Reviewerly/exHarmony huggingface dataset:
3D SEARCHING TECHNOLOGY

The World Wide Web is full of digital information and as the Web grows the number of 3D models also grows in the corporate database. Traditional text based searching technique is not always effective to search 3D data. So there is a great need for searching techniques to search 2D, 3D models available on web. 3D searching generally includes: query formulation, search process and finally search result. Typically web search engine Google uses Google Doodle for searching and retrieving 3D objects.
Automate a Little

In the previous chapter, we learned all about conditions, if, else, and elif statements, and combining multiple conditions to create complex commands.
Targeted Data Acquisition for Evolving Negotiation Agents

Successful negotiators must learn how to balance optimizing for self-interest and cooperation. Yet current artificial negotiation agents often heavily depend on the quality of the static datasets they were trained on, limiting their capacity to fashion an adaptive response balancing self-interest and cooperation. For this reason, we find that these agents can achieve either high utility or cooperation, but not both. To address this, we introduce a targeted data acquisition framework where we guide the exploration of a reinforcement learning agent using annotations from an expert oracle. The guided exploration incentivizes the learning agent to go beyond its static dataset and develop new negotiation strategies. We show that this enables our agents to obtain higher-reward and more Pareto-optimal solutions when negotiating with both simulated and human partners compared to standard supervised learning and reinforcement learning methods. This trend additionally holds when comparing agents using our targeted data acquisition framework to variants of agents trained with a mix of supervised learning and reinforcement learning, or to agents using tailored reward functions that explicitly optimize for utility and Pareto-optimality.
Perancangan Data Warehouse Pada Bagian Akademik Universitas di Bandung

Data Warehouse is a centralized database, the source of data warehouse is a history of operational database and any other source data. Data Warehouse generates reports and analyses related to organizational or user needs, and it's used as a decision making tool. University in Bandung have an Academic Directorate which records and processes information about new students, active students, accreditation of study programs, and student graduation. The Academic Directorate has three external data sources for Online Transaction Processing (OLTP), namely the New Student Admissions System (PMB), Integrated Academic Information System (SAT), and Graduate System. Academic Directorate doesn't have an Online Analytical Processing (OLAP), The data processing in the Academic Directorate still uses the manual way of query the OLTP databases and imported to Microsoft Excel format. Academic Directorate takes about 1 (one) to 3 (three) days to process the data into information because the required data is scattered in some storage media that has not integrated well as a data storage. This research have a purpose to build data warehouse as an OLAP system for Academic Directorate of University in Bandung.Keywords: Academic, Database, Data Warehouse.
Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?

Can models with particular structure avoid being biased towards spurious correlation in out-of-distribution (OOD) generalization? Peters et al. (2016) provides a positive answer for linear cases. In this paper, we use a functional modular probing method to analyze deep model structures under OOD setting. We demonstrate that even in biased models (which focus on spurious correlation) there still exist unbiased functional subnetworks. Furthermore, we articulate and demonstrate the functional lottery ticket hypothesis: full network contains a subnetwork that can achieve better OOD performance. We then propose Modular Risk Minimization to solve the subnetwork selection problem. Our algorithm learns the subnetwork structure from a given dataset, and can be combined with any other OOD regularization methods. Experiments on various OOD generalization tasks corroborate the effectiveness of our method.
동해의 해양침적쓰레기 성상 및 공간 분포 특성 연구

The Korean Peninsula is surrounded on three sides by the East Sea, West Sea and South Sea which are connected to many rivers and streams, thereby facilitating easy inflow of debris from land. Furthermore, excessive debris inflow to the sea because of active fishing and various recreational activities. Debris entering the sea are weighted over time and settle in the seabed, thus, making direct monitoring of debris impossible and its collection difficult. Uncollected submerged marine debris affects the seabed ecosystem and water quality and can cause ghost fishing and ship accidents, especially due to waste net ropes and waste fishing gears. Therefore, understanding the debris distribution characteristics is necessary to assist quick collection of these debris (waste net ropes and waste fishing gears). Thus, this study conducted a survey of debris deposited in the seas of 39 ports. Furthermore, distribution characteristics and compositions of submerged marine debris were identified by a map prepared through GIS-based spatial analysis of the East Sea. Consequently, 58% of waste tires in the East Sea were concentrated in breakwaters and ship berthing facilities. Moreover, 26 % of waste plastics were distributed outside the port. Identifying the distinct distribution characteristics of submerged marine debris was difficult; however, compared with others, the distribution of waste plastics was possible outside the port. The findings of this study can serve as baseline data to assist the collection of submerged marine debris using the distribution characteristics.
СОВРЕМЕННЫЕ ТЕНДЕНЦИИ В ФОРМИРОВАНИИ ОСНОВ ПРОФЕССИОНАЛИЗМА У БУДУЩИХ СПЕЦИАЛЬНЫХ ПЕДАГОГОВ

In the article, the leading approaches of the organization of the pedagogical process are shown, pedagogical conditions for the successful formation of the foundations of professionalism in defectology students in the university. The existence of graduates of defectologists with the foundations of professionalism will allow them in practice to create optimal comfortable conditions for children with special educational needs for development and education, to choose the development trajectory appropriate for the real capabilities of children, ensure the success of mastering program requirements, and the lack or smoothness of adaptation problems. Organizational-didactic conditions for the successful formation of the foundations of professionalism in the future specialist in the field of special pedagogy should become the creation of an organized vocational and educational innovative development environment and the provision. High-quality professional practical activities of students will allow to create professional competences of future expert. The article highlights the issues of professional training of special teachers., The experience of the offices of psychological and pedagogical correction which was opened in 2018 on the base of Kazakh State Women's Teacher Training University in the implementation of the practice-oriented approach in teaching is presented. The results of the practice-oriented approach, conducting seminar (practical) classes at the base, teaching practices, reflected in the graduate and master studies of students. The result is professional training for students with 3 courses on working with children with special educational needs.
Omilia - The Sign Language Converter

Omilia - The sign language converter is a tool used for converting sign language into text as well as speech. This proves to be of great use to people who wish to interact using sign language but have no prior knowledge of the same. In India, about 63 million (6.3 percent) of the population suﬀers from auditory loss. This model aims to mitigate the stigma around deaf people in society and help in easing the process of communication. The model is a real time sign language converter, where the signer stands in front of a camera and signs, the gesture is then translated into a visual and auditory signal. This allows a person with no knowledge of sign language to understand the signer. This model uses a webcam, Logitech C310 HD Webcam, for capturing the signs and with the help of deep learning algorithms, the gesture is recognized. Once the gesture recognition gets completed, it is used to generate text that is displayed on a screen. The text is then converted into speech whose output is provided by a speaker. The whole model is executed on a Raspberry Pi.
Civil Society as a Means against Hate Speech

On closer examination, the concept of civil society becomes blurred, for instance, on the crucial question of who belongs to civil society. The different answers to this question show how controversial the concept of civil society is, since it cannot be assumed that members of civil society would be morally particularly preferable actors. If, however, the conceptual foundations of and the question of membership in civil society already raise doubts about this concept, the recourse to civil society as a means against fake news and hate speech itself, which is often heard in public debates, becomes doubtful.
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning

This paper considers batch Reinforcement Learning (RL) with general value function approximation. Our study investigates the minimal assumptions to reliably estimate/minimize Bellman error, and characterizes the generalization performance by (local) Rademacher complexities of general function classes, which makes initial steps in bridging the gap between statistical learning theory and batch RL. Concretely, we view the Bellman error as a surrogate loss for the optimality gap, and prove the followings: (1) In double sampling regime, the excess risk of Empirical Risk Minimizer (ERM) is bounded by the Rademacher complexity of the function class. (2) In the single sampling regime, sample-efficient risk minimization is not possible without further assumptions, regardless of algorithms. However, with completeness assumptions, the excess risk of FQI and a minimax style algorithm can be again bounded by the Rademacher complexity of the corresponding function classes. (3) Fast statistical rates can be achieved by using tools of local Rademacher complexity. Our analysis covers a wide range of function classes, including finite classes, linear spaces, kernel spaces, sparse linear features, etc.
Debiasing a First-order Heuristic for Approximate Bi-level Optimization

Approximate bi-level optimization (ABLO) consists of (outer-level) optimization problems, involving numerical (inner-level) optimization loops. While ABLO has many applications across deep learning, it suffers from time and memory complexity proportional to the length $r$ of its inner optimization loop. To address this complexity, an earlier first-order method (FOM) was proposed as a heuristic that omits second derivative terms, yielding significant speed gains and requiring only constant memory. Despite FOM's popularity, there is a lack of theoretical understanding of its convergence properties. We contribute by theoretically characterizing FOM's gradient bias under mild assumptions. We further demonstrate a rich family of examples where FOM-based SGD does not converge to a stationary point of the ABLO objective. We address this concern by proposing an unbiased FOM (UFOM) enjoying constant memory complexity as a function of $r$. We characterize the introduced time-variance tradeoff, demonstrate convergence bounds, and find an optimal UFOM for a given ABLO problem. Finally, we propose an efficient adaptive UFOM scheme.
Exploring Android Concepts: Core UI Widgets

In Part 1 of the book, you had a whirlwind tour of the steps required to create a new Android Studio project for your Android application and explored the structure, files, and purpose of the various parts of a default Android project. You delved into the initial steps of creating your own behavior with the XML configuration for your application's color scheme and leveraged the utility of text resources to personalize the welcome message in your application's main activity.
Text Classification Feature Extraction Using SVM

Text Classification is an automated procedure of ordering Text into classifications. We can characterize Emails into spam or non-spam, nourishment into frank or not sausage, and so on. Text Classification should be possible with the assistance of Natural Language Processing and various calculations. The center target of this exploration is to examine the presentation of classification calculations utilizing the Scopus data set. In-text classification, classification, and highlight extraction from the archive using extricated highlights are the significant issues for diminishing the exhibitions in various calculations. In this paper, displays of classification calculations, for example, KNN and Support vector Machine(SVM), indicated better improvement utilizing Bayesian lift and sacking. The presentation results were broken down through chosen classification calculations over various archives from Scopus. They were analyzed using F-measure and delivered examination networks to assess exactness, accuracy, and review utilizing N.B. and SUPPORT VECTOR MACHINE (SVM) classifier. Further, information pre-processing and cleaning steps are incited on the chose data set, and class unevenness issues are examined to build the presentation of text classification calculations. Test results indicated exhibitions over 9% utilizing SVM and uncovered better when contrasted with KNN.
Functional Space Analysis of Local GAN Convergence

Recent work demonstrated the benefits of studying continuous-time dynamics governing the GAN training. However, this dynamics is analyzed in the model parameter space, which results in finite-dimensional dynamical systems. We propose a novel perspective where we study the local dynamics of adversarial training in the general functional space and show how it can be represented as a system of partial differential equations. Thus, the convergence properties can be inferred from the eigenvalues of the resulting differential operator. We show that these eigenvalues can be efficiently estimated from the target dataset before training. Our perspective reveals several insights on the practical tricks commonly used to stabilize GANs, such as gradient penalty, data augmentation, and advanced integration schemes. As an immediate practical benefit, we demonstrate how one can a priori select an optimal data augmentation strategy for a particular generation task.
Modeling Alignment of Software Projects Needs with Software Project Teams’ Needs: It is a Balancing Act

Project managers face a challenge in aligning project requirements with project team members’ due to the fast-paced, competitive world in which information and communication technology (ICT) projects are run, as well as ICT professionals' constant evolving requirements. A project client measures a project success by how well has a project manager been able to meet project goals, while on the other hand software project teams (SPTs) measure project success by a manager’s ability to satisfy their project associated needs. This chapter purports that if projects’ requirements are properly aligned with team members’ requirements, then this will result in a balanced decision approach. Moreover, such an alignment will result in the realization of SPTs’ needs as well as meeting project’s needs. The chapter presents a Software Project’s needs and Software Project Team’s needs (SPnSPTn) alignment model and argues that a balanced decision, which meets both software project’s requirements and team members’ requirements can be achieved through the application of the SPnSPTn alignment model.
Machine Learned Resume Scrutiny for Job Recommendations

This study adopts machine learning and text mining technology to analyse a candidate profile and resume, developing a system that can be applied where numerous applicants seek to match with the job vacancies provided by companies. The developed system conducts personal competitiveness analysis, personality analysis, and gives recommendations according to the resumes applicants submit.
Designing the Quality Assurance Agency E-Document Application at the National University Website Based: Designing the Quality Assurance Agency E-Document Application at the National University Website Based

The National University Quality Assurance Agency is an agency that has the function of maintaining and improving quality so that quality is always guaranteed as well as possible. One of its duties is collecting documents for lecturers and employees. Documents are accurate evidence of recording information sources used for selecting, processing or storing information. This document is very important for seeing the level of achievement of organized activities. The purpose of this E-Document Application was made to optimize the creation of an electronic document processing system for the Quality Assurance Agency (BPM) at the National University in collecting the documents needed so that the documents are stored properly, facilitating the search process and maintaining because often this document processing is still carried out Manually, difficulty in searching and disorganized in their storage. The design of this application uses software, namely the programming language used is PHP, bootstrap, XAMPP, MySQL, using the Unified Modeling Language (UML) method and testing using black box testing. The result of designing this application is a Website-based E-Document information system and is expected to help the work unit to find documents quickly and does not take a long time in searching and stored regularly. using the Unified Modeling Language (UML) method and testing using black box testing. The result of designing this application is a Website-based E-Document information system and it is hoped that it can help the work unit to find documents quickly and does not take a long time in searching and stored regularly. using the Unified Modeling Language (UML) method and testing using black box testing. The result of designing this application is a Website-based E-Document information system and is expected to help the work unit to find documents quickly and does not take a long time in searching and stored regularly.
Implementation of the school literacy movement during the covid-19 pandemic

&lt;div&gt;&lt;p&gt;The implementation of the school literacy movement, especially in elementary schools, has many obstacles. Moreover, during the Covid-19 pandemic, the problem of implementing the school literacy movement became increasingly complex. This research is a qualitative research, which is carried out in elementary schools in Banjarharjo District. The data collection instruments consisted of interviews, questionnaire sheets, and observation sheets. The results showed 1) the pattern of literacy movements during the Covid-19 pandemic was adjusted to the level of developmental age of students and the situation and conditions and stages of the literacy movement carried out online; 2) The driving factors for the implementation of the school literacy movement during the Covid-19 pandemic include the availability of good facilities and cooperation with parents. Meanwhile, The inhibiting factors for the school literacy movement are the lack of supporting facilities and the lack of teachers to directly control the development of students' literacy. The school literacy movement during the Covid-19 pandemic should be supported by digital-based reading materials and parental understanding regarding the implementation of the school literacy movement during the Covid-19 pandemic.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;/div&gt;
Sustainable Development of Society and Russian Philosophy: Educational Aspect

The purpose of the article is to project the general statements of the philosophical tradition and domestic psychological schools of thought on the specific situation of teaching and education, as well as to compare the historically emerging ideas of upbringing and education with modern approaches, and to trace the genetic connection and the continuity of ideas. In this context, it is possible to draw conclusions and learn lessons for the development of the modern pedagogical situation. The research results are novel and practically significant, since the main conclusion is the choice of a methodological basis for teaching natural sciences with a view to the sustainable development of society as a whole and within the framework of subject-subject relationship in particular. According to the chosen methodology, teaching information about sustainable development involves a set of interconnected methods for the purpose of their application in the educational process so to access each student individually. A special contribution of the authors is a comparative analysis of approaches to training and education, which result in the sustainable development of society.
Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings

Word embeddings are the standard model for semantic and syntactic representations of words. Unfortunately, these models have been shown to exhibit undesirable word associations resulting from gender, racial, and religious biases. Existing post-processing methods for debiasing word embeddings are unable to mitigate gender bias hidden in the spatial arrangement of word vectors. In this paper, we propose RAN-Debias, a novel gender debiasing methodology which not only eliminates the bias present in a word vector but also alters the spatial distribution of its neighbouring vectors, achieving a bias-free setting while maintaining minimal semantic offset. We also propose a new bias evaluation metric - Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections. Experiments based on a suite of evaluation metrics show that RAN-Debias significantly outperforms the state-of-the-art in reducing proximity bias (GIPE) by at least 42.02%. It also reduces direct bias, adding minimal semantic disturbance, and achieves the best performance in a downstream application task (coreference resolution).
An Appeal to the People: Politeness Strategies in the Malaysian Prime Minister’s Maiden Speech

The use of politeness strategies has received increasing attention in the political discourse as a powerful persuasion tool. These strategies became critical for the newly appointed prime minister of Malaysia, Tan Sri Muhyiddin Yassin, to convince Malaysians that he is qualified to lead the country during a political upheaval and global pandemic. Thus, the objectives of this paper are to identify the types and frequency of the politeness strategies used by Muhyiddin Yassin in his maiden speech as the eighth prime minister of Malaysia. Two categories of politeness strategies based on Brown and Levinson’s (1987) were examined, which are negative strategies and positive strategies. The speech text was obtained from the official website of Prime Minister’s Office and was translated to English for analysis. The translated speech was subsequently checked for validity. Document analysis was used to analyse the translated speech text to determine the types and frequency of the politeness strategies. The analysis revealed that positive politeness strategies were significantly used (88%) compared to negative politeness strategies (12%). The most dominantly used positive politeness strategy was “notice and attend to the receiver’s need, interest, or want” while “use of exaggeration”, “seek agreement”, and “avoid disagreement” were the least used strategy. In contrast, “question” and “giving deference” were identified to be the most frequently used negative politeness strategy. The present study extends our knowledge on the use of politeness strategies in a political speech in an Asian setting, which is notably lacking in the literature.