Reviewerly/exHarmony huggingface dataset:
Research on Innovation and Entrepreneurship Education Reform and University Talent Cultivation Model Innovation

In recent years, in the process of continuous improvement and advancement of education reform, the importance of innovation and entrepreneurship for talent training and the improvement of students' thinking literacy have been gradually emphasized.Therefore, in this context, specific educational models and the teaching methods are improved and optimized to give full play to the training effect of applied talents.For this reason, the innovation and entrepreneurship education reform and the training model of university talents need to be more perfected, and the curriculum construction, teaching staff and teaching activities need to be optimized in many aspects, which are for reference only.
Wavelet Transform-Assisted Adaptive Generative Modeling for Colorization

Unsupervised deep learning has recently demonstrated the promise of producing high-quality samples. While it has tremendous potential to promote the image colorization task, the performance is limited owing to the high-dimension of data manifold and model capability. This study presents a novel scheme that exploits the score-based generative model in wavelet domain to address the issues. By taking advantage of the multi-scale and multi-channel representation via wavelet transform, the proposed model learns the richer priors from stacked coarse and detailed wavelet coefficient components jointly and effectively. This strategy also reduces the dimension of the original manifold and alleviates the curse of dimensionality, which is beneficial for estimation and sampling. Moreover, dual consistency terms in the wavelet domain, namely data-consistency and structure-consistency are devised to leverage colorization task better. Specifically, in the training phase, a set of multi-channel tensors consisting of wavelet coefficients is used as the input to train the network with denoising score matching. In the inference phase, samples are iteratively generated via annealed Langevin dynamics with data and structure consistencies. Experiments demonstrated remarkable improvements of the proposed method on both generation and colorization quality, particularly in colorization robustness and diversity.
Application of Deep Learning in Counting WBCs, RBCs, and Blood Platelets Using Faster Region-Based Convolutional Neural Network

Convolutional neural network (CNN) is a part of deep neural network, mainly aimed at developing machine learning (ML) models on visual imagery, which is also referred to as the shift-invariant artificial neural network due to a shared weight architecture. One such application that this chapter will be discussing is the CBC test using faster region-based convolutional neural network (RCNN). The blood consists of three major types of cells: RBCs or erythrocytes, WBCs or leukocytes, and blood platelets or thrombocytes. Traditionally, techniques such as “Coulter Counters and Laser Flow Cytometry” were used, which required a very complicated, costly, and time-consuming system for performing the CBC test. The work presented uses the power of a CNN to develop the model to count the blood cells. It uses an optimized version of CNN, known as the Faster RCNN, which processes the images quickly and efficiently. Convolutional function is a linear function used as an activation function in CNN.
Readability Level of Arabic Language Textbook of the Sixth Grade in the State of Kuwait

The current study aimed to identify the level of readability of the sixth-grade Arabic language textbooks in the State of Kuwait from the viewpoint of Arabic language teachers, and in light of the students 'performance in reading the Cloze test of these books. For the current study, a tool was prepared that was applied to a sample of (200) male and female Arabic language teachers in the middle stage. The reading Close test was applied to a sample of (320) male and female sixth-grade students. The results of the study revealed that teachers' estimates of the level of readability of reading textbooks prescribed in the book entitled "My Arabic language" were of a moderate degree, with relative weight (57.8%). The results also showed that most of the students were distributed within the depressive level of readability, with a percent of (76.9%) and (16.6%) within the educational level, and (6.5%) within the independent level. It is also showed that there were statistically significant differences between the mean individuals of the sample of both sexes on the reading Cloze test in favor of females and that there were no differences based on the educational Zone. In light of the study results, a set of recommendations was presented. Received: 18 March 2021 / Accepted: 5 June 2021 / Published: 8 July 2021
Deceptive Opinions Detection Using New Proposed Arabic Semantic Features

Some users try to post false reviews to promote or to devalue other’s products and services. This action is known as deceptive opinions spam, where spammers try to gain or to profit from posting untruthful reviews. Therefore, we conducted this work to develop and to implement new semantic features to improve the Arabic deception detection. These features were inspired from the study of discourse parse and the rhetoric relations in Arabic. Looking to the importance of the phrase unit in the Arabic language and the grammatical studies, we have analyzed and selected the most used unit markers and relations to calculate the proposed features. These last were used basically to represent the reviews texts in the classification phase. Thus, the most accurate classification technique used in this area which has been proven by several previous works is the Support Vector Machine classifier (SVM). But there is always a lack concerning the Arabic annotated resources specially for deception detection area as it is considered new research area. Therefore, we used the semi supervised SVM to overcome this problem by using the unlabeled data.
Exploring Immersive Technology in Education for Smart Cities

Today, there is a revolutionary impact on the lines of research in the field of augmented reality and virtual reality with numerous scientific applications and educational domains. As education is one of the key industries being disrupted by technology, the Fourth Industrial Revolution will see a tremendous increase in workflow automation. In this changing era, virtual reality in education systems must ensure that students are equipped with the right skills through technology that can offer a wealth of rich experimental learning opportunities, through its ability to create a digital learning environment. Recent research of augmented reality in education shows deeper student engagement, motivated learning, and better learning performance by modifying and enhancing real-life education by computer-generated sound and sights. Mixed reality integrates digital information and the real space and manipulates as though it was a physical object. In summary, augmented reality with mixed reality and virtual reality integrates digital information with digital real-world objects and puts the student in a virtual world. This chapter will give a shared understanding of the current and future applications of Immersive Technology in the education sector.
Fitness first and fatherless crossover

When programs are very large and fitness evaluation is fast (e.g. due to evaluating only a tiny fraction of the program) it can be efficient to evaluate fitness before creating the program, as only individuals with children need be created. The saving can be > 50% and even in highly converged populations, it saves e-2 = 13.5%.
YOLOv2 Deep Learning Model and GIS Based Algorithms for Vehicle Tracking

The latest advances in Deep Learning based methods and computational capabilities provide new opportunities for vehicle tracking.In this study, YO-LOv2 (You Only Look Once-version 2) is used as an open source Convolutional Neural Network (CNN), to process high-resolution satellite images, in order to generate the spatio-temporal GIS (Geographic Information System) tracks of moving vehicles.At first step, YOLOv2 is trained with a set of images of 1024 × 1024 resolution from the VEDAI database.The model showed satisfactory results, with an accuracy of 91%, and then at second step, is used to process aerial images extracted from aerial video.The output vehicle bounding boxes have been processed and fed into the GIS based LinkTheDots algorithm, allowing vehicles identification and spatio-temporal tracks generation in GIS format.
Research on Quick Inventory Framework Based on Deep Neural Network

In view of the disadvantages of the existing popular RFID (Radio Frequency Identification) book inventory in the industry, such as expensive equipment, low accuracy, and high business threshold, this paper proposes a deep neural network-based inventory framework. By loading different deep neural network algorithm modules, the proposed framework is able to complete target detection of 1D/2D barcodes in photos or video streams. Faster R-CNN is used for 2D barcode and SSD-ResNet for 1D barcode. Then the target barcode and the coordinates of the barcode are intercepted, the books are sorted by the barcode coordinates, and the values are obtained through the barcode recognition to realize the book inventory. Through inventory test on 1D/2D barcode book, the accuracy and recall rate of the framework reached above 99%, with precision rate close to 100%. Compared with RFID inventory, the proposed deep neural network based inventory method is more accurate and precise, and increased the processing speed by around 35%.
Explicit no arbitrage domain for sub-SVIs via reparametrization

The no Butterfly arbitrage domain of Gatheral SVI 5-parameters formula for the volatility smile has been recently described. It requires in general a numerical minimization of 2 functions altogether with a few root finding procedures. We study here the case of some sub-SVIs (all with 3 parameters): the Symmetric SVI, the Vanishing Upward/Downward SVI, and SSVI, for which we provide an explicit domain, with no numerical procedure required.
Learning from Users and their Interaction with a Dual-interface Shape-grammar Implementation

We present a shape grammar implementation with two new characteristics.One is that it is visual and directly manipulable: users draw the shapes and rules in a modeling application.The other characteristic is advanced technical capabilities, such as non-visual attributes and higher-order elements like surfaces.It consists of three components running in Rhinoceros3d.We also report on workshops that introduced the implementation.
Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis

Alzheimer’s Disease (AD) is associated with many characteristic changes, not only in an individual’s language but also in the interactive patterns observed in dialogue. The most indicative changes of this latter kind tend to be associated with relatively rare dialogue acts (DAs), such as those involved in clarification exchanges and responses to particular kinds of questions. However, most existing work in DA tagging focuses on improving average performance, effectively prioritizing more frequent classes; it thus gives a poor performance on these rarer classes and is not suited for application to AD analysis. In this paper, we investigate tagging specifically for rare class DAs, using a hierarchical BiLSTM model with various ways of incorporating information from previous utterances and DA tags in context. We show that this can give good performance for rare DA classes on both the general Switchboard corpus (SwDA) and an AD-specific conversational dataset, the Carolinas Conversation Collection (CCC); and that the tagger outputs then contribute useful information for distinguishing patients with and without AD
LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction

This paper describes team LCP-RIT's submission to the SemEval-2021 Task 1: Lexical Complexity Prediction (LCP).The task organizers provided participants with an augmented version of CompLex (Shardlow et al., 2020), an English multi-domain dataset in which words in context were annotated with respect to their complexity using a five point Likert scale.Our system uses logistic regression and a wide range of linguistic features (e.g.psycholinguistic features, n-grams, word frequency, POS tags) to predict the complexity of single words in this dataset.We analyze the impact of different linguistic features on the classification performance and we evaluate the results in terms of mean absolute error, mean squared error, Pearson correlation, and Spearman correlation.
Model-Based Bayesian Deep Learning Architecture for Linear Inverse Problems in Computational Imaging

We propose a neural network architecture combined with specific training and inference procedures for linear inverse problems arising in computational imaging to reconstruct the underlying image and to represent the uncertainty about the reconstruction. The proposed architecture is built from the model-based reconstruction perspective, which enforces data consistency and eliminates the artifacts in an alternating manner. The training and the inference procedures are based on performing approximate Bayesian analysis on the weights of the proposed network using a variational inference method. The proposed architecture with the associated inference procedure is capable of characterizing uncertainty while performing reconstruction with a modelbased approach. We tested the proposed method on a simulated magnetic resonance imaging experiment. We showed that the proposed method achieved an adequate reconstruction capability and provided reliable uncertainty estimates in the sense that the regions having high uncertainty provided by the proposed method are likely to be the regions where reconstruction errors occur .
Building a Knowledge Graph with Inference for a Production Machine Using the Web of Things Standard

This paper presents an architecture for a knowledge-based system (KBS) that consists of a Knowledge Graph, an inference engine and services. The Web of Things standard is used to translate sensor data from a production machine into an RDF graph. The KBS architecture is implemented for a physical twin of a real machine using open-source Semantic Web technologies. The performance evaluation of the system reveals limitations for the application in production.
A novel method for developing post-quantum cryptoschemes and a practical signature algorithm

Purpose The practical purpose of this research is to propose a candidate for post-quantum signature standard that is free of significant drawback of the finalists of the NIST world competition, which consists in the large size of the signature and the public key. The practical purpose is to propose a fundamentally new method for development of algebraic digital signature algorithms. Design/methodology/approach The proposed method is distinguished by the use of two different finite commutative associative algebras as a single algebraic support of the digital signature scheme and setting two different verification equation for a single signature. A single public key is computed as the first and the second public keys, elements of which are computed exponentiating two different generators of cyclic groups in each of the algebras. Findings Additionally, a scalar multiplication by a private integer is performed as final step of calculation of every element of the public key. The same powers and the same scalar values are used to compute the first and the second public keys by the same mathematic formulas. Due to such design, the said generators are kept in secret, providing resistance to quantum attacks. Two new finite commutative associative algebras, multiplicative group of which possesses four-dimensional cyclicity, have been proposed as a suitable algebraic support. Originality/value The introduced method is novel and includes new techniques for designing algebraic signature schemes that resist quantum attacks. On its base, a new practical post-quantum signature scheme with relatively small size of signature and public key is developed.
Financial Fraud Detection in Plastic Payment Cards using Isolation Forest Algorithm

The need for technology has always found space in Financial Transaction as the number of fraud in financial transactions increases day by day. In this research we have proposed a new methodology by using the isolation forest algorithm and local outlier detection algorithm to detect the financial fraud. A standard data set is used in experimentation to classify a transaction occurred is a fraudulent or not. We have used neural networks and machine learning for classification. We have focused on the deployment of anomaly detection algorithms that is Local Outlier Factor and Isolation Forest algorithm (IFA) on financial fraud transactions data.
Night Patrolling Robot

Patrolling should be done on a regular basis; the more the premises are under surveillance, the more difficult it is for an intruder to commit illegal crimes.They are often done by security guards or police and via CCTV monitoring. However, these are quite expensive.The introduction of autonomous mobile robots, which are intended to handle patrolling and protection difficulties, cut expenses, and improve the quality of restricted-area patrols. Here we propose a robot patrolling security that uses a night vision camera to guard any premises. Our aim is to provide a security robot built using Raspberry PI that moves around a restricted area automatically by following a visual line embedded on the floor. It has the power to watch sound within the premises. If any sound is detected, it turns towards that side.It then uses its camera to scan the environment, then collects and transmits the photographs of the scenario right away.Also if it detects any obstacles in its path, it will capture the image of the obstacle and then send it to the client. This robot will in turn address the challenges of protection and patrolling.
Recognition of Handwritten English Numerals Based on Combining Structural and Statistical Features

Generally, pattern recognition considered a strong challenge in many information processing research fields. The aim of this paper is to propose a highly accurate model for recognizing a handwritten English numeral through efficiently extracting the most valuable features of a certain handwritten numeral or digit. The handwritten English Numerals Recognition Model (HENRM) is proposed in this paper. The features extraction of the proposal based on combining both statistical and structural features of the certain numeral sample image. Mainly, the proposed HENCM has four phases which are image acquisition, image preprocessing, features extraction, and classification. In fact, four feature extraction approaches are utilized in this paper, which are the number of intersection points, the number of open-end points, calculation of density feature, and determining the chain code for each of the English numerals. The latter phase gives a features vector of 26-element size to be fed into the classifier that uses the Multi-class Support Vector Machine (MSVM) for the classification process. The experimental results showed that the proposed HENCM exhibits an average recognition rate equals to 97%. Index Terms—Chain Code, Density feature, MSVM, Recognition.
On the Edge and Cloud: Recommendation Systems with Distributed Machine Learning

Recently, recommendation systems have gained more popularity, especially in online e-commerce and offline brick-and-mortar businesses such as retailers. In these systems, the users and services transaction data are analyzed to provide reasonable and timely recommendations on products or services to the users. However, with the increasing complexity and volume of processing data, the recommendation system model must be adapted. Edge and cloud computing technologies have been adopted recently in recommendation systems to leverage such systems’ scalability. However, with such a large amount of processing data, efficient data processing techniques are still required. In this paper, we propose a distributed machine learning approach (DE-SVD) for recommendation systems that comprises two steps: a) preliminary data processing on edge servers (edge computing) and b) final data processing on centralized servers (cloud computing). Performance evaluation demonstrates the benefits of our approach in reducing data transfer size between edge servers and cloud servers and training time while preserving the quality of the model (with an insignificant reduction in recommendation performance).
Quality Evolvability ES: Evolving Individuals With a Distribution of Well Performing and Diverse Offspring

One of the most important lessons from the success of deep learning is that learned representations tend to perform much better at any task compared to representations we design by hand. Yet evolution of evolvability algorithms, which aim to automatically learn good genetic representations, have received relatively little attention, perhaps because of the large amount of computational power they require. The recent method Evolvability ES allows direct selection for evolvability with little computation. However, it can only be used to solve problems where evolvability and task performance are aligned. We propose Quality Evolvability ES, a method that simultaneously optimizes for task performance and evolvability and without this restriction. Our proposed approach Quality Evolvability has similar motivation to Quality Diversity algorithms, but with some important differences. While Quality Diversity aims to find an archive of diverse and well-performing, but potentially genetically distant individuals, Quality Evolvability aims to find a single individual with a diverse and well-performing distribution of offspring. By doing so Quality Evolvability is forced to discover more evolvable representations. We demonstrate on robotic locomotion control tasks that Quality Evolvability ES, similarly to Quality Diversity methods, can learn faster than objective-based methods and can handle deceptive problems.
Gauss-Lintel, an Algorithm Suite for Exploring Chord Diagrams

Gauss diagrams, or more generally chord diagrams are a well-established tool in the study of topology of knots and of planar curves. In this paper we present a system description of Gauss-lintel, our implementation in SWI-Prolog of a suite of algorithms for exploring chord diagrams. Gauss-lintel employs a datatype which we call “lintel”, which is a list representation of an odd-even matching for the set of integers [0,...,2n–1], for efficiently generating Gauss diagrams and testing their properties, including one important property called realizability. We report on extensive experiments in generation and enumeration of various classes of Gauss diagrams, as well as on experimental testing of several published descriptions of realizability.
Universal Framework for Quantum Error-Correcting Codes

We present a universal framework for quantum error-correcting codes, i.e., a framework that applies to the most general quantum error-correcting codes. This framework is based on the group algebra, an algebraic notation associated with nice error bases of quantum systems. The nicest thing about this framework is that we can characterize the properties of quantum codes by the properties of the group algebra. We show how it characterizes the properties of quantum codes as well as generates some new results about quantum codes.