Reviewerly/exHarmony huggingface dataset:
XIRL: Cross-embodiment Inverse Reinforcement Learning

We investigate the visual cross-embodiment imitation setting, in which agents learn policies from videos of other agents (such as humans) demonstrating the same task, but with stark differences in their embodiments -- shape, actions, end-effector dynamics, etc. In this work, we demonstrate that it is possible to automatically discover and learn vision-based reward functions from cross-embodiment demonstration videos that are robust to these differences. Specifically, we present a self-supervised method for Cross-embodiment Inverse Reinforcement Learning (XIRL) that leverages temporal cycle-consistency constraints to learn deep visual embeddings that capture task progression from offline videos of demonstrations across multiple expert agents, each performing the same task differently due to embodiment differences. Prior to our work, producing rewards from self-supervised embeddings has typically required alignment with a reference trajectory, which may be difficult to acquire. We show empirically that if the embeddings are aware of task-progress, simply taking the negative distance between the current state and goal state in the learned embedding space is useful as a reward for training policies with reinforcement learning. We find our learned reward function not only works for embodiments seen during training, but also generalizes to entirely new embodiments. We also find that XIRL policies are more sample efficient than baselines, and in some cases exceed the sample efficiency of the same agent trained with ground truth sparse rewards.
Semi Fragile Watermarking with Tamper Localization and Recovery using AMBTC Method

The watermarking scheme in digital media communication has become an essential tool in helping content creators prove ownership if any dispute arises in copyright infringement. In this paper, Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) is performed in the watermarking process which improves the authentication of the image and the detection of the tampered region. The semi-fragile watermarking scheme provides robustness to the watermark during extraction process even after the unintentional compression attacks like JPEG compression. The watermark payload is significantly lowered in order to increase the quality of the image. The recovery is done using the absolute moment block truncation coding (AMBTC) of the image in the untampered region. Thus, both the mean and first absolute moment are used in recovering the tampered regions in the watermarked image.
PERANCANGAN APLIKASI PEMBELAJARAN PENGENALAN NAMA WARNA BERBASIS MULTIMEDIA DI TK LESTARI DESA TAMAONA

This research aims to designing apps to learn to know colors in children and implement a color name recognition learning app for children with basic color merging.Research method in the design of multimedia-based color recognition learning application using Multimedia development method Luther-Sutopo Version multimedia development life cycle (MDLC) with Concept stage, Design using Macromedia Flash Fropessional 8.0, Material Collecting, Assembly, and Testing using Black Box. The method of data collection in research with the stages of Observation, Interview, and Study Library..The results of this multimedia-based color recognition learning application conclude that : (1) Has been designed multimedia-based color recognition learning application for teaching learning process in Lestari Kindergarten Tamaona Village, especially to recognize basic colors, secondary colors, and tertiary colors in indonesia and english that can help teachers in conveying their materials, with this learning medium children more easily recognize colors, (2) It has facilities in primary, secondary and tertiary color recognition learning applications provided in the form of images, animations and sounds, as well as a game that contains questions related to getting to know primary, secondary and tertiary colors
Probabilistic program induction as a model of humanconcept learning

Humans have a unique ability to learn concepts thatrely on hidden, abstract rules, that require relational reasoning,or that have latent combinatorial structure. Often these conceptssystematically extend to provide infinite expressive capacity. Thecanonical example of such a concept is natural language, but theseproperties can be observed even in simple laboratory tasks. Suchconcept learning is interesting because it is not found in anyother animal species, and it has also proven an elusive target forcomputational models. This thesis proposes that people representconcepts as probabilistic programs. Thus, learning such conceptscorresponds to inducing a latent probabilistic program thatgenerates, and thus explains the observed data. We explore twocomputational models of probabilistic program induction. The firstis based on the notion of a Language of Thought, a grammar-basedapproach to rule-based concept learning. We adapt this framework tolearn probabilistic programs and compare its predictions to theresults of a human concept learning behavioral experiment. Thesecond model is based on a novel representation of probabilisticprograms as graphs. This representation brings together conceptsfrom functional programming with the theory of probabilisticgraphical models, or Bayesian networks. We test the model on tworelational concept learning tasks.
Why Code-switch on WhatsApp? A Quantitative Analysis of Types and Influences of Code-switching

This paper examines an asynchronous computer-mediated communication (CMC) between bilingual university students in Malaysia, in particular via Whatsapp by appropriating a functional approach in scrutinizing the diverse types and influences for codeswitching (CS). A quantitative methodology was employed wherein a survey was designed and administered to undergraduate students from the Universiti Teknologi MARA (UiTM). A dataset from a total of 90 respondents was collected from five faculties; Academy of Language Studies, Faculty of Law, Faculty of Communication and Media Studies and Faculty of Education. The yielded findings postulate that inter-sentential code-switching was the most used type of code-switching among the respondents and habitual expression is the main factor that influence them to code-switch.
Teaching Students in a Multicultural Environment

Teaching students in a multicultural environment on the one hand requires significant financial resources to recruit, support, retain students, and on the other - students' self-motivation. The need to study different cultures affects the level of motivation to study abroad. The study aims to analyze quantitatively financial incentives as a factor in student learning in a multicultural environment, namely the relationship between financial aid, EU public spending on education and student mobility from Europe, Africa and Asia. The research methodology is based on correlation analysis and statistical analysis of student mobility indicators in Germany, France, Austria and Poland for 2013-2019. The results show differences in the policy of encouraging foreign students to study in a multicultural environment. Germany and France have the highest levels of student mobility, along with inefficient multicultural environments. Inefficiency of funding is confirmed by the low share of degree mobile graduates from abroad in mobile students from abroad enrolled in tertiary education, as well as the high share of foreign youth who do not work and study. Austria and Poland have lower levels of international student involvement, in particular, due to lower levels of financial support. This study proves that the effectiveness of multiculturalism policy will depend on the optimization and balance between funding of training programs for social inclusion, responsibility of students for the financial resources of countries financing their training, and motivation to study abroad. Governments should direct policies to encourage international students seeking to pursue their careers in a foreign country (country of study). The practical value lies in the possibility of incorporating the results into national strategies for the recruitment and retention of foreign students by strengthening the responsibility for funding tuition.
Boosting Local Recommendations With Partially Trained Global Model

Building recommendation systems for enterprise software has many unique challenges that are different from consumer-facing systems. When applied to different organizations, the data used to power those recommendation systems vary substantially in both quality and quantity due to differences in their operational practices, marketing strategies, and targeted audiences. At Salesforce, as a cloud provider of such a system with data across many different organizations, naturally, it makes sense to pool data from different organizations to build a model that combines all values from different brands. However, multiple issues like how do we make sure a model trained with pooled data can still capture customer specific characteristics, how do we design the system to handle those data responsibly and ethically, i.e., respecting contractual agreements with our clients, legal and compliance requirements, and the privacy of all the consumers. In this proposal, We present a framework that not only utilizes enriched user-level data across organizations, but also boosts business-specific characteristics in generating personal recommendations. We will also walk through key privacy considerations when designing such a system.
Semi-supervised Learning Based Right Ventricle Segmentation Using Deep Convolutional Boltzmann Machine Shape Model

Automated Right Ventricle (RV) segmentation is a challenge due to the RV’s variable shape and the lack of labelled data. This paper proposes a semi-supervised learning method based on a convolutional deep Boltzmann machine (CDBM). A CDBM is constructed to learn the complex shape of RV using the short-run MCMC. Next, a semi-supervised learning network composing of a CDBM and two CNNs is proposed. The CNNs and the CDBM have trained alternatively; labelled data are used to train the CNNs, and CDBM reconstructs the predicted results of unlabelled data using the CNNs to guide the training of CNNs further. During this procedure, the CDBM is trained at the same time. Our approach’s main idea is to extract the shape information of RV and use the shape information to improve CNN’s performance. Our approach takes advantage of avoiding overfitting and requiring less labelled data. Besides, our approach does not increase any extra computational cost and parameters during inference. The experiment results show that our approach can improve segmentation accuracy when the labelled training data is small.
Forensic Accounting: Review on Global Organization’s Scenario

Forensic accounting popularity and application over the decade has intensified immensely though it is not a new concept. Its application is of many importance provided that accounting fraudulent practices are increasing immensely throughout the globe.  Forensic accounting is the combination of accounting, auditing and investigative skills. It provides investigative functions and litigation support services to understand the depth and width of the financial scams happening in any economy. This research paper discusses some of well-known global cases of financial fraudulent practices like Worldcom, Krispy Kreme and Enron.  The research is based on secondary data collected from published journals, periodicals and books.
Duality in Intuitionistic Propositional Logic

It is known that provability in propositional intuitionistic logic is Pspace-complete. As Pspace is closed under complements, there must exist a Logspace-reduction from refutability to provability. Here we describe a direct translation: given a formula φ, we define φ so that φ is provable if and only if φ is not.
Physical Reasoning Using Dynamics-Aware Models

A common approach to solving physical-reasoning tasks is to train a value learner on example tasks. A limitation of such an approach is it requires learning about object dynamics solely from reward values assigned to the final state of a rollout of the environment. This study aims to address this limitation by augmenting the reward value with additional supervisory signals about object dynamics. Specifically,we define a distance measure between the trajectory of two target objects, and use this distance measure to characterize the similarity of two environment rollouts.We train the model to correctly rank rollouts according to this measure in addition to predicting the correct reward. Empirically, we find that this approach leads to substantial performance improvements on the PHYRE benchmark for physical reasoning: our approach obtains a new state-of-the-art on that benchmark.
Analisis Sentimen Media Sosial Twitter Dengan Algoritma K-Nearest Neighbor Dan Seleksi Fitur Chi-Square (Kasus Omnibus Law Cipta Kerja)

The use of information technology is growing rapidly, marked by public opinion that can be conveyed indefinitely through social media. One of the social media that is Twitter. Twitter is considered easier to retrieve information related to existing opinions and sentiments due to the limited character in a tweet made by users and there is the hashtag # which can be searched easily regarding the current hotly discussed situation. Some time ago, there was a lot of discussion regarding the ratification of the omnibus work copyright law. Tweets in the form of lively sentiments adorn the hashtag “#omnibuslaw” and other related hashtags. This study discusses reviews in the form of tweets related to omnibus law with the Chi Square selection feature and the K-Nearest Neighbor algorithm using R Studio. Data were taken as many as 500 tweets related to the omnibus law. The methodology used is Knowledge Discovery in Databases. Data labeling is carried out by experts who are divided into positive and negative sentiments. The results of the modeling using K-Fold Cross Validation, the highest accuracy is obtained with a 25% feature use scheme (Chi Square feature selection), and the value of k = 5 in KNN is 81.4%. Testing on the model was carried out using 100 random data and obtained 83% accuracy, 100% precision, 15% recall and 26.08% F-Measure value. Of the 500 data taken, the word people is the most dominating word. Of the 500 data taken, 78.8% negative sentiments and 21.2% positive sentiments.
Securing IoT Communications Using Blockchain Technology

The Internet of Things (IoT) is logically an actuality today. Eventually, particular key moves despite everything ought to be given explicit thought so that IoT courses of action further assist creating enthusiasm for related contraptions and the organizations publicized. Due to the conceivable relevance and affectability of organizations, IoT courses of action should address the security and assurance concerns enveloping these devices and the data they assemble and make and the procedure. Starting late, blockchain advancement has expanded a great deal of thought in IoT courses of action. Its fundamental use circumstances are in the budgetary space, where blockchain makes an encouraging request worldwide and can be used to understand security and assurance issues. In any case, this rising development has an unfathomable potential in the most different mechanical domains and would altogether be able to help achieve the Internet of Things see in different points of view, extending the restriction of decentralization, empowering interchanges, enabling new trade models, and allowing self-administering coordination of the devices.
AR-Powered Computer Science Education

There has been a huge spike in the public’s interest in computer science and programming; hence, there is a growing demand for computer science education. While there are many graphical elements that are utilized today in the demonstration of data structures and algorithms, these visualizations need time to make and have various limitations, particularly in the representation of complex data structures and algorithms. This chapter aims to provide a tool to educators and programmers to render the data structures and algorithms of their programs in augmented reality, making it easier for everyone to teach and learn the basic foundation of computer programming. Augmented reality is the future for all forms of visualization, which provides an interactive 3D stage for immersive experiences. The augmented reality industry has been growing rapidly of late and is projected to continue growing exponentially. The chapter provides a python library that allows users to create programs whose elements can be rendered in augmented reality and a mobile application that can render the data structures and algorithms based on user input in real time. The chapter also provides a repository of algorithms. This repository, along with the open-source nature of the project (https://argorithm.github.io/), is designed to boost the project’s growth and outreach as a tool that anyone can understand and use. This chapter offers ideas regarding the visualization of data structures and algorithms in augmented reality for education. We hope to introduce the subject of computer science and programming to students in a manner and environment in which they and their teachers are comfortable and feel less pressured.
Artificial Intelligence and the Temporality of Machine Images

DeepMind, a recent artificial intelligence technology created at Google, references in its name the relationship in AI between models of cognition used in this technology‘s development and its new deep learning algorithms. This chapter shows how AI researchers have been attempting to reproduce applied learning strategies in humans but have difficulty accessing and visualizing the computational actions of their algorithms. Google created an interface for engaging with computational temporalities through the production of visual animations based on DeepMind machine-learning test runs of Atari 2600 video games. These machine play animations bear the traces of not only DeepMind‘s operations, but also of contemporary shifts in how computational time is accessed and understood.
ORGANISATION OF THE STUDENTS’ EDUCATIONAL PROCESS (IN THE MOSCOW SCHOOL)

ORGANISATION OF THE STUDENTS&rsquo; EDUCATIONAL PROCESS&nbsp;(IN THE MOSCOW SCHOOL) Alina Chekalkina student, Peoples&rsquo; Friendship University of Russia, Russia, Moscow Anna Tadjieva candidate of science, associate professor, Peop
Tiroler Wortarbeit

Recently, the manuscript collections of the ULB Tyrol have been completely catalogued. Now that the last three volumes have been published, this paper examinates the project as a whole and, looking ahead, asks about the conditions for the success of modern cataloging. In den vergangenen Jahren sind die Handschriftenbestände der ULB Tirol vollständig katalogisiert worden. Ausgehend von den letzten drei Katalogen, würdigt der Beitrag das Gesamtprojekt und fragt ausblickend nach den Gelingensbedingungen moderner Katalogisierungsprojekte.
ANALYSIS OF INTERNATIONAL EXPERIENCE IN THE DEVELOPMENT AND USE OF MODELS OF DIGITAL TRANSFORMATION OF HIGHER EDUCATION ORGANIZATIONS

The article presents an analysis of five international and national framework models for digital transformation of higher education organizations, identifies priority areas of university transformation in the context of an evolving digital environment, identifies typical deficiencies found in national programs and strategies for digital transformation of higher education organizations. The article is aimed at identifying the main directions of changes in the activities of educational organizations provided for in these frameworks, as well as correlating them with the activities provided for in national programs and strategies for digital transformation of higher education organizations and identifying systemic deficits. The article describes the digital strategies of individual universities, which are developed in accordance with the goals and objectives of a particular university, its competencies and deficits, as well as the expectations of its main stakeholders.
Gesture Recognition with Multi-dimensional Parameter Using FMCW Radar

A multi-parameter convolutional neural network method is proposed for gesture recognition based on Frequency Modulated Continuous Wave (FMCW) radar. A multidimensional parameter dataset is constructed for gestures by performing time-frequency analysis of the radar signal to estimate the distance, Doppler and angle parameters of the gesture target. To realize feature extraction and classification accurately, an end-to-end structured Range-Doppler-Angle of Time (RDA-T) multi-dimensional parameter convolutional neural network scheme is further proposed using multi-branch network structure and high-dimensional feature fusion. The experimental results reveal that using the combined gestures information of distance, Doppler and angle for multi-parameter learning, the proposed scheme resolves the problem of low information quantity of single-dimensional gesture recognition methods, and its accuracy outperforms the single-dimensional methods in terms of gesture recognition by 5%～8%.
Application of Project-based Teaching in Basic English in Higher Vocational Colleges

With the deepening of teaching reform in higher vocational colleges, project-based teaching has been widely used in basic English in higher vocational colleges.This paper first summarizes the concept of project-based English teaching, then analyzes the current situation of basic English teaching in higher vocational colleges, and finally discusses the application path of project-based teaching in basic English in higher vocational colleges.
SPI Transactional Database Using Secure Elastic Cloud Access with OOB

Security is the major issue in cloud computing that connects electronic media of diversified location. It is accessed by the privileged and authenticated user as the recent cloud computing trends proceeds towards server access which make the security concept more vital. Another factor considered in cloud access is the cloud virtualization that entirely depends on server along with the concept of distributed computing. This work focus on cloud authentication mechanism using Out of Band (OoB) mechanism that is used in cloud SPI model. In order to make the authentication mechanism stronger the proposed method takes the challenge of handling storing authentication mechanism for SPI cloud model instead of using two phase authentication model.
Underwater image processing method based on red channel prior and Retinex algorithm

Due to light being absorbed by water and scattered by suspended particles, color distortion, low image contrast, and detail loss are the common problems found in underwater images. We present an underwater image processing method, which based on an underwater imaging model, makes initial image restoration with the improved red channel prior combining transmission reduction, background light correction, and color compensation to acquire clearer underwater images, and then combines multiscale retinex (MSR) enhancement and hue saturation value to enhance image color contrast and saturation. Results indicate that, compared with the original images, the method proposed in this paper is capable of significantly enhancing image definition, saturation, contrast, and luminance. In comparison with dark channel prior, single-scale retinex (SSR), MSR, and multiscale retinex with color restoration (MSRCR), this method greatly improved the average gradient of images while keeping the information entropy of images unchanged. The average gradient calculated with the proposed method increased by 239%, 199%, 200%, and 220% for the three given images, respectively, compared with the other methods above.