Reviewerly/exHarmony huggingface dataset:
An Exploration of the Relationship Between Mathematics Self-efficacy and Mathematics Learning Achievement Based on the Learning Style of Field and Silverman Model

This research aims toknow the learning styles according to the model of Felder and Silverman and its relationship to effectively self-perceived mathematicalamong students of the Faculty of Education Pure Sciences -Ibn al-Haytham. By answering the following questions: 1. What are the preferred methods of learning among students in the mathematics department according to the model Felder and Silverman? 2. What is the mathematicalself-perceived levelof the students at the Department of Mathematics effectiveness level? 3. What is the relationship between learning styles according to the Felder model and Silverman and the effectiveness of mathematical self-perceived of the students of the Department of Mathematics? The research sample consisted of 80 male and female students from the fourth grade students in the Department of Mathematics in the College of Education Pure Sciences -Ibn al-Haytham for the academic year 2015/2016. As for the two tools the first search tools is a measure of learning methods developed by (Felder and Silverman, 1988) Translation (Al-Zubaidi, 2015) which consisted of 44 items distributed on four areas: (practical -Contemplative) -(sensory -intuitive) -(optical -pronunciation) -(sequential -total).And the second tool and is a measure of the effectiveness of sports self-perceived, which consists of a paragraph (30), were reliability and validity account and conducted statistical analyzes to calculate the difficulty and ease transactions. After the use of appropriate statistical methods to analyze the results, the results indicated that (1) 37.5% of the students have more than one level indicating no preference for any style of learning. (2) 30% of the students' own (visual -verbal)method (3) 16.25% of the students' own (sensory -intuitive)method. (4) 11.25% of the students' own (sequential -total)method. (5) 5% of the students' own (practical -Contemplative)method. (6) Students did not prefer any of the styles (intuitive -verbal -total). (7) the students in grade IV Department of Mathematics in the College of Education Pure Sciences / Ibn al-Haytham students have aware of the effectiveness of mathmatics. (8) There is no relationship between the learning styles according to the model Felder and Silverman and the effectiveness of sports self-conscious of the students of the Department of MathematicsTherefore, the researcher recommends that students should be aware of the methods and activities that are compatible with their learning methods, as well as the development of their perceived mathematical effectiveness to enhance their ability to solve any mathematical task, no matter how difficult and flexible it may be.In the light of the results of the research, a number of proposals were developed.
Proving Obliviousness of Probabilistic Algorithms with Formal Verification

Obliviousness is a security feature to protect sensitive information from an algorithm’s observable behaviours. For better run-time performance, many oblivious algorithms published recently are probabilistic instead of deterministic, but this also brings difficulties to reason about them. We introduce some challenges, works and further ideas in this paper, including the concrete contribution of extending PSL for verifying one of the random oblivious algorithms. We also present my further PhD research plan on this topic.
Explicit Code Reuse Recommendation

Code reuse is a common and recommended practice. However, to avoid code duplication reuse may require substantial refactoring effort, especially in legacy code with which the developer is not profoundly familiar. For this reason, developers often prefer to reimplement simple code fragments rather than properly reuse them. In this work, we describe the use of annotations to recommend a useful piece of code for subsequent reuse. These annotations then guard the code against changes that can complicate its extraction, thus allowing subsequent developers to easily locate and reuse it.
Verification of Hardware and Software with Fuzzing and Proofs

Automating formal verification of safety and security prop-erties in both hardware and software systems is challengingdue to a number of issues. In this paper, we tried to addresstwo important challenges in this regard. The first challengethat we discuss is the scalable co-verification of hardwareand firmware in a modern system-on-chip (SoC) platformto guarantee end-to-end security of the system. We havediscussed two specific problems in this regard that we havetried to solve and our approach to address this challenge, (i) we designed a verification methodology for end-to-end secu-rity property verification for authenticated firmware loaderprotocol running on a SoC platform, and (ii) we proposeda unified framework called HyperFuzzing, for specifyinghardware security properties and automatically testing forviolations of these properties using fuzzing. The second chal-lenge we have discussed in this paper is to verify hardwareor software systems that use closed-box functions or opera-tions. To address this challenge, we have introduced a newtheory for SMT solvers, called closed-box function theory,and have implemented it in our prototype solver Sādhak. Our solver Sādhak can handle SMT constraints with closed-box functions, which can be used by verification and testingtools for solving closed-box constraints.
Historical perspective of code clone refactorings in evolving software

Cloning in software is generally perceived as a threat to its maintenance and that is why it needs to be managed properly. Understanding clones from a historical perspective is essential for effective clone management. Analysis of code refactorings performed on clones in previous releases will help developers in taking decisions about clone refactoring in future releases. In this paper we perform a longitudinal study on the evolution of clone refactorings in various versions of five software systems. To perform a systematic study on clone refactoring evolution, we define clone evolution patterns for studying refactorings in a formal notation. Our results show that only a small proportion of code clones are refactored between the versions and most of the refactorings are inconsistent within clone classes. Moreover, clone refactorings may cause clone removal. Analysis of the source code of refactored clones reveals similar reasons of inconsistent refactorings and clone removal for five Java systems. This analysis will help in devising appropriate strategies for managing clone refactorings in software and hence provide foundation for devising better clone management tools.
Smart OCR for Recognizing Bangla Characters with CRAFT and Deep Learning Models

Optical Character Recognition (OCR) is a popular technology for a variety of applications. The purpose of implementing optical character recognition is to turn printed text into editable text. OCR accuracy can be influenced by text preparation and segmentation techniques. Occasionally, it might be difficult to obtain text from an image due to its varying size, style, orientation, and intricate backdrop, among other factors. If the printed or handwritten texts are in Bangla font, the work gets more difficult. Because Bangla language has 50 characters and 24 scrape characters for example, etc. Moreover, in bangla language there are several dipthong or coumpound letter for example, etc. Therefore, even though there are several OCR-related works really good for the English font, for Bangla, we do not have without NLP (Natural Language Processing). So here We have tried to cover both the English and the Bangla font with our proposed model using crafting, VGGNet and various other machine learning algorithms such as LSTM and CTC.
Integral Sampler and Polynomial Multiplication Architecture for Lattice-based Cryptography

With the surge of the powerful quantum computer, lattice-based cryptography proliferated the latest cryptography hardware implementation due to its resistance against quantum computers. Among the computational blocks of lattice-based cryptography, the random errors produced by the sampler play a key role in ensuring the security of these schemes. This paper proposes an integral architecture for the sampler, which can reduce the overall resource consumption by reusing the multipliers and adders within the modular polynomial computation. For instance, our experimental results show that the proposed design can effectively reduce the discrete Ziggurat sampling method in DSP usage.
On ensuring data integrity in data aggregation protocols in IoT environments

Data aggregation is the process of summarizing and combining data to reduce the amount of data transmission in the network. Data aggregation has become an absolute necessity for some IoT applications. In this sense, several aggregation schemes have been proposed but have dealt with security issues. Data confidentiality, authentication, and integrity are the primary security requirements for this type of aggregation scheme. In this paper, we are interested in authentication and data integrity. We present the most used signature algorithms, namely, ECDSA and BLS. We analyze these two signature schemes regarding security, the efficiency of calculations, and communications. In fact, we show that in the case of200 IoT devices, the aggregator needs 48,24 ms to perform a batch verification with ECDSA, while 289,44 ms are needed with BLS. Finally, we present a comparative study and recommendations for use.
Pseudo Random Number Generator Based on Chaos Theory and Artificial Neural Networks

In recent years, developing new methods for designing Pseudo-Random Number Generators (PRNGs) has attracted the attention of researchers for their efficiency in many crypto-graphic applications such as communication and data security. In this work, an innovative hybrid design of a PRNG based on Artificial Neural Networks (ANNs) and chaos theory is proposed. The training data for fitting the proposed ANN model was generated from the numerical solution of the system of differential equations driving the dynamics of the Lorenz system. After that, this model is evaluated using the mean square error (MSE) metric. The work’s major contributions are focused on enhancing the MSE and expanding the keyspace in order to better prevent brute force attacks. In comparison to prior research, the suggested ANN model improved efficiency the MSE criteria, by reaching a value of $2.3751\times10^{-07}$, and increased the keyspace dimension by a factor up to 3 folds. Moreover, the statistical performances of the proposed PRNG have been evaluated. Generated sequences have been successfully passed the NIST SP 800-22 tests battery. Comparing to traditional chaos-based PRNGs designing methods, these results confirm that the ANN-based approach is a suitable and efficient alternative for developing future PRNGs dedicated to cryptographic applications.
Combined textural-Gabor Wavelet descriptors for Sign and Hand Gesture Recognition

Sign language, also known as the silent language, is a vital communication tool for the hearing-impaired society where the communication represents a significant challenge. In this paper, we propose a gesture recognition system implementation for both static and dynamic gesture (Jochen Triesch’s static dataset for American Sign Language ASL, and Sebastien Marce’s dynamic database) in a uniform background. The system is based on Gabor features, Histogram of Oriented Gradients (HOG), Center-Symmetric Local Binary Patterns (CSLBP), Local Directional ZigZag Pattern (LDZP) and Local Energy based Shape Histogram (LESH) where the recognition task is applied using the one against all Support Vector Machine (SVM) classification technique. Individual textural and combined Gabor-textural descriptors are also studied to show the efficiency of the oriented Gabor filters. The combined Gabor-HOG architecture achieved the best recognition rate where 98.33 percent was obtained for ASL.
Fine-Grained Graptolite Image Classification Based on Multi-Scale Deep Learning

Graptolite is the fossil of the graptolite fauna. Experts can identify the chronological order of strata through graptolite species. Graptolite image classification is much more challenging than traditional fine-grained image classification tasks due to the impacts of geological layer mining extrusion, light and noise. In this paper, we propose a multi-scale deep learning method to tackle these problems. By integrating feature from different scale learning, we can accurately locate the discriminative regions. Training these discriminative part images can further identify the subtle differences, and the proposed model is optimal for adapting to the graptolite classification task. Experimental results on the benchmark graptolite dataset show that our method achieves the state-of-the-art performance.
Ship Image Compression Method Based on HSV Color Space and Kernel Principal Component Analysis

With the development of maritime communication technology, the demand for image data from ships is increasing. However, compared with land communication technology, marine communication technology still has the disadvantages of limited transmission bandwidth, high transmission cost, and poor transmission stability, which are very unfavorable to the transmission of ship images. Therefore, it is necessary to study new image compression technology without significant changes in image quality. This paper proposes a ship image compression algorithm based on HSV-KPCA to improve the transmission efficiency of ship images. Through the KPCA algorithm, the image data can be mapped to a high-dimensional space, and the initially linear and indistinguishable data in the image becomes linear and distinguishable so that the main nonlinear features can be extracted effectively. By setting a fixed cumulative contribution rate threshold, only the principal components containing a large amount of information can be retained, and other main parts containing unimportant information can be discarded to reduce the data volume of the image. The reconstructed image can be obtained by using these retained feature vectors. Then the reconstructed image data is converted from RGB color space to HSV color space to improve the value component of the reconstructed image. Finally, the reconstructed image is converted to RGB color space again to obtain the compressed image. Finally, the compression method’s performance was evaluated using the compression ratio, mean square error, peak signal-to-noise ratio, and structural similarity to compare the algorithm’s performance. The experimental results show that the HSV-KPCA compression algorithm proposed in this paper has a better compression effect compared with the current popular JPEG and PCA image compression algorithm.
Wearable devices for Health Remote Monitor System

It is feasible to see how communication and information technology have advanced at a rapid pace in today’s world. The introduction of wearable technology is one aspect contributing to this progress and has the potential to be an innovative solution to healthcare challenges since it may be utilized for illness prevention and maintenance, such as physical monitoring, as well as patient management. In order to solve some of the healthcare challenges, this paper proposes the development of an intelligent health monitoring system with alerts and continuous monitoring using wearable devices capable of collecting biometric data on human health. The concept was then proven by the development of a prototype using sensors connected to a micro-controller which transmits its information via MQTT to a Node-RED powered dashboard that handles the health metrics monitoring. The designed prototype has proven satisfactory to provide evidences that support the developed research questions.
Interoperability Between Information Systems Concerning Electronic Records of Patients

The problematic over the interoperation between institutions in underdeveloped countries always presents good opportunities for science to contribute to substantial improvements in the real-world issues. The basic support systems such as the health institutions are among the ones that can most benefit from scientific advances. This paper reports an analysis done over the interoperability between health institutions, specifically regarding the interaction between medical centres and clinical laboratories where the main interoperability instrument is the patient record. This research was validated with a real practical use case that is presented in this paper. In order to make the information stored in different software applications of the national health system (SIS), more specifically in the Dr. Ayres de Menezes hospital, in the country of São Tomé and Príncipe, interoperable with the information systems of the clinical laboratories that support the hospital, two different applications were developed (Patient Management System and Clinical Analysis Laboratory Management System) to implement the interoperability between them. The Patient Management application requests medical exams from your Medical Appointment dashboard. The second application receives the exam request and after exams are processed and validated, the second system sends the result to the requesting application.To make the interoperability service effective, the SOAP protocol was used, which allowed the exchange of information synchronously between these two applications, allowing for faster transactions of patients' pathological data, and greater confidentiality of this same information.
A Multiplication-Free FPGA Implementation of Multiple RLWE Encryption using Anti-Circulant Matrix

With the expansion of cloud services and privacy security becomes more crucial, fully homomorphic encryption (FHE) scheme which operates data in ciphertext domain has been widely concerned. Lattice-based cryptography ring learning with errors (RLWE), together with its variants RLWE’ and RGSW, form the core component of main-stream homomorphic encryption schemes. This paper presents a universal RLWE encryption module supports ciphertext generation of three cryptographic schemes above. The design uses anti-circulant matrix to convert polynomial multiplication to N-level inner product between vector and anti-circulant matrix to wipe off additional modular reduction. Meanwhile, Montgomery reduction is used to realize multiplicationfree polynomial multiplication. The results show that the utilizations of LUT and FF are reduced by 51% and 30% respectively, as well as completely avoiding DSP usage.
Personalized News Recommendation with CNN and Multi-Head Self-Attention

With the globalization of information dissemination and information reception, it is especially important to obtain accurate user and news representations to recommend limited news that matches users’ real interests in the infinite richness of news. Existing user representations are usually single and ignore the interrelationship between different components (e.g., titles, categories, and bodies) in the news. In this paper, we propose a personalized news recommendation approach using a convolutional neural network (CNN) and multi-head self-attention. The core of our approach is a user encoder and a news encoder, In the user encoder, we use multi-head self-attention to learn user representations from the news that users have browsed. In the news en-coder, we use convolutional neural networks to obtain local contextual information of words from news components and then use multi-head self-attention to model the interrelationship of words between different components of the news to learn news representations from multi-view news components. In addition, we use attention to select more important words and news components to enrich the user and news representations, and the experimental results show the advancement of our approach on real-world dataset.
Evaluation of Multiple Convolutional Neural Networks in Training the NVIDIA JetBot

Deep Learning has risen in popularity with the idea of "teaching" a robot to drive itself. Knowing where to begin research in this category can be difficult. This paper aims to discover the optimal Convolutional Neural Network(CNN) to use in a Deep Learning-based Self-Driving robot(JetBot). The JetBot intends to use the fully trained CNN to drive across a fake road without any human intervention. The CNNs used in this paper are imported from PyTorch for Reinforcement-Learning style training. The pre-trained models are imported to decrease the margin of error of the results. In order to find this optimal CNN combination, a "for each" training style is taken. For each CNN, every available PyTorch Optimizer Algorithm is tested, and the results are recorded. After all experimentation concludes, the highest performing Optimizer Algorithm for each CNN is found and graphed to compare to the other combinations. Numerical results show that ResNet18 with the Adam optimizer performs best for the JetBot.
Echocardiography Segmentation Based on Cross-modal Data Augmentation Method

Deep learning algorithms have been widely applied in the field of medical image analysis. However, it is very difficult and time-consuming to obtain a large number of accurate samples and their corresponding labels. Deep neural networks can be difficult to train without a large number of samples, which may lead to unsatisfactory performance. Currently, synthesizing medical image samples is an effective solution to this problem. One of the commonly adopted methods for synthesizing labeled ultrasound images is simulation-based approach, such as Field II software. Although these simulation-based methods have demonstrated acceptable performance, they are not only time-consuming, but also require expert experience to fine-tune parameters. Therefore, we propose a CycleGAN-based method to synthesize annotated ultrasound images from computed to-mography (CT) images. Our proposed method is validated on the left ventricle segmentation task. Experimental analyses demonstrate that our proposed annotated sample generation method outperforms the general CycleGAN method in the context of segmentation results.
Type System for Four Delimited Control Operators

The operational behavior of control operators has been studied comprehensively in the past few decades, but type systems of control operators have not. There are distinct type systems for shift, control, and shift0 without any relationship between them, and there has not been a type system that directly corresponds to control0. This paper remedies this situation by giving a uniform type system for all the four control operators. Following Danvy and Filinski’s approach, we derive a monomorphic type system from the CPS interpreter that defines the operational semantics of the four control operators. By implementing the typed CPS interpreter in Agda, we show that the CPS translation preserves types and that the calculus with all the four control operators is terminating. Furthermore, we show the relationship between our type system and the previous type systems for shift, control, and shift0.