Reviewerly/exHarmony huggingface dataset:
Experienced Challenges of Adopting Agile Scaling Frameworks

Background: The adoption of agile scaling frameworks has become increasingly prevalent in the software industry as organizations seek to extend the use of agile to large and complex projects. The frameworks claim to provide the necessary structure and guidance for scaling agile practices across multiple teams and domains. Despite their growing popularity, the challenges of adopting the frameworks are poorly understood. Aims: In this study, we analyze the experienced challenges related to adopting agile scaling frameworks to understand whether they are different for the different frameworks or between industries or roles within the adopting organization. Method: We conducted a survey targeting software practitioners with experience adopting agile scaling frameworks. We received 204 valid responses, representing ten frameworks adopted in 26 countries and six continents. Results: The most salient challenge, regardless of scaling framework, industry, or role, was organizational politics. Across frameworks, the challenge of forming agile teams emerged as the second most significant and change resistance as the third. We found significant differences in the challenges experienced by organizations based on their chosen framework and significant differences in the challenges between different industries and organizational roles, highlighting the importance of context when adopting agile scaling frameworks. Conclusions: This study provides the first quantitative assessment of the challenges of adopting specific agile scaling frameworks in various contexts. By comparing the challenges associated with different frameworks, industries, and organizational roles, other organizations can better understand the most significant obstacles they may face and improve their framework fit to mitigate them. Future studies could build on these findings to provide additional insights and recommendations for organizations seeking to adopt agile scaling frameworks, including identifying strategies for overcoming common challenges and improving the overall effectiveness of these frameworks in a diverse range of contexts.
Assessing the Use of AutoML for Data-Driven Software Engineering

Background.Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies.In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate the building of end-to-end AI/ML pipelines that would normally be engineered by specialized team members.Aims.Despite the growing interest and high expectations, there is a dearth of information about the extent to which AutoML is currently adopted by teams developing AI/ML-enabled systems and how it is perceived by practitioners and researchers.Method.To fill these gaps, in this paper, we present a mixed-method study comprising a benchmark of 12 end-to-end AutoML tools on two SE datasets and a user survey with follow-up interviews to further our understanding of AutoML adoption and perception.Results.We found that AutoML solutions can generate models that outperform those trained and optimized by researchers to perform classification tasks in the SE domain.Also, our findings show that the currently available AutoML solutions do not live up to their names as they do not equally support automation across the stages of the ML development workflow and for all the team members.Conclusions.We derive insights to inform the SE research community on how AutoML can facilitate their activities and tool builders on how to design the next generation of AutoML technologies.
ToxiSpanSE: An Explainable Toxicity Detection in Code Review Comments

Background: The existence of toxic conversations in open-source platforms can degrade relationships among software developers and may negatively impact software product quality. To help mitigate this, some initial work has been done to detect toxic comments in the Software Engineering (SE) domain. Aims: Since automatically classifying an entire text as toxic or non-toxic does not help human moderators to understand the specific reason(s) for toxicity, we worked to develop an explainable toxicity detector for the SE domain. Method: Our explainable toxicity detector can detect specific spans of toxic content from SE texts, which can help human moderators by automatically highlighting those spans. This toxic span detection model, ToxiSpanSE, is trained with the 19,651 code review (CR) comments with labeled toxic spans. Our annotators labeled the toxic spans within 3,757 toxic CR samples. We explored several types of models, including one lexicon-based approach and five different transformer-based encoders. Results: After an extensive evaluation of all models, we found that our fine-tuned RoBERTa model achieved the best score with 0.88 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F1$</tex> , 0.87 precision, and 0.93 recall for toxic class tokens, providing an explainable toxicity classifier for the SE domain. Conclusion: Since ToxiSpanSE is the first tool to detect toxic spans in the SE domain, this tool will pave a path to combat toxicity in the SE community.
Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition

When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent of such impact, we perform and briefly present a differential analysis against three DNNs widely used for image recognition (MobileNetV2, ResNet101, and InceptionV3)converted across four well-known deep learning frameworks (PyTorch, Keras, TensorFlow (TF), and TFLite), which revealed numerous model crashes and output label discrepancies of up to 72%. To mitigate such errors, we present a novel approach towards fault localization and repair of buggy deep learning framework conversions, focusing on pre-trained image recognition models. Our technique consists of four stages of analysis: 1) conversion tools, 2) model parameters, 3) model hyperparameters, and 4) graph representation. In addition, we propose various strategies towards fault repair of the faults detected. We implement our technique on top of the Apache TVM deep learning compiler, and we test it by conducting a preliminary fault localization analysis for the conversion of InceptionV3 from TF to TFLite. Our approach detected a fault in a common DNN converter tool, which introduced precision errors in weights, reducing model accuracy. After our fault localization, we repaired the issue, reducing our conversion error to zero.
Extended reality (<scp>XR</scp>)—A magic box of digitalization in driving sustainable development of the construction industry: A critical review

Abstract Against a background of global resource shortage and climate change, there is an urgency for the construction industry to aim towards sustainable development. With the rapid development of information technology, the emergence of extended reality (XR) has provided new ideas and options for sustainable construction (SC). Such technologies are applied in research and practice of the whole construction lifecycle, which can well meet the requirements of SC. This paper presents a critical review of XR applications in SC, aiming to systematically summarize the specific uses and advantages of virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies. This study also examines the gap in research into the imbalance between the use of technology and the construction stages involved. Most XR applications focus on the preconstruction stage, while visualization and interactivity are the main features for further application. Through qualitative analyses, different applications of XR are introduced in stages throughout the entire lifecycle of building projects. In addition, the current challenges and limitations are presented, and several suggestions are proposed to facilitate further advancements in SC research and practice in the near future. This review provides valuable information to help stakeholders understand the advances in XR research and benefit future exploration and practice in the field.
Calibration of uncertainty in the active learning of machine learning force fields

Abstract FFLUX is a machine learning force field that uses the maximum expected prediction error (MEPE) active learning algorithm to improve the efficiency of model training. MEPE uses the predictive uncertainty of a Gaussian process (GP) to balance exploration and exploitation when selecting the next training sample. However, the predictive uncertainty of a GP is unlikely to be accurate or precise immediately after training. We hypothesize that calibrating the uncertainty quantification within MEPE will improve active learning performance. We develop and test two methods to improve uncertainty estimates: post-hoc calibration of predictive uncertainty using the CRUDE algorithm, and replacing the GP with a student- t process. We investigate the impact of these methods on MEPE for single sample and batch sample active learning. Our findings suggest that post-hoc calibration does not improve the performance of active learning using the MEPE method. However, we do find that the student- t process can outperform active learning strategies and random sampling using a GP if the training set is sufficiently large.
An Automatic Privacy-Aware Framework for Text Data in Online Social Network Based on a Multi-Deep Learning Model

With the increasing severity of user privacy leaks in online social networks (OSNs), existing privacy protection technologies have difficulty meeting the diverse privacy protection needs of users. Therefore, privacy-aware (PA) for the text data that users post on OSNs has become a current research focus. However, most existing PA algorithms for OSN users only provide the types of privacy disclosures rather than the specific locations of disclosures. Furthermore, although named entity recognition (NER) technology can extract specific locations of privacy text, it has poor recognition performance for nested and interest privacy. To address these issues, this paper proposes a PA framework based on the extraction of OSN privacy information content. The framework can automatically perceive the privacy information shared by users in OSNs and accurately locate which parts of the text are leaking sensitive information. Firstly, we combine the roformerBERT model, BI_LSTM model, and global_pointer algorithm to construct a direct privacy entity recognition (DPER) model for solving the specific privacy location recognition and entity nesting problems. Secondly, we use the roformerBERT model and UniLM framework to construct an interest privacy inference (IPI) model for interest recognition and to generate interpretable text that supports this interest. Finally, we constructed a dataset of 13,000 privacy-containing texts for experimentation. Experimental results show that the overall accuracy of the DPER model can reach 91.80%, while that of the IPI model can reach 98.3%. Simultaneously, we compare the proposed model with recent methods. The analysis of the results indicates that the proposed model exhibits better performance than previous methods.
PROSTATA: a framework for protein stability assessment using transformers

Accurate prediction of change in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is dominant in many fields of machine learning.In this work, we introduce PROSTATA, a predictive model built in a knowledge-transfer fashion on a new curated dataset. PROSTATA demonstrates advantage over existing solutions based on neural networks. We show that the large improvement margin is due to both the architecture of the model and the quality of the new training dataset. This work opens up opportunities to develop new lightweight and accurate models for protein stability assessment.PROSTATA is available at https://github.com/AIRI-Institute/PROSTATA and https://prostata.airi.net.
Breaking Barriers in Sentiment Analysis and Text Emotion Detection: Toward a Unified Assessment Framework

Sentiment analysis (SA) and text emotion detection (TED) are two computer techniques used to analyze text. SA categorizes text into positive, negative, or neutral opinions, while TED can identify a wide array of emotional states, allowing an automated agent to respond appropriately. These techniques can be helpful in areas such as employee and customer management, online support, and customer loyalty, where identifying human emotions is crucial. Among other approaches, research has been conducted using machine learning (ML) algorithms, and labeled datasets have been created to train these models. Current state-of-the-art research for supervised ML algorithms reports good performance for TED (approximately 80% accuracy) and even better results for SA (above 90%). After conducting an extensive review of 30 survey articles, the primary objective of this manuscript is to highlight the disproportionate emphasis placed on comparing computational approaches, as evidenced by 94% of the articles surveyed that feature algorithmic aspects in their summaries. %), the corpora utilized for training (30%), and the data source employed during analysis and evaluation (20%). The lack of standardization across these essential elements presents a significant challenge when performing meaningful performance comparisons among algorithms. Consequently, the absence of a unified framework for comparison hampers the practical implementation of SA and TED techniques within mission-critical scenarios within real-world mission-critical scenarios.
Pair Programming Practiced in Hybrid Work

Pair programming (PP) has been a widespread practice for decades and is known for facilitating knowledge exchange and improving the quality of software.Many agilists advocated the importance of collocation, face-to-face interaction, and physical artifacts incorporated in the shared workspace when pairing.After a long period of forced workfrom-home, many knowledge workers prefer to work remotely two or three days per week, which is affecting practices such as PP.In this revelatory single-case study, we aimed to understand how PP is practiced during hybrid work when team members alternate between on-site days and working from home.We collected qualitative and quantitative data through 11 semistructured interviews, observations, feedback sessions, and selfreported surveys.The interviewees were members of an agile software development team in a Norwegian fintech company.The results presented in this paper indicate that PP can be practiced through on-site, remote, and mixed sessions, where the mixed mode seems to be the least advantageous.The findings highlight the importance of adapting the work environment to suit individual work mode preferences when it comes to PP.In the future, we will build on these findings to explore PP in other teams and organizations practicing hybrid work.
GVFs in the real world: making predictions online for water treatment

Abstract In this paper we investigate the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant. Developing such a prediction system is a critical step on the path to optimizing and automating water treatment. Before that, there are many questions to answer about the predictability of the data, suitable neural network architectures, how to overcome partial observability and more. We first describe this dataset, and highlight challenges with seasonality, nonstationarity, partial observability, and heterogeneity across sensors and operation modes of the plant. We then describe General Value Function (GVF) predictions—discounted cumulative sums of observations–and highlight why they might be preferable to classical n-step predictions common in time series prediction. We discuss how to use offline data to appropriately pre-train our temporal difference learning (TD) agents that learn these GVF predictions, including how to select hyperparameters for online fine-tuning in deployment. We find that the TD-prediction agent obtains an overall lower normalized mean-squared error than the n-step prediction agent. Finally, we show the importance of learning in deployment, by comparing a TD agent trained purely offline with no online updating to a TD agent that learns online. This final result is one of the first to motivate the importance of adapting predictions in real-time, for non-stationary high-volume systems in the real world.
Analyzing and Comparing DNS Lookup Tools in Python

The performance of Domain Name System (DNS) resolvers is crucial, as the majority of the communication in the Internet starts with a DNS lookup to resolve a domain an IP address to reach the desired content. In this sense, the academia has been devoted to measure and analyze the performance of DNS resolvers using different tools. However, such tools might present different results due to their implementation and affect the measurements. Hence, this paper provides an analysis and comparison of there different DNS lookup tools employed in the literature and discuss the impact of the tool selection.
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders

Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transformer-based language models, which are notoriously inefficient in terms of resources and latency. We propose Fast-Forward indexes—vector forward indexes which exploit the semantic matching capabilities of dual-encoder models for efficient and effective re-ranking. Our framework enables re-ranking at very high retrieval depths and combines the merits of both lexical and semantic matching via score interpolation. Furthermore, in order to mitigate the limitations of dual-encoders, we tackle two main challenges: Firstly, we improve computational efficiency by either pre-computing representations, avoiding unnecessary computations altogether, or reducing the complexity of encoders. This allows us to considerably improve ranking efficiency and latency. Secondly, we optimize the memory footprint and maintenance cost of indexes; we propose two complementary techniques to reduce the index size and show that, by dynamically dropping irrelevant document tokens, the index maintenance efficiency can be improved substantially. We perform evaluation to show the effectiveness and efficiency of Fast-Forward indexes—our method has low latency and achieves competitive results without the need for hardware acceleration, such as GPUs.
Australian Public and Private Crime Prevention Partnerships in Cyberspace

This chapter explores the growing trends in cybercrime and the role that public and private partnerships can and do play in addressing the scourge of criminal activity in cyberspace. The amount of digital information stored and available to the world expands exponentially. Global technological innovations are ubiquitous and growing. The nature and quality of data are changing. Each of these trends, however, threatens to leave policing in its wake as digital information becomes vulnerable to improper usage and theft. That being the case, the private sector has been and will continue to be co-opted in the public policing spheres. In significant ways its assistance has been desirable and useful. There is a developing trust between public and private agencies, for example, in relation to the collection, use and storage of metadata and the monitoring of visual digital data. However, given the potential of some corporate entities, particularly in the processing, use and storage of private digital data records, to push legal and ethical boundaries, governments cannot adopt a ‘hands-off’ approach. In the quest to defeat cybercrime, governments must continue to develop partnerships with a clear over-arching framework to require the compliance of private owners of surveillance tools and data managers in the same way as controls are in place to protect the private nature of government-collected data. These partnerships are not just with private entities committed to the business of policing. Partnerships must be developed with those who are vulnerable to cybercrime, and that is everyone’s business. In this way, governments must enhance the capacity of potential victims to be self-policing. This chapter explores these relationships, the legislative initiatives that are now in place in Australia, and the imperatives that flow therefrom.
Trajectory generation and tracking control for aggressive tail-sitter flights

We address the theoretical and practical problems related to the trajectory generation and tracking control of tail-sitter UAVs. Theoretically, we focus on the differential flatness property with full exploitation of actual UAV aerodynamic models, which lays a foundation for generating dynamically feasible trajectory and achieving high-performance tracking control. We have found that a tail-sitter is differentially flat with accurate (not simplified) aerodynamic models within the entire flight envelope, by specifying coordinate flight condition and choosing the vehicle position as the flat output. This fundamental property allows us to fully exploit the high-fidelity aerodynamic models in the trajectory planning and tracking control to achieve accurate tail-sitter flights. Particularly, an optimization-based trajectory planner for tail-sitters is proposed to design high-quality, smooth trajectories with consideration of kinodynamic constraints, singularity-free constraints, and actuator saturation. The planned trajectory of flat output is transformed into state trajectory in real time with optional consideration of wind in environments. To track the state trajectory, a global, singularity-free, and minimally parameterized on-manifold MPC is developed, which fully leverages the accurate aerodynamic model to achieve high-accuracy trajectory tracking within the whole flight envelope. The proposed algorithms are implemented on our quadrotor tail-sitter prototype, “Hong Hu,” and their effectiveness are demonstrated through extensive real-world experiments in both indoor and outdoor field tests, including agile SE(3) flight through consecutive narrow windows requiring specific attitude and with speed up to 10 m/s, typical tail-sitter maneuvers (transition, level flight, and loiter) with speed up to 20 m/s, and extremely aggressive aerobatic maneuvers (Wingover, Loop, Vertical Eight, and Cuban Eight) with acceleration up to 2.5 g. The video demonstration is available at https://youtu.be/2x_bLbVuyrk .