Reviewerly/exHarmony huggingface dataset:
The Quest for NLP Applications and Tools: The Case of Standard Arabic and the Dialects

The rapid advances of natural language processing (NLP) research bring about applications and tools that serve automating Arabic at different linguistic levels. However, there is a focus on the modern standard Arabic (MSA) more than the dialects which are used heavily in day-to-day communication and require special handling. This paper introduces research efforts in Arabic NLP with emphasis on tools, applications, and resources related to core NLP areas. Some research and scholarly work come from researchers working on extended projects to compute Arabic. The findings demonstrated that research on Arabic NLP can be characterized by emphasis on morphological analysis and processing of MSA especially tokenization and tagging. Recently there has been a growing interest in processing and identifying Arabic dialects. The proposed solutions were adapted from other solutions used for MSA or designed mainly for certain dialects. Also, advances in developing transformers were applied to Arabic NLP in limited research. It appeared that ambiguity is still a big challenge to overcome and there is a need to innovate applications and tools for Arabic MSA and the dialects in parallel.
Untangling the XRP Ledger: Insights and Analysis

Over the last few years, the interest in blockchain platforms has fostered the implementation of a number of distributed ledger-based solutions for the exchange of information, assets and digitized goods in both the private and the public sectors. While proposing promising alternatives to the original Bitcoin protocol is an important goal that the bulk of the effort in blockchain community has been focused on, it may not be enough. A major challenge faced by blockchain systems goes beyond the ability to superficially explore their attack surface, and firstly must consider the importance of studying the functioning of their underlying consensus protocols also in the form of non-functional properties such as security and safety. It is to this extent that recent research has started to rigorously analyze the Bitcoin protocol and its close variants, whilst BFT-like systems have not received equal attention so far. In this paper, we focus on the XRP Ledger with the aim to lay down the first steps towards the complete formalization of its unique consensus mechanism. We provide a thorough description of its different phases and present an analysis of some of its properties, which will be suitable as a basis for future research in the same vein.
Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

Companies that produce energy transmit it to any or all households via a power grid, which is a regulated power transmission hub that acts as a middleman. When a power grid fails, the whole area it serves is blacked out. To ensure smooth and effective functioning, a power grid monitoring system is required. Computer vision is among the most commonly utilized and active research applications in the world of video surveillance. Though a lot has been accomplished in the field of power grid surveillance, a more effective compression method is still required for large quantities of grid surveillance video data to be archived compactly and sent efficiently. Video compression has become increasingly essential with the advent of contemporary video processing algorithms. An algorithm’s efficacy in a power grid monitoring system depends on the rate at which video data is sent. A novel compression technique for video inputs from power grid monitoring equipment is described in this study. Due to a lack of redundancy in visual input, traditional techniques are unable to fulfill the current demand standards for modern technology. As a result, the volume of data that needs to be saved and handled in live time grows. Encoding frames and decreasing duplication in surveillance video using texture information similarity, the proposed technique overcomes the aforementioned problems by Robust Particle Swarm Optimization (RPSO) based run-length coding approach. Our solution surpasses other current and relevant existing algorithms based on experimental findings and assessments of different surveillance video sequences utilizing varied parameters. A massive collection of surveillance films was compressed at a 50% higher rate using the suggested approach than with existing methods.
E-Aid: Open Wound Identifier and Analyzer Using Smartphone Through Captured Image

E-Aid is a study that aims to develop an application based on the convolutional neural network algorithm. The central idea for the creation of E-Aid is to provide a mobile application which offers more advanced capabilities and leads to a strong emergence for the medical health applications in the market. The reliability for the usage of CNN as an algorithm produces positive results which is essential for this study. The researchers trained CNN model that will be used later on during the execution of the CCN algorithm, and this CNN model must be able to identify 4 types of open wounds (laceration, puncture, abrasion and avulsion) and 4 types of skin burns (1st-, 2nd-, 3rd- and 4th-degree burn) and also must be able to classify it whether the wound is infected or not infected. The researchers tested the accuracy of the CNN model before sending to our respondents. The researchers tested the accuracy by getting a random image of open wounds and skin burns in the Internet and run it on the E-Aid app. After the researchers finish testing the accuracy of the app, they distributed the app to their respondents to test furthermore the accuracy and reliability of the app. The researchers’ respondents are composed of 6 medical professionals (doctors/nurses), 5 IT/CS professionals and 14 students (in the field of medicine and computer studies).
Image Multi-Label Classification Based on Pyramid Convolution and Split-Attention Mechanism

Image multi-label classification is a critical task in the field of computer vision. The primary difficulty is that multi-label classification relies on the complex information in the image to differentiate different labels, significantly increasing the classification difficulty. We proposed a method for modifying previous models. First, we use TResNet as the benchmark model, replacing ordinary convolution with pyramid convolution in the original model and the attention mechanism in the model with the split-attention method. Then the model was trained on the VOC2007 and MS-COCO data sets. The process of selecting the model's parameters and determining the optimal modification method was demonstrated through comparative experiments. Finally, by comparing the performance of the modified model with the performance of the unmodified model, it is proved that our two modification methods can effectively improve the performance of the model. On the VOC data set, the modified model by the two methods increased by 1% and 1.6%, respectively.
Educational Artificial Intelligence (EAI) Connotation, Key Technology and Application Trend -Interpretation and analysis of the two reports entitled “Preparing for the Future of Artificial Intelligence” and “The National Artificial Intelligence Research and Development Strategic Plan”

In order to further the application,research and development of artificial intelligence (AI), The Office of Science and Technology Policy of the White House released two reports commissioned “Preparation for the Future of Artificial Intelligence“ and “The National Artificial Intelligence Research and Development Strategic Plan” in October 2016. According to the reports, AI has been playing a growing role in every area of society, among which education is an important one. Educational artistic intelligence (EAI) is a new field which combinations AI with learning science. Current, the key technologies of EAI are knowledge representation, machine learning, deep learning, natural language processing, intelligent agent, affective computing, etc. The development of EAI in education focuses on the intelligent tutor and assistant, intelligent evaluation, learning partner, data mining, learning analysis, etc.On this account, there is a urgent need to strengthen the training of the workforce of EAI at all levels, in accordance with the rapid development of AI.
Creation of EMA-KN – A Knowledge Network for Ecological Momentary Assessment

Domain-specific knowledge is necessary for critical analysis and decision-making in any scientific field. As a result, it is important that we have mechanisms for collecting and applying knowledge contributed by the larger scientific community. The current paradigm involves collecting knowledge in human-readable scientific papers across various scientific journals. Extracting useful information from these papers is a labor-intensive task and the growing population of papers makes it difficult to consider older works. The implementation of a knowledge network would allow for the automation of this process, but there is no existing pipeline for the reorganization of data collected through Ecological Momentary Assessment (EMA) into knowledge graphs. In this paper, we present EMA-KN, an automatically generated knowledge graph built using the AI-KG architecture. This architecture features state-of-the-art extraction by employing the DyGIE++ and StanfordCoreNLP tools. We test our pipeline using a dataset of 74 EMA-related papers and compare the output to that of AI-KG using a dataset of 74 CS-related papers to capture the success of knowledge graph construction. Further, we evaluate knowledge graph embedding using different metrics. Results show that our pipeline has a slightly lower performance rate than AI-KG, sacrificing triple quality for domain plug-ability. In the future, we seek to improve the system to match the performance of dedicated domain-specific solutions.
Introduction to Advances in Machine Learning/Deep Learning-Based Technologies

The field of Machine Learning and its sub-field of Deep Learning are most active areas of research in Artificial Intelligence, as researchers worldwide continuously develop and announce both new theoretical results and innovative applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its readers to some of the most significant recent advances in Machine Learning/Deep Learning-based technologies. At the same time, the book aims at honouring Professor Nikolaos G. Bourbakis, an outstanding researcher in this area who has contributed significantly to the development of Machine Learning/Deep Learning-based technologies. As such, the book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent progress in Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their areas of interest.
Analysis of Algorithms for Searching Objects in Images Using Convolutional Neural Network

The problem of finding objects in images using modern computer vision algorithms has been considered. The description of the main types of algorithms and methods for finding objects based on the use of convolutional neural networks has been given. A comparative analysis and modeling of neural network algorithms to solve the problem of finding objects in images has been conducted. The results of testing neural network models with different architectures on data sets VOC2012 and COCO have been presented. The results of the study of the accuracy of recognition depending on different hyperparameters of learning have been analyzed. The change in the value of the time of determining the location of the object depending on the different architectures of the neural network has been investigated.
A Deep Learning Approach To Analyze The Sentiment Of Online Game Users

The policies to be set by online game developers can depend on user sentiment. This study aims to obtain information about the sentiment of online game users as one of the bases for decision making. The Convolutional Neural Network (CNN) algorithm is a method of deep learning that is used to obtain classification results regarding game user sentiment. The methodology used is the Cross-Industry Standard Process for Data Mining. Then preprocessing and weighting was carried out using GloVe. The accuracy value of the CNN algorithm is 81%. And it shows 80.4% is positive and 19.6% is negative. The results of this study can be used to assist in decision-making, which is then seen from the opinions of game players.
Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.
Performance Analysis of Imperialist Competitive Algorithm for Lossless Image Compression

Quite possibly the main reasons of the presence of various sorts of records with medium (sound or video) content, is accomplishing compression and a smaller amount mass, however safeguarding quality. As far as quick transportation of documents among gear and organizations and diminishing of the necessary extra room, compression has consistently been under consideration and activity. Taking into account what was referenced, as a rule, the idea of sinking the mass can be parted as two extents with loss and deprived of loss. With loss stratagem, a section of gen is superfluous, however without loss stratagems. no gen is barred for sinking the mass. On the way to glancing the section, without loss sinking stratagem is familiarized manipulating Imperialist pitiless scheming to sinking the mass. The anticipated scheming endeavors to accomplish more unconventional shading for the depiction masking enroute. it inflates the sinking proportion. Depicted recreation fallouts validate that the anticipated scheming may besieged sinking the mass by 43% and show its prevalence thought about over other comparative techniques
Designing and Implementation Quantum Half Subtractor and Full Subtractor Using IBM Quantum Experience

Today there is a lot of advent in the digital electronics with different type of circuits using quantum technology. Different quantum gates can be designed and implemented for several combinational logic circuits such as multiplexers, Demultiplexer and ALU. These are the circuits used particularly in data processing and for data manipulation. The aim of paper is to implement half subtractor and full subtractor on quantum qiskit and IBM Quantum Experience interface.
Adaptive Web Information Extraction and Intelligent Analysis Based on Improved Neural Network

With the rapid development of the Internet, the Web has become one of the most important knowledge bases. Extracting and using the knowledge efficiently and quickly has good application prospects and application value. The goal of Web information extraction is to locate and identify information of interest from multiple heterogeneous Web sites, and express the extraction results in a unified structured form. The inherent huge, heterogeneous and dynamic characteristics of Web pages on the Internet bring complexity, scalability, and adaptability to Web information extraction. This paper studies the adaptive Web information extraction and intelligent analysis based on the improved neural network.
User Interface Design Patterns for Marketplace Mobile Application in Indonesia Based on User Behavior

The number of mobile applications for online marketplace is kept increasing along with the transaction in online shopping. Each marketplace application has its user interface (UI) design pattern. Currently, there is no design pattern preference for marketplace mobile app, especially in Indonesia, which is based on user behavior. This creates a need for a recommendation of UI design patterns to help the designers building the mobile application based on user behavior. Thus, this study aims to propose a recommendation for the design patterns. Mix method was used in this study to collect data from the users of three mobile marketplace applications, which are Shopee, Tokopedia, and Bukalapak. Quantitative data was collected through an online survey, and qualitative data was gathered from interviews with respondents. Throughout the study, information about different segments in terms of personal preference design and pattern of behavior and details related to the design and behavior towards each marketplace was discovered. Those data were then analyzed to generate a collection of design patterns that can be used as reference and consideration for the next related studies and implementations in constructing a marketplace mobile application.
Learning to Fly with a Video Generator

This paper demonstrates a model-based reinforcement learning framework for training a self-flying drone. We implement the Dreamer proposed in a prior work as an environment model that responds to the action taken by the drone by predicting the next video frame as a new state signal. The Dreamer is a conditional video sequence generator. This model-based environment avoids the time-consuming interactions between the agent and the environment, speeding up largely the training process. This demonstration showcases for the first time the application of the Dreamer to train an agent that can finish the racing task in the Airsim simulator.
Hardware Architectures of Multipliers in Binary Galois Fields with Characteristic Less Than 1024

The multiplication in Galois field is actively used on various steps of algebraic decoders of BCH codes. The short codes are usually used as the component codes of more complex structures, like Open FEC. The decoding of the overall construction is usually iterative and thus requires very fast decoders of component codes. The fast hardware architectures of multipliers for short code decoders are considered in this paper and carefully compared in terms of gates consumption and critical path length for various field sizes. The best architecture candidates are proposed depending on the field size and requirements.
Ignoring messages in a digital environment: Analyze of flash mob #неотвечатьгрубо

This article provides an analysis of the statistical data of the flash mob #неотвечатьгрубо. Its essence lies in the fact that one has to respond to a long-forgotten and ignored social media messages. According to the research 79% of the people ignored strangers. 66% of those who ignored were women, the largest number (41%) of ignored messages included messages from acquaintances who shared their plans or were interested in the life of the interlocutor, the most popular reaction to an unexpected message (53%), unfortunately, was ignoring this message. The reason for this might be the fact that the majority of the participants took a snapshot of the chats immediately after sending the message. Most likely, the addressees have not yet had time to read it.
Bayesian Inference for Estimating Subset Proportions using Differentially Private Counts

Abstract Recently, several organizations have considered using differentially private algorithms for disclosure limitation when releasing count data. The typical approach is to add random noise to the counts sampled from, for example, a Laplace distribution or symmetric geometric distribution. One advantage of this approach, at least for some differentially private algorithms, is that analysts know the noise distribution and hence have the opportunity to account for it when making inferences about the true counts. In this article, we present Bayesian inference procedures to estimate the posterior distribution of a subset proportion, that is, a ratio of two counts, given the released values. We illustrate the methods under several scenarios, including when the released counts come from surveys or censuses. Using simulations, we show that the Bayesian procedures can result in accurate inferences with close to nominal coverage rates.
Triplet Mapping for Continuously Knowledge Distillation

Knowledge distillation is regarded as a widely used technology that trains a small high-performance network and knowledge is transferred from a large network (teacher) to a miniature network (student). However, the student network simulates the teacher network quickly during training, which leads the effect of knowledge transmission is extremely reduced. In this paper, we propose a triplet mapping knowledge distillation (TMKD) for continuing and efficient knowledge transfer, which contains a new structure, an assistant network. The teacher and the assistant are employed to supervise the training of the student, helping it to gain expressive knowledge from the teacher and discard the useless knowledge of the assistant. To maintain effectiveness of knowledge transfer, we map the original features of the above three networks by the multilayer perceptron (MLP). The metric function is used to calculate the difference between the original features and the mapped features that have a large difference. The experimental results demonstrate that the TMKD can obviously enhance the capability of the student. The student VGGNet13 achieves 1.67%, 3.73%, 0.42%, and 2.89% performance improvements on the CIFAR-10, CIFAR-100, SVHN, and STL-10 datasets, reaching 94.72%, 76.22%, 95.55%, and 88.18% accuracies respectively.
Information Privacy and Emerging Technologies in the UAE

The rapid developments of emerging technologies, including Big Data, Cloud Computing, and Internet of Things, are causing many societies to struggle whilst trying to keep up with, and adopt them. As a consequence, serious concerns and issues are being raised. The threat to personal information privacy is one of these issues. This review paper briefly introduces the aforementioned technologies and explores concepts related to concerns on information privacy and disclosure in the U.A.E. in the context of these technologies. In addition, related research themes that could be interesting to explore are identified, with a focus on the local environment.