Reviewerly/exHarmony huggingface dataset:
Maximizing Windows Interoperability

When working with WSL, you may find you need to use utilities or files from Windows in your workflow or vice versa. Thankfully, there are several ways that you can blur the barrier between your WSL distro and Windows allowing a much more productive environment than Windows, or a Linux Distro, would provide on their own.
Itemset Representation and Mining the Rules for Huntington’s Dataset

Association rule mining does not restrict to market basket application but it is also employed in many applications such as health, industrial, network domain and etc. In this paper, an association mining algorithm is applied to the health management domain. It helps in the decision making by producing the rules for the early detection of the disease. By checking the personal details and symptoms of the patient, association rule mining will help in prediction and diagnosing the disease at an early stage. The dataset used in this experiment is the Huntington Disease (HD) dataset, which is one of the rare diseases. The dataset needs to be stored in the memory for the computation and generation of rules. Storing the items in the memory will take 4 bytes if the array data structure is used. Furthermore, if the dataset is very large, storing each and every detail in the memory becomes speculative. It is also not cost-effective and consumes a lot of resources. One of the solutions is to present the itemset in such a way that the memory consumed is concise. The items are represented using the set representation that takes less time and memory as compared to the traditional methods. The dataset is mine using the Apriori Algorithm which produces only those itemsets which are more frequent or have a high probability of occurrence. The algorithm gives a prior knowledge of the frequent itemsets. Then, the rules will be generated from these frequent itemsets. The memory and time consumption using the set representation is compared with the array representation of itemsets. Doi: 10.28991/esj-2021-01284 Full Text: PDF
Digital University Act.pdf

Text of the lecture of Prof. Karen Maex, Rector Magnificus of the University of Amsterdam, at the Dies Natalis of the university, January 2021. In this lecture, she expresses concerns about the independence of science in the current digital age and advocates a European 'Digital University Act'.
Factors of academic resistance formation

The article is devoted to the analysis of factors for forming academic resilience, defined by the author as students’ ability to cope with educational tasks successfully, despite the failures and problems typical of the usual course of study. While considering the educational process through the prism of forming this result, the author determines not so much the acquisition of knowledge as the complex development of the personality, but the formation of superimposed skills necessary for solving life problems. The article provides evidence that academic residency contributes to the formation of the personality of learners and the development of such vital qualities as the ability to control their efforts and achieve planned results. Factors for the formation of academic resistivity are considered in the hierarchy. The change of educational targets in accordance with the challenges of the post-industrial era is considered as a system-forming factor, the concept of metacognitive is called an epistemological factor, didactic factors include: didactic content, didactic relationships and academic productivity of students. The theoretical and practical significance of the materials of the article is due to the isolation, justification and description of the drivers of academic resistance development, which determine the nature and individual features of this process. The analysis of the author's own pedagogical experience allows us to argue that the result of the combined action of the factors described in the article is students’ ability not to fear errors, to build and test hypotheses, to rely on logic and think beyond frames. This article continues the discussion started earlier on the pages of Yaroslavl Pedagogical Bulletin journal and within the framework of the activities of the inter-university research center «New Didactics» on issues of methodology and practice in the field of didactics of pedagogical education and higher education in general.
Dropout: Explicit Forms and Capacity Control

We investigate the capacity control provided by dropout in various machine learning problems. First, we study dropout for matrix completion, where it induces a data-dependent regularizer that, in expectation, equals the weighted trace-norm of the product of the factors. In deep learning, we show that the data-dependent regularizer due to dropout directly controls the Rademacher complexity of the underlying class of deep neural networks. These developments enable us to give concrete generalization error bounds for the dropout algorithm in both matrix completion as well as training deep neural networks. We evaluate our theoretical findings on real-world datasets, including MovieLens, MNIST, and Fashion-MNIST.
REAL TIME TRANSLATION OF SIGN LANGUAGE TO SPEECH AND TEXT

Creating a desktop application that uses a computer webcam to capture a person signing gestures for Indian Sign Language (ISL) and translate it into corresponding text and speech in real time.The translated sign language gesture will be acquired in text which is further converted into audio.In this manner we are implementing a finger spelling language translator.To enable the detection gestures, we are making use of a Convolution neural network (CNN).A CNN is highly efficient in tackling computer vision problems and is capable of detecting the desired features with high degree of accuracy upon sufficient training.This project is about converting the hand gesture of sign language to voice or text using Machine Learning Techniques and vice versa.In this we are going to capture a real time translation of indian sign language using single and double hand gestures and recognize the words and convert it into text and then to speech.If the person gives speech as input it is first converted to text and then it displays the suitable sign as output and vice versa.
Kernel Stein Discrepancy Descent

Among dissimilarities between probability distributions, the Kernel Stein Discrepancy (KSD) has received much interest recently. We investigate the properties of its Wasserstein gradient flow to approximate a target probability distribution $\pi$ on $\mathbb{R}^d$, known up to a normalization constant. This leads to a straightforwardly implementable, deterministic score-based method to sample from $\pi$, named KSD Descent, which uses a set of particles to approximate $\pi$. Remarkably, owing to a tractable loss function, KSD Descent can leverage robust parameter-free optimization schemes such as L-BFGS; this contrasts with other popular particle-based schemes such as the Stein Variational Gradient Descent algorithm. We study the convergence properties of KSD Descent and demonstrate its practical relevance. However, we also highlight failure cases by showing that the algorithm can get stuck in spurious local minima.
Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases

NoSQL databases were created for the purpose of manipulating large amounts of data in real time. However, at the beginning, security was not important for their developers. The popularity of SQL generated the false belief that NoSQL databases were immune to injection attacks. As a consequence, NoSQL databases were not protected and are vulnerable to injection attacks. In addition, databases with NoSQL queries are not available for experimentation. Therefore, this paper presents a new method for the construction of a NoSQL query database, based on JSON structure. Six classification algorithms were evaluated to identify the injection attacks: SVM, Decision Tree, Random Forest, K-NN, Neural Network and Multilayer Perceptron, obtaining an accuracy with the last two algorithms of 97.6%.
Designing Improvisation in Privacy Protection for Personalized Web Search using deep learning approach

Abstract—Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. However, evidences show that users’ reluctance to disclose their private information during search has become a major barrier for the wide proliferation of PWS. We study privacy protection in PWS applications that model user preferences as hierarchical user profiles. We propose a PWS framework called UPS that can adaptively generalize profiles by queries while respecting user- specified privacy requirements. Our runtime generalization aims at striking a balance between two predictive metrics that evaluate the utility of personalization and the privacy risk of exposing the generalized profile. We present two greedy algorithms, namely GreedyDP and GreedyIL, for runtime generalization. We also provide an online prediction mechanism for deciding whether personalizing a query is beneficial. Extensive experiments demonstrate the effectiveness of our framework. The experimental results also reveal that GreedyIL significantly outperforms GreedyDP in terms of efficiency.&#x0D; &#x0D; Index Terms—Privacy protection, personalized web search, utility, risk, profile
Style Transfer Using Convolutional Neural Network and Image Segmentation

In this paper, we present a style transfer-based artwork using deep learning. Our proposed artwork aims to perform a different style transfer to the foreground and background using images of different styles. The framework of our proposed artwork comprises three steps: i) human detection and segmentation, ii) style transfer to the human and background regions, and iii) fusion of the style-transferred result images. The resultant style transferred image is preserved the content of the original image while changing the original style with the target style. The proposed artwork can provide a different artistic effect and style with an esthetic sense to human and background regions using different style images.
Software products for creating educational video courses for people with physical disabilities

The article identifies the benefits of implementing information and communication technologies in inclusive education, areas, in which information and communication technology support are recommended, as well as the main types of information and communication technologies suitable for use as a support tool for inclusive learning. The authors characterize the main problems of implementing information and communication technologies to support inclusive education and the conditions, the implementation of which will help to prevent them.
Irritation-free optical 3D-based measurement of tidal volume

The measurement of breathing biomechanics, such as tidal volume, can be used to assess both the breathing performance and the respiratory health of individuals. State-of-the-art methods like spirometry or body plethysmography require a mouthpiece or facemask., which can be uncomfortable to the test person. As an alternative, we propose to use the change of the geometric shape of the subject’s torso while breathing. By acquiring 3D point clouds of the person with a real-time near-infrared (NIR) 3D scanner, we measure those changes in a comfortable, irritation-free, and contact-free manner. Accordingly, two continuously measuring structured light 3D sensors, using a GOBO-based aperiodic sinusoidal pattern projector at a wavelength of 850 nm, simultaneously capture the upper front and side torso of the subject at a frame rate of 200 Hz. Both 3D scanners are calibrated and operated in a sensor network fashion, yielding a unified data stream within a global coordinate system. This results in increased coverage and reduced occlusion of the patient’s body shape, enabling robust measurements even in the presence of loose clothing and varying body figure. We collected data from 16 healthy participants in an upright sitting position, wearing everyday clothing during the measurements. For reference, we simultaneously recorded spirometry readings. An algorithm (“OpTidal”) tracks the volume of the subject’s torso from the 3D data. Comparison whith the reference data shows high correlation and low mean error for the absolute tidal volume readings. As such, our method is a viable, safe, and accurate alternative to spirometry and plethysmography.
Biometric Authentication in E-Voting through RNN

In a democratic country the election process and the right to vote are one of the most significant aspects. This is due to the fact that in a democratic country the sole administrator and the decision maker for the entire country needs to be elected in a fair and just manner from amongst the citizens of the country. This procedure has been effectively performed manually and physically by the utilization of ballot elections. This is a highly inefficient form of election that needs to be upgraded in this day and age of information and electronic supremacy. But the considerable concerns for developing an electronic voting scheme was the security concerns regarding multiple voting performed by a single person. Therefore to introduce an effective and useful technique which also addresses the security concerns an effective methodology for electronic voting through facial recognition has been proposed in this research article. The proposed methodology utilizes the open-source open CV library along with Recurrent Neural Networks to achieve highly accurate facial recognition for a secure electronic voting system. This approach has been significantly e evaluated for their performance metric which has achieved suitable results.
Expert System for Diagnosing Worms in Animal Husbandry using the Bayes Method

Expert systems have been widely used to solve problems in various fields such as medicine, mathematics, engineering, chemistry, pharmacy, computer science, business, law, education, and defense. If the expert system is related to the doctor's ability to diagnose the patient's health, a computer system can be created that can identify and analyze the symptoms that occur in patients and provide suggestions for overcoming them. Worms seem trivial, but if we look closely, it has a serious impact on affecting the health of livestock which in turn will have a direct result in decreased production. Goats that experience worms will experience a lack of nutrition or nutrition, so that their resistance to disease will decrease, so they are prone to other diseases, especially infectious diseases. Bayesian probability theory is a branch of the mathematical-statistical theory that allows us to create a model of the uncertainty of an event that occurs by combining general knowledge with facts from observations.
Nonparametric Regression with Shallow Overparametrized Neural Networks Trained by GD with Early Stopping

We explore the ability of overparameterized shallow neural networks to learn Lipschitz regression functions with and without label noise when trained by Gradient Descent (GD). To avoid the problem that in the presence of noisy labels, neural networks trained to nearly zero training error are inconsistent on this class, we propose an early stopping rule that allows us to show optimal rates. This provides an alternative to the result of Hu et al. (2021) who studied the performance of l2 -regularized GD for training shallow networks in nonparametric regression which fully relied on the infinite-width network (Neural Tangent Kernel (NTK)) approximation. Here we present a simpler analysis which is based on a partitioning argument of the input space (as in the case of 1-nearest-neighbor rule) coupled with the fact that trained neural networks are smooth with respect to their inputs when trained by GD. In the noise-free case the proof does not rely on any kernelization and can be regarded as a finite-width result. In the case of label noise, by slightly modifying the proof, the noise is controlled using a technique of Yao, Rosasco, and Caponnetto (2007).
Testing and Refining Scholarly Communications Workflows and Work Habits for the Digital Age

Greenhouse Studios | Scholarly Communications Design at UConn is a shared venture of the School of Fine Arts, University Library, and College of Liberal Arts and Sciences at the University of Connecticut. Greenhouse Studios’ core research mission is the development of workflows that bring diverse interdisciplinary teams together to create works of digital and non-traditional scholarship while also cultivating a collaborative work culture. This article summarizes the implementation, assessment, and refinement of those workflows, which together constitute Greenhouse Studios’ design-based, inquiry-driven, collaboration-first model of scholarly production. Findings from this research, undertaken with support from the Andrew W. Mellon Foundation, include modifications to Greenhouse Studios’ operations, specifically to the terminology used in its design-process model, the composition of team personnel, approaches to project management, tactics to foster divergent thinking, and our relationships to press partners.
Face Verification Technology Based on FaceNet Similarity Recognition Network

The background complexity of face images is high in actual scenes, and there are a series of problems such as illumination and occlusion, which greatly reduces the performance of the face verification model. This paper proposes a face verification algorithm FaceNetSRM based on the FaceNet similarity recognition network to improve the performance of the face verification model and the accuracy of Chinese face verification. Firstly, the deep convolutional neural network framework in FaceNet is determined, and the similarity recognition module is used to replace the Euclidean distance module in FaceNet. Then, the CASIA-WebFace face dataset and the self-made face dataset C-facev1 are used to train the face verification algorithm of this article. Finally, the trained model is tested and evaluated on the face dataset LFW and CASIA-FaceV5 to show the effectiveness of the face verification method in this article, and the face verification effect of the algorithm is compared with the face verification effect of FaceNet. The experimental results show that the face verification accuracy rate of the FaceNetSRM algorithm in this paper is 1.5% higher than that of FaceNet, and the accuracy rate of Chinese face verification is improved by 2.8%. The algorithm has good robustness and generalization ability, which can be applied in face verification systems.
Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text

Understanding who blames or supports whom in news text is a critical research question in computational social science.Traditional methods and datasets for sentiment analysis are, however, not suitable for the domain of political text as they do not consider the direction of sentiments expressed between entities.In this paper, we propose a novel NLP task of identifying directed sentiment relationship between political entities from a given news document, which we call directed sentiment extraction.From a million-scale news corpus, we construct a dataset of news sentences where sentiment relations of political entities are manually annotated.We present a simple but effective approach for utilizing a pretrained transformer, which infers the target class by predicting multiple question-answering tasks and combining the outcomes.We demonstrate the utility of our proposed method for social science research questions by analyzing positive and negative opinions between political entities in two major events: 2016 U.S. presidential election and COVID-19.The newly proposed problem, data, and method will facilitate future studies on interdisciplinary NLP methods and applications. 1
Synthetic Data for Basic Computer Vision Problems

It is time to put the pedal to the metal: starting from this chapter, we will discuss the current state of the art in various aspects of synthetic data. This chapter is devoted to basic computer vision problems: we begin with low-level problems such as optical flow estimation and stereo image matching, proceed to datasets of basic objects that can be used to train computer vision models, discuss in detail the case study of synthetic data for object detection, and finish with several different use cases such as synthetic datasets of humans, OCR, and visual reasoning.
Novel Features of Considerable Biologic Interest: the Fourth Amendment and the Admissibility of Abandoned DNA Evidence

DNA has become a fixture of modern society, so much so that much of the recent debate on the so-called “CSI effect” was actually a debate on the extent to which the general public has come to expect that DNA will play a role in even the most routine criminal cases. Expectations notwithstanding, DNA is unarguably a powerful forensic tool, one that almost seems to beg for its own set of rules—a “genetic exceptionalism” perhaps. In no area is this more debated than in the realm of “abandoned DNA,” i.e., DNA that is abandoned in the course of everyday activities, often unconsciously and unwittingly. Abandoned DNA, like abandoned property generally, falls outside of the protection of the Fourth Amendment by virtue of having been abandoned, and it may be collected by law enforcement without the benefit of a warrant. Many scholars argue that abandoned DNA is particularly in need of an exception that would bring it within the Fourth Amendment’s protection rather than to exclude it like other abandoned property, but there is no ready legal analogue for such action, and we should be wary of crafting one. Abandoned DNA, as its name implies, has its legal roots deep in the soil of abandoned property, and the existing body of abandonedproperty law provides a workable framework with which to analyze DNA issues. In this regard, abandoned DNA differs very little from other unconsciously and unwittingly abandoned human markers, such as fingerprints. To adopt an exception whereby abandoned DNA is given Fourth Amendment protection leads logically to an unworkable end state. If protection is sought for DNA privacy, it needs to come through statute rather than shoehorning it in under the Fourth Amendment.
Conceptual framework: The role of cognitive absorption in delone and mclean success model in online learning in united arab emirates

As COVID-19 pandemic has taken the world by surprise, it has resulted in an alteration to human activities that have never been seen before. Among the many areas that have been mostly affected is the education industry where institutions have resorted to online learning to maintain the educational process during lockdowns and other strict measures that governments around the world have implemented. Performance impact of such technology has risen as the main concern in that context; this study is making use of Delone & Mclean information success model to examine performance impact of online learning with the addition of cognitive absorption as a moderating variable to come out with a proposed conceptual framework for future empirical undertaking. The intended context of this study will be students within the public universities in the United Arab Emirates and implications are thoroughly discussed as well.
P‐78: Simulator‐Based Efficient Panel Design and Image Retrieval for Under‐Display Cameras

Images of an under‐display camera (UDC) are distorted by a transparent display's diffraction. Conventional panel design and image retrieval require high‐cost panel fabrication and time‐consuming image acquisition for deep learning. We utilize a UDC image simulator to perform pixel structure optimization and image retrieval efficiently. Four typical pixel structures are investigated and compared.