Reviewerly/exHarmony huggingface dataset:
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks

Conditional computation and modular networks have been recently proposed for multitask learning and other problems as a way to decompose problem solving into multiple reusable computational blocks. We propose a new approach for learning modular networks based on the isometric version of ResNet with all residual blocks having the same configuration and the same number of parameters. This architectural choice allows adding, removing and changing the order of residual blocks. In our method, the modules can be invoked repeatedly and allow knowledge transfer to novel tasks by adjusting the order of computation. This allows soft weight sharing between tasks with only a small increase in the number of parameters. We show that our method leads to interpretable self-organization of modules in case of multi-task learning, transfer learning and domain adaptation while achieving competitive results on those tasks. From practical perspective, our approach allows to: (a) reuse existing modules for learning new task by adjusting the computation order, (b) use it for unsupervised multi-source domain adaptation to illustrate that adaptation to unseen data can be achieved by only manipulating the order of pretrained modules, (c) show how our approach can be used to increase accuracy of existing architectures for image classification tasks such as ImageNet, without any parameter increase, by reusing the same block multiple times.
Automated Online Course Recommendation System using Collaborative Filtering

In the digital world of online information huge amounts of Massive Open Online Courses (MOOCs) are available of different category and domain. Multiple online courses are available on different platform finding appropriate course from this massive available course is difficult for students. Recommender system plays vital role in finding appropriate courses to students. Managing massive amount of information and identifying individual users' choice and behavior has become tedious task nowadays, so the aim of recommender system is to suggest relevant course to student based on user behavior and similarity with another course. Several recommender system techniques are being implemented like content based, collaborative, Knowledgebased.This paper aims to build a hybrid approach using collaborative filtering with content base filtering.This system recommendation is based on course description and ratings.Experiments were conducted on real datasets to get the overall performance of proposed system.
E-Commerce Human Capital Performance Evaluation Model based on Fuzzy Pattern Recognition

Under the traditional model of human capital performance evaluation system, the promotion of e-commerce commercialization is poor. Therefore, a performance evaluation model of e-commerce human capital based on fuzzy pattern recognition is proposed. By determining the logical relationship of human capital performance evaluation, obtaining model evaluation indicators, dividing their evaluation grades, using fuzzy pattern recognition to calculate indicator weights, multiplying the standardized index values and weights to obtain the overall evaluation value of human capital performance, and completing the model design. Experimental results show that compared with the two traditional models, this model has increased the market share of enterprises and promoted the commercialization of e-commerce.
Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates

Off-policy deep reinforcement learning (RL) has been successful in a range of challenging domains. However, standard off-policy RL algorithms can suffer from low signal and even instability in Q-learning because target values are derived from current Q-estimates, which are often noisy. To mitigate the issue, we propose ensemble-based weighted Bellman backups, which re-weight target Q-values based on uncertainty estimates from a Q-ensemble. We empirically observe that the proposed method stabilizes and improves learning on both continuous and discrete control benchmarks. We also specifically investigate the signal-to-noise aspect by studying environments with noisy rewards, and find that weighted Bellman backups significantly outperform standard Bellman backups. Furthermore, since our weighted Bellman backups rely on maintaining an ensemble, we investigate how weighted Bellman backups interact with UCB Exploration. By enforcing the diversity between agents using Bootstrap, we show that these different ideas are largely orthogonal and can be fruitfully integrated, together further improving the performance of existing off-policy RL algorithms, such as Soft Actor-Critic and Rainbow DQN, for both continuous and discrete control tasks on both low-dimensional and high-dimensional environments.
Application of intellectual data analysis methods for digital educational platform

This article describes the possibility of using data mining techniques. In order to join new carpet participants, it is necessary to understand that the system of interaction with them is public educational services. To implement digital educational platforms, it is proposed to create an agent that collects information about sites, and also selects and tests the architecture of the neural network to build an individual trajectory that is trained using the competency-based model.
Cloud management and monitoring - a systematic mapping study

&lt;p&gt;&lt;span id="docs-internal-guid-b5d33f27-7fff-a26f-acaf-91883bdbd4c0"&gt;&lt;span&gt;A key component of ensuring that services are available on the cloud at the right time in the right manner is adequate cloud management. This makes it possible to provide services that meets user demands. The purpose of this research is to carry out a systematic study of management and monitoring on the cloud. Three facets were applied in conducting the categorization. These are the contribution, research, and topic facets. The purpose was to determine the level of work so far carried out in the field of cloud management. This enabled the creation of a pictorial representation of the research coverage. The result of the study showed that there are no opinion research on cloud management. Generally, articles on experience research, philosophical research and metric are the lowest at 6.62%, 4.41% and 1.90% respectively, while articles on models, solution research and evaluation research are the highest with 52.38%, 46.32% and 31.62% respectively. The outcome of this study will stimulate further research in the area cloud management and systematic studies.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
A hybrid approach for load balancing in cloud computing

Cloud computing is one of the most promising technical developments in recent days. It emerged as a dominant and transformational paradigm. Cloud computing can be considered a vital form of information technology that allows the delivery of services to users via the Internet upon request from the user and based on immediate payment. One of the main challenges and important fields for research in the cloud computing environment is load balancing. Load balancing has become an important point for stability and good system performance. Therefore, the main goal is to establish an effective load balancing algorithm for task scheduling. In this paper, we introduce the Hybrid Algorithm for Load Balancing (HALB), which aims to balance effective loading among virtual machines by balancing the percentage of data usage from RAM in each VM. As the results of the percentages were close, and all percentages did not reach the condition of overload. The proposed hybrid algorithm also reduces average waiting time and turnaround time. As a mechanism of work of our hybrid algorithm relies on two types of scheduling algorithms, one dependent on the other, namely the lottery algorithm and the Shortest Job First algorithm. A specific mechanism has been implemented to allocate tasks resulting from scheduling each algorithm separately, when calculating the total size of data tasks in each VM, the results showed that the volume of data allocated within the VM. Data sizes converge across all VMs.
An Analysis of the Concepts Closely Related to Financial Audit Using NLP Tools

In this paper the authors have analyzed scientific data regarding publications in the field of Financial Audit available on Web of Science online platform, in the Core Collection database. Starting from concatenations of abstracts, titles and keywords provided by both the creators and the journals, the authors of this paper extracted related concepts and corresponding importance scores using IBM Watson’s public Natural Language Processing tool and then they aggregated them using SQL queries. The results show the best associated topics with term they were looking for, namely “Financial Audit”. They also reveal the evolution of the frequency of publications considering the aforementioned topic. The authors of this paper planed and succeeded in finding the evolution of the publication frequency for the starting concept and also for the ones identified as most related using the same online platform and the database above, and further perform regression analyzes of the resulting multiple time series using Data Mining and Statistical tools.
Prototype of software and technological support quality management of knowledge acquisition by university students

Currently there are a large number of services and platform for organizing remote learning and manag-ing the quality of knowledge acquisition by university students. For example, Moodle, Ispring, Edmodo, Ёстади. All of them have a testing device, the ability to store and present theoretical material in different formats: in the form of audio, video, test, etc.Despite this, some areas are developing related to the improvement of tools and tools for knowledge control, analysis ofthe quality of the learning process, analysis of the quality of educational and methodological materials. There is a need for mobile components that allow students to evaluate and analyze the acquired knowledge in the learning process, actively identify missing knowledge, receive recommendations and advice at the right time, and thus effectively use classroom and extracurricular time.
Quantization-Guided Training for Compact TinyML Models.

We propose a Quantization Guided Training (QGT) method to guide DNN training towards optimized low-bit-precision targets and reach extreme compression levels below 8-bit precision. Unlike standard quantization-aware training (QAT) approaches, QGT uses customized regularization to encourage weight values towards a distribution that maximizes accuracy while reducing quantization errors. One of the main benefits of this approach is the ability to identify compression bottlenecks. We validate QGT using state-of-the-art model architectures on vision datasets. We also demonstrate the effectiveness of QGT with an 81KB tiny model for person detection down to 2-bit precision (representing 17.7x size reduction), while maintaining an accuracy drop of only 3% compared to a floating-point baseline.
Design of Risk Assessment System for College Students' Mainstream Cultural Identity in the Era of Micro Communication

In view of the influence of the differences between Chinese and western mainstream culture on the risk assessment of College Students' mainstream culture identity, in order to improve the feasibility of risk assessment of College Students' mainstream cultural identity, this paper puts forward the design of risk assessment system of College Students' mainstream cultural identity in the era of micro communication. By extracting the risk characteristics of College Students' mainstream cultural identity, this paper constructs the risk assessment model of College Students' mainstream cultural identity, and adopts the method of minimum mean square error compensation to carry out adaptive quantitative evaluation, so as to realize the quantitative assessment of College Students' mainstream cultural identity risk. The test results show that the risk assessment system of College Students' mainstream culture identity in the era of micro communication is feasible and accurate.
Efficient Estimators for Heavy-Tailed Machine Learning

A dramatic improvement in data collection technologies has aided in procuring massive amounts of unstructured and heterogeneous datasets. This has consequently led to a prevalence of heavy-tailed distributions across a broad range of tasks in machine learning. In this work, we perform thorough empirical studies to show that modern machine learning models such as generative adversarial networks and invertible flow models are plagued with such ill-behaved distributions during the phase of training them. To alleviate this problem, we develop a computationally-efficient estimator for mean estimation with provable guarantees which can handle such ill-behaved distributions. We provide specific consequences of our theory for supervised learning tasks such as linear regression and generalized linear models. Furthermore, we study the performance of our algorithm on synthetic tasks and real-world experiments and show that our methods convincingly outperform a variety of practical baselines.
THE STRATEGIES USED BY ENGLISH TEACHER TO TEACH VOCABULARY AT GENTA ENGLISH COURSE PARE-KEDIRI

This study was aimed to find out the used of strategies used by English teacher to teach vocabulary. for those who are learning English, it is essential to learn about vocabulary mastery in order they can use it to communicate and to express their ideas, opinions, thoughts by using words, sentences, paragraphs through written form without having face to face with other people directly. The design of this research in qualitative descriptive. This research was conducted in Genta English course. The students were 10 students. It consist of 9 female and 1 male. To know the result of the student activity, the researcher used observation checklist, field note and interview. Teacher strategies in teaching vocabulary is the ways or steps that help learners to learning vocabulary by acting one of the strategies as steps formula, so that strategy become a facility which increasing learners understanding in learning vocabulary. It means use the strategies is good in teaching vocabulary.
Autonomy and Participation: The Challenges of Education on Virtual Modality

The autonomy of students in virtual classes has been a challenge since virtual education took place because of the current pandemic situation, bringing new challenges to educational workers. This article is derived from the secondary school practice experience of a group of students from a public university in Bogotá; the researchers of this study present the preliminary results of the ongoing practicum experience subject. It will present the importance of autonomy in the virtual educational context and how it affects the development of classes. This is qualitative research, a case study; the data were collected through field notes and class observations from seven secondary school groups. Findings suggest that participation is a chief variable in virtual class development and the challenges that teachers face are gaining a permanent place in the current educational reality.
Effect of Banking Internal Controls on Reducing Non-Performing Loans in Commercial Banks in Kosovo Using Linear Regression Model and Mediation Analysis

The purpose of this research is to identify the effect of internal banking controls as mediation variables on reducing non-performing loans in banks in Kosovo. The methodology used in this research paper is the linear regression model and mediation analysis. Non-performing loans are determined as dependent variable and lending interest rate as independent variable whereas detective control, preventive control and corrective control are used as mediation variables.
DATA PROTECTION & RIGHT TO PRIVACY: LEGISLATIVE FRAMEWORK IN INDIA

The amount of data generated in the current digital era has increased significantly. Companies are deriving substantial value and making a lot of profit just by evaluating data. Recent headlines have shed light on how corporate giants and popular social networking sites are minting money by compromising data privacy of their customers and users, respectively. Surprisingly, these companies are willing to shed millions and billions to gain access to data. Indeed, data is the new currency in contemporary times.We, users, are voluntarily and involuntarily sharing our data on the internet during online shopping, e-banking, placing online food orders, or even while making our presence on social networking sites and instant messaging platforms. We do not realize how conveniently we are giving up our privacy rights, by clicking on, 'terms and conditions that may apply' or 'accept' option on these online sites. Regardless of so many data scandals across the globe, there is a steeping rise in the number of users using the World Wide Web. In 2018, data of millions of Facebook users were compromised and used by Cambridge Analytica for political advertisement. Recently, amidst the coronavirus outbreak, another instance of infringement of data privacy of users of Zoom, an application for virtual meeting, web-conferencing and webinars has been in the news. During this pandemic, schools, colleges, and offices are operating in online mode, and Zoom application became quite popular because of its ease in use, and its free plans. Security flaws and inefficient encryption policy in the app led to instances of zoom bombings and unauthorized entry in the zoom meetings. It's been reported that the personal details of more than half a million Zoom users have been compromised and traded on the dark web at a throwaway price.The right to privacy derives its significance from Article 21, 'protection of life and personal liberty' of the Indian Constitution. The premise of dignity, equality, and liberty lays down the basic structure of the Indian Constitution, in which privacy is the underlying foundation of human dignity. In this information age, with the increasing use of data, safeguarding data privacy has become increasingly difficult. The general public ignores the subtleties and unintentionally share personal data with third parties subjecting themselves to privacy infringement. This article is an attempt to shed light on the existing legal regime concerning data protection and the right to privacy in India.
Research Activities As a Mean of Intellectual Development of Primary Schoolchildren

The article reveals the results of research on the design of the educational process, which provides productive search and research activities of younger students. The problems arising for students in the course of solving educational problems in the lessons of mathematics and the surrounding world are analyzed. The value of search, research for the intellectual development of primary school children is revealed. Special attention is paid to the formation of logical thinking, speech-reasoning, methods of forming the student’s ability to control and self-control.
Image Inpainting using Weighted Mask Convolution

Despite of many efforts for handling various holes, it has been not sufficiently resolved and the instability and normalization issues exists due to the presence of the invalid pixels. We proposed the weighted convolution that balances the valid and invalid pixels throughout the networks to help the network efficiently cope with various hole shapes. In our convolution layer, the mask is utilized to store the validity of the features by using the real-valued mask. A weighted scheme for the normalization layers is also proposed to adaptively operate along with the weighted convolution. By balancing upon the invalid pixels caused by the holes and zero-paddings, the network can be trained more robust to the hole shapes. The experimental results verified that our method achieved improvements over the state-of-the-art inpainting methods.
Probabilistic Multimodal Representation Learning

Learning multimodal representations is a requirement for many tasks such as image--caption retrieval. Previous work on this problem has only focused on finding good vector representations without any explicit measure of uncertainty. In this work, we argue and demonstrate that learning multimodal representations as probability distributions can lead to better representations, as well as providing other benefits such as adding a measure of uncertainty to the learned representations. We show that this measure of uncertainty can capture how confident our model is about the representations in the multimodal domain, i.e, how clear it is for the model to retrieve/predict the matching pair. We experiment with similarity metrics that have not been traditionally used for the multimodal retrieval task, and show that the choice of the similarity metric affects the quality of the learned representations.
ROLE OF EDUCATION IN DEVELOPING PEACE AND HARMONY

Education has been always supposed to promote peace and harmony all over the world. The universal human values are always focused on by education in schools and colleges. Peace and Harmony are two distinct values of these universal human values that need to be developed among the human kind through education. Madam Today both these concepts are given less importance in education and its curriculum. Today’s world is full of conflicts, insecurities, violence, unharmonious conduct based on political, racial, religious and ethnic factors which make a negative impact on the humanity. In the contemporary time, the major aim of education should be to help people to develop themselves as global citizens who can make continuous efforts for building one peaceful and harmonious world. There is a need of learning to live together with peace and harmony in today’s world and education is the only tool that can approach to this aim. This article focuses on how education can help developing peace and harmony in this age of globalization.
Semi-Supervised Learning via Clustering Representation Space

We proposed a novel loss function that combines supervised learning with clustering in deep neural networks. Taking advantage of the data distribution and the existence of some labeled data, we construct a meaningful latent space. Our loss function consists of three parts, the quality of the clustering result, the margin between clusters, and the classification error of labeled instances. Our proposed model is trained to minimize our loss function by backpropagation, avoiding the need for pre-training or additional networks. This guides our network to classify labeled samples correctly while able to find good clusters simultaneously. We applied our proposed method on MNIST, USPS, ETH-80, and COIL-100; the comparison results confirm our model's outstanding performance over semi-supervised learning.