Reviewerly/exHarmony huggingface dataset:
Adaptive Control of Thought-Rational (ACT-R): Applying a Cognitive Architecture to Neuroergonomics

For neuroergonomists who wish to apply Adaptive Control of Thought-Rational (ACT-R)Adaptive Control of Thought-Rational (ACT-R) to investigate the human mind and its structure including learning, performance, and problem-solving skills, this chapter aims at providing an overview of ACT-R with an emphasis on its modules, buffers, and sub-symbolic levels. ACT-R is a high-level computational simulation of human cognitive processing and one of the cognition theories that seek to predict human performance in real-world settings. A group of previous studies on behavioral- and neural-based cognitive modelingCognitive modeling of human cognition using ACT-R will also be discussed. Finally, this chapter presents future directions of ACT-R for neuroergonomicsNeuroergonomics research.
Use of Data Mining Techniques for Data Balancing and Fraud Detection in Automobile Insurance Claims

A novel hybrid data balancing method based on both undersampling and oversampling with ensemble technique has been presented in this paper for efficiently detecting the auto insurance frauds. Initially, the skewness from the original imbalance dataset is removed by excluding outliers from the majority class samples using Box and Whisker plot and synthetic samples are generated from the minority class samples by using synthetic minority oversampling (SMOTE) technique. We employed three supervised classifiers, namely, support vector machine, multilayer perceptron, and K-nearest neighbors for classification purpose. The final classification results are obtained by aggregating the results obtained from these classifiers using the majority voting ensemble technique. Our model has been experimentally evaluated with a real-world automobile insurance dataset.
EXPERIENCE OF ICT IMPLEMENTATION IN PRIMARY CURRICULUM IN UKRAINE AND THE UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND

The study was formulated within the context of an increasing recognition of ICT as curriculum priorities in primary schools of many European countries. The implementation of ICT-centered curriculum is a step towards realizing the goals of the new Law of Ukraine “On Education” (2017), National Strategy for the Development of Education in Ukraine until 2021, European strategic programme “Education and Training 2020”. Actuality of approaches to ICT implementation in present-day primary schools is caused by the substantial development of digital technologies and requirement of digital literacy for people’s work, social, and personal lives. The unique opportunity of primary education as a large sub-sector of any education system is to contribute to the renewal of societies through education of the young. The paper aims to study implementation of Computer science into national standard of primary education in Ukraine and the UK. In the recent years, many European countries have seen some changes of the content one of which relates to the area of computer science education that resulted in implementing an ambitious new curriculum in this subject. The author gives a comparative analysis of primary ICT within national standards in term of the Computer science development from the initial stage to present day situation. The comparative analysis specifies areas of similarities (aims, objectives, approaches to implementation, priorities of developing digital skills, teaching hours) and differences (programme topics, characterization of learning outcomes) in the study of ICT covering the subject content, expected learning results and general principles of ICT in education, as well as examples of ICT implementation.&#x0D; The paper is also focused at discussing the role of the ICT curriculum in modern-day primary classroom; advantages and disadvantages of ICT integration at primary stage.&#x0D; Much attention is paid to how it is integrated into daily learning modes to allow and encourage active learning. In primary education there are two main models of ICT implementation into curriculum, these are: ICT integrated across the curriculum; ICT (or Informatics, Computer Science and Computing) as a discrete subject within national standard.
COMPUTER SCIENCE TEACHERS’ READINESS TO DEVELOP AND USE COMPUTER DIDACTIC GAMES IN EDUCATIONAL PROCESS

The paper reports research on computer science teachers’ readiness to develop computer didactic games and implement them into the educational process. Today’s teacher of computer science has to acquire and improve professional competencies in line with modern trends of high technological society. It is a process that we consider in the context of life-long education. The use of computer didactic games is one of the forms of innovative education, which increases the attractiveness of education, generates the development of learners’ endeavour and creativity. The authors emphasize that computer science teachers’ readiness to use computer didactic games becomes impossible without mastering the methodology of pedagogical endeavours and gaming technologies in education. By this, we mean a search for more efficient ways of achieving pedagogical goals, the ability to select computer didactic games and a search for ways of their successful realization in professional activities. Development of computer didactic games requires a computer science teacher to learn methodology and standards within this domain, and to obtain the basic knowledge of environment and skills to use in games development. Our survey revealed that the use of computer didactic games is sporadic and inadequately prioritized by the computer science teachers, who appeared to have very little knowledge in techniques aimed at their development and implementation into the educational process. The main reason is that prospective computer science teachers do not learn it sufficiently in higher educational establishments. This sphere remains underestimated by those who educate prospective computer science teachers, as well as by computer science teachers themselves. As a result, we observe a lack of knowledge in development, selection and implementation of computer didactic games. Theoretical analysis of the use of computer didactic games and their implementation into the educational process remains out of scholars’ attention, despite the issue becoming more and more attractive. The examination of computer science teachers’ readiness to develop computer didactic games and implement them into the educational process allows us to suggest ways for enhancing the level of teachers’ readiness for this training activity.
Location-Based Services

In the current information technology trend, GIS (Geographic Information Systems) and GPS (Global Positioning Systems) are potential systems providing convenient, valuable services. They are a fundamental base in location-based services (LBS). LBS is an object positioning and monitoring system with a major combination of advanced technologies such as mobile communications, GPS technology, GIS and the Internet. The article consists of two sections in which the overview of the LBS system is presented first, followed by the introduction of the LBS system structure used to build the VTS - Vehicle Tracking System at Dong A Bank. &#x0D; &#x0D; &#x0D; &#x0D;
Peregrine Preying Pattern-Based Differential Evolution

Jain, Sanjay Sharma, Vivek Kumar Kumar, SandeepThe differential evaluation (DE) algorithm is an evolutionary algorithm. It is a popular metaheuristics that efficiently solved various complex optimization problems. This paper proposed modification in DE, motivated by the flying pattern of peregrine falcon while preying and named as peregrine preying pattern-based DE algorithm (P3DE). In P3DE, the flying pattern of peregrine ameliorate the exploration potential, while preserving the novel exploitation potential of the DE. The competence, precision, robustness and trustworthiness of the projected P3DE algorithm is analyzed while simulating it over 12 composite benchmark functions of diverse characteristics. The results of P3DE compared with DE and artificial bee colony algorithm to prove its competitiveness for selected problems.
ADAM &amp; RAL: Adaptive Memory Learning and Reinforcement Active Learning for Network Monitoring

Network-traffic data commonly arrives in the form of fast data streams; online network-monitoring systems continuously analyze these kinds of streams, sequentially collecting measurements over time. Continuous and dynamic learning is an effective learning strategy when operating in these fast and dynamic environments, where concept drifts constantly occur. In this paper, we propose different approaches for stream-based machine learning, able to analyze network-traffic streams on the fly, using supervised learning techniques. We address two major challenges associated to stream-based machine learning and online network monitoring: (i) how to dynamically learn from and adapt to non-stationary data and patterns changing over time, and (ii) how to deal with the limited availability of ground truth or labeled data to continuously tune a supervised learning model. We introduce ADAM * RAL, two stream-based machine-learning approaches to tackle these challenges. ADAM implements multiple stream-based machine-learning models and relies on an adaptive memory strategy to dynamically adapt the size of the system's learning memory to the most recent data distribution, triggering new learning steps when concept drifts are detected. RAL implements a stream-based active-learning strategy to reduce the amount of labeled data needed for stream-based learning, dynamically deciding on the most informative samples to integrate into the continuous learning scheme. Using a reinforcement learning loop, RAL improves prediction performance by additionally learning from the goodness of its previous sample-selection decisions. We focus on a particularly challenging problem in network monitoring: continuously tuning detection models able to recognize network attacks over time.By continuously learning from and detecting concept drifts within real network measurements, we show that ADAM * RAL can continuously achieve high detection accuracy and limit the amount of training data needed to detect attacks over dynamic network data streams.
A Novel Bayesian Patch-Based Approach for Image Denoising

Recently patch-based image denoising techniques have gained the attention of researchers as it is being used in numerous image denoising applications. This article is proposing a new Bayesian Patch-based image denoising algorithm using Quaternion Wavelet Transform (QWT) for grayscale images. In the proposed work, a patch model has been used instead of the Gibbs distribution based energy model. Experimental results indicate that the proposed algorithm effectively diminishes noise. The results of the developed approach are also compared with other efficient image denoising algorithms such as Expected Patch Log Likelihood (EPLL), Block-matching and 3D filtering (BM3D), Patch-Based Locally Optimal Wiener (PLOW), Weighted Nuclear Norm Minimization (WNNM), Hybrid Robust Bilateral Filter-Total Variation Filter (RBF-TVF) and Hybrid Total Variation Filter-Weighted Bilateral Filter (TVF-WBF) methods. The comparison revealed that the outcomes of the given approach are much sharper, clearer, and having the highest quality in comparison with other patch-based methods.
Implicitly Defined Layers in Neural Networks

In conventional formulations of multilayer feedforward neural networks, the individual layers are customarily defined by explicit functions. In this paper we demonstrate that defining individual layers in a neural network \emph{implicitly} provide much richer representations over the standard explicit one, consequently enabling a vastly broader class of end-to-end trainable architectures. We present a general framework of implicitly defined layers, where much of the theoretical analysis of such layers can be addressed through the implicit function theorem. We also show how implicitly defined layers can be seamlessly incorporated into existing machine learning libraries. In particular with respect to current automatic differentiation techniques for use in backpropagation based training. Finally, we demonstrate the versatility and relevance of our proposed approach on a number of diverse example problems with promising results.
Generalized Adaptation for Few-Shot Learning

Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model. In this paper, we propose to use closed-form base learner, which constrains the adapting stage with pre-trained base model to get better generalized novel model. Following theoretical analysis proves its rationality as well as indication of how to train a well-generalized base model. We then conduct experiments on four benchmarks and achieve state-of-the-art performance in all cases. Notably, we achieve the accuracy of 87.75% on 5-shot miniImageNet which approximately outperforms existing methods by 10%.
Categorizing Patient Disease into ICD-10 with Deep Learning for Semantic Text Classification

How to leverage insights into big electronic health records (EHRs) becomes increasingly important for accomplishing precision medicine to improve the quality of human healthcare. When analyzing big Chinese EHRs, there are a lot of applications that we need to categorize patients' diseases according to the medical coding standard. In this paper, we develop natural language processing (NLP), deep learning, and machine learning algorithms to automatically categorize each patient's individual diseases into the ICD-10 standard. Experimental results show that the convolutional neural network (CNN) algorithm outperforms the recurrent neural network (RNN)-based long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms, and it generates much better results than the support vector machine (SVM), one of the most popular conventional machine learning algorithms, demonstrating the great impact of deep learning on medical big data analysis.
Grid text classification method based on DNN neural network

With the rapid development of network technology, the electric power Internet of Things needs to face a large number of electronic texts and a large number of distributed data access and analysis requirements. If the system wants to complete accurate and efficient data analysis and build an existing data and service standard system covering the entire chain of energy and power business on the existing basis, it must implement massive electronic text retrieval, information extraction and classification in the power grid system. In order to achieve this purpose, a DNN neural network classification model is constructed to classify the text information of the power grid, and the effectiveness of the method is verified by experiments based on data from the substation information system.
COMPARISON BETWEEN CLOUD ERP AND TRADITIONAL ERP

The Enterprise Resource Planning (ERP) system is one of the most important systems that institutions of different types, whether private or governmental, implement. Because of the great development in Internet services and increased dependence on the infrastructure of companies that provide Cloud services, which led to the development of ERP design, and the emergence of many types of ERP systems, which rely on Internet services. The most important of the ERP types such as Web-based ERP and Cloud ERP in addition to the traditional type of ERP system, so designers and vendors of ERP systems such as Oracle and SAP are turning to ERP system design based on Cloud technology and providing the ERP system as a service for monthly and annual subscription, where the system is outside the organizations without need to exist within the organization. The purpose of this paper is to study the various types of ERP systems and to explain the most important differences between Cloud ERP and Traditional ERP. This helps organizations to choose the right type of ERP system that organizations seek to implement to improve their performance and achieve their different objectives by selecting the appropriate application type for the planning system Enterprise resources to achieve their goals.
A Decentralized Protocol for Securely Storing and Sharing Health Records

Cloud computing allows for on demand storage and sharing of records with high degree of availability. However, storing a health record in a cloud provider requires trusting it for the record security. By mitigating it, current approaches focus on confidentiality and access control while not properly handling data integrity. This paper presents a protocol that employs attribute-based cryptography and decentralized networks for secure storage and sharing of health records. The solution addresses confidentiality, access control and integrity of records. A proof of concept of the protocol was implemented and load tests were executed in order to demonstrate its practical feasibility.
JobChain: An Integrated Blockchain Model for Managing Job Recruitment for Ministries in Sultanate of Oman

Industries around the world has revolutionized with the arrival of blockchain technology.Blockchain applications and use cases are in the process of development in different domains.This research presents a blockchain platform "JobChain" to manage the job recruitments.The case study is conducted for the job recruitments in various Ministries in Sultanate of Oman.Currently, in Oman, citizens are aware of the job vacancies through the advertisements posted in newspapers or social media.A job seeker then applies for the desired job and thereafter the qualified candidates are called for tests/ interviews.To ease this process, a solution based on blockchain which includes various Ministries and the citizens/ residents of Sultanate of Oman is proposed in this research.Ministries can post the job vacancies in the blockchain and qualified citizen(s) can submit their application.Relevant cryptographic functions are used to verify the authenticity of the participants in the blockchain network.The citizens feel the existence of a trusted secure government, which is mandatory for the development of a country.Unlike traditional models, blockchain eliminates the need of intermediary agents (e.g.Job Consultancies) thereby providing direct communication between the participants of the blockchain.The proposed blockchain framework helps the citizens in Oman to get updated about the job vacancies.Hyperledger Composer Playground is used to design and test the proposed blockchain business network.Preliminary results show that the participants and assets are created successfully and the transactions to approve a job vacancy and a job application is done through the proposed blockchain network.
Despeckling method of ultrasound images using closed-form shrinkage function based on cauchy distribution in wavelet domain

Speckle suppression and elimination are very important to improve the visual quality of ultrasound image and the diagnostic ability of the diseases. An effective technique of image denoising based on discrete wavelet transform is to employ a Bayesian maximum a posteriori (MAP) estimator. To suppress and remove the speckle noise using MAP estimator effectively, it must assign correctly the shrinkage function based on appropriate probability density functions (PDFs) for the wavelet coefficients of logarithmically transformed noise-free ultrasound image and speckle noise. In this paper, we introduce a new closed-form shrinkage function that is an analytical solution of a Bayesian MAP estimator for despeckling of the ultrasound images effectively in wavelet domain. We employ a Cauchy prior and Gaussian PDF to model the wavelet coefficients of logarithmically transformed noise-free ultrasound image and speckle noise, respectively. Firstly, we derive the CauchyShrinkGMAP that is a closed-form shrinkage function. In addition, we estimate the noise variance and parameter of MAP estimator. Next, we evaluate the despeckling performance of wavelet image denoising method using the CauchyShrinkGMAP compared to various despeckling method using median and Wiener filters, hard and soft thresholding and GaussShrinkGMAP and MCMAP3N shrinkage function. The experiment results show that PSNR of new closed-form shrinkage function is highest, MSE is smallest, and the correlation coefficient ([Formula: see text]) and SSIM are closer to one than the other existing image denoising methods for noisy synthetic ultrasound images at different speckle noise levels. Also, experiment results show that ENL of new closed-form shrinkage function is highest and that of EN and SD is smallest than the other existing image denoising methods for noisy real ultrasound image.
Distilling portable Generative Adversarial Networks for Image Translation

Despite Generative Adversarial Networks (GANs) have been widely used in various image-to-image translation tasks, they can be hardly applied on mobile devices due to their heavy computation and storage cost. Traditional network compression methods focus on visually recognition tasks, but never deal with generation tasks. Inspired by knowledge distillation, a student generator of fewer parameters is trained by inheriting the low-level and high-level information from the original heavy teacher generator. To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators. An adversarial learning process is therefore established to optimize student generator and student discriminator. Qualitative and quantitative analysis by conducting experiments on benchmark datasets demonstrate that the proposed method can learn portable generative models with strong performance.
Trinetra: a solution to handle cross-VM time-driven attack

The efficient utilization of hardware and software resources plays a vital role in a high-performance computing environment. Where on the one side, a shared pool of resources facilitates faster processing with limited resources, this mechanism also widens the scope of many kinds of security attacks on the other side. Side-channel attack (SCA) is one such attack where methods to monitor the activity of exploited shared resource is carried out to extract the private key. One such SCA, branch prediction analysis attack, launched with time-driven attack (TDA) method is considered in this paper. With the consideration of the virtualization environment, proposal of an algorithm Trinetra, to detect the presence of cross-VM TDA, is the primary focus of our paper. We have tested the performance of Trinetra with experimental analysis in addition to the attack simulation. Performance evaluation has found the Trinetra very effective with negligible performance overhead.
Lossless Monolithically Integrated Photonic InP Neuron for All-Optical Computation

We demonstrate a monolithically integrated SOA-based photonic neuron, including both the weighted addition and a wavelength converter with tunable laser as nonlinear function, allowing for lossless computation of 8 Giga operation/s with an 89% accuracy.
Heterogeneous Dimensionality Reduction for Efficient Motion Planning in High-Dimensional Spaces

Increasing the dimensionality of the configuration space quickly makes trajectory planning computationally intractable. This paper presents an efficient motion planning approach that exploits the heterogeneous low-dimensional structures of a given planning problem. These heterogeneous structures are obtained via a Dirichlet process (DP) mixture model and together cover the entire configuration space, resulting in more dimensionality reduction than single-structure approaches from the existing literature. Then, a unified low-dimensional trajectory optimization problem is formulated based on the obtained heterogeneous structures and a proposed transversality condition which is further solved via SQP in our implementation. The positive results demonstrate the feasibility and efficiency of our trajectory planning approach on an autonomous underwater vehicle (AUV) and a high-dimensional intervention autonomous underwater vehicle (I-AUV) in cluttered 3D environments.