Reviewerly/exHarmony huggingface dataset:
Effects of Open Board Lecture Videos on Anatomy

COVID-19 has forced anatomists to perform non-face-to-face education using lecture videos. A Korean anatomist has given (white and black) board lectures and distributed lecture videos to the public for many years. This study was to verify the effects of open board lecture videos in the anatomy field. A questionnaire survey was carried out with the help of medical students who were exposed to the board lecture videos. The video provider uploaded the lecture videos on YouTube, where the viewing numbers were counted. At a medical school where the video provider belonged, the students mainly watched the lecture videos before the anatomy class. The watching hours of the lecture videos were related to the written examination scores. Students gave positive and negative comments on the board lectures. At the other two medical schools, students partly watched the lecture videos regardless of the teacher who delivered the lectures. The results suggested that students understood the board lectures themselves. On YouTube, the lecture videos were viewed by approximately 1,000 students. This paper introduces the desirable aspects of open board lecture videos on anatomy. The videos could enhance the quality of both students and teacher.
APIs

The subject of this chapter is APIs that are the cornerstones of your app. This includes
Throwing Activity Detection Using CCTV and Video Analytics for Safety and Security in Railway Station

CCTV and video analytics could assist in maintaining the safe and secure aspect of smart city. One necessity of keeping safety and security in railway stations is aggression monitoring such as throwing. A video analytics feature using video analytics and OpenPose is proposed to be added to an existing VIANA platform for railways station in Bandung. Deep learning processes to get this feature integrated in VIANA must be done including dataset preparation, training, inferencing design, and integration. This feature is proven to be effective functionally and nonfunctionally with a 76% precision, 86% recall, and 79% accuracy as well as 28% GPU utilization, 6% memory utilization and 61°C GPU temperature.
Research on the Importance of Constructing Digital Resources of Doctoral Dissertations and Master’s Theses Written in Mongolian

In this paper we focus on the importance of the construction of the characteristic resource library of Doctoral Dissertations and Master's Theses written in Mongolian of Inner Mongolia Normal University, and analyzes the opportunities and challenges brought by the construction of the characteristic resources of university libraries combined with the characteristics of the era of big data, and puts forward some countermeasures.
FaceSwap based DeepFakes Detection

The progression of Machine Learning (ML) has introduced new trends in the area of image processing. Moreover, ML presents lightweight applications capable of running with minimum computational resources like Deepfakes, which generates widely manipulated multimedia data. Deepfakes introduce a serious danger to the confidentiality of humans and bring extensive religion, sect, and political anxiety. The FaceSwapp-based deepfakes are problematic to be identified by people due to their realism. Hence, the researchers are facing serious issues to detect visual manipulations. In the presented approach, we have proposed a novel technique for recognizing FaceSwap-based deepfakes. Initially, landmarks are computed from the input videos by employing Dlib-library. In the next step, the computed landmarks are used for training two classifiers namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). The reported results demonstrate that SVM works well than ANN in classifying the manipulated samples due to its power to deal with over-fitted training data.
Design and Implementation of Family Education Intelligent Management System

the traditional family education model is that children first inform their parents that they have a need for family education, and then parents take the initiative to find suitable teachers on various platforms. This model does not have a strict system. At this time, a family education management system is needed to improve it. The family education management system is systematically analyzed and designed according to the disadvantages of the traditional family education model. It can quickly match the tutor to save the time of children and parents, and also give college students the opportunity to exercise. A total of four roles have been designed for users, applicant, publisher, general administrator and super administrator. Different users have different permissions. Users can browse related education consultation on the home page. Candidates can only be approved by the publisher. Only then can you be a tutor, ordinary administrators can manage all related information, and super administrators have the authority to add or delete ordinary administrators. This system uses Microsoft Visual Studio as the system development tool, and establishes the background database through Microsoft SQL Server. After the development of the system was completed, the system was tested to ensure that it was functioning properly.
David – A Novel Approach to an Industrial Recommender System

A conceptual approach to a recommender system that is industrial oriented and optimized for business-to-business. With particular needs, industrial datasets seek assertiveness and contextualization to capitalize on recommender systems. Muscle memory must be implanted on business users, enabling them to harvest the benefits of such technologies. These types of users, which differ from standard ones, are fuzzy and vague in their choices, avoiding explicit forms of feedback. This paper addresses state-of-the-art of such industrial systems, its users and explores a potential solution.
COMPARATIVE STUDY ON THE CHARACTERISTICS OF METAVERSE EXPERIENCE: AN EXPERIENCE ECONOMY MODEL PERSPECTIVE

The previous studies in areas of the experience economy investigate the relationship between 4 realms of experience and the consumers' attitudes or behaviors.However, with the development of Mixed reality and Metaverse, human-oriented experiences expanded from the physical world to the virtual world.Thus, there are critical changes in the ways people participate in the experience and the contents of the experience.In this study, we aim to explore whether there are any recent changes in the experience economy under the VR/AR context.We utilized the Term Frequency Inverse Document Frequency method (TF-IDF) to extract keywords from two datasets consisting of questionnaire items for related papers.The results show that the core contents of the 4 realms of experience have changed in the VR/AR context.This study provides inspiration for us to refine the classification of the experience economy and optimize the experience design.
<i>All about</i> … Persona Dolls

What is the purpose of Persona Dolls in helping to tackle issues such as diversity, and how do practitioners use them in early years settings? Vicky Hutchin explains
Self-Augmentation Based on Noise-Robust Probabilistic Model for Noisy Labels

Learning deep neural networks from noisy labels is challenging, because high-capacity networks attempt to describe data even with noisy class labels. In this study, we propose a self-augmentation method without additional parameters, which handles noisy labeled data based on small-loss criteria. To this end, we use small-loss samples by introducing a noise-robust probabilistic model based on a Gaussian mixture model (GMM), in which small-loss samples follow class-conditional Gaussian distributions. With this sample augmentation using the GMM-based probabilistic model, we can effectively solve over-parameterization problems induced by label inconsistency in small-loss samples. We further enhance the quality of the small-loss samples using our data-adaptive selection strategy. Consequently, our method prevents networks from over-parameterization and enhances their generalization performance. Experimental results demonstrate that our method outperforms state-of-the-art methods for learning with noisy labels on several benchmark datasets. The proposed method produced a remarkable performance gap of up to 12% compared with the previous state-of-the-art methods on CIFAR dataset.
UX Design and Evaluation on Conversational Bot Supporting Multi-Turn and Multi-Domain Dialogues

In this paper, UX design and evaluation metrics for a conversational bot, especially, a voice-bot supporting multi-turn and multi-domain conversations, are studied. For a voice-bot, user satisfaction can be affected by the number of dialogue turns, the number of conversation topics, and the amount of information. New evaluation metrics for UX on the voice-bot are introduced under a persona design framework for the public sector. Also, comparison of evaluation metrics among a text-bot, a voice-bot with few-turn and single-domain, and a voice-bot with multi-turn and multi-domain are presented. By referring to the presented UX evaluation metrics, it is possible to design a conversational bot to improve user experience and provide human-centered services.
E-Commerce For Village Information System Using Agile Methodology

With the entry into the era of Industrial Revolution 4.0, the development of digitization of various aspects at the village level began. The level of use of mobile devices in the commercial transactions of society is now a massive number of users. It happens not only in large transactions but also in small transactions. With the community's high interest in the use of smartphone devices, this is a different opportunity to explore the potential of each village by helping the community, tiny and medium enterprises in conducting transactions, sales, and marketing online through the village government website. The village information system itself requires an e-commerce feature on its page to help small and medium enterprises in the area to sell products online through a simple page display. This research aims to design and develop new features of the village system that plays a role in the field of e-commerce with the Direct Message transaction method. The system development methodology used is Agile with Scrum as a framework. The Agile Model is a short-term development model that requires rapid adaptation and development to changes in any form. This e-commerce feature is for local communities, especially Micro, Small, and Medium Enterprises, so their products' marketing reach is even more outstanding while being recorded in the village system.
Mining Better Samples and Semantic Consistency for Contrast Learning in Forest Semantic Segmentation

Image segmentation has impressive progress in the past several years. But good segmentation usually follows pixel-wise well-annotated labels which is energy-consuming. Moreover, the robustness would not be guaranteed due to lack-of-diversity datasets. Previous work usually focuses on pixels individually and pay less attention to the neighbor pixels. The local context would be scarce and the global context is not utilized following these ways. We proposal a method, named Forest Semantic Segmentation Network(FSSNet) to address these issues. FSSNet organizes original version and augmented version of images, as two inputs into student branch and teacher branch, and force the two outputs being consistent to strengthen the robustness of our model. Moreover, we not only consider pixel itself and also the neighbor pixels because the context of neighbor pixels helps understanding the pixel. FSSNet utilizes contrastive loss with memory bank to involve global context in training which will make pixels closer to others in same category and far away from pixels of different categories. A bank filter is suggested to improve the quality of features in the memory bank. we also proposal a new sample strategy to improve the effect of contrastive loss and reduce the computation. Our method can improve accuracy and strengthen the robustness with affordable extra computation during training process, and no additional computation during inference toward baseline. Comparing to benchmark, the proposed approach can improve the mIoU by 3.1% on our challenging dataset.
THE IMPORTANCE OF ZINC IN COMBATING CLIMATE CHANGE AND HOW U.S. RESOURCES COULD CONTRIBUTE

Zinc (Zn) is primarily used for galvanization and is essential to prevent corrosion of wind turbines used for energy generation; therefore, Zn is an indispensable component of the low-carbon energy transition. To meet a mitigation scenario where average global temperature increase is limited to 2°C by 2100, which is less ambitious than the Paris Agreement, total Zn demand will need to increase ~80% by 2050. The current five-year average of annual apparent consumption of refined Zn in the U.S. is 887,000 metric tons. If Zn mining in the U.S. is to meet the future demands of the 2°C scenario, annual apparent consumption will need to increase to 1,597,000 metric tons by 2050. The USGS Mineral Deposit Database (USMIN) project creates geospatial databases that are authoritative sources of the most important mines, mineral deposits, and mineral districts in the U.S. USMIN has focused on critical minerals since 2017. Zinc was added to the 2022 Final list of critical minerals due to increasing supply risk as determined by quantitative assessment. Accordingly, USMIN has started compilation of data on Zn deposits, including attributes such as resources, production, and grade. USMIN has gathered data for 30 Zn deposits in the U.S. that have contained resources plus production values that exceed the five-year average of annual apparent consumption. Only 17 of these deposits have reported resources and when combined, they amount to 45.4 Mt Zn. The five deposits with the highest grades (>6.5% Zn) have combined resources of 19.5 Mt. At the current rate of consumption, U.S. resources could theoretically supply more than 50 years of Zn. If annual production from U.S. Zn mines were to increase linearly to meet the future requirements of the 2°C scenario, the cumulative amount of Zn mined by the year 2050 would increase from 24.8 Mt (current production rate) to 34.7 Mt. The U.S. has sufficient resources to meet this demand when including deposits of all grades. However, only three of the five highest-grade deposits are currently being mined and it is estimated that the energy consumption for Zn mining doubles if ore grades decrease from 6.5 to 0.24 wt.%. Should the highest-grade deposits be mined first to realize the greatest margin between the energy expended for mining against the subsequent energy produced from wind generation?
Design and implementation of ecological data collection management software based on DOI mechanism

The Three-River Source region is located at a high altitude in the hinterland of the Tibetan Plateau, the complexity of the region and its biodiversity make its ecological data of the region very invaluable. In order to realize data sharing and further processing of the Three-River Source’s ecological data, it is essential to formulate a unified data collection and management standard for ecological data. In the paper, a method based on DOI mechanism for ecological data collection and management of the Three-River Source is proposed, and the corresponding software model is implemented and verified. Specifically, for the collection and transmission of ecological data, the metadata-based data collection model and a TCP/IP protocol-based data transmission structure are adopted to ensure the effectiveness, integrity and availability of ecological data collection and transmission between different sites. In respect of data management and storage, the data identification method based on the Digital Object Unique Identifier (DOI) is adopted in the software, meanwhile MySQL is used to realize the storage of descriptive information under DOI flag data, so as to realize the efficient management of multi-source ecological data, as well as improve the accuracy of data retrieval by users. Through testing results, the specific software model implemented based on the design scheme proposed in the paper is consistent with the expected results and can meet the requirements.
Construction and practice of practical teaching system for intelligent health care service and management in vocational colleges

In the context of an aging population, the shortage of senior care practitioners and the lack of professional capabilities have become increasingly prominent.In order to adapt to the new situation of the development of senior care services, new standards and new requirements have been put forward for the practical teaching of smart and healthy senior care services and management.Through the construction and practice of the practical teaching system of smart and healthy elderly care services and management, the basic framework of the practical teaching system, the curriculum system, the platform construction and evaluation mechanism, and the construction effect are discussed, forming a "four-in-one" practice.The teaching system lays the foundation for deepening the practical teaching reform of smart and healthy elderly care services and management, and cultivating high-quality technical and skilled talents.
Linear Optimal Kalman Predictor for Descriptor System With Packet Dropouts and Colored Measurement Noises

This paper addresses the optimal linear estimation problem for a descriptor system with missing measurements, packet dropouts and colored measurement noises. By singular value decomposition, fictitious noise compensation and augmented state approach, the original system can be transformed into two reduced-order subsystems with augmented correlated noises. Then the problem of optimal Kalman predicting is transformed into prediction problem for augmented systems. Based on Kalman filtering theory, the steady-state Kalman predictor of the original descriptor system is presented. The effectiveness of the proposed algorithm is verified by a numerical simulation example.
Task-driven Dialogue System based on Dynamic Intention Capture

With the continuous development of artificial intelligence technology, dialogue system has attracted more and more attention because of its strong applicability and wide application scenarios. It has gradually entered all aspects of people's lives. The development of science and technology such as speech recognition and synthesis, natural language processing, machine learning, deep neural network and soon has also accelerated the transformation of this process, Make the machine closer to the goal of smooth dialogue with people. This paper mainly studies the task driven human-computer interaction system. Aiming at the sequence track of humancomputer conversation, we use the encoding and decoding model to capture human's dynamic intention. From the final system, this way can significantly improve the accuracy of machine response compared with the previous way of only recommending answers through single state conversation.
Creating Plugins

Although Eleventy comes with a host of features built-in as standard, there will come a time when we reach the limit of what is possible, and need to take it a step further. The best way to do this is with plugins - thanks to Eleventy being based around JavaScript, it is easy to extend or configure Eleventy with new features. What’s even better is that as Eleventy runs on Node, we can make use of any one of thousands of plugins available on npm! Over the next few pages I’ll put a few examples to use on our site, and show you how we can create and test our own plugin as part of developing our site.
An Approach for Predicting Admissions in Post-Graduate Programme by Using Machine Learning

Machine Learning (ML) has spread its wings over a wide range of fields nowadays. For all data-driven areas, machine learning is a true game-changer. Today's most powerful firms are those that have concentrated their resources and energy on understanding client data. Machine Learning is a branch of Artificial Intelligence that contains a collection of sophisticated algorithms. Machine learning aids in machine training by mapping the link between input/inputs and output. Machine Learning is used in the proposed study to estimate the probability of admission to a master's programme from the perspective of Indian students. This work is presented as a web-based interactive application. This web-application was created using the platforms ‘R’ and ‘Shiny’. The Web-App was created to provide students with a platform to get insight into their chances of acceptance. In this study, Support Vector Regression is utilised to train the model for predicting admission probability. Since, the R-squared Value of Support Vector Regression has been greater than 0.8 for both the training and testing sets. As a result, it's a good algorithm to use in the proposed project.
Information retrieval and data mining through library OPAC keyword searching: An experience of recent trends and JNU, New Delhi, Library

This paper aims to explain Library Web OPAC keyword searching and data mining techniques. Data mining techniques recommend digital library services based on the user's profile and search history. This paper also discusses the web OPAC service and keyword searching technique. Moreover, the paper provides a brief description of the various search option facilities provided by the Koha Web OPAC, a service at Dr. BR Ambedkar Central Library, JNU, New Delhi and highlights the recent trend that reflects increasing use of various software applications in libraries to interact with library collections and searching the pin-pointed information.
Formation of legal conscious personality in the context of socialization and legal education of children and youth

The article analyzes the legal, psychological, social and pedagogical literature on the problems of legal socialization and education of the individual, clarifies the key concepts of the study. Legal socialization of the individual is defined as a process that ensures its adaptation in the legal life of society, integration into the legal environment and legal relations, the formation of legal qualities of the individual and his legal activity for adequate functioning of the individual in legal reality. The interaction of educational institutions with the family in the legal education of children is considered a complex process of interaction of these social institutions of legal socialization on each other, which is characterized by the implementation of ties and relations of public institutions and small groups. preschool in primary school age. Under the legal upbringing of children and youth we understand personal education, which is determined by the degree of mastery of legal ideas about their own rights and responsibilities and the rights of others, formed attitude to law and legal law as values, mastery of skills to exercise their rights, perform duties and adhere to prohibitions of illegal behavior in society. The results of the study show a dynamic positive changes in the formation of legal education of children and youth of experimental groups under the influence of implemented measures in the educational process for the prevention of violations of children's rights in the family and society.