Miracle-dz/UniFAR-FTUs huggingface dataset:
Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems

Continuous integration and delivery (CI/CD) have been shown to be very useful to improve the quality of software products (e.g., increasing their reliability or maintainability), and their development processes, e.g., by shortening release cycles. Applying CI/CD in the context of Cyber-Physical Systems (CPSs) can be particularly important, given that many of those systems can have safety-critical properties, and given their interaction with hardware or simulators during the development phase. This paper empirically analyzes how CI/CD is enacted in CPSs when considering the context of open-source projects, that often (also) rely on hosted CI/CD so-lutions, and benefit of an open-source development community. We qualitatively analyze a statistically significant sample of 670 pull requests from 20 open-source CPSs hosted on GitHub, to identify and categorize-also keeping into account catalogs from previous literature-bad practices, challenges, mitigation, and restructuring actions. The study reports and discusses the relationships we found between bad practices/challenges and CI/CD restructuring/mitigation strategies, reporting concrete examples, especially those emerging from the intrinsic complexity of CPSs.
Deep Learning Based on Ensemble to Diagnose of Retinal Disease using Optical Coherence Tomography

Optical coherence tomography (OCT) image plays an essential modality role in medical image analysis to diagnose various retinal diseases. Through the retinal cross-sectional OCT images, ophthalmologists need much time and effort in manual analysis to extract diagnostic features. In this study, we propose a method for the automatic diagnosis of five retinal diseases based on an ensemble of two transfer learning models to analyze OCT images. The individual predicted class probabilities of MobileNetV3Large are fused with the predicted class probabilities of ResNet50 to ensure robustness in the prediction. An image processing technique such as contrast limited adaptive histogram equalization (CLAHE) is applied to enhance the quality of OCT images before feeding them to neural network models. In addition, 5-fold cross-validation and EarlyStopping techniques are also used to prevent the overfitting of the training model. To conduct the experiment, a dataset containing 1999 OCT images is constructed.
An Overview of Selected Autoencoders and Their Potential Application in Smart Cities

The following work gives an overview of a special type of neural networks, autoencoders, that can be of great interest to researchers and practitioners in the field of smart city, due to their numerous application possibilities in this context. Given the fact that these networks can be trained in an unsupervised fashion, autoencoders are immediately applicable to practically collected data sets that often lack labels, not requiring the tedious process of data labeling. In addition to the classical autoencoder, we present two other types, and highlight their differences in architecture and in areas of application. In doing so, the benefits of the respective autoencoders and their possible application, especially in the context of smart cities, are presented.
Glowinski-Pironneau method for the 3D ω-Ψ equations

Glowinski-Pironneau method for solving the 2D Stokes problem as two uncoupled scalar Poisson equations for the vorticity and the stream function is extended to the three-dimensional problem. The determination of the two tangential components of vorticity over the boundary is achieved by solving an auxiliary boundary problem characterized by a symmetric definite positive linear operator. In the discrete case, the explicit determination of the corresponding matrix and/or its solution involves the computation of solenoidal fields for the vorticity and the stream vector which are solution to Poisson equations supplemented by both essential and natural boundary conditions, the latter implying a coupling between the three Cartesian components of each vector unknown.
The Ineffectiveness of Domain-Specific Word Embedding Models for GUI Test Reuse

Reusing test cases across similar applications can significantly reduce testing effort. Some recent test reuse approaches successfully exploit word embedding models to semantically match GUI events across Android apps. It is a common understanding that word embedding models trained on domain-specific corpora perform better on specialized tasks. Our recent study confirms this understanding in the context of Android test reuse. It shows that word embedding models trained with a corpus of the English descriptions of apps in the Google Play Store lead to a better semantic matching of Android GUI events. Motivated by this result, we hypothesize that we can further increase the effectiveness of semantic matching by partitioning the corpus of app descriptions into domain-specific corpora. Our experiments do not confirm our hypothesis. This paper sheds light on this unexpected negative result that contradicts the common understanding.
Enhancing the Resolution of Satellite Imagery Using a Generative Model

Recent breakthroughs in deep learning algorithms introduced the image super-resolution technique that maps the low-resolution image to generate a high-resolution image. These techniques increase various surveillance applications by providing finer spatial details than data from original sensors. Satellite images obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) observation offer essential information about the earth’s landscape, ocean, and ecosystem, contributing to monitoring various applications in the scientific field. The spatial resolution of satellite images has a significant impact on image accuracy. This paper focuses on improving image resolution by training a convolutional neural network to produce super-resolution images from low-resolution images. We present an implementation of Super Resolution Generative Adversarial Network (SRGAN), a GAN-based approach that uses a perceptual loss function that includes an adversarial loss and a content loss. Using a discriminator network that is designed for discerning between super-resolved images and original photo-realistic images, the adversarial loss drives the solution of this architecture to natural images. Moreover, the content loss is driven by perceptual similarity rather than pixel space similarity. We used this architecture to satellite images collected from NASA MODIS devices and found satisfactory results. Our key finding is that our system’s result can now be used to improve a variety of low-resolution images.
Sixth International Joint Conference on Autonomous Agents and Multiagent Systems

Registration 8 am 5 pm, 3rd floor Registration 8 am 2 pm, 5-8 pm, 3rd floor Registration 7:30 am 5 pm, 3rd floor Registration 7:30 am 2 pm, 3rd floor Registration 8 am 12 noon, 3rd floor 8:15 303A Exhibits 8 am 5 pm 303A Exhibits 8 am 5 pm 303A Exhibits 8 am 5 pm 8:30 303B Agent Reputation and Trust, 10 4 303B Agent Reputation and Trust, 10 4 301B TRUST workshop ART papers ALAg 317A T1 COOP 307A ABSHLE 303A T7 DESIGN (AM) 307A 8:45 311 ABUC 303A T2 LANG 307B AP2PC 303B T8 NEGOTIATE(AM) 307B 9:00 311 Invited Talk 1 311 311 AMEC 303B T4 PLAN(AM) 305A ARGMAS 305A T9 DEVEL(AM) 308A AOSE 301A CCMMS 305B ATSN 301B MABS 317B COIN 305B ProMAS 301A DALT 308A TRUST 301B 10:30 313A 313A 313A MRABS 317B CAPS(AM) 308B 313B 313B 313B SOCASE 308B 313C 313C 313C 10:30 301 301 301 312 312 312 11:00 workshops tutorials workshops tutorials 317 continue continue continue continue 12:00
Weakly Supervised Crowdsourcing Learning Based on Adversarial Consensus

Crowdsourcing provides an efficient way to obtain labels for large datasets in the deep learning era. However, due to the non-expert workers, the annotations are usually noisy.Besides, concerning the labeling cost, sparse annotations are common. To face this challenge, we propose an approach based on adversarial consensus, which trains one classifier for each worker, and enforces their predictions over the ground-truth labels to be maximally consistent by exploiting the generative adversarial learning idea. We give two implementations respectively for the light and heavy noise cases. Extensive experiments on real-world and synthetic datasets demonstrate the effectiveness of our approach.
SYMTUNER: Maximizing the Power of Symbolic Execution by Adaptively Tuning External Parameters

We present SYMTUNER, a novel technique to automatically tune external parameters of symbolic execution. Practical symbolic execution tools have important external parameters (e.g., symbolic arguments, seed input) that critically affect their performance. Due to the huge parameter space, however, manually customizing those parameters is notoriously difficult even for experts. As a consequence, symbolic execution tools have typically been used in a suboptimal manner that, for example, simply relies on the default parameter settings of the tools and loses the opportunity for better performance. In this paper, we aim to change this situation by automatically configuring symbolic execution parameters. With Symtuner that takes parameter spaces to be tuned, symbolic executors are run without manual parameter configurations; instead, appropriate parameter values are learned and adjusted during symbolic execution. To achieve this, we present a learning algorithm that observes the behavior of symbolic execution and accordingly updates the sampling probability of each parameter space. We evaluated Symtuner with KLEE on 12 open-source C programs. The results show that Symtuner increases branch coverage of KLEE by 56% on average and finds 8 more bugs than KLEE with its default parameters over the latest releases of the programs.
Fusing User Reviews into Heterogeneous Information Network Recommendation Model

With the advent of the information epoch and the development of Big Data, users are constantly overwhelmed in massive information online. As an effective method to deal with this dilemma of information overload, recommendation system has become a popular area of research for the past few years. However, most of the current recommendation models fail to make the most of the ample resources hidden behind auxiliary data and user reviews. For this reason, we put forward the FHRec model. We try to combine heterogeneous information network with deep learning technology to improve recommendation performance. The working mechanism is to represent the rich auxiliary data by using heterogeneous information network, learning the features of entities through network embedding; in the meantime, by adopting deep learning technology to mine the features of entities from review texts, using the attention mechanism to fuse entity feature vectors, inputting them into the neural network for recommendation; the last step is that we use Yelp dataset and Douban movie dataset to verify the FHRec model. The experimental results show that the FHRec behaves better than the traditional comparison algorithm on the Yelp dataset and Douban movie dataset.
Complex Python Features in the Wild

While Python is increasingly popular, program analysis tooling for Python is lagging. This is due, in part, to complex features of the Python language-features with difficult to understand and model semantics. Besides the “usual suspects”, reflection and dynamic execution, complex Python features include context managers, decorators, and generators, among others. This paper explores how often and in what ways developers use certain complex features. We analyze over 3 million Python files mined from GitHub to address three research questions: (i) How often do developers use certain complex Python features? (ii) In what ways do developers use these features? (iii) Does use of complex features increase or decrease over time? Our findings show that usage of dynamic features that pose a threat to static analysis is infrequent. On the other hand, usage of context managers and decorators is surprisingly widespread. Our actionable result is a list of Python features that any “minimal syntax” ought to handle in order to capture developers' use of the Python language. We hope that understanding the usage of Python features will help tool-builders improve Python tools, which can in turn lead to more correct, secure, and performant Python code.
Middleware to Integrate Patient Data from Heterogeneous Distributed Databases and Its Efficacy

The health organizations store the patient data in different repositories and scattered in diverse locations. In the healthcare domain, the problem is that each hospital or even each department under a hospital maintains its own database having various data models (SQL, NoSQL, etc.). In this situation, existing or new applications require to grant healthcare actors to locate and share patient data from those pre-existing distributed databases (DDBs) remotely for the needs of patient quality treatment, daily operations of the health centers. However, data integration from distributed data sources is raising concern for data model variability. Therefore, it is significant to identify that how much an application like middleware is efficient to reconstruct and share patient data remotely from heterogeneous DDBs over the networks. The health organizations could also require to ensure whether their existing database model performs well or should replace by another one. So, this paper aims to design a system using different databases consisting of distinct data structures and an algorithm for middleware to integrate data from them with testing the system performance. The experimental results of this research work show that the patient data could be shared from various distributed data sources efficiently. Therefore the study could direct the healthcare organizations for sharing patient data from heterogeneous distributed databases without replacing the existing data model.
A data science approach to readability

In this paper we study and test a novel hypothesis: namely, sentence complexity is a measure of readability. Our goal is not to replace nor necessarily improve readability tests but to investigate an experimental, data science application of word complexity. Word complexity is the number of words needed to define a word according to a reference dictionary. Sentence complexity is the aggregation or reduction of word complexity for words in a sentence. With the aid of natural language processing, we test our hypothesis using a corpus of 24 classic fairy tales. We chose these stories for their accessibility, popularity, and brevity. The data shows their reading difficulty to be centered around the 6th-7th grade level. The data also show the Flesch-Kincaid test and sentence complexity are negatively correlated in reading ease (r=-0.31, P<0.01) and positively correlated in grade level (r=0.46, P<0.01), respectively. That the correlations are moderate suggests sentence complexity is related to Flesch-Kincaid but not a proxy for it. Finally, the data further show that sentence complexity is weakly correlated with maximum number of syllables in a sentence (r=0.12, P<0.01), weakly correlated with maximum word length in a sentence (r=0.20, P<0.01), and moderately correlated with the number of words in a sentence (r=0.52, P<0.01). These findings support our hypothesis.
Is Refactoring Always a Good Egg? Exploring the Interconnection Between Bugs and Refactorings

Bug fixing and code refactoring are two distinct maintenance actions with different goals. While bug fixing is a corrective change that eliminates a defect from the program, refactoring targets improving the internal quality (i.e., maintainability) of a software system without changing its functionality. Best practices and common intuition suggest that these code actions should not be mixed in a single code change. Furthermore, as refactoring aims for improving quality without functional changes, we would expect that refactoring code changes will not be sources of bugs. Nonetheless, empirical studies show that none of the above hypotheses are necessarily true in practice. In this paper, we empirically investigate the interconnection between bug-related and refactoring code changes using the SmartSHARK dataset. Our goal is to explore how often bug fixes and refactorings co-occur in a single commit (tangled changes) and whether refactoring changes themselves might induce bugs into the system. We found that it is not uncommon to have tangled commits of bug fixes and refactorings; 21% of bug-fixing commits include at least one type of refactoring on average. What is even more shocking is that 54% of bug-inducing commits also contain code refactoring changes. For instance, 10% (652 occurrences) of the Change Variable Type refactorings in the dataset appear in bug-inducing commits that make up 7.9% of the total inducing commits.
Influence of n+ and p+ doping on the lattice sites of implanted Fe in Si

We report on the lattice location of implanted 59Fe in n+- and p+-type Si by means of emission channeling. We found clear evidence that the preferred lattice location of Fe changes with the doping of the material. While in n+-type Si Fe prefers displaced bond-centered (BC) sites for annealing temperatures up to 600 °C, changing to ideal substitutional sites above 700 °C, in p+-type Si, Fe prefers to be in displaced tetrahedral interstitial positions after all annealing steps. The dominant lattice sites of Fe in n+-type Si therefore seem to be well characterized for all annealing temperatures by the incorporation of Fe into vacancy-related complexes, either into single vacancies which leads to Fe on ideal substitutional sites, or multiple vacancies, which leads to its incorporation near BC sites. In contrast, in p+-type Si, the major fraction of Fe is clearly interstitial (near-T or ideal T) for all annealing temperatures. The formation and possible lattice sites of Fe in FeB pairs in p+-Si are discussed.
SOSum: A Dataset of Stack Overflow Post Summaries

Stack Overflow (SO) is becoming an indispensable part of modern software development workflow. However, given the limited time, attention, and memory capacity of programmers, navigating SO posts and comparing different solutions is time-consuming and cumbersome. Recent research has proposed to summarize SO posts to concise text to help programmers quickly assess the relevance and quality of SO posts. Yet there is no large dataset of high-quality SO post summaries, hindering the development and evaluation of post summarization techniques. We present SOSum, a dataset of 2,278 popular SO answer posts with manually labeled summative sentences. Questions in SOSum cover 669 tags with a median view count of 253K and a median post score of 17. This dataset will foster research on sentence-level summarization of SO posts and has the potential to facilitate text summarization research on other types of textual software artifacts such as programming tutorials.
A High-Performance 5G/6G Infrastructure for Augmented, Virtual, and Extended Reality

Fully developed 5G application delivery services at the mobile edge with reliable "round trip" low latency such as the "motion to photon" measurements in augmented, virtual, and extended reality (AR/VR/XR) systems are not commercially available yet. In fact, these features may well be a part of 6G infrastructures since viable business models, applications, and supporting technology such as military tactical headsets capable of AR/VR/XR in day/night operations are just coming to market and becoming available. Additionally, the first generations of AR/VR devices and 5G infrastructure have provided lessons-learned for technology developers eager to field useful systems with customer appeal. This paper describes the infrastructure for one of the Department of Defense 5G programs focused on AR/VR for military training.
The Unsolvable Problem or the Unheard Answer? A Dataset of 24,669 Open-Source Software Conference Talks

Talks at practitioner-focused open-source software conferences are a valuable source of information for software engineering researchers. They provide a pulse of the community and are valuable source material for grey literature analysis. We curated a dataset of 24,669 talks from 87 open-source conferences between 2010 and 2021. We stored all relevant metadata from these conferences and provide scripts to collect the transcripts. We believe this data is useful for answering many kinds of questions, such as: What are the important/highly discussed topics within practitioner communities? How do practitioners interact? And how do they present themselves to the public? We demonstrate the usefulness of this data by reporting our findings from two small studies: a topic model analysis providing an overview of open-source community dynamics since 2011 and a qualitative analysis of a smaller community-oriented sample within our dataset to gain a better understanding of why contributors leave open-source software.
Is Open Source Eating the World's Software? Measuring the Proportion of Open Source in Proprietary Software Using Java Binaries

That open source software comprises an increasingly large percentage of modern software applications has become conventional wisdom. The exact extent to which open source software constitutes today's applications is indeterminate, however, at least by the standards of the academic software engineering research community. This paper proposes a methodology and associated tool that can analyze Java binaries and determine the proportion of open source that comprises them. This paper also presents empirical measurements of 5 commercial Java software systems, reporting OSS proportions between 76.2% to 99.9% among these 5 systems, including a historical analysis covering 6 versions and 12 years for one of the subject systems.
Design Methodology of Bidirectional Resonant CLLC Charger for Wide Voltage Range Based on Parameter Equivalent and Time Domain Model

The bidirectional CLLC resonant converter has distinguished potential in battery chargers and energy storage systems for its advantages in soft switching and bidirectional power flow capability. However, traditional CLLC converters generally adopt symmetrical design to maintain bidirectional symmetrical characteristics, which means the secondary LC network is designed to be equal to the primary LC network after reflection. The symmetrical design is only suitable for voltage grade matching scenarios where a wide voltage range is not required, such as CLLC-type dc transformers. Whereas in the field of bidirectional chargers, due to the wide voltage range of battery, there are significant differences in the characteristics required between charging and discharging mode, which makes symmetrical design no longer applicable. To cope with this issue, this paper proposes a novel design methodology of CLLC based on parameter equivalent and time domain model. With the parameter equivalent principle, the CLLC resonant tank with arbitrary parameters is investigated to satisfy the requirements of wide voltage range for bidirectional charger application. Compared with the symmetrical design, the proposed method can meet the requirements of bidirectional gain within preset frequency range and guarantee the achievement of zero-voltage switching under required load conditions with the minimum reactive power. In addition, the area product capacity of the magnetic part of the CLLC resonant tank is minimized based on the parameter equivalent principle. Finally, experiments have been performed on a 1 kW prototype to confirm the validity and feasibility of the proposed design methodology.