Reviewerly/exHarmony huggingface dataset:
Secured E-Health Care Provider Using Java Script

We are implementing a health records (HISs) architecture in cells in cloud computing environments to overcome this problem. This system offers healthcare vendors, patient and physician an excessive degree of collaboration, interoperability, availability and exchange of health records. Included and stored within the Cloud garage area are electronic science records (EMRs) of patients scattered among special care organizations (CDOs), thus creating a digital healthcare record (EHRs) for any person affected. In the literature, we have seen the rising need and acceptance of cloud-environment technological improvement within the health care quarter. We have been able to see in the literature that the existing and future needs in the healthcare sector are being addressed and improved infrastructure is being implemented in the cloud world. In this paper we advocate a cloud-based platform for the health care environment that is scalable, safe, valuable and stable. In order to enforce the entry for regulatory regulation, we advise the government EHR machine on a comfortable and productive environment in which satisfactoriness can be given for handling on the basis of multi authoritarian cypher text-policy character-based encryption (CP ABE).The system proposed would enable Saudi Arabia selection makers to extend the health care sector and enjoy the current Yasser e-Authorities cloud computing platform to provide joint services in an incredibly effective, stable and reliable environment. This system seeks to provide people with fitness opportunities and amenities (G2C). In addition, in collaboration with two government dependents, multifactor applicant authentication was established and demonstrated. The frameworks involved were carried out.
Differentially Private Link Prediction in Social Networks

With the thriving of network embedding, link prediction based on network embedding has received increasing attention in recent years. Network embedding aims to learn the low dimensional vertex representations in a network while preserving the structures and inherent properties of the network. While the latent benefits of learning low dimensional representations can be tremendous for link prediction, improper releasing vertex representations may pose privacy of network individuals. Presently, protecting individual's privacy in network embedding has been largely unexplored. Addressing this goal, we propose a spectral clustering (SC) based differentially private network embedding for link prediction, namely DPHWSC. In this approach, we first present a SC based solution to reap the vertex representations. Meanwhile, the proximities of arbitrary orders can be captured and different orders of proximities can be arbitrarily weighted. Then, we leverage a sampler to approximate global sensitivity of vertex representations with high probability, achieving random differential privacy. Extensive experiments demonstrate that DPHWSC can not only yield high prediction precision, but also ensure a reliable level of privacy protection.
Super-resolution reconstruction of image based on generative adversarial network with attention module

In order to solve the problems of unstable training and texture blurring of generated images, we proposed a generative adversarial network combining residual and attention block. The attention module is added to the network, which reduces the dependence on the network depth and reduces the depth of the model. The dense connection in the residual module can extract richer image details. The number of parameters is reduced and the calculation efficiency is greatly improved. Generative adversarial network is used to further improve the texture details of the image. Generator loss functions include a content loss, a perceptual loss, a texture loss and an adversarial loss. The texture loss is used to enhance the matching degree of local information, and the perceptual loss is used to obtain more detailed features by using the feature information before an activation layer. The experimental results show that the peak signal to noise ratio is 32.10 dB, and the structural similarity is 0.92. Compared with bicubic, SRCNN, VDSR and SRGAN, the proposed algorithm improves the texture details of reconstructed images.
Artificial Intelligence: How Carbon-Based Life Has Created Silicon-Based Life

The paper considers the main stages of the development of artificial intelligence (AI). The aim of the paper is to reveal the influence of computerization on social life at present and in the future. The historical analysis method was used to identify the main stages in the development of neural networks and to compare some predictions and real achievements in the field of AI modeling. The article identifies three main periods, widely known as the first and second spring of the era of artificial intelligence and the “winter of AI” – a period of stagnation. The earliest promising developments ran into technical difficulties. At the first stage, the developers tried to copy nature, that is, the neurons of the natural brain, and the tasks turned out to be too complicated. In addition, the first algorithms for training neural networks encountered fundamental difficulties, such as exponential decay of the error vector when propagating into the inner layers of the neural network. It took years to find new algorithmic solutions, to accumulate data for training neural networks, for the next technological breakthrough, known as the “second spring of AI.” The result of the analysis showed that predictions of AI capabilities were often more modest than actual achievements. In the present paper, we make new predictions, according to which computer technology is going play not only an instrumental role but will also have a significant impact on the ideological values of society. This will happen not only due to increasing automation but also due to substitution of creative professions by machines, which will increase the segregation of society and the trend towards individualism. The problem of realism and the search for criteria of truth will also gain importance as self-programmable neural networks evolve.
Automatic Public Issue Sorting System Implemented using ML Methods

In our day to day life there are several civic issues which are being faced by each&amp; every person in the world.&amp; if we take countries just like India or any other country where population is too much, in such countries there are several civic issues which are faced by each citizen in such countries. There is another kind of people also present in respective countries, they are very sciatic who really wants to act regarding such as garbage wasting or any other issues. They register their complaint but they don’t know the status of their complaint, just because of that they leave from that topic &amp; easily move on.So, this is just an effort for finding remedy for all above mentioned issues.
Analysis of Evolved Response Thresholds for Decentralized Dynamic Task Allocation

In this work, we investigate the application of a multi-objective genetic algorithm to the problem of task allocation in a self-organizing, decentralized, threshold-based swarm. We use a multi-objective genetic algorithm to evolve response thresholds for a simulated swarm engaged in dynamic task allocation problems: two-dimensional and three-dimensional collective tracking. We show that evolved thresholds not only outperform uniformly distributed thresholds and dynamic thresholds but achieve nearly optimal performance on a variety of tracking problem instances (target paths). More importantly, we demonstrate that thresholds evolved for some problem instances generalize to all other problem instances, eliminating the need to evolve new thresholds for each problem instance to be solved. We analyze the properties that allow these paths to serve as universal training instances and show that they are quite natural. After a priori evolution, the response thresholds in our system are static. The problem instances solved by the swarms are highly dynamic, with schedules of task demands that change over time with significant differences in rate and magnitude of change. That the swarm is able to achieve nearly optimal results refutes the common assumption that a swarm must be dynamic to perform well in a dynamic environment.
Petrography, mineralogy, litho-geochemistry and fluid inclusion gold-copper deposit Gheshlaghe Mil, northwest of Saveh

The Gheshlaghe mil deposit is located in the Urumieh-Dokhtar magmatic belt. Vein- type mineralization is concordance within the rhyodacitic and rhyolite meta-tuff with Eocene age. Alteration zones of sericite, silica, argillic and iron oxides are observed in the investigated area. Primary sulfide minerals of pyrite, fine-grained chalcopyrite and Oxidized specularite and Secondary minerals goethite, limonite, malachite and quartz in Microscopic and field studies have been identified. Based on X-ray diffraction analysis studies minerals such as chlorite, illite, kaolinite, hematite, quartz, mica group and calcite have been observed in the bearing gold veins. According litho-geochemical studies, the gold element has a positive correlation with iron, molybdenum, nickel and lead. A number of 157 fluid inclusion studies in the Gheshlaghe Mil ore mineralizaion area has been showed mineralization temperature of 99 to 299 ° C, rate of salinity ore deposit 1.81 and 12.30 equivalent to the Nacl weight percent. Using Raman laser studies (13 points), the presence of gaseous phases (CO2, N2, H2O) in the fluid inclusions have been demonstrated. Regarding to studies have been done in this area, the Gheshlaghe Mil ore mineralization can to know to a vein type- hydrothermal bearing gold-copper ore deposit.
A Smart Home For All Supported By User And Context Adaptation

Homes are becoming increasingly smarter, enabling us to control their smart appliances and devices, as well as obtain relevant information on the home. However, accessibility in smart homes for all is still a challenge, with information being presented in the same way to the different users and in distinct contexts. If the interaction is not adapted to the user, certain citizens (e.g., children, and older/impaired people) can be excluded from exploiting the full potential of smart homes. Furthermore, without adaptation to the context, interaction becomes more difficult in some situations (e.g., noisy or low-light environments). With the aim of enhancing smart home accessibility, we propose a solution for adapting the information presented during interaction with the home to the user's characteristics, capabilities and preferences, as well as to the context, namely the environment's noise and luminosity, and user distance.
Up to 8k-bit Modular Montgomery Multiplication in Residue Number Systems with Fast 16-bit Residue Channels

Hardware realization of public-key cryptosystems often entails Montgomery modular multiplication (MMM), which is more efficient in residue number systems (RNS). A large pool of co-prime moduli allows for higher number of dynamically changeable moduli-set pairs for the required base extension, leading to ultra-wide key-lengths to accommodate the indispensable resistance to differential power-analysis (DPA) attacks. The moduli are often of the form 2^r-, where r denotes the width of residue channels. In a previous relevant RNS MMM design, with r=64, probability of a successful DPA attack is less than 2^(-66), where efficient arithmetic is obtained only for a limited set of moduli that are insufficient for key-lengths over 1024 bits. Here we propose a free- RNS MMM scheme, for up-to 8192-bit key-lengths and fast 16-bit residue channels, based on the proposed -independent modulo-(2^r-) adders and multipliers. Moreover, we propose an especial method for moduli selection that is required for base extension, leading to the same aforementioned DPA-resistance measure and much lower measures for key-lengths over 1024. The implementation results show 82%,69%,44% less RSA delay, for key-lengths 512,1024,2048, respectively of the home designs versus the 512-bit main reference design, and more than 5%,100% for 4096,8192 key-lengths, respectively, all per 512-bit encrypted messages.
Research on an Improvement of Images Haze Removal Algorithm Based on Dark Channel Prior

In view of the image reduction in haze weather due to aerosol scattering, the reduction of information volume and recognition accuracy of outdoor images, this paper improves the dark channel prior algorithm on the basis of the analysis of the image recovery algorithm, puts forward a solution strategy to obtain the atmospheric light value and the transmission based on the dark channel prior algorithm with different resolutions, and carries on the relevant experimental research, which effectively solves the problems of color distortion and poor real time of image haze removal.
Design of True Random Number Generator Using Fingerprint as an Entropy Source and Its Implementation in S-Box

This paper deals with the design of a true random number generator (TRNG) using the fingerprint as an entropy source and its implementation in substitution box (S-box) of Advanced Encryption Standard (AES). Considering fingerprint as a unique and random arrangement of minutiae, these minutiae points are used as the source of entropy. The proposed design utilizes fewer resources minimizing hardware redundancy and enhancing the level of randomness. This TRNG has been designed and validated using Artix-7 FPGA. The data rate, speed and latency have been obtained as 40 Mbps, 5 Mbps and 305 ns, respectively. The generated random bit stream had also been sampled and converted to a binary format in MATLAB and tested through the National Institute of Standards and Technology (NIST) 800.22 statistical suite for validation. The proposed TRNG design pass efficiency achieved is more than 95% for a sample size of 10 binary sequences.
The digitalization of training of bachelors - the future science teachers

The types of digitalization in the training of bachelors - the future science teachers are identified. The peculiarity of digitization of methodological training of future teachers is considered on the example of the author's digital educational resource of chemical education. The technology of working with the developed digital resource is presented.
rAVA: A Robot for Virtual Support of Learning

This paper proposes a strategy to improve the quality of chatbots interactions by dynamically and automatically learning new information to deal more appropriately with new discourse domains. We show some experiments in the school learning domain where our chatbot, named \(r\)AVA, was able to, after reading a book, not only respond correctly to many questions presented by the students but also to formulate its questions regarding the book’s content to the students.
Non-invertible-element constacyclic codes over finite PIRs

In this paper we introduce the notion of λ-constacyclic codes over finite rings R for arbitrary element λ of R. We study the non-invertible-element constacyclic codes (NIE-constacyclic codes) over finite principal ideal rings (PIRs). We determine the algebraic structures of all NIE-constacyclic codes over finite chain rings, give the unique form of the sets of the defining polynomials and obtain their minimum Hamming distances. A general form of the duals of NIE-constacyclic codes over finite chain rings is also provided. In particular, we give a necessary and sufficient condition for the dual of an NIE-constacyclic code to be an NIE-constacyclic code. Using the Chinese Remainder Theorem, we study the NIE-constacyclic codes over finite PIRs. Furthermore, we construct some optimal NIE-constacyclic codes over finite PIRs in the sense that they achieve the maximum possible minimum Hamming distances for some given lengths and cardinalities.
The Structures for Policing Fraud in the UK : A Critical Assessment

Due to the revolutionary development of the Internet and communication technology, fraud and related economic crimes have exploded over the past 30 years. In England and Wales, the likelihood of being a victim of fraud and computer fraud is four times higher than that of robbery. Law enforcement structures in many countries around the world are adapting from traditional crimes such as theft and robbery to new crimes such as fraud, hacking, and ransomware. In the UK, a new millennium began responding to these changes from 2000 to 2010 and new organizations such as National Fraud Intelligence Bureau (NFIB) and Action Fraud to enhance the existing limited capacity for fraudulent crime. The purpose of this study is to explain the changes in the police and judicial system in the UK to cope with the increasing number of fraudulent crimes, and to make a critical evaluation from this important point of view. The UK's fraud prevention system is theoretically very good with the establishment of the Integrated Fraud Reporting Center, Action Fraud, and NFIB, which preemptively blocks fraudulent phone numbers by analyzing and utilizing big data However, it was analyzed as a major problem that the actual manpower and resources administered to the police were very short, and that the priority of the police regarding fraud prevention was much lower than that of other crimes. It is hoped that this study will help to derive useful lessons and policy implications for Korea and other similar countries fighting the explosively increasing tsunami of fraudulent crimes.
Evaluating a Blockchain-based Method for Industrial IoT Data Confidentiality: Proof of Concept

Utilizing the Internet of Things in the industry has led to an event called IIoT (Industrial Internet of Things) due to make smart cities, communication routes, smart grids, etc. IIoT deals with various sensors, devices scattered on the edges, and cloud servers by identified standards and protocols in decentralized networks. Besides all benefits the IIoT has carried out, the data stream's security and privacy remain a debatable subject of this technology. There are many solutions to overcome security issues and confidentiality breaches, but some do not completely consider the purpose. Factors like speed, integrity, security, and power consumption must be considered, and of course, the cost factor is a significant role in achieving the goal. The purpose of this article is to introduce a new scheme evolved from Blockchain methodology to overcome privacy and data confidentiality challenges.
Semi-Supervised Learning by Exploiting Unlabeled Data Correlations in a Dual-Branch Network

How to utilize more abundant unlabeled data to effectively boost the performance of a model with limited labeled training data has attracted much attention. Recent semi-supervised learning works have achieved great success through assigning pseudo-labels to strongly-augmented versions of unlabeled data, and fitting their predictions from a model to the pseudo-labels. But the slow convergence of optimization and the neglect of unlabeled data with low confidence limit the performances of these methods. In this work, we proposed a novel framework of semi-supervised learning by Exploiting the Correlations of unlabeled data in a Dual-Branch Network (EC-DBN). In the proposed ECDBN, the augmented unlabeled data are explored to accelerate the convergence of the methods with data augmentation. Specifically, we apply strong data augmentation to one unlabeled image twice so that the model can obtain more information about unlabeled data from two strongly-augmented versions shared with the same pseudo-label. What’s more, we contract the distances between weakly-augmented version of unlabeled data and their strongly-augmented versions. To align the feature distributions of labeled and unlabeled data, a dual-branch network is also presented by introducing a consistency loss between these two branches. Our experiments demonstrate the effectiveness and better performance of our method on several standard benchmarks compared to other methods.
The experience of distance education in system of higher education in Azerbaijan

The purpose of this study is to study and analyze the experience of distance education in the higher education system of Azerbaijan. This study is a qualitative study that explores distance learning practices, and document analysis of quality methods has been used. The study identified the history of distance education, the world's first distance education schools, higher education institutions. At the same time, during the pandemic, statistical indicators of the change of the form of education and the transition to distance education and the number of students were determined in connection with the suspension of the teaching and learning process in Azerbaijan. The decisions of the Ministry of Education regarding the introduction of distance learning were commented in the study. During the pandemic, the difficulties encountered in starting distance education were identified. Complex events held in educational institutions were discussed. It reflects how distance education is applied in higher education institutions and a number of measures taken by the Ministry of Education to adapt to this form of education, which is a sudden transition for students and educators. Key words: distance education, higher education, pandemic, education system,educational platforms
Zero-Shot Learning Based on Knowledge Sharing

Zero-Shot Learning (ZSL) is an emerging research that aims to solve the classification problems with very few training data. The present works on ZSL mainly focus on the mapping of learning semantic space to visual space. It encounters many challenges that obstruct the progress of ZSL research. First, the representation of the semantic feature is inadequate to represent all features of the categories. Second, the domain shift problem still exists during the transfer from semantic space to visual space. In this paper, we introduce knowledge sharing (KS) to enrich the representation of semantic features. Based on KS, we apply a generative adversarial network to generate pseudo visual features from semantic features that are very close to the real visual features. Abundant experimental results from two benchmark datasets of ZSL show that the proposed approach has a consistent improvement.
Interval Type-2 Fuzzy Observers Applied in Biodegradation

There exist processes difficult to control because of the lack of inline sensors, as occurs in biotechnology engineering. Commonly the sensor is expensive, damaged, or even they do not exist. It is important to build an observer to have an approximation of the process output to have a closed-loop control. The biotechnological processes are nonlinear, thus in this work is proposed a fuzzy observer to endure nonlinearities. To improve the results reported in the literature, type-2 fuzzy logic was used to justify the membership functions used. The observer's gains were computed via LMIs to guarantee the observer's stability. To facilitate the fuzzy inference computation, interval type-2 fuzzy sets were implemented. The results obtained with the interval type-2 fuzzy observer were compared with a similar technique that uses a fuzzy sliding mode observer; this new approach gives better results obtaining an error 60% lower than the obtained with the other technique. They were designed three observers that work ensemble via a fuzzy relation. The best approximation was to estimate the intermediate concentration. It is important to know this variable because this sub-product was also toxic. It was concluding that by using the oxygen concentration and the liquid volume inside the reactor, the other concentrations were estimated. Finally, this result helps to design a fuzzy controller by using the estimated state. Using this approach, the estimation errors for the phenol and biomass concentrations were 49.26% and 21.27% lower than by using sliding modes.