Reviewerly/exHarmony huggingface dataset:
Arabic Handwritten Recognition System Using Deep Convolutional Neural Networks

The use of traditional chalkboards, the illnesses that result from the use of chalk such as allergies, students who find it difficult to copy their lessons written by the teacher, make our daily classes lack intelligence, however, these difficulties manifest themselves in the difficult recognition of handwritten letters and words. Hence, we are discussing these current problems by providing future solutions that are smart, to use a new term it is "the smart classroom". This has prompted extensive research into the use of deep learning in the fields of computer vision and image processing based on artificial neural networks. In this article, a new approach to the recognition of Arabic handwritten words is presented. Certainly, the recognition of Arabic handwriting is a very recent research topic in computer vision, which has several attractive applications, in particular intelligent systems, video conferencing, real-time applications etc.… In our proposed approach, Deep Convolutional Neural Networks (DCNN) are adapted to perform the classification phase. Notably the architecture "ResNet", and "VGG16", using an enriched dataset containing images extracted from the IFN/ENIT database (The names of Tunisian villages). This technique has shown very good performance for the frequent recognition problems. According to the results obtained, the developed system gives very interesting recognition rates.
High Accuracy Phishing Detection Based on Convolutional Neural Network

There are numerous web security dangers yet one of the significant web security issues is Phishing sites that focus on the human weaknesses instead of programming weaknesses. It tends to be depicted as the way toward acquiring the online clients to get their touchy data, for example, usernames and passwords. These days, phishing is one of the greatest regular web dangers as for the critical increase of the World Wide Web in volume over the long run. Phishing aggressors consistently utilize new (multi day) and complex procedures to beguile online clients. Thus, it is important that the counter phishing framework is ongoing and quick and furthermore influences from a shrewd phishing recognition arrangement. Here, we build up a very much established location framework which can adaptively coordinate with the changing climate and phishing sites. Our strategy is an on the web and highlight rich AI procedure to separate the phishing and real sites. Since the proposed approach removes various sorts of various highlights from URLs and pages source code, it is a totally customer side arrangement and doesn't need any assistance from the outsider. In this task, we offer a clever framework for finding phishing sites. The framework depends on an AI technique, explicitly managed learning. We have chosen the Logistic Regression strategy because of its great presentation in grouping. Our point is to acquire a better classifier by considering the attributes of phishing site and pick the better mix of them to prepare the grouped.
A Preliminary Study on the Construction Thought and Path of First-Class Undergraduate Major with Course Construction as the Starting Point

This paper expounds the meaning of first-class undergraduate major construction and its relationship with course construction, points out that course construction is the foundation of discipline construction and major construction, combined with the current situation of product design major construction, explores the core idea and implementation path of first-class course construction.
Advances in Machine Learning Approaches in Cancer Prognosis

Machine Learning (ML) methods have numerous promising applications in medicine, including cancer risk assessment, lesion detection using biomarkers and image segmentation, prediction of disease grading, staging, prognosis and therapy response and so on. The ML methods have the potential to improve analysis of various medical data, such as multidimensional numerical, visual and text data, compared to conventional statistical analysis. A brief overview presented in this chapter discusses the trends in predicting some types of cancer, including the application of Deep Learning (DL) methods.
CHAT BOTS AS A TOOL FOR POPULARIZATION OF MEDIA MATERIALS

The development of social networks, personalized assistants, chatbots, and computational algorithms makes the media think about new prospects. Users have long departed from the format of receiving information through one channel. Today, audiences consume content through a variety of channels, from paper noses to receiving data through eyepiece lenses. Under these conditions, journalism must experiment, because no one can predict the trajectory of further transformations of the media system. Bot programs have become indispensable assistants for media outlets. Chatbots are specially programmed computer programs that work according to a certain algorithm of actions. Nowadays, there are new types of bot programs - simple and smart. After all, with their help, you can quickly publish posts on social networks and messengers, search for the most popular topics, quickly answer questions from the audience, track the reaction to posts, and more. As well as smart programs can write journalistic material. And they work 24/7 and process a lot of information in seconds. Such bot programs provide additional tools for the media, which facilitate and optimize the work of journalists. The first bot programs were created in the 60-the 70s of the XX century. And in messengers chatbots were in the late '90s of the XX century - ICQ. But a significant impetus for the use and development of chatbots was the messenger Telegram, which has a whole bot farm. Such programs have prospects, so the editors of the leading media invest in the creation and training of such bots because they optimize the editorial process. In the study, we analyze the prospects of using media chatbots to disseminate and promote their materials and optimize editorial processes.
A New $k$-Nearest Neighbors Algorithm for Learning from Multiple Experts' Uncertain Decisions

k-nearest neighbors algorithm ( k-NN) is a simple yet powerful method that can predict an unlabeled object using the k closest samples whose labels are already known. However, in some cases such as medical treatment decision making, multiple experts can provide their individual decisions and there is no single ground truth for the labels of samples. As a result, the accuracy of the predictions can be affected by the uncertainties of historical decisions. In order to learn from uncertain decisions from multiple experts, we propose a new k-NN algorithm. This algorithm firstly measures the uncertainties of labels. Then, the prediction is made based on the uncertain labels of the k closest samples based on the Dempster-Shafer theory. We also introduce two methods to compute the optimal k value. Experiments on a real-world medical treatment dataset prove that our algorithm has better performance compared with other algorithms.
Foundations of Functional Encryption

Functional encryption is an emerging paradigm of public-key encryption that permits the enormous flexibility in accessing the ciphertext and helps to compute the function of the ciphertext using a decryption key. Nowadays, it contributes to protect the more large and complex data in the cloud. In this chapter, we will discuss the functional encryption, its relationship with fully homomorphic encryption, challenges, and future directions.
Atomic Restriction: Hardware Atomization to Defend Against Preemption Attacks

Attacks aiming at parallelism and concurrency system have a common basis, which is preemptive execution among multi-tasks. We call them preemptive execution based attacks, which include some side channel attacks and TOCTTOU exploits. For this type of attack, attackers and victims are parallel or concurrent processes or threads in general. An attacker needs to complete a series of malicious actions in the preemption window of a victim's execution. This paper summarizes the victim's preemption windows in some attacks and proposes Atomic Restriction. Based on Intel TSX, Atomic Restriction blocks the victim's preemption window by atomizing critical operations. As a universal solution, Atomic Restriction can protect the victim from preemptive execution based attacks. We verified Atomic Restriction on page cache attacks and shadow stack TOCTTOU exploits by using LLVM to implement Atomic Restriction as an automated instrumentation tool ARES. The analysis and evaluation results show that Atomic Restriction can effectively defend against preemptive execution based attacks with low performance overhead in most scenarios.
Application Research of Terrain Mapping Based on RIEGL 3D Scanning System

Abstract Based on the RIEGL 3D scanning system, this paper studies the key technical methods of 3D laser scanning technology in the whole process of data acquisition, data processing and topographic mapping, and forms an effective technical route of 3D laser scanning technology in large scale topographic mapping.The transformation relationship among the coordinate system of the scanner itself, the engineering coordinate system, the global coordinate system and the image machine coordinate system is explained.The point cloud data registration, “non-topographic point” filtering technology, arbitrary point cloud data network construction technology and other technical methods are introduced.Better results can be achieved through multi-station acquisition and the combination of GPS and ground 3D laser scanning system.
Research on the Elite Genetic Particle Filter Algorithm and Application on High-Speed Flying Target Tracking

Resampling is an inevitable process in the standard particle filter, but it also can lead to particles vanish diversity and degenerate the performance. In order to solve this problem, an elite genetic resampling particle filter is proposed in this paper. The global optimization of the genetic algorithm could keep particles move towards real state probability density function. The state estimate is corresponding to the maximum fitness state after several evolution generations. As the maximum fitness of every generation of the algorithm constitutes a non-negative bounded sub-martingale, this algorithm theoretically converges to the optimal global solution with probability 1. The estimate expression of absolute error is also concluded. The simulation demonstrates that this algorithm outperforming the particle filter using genetic operation in resampling could improve the estimation accuracy of high-speed flying targets tracking in the non-Gaussian background.
A CBRD Technique for Energy Efficient Bandwidth Image Sharing

Conventional methods for picture recovery are not upheld for the consistently far reaching picture information base. These drawbacks can be eliminated by using substance of the picture for picture retrieval. Such a picture recovery is called as Cross Batch Redundancy Detection (CBRD). Honey bees is works with CBRD is engaged around the visual highlights like shape, shading and surface. The Density-Bandwidth Energy Efficient Sharing(BEES) is a stand apart among the most locally highlight indicator and descriptors which is used as a piece of most of the vision programming. We center texture, color, shape, size, string based picture coordinating with better precision. These highlights incorporate Texture, Color, Shape and Region. It is a hot exploration zone and specialists have created numerous methods to utilize these element for precise recovery of required pictures from the information bases. In this paper we present a writing study of the Cross Batch Redundancy Detection (CBRD) procedures dependent on Texture, Color, Shape and Region. We likewise survey a portion of the cutting edge apparatuses created for CBRD.
Digital currencies: The present and the future

Subject. The article considers digital currencies in the context of global and national financial system development. Objectives. The purpose is to identify and justify the role of digital currencies in the global finance development and the place of modern central bank policies in the development of digital currencies, to underpin the need for changing approaches to digital currencies on the part of central banks, and to predict possible scenarios for the development of digital currencies on the global scale. Methods. The study applies methods of logical and statistical analysis, the comparative approach, and systematization. Results. The paper describes major pros and cons of digital currencies by comparing them with cryptocurrencies; identifies the beneficiaries of the introduction of digital currencies in countries with the most developed strategies for the introduction of national digital currencies. It also unveils barriers to a large-scale introduction of digital currencies to the world market, and highlights the main areas of their use. I developed three scenarios of digital currencies introduction to the world market. Conclusions. A large-scale introduction of digital currencies as a means of international settlements is not possible at the present stage, however, their use as a tool for dealing with financial crises is the most realistic scenario.
Digital Archiving of Archaeological Resources in Flanders (Belgium): a brief review

This article provides an overview of the current state of archaeological digital archiving in Flanders (Belgium). It briefly outlines the legally defined stakeholders and actors through which archaeological fieldwork is regulated and carried out. It subsequently describes related digital documentation and archiving practices, and guidelines. This is followed by a discussion on compliance with the FAIR Guiding Principles, and the article concludes with notes on the past and future of archaeological digital archiving in Flanders.
Marketing Strategies Design for Crowdsourcing Application in Indonesia

In an era that is very spoiled by this technology, all entrepreneurs in the digital and non-digital fields are competing to make their best products that attract the hearts of their consumers. Apart from things that are beneficial to a company, every individual has a desire to share in their daily life, but sometimes they are confused about how to channel it. One of the things people want to do is share the food they have because they have too much food at home and it has to be eaten quickly because it doesn't last long when stored. It is presented simple problems that mostly customer often encounter in everyday life, which the target market is chosen by crowdsourcing apps with social media users and search engine users. This study aims to compare how many people find out about a new application through their search engine and how many people can be influenced by advertisements on social media and are interested in finding out about the relevant information.
A Super Resolution Algorithm Based On Content Regional Division

In the field of image process research, super-resolution algorithm is very important and widely used in practice. Some traditional super-resolution algorithms run fast and keep a consistent structure between the original image and super-resolution result, but cannot rebuild the detail information. Other deep learning algorithms can achieve better results, but are time-consume. We propose a new super-resolution algorithm based on content regional division. According to the similarity of image content, the image is divided into several parts. If patches of these areas can match with the patches in the database, the corresponding algorithm is used to replace them; otherwise, the self-similar method is used for super-resolution processing. The experiment shows that we can get acceptable high-resolution images at higher speed.
A Secure Live Signature Verification with Aho-Corasick Histogram Algorithm for Mobile Smart Pad

There is a long history of using handwritten signatures to verify or authenticate a “signer” of the signed document. With the development of Internet technology, many tasks can be accomplished through the document management system, such as the applications of digital contracts or important documents, and more secure signature verification is demanded. Thus, the live handwriting signatures are attracting more interest for biological human identification. In this paper, we propose a handwriting signature verification algorithm by using four live waveform elements as the verification features. A new Aho-Corasick Histogram mechanism is proposed to perform this live signature verification. The benefit of the ACH algorithm is mainly its ability to convert time-series waveforms into time-series short patterns and then perform a statistical counting on the AC machine to measure the similarity. Since AC is a linearly time complexity algorithm, our ACH method can own a deterministic processing time. According to our experiment result, the proposed algorithm has satisfying performance in terms of speed and accuracy with an average of 91% accuracy.
Corpus-Aware Graph Aggregation Network for Sequence Labeling

Current state-of-the-art sequence labeling models are typically based on sequential architecture such as Bi-directional LSTM (BiLSTM). However, the structure of processing a word at a time based on the sequential order restricts the full utilization of non-sequential features, including syntactic relationships, word co-occurrence relations, and document topics. They can be regarded as the corpus-level features and critical for sequence labeling. In this paper, we propose a Corpus-Aware Graph Aggregation Network. Specifically, we build three types of graphs, i.e., a word-topic graph, a word co-occurrence graph, and a word syntactic dependency graph, to express different kinds of corpus-level non-sequential features. After that, a graph convolutional network (GCN) is adapted to model the relations between words and non-sequential features. Finally, we employ a label-aware attention mechanism to aggregate corpus-aware non-sequential features and sequential ones for sequence labeling. The experimental results on four sequence labeling tasks (named entity recognition, chunking, multilingual sequence labeling, and target-based sentiment analysis) show that our model achieves state-of-the-art performance.
Activity Emersion Algorithm Based on Multi-Sensor Fusion

With the popularity of wearable intelligent devices, the research of activity recognition using wearable sensors has also been developed. This paper proposes an activity emersion algorithm based on multiple movement sensors. The authors gather movement data with wearable sensors and pre-process the data. They extract 57 features in both time-domain and frequency-domain, and select useful features with an SVM-RFE algorithm. SVM is used as the classifier. Recognition accuracy for the waist sensor is 93.4%, and that for the leg sensor is 92.1%. Then, the authors fuse the recognition results and achieve an accuracy of 95.6%, which is better than either single sensor.
DTC: A Dynamic Transaction Chopping Technique for Geo-Replicated Storage Services

Replicating data across geo-distributed datacenters is usually necessary for large scale cloud services to achieve high locality, durability and availability. One of the major challenges in such geo-replicated data services lies in consistency maintenance, which usually suffers from long latency due to costly coordination across datacenters. Among others, transaction chopping is an effective and efficient approach to address this challenge. However, existing chopping is conducted statically during programming, which is stubborn and complex for developers. In this article, we propose Dynamic Transaction Chopping (DTC), a novel technique that does transaction chopping and determines piecewise execution in a dynamic and automatic way. DTC mainly consists of two parts: a dynamic chopper to dynamically divide transactions into pieces according to the data partition scheme, and a conflict detection algorithm to check the safety of the dynamic chopping. Compared with existing techniques, DTC has several advantages: transparency to programmers, flexibility in conflict analysis, high degree of piecewise execution, and adaptability to data partition schemes. A prototype of DTC is implemented to verify the correctness of DTC and evaluate its performance. The experiment results show that our DTC technique can achieve much better performance than similar work.
Verification of the RRA-Algorithm Regularization for the Analysis of Stochastic Structures in Bioinformatic Intelligent Systems

This article deals with methods for the analysis of experimental data in intelligent systems for medical monitoring, the control of diagnostic and therapy processes and biomedical facilities. These methods are relevant for creating mathematical models for dynamic systems with stochastic properties as well as for managing complex subsystems of bio information systems (BIS). The nonlinear dynamic properties of such structures and their evolution in terms of random processes and values manifest themselves as complex (non-Gaussian, polymodal) distributions to be subject to correct identification. The distribution law for stochastic characteristics is necessary to form a mathematical probabilistic model of a local object for the BIS control. Based thereon it is possible to present an experimental-mathematical model of the human respiratory or digestive system functioning, to create systems for monitoring of critical parameters in the course of therapy, to predict and synthesize medicines. When identifying the distribution laws for stochastic structures, it is necessary to solve systems of ill-conditioned equations.BIS models in the form of differential or integral equations also require to solve high-order approximating algebraic systems. Algorithms for solving ill-conditioned systems are based on regularization methods. For these purposes the article suggests the RRA-algorithm procedure. The RRA-algorithm is a variation of the Tikhonov method and it is suggested as a regularization method for first kind Fredholm integral equation. To verify the RRA-algorithm for solving systems of algebraic equations with conditioning numbers of level 1012 and higher, solutions of test first kind Fredholm integral equation are presented.
A Secure E-wallet System Using Block Chain

In 2016, the Indian government, diode by Prime Minister Narendra Singh Modi, academic degree announced that the nations two highest-denomination bank notes would stop to be legal tenders. At the time, the two denominations accounted for roughly eighty 600 of cash in circulation in India. those that possessed the banknotes were to deposit them among the bank. With the move, the Indian government aimed to penalize tax evaders wanting back. The logic was that people with hoards of black money would have to be compelled to answer queries if they tried to deposit the demonetized banknotes. Banking and technology unit really closely associated and innovations have changed banking drastically over the number of it slow. The digital innovations among the banking sector started with the introduction of money that replaced the barter system then the gradual replacement of wax seal with digital signatures. One such troubled innovation that's dynamic the banking sector globally is Block chain Technology (BCT). Block chain is shared distributed ledger that stores business group action to a permanent unbreakable chain which may be viewed by the parties during a group action. Block chain technology has the potential to disrupt the nancial business applications because it provides permanent and tamper proof recording of transactions during a distributed network.