Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Transformer-Based Neural Text Representation Techniques on Bug Triaging

Bug report management has been shown to be an important and time consuming software maintenance task. Often, the first step in managing bug reports is related to triaging a bug to the appropriate developer who is best suited to understand, localize, and fix the target bug. Additionally, assigning a given bug to a particular part of a software project can help to expedite the fixing process. However, despite the importance of these activities, they are quite challenging, where days can be spent on the manual triaging process. Past studies have attempted to leverage the limited textual data of bug reports to train text classification models that automate this process - to varying degrees of success. However, the textual representations and machine learning models used in prior work are limited by their expressiveness, often failing to capture nuanced textual patterns that might otherwise aid in the triaging process. Recently, large, transformer-based, pre-tained neural text representation techniques (i.e., large language models or LLMs) such as BERT and CodeBERT have achieved greater performance with simplified training procedures in several natural language processing tasks, including text classification. However, the potential for using these techniques to improve upon prior approaches for automated bug triaging is not well studied or understood. Therefore, in this paper we offer one of the first investigations that fine-tunes transformer-based language models for the task of bug triaging on four open source datasets, spanning a collective 53 years of development history with over 400 developers and over 150 software project components. Our study includes both a quantitative and qualitative analysis of effectiveness. Our findings illustrate that DeBERTa is the most effective technique across the triaging tasks of developer and component assignment, and the measured performance delta is statistically significant compared to other techniques. However, through our qualitative analysis, we also observe that each technique possesses unique abilities best suited to certain types of bug reports.
LiSum: Open Source Software License Summarization with Multi-Task Learning

Open source software (OSS) licenses regulate the conditions under which users can reuse, modify, and distribute the software legally. However, there exist various OSS licenses in the community, written in a formal language, which are typically long and complicated to understand. In this paper, we conducted a 661-participants online survey to investigate the perspectives and practices of developers towards OSS licenses. The user study revealed an indeed need for an automated tool to facilitate license understanding. Motivated by the user study and the fast growth of licenses in the community, we propose the first study towards automated license summarization. Specifically, we released the first high quality text summarization dataset and designed two tasks, i.e., license text summarization (LTS), aiming at generating a relatively short summary for an arbitrary license, and license term classification (LTC), focusing on the attitude inference towards a predefined set of key license terms (e.g., Distribute). Aiming at the two tasks, we present LiSum, a multi-task learning method to help developers overcome the obstacles of understanding OSS licenses. Comprehensive experiments demonstrated that the proposed jointly training objective boosted the performance on both tasks, surpassing state-of-the-art baselines with gains of at least 5 points w.r.t. F1 scores of four summarization metrics and achieving 95.13% micro average F1 score for classification simultaneously. We released all the datasets, the replication package, and the questionnaires for the community.
Generative Type Inference for Python

Python is a popular dynamic programming language, evidenced by its ranking as the second most commonly used language on GitHub. However, its dynamic type system can lead to potential type errors, leading researchers to explore automatic type inference approaches for Python programs. Existing type inference approaches can be generally grouped into three categories, i.e., rule-based, supervised, and cloze- style approaches. The rule-based type inference approaches can ensure the accuracy of predicted variable types, but they suffer from low coverage problems caused by dynamic features and external calls. Supervised type inference approaches, while feature-agnostic and able to mitigate the low coverage problem, require large, high- quality annotated datasets and are limited to pre-defined types. As zero-shot approaches, the cloze-style approaches reformulate the type inference problem into a fill-in-the-blank problem by leveraging the general knowledge in powerful pre-trained code models. However, their performance is limited since they ignore the domain knowledge from static typing rules which reflect the inference logic. What is more, their predictions are not interpretable, hindering developers' understanding and verification of the results. This paper introduces Typegen, a few-shot generative type inference approach that incorporates static domain knowledge from static analysis. Typegen creates chain-of-thought (COT) prompts by translating the type inference steps of static analysis into prompts based on the type dependency graphs (TDGs), enabling language models to learn from how static analysis infers types. By combining COT prompts with code slices and type hints, TypegEnconstructs example prompts from human annotations. Typeg Enonly requires very few annotated examples to teach language models to generate similar COT prompts via in-context learning. Moreover, Typeg Enenhances the interpretability of results through the use of the input- explanation-output strategy, which generates both explanations and type predictions in COT prompts. Experiments show that Typegen outperforms the best baseline Type4Py by 10.0% for argument type prediction and 22.5 % in return value type prediction in terms of top-l Exact Match by using only five examples. Furthermore, Typeg Enachieves substantial improvements of 27 % to 84 % compared to the zero-shot performance of large language models with parameter sizes ranging from 1.3B to 175B in terms of top-I Exact Match.
Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction

Traditional Click-Through Rate (CTR) prediction models are usually trained and deployed in a single scenario. However, large-scale commercial platforms usually contain multiple recommendation scenarios, the traffic characteristics of which may be significantly different. Recent studies have proved that learning a unified model to serve multiple scenarios is effective in improving the overall performance. However, most existing approaches suffer from various limitations respectively, such as insufficient distinction modeling, inefficiency with the increase of scenarios, and lack of interpretability. More importantly, as far as we know, none of existing Multi-Scenario Modeling approaches takes explicit feature interaction into consideration when modeling scenario distinctions, which limits the expressive power of the network and thus impairs the performance. In this paper, we propose a novel Scenario-Adaptive Feature Interaction framework named SATrans, which models scenario discrepancy as the distinction of patterns in feature correlations. Specifically, SATrans is built on a Transformer architecture to learn high-order feature interaction and involves the scenario information in the modeling of self-attention to capture distribution shifts across scenarios. We provide various implementations of our framework to boost the performance, and experiments on both public and industrial datasets show that SATrans 1) significantly outperforms existing state-of-the-art approaches for prediction, 2) is parameter-efficient as the space complexity grows marginally with the increase of scenarios, 3) offers good interpretability in both instance-level and scenario-level. We have deployed the model in WeChat Official Account Platform and have seen more than 2.84% online CTR increase on average in three major scenarios.
Mathematical Treatment for Real-Time Vehicle Recognition Using Traditional Road Surveillance Cameras

Intelligent transportation systems require advanced vehicle tracking and surveillance tools, but hardware upgrades such as sensors, radars, and high-resolution cameras may be economically unfeasible in some regions. This paper introduces a mathematical approach for extracting traffic information using only traditional stationary single-lens traffic cameras. The method features vehicle recognition and tracking, speed estimation, traffic condition awareness, and abnormal behavior detection. Though machine learning is used for vehicle detection, information extraction, and analysis rely on mathematical deductions. The method’s effectiveness provides a viable solution for medium-to-small scale transportation systems, supplementing existing surveillance measures. Implementing this approach significantly improves real-time traffic surveillance efficiency and reduces infrastructure upgrade costs, offering a practical, cost-effective solution for enhancing transportation systems.
Blockchain Technology Adoption in Construction Projects Logistics in South Africa: Militating Factors

Construction logistics are said to be particularly complicated since there are so many deliveries and varied routes to coordinate, all of which must be done in a safe and effective manner to ensure that materials are transported from one construction site to another in good time and with accuracy. To this end, complex logistics solutions that will help manage effectively the entire logistics processes have been advocated. Blockchain technology has been identified as this logistics solution. However, there are concerns in some quarters as regards the barriers to the adoption of this solution in the management of construction logistics. Hence, this study evaluates the militating factors against the adoption of blockchain technology in construction project logistics in South Africa (SA). A Survey design was adopted. A total number of one hundred and forty (140) questionnaires were administered to construction professionals in Guateng Province, SA, and ninety (90) were returned and found suitable for analysis. Collected data were analyzed using percentage, mean item score, standard deviation, one sample t-test, and Kruskal-Wallis. The findings reveal the militating factors to the adoption of this solution in the management of construction logistics in SA which are lack of trust, skepticism, lack of data storage capability, cost and complexity, lack of data standardization, low understanding toward blockchain technology, low support from high-level management, and low awareness to related legal issues. This finding could inform construction stakeholders in the country on the present militating factors to the adoption of this solution in the management of construction logistics and these challenges identified must encourage stakeholders to take significant steps towards addressing them such that the SA construction industry benefit from this all-important approach.
Exploring Saudi Higher Education Issues by using Sentiment Analysis of Saudi Dialect Tweets

This study used a sentiment analysis to identify the polarity of the tweets in order to address any of the higher education issues in Saudi Arabia and to track the sentiments over time. Shaqra University has been selected to be a sample for the Saudi higher education system; thus, this study detects social media content regarding Shaqra University and applies sentiment analysis techniques to classify the sentiment and subject of the posts. The study applies this technique to social media content in the Arabic language, which is more difficult than languages such as English, and shows its application to uncover previously unknown issues for a target organization. Unlike previous studies, this study did not begin with known concerns but rather discovered unknown concerns. It applies a structured vocabulary model to a language that is typically difficult to structure programmatically and uses this technique to structure knowledge found in social media content. In such a case, we have already built our lexicon (SaudiSentiPlus). It is a Saudi dialect sentiment lexicon that was developed early in the author's prior study and contains 7,139 words. SaudiSentiPlus and its algorithms have been used in this study to investigate and track sentiments toward Shaqra University’s performance over the seven periods. This study created its own dataset from Twitter. The dataset contains tweets regarding sentiments toward Shaqra University collected using the Twitter API for seven periods, starting from 1–2016 to 7–2019. A total of 49,270 Arabic tweets were crawled and indexed. This study proposes a new application for sentiment analysis on social media. This shows that sentiment analysis can play an important role in developing higher education. By using sentiment analysis, the study identified 18 university-related issues. Half of them accounted for 66% of the weight of the university’s issues.
The Impact of Blockchain Technology in Public Services: Lessons Learned

The public sector is still considered a potential domain to increase its level of digitization. Blockchain/DLT-based solutions are a valid option to increase efficiency, transparency, persistence, and, generally, increase trust in the digitized solution. Unfortunately, solutions based on blockchain/DLT present technological and organizational barriers that come along with the distributed nature of that technology. The TOKEN project was established to tackle such challenges. It aims to develop a service platform and components to shield all blockchain-related complexities from public services and provide easily accessible blockchain-based services. The areas that TOKEN was planned to cover are notarization, persistent storage, identity management, and messaging and event streaming. To prove the usefulness of TOKEN, four pioneer use cases of selected public services had to be developed on top of TOKEN. The lessons learned from integrating TOKEN into public services showed that these public services mostly only used a small subset of the available service and that their use was often not essential for the success of the public service. Additionally, the shielding of the blockchain by TOKEN shifted trust in the blockchain network away from trust in the correct operation of TOKEN. That is no longer experienced as a distributed-decentralized solution but only as a single access point. Further, the launch of the European Blockchain Services Infrastructure (EBSI) with a similar set of services and the lack of early adopters of TOKEN led to a loss of interest in governing the results in the future as a community-driven organization.
Semi-supervised Single-Shot Object Detection for Table Detection in Scanned Documents

Table detection played a vital role in scanned document mining and understanding. The complex nature of tables has made table detection cumbersome and resource-intensive. In this paper, a novel two-path semi-supervised single-shot object detection framework was proposed for automatic detection of table in scanned documents. The proposed framework includes a shared VGG16 network and a two-path single-shot object detection model comprising of supervised and unsupervised subnetworks for table detection and classification. CascadeTabNet General dataset was employed to validate the effectiveness of the proposed framework. Experimental results demonstrated that the model implemented under the proposed framework is robust across various amount of available annotated documents (labeled data) for training. It can detect and classify tables in scanned document effectively even when training on very limited labeled data. For example, the precision of the proposed model is within 3% of a supervised model (SSD300) when training on only 10% of labeled data and 90% of unlabeled data.
Sliding Mode Control for UAV Trajectory Tracking: Focus on Virtual Learning Environments

The present work presents a cascade control scheme, in the initial stage, there is a kinematic controller based on the sliding mode technique for the trajectory tracking of a UAV. Secondly, a dynamic controller is cascaded; this controller allows to compensate the perturbation and velocity errors of the robot. For this purpose, the simplified dynamic model of the UAV is used. For testing purposes, a virtual reality simulator is presented to test the proposed control scheme at laboratory level. The virtual reality environment will allow evaluating the UAV performance as much as possible. The virtual reality environment has the digitized physical robot, with its dynamic model and all the movement characteristics. The controller is simulated and then experimental tests are performed with the physical UAV in a partially structured environment and with wind disturbances at the time of the experiment, an $$RMSE=0.32$$ [m] is obtained for the UAV position.
Education Robotics in Virtual Environment

Today, robotic toys and kits are one of the ways to build children’s positive attitude towards technology and robotics, from kindergarten or primary school to university. We take into account not only the age of the learners but also the availability and simplicity of the robots for the youngest students. In the upper grades of primary and secondary schools, pupils gradually work with increasingly complex robots and create more complex programs. At universities, students often design and program real robots that occur in practice. At technical universities, students often design a complete robotic workplace. Robotics is a tool for developing the key competencies of the 21st century. The task of teachers is not only education, but also the preparation of students for the future and their guidance in choosing a future profession. Robotics develops computer, algorithmic, programming and logical thinking, mathematics and digital literacy, creativity, diligence, manual dexterity, patience and a positive attitude towards technology and self-education. It allows the students and users to make decisions, react to changes, work in a team, present and defend their own ideas and solutions. The article presents specific examples and possibilities of teaching robotics and programming robots in primary school using virtual robots and others virtual programmable kits and toys, which are a suitable tool for teaching construction and programming of robots even in distance education. The use of virtual tools is not a goal, but an emergency solution to a situation when face-to-face teaching must be replaced by a distance study.
Unsupervised, Self-supervised, and Supervised Learning for Histopathological Pattern Analysis in Prostate Cancer Biopsy

Prostate cancers are often non-aggressive, which makes it difficult to determine a treatment such as surgical prostate removal. The survival rate can be significantly enhanced with early detection of cancer so that appropriate intervention can be administered. This paper presents a state-of-the-art system that conducts a multi-class classification, from unsupervised to supervised learning techniques to predict the tissue components, namely stroma benign, and cancer in whole slide image (WSI) of prostate cancer. First, the unsupervised classifier learns from an unlabeled dataset to extract meaningful information from tissue images. For that, we used a modified K-means algorithm without any supervision to generate the labels using scale-invariant feature transform (SIFT), Histogram of oriented gradients (HOG), Gray-level co-occurrence matrix (GLCM), and Edge-based (i.e., Sobel, Roberts, Scharr, and Prewitt) features. Further, our proposed model, Bi-directional ConvLSTM Convolutional Neural Network (BCACNN) is used for self-supervised and supervised learning on unlabeled and unsupervised labeled data, respectively, to differentiate the regions in WSI of prostate cancer. The proposed model achieved the highest accuracy, precision, recall, f1-score, and area under the curve (AUC) of 0.8655, 0.8597, 0.8524, 0.8560, and 0.9748, respectively, on the internal test dataset, and 0.8915, 0.8880, 0.8904, 0.8891, and 0.9764, respectively, on the external test dataset.
Kube-Volttron: Rearchitecting the VOLTTRON Building Energy Management System for Cloud Native Deployment

Managing the energy consumption of the built environment is an important source of flexible load and decarbonization, enabling building managers and utilities to schedule consumption to avoid costly demand charges and peak times when carbon emissions from grid generated electricity are highest. A key technology component in building energy management is the building energy management system. Eclipse VOLTTRON is a legacy software platform which enables building energy management. It was developed for the US Department of Energy (DOE) at Pacific Northwest National Labs (PNNL) written in Python and based on a monolithic build-configure-and-run-in-place system architecture that predates cloud native architectural concepts. Yet the software architecture is componentized in a way that anticipates modular containerized applications, with software agents handling functions like data storage, web access, and communication with IoT devices over specific IoT protocols such as BACnet and Modbus. The agents communicate among themselves over a message bus. This paper describes a proof-of-concept prototype to rearchitect VOLTTRON into a collection of microservices suitable for deployment on the Kubernetes cloud native container orchestration platform. The agents are packaged in redistributable containers that perform specific functions and which can be configured when they are deployed. The deployment architecture consists of single Kubernetes cluster containing a central node, nominally in a cloud-based VM, where a microservice containing the database agent (called a “historian”) and the web site agent for the service run, and remote site nodes running on sites in buildings where a microservice containing IoT protocol-specific agents handles control and data collection to and from devices, and communication back to the central node.
A New Query Expansion Algorithm for Enriching the AGROVOC Vocabulary

Query expansion algorithms provide the ability to extend the vocabulary of knowledge sources, such as ontologies and knowledge graphs, and thus improve the performance of natural language processing tasks that rely on them. These algorithms are built on the automatic analysis of the relationships between words to generate new synonyms. The objective of this study is to propose a new query expansion algorithm to enrich the AGROVOC vocabulary, by taking advantage of recent advances in natural language processing. Our proposal is executed in two main steps, namely synonym candidate generation and valid synonym prediction. In the first step, our approach generates a set of synonym candidates, based on the knowledge on the hierarchical relationships in AGROVOC. To improve the quality of generated synonyms, in the second step our approach leverages the AgricultureBERT model to predict the most similar synonyms to the existing terms in AGROVOC. Only candidate synonyms whose AgricultureBERT encoding vectors are similar to those of the ontological terms are considered valid. From 17500 randomly selected AGROVOC concepts, our method extended AGROVOC with 2426 new candidates and predicted that 83% of these candidates were high-quality synonyms. A domain expert manually validated a random sample of the predicted synonyms and a precision of 92% was achieved. From the results, we can conclude that our query expansion algorithm predicts high-quality multiword synonyms, which has a positive impact on improving the clarity and accessibility of AGROVOC.