Reviewerly/exHarmony huggingface dataset:
Data privacy and data protection security algorithms for big data in cloud

Data security is the primary concern in the world, while storing large and confidential data in the cloud. Cloud Computing is a powerful internet-oriented technology which gives demand-based services to the users. But security is the major problem here when we use cloud computing networks. So, using secured cryptography algorithms only will protect data from attackers and gives security to the big data in cloud. It is mainly used for transmission of data in secured format. This paper proposed new enhanced hybrid security algorithm to give better security for Big Data in cloud. They are Hybrid Matrix hash key generation algorithm (HMHA) and Anonymous Multi-Fusion Shuffling Algorithm (AMSA).
The role of economic prosperity on informality in Africa: evidence of corruption thresholds from PSTR

This paper is interested in explaining the causes of the simultaneous evolution between economic growth and informality. Using a large annual panel of African countries with a time series of 25 years, ours results show that when the corruption rate is above (below) a threshold of 1.3577, economic growth reduces (increases) informal economic sector. The corruption proxy is measured as a decreasing function of corruption such that higher levels of the corruption proxy translate lower levels of corruption. It is therefore desirable for policymakers to improve the transparency of interactions between firms, public and private agents to fight corruption, in view of decreasing the informal economic sector through economic growth.
Extending the Exposure Score of Web Browsers by Incorporating CVSS

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Yet its content differs from one browser to another. Despite the privacy and security risks of User-Agent strings, very few works have tackled this problem. Our previous work proposed giving Internet browsers exposure relative scores to aid users to choose less intrusive ones. Thus, the objective of this work is to extend our previous work through: first, conducting a user study to identify its limitations. Second, extending the exposure score via incorporating data from the NVD. Third, providing a full implementation, instead of a limited prototype. The proposed system: assigns scores to users’ browsers upon visiting our website. It also suggests alternative safe browsers, and finally it allows updating the back-end database with a click of a button. We applied our method to a data set of more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available here [4].
ADaPT-ML: A Data Programming Template for Machine Learning

Classification is a task that involves making a prediction about which class(es) a data point belongs to; this data point can be text, an image, audio, or can even be multimodal.This task can become intractable for many reasons, including:• Insufficient training data to create a data-driven model; available training data may not be appropriate for the domain being studied, it may not be of the right type (e.g.only text but you want text and images), it may not have all of the categories you need, etc.• Lack of available annotators with domain expertise, and/or resources such as time and money to label large amounts of data.• Studying a phenomenon that changes rapidly, so what constitutes a class may change over time, making the available training data obsolete.ADaPT-ML (Figure 1) is a multimodal-ready MLOps system that covers the data processing, data labelling, model design, model training and optimization, and endpoint deployment, with the particular ability to adapt to classification tasks that have the aforementioned challenges.ADaPT-ML is designed to accomplish this by:• Using Snorkel (Ratner et al., 2020) as the data programming framework to create large, annotated, multimodal datasets that can easily adapt to changing classification needs for training data-driven models.
Structured Prediction Problem Archive

Structured prediction problems are one of the fundamental tools in machine learning. In order to facilitate algorithm development for their numerical solution, we collect in one place a large number of datasets in easy to read formats for a diverse set of problem classes. We provide archival links to datasets, description of the considered problems and problem formats, and a short summary of problem characteristics including size, number of instances etc. For reference we also give a non-exhaustive selection of algorithms proposed in the literature for their solution. We hope that this central repository will make benchmarking and comparison to established works easier. We welcome submission of interesting new datasets and algorithms for inclusion in our archive.
A theoretical framework and some promising findings of grey wolf optimizer, part I: analytical model of sampling distribution and stability analysis

This paper proposes a theoretical framework of the grey wolf optimizer (GWO) based on several interesting theoretical findings, involving sampling distribution, order-1 and order-2 stability, and global convergence analysis. In the part I of the paper, the characteristics of the sampling distribution of the new solution and the probabilistic stability of the GWO are carefully discussed based on the well-known stagnation assumption for simplification purposes. Firstly, the characteristics of the sampling distribution of the new solution, mainly related to the shape of the joint probability density function (PDF), are discussed under the assumption that the original solution before updating is constant. Then, the assumption that the original solution is constant is eliminated to perform the sampling distribution analysis, based on which several characteristics of the new solution are provided, containing the shape of the joint PDF and central moments of any positive integer order. Finally, the order-1 and order-2 stability of the GWO under stagnation assumption is introduced and proved as the inference of conclusions above, which are all verified by numerical simulations.
3D-Aware Indoor Scene Synthesis with Depth Priors

Despite the recent advancement of Generative Adversarial Networks (GANs) in learning 3D-aware image synthesis from 2D data, existing methods fail to model indoor scenes due to the large diversity of room layouts and the objects inside. We argue that indoor scenes do not have a shared intrinsic structure, and hence only using 2D images cannot adequately guide the model with the 3D geometry. In this work, we fill in this gap by introducing depth as a 3D prior. Compared with other 3D data formats, depth better fits the convolution-based generation mechanism and is more easily accessible in practice. Specifically, we propose a dual-path generator, where one path is responsible for depth generation, whose intermediate features are injected into the other path as the condition for appearance rendering. Such a design eases the 3D-aware synthesis with explicit geometry information. Meanwhile, we introduce a switchable discriminator both to differentiate real v.s. fake domains and to predict the depth from a given input. In this way, the discriminator can take the spatial arrangement into account and advise the generator to learn an appropriate depth condition. Extensive experimental results suggest that our approach is capable of synthesizing indoor scenes with impressively good quality and 3D consistency, significantly outperforming state-of-the-art alternatives.
Adaptation of Distilling Knowledge Method in Natural Language Processing for Sentiment Analysis

This paper describes how to adapt an application method of “ knowledge distillation “ for sentiment analysis for Ukrainian and Russian languages. It is demonstrated how to minimize resources without losing much accuracy, but speeding up the text sentiment recognition, and how to decrease expenses on cloud by using the method of “knowledge distillation”. For research we used two types of different neural networks architecture for natural language processing: BERT instead of ensemble models and FastText like a small model. Combination of these two neural networks (BERT as a teacher and FastText as a learner) allowed us to achieve the speedup up to 5 times and without sacrificing much accuracy in sentiment analysis task.
Current crowding in nanoscale superconductors within the Ginzburg-Landau model

The current density in a superconductor with turnarounds or constrictions is non-uniform due to a geometrical current crowding effect. This effect reduces the critical current in the superconducting structure compared to a straight segment and is of importance when designing superconducting devices. We investigate the current crowding effect in numerical simulations within the generalized time-dependent Ginzburg-Landau (GTDGL) model. The results are validated experimentally by measuring the magnetic field dependence of the critical current in superconducting nanowire structures, similar to those employed in single-photon detector devices. Comparing the results with London theory, we conclude that the reduction in critical current is significantly smaller in the GTDGL model. This difference is attributed to the current redistribution effect, which reduces the current density in weak points of the superconductor and counteracts the current crowding effect. We numerically investigate the effect of fill factor on the critical current in a meander and conclude that the reduction of critical current is low enough to justify fill factors higher than $33\,\%$ for applications where detection efficiency is critical. Finally, we propose a novel meander design which can combine high fill factor and low current crowding.
CLINICAL DIAGNOSIS OF BREAST CANCER SAMPLES USING NEGATIVE SELECTION ALGORITHM

This chapter uses an artificial immune system applied for diagnosing breast cancer samples. Taking as basis an immunological process, the Negative Selection Algorithm was used to discriminate the samples, attaining a classification for benign or malignant cases. The main application of the method is to assist professionals in the breast cancer diagnostic process, thereby providing decision-making agility, efficient treatment planning, reliability and the necessary intervention to save lives. To evaluate this method, the Wisconsin Breast Cancer Diagnosis database was used. This is an actual breast cancer database. The results obtained using the method, when compared with the specialized literature, show accuracy, robustness and efficiency in the breast cancer diagnostic process.
THE DEVELOPMENT OF ORACY SKILLS IN AN EFL CONTEXT USING YOUTUBE VIDEOS: A CASE STUDY

The present study aimed to examine the effectiveness of YouTube videos in the oracy skills of a young learner through the implementation of differentiated instruction.The research which was conducted was a case study and qualitative data were collected through the use of semi-structured interviews.Action research was also conducted through a series of ten YouTube-video based lessons grounded upon the principles of differentiated instruction.The efficiency of the methods and approaches which were implemented were verified by the results of the pre-and post-listening and speaking test which were both applied before and after the intervention of the ten lessons.To triangulate the study, qualitative research was conducted in the form of semi-structured interviews to the student and his mother before and after the intervention.The findings showed that there was an improvement in the student's performance as regards the listening, the speaking, and the grammar-lexis structure skills.
PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research

There has been significant progress in machine learning algorithms for human pose estimation that may provide immense value in rehabilitation and movement sciences. However, there remain several challenges to routine use of these tools for clinical practice and translational research, including: 1) high technical barrier to entry, 2) rapidly evolving space of algorithms, 3) challenging algorithmic interdependencies, and 4) complex data management requirements between these components. To mitigate these barriers, we developed a human pose estimation pipeline that facilitates running state-of-the-art algorithms on data acquired in clinical context. Our system allows for running different implementations of several classes of algorithms and handles their interdependencies easily. These algorithm classes include subject identification and tracking, 2D keypoint detection, 3D joint location estimation, and estimating the pose of body models. The system uses a database to manage videos, intermediate analyses, and data for computations at each stage. It also provides tools for data visualization, including generating video overlays that also obscure faces to enhance privacy. Our goal in this work is not to train new algorithms, but to advance the use of cutting-edge human pose estimation algorithms for clinical and translation research. We show that this tool facilitates analyzing large numbers of videos of human movement ranging from gait laboratories analyses, to clinic and therapy visits, to people in the community. We also highlight limitations of these algorithms when applied to clinical populations in a rehabilitation setting.
Abstractive summarization of hospitalisation histories with transformer networks

In this paper we present a novel approach to abstractive summarization of patient hospitalisation histories. We applied an encoder-decoder framework with Longformer neural network as an encoder and BERT as a decoder. Our experiments show improved quality on some summarization tasks compared with pointer-generator networks. We also conducted a study with experienced physicians evaluating the results of our model in comparison with PGN baseline and human-generated abstracts, which showed the effectiveness of our model.
Morphological Reinflection with Multiple Arguments: An Extended  Annotation schema and a Georgian Case Study

In recent years, a flurry of morphological datasets had emerged, most notably UniMorph, a multi-lingual repository of inflection tables. However, the flat structure of the current morphological annotation schema makes the treatment of some languages quirky, if not impossible, specifically in cases of polypersonal agreement, where verbs agree with multiple arguments using true affixes. In this paper, we propose to address this phenomenon by expanding the UniMorph annotation schema to a hierarchical feature structure that naturally accommodates complex argument marking. We apply this extended schema to one such language, Georgian, and provide a human-verified, accurate and balanced morphological dataset for Georgian verbs. The dataset has 4 times more tables and 6 times more verb forms compared to the existing UniMorph dataset, covering all possible variants of argument marking, demonstrating the adequacy of our proposed scheme. Experiments with a standard reinflection model show that generalization is easy when the data is split at the form level, but extremely hard when splitting along lemma lines. Expanding the other languages in UniMorph to this schema is expected to improve both the coverage, consistency and interpretability of this benchmark.
VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation

For online video instance segmentation (VIS), fully utilizing the information from previous frames in an efficient manner is essential for real-time applications. Most previous methods follow a two-stage approach requiring additional computations such as RPN and RoIAlign, and do not fully exploit the available information in the video for all subtasks in VIS. In this paper, we propose a novel single-stage framework for online VIS built based on the grid structured feature representation. The grid-based features allow us to employ fully convolutional networks for real-time processing, and also to easily reuse and share features within different components. We also introduce cooperatively operating modules that aggregate information from available frames, in order to enrich the features for all subtasks in VIS. Our design fully takes advantage of previous information in a grid form for all tasks in VIS in an efficient way, and we achieved the new state-of-the-art accuracy (38.6 AP and 36.9 AP) and speed (40.0 FPS) on YouTube-VIS 2019 and 2021 datasets among online VIS methods. The code is available at https://github.com/SuHoHan95/VISOLO.
Methodology for identifying the effective proportion of educational activities for the purpose of conducting distance learning without losing the quality of the educational process

The pandemic of the new coronavirus infection has significantly affected all areas of society in all countries of the world, the education sector is no exception. In view of the rapid spread of the growth of morbidity, many universities were forced to abandon the traditional educational model and switch to a remote mode of operation. However, such a transition raises a serious question about the quality of training of students. This paper analyzes the effectiveness of distance learning in one of the technical specialties of the Moscow Aviation Institute (National Research University). This analysis contains various statistical data. This study shows that although distance learning forms have a number of positive aspects, they also have a number of negative factors, in view of which a complete transition from the classical educational model to the distance form is impossible.
Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection

While deep learning models have seen recent high uptake in the geosciences, and are appealing in their ability to learn from minimally processed input data, as black box models they do not provide an easy means to understand how a decision is reached, which in safety-critical tasks especially can be problematical. An alternative route is to use simpler, more transparent white box models, in which task-specific feature construction replaces the more opaque feature discovery process performed automatically within deep learning models. Using data from the Groningen Gas Field in the Netherlands, we build on an existing logistic regression model by the addition of four further features discovered using elastic net driven data mining within the catch22 time series analysis package. We then evaluate the performance of the augmented logistic regression model relative to a deep (CNN) model, pre-trained on the Groningen data, on progressively increasing noise-to-signal ratios. We discover that, for each ratio, our logistic regression model correctly detects every earthquake, while the deep model fails to detect nearly 20 % of seismic events, thus justifying at least a degree of caution in the application of deep models, especially to data with higher noise-to-signal ratios.
Leveraging Emotion-specific Features to Improve Transformer Performance for Emotion Classification

This paper describes the approach to the Emotion Classification shared task held at WASSA 2022 by team PVGs AI Club. This Track 2 sub-task focuses on building models which can predict a multi-class emotion label based on essays from news articles where a person, group or another entity is affected. Baseline transformer models have been demonstrating good results on sequence classification tasks, and we aim to improve this performance with the help of ensembling techniques, and by leveraging two variations of emotion-specific representations. We observe better results than our baseline models and achieve an accuracy of 0.619 and a macro F1 score of 0.520 on the emotion classification task.
Designing a Sequential Recommendation System for Heterogeneous Interactions Using Transformers

While many production-ready and robust algorithms are available for the task of recommendation systems, many of these systems do not take the order of user's consumption into account. The order of consumption can be very useful and matters in many scenarios. One such scenario is an educational content recommendation, where users generally follow a progressive path towards more advanced courses. Researchers have used RNNs to build sequential recommendation systems and other models that deal with sequences. Sequential Recommendation systems try to predict the next event for the user by reading their history. With the massive success of Transformers in Natural Language Processing and their usage of Attention Mechanism to better deal with sequences, there have been attempts to use this family of models as a base for a new generation of sequential recommendation systems. In this work, by converting each user's interactions with items into a series of events and basing our architecture on Transformers, we try to enable the use of such a model that takes different types of events into account. Furthermore, by recognizing that some events have to occur before some other types of events take place, we try to modify the architecture to reflect this dependency relationship and enhance the model's performance.
PROFESSIONAL CAREER, SUCCESS AND HIGHER EDUCATION IN THE VALUE SYSTEM OF REGIONAL UNIVERSITY STUDENTS (BASED ON THE SOCIOLOGICAL SURVEY MATERIALS)

The relevance of studying the ideas of the regional university students about professional success, career, and the role of education is due to the fact that the choice of professional journey, building a future career in the educational process among modern students remains rather low.At the same time, the problem of personnel outflow in the regions of Russia is regularly discussed at all levels of government bodies and in the scientific community.The purpose of this article is to study the ideas of modern students of a regional university about professional success, career and education.The authors suggest that the research data can help clarify the ideas about the value orientations of modern student youth, study various aspects related to the career of young people, and contribute to the career strategies construction for young people.So, a modern employer is interested in highly qualified specialists, therefore, today higher educational institutions are faced with the task of professional training of graduates in accordance with those competencies that will not only correspond to the labor market, but even outstrip the requirements of the market.The conducted survey allows us to investigate the perceptions of respondents at a given time stage.The result of such a study can be designed career guidance work, starting with school, with the involvement of employers of city-forming enterprises and the administration of universities.