Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of HMDSO Addition on Discharge Characteristics and Uniformity of Nanosecond-Pulsed Plasma Jet Array

Atmospheric-pressure plasma jet (APPJ) array is considered as an efficient method to enlarge treatment and improve treatment efficiency. With the addition of precursors containing Si, the APPJ array can be used for improving the surface hydrophobicity and insulating performance of insulating materials. In this article, the influence of hexamethyl disiloxane (HMDSO) addition on the discharge characteristics and uniformity of a 1-D nanosecond-pulsed jet array in Ar has been investigated through electrical and optical methods. It is found that the addition of HMDSO in a small amount can promote the plume length, discharge power, transported charge, and emission intensity through the Penning effect. When the HMDSO content is higher than 0.04%, the discharge of jet array is weaken, especially the middle unit, which results in the decrease of the uniformity, plume length, discharge power, transported charge, and emission intensity. With the intensified charge-coupled device (ICCD) measurement of the ionization wave propagations of jet arrays in pure Ar, 0.04%, and 0.50% HMDSO, it shows that the 0.04% HMDSO enhances the spatial electric field strength in jet array and promotes plasma bullets propagation. However, more HMDSO addition reduces the space charges and plasma bullets propagation speed. The competition of Penning effect and electron absorption by the HMDSO addition results that the Ar jet array has maximum values of plume length, discharge power, transported charge, and emission intensity at 0.04% HMDSO content.
Characterizing Human Aspects in Reviews of COVID-19 Apps

To successfully satisfy user needs, software developers need to suitably capture and implement user requirements. A critical and often overlooked characteristic of user requirements are “human aspects”, which are personal circumstances affecting the use of software (e.g., age, gender, language, etc.). To better understand how human aspects can impact the use of software, this work presents an empirical study focusing on app reviews of COVID-19 contact tracing apps. We manually analyzed a dataset of 2,611 app reviews sampled from the reviews associated with 57 COVID-19 apps. To analyze the reviews, we performed qualitative and quantitative analyses. The analyses characterize the human aspects contained in the reviews and investigate whether the apps suitably address the human aspects. We identified 716 reviews related to human aspects and grouped these into nine categories. Of these 716 reviews, 8% report bugs, 14% describe future/improvement requests, and 22% detail the user experience. Our analysis of the results reveal that human aspects are important to users and we need better support to account for them as software is developed.
Training Data Debugging for the Fairness of Machine Learning Software

With the widespread application of machine learning (ML) software, especially in high-risk tasks, the concern about their unfairness has been raised towards both developers and users of ML software. The unfairness of ML software indicates the software behavior affected by the sensitive features (e.g., sex), which leads to biased and illegal decisions and has become a worthy problem for the whole software engineering community. According to the “data-driven” programming paradigm of ML software, we consider the root cause of the unfairness as biased features in training data. Inspired by software debugging, we propose a novel method, Linear-regression based Training Data Debugging (LTDD), to debug feature values in training data, i.e., (a) identify which features and which parts of them are biased, and (b) exclude the biased parts of such features to recover as much valuable and unbiased information as possible to build fair ML software. We conduct an extensive study on nine data sets and three classifiers to evaluate the effect of our method LTDD compared with four baseline methods. Experimental results show that (a) LTDD can better improve the fairness of ML software with less or comparable damage to the performance, and (b) LTDD is more actionable for fairness improvement in realistic scenarios.
So who is impacted anyway - a preliminary study of indirect stakeholder identification in practice

Due to the proliferation of disruptive technologies such as AI into almost every aspect of modern society, software systems increasingly affect the lives of people who do not directly use these systems -with potentially serious and harmful consequences. However, current software development practices do not yet account for this trend sufficiently well and frequently overlook indirect stakeholders. This paper presents the results of a preliminary interview-based study of software professionals aimed at understanding the state-of-practice of indirect stakeholder identification in the software industry. Our initial findings confirm that indirect stakeholders are often overlooked due to customer expectations, project constraints, the prevailing technology-centric software engineering culture and a lack of practical methods and tools. Based on these findings, we outline a roadmap for the investigation of methods and tools for the effective and efficient identification of indirect stakeholders.
Missing Value Recovery for Single Cell RNA Sequencing Data

The emergence of single-cell sequencing technologies has enabled the production of high-resolution data at the individual cell level, providing unprecedented opportunities to capture cell population diversity and dissect the cellular heterogeneity of complex diseases. At the same time, relatively high biological and technical noise poses new challenges for single-cell data analysis. The single-cell RNA sequencing (scRNA-seq) data often contains substantial missing values due to gene dropout events. Here, we developed a convolutional neural network based model to recover missing values for scRNA-seq data. We first calculated the probability of dropout employing gamma-normal expectation maximum algorithm. Unlike most existing approaches, our model only recovered the expression values that have a dropout probability larger than a threshold. The mean square error and Pearson correlation coefficient were used to assess the accuracy of predicted expression values. The purity and entropy were computed to measure the homogeneity of cell clusters using imputed gene expression profiles. Across various scRNAseq datasets, our model demonstrated robust performance and achieved comparable or better results compared to the other imputation methods.
aMDH and TWIN: Two original honeypot-based approaches to protect swarms of drones

Drones and swarms of drones are now considered an additional tool for both civilian and military applications. As any computer-based system they can thus be (and are) the target of attacks and the consequences of such attacks can be dramatic for assets and people. We believe an approach based on honeypots that would attract the attention of attackers and would behave so that these attackers could not even understand they are in a honeypot and not in a real drone, would be a significant step towards the protection of these systems. Even though some prototypes exist, they do not fully address the fact of luring the attacker to believe he/she controls a real drone. In this paper we present our work to address this issue.
How developers feel about tools: an investigation on software startup professionals experience with virtual kanban boards

Virtual kanban board is a tool widely used by software teams. The board provides a rich source of information about the teams' work and the project work-in-progress. Software startups often work with small teams with few resources and under constant time pressure. In this fast-paced environment of startups, the virtual kanban boards are helpful to improve communication keeping the whole organization members informed about the workflow and the allocation of tasks. However, startup software professionals often have little experience, and consequently, they can have difficulties maximizing the benefits of the virtual kanban to manage their work. In this paper, we present an empirical study with software startup professionals in which we analyzed qualitative data using the developer experience framework. This framework allows us to find out how development-related professionals perceive the resources of the virtual kanban tools and feel and see the board's contribution to the activities of their daily work. Our results revealed that the participants agreed that the virtual kanban boards are essential to tracking ongoing software projects and performing their work. However, they reported difficulties handling the tools, which can cause the professionals frustration. Besides, our findings showed that the participants saw the kanban virtual boards as a stimulus for their autonomy in routine activities. As the main contribution, our work provides a set of startup-related recommendations that can help startup teams better use virtual kanban board resources and, consequently, impact the software professionals' experience.
Dynamic model change analysis of social behavior processes during the COVID19 pandemic

In this paper, an analysis of changes in dynamic process models described by variables that represent social behavior from the point of view of people’s mobility and of economic indices in the framework of the COVID19 pandemic is presented. Here, the mobility described by Google and Apple is used as a proxy for the social behavior to correlate it with the dynamic evolution of daily COVID19 infections. In addition, indices related from the global economy are used as a proxy of the socio-economic process, where two of ascending evolution (MSFT Microsoft and NASDAQ, Inc.) and another with smooth evolution (WTI oil gallon price) are analyzed. The evolution of such proxies are related to the daily COVID19 cases. In the latter case, it is difficult to detect a territorial region of influence given the number of origins of influences that the selected indices have, but the impact of the first peak in China and the subsequent evolution in the world can be studied, especially in our country and in the Netherlands. The main findings include that the underlying model for social behavior has changed in different stages, depending on the months of the year and that after mid-2021 an unstable equilibrium is on the track, with the addition of the new possibilities provided by the vaccination process and the rules of social coexistence. It is concluded that it is necessary to analyze which decision should be taken at the social level of public policy and which personal decisions for each individual.
Realistic Deployment of Hybrid Wireless Sensor Networks based on ZigBee and LoRa for Search and Rescue Applications

Search and Rescue operations in emergency response to natural or human catastrophes have the main objective of locating and rescuing potential victims as fast as possible, thus quick response and accurate actions being mandatory. While standard communications may be affected, a Wireless Sensor Network can be deployed to support the rescue team. This kind of network allows data acquisition close to events and enable persistence over time, among other advantages. However, to adapt to the requirements of this kind of scenario, enhancements must be made. This work presents two Hybrid Wireless Sensor Networks, based on ZigBee and LoRa, developed to address some of the challenges that Search and Rescue operations pose to the use of Wireless Sensor Networks, and tested in realistic scenarios in cooperation with first responders. Likewise, several software developments that increase the performance of the networks are described. Finally, the conclusions presented, and the lessons learnt are supported by a high amount of data, gathered in realistic exercises in cooperation with civilian and military first responders.
Network Intrusion Detection System Using Principal Component Analysis Algorithm and Decision Tree Classifier

Network Intrusion Detection Systems (IDS) have become expedient for network security and ensures the safety of all connected devices. Network Intrusion Detection System (IDS) alludes to observing network data information swiftly, detecting any intrusion pattern and preventing any harmful effect of anomaly intrusion that will cost the network. To combat this issue, we present in this concept paper an IDS based on the Principal Component Analysis (PCA) and Decision Tree Classifier algorithm, a supervised machine learning model to detect intrusion in the Network.
Extending EcoAndroid with Automated Detection of Resource Leaks

When developing mobile applications, developers often have to decide when to acquire and when to release resources. This leads to resource leaks, a kind of bug where a resource is acquired but never released. This is a common problem in Android applications that can degrade energy efficiency and, in some cases, can cause resources to not function properly. In this paper, we present an extension of EcoAndroid, an Android Studio plugin that improves the energy efficiency of Android applications, with an inter-procedural static analysis that detects resource leaks. Our analysis is implemented using Soot, FlowDroid, and Heros, which provide a static-analysis environment capable of processing Android applications and performing inter-procedural analysis with the IFDS framework. It currently supports the detection of leaks related to four Android resources: Cursor, SQLite-Database, Wakelock, and Camera. We evaluated our tool with the DroidLeaks benchmark and compared it with 8 other resource leak detectors. We obtained a precision of 72.5% and a recall of 83.2%. Our tool was able to uncover 191 previously unidentified leaks in this benchmark. These results show that our analysis can help developers identify resource leaks.
C for Yourself: Comparison of Front-End Techniques for Formal Verification

With the improvement of hardware and algorithms, the main challenge of software model checking has shifted from pure algorithmic performance toward supporting a wider set of input programs. Successful toolchains tackle the problem of parsing a wide range of inputs in an efficient way by reusing solutions from existing compiler technologies such as Eclipse CDT or LLVM. Our experience suggests that well-established techniques in compiler technology are not necessarily beneficial to model checkers and sometimes can even hurt their performance. In this paper, we review the tools mature enough to participate in the Software Verification Competition in terms of the employed analysis and frontend techniques. We find that successful tools do exhibit a bias toward certain combinations. We explore the theoretical reasons and suggest an adaptable approach for model checking frameworks. We validate our recommendations by implementing a new frontend for a model checking framework and show that it indeed benefits some of the algorithms.
Towards Reliable Agile Iterative Planning via Predicting Documentation Changes of Work Items

In agile iterative development, an agile team needs to analyze documented information for effort estimation and sprint planning. While documentation can be changed, the documentation changes after sprint planning may invalidate the estimated effort and sprint plan. Hence, to help the team be aware of the potential documentation changes, we developed DocWarn to estimate the probability that a work item will have documentation changes. We developed three variations of DocWarn, which are based on the characteristics extracted from the work items (DocWarn-C), the natural language text (DocWarn-T), and both inputs (DocWarn-H). Based on nine open-source projects that work in sprints and actively maintain documentation, DocWarn can predict the documentation changes with an average AUC of 0.75 and an average F1-Score of 0.36, which are significantly higher than the baselines. We also found that the most influential characteristics of a work item for determining the future documentation changes are the past tendency of the developers and the length of description text. Based on the qualitative assessment, we found that 40%-68% of the correctly predicted documentation changes were related to scope modification. With the prediction of DocWarn, the team will be better aware of the potential documentation changes during sprint planning, allowing the team to manage the uncertainty and reduce the risk of unreliable effort estimation and sprint planning.
DISCO: A Dataset of Discord Chat Conversations for Software Engineering Research

Today, software developers work on complex and fast-moving projects that often require instant assistance from other domain and subject matter experts. Chat servers such as Discord facilitate live communication and collaboration among developers all over the world. With numerous topics discussed in parallel, mining and analyzing the chat data of these platforms would offer researchers and tool makers opportunities to develop software tools and services such as automated virtual assistants, chat bots, chat summarization techniques, Q&A thesaurus, and more. In this paper, we propose a dataset called DISCO consisting of the one-year public DIScord chat COnversations of four software development communities. We have collected the chat data of the channels containing general programming Q&A discussions from the four Discord servers, applied a disentanglement technique [13] to extract conversations from the chat transcripts, and performed a manual validation of conversations on a random sample (500 conversations). Our dataset consists of 28, 712 conversations, 1,508,093 messages posted by 323, 562 users. As a case study on the dataset, we applied a topic modelling technique for extracting the top five general topics that are most discussed in each Discord channel.
The application of artificial intelligence on different types of literature reviews - A comparative study

The growing number of published academic literature poses challenges to the research community which struggles to keep up with the vast amount of publications through traditional research methods that are highly manual in nature. Researchers are struggling to determine the most relevant research gaps, yielding insignificant publications that constitute a waste of resources. As a consequence, AI applications are being applied increasingly to automate and facilitate the review process of these vast amounts of papers. However, scholars have so far only addressed a limited number of scientific fields and focused their efforts on one end of the spectrum in automating systematic literature reviews (SLRs). Yet, these are not sufficient to cover the full range of research questions and available data sources. This paper offers a comparative study of systematic and semi-systematic literature reviews to determine the potential of AI applications in both types of literature review processes. The analysis addresses the status quo and discusses apparent limitations of AI to automate reviews. Results are synthesized in proposing a new tool integrating various AI applications along the research process that improve the speed, quality, and cost-efficiency of the overall research process.
Analyzing User Perspectives on Mobile App Privacy at Scale

In this paper we present a methodology to analyze users‘ con-cerns and perspectives about privacy at scale. We leverage NLP techniques to process millions of mobile app reviews and extract privacy concerns. Our methodology is composed of a binary clas-sifier that distinguishes between privacy and non-privacy related reviews. We use clustering to gather reviews that discuss similar privacy concerns, and employ summarization metrics to extract representative reviews to summarize each cluster. We apply our methods on 287M reviews for about 2M apps across the 29 cate-gories in Google Play to identify top privacy pain points in mobile apps. We identified approximately 440K privacy related reviews. We find that privacy related reviews occur in all 29 categories, with some issues arising across numerous app categories and other issues only surfacing in a small set of app categories. We show empirical evidence that confirms dominant privacy themes - concerns about apps requesting unnecessary permissions, collection of personal information, frustration with privacy controls, tracking and the selling of personal data. As far as we know, this is the first large scale analysis to confirm these findings based on hundreds of thousands of user inputs. We also observe some unexpected findings such as users warning each other not to install an app due to privacy issues, users uninstalling apps due to privacy reasons, as well as positive reviews that reward developers for privacy friendly apps. Finally we discuss the implications of our method and findings for developers and app stores.
Simple or Complex? Together for a More Accurate Just-In-Time Defect Predictor

Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can be classified into two categories: 1) simple models using traditional machine learning classifiers with hand-crafted features, and 2) complex models using deep learning techniques to automatically extract features. Hand-crafted features used by simple models are based on expert knowledge but may not fully represent the semantic meaning of the commits. On the other hand, deep learning-based features used by complex models represent the semantic meaning of commits but may not reflect useful expert knowledge. Simple models and complex models seem complementary to each other to some extent. To utilize the advantages of both simple and complex models, we propose a combined model namely SimCom by fusing the prediction scores of one simple and one complex model. The experimental results show that our approach can significantly outperform the state-of-the-art by 6.0-18.1%. In addition, our experimental results confirm that the simple model and complex model are complementary to each other.
Branching Programs and Binary Deci-sion Diagrams-Theory and Applications

Preface Introduction 1. Introduction 2. BPs and Decision Trees (DTs) 3. Ordered Binary Decision Diagrams (OBDDs) 4. The OBDD Size of Selected Functions 5. The Variable-Ordering Problem 6. Free BDDs (FBDDs) and Read-Once BPs 7. BDDs with Repeated Tests 8. Decision Diagrams (DDs) Based on Other Decomposition Rules 9. Integer-Valued DDs 10. Nondeterministic DDs 11. Randomized BDDs and Algorithms 12. Summary of the Theoretical Results 13. Applications in Verification and Model Checking 14. Further CAD Applications 15. Application in Optimization, Counting, and Genetic Programming Bibliography Index.
An Efficient CNN-based Approach for Automatic Brazilian License Plate Recognition

Automatic License Plate Recognition (ALPR) is increasingly becoming the target of many studies in computer vision due to its great applicability in urban environments. In Brazil, a new type of license plate called Mercosul started being used in 2020, and only few works proposed to include the detection of this kind of plate. To address this issue, we trained models using approximately sixty-five thousand unlabeled images from the Federal Highway Police (PRF) captured by the institution’s radars. Using three Convolutional Neural Networks (CNN) it was possible to build an ALPR system with an accuracy of 97.91% in the UFPR-ALPR Dataset, 95% in the SSIG SegPlate Dataset and 79.77 % in the UFRN Dataset, presented in this paper. These results outperform the most reliable studies for Brazilian license plates so far.