Reviewerly/exHarmony huggingface dataset:
Validation of Practices for Effective Communication during Requirements Elicitation in Global Software Development

Effective communication play an important role during requirements collection and implementation for any software system.In Global Software Development (GSD), its significance increase more as stakeholders are far away across the globe.In GSD challenges such as language differences and time zone differences exist and thus proper and timely communication become more difficult.There is need of practices for effective communication during proper requirements elicitation and implementation in GSD.This study address validation of possible solutions and practices for effective communication from our previous study.
Opposition based Spiral Dynamic Algorithm with an Application to a PID Control of a Flexible Manipulator

This paper presents an improved version of a Spiral Dynamic Algorithm (SDA). The original SDA is a relatively simple optimization algorithm. It uses a spiral strategy to move search agents within the feasible search space. However, SDA suffers from a premature convergence due to an unbalanced diversification and intensification throughout its search operation. Hence, the algorithm unable to acquire an optimal accuracy solution. An Opposition learning is adopted into SDA to improve the searching strategy of the SDA agents. Therefore in the proposed strategy, a random and a deterministic approaches are synergized and complement each other. The algorithm is tested on several benchmark functions in comparison to the original SDA. A statistical nonparametric Wilcoxon sign rank test is conducted to analyze the accuracy achievement of both algorithms. For solving a real world application, the algorithms are applied to optimize a PID controller for a flexible manipulator system. Result of the test on the benchmark functions shows that the Opposition based SDA outperformed the SDA significantly. For solving the PID control design, both algorithms acquire PID parameters and hence can control the flexible manipulator very well. However, the proposed algorithm shows a better control response.
Estimating the Case Fatality Rate for COVID-19: A Markov Model Application

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An Overview of Smart Classroom using the Augmented Reality Technology

This paper describes the rapid advancements being made in the development of Augmented Reality (AR) based systems. Augmented reality has proven to be a great asset to the smart electronics technology and in this paper we have discussed on how the AR technology can benefit the education field and on how the use of AR technology in classrooms will help the student community in acquiring the knowledge about the subjects and enhance their perspectives on complex topics.
Investigation of the algorithm for detecting deterministic anomalies in complex structure images using a doubly stochastic model

The article proposes an algorithm for detecting deterministic anomalies against the background of satellite images with a complex structure, containing a combination of several natural objects. To describe such images, doubly stochastic models of random fields are proposed, which provide the formation of inhomogeneous images. The effectiveness of the algorithm is estimated by the probability of correct detection under various conditions of the interference. The dependences of the probability of the correct detection on the signal-to-noise ratio at various thresholds are presented. We present the detection efficiency studies on simulated and real images. The results are compared with the algorithm based on autoregressive random fields. We demonstrate that the proposed algorithm provides the efficacy in reducing the signal-to-noise ratio to achieve the given probability of the correct detection. Andriyanov N. A., Gavrilina Yu. N. Investigation of the algorithm for detecting deterministic anomalies in complex structure images using a doubly stochastic model. Ural Radio Engineering Journal . 2020;4(1):18–32. DOI: 10.15826/urej.2020.4.1.002
EXCON Teams in Cyber Security Training

A cyber range is an arena where exercise will be used to expose individuals, public and private organizations, and government agencies to simulate socio-technical cyber security events and situations in a realistic but safe environment. Running these exercises is a demanding task, and the exercise control (EXCON)-team have vast and detailed tasks to run and coordinate during the exercise. Often the team-members in EXCON sit upon tacit knowledge and inherited experience rather than formal pedagogical knowledge. As cyber rangers will provide full-scaled cyber exercises for different organizations; on strategic, tactical and operational levels, there will also be a need of bringing in diverse experts in the EXCON-teams, such as experts from CERT's and other real-life stakeholders. These vast tasks require excellent capabilities to manage such teams and will be one of the most important roles to frame. In this paper we suggest a framework for EXCON-team roles running full scaled cyber-incident exercises and want to test this framework during running the exercises.
Teaching Cyber Security Topics Effectively in a College or University with Limited Resources

To handle cyber security threats, we need to develop courses to educate students about cyber security concepts, methods to handle various attacks in the cyber space. In this paper, we address what resources would be required to develop courses to effectively teach students the cyber security concepts and methods at small colleges or universities with limited resources.
Case Study: Image Processing

This chapter introduces techniques for working with imagery in Python. First, we review the data formats commonly used for storing images. Popular formats such as JPG, PNG, and TIF can all be treated as multidimensional arrays. Converting images to a format that can be used in tabular data analysis requires additional steps to extract features of interest.
Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet

Rubber plantation sector is one of the leading commodities in East Kalimantan Province contributing greatly to non-oil and gas exports. Currently, the price of rubber in the world is increasingly competitive. The aim of this research is to predict the rubber prices as a reference for the government and companies in making policies and preparing work plans. Data of 60 months during the period of 2014-2018 taken from Plantation office of East Kalimantan Province has been analyzed using Backpropagation Neural Network (BPNN) algorithm in predicting rubber prices. Based on the testing results, parameters of the BPNN algorithm with ratio of 4: 1, architectural models 5-10-10-10-1, trainlm learning function, learning rate of 0.5, error tolerance of 0.01, and epoch of 1000 have gained good accuracy with a mean square error (MSE) of 0.00015464. The results showed that the BPNN algorithm can be used as an alternative method in forecasting.
Coverless image steganography framework using distance local binary pattern and convolutional neural network

Steganography in digital images commonly uses a carrier image and embeds secret data into to create the stego-image by spatial or frequency domain methods, which directly modifies the bits of the carrier image, altering the intensity of the pixels and leaving traces of modification caused by the embedding of data in the carrier image, which makes successful steganalysis possible. This paper proposes a digital image steganography framework without embedding data directly into the images that extracts the secret-data from the convolutional neural network trained with the distance local binary pattern images from an indexed image database. Experimental results demonstrate that the proposed framework is resistant to common steganalysis tools, intentional and unintentional image attacks such as luminance and contrast changes, rescaling, noise addition and compression.
Digital Text Feature Extraction Using Singular Value Decomposition and Principal Component Analysis

The management and automated processing of digital information is an increasingly significant topic of research. Digital text information is becoming particularly pervasive and poses particular challenges to processing methods. In this paper, we present a feature extraction method using singular value decomposition and principal component analysis for effective information reconstruction. We first use singular value decomposition to modify the small eigenvalues of the test matrix. This new test matrix may contain redundant information and contribute to increased computational complexity. Thus, we use principal component analysis in a second stage of processing. This method reduces the amount of redundant information and the dimensionality of the data, and eliminates the correlation between each input variable. This greatly simplifies the computational complexity of the feature extraction.
Divisive display augmented reality (ddAR) for real-world soldier performance

Commercial augmented reality (AR) and mixed reality (MR) displays are designed for indoor nearfield gaming tasks. Conversely, outdoor warfighter tasks have a different set of needs for optimal performance, e.g. for aided target recognition (AiTR). The information display needs to be visible across a wide luminance range, from mesopic to photopic (0.001 to 100,000 cd/m<sup>2</sup>), including maxto- min luminance ratio exceeding 10,000-to-1 within a single scene (high dynamic range luminance, HDR). The information display also should not distract from other tasks, a difficult requirement because the saliency of the information display depends on its relationship to the HDR background texture. We suggest that a transparency-adjustable divisive display AR (ddAR) could achieve these luminance and saliency needs, with potentially less complexity and processing power than current additive displays. We report measurements of acuity to predict how such ddAR might affect low contrast visibility under gaze shifts, which often result in 10- or 100-fold changes in luminance. We developed an HDR display projection system with up to 100,000-to-1 luminance contrast ratio and assessed how luminance dynamics affect acuity to semi-transparent letters against a uniform background. Immediately following a luminance flash, visual acuity is unaffected at 20% letter contrast, and it is only weakly affected at 10% letter contrast (+0.10 and +0.12 LogMAR for flashes of 25× and 100× luminance). The resilience of low contrast letter acuity across luminance changes suggests that soft highlighting and ddAR could effectively convey information, to improve AiTR under real world luminance.
Knowledge Refactoring for Program Induction

Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the \textit{knowledge refactoring} problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it. We focus on inductive logic programming, where the knowledge base is a logic program. We introduce Knorf, a system which solves the refactoring problem using constraint optimisation. We evaluate our approach on two program induction domains: real-world string transformations and building Lego structures. Our experiments show that learning from refactored knowledge can improve predictive accuracies fourfold and reduce learning times by half.
Comparison of Evolutionary Algorithms for AUVs for Path Planning in Variable Conditions

Ocean exploration has always attracted research interest. One of the most significant advances in the area of underwater navigation is an unmanned, self-propelled vehicle, namely the Autonomous Underwater Vehicle (AUV). Since the level of autonomy is crucial for AUVs, path planning is identified as one of the core components to improve AUV persistence. This study examines the optimization problem of underwater rendezvous through a cluttered and variable operating field. An improved Particle Swarm Optimization (PSO) algorithm is introduced for underwater path planning and assessed against the classic PSO with respect to optimal solution quality and energy efficiency. Our results based on extended Monte Carlo simulations demonstrate robustness and efficiency of the proposed planners for optimal and collision free path planning. Finally, we set the scene for further enhancement in the area of evolutionary algorithms.
Performance Evaluation for Face Database

The growing interest in the database generated many techniques in this area and the Intelligent Systems Laboratory Where graphical models were used in the theoretical developments of computer vision and reasoning, and their application in various fields. Our proposal provides a database of the face under the circumstances of the real, without knowing the people who are taking pictures of them. The present study focuses on the which recording images and places them in the database through active inference and effective reasoning. We are interested in active reasoning because it manages sensor algorithms and guidance unit until the visual translation process is completed.Thus, this sophisticated capture technique processes each frame whenever a face or eye is selected. We are developing a face detection and eye identification process by building a facial recognition algorithm using databases collected from previous experiments. This procedure was applied to a database of a previous set of 40,000 images for 40 people, which illustrates how difficult it is to identify faces. This paper includes a detailed research methodology. In Section 3, face evaluation is discussed, depending on key characteristics within a specific protocol, followed by a definition of the most accurate performance criteria for face verification, and identification through statistical measures. The evaluation protocols in our paper provide researchers the means to recognize faces using modern methods. The results obtained were mentioned in the figure and indicated the strength of the technique used.
Identifying Customer Interest from Surveillance Camera Based on Deep Learning

This study proposes a method to identify a customer's interest in the product. Specifically, we applied the state-of-the-art deep learning algorithms to the real-world surveillance videos for analyzing customer interest in the product and evaluated the accuracy. For this, we first introduce a new first of its kind dataset called ICI (items of customer's interest) that includes various shopping situations. We experimented the state-of-the-art deep learning algorithms on the ICI dataset to determine a suitable algorithm for identifying a customer's interest. The experimental results demonstrated that the estimation accuracy is 71% on the average, meaning that a customer's interest can be measured effectively.
Emerging Trends and Patterns of Cyber Economic Crimes

This chapter maps various trends and patterns of CybercrimesCybercrime pattern and Cyber Economic CrimesCyber economic crime . TrendsCybercrime trends and pattern are analysed in the registration of crime, profile of criminals, gender, age of accused, types of cyber economic crimesCyber economic crime . The case studies from the State of Maharashtra and Mumbai are presented for comparative analysis, as Maharashtra and Mumbai have accounted for 16 and 6% of the total incidences of the cybercrimeCybercrime in India. It is very interesting to note that in Maharashtra, out of the total registered cases of cybercrimeCybercrime , 66% of the cases are Cyber Economic crimeCyber economic crime cases and in Mumbai, this pattern further deepens as 93% of the total cases registered are cyber economic crimeCyber economic crime cases. The analysis is supported by statistics from India, Maharashtra, and Mumbai. A comparative analysis of six metro cities with Mumbai is also presented. The Crime data considered in this book is from 2002 to 2016. A comparative study of various metro cites with Mumbai has also been undertaken.
Discovering Research Trends in the Computer Science Area of Ecuador: an approach using semantic knowledge bases

We present a study of research trends for the area of Computer Sciences in Ecuador in recent years. This analysis was performed through a new method that leverages on semantic web technologies and external knowledge bases (i.e. DBpedia and UNESCO nomenclature) for identifying research topics within articles' metadata. This information takes into account the documents' publication date in order to construct time series which are analyzed and interpreted looking for trends. Concretely, we focused our study on the REDI (Semantic Repository of Ecuadorian Researchers) knowledge base which compiles most of the scholarly assets produced in Ecuador and more specifically on the Computer Science subset of publications. This study found that most of the research topics have shown an steady growth in the volume of publications over time, whereas the Semantic Web and E-Government research topics had a great impact initially and now have been slightly reducing its share in favor of new topics such as Information Integration, Machine Learning and Data Mining.
Design of a Multi-Dimensional Model for UGC-Based Emotion Analysis

Issues concerning social network analysis have drawn significant attentions from the public. One of the most eye-catching issues is the gradually-improving studies regarding text analysis on social network services. Studies pointed out that users' emotion status can be recognized, and modeled as well, through sentiment analysis, and furthermore, prompted social awareness such as trend/opportunity exploration, business strategy development, and so on. It is found that not only the text, e.g., tweet, post, etc., itself may lead to the changes in users' emotion but also any bi-directional activities during the whole social interaction process. Considering emotion as the result of human judgment on the overall feeling in the cognitive theory of emotion, this study targets to design a universal model that supports the monitoring of users' emotion change on social network service. This model employs a new multi-dimension mapping method to detect and analyze users' emotion changes based on machine learning technique, and target to prompt further understanding of human behavior in online society.
An Accurate Breast Cancer Detection System Based on Deep Learning CNN

Deep learning of multilayered computational models allowed processing to recognize the representation of data at multiple levels of abstraction. These technologies have significantly improved to identify breast cancer. Convolutional Neural Network (CNN) is a type of radiological basis for classification problems and is based on the Bayes decision-making base, which reduces the expected error in classification. In this paper,it is proposed to detect breast cancer using CNN of the mammography system to classify the mammogram to noncancerous abnormality and cancerous abnormality. The goal of Breast Cancer Detection based on CNN for speeding up diagnosis by helping a particular diagnose and classify breast cancer.A set of images of a mammogram is used to perform the pre-processing of the histogram equalization and adjust the appropriate parameters for the CNN work. Then, the whole changed images are set in CNN to classify as a training source. A CNN classifies can be produced as a model foridentifying the mammogram. the BCDCNN method with the mammography classification utilizing MCCANN comparison with BCDCNN improved the classificationaccuracy on mammographic. Therefore, the results showed that the proposed system has a higher resolution than the other recently existing systems, and the only mass containing all was maximized from 0.84 to 0.88 and 0.70 to 0.82 accuracy.
Combating Online Impersonation in the United States

Online impersonation is one of the major problems of the computer age. As technology advances, impersonation becomes a greater problem. Online impersonation can cause serious damages to victims. The laws badly need to be updated to deal with impersonation in the information age. This research paper makes seven recommendations to effectively combat online impersonation in the United States.
User Authentication of IoT Devices for Decentralized Architecture Using Blockchain

These days, IoT devices are deployed at an ever-increasing rate worldwide. It is estimated that the growth of IoT devices will increase up to 31 billion devices by the end of next year. Due to this ever alarming increase in cases of identity theft and data leaks all around the world, authentication has become a major concern. As the IoT devices are resource constraint and incapable of defending, they offer potential points of failure within an IoT-based system themselves. Hence, device authentication and authorization is critical for securing IoT systems, especially when the IoT device holds crucial information like medical data or defense-related data. In this paper, authentication for IoT devices using Ethereum smart contract which is a decentralized approach is discussed. Decentralized application instead of centralized system architecture is preferred much. This system makes use of Ethereum smart contracts to ensure secure authentication of device by the respective user. The key aspects of how the system works, its architecture and the implementation of smart contracts on a local blockchain are discussed.
Perturbed gradient descent with occupation time.

This paper develops further the idea of perturbed gradient descent, by adapting perturbation with the history of state via the notation of occupation time for saddle points. The proposed algorithm PGDOT is shown to converge at least as fast as perturbed gradient descent (PGD) algorithm, and is guaranteed to avoid getting stuck at saddle points. The analysis is corroborated by experimental results.
4D semantic cardiac magnetic resonance image synthesis on XCAT anatomical model

We propose a hybrid controllable image generation method to synthesize anatomically meaningful 3D+t labeled Cardiac Magnetic Resonance (CMR) images. Our hybrid method takes the mechanistic 4D eXtended CArdiac Torso (XCAT) heart model as the anatomical ground truth and synthesizes CMR images via a data-driven Generative Adversarial Network (GAN). We employ the state-of-the-art SPatially Adaptive De-normalization (SPADE) technique for conditional image synthesis to preserve the semantic spatial information of ground truth anatomy. Using the parameterized motion model of the XCAT heart, we generate labels for 25 time frames of the heart for one cardiac cycle at 18 locations for the short axis view. Subsequently, realistic images are generated from these labels, with modality-specific features that are learned from real CMR image data. We demonstrate that style transfer from another cardiac image can be accomplished by using a style encoder network. Due to the flexibility of XCAT in creating new heart models, this approach can result in a realistic virtual population to address different challenges the medical image analysis research community is facing such as expensive data collection. Our proposed method has a great potential to synthesize 4D controllable CMR images with annotations and adaptable styles to be used in various supervised multi-site, multi-vendor applications in medical image analysis.