Reviewerly/exHarmony huggingface dataset:
Combination of NumPy, SciPy and Matplotlib/Pylab -a good alternative methodology to MATLAB - A Comparative analysis

Python is a simple, dominant and well-organized interpreted language. Python is used to develop the very high performance scientific related application and it is used to develop an application for Numeric computation, it is frequently used. Using python we can develop a high performance applications Python often utilizes external libraries to perform scientific computing. The most important libraries used are NumPy, SciPy and Matplotlib to perform scientific and numeric applications. The Python libraries are open-source tag-on modules, which do additional frequent mathematical and numerical routines in pre-compiled, high-speed tasks. These two modules are providing the functionality that meets, or perhaps exceeds, that related with common commercial software like MATLAB. The python package NumPy (Numeric Python) is used for manipulating large arrays and matrices of numeric data. The Scientific Python (SciPy) extends the functionality of NumPy with a considerable collection of valuable algorithms, like minimization, Fourier transformation, regression, and other applied mathematical techniques.Matrix Laboratory (MATLAB) is a multiple programming paradigm. it is used for numerical computing environment and it is a proprietary programming language. The following various operations are performed by the MATLAB, operations are plotting of functions and data, matrix manipulations, python use an interface for user to interact with an application, some type of algorithms are used for implementation purpose and another type of programming languages are used to create interfacing with programs. The combination of the following three packages NumPy, SciPy and Matplotlib/Pylab are alternatively used as a MATLAB, so this three packages give the alternative to MATLAB, MATLAB environment is best suit for scientific works. Python provide a alternative to MATLAB.
Semi-supervised learning for hierarchically structured networks

A set of data can be obtained from different hierarchical levels in diverse domains, such as multi-levels of genome data in omics, domestic/global indicators in finance, ancestors/descendants in phylogenetics, genealogy, and sociology. Such layered structures are often represented as a hierarchical network. If a set of different data is arranged in such a way, then one can naturally devise a network-based learning algorithm so that information in one layer can be propagated to other layers through interlayer connections. Incorporating individual networks in layers can be considered as an integration in a serial/vertical manner in contrast with parallel integration for multiple independent networks. The hierarchical integration induces several problems on computational complexity, sparseness, and scalability because of a huge-sized matrix. In this paper, we propose two versions of an algorithm, based on semi-supervised learning, for a hierarchically structured network. The naïve version utilizes existing method for matrix sparseness to solve label propagation problems. In its approximate version, the loss in accuracy versus the gain in complexity is exploited by providing analyses on error bounds and complexity. The experimental results show that the proposed algorithms perform well with hierarchically structured data, and, outperform an ordinary semi-supervised learning algorithm.
Authorship Identification using Recurrent Neural Networks

Authorship identification is the process of revealing the hidden identity of authors from a corpus of literary data based on a stylometric analysis of the text. It has essential applications in various fields, such as cyber-forensics, plagiarism detection, and political socialization. This paper aims to use a deep learning approach for the task of authorship identification by defining a suitable characterization of texts to capture the distinctive style of an author. The proposed model uses an index based word embedding for the C50 and the BBC datasets, applied to the input data of article level Long Short Term Memory (LSTM) network and Gated Recurrent Unit (GRU) network models. A comparative study of this new variant of embeddings is done with the standard approach of pre-trained word embeddings.
Using a translanguaging approach in teaching paraphrasing to enhance reading comprehension in first-year students

Background: The teaching of reading strategies that enhance comprehension is a priority for many practising lecturers involved in academic literacy programmes. However, due to the unprepared nature of a large section of students entering the Higher Education system, a number of students have been found to lack basic literacy skills such as reading for comprehension. A plethora of studies has been conducted locally and internationally with the aim of finding solutions to help students who struggle with text comprehension, especially at university level.Objectives: The purpose of this research is to show how paraphrasing as a strategy can be combined with a translingual approach to instruct students explicitly on how to read and comprehend textsMethod: Using a group of first-year multilingual medical students, this research included translanguaging as an additional component to the use of paraphrasing as a reading comprehension strategy.Results: The paper provides insight into how translanguaging can be used to assist multilingual students to understand texts through paraphrasing. Drawing empirical data from classroom activities, this paper shows how lecturers can help students to use translanguaging and paraphrasing as metacognitive strategies to help improve their comprehension of texts.Conclusion: Finally, the importance of allowing students to utilise their multilingual repertoires is emphasised and reiterated, through translanguaging, in order to enhance learning.
Biometric data on the edge for secure, smart and user tailored access to cloud services

We are living an era in which each of us is immersed in a fully connected environment. The smart devices worn everyday by everyone let the users be projected into the so called IoT world, whose main aim is to provide tools and strategies to solve problems of everyday life as well as to improve well-being and quality of life. Such a goal can be achieved in several ways, but consumer specific services should be provided according to their daily habits, as well as temporary needs. This can be made possible by current mobile devices and their built-in sensors, which can infer the context where the owner operates and the performed activities to build up a user profile. In this work, an architecture for cloud services supply is proposed, based on subject continuous authentication and context (status/activity) recognition. A subject and the context are authenticated/recognised by means of the cyclic recognition of signals captured by the sensors of his/her smartphone, e.g., accelerometer and gyroscope. In order to test the accuracy of the proposed context recognition, the H-MOG dataset has been used, which provides joint acquisitions of status data (Sitting or Walking) related to the activity performed (Reading, Writing, or Navigating Map).
ESCAPE Data Collection for Multi-Modal Data Fusion Research

Over the last decade there has been a technological explosion of advanced, digital, solid state, software controlled, and low size, weight, power and cost (SWaPC) sensors and payloads. The sensor advancement allowed engineering practitioners the ability to easily perform a variety of remote sensing operations and consider a wide range of modalities measuring the environment simultaneously. However, there has not been a common multi-modal data set to compare data fusion methods. This paper describes a multi-mode data set performed by the Air Force Research Laboratory, Information Directorate, to enable multi-modal signature data-fusion research. The Experiments, Scenarios, Concept of Operations, and Prototype Engineering (ESCAPE)collection brings together electro-optical, infrared, distributed passive radio-frequency, radar, acoustic and seismic data in a common scenario for the application of advanced fusion methods for aerospace systems. The paper details hardware, scenarios, and data collection specifics. Scenarios involved disparate moving emitting ground vehicles, challenging vehicle path patterns, and differing vehicle noise profiles. The purpose of the data collection, and the resulting data sets, is to engage the data fusion community in advanced upstream heterogeneous data analytics, design, and understanding.
Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning

Consider a device that is connected to an edge processor via a communication channel. The device holds local data that is to be offloaded to the edge processor so as to train a machine learning model, e.g., for regression or classification. Transmission of the data to the learning processor, as well as training based on stochastic gradient descent (SGD), must be both completed within a time limit. Assuming that communication and computation can be pipelined, this letter investigates the optimal choice for the packet payload size, given the overhead of each data packet transmission and the ratio between the computation and the communication rates. This amounts to a tradeoff between bias and variance, since communicating the entire data set first reduces the bias of the training process but it may not leave sufficient time for learning. Analytical bounds on the expected optimality gap are derived so as to enable an effective optimization, which is validated in numerical results.
User Behavior Modelling for Fake Information Mitigation on Social Web

The propagation of fake information on social networks is now a societal problem. Design of mitigation and intervention strategies for fake information has received less attention in social media research, mainly due to the challenge of designing relevant user behavior models. In this paper we lay the groundwork towards such models and present a novel, data-driven approach for user behavior analysis and characterization. We leverage unsupervised learning to define user behavioral categories over key behavior dimensions. We then relate these categories to content-based, user-based, and network-based features that can be extracted in near-real time and identify the most discriminative features. Finally, we build predictive models via supervised learning that leverage these features to determine a user’s behavior category. Rigorous evaluation indicates that the constructed models can be valuable in predicting user behavior from recent activity. These models can be employed to rapidly identify users for intervention in mitigation strategies, crisis communication, and brand management.
Credit Card Fraud Detection Using Anomaly Techniques

Credit card fraud transaction detection system is a method used for determining the fraudulent transactions that take place every once in a while. The project uses a test data set of around 27,000 credit card transactions which have been taken from Caltech (Kaggle). The project comprises of primarily 2 major algorithms and uses anomaly detection as a method to classify the fraudulent transactions. The Local Outlier Factor (LoF) defines the various parameters that have to be used in determining the criteria for fraudulent transactions. It then checks upon the different transactions for the various parameters present in the given LoF. This factor then gives each transaction a score based on the various transactions that have or will have taken place. These scores can range from 0 - 1. Each transaction is thus given a score which is based on the various parameters given in the LoF. The second part of the project is isolation forest algorithms which is an algorithm that isolates the transaction which have a high rate of anomaly detected in them. Thus, these transactions are isolated and then checked with various parameters to be labelled as either fraudulent or real transactions. The algorithm also uses charts to check for spikes in the average transaction. We also uses technique like data visualization in order to show the output in more understandable ways which may include histograms, graphs and matrix .Through these two algorithms and with help of data visualization technique we can detect the fraudulent transactions from correct transaction and obtain results in quick time, Since these algorithms are much more time efficient than other machine learning algorithms in this type of tasks.
Synthetic-analytic behavior-based control framework: Constraining velocity in tracking for nonholonomic wheeled mobile robots

This work presents a genetic programming control design methodology that extends the traditional behavior–based control strategy towards a synthetic-analytic perspective. The proposed approach considers the internal and external dynamics of the system, providing solutions to a general structure, and including analytic functions, which can be studied within the Control Theory framework. The method is illustrated for the tracking control problem under bounded velocity restrictions of a nonholonomic wheeled mobile robot. A classic Control Theory (CT) based controller that solves the tracking problem (but not the velocity constraint requirement) is chosen from the literature; based on its stability properties, a modified structure where the search of suitable analytic basis behaviors, fulfilling both control objectives simultaneously, can be introduced. The proposed framework takes the form of a learning process based on Genetic Programming (GP) which generates a set of nonlinear tracking controllers satisfying pre-specified velocity bounds. A collection of 9113 suitable nonlinear solutions were obtained to augment the ground controller. Simulations and real–time experiments are performed to illustrate the effectiveness of the methodology through the testing of the models with the best performance, as well as those with lower structural complexity.
An adaptive doctor-recommender system

Recommender systems use machine-learning techniques to make predictions about resources. The medical field is one where much research is currently being conducted on recommender system utility. In the last few years, the amount of information available online that relates to healthcare has increased tremendously. Patients nowadays are more aware and look for answers to healthcare problems online. This has resulted in a dire need of an effective reliable online system to recommend the physician that is best suited to a particular patient in a limited time. In this article, a hybrid doctor-recommender system is proposed, by combining different recommendation approaches: content base, collaborative and demographic filtering to effectively tackle the issue of doctor recommendation. The proposed system addresses the issue of personalization through analysing patient's interest towards selecting a doctor. It uses a novel adoptive algorithm to construct a doctor's ranking function. Moreover, this ranking function is used to translate patients’ criteria for selecting a doctor into a numerical base rating, which will eventually be used in the recommendation of doctors. The system has been evaluated thoroughly, and result show that recommendations are reasonable and can fulfil patient's demand for reliable doctor's selection effectively.
Snoring: A Noise in Defect Prediction Datasets

In order to develop and train defect prediction models, researchers rely on datasets in which a defect is often attributed to a release where the defect itself is discovered. However, in many circumstances, it can happen that a defect is only discovered several releases after its introduction. This might introduce a bias in the dataset, i.e., treating the intermediate releases as defect-free and the latter as defect-prone. We call this phenomenon as "sleeping defects". We call "snoring" the phenomenon where classes are affected by sleeping defects only, that would be treated as defect-free until the defect is discovered. In this paper we analyze, on data from 282 releases of six open source projects from the Apache ecosystem, the magnitude of the sleeping defects and of the snoring classes. Our results indicate that 1) on all projects, most of the defects in a project slept for more than 20% of the existing releases, and 2) in the majority of the projects the missing rate is more than 25% even if we remove 50% of releases.
Adversarial Structure Matching for Structured Prediction Tasks

Pixel-wise losses, i.e., cross-entropy or L2, have been widely used in structured prediction tasks as a spatial extension of generic image classification or regression. However, its i.i.d. assumption neglects the structural regularity present in natural images. Various attempts have been made to incorporate structural reasoning mostly through structure priors in a cooperative way where co-occurring patterns are encouraged. We, on the other hand, approach this problem from an opposing angle and propose a new framework, Adversarial Structure Matching (ASM), for training such structured prediction networks via an adversarial process, in which we train a structure analyzer that provides the supervisory signals, the ASM loss. The structure analyzer is trained to maximize ASM loss, or to emphasize recurring multi-scale hard negative structural mistakes usually among co-occurring patterns. On the contrary, the structured prediction network is trained to reduce those mistakes and is thus enabled to distinguish fine-grained structures. As a result, training structured prediction networks using ASM reduces contextual confusion among objects and improves boundary localization. We demonstrate that ASM outperforms its pixel-wise counterpart and commonly used structure priors, GAN, on three different structured prediction tasks, namely, semantic segmentation, monocular depth estimation, and surface normal prediction.
Mining the Technical Roles of GitHub Users

Context:Modern software development demands high levels of technical specialization. These conditions make IT companies focus on creating cross-functional teams, such as frontend, backend, and mobile developers. In this context, the success of software projects is highly influenced by the expertise of these teams in each field. Objective:In this paper, we investigate machine-learning based approaches to automatically identify the technical roles of open source developers. Method:For this, we first build a ground truth with 2284 developers labeled in six different roles: backend, frontend, full-stack, mobile, devops, and data science. Then, we build three different machine-learning models used to identify these roles. Results:These models presented competitive results for precision (0.88) and AUC (0.89) when identifying all six roles. Moreover, our results show that programming-languages are the most relevant features to predict the investigated roles. Conclusion:The approach proposed in this paper can assist companies during their hiring process, such as by recommending developers with the expertise required by job positions.
Blockchain and Its Role in the Internet of Things

This paper provides an overview of blockchain architectures designed for Internet of Things (IoT) networks, namely, Internet of Healthcare Things (IoHT), Internet of Vehicles (IoV), Internet of Energy (IoE), and Internet of Cloud (IoC). This paper starts by providing a horizontal overview of the blockchain mechanism. Moreover, it explores the relation between the IoT and other emerging technologies including, blockchain, IoHT, IoV, IoE, and IoC. For each subsector of IoT, it gives a taxonomy and a side-by-side comparison of the state-of-the-art methods towards secure blockchain mechanism with respect to the specific security goals, performance, limitations, and complexity. The main objective of this article is to provide a thorough summary of the blockchain protocols designed for IoT and published during 2018.
Global solar radiation prediction for Makurdi, Nigeria, using neural networks ensemble

In this work, various artificial neural networks (ANNs) used for predicting the solar radiation in Makurdi city, Nigeria (7°7ʹ N long. 8°6ʹ) have been developed: Feed-forward back-propagation neural network (FFNN), radial basis function network (RBFN), and generalized regression neural network (GRNN). The main objective of this study is the use of an ANN’s ensemble for the prediction of average monthly global solar radiation of Makurdi in order to improve the prediction accuracy. The training and testing data were obtained from the Nigeria metrological station (NIMET) Makurdi. For each neural network type, average R2 = 0.998 and MSE = 0.0142, performance measures were obtained when the networks were analyzed. In order to improve prediction accuracy, an ensemble of neural networks was examined which gave an R2 = 1.0 and MSE = 0.0139. All the proposed neural networks predicted solar radiation with great accuracy; nevertheless, ensemble achieved better results implying high dependency of the model for the evaluation of solar radiation in the locations where solar radiation instruments are not available or faulty.
Sexual Online Grooming as an Object of Psychological Research

Sexual exploitation of children and adolescents has acquired new forms with development of the Internet. Sexual online grooming, or solicitation through the Internet, is one of such forms. The number of online grooming cases increases in Russia, but there is little research on this phenomenon. A few studies concern predominantly its prevalence and problems of joint forensic psychological-linguistic expertise of interactions between the groomer and the child. The range of research abroad is wider, from the structure of the grooming process, manipulative strategies and tactics of the offender to the factors influencing the child’s vulnerability. Special attention is paid to the linguistic features of the online grooming with the goal to develop computer systems for its detection. On the basis of the review of the modern studies the necessity of further interdisciplinary research of the online grooming is argued. It will provide new information for criminal psychology and victimology, promote forensic expertise and prevention of sexual crimes against children.
Cybersecurity as a Centralized Directed System of Systems Using SoS Explorer as a Tool

The vast advancements in computing technology and the profound reliance on inter-connected systems has led to a significant growth of cyber-attack incidents. The incidents can propagate from within or outside of organizations, thus ensuing detrimental consequences such as business conducting disruptions, market brand degradation, settlement charges, regulatory compliance issues, etc. The ability to withstand such interference is essential to maintaining a vital and competitively advantageous organization. This paper analyzes coexisting vulnerabilities and the utilization of innovative and effective mechanisms to identify and block undesirable emergence through a centrally controlled and directed SoS approach. The paper will use SoS Explorer as a tool to architect, model and optimize the many objective cybersecurity complex system dealing with securing from exploiting threats through threat-inhibiting systems. The tool provides an intuitive and interactive visualization enabling the user to produce “what-if” analysis on various scenarios, especially for time evolving dynamics of cybersecurity, through generated optimization functional architecture solutions. The paper will further discuss vulnerability exploitation and cybersecurity implications on banking domain describing speculative observations followed by future research directions.