Reviewerly/exHarmony huggingface dataset:
Linear Recurrences over a Finite Field with Exactly Two Periods

In this paper, we study the periodicity structure of finite field linear recurring sequences whose period is not necessarily maximal and determine necessary and sufficient conditions for the characteristic polynomial~\(f\) to have exactly two periods in the sense that the period of any sequence generated by~\(f\) is either one or a unique integer greater than one.
Random access codes via quantum contextual redundancy

We propose a protocol to encode classical bits in the measurement statistics of many-body Pauli observables, leveraging quantum correlations for a random access code. Measurement contexts built with these observables yield outcomes with intrinsic redundancy, something we exploit by encoding the data into a set of convenient context eigenstates. This allows to randomly access the encoded data with few resources. The eigenstates used are highly entangled and can be generated by a discretely-parametrized quantum circuit of low depth. Applications of this protocol include algorithms requiring large-data storage with only partial retrieval, as is the case of decision trees. Using $n$-qubit states, this Quantum Random Access Code has greater success probability than its classical counterpart for $n\ge 14$ and than previous Quantum Random Access Codes for $n \ge 16$. Furthermore, for $n\ge 18$, it can be amplified into a nearly-lossless compression protocol with success probability $0.999$ and compression ratio $O(n^2/2^n)$. The data it can store is equal to Google-Drive server capacity for $n= 44$, and to a brute-force solution for chess (what to do on any board configuration) for $n= 100$.
Vyākarana: A Colorless Green Benchmark for Syntactic Evaluation in Indic Languages

While there has been significant progress towards developing NLU resources for Indic languages, syntactic evaluation has been relatively less explored. Unlike English, Indic languages have rich morphosyntax, grammatical genders, free linear word-order, and highly inflectional morphology. In this paper, we introduce Vy\=akarana: a benchmark of Colorless Green sentences in Indic languages for syntactic evaluation of multilingual language models. The benchmark comprises four syntax-related tasks: PoS Tagging, Syntax Tree-depth Prediction, Grammatical Case Marking, and Subject-Verb Agreement. We use the datasets from the evaluation tasks to probe five multilingual language models of varying architectures for syntax in Indic languages. Due to its prevalence, we also include a code-switching setting in our experiments. Our results show that the token-level and sentence-level representations from the Indic language models (IndicBERT and MuRIL) do not capture the syntax in Indic languages as efficiently as the other highly multilingual language models. Further, our layer-wise probing experiments reveal that while mBERT, DistilmBERT, and XLM-R localize the syntax in middle layers, the Indic language models do not show such syntactic localization.
Fast Adaptation with Linearized Neural Networks

The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings. We study the inductive biases of linearizations of neural networks, which we show to be surprisingly good summaries of the full network functions. Inspired by this finding, we propose a technique for embedding these inductive biases into Gaussian processes through a kernel designed from the Jacobian of the network. In this setting, domain adaptation takes the form of interpretable posterior inference, with accompanying uncertainty estimation. This inference is analytic and free of local optima issues found in standard techniques such as fine-tuning neural network weights to a new task. We develop significant computational speed-ups based on matrix multiplies, including a novel implementation for scalable Fisher vector products. Our experiments on both image classification and regression demonstrate the promise and convenience of this framework for transfer learning, compared to neural network fine-tuning. Code is available at https://github.com/amzn/xfer/tree/master/finite_ntk.
Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing

Recent research has shown that graph neural networks (GNNs) can learn policies for locomotion control that are as effective as a typical multi-layer perceptron (MLP), with superior transfer and multi-task performance (Wang et al., 2018; Huang et al., 2020). Results have so far been limited to training on small agents, with the performance of GNNs deteriorating rapidly as the number of sensors and actuators grows. A key motivation for the use of GNNs in the supervised learning setting is their applicability to large graphs, but this benefit has not yet been realised for locomotion control. We identify the weakness with a common GNN architecture that causes this poor scaling: overfitting in the MLPs within the network that encode, decode, and propagate messages. To combat this, we introduce Snowflake, a GNN training method for high-dimensional continuous control that freezes parameters in parts of the network that suffer from overfitting. Snowflake significantly boosts the performance of GNNs for locomotion control on large agents, now matching the performance of MLPs, and with superior transfer properties.
Maximal function pooling with applications

Inspired by the Hardy-Littlewood maximal function, we propose a novel pooling strategy which is called maxfun pooling. It is presented both as a viable alternative to some of the most popular pooling functions, such as max pooling and average pooling, and as a way of interpolating between these two algorithms. We demonstrate the features of maxfun pooling with two applications: first in the context of convolutional sparse coding, and then for image classification.
Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset

Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several interesting mobile applications have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community's response to the applications by analyzing information from different sources, such as news and users' reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users' reviews and extracts the evoked sentiments. In this work, we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users' reviews. In total, we employ eight different methods achieving up to an average F1-Scores 94.8% indicating the feasibility of automatic sentiment analysis of users' reviews on the COVID-19 contact tracing applications. We also highlight the key advantages, drawbacks, and users' concerns over the applications. Moreover, we also collect and annotate a large-scale dataset composed of 34,534 reviews manually annotated from the contract tracing applications of 46 distinct countries. The presented analysis and the dataset are expected to provide a baseline/benchmark for future research in the domain.
NavTuner: Learning a Scene-Sensitive Family of Navigation Policies

The advent of deep learning has inspired research into end-to-end learning for a variety of problem domains in robotics. For navigation, the resulting methods may not have the generalization properties desired let alone match the performance of traditional methods. Instead of learning a navigation policy, we explore learning an adaptive policy in the parameter space of an existing navigation module. Having adaptive parameters provides the navigation module with a family of policies that can be dynamically reconfigured based on the local scene structure, and addresses the common assertion in machine learning that engineered solutions are inflexible. Of the methods tested, reinforcement learning (RL) is shown to provide a significant performance boost to a modern navigation method through reduced sensitivity of its success rate to environmental clutter. The outcomes indicate that RL as a meta-policy learner, or dynamic parameter tuner, effectively robustifies algorithms sensitive to external, measurable nuisance factors.
Sentiment Analysis on Covid-19 Using Deep Learning

Abstract: The social media has immense popularity among all the services today. Twitter is one of the widely used platform by people to express their opinions and display sentiments on different occasions. Sentiment Analysis is a classification task in order to identify public reviews about different issues like reviews about movies, restaurants and other current issues by extracting the public reviews from social media. As we all know the world was hit by a global pandemic of COVID-19 and it is still a global issue. Due to rapid increase in the infection people were expressing their emotions, thoughts, and were having mixed feelings regarding the situation. The main objective of this research paper is to analyze the emotions expressed by people using twitter data. The corona specific tweets are collected from twitter and pre-processing is done to clean the data and later word embedding pre-trained model is used and finally CNN, LSTM and CNN-BiLSTM hybrid deep learning approaches are applied. The mode l is evaluated using accuracy, precision and recall techniques.
Application Development for AR and VR devices

Abstract: Artificial Reality and Virtual Reality have been the latest trends in the market. They are finding applications in almost all the sectors of the society. Augmented Reality uses CV along with tracking of the depth show the content to the user. AR is designed for unrestricted movement while projecting objects that you are looking at and changes the user’s physical environment with relevant digital data in real-time. A user can experience Augmented Reality with his mobile or special hardware. Virtual Reality allow users to experience scenarios through simulated counterparts and obtain practical knowledge that would else be difficult to obtain in a real environment. Since the field of VR and AR is ever expanding, it is important to provide a very smooth and innovative experience to the end user. Therefore, application development for VR and AR devices is becoming the latest trend and there is a necessity to keep upgrading the content and experience being provided to the user.
Review on Machine Translation from English to Kannada

Abstract: Interlingual is a machine translation tool that uses anartificial language to convey the meaning of reallanguages. The process of converting text from one language to another is known as machine translation.This study provides a better model of machine translation system for English-to-Kannada sentence translation that employs statistically based techniques. Here, we use Moses approach. Moses is a statistical machine translation system. systems are trained on huge amounts of parallel data as well as even bigger amounts of monolingual data in statistical machine translation. Parallel data is a set of sentences in two languages that are sentence- aligned, meaning that each sentence in one languageis matched with its translated counterpart in the other. Moses training technique takes the parallel data and infers translation correspondences betweenthe two languages of interest by looking for co- occurrences of words and segments. The two main components in Moses are the training pipeline and the decoder. The training pipeline consists of a set oftools that take raw data and convert it into a machinetranslation model. The Moses decoder determines thehighest scoring sentence in the target language that matches a given source sentence.
Cyber Bullying Detection Using Text, Image and Voice

Abstract: Through social media, users may have access to a lot of information as well as a pleasant communication experience. Social media, on the other hand, can have negative implications, such as online bullying, which can have a negative impact on people's life, particularly children and teenagers. Cyberbullying is defined as an individual or a group of individuals engaging in aggressive, deliberate acts against a victim using digital communication channels such as sending texts and writing comments. Unlike traditional bullying, which occurs in a face-to-face interaction at school, online bullying on social media sites can occur anywhere at any time.
“Let us Read Amidst the Pandemic Campaign” Improving Reading Comprehension Among Grade Five Pupils

This Practical Action Research aimed at determining the effectiveness of the reading campaign among the Grade V pupils. It employed the narrative. Action research methods was applied where multiple choice questions were deployed for pre-evaluation. There were 10 pupils who participated in this study. The result showed that they performed very poor in all dimensions of reading comprehension, However, the intervention was done after 8 weeks through interviews, there was a significant increase in their performance. Thus, the reading program changed the lives of the pupils. It was an effective approach based on the different activities provided by the teacher. Aside from that, her presence made a difference because they were able to ask questions that are difficult to be done just by themselves. Therefore, teachers played a crucial role during the time of the uncertainties in helping their learners enhance their reading skills in language.
Social risks of educational space digitalization in regional universities: identification and institutionalization problems

Insufficient conceptualization regarding the analysis of social risks of the educational space digitalization in universities and the ambiguity of their initial prerequisites necessitate the study of this problem. The article analyzes the prerequisites for social risks of the educational space digitalization in regional universities, based on the provisions on their normativity for modern social reality, transformation into a permanent factor of the functioning of complex sociobiotechnical systems, ambivalence of risk, the need for risk genesis process regulation. The well-established statement “risk = uncertainty” is considered, and the author′s own vision of the social risks of the educational space digitalization in regional universities is formulated. It is emphasized that the process of digitalization leads to mass reproduction of uncertainties and, as a consequence, social risks at all levels of social self-organization, including universities, which are open to the external environment. The author describes the specific uncertainties that digitalization creates at the level of the education system and individual universities. The author’s definition of the social risks of the educational space digitalization in a regional university is given as the awareness of its actors of the probability of events that determine the deviation from their goals due to the introduction of digital technologies and encouraging subjects to take actions that do not guarantee the achievement of the planned result. The necessity of risk genesis management as a regulatory influence combining administrative regulation and self-regulation of subjects and counterparties of the educational space of universities in order to identify and use positive opportunities of social risks of digitalization and exclude negative consequences is substantiated. The idea of “risk making” is proposed as an activity to identify and use the constructive potential of social risks of the educational space digitalization and minimize their negative consequences.
The Use of Social Media by Higher Education Lectures in a Language Teaching Course during Covid-19 Pandemic

The objective of the current study was to explore the use of social media by higher education lectures in a language learning course. This study involved 85 lecturers from across Malaysia who were purposefully selected. This mixed method study used a survey with closed-ended and open-ended questions to collect data. The survey data was analyzed through descriptive statistics using SPSS 23, while the qualitative data was analyzed through content analysis and emergent coding. The findings show that they use social media for formal academic communication, using different types of social media platforms (Google Meet, Facebook, WhatsApp, YouTube, Telegram, etc.), where they are used for different purposes including social media usage (teaching and learning, student support, building community &amp; connection). The findings of the study have implications for language learning in particular and education in general in view of the pandemic global crisis.
Long-Tail Distribution Financial Recommender Systems Embedded into Social Networks

Modern financial recommender systems of online social networks have switched to using extended databases that take into account not only mass and typical, but also peripheral and personalized requests of network users. Such systems use ‘long-tail’ technology.
Cyber Range as a Competency Based Education Instrument in Cyber Security

The importance of Cyber Security is becoming more and more widely known among companies and government institutions.This came at a cost that many business and public administration entities have paid in recent years.This indirect cost is due to the multitude of cyberattacks to which they have been subjected, attacks directed to the IT infrastructures of these entities.Recent studies show that most cyberattacks were based on vulnerabilities caused by the human factor: either directly, through the mishandling of technology (hardware and software), or indirectly, through the misconfiguration of cyber services exposed on the Internet.Cyber Range systems can offer multiple functionalities depending on the field of activity for which they are implemented.One of the main advantages of Cyber Ranges is that these systems can make a major contribution to the digitization of educational platforms, regardless of the field of study.This study aims to contribute to the process of educational environment optimization by addressing the influence of implementing Cyber Range systems as part of the digitalization of the educational process, particularly in the sphere of cybersecurity training.The conclusions presented in this article might have a major positive influence on educational effectiveness if they are implemented.The immediate impact in the field of cybersecurity is a financial one, as trained users reduce the risk of costly cyberattacks.
Web-Based ManagementSystem for Student ScorePassing Grades

In the current digital era, all schools from elementary to tertiary levels certainly require computer equipment and supporting applications to support teaching and learning activities. One indicator of learning success is seen from the students or their learning outcomes. Learning outcomes are the success achieved by students, namely student learning achievements in schools thatmanifest in numbers or grades. Many schools in managing grades currently use a media system for storing student data, teacherdata, class data, lesson schedule, and score data that are still done offline without a database, as well as what happened at theSanta Ursula Junior High School of Jakarta. Filling in the assignments, daily tests, semester test scores, and final semesterexaminations is always done in writing on paper, which results in a slow score calculation process, so it takes a long time. Based on this condition, the researchers designed and created a web-based information system that precisely handled the management of student scores and printed them out. The design of the grading management information system for the academic administration of grading management was built using the PHP programming language with a database with MySQL and XAMPP Server. This system aimed tofacilitate the academic administration of schools, teachers, students, and homeroom teachers.
Modern trends in building low-code platforms

The paper deals with the problems of developing BPM systems using Low-Code platforms. To find solutions to the identified problems, a study of trends in the processes of building Low-Code platforms was carried out. A two-level method for building a Low-Code platform is proposed, in which there is a subject and proto-subject level. It is assumed that at the subject level, the implementation of the functionality of the Low-Code platform is performed based on the method for solving a specific business problem, defined by users at the proto-subject level. The main feature of the proposed method is that the method for solving a specific business problem is converted into a methodology for constructing a subject-level functionality. An example of the implementation of the proposed method for building a platform is considered.
FineNet: Frame Interpolation and Enhancement for Face Video Deblurring

The objective of this work is to deblur face videos. We propose a method that tackles this problem from two directions: (1) enhancing the blurry frames, and (2) treating the blurry frames as missing values and estimate them by interpolation. These approaches are complementary to each other, and their combination outperforms individual ones. We also introduce a novel module that leverages the structure of faces for finding positional offsets between video frames. This module can be integrated into the processing pipelines of both approaches, improving the quality of the final outcome. Experiments on three real and synthetically generated blurry video datasets show that our method outperforms the previous state-of-the-art methods by a large margin in terms of both quantitative and qualitative results.
Anomalies in $b\to s \ell\ell$ transitions and Global Fits

We review the status of the anomalies in $b\to s \ell\ell$ transitions, and comment on the impact of the most recent measurements in 2019 and 2020 on the global fits. We also discuss a few developments in the theory calculation of local and non-local form factors.
Automated data-driven approach for gap filling in the time series using evolutionary learning

In the paper, we propose an adaptive data-driven model-based approach for filling the gaps in time series. The approach is based on the automated evolutionary identification of the optimal structure for a composite data-driven model. It allows adapting the model for the effective gap-filling in a specific dataset without the involvement of the data scientist. As a case study, both synthetic and real datasets from different fields (environmental, economic, etc) are used. The experiments confirm that the proposed approach allows achieving the higher quality of the gap restoration and improve the effectiveness of forecasting models.
Optimal Linear Combination of Classifiers

The question of whether to use one classifier or a combination of classifiers is a central topic in Machine Learning. We propose here a method for finding an optimal linear combination of classifiers derived from a bias-variance framework for the classification task.