Reviewerly/exHarmony huggingface dataset:
Measures Driven Change Impact Analysis Approach for Prioritizing Software Requirements Changes

In today’s world, software organization survival depends not only on rapid development but also on adaptability. Modern software development approaches are used to advocate rolling wave planning, evolutionary development, early delivery, and continual improvement, with rapid and flexible response to change. This forces attention to the way requirement change requests are evaluated. However, the lack of a detailed change impact analysis raises questions about the effectiveness of the evaluation process and the decisions made. Important decisions are made to identify whether the Requirement Change (RC) requests could be handled within the estimated project budget and time. Thus, a well-defined change measure and a right and accurate decision making are essential to software project success.RC requests may affect functional requirements (FUR), Non-functional requirements (NFR).As the project evolves over time, the description of the expected software behavior in FR will become more detailed. Most of the software life cycles highlight that there are different phases with different levels of granularity in the description of functional requirements. In early phases of a software project, functional requirements are often unclear, ambiguous and not entirely defined, while in later phases, these requirements will become more detailed and clearer. Thus, functional requirements tend to expand throughout the software life cycle. Many software projects suffer from “scope creep” that can have a negative impact on the project success. In most cases, scope creep means added effort that results in cost overruns and delays, and it may also undermine software quality. Thus, defining a sound changes prioritizing approach (called in this paper Measures Driven Change Impact Analysis) based on measures at different levels of granularity ( i.e., at functional level, control structure level, and process level) of a requirement change RC across the software life cycle is a major challenge for software development teams, testers, and managers.
Future Image Prediction for Mobile Robot Navigation: Front-Facing Camera Versus Omni-Directional Camera

When we perform a task, we select the action by imagining its future consequences. Hence, the ability to predict future states would also be an essential feature for robotic agents because it would allow them to plan effective actions to accomplish given tasks. In this research, we explore an action-conditioned future image prediction model considering its application to navigation tasks for a mobile robot. We investigate the image prediction performance of deep neural network architectures and training strategies with two different camera systems. One camera system is a conventional front-facing camera that has a narrow field of view with high definition, and the other is an omni-directional camera that has a wide field of view with low definition. We compare the performances of prediction models for these two camera systems, and propose using an image prediction model with the omni-directional camera for the navigation tasks of a robot. We evaluate the prediction performance of each camera system through experiments conducted in a complex living room-like environment. We demonstrate that models with an omni-directional camera system outperform models with a conventional front-facing camera. In particular, the model comprising a combination of action-conditioned long short-term memory successfully predicts future images for states of more than 100 steps ahead in both simulation and real-world scenarios. Further, by integrating the proposed system into image-prediction-based navigation algorithm, we demonstrate that navigation based on a model with an omni-directional camera can successfully navigate the robot in cases where one with a conventional front-facing camera fails.
Performance testing in lexical analysis on latest Twitter trends for enterprise network using PIG

Twitter usage is outgrowing each day and so is the ever changing trend. The data flow keeps on increasing each day through tweets and thus analysis on the latest trends are done by using most widely used framework for processing large datasets-Apache Hadoop. This paper presents an overview of Twitter Trend analysis using Apache PIG in Hadoop framework and its performance will be tested using lexical analysis. Hadoop is preferred over other frameworks due to its robustness, scalability, cost effectiveness and high speed which makes parallel processing over distributed network easy. The paper also shows results obtained through lexical analysis done on the Hadoop cluster to show the efficiency of the program.
DUET dual-mode scanning of H&amp;E slides reveals novel contrast

Hematoxylin- and eosin-stained tissue sections are central to patient diagnosis and management guidance. Direct viewing of these slides using a microscope is slowly but seemingly inevitably giving way to the new world of digital pathology. Having the histology images in digital format facilitates deployment of digital analysis tools, ranging from basic image enhancement to straightforward assessment of quantifiable metrics (area, intensity, molecular marker intensity and distribution) all the way to applications of novel machine learning and artificial intelligence tools. However, to date, virtually all such H&E whole-slide scans have been accomplished only in brightfield mode. We have observed that simple fluorescence imaging of H&E-stained slides can provide a great deal of useful histology content. We have developed a scanner that can acquire pixel-matched brightfield and fluorescence images in area-scanning mode, a process we termed DUET, for DUal-mode Emission and Transmission, i
Potential Impacts of Era 4.0 on Agricultural Sustainability

Achieving sustainable development goals is one of today’s main global challenges which includes the agricultural sector, namely in dealing with climate change and food security. Several strategies have been designed to make an increase in farming productivity compatible with environmental protection. In these frameworks, smart farming and digital technologies seem to have been recognised by national institutions and international organisations as sustainable solutions to transform agriculture into something better linked to sustainability. From this perspective, the aim of this study is to compare the Digital Adoption Index with agricultural indicators which may highlight the sustainability of this sector on a worldwide scale. In other words, the intention is to assess if digital adoption is interconnected with the sustainable development of farms. For these objectives, data from The World Bank was considered for the Digital Adoption Index and for the agricultural indicators for the year 2016. This statistical information was analysed through descriptive assessments and matrices of correlation to show the different associations. This research highlights interesting interrelationships which may support the design of future strategies for a better interconnection between digital adoption and agricultural sustainability.
Leveraging Non-negative Matrix Factorization for Document Summarization

This paper outlines the doctoral research work carried out to develop unsupervised approaches for extractive single document summarization using Non-negative Matrix Factorization (NMF). NMF is a popular topic modeling technique, which divulges inter-relation between three prime semantic units of the document. We exploit the inter-relationship among the three semantic units, viz. terms, sentences, and latent topics, in a novel manner to extract informative sentences from the salient topics in the document. The three methods developed during this doctoral study are language-, domain- and collection-independent.
Applying Monte Carlo Search and Monte Carlo Tree Search on Embedded Systems to Play Connect Four with a Robotic Arm

Monte Carlo based methods have brought a remarkable improvement in performance of artificial intelligence in the realm of games in recent years, whereby human champions could be beaten in several board games of high complexity. In this work, two Monte Carlo based approaches, the Monte Carlo Search and the Monte Carlo Tree Search, are applied to the game of Connect Four. To test and compare these two approaches, games against each other are simulated. The better approach is then further placed against human opponents using a website ( https://carloconnect.com/ ) and a robotic arm to evaluate the algorithm’s strength. All tests are conducted on a Raspberry Pi 3 Model B to investigate the approaches’ performance in an environment with limited available computing power and memory. The Experiments show that under these conditions the Monte Carlo tree search significantly outperforms the Monte Carlo search and is therefore the better solution. This approach is called CarloConnect and proves itself against human opponents by winning most of the games, thus playing at human level even while having only low computing resources.
Recombination of Ideas and Direction of Inventive Activity in Economic Growth Research: Evidence from the Journal of Economic Growth

We explore the inventive activity and recombinant ideas in the field of economic growth research by analyzing the Journal of Economic Literature (JEL) classification codes assigned to articles published in the Journal of Economic Growth. The average number of JEL codes, authors, keywords, pages and references per article have increased over time being consistent with increasingly complex idea combinations. Research is concentrated around specific JEL codes (O4 and O1) but the concentration has decreased, particularly after the financial crisis 2007-2008, suggesting an increasing variety of field-crossing idea combinations. We observe a negative association between the number of JEL codes and received citations. Moreover, having no JEL code from O class is negatively associated with received citations. The findings suggest that, on average, articles combining ideas from more fields and articles focusing on atypical topics are less likely to become influential.
Digital strategy with blockchain in healthcare ecosystem using service-dominant architecture

The research explores the relationship between the health industry and blockchain to develop a digital strategy that can create value for the healthcare ecosystem. To fulfil the same, interpretive qualitative case studies were used for content analysis where the 'texture of practices' emerged from the data triangulation. The design science research methodology was applied to devise a digital strategy, focusing on problem-centred initiation in the healthcare ecosystem. The results confirmed that considering the transparent and immutable nature of blockchain technology, it could help the health sector develop 'digital trust' and protect patients' privacy. Considering the socio-material view and based on service-dominant logic, blockchain can deliver mutual benefits, thereby resulting in precision medicines, targeted research, and population-level insights into medical treatment. Based on the insights, the study identified the impact of using blockchain as a technological enabler and reflected the value co-creation mechanism driven by a digital strategy.
Mobile Forensics for iOS and Android Platforms: Chrome App Artifacts Depending on SQLite

This chapter starts by comparing and contrasting the architectures of Android and iOS that are discussed in the first chapter. As a result, we implement and utilize mobile forensics methodology to analyze SQLite files from the applications that install on the mobile device, we discuss some of the techniques and tools used to extract information, and we present a case study of Chrome application. In terms of forensic analysis, the chapter will also emphasize the necessity of examining all SQLite files that come under the apps in order to extract the most amount of digital evidence feasible. We investigated practical forensic analysis for the Chrome app for iOS and Android, and forensic procedures were carried out using the three phases (seizure, acquisition, examination & analysis) methodology. This chapter aims to extract artifacts from Chrome applications using many tools such as iBackup, iExplorer, iTunes, Belkasoft, and FinalMobile software for iOS. We use ADB, Belkasoft, AXIOM, FinalMobile, and MOBILedit for Android. SQLiteStudio is used to view SQLite database files which are extracted from both Android and iOS.
Chaotic-Integrated Harris Hawks Optimization Algorithm for Unconstrained Problems Optimization

The complexity of the real-world problems is increasing exponentially and prior approaches are losing their efficacy. Therefore, researchers are developing new methods for solving problems. One of these state-of-the-art approaches is the use of meta-heuristic optimization algorithms. The optimization algorithms are the most recent approaches to deal with the complexity of real-world problems, which have been shown an abundant performance in solving many issues. Hence, much attention has been paid to these algorithms recently, as numerous optimization algorithms have been developed. The Harris Hawks Optimization (HHO) algorithm is the cutting-edge optimization algorithms which have been inspired by the intelligent behavior and chasing strategies of Harris's hawks in nature and has an extraordinary performance in solving various problems. In this paper, the effect of eleven eminent chaotic maps has been investigated on the proficiency of the HHO. The experiments were performed on fifty-three benchmark functions and the obtained results have been compared with the original HHO in terms of mean and standard deviation. The simulation results show that in the unimodal benchmark functions, Sinusoidal and Singer maps, in the multimodal and the fix-dimension benchmarks, Liebovitch map, in the shifted and rotated benchmarks, Intermittency map, and in the hybrid and composite benchmarks Tent map is outperformed. In addition, the convergence speed of the chaotic HHO algorithm is discussed in all chaotic maps over each benchmark function. The results represent the chaotic maps can enhance the exploration and exploitation capabilities of the HHO in almost all cases.
Particle Swarm Optimization Based Feature Selection for Educational Data Mining

The domain of Educational Technology in recent days is becoming personalized. The learning mechanism of each student varies significantly from time-to-time based on the learning styles and the ICT technology adapted by the educators. Toolkits and digitized environment has been established by most of the universities with the intention to provide supportive deliverables in educational technology. Predicting the learning ability among the student’s community is becoming a challenging task in a learner-centric environment. This research completely focuses in the development of a decision support system with the intention to determine the performance and ability of the student’s upon multiple decisive factors. The model has been developed using machine learning and swarm intelligence technique involving Chaotic Particle Swarm Optimization with Henon execution. The experimental results shown that the features corresponding to the use of ICT for their own progress with the intention of evaluating the learning outcomes and they can carry out effective presentations for their course learning, assessment and evaluation. Significant statistical analysis is also made using Sphearman Rank correlation and it has been validated by the test hypothesis stating that the P-value is found to be significant and always <0.001 respectively. this mechanism can be modelled out for the students facing different process during their learning in each semester and the most significant and adherent risk factors can be determined effectively.
Mobile Application of Registry Information for Urban Planning Context with Augmented Reality and QR Codes

Augmented reality technologies present a different experience from the real thing. Using new technologies for management activities in urban environments represents a change of bet on the digital development of a city. This work presents a prototype of a mobile application for obtaining housing registry information through augmented reality and QR codes. The objective of the study is to develop a tool for the generation of authorized public information on housing in the city of Guayaquil, Ecuador, in an interactive way using a combination of new technologies as a management strategy in the context of urban planning. The research methodology is a quantitative, quasi-experimental approach. It uses the survey technique to determine the perception of the use of the mobile application and the levels of satisfaction of 400 inhabitants of the urban sector of the city of Guayaquil. With a confidence percentage of 95% and a margin of error of 4.905%, the study reveals an 88% interest in the use of the application by the participants and an 84% ease of use of the BuildingQR mobile application in its first version. The data and results are available at https://github.com/mmoraa25/Building_QR . The level of satisfaction with the proposal exceeds 90% in the medium. It is concluded that new technologies allow new experiences that complement the development of smart cities by breaking the barriers of digitization. The scope of applicability of this research work to territories or pre-divisions in rural sectors is foreseen for future works.
Deep-Learning Approach for Sentiment Analysis in Software Engineering Domain

Sentiment analysis (SA) is an automated process that is increasingly being applied on software engineering (SE) text to study developers’ sentiments and to improve productivity. Prior studies have reported varying performance for software engineering datasets depending on the techniques employed. In this paper, we propose deep-learning approach to perform sentiment analysis to identify the sentiment polarities of Jira [20] dataset. We developed and compared the performance of stacked CNN, stacked LSTM and stacked BiLSTM classifiers and found that the stacked BiLSTM classifier outperformed the other classifiers developed on the above-mentioned dataset. Additionally, this study investigated the impact of domain-customization on the performance of the classifiers by employing SE-specific word embeddings learnt from StackOverflow posts and compared with the pre-trained open-domain word embeddings learnt from google news posts. The classifiers employing Google News (GN) embeddings outperformed the SE-customized based classifiers. The better performance of GN embedding classifiers is due to the large generic training corpus of the GN word embedding model which identifies the sentiments expressed accurately when compared to SE-specific word embedding.
Personalizing digital services as exemplified by library recommendation service

The possibility of designing recommendation system for library e-catalog as a recommendation service is examined. Several options for recommendations were considered, i.e. collaborative filtering method and content-based recommendations. The findings were used for building the recommendation service based on two recommendation algorithms – document-based collaborative filtering and content-based recommendations. Anonymized data on fulfilled orders and library’s e-catalog data are used as input data for recommendation system.
Topic Identification of Instagram Hashtag Sets for Image Tagging: An Empirical Assessment

Images are an important part of collection items in any digital library. Mining information from social media networks, and especially the Instagram, for Image description has recently gained increased research interest. In the current study we extend previous work on the use of topic modelling for mining tags from Instagram hashtags for image content description. We examine whether the hashtags accompanying Instagram photos, collected via a common query hashtag (called ‘subject’ hereafter), vary in a statistically significant manner depending on the similarity of their visual content. In the experiment we use the topics mined from Instagram hashtags from a set of Instagram images corresponding to 26 different query hashtags and classified into two categories per subject, named as ‘relevant’ and ‘irrelevant’ depending on the similarity of their visual content. Two different set of users, namely trained students and generic crowd, assess the topics presented to them as word clouds. To invest whether there is significant difference between the word clouds of the images considered as visually relevant to the query subject compared to those considered visually irrelevant. At the same time we investigate whether the word cloud interpretations of trained students and generic crowd differ. The data collected through this empirical study are analyzed with use of independent samples t-test and Pearson rho. We conclude that the word clouds of the relevant Instagram images are much more easily interpretable by both the trained students and the crowd. The results also show some interesting variations across subjects which are analysed and discussed in detail throughout the paper. At the same time the interpretations of trained students and the generic crowd are highly correlated, denoting that no specific training is required to mine relevant tags from Instagram hashtags to describe the accompanied Instagram photos.
A Verifiable Assured Deletion Scheme of Cloud Data Based on Blockchain

With the rapid development of cloud computing technology, more and more companies and individuals choose to store their data in the cloud to make up for the lack of local storage. However, once data is outsourced to the cloud service provider, users will lose control of the data, the confidentiality and security of the data will therefore have security risks. This paper proposes A Verifiable Assured Deletion Scheme of Cloud Data Based on Blockchain to achieve fine-grained access control, secure data sharing and effective and traceable assured deletion. Using CP-ABE to realize data outsourcing encryption storage and fine-grained access control; The audit model based on blockchain and consensus based on VRF and reputation are established to realize credible authority verification; Finally, the deletion verification function is provided. The data owner can verify the deletion results according to the deletion evidence on the blockchain and Merkel hash tree structure. The decentralized and tamper proof characteristics of blockchain ensure the effectiveness and traceability of deletion. Security analysis, function comparison analysis and efficiency analysis show that the scheme is safe and efficient, and realizes more comprehensive functions with acceptable overhead.
Rational activation functions in neural network with uniform norm based loss function and its application in classification

In this paper, we demonstrate an application of generalised rational uniform (Chebyshev) approximation to neural networks.In particular, our activation functions are rational functions of degree (1, 1) and the loss function is based on the uniform norm.In this setting, when the coefficients of the rational activation function are fixed, overall optimisation problem of the neural network can be formulated as a generalised rational approximation problem with additional linear constraints.In this case, the weights and the bias of the network are the decision variables.To optimise the decision variables, we suggest using two prominent methods: bisection method and differential correction algorithm.We illustrate the efficiency of this application by performing numerical experiments on classification problems with two classes and report the classification accuracy obtained by the network using the bisection method, differential correction algorithm along with the standard MATLAB toolbox which uses least square loss function.We show that the choice of the uniform norm based loss function with rational activation function leads to a better classification accuracy when the training dataset is either very small or if the classes are imbalanced.