Reviewerly/exHarmony huggingface dataset:
Chatbot, Speech, and NLP

Chatbots are programs that are capable of conversing with people trained for their specific tasks, such as providing parents with information about a school. This chapter will provide the skills required to create a basic chatbot that can converse through speech. Speech to text tools will be used to convert speech data into text data. An encoder-decoder architecture model will be trained using Long-Short Term Memory units for a question and answer task for conversation.
ORGANIZATIONAL AND PEDAGOGICAL CONDITIONS FOR FORMATION OF READER INTEREST IN FICTION AMONG OLDER PRESCHOOLERS

The issue of reading interest formation among older preschoolers is relevant due to the general decline in reading culture in modern society, the underestimation of the fiction role by parents and the introduction of children to reading for their intellectual, social, artistic, aesthetic and psycho-emotional development. Parents stopped reading books to their children, using electronic audiovisual products as a means of children's knowledge of the world around them; as a result, modern preschoolers develop outside the context of book culture, which negatively affects their mental development. The aim of the study is to develop organizational and pedagogical conditions that ensure formation effectiveness of reader interest in fiction among older preschoolers based on the scientific sources analysis. Reader interest in senior preschool age is a dynamic personal formation, which manifests itself in the child's conscious and selective attitude in choosing the types and genres of works of fiction to meet cognitive and artistic and aesthetic needs. The effectiveness of the reader interest formation among 7-year-old children will be facilitated by the implementation of complex with organizational and pedagogical conditions in the preschool institution educational practice. The complex includes development of a model and program of pedagogical support for reader interest formation in fiction among older preschoolers, social partnership relations with a children's library, developing subject-spatial environment in the preschool group, enriched with didactic and subject material focused on the literary development of older preschoolers.
Predictive models of epidemiological outcomes for patients with subarachnoid hemorrhage

Subarachnoid hemorrhage-SAH is considered a serious disease with a high potential for mortality and disability worldwide.This study aimed to evaluate the epidemiological outcomes in patients with SAH from 2006 to 2018 through a predictive equation, considering the following variables: risk factors, early manifestations, complementary needs, neurological and systemic complications.The subjects of this study were hospitalized patients with a proven diagnosis of SAH, evaluated by the Hunt-Hess and Fisher scales and classified by the modified Rankin scale (mRS).To reach this objective, binary logistic regression was applied, with risk factors as independent variables and epidemiological outcomes as dependent.A total of 148 patients were documented, 65% female and 35% male, with a mean age of 53.7 years.Heart disease was an important predictor for the severe disability outcome, the combination of sensory impairment and syncope explained cases of death by 75.7%.Among neurological complications, hydrocephalus and vasospasm explained the severe cases and ischemic injury and rebleeding explained the death.For systemic complications, hyperglycemia and aspiration pneumonia together represented predictors for severe cases and death events, which can be explained by the presence of sepsis and fever.There is a need to intensify health prevention programs for chronic diseases and women's health, as well as implement hospital assessment protocols. I.
INFORMATION COMMUNICATION TECHNOLOGIES IN TEACHING ASTRONOMY IN PROFESSIONAL EDUCATION ORGANIZATIONS

The problem of using information communication technologies in teaching astronomy in professional education organizations is actualized. Classification of internet resources and specialized software in astronomy is considered, that can be used in the conditions of educational process informatization. Results of a pedagogical experiment aimed at studying the impact of the used internet resources and specialized software on student’s performance are presented. It has been found that application of modern communication technologies and specialized software makes it possible to imitate various processes and phenomena when studying the Universe, making the process of teaching astronomy exiting, interesting, absorbing and increases the motivation to studies and knowledge acquisition.
COVIBOT: A ChatBot for Covid-19 Related Information

Covid-19 was declared as a pandemic by World Health Organization (WHO) on March 11, 2020, creating chaos among all the countries around the world. Millions of people have lost their lives with this outbreak. It’s been a year and still countries like India are fighting this global pandemic. Under such circumstances, having the right information about the cases and facilities around would be of great use. In this context, we aim to develop a chatbot for the people of Bangalore to gain the necessary information about the situation. The proposed application includes statistics about the covid cases in Karnataka, Bruhat Bengaluru Mahanagara Palike (BBMP) Help-Line Number for specific zones, Home Intensive Care Unit (ICU) Service Providers, Online Doctor consultation, Oxygen Providers Information and availability of vaccine slots. In this way, our bot can help people gain better understanding of current condition and take informed decisions.
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training

Understanding how the predictions of deep learning models are formed during the training process is crucial to improve model performance and fix model defects, especially when we need to investigate nontrivial training strategies such as active learning, and track the root cause of unexpected training results such as performance degeneration. In this work, we propose a time-travelling visual solution DeepVisualInsight (DVI), aiming to manifest the spatio-temporal causality while training a deep learning image classifier. The spatio-temporal causality demonstrates how the gradient-descent algorithm and various training data sampling techniques can influence and reshape the layout of learnt input representation and the classification boundaries in consecutive epochs. Such causality allows us to observe and analyze the whole learning process in the visible low dimensional space. Technically, we propose four spatial and temporal properties and design our visualization solution to satisfy them. These properties preserve the most important information when projecting and inverse-projecting input samples between the visible low-dimensional and the invisible high-dimensional space, for causal analyses. Our extensive experiments show that, comparing to baseline approaches, we achieve the best visualization performance regarding the spatial/temporal properties and visualization efficiency. Moreover, our case study shows that our visual solution can well reflect the characteristics of various training scenarios, showing good potential of DVI as a debugging tool for analyzing deep learning training processes.
U-Shift II Vision and Project Goals

The driverless and electrically powered vehicle concept U-Shift enables a new type of flexibility and efficiency for the mobility of tomorrow by separating the drive unit (driveboard) and transport capsule. In combination with various capsule types, the driveboard is used to transport both people and goods. In a first step, a rollable Mock-up was built (U-Shift I project). The development of another prototype with extended functions for driverless driving (U-Shift II project) is described in this article. Automation – implementation of driverless operation based on sensor modules in the driveboard, supported by sensors in capsules and infrastructure. Chassis – integration of a lifting unit for flexible and quick capsule changes as well as a compact design of the wheel suspension. Drive train – design and implementation of a compact drive concept with wheel hub motors and a modular traction battery in connection with holistic energy and thermal management. Electrics/Electronics Architecture – Conception of a service-oriented architecture with distributed services in the driveboard, capsule and infrastructure. Vehicle structure – optimization of the load distribution in the driveboard and the capsules, as well as secure locking of the capsules on the vehicle. Citizen Participation – Dialogue on the Concept.
Intermediate Python

Codes covered in Basic Python were generally limited to short snippets. Intermediate Python will cover concepts that will allow codes to be reused such as developing functions, classes, objects, modules, and packages. Functions are callable modules of code that are written to take in parameters to perform a task and return a value. Decorators and closures can be applied to modify function behaviors. Objects encapsulate both variables and functions into a single entity which are defined in classes. A module in Python is a set of classes or functions that encapsulate a single, and related, set of tasks. Packages are a set of modules collected together into a single focused unit.
An Extensive Survey on Outlier Prediction Using Mining and Learning Approaches

The predictions of outliers are considered the foremost issue in various real-time applications. This work provides an extensive survey on outlier prediction that captures the researchers’ attention to handle the issue substantially. The investigators intend to model various feasible and robust models to formulate the solution and generically predict the outliers. Some of the foremost baseline approaches are discussed, and the outlier's prediction process classification is discussed comprehensively. In every section, various outlier prediction processes are analyzed, and their prediction performance is validated. The associated merits and demerits of the models and the research challenges are highlighted to extend future research works and provide future research ideas for young researchers. The solution for specific research questions is consolidated provided for the experts to gain knowledge with the various prediction approaches. The open research problems and extension ideas are provided for the upcoming researchers to make a clear outlier for the prediction process.
An Augmented Reality Visualization Aid Associated to a Locker Reservation System

Finding an available locker at the university is a time-consuming process; that is, finding an empty locker on the university campus where you may store your possessions takes a significant amount of effort. The majority of students have difficulty finding a locker since they can't find one easily and don't want to waste their time hunting for lockers in other buildings. As a result, this article recommended a locker reservation system in order to make it easier for students to find accessible unoccupied lockers. This approach can minimize the challenges that certain students who are seeking to obtain a locker are experiencing, such as the fact that they are unable to readily find an available locker and that they are wasting their time searching in other buildings for an available locker. In order to make finding a locker easier, a locker reservation system has been established that allows students to immediately locate a locker that is available without having to waste time. The locker reservation system allows the student to select a date and time for the locker to be constructed, as well as examine information about the reserved locker for the student if one is available. Additionally, the student may locate their locker utilizing augmented reality technology, as the system strives to assist student to the precise location of an empty locker by displaying arrows that point to the student in the right direction.
Exploring the Context of Converged Learning: A Case Study in a Polytechnic University

New Jersey Institute of Technology (NJIT), a four-year polytechnic R01 research university in the United States utilized a participatory strategic planning process to implement an innovative approach to the modes of delivery for instruction that exist between face-to-face and online instruction. NJIT defines the spectrum of integration of online and on-ground instruction as Converged Education. This spectrum allows students to either participate face to face, join remotely through real-time video conferencing technology synchronously, or watch classroom instruction asynchronously. The article opens with a general background of NJIT's approach to the new idea of converged instructional delivery. It provides a brief history, context, and explanation of its interdisciplinary participatory strategic planning process. Then the paper describes the process of pilot testing that was conducted to determine how best to adopt the new modes of instruction across all disciplines. Next, the process for defining and clarifying the terms and conceptions of the various modes to be adopted is presented. After this, the paper discusses the impact of the shift of Web 1.0 to web 6.0 and how the different departments and sectors of NJIT worked on strategic planning together. Finally, the resulting implementation of the new policy and its reflection in course offerings is shown and discussed.
Multi-view Deep Learning for Weather Recognition

Multi-class problem for weather recognition is a challenging task in the area of computer vision and machine learning. This research work, a novel multi-view deep learning method called MMDeep, has been proposed to handle the weather recognition problem in a multi-class scenario. The proposed method obtains the multiple views (multiple distinct interpretations) from the images by using pixel-wise computer vision operations and then deploys multi-view deep learning using the same views. In this method, natural views and mathematical views are utilized to learned models, and collective performance is obtained using ensemble of results obtained from each of the views. The proposed method is compared to the baseline methods as well as other models proposed for the same task. The model produces mean accuracy of 0.9746 with a standard deviation of 0.0127, which is significantly better than all the models compared with this method. The statistical analysis of the results of proposed method also shows its effectiveness as compared to state-of-the-art methods.
Overview of the Application of Interactive Teaching in College English Teaching

The current university English teaching classroom, students participate in English learning is a situation that university &#x0D; English teachers must pay great attention to.Only in the college English learning, students can always maintain a strong independent &#x0D; learning ability, in order to achieve a better learning effect.In the college English teaching, teachers need to choose the multiple &#x0D; interactive teaching mode and adopt the interactive teaching method, so as to successfully guide the interaction between students &#x0D; and other students and teachers, and completely realize the interaction between English learning methods and English learning &#x0D; materials.Only in the more opportunities and exercises of English practice expression provided by teachers can college students &#x0D; completely enhance their students’ English application ability.
INFORMATION COMMUNICATION TECHNOLOGIES IN TEACHING ASTRONO-MY IN PROFESSIONAL EDUCATION ORGANIZATIONS

The article is aimed at substantiating the content of practices in the preparation of Bachelors of Vocational Training in the context of practice-oriented, competence and process approaches, taking into account the requirements of educational and professional standards and the need to use digital learning technologies at all stages of training, including conducting practices. A model of organizational and pedagogical conditions for conducting practices is proposed. In the model, the target component, organizational conditions, pedagogical conditions and the effective component are identified as interrelated and interdependent blocks. Each block is disclosed in terms of the tools necessary and sufficient for the practice. As tools for implementing the model, an algorithm was developed, organizational charts made in the ideology of the process approach and digital tools in the form of a theoretical and practical course on the electronic platform of the university.
Conclusions of Type-3 Fuzzy Systems

In this chapter the conclusions about the presented work in the area of the type-3 fuzzy systems are presented. The basic concepts and methods for the type-2 fuzzy sets, membership functions, inference and fuzzy systems are proposed in this book. Also, possible future trends that we can envision based on the review of the research work done in this area are presented. It is well-known that designing optimal fuzzy systems is a difficult task, and this is especially true in the case of the type-2 fuzzy systems. In the case of designing the type-3 fuzzy systems the problem is more complicated due to a higher number of parameters to consider, and we envision the use of bio-inspired optimization techniques in the future for the optimal design of this sort of systems, as it has already been the case for the type-1 and type-2 fuzzy systems.
Unikernel and Advanced Container Support in the Socker Tool

Linux containers, with the build-once-run-anywhere approach, are becoming popular among scientific communities for software packaging and sharing. Docker is the most popular and user friendly platform for running and managing Linux containers. Unikernels are single-application fully virtualised lightweight packages designed to run as virtual machines. For some applications, unikernels can be alternative to containers due to the benefits they provide in terms of performance and security. presents an update for Socker, a wrapper for running Docker containers on Slurm that enforces running unpriviliges containers within Slurm jobs. The update to Socker includes: Improved security, MPI support, and support for OSv unikernels.
Arrest Authority by Police Investigators and BNN Investigators on Narcotics Crimes

Agents from the Indonesian National Police (Polri) and agents from the National Narcotics Agency (BNN) have the specialist to conduct examinations into narcotics crimes.However, in the case of arrests, there are differences in authority regarding the time limit for arrests.This study aims to find the ideal arrangement regarding the difference in the time limit for arresting authority by Polri investigators and BNN investigators on narcotics crimes.This sort of investigation is standardizing legitimate investigation employing a statutory approach.Sources of information utilized are essential information through interviews and auxiliary information within the frame of library materials.Legitimate materials were analyzed utilizing qualitative descriptive techniques.This comes about from the considered contrasts within the specialist of the time restrained for captures between Polri investigators and BNN investigators.The specialist of Polri investigators on opiates wrongdoings is expressed in Article 16 jo.Article 19 of Law Number 8 of 1981 concerning the Criminal Strategy Code (KUHAP), specifically captures are made no afterward than 1 x 24 hours, whereas BNN is given specialist by Article 75 letter (g) jo.Article 76 of Law Number 35 of 2009 on the Narcotics, specifically the execution of captures for a greatest of 3 x 24 hours and can be expanded for a greatest of 3 x 24 hours.With the difference in the time limit for arresting authority by Polri and BNN investigators, it is necessary to use the same legal basis for narcotics crimes regarding the regulation of the authority for arresting time limits between Polri investigators and BNN investigators.
Route Planning of Flight Diversion Based on Genetic Algorithm and Gene Recombination

In this paper, genetic algorithm and gene recombination algorithm are used to search the optimal path or suboptimal path by constructing the navigation environment model in two-dimensional plane. Then, the gene recombination quadratic optimization path is used to optimize the large rotation angle to make it close to the smooth path of plane angle under the premise of not intersecting with any flight restricted area. The experimental results show that the combination of GA and GA is better than GA in blocky and scattered flight restricted area environment, which reduces the complexity of GA, shortens the path length, and obtains relatively optimal collision free path.
Data Augmentation for Communication Emitter Identification Using Generative Adversarial Nets

Specific emitter identification has been widely applied in military reconnaissance, wireless security and other fields. With the help of deep learning technology, it has realized the automatic identification of emitter and achieved good performance. However, communication emitter identification is often affected by insufficient data and overfitting in practical application. To address the challenges, we propose a data augmentation method for communication emitter identification based on generative adversarial nets, which generates additional synthetic data to expand training set. With the result of original small signal set as our baseline, we will evaluate the result of emitter identification on the enlarged data set to validate performance of the proposed method. The result shows that training set augmentation increases the accuracy of emitter identification significantly.
Weighted minimum norm algorithm for improved phase unwrapping

Phase unwrapping is an integral part of multiple imaging techniques, and as a result, a wide range of algorithms have been created to unwrap phases.One such algorithm is the minimum L pnorm phase unwrapping algorithm.This algorithm transforms the phase unwrapping problem into a minimization problem of a certain functional, which it solves with an iterative method.However, the problem is usually not convex, and when there are many sharp edges in the data to be unwrapped, the algorithm often produces a local minimum with new discontinuities in originally smooth areas.To prioritize solutions which minimize the functional better in smooth areas, we use weights to deprioritize data lying along edges in the ground-truth image.This requires a method to find ground-truth edges using the wrapped image, which we describe.When using the modified algorithm, we generally obtain improved results on images with multiple edges (both lower errors and more correct edge placement).
Data Manipulation

In this chapter, we introduce data manipulation, its procedures and methods in Python. In particular, we explore several methods for data loading and writing, data indexing and selection, data merging and combination, data cleaning and preparation, and data computation and aggregation. In the final section of this chapter, we introduce some basic ways of working with text and datetime data.