Reviewerly/exHarmony huggingface dataset:
The Fundamental of Al-Quran Braille in Rasm Uthmani

Al-Quran is a miracle sent to human kind by Allah. As a Muslim, once should devote himself to learn, read, vocalize and understand the content of the book and this has no exception for Muslim with visual disability. The current printing of al-Quran for the blind is in the form of Braille, following the principle of rasm Al-Imla'i. However, there are some mistakes and inconsistencies in the orthogonal aspect in writing the Quran using this method. Thus, the principal and methods of Uthmanic model, is a solution to be used for al-Quran Braille. In rasm Uthmani, braille codes are added to adhere to the principle including diacritical marks and new waqf signs. The process of learning Al-Quran in Braille in its current form is already a big challenge, a transition to a new principle of rasm Uthmani will create new challenges. Thus, there is a need to have a proper learning tool to learn Al-Quran in Braille with rasm Uthmani. In this paper we highlight the challenges of learning al-Quran in braille and the additional challenges when principle of rasm Uthmani is adopted. There is a need to use assistive device to teach the basic of Al-Quran Braille to ease the transition of learning Al-Quran written in rasm Uthmani. This research is an initial step to enable the development of an assistive device that can be used for the blind people to read al-Quran with the principle of rasm Uthmani.
Mobile MoneyWallet Attack Resistance using ID-based Signcryption Cryptosystem with Equality Test

This paper is an extension of a research work presented at ICSIoT 2019.An attack continuum against the insider attack in mobile money security in Ghana using a witness based cryptographic method proposed by Alornyo et al. resisted the service provider from peddling with users data for economic gains.Our improved scheme achieves a simultaneous benefit of digital signature in public key encryption (PKE).The adoption of signcryption cryptosystem in our scheme achieved a desired security property of EUF-CMA using the random oracle model.
A new approach for increasing K-nearest neighbors performance

K-nearest neighbors is one of the most popular classification algorithms. It assumes that similar things or people are near to each other. One of the primordial steps in this algorithm is K value, which is given by the user. This value influences the algorithm result performance. In this paper, we suggest an enhanced approach that eliminates the use of K value with keeping the same performance and increasing it for a specific datasets type. We propose a combined approach named `Zone classifier' that provides an excellent performance which is estimated, in average, by more than 85%; for Iris, Wine, Digits, The breast cancer and Olivetti faces dataset.
Interval Differential Evolution Algorithm

By introducing the interval model into the existing differential evolution, this chapter proposes a novel interval differential evolution algorithm, which can directly solve the original interval optimization problem rather than transforming it to a deterministic optimization problem first.
Performance Assessment of Agile Hardware Co-development Process

This paper investigates how different Hybrid Agile Product Development Process (hybrid PDP) architectures affect project performance in space hardware development. The performance benchmark of alternative hybrid Agile PDPs offers valuable information for structuring Minimum Viable Products in order to meet customer needs while improving effectiveness and efficiency of the PDP. We propose a modeling framework to simulate iterations through the product development process, and therefore optimize tasks definition in Agile frameworks while accounting for product interdependencies between components, subsystems and relevant lifecycle processes such as assembly, integration, testing, procurement, and validation. The framework has been verified on a case study of an undisclosed telecommunication payload hosted onboard a nanosatellite mission. The results provide an initial understanding of the applicability of Agile methods to the co-development of complex engineering systems based on experimental data (notably tasks, task durations, dependencies, and cost) that have been collected on the field as well as simulation outputs. The model we propose also represents an effective decision making support instrument to evaluate the programmatic and technical tradeoffs.
Deterministic generation of a cluster-state of indistinguishable entangled photons.

We use periodic excitation of a semiconductor quantum-dot-confined hole to demonstrate gigahertz rate deterministic generation of long strings of entangled indistinguishable photons in a cluster state. The entanglement lasts for 11 consecutive photons.
Motion Blur Super-Resolution Reconstruction Based on Accelerated Deep Network

In order to meet the demand of super-resolution reconstruction of low-resolution motion blur image, an accelerated deep network architecture was proposed. In this method, the high frequency residual information between low-resolution motion blur image and high-resolution image was trained by deep network. In addition, a shrinkage layer was added in front of the nonlinear mapping structure of the series of multi-layer small-size filters to reduce dimension of the extracted feature map. Then an extension layer was added to increase dimension after nonlinear mapping. Finally, the residual information obtained from network was added to the low-resolution motion blur image to obtain the reconstructed high-resolution image. The peak signal to noise ratio (PSNR) and structural similarity (SSIM) were used to evaluate the reconstruction quality. Taking set5 test dataset as an example, the reconstructed mean result is 29.05/0.8339, which is greatly improved compared with VDSR and other deep learning methods. The reconstruction speed is also improved compared with these methods. It shows that this method can effectively solve the problem of super-resolution reconstruction of motion blur image.
Hierarchical Design and Execution of Smart Contracts in Blockchain

In light of the multiple legal issues, compliance and disruptions caused by the pandemic, organisations searching for new solutions need to know what smart contracts are and how they would function under the legal doctrine of force majeure in light of COVID-19. The Blockchains which use Bitcoin type of scripts have been popular as payment solutions, but it is less used as smart contracts. In the case of multi-level games and incremental project payments, there is a high potential to use Bitcoin type of scripts, but it is not being used currently. Interestingly, there have been attempts to associate smart contract mainly using Ethereum Blockchain but not with Bitcoin type of scripts. This article intends to demonstrate the novelty of designing smart contracts using Bitcoin type of scripts for hierarchical execution of smart contracts. An attempt is done to show its application in two use cases (multi-level reward games payment and incremental project payment). An evaluation is done with three methods each having a combination of pros and cons based on the requirements which aids in understanding for transparency and control over funds through Blockchain.
Computational Tool for Analysis of Strains Based on Optical Flow Approach

At present, there is an increase in the quantity of computer vision method applications in the industry, robotics, in systems of virtual and augmented reality, geoinformation systems and so on. This leads to the need for the creation and development of efficient methods and algorithms for solving the problems of computer vision. In the paper, we present an approach to solve the problem of the finding of displacement vectors in the two consecutive video frames of the strain test process video. To analyse video we use the optical flow approach and propose improvements to feature extraction method of establishing of robust point correspondences between pair of key points in video frames.
Design and Implementation of a Hand-held Lensless Light Field Camera

In this paper, we design a lensless light field camera by replacing the camera lens with a pinhole array mask attached to the sensor. Without a camera lens, the cost, weight and volume of a camera can thus be reduced. The proposed camera can be regarded as a miniature-sized camera array consisting of many small pinhole cameras, which means we are able to obtain a set of sub-images from the sensor with different view angles in a single shot. Then we can synthesize a series of larger digitally-refocused images from these sub-images using a stereo vision technique based on light field properties.
Leveraging Neural Network-Based Model for Context Classification of Classroom Dialogue Text

Classroom dialogue is a common strategy for teaching and learning. Technology-assisted classroom dialogue has drawn increasing attentions, where the classification of classroom dialogue is one of active research topics. However, existing studies mainly paid much attention to dialogue manners rather than dialogue contexts. This paper conducts a deep learning-based experiment on the classification of classroom dialogue context in text format. A hybrid neural network-based model namely CNN-BiLSTM-Attention is proposed for context classification of classroom dialogue text. The hybrid model consists of a Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory Network (BiLSTM) by leveraging an attention mechanism. The CNN-BiLSTM-Attention model is able to capture and learn both the local and global features of classroom dialogue texts for learning semantic information of dialogue contexts. To test the effectiveness of the model, an annotated classroom dialogue text dataset is built based on a well-established coding framework through collecting 155 lessons in Chinese language. Compared with eleven baseline methods, including commonly-used machine learning models and deep learning models, the evaluation results demonstrate that the CNN-BiLSTM-Attention model achieves the best performance with an overall F1-score of 0.7006.
A Semantic Definition of Faults and Its Implications

Given that faults are the focus of much software quality assurance (fault avoidance, fault removal, fault tolerance, fault prediction/ forecasting), we argue that a formal definition of faults ought to help us enhance the state of the art in this field. In this paper we consider a formal semantic definition of faults, and explore the insights that this definition gives us, and how these insights can be used in practice. Some of these insights are counter-intuitive, which makes them all the more interesting/useful.
Deep Iris Compression

Lossy image compression can reduce the space and bandwidth required for image storage and transmission, which is increasinly in demand by the iris recognition systems developers. Deep learning techniques (i.e. CNN, and GAN networks) are quickly becoming a tool of choice for general image compression tasks. But some key quality criteria, such as high perceptual quality and the spatial precision of the images, need to be satisfied when applying such modules for iris images compression tasks. We investigate and evaluate the expediency of a deep learning based compression model for iris data compression. In particular, we relate rate-distortion performance as measured in PSNR, and Multi-scale Structural Similarity Index (MS-SSIM) to the recognition scores as obtained by a concrete recognition system. We further compare the model performance against a state-of-the-art deep learning base image compression technique as well as some lossy compression algorithms currently used for iris compression (namely: the current ISO standard JPEG2000, JPEG, H.265 derivate BPG, and WEBP), to figure out the most suited compression algorithm which can be used for this purpose. The experimental results show superior compression, and promising recognition performance of the model over all other techniques on different iris data.
Real-Time Person Tracking and Association on Doorbell Cameras

This paper presents key techniques for real-time, multi-person tracking and association on doorbell surveillance cameras at the edge. The challenges for this task are: significant person size changes during tracking caused by person approaching or departing from the doorbell camera, person occlusions due to limited camera field and occluding objects in the camera view, and the requirement for a lightweight algorithm that can run in real time on the doorbell camera at the edge. To address these challenges, we propose a multi-person tracker that uses a detect-track-associate strategy to achieve good performance in speed and accuracy. The person detector only runs at every n-th frame, and between person detection frames a low-cost point-based tracker is used to track the subjects. To maintain subject tracking accuracy, at each person detection frame, a person association algorithm is used to associate persons detected in the current frame to the current and recently tracked subjects and identify any new subjects. To improve the performance of the point-based tracker, human-shaped masks are used to filter out background points. Further, to address the challenge of drastic target scale change during the tracking we introduced an adaptive image resizing strategy to dynamically adjust the tracker input image size to allow the point-based tracker to operate at the optimal image resolution given a fixed number of feature points. For fast and accurate person association, we introduced the Sped-Up LOMO, a fast version of the popular local maximal occurrence (LOMO) person descriptor. The experimental results on doorbell surveillance videos illustrate the efficacy of the proposed person tracking and association framework.
Digital and media literacy of primary education teachers

This contribution is focused on the intertwining and peculiarities of the given literacy, at the same time is showing a partial results of our research, which examined the self-assessment of teachers' readiness in the field of media literacy.The quantitative part of the study was attended by 151 teachers of primary education represented by each region of Slovakia.In the case of teachers, we used a questionnaire and, from qualitative methods, observation and interview with school management.It also describes the problems in practice that teachers and future primary school teachers is facing.The results showed that teachers consider their media literacy skills to be average and consider their undergraduate training to be insufficient.The results of the research show the need for further education of teachers and the improvement of the training of future teachers in the field of digital and media literacy and in general in the development of competencies in the field of the use of ICT in education.
A polyomino puzzle for arithmetic practice

Recent trends in mathematics education emphasize discovery learning over drill. This has proven to be a bad idea in some cases, for the simple reason that practice is required to learn basic arithmetic skills. Drills in arithmetic skills can be made interesting through gamification. This study proposes a family of puzzles that gamify arithmetic practice. The puzzles are designed with an evolutionary algorithm forming an instance of automatic content generation. Two methods of evolutionary puzzle design are presented and discussed. The first method used transformed the problem into an almost trivial optimization. The second algorithm was designed to avoid the flaws of the first and produced a huge variety of puzzles. A hardness measure, based on the difficulty experienced by the evolutionary puzzle generator, is employed. The hardness measure is tested on a large collection of puzzles produced with the evolutionary automatic content generation system. An initial assumption, that all the pieces in the puzzle must be used to achieve a maximum score, was shown to be incorrect in puzzles located via automatic search. Two classes of puzzle are defined: those where the optimal solution uses all pieces and those where the optimal solution fails to use at least one piece. The latter sort of puzzle were found to be far more common in the search space.
Multiplatform collaborative detection resource scheduling method using K‐means clustering algorithm and Hungarian algorithm

Summary According to the different situation electromagnetic environment, the multiplatform collaborative detection task is planned under the condition of meeting the requirements of multiplatform performance constraints and detection and positioning accuracy. In this article, the 6‐tuples of multiple‐platform detection attributes is defined, and then a multiple‐platform cooperative detection resource scheduling algorithm is established, and a cooperate adaptive evaluation method is proposed for coordination among all the platforms. Calculate the cooperate value among all the platforms. According to the cooperate value, the platform is clustered into a set of the same amount of objects, and then the expected core equipment of each set will be solved. The K‐means‐clustering technology and Hungarian technology are used to calculate and solve the matrix of the collaborative revenue value of the expected core equipment for the detection target. The feasibility, high adaptability, and the best distribution relationship of the collaborative revenue value are obtained. A kind of resource represented by the expected core equipment is allocated to the corresponding target for work, and the resource scheduling is completed. Multiplatform cooperative electronic detection tasks will be completed effectively in real time, and identify the targets quickly and accurately. Finally, we could complete the countertask.
Study the Significance of ML-ELM Using Combined PageRank and Content-Based Feature Selection

Scalable big data analysis frameworks are of paramount importance in the modern web society, which is characterized by a huge number of resources, including electronic text documents. Hence, choosing an adequate subset of features that provide a complete representation of the document while discarding the irrelevant one is of utmost importance. Aiming in this direction, this paper studies the suitability and importance of a deep learning classifier called Multilayer ELM (ML-ELM) by proposing a combined PageRank and content-based feature selection (CPRCFS) technique on all the terms present in a given corpus. Top \(k\%\) terms are selected to generate a reduced feature vector which is then used to train different classifiers including ML-ELM. Experimental results show that the proposed feature selection technique is better or comparable with the baseline techniques and the performance of Multilayer ELM can outperform state-of-the-arts machine and deep learning classifiers.
The Discrete Quincunx Wavelet Packet Transform

This chapter aims to present an efficient compression algorithm based on quincunx wavelet packet transform that can be applied on any image of size 128×128 or bigger. Therefore, a division process into sub-images of size 128×128 was applied on three gray-scale image databases, then pass each sub-image through the wavelet transform and a bit-level encoder, to finally compress the sub-image with respect to a fixed bit rate. The quality of the reconstructed image is evaluated using several parameters at a given bit rate. In order to improve the quality in sense of the evaluation quality, an exhaustive search has led to the best packet decomposition base. Two versions of the proposed compression scheme were performed; the optimal version is able to decrease the effect of block boundary artifacts (caused by the image division process) by 27.70% considering a natural image. This optimal version of the compression scheme was compared with JPEG standard using the quality evaluation parameters and visual observation. As a result, the proposed compression scheme presents a competitive performance to JPEG standard; where the proposed scheme performs a peak signal to noise ratio of 0.88dB over JPEG standard at a bit rate of 0.50bpp for a satellite image.
Collaborative Filtering Recommendation Systems Algorithms, Strengths and Open Issues

Recommendation systems recommender systems are a subcategory of information filtering that is utilized to determine the preferences of users towards certain items. These systems emerged in the 1990’s and they have since changed the intelligence of both the web and humans. Vast amounts of research papers have been published in various domains. Recommendation systems suggest items to users and their principal purpose is to recommend items that are predicted to be suitable for users. Some of the most popular domains where recommendation systems are used include movies, music, jokes, restaurants, financial services, life insurance, Instagram Facebook and twitter followers. This paper explores different collaborative filtering algorithms. In so doing, the paper looks at the strengths and challenges (open issues) faced by this technique. The open issues give direction of future research work to researchers and also provide information of where to use collaborative filtering recommender systems applications.
Dialectics of social development and consciousness in the context of systemic approach

The present article deals with the dialectics of social development and social consciousness. The author also considers the problems of public consciousness in the context of a systematic approach, in particular, and generality of the process of social development. The author considers the history of the development of human society, the global goals of education and upbringing, as well as their dynamics in accordance with philosophical, methodological concepts, paradigms of education and other reasons. Philosophical categories change in accordance with the expansion of the object of study. For example, modern scientific knowledge studies complex systemic objects, and some classical philosophical ideas cannot provide adequate knowledge. Therefore, such philosophical categories as determinism, randomness, uncertainty, chaos, society, state, human being, good and evil, beautiful and ugly, and others acquire somewhat different functions in the study of modern complex systems compared to their interpretation as classical categories of philosophy.
Some general observations about handwriting identification

This chapter sets the background for the following chapter that deals with the process of comparison. The topics discussed in this chapter examined in detail in the next chapter. The concepts and principles set forth in this chapter are essential to a complete understanding of the process of comparison.
Distribution-based Regression Models for Semi-Bounded Data Analysis

Positive vectors appear naturally in many applications, yet heir modeling has not been adequately addressed in the past. Inverted Dirichlet offers a good representation and modeling of positive non-Gaussian vectors, and its generalizations offer even more practical and flexible alternatives. In this paper, we propose three regression models based on flexible distributions for semi-bounded data, namely, inverted Dirichlet, inverted generalize Dirichlet, and inverted Beta-Liouville. The efficiency of these models is tested via real-world applications, including software defects prediction, age estimation, spam filtering, and disease diagnosis. Our results show that the three proposed regression models outperform other commonly used regression approaches.
Advanced Fuzzy Cognitive Maps Modelling a Nearly Zero Energy Building

Fuzzy Cognitive Maps (FCM) are developed in recent decades in order to model complex systems, combining Fuzzy Logic and Neural Networks. They have been used in various systems and applications and they have been combined with learning algorithms and experts' knowledge. This paper presents new equations for the concept values calculation and uses the state-space approach in FCMs, separating the concepts as inputs, states and outputs. All the above are applied on a FCM, modelling a Nearly Zero Energy Building (nZEB), creating a strong base for further improvement and research.