Reviewerly/exHarmony huggingface dataset:
Assessing the Maturity of DevOps Practices in Software Industry: An Empirical Study of HELENA2 Dataset

Currently, the software development organizations are adopting DevOps practices in order to develop quality product. Due to the lack of definition of DevOps, the principles, practices, and methods adopted in DevOps to determine success have changed substantially. There are several benefits of DevOps can be achieved if the DevOps practices are implemented effectively and efficiently. This study has been conducted to identify the DevOps practices that contribute to achieve a high level of DevOps. The qualitative and interpretive approach have been used for analyzing the HELENA2 dataset. The maturity is calculated using 36 DevOps practices based on the four different classifications: (1) rarely used DevOps, (2) sometimes used DevOps, (3) often used DevOps, and (4) always used DevOps with the help of degrees of addressing 18 goals. The maturity score for the different categories indicates that organizations that use DevOps always are more mature to achieve goals than organizations that use it rarely.
Automated evaluation of comments to aid software maintenance

Abstract Approaches to evaluate comments based on whether they increase code comprehensibility for software maintenance tasks are important, but largely missing. We propose Comment for automated classification and quality evaluation of code comments of C codebases based on how they can help to understand existing code. We conduct surveys and document developers' perceptions on the type of comments that prove useful to maintaining software in the form of comment categories. A total of 20,206 comments have been collected from open‐source Github projects and annotated with assistance from industry experts. We develop features to semantically analyze comments to locate concepts related to categories of usefulness. Additionally, features based on code and comment correlation are designed to infer whether the comment is also consistent and not superfluous. Using neural networks, comments are classified as useful , partially useful , and not useful with precision and recall scores of 86.27% and 86.42%, respectively. The proposed framework for comment quality evaluation incorporates industry practices and adds significant value to companies wanting to formulate better code commenting strategies. Furthermore, large codebases can be de‐cluttered by removing comments not helpful in maintaining code.
PLAYPEN: Plug-and-Play Visual Graph Query Interfaces for Top-down and Bottom-Up Search on Large Networks

Visual graph query interfaces (VQI) facilitate non-programmers to query graph data effortlessly. The construction of these interfaces for large networks is typically not data-driven. That is, they do not exploit the underlying networks to automatically generate the contents of various panels of a VQI. Such data-driven construction has several benefits such as facilitating efficient top-down and bottom-up query formulation and portability of an interface across different application domains and sources. In this demonstration, we present a novel plug-and-play visual subgraph query interface construction engine called PLAYPEN that can be plugged on any large network G with a plug specification b to automatically generate the VQI for G that satisfies b by populating various components of the interface.
Global-Local AI Coordinated Learning over Optical Access Networks for Scalable H2M/R Collaborations

While pre-fifth and fifth generation networks have been designed mainly to support machine-to-machine and human-to-human communications via data, audio, and video communications, a major thrust of beyond fifth generation Networks is the abutment of human-to-machine/robot (H2M/R) collaboration. Such collaborations are deeply anchored in the vision of Society 5.0, which promotes a human-centered society that integrates virtual and physical real spaces to resolve social problems, and Industry 5.0, which promotes intelligent manufacturing processes through collaboration between humans and cyber-physical systems. It is expected that real-time H2M/R collaborations will drive new technologies that facilitate the low-latency exchange of control signals and haptic feedback between humans and machines/robots. The low-latency constraints for real-time interaction will, however, place limits on deployable distance and the flexibility of adding new machines/robots to the system. The scalability of H2M/R collaborations across large geographical distances that also allow rapid onboarding of new machines/robots is therefore an open challenge. This article focuses on global-local AI coordinated learning (GLAD), a solution framework that supports the dynamic addition of new machines/robots and their rapid onboarding. The GLAD framework is based on learning the traffic characteristics and correlation of supported robots/machines at the local edge and global cloud. Harnessing data samples from a real H2M/R application for training and validation, GLAD enables human operators to remotely control machines/robots over extended distances through accurately forecasting their haptic feedback. By globally coordinating and sharing this learning across the network, up to 72 percent of the time can be saved when onboarding new machines/robots.
A GAN-based method for time-dependent cloud workload generation

• An GAN-based method for time-dependent cloud workload generation is proposed. • The method does not rely on any prior knowledge to capture the data distribution. • Spectral normalization is used to stabilize the adversarial training. • Achieving better results than state-of-art models. • Enabled conditional workload generation with simple changes on models. To design repeatable and comparable resource management policies for data centers, researchers mainly conduct experiments in the simulation environment, which requires large-scale workload traces to simulate real scenes. However, issues related to data collection, security and privacy hinder the public availability of cloud workload datasets. Though workload generation is a promising solution, due to the unpredictable time dependency, cloud workloads are difficult to model. In light of this, we propose a novel end-to-end model for time-dependent cloud workload generation using Generative Adversarial Networks, which adopts improved Temporal Convolution Networks and Spectral Normalization to capture the time dependency and stabilize the adversarial training. Experimental results on real cloud datasets demonstrate that our model can efficiently generate realistic workloads that fulfill the diversity, fidelity and usefulness. Further, we also propose a conditional GAN which is trained with labeled data and can generate specific kind of workloads according to the input.
Substantial improvement of histopathological diagnosis by whole-slide image-based remote consultation

Consultation by subspecialty experts is the most common mode of rendering diagnosis in challenging cases in pathological practice. Our study aimed to highlight the diagnostic benefits of whole-slide image (WSI)-based remote consultation. We obtained diagnostically challenging cases from two institutions from the years 2010 and 2013, with histological diagnoses that contained keywords "probable," "suggestive," "suspicious," "inconclusive," and "uncertain." A total of 270 cases were selected for remote consultation using WSIs scanned at 40 × . The consultation process consisted of three rounds: the first and second rounds each with 12 subspecialty experts and the third round with six multi-expertise senior pathologists. The first consultation yielded 44% concordance, and a change in diagnosis occurred in 56% of cases. The most frequent change was from inconclusive to definite diagnosis (30%), followed by minor discordance (14%), and major discordance (12%). Out of the 70 cases which reached the second round, 31 cases showed discrepancy between the two consultants. For these 31 cases, a consensus diagnosis was provided by six multi-expertise senior pathologists. Combining all WSI-based consultation rounds, the original inconclusive diagnosis was changed in 140 (52%) out of 266 cases. Among these cases, 80 cases (30%) upgraded the inconclusive diagnosis to a definite diagnosis, and 60 cases (22%) changed the diagnosis with major or minor discordance, accounting for 28 cases (10%) and 32 cases (12%), respectively. We observed significant improvement in the pathological diagnosis of difficult cases by remote consultation using WSIs, which can further assist in patient healthcare. A post-study survey highlighted various benefits of WSI-based consults.
Shedding new light on the foundations of abstract argumentation: Modularization and weak admissibility

In his seminal 1995 paper, Dung laid the foundations of abstract argumentation, a by now major research area in knowledge representation. He pointed out that there is a problematic issue with self-defeating arguments underlying all traditional semantics. A self-defeat occurs if an argument attacks itself either directly or indirectly via an odd attack loop, unless the loop is broken up by some argument attacking the loop from outside. Motivated by the fact that such arguments represent self-contradictory or paradoxical arguments, he asked for reasonable semantics which overcome the problem that such arguments may indeed invalidate any argument they attack. This paper provides a solution to this problem. More precisely, we introduce alternative foundations for abstract argumentation, namely weak admissibility and weak defense. After showing that these key concepts are compatible as in the classical case we introduce new versions of the classical Dung-style semantics including complete, preferred and grounded semantics. We provide a rigorous study of these new concepts including interrelationships as well as the relations to their Dung-style counterparts. We also conduct an analysis of the relationship of our concepts with similar concepts found in the literature. We show that weak admissibility, although defined in entirely different terms, is in fact equivalent to a notion of acceptability which was defined by Kakas and Mancarella yet never studied in depth. The new semantics presented here overcome the issue with self-defeating arguments, and they are semantically insensitive to syntactic deletions of self-attacking arguments, a special case of self-defeat.1
Sevi: Speech-to-Visualization through Neural Machine Translation

Data visualization is a powerful tool for understating information through visual cues. However, allowing novices to create visualization artifacts for what they want to see is not easy, just as not everyone can write SQL queries. Arguably, the most natural way to specify what to visualize is through natural language or speech, similar to our daily search on Google or Apple Siri, leaving to the system the task of reasoning about what to visualize and how.
Extending the Unified Theory of Acceptance and Use of Technology for COVID-19 Contact Tracing Application by Malaysian Users

The Malaysian government has mobilized its strength to confront the current COVID-19 pandemic and has sought to develop and implement a digital contact tracking application, making it an integral part of the exit strategy from the lockdown. These applications record which users have been near one another. When a user is confirmed with COVID-19, app users who have recently been near this person are notified. The effectiveness of these applications is determined by the users’ willingness to install and use them. Therefore, this research aims at identifying the factors that would stimulate or slow down the adoption of a contact-tracing app. It proposes solutions to mitigate the impact of the factors affecting the user’s acceptance of COVID-19 Digital Contact Tracing Apps. A quantitative approach was followed in this research, where an electronic survey was spread in Malaysia, for the objective of data collection, considering the previous discussion of the results. Then, using PLS-SEM, the collected data were analyzed statistically. The findings of this study indicate that the unified theory of acceptance and use of technology (UTAUT) factors (Performance Expectancy, Effort Expectancy, Social Influence, Facilities Condition) were significant predictors of MySejahtera application adoption among citizens in Malaysia. On the other hand, the factors of app-related privacy concern were found to be insignificant for MySejahtera application adoption.
Cross-domain multi-task learning for sequential sentence classification in research papers

Sequential sentence classification deals with the categorisation of sentences based on their content and context. Applied to scientific texts, it enables the automatic structuring of research papers and the improvement of academic search engines. However, previous work has not investigated the potential of transfer learning for sentence classification across different scientific domains and the issue of different text structure of full papers and abstracts. In this paper, we derive seven related research questions and present several contributions to address them: First, we suggest a novel uniform deep learning architecture and multi-task learning for cross-domain sequential sentence classification in scientific texts. Second, we tailor two common transfer learning methods, sequential transfer learning and multi-task learning, to deal with the challenges of the given task. Semantic relatedness of tasks is a prerequisite for successful transfer learning of neural models. Consequently, our third contribution is an approach to semi-automatically identify semantically related classes from different annotation schemes and we present an analysis of four annotation schemes. Comprehensive experimental results indicate that models, which are trained on datasets from different scientific domains, benefit from one another when using the proposed multi-task learning architecture. We also report comparisons with several state-of-the-art approaches. Our approach outperforms the state of the art on full paper datasets significantly while being on par for datasets consisting of abstracts.
Towards Interpretable Anomaly Detection: Unsupervised Deep Neural Network Approach using Feedback Loop

As telecom networks generate high-dimensional data, it becomes important to support large numbers of co-existing network attributes and to provide an interpretable and eXplainable Artificial Intelligence (XAI) anomaly detection system. Most state-of-the-art techniques tackle the problem of detecting network anomalies with high precision but the models don’t provide an interpretable solution. This makes it hard for operators to adopt the given solutions. The proposed Cluster Characterized Autoencoder (CCA) architecture improves model interpretability by designing an end-to-end data driven AI-based framework. Candidate anomalies identified using the feature optimised Autoencoder and entropy based feature ranking are clustered in reconstruction error space using subspace clustering. This clustering is seen to separate true positives and false positives and how well this is done is evaluated using entropy and information gain. A two dimensional t-SNE representation of anomaly clusters is used as a graphical interface to the analysis and explanation of individual anomalies using SHAP values. The solution provided by this unsupervised approach helps the analyst in the categorisation, identification and feature explanation of anomalies providing faster root cause analysis. Therefore, our solution provides better support for the network domain analysts with an interpretable and explainable Artificial Intelligence (AI) anomaly detection system. Experiments on a real-world telecom network dataset demonstrate the efficacy of our proposed algorithm.
XQsim

10+K qubit quantum computer is essential to achieve a true sense of quantum supremacy. With the recent effort towards the large-scale quantum computer, architects have revealed various scalability issues including the constraints in a quantum control processor, which should be holistically analyzed to design a future scalable control processor. However, it has been impossible to identify and resolve the processor's scalability bottleneck due to the absence of a reliable tool to explore an extensive design space including microarchitecture, device technology, and operating temperature.
Virtual Reality and Productivity in Knowledge Workers

Productivity has a significant impact on success and monetary wellbeing of every organisation. Over the past few years, the substantial developments of digital technologies have encouraged a shift in the way we work and produce, from an office-based environment to “virtual work”. However, very little is known as to how virtual work and productivity can be supported by virtual reality (VR). We conducted two studies to extend previous productivity research in relation to VR: Study one examined the routes that connect the organisational context with the individual productivity position through the lens of remote working and distributed collaboration; Study two explored the nature of and connections between productivity in individuals and teams working in VR. Based on the findings we explored how the future of VR could enact in knowledge workers’ daily productivity. This was done by developing a VR productivity framework that represents physical, environmental, cognitive, and behavioural needs to ensure productivity and organisational growth. Theoretical and practical implications of the findings are discussed.
An Optimal Transport Approach to Personalized Federated Learning

Federated learning is a distributed machine learning paradigm, which aims to train a model using the local data of many distributed clients. A key challenge in federated learning is that the data samples across the clients may not be identically distributed. To address this challenge, personalized federated learning with the goal of tailoring the learned model to the data distribution of every individual client has been proposed. In this paper, we focus on this problem and propose a novel personalized Federated Learning scheme based on Optimal Transport (FedOT) as a learning algorithm that learns the optimal transport maps for transferring data points to a common distribution as well as the prediction model under the applied transport map. To formulate the FedOT problem, we extend the standard optimal transport task between two probability distributions to multi-marginal optimal transport problems with the goal of transporting samples from multiple distributions to a common probability domain. We then leverage the results on multi-marginal optimal transport problems to formulate FedOT as a min-max optimization problem and analyze its generalization and optimization properties. We discuss the results of several numerical experiments to evaluate the performance of FedOT under heterogeneous data distributions in federated learning problems.
Familiarity ratings for 24,325 simplified Chinese words

The present work collected familiarity norms for 20,275 two-character, 1231 three-character, and 2819 four-character simplified Chinese words from 1300 native speakers of Mandarin Chinese. The familiarity of each word was rated on a 7-point scale by at least 21 participants. The results supported the reliability and validity of the present familiarity ratings, which is the first large familiarity database for Chinese in the field. These familiarity norms can be downloaded from the supplemental materials. Furthermore, the contribution of familiarity to Chinese lexical processing was investigated using the present familiarity ratings and previous data (lexical features and visual lexical decision), mainly from two major Chinese lexicon projects, MELD-SCH and CLP. Regression analysis suggests that familiarity explained a substantial percentage of the variance in lexical processing of both simplified and traditional Chinese words, over and above the effects of word frequency and other lexical features, including age of acquisition (AoA). Further analysis identified a significantly greater familiarity effect for lower-frequency words than that for higher-frequency words. Together, among the first, our findings support the important contribution of familiarity with Chinese words to lexical processing, especially for low-frequency words.
Moving Object Tracking in Satellite Videos by Kernelized Correlation Filter Based on Color-Name Features and Kalman Prediction

This paper studies moving object tracking in satellite videos. For the satellite videos, the object size in the images may be small, the object may be partly occluded, and the image may contain an area resembling dense objects. To handle the above problems, this paper puts forward a kernelized correlation filter based on the color-name feature and Kalman prediction. The original image is mapped to the color-name feature space so that the tracker can process the image with multichannel color features. The Kalman filter is used to predict the moving object position in the tracking process, and the detection area is determined according to the predicted position. The Kalman filter is updated with the detection results to improve the tracking accuracy. The proposed algorithm is tested on Jilin-1 datasets. Compared with the other seven tracking algorithms, the experiment results show that the proposed algorithm has stronger robustness for several complex situations such as rapid target motion and similar object interference. Besides, it is also shown that the proposed algorithm can prevent the problem of tracking failure when the moving object is partially occluded.
Video augmentation to support video-based learning

Multimedia content and video-based learning are expected to take a central role in the post-pandemic world. Thus, providing new advanced interfaces and services that further exploit their potential becomes of paramount importance. A challenging area deals with developing intelligent visual interfaces that integrate the knowledge extracted from multimedia materials into educational applications. In this respect, we designed a web-based video player that is aimed to support video consumption by exploiting the knowledge extracted from the video in terms of concepts explained in the video and prerequisite relations between them. This knowledge is used to augment the video lesson through visual feedback methods. Specifically, in this paper we investigate the use of two types of visual feedback, i.e. an augmented transcript and a dynamic concept map (map of concept's flow), to improve video comprehension in the first-watch learning context. Our preliminary findings suggest that both the methods help the learner to focus on the relevant concepts and their related contents. The augmented transcript has an higher impact on immediate comprehension compared to the map of concepts' flow, even though the latter is expected to be more powerful to support other tasks such as exploration and in-depth analysis of the concepts in the video.