Reviewerly/exHarmony huggingface dataset:
Software Fault Proneness Prediction Using Genetic Based Machine Learning Techniques

This work is an attempt to propose a software replica to predict fault proneness by means of genetic based method implementing machine learning. The underlying method is collection of data from open source software, where the data will be in form of object oriented metrics. The said data would be used to create model for forecasting the faults. These techniques are known as genetic based Classifier Systems or learning classifier systems. Later in this work, there is in detail description about data collection technique and stepwise algorithm to get the results. In the end it can be concluded that these techniques can be used to make prediction model on object oriented data of software and can be useful pertaining to fault proneness prediction in the near the beginning stages in the development sequence. of any software (SDLC).
Comparison of Different CI/CD Tools Integrated with Cloud Platform

A microservice architecture is a routine of enhancing cloud applications which enable large-scale enterprises to scale their application as per the demand. These microservices can be deployed using virtual machines, Docker containers or as a serverless function such as AWS lambda. However, with an increase in the number of microservices, it becomes strenuous to manage and deploy these services. Therefore, to overcome the problem, continuous integration and continuous delivery tool are used to deploy the microservices with minimum possible downtime. In this paper, we will be comparing different continuous integration and delivery tools taking into consideration different parameters like performance monitoring post-deployment, pipeline integration, cloud compatibility, and server monitoring.
Video saliency detection by gestalt theory

Abstract Image saliency detection has been widely explored in recent decades, but computational modeling of visual attention for video sequences is limited due to complicated temporal saliency extraction and fusion of spatial and temporal saliency. Inspired by Gestalt theory, we introduce a novel spatiotemporal saliency detection model in this study. First, we compute spatial and temporal saliency maps by low-level visual features. And then we merge these two saliency maps for spatiotemporal saliency prediction of video sequences. The spatial saliency map is calculated by extracting three kinds of features including color, luminance, and texture, while the temporal saliency map is computed by extracting motion features estimated from video sequences. A novel adaptive entropy-based uncertainty weighting method is designed to fuse spatial and temporal saliency maps to predict the final spatiotemporal saliency map by Gestalt theory. The Gestalt principle of similarity is used to estimate spatial uncertainty from spatial saliency, while temporal uncertainty is computed from temporal saliency by the Gestalt principle of common fate. Experimental results on three large-scale databases show that our method can predict visual saliency more accurately than the state-of-art spatiotemporal saliency detection algorithms.
Awareness, Trust, and Adoption of Blockchain Technology and Cryptocurrency among Blockchain Communities in Malaysia

Blockchain technology and cryptocurrency are attracting increasing attention from consumers, investors, investment industry and regulators. Cryptocurrency has great potential to be used for transaction or investment in the future. However, level of awareness of the blockchain technology and cryptocurrency is still at infant stage, specifically in developing countries. Thus, this study aims to investigate the level of awareness, trust and adoption of blockchain technology among blockchain community in Malaysia. Quantitative approach was adopted in this study where a new questionnaire was developed in the first phase to measure the level of awareness, adoption, and trust of blockchain technology applications among Malaysian blockchain communities. The resulting questionnaire consists of items on respondents’ demographic, their awareness, trust, and adoption of FinTech particularly on blockchain technology and cryptocurrency. In the second phase, a pilot study was conducted to validated the new questionnaire from 304 respondents. Reliability test using Cronbach’s alpha with a value of 0.908. A real survey was also conducted in this phase using the validated queationnaire and data were obtained online from 304 respondents. Descriptive statistics were used in the analysis during the third phase of the study, and results demonstrate that the awareness level of blockchain technology and cryptocurrency are at the intermediate level. Nevertheless, the majority of respondents are confident and trust that the blockchain technology can offer a stable and secure platform, which gives positive impact on the application of the technology. Empirical results provide significant insights into the development of the blockchain technology industry in the country.
Multi-scale Capsule Attention-Based Salient Object Detection with Multi-crossed Layer Connections

With the popularization of convolutional networks being used for saliency models, saliency detection performance has achieved significant improvement. However, how to integrate accurate and crucial features for modeling saliency is still underexplored. In this paper, we present CapSalNet, which includes a multi-scale Capsule attention module and multi-crossed layer connections for Salient object detection. We first propose a novel capsule attention model, which integrates multi-scale contextual information with dynamic routing. Then, our model adaptively learns to aggregate multi-level features by using multi-crossed skip-layer connections. Finally, the predicted results are efficiently fused to generate the final saliency map in a coarse-to-fine manner. Comprehensive experiments on four benchmark datasets demonstrate that our proposed algorithm outperforms existing state-of-the-art approaches.
Software quality assessment model: a systematic mapping study

Quality model is regarded as a well-accepted approach for assessing, managing and improving software product quality. There are three categories of quality models for software products, i.e., definition model, assessment model, and prediction model. Quality assessment model (QAM) is a metric-based approach to assess the software quality. It is typically regarded as of high importance for its clear method on how to assess a system. However, the current state-of-the-art in QAM research is under limited investigation. To address this gap, the paper provides an organized and synthesized summary of the current QAMs. In detail, we conduct a systematic mapping study (SMS) for structuring the relevant articles. We obtain a total of 716 papers from the five databases, and 31 papers are selected as relevant studies at last. In summary, our work focuses on QAMs from the following aspects: software metrics, quality factors, aggregation methods, evaluation methods and tool support. According to the analysis results, our work discovers five needs that researchers in this area should continue to address: (1) new method and criteria to tailor a quality framework (i.e., structure of software metrics and quality factors) according to different specifics, (2) systematic investigations on the effectiveness, strength and weakness of different aggregation methods to guide the method selection in different context, (3) more investigations on evaluating QAMs in the context of industrial cases, (4) further investigations or real-world case studies on the QAMs related tools, and (5) building a public and diverse software benchmark which can be adopted in different application context.
Development of a Regional Innovation Ecosystem: The Role of a Pillar University

This paper is devoted to the study of the university’s role in the development of a regional innovation ecosystem. It is shown that the basis hereof is a triple helix model connecting business, government and universities. Therewith, universities have an advantage: their impact can embrace all stages of the innovation process. The authors of this study focused attention on universities that have with a special status of pillar university in the Russian region. Pillar universities appeared in the Russian education system in 2015 were designated as research and education complexes that conduct studies and train staff in groundbreaking lines of development. The study highlights the issues of the Russian innovation ecosystem development, the solution hereof mostly depends on performance of pillar university.
Data-Driven Decision-Making (D<sup>3</sup>M): Framework, Methodology, and Directions

A decision problem, according to traditional principles, is approached by finding an optimal solution to an analytical programming decision model, which is known as model-driven decision-making. The fidelity of the model determines the quality and reliability of the decision-making; however, the intrinsic complexity of many real-world decision problems leads to significant model mismatch or infeasibility in deriving a model using the first principle. To overcome the challenges that are present in the big data era, both researchers and practitioners emphasize the importance of making decisions that are backed up by data related to decision tasks, a process called data-driven decision-making (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> M). By building on data science, not only can decision models be predicted in the presence of uncertainty or unknown dynamics, but also inherent rules or knowledge can be extracted from data and directly utilized to generate decision solutions. This position paper systematically discusses the basic concepts and prevailing techniques in data-driven decision-making and clusters-related developments in technique into two main categories: programmable data-driven decision-making (P-D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> M) and nonprogrammable data-driven decision-making (NP-D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> M). This paper establishes a D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> M technical framework, main methodologies, and approaches for both categories of D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> M, as well as identifies potential methods and procedures for using data to support decision-making. It also provides examples of how D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> M is implemented in practice and identifies five further research directions in the D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> M area. We believe that this paper will directly support researchers and professionals in their understanding of the fundamentals of D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> M and of the developments in technical methods
An application for Cultural Heritage using a Chatbot

Italy is one of the nations in the world with the largest number of archaeological sites. Every year millions of tourists visit the Italian peninsula to come and see the wonders that the country offers. Nowadays, many technologies are used to help and simplify the process of finding the information related to a specific archaeological site. Just think of all the applications like the Artificial Vision, the Interactive Information Systems and Context Aware technologies. In this scenario, support and recommendation systems are becoming more and more present. in fact, thanks to the use of automatic assistants, it is possible to help the tourist in the most disparate situations such as getting the right information at the right time or helping him make decisions about what to do. In this context it is possible to introduce a Chatbot systems to support users to provide right information in the right moment. In this paper a Chatbot system able to provide contextual information to users is described through semantic analysis, into an archeological park.
Challenges in Adopting Continuous Delivery and DevOps in a Globally Distributed Product Team: A Case Study of a Healthcare Organization

This paper presents our experiences in a project of a software engineering team spread across three countries that successfully established continuous delivery, DevOps and short release cycles with agile scrum. We had the challenge to find a way from established regulatory heavy-weight processes, long release strategies, legacy tools and technologies and people mindset towards adopting continuous delivery and DevOps. We are describing our experiences in the journey towards timeboxed release strategies compared to legacy fixed scope-based releases; value stream-based execution compared to traditional milestone-based execution; operation, test, and infrastructure as a code compared to executing these activities manually. This paper also describes experiences in transforming traditional scrum team into a DevOps team, technological landscape into lightweight tools. The authors bring their experiences as a Project Manager, Quality Manager, and an Architect, who has been an integral part of this journey. These practices have helped in stabilizing processes and methods to an extent where we have released several products versions within a year. The other business units are adopting our practices for continuous delivery and DevOps. This paper also summaries our lessons learned, and recommendations.
A covariance matrix adaptation evolution strategy variant and its engineering application

This paper proposes a novel covariance matrix adaptation evolution strategy (CMA-ES) variant, named AEALSCE, for single-objective numerical optimization problems in the continuous domain. To avoid premature convergence and strengthen the exploration capacity of the basic CMA-ES, AEALSCE is obtained by integrating the CMA-ES with two strategies that can adjust the evolutionary directions and enrich the population diversity. The first strategy is named the anisotropic eigenvalue adaptation (AEA) technique, which adapts the search scope towards the optimal evolutionary directions. It scales the eigenvalues of the covariance matrix anisotropically based on local fitness landscape detection. The other strategy is named the local search (LS) strategy, which is executed under the eigen coordinate system and can be subdivided into two parts. In the first part, the new candidates of superior solutions are sampled around the best solution to perform local exploration. In the other part, the new candidates of inferior solutions are generated using a modified mean point along the fitness descent direction. The proposed AEALSCE algorithm is compared with other top competitors, including the CEC 2014 champion, L-SHADE, and the promising NBIPOP-aCMA-ES, by benchmarking the CEC 2014 testbed. Moreover, AEALSCE is applied in solving three constrained engineering design problems and parameter estimation of photovoltaic (PV) models. According to the statistical results of the experiments, our proposed AEALSCE is competitive with other algorithms in convergence efficiency and accuracy. AEALSCE benefits from a good balance of exploration and exploitation, and it exhibits a potential to address real-world optimization problems.
The support designs of the triply even binary codes of length 48

Abstract In this paper, we present examples of codes all of whose weight classes support 1‐designs, with duals whose classes include two that support 2‐designs. We can find these examples in the triply even binary codes of length 48, which have been classified by Betsumiya and Munemasa.
The use of prototypes to bridge knowledge boundaries in agile software development

Abstract Bridging knowledge boundaries among project team members is essential to prevent delays or complete failure of software development projects. Prior researchers have reported that software prototypes can be used to help bridge knowledge boundaries between team members in traditional software development settings, yet their use in an agile development setting remains unexplored. Agile development centers the interactions between team members on emerging representations of the prototype whose properties are prone to change over time. Therefore, we conducted an in‐depth study of an agile development project to enhance our understanding on how software prototypes are used as boundary objects in a distributed team setting. Our analyses of team member interactions during 46 virtual meetings that took place over a period of 6 months revealed four different prototype use practices (exemplifying, contrasting, relating, framing) that were effective in bridging syntactic, semantic, or pragmatic knowledge boundaries. We also provide empirically grounded evidence of how variations in object properties can afford different use practices, how the use practices take advantage of these properties, and how object properties are reshaped through these use practices as different types of knowledge boundaries are bridged. These findings bear important implications for research on prototypes as boundary objects in software development in particular and boundary object use in general.
Evaluating the reliability of sources of evidence with a two-perspective approach in classification problems based on evidence theory

Conflict management and accuracy improvement are the two main concerns of classification problems based on the evidence theory. High conflict among sources of evidence can be solved effectively using discounting methods based on source-reliability evaluations. However, these methods may not ensure efficient performance of a classification model. To relieve high conflict and improve accuracy, a two-perspectives approach for reliability evaluation is presented to generate discounting rules. An independent reliability evaluation (IRE) is used to assess the independent reliability of an individual source, under the assumption that the source works independently. The other perspective is the combination reliability evaluation (CRE). It evaluates all the sources by considering the combination relationship among them. Both methods are designed as supervising methods and integrate a new dissimilarity measure proposed in this paper—decision dissimilarity—with the Jousselme distance. The ability of the new dissimilarity measure to effectively discriminate evidence from the truth can be experimentally verified. The proposed approach is not only effective for conflict management but also for the improvement of the performance of classification models based on the evidence theory as it helps implement the correct and specific decisions.
How to Build Pseudorandom Functions from Public Random Permutations

Pseudorandom functions are traditionally built upon block ciphers, but with the trend of permutation based cryptography, it is a natural question to investigate the design of pseudorandom functions from random permutations. We present a generic study of how to build beyond birthday bound secure pseudorandom functions from public random permutations. We first show that a pseudorandom function based on a single permutation call cannot be secure beyond the $$2^{n/2}$$ birthday bound, where n is the state size of the function. We next consider the Sum of Even-Mansour (SoEM) construction, that instantiates the sum of permutations with the Even-Mansour construction. We prove that SoEM achieves tight $$2n{/}3$$ -bit security if it is constructed from two independent permutations and two randomly drawn keys. We also demonstrate a birthday bound attack if either the permutations or the keys are identical. Finally, we present the Sum of Key Alternating Ciphers (SoKAC) construction, a translation of Encrypted Davies-Meyer Dual to a public permutation based setting, and show that SoKAC achieves tight $$2n{/}3$$ -bit security even when a single key is used.
Probability representation of quantum dynamics using pseudostochastic maps

In this work, we consider a probability representation of quantum dynamics for finite-dimensional quantum systems with the use of pseudostochastic maps acting on true probability distributions. These probability distributions are obtained via symmetric informationally complete positive operator-valued measure (SIC-POVM) and can be directly accessible in an experiment. We provide SIC-POVM probability representations both for unitary evolution of the density matrix governed by the von Neumann equation and dissipative evolution governed by Markovian master equation. In particular, we discuss whereas the quantum dynamics can be simulated via classical random processes in terms of the conditions for the master equation generator in the SIC-POVM probability representation. We construct practical measures of nonclassicality non-Markovianity of quantum processes and apply them for studying experimental realization of quantum circuits realized with the IBM cloud quantum processor.
Redirecting View Rotation in Immersive Movies with Washout Filters

Immersive movies take advantage of virtual reality (VR) to bring new opportunities for storytelling that allow users to naturally turn their heads and bodies to view a 3D virtual world and follow the story in a surrounding space. However, while many designers often assume scenarios where viewers stand and are free to physically turn without constraints, this excludes many commonly desired usage settings where the user may wish to remain seated, such as the use of VR while relaxing on the couch or passing the time during a flight. For such situations, large amounts of physical turning may be uncomfortable due to neck strain or awkward twisting. Our research investigates a technique that automatically rotates the virtual scene to help redirect the viewer's physical rotation while viewing immersive narrative experiences. By slowly rotating the virtual content, viewers are encouraged to gradually turn physically to align their head positions to a more comfortable straight-ahead viewing direction in seated situations where physical turning is not ideal. We present our study of technique design and an evaluation of how the redirection approach affects user comfort, sickness, the amount of physical rotation, and likelihood of viewers noticing the rotational adjustments. Evaluation results show the rotation technique was effective at significantly reducing the amount of physical turning while watching immersive videos, and only 39% of participants noticed the automated rotation when the technique rotated at a speed of 3 degrees per second.
Conversation considered harmful?

As a concept, 'conversation' is rife with troublemaking potential. It is not that we should necessarily abandon use of 'conversation' in conversational user interface (CUI) research, but rather treat it with a significant measure of care due to the varied conceptual problems it introduces---problems sketched in this paper. I suggest an alternative, possibly safer articulation and conceptual shift: conversation-sensitive design.