Reviewerly/exHarmony huggingface dataset:
Variational Auto-Regressive Gaussian Processes for Continual Learning

Through sequential construction of posteriors on observing data online, Bayes' theorem provides a natural framework for continual learning. We develop Variational Auto-Regressive Gaussian Processes (VAR-GPs), a principled posterior updating mechanism to solve sequential tasks in continual learning. By relying on sparse inducing point approximations for scalable posteriors, we propose a novel auto-regressive variational distribution which reveals two fruitful connections to existing results in Bayesian inference, expectation propagation and orthogonal inducing points. Mean predictive entropy estimates show VAR-GPs prevent catastrophic forgetting, which is empirically supported by strong performance on modern continual learning benchmarks against competitive baselines. A thorough ablation study demonstrates the efficacy of our modeling choices.
Rule-based Named Entity Recognition (NER) to Determine Time Expression for Balinese Text Document

Named Entity Recognition (NER) is a process to identify words or phrases as a named entity, such as a person, location, time expression, or organization. In this research, we are interested in developing a NER which able to identify the time expression entity in Balinese text documents. The time expression entity becomes an important component in the text because it is usually followed by important facts and information. NER was built using a rules-based approach. The rules are built based on direct observation of documents and pay attention to the morphological and contextual structures. Based on the experiments conducted, the average results of the precision, recall, and f-measure values were 0.85, 0.87, and 0.85.
Database Encryption for Balance Between Performance and Security

In an increasingly digital world, information security is a very recurring theme and a growing concern for companies. This involves the protection of data and confidential or non-confidential information of a company, which transit between all its sectors and between the organization and its stakeholders.
Question Answering techniques for detecting needs of people in crisis

The use of social networks has become one of the basics of daily life to follow the news, and also in the case of inquiries and requesting assistance. In case of a crisis, information about the event begins to spread on social networks with the aim of raising awareness and knowing all the important instructions and requesting assistance. This information can be used to respond to people with the required needs depending on the type of assistance or inquiry requested. In this paper, an approach was proposed that uses Question Answering techniques based on natural language techniques and neural networks to extract the needs of affected people during a crisis and provide the proper response. The approach was tested on twitter data from different types of crises. The results showed that the suggested approach can answer with suitable guidelines with a precision of 0.81, a recall of 0.76 and an f-score of 0.78.
Human Activity Recognition using Resnet-34 Model

Activity recognition has been an emerging field of research since the past few decades. Humans have the ability to recognize activities from a number of observations in their surroundings. These observations are used in several areas like video surveillance, health sectors, gesture detection, energy conservation, fall detection systems and many more. Sensor based approaches like accelerometer, gyroscope, etc., have been discussed with its advantages and disadvantages. There are different ways of using sensors in a smartly controlled environment. A step-by-step procedure is followed in this paper to build a human activity recognizer. A general architecture of the Resnet model is explained first along with a description of its workflow. Convolutional neural network which is capable of classifying different activities is trained using the kinetic dataset which includes more than 400 classes of activities. The videos last around tenth of a second. The Resnet-34 model is used for image classification of convolutional neural networks and it provides shortcut connections which resolves the problem of vanishing gradient. The model is trained and tested successfully giving a satisfactory result by recognizing over 400 human actions. Finally, some open problems are presented which should be addressed in future research.
The Influence of BYOD on Results of Students' Learning

This work analyzes the influence and efficiency of BYOD for learning students and academic perfection. The relevance of mobile technologies for learning has been substantiated. The influence of BYOD on academic performance of undergraduate students in terms of their test scores and final course marks, as well as on quality of work on selected course projects and on level of satisfaction with the BYOD course, has been experimentally studied. It has been proved that BYOD exerts positive influence on learning and academic achievements of students measured by their academic test scores, by their final marks and quality of their classroom work, as well as that the implementation of BYOD will stimulate and motivate students, thus affecting their level of involvement and creation of independent learning. The experimental results should be used as a guide for persons making decisions as well as for practical specialists in the field of education, especially in the area of BYOD and mobile learning, which will save significant expenses for stationary learning (for instance, concerning desktop computers, projectors, etc.). Scientific novelty of the studies is that the influence of BYOD on academic performance of students in terms of their test scores and final course marks, as well as on quality of students’ work on selected course projects and on level of satisfaction with the BYOD course, has been comprehensively analyzed.
The “Black Swan” of COVID-19 and the Security Issues in Digital Learning

The social security problems come to the fore as the relationships between individuals, society and modern technologies become more and more complicated. In the context of the pandemic social security issues turned out to be decisive for the education system, the transformation of which caused significant difficulties. The article covers the topic of the security of digital education and focuses on theoretical concepts as well as on the analysis of practical cases found in the media. The author analyzes the experience of educational organizations and education management institutions in the area of protection against risks in the information environment, and identifies the most successful approaches. As a result, the conclusion is made that it is necessary to create a system of safe information and communication environment that should be designed to ensure the rights and quality of life of individuals in the context of the digital transformation of educational and upbringing processes.
Deep Understanding of Technical Documents: Part II. Automatic Extraction of Pseudocode

Humans have the privilege to automatically have a deep understanding of technical documents, since they have the ability to deal with complex concepts coming from many different modalities, like diagrams, text, tables, formulas, graphics, pictures, etc. For many years researchers are working to transfer such potential to AI based machines. This paper takes the advantage of the synergistic and interactive enrichment of two TD modalities, the block diagrams and the associated natural language text, obtained to automatically generate pseudocode that describes the functionality of the system under study. The methodology for generating the code is mainly based on the mapping of the TD modalities into Stochastic Petri-nets (SPN) that enriches the system diagrams, from which the pseudocode is generated. The overall methodology will contribute to an automatic deep understanding of technical documents (TD) without the main involvement of humans. Two illustrative examples are also provided for describing the methodology.
Retracted: Quality of experience (QOE) content aware hybrid lean predictive models for medical video transmission over Internet of things (IOT) networks

Lavanya K, Vimala Devi K, Subramaniam M. Quality of experience (QOE) content aware hybrid lean predictive models for medical video transmission over Internet of things (IOT) networks, International Journal of Communication Systems 2021, (https://doi.org/10.1002/dac.4833). The above article published online on 3 June 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor and John Wiley and Sons Ltd. Following publication, it has come to our attention that the people named as the Guest Editors of the Special Issue were being impersonated by a fraudulent entity and the articles were not reviewed in line with the journal's peer review standards. The Editor has determined that the articles do not meet the required scholarly standards of the journal, and therefore has taken the decision to retract the article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.
An Analytical Study of ICT Based Library and Information Services in Maharashtra University of Health Science Nasik

Information is dynamic and unending resource that affects all disciplines and allwalks of life. ICT has revolutionaries the field of library and information services.ICT has developed to such a stage that it has given access to information atfingertips. UNESCO defines Information Technology as “The scientific,technological and engineering disciplines and the managerial techniques used ininformation handling and processing; their applications; computers and theirinteraction with men and machines and associated social, economic and culturalmatters.
Advantages of using paleontological components in extracurricular activities in the english language for middle-school students

The article explores the use of the elements of paleontology in extracurricular activities in the English language, since the materials enriched with the linguocultural and historical components contribute to the introduction of students to the linguistic cultures of other ethnic groups within a single state and increase the motivation for learning languages. The correct use of such materials in the project activities of schoolchildren provides opportunities for developing social activism and improving cross-cultural and linguistic competencies. The elements of paleontology in extracurricular activities in the English language could also contribute positively to the expansion of knowledge base in such school subjects as English and Biology and could potentially provide a deeper immersion in the culture and history of their country and their small homeland.
Model for Assigning a Pupil to a Mentor in the Staff Training System of the Organization

This article will focus on the mentoring process, which is interconnected with such HR functions as adaptation, motivation and training. And recently, many employers from a variety of alternatives have made a choice in its favor. Aim. The work examines the main functions of mentoring, its practical benefits and areas of application, and the main attention will be focused on the issue associated with the formation of pairs of mentors and students in order to achieve the greatest effect from their interaction. Materials and methods. In the process of achieving the research goal at the initial stage, it was planned to solve the problem of assigning a student to a mentor by forming the most “effective” pairs of mentors and students. These tasks were solved using the Hungarian method. But all the solutions obtained testified to the optimality of any solution to this problem. From this, it was concluded that there was no significance in combining mentors and students, based on a comprehensive assessment of their competencies, skills, etc. As a consequence of the result obtained and analysis of various literary sources, it was concluded that the most effective criterion for composing pairs “mentor – teaching” is compatibility (psychological and socio-psychological), the presence of which will favorably affect the effectiveness of the interaction between the mentor and the student. Consequently, the human resource management department needs, in addition to assessing the competencies, skills and other parameters of the effectiveness of mentors and students, to assess their psychotypes and/or apply various methods to determine the compatibility of the participants in the mentoring process. Results. The main result of the work is a mechanism for securing a student for a mentor and a refutation of the fact that the “effectiveness” of mentors and students, i.e. a point assessment of their competencies, skills and qualities can be a determining factor in the formation of pairs of mentors and students. Conclusion. So, when forming pairs of mentors and students, i.e. distribution of students by mentors, it is proposed to focus on their psychological and socio-psychological compatibility.
Probing Pre-Trained Language Models for Disease Knowledge

Pre-trained language models such as Clini-calBERT have achieved impressive results on tasks such as medical Natural Language Inference.At first glance, this may suggest that these models are able to perform medical reasoning tasks, such as mapping symptoms to diseases.However, we find that standard benchmarks such as MedNLI contain relatively few examples that require such forms of reasoning.To better understand the medical reasoning capabilities of existing language models, in this paper we introduce DisKnE, a new benchmark for Disease Knowledge Evaluation.To construct this benchmark, we annotated each positive MedNLI example with the types of medical reasoning that are needed.We then created negative examples by corrupting these positive examples in an adversarial way.Furthermore, we define training-test splits per disease, ensuring that no knowledge about test diseases can be learned from the training data, and we canonicalize the formulation of the hypotheses to avoid the presence of artefacts.This leads to a number of binary classification problems, one for each type of reasoning and each disease.When analysing pre-trained models for the clinical/biomedical domain on the proposed benchmark, we find that their performance drops considerably.
Research on the Training Model of Broadcasting and Hosting Talents under the Background of AI Anchors

With the development of artificial intelligence in the media field, the traditional art of broadcasting and hosting is facing huge challenges in teaching and practice.In particular, the advent of artificial intelligence synthetic anchors has caused a huge impact on the host and announcer industry.Many practitioners are full of doubts about whether they will be replaced by artificial intelligence anchors.AI anchors can not only broadcast news without error, spread information across languages but also work uninterrupted 24 hours a day, 365 days a day, which greatly improves work efficiency.In 2019, foreign media such as The Times, Newsweek, and Los Angeles Times reported that news anchors in China may face some new competition because AI anchors can imitate human facial expressions and behaviors when broadcasting news.This means that from the screen, the simulation level of the artificial intelligence anchor is almost the same as that of a real person.This undoubtedly puts those live-action anchors facing the risk of being eliminated, and it puts forward new requirements for the teaching and training of the art of broadcasting and hosting.Therefore, in the context of AI anchors, it is urgent to explore how to train competitive announcers and hosts.This paper studies the development of the talent training model for broadcasting and hosting under the background of artificial intelligence.The results will help to cultivate industry-competitive broadcasting and hosting talents and greatly improve the professional ability of students majoring in broadcasting and hosting arts.
The Role of Active Teaching Methods in the Formation of Students’ Cognition and Creativity

Higher education as a subject of study of this article in the summer provides for the process of teaching pedagogical disciplines. When conducting seminars in a higher educational institution, the cognitive activity and creativity of students of individual methods of developing physical activity for the purpose of research. Recognition of students and creative activity of children. conversation “brainstorming”, “game activities” “round table” competition of practical works and methods of their discussion is used. It recognizes students on the basis of methods, determining the conditions for the development of culture and creativity. Specific recommendations are given. Student creativity the fact that active learning has a great influence on improvement, also identified. Results in higher educational institution to improve the process of teaching pedagogical disciplines contributes to. This article is recommended for university professors for use in seminars.
Exponentially Many Local Minima in Quantum Neural Networks

Quantum Neural Networks (QNNs), or the so-called variational quantum circuits, are important quantum applications both because of their similar promises as classical neural networks and because of the feasibility of their implementation on near-term intermediate-size noisy quantum machines (NISQ). However, the training task of QNNs is challenging and much less understood. We conduct a quantitative investigation on the landscape of loss functions of QNNs and identify a class of simple yet extremely hard QNN instances for training. Specifically, we show for typical under-parameterized QNNs, there exists a dataset that induces a loss function with the number of spurious local minima depending exponentially on the number of parameters. Moreover, we show the optimality of our construction by providing an almost matching upper bound on such dependence. While local minima in classical neural networks are due to non-linear activations, in quantum neural networks local minima appear as a result of the quantum interference phenomenon. Finally, we empirically confirm that our constructions can indeed be hard instances in practice with typical gradient-based optimizers, which demonstrates the practical value of our findings.
An Algorithm for Stochastic and Adversarial Bandits with Switching Costs

We propose an algorithm for stochastic and adversarial multiarmed bandits with switching costs, where the algorithm pays a price $\lambda$ every time it switches the arm being played. Our algorithm is based on adaptation of the Tsallis-INF algorithm of Zimmert and Seldin (2021) and requires no prior knowledge of the regime or time horizon. In the oblivious adversarial setting it achieves the minimax optimal regret bound of $O\big((\lambda K)^{1/3}T^{2/3} + \sqrt{KT}\big)$, where $T$ is the time horizon and $K$ is the number of arms. In the stochastically constrained adversarial regime, which includes the stochastic regime as a special case, it achieves a regret bound of $O\left(\big((\lambda K)^{2/3} T^{1/3} + \ln T\big)\sum_{i \neq i^*} \Delta_i^{-1}\right)$, where $\Delta_i$ are the suboptimality gaps and $i^*$ is a unique optimal arm. In the special case of $\lambda = 0$ (no switching costs), both bounds are minimax optimal within constants. We also explore variants of the problem, where switching cost is allowed to change over time. We provide experimental evaluation showing competitiveness of our algorithm with the relevant baselines in the stochastic, stochastically constrained adversarial, and adversarial regimes with fixed switching cost.
Person Retrieval in Physical World

Person re-identification (re-ID) gains plenty of achievements as a retrieval problem in constrained camera networks. However, most of the researches are concentrated on visual appearance, they still suffer from the complicated environments in unconstrained urban/campus surveillance scenario due to unreliable visual representations with extremely challenging problems as lack of training samples, amounts of irrelevant crowds, etc. Besides, most of the existing person re-ID datasets neglect the physical truth of realistic investigation application: 1) investigators search only few suspects among amounts of crowds. Moreover, he may not go through by every camera in the surveillance area and may appear in the same camera several times; and 2) the corresponding characteristic in multi-space of the same ID can be verified with each other. Therefore, we propose a person retrieval in physical world (PRPW) dataset with large-scale unconstrained surveillance scenario. It contains over 1.4 million bounding boxes, including 20 labeled IDs and numerous irrelevant crowds captured by 86 cameras. Furthermore, over 30,000 records of 20 mobile trajectories are collected in this dataset, and the 20 mobile trajectories are partially overlapped while passing by 86 cameras. Finally, based on two common senses and a verification experiment, we provide a proposal to tackle with PRPW task on the basis of trajectory association which utilizes global optimization to compensate for the errors caused by visual expression on local observation points. The comparison experiments with two typical unsupervised person re-ID methods are implemented on the constructed dataset.
The Use of ELLLO in Improving Teaching Listening to Students

Listening is one of English's essential skills. Listening is a very valuable skill because in communicating with others, this skill can gain awareness, understanding, information and performance for humans. Listening involves paying attention to motion or tone. Listening requires recognizing and translating the sounds of speech into words and sentences. It is a general term used to refer to complex attraction, preception and behavior processes. The listening needs to be all-ears focused to the listening portion while practicing listening skills. In this method, it is important to use application as a teaching medium. In this method, it can capture the attention of students. In order to apply this analysis, pre-experimental design was used as samples in the quantitative process. In order to assess the improvement that has happened to students, the Pre-Test would offer students, the care was given to the students, and then Post-Test was provided to classes. Pre-experimental design is being used in these studies. In the Pre-Test and Post-Test, students scored an average Pre-Test score of 59.03 and an average Post-Test score of 81.96. The students were very passionate about this learning process.
Guided Exploration with Proximal Policy Optimization using a Single Demonstration

Solving sparse reward tasks through exploration is one of the major challenges in deep reinforcement learning, especially in three-dimensional, partially-observable environments. Critically, the algorithm proposed in this article uses a single human demonstration to solve hard-exploration problems. We train an agent on a combination of demonstrations and own experience to solve problems with variable initial conditions. We adapt this idea and integrate it with the proximal policy optimization (PPO). The agent is able to increase its performance and to tackle harder problems by replaying its own past trajectories prioritizing them based on the obtained reward and the maximum value of the trajectory. We compare variations of this algorithm to different imitation learning algorithms on a set of hard-exploration tasks in the Animal-AI Olympics environment. To the best of our knowledge, learning a task in a three-dimensional environment with comparable difficulty has never been considered before using only one human demonstration.