Reviewerly/exHarmony huggingface dataset:
Setting Up Interactors and Virtual Hands

With your Camera Rig setup complete, you now need to set up your Left and Right controller game objects, which will mimic your real-life hand movements and map to the transform of your VR controllers. In this chapter, we will first go over what an Interactor and an Interactable are. We will then set up the default controller models provided by the VRTK, which are two cuboid objects representing your hands. However, to increase immersion in Virtual Reality, we will replace these cuboid hands with virtual animated hands. After reviewing how to set up the Oculus-provided hands, you’ll go on to set up your very own custom hands that can be used with the Spatial Simulator and the Camera Rigs, Unity XR setup.
Dual-arm Robot Fast Pick-and-Place on a Moving Conveyor

Abstract The pick-and-place (PNP) system of multi-robots on moving con- veyors have a wide range of industrial applications. In practice, the most used is first in first out (FIFO) algorithm. However, the efficiency of FIFO is low when the number of objects to be picked is large. To improve the PNP efficiency, we first build a kinematic model of the dual-arm robot and establish a PNP time lookup table based on the inverse kinematic algorithm. Then, we develop a PNP shortest time first out (STFO) algorithm to calculate the high-efficiency picking sequence. Dynamic experiments show that compared with FIFO, we improve the dual-arm robot PNP system efficiency by 20%.
AlphaQO: Robust Learned Query Optimizer

Recently, learned query optimizers typically driven by deep learning models have attracted wide attention as they can offer similar or even better performance than state-of-the-art commercial optimizers.A successful learning optimizer often relies on enough high-quality load queries as training data, and poor-quality training will lead to the query failure of learned query optimizers.In this paper, we propose a novel training framework AlphaQO for robust learned query optimizers based on Reinforcement Learning (RL), and the robustness of the optimizers can be improved by finding the bad queries in advance.AlphaQO is a loop system consisting of two main components, namely the query generator and the learned optimizer.A query generator aims at generating "difficult" queries (i.e., queries that the learned optimizer provides poor estimates).The learned optimizer will be trained using these generated queries, as well as providing feedback (in terms of numerical rewards) to the query generator for updates.If the generated queries are good, the query generator will get a high reward; otherwise, the query generator will get a low reward.The above process is performed iteratively, with the main goal that within a small budget, the learned optimizer can be trained and generalized well to a wide range of unseen queries.Extensive experiments show that AlphaQO can generate a relatively small number of queries and train a learned optimizer to outperform commercial optimizers.Moreover, learned optimizers require much fewer queries from AlphaQO than randomly generated queries for the quality training of the learned optimizer.
Classification and retrieval of CAD three dimensional models based on neural network

Several neural networks corresponding to feature space in the paper were formed by Boosting method variant and RBF neural network based on particle swarm optimization (PSO), and these neural networks were integrated, so that the classification information of CAD three dimensional (3D) models was given. In the retrieval of CAD 3D model, the distance of the output results for the classifier and the distance for the feature space were weighted to calculate, which not only considered the difference of between the model's content and features, at the same time, and appended classification information parameters,but also took into account the semantic classified information of model. The experimental results showed that the classification method based on neural network ensemble could effectively improve the classification accuracy of CAD 3D model as well as consider the distance between models in feature space and the distance between models at semantic classification level, so that the 3D CAD model retrieval could be greatly improved accuracy.
College Management System

Abstract: This process is based on COLLEGE MANAGEMENT SYSTEM. College management system is helpful for students as well as the colleges. In the existing system all the activities are done manually. It is very time consuming.It manage the college information , student information, Admission management ,Attendance management and various different types of event going on in our college. . In our proposed system, The data will be stored in the college server. To store the data SQL server will be used. The Admin, Faculty or the student should be a register user. Keywords: College Management, SQL Server, Information Management, Result, Notification
Systematic Review of Risks in Domestic and Global IT Projects

The development of information technology projects is no longer limited to the domestic sphere. This study identifies the differentiation of risk categories between global and domestic projects through an exploratory research carried out by means of a systematic literature review. 1367 risks were identified in 37 articles and classified within 22 categories. The major concern regarded in domestic project management was the client (external risk) and scope (internal risk) and, in global project management, the psychic distance (external) and coordination and control (internal). The main difference between the risk categories for each project type refers to the psychic distance category, which was identified almost exclusively in global projects, thus making the external risks more relevant than those in domestic projects. On the other hand, it makes risks such as client, supplier and stakeholders be underestimated. The results indicate that project managers should focus on different risks depending on the type of IT project: global or domestic.
Implementasi Algoritma C.45 Pada Data Pengolahan Limbah Kelapa Sawit

The problems faced at PT. PP. London Sumatra Indonesia Tbk, Bagerpang Palm Oil Mill, is that there are still errors in the grouping of the liquid waste which have a bad impact and cause environmental damage and disrupt activities around the company. One way to process the data is to apply or use data mining techniques. Where data mining is the process of analyzing large data into information to determine the pattern of the conclusions of the data being analyzed. The application of the C.45 algorithm which later will explore existing data and will form a decision tree, where this decision tree will later help the company to know the grouping and processing of the liquid waste so as not to have a bad impact on the surrounding company
Increasing Interpretability and Prediction Rate by combining Self-organizing Maps with Modeling Algorithms

We consider supervised learning problems, for which we need not only the accurate model, but also the model, that explains the relation between inputs and a target variable. There are modeling problems, when production experts can measure their confidence in the modeling results by modeling metrics, such as accuracy, but need an explanation for what was the reason of desirable or undesirable situation or system state in the past. In this study we utilize a combination of self-organizing maps and multiple linear modeling to increase the interpretability and accuracy. We assume that the target variable can be explained differently by different patterns that characterizes inputs data. By solving clustering problem for subset of inputs, we have structured data and can relate each cluster to its representative or cluster profile, which explains the cluster. Based on that structure we build linear model for each cluster dataset, and coefficients of this model explain the influence of factors for particular inputs characteristics. To cut the number of inputs we use L1-regularization for linear model. Proposed approach was tested on several industry related problems and implemented in application.
Written English Syntactic Complexity Analysis Based on AES System and Big-data Technology

Based on the data from the Automatic Essay Scoring(AES) system, this research used Second Language Syntactic Complexity Analyzer(L2SCA) to analyze the English and non-English majors’(EMJ and NEMJ)written English syntactic complexity and examined the predictive power of syntactic complexity measures on the AES scores of participants’ essays. The results show that MLC, MLT and MLS in NEMJs’ essays were significantly higher than those in EMJs’ essays. EMJs’ essays were significantly higher than NEMJs’ in DC/C, DC/T, CT/T and C/T. NEMJs’ essays were significantly higher than EMJs’ in CP/C and CP/T. NEMJs’ essays were significantly higher than EMJs’ in CN/C. Subordination measures DC/C, DC/T, CT/T and C/T have predictive power on the AES scores of participants’ essays.
PAKPAK LANGUAGE TRANSLATOR APPLICATION INTO INDONESIAN USING ALGORITHM BOYER MOORE BASED ON ANDROID

The lack of preservation is also the knowledge of the Pakpak Language in Indonesia, which causes the Pakpak Language to be less preserved, especially for young people who continue to keep abreast of the times. This causes the process of globalization and urbanization which causes assimilation and acculturation, especially in urban areas. This situation triggers a new language that is very popular, especially for young people who unconsciously, it makes us lose our identity as a society that has tribes and customs each – and the emergence of international and national schools that require students to speak foreign languages. Therefore, learning is needed to preserve Pakpak Language. By making a Pakpak to Indonesian translator application that uses the Android Based Boyer Moore algorithm. This translator application was made for the introduction of the PakpakLangugae to the wider community so that the sustainability of the Pakpak Language is maintained and this translator application can translate the Pakpak Language into Indonesian or vice versa based on the prevailing Pakpak – Indonesian dictionary.
Feed-Forward Neural Networks

In the last chapter we did some amazing things with one neuron, but that is hardly flexible enough to tackle more complex cases. The real power of neural networks comes into light when several (thousands, even millions) neurons interact with each other to solve a specific problem. The network architecture (how neurons are connected to each other, how they behave, and so on) plays a crucial role in how efficient the learning of a network is, how good its predictions are, and what kind of problems it can solve. There are many kinds of architectures that have been extensively studied and that are very complex, but from a learning perspective, it is important to start from the simplest kind of neural network with multiple neurons. It makes sense to start with a feed-forward neural network, where data enters at the input layer and passes through the network, layer by layer, until it arrives at the output layer (this gives the networks their name: feed-forward neural networks).
Virtual Environments as Memory Anchors

In the future, the past will be as accessible as the present. This statement is becoming as true with respect of the near past, as it is true for ancient history, following the previous two decades of digital transformation of archaeological research. Institutions have a tendency to favour recording large historical events over smaller, less significant happenstance. The same institutions endorse more distant material cultures over contemporary artefacts, trivialising that which is in abundance over rare historical objects. Yet, the memory of every individual has value, at least to that person and their family, whilst each individual has a moral responsibility to remember their own pasts. In this context, the chapter presented here discusses the notion of memory anchors and how immersive virtual environments can facilitate the process of remembering, reliving, sharing, and retention of individual memory. It examines what virtual reality can offer to remembrance, and the resilience of memory anchors within such environments. The chapter argues that physical objects and spaces associated with each individual must be captured as a moral obligation, and that the responsibility of such activities should rest at the level of family units where these memories reside.
Research on Higher Vocational College Public English Blending Teaching Based on “Internet Plus”

“Internet era”not only brings more opportunities for higher vocational public English teaching, but also lets it face with &#x0D; new challenges. The traditional teaching mode has been unable to meet the requirements of English teaching, and it is urgent to &#x0D; promote and apply the blending teaching mode. This paper firstly introduces the connotation and the significance of the blending &#x0D; teaching mode, and then analyzes present situation, the problems and reasons, and finally proposes the strategies to improve the &#x0D; blending teaching of public English in higher vocational colleges.
Analyzing the Methods for Detecting Deepfakes

Progress in the field of deep counterfeiting is equally impressive and worrisome on the upside. We can change the fidelity of the media that will undoubtedly lead to some world memes, but in the hands of the bad guys. Deepfake technology can be used to spread misinformation and destroy public trust in. It is almost like science fiction identity theft. You can allow anyone to tell what they want, but the reverse is true. In this paper, Detailed study has been carried out that proposed different deepfake detection models, this document finds DEFAKEHOP as the best method in , which uses spatial dimensionality reduction and soft classification to obtain a more concise description of the face for each detection channel. This article finally looks at ’s research direction in this field, as well as the development of more powerful technologies to deal with the growing threat surrounding Deepfake technology.
Image Provenance Analysis

Abstract The literature of multimedia forensics is mainly dedicated to the analysis of single assets (such as sole image or video files), aiming at individually assessing their authenticity. Different from this, image provenance analysis is devoted to the joint examination of multiple assets, intending to ascertain their history of edits, by evaluating pairwise relationships. Each relationship, thus, expresses the probability of one asset giving rise to the other, through either global or local operations, such as data compression, resizing, color-space modifications, content blurring, and content splicing. The principled combination of these relationships unveils the provenance of the assets, also constituting an important forensic tool for authenticity verification. This chapter introduces the problem of provenance analysis, discussing its importance and delving into the state-of-the-art techniques to solve it.
SparseMeshCNN with Self-Attention for Segmentation of Large Meshes

In many clinical applications, 3D mesh models of human anatomies are important tools for visualization, diagnosis, and treatment planning. Such 3D mesh models often have a high number of vertices to capture the complex shape, and processing these large meshes on readily available graphic cards can be a challenging task. To accommodate this, we present a sparse version of MeshCNN called SparseMeshCNN, which can process meshes with more than 60 000 edges. We further show that adding non-local attention in the network can mitigate the small receptive field and improve the results. The developed methodology was applied to separate the Left Atrial Appendage (LAA) from the Left Atrium (LA) on 3D mesh models constructed from medical images, but the method is general and can be put to use in any application within mesh classification or segmentation where memory can be a concern.
Enhancing the Students’ Reading Comprehension Achievement through Skimming and Scanning Techniques in Senior High School

This research was intended to enhance the students’ participation and reading comprehension achievement by using Skimming and Scanning Techniques at MAN 1 Jember that was conducted collaboratively with the English teacher. The design of the research was classroom action research. The research participants was the X IPA 4 grade students of MAN 1 Jember. The implementation of skimming and scanning techniques could improve the tenth IPA 4 grade students’ active participation in the reading teaching learning process. The result of the observation showed that the percentage of students’ active participation improved from 75.76% in meeting 1 to 78.79% in meeting 2. Furthermore, the implementation of skimming and scanning techniques could enhance the tenth IPA 4 grade students’ reading comprehension achievement in the reading teaching learning process. The result of the reading test showed that the percentage of the students who got at least 75 was 78.79% or 26 students of 33 students. Based on the result above, it could be concluded that both the result of observation and reading comprehension achievement test had achieved the success criteria of this classroom action research in cycle 1.
FORMATION OF WAYS OF ROLE INTERACTION OF FUTURE LEADERS IN THE LEARNING PROCESS AS AN IMPORTANT CONDITION FOR THEIR SUCCESSFUL ADAPTATION TO THE PROFESSIONAL ACTIVITY

the meaning of the terms “manager”, “manager’s role” and “role interaction” is revealed in the article. It is shown that the process of successful adaptation to the professional activity of future leaders is based on leading personal innovations, the formation of which is necessary in the process of studying in higher education institutions. The results of a study in which the characteristics of their emotional, communicative and regulatory qualities were considered are noted in the article. The obtained results allow us to conclude that the future leaders lack a number of qualities that contribute to their successful adaptation to professional management activity. Their formation requires improvement of methods, tools and organizational forms that ensure this process.&#x0D; The use of role-playing games in the form of video materials and presentations plays an important role in the training sessions. The objectives, principles and role of training as a methodological tool to enhance the adaptation of future leaders to successful professional activity are highlighted in the article.
Deep Fake Face Detection Using Machine Learning and Various GAN Models

Remarkable advancements in Deep learning, incredibly genuine AI generated pretended videos have been created. To appropriately construct the Deep fake phenomenon, new Deep fake detection algorithms must be designed for the exploitation of this powerful A.I. technology has a major effect on person lives. However, traces left by Generative Adversarial Network (GAN) engines during the construction of Deep fakes can be discovered by studying ad-hoc frequencies. We'll utilize the Deep Convolutional Generative Adversarial Network (DCGAN) and Style GAN, which have both shown to be quite good at creating images. We covered the theoretical aspects of GAN as well as our methods for developing a DCGAN Model using MNIST and CelebA data sets.
SPECIFICS OF CONDUCTING ONLINE BINARY CLASSES IN THE CONDITIONS OF DIGITALIZED EDUCATIONAL PROCESS IN THE TRAINING OF ECONOMISTS

The article focuses on the introduction of resource-based learning in a digitalized educational process in the training of economists. It is argued that one of the forms of resource-oriented learning is online binary classes. Online binary lesson is considered as a form of resource-based learning, built not only on close interdisciplinary links, conducted jointly by two teachers of relevant disciplines with different didactic purposes, but also on the integrated application of various digital technologies (distance learning platforms and digital content for distance learning courses, online services, any digital narratives, etc.). The general organizational structure of online binary classes and the approximate scheme of conducting online binary classes in the disciplines "Economic Informatics" and "Higher and Applied Mathematics" are proposed.
UNDIKSHA VIRTUAL ASSISTANT (SHAVIRA): INTEGRATION FREQUENCY ASKED QUESTION WITH RASA FRAMEWORK

Nowadays, the implementation of chatbots in information systems is getting popular to increase user experience. In our previous research, we developed Undiksha Virtual Assistant (Shavira). Shavira is a knowledge management system (KMS) as Frequency Asked Question (FAQ) system to facilitate users to get an answer about their questions. The challenge on the existing Shavira system is that the users must open a website and type queries to get a list of answers. The text query must match with the keyword in systems. To address this problem and to avoid cognitive load, in this research we implemented a chatbot in Shavira. The purpose of the chatbot implementation in Shavira is to make information easier to be accessed and to increase user experience. The users can ask a question in natural language and get an answer immediately on their chat application such as: Telegram, Facebook, or Messenger. We used Rasa Framework as a chatbot engine in Shavira. Rasa Framework is an open-source virtual assistant engine based on artificial intelligence. The challenge in conducting this research is in integrating the existing FAQ system with Rasa Framework. This research consists of four phases, they are: adaptation data structure in the existing FAQ with data structure requested by Rasa framework, data integration and mapper, and evaluation. The result of this research is a new Shavira that is integrated with a chatbot and can be accessed from chat applications such as Telegram.Â We evaluated the accuracy of Shavira powered by Rasa Framework with ten topics about academic information. The results showed that the accuracy of Shavira is 90%. We also evaluated the usability of Shavira with Software Usability Scale (SUS) Questionnaire. The usability evaluation showed that Shavira satisfied users with valueÂ 81 (threshold 68) and categorized Excellent, Acceptable and Prometer.
Does non-linear factorization of financial returns help build better and stabler portfolios?

A portfolio allocation method based on linear and non-linear latent constrained conditional factors is presented. The factor loadings are constrained to always be positive in order to obtain long-only portfolios, which is not guaranteed by classical factor analysis or PCA. In addition, the factors are to be uncorrelated among clusters in order to build long-only portfolios. Our approach is based on modern machine learning tools: convex Non-negative Matrix Factorization (NMF) and autoencoder neural networks, designed in a specific manner to enforce the learning of useful hidden data structure such as correlation between the assets' returns. Our technique finds lowly correlated linear and non-linear conditional latent factors which are used to build outperforming global portfolios consisting of cryptocurrencies and traditional assets, similar to hierarchical clustering method. We study the dynamics of the derived non-linear factors in order to forecast tail losses of the portfolios and thus build more stable ones.