Reviewerly/exHarmony huggingface dataset:
Machine Component Clustering with Connection Correlation Method

This study aims to introduce a new method for modular design, which is called Connection Correlation Method (CCM), to clustering machine parts with inter-part connection conditions to classify independent modules for the Industrial Printer Powertrain set with 105 components.With the CCM technique, module members would be appropriately assigned for each module so that machine modules are independent of their function.The CCM technique should first calculate the distance coefficient matrix with surface contact conditions of bonded, rough, separation, and frictionless for machine components.Finally, this distance coefficient matrix is used to generate the machine dendrogram with the dependency coefficient between 2.4945 and 3.5593.At the dependency coefficient of 2.7913, the 105 components of the Industrial Printer Powertrain set are clustered into 7 modules: Module 1 with 75 interconnection surfaces and 8 parts, Module 2 with 136 interconnection surfaces and 14 parts, Module 3 with 176 interconnection surfaces and 17 parts, Module 4 with 61 interconnection surfaces and 8 parts, Module 5 with 200 interconnection surfaces and 21 parts, Module 6 with 209 interconnection surfaces and 19 parts, and Module 7 with 18 interconnection surfaces and 18 parts.The results show that the CCM technique can apply to design a modular machine like DSM technique, and multitudinous connectivity factors can also be analysed together with general factors.
The Development of Applications using Augmented Reality Technology as the Teaching Media of Special Mirror Lights

This research was conducted due to the low interest of researchers to develop applications using smartphone-based Augmented Reality technology. The objective of this study was to produce a teaching media based on smartphone applications using augmented reality technology. The research method used was research and development with a 4D (Four-D) development model, which consisted of 4 stages, namely Define, Design, Develop, and Disseminate, which aimed to produce products with experts validity, namely media and material experts and also students’ responses. The results of assessment in the teaching media development conducted by the media experts was 4.273 (Good), while the assessment value from the material experts was 4.291 (Good). The average value of students' responses was 4.083, and it can also classified as Good. It means that the applications can be used as teaching media in the classroom to boost innovation for efficient and interactive teaching. Therefore, it can be concluded that the applications are suitable for use and apply in learning activities .
Design and Implementation Considerations for Virtual Reality in Human Services

As coeditors, we are humbled to contribute to the literature and knowledge regarding the creation of virtual reality (VR) technologies, and their integration into the professional domains of social...
The Role And Advantages Of Distance Courses In The Innovative Educational System

The article focuses on the specifics of an open online course, which is one of the ways to continue the education of pupils and students in today's pandemic, which includes the technologies and types of distance learning. suspended. It is also thought that the changes caused by the pandemic and new solutions in education will bring the necessary innovative changes to the industry. Continuing the education of pupils and students in the article of a pandemic is a global problem today. The pandemic led to the closure of all educational institutions around the world, preventing students from learning. In this article, there is a problem of pupils and students. With the advent of distance education in the modern educational space, there have been significant changes in the education system. The article is devoted to the peculiarities of distance education, focusing on the technologies and types of distance education. The article is devoted to the analysis of public open online courses, the features of the creation and implementation of public open online courses in the educational process of a modern university. It is also thought that the changes caused by the pandemic and new solutions in education will bring the necessary innovative changes to the industry. These changes have undoubtedly brought about positive aspects of education in a certain order. The advantages of distance education are better organization of the learning process, ease and mobility of teaching, the implementation of professional and personal goals of the teacher, as well as increasing the efficiency of resource use. The materials presented and conclusions may be useful in discussing models for incorporating public open online courses into the university curriculum.
SA-GAN: Structure-Aware Generative Adversarial Network for Shape-Preserving Synthetic CT Generation.

In medical image synthesis, model training could be challenging due to the inconsistencies between images of different modalities even with the same patient, typically caused by internal status/tissue changes as different modalities are usually obtained at a different time. This paper proposes a novel deep learning method, Structure-aware Generative Adversarial Network (SA-GAN), that preserves the shapes and locations of in-consistent structures when generating medical images. SA-GAN is employed to generate synthetic computed tomography (synCT) images from magnetic resonance imaging (MRI) with two parallel streams: the global stream translates the input from the MRI to the CT domain while the local stream automatically segments the inconsistent organs, maintains their locations and shapes in MRI, and translates the organ intensities to CT. Through extensive experiments on a pelvic dataset, we demonstrate that SA-GAN provides clinically acceptable accuracy on both synCTs and organ segmentation and supports MR-only treatment planning in disease sites with internal organ status changes.
Freelancerâ€™s E-Marketplace Information System Using Profile Matching Method

Now development of information technology and internet is very high and increasingly widespread. With the technology which is connected without limits time and place. The development of online transactions is very much in demand compared to conventional transactions, without a medium to make the right transactions, it will hamper the efficiency and effectivenes, so that transaction activities between sellers and buyers become inhibited. Therefore we need a solution to solve these problems, the progress of e-commerce in Indonesia is needed for the community. Developing technology can be implemented into the business field. So an e-marketplace application is needed to be built and designed as a web to transact between sellers and buyers. This website is developed using the profile-matching method and with the design of RAD (Rapid Application Development) which is an object-oriented approach to the system with the main goal of shortening the application processing time and process so that as soon as possible empowering the software system precisely and quickly which consists of the requirements planning, design system, instruction and implementation. The e-marketplace information system can increase freelancer revenue and the level of customer convenience in finding workers according to what is needed and can make the transaction process efficient and effective. As a result, it helps to provide decision criteria for the client selection.
The social dimension of peripersonal space

Peripersonal space (PPS) is a dynamic representation of the space around the body subserving primarily the organization of goal-directed behaviours towards stimuli with the highest reward value. It must also be viewed as a space where potentially harmful stimuli receive specific attention in order to protect the body from the hazards ahead. In the present chapter, we will highlight the anticipatory motor nature of PPS representation and its dynamic properties. We will also show that stimuli in PPS receive particular attention that fosters perceptual and cognitive processes. Finally, we will propose that PPS serves as a mediation zone between the body and the environment, protecting the body from external threats and, as such, contributing to the organization of the social life.
Dynamic Preservation for a Class of Semi-Discrete Recurrent Neural Networks

The dynamic preservation in discrete simulations of the recurrent neural networks (RNNs) with discrete and infinite distributive asynchronous time delays is addressed. Firstly, we formulate the corresponding discrete-time model by semi-discrete technique. Secondly, we derive several mild algebra conditions to guarantee that the discussed discrete-time system is the global exponential stability in Lyapunov sense. It is shown analytically that the discrete-time technique is able to maintain the uniqueness of equilibrium point and its dynamic behavior of the continuous-time model under the same conditions. Meanwhile, we also show that there exists some degree of deviation in the spatial position among the equilibrium points of the continuous-time model and its discrete-time analogue due to the rounding error. Finally, we verify the validity of the main obtained results by comparing one continuous-time numerical example with its discrete-time counterpart.
Vers une écologie linguistique des anciens chasseurs-cueilleurs du bassin central congolais

The article attempts to provide some sociolinguistic information about the former Hunter-Gatherers of the Central Congo Basin in the hope that they could arouse the same scientific interest as that which was carried out on the Bambuti Pygmies of Ituri and Aka of the borders of the Democratic Republic of Congo, the Central African Republic, and Southern Cameroon. It emerges that all the questions raised and discussed so far in relation to the status of the languages spoken by these latter Pygmy tribes, their structure and their genetic affiliation also apply to the Pygmies of the Central Congo Basin. Thus, the article addresses, first and foremost, the problem of their near and distant origins and their current sociolinguistic situation, before going on to review the current state of linguistic research. A subclassification is then sketched based on common phonetic, morphosyntactic, and lexical characteristics to their languages as well as the traditions collected by the colonial administration.
Implementation of Deep Learning-based Object Recognition and Tracking for Intelligent Video Surveillance

As research on artificial intelligence technology is actively conducted in recent years, research on deep learning technology that recognizes and classifies images in real time on behalf of humans is required. Object recognition has difficulties in finding an object of interest from a video clip or image, and classifying several detected objects for each object. To solve this problem, research is needed to detect and track objects using a CNN-based deep learning method. Among the CNN-based multi objects detection techniques, the most well-known methods are R-CNN and Faster R-CNN. These methods are based on ROI-based detection techniques to perform verification work to reduce candidate groups through pre-work in the ROI of the object. However, since the process of classifying objects for each region of interest is performed, the detector speed decreases. Real-time processing is not possible due to this speed problem. In this paper, to overcome these issues, we have proposed multi object detection, classification, and tracking method using YOLO, a single step technique that performs a single CNN to determine the location and type of objects in an image. Experimental results depict that it can detect and classify objects robustly in various environments, and that real-time tracking is possible because the calculation speed is faster than the conventional method.
Attentional Learn-able Pooling for Human Activity Recognition

Human activity/behaviour monitoring and recognition is a key for facilitating humans robot interaction, and allows robots for a better scheduling of future operations. It is challenging and often addressed at different levels, such as human activity classification, future activity prediction and monitoring of the on-going activities. The paper proposes a novel attention-based learn-able pooling mechanism for human activity classification from RGB videos. Recently, most of the best performing human activity recognition approaches are based on 3D skeleton positions. The 3D skeleton positions are not always available in videos captured using RGB cameras, which are widely used in robotics applications. RGB videos contain rich spatio-temporal information and processing them semantically is a difficult task. Moreover, accurately capturing spatial information and long-term temporal dependencies is the key to achieving high recognition accuracy. We use an existing Convolutional Neural Network for image recognition to extract video features which are then processed using our innovative application of attention mechanism to focus the network on features that are more important for discrimination. Afterwards, we use a novel learn-able pooling mechanism to extract activity-aware spatio-temporal cues for efficient activity recognition. The proposed pooling mechanism learns the structural information from hidden states of a bidirectional Long Short-Term Memory network via Fisher Vectors.
How to Prepare Students for the 4th Industrial Revolution Society

This paper aims to examine how universities prepare students for meeting the job needs in the 4th industrial revolution society and suggest a curriculum to meet the students’ needs. In order to achieve the research goal, a survey method was used. According to Wikipedia, the 4th industrial revolution consists of various technological components such as mobile devices, Internet of things Platforms, big data, and augmented reality. This study attempted to determine whether the university’s curriculum is designed to prepare students for obtaining the knowledge and skills necessary in society. It also examined how students perceive their abilities to handle technologies and get jobs in the changing society. Based on the data analysis, suggestions were made to adjust a curriculum design for future changes.
UO @ HaSpeeDe2: Ensemble Model for Italian Hate Speech Detection

This document describes our participation in the Hate Speech Detection task at Evalita 2020. Our system is based on deep learning techniques, specifically RNNs and attention mechanism, mixed with transformer representations and linguistic features. In the training process a multi task learning was used to increase the system effectiveness. The results show how some of the selected features were not a good combination within the model. Nevertheless, the generalization level achieved yield encourage results.
APPLICATION OF PICTURE &amp; PICTURE MODEL IN INTRODUCING ENGLISH VOCABULARY (NUMBER) AT EARLY CHILDHOOD (Case Study in term of Motivation Aspect)

The golden age is a sensitive period that is very important for children to develop all aspects of development such as language, one of which is English so they can compete in the global world while growing up. Introducing activity must be carried out with a learning model that is and are concrete so that children can understand what is meant for example the picture and picture model learning. This study aims to describe how the contribution of the application of picture and picture model in introducing English vocabulary (number) at early childhood to increase children's motivation. The research approach used is qualitative with the type of case study (case study). Data collection using in-depth interview, observation and documentation. The result showed that the application of picture and picture model in introducing English vocabulary (number) contribute to the improvement of children's motivation such as children's attention during activity (attention), children's interest in English (interest), trust the child's self in knowing English vocabulary (confidence) and the satisfaction with fun learning (satisfaction). This research can be used as input for the teacher to create English learning that can increase children's motivation in class.
Combination of Fuzzy C-Means Clustering Methods and Simple Additive Weighting in Scholarship of Decision Support Systems

Scholarships are educational assistance given to individuals or students with the aim of providing relief from financial costs for the continuation of the education pursued.Scholarships at SMK Auto Matsuda There are two types of scholarships which are grouped based on achievement criteria, status and family economic conditions.The decision-making system has not been objective and transparent in determining foundation scholarship recipients, this is the background of the research using a combination of two methods, namely theMethod Fuzzy C-Means (FCM) and Simple Additive Weighting (SAW).The combination of the Fuzzy C-Means (FCM) method is used for clustering to determine the membership weights objectively based on each variable criterion, while the Simple Additive Weighting (SAW) method is used for the weighted addition of each alternative on all alternative criteria to find a weighted addition.with the performance appraisal of the overall highest score by taking the results of the clustering, the best alternative will be taken.provide information and consideration to anticipate in classifying prospective recipients of Foundation Education Fee Assistance Scholarships (BBP Foundation) and Learning Achievement Enhancement Scholarships (PPB Foundation).Sampel data as much as 10 student data obtained three clusters based on the average value of the determination of the scholarship then processed by Simple Additive Weighting (SAW) for the ranking of each cluster classified based on which criteria are prioritized with the greatest value at the final distance is the cluster that receives the scholarship, while The cluster with the smallest score is the cluster that is not eligible to receive a scholarship.This study aims to implement the Simple Additive Weighting (SAW) method and the Fuzzy C-Means (FCM) method by categorizing variable criteria and using weighting in the selection of participants who receive the Foundation Education Fee Assistance (BBP Foundation) scholarship and Learning Achievement Improvement (PPB Foundation) scholarship.can display the final results of prospective scholarship recipients from the greatest value (feasible) to the smallest (not feasible).
Lawyer Ethics for Innovation

Law struggles to keep pace with innovation. Twenty-first century advancements like artificial intelligence, block chain, and data analytics are already in use by academic institutions, corporations, government entities, health care providers, and others but many questions remain about individual autonomy, identity, privacy, and security. Even as new laws address known threats, future technology developments and process improvements, fueled by consumer-demand and globalization, inevitably will present externalities that the legal community has yet to confront.How do we design laws and systems to ensure accountability, equality, and transparency in this environment of rapid change? A solution can be found in a surprising source — the regulation of professional ethics. Lawyers have the capacity to play a critical role both in assessing the risks and benefits of innovation generally and also in deploying innovative tools to enhance the delivery of legal services. This Article is the first to articulate a formal obligation of ethical innovation as a component of professional discipline and licensing rules. This proposal comes at a time when the legal profession is increasingly immersed in innovation — whether measured by the number of “NewLaw” providers, exponentially increasing financial investment in legal tech, or by the American Bar Association’s 2020 Resolution supporting innovation to address the access-to-justice crisis.Rather than taking a particular side in the debate over whether lawyers and judges should adopt innovations like artificial intelligence or machine learning, this Article acknowledges that technology advancements inevitably are part of modern society, including the practice of law, and advocates for reforms to professional conduct rules to protect individuals in the midst of innovation. This protection is especially warranted when innovation is forced amidst a moment of crisis, for example as seen when the 2020 coronavirus pandemic abruptly halted law practice in its traditional form, canceling office meetings and jury trials and other in‑person interactions. Some lawyers and courts were prepared, others were not. Some clients received the legal advice through virtual consultations or apps, and had their cases decided by judges via Zoom hearings, but many found themselves without the justice they needed. The lawyers and judges at the forefront of ethical innovation before the pandemic hit were the ones best able to serve their clients. Formalizing a duty to innovate as an ethical obligation will make the profession better prepared to serve the public in the future.
A Comparison of Classical and Deep Learning-based Techniques for Compressing Signals in a Union of Subspaces

Many natural signals lie in a union of subspaces, which we can exploit when compressing these signals to maintain a high level of fidelity while significantly reducing the storage size. Standard compression techniques for natural signals such as images follow a general pipeline which uses predetermined transformations to encode and decode data. Recent advances in deep learning-based techniques for compressing image data have shown results which compete with existing compression standards. Inspired by the success of these deep learning methods, we evaluate various classical and deep learning-based methods for encoding and decoding signals which follow a union-of-subspaces structure. On the classical side, we evaluate compressed sensing with a learned dictionary, whereas for deep learning-based techniques, we consider an autoencoder and a deep generative model-based variant of compressed sensing. Our results suggest that while classical compressed sensing-based methods work well, deep learning-based techniques perform better as the union-of-subspaces signal structure becomes more complex.
An Collaborative and Early Detection of Email Spam Using Multitask Learning

Currently, E-mail is one of the most important methods of communication.The increasing of spam e-mails causes traffic congestion, decreasing productivity, which has become a serious problem for our society.The problem of Email spam has grown significantly over the past few years.It is not just a nuisance for users but also it is damaging for those who fall for scams and other attacks.The complexity intensification of Email spamming techniques which are advancing from traditional spamming (direct spamming) techniques to a more scalable and indirect approach of botnets for distributing Email spam message is the major reason for it.The aim of this research is to find an effective solution to filter possible spam e-mails.In this paper a hybrid solution which uses machine learning algorithms like Deep Neural Network, Convolution Neural Network are used to produce an improved result and efficiency compared to existing system.The experimental results show that the proposed algorithm has 92.8% accuracy.