Reviewerly/exHarmony huggingface dataset:
Digitalization in the Higher Education System: Sociological analysis

Examined are problems of modern digital transformation of Russian universities and the impact of restrictive measures related to the pandemic on the acceleration of these processes. The work is based on the results of two stages of the sociological study “Peculiarities of the work organization of Russian universities in the period of restrictions in connection with the COVID-19 pandemic (opinions of students and teachers)”, conducted by the researchers of the MSU and RUDN in 2020-2021. The purpose of the study was a comprehensive analysis of the opinions of teachers and students of Russian universities about working in a remote format during the period of forced restrictions, their assessment of their digital skills and competencies and the level of digitalization of their universities. The article analyzes the main achievements and problems of the transition to modern digital tools for organizing the educational process in the country's universities. As practical recommendations for improving the process of digital transformation of higher education, the authors propose a number of possible measures to optimize the digitalization of universities and that are aimed at smoothing out a number of restrictions that imminently comes amid this process. The work is addressed to a wide range of readers, all those who are interested in the problems of higher education, the introduction of digital technologies in the organization of training.
Near-Optimal Deterministic Single-Source Distance Sensitivity Oracles

Given a graph with a distinguished source vertex s, the Single Source Replacement Paths (SSRP) problem is to compute and output, for any target vertex t and edge e, the length d(s,t,e) of a shortest path from s to t that avoids a failing edge e. A Single-Source Distance Sensitivity Oracle (Single-Source DSO) is a compact data structure that answers queries of the form (t,e) by returning the distance d(s,t,e). We show how to deterministically compress the output of the SSRP problem on n-vertex, m-edge graphs with integer edge weights in the range [1,M] into a Single-Source DSO that has size O(M^{1/2} n^{3/2}) and query time O(1). We prove that the space requirement is optimal (up to the word size). Our techniques can also handle vertex failures within the same bounds.Chechik and Cohen [SODA 2019] presented a combinatorial, randomized O(m√n+n²) time SSRP algorithm for undirected and unweighted graphs. We derandomize their algorithm with the same asymptotic running time and apply our compression to obtain a deterministic Single-Source DSO with O(m√n+n²) preprocessing time, O(n^{3/2}) space, and O(1) query time. Our combinatorial Single-Source DSO has near-optimal space, preprocessing and query time for unweighted graphs, improving the preprocessing time by a √n-factor compared to previous results with o(n²) space.Grandoni and Vassilevska Williams [FOCS 2012, TALG 2020] gave an algebraic, randomized O(Mn^ω) time SSRP algorithm for (undirected and directed) graphs with integer edge weights in the range [1,M], where ω < 2.373 is the matrix multiplication exponent. We derandomize it for undirected graphs and apply our compression to obtain an algebraic Single-Source DSO with O(Mn^ω) preprocessing time, O(M^{1/2} n^{3/2}) space, and O(1) query time. This improves the preprocessing time of algebraic Single-Source DSOs by polynomial factors compared to previous o(n²)-space oracles.We also present further improvements of our Single-Source DSOs. We show that the query time can be reduced to a constant at the cost of increasing the size of the oracle to O(M^{1/3} n^{5/3}) and that all our oracles can be made path-reporting. On sparse graphs with m = O(n^{5/4-e}/M^{7/4}) edges, for any constant e > 0, we reduce the preprocessing to randomized O(M^{7/8} m^{1/2} n^{11/8}) = O(n^{2-e/2}) time. To the best of our knowledge, this is the first truly subquadratic time algorithm for building Single-Source DSOs on sparse graphs.
Improved Rapid Automatic Keyword Extraction for Voice-based Mechanical Arm Control

As smart voices gradually enter the human living environment, people can interact with controlled systems through simple commands without the need for a screen.Even people without specialist training can use voice sensing control to easily complete the operation of a robot arm while avoiding incorrect operation caused by insufficient professional knowledge.In this study, we use the Google speech recognition engine to convert input speech into text information, and then perform speech recognition, word segmentation, and part-of-speech (POS) tagging.We also propose an improved rapid automatic keyword extraction (IRAKE) method, which uses the word string matching feature in the dictionary method to correspond to the relevant execution action function.The experimental results show that the accuracy rate of the proposed IRAKE method is 16% higher than that of the traditional rapid automatic keyword extraction (RAKE) method.
Optical Character Recognition Engines Performance Comparison in Information Extraction

Named Entity Recognition (NER) is often used to acquire important information from text documents as a part of the Information Extraction (IE) process.However, the text documents quality affects the accuracy of the data obtained, especially for text documents acquired involving the Optical Character Recognition (OCR) process, which never reached 100% accuracy.This research tried to examine which OCR engine with the highest performance for IE using NER by comparing three OCR engines (Foxit, PDF2GO, Tesseract) over 8,562 government human resources documents within six document categories, two document structures, and four measurements.Several essential entities such as name, employee ID, document number, document publishing date, employee rank, and family member's name were trying to be extracted automatically from the documents.NER processes were done using Python programming language, and the preprocessing tasks were done separately for Foxit, PDF2GO, and Tesseract.In summary, each OCR engine has its drawbacks and benefit, such as Tesseract has better NER extraction and conversion time with better accuracy but lack in the number of entities acquired.
Developing access to confidential data in France: results and new challenges

Over the past twenty years, in various countries, secure access to data for the members of the research community was eased in a significant manner. Such data involve microdata and granular data from administrative records and detailed individual surveys. While some difficulties remain, the scene has been extensively redesigned, and new players emerged on both sides of the fence: data holders and users, both challenging what seemed to be well-established boundaries. In the French case, access to confidential data for research purposes has been carefully facilitated. The paper analyses the French developments and current achievements, providing insights into how obstacles can be overcome and newly emerging issues
A Numerical Verification Framework for Differential Privacy in Estimation

This letter proposes an algorithmic method to verify differential privacy for estimation mechanisms with performance guarantees. Differential privacy makes it hard to distinguish outputs of a mechanism produced by adjacent inputs. While obtaining theoretical conditions that guarantee differential privacy may be possible, evaluating these conditions in practice can be hard. This is especially true for estimation mechanisms that take values in continuous spaces, as this requires checking for an infinite set of inequalities. Instead, our verification approach consists of testing the differential privacy condition for a suitably chosen finite collection of events at the expense of some information loss. More precisely, our data-driven, test framework for continuous range mechanisms first finds a highly-likely, compact event set, as well as a partition of this event, and then evaluates differential privacy wrt this partition. This results into a type of differential privacy with high confidence, which we are able to quantify precisely. This approach is then used to evaluate the differential-privacy properties of the recently proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$W_{2}$ </tex-math></inline-formula> Moving Horizon Estimator. We confirm its properties, while comparing its performance with alternative approaches in simulation.
Storing information of stroke rehabilitation patients using blockchain technology

Stroke patients usually experience damage to multiple functions and a long rehabilitation period. Hence, there is a large volume of patient clinical information. It thus takes a long time for clinicians to identify the patient's information and essential pieces of information may be overlooked. To solve this, we stored the essential clinical information of stroke patients in a blockchain and implemented the blockchain technology using the Java programming language.We created a mini blockchain to store the medical information of patients using the Java programming language.After generating a unique pair of public/private keys for identity verification, a patient's identity is verified by applying the Elliptic Curve Digital Signature Algorithm based on the generated keys. When the identity verification is complete, new medical data are stored in the transaction list and the generated transaction is verified. When verification is completed normally, the block hash value is derived using the transaction value and the hash value of the previous block. The hash value of the previous block is then stored in the generated block to interconnect the blocks.We demonstrated that blockchain can be used to store and deliver the patient information of stroke patients. It may be difficult to directly implement the code that we developed in the medical field, but it can serve as a starting point for the creation of a blockchain system to be used in the field.
MERN Stack Web Application

Teams Application is a responsive web application that can also be used in mobile phones is developed using React JS as the front end and MongoDB[4] as database. The main objective of the application is to form small teams and assign some duties to the members for which the members are notified through email and they can notify the team members about the completion status with comments via email. This application is developed using MERN Stack i.e., Mongoose, Express, ReactJs and NodeJs. We propose a three-tier architecture which has client-tier, business-logic tier and database-tier[5]. The client-tier will have the user experience and interface elements. It consists of ReactJs (jsx elements), html and the browser[11]. Our web application is a single page web application which means that only one html page is rendered. Using ReactJs, different components are rendered in that single html element. The business- logic tier consists of the functionality elements which will be done using  NodeJs and express server. NodeJs is the platform and ReactJs is the framework used for User Interface. ExpressJs is a library that acts as a server for the application. The last will be the database-tier which consists of the MongoDB database[7]. The data from the database is fetched using Mongoose ODM. The important goal of the application is to provide better communication among the members of a team which helps the team to perform better and come up with better ideas that may lead to increase in productivity. The user’s data is also kept safe in the application and managed properly. We have achieved this connectivity among the members using email notifications. Every move of the user is kept track and the members are notified about it. The members are given proper information about the status of the task that is live. This application can also have a lot of add-on features which can be done in the future enhancements.
Grammatical Error Correction with Denoising Autoencoder

A denoising autoencoder sequence-to-sequence model based on transformer architecture proved to be useful for underlying tasks such as summarization, machine translation, or question answering.This paper investigates the possibilities of using this model type for grammatical error correction and introduces a novel method of remark-based model checkpoint output combining.This approach was evaluated by the BEA 2019 shared task.It was able to achieve state-of-the-art F-score results on the test set 73.90 and development set 56.58.This was done without any GEC-specific pre-training, but only by fine-tuning the autoencoder model and combining checkpoint outputs.This proves that an efficient model solving GEC might be trained in a matter of hours using a single GPU.
Post-COVID-19 World Politics and Conservative-Progressive Competition: Why Africa Might Find Itself in a Complex Dilemma?

This paper intends to briefly present the most possible scenario of the post-COVID-19 world politics situation, and then to explain why the latter with the "conservative-progressive competition" is more likely to place Africa in a "complex dilemma".After a short debate and discussion, our result shows that, we are moving towards a more complex world politics than those of before and during COVID-19.A world that is going to be dominated by "conservativeprogressive competition".Projected as: a more suspicion and uncertainty, and a more competitive and clearly divided world, is more likely to place Africa in a "complex dilemma".Just because the various Africa's weaknesses and problems-its "complex dependence" on the "conservatives" and "progressives", its lack of unity or fragile unity, and its political governance and leadership's problems, make the costs of all its possible choices complex.It is up to Africa to be well prepared for its best survival.
Genetic Algorithm and Ensemble Learning Aided Text Classification using Support Vector Machines

Text classification is one of the areas where machine learning algorithms are used.The size of the dataset and the methods used for converting the textual words into vectors play a major role in classifying them.This paper proposes a heuristic based approach to classify the documents using Genetic Algorithm aided Support Vector Machines (SVM) and Ensemble Learning approach.The real valued representation of the textual data into vectors is done on applying Term Frequency -Inverse Document Frequency (TF-IDF) and Bi-Normal Separation (BNS).However, in this paper, the common data misclassification issue in SVM is overcome by introducing two algorithms that adds weightage to accurate classification.The first algorithm applied BNS and TF-IDF along with ensemble learning and constructs a voting classifier for classifying the textual documents.The results produced justify that TF-IDF produces good results with voting classifier than BNS for classification.Henceforth TF-IDF is applied in the subsequent approach for vector generation.Secondly, genetic algorithm is applied along with OneVsRest strategy in SVM to overcome the drawback of multiclass multilabel classification.The results show that Genetic algorithm improves the accuracy of classification even with a very small labelled dataset, as genetic algorithm applies the process of Mutation and Cross over across many generations to understand the pattern of right classification.
Entangling-gate error from coherently displaced motional modes of trapped ions

Entangling gates in trapped-ion quantum computing have primarily targeted stationary ions with initial motional distributions that are thermal and close to the ground state. However, future systems will likely incur significant non-thermal excitation due to, e.g., ion transport, longer operational times, and increased spatial extent of the trap array. In this paper, we analyze the impact of such coherent motional excitation on entangling-gate error by performing simulations of Molmer-Sorenson (MS) gates on a pair of trapped-ion qubits with both thermal and coherent excitation present in a shared motional mode at the start of the gate. We discover that a small amount of coherent displacement dramatically erodes gate performance in the presence of experimental noise, and we demonstrate that applying only limited control over the phase of the displacement can suppress this error. We then use experimental data from transported ions to analyze the impact of coherent displacement on MS-gate error under realistic conditions.
Bag of Tricks for Building an Accurate and Slim Object Detector for Embedded Applications

Object detection is an essential computer vision task that possesses extensive application prospects in on-road applications. Copious novel methods have been proposed in this branch recently. However, the majority of them have high computational cost, making them intractable to be deployed on embedded devices. In this paper, taking YOLOv5s, the smallest model in the YOLOv5 family, as the baseline, we explore a bag of tricks that improve the detection performance for a specified on-road application, under the premise of ensuring that it does not increase the computational cost of YOLOv5s. Specifically, we introduce relevantly external data to deal with the problems of sample imbalance. Meanwhile, knowledge distillation is employed to transfer knowledge from a cumbersome model to a compact model, where a united distillation scheme is developed to enhance the effectiveness. In addition, a pseudo-label based training strategy is utilized to further learn from the biggest YOLOv5 model. We have applied the above tricks to the Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries held in conjunction with ICMR 2021. The experiments have shown that all the tricks are useful. Their combination have built an accurate and slim detection model. It is highly competitive and has been ranked 2nd place in the competition. We believe the tricks are also meaningful for building other application-oriented object detectors.
Importance weighted Ranking Methods for Preference the Covid 19 Pandemic Social Assistance

Issues importance-weighted value is a critical aspect of decision making. Differences in weight, even the slightest change in weight assignment, can drastically change the final decision. Moreover, in the case of distributing social assistance during the Covid-19 pandemic, objectivity and accuracy of weighting the criteria for potential recipients are very important applied for the welfare of the community. The proposes study 3 popular models of ranking methods for weighting criteria in the internet data package assistance cases. Weighting is given to 390 alternatives with 5 decision-making criteria based on online learning needs and economic cost capabilities. The decision analysis method uses the reference point and optimization from Moora. The study results were found accuracy, precision and error rate performance each method using a confusion matrix approach. The study results discussed raised several important points of findings, that the three ranking methods (RS, RR, ROD) have their respective characteristics in weighting importance, where the level of accuracy and precision of the rank-sum method is better than the RR and ROD methods (for the case: 5 criteria; 390 alternatives). Other things in giving weight value from important to most important are comparable, and the weight value of the non-benefit (cost) criteria in the ranking method have a significant effect on performance results. These three methods are simple in use and with the assessment of replacement weights that can be determined how important these variables are to the principal of these criteria.
Common Coherence Witnesses and Common Coherent States

We show the properties and characterization of coherence witnesses. We show methods for constructing coherence witnesses for an arbitrary coherent state. We investigate the problem of finding common coherence witnesses for certain class of states. We show that finitely many different witnesses W1,W2,⋯,Wn can detect some common coherent states if and only if ∑i=1ntiWi is still a witnesses for any nonnegative numbers ti(i=1,2,⋯,n). We show coherent states play the role of high-level witnesses. Thus, the common state problem is changed into the question of when different high-level witnesses (coherent states) can detect the same coherence witnesses. Moreover, we show a coherent state and its robust state have no common coherence witness and give a general way to construct optimal coherence witnesses for any comparable states.
GeoACT: Augmented Control Tower using Virtual and Real Geospatial Data

Despite prior efforts to solve urban problems and build smart cities using large-scale urban information, researchers have yet to develop tools for city administrators to easily access a diverse mix of city information. To address this limitation, this article proposes an augmented reality visualization system, GeoACT, which can develop a digital twin to present city information from both macro and micro perspectives. GeoACT employs a 3D virtual city miniature with real and virtual IoT information to provide a macro perspective view, and a 360-degree-video-based visualization technique is used for a micro perspective view. We applied GeoACT to an urban space and demonstrated that it could effectively visualize city information through an augmented reality Head Mounted Display (HMD). Furthermore, GeoACT can be used in urban control centers.
Sense representations for Portuguese: experiments with sense embeddings and deep neural language models

Sense representations have gone beyond word representations like Word2Vec, GloVe and FastText and achieved innovative performance on a wide range of natural language processing tasks. Although very useful in many applications, the traditional approaches for generating word embeddings have a strict drawback: they produce a single vector representation for a given word ignoring the fact that ambiguous words can assume different meanings. In this paper, we explore unsupervised sense representations which, different from traditional word embeddings, are able to induce different senses of a word by analyzing its contextual semantics in a text. The unsupervised sense representations investigated in this paper are: sense embeddings and deep neural language models. We present the first experiments carried out for generating sense embeddings for Portuguese. Our experiments show that the sense embedding model (Sense2vec) outperformed traditional word embeddings in syntactic and semantic analogies task, proving that the language resource generated here can improve the performance of NLP tasks in Portuguese. We also evaluated the performance of pre-trained deep neural language models (ELMo and BERT) in two transfer learning approaches: feature based and fine-tuning, in the semantic textual similarity task. Our experiments indicate that the fine tuned Multilingual and Portuguese BERT language models were able to achieve better accuracy than the ELMo model and baselines.
A note on Low Order assumptions in RSA groups

In this short note, we show that substantially weaker Low Order assumptions are sufficient to prove the soundness of Pietrzak's protocol for proof of exponentiation in groups of unknown order.This constitutes the first step to a better understanding of the asymptotic computational complexity of breaking the soundness of the protocol.Furthermore, we prove the equivalence of the (weaker) Low Order assumption(s) and the Factoring assumption in RSA groups for a non-negligible portion of moduli.We argue that in practice our reduction applies for a considerable amount of deployed moduli.Our results have cryptographic applications, most importantly in the theory of recently proposed verifiable delay function constructions.Finally, we describe how to certify RSA moduli free of low order elements.