Reviewerly/exHarmony huggingface dataset:
Verifying Measures of Quantum Entropy

This paper introduces a new measure of quantum entropy, called the effective quantum entropy (EQE). The EQE is an extension, to the quantum setting, of a recently derived classical generalized entropy. We present a thorough verification of its properties. As its predecessor, the EQE is a semi-strict quasi-concave function; it would be capable of generating many of the various measures of quantum entropy that are useful in practice. Thereafter, we construct a consistent estimator for our proposed measure and empirically test its estimation error, under different system dimensions and number of measurements. Overall, we build the grounds of the EQE, which will facilitate the analyses and verification of the next innovative quantum technologies.
What's bothering developers in code review?

The practice of code review is widely adopted in industry and has been studied to an increasing degree in the research community. However, the developer experience of code review has received limited attention. Here, we report on initial results from a mixed-method exploratory study of the developer experience.
Bug Tracking Process Smells In Practice

Software teams use bug tracking (BT) tools to report and manage bugs. Each record in a bug tracking system (BTS) is a reporting entity consisting of several information fields. The contents of the reports are similar across different tracking tools, though not the same. The variation in the workflow between teams prevents defining an ideal process of running BTS. Nevertheless, there are best practices reported both in white and gray literature. Developer teams may not adopt the best practices in their BT process. This study investigates the non-compliance of developers with best practices, so-called smells, in the BT process. We mine bug reports of four projects in the BTS of JetBrains, a software company, to observe the prevalence of BT smells in an industrial setting. Also, we survey developers to see (1) if they recognize the smells, (2) their perception of the severity of the smells, and (3) the potential benefits of a BT process smell detection tool. We found that (1) smells occur, and their detection requires a solid understanding of the BT practices of the projects, (2) smell severity perception varies across smell types, and (3) developers considered that a smell detection tool would be useful for six out of the 12 smell categories.
Towards Build Verifiability for Java-based Systems

Build verifiability refers to the property that the build of a software system can be verified by independent third parties and it is crucial for the trustworthiness of a software system. Various efforts towards build verifiability have been made to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{C}/\mathrm{C}++$</tex> -based systems, yet the techniques for Java-based systems are not systematic and are often specific to a particular build tool (e.g., Maven). In this study, we present a systematic approach towards build verifiability on Java-based systems. Our approach consists of three parts: a unified build process, a tool that dynamically controls non-determinism during the build process, and another tool that eliminates non-equivalences by post-processing the build artifacts. We apply our approach on 46 unverified open source projects from Reproducible Central and 13 open source projects that are widely used by Huawei commercial products. As a result, 91% of the unverified Reproducible Central projects and 100% of the commercially adopted OSS projects are successfully verified with our approach. In addition, based on our experience in analyzing thousands of builds for both commercial and open source Java-based systems, we present 14 patterns that introduce non-equivalences in generated build artifacts and their respective mitigation strategies. Among these patterns, 11 (78%) are unique for Java-based system, whereas the remaining 3 (22%) are common with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{C}/\mathrm{C}++$</tex> -based systems. The approach and the findings of this paper are useful for both practitioners and researchers who are interested in build verifiability.
How Does Code Reviewing Feedback Evolve?: A Longitudinal Study at Dell EMC

Code review is an integral part of modern software development, where fellow developers critique the content, premise, and structure of code changes. Organizations like DellEMC have made considerable investment in code reviews, yet tracking the characteristics of feedback that code reviews provide (a primary product of the code reviewing process) is still a difficult process. To understand community and personal feedback trends, we perform a longitudinal study of 39,249 reviews that contain 248,695 review comments from a proprietary project that is developed by DellEMC. To investigate generalizability, we replicate our study on the OpenStackn Ova project. Through an analysis guided by topic models, we observe that more context-specific, technical feedback is introduced as the studied projects and communities age and as the reviewers within those communities accrue experience. This suggests that communities are reaping a larger return on investment in code review as they grow accustomed to the practice and as reviewers hone their skills. The code review trends uncovered by our models present opportunities for enterprises to monitor reviewing tendencies and improve knowledge transfer and reviewer skills.
Using Natural Language Processing Techniques to Improve Manual Test Case Descriptions

Despite the recent advancements in test automation, testing often remains a manual, and costly, activity in many industries. Manual test cases, often described only in natural language, consist of one or more test steps, which are instructions that must be performed to achieve the testing objective. Having different employees specifying test cases might result in redundant, unclear, or incomplete test cases. Manually reviewing and validating newly-specified test cases is time-consuming and becomes impractical in a scenario with a large test suite. Therefore, in this paper, we propose an automated framework to automatically analyze test cases that are specified in natural language and provide actionable recommendations on how to improve the test cases. Our framework consists of configurable components and modules for analysis, which are capable of recommending improvements to the following: (1) the terminology of a new test case through language modeling, (2) potentially missing test steps for a new test case through frequent itemset and association rule mining, and (3) recommendation of similar test cases that already exist in the test suite through text embedding and clustering. We thoroughly evaluated the three modules on data from our industry partner. Our framework can provide actionable recommendations, which is an important challenge given the widespread occurrence of test cases that are described only in natural language in the software industry (in particular, the game industry).
Design and development of a sensor assembly based on 1-D photodiode arrays for distance offset measurements

This paper proposes a sensing device to accurately measure lateral offsets for applications in calibration and alignment activities. The proposed sensor assembly is primarily designed and developed for use in volumetric calibration procedures of custody tanks in the energy industry. In order to achieve sub-millimeter accuracy levels for offset measurements, the sensor device utilizes multiple 1-D photodiode arrays in a specific configuration that enables improvement upon currently utilized procedures and devices. The presented design is also modular such that it can be easily modified to address applications involving robotic vehicles, and other systems that require tracking, measurement, alignment and calibration activities. The experimental evaluation of the proposed sensor assembly showed that it can measure distance offset measurements with a resolution of 125μm and a measured accuracy within 730μm. The promising results show that the proposed sensing device can be integrated into existing procedures that require high accuracy measurements.
Application of Key Technologies of College English Online Teaching Platform in Deep Learning

With the rapid development of information technology, the network has become a part of the daily life of current college students. The use of network tools to develop college English teaching platform is necessary and beneficial. Various versions of online English teaching platforms have been developed in China and used by many Chinese universities. Through research and analysis, according to the theory of communicative learning, this paper puts forward the design plan of English university teaching platform for Chinese colleges and universities. The article first summarizes the research origin and importance of the college English online teaching platform. It then outlines the current situation of college English online teaching platforms and provides a demonstration of a specific platform for a specific teaching network. This article discusses the research on deep learning using advanced technology on college English teaching platforms. Through questionnaires, group discussions, in-depth interviews, classroom observations, and other research methods, we can understand the current situation of school computer networks from a macro perspective. It integrates foreign language courses and the problems teachers and students encounter in the process. The results show that 56% of the students said that this method can promote their own learning, and 12% of the students think that there will be no good learning effect. For this new test method, 86% of the teachers think that it can achieve a certain learning effect, which is a good similar to an adaptive test method.
Learning-Based Non-rigid Video Depth Estimation Using Invariants to Generalized Bas-Relief Transformations

Abstract We present a method to locally reconstruct dense video depth maps of a non-rigidly deformable object directly from a video sequence acquired by a static orthographic camera. The estimation of depth is performed locally on spatiotemporal patches of the video, and then, the full depth video is recovered by combining them together. Since the geometric complexity of a local spatiotemporal patch of a deforming non-rigid object is often simple enough to be faithfully represented with a parametric model, we artificially generate a database of small deforming rectangular meshes rendered with different material properties and light conditions, along with their corresponding depth videos, and use such data to train a convolutional neural network. Since the database images are rendered with an orthographic camera model, linear deformations along the optical axis cannot be recovered from the training images. These are known in the literature as generalized bas-relief (GBR) transformations. We address this ambiguity problem by employing the invariant-theoretic normalization procedure in order to obtain complete invariants with respect to this group of transformations, and use them in the loss function of a neural network. We tested our method on both synthetic and Kinect data and experimentally observed that the reconstruction error is significantly lower than the one obtained using conventional non-rigid structure from motion approaches and state-of-the-art video depth estimation techniques.
The role of incentives on improving performance of human resources in Nesma holding company in Saudi Arabia

The importance of incentives lies in their effect on human behaviour, as it is considered an important factor in improving the performance of employees, and this study aims to identify the role of material and moral incentives on the performance of the human resource in Nesma Holding Company in Jeddah. We have relied on the descriptive and analytical approach in the theoretical side; where the study sample consisted of 60 workers in Nesma Holding Company, 60 questionnaires were distributed to the workers of the company, and they were completely retrieved. In conclusion, the results show that having appropriate materials and moral incentives, and good training can have a significant impact on employee motivation, knowledge and job performance.
Challenges in Applying Continuous Experimentation: A Practitioners' Perspective

Background: Applying Continuous Experimentation on a large scale is not easily achieved. Although the evolution within large tech organisations is well understood, we still lack a good understanding of how to transition a company towards applying more experiments. Objective: This study investigates how practitioners define, value and apply experimentation, the blockers they experience and what to do to solve these. Method: We interviewed and surveyed over one hundred practitioners with regards to experimentation perspectives, from a large financial services and e-commerce organization, based in the Netherlands. Results: Many practitioners have different perspectives on experimentation. The value is well understood. We have learned that the practitioners are blocked by a lack of priority, experience and well functioning tooling. Challenges also arise around dependencies between teams and evaluating experiments with the correct metrics. Conclusions: Organisation leaders need to start asking for experiment results and investing in infrastructure and processes to actually enable teams to execute experiments and show the value of their work in terms of value for customers and business.
Twin-Field Quantum Digital Signature with Fully Discrete Phase Randomization

Quantum digital signatures (QDS) are able to verify the authenticity and integrity of a message in modern communication. However, the current QDS protocols are restricted by the fundamental rate-loss bound and the secure signature distance cannot be further improved. We propose a twin-field quantum digital signature (TF-QDS) protocol with fully discrete phase randomization and investigate its performance under the two-intensity decoy-state setting. For better performance, we optimize intensities of the signal state and the decoy state for each given distance. Numerical simulation results show that our TF-QDS with as few as six discrete random phases can give a higher signature rate and a longer secure transmission distance compared with current quantum digital signatures (QDSs), such as BB84-QDS and measurement-device-independent QDS (MDI-QDS). Moreover, we provide a clear comparison among some possible TF-QDSs constructed by different twin-field key generation protocols (TF-KGPs) and find that the proposed TF-QDS exhibits the best performance. Conclusively, the advantages of the proposed TF-QDS protocol in signature rate and secure transmission distance are mainly due to the single-photon interference applied in the measurement module and precise matching of discrete phases. Besides, our TF-QDS shows the feasibility of experimental implementation with current devices in practical QDS system.
Testing Machine Learning Systems in Industry: An Empirical Study

Machine learning becomes increasingly prevalent and integrated into a wide range of software systems. These systems, named ML systems, must be adequately tested to gain confidence that they behave correctly. Although many research efforts have been devoted to testing technologies for ML systems, the industrial teams are faced with new challenges on testing the ML systems in real-world settings. To absorb inspirations from the industry on the problems in ML testing, we conducted an empirical study including a survey with 87 responses and interviews with 7 senior ML practitioners from well-known IT companies. Our study uncovers significant industrial concerns on major testing activities, i.e., test data collection, test execution, and test result analysis, and also the good practices and open challenges from the perspective of the industry. (1) Test data collection is conducted in different ways on ML model, data, and code and faced with different challenges. (2) Test execution in ML systems suffers from two major problems: entanglement among the components and the regression on model performance. (3) Test result analysis centers on quantitative methods, e.g., metric-based evaluation, and is combined with some qualitative methods based on practitioners’ experience. Based on our findings, we highlight the research opportunities and also provide some implications for practitioners.
Surveying the Developer Experience of Flaky Tests

Test cases that pass and fail without changes to the code under test are known as flaky. The past decade has seen increasing research interest in flaky tests, though little attention has been afforded to the views and experiences of software developers. In this study, we utilized a multi-source approach to obtain insights into how developers define flaky tests, their experiences of the impacts and causes of flaky tests, and the actions they take in response to them. To that end, we designed a literature-guided developer survey that we deployed on social media, receiving 170 total responses. We also searched on StackOverflow and analyzed 38 threads relevant to flaky tests, offering a distinct perspective free of any self-reporting bias. Through a mixture of numerical and thematic analyses, this study reveals a number of findings, including (1) developers strongly agree that flaky tests hinder continuous integration; (2) developers who experience flaky tests more often may be more likely to ignore potentially genuine test failures; and (3) developers rate issues in setup and teardown to be the most common causes of flaky tests.
Issues in the Adoption of the Scaled Agile Framework

Agile methods were originally introduced for small sized, colocated teams. Their successful products immediately brought up the issue of adapting the methods also for large and distributed organizations engaged in projects to build major, complex products. Currently the most popular multi-teams agile method is the Scaled Agile Framework (SAFe) which, however, is subject to criticism: it appears to be quite demanding and expensive in terms of human resource and project management practices. Moreover, SAFe allegedly goes against some of the principles of agility. This research attempts to gather a deeper understanding of the matter first reviewing and analysing the studies published on this topic via a multivocal literature review and then with an extended empirical investigation on the matters that appear most controversial via the direct analysis of the work of 25 respondents from 17 different companies located in eight countries. Thus, the originality of this research is in the systemic assessment of the “level of flexibility” of SAFe, highlighting the challenges of adopting this framework as it relates to decision making, structure, and the technical and managerial competencies of the company. The results show that SAFe can be an effective and adequate approach if the company is ready to invest a significant effort and resources into it both in the form of providing time for SAFe to be properly absorbed and specific training for individuals.
Access Control Based on Log File for Internet of Things Devices

The use of Internet of Things devices has lately increased significantly, leading to the management of a diverse set of nodes and a vast number of data. Most Internet of Things nodes have limited resources and are vulnerable to a variety of threats and failures. Therefore, numerous novel techniques have been conducted to secure resource-constrained devices such as access control. In this work, we proposed an access control mechanism by using the user log files when they interact with their Internet of Things devices. Where it is possible to define and enforce access control restrictions and follow logs through log files to monitor the user accessing behaviors. This mechanism can be applied as an extra security layer along with any traditional user authentication access control to have the effective and accurate access control to prevent intrusion reveal information in the Internet of Things devices. To do this, we developed three Internet of Things applications on mobile, table, and website pages with different functionalities and goals to store the user log file features. We collected a large-scale date-set from over a thousand participants. Three machine learning algorithms: J48, Part, and Naive Bayes are applied and compared to predict the legitimate users. Several experiments were performed with significant results.
Building Virtual and Augmented Reality Passenger Experiences

Virtual and Augmented Reality (together XR) headsets enable the rendering of virtual content intermixed with reality. They have the capacity to allow passengers to break free from small physical displays in constrained environments such as cars, trains and planes, allowing them to escape to new experiences. They can allow passengers to make better use of their time by making travel more productive and enjoyable, supporting both privacy and immersion. This is of particular note given the predicted adoption of autonomous vehicles. The ViAjeRo project (www.viajero-project.org) is conducting breakthrough research in HCI and neuroscience to enable passenger usage of XR headsets, with the underlying goal of making more effective, comfortable and productive use of travel time. This paper sets out the limitations of current state-of-the-art and how ViAjeRo is opening new possibilities for passenger XR experiences.
Mobile Robot Path Planning Based on Improved Coyote Optimization Algorithm

An improved coyote optimization algorithm (ICOA) is proposed to meet the requirements of global search capability, convergence speed, and stability for mobile robot path planning problems. Firstly, the population is divided into the elite group and the general groups after initialization, and the coyote individuals in the general groups are evolved by introducing the strategy of the best leading the poor. Secondly, after returning to the original position, coyotes adopt a new growth mode, and each individual makes a certain contribution to improve the level of the whole group. Thirdly, different from the original greedy algorithm, the survival of the fittest is carried out after each group of coyotes grows up, and the individuals do not affect each other. Finally, ICOA and the other seven optimization algorithms are simulated on four maps for mobile robot path planning. The simulation results show that ICOA can keep the diversity of the population, and has a strong global search ability, better stability, fast convergence speed, which reflects the strong optimization ability.