Reviewerly/exHarmony huggingface dataset:
Improving Automated Arabic Essay Questions Grading Based on Microsoft Word Dictionary

There are three main types of questions: true/false, multiple choice, and essay questions; it is easy to implement automatic grading system (AGS) for multiple choice and true/false questions because the answers are specific compared with essay question answers. Automatic grading system (AGS) was developed to evaluate essay answers using a computer program that solves manual grading process problems like high cost, time-consuming task, increasing number of students, and pressure on teachers. This chapter presents Arabic essay question grading techniques using inner product similarity. The reason behind this is to retrieve students’ answers that more relevance to teachers’ answers. NB (naive Bayes) classifier is used because it is simple to implement and fast. The process starts by preprocessing phase, where tokenization step divides answers for small pieces of tokens. For normalization step, it is used to replace special letter shapes and remove diacritics. Then, stop word removal step removes meaningless and useless words. Finally, stemming process is used to get the stem and root of the words. All the preprocessing phase is meant to be implemented for both student answer and dataset. Then, classifying by naive Bayes classifier to get accurate result also for both students’ answers among with dataset. After that, using Microsoft Word dictionary to compare and get enough synonyms for both students’ answers and model answers in order to have exceptional results. Finally, showing results with the use of inner product similarity then compare the results showed by inner product similarity with human score results so the evaluation among with the efficiency of the proposed technique can be measured using mean absolute error (MAE) and Pearson correlation results (PCR). According to the experimental results, the approach leads to positive results when using MS dictionary and improvement Automated Arabic essay questions grading, where experiment results showed improvement in MAE is 0.041 with enhanced accuracy is 4.65% and PCR is 0.8250.
Construction of the EFL Mobile Learning Model in the Hybrid Distributed Terminal

With the integration of technology in higher education, mobile learning plays an important role in EFL classes. In order to optimize the efficiency of EFL learning, a mobile learning model in the hybrid distributed terminal is constructed. The online platform supporting both synchronous and asynchronous learning with three types of interaction helps build a blended learning model and creates a closed loop for ubiquitous online and offline learning with multiple evaluation, which also builds a community of inquiry-based learning to facilitate collaborative study and deep thinking. Aiming to improve the resource scheduling performance of learning information sharing mode, based on the traditional sharing mode hardware, the mobile learning software for the hybrid distributed terminal is thus optimized. Learning resources are mined and fused to realize the scheduling of learning information resources, and user interface management function is designed to construct the EFL mobile learning mode with the hybrid distributed terminal.
BLOCKCHAIN IN PUBLIC SECTOR

The Internet has radically transformed access to and exchange of information, and it plays a fundamental role in today's society and economy. Yet it was difficult to imagine all the services offered today when the first tools necessary for its democratization were introduced thirty years ago. The blockchain, since the publication of the white paper of bitcoin as the first digital asset, has considerably broadened the range of its fields of application and its potential use cases in ten years; it could play, in different forms, a role comparable to that of the Internet in an area that the latter does not cover: trusted IT and legal transactions. Indeed, while the Internet is the privileged vehicle for the exchange of all digitized forms of information, it is not the guarantor, as a network infrastructure, of its uniqueness or its functioning. Blockchain technology has evolved from a niche subject to the hottest tech disruption buzzword, but there is still a lot of confusion about the subject. Without a clear understanding about what Blockchains are, their potential public sector potential impact is sometimes misunderstood or, more often, ignored. Questions related to their technical complexity, risk, security, and appropriateness often serve as obstacles to government officials’ ability to truly engage with this emerging technology. In this article, we consider the key features and types of blockchain technology and describe the potential use of blockchain technology in the public sector. In general, the blockchain can be used in the public sector to address the following tasks: authentication, traceability and uniqueness. We have identified ten potential directions of using blockchain technologies in public sector: self-sovereign identity, contract and vendor management, notarization, aid management, secure data sharing, financial services and banking, voting, verifiable diplomas and certificates, energy utilities, copyrights.
On the Longest Common Cartesian Substring Problem

Abstract A Cartesian tree is associated with a string of numbers and is structured as a heap from which the original string can be recovered. Although Cartesian trees have been introduced 40 years ago, the Cartesian tree matching problem appeared very recently. It consists in finding all substrings of given text, which have the same Cartesian tree as that of a given pattern. In this paper, we address the problem of computing the longest common Cartesian substrings of two strings and present three methods for such problem. Our first method is based on a classical suffix tree construction and solves the problem in randomized linear time and linear space, although the space overhead is quite prohibitive in the case of large strings. Our second solution is based on classical dynamic programming, and our third solution is based on a constructive approach. Both of them run in quadratic worst case time but are more space economical in practice. From our experimental results, it turns out that our second solution runs faster than the standard suffix tree solution for short strings, whereas our third solution is more suitable for large strings, when storing a full suffix tree becomes prohibitive.
Can you sign a quantum state?

Cryptography with quantum states exhibits a number of surprising and counterintuitive features. In a 2002 work, Barnum et al. argue that these features imply that digital signatures for quantum states are impossible (Barnum et al., FOCS 2002). In this work, we ask: can all forms of signing quantum data, even in a possibly weak sense, be completely ruled out? We give two results which shed significant light on this basic question. First, we prove an impossibility result for digital signatures for quantum data, which extends the result of Barnum et al. Specifically, we show that no nontrivial combination of correctness and security requirements can be fulfilled, beyond what is achievable simply by measuring the quantum message and then signing the outcome. In other words, only classical signature schemes exist. We then show a positive result: a quantum state can be signed with the same security guarantees as classically, provided that it is also encrypted with the public key of the intended recipient. Following classical nomenclature, we call this notion quantum signcryption. Classically, signcryption is only interesting if it provides superior performance to encypt-then-sign. Quantumly, it is far more interesting: it is the only signing method available. We develop "as-strong-as-classical" security definitions for quantum signcryption and give secure constructions based on post-quantum public-key primitives. Along the way, we show that a natural hybrid method of combining classical and quantum schemes can be used to "upgrade" a secure classical scheme to the fully-quantum setting, in a wide range of cryptographic settings including signcryption, authenticated encryption, and CCA security.
Shortest covers of all cyclic shifts of a string

A factor C of a string S is called a cover of S, if each position of S is contained in an occurrence of C. Breslauer (1992) [3] proposed a well-known O(n)-time algorithm that computes the shortest cover of every prefix of a string of length n. We show an O(nlog⁡n)-time and O(n)-space algorithm that computes the shortest cover of every cyclic shift of a string of length n and an O(n)-time algorithm that computes the shortest among these covers. We also provide a combinatorial characterization of shortest covers of cyclic shifts of Fibonacci strings that leads to efficient algorithms for computing these covers. We further consider the bound on the number of different lengths of shortest covers of cyclic shifts of the same string of length n. We show that this number is Θ(log⁡n) for Fibonacci strings.
Fractional Stochastic Gradient Descent Based Learning Algorithm For Multi-layer Perceptron Neural Networks

Neural Networks are indispensable tools in adaptive signal processing. Multi-layer perceptron (MLP) neural network is one of the most widely used neural network architecture. The performance is highly subjective to the optimization of learning parameters. In this study, we propose a learning algorithm for the training of MLP models. Conventionally back-propagation learning algorithm also termed as (BP-MLP) is used. It is a type of stochastic gradient descent algorithm where performance is governed by eigen spread of the input signal correlation matrix. In order to accelerate the performance, we design a combination of integral and fractional gradient terms. The proposed fractional back-propagation multi-layer perceptron (FBP-MLP) method is based on fractional calculus and it utilizes the concept of fractional power gradient which provides complementary information about the cost function that helps in rapid convergence. For the validation of our claim, we implemented leukemia cancer classification task and compared our method with standard BPMLP method. The proposed FBP-MLP method outperformed the conventional BP-MLP algorithm both in terms of convergence rate and test accuracy.
Security-Induced Lock-In in the Cloud

Cloud services providers practice security-induced lock-in when employing cryptography and tamper-resistance to limit the portability and interoperability of users’ data and applications. Moreover, security-induced lock-in and users’ anti-lock-in strategies intersect within the context of platform competition. When users deploy anti-lock in strategies, such as using a hybrid cloud, a leader–follower pricing framework increases profits for cloud services providers relative to Nash equilibrium prices. This creates a second-mover advantage, as the follower’s increase in profits exceeds that of the leader owing to the potential for price undercutting. By contrast, introducing or enhancing security-induced lock-in creates both an increase in profits and a first-mover advantage. Cloud services providers therefore favor security-induced lock-in over price leadership. More broadly, we show why standardization of semantics, technologies, and interfaces is a nonstarter for cloud services providers.
Contactless Attendance Tracking using Face Recognition and Sensor based Techniques: A Pilot Study

Stepping out of home without mask is impossible in today’s situation due to the global pandemic. This crisis has forced the world to shut down affecting its economy, day-to-day routine, and people’s livelihood. Today, people are adapting to this new normal in a gradual manner. Therefore, making contactless attendance tracking and screening is an essential part in all the organizations. This will be solved by building a technology which will do multiple functions like detecting faces of people with their masks on and reading their body temperature involving various techniques computer vision and deep learning using Python, OpenCV, and TensorFlow/Keras and also some sensors like Infrared Temperature Sensor. The output values of these modules will be uploaded in cloud storage and can be viewed remotely. It is highly possible to reduce the spread of the virus and promote social distancing as per the government norms by using such technology driven solutions. The aim of this paper is to provide a technical review on such existing technologies and suggesting some solutions for additional features.
Just-in-Time Defect Prediction Technology based on Interpretability Technology

In recent years, in the field of software defect prediction, researchers have proposed the Just-in-Time defect prediction technology, which can predict whether there are defects in each code change submitted by developers. This method is instant and easy to trace. However, the accuracy of Just-in-Time defect prediction is affected by the imbalance of data set categories. 20% of the defects in the software engineering field may exist in 80% of the modules. In most cases, code changes that do not cause defects account for a larger proportion. Therefore, there is an imbalance rate in the data set, that is, the imbalance between the minority and majority categories, which will affect the classification prediction effect of the model. Most types, that is, code changes that will not produce defects will make the model have an artificially high prediction accuracy, and it is difficult to obtain the expected results in practical applications. Moreover, the data set features contain many irrelevant features and redundant features, which will also increase the complexity of the prediction model. In order to improve the prediction efficiency of just in time defect prediction. Improve the interpretability and transparency of the model and establish the trust relationship between users and decision-making model. For this reason, we have established a RandomForest defect prediction model, using multiple different types of change features to study 6 open source projects from different fields. The model is explained to a certain extent using LIME interpretability technology . Using interpretability methods to extract features and trying to reduce the developer’s workload as much as possible. Our research results show that through the interpretability of the defect prediction model and identifying key features, 45% of the original workload can be used, and 96% of the original work effect can be achieved.
Learning to complete incomplete hearts for population analysis of cardiac MR images

Cardiac MR acquisition with complete coverage from base to apex is required to ensure accurate subsequent analyses, such as volumetric and functional measurements. However, this requirement cannot be guaranteed when acquiring images in the presence of motion induced by cardiac muscle contraction and respiration. To address this problem, we propose an effective two-stage pipeline for detecting and synthesising absent slices in both the apical and basal region. The detection model comprises several dense blocks containing convolutional long short-term memory (ConvLSTM) layers, to leverage through-plane contextual and sequential ordering information of slices in cine MR data and achieve reliable classification results. The imputation network is based on a dedicated conditional generative adversarial network (GAN) that helps retain key visual cues and fine structural details in the synthesised image slices. The proposed network can infer multiple missing slices that are anatomically plausible and lead to improved accuracy of subsequent analyses on cardiac MRIs, e.g., ventricle segmentation, cardiac quantification compared to those derived from incomplete cardiac MR datasets. For instance, the results obtained when compensating for the absence of two basal slices show that the mean differences to the reference of stroke volume and ejection fraction are only -1.3 mL and -1.0%, respectively, which are significantly smaller than those calculated from the incomplete data (-26.8 mL and -6.7%). The proposed approach can improve the reliability of high-throughput image analysis in large-scale population studies, minimising the need for re-scanning patients or discarding incomplete acquisitions.
SBOF: An End-to-End Framework for Simulative Black-Box Optimization of Hybrid Perceptive Functions

The adaptation of perceptive functions to new tasks or new domains is currently a major challenge in the field of autonomous driving. Since the creation of manually labeled datasets is generally time-consuming and cost-intensive, training approaches based on virtual simulation are becoming more relevant. Current approaches focus on the usage of virtual simulation to adapt machine learning models. However, in many automotive applications, perceptive functions consist of combined machine learning and traditional-rule-based approaches, called hybrid approaches. Rather than changing underlying machine learning models, we approach the task of domain adaptation of such hybrid perceptive functions by the optimization of often neglected and arbitrary black-box parameters. We therefore present a general methodology for end-to-end black-box optimization of hybrid perceptive functions for a specific target domain using virtual simulation. In a proof-of-concept, we carried out experiments to adapt a hybrid 3D object detector to the KITTI domain, using the simulator CARLA. Without the need for manually labeled data, our proposed methodology cannot only improve performance in the simulated target domain but can also improve performance in the real target domain. Furthermore, we compare the effectiveness of different black-box optimization algorithms for this specific task. Based on convergence behavior and the maximum achieved performance, we recommend the Covariance Matrix Adaptation Evolution Strategy (CMA-ES).
People, Ideas, and the Path Ahead

While recent advances in machine learning have yielded impressive results, researchers, practitioners, and even companies are beginning to recognize that true artificial intelligence requires much more sophisticated reasoning capabilities. Knowledge representation and declarative programming are arguably in a premier position to aid in the achievement of such capabilities. In this paper, I reflect on people and ideas that have had a great influence on my view of knowledge representation and of declarative programming. Through these lenses, I will discuss what I consider to be some of the most important milestones in the evolution of the field over the past years. I will conclude my reflection with my take on what this may tell us about the path that lies ahead and about areas where research efforts may yield considerable benefits.
Assisting the Visually Challenged People Using Faster RCNN with Inception ResNet V2 Based Object Detection Model

Visual impairment is a crucial issue which affects numerous people all over the globe. The arise of Artificial Intelligence (AI) and computer vision approaches have focused on the development of useful technologies for Visually Challenged people. This paper aims to design an effective Faster Region-based Convolutional Neural Network (Faster-RCNN) with Inception-ResNet-V2 (FRCNN + IRNV2) based object detection model for a given video input for the assisting visually challenged people. After detecting an object, a SoftMax (SM) classifier is applied to classify the objects. In addition, Inception–ResNet-v2 model is utilized as a shared base network for Region Proposal Network (RPN). Also, FRCNN + IRNV2 generates an audio message about the detected objects. A comprehensive experimental analysis was carried out to investigate the effectual detection performance of the proposed model on the three datasets namely, Indoor Scene, Human4 Dataset, and UTrento2 dataset. From the simultaion results, it is evident that the presented FRCNN + IRNV2 model has effectively detected the objects by attaining a higher sensitivity and specificity under all the three applied datasets.
Augmented Reality using Natural Feature Tracking Method to Introduce Science Verses in Qur’an

Technology support for multimedia learning is interesting ways as knowledge transfer to student, including Augmented Reality (AR). Qur’an as main life guidance source for Muslim which not only contains instructions for life but also contains various sciences. Therefore, this study aims to discuss about AR implementation as a technology that can be used as a medium for introducing and learning science verses from the Qur'an. The method used is Natural Feature Tracking (NFT) to detect markers of object. Three Dimension (3D) objects and simulations can help users easily understand the meaning of the science verses of the Qur'an on the marker. The implementation of AR multimedia for learning science verses of Qur’an is developed in mobile-based application with Android operating system using Vuforia. The experiment result showed that NFT can detect all of objects with speed test on 20 markers have an average speed of 0.306 s/marker. Besides, this research conducted the user acceptance with several respondents from students and teachers. The user acceptance test showed that this AR multimedia learning application is accepted by users and can attract interest and make it easier to understand and recognize science verses in the Qur’an by obtaining an assessment percentage of 87%.
Improving Object Distance Estimation in Automated Driving Systems Using Camera Images, LiDAR Point Clouds and Hierarchical Clustering

Data fusion plays a significant role in autonomous driving domain. Using an efficient combination of sensors like LiDAR, radar, and cameras could determine how quickly and accurately a vehicle makes all kinds of decisions related to road safety. In this article, we propose two approaches to improve object distance estimation by combining camera and LiDAR sensors. This work is inspired by the work presented in [2]. We propose to use instance segmentation and hierarchical clustering algorithms to resolve estimation errors generated when two or several bounding boxes (bbox) of detected objects overlap with each other. KITTI and Waymo databases were used to evaluate the accuracy of the proposed approaches. Finally, we compare the accuracy of our approaches with the accuracy proposed in [2] for some specific scenarios.
Image Reconstruction from Geometric Moments via Cascaded Digital Filters

Geometric moments belong to the family of non-orthogonal moments that do not have the ability to reconstruct the original image directly from it. Because of this, indirect methods are used to reconstruct images from their geometric moments. In this paper, we introduce a direct reconstruction method that uses cascaded digital filters where each digital filter operates as a subtractor. The proposed method has a less intensive computational process and has a great advantage as it can deal with geometric moments computed from any coordinate format. The proposed method is tested on \(N \times N\) binary and gray-scale images, and the performance of the proposed reconstruction process is compared with the existing methods. The results show that the proposed method takes less computational time and reconstruction error when compared with the two current methods.