Reviewerly/exHarmony huggingface dataset:
Nonlocal total variation regularization with Shape Adaptive Patches for image denoising via Split Bregman method

Abstract Non-Local or patch-based approaches are used in most of the state-of-the-art methods for image denoising. Denoising with regular square patches may cause noise halos around edges, particularly high contrasted edges. In order to overcome this drawback, this work presents an extension of the Non-Local TV framework that effectively exploits the potential local geometric features of the image by replacing the simple square patches with different oriented shapes. We first solve the denoised models of ROF-NLTV with different oriented patch shapes by Split Bregman algorithm. Then, use exponentially weighted aggregation method based on Stein’s unbiased risk estimate to combine the estimators obtained in the first step. The numerical results show our method outperforms some previous ones. Moreover, common noise halos around edges usually observed by denoising with Non-Local TV method are reduced thanks our approach.
Open Data Consumption Through the Generation of Disposable Web APIs

The ever-growing amount of information in today's world has led to the publication of more and more open data, i.e., that which is available in a free and reusable manner, on the Web. Open data is considered highly valuable in situational scenarios, in which thematic data is required for a short life cycle by a small group of consumers with specific needs. In this context, data consumers (developers or data scientists) need mechanisms with which to easily assess whether the data is adequate for their purpose. SPARQL endpoints have become very useful for the consumption of open data, but we argue that its steep learning curve hampers open data reuse in situational scenarios. In order to overcome this pitfall, in this paper, we coin the term disposable Web APIs as an alternative mechanism for the consumption of open data in situational scenarios. Disposable Web APIs are created on-the-fly to be used temporarily by a user to consume open data. In this paper we specifically describe an approach with which to leverage semantic information from data sources so as to automatically generate easy-to-use disposable Web APIs that can be used to access open data in a situational scenario, thus avoiding the complexity and learning curve of SPARQL and the effort of manually processing the data. We have conducted several experiments to discover whether non-experienced users find it easier to use our disposable Web API or a SPARQL endpoint to access open data. The results of the experiments led us to conclude that, in a situational scenario, it is easier and faster to use the Web API than the corresponding SPARQL endpoint in order to consume open data.
B-spline fitting of sunglasses lens contour based on adaptive sampling

Abstract To solve the problems of low accuracy and low efficiency of B-spline fitting in sunglasses lens contour cutting, a sampling method based on the characteristic function of a weighted combination of arc length and curvature square root was proposed, and then perform B-spline fitting according to the sampling points to improve the accuracy and efficiency. First, read and identify the sunglasses lens data in the DXF file, and disperse it to obtain a series of coordinate points; combine the defined feature function for uniform sampling, and adjust the parameter weight in the feature function to obtain a more reasonable sampling point for the sunglasses lens contour; Then use the LU decomposition and interpolation method to obtain the B-spline curve through all the sampling points; the maximum absolute error (MAE) and the root-mean-square error (RMSE) are used as the accuracy criteria, by increasing the number of sampling points iteratively until the accuracy is satisfied to construct a reasonable fitting curve.
Semantic Features of Students’ Attitude to Interaction in the Digital Educational Environment

The characteristics of the main content of the resources and risks of the digital educational environment are considered. The features of understanding the nature and determinants of the success of the interaction of future specialists in the digital educational environment are shown. The article deals with the use of information resources and interaction in the network as a factor of professional and personal growth of students. An idea is given about the features of the semanticization of relations in the digital educational environment. The role of an adequate understanding of the semantic core of the problem in the productivity of students ‘ project activities is shown. The conditions and ways of achieving success in interaction in the information space within the framework of educational and professional activities are described. The relationship of success with information competence and semantic features of the attitude to interaction in the information space, the motivation of achievement and the expression of the desire for self-development is shown. The results of an empirical study of the peculiarities of students ‘ attitudes to interaction in the digital environment are described, and their ideas about the limitations and risks of this format of activity are shown. The article presents the material describing the main content of students ‘ ideas about the characteristics of a successful teacher and a successful student as subjects of interaction in the digital educational environment.
Autonomous flood barrier using Arduino

Flood is an overflow of huge amount of water covering large areas causing destruction at the places affected. Many regions across the globe face the problem of floods each year. Flood occurs due to excessive downpour and lack of proper drainage system. The severity of flood may vary from region to region and the destruction caused due to the same varies accordingly. The civic bodies have a major role to play when it comes to minimizing the effect of floods, especially in areas that are known to be vulnerable to flooding.Flood barriers exemplify the automation of flood control solution for the sake of quick but efficient response. These ones are self-activating, self-closing floodgates built on the top portions of large buildings. These flood barriers can rise automatically with the rising flood waters or by push button in advance keeping you in control for complete peace of mind.
Directional sensitivity of track initialisation of a multistatic Doppler‐only passive radar network

A multistatic passive radar network originally presented in Ptak et al. for long-distance aircraft tracking is sensitive to track initialisation errors. When these errors exceed 50 km, target tracking often diverges. Initialisation can be improved by even coarse directional information. The impact of binary directional information was studied, which allows flights to be assigned to start from east or west of the multistatic network. Substantial improvement to the performance of flight tracking is observed on reference data based on Automatic Dependent Surveillance–Broadcast information.
Models of lexical semantics in the algorithms for natural language processing

The aim of this study was to determine whether some of the approaches of lexical semantics for studying word meaning could be identified in word2vec and recurrent neural networks (RNN), the algorithms for natural language processing (NLP). Linguistic concepts from the field of lexical semantics were decompositional, holistic, and relational. Although it is assumed that algorithms for natural language processing cannot be written only on the basis of mathematical knowledge, but also linguistic, this analysis was carried out so as to determine the exact models used in the above-mentioned algorithms. First, the aforementioned linguistic models were concisely explained through descriptive research. In describing those, the authors of the paper referred to the studies of the most prominent linguists within the field of lexical semantics such as Fillmore, Firth and Lyons. Then, in a similar fashion, the architecture of NLP algorithms was introduced and described. For this intent, the authors of the paper relied mostly on the studies of Mikolov et al., who designed word2vec. Next, a comparative analysis was conducted between approaches of lexical semantics on one hand and the algorithms in question on the other. This analysis confirmed the underlying assumption of the paper that the characteristics of decompositional and relational approach to studying word meaning could be recognized in word2vec and that the properties of the holistic model could be observed in RNN algorithms. More than that, the analysis showed a considerable overlap in processing natural language, as if the models of lexical semantics were taken and mathematically implemented in the algorithms examined. It should be emphasized that for the purposes of this paper only the basic principles of lexical semantics and NLP algorithms were taken into account. The aim of the paper was not to describe edge cases or to talk in detail about the mechanisms of these structures and their advantages and disadvantages. Essentially, this study sought to examine whether the basic ideas and characteristics of lexical semantics could be found in the architecture of the above-noted algorithms.
An Analysis of Data Hidden in Bitcoin Addresses

The main purpose of Bitcoin address is a representation of a possible source or destination for a payment. However, some users use it not for the transfer of cryptocurrencies, but to record some arbitrary data, ranging from short messages to website links. We provide the first systematic analysis, both quantitative and qualitative, of data hidden in Bitcoin addresses. In this paper, we explore all addresses existing in the Bitcoin and Ethereum blockchain in the purpose to discover those with some hidden content. Results of our research are publicly available in the projects THIBA and THIEA (Thinks/Texts Hidden In Bitcoin/Ethereum Addresses).
Fundamentals of Functioning of Global Contact Monitoring Systems in the Context of COVID-19 Spread Prevention

Coronavirus disease 2019 (COVID-19) has a clear potential for a prolonged global pandemic, high mortality and overload of health systems. Until vaccines become widely available, the only available approaches to infection prevention are case isolation, contact and quarantine tracking, physical distancing, decontamination, and hygiene measures. The paper considers the conceptual principles of combining the bases of transparency and confidentiality of personal life and the need to track personal contacts, substantiates the need for such a combination and provides a possible technical solution using blockchain technology. The practical development and deployment of the system based on such protocols will allow each citizen to track the personal history of probable contact with infected persons or other potential risks (including deciding on the need for self-isolation or additional examination). In this case, the decentralized system guarantees the privacy of such information both for each participant (due to the inability to determine the personal data of a potentially infected person) and for the system as a whole.
Relevance Transfer: Towards Robust Distillation in Person Re-Identification

Abstract Person Re-Identification(ReID) is developing rapidly these years powered by deep learning. However most existing methods with state-of-the-art performance are highly dependent on “heavy” CNN backbones, which are hard to deploy on lightweight computing devices. In this paper, we propose a novel feature distillation method called Relevance Transfer, which aims to transfer the knowledge hidden inside the relation of different feature parts from teacher network to tiny student network. And we conduct a Multi-Granularity Horizontal Partition (MGHP) strategy to make training more efficient and robust. As results on two mainstream ReID datasets show, our method achieves competitive performance with original logits Knowledge Distillation (logits KD), and gains even more aggressive performance when works along with logits KD.
A flexible multivariate model for high-dimensional correlated count data

Abstract We propose a flexible multivariate stochastic model for over-dispersed count data. Our methodology is built upon mixed Poisson random vectors ( Y 1 ,…, Y d ), where the { Y i } are conditionally independent Poisson random variables. The stochastic rates of the { Y i } are multivariate distributions with arbitrary non-negative margins linked by a copula function. We present basic properties of these mixed Poisson multivariate distributions and provide several examples. A particular case with geometric and negative binomial marginal distributions is studied in detail. We illustrate an application of our model by conducting a high-dimensional simulation motivated by RNA-sequencing data.
Development of Solar-Powered Microcontroller-Relay-Based Control System Omnidirectional Wheelchair

A solar-powered omnidirectional wheelchair is implemented for physically challenged persons. The framework was mounted on the wheels that were connected with two direct current (DC) motors. The ratings of the battery and solar module were determined using system voltage (12V). A 7,805-voltage regulator was used to supply 5VDC to the AT89352 microcontroller. The microcontroller was programmed to provide a reference signal to the motor. The motor provides the needed torque to drive the wheels through interconnected relays. The relays are energized by the microcontroller and omnidirectional movement achieved through relays connected with microprocessor and micro switches, eliminating the need for joysticks and complex control mechanisms. System performance test result showed that the auxiliary solar power supply of the wheelchair increased the travel range by approximately 86% compared with that of a wheelchair powered by battery alone.
Optical Testing of Optical Elements

Optical Testing of Optical Elements is of utmost importance in the optical instrumentation. The pioneering work on providing the simplest method for testing an optical instrument was done by the Italian physicist Vasco Ronchi.
Multi-modal Data-Driven Analytics for Health Care

In this day and age, Artificial Intelligence has crept into every domain of work. It is only fitting that it be made a part of the medical domain as well. With the vast amount of healthcare data available in various forms such as image, video, text, and due to emerging technologies and advancements in data science, tools are being created to ease the process of medical analysis and provide an efficient and accurate diagnosis of the patient. Such techniques save lots of human efforts and provide a rather accurate result. In particular, the chapter focuses on emphasizing the importance of multimodal system where instead of relying on a particular data source or a particular field of data analytics; one can combine multiple sources and apply multi-domain techniques to extract information to an even greater extent. The high-quality information retrieved from the analysis can be further used to determine and diagnose more symptoms and hence help in providing accurate solutions.
Anemia Multi-label Classification Based on Problem Transformation Methods

The CBC report is considered to be the most important report for assessing the overall health of the human body which is an important test for the diagnosis of diseases such as anemia, cancer, infections, vitamin, and mineral deficiencies. Anemia is a common health problem among people worldwide. Anemia is not a disease but a sign of a serious illness. Therefore, it can be prevented from serious diseases diagnosed at an earlier stage. The pattern of CBC parameters is found to be very complex. Therefore, a multi-label classification of the types of IDA, Vitamin B12, aplastic, and sickle cell anemia has been made by the machine learning problem transformation method which gives an idea of the type of anemia that is likely to occur due to the abnormal condition of CBC parameters. The use of the model can help predict anemia at an earlier stage. And serious diseases can be avoided. Ultimately, people can be saved from financial costs, kept mentally healthy, and concentration and regularity in teaching children can also be improved. This multi-label classification inspires the application of methods to classify these anemia types. Binary relevance, classifier chains, and label power set methods are used with different base classifications, and the results are analyzed. The results obtained by the SVM model based on the classifier chains method of multi-label classification have proved to be superior to other methods.
The Path to Living Knowledge Based on the Application of Teaching Information Transformation Techniques

The author summarizes the author’s techniques used in psychology classes with the aim of meaningfully transforming educational information by bachelors into their personal knowledge. The methodology for developing techniques was based on idiographic and qualitative approaches. The productive ideas of V.P. Zinchenko about “living” knowledge and the fundamental difference between information and knowledge, his original and fruitful metaphor about the fifth dimension of being, which he associated with meaning; a consequence of this metaphor about the difficulties of the process of generating an affectively colored space of meanings by a person turned out to be useful. The procedural aspect and content of four specific techniques for transforming educational psychological information are described. Examples of the use of techniques in the study of specific psychological disciplines are given. A special technique of creating and accumulating knowledge about his person by transforming educational psychological information into verbal self-portraits is highlighted. The question is raised about the correspondence of the knowledge obtained by bachelors as a result of the application of the proposed techniques to the content of the concept of “living” knowledge in the understanding of V.P. Zinchenko. The author’s version of the answer to this question is offered. A hypothesis is formulated about the possible mechanisms of transformation of educational psychological information into meaningful knowledge of bachelors. Two promising directions for the continuation of the empirical research pre sented in the article are highlighted.
Learning To Recover Sharp Detail From Simulated Low-Dose Ct Studies

Radiology exams require exposing a patient to a variable dosage of radiation. Importantly, the amount of radiation used during the exam directly corresponds to the level of noise in the resulting image, and increased amounts of radiation can pose health risks to patients. This results in a tradeoff, as radiologists need a high-quality image to make a diagnosis. In this work, we propose a method to recover image fidelity given a noisy, or low-dose, sample. Using a two-part criterion that consists of a pixel-wise loss and an adversarial loss, we are able to recover the structure and fine detail of the normal-dose sample. To evaluate the denoising method, we implement simulations of realistic low-dose noise for a computed tomography exam, which may be of independent interest. Quantitative and qualitative results highlight the performance of our approach as compared to existing baselines.
A Study on the Awareness of Artificial Intelligence Among Youth and Its Impact on Employment

Artificial Intelligence (A.I.) is the Engineering and Science of making Intelligent Machines and Intelligent Computer Programs. It develops Intelligence in Machines and enables them to think like Humans. The study was conducted to find out level of awareness about artificial intelligence among youth and its impact on employment. From the study it was found that artificial intelligence is career threatening and the youth shows a negative attitude towards it. As a result of development of artificial intelligence the employment sector now demands more technological skills from the employees. It was found that majority of the respondents are of the opinion that artificial intelligence will replace the human workers in the near future. It is evident from the secondary analysis that low skilled jobs are at risk due to artificial intelligence, because the replacement is more in low skilled jobs as compared to high skilled jobs. It can be conclude that , demand of skilled jobs is on the rise and there might be a temporary shortage of skilled labor but if the current pace of development in education and retraining continues it will be resolved soon.
GCompris As a Didactic Resource to Solve Mathematical Addition, Subtraction, and Language Problems in 2nd Grade Elementary Children

ICT as educational tools are very beneficial for the teacher and for students in the 2nd grade of the primary level since through the GCompris software. The purpose of this research is to analyze how students in the second grade of primary school implement the GCompris program as a teaching resource in the area of mathematics to solve problems of addition, subtraction and mathematical language. The sample was of 36 students were used as an experimental group (pre-test - post-test), the participants were between 8 and 9 years old. Finally, an exit evaluation was applied to compare the result and see if the software was relevant to improve its mathematical performance. Finally, the results showed that through the comparative chart students managed to improve their mathematical performance.
Chosen Ciphertext Secure Functional Encryption from Constrained Witness PRF

Functional encryption generates sophisticated keys for users so that they can learn specific functions of the encrypted message. We provide a generic construction of chosen ciphertext attacks (CCA) secure public-key functional encryption (PKFE) for all polynomial-size circuits. Our PKFE produces succinct ciphertexts that are independent of the size and depth of the circuit class under consideration.We accomplish our goal in two steps. First, we define a new cryptographic tool called constrained witness pseudorandom function (CWPRF) which is motivated by combining WPRF of Zhandry (TCC 2016) and constrained PRF of Boneh and Waters (ASIACRYPT 2013). More specifically, CWPRF computes pseudorandom values associated with NP statements and generates constrained keys for boolean functions. We can recompute the pseudorandom value corresponding to a particular statement either using a public evaluation key with a valid witness for the statement or applying a constrained key for a function that satisfies the statement. We construct CWPRF by coupling indistinguishability obfuscation (\(i\mathcal {O}\)) and CPRF supporting all polynomial-size functions.In the second and main technical step, we show a generic construction of a CCA secure PKFE for all circuits utilizing our CWPRF. It has been observed that obtaining PKFE supporting all circuits is already a complex task and \(i\mathcal {O}\)-based constructions of PKFEs are only proven to be chosen plaintext attacks (CPA) secure. On the other hand, existing CCA secure functional encryption schemes are designed for specific functions such as equality testing, membership testing, linear function etc. We emphasize that our construction presents the first CCA secure PKFE for all circuits along with succinct ciphertexts.