Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Detection of Security-Relevant Bug Reports using Machine Learning: How Far Are We?

Bug reports are common artefacts in software development. They serve as the main channel for users to communicate to developers information about the issues that they encounter when using released versions of software programs. In the descriptions of issues, however, a user may, intentionally or not, expose a vulnerability. In a typical maintenance scenario, such security-relevant bug reports are prioritised by the development team when preparing corrective patches. Nevertheless, when security relevance is not immediately expressed (e.g., via a tag) or rapidly identified by triaging teams, the open security-relevant bug report can become a critical leak of sensitive information that attackers can leverage to perform zero-day attacks. To support practitioners in triaging bug reports, the research community has proposed a number of approaches for the detection of security-relevant bug reports. In recent years, approaches in this respect based on machine learning have been reported with promising performance. Our work focuses on such approaches, and revisits their building blocks to provide a comprehensive view on the current achievements. To that end, we built a large experimental dataset and performed extensive experiments with variations in feature sets and learning algorithms. Eventually, our study highlights different approach configurations that yield best performing classifiers.
Anisotropies in cosmological 21 cm background by oscillons/I-balls of ultra-light axion-like particle

Ultra-light axion-like particle (ULAP) with mass m ∼ 10-22 eV has recently been attracting attention as a possible solution to the small-scale crisis. ULAP forms quasi-stable objects called oscillons/I-balls, which can survive up to a redshift z ∼ 10 and affect the structure formation on a scale ∼ 𝒪(0.1) Mpc by amplifying the density fluctuations. We study the effect of oscillons on 21 cm anisotropies caused by neutral hydrogen in minihalos. It is found that this effect can be observed in a wide mass range by future observations such as Square Kilometer Array (SKA) if the fraction of ULAP to the total dark matter density is 𝒪(0.01 – 0.1).
Sub-100uW Multispectral Riemannian Classification for EEG-based Brain--Machine Interfaces

motor imagery (MI) brain–machine interfaces (BMIs) enable us to control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the classification of the brain signals is done locally near the sensor using machine learning models embedded on energy-efficient microcontroller units (MCUs), for assured privacy, user comfort, and long-term usage. In this work, we provide practical insights on the accuracy-cost trade-off for embedded BMI solutions. Our multispectral Riemannian classifier reaches 75.1% accuracy on a 4-class MI task. The accuracy is further improved by tuning different types of classifiers to each subject, achieving 76.4%. We further scale down the model by quantizing it to mixed-precision representations with a minimal accuracy loss of 1% and 1.4%, respectively, which is still up to 4.1% more accurate than the state-of-the-art embedded convolutional neural network. We implement the model on a low-power MCU within an energy budget of merely 198μJ and taking only 16.9 ms per classification. Classifying samples continuously, overlapping the 3.5 s samples by 50% to avoid missing user inputs allows for operation at just 85μW. Compared to related works in embedded MI-BMIs, our solution sets the new state-of-the-art in terms of accuracy-energy trade-off for near-sensor classification.
Experimental and Numerical Results on Earthquake-Induced Rotational Ground Motions

Stimulated by the lack of direct measurements of earthquake-induced ground rotations, a set of experimental and numerical results on rotational ground motion is illustrated. The results cover a relatively broad range of magnitude (4–6.5), and regard both far field and near source conditions. Experimentally, results are derived through a suitable spatial interpolation procedure of displacement records collected from two dense arrays, Parkway Valley (New Zealand) and UPSAR (California). Validation checks both with other array-derived methods and with numerical simulations are carried out, to verify the reliability of our estimations and the capability of numerical wave propagation analysis codes to provide realistic estimates of rotational ground motions over a reasonable frequency band. Peak Ground Velocity (PGV) and Peak Ground Rotation (PGR) values obtained via the spatial interpolation procedure over dense seismic arrays are then put in comparison with the numerical results computed along two representative cross-sections of the Grenoble Valley (France), in the near field of a MW 6 strike-slip fault. In this case, a combination of topographic, rupture directivity, and site effects produces rotations of remarkable amplitudes, of the order of 10−3 rad. Finally, PGV-PGR values resulting from both the experimental procedure and the numerical simulations are discussed. A common linear trend between PGV and PGR is found and turns out to be in reasonable agreement with other published results.
Weak-gravity bound in asymptotically safe gravity-gauge systems

The weak-gravity bound has been discovered in asymptotically safe gravity-matter systems, where it limits the maximum strength of gravitational fluctuations. In the present paper, we explore it for the first time in systems with more than one gauge field, to discover whether systems with 12 gauge fields (like the Standard Model) exhibit a weak-gravity bound and whether the gravitational fixed point evades it. Further, we test the robustness of the present and previous results on the weak-gravity bound by exploring their dependence on a gravitational gauge parameter. Finally, the existence of the weak-gravity bound also has important phenomenological consequences: it is key to a proposed mechanism that bounds the spacetime dimensionality from above to four or five dimensions. In this paper, we strengthen the evidence for this mechanism. Thus, the predictive power of the asymptotic safety paradigm could extend to parameters of the spacetime geometry, such that the four-dimensionality of our universe could be explained from first principles.
Low-energy structure of spiral spin liquids

In this work we identify a previously unexplored type of topological defect in spiral spin liquids -- the momentum vortex -- and reveal its dominant role in shaping the low energy physics of such systems. Spiral spin liquids are a class of classical spin liquids featuring sub-extensively degenerate ground states. They are distinct from spin liquids on geometrically frustrated lattices, in which the ground state degeneracy is extensive and connected by local spin flips. Despite a handful of experimental realizations and many theoretical studies, a concrete physical picture of their spin liquidity has not been established so far. In this work, we study a 2D spiral spin liquid model to answer this question. We find that the local momentum vector field can carry topological defects in the form of vortices, which, however, have very different properties from the commonly known spin vortices. The fluctuations of such vortices lead the system into a liquid phase at intermediate temperatures. Furthermore, the effective low energy theory of such vortices indicates their equivalence to quadrupoles of fractons in a rank-2 U(1) gauge theory or, alternatively, to quadrupoles of disclinations in elasticity theory. At very low temperatures, the system freezes into a glassy state in which these vortices form a rigid network with straight-line domain walls. Our work sheds light on the nature of spiral spin liquids and also paves the way toward understanding their quantum limit.
Automated Vision-Based Wellness Analysis for Elderly Care Centers

The growth in the aging population require caregivers to improve both efficiency and quality of healthcare. In this study, we develop an automatic, vision-based system for monitoring and analyzing the physical and mental well-being of senior citizens. Through collaboration with Haven of Hope Christian Service, we collect video recording data in the care center with surveillance camera. We then process and extract personalized facial, activity, and interaction features from the video data using deep neural networks. This integrated health information systems can assist caregivers to gain better insights into the seniors they are taking care of. These insights, including wellness metrics and long-term health pattern of senior citizens, can help caregivers update their caregiving strategies. We report findings of our analysis and evaluate the system quantitatively. We also summarize technical challenges and additional functionalities and technologies needed for offering a comprehensive system.
Efficient and Scalable Graph Pattern Mining on GPUs

Graph Pattern Mining (GPM) extracts higher-order information in a large graph by searching for small patterns of interest. GPM applications are computationally expensive, and thus attractive for GPU acceleration. Unfortunately, due to the complexity of GPM algorithms and parallel hardware, hand optimizing GPM applications suffers programming complexity, while existing GPM frameworks sacrifice efficiency for programmability. Moreover, little work has been done on GPU to scale GPM computation to large problem sizes. We describe G2Miner, the first Graph Pattern Mining (GPM) framework that runs on multiple GPUs. G2Miner uses pattern-aware, input-aware and architecture-aware search strategies to achieve high efficiency on GPUs. To simplify programming, it provides a code generator that automatically generates pattern-aware CUDA code. G2Miner flexibly supports both breadth-first search (BFS) and depth-first search (DFS) to maximize memory utilization and generate sufficient parallelism for GPUs. For the scalability of G2Miner, we use a customized scheduling policy to balance work among multiple GPUs. Experiments on a V100 GPU show that G2Miner achieves average speedups of 5.4x and 7.2x over two state-of-the-art single-GPU systems, Pangolin and PBE, respectively. In the multi-GPU setting, G2Miner achieves linear speedups from 1 to 8 GPUs, for various patterns and data graphs. We also show that G2Miner on a V100 GPU is 48.3x and 15.2x faster than the state-of-the-art CPU-based system, Peregrine and GraphZero, on a 56-core CPU machine.
Conformal Isometries and Curvature Collineations of an Impulsive Plane Wave: a distributional approach

By extending the notion of Lie derivative to distribution-valued tensor fields of order m, Lie derivatives with respect to C vector fields, k > m+1, can be shown to be well defined. Geometric symmetries, definable in terms of these Lie derivatives, can then be considered. In particular, for spacetimes of low regularity, geometric symmetries generated by vector fields of regularity as low as C are definable. We find that the conformal algebra of the impulsive plane wave spacetime (with + polarization) described by the continuous Baldwin-Jeffery-Rosen form of the metric has the maximal dimension seven. The curvature of this metric is well defined as a distribution and since this spacetime is Ricci-flat, we show that all the vector fields in the conformal algebra are special curvature collineations. We find also the general form of these last ones. Finally, we find the conformal Killing vector fields of this spacetime when using the distributional Brinkmann form of the metric.
Evaluation of building period formulas for seismic design

Building period formulas in seismic design code are evaluated with over 800 apparent building periods from 191 building stations and 67 earthquake events. The evaluation is carried out with the formulas in ASCE 7‐05 for steel and RC moment‐resisting frames, shear wall buildings, braced frames, and other structural types. Qualitative comparison of measured periods and periods calculated from the code formulas shows that the formula for steel moment‐resisting frames generally predicts well the lower bound of the measured periods for all building heights. But the differences between the periods from code formula and measured periods of low‐ to‐medium rise buildings are relatively high. In addition, the periods of essential buildings designed with the importance factor are about 40% shorter than the periods of non‐essential buildings. The code formula for RC moment‐resisting frames describes well the lower bound of measured periods. The formula for braced frames accurately predicts the lower bound periods of low‐to‐medium rise buildings. The formula for shear wall buildings overestimates periods for all building heights. For buildings that are classified as other structural types, the measured building periods can be much shorter than the periods calculated with the code formula. Based on these observations, it is suggested to use Cr factor of 0.015 for shear walls and other structural types. Copyright © 2010 John Wiley & Sons, Ltd.
hybrid-Flacon: Hybrid Pattern Malware Detection and Categorization with Network Traffic andProgram Code

—Nowadays, Android is the most dominant operating system in the mobile ecosystem, with billions of people using its apps daily. As expected, this trend did not go unnoticed by miscreants, and Android became the favorite platform for discovering new victims through malicious apps. Moreover, these apps have become so sophisticated that they can bypass anti-malware measures to protect the users. Therefore, it is safe to admit that traditional anti-malware techniques have become cumbersome, sparking the urge to develop an efﬁcient way to detect Android malware. This paper presents hybrid-Flacon , a hybrid pattern Android malware detection and categorization framework. It combines dynamic and static features of Android malware, which are from network trafﬁc and code graph structure. In hybrid-Flacon , we treat network trafﬁc as a dynamic feature and process it as a 2D-image sequence. Meanwhile, hybrid-Flacon handles each network ﬂow in the packet as a 2D image and uses a bidirectional LSTM network to process those 2D-image sequences to obtain vectors representing network packets. We use the program code graph for a static feature and introduce natural language processing (NLP) inspired techniques on function call graph (FCG). We design a graph neural network-based approach to convert the whole graph structure of Android apps to vectors. Finally, We utilize those converted vectors, both network and program code features, and concatenate them to detect and categorize the malware. Our results reveal that hybrid-Flacon yields better results as we get 97.16% accuracy on average for malware detection and 88.32% accuracy for malware categorization. Additionally, we release a dataset AndroNetMnist , which converts the network trafﬁc to a 2D-image sequence and helps to accomplish malware detection on a 2D-image sequence.
Automated Detection of Spatial Traffic Violations through use of Video Sensors

It is plausible that road use behavior that leads to traffic violations can be a contributing factor to the failure mechanism that prompts a collision. The detection and the understanding of traffic violations, therefore, can be key components of a sound safety diagnosis. Recent advances in computer vision techniques have enabled the conduct of automated and large-scale analyses of various approaches to safety diagnoses, including traffic conflicts and violation analysis. This paper proposes and compares two approaches to detect vehicular spatial violations. The approaches are (a) k-means clustering and (b) pattern matching with use of the longest common subsequence (LCSS) similarity measure. The purpose of this study was to learn what constituted normal movement patterns and to interpret any discrepancy between these patterns and observed tracks as an indication of a traffic violation. Each of the approaches was applied to detect U-turn violations on an urban intersection in Kuwait City, Kuwait. Violation detection on the basis of LCSS generally was superior to detection with piecewise k-means clustering, especially when low false detection rates were desirable. This expected differential was the result of the performance of LCSS matching on observed road user positions, and not on an approximated model.
Anomalous multifractality in quantum chains with strongly correlated disorder

We demonstrate numerically that a robust and unusual multifractal regime can emerge in a one-dimensional quantum chain with maximally correlated disorder, above a threshold disorder strength. This regime is pre-ceded by a mixed and an extended regime at weaker disorder strengths, with the former hosting both extended and multifractal eigenstates. The multifractal states we ﬁnd are markedly different from conventional multifractal states in their structure, as they reside approximately uniformly over a continuous segment of the chain, and the lengths of these segments scale non-trivially with system size. This anomalous nature also leaves imprints on dynamics. An initially localised wavepacket shows ballistic transport, in contrast to the slow, generally sub-diffusive, transport commonly associated with multifractality. However, the timescale over which this ballistic transport persists again scales non-trivially with the system size.
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods

Graph representation learning is a fast-growing field where one of the main objectives is to generate meaningful representations of graphs in lower-dimensional spaces. The learned embeddings have been successfully applied to perform various prediction tasks, such as link prediction, node classification, clustering, and visualization. The collective effort of the graph learning community has delivered hundreds of methods, but no single method excels under all evaluation metrics such as prediction accuracy, running time, scalability, etc. This survey aims to evaluate all major classes of graph embedding methods by considering algorithmic variations, parameter selections, scalability, hardware and software platforms, downstream ML tasks, and diverse datasets. We organized graph embedding techniques using a taxonomy that includes methods from manual feature engineering, matrix factorization, shallow neural networks, and deep graph convolutional networks. We evaluated these classes of algorithms for node classification, link prediction, clustering, and visualization tasks using widely used benchmark graphs. We designed our experiments on top of PyTorch Geometric and DGL libraries and run experiments on different multicore CPU and GPU platforms. We rigorously scrutinize the performance of embedding methods under various performance metrics and summarize the results. Thus, this paper may serve as a comparative guide to help users select methods that are most suitable for their tasks.
Branched Polymers with Excluded Volume Effects/ Configurations of Comb Polymers in Two- and Three-dimensions

Abstract: We investigate the excluded volume effects in good solvents for the isolated comb polymers having ν0 = 1/4. In particular, we investigate the change of the size exponent, ν, defined by 〈s 2 N 〉 ∝ N 2ν , for the various fully-expanded configurations. The results show that, given the fully stretched backbone and side chains, the exponent takes the same value, ν = 1/2, irrespective of the configurational isomerization of side chains; only the pre-exponential factor changes.
A diagram-free approach to the stochastic estimates in regularity structures

In this paper, we explore the version of Hairer’s regularity structures based on a greedier index set than trees, as introduced in [32] and algebraically characterized in [30]. More precisely, we construct and stochastically estimate the renormalized model postulated in [32], avoiding the use of Feynman diagrams but still in a fully automated, i. e. inductive way. This is carried out for a class of quasi-linear parabolic PDEs driven by noise in the full singular but renormalizable range. We assume a spectral gap inequality on the (not necessarily Gaussian) noise ensemble. The resulting control on the variance of the model naturally complements its vanishing expectation arising from the BPHZ-choice of renormalization. We capture the gain in regularity on the level of the Malliavin derivative of the model by describing it as a modelled distribution. Symmetry is an important guiding principle and built-in on the level of the renormalization Ansatz. Our approach is analytic and topdown rather than combinatorial and bottom-up.
Learning Semi-Structured Representations of Radiology Reports

Beyond their primary diagnostic purpose, radiology reports have been an invaluable source of information in medical research. Given a corpus of radiology reports, researchers are often interested in identifying a subset of reports describing a particular medical finding. Because the space of medical findings in radiology reports is vast and potentially unlimited, recent studies proposed mapping free-text statements in radiology reports to semi-structured strings of terms taken from a limited vocabulary. This paper aims to present an approach for the automatic generation of semi-structured representations of radiology reports. The approach consists of matching sentences from radiology reports to manually created semi-structured representations, followed by learning a sequence-to-sequence neural model that maps matched sentences to their semi-structured representations. We evaluated the proposed approach on the OpenI corpus of manually annotated chest x-ray radiology reports. The results indicate that the proposed approach is superior to several baselines, both in terms of (1) quantitative measures such as BLEU, ROUGE, and METEOR and (2) qualitative judgment of a radiologist. The results also demonstrate that the trained model produces reasonable semi-structured representations on an out-of-sample corpus of chest x-ray radiology reports from a different medical provider.
Wiener Guided DIP for Unsupervised Blind Image Deconvolution

Blind deconvolution is an ill-posed problem arising in various fields ranging from microscopy to astronomy. Its ill-posed nature demands adequate priors and initialization to arrive at a desirable solution. Recently, it has been shown that deep networks can serve as an image generation prior (DIP) during unsupervised blind deconvolution optimization, however, DIP’s high frequency artifact suppression ability is not explicitly exploited. We propose to use Wiener-deconvolution to guide DIP during optimization in order to better leverage DIP’s ability for blind image deconvolution. Wiener-deconvolution sharpens an image while introducing high-frequency artifacts, which are reproduced by DIP with a delay compared to low-frequency features and sharp edges, similar to what has been observed for noise. We embed the computational process in a constrained optimization problem together with an automatic kernel initialization method and show that the proposed method yields higher performance and stability across multiple datasets.