Miracle-dz/UniFAR-FTUs huggingface dataset:
AutoTransform: Automated Code Transformation to Support Modern Code Review Process

Code review is effective, but human-intensive (e.g., developers need to manually modify source code until it is approved). Recently, prior work proposed a Neural Machine Translation (NMT) approach to automatically transform source code to the version that is reviewed and approved (i.e., the after version). Yet, its performance is still suboptimal when the after version has new identifiers or literals (e.g., renamed variables) or has many code tokens. To address these limitations, we propose AutoTransform which leverages a Byte-Pair Encoding (BPE) approach to handle new tokens and a Transformer-based NMT architecture to handle long sequences. We evaluate our approach based on 14,750 changed methods with and without new tokens for both small and medium sizes. The results show that when generating one candidate for the after version (i.e., beam width = 1), our AUTOTRANSFORM can correctly transform 1,413 changed methods, which is 567% higher than the prior work, highlighting the substantial improvement of our approach for code transformation in the context of code review. This work contributes towards automated code transformation for code reviews, which could help developers reduce their effort in modifying source code during the code review process.
ReCover: a Curated Dataset for Regression Testing Research

It is recognized in the literature that finding representative data to conduct regression testing research is non-trivial. In our experience within this field, existing datasets are often affected by issues that limit their applicability. Indeed, these datasets often lack fine-grained coverage information, reference software repositories that are not available anymore, or do not allow researchers to readily build and run the software projects, e.g., to obtain additional information. As a step towards better replicability and data-availability in regression testing research, we introduce ReCover, a dataset of 114 pairs of subsequent versions from 28 open source Java projects from GitHub. In particular, ReCover is intended as a consolidation and enrichment of recent dedicated regression testing datasets proposed in the literature, to overcome some of the above described issues, and to make them ready to use with a broader number of regression testing techniques. To this end, we developed a custom mining tool, that we make available as well, to automatically process two recent, massive regression testing datasets, retaining pairs of software versions for which we were able to (1) retrieve the full source code; (2) build the software in a general-purpose Java/Maven environment (which we provide as a Docker container for ease of replication); and (3) compute fine-grained test coverage metrics. ReCover can be readily employed in regression testing studies, as it bundles in a single package full, buildable source code and detailed coverage reports for all the projects. We envision that its use could foster regression testing research, improving replicability and long-term data availability.
Convolutional Neural Networks for Morphologically Similar Fish Species Identification

Seafood comprises the largest globally traded food commodity in the world. Its supply chains are complex, focus on quick distribution, and rely on processing practices that make it difficult to trace products to their source. This has resulted in seafood mislabeling, with investigations revealing mislabeling of more than 30% of marketed seafood products, though the full extent of seafood mislabeling in the U.S. is unknown. When two species are morphologically similar, it is difficult for humans to visually distinguish between them, thus making mislabeling difficult to detect. To address this problem, we present a novel deep-learning-based model to distinguish between morphologically similar fish species in images. Our approach uses transfer learning with state-of-the-art convolutional neural networks (CNN) to build upon previously learned features on millions of images, thereby improving the model’s classification accuracy. We compare three pretrained CNNs: VGG, ResNet, and RegNet. For evaluation, we utilize the FishNet Open Image Database, containing over 85,000 images from electronic monitoring footage of fisheries. We train and test two models: a 4-species classifier of visually-similar tuna species, and a binary classifier of visually-indistinguishable tuna often mislabeled. Our results show CNNs can be used to distinguish between morphologically similar fish species with high accuracy, which otherwise would often be mislabeled by humans.
Simulating Realistic IoT Network Traffic Using Similarity-based DSE

In the research for novel network technologies and protocols, network simulators play a crucial role by offering a fast and safe evaluation environment. Here, it is crucial that the simulated traffic is similar to representative and realistic network traffic. Such traffic usually strongly depends on the set of connected devices and their application behaviors. These behaviors are often modeled by simulation parameters and inducing a vast design space—comprising all possible parameter settings that each may lead to different simulated traffic traces. To tackle the vast parameter design space, we propose a general methodology that efficiently explores this design space to find optimal parameter settings that result in simulated traffic traces that are most similar to a given trace. For an Internet of Things case study and two state-of-the-art similarity measures, we show that the proposed method can improve trace similarity by up to a factor of 184 and 4.92, respectively.
An Empirical Study on the Survival Rate of GitHub Projects

The number of Open Source projects hosted in social coding platforms such as GitHub is constantly growing. However, many of these projects are not regularly maintained and some are even abandoned shortly after they were created. In this paper we analyze early project development dynamics in software projects hosted on GitHub, including their survival rate. To this aim, we collected all 1,127 GitHub repositories from four different ecosystems (i.e., NPM packages, R packages, WordPress plugins and Laravel packages) created in 2016. We stored their activity in a time series database and analyzed their activity evolution along their lifespan, from 2016 to now. Our results reveal that the prototypical development process consists of intensive coding-driven active periods followed by long periods of inactivity. More importantly, we have found that a significant number of projects die in the first year of existence with the survival rate decreasing year after year. In fact, the probability of surviving longer than five years is less than 50% though some types of projects have better chances of survival.
Chips, Dies, Chiplets and Dielets and Heterogeneous Integration

Packaging is undergoing a major paradigm shift and promises to take up the lag caused by the slowing down of CMOS scaling. In this paper, we examine these shifts that have been driven by the scaling of key packaging metrics such as bump pitch, trace pitch, inter-die spacing and alignment. The goal of advanced packaging is to enable the same benefits that Moore/Dennard scaling has accomplished for CMOS viz. density, performance, power, and cost. The vehicles that advanced packaging employs are somewhat different: dielets/chiplets, advanced assembly techniques, simplified inter-chip communication protocols and cost optimization via the use of optimized heterogeneous technologies. Another important aspect of advanced packaging is the adoption and adaptation of silicon technology methods to packaging.
Gain and DC Offset Mismatch Correction in TI-ADC System Using Adaptive Filter

For digital processing of signals, Analog to Digital Converters (ADCs) are used to convert analog signals to digital signals. The sampling frequency of the ADC should be at least two times the highest frequency of an analog signal. To increase the sampling rate for super high frequency signals, time interleaved ADC (TI-ADC) systems are used. In a TI-ADC system, M ADCs are time interleaved to increase the sampling frequency by factor of M. Ideally all ADCs should have the same gain and dc offsets. However, practical ADCs that are interleaved have different gains and dc offsets which result in mismatch. This mismatch in turn gives spurious peaks in the frequency spectrum. In this paper, least mean square (LMS) and recursive least square (RLS) adaptive filters are used to correct the mismatch. The simulation results show that at least 20 dB attenuation in the spurious peaks after correction using adaptive filters.
Weight Perception Analysis using Electroencephalographic Signals : Full/regular papers, CSCI-ISPC

Haptics is the study of coupling the human sense of touch and motion with computer systems. In this study, we analyze weight perception of the brain based on electroencephalographic signals, which is obtained using a pressure tactile as a stimulus. The state of weight perception is represented by the relative power of alpha and beta sub-bands of subjects. Independent Component Analysis is applied for post-processing. In the final stage, Linear Discriminant Analysis and Support Vector Machine techniques are utilized for classification of the brain signals as either holding a weight or being in a resting state. Our proposed method achieved satisfactory performance when Support Vector Machine with Radial Basis Function kernel classifier and relative power features, which are obtained from the alpha sub-band of four EEG channels, were used.
History-Driven Test Program Synthesis for JVM Testing

Java Virtual Machine (JVM) provides the runtime environment for Java programs, which allows Java to be “write once, run anywhere”. JVM plays a decisive role in the correctness of all Java programs running on it. Therefore, ensuring the correctness and robustness of JVM implementations is essential for Java programs. To date, various techniques have been proposed to expose JVM bugs via generating potential bug-revealing test programs. However, the diversity and effectiveness of test programs generated by existing research are far from enough since they mainly focus on minor syntactic/semantic mutations. In this paper, we propose JavaTailor, the first history-driven test program synthesis technique, which synthesizes diverse test programs by weaving the ingredients extracted from JVM historical bug-revealing test programs into seed programs for covering more JVM behaviors/paths. More specifically, JavaTailor first extracts five types of code ingredients from the historical bug-revealing test programs. Then, to synthesize diverse test programs, it iteratively inserts the extracted ingredients into the seed programs and strengthens their interactions via introducing extra data dependencies between them. Finally, JavaTailor employs these synthesized test programs to differentially test JVMs. Our experimental results on popular JVM implementations (i.e., HotSpot and OpenJ9) show that JavaTailor outperforms the state-of-the-art technique in generating more diverse and effective test programs, e.g., test programs generated by JavaTailor can achieve higher JVM code coverage and detect many more unique inconsistencies than the state-of-the-art technique. Furthermore, JavaTailor has detected 10 previously unknown bugs, 6 of which have been confirmed/fixed by developers.
Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning

Physics-informed machine learning (PIML) has been emerging as a promising tool for applications with domain knowledge and physical models. To uncover its potentials in power electronics, this article proposes a PIML-based parameter estimation method demonstrated by a case study of dc–dc Buck converter. A deep neural network and the dynamic models of the converter are seamlessly coupled. It overcomes the challenges related to training data, accuracy, and robustness which a typical data-driven approach has. This exemplary application envisions to provide a new perspective for tailoring existing machine learning tools for power electronics.
Pulsar Candidate Selection Using Gaussian Hellinger Extremely Fast Decision Tree

Radio wave data gathered by pulsar finding telescopes are required to be classified while being streamed. The reason for that is the practical constraints of traditional machine learning algorithms on streaming datasets. Traditional machine learning algorithms would take considerable compute power, memory and time to give pragmatic results. (recent surveys collect data at the rate of 0.5 – 1 terabyte per second) Stream classification algorithms are specifically developed to address the above limitations and can classify data streams without taking up a lot of memory or training time. They relate with characteristics of data streams such as concept drift and limited memory. Extremely Fast Decision Tree is one of the stream classification algorithms that can learn incrementally when it sees new data. However, data from pulsar detecting datastreams are highly imbalanced (there are less examples of pulsars in the data than non-pulsar objects). Learning incrementally from such a datastream would be a destructive interference for the model’s precision (of detecting pulsars). In this research, we introduce an improved version of the Extremely Fast Decision Tree, that is able to learn imbalanced data streams. Our approach is fast, accurate, and avoids the pitfalls of class imbalance and concept drift.
Comparative gait analysis of parameters of the knee joint after CR and PS implantations

This study presents a methodology for functional comparison of motion parameters of the knee joint after posterior-stabilized (PS) and posterior cruciate ligament retaining (CR) endoprosthesis implantations. In this study, 14 patients participated. The data was collected using the 3D OptiTrack System. Furthermore, data processing continued using the MATLAB program in order to define mathematical models for determining the kinematic motion parameters (e.g, Lateral gap-LatGAP, Medial gap-MedGAP, Medial Lateral-ML translation). The results were shown using the mean values of change in the mentioned motion parameters and using the movement curves shown in graphics. Comparative analysis of the data revealed that there were no significant differences in the knee joint kinematics after implantation of both endoprosthesis designs.
Detection and Classification of GNSS Jammers Using Convolutional Neural Networks

Global Navigation Satellite Systems (GNSSs) have been established as one of the most significant infrastructures in today's world and play an important role in many critical applications. It is known that the power of the GNSS signals at the receivers' antenna is extremely weak and the transmitted signals are vulnerable to interference, which can cause degraded positioning and timing accuracy or even a complete lack of position availability. Thus, it is essential for GNSS applications to detect interference and further recognize the types of it for the mitigation in GNSS receivers to guarantee reliable solutions. In this paper, the focus is on the automatic detection and classi-fication of chirp signals, known as one of the most common and disruptive interfering signals. The classifier is a Convolutional Neural Networks (CNN) based on multi-layer neural networks that operate on the representation of the signals in transformed domains, Wigner- Ville and Short Time Fourier transforms. The representation of signals is fed to a CNN algorithm to classify the different shapes of chirp signals. The proposed method is performed in two case-study scenarios: the monitoring and classification by a terrestrial interference monitor and from a Low-Earth-Orbit (LEO) satellite. The experimental results demonstrate that the CNN model has a classification accuracy of 93 % and can be a suitable approach to classify different shapes of chirp signals.
A Side Channel Attack Detection System Using Processor Core Events and a Support Vector Machine

A method for the detection and suppression of side band channel attacks has been proposed and evaluated using machine learning and processor core events. A supervised learning model is used in the implementation of a system based on hardware event counters to detect malicious exploits such as SPECTRE variants running in a process on a Linux based system operating as an Edge computing device. The approach uses existing on-chip hardware to detect variants of malicious exploitation in the midst of other application processes and suspend the offending process. In this work we analyze multiple variants of side channel attack and demonstrate how our detection system can be trained to detect and react to multiple attacks simultaneously. We demonstrate this prototype on variants of the classic SPECTRE attack such as the micro-ops cache attack based on x86 based machines. We use dimensionality reduction techniques and feature selection techniques from a large set of counter data to improve performance results. The detection system has successfully performed on multiple instruction set architectures including x86 as well as Cortex-A class ARM architectures.
Replication of alphaviruses requires a pseudoknot that involves the poly(A) tail

Alphaviruses, such as Sindbis virus and Chikungunya virus, are RNA viruses with a positive sense single-stranded RNA genome that infect various vertebrates including humans. A conserved sequence element (CSE) of ∼19 nucleotides (nts) in the 3’ non-coding region is important for replication. Despite extensive mutational analysis of the CSE no comprehensive model of this element exists to date. Here it is shown that the CSE can form an RNA pseudoknot with part of the polyA tail and is similar to the human telomerase peudoknot with which it shares 17 nts. Mutants that alter the stability of the pseudoknot were investigated in the context of a replicon of Sindbis virus and by native gel electrophoresis. These studies reveal that the pseudoknot is required for virus replication and is stabilized by UAU base triples. The new model is discussed in relation to previous data on Sindbis virus mutants and revertants lacking (part of) the CSE.
Evolution of sperm morphology in a crustacean genus with fertilization inside an open brood pouch

Sperm is the most fundamental male reproductive feature. It serves the fertilization of eggs and evolves under sexual selection. Two components of sperm are of particular interest, their number and their morphology. Mode of fertilization is believed to be a key determinant of sperm length across the animal kingdom. External fertilization, unlike internal, favors small and numerous sperm, since sperm density is thinned out in the environment. Here, we study the evolution of sperm morphology in the genus Daphnia, where fertilization occurs in a receptacle, the brood pouch, where sperm can constantly be flushed out by a water current. Based on microscopic observations of sperm morphologies mapped on a phylogeny with 15 Daphnia and 2 outgroup species, we found that despite the internal fertilization mode, Daphnia have among the smallest sperm recorded, as would be expected with external fertilization. Despite being all relatively small compared to other arthropods, sperm length diverged at least twice, once within each of the Daphnia subgenera Ctenodaphnia and Daphnia. Furthermore, species in the latter subgenus also lost the ability of cell compaction by extracellular encapsulation and have very polymorphic sperm with long, and often numerous, filopodia. We discuss the different strategies that Daphnia evolved to achieve fertilization success in the females’ brood pouch.
Extensive substrate recognition by the streptococcal antibody-degrading enzymes IdeS and EndoS

Enzymatic cleavage of IgG antibodies is a common strategy used by pathogenic bacteria to ablate immune effector function. The Streptococcus pyogenes bacterium secretes the protease IdeS and the glycosidase EndoS, which specifically catalyse cleavage and deglycosylation of human IgG, respectively. IdeS has received clinical approval for kidney transplantation in hypersensitised individuals, while EndoS has found application in engineering antibody glycosylation. Here, we present crystal structures of both enzymes in complex with their IgG1 Fc substrate, which was achieved using Fc engineering to disfavour preferential Fc crystallisation. The IdeS protease displays extensive Fc recognition and encases the antibody hinge. Conversely, the glycan hydrolase domain in EndoS traps the Fc glycan in a flipped-out conformation, while additional recognition of the Fc peptide is driven by the so-called carbohydrate binding module. Understanding the molecular basis of antibody recognition by bacterial enzymes will facilitate the development of next-generation enzymes for clinical and biotechnological use.
Functional Redundancy of the Posterior Hippocampi, but not Anterior Hippocampi or Left Frontal Cortex, is Disrupted in Pathological Brain Aging

As prevalence rates of Alzheimer’s disease (AD), the leading cause of dementia, are projected to more than double by 2050, emphasis has been placed on early intervention strategies that target resilience mechanisms to delay or prevent the onset of clinical symptoms. Several neural mechanisms underlying brain resilience to AD have been proposed, including redundant neural connections between the posterior hippocampi (HC) and all other brain regions, and global functional connectivity of the left frontal cortex (LFC). It remains unknown, however, if regional redundancy of the HC and LFC underscores neural resilience in the presence of AD pathologies. From the ADNI database, 363 cognitively normal older adults (CN) (N = 220; 36% Aβ+) and patients with Mild Cognitive Impairment (MCI) (N = 143; 51% Aβ+) were utilized. Regional redundancy was calculated from resting state fMRI data using a graph theoretical approach by summing the direct and indirect paths (path lengths=1-4) between each ROI and its 262 functional connections. The results showed that Aβ-status significantly disrupted posterior HC, but not anterior HC or LFC, redundancy. Aβ- groups showed higher redundancy of the bilateral posterior HC than Aβ+. In regard to redundancy-cognition relationships, higher posterior HC redundancy was related to better episodic memory performance, an effect which was primarily driven by the Aβ- group. Despite the positive relationship between posterior HC redundancy and cognition, we did not find compelling evidence that redundancy of the posterior HC serves in a resilience manner, as posterior HC redundancy did not moderate the potentially deleterious relationship between Aβ deposition and cognition. No relationships were found between anterior HC or LFC redundancy and cognitive performance. Together, these findings suggest that redundancy of the LFC does not underpin its role in resilience and that posterior HC redundancy may capture disruptions to network connectivity that occur as a result of Aβ deposition.
Mutant SF3B1 promotes PDAC malignancy through TGF-β resistance

The splicing factor SF3B1 is recurrently mutated in various tumors, including pancreatic ductal adenocarcinoma (PDAC). The impact of the hotspot mutation SF3B1K700E on the PDAC pathogenesis, however, remains elusive. Here, we demonstrate that Sf3b1K700E alone is insufficient to induce malignant transformation of the murine pancreas, but increases aggressiveness of PDAC if it co-occurs together with mutated KRAS and p53. We further demonstrate that SF3B1K700E reduces epithelial–mesenchymal transition (EMT) and confers resistance to TGF-β1-induced cell death, and provide evidence that this phenotype is in part mediated through aberrant splicing of Map3k7. Taken together, our work suggests that SF3B1K700E acts as an oncogenic driver in PDAC through enhancing resistance to the tumor suppressive effects of TGF-β.