Reviewerly/exHarmony huggingface dataset:
Temporal dynamics of requirements engineering from mobile app reviews

Opinion mining for app reviews aims to analyze people's comments from app stores to support data-driven requirements engineering activities, such as bug report classification, new feature requests, and usage experience. However, due to a large amount of textual data, manually analyzing these comments is challenging, and machine-learning-based methods have been used to automate opinion mining. Although recent methods have obtained promising results for extracting and categorizing requirements from users' opinions, the main focus of existing studies is to help software engineers to explore historical user behavior regarding software requirements. Thus, existing models are used to support corrective maintenance from app reviews, while we argue that this valuable user knowledge can be used for preventive software maintenance. This paper introduces the temporal dynamics of requirements analysis to answer the following question: how to predict initial trends on defective requirements from users' opinions before negatively impacting the overall app's evaluation? We present the MAPP-Reviews (Monitoring App Reviews) method, which (i) extracts requirements with negative evaluation from app reviews, (ii) generates time series based on the frequency of negative evaluation, and (iii) trains predictive models to identify requirements with higher trends of negative evaluation. The experimental results from approximately 85,000 reviews show that opinions extracted from user reviews provide information about the future behavior of an app requirement, thereby allowing software engineers to anticipate the identification of requirements that may affect the future app's ratings.
Ethical Hacking: Importance, Controversies and Scope in the Future

With the ongoing digitalisation of the modern world and our quest to digitalise and automate everything, issues related to cybersecurity such as data breaches, security breaches etc., will be in the spotlight. Therefore, ethical hacking and its importance in the future can&#x0027;t be undermined. Ethical hacking technology has spread to almost all fields of the life and especially to all paths of computer industry; the need to protect the important data of the same should be addressed with right technology. In ethical hacking (i.e., white hat hacking) the objective in hand is to find weaknesses in the security systems and find potential data breaches and is in stark contrast to the almost universal definition of hacking i.e., to breach the security systems of individuals or companies with malicious intent and to steal data and plant viruses (black hat hacking). ethical hacking is a way of combatting and neutralising black hat hackers. With this research paper, my objective is to analyse and establish the importance of ethical hacking and also to analyse the controversies surrounding it, its pros and cons, its ethical and moral boundaries and its scope in the future.
A Systematic Approach on Visibility Analysis for Video Surveillance

Nowadays, video observation is normally utilized to watch and record events in stores, displays, colleges, parking garages, and public limits. The objective of this undertaking is to make a perceivability application for video surveillance that decides the quantity of cameras and their positions to cover the ground, with a reduced expense in the camera origination framework. While grounds can be in any structure or size, this research focuses on basic polygonal shaped structures. The Art Gallery Theorem and Triangulation are utilized in this implementation. The 2-Ear triangulation theorem and 3-color models are used in the primary procedure to decide the quantity of cameras and their arrangements in a polygon of vertices N. Quantity of occupations ought to be kept to an absolute minimum. The polygon is apportioned into monotone parts in the subsequent methodology, which works on the computational speed of triangulation. Every monotone is then located in straight time. This application is made using the C/C++ program.
COVID-19 Information-Tracking Solutions: A Qualitative Investigation of the Factors Influencing People’s Adoption Intention

Numerous information-tracking solutions have been implemented worldwide to fight the COVID-19 pandemic. While prior work has heavily explored the factors affecting people’s willingness to adopt contact-tracing solutions, which inform people when they have been exposed to someone positive for COVID-19, numerous countries have implemented other information-tracking solutions that use more data and more sensitive data than these commonly studied contact-tracing apps. In this work, we build on existing work focused on contact-tracing apps to explore adoption and design considerations for six representative information-tracking solutions for COVID-19, which differ in their goals and in the types of information they collect. To do so, we conducted semi-structured interviews with 44 participants to investigate the factors that influence their willingness to adopt these solutions. We find four main categories of influences on participants’ willingness to adopt such solutions: individual benefits of the solution, societal benefits of the solution, functionality concern, and digital safety (e.g., security and privacy) concerns. Further, we enumerate the factors that inform participants’ evaluations of these categories. Based on our findings, we make recommendations for the future design of information-tracking solutions and discuss how different factors may balance against benefits in future crisis situations.
Reliability assessment of digital forensic investigations in the Norwegian police

This case study presents a qualitative assessment of the reliability of digital forensic investigation in criminal cases in Norway. A reliability validation methodology based on international digital forensic standards was designed to assess to what extent those standards are implemented and followed by law enforcement in their casework. 124 reports related to the acquisition, examination, and analysis of three types of digital data sources - computers, mobile phones, and storage devices were examined. The reports were extracted from the criminal case management system used by the police and prosecution services. The reports were examined on technology, method, and application level in order to assess the reliability of digital evidence for criminal proceedings. The study found that digital forensic investigation in 21 randomly sampled criminal cases in Norway were insufficiently documented to assess the reliability of the digital evidence. It was not possible to trace the digital forensic actions performed on each item or link the digital evidence to its source. None of the cases were shown to comply with digital forensic methodology, justify the methods and tools used, or validate tool results and error rates.
Spectral integration of clear-sky atmospheric transmittance: Review and worldwide performance

Broadband atmospheric transmittance is a key component in many broadband clear-sky solar radiation models, whose performance is inevitably bound to the goodness of their atmospheric transmittance parameterizations. A key element in the development of these parameterizations, that has been traditionally overlooked, is the spectral integration to the broadband range. The related scientific literature reveals that two different spectral integration approaches have been proposed, but they have been vaguely evaluated despite the central role of broadband transmittance. This work discusses such approaches, proposes new ones that mitigate current issues and conducts an unprecedented worldwide evaluation study that analyzes the direct normal irradiance evaluation error that is directly attributable to the choice of spectral integration approach. The study makes use of more than 7 million atmospheric transmittance spectra and a 1-year hourly simulation across a 2-degree spatial grid worldwide. The results show that the independent integration scheme, which is the one virtually used always, results in deviations as high as ±40 W/m2 in dry and clean, and in humid and turbid environmental conditions. Overall, while the error stays within ±20 W/m2 for 90% of the cases worldwide using the independent scheme, it is reduced to only ±1.5 W/m2 using one of the alternative integration schemes here proposed. Hence, as a matter of conclusion, it is recommended that any broadband clear-sky solar irradiance model intended for global applications considers a higher-performance spectral integration scheme for atmospheric transmittance, such as the ones proposed here.
Using chains of bottleneck transitions to decompose and solve reinforcement learning tasks with hidden states

Reinforcement learning is known to underperform in large and ambiguous problem domains under partial observability. In such cases, a proper decomposition of the task can improve and accelerate the learning process. Even ambiguous and complex problems that are not solvable by conventional methods turn out to be easier to handle by using a convenient problem decomposition, followed by the incorporation of machine learning methods for the sub-problems. Like in most real-life problems, the decomposition of a task usually stems from the sequence of sub-tasks that must be achieved in order to get the main task done. In this study, assuming that unambiguous states are provided in advance, a decomposition of the problem is constructed by the agent based on a set of chains of bottleneck transitions, which are sequences of unambiguous and critical transitions leading to the goal state. At the higher level, an agent trains its sub-agents to extract sub-policies corresponding to the sub-tasks, namely two successive transitions in any chain, and learns the value of each sub-policy at the abstract level. Experimental study demonstrates that an early decomposition based on useful bottleneck transitions eliminates the necessity for excessive memory and improves the learning performance of the agent. It is also shown that knowing the correct order of bottleneck transitions in the decomposition results in faster construction of the solution.
Leveraging Narrative to Generate Movie Script

Generating a text based on a predefined guideline is an interesting but challenging problem. A series of studies have been carried out in recent years. In dialogue systems, researchers have explored driving a dialogue based on a plan, while in story generation, a storyline has also been proved to be useful. In this article, we address a new task—generating movie scripts based on a predefined narrative. As an early exploration, we study this problem in a “retrieval-based” setting. We propose a model (ScriptWriter-CPre) to select the best response (i.e., next script line) among the candidates that fit the context (i.e., previous script lines) as well as the given narrative. Our model can keep track of what in the narrative has been said and what is to be said. Besides, it can also predict which part of the narrative should be paid more attention to when selecting the next line of script. In our study, we find the narrative plays a different role than the context. Therefore, different mechanisms are designed for deal with them. Due to the unavailability of data for this new application, we construct a new large-scale data collection GraphMovie from a movie website where end-users can upload their narratives freely when watching a movie. This new dataset is made available publicly to facilitate other studies in text generation under the guideline. Experimental results on the dataset show that our proposed approach based on narratives significantly outperforms the baselines that simply use the narrative as a kind of context.
Ethical aspects of AI robots for agri-food; a relational approach based on four case studies

Abstract These last years, the development of AI robots for agriculture, livestock farming and food processing industries is rapidly increasing. These robots are expected to help produce and deliver food more efficiently for a growing human population, but they also raise societal and ethical questions. As the type of questions raised by these AI robots in society have been rarely empirically explored, we engaged in four case studies focussing on four types of AI robots for agri-food ‘in the making’: manure collectors, weeding robots, harvesting robots and food processing robots which select and package fruits, vegetables and meats. Based on qualitative interviews with 33 experts engaged in the development or implementation of these four types of robots, this article provides a broad and varied exploration of the values that play a role in their evaluation and the ethical questions that they raise. Compared to the recently published literature reviews mapping the ethical questions related to AI robots in agri-food, we conclude that stakeholders in our case studies primarily adopt a relational perspective to the value of AI robots and to finding a solution to the ethical questions. Building on our findings we suggest it is best to seek a distribution of tasks between human beings and robots in agri-food, which helps to realize the most acceptable, good or just collaboration between them in food production or processing that contributes to realizing societal goals and help to respond to the 21 century challenges.
Patch-Based Image Restoration Using Expectation Propagation

This paper presents a new Expectation Propagation (EP) framework for image restoration using patch-based prior distributions. While Monte Carlo techniques are classically used to sample from intractable posterior distributions, they can suffer from scalability issues in high-dimensional inference problems such as image restoration. To address this issue, EP is used here to approximate the posterior distributions using products of multivariate Gaussian densities. Moreover, imposing structural constraints on the covariance matrices of these densities allows for greater scalability and distributed computation. While the method is naturally suited to handle additive Gaussian observation noise, it can also be extended to non-Gaussian noise. Experiments conducted for denoising, inpainting, and deconvolution problems with Gaussian and Poisson noise illustrate the potential benefits of such a flexible approximate Bayesian method for uncertainty quantification in imaging problems, at a reduced computational cost compared to sampling techniques.
Analysis of platform business and secure business intelligence

Platform is a business stage where people and technology share the information and make the business ecosystem through the network effect. It is a state-of-the-art technology for business. Platform business is an interaction of the network effects which creates profit in multidimensional way. It encourages business growth and efficiency, which is both exponentially and asymptotically. A platform is a creation which helps and empowers other products and amenities. Digital platforms occur at many levels in internet-based businesses and business models with blockchain technology. They array from high-level platforms to enable a platform business model to a low-level platform which provides an assembly line of business and technology dimensions that other products or services consume to deliver their own business capabilities. This paper deliberates the analysis of platform learning business approaches and secure business transactions like blockchain technology. Platform business along with secure business intelligence (BI) is now a prerequisite of the frontrunners for noble organizations to keep customers active, alive and compete the business in the digital age. Blockchain technology plays a vital role in this issue. Presently, businesses implement attract data-driven, network-driven, cloud-driven and blockchain technology platform-intensive approaches. Artificial intelligence, deep learning and machine learning are playing a great role in analyzing the data and classifying customer patterns and retaining customers in the business principles of market. Currently, we are competent enough to analyze online and offline data using these intelligent business tools. These business tools are very useful to identify customers’ marketing trends and their buying patterns. Securities in the business are more demanding and need professional ventures. Business people are now copious, focused and vigilant for secure transactions and demand blockchain technology-based platforms. Blockchain technology is nowadays becoming a boon for industrialists to keep their transactions more secure. As businesses and industries grow, they are now using blockchain technology to secure and track the items in supply chain management using digital platforms and BI. This concept is very relevant in the current scenario and state-of-the-art business platforms. We have exasperated the study in this direction, that is, how to concentrate on strategic revolution in financial sector with various innovative econometrics and simulator tools. This paper also addresses the recent research gap in the secure digital platform and analyzes the state-of-the-art methodology for the optimized solutions for the industry.
Random Replacement Crisscross Butterfly Optimization Algorithm for Standard Evaluation of Overseas Chinese Associations

The butterfly optimization algorithm (BOA) is a swarm intelligence optimization algorithm proposed in 2019 that simulates the foraging behavior of butterflies. Similarly, the BOA itself has certain shortcomings, such as a slow convergence speed and low solution accuracy. To cope with these problems, two strategies are introduced to improve the performance of BOA. One is the random replacement strategy, which involves replacing the position of the current solution with that of the optimal solution and is used to increase the convergence speed. The other is the crisscross search strategy, which is utilized to trade off the capability of exploration and exploitation in BOA to remove local dilemmas whenever possible. In this case, we propose a novel optimizer named the random replacement crisscross butterfly optimization algorithm (RCCBOA). In order to evaluate the performance of RCCBOA, comparative experiments are conducted with another nine advanced algorithms on the IEEE CEC2014 function test set. Furthermore, RCCBOA is combined with support vector machine (SVM) and feature selection (FS)—namely, RCCBOA-SVM-FS—to attain a standardized construction model of overseas Chinese associations. It is found that the reasonableness of bylaws; the regularity of general meetings; and the right to elect, be elected, and vote are of importance to the planning and standardization of Chinese associations. Compared with other machine learning methods, the RCCBOA-SVM-FS model has an up to 95% accuracy when dealing with the normative prediction problem of overseas Chinese associations. Therefore, the constructed model is helpful for guiding the orderly and healthy development of overseas Chinese associations.
A New Parameterized Monogamy Relation between Entanglement and Equality

Abstract A generalized definition of the monogamy relation for entanglement measures is provided. A monogamy equality rather than the usual inequality is presented based on the monogamy weight, from which monogamy relations are given satisfied by the αth power of the entanglement measures. Taking concurrence as an example, the significance and advantages of these relations are further demonstrated. In addition, it is shown that monogamy relations can be recovered by considering multiple copies of states for every non‐additive entanglement measure that violates the inequalities. It is also demonstrated that such relations for tripartite states can be generalized to multipartite systems.
A multi features based background modeling approach for moving object detection

Background subtraction always remains an important and challenging task for different applications. Our previous work established the effectiveness of hybrid model by exploiting the oriented patterns present in a video sequences over other statistical method. To extend this approach further, we have proposed a novel approach herein by eliminating GLCM based features with an improved local Zernike moment and color components of intensity. These features are clubbed with the orientation based features extracted from angle co-occurrence matrices (ACMs) to model the background. Furthermore the Mahalanobis distance measure is replaced by Canberra distance to categorized foreground and background pixels, which significantly reduces the computational complexity of the proposed method due to the absence of covariance matrix measure. Comparative results have shown that our proposed method is effective than other competing method on different set of video sequences.
Perceptions of users and non-users of an early contact tracing mobile application to fight COVID-19 spread: a value-based empirical investigation

Purpose The purpose of this study is to describe a comparative study of the perceptions of users and non-users of an early contact tracing application helping to prevent the spread of the COVID-19 pandemic. The unprecedented incidence of this disease warrants investigating theoretically the use of mobile contact tracing applications as a promising approach to curtail its transmission. Design/methodology/approach A consumption value-based model of the adoption and use of a contact tracing mobile application was built and tested through a cross-sectional survey conducted with 2 samples (of 309 already users and 306 non-users) in the Province of Alberta, Canada. Findings Utilitarian and social values together with health information seeking and perceived critical mass drive the use of the application while perceived privacy risk is an obstacle to usage for both users and non-users. Research limitations/implications Study participants self-assessed their risk category of potential exposure to the COVID-19 virus, and this was a subjective measure including an emotional component. Practical implications No major differences in the approaches targeting users and non-users of a mobile contact tracing application to encourage its adoption and use are necessary. Social implications Additional efforts are required to convey to people information on the benefits and current rate of use of such an application and to mitigate privacy risk concerns. Originality/value Overall, the study offers theoretical and practical contributions that may help improve the adoption and usage of contact tracing applications addressing the COVID-19 pandemic or other possible public health crises.
Sampling-based unmanned aerial vehicle air traffic integration, path planning, and collision avoidance

Unmanned aircraft or drones as they are sometimes called are continuing to become part of more real-life applications. The integration of unmanned aerial vehicles in public airspace is becoming an important issue that should be addressed. As the number of unmanned aerial vehicles and their applications are largely increasing, air traffic with more unmanned aircraft has to be given more attention to prevent collisions and maintain safe skies. Unmanned aerial vehicle air traffic integration and regulation has become a priority to different regulatory agencies and has become of greater interest for many researchers all around the world. In this research, a sampling-based air traffic integration, path planning, and collision avoidance approach is presented. The proposed algorithm expands an existing 2D sampling-based approach. The original 2D approach deals with only two unmanned aircraft. Each of the two aircraft shares location information with a ground-based path planner computer, which would send back the avoidance waypoints after performing the 2D sampling. The algorithm proposed in this article can handle any number of drones in the 3D space by performing either 2D or 3D sampling. The proposed work shows a 10-fold enhancement in terms of the number of unmanned aerial vehicle collisions. The presented results also contribute to enabling a better understanding of what is expected of integrating more drones in dense skies.