Reviewerly/exHarmony huggingface dataset:
On the Relationship Between Organizational Structure Patterns and Architecture in Agile Teams

Forming members of an organisation into coherent groups or teams is an important issue in any large-scale software engineering endeavour, especially so in agile software development where teams rely heavily on self-organisation and organisational flexibility.But is there a recurrent organisational structure pattern in agile software engineering teams?and if so what does that pattern imply, in terms of software architecture quality?We address these questions using mixed-methods research in industry featuring interviews, surveys, and Delphi studies of real agile teams.In our study of 30 agile software teams we found that, out of seven organisational structure patterns that recur across our dataset, a single organisational pattern occurs over 37% of the time.This pattern: (a) reflects young communities (1-12 months old); (b) disappears in established ones (13+ months); and (c) reflects the highest number of architecture smells reported.Finally, we observe a negative correlation between a proposed organisational measure and architecture smells.On the one hand, these insights may serve to aid architects in designing not only their architectures but also their communities to best support their co-evolution.On the other hand, we observe that organisational structures in software engineering influence much more than simply software architectures, and we expect our results to lay the foundations of more structured and rigorous approaches to organisational structure studies and use in software engineering research and practice.
Copy Move Forgery Detection-A Robust Technique

This paper discusses the Techniques for the Ro-bust Copy Move Forgery detection for the different datasets MICCF8multi,MICCF600,MICCF220,CoMoFoD DB. Thepro-posedmethodology computes two thresholds dynamically, for each input image one to detect the candidate block &#x0026; another to detect the forged block. The performance of the proposed algorithm is evaluated along with the different state-of-art techniques. The results have shown that the proposed SWT-SVD algorithm out-performs 2D-DWT,BDF,YU-SUN,DRHFMS in terms of accuracy &#x0026; computational time.
Reactive Power Optimization Using New Enhanced Whale Optimization Algorithm

The standard Whale Optimization Algorithm (WOA) involves exploitation and exploration operations which require to be balanced for improved performance. This paper suggests a new enhanced WOA to improve the convergence speed and enhance the global optimum by balancing exploitation and exploration processes. New stages have been suggested at the hunting stages of the WOA to increase the exploitation capability. The performance of the modified algorithm has been analyzed on few commonly used test functions and Reactive Power Optimization (RPO) problem. The numerical studies have shown that the proposed WOA variant has outperformed other compared optimization algorithms in terms of global optimum and convergence speed.
Perceptual Aliasing++: Adversarial Attack for Visual SLAM Front-End and Back-End

Similar appearing places are known to confuse place recognition methods (a.k.a perceptual aliasing). Robust visual SLAM methods have shown impressive resilience against scenarios having moderate perceptual aliasing. In this work, we evaluate visual SLAM in scenarios where perceptual aliasing occurs with greater frequency. Firstly we evaluate visual SLAM front-ends. We modify the environment by replicating a simple high textured patch. Surprisingly, this simple patch manages to by-pass all the geometric checks in the state of the art ORB-SLAM, resulting in false loop closures and hence corrupting the map. The patch can be printed on a simple paper, using a simple printer, or it can be digital content displayed on screens. We show that closeups (approaching doors, turning a corridor, interacting with an object) are the most vulnerable situations. This vulnerability exists in multiple loop closing pipelines. Secondly, we evaluate robust SLAM back-ends. These back-ends have shown the ability to recover from false positives under multiple policies (random, local, random grouped and locally grouped). We propose a novel policy, i.e. locally symmetric, for generating false loop closures which successfully attacks multiple robust back-ends and public datasets. Surprisingly, for commonly occurring weaker structures (multiple floors) even a single false loop manages to fool multiple robust back-ends. We hope these findings will motivate the community in evolving SLAM solutions for adversarial environments. To further enhance progress in this direction, we release a novel dataset with adversarial content in both visual SLAM front-ends and back-ends <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Severity Prediction of Software Vulnerabilities based on their Text Description

Software vulnerabilities represent a real challenge nowadays, often resulting in disruption of vital systems and data loss. Due to the multitude of software applications used within a company, system administrators often end up in the situation of facing multiple vulnerabilities at the same time, having no choice but to prioritize the most critical ones. Administrators commonly use vulnerability databases and metric systems to rank vulnerabilities; however, it usually takes from days to weeks for the metrics to be published since these metrics are established by human security analysts and the number of daily discovered exploits is constantly increasing. Therefore, newly discovered vulnerabilities, especially those without an available patch, represent the largest problem. In this paper, we propose a deep learning approach to predict the severity score and other metrics of a vulnerability using only its text description, which is available on discovery. We use a Multi-Task Learning architecture with a pre-trained BERT model for computing vector-space representations of words. Our best configuration achieves a mean absolute error of 0.86 for the severity score and an accuracy of 71.55&#x0025; for the severity level.
An Integrated Narrative Generation System

This chapter introduces an integrated narrative generation system (INGS), which is one of the central parts in the author's study that aims to define an integrated approach to narrative generation. In the narrative generation study, all parts are considered as components that serve the extreme generative and productive goals, and the most important element of the goals corresponds to the design and development of INGS. This chapter divides INGS into two parts: mechanisms and contents. The main generation modules in INGS are for story, narrative discourse, and expression including language, music, and picture images, which are performed using various narrative techniques. On the other hand, INGS has dictionaries, including conceptual and language, and various knowledge, including story content and state-event transformation knowledge bases, which correspond to the aspect of contents. This chapter explains the mechanisms in the order of the entire process and states in detail the conceptual dictionaries, mainly noun and verb conceptual dictionaries.
Assessing international research activities in library studies (2011–2020) on SciVal online platform

The author discusses the findings of the study accomplished within “The Library. Librarians. Information” topic cluster of Elsevier’s SciVal online platform and assesses international research activities in bibliometric studies in 2011–2020. She analyzes the dynamics of research activities in library studies and contributions made by individual players; characterizes the most relevant topics in the modern library studies. The methodologies is determined by SciVal metrics applicability and bibliometrics on the whole. The author concludes on the trends of publication activity slow decrease and the U. S.’ dominating in research output contributions in the given domain; she characterizes 10-top actors in research output in the aspects of countries and individual organizations; identifies journals providing the core of articles with the citation rate above the worldwide average. Based on the prominence term, the author identifies 14 top topics of library studies and research. For the first time in the national publications, the functionality of SciVal online platform is revealed to get acquainted with the research trends in library studies.
SSD Forensic: Evidence Generation and Forensic Research on Solid State Drives Using Trim Analysis

Traditional hard drives consisting of spinning magnetic media platters are becoming things of the past as with the emergence of the latest digital technologies and electronic equipment, the demand for faster, lighter, and more reliable alternate storage solutions is imperative. To attain these requirements, flash storage technologies like Solid State Drive (SSD) has overtaken traditional hard disk drives. In a forensic analysis of flash storage devices, forensic investigators are facing severe challenges for the reason that the sovereign behavior of solid-state storage media does not look favorable compared to traditional storage media devices. Wear Leveling, a fundamental mechanism in Solid State Drive (SSD), plays a severe challenge that most often destroys forensic evidence in many cases. It makes it complicated for forensic investigators to recover the necessary evidence. Persistence of deleted data in flash storage media depends on various factors like the Garbage Collection process, TRIM command, flash media type, manufacturer, capacity, file system, type of file saved, and the Operating System, etc. In view of this, extensive experiments conducted to identify the probability of data recovery and carving. Analyzed effects of Wear Leveling and Garbage Collection processes in Solid State Drive (SSD) of different manufacturers, having the same storage capacities and with a different type of files utilized. In conclusion, experimental findings established the fact that Wear Leveling in solid-state media can obfuscate digital evidence, and a conventional assumption regarding the behavior of storage media is no more valid. Moreover, data persistency also depends on the manufacturers, time-lapse of forensic analysis after data deletion, type of files, and size of files stored in Solid State Drives (SSD).
Lightweight deep learning

Deep convolutional neural networks have been excelling continuously on various challenging visual analysis tasks. Deep models with parameter-heavy architectures have been successfully trained and deployed on a multitude of applications, and owe their success partly due to the continuous development of increasingly more powerful graphical processing units. However, the power consumption and sheer size of such models hinder their applicability in robotics applications. Thus recent research has steered toward the optimization of deep learning architectures for deployment on devices with limited resources. This entails using modules with fewer parameters and fewer floating point operations, as well as careful tuning of such models to train models, which are both efficient and effective. This chapter aims to provide insight into various methods geared toward improving the performance of deep lightweight neural networks, starting by introducing lightweight models, which can run on embedded devices. Subsequently, various methods, which aim to improve the effectiveness of such models are presented, including regularization techniques and architectural modifications.
A Novel Approach in Credit Card Fraud Detection System Using Machine Learning Techniques

With the rapid expansion of daily life, the use of credit cards for online purchases is steadily increasing and credit card fraud is on the rise. Nowadays, in the social distancing environment, due to covid-1, 9online shopping has become important. Credit card credentials are used to make online payments, and then deduct money which does not involve any contact and makes people’s life difficult. Because of this, finding the most effective method of detecting scams in online systems is essential. To prevent customers from being charged for goods they have not purchased, credit card companies must be able to identify fraudulent credit card transactions. Therefore, there are several theories either completed or proceeding to detect these kinds of frauds. This study is an approach to identify non-legitimate transactions using semi-supervised machine learning models by explaining how to deal with imbalanced datasets, using a wide variety of models to better understand which ones work better.
Sentiment Analysis on Movie Review using Ensemble Stacking Model

Movie reviews are very essential to people because they can help viewers get an overview of the movie and provide feedback to the movie directors about their movies based on public sentiment. One way to determine whether the sentiment is positive or negative from an opinion can be solved by sentiment analysis. However, analyzing sentiment manually is difficult due to a large amount of data, so it is necessary to implement automation that can make this easier. In this research, we investigate a machine learning method for classifying movie review sentiment. Then Ensemble Stacking model was chosen as a classification method for this sentiment analysis case. In the classification stage, three algorithms (Naïve Bayes, K-Nearest Neighbors, and Logistic Regression) are implemented as the base-learners, then the Logistic Regression algorithm is implemented as a meta-classifier to improve the performance of the base-learners in the Ensemble Stacking model. Therefore, testing and comparison between single classification such as the base-learners with the classification method using Ensemble Stacking are the main focus of this research. The results of this research show that the accuracy of this stacking model reaches 89.40%, an increase of 0.71% from the highest accuracy on the base-learners.
Risks in Student Projects

Anecdotally, tertiary educators in software related topics experience challenges during project-based group assessments. To address the range of issues experienced, some educators have introduced aspects of project management (PM) into their course. However, the key focus for software courses is not project management and time spent teaching PM techniques means less time is available for addressing course objectives. In this paper, we describe a study to investigate the use of PM reference models to identify the main areas of risk for student projects. We evaluated some common reference models and selected the Project Management Body of Knowledge (PMBOK®) as being the most suitable. We mapped the issues as reported in the computer science education (CSE) literature on group projects to the PMBOK® Knowledge Areas (KAs) and found that only a subset of the KAs was relevant. We used these KAs to categorise the key risks for student projects. Our contributions are the exposure of a gap in the literature on student project risk and the identification of the main sources of risk. There is a need for researchers to further investigate project risk in tertiary group projects in order to help instructors introduce appropriate PM concepts to support their projects.
Fake news detection platform—conceptual architecture and prototype

Abstract Countering the fake news phenomenon has become one of the most important challenges for democratic societies, governments and non-profit organizations, as well as for the researchers coming from several domains. This is not a local problem and demands a holistic approach to analyzing heterogeneous data and storing the results. The research problem we face in this paper is the proposition of an innovative distributed architecture to tackle the above-mentioned problems. The architecture uses state-of-the-art technologies with a focus on efficiency, scalability and also openness, so that community-created components and digital content analyzers could be added. Moreover, we prove the usability of the prototype on Kaggle Fake News dataset. In particular, we consider different configurations of the proposed deep neural network and present the results reflecting the effectiveness, scalability and transferability of the proposed solution.
The digital divide in the US criminal justice system

The growing use of digital evidence from smartphones and social media has led to a digital divide in the US criminal justice system that advantages law enforcement and prosecutors while further increasing the vulnerability of poor people and people of color who rely on public legal assistance. Drawing on a year-long ethnographic study of one of the first digital forensics laboratories in a public defender office, I argue that digital inclusion in the form of better resources for public defenders is necessary for equitable and fair representation in today’s criminal justice system. Findings show that access to digital forensic technologies is an important equalizing tool that allows public defenders to (1) mount strong, data-driven cases; (2) create counter narratives that challenge depictions of marginalized defendants as dangerous; and (3) engage in nuanced storytelling to highlight the complexities of human relationships and life circumstances that shape cases.
Deep reinforcement learning

Reinforcement learning is a wide ranging subfield of machine learning, which has been brought to the forefront of research after the unprecedented rise of deep learning. The improvements introduced by deep learning have been applied to many facets of reinforcement learning and have led to the dramatic improvement in many existing methods that were missing the complex dissemination of information of deep learning. In this chapter, we explore some of the reinforcement learning methods that have been crucial to solving tasks previously thought impossible to be solved by machine learning and even surpassing human performance in many of them, such as Atari games, chess, go, and others.
Hybrid machine learning classification scheme for speaker identification

Motivated by the requirement to prepare for the next generation of "Automatic Spokesperson Recognition" (ASR) system, this paper applied the fused spectral features with hybrid machine learning (ML) strategy to the speech communication field. This strategy involved the combined spectral features such as mel-frequency cepstral coefficients (MFCCs), spectral kurtosis, spectral skewness, normalized pitch frequency (NPF), and formants. The characterization of suggested classification method could possibly serve in advanced speaker identification scenarios. Special attention was given to hybrid ML scheme capable of finding unknown speakers equipped with speaker id-detecting classifier technique, known as "Random Forest-Support Vector Machine" (RF-SVM). The extracted speaker precise spectral attributes are applied to the hybrid RF-SVM classifier to identify/verify the particular speaker. This work aims to construct an ensemble decision tree on a bounded area with minimal misclassification error using a hybrid ensemble RF-SVM strategy. A series of standard, real-time speaker databases, and noise conditions are functionally tested to validate its performance with other state-of-the-art mechanisms. The proposed fusion method succeeds in the speaker identification task with a high identification rate (97% avg) and lower equal error rate (EER) (<2%), compared with the individual schemes for the recorded experimental dataset. The robustness of the classifier is validated using the standard ELSDSR, TIMIT, and NIST audio datasets. Experiments on ELSDSR, TIMIT, and NIST datasets show that the hybrid classifier produces 98%, 99%, and 94% accuracy, and EERs were 2%, 1%, and 2% respectively. The findings are then compared with well-known other speaker recognition schemes and found to be superior.
Web-based systems for inventory control in organizations: A Systematic Review

Currently, large companies in the sales and warehouse environment, there are realities where they perform their inventories in a physical way, is have technological service, therefore, not running a stock management, etc. That is why Web Systems or Web Applications for inventory control, is very important for the efficiency of the areas they represent in small, medium and large organizations, where a change is reflected through the use of web technologies. The objective of a Web System is to manage the different processes that are represented in a company, where primarily based on their requirements, it is necessary to study, analyze, apply, validate, all the factors that compose it, and thus generates an impact, with the benefit of generating cost savings in hardware and software, and as a result there is efficiency, at the same time can include all the needs of the business. Therefore, the present work aims to collect and review the approaches made for the implementation of a Web System, the various methodologies or frameworks and frameworks that are used in the implementation of web systems, from a literature review and an analysis of the current state of the art of web systems. Eighty-five articles were found and 26 articles were selected, including those meeting the inclusion and exclusion criteria corresponding to the research questions. As a result, we obtained the proposed information, as a proposal for a Web system that can be implemented and evaluated later in another research.
Enabling Privacy-Preserving Parallel Outsourcing Matrix Inversion in IoT

With the rapid development of Internet of Things (IoT), edge computing has been widely applied as a novel computing paradigm. Securely outsourcing intensive tasks to edge servers is becoming increasingly pervasive. It is a nice approach for resource-limited IoT devices to accomplish heavy computing tasks. Matrix inversion is a basic but time-consuming operation, which has a wide range of applications in IoT. The current privacy-preserving outsourcing schemes for matrix inversion cannot support parallel computing based on multiple edge servers. As a result, they cannot well satisfy the requirement of fast response for computation in IoT. In order to deal with this problem, we propose two privacy-preserving parallel outsourcing schemes for matrix inversion in IoT. In the first scheme, we design a novel method to generate a random matrix, which is used to blind the inputted original matrix. In this scheme, two edge servers compute the inversion of the encrypted matrix in parallel to improve the computational efficiency. To further improve the efficiency, we design a novel subtasks partitioning and assignment strategy and propose the second scheme by balancing the computing load of edge servers. We analyze the correctness, security, and verifiability of the proposed schemes. And we provide theoretical analysis and experimental results to demonstrate the performance advantages of the proposed schemes.
Conversational Artificial Intelligence in Healthcare

Innovative conversational artificial intelligence (AI) powered systems have been gaining momentum in the healthcare industry in recent years. Automated artificial intelligence programs are built with the purpose of allowing effective communication by providing an interface between the computer and the user. Conversational AI are making a significant impact on the healthcare industry for both medical health providers and patients. Several natural language processing (NLP) platforms, in particular using natural language understanding (NLU), such as Google Dialogflow, IBM Watson and Rasa are used in conversational AI. This paper intends to present an architecture adopted to deploy a successful conversational AI agent, named Ainume, using Google Dialogflow on the Google Cloud Platform (GCP). Ainume identifies symptoms of common and chronic diseases, accordingly, suggesting nutraceutical solutions to reduce the symptoms of these diseases. The focus of this paper is on one aspect that Ainume is equipped to deal with, that is, cardiovascular diseases.