prof. Jan Baeyens Beijing University of Chemical Technology, China KULeuven, Belgium baeyens.j@gmail.com
Introduction

This chapter divides into four groups, each representing a general area of inquiry within cognitive science: the nature and content of psychological theories, mental representation, cognitive development, and semantic theory. Cognitive science is an interdisciplinary study of the nature of human information processing. It seeks to describe the ways in which knowledge and goals are represented and the faculties and algorithms that govern the transitions of mental representations in the acquisition of sensory knowledge, and in theoretical and practical reasoning. Cooper's analysis of cognitive skill is an argument for the existence of multiple cognitive models addressing a single representational problem. Individuals differ in their ability to judge whether different two-dimensional representations of an object are equivalent, i.e. whether they represent the same object. Cognitive science seeks to know the information-processing procedures by which we arrive at representations of the external world, and it would like to know the nature of the meaning and truth conditions of those representations.
Performance Assessment Using Supervised Machine Learning Algorithms of Opinion Mining on Social Media Dataset

Opinion mining is an exclusive technique used to identify the emotional tone behind a body of text. Twitter is a social media platform to send and receive short posts called tweets. Companies use opinion mining to develop their business strategies by knowing the public feelings towards a product or a brand with their tweets. Opinion mining is also called sentiment analysis, which is implementing a model that collects and examines comments about a topic made in tweets. Some data mining methods (such as clustering, classification, regression, and so on) are available for the sentiment analysis task (Dey and Chakraborty in International Scholarly Research Notices 2014:1–11, 2014; Dey and Chakraborty in Convex-hull & DBSCAN clustering to predict future weather, pp. 1–8, 2015). The tweets are categorized as either positive or negative by analyzing the performance concerning the parameters such as accuracy, precision, and recall. The technologies used to implement this is data mining and machine learning. Many algorithms came into existence to extract the opinions from tweets. This paper focused on the performance assessment of supervised learning models like logistic regression, Naïve Bayes’, and support vector machine (SVM). The SVM with kernel radial basis function (RBF) resulted in better performance.
Detection of Fake Profiles in Instagram Using Supervised Machine Learning

The number of followers on Instagram is a popular measure for determining one’s level of success. Consequently, a significant part of the company’s workforce is comprised of believers who are providing services. Influencers are swamped with phone supporters, which force a company to overpay to get branded enterprise services. It is essential to identify false supporters in order to evaluate the credibility of an influence. This research attempts to identify fraudulent customer behavior and proposes the use of machine learning algorithm based models in order to evaluate trustworthy and fraudulent consumers. However, despite the fact that the Internet and online media have made enormous quantities of information accessible to many people, the growth of digital misbehavior, especially among women, has risen dramatically in recent years. According to a report published in the Economic Times in 2019, cybercrime in India increased by 457 percent between 2011 and 2016. Most people are aware of the effect that social media sites such as Facebook, Instagram, and Twitter have on their lives. This is a result of our everyday activities. It is the purpose of false characters, as well as ways of detecting phoney online media identities, that are discussed in this article in the context of the present cutting-edge threats. We will investigate the effect of three regulated AI calculations on the prediction of false or real profiles. These calculations will be: The Random Forest Algorithm based on Linear Regression.
Author Processing Charges: Barriers to Open Research, Barriers to Progress

Presentation slides from Metropolitan New York Library Council Open Access Symposium: "The Future Is Open Access, but How Do We Get There?: A Symposium." September 12-13, 2019. New York. NY.
TEACHER’S EFFORTS TO APPLY VISUAL MEDIA IN ENGLISH LEARNING PROCESS

An effort is one of the efforts or conditions to achieve a certain purpose. Visual media is a learning medium that distributes messages by sight. Teachers more often use visual media in learning. This study discusses the teacher's efforts to apply visual media in learning English. The tool used in the visual media process is an LCD projector. This study uses a qualitative approach with a descriptive research design. Data were obtained from English teachers through interviews and documentation. The various benefits of using visual media are that it can attract students' attention, motivate students to learn more, introduce technological tools, and many others.
A Survey on Data Integrity Methods in Cloud Storage

Cloud computing offers different kind of servicesto the end user, so that end user can store the data and access iton demand where ever they need it. Now days IT industries areoutsourcing their data by storing it remotely in cloud forreducing the load on local storage there by reducing thehardware, software and maintenance cost. In spite of thesebenefits, the major problem with data storage is that there is noguarantee of data consistency and integrity, which has become amajor hurdle for using the services offered by cloud. So thispaper surveys existing schemes that proves data integrity andretrievability.
THE INDONESIAN WEALTH

All glory be to Allah Lord of the worlds, the merciful, the beneficent who has bestowed upon us guidance, health, and ability to complete an English book entitled “Learning English through Popular Indonesian Holiday Destinations: Junior High School Edition.” The completion of this book aims at empowering English course book for junior high school students. Preparations for this book utilize materials using Genre Based Approach (G.B.A), which is an approach that emphasizes process and production. The Genre Based Approach is a learning method that has four main stages, namely: Building the Knowledge of the Field (B.K.O.F), Modelling of the Text (M.O.T), Joint Construction of the Text (J.C.O.T), and Independent Construction of the Text (I.C.O.T). Through this book, we hope students will be able to learn English effectively and efficiently while enjoying themselves. Furthermore, through this book, we hope to promote the students‟ creativity and enthusiasm towards learning English. Moreover, we are always open to suggestions and critiques to help improve this book.
Sign Language Recognition Using Convolution Neural Network

Sign language is one of the medias to communicate with deaf and dumb people; usually, it is not known to normal people. So, it becomes a challenging task to establish a communication between normal people and hearing impaired person. Many tools are developed to help them, but unfortunately not produce accurate results. To interact with them, we are using various fingers gestures; then, designed model will convert those gesture into words or alphabets into specific language. The predicted model is helpful to reduce the gap between the normal people and hearing impaired person. In our proposed sign language recognition algorithm, we focused on deep convolution neural network to produce better accuracy.
An Exploratory Study on the Settlement of Work and Learning Dual System at Junior College Students

The purpose of this study is to prepare basic data for the stable settlement of Work and Learning Dual System in the hospitality field of junior college students and to secure the competitiveness of colleges. The subject of the study was limited to hotel companies, and an exploratory study was conducted through in-depth interviews with experts. In addition, the problems of hotel companies were analyzed, and methods for improvement and activation of the work-learning system were presented. <BR> As a result of the study, the hotel industry is currently experiencing difficulties due to abnormal business conditions due to COVID-19. Also, as for the improvement of the work-study dual system at the junior college student level, simplification of administrative requirements, diversification of training courses, application of the IPP concept, and listening to the demands of human resources managers and learning companies were suggested. Promotion of the system, development of best practices, easing of barriers to entry, and strengthening of competitiveness compared to similar projects were suggested as revitalization measures.
A Feature Extraction and Heatmap Generation Approach Based on 3D Object Models and CNNS

The reliance on individual companies’ collection and ownership of object’s 3D models and their annotations has become a serious issue for developing new algorithms for autonomous vehicles (AV). This thesis proposes a method for directly representing real-world objects with open-source graphics model datasets as prior knowledge. This method employs machine learning (ML) methods to extract 3D keypoints (feature points), and annotations are generated from graphics models to verify variable and static objects and to identify dynamic objects. Furthermore, it creates heatmaps in real-time images to eliminate variable and static objects before working on recognizing dynamic objects. This proposed approach helps in the progress of novel algorithms related to self-driving vehicles by bridging the gap between the physical and virtual worlds.
MEASURING LISTENING SKILLS THROUGH STRATEGY TO NOTE (NOTETAKING) ON KEYWORDS AT SMK N 1 PEMATANGSIANTAR

&#x0D; This study aims to determine the role of note taking strategies on listening skills (listening). The research method used in this study is a True research method Experimental Design whose implementation uses the Posttest-Only Control Design type compares the experimental class with the control class. At SMK N 1 PEMATANGSIANTAR, the population of all students in class XI Program Mechanical Engineering Expertise. Samples were taken as many as 52 with a random class technique sampling. The instruments used are MGMP questions and UN questions (Listening). From data processing the results obtained: there are differences in listening learning achievement between before and after using note-taking strategy.&#x0D;
KSL Syntax and Semantics

This chapter discusses the acquisition of Korean syntax and semantics by L1 English and heritage speakers. After presenting an overview of Korean grammar, focusing on the syntax–semantics mismatch between L1 English and L2 Korean, we survey recent KSL literature investigating the acquisition of clause structure; case, number, and topic markings on nominals; relative clauses; honorifics; tense and aspect markings on verbals, and wh-questions in L2 Korean. Influential hypotheses in SLA, such as the subset principle, the shared structure hypothesis, the feature (re)assembly hypothesis, and the interface hypothesis, are examined in light of Korean acquisition data.