Reviewerly/exHarmony huggingface dataset:
Development and Application of AI-Based Chatbot for Place-Based Learning of Global Region: Focusing on Understanding Cultural Diversity

This study developed an AI chatbot for place-based microlearning in Haeundae to help learners build knowledge of global region and applied it to the class. The results of big data analysis techniques for identifying regions and topics for the place-based microlearning were Southeast Asian countries and cultural topics such as food, art, performances, and tourism etc. In regards to the results of the big data analysis, a total of 1,070 questions and answers were created as microcontents including texts, and photos, maps, and videos to serve the purpose of learning global regions. The results of the study indicated that learners could learn how closely the Haeundae area, where they live, is connected with many countries in Southeast Asia, in terms of food, clothing and shelter, which students are difficult to understand in traditional classroom environments. In addition, learners showed high level of understanding and satisfaction in the study because the classroom activities were self-directed, customized, individualized. Appying the principle of flexible space expansion to the classroom activity resulted in creative learning and overcoming the limitation of textbooks. The study showed that the learners were able to cultivate the qualities of global citizenship of living together in the complexity of global similarity and differences.
Immersive display for remote vehicle operation

An immersive display system is presented for remote vehicle operation, the “last mile” problem for autonomous trucks. The display creates a wraparound virtual image with a 152-degree horizontal field of view and a 36-degree vertical field of view. The user does not wear any headgear and is comfortable to use for long periods of time. The display allows the user to see the controls for the remote vehicle. The overall system size is less than 32” &times; 16” &times; 16” (600mm &times; 400 mm &times; 400 mm). The system is light weight and has low power consumption. The display system has eye limited resolution over the wide field of view. To accurately operate the remote vehicle, the overall system latency must be minimized. The system uses a fast OLED based display system. The components of latency of the display system are measured including the remote camera to image processing computer and the computer to display output.
The Effect of Pilgrimage to Waliyullah's Tomb on the Spiritual Motivation Student of Jagad 'Alimussirry

The modern world, which is full of increasing human needs, is more easily met with the rapid advancement of science and technology. However, in reality the desired happiness is getting further away and causes a spiritual crisis. As for the signs of a spiritual crisis, material difficulties are replaced by mental difficulties which cause the mental burden to become heavier, anxiety and tension and feeling pressure are felt more often and are more pressing, thereby reducing happiness.&#x0D; Making a pilgrimage to the tomb of waliyullah’s is like being in the house of someone who is loved by Allah so that it can be a medicine for those who make pilgrimages and understand their pain and suffering. There he complained about all the problems he faced. In that house, he will feel at ease because he feels no longer alone and is under the auspices of Allah's grace and power.&#x0D; The results of previous research related to the research to be carried out are Fitriana's Thesis, (2014) entitled The Impact of Pilgrimage to the Tomb of Habib Sholeh Tanggul on Community Morals, and Selvi Raudatut Tilawah's Thesis, (2015) entitled Study of Living Hadith: The Phenomenon of Pilgrimage to the Grave in Village Author Kab. Bondowoso.&#x0D; so the author is interested in researching and studying more deeply about this problem by proposing the title of the research, namely the influence of pilgrimage to the tomb of waliyullah’s on the spirituality student of Jagad ‘Alimussirry. This type of research includes quantitative research that collects, collects and processes data in numerical form. The target population of the researcher is the active students of the Jagad 'Alimussirry Islamic Boarding School. Collecting data using questionnaires and interviews.
Breast cancer detection from histopathological images using machine learning models

Breast cancer is the most common cancer in women worldwide, accounting for more than 25% of all cancer cases and affecting more than 2.1 million people each year. According to the WHO, early detection is crucial to improving patient outcomes and survival. However, prognosis by histology of biopsy tissue is a complex procedure and the ultimate interpretation can be controversial. Therefore, machine learning algorithms are deployed to generate techniques that can be used by technicians, radiologists, and physicians as tools to unequivocally detect and diagnose breast cancer at an early stage. This will help to significantly increase the survival rate of the patients and their subsequent quality of life.
College Enquiry Chatbot System

At the time of the admission process students and parents have a lot of questions in their minds for which they either call the administration or have to go to the college which may be miles away from them. Again, it would not be possible for one person to answer 500 calls at a time. The best solution to this problem is a Chatbot that answers all the questions. This project is based on developing a Chatbot that would be a saviour for the college staff at the time of admissions, upcoming exam details, date of release of the result etc.
Language Interpreter and Speaker

Language Interpreter and Speaker is a device for identifying the language of the written image text and then converting the same text to speech format. This device would surely be useful for blind and visually impaired people. Language identification (LI) is the method in which we identify the natural language of the given content. It is the process of categorizing a document on the basis of its language. In this generation, we are heading towards a phase where computers would be capable of doing all things that humans can do. Recognition of language used is the initial requirement before reading or learning. To start with any of the tasks, humans first try to understand the task and then process the task. Similarly, for language identification, the machine needs to learn the language and once learning is complete, it should be able to recognize the language. The project is divided into three parts. Initially, the handwritten image text would be converted to normal text. In the second part, the language would be identified from the converted text and last, the text would be converted to audio format. This paper discusses the implementation of this idea, gives an approach to problems and challenges that we came across, and some solutions.
Design Schema to Offer Security and Confidentiality to Healthcare Data in Cloud Environment

The cloud environment is an emerging technology dueto its broad utilization nowadays because of the availability of the internet for processing and storing the information. It offers abundant support such as availability since it eliminates the necessity to provide expensive applications, software, or intricate software. Alternatively, it carries out the operations by making use of cloud-based services, applications, and communications that can be employedbased on theneed. The cloud environmentprovides thepossibility to store abundant data in terms of inexpensive manner. Overcloud users can make use of the services or applications regardless of their position or devices required for accomplishing analysis. Furthermore, spaced out from gains accumulated from the cloud environment there emerge several threats like safety and confidentiality which lingers to the greatest threat to the system. The confidentiality-related threats might emerge because of the trouble-free access to the cloud resources, protecting the privacy of the 180data prevailing over the cloud, and examining the cloud routines. Some threat model believes that the service providers of the cloud are intricate to believe since trust is essential for carrying out the encoding of data within the cloud environment but some again believe that the service suppliers of the cloud could be considered while the threats arise from the outer illicit users and the users of the cloud. Various public cloud-related services are currently being implemented over massively consistent companies such as Google and IBM. Furthermore, these firms can design their private clouds in term of their open-source software which is overall the entrusted service supplier model matching the conditions. Consequently, the belief model can trigger the modeling of confidential and safe frameworks that are reliant on the belief-based cloud service providers for accomplishing executable modules within the framework. It pays attention to constructing enormous aggregated confidential and privacy offering framework with trouble-free access to the users.
When to retrain? assessing production ML model performance using uncertainty, out-of-distribution detection, and concept drift detection

Submitting this abstract for an invited talk. The Laboratory for Physical Sciences has recently been conducting research in ML model uncertainty and confidence, detecting out-of-distribution data, and detecting concept drift. As we deploy ML models into operations, we must be constantly assessing whether the models are still effective and performing as expected in the current data environment. This is relevant in all cases, but especially critical in cybersecurity applications, because the data, technology, actors and behaviors are all evolving so rapidly. This talk will review several algorithmic techniques developed to address this problem.
Efforts to Increase Student Learning Activity in Materials Strengthen National Commitment in Online Learning Through Quizizz Educational Game Media in the New Normal Era

This study aims to determine the process and results of increasing student learning activity in online learning in the new normal era on the material to strengthen national commitment through the Quizizz Educational Game Media. Methods of data collection in the form of observation, questionnaires, and tests. Observations and questionnaires were used to determine the increase in student learning activities in learning and evaluation tests were used to determine student achievement in the material to strengthen national commitment. Based on the results of observations, student learning activities and achievements have increased significantly. Based on student response questionnaires on the use of educational game media quizizz in learning shows that students' responses in learning increase. Thus, overall, the application of quizizz educational game learning media can increase student activity and learning outcomes in Civics learning on materials to strengthen national commitment in this new normal era. For this reason, it is recommended, especially for PPkn’s teachers, the results of this study should be used as information and references in the development of education so that they become creative and innovative learning alternatives
Introduction

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
Social Media

Freedom of speech, social media, cancel culture have become under fire in our new form of ethical judgment. Celebrating everyone's differences is something that our country was built upon. The intolerance created by the cancel culture movement is threatening our diversity of speech and dividing our country. It's violating our most powerful right and most importantly is putting our democracy at risk. This chapter will consider all of the factors while trying to decipher the power or lack of voice.
Corpus Stylistics in Contemporary English Dramas: Keywords and Semantic Fields of Delusions

As reflected through contemporary Anglo-American dramas, delusion is considered a repertoire afflicted by the societal depression after World War II. Since post-war depression can be vocally expressed, it poses a question of how the theme of delusion could be portrayed through emotive phrases and how they are interconnected with the literary themes in dramas. This study investigates semantic fields demonstrated through corpus-based methodologies on a collection of contemporary dramas via Lancsbox and Wmatrix . Ten contemporary British and American dramas are constructed as a specialized corpus to compare with the reference corpus, built upon BNC and COCA imaginative texts. The comparison can generate keywords, lexical bundles, shared collocates, and extract the words indicating what the specialized corpus is about, or “aboutness.” The meta-tiered results show that “Negative,” “Wanted,” “Getting and possession,” and “Diseases” are the fields that relate to the aboutness of the contemporary dramas. The top 20 keywords can refer to the specialized corpus’s dialogic structure and highlight four characters with dilemmas regarding the delusional disorder. The collocation network, measured by GraphColl , can raise stylistic awareness where these keywords and semantic fields are interconnected and substantiate the theme of delusion. Though literary texts are interpreted through close reading, this study argues that quantitative and qualitative aspects can be integrated to undermine the absence of positivism.
Identification and Ranking of Factors Affecting Hospital Information Acceptance System Using Network Analysis Process

Identification and Ranking of Factors Affecting Hospital Information Acceptance System Using Network Analysis Process
A Local Mixup to Prevent Manifold Intrusion

Deployed in the context of supervised learning, Mixup is a data-dependent regularization technique that consists in linearly interpolating input samples and associated outputs. It has been shown to improve accuracy when used to train on stan-dard machine learning datasets. However, authors have pointed out that Mixup can produce out-of-distribution virtual samples and even contradictions in the augmented training set, potentially resulting in adversarial effects. In this paper, we introduce Local Mixup in which distant input samples are weighted down when computing the loss. In constrained settings we demonstrate that Local Mixup can create a trade-off between bias and variance, with the extreme cases reducing to vanilla training and classical Mixup. Using standardized computer vision benchmarks, we also show that Local Mixup can improve accuracy.
Review based on Variations of DBSCAN algorithms

Clustering is among the most broadly used algorithms that works by separating the data points into many different groups (clusters) having similar data points and properties. Density Based Spatial Clustering of Applications with noise (DBSCAN) is one such clustering algorithm used for identifying distinctive clusters in the given dataset. The algorithm works on a dataset which has noise and outliers, clusters of various sizes and shapes are discovered in a large dataset. DBSCAN has certain issues when it comes to the time complexity (O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of the algorithms and also it produces incorrect results when the database is large and that too with varied densities. In order to overcome these limitations one needs certain variations or improvements. In this paper a review of some of the variations of this algorithm that have been modified or those which have a high efficiency on varied densities have been presented. Eight variations have been discussed in this report, after which it was concluded that these variations are better performing than the conventional DBSCAN algorithms.
Application scenarios and usability for modern 360 degree video projection rooms in modern meeting, incentive, conventions, and exhibitions (MICE) industry

360-degree image and movie content has gained popularity over the media and the MICE (Meeting, Incentive, Conventions, and Exhibitions) industry in the last few years. There are three main reasons for this development. First, on the one hand, it is the immersive character of this media form, and, on the other hand, the development of recording and presentation technology has made significant progress in terms of resolution and quality. Third, after a decade of dynamic rising, the MICE Industry focuses on a disruptive change for more digital-based solutions. 360-degree panoramas are particularly widespread in VR and AR technology. However, despite the high immersive potential, these forms of presentation have the disadvantage that the users are isolated and have no social contact during the performance. Therefore, efforts have been made to project 360-degree content in specially equipped rooms or planetariums to enable a shared experience for the audience.
Real-time 3D video imaging with adaptive phase unwrapping method

This paper presents a novel method that does not reqire additional images for 3D video imaging with phase unwrapping technique using geometric constrain based on graident field. Specifically, we creat an artificial absolute phase map &Phi;<sub>min</sub> at given depth z = z<sub>min</sub> from the former frame. We optimize the z<sub>min</sub> by validate the continuity of computed unwrapped phase with gradient field and deliver the true minimum z value z<sub>min</sub> to next frame. The first frame is reconstructed by a static method. Experiments demonstrate that only three phase-shifted fringe patterns are required to measure moving objects.
The Ethics of the New Wave of Censorship

Within the emerging media ecosystem the major technology companies have a key role in information circulation upending traditional news sources through their distribution and advertising algorithms. These technologies have spawned some radicalized user generated content (UGC) that is a breeding ground for misinformation and even disinformation. Is the answer censorship? And by whom? When censorship takes place is someone's voice then not heard and open and meaningful dialogue among differing opinions limited? Does censorship limit the individual person's ability to be media mindful? By evaluating the issues around censorship according to media literacy principles and ethics, this chapter will lay out the media mindfulness strategy as a tool to support the social justice principles of equity, access, participation, and rights.
A Cognitive Study on One District One Product Yojana Uttar Pradesh Government

In this scheme of large amount of people have no any type of information, what about this scheme. And second the problem of money problem of rural area. Firstly, this scheme to share the information of rural and urban people like advertisement poster etc. and provide some fund, any type of loan to start the business or growth the economy of district. This scheme is very useful for future or improves of economy of district after year and every district have tag to introduce itself. This scheme has given a new digestion every district
The Role of Block Chain in Fog Computing

The role of block chain in fog computing is an increasing concern in security management. The purpose of the study is to analyze the roles of key management techniques and fog computing cross-platform support for several block chain framework operating systems. This chapter presents the cipher key management in block chain based fog computing between user file and encrypted key. The use of a meta key model has been analyzed by the attack of collusion and proxy encryption development. The encrypted data shares the compatible protocols for block chain key management. For authentication of information, hashing techniques have been applied to complete nodes in the structure. This approach gives secure shared data in the distrusted environment by the integration of block chain based fog computing looking towards successful integration.
Automated detection of common IED components on resource constrained computing devices

Developing automated threat detection algorithms for imaging equipment used by explosive ordnance disposal (EOD) and public safety personnel has the potential to improve mission efficiency and safety by automatically drawing a user’s attention to potential threats. To demonstrate the value of automated threat detection algorithms to the EOD community, Deep Analytics LLC (DA) developed an object detection algorithm that runs in real-time on resource constrained devices. The object detection algorithm identifies 10 common classes of improvised explosive device (IED) components in live video and alerts a user when an IED component is detected. In this paper we discuss the development of the IED component dataset, the training and evaluation of the object detection algorithm, and the deployment on the algorithm on resource constrained hardware.
Expert System for Diagnosis Papaya Plant Disease with Backward Chaining and Dempster Shafer

Papaya plant (Carica papaya L.) it is one of leading commodities in East Kalimantan. Papaya is widely cultivated in Samarinda, Balikpapan, and Samboja. However, farmers are often faced with the problem of papaya plant diseases that can damage and inhibit the growth of papaya plants and fruit. To solve this problem, an expert system was created to diagnose diseases in papaya plants. This expert system was created using two methods, namely Backward Chaining Reasoning and to determine the level of certainty in the diagnosis of papaya plant diseases, the Dempster Shafer method was used. The excepted result of making this expert system is to build an expert system application that can diagnose web-based diseases in papaya plants so that it can make it easier for farmers to consult and get the right solution to overcome various disease problems that attack papaya plants.
Ensemble Methods: Bagging and Boosting

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.