Miracle-dz/UniFAR-FTUs huggingface dataset:
A Concept Drift Based Ensemble Incremental Learning Approach for Intrusion Detection

Faced with various malicious intrusions, the design of intrusion detection system (IDS) has always being important in the area of network security. Recently, machine learning methods using network status data as input features are widely used to detect abnormality in the network. However, existing work does not consider the variance of the intrusions and thus is not robust to detect new types of intrusions. Therefore, considering the statistical properties of the status data change over time in unforeseen ways if intrusions occur, we propose a concept drift based ensemble incremental learning approach in IDS (CDIL). Concept drift detection technology is firstly used to detect the variance of the statistical properties of the status data in real time, and then an incremental learning is triggered to diagnose if an intrusion happens. In this way, CDIL can adapt the learning model to the changing input network status data in real time. We conduct extensive experiments on real-world network intrusion experimental data set and verify the effectiveness and real-time performance of the proposed CDIL; the accuracy of intrusion detection reaches up to 94.91%.
Mesh R-CNN

Rapid advances in 2D perception have led to systems that accurately detect objects in real-world images. However, these systems make predictions in 2D, ignoring the 3D structure of the world. Concurrently, advances in 3D shape prediction have mostly focused on synthetic benchmarks and isolated objects. We unify advances in these two areas. We propose a system that detects objects in real-world images and produces a triangle mesh giving the full 3D shape of each detected object. Our system, called Mesh R-CNN, augments Mask R-CNN with a mesh prediction branch that outputs meshes with varying topological structure by ﬁrst predicting coarse voxel representations which are converted to meshes and reﬁned with a graph convolution network operating over the mesh’s vertices and edges. We validate our mesh prediction branch on ShapeNet, where we out-perform prior work on single-image shape prediction. We then deploy our full Mesh R-CNN system on Pix3D, where we jointly detect objects and predict their 3D shapes.
A Systematic Chart Review of Inpatient Population with Childhood Dissociative Disorder

Objective: To study the socio-demographic factors, clinical characteristics, and long-term outcome of dissociative disorders in inpatient children and adolescents. Methods: Chart data of forty-four subjects (8-15 years) with a diagnosis of dissociative disorder admitted to a specialist Child and Adolescent Psychiatry (CAP) unit between September 2001 and August 2002 were reviewed. Results: Eighty-nine percent of the subjects were above 10 years of age, and 61% were females. Difficult or anxious temperament was found in half of the subjects. Precipitating stressors were present in a majority (82%) and half of the subjects had an acute onset of symptoms. Mean duration of illness was 3.6 weeks. The most common symptom was pseudoseizure. Sixty-eight percent of patients had a co-morbid psychiatric diagnosis, the commonest being a depressive disorder. Remission in symptoms was seen in about 80% of subjects at the time of discharge. Two-thirds of the twenty-four subjects available for follow-up had resumed academics and had good global functioning. Conclusions: Inpatient children and adolescents with dissociative disorders had an acute onset, with obvious precipitating stressors and had a favourable short- and medium-term outcome. Majority of the subjects had psychiatric co-morbidity. Early diagnosis and presence of precipitating factors determined a favourable outcome.
An End-to-End Learning-based Cost Estimator

Cost and cardinality estimation is vital to query optimizer, which can guide the query plan selection. However traditional empirical cost and cardinality estimation techniques cannot provide high-quality estimation, because they may not effectively capture the correlation between multiple tables. Recently the database community shows that the learning-based cardinality estimation is better than the empirical methods. However, existing learning-based methods have several limitations. Firstly, they focus on estimating the cardinality, but cannot estimate the cost. Secondly, they are either too heavy or hard to represent complicated structures, e.g., complex predicates. To address these challenges, we propose an effective end-to-end learning-based cost estimation framework based on a tree-structured model, which can estimate both cost and cardinality simultaneously. We propose effective feature extraction and encoding techniques, which consider both queries and physical operations in feature extraction. We embed these features into our tree-structured model. We propose an effective method to encode string values, which can improve the generalization ability for predicate matching. As it is prohibitively expensive to enumerate all string values, we design a patten-based method, which selects patterns to cover string values and utilizes the patterns to embed string values. We conducted experiments on real-world datasets and experimental results showed that our method outperformed baselines.
Stochastic Bandits with Context Distributions

We introduce a novel stochastic contextual bandit model, where at each step the adversary chooses a distribution over a context set. The learner observes only the context distribution while the exact context realization remains hidden. This allows for a broader range of applications, for instance when the context itself is based on predictions. By leveraging the UCB algorithm to this setting, we propose an algorithm that achieves a $\tilde{\mathcal{O}}(d\sqrt{T})$ high-probability regret bound for linearly parametrized reward functions. Our results strictly generalize previous work in the sense that both our model and the algorithm reduce to the standard setting when the environment chooses only Dirac delta distributions and therefore provides the exact context to the learner. We further obtain similar results for a variant where the learner observes the realized context after choosing the action, and we extend the results to the kernelized setting. Finally, we demonstrate the proposed method on synthetic and real-world datasets.
Renaissance: benchmarking suite for parallel applications on the JVM

Established benchmark suites for the Java Virtual Machine (JVM), such as DaCapo, ScalaBench, and SPECjvm2008, lack workloads that take advantage of the parallel programming abstractions and concurrency primitives offered by the JVM and the Java Class Library. However, such workloads are fundamental for understanding the way in which modern applications and data-processing frameworks use the JVM's concurrency features, and for validating new just-in-time (JIT) compiler optimizations that enable more efficient execution of such workloads. We present Renaissance, a new benchmark suite composed of modern, real-world, concurrent, and object-oriented workloads that exercise various concurrency primitives of the JVM. We show that the use of concurrency primitives in these workloads reveals optimization opportunities that were not visible with the existing workloads. We use Renaissance to compare performance of two state-of-the-art, production-quality JIT compilers (HotSpot C2 and Graal), and show that the performance differences are more significant than on existing suites such as DaCapo and SPECjvm2008. We also use Renaissance to expose four new compiler optimizations, and we analyze the behavior of several existing ones. We use Renaissance to compare performance of two state-of-the-art, production-quality JIT compilers (HotSpot C2 and Graal), and show that the performance differences are more significant than on existing suites such as DaCapo and SPECjvm2008. We also use Renaissance to expose four new compiler optimizations, and we analyze the behavior of several existing ones.
A method for the classification of chimera states of coupled oscillators and its application for creating a neural network information converter

This paper presents a new method for evaluating the synchronization of quasi-periodic oscillations of two oscillators, termed “chimeric synchronization”. The family of metrics is proposed to create a neural network information converter based on a network of pulsed oscillators. In addition to transforming input information from digital to analogue, the converter can perform information processing after training the network by selecting control parameters. In the proposed neural network scheme, the data arrives at the input layer in the form of current levels of the oscillators and is converted into a set of non-repeating states of the chimeric synchronization of the output oscillator. By modelling a thermally coupled VO2-oscillator circuit, the network setup is demonstrated through the selection of coupling strength, power supply levels, and the synchronization efficiency parameter. The distribution of solutions depending on the operating mode of the oscillators, sub-threshold mode, or generation mode are revealed. Technological approaches for the implementation of a neural network information converter are proposed, and examples of its application for image filtering are demonstrated. The proposed method helps to significantly expand the capabilities of neuromorphic and logical devices based on synchronization effects.
Quality Management Systems within the Public Sector: The Case of ISO 9000 Implementation Barriers in Malaysian Local Government

Implementing ISO 9000 in the public sector is normally seen to be wrought with various obstacles that usually impede the entire process. The objective of this study is to explore the main barriers of ISO 9000 implementation in Malaysian Local Government (LG) organizations. Relevant information had been gathered from literature in developing the survey tool to gather the related data. Quantitative analysis was then used to analyse the data. There were three aspects discovered to be the main barriers which have hampered the ISO 9000 implementation process. These barriers are lack of cooperation among internal departments, negative perception or attitude towards quality among employees and employees' culture toward quality. Keywords- Quality management systems, public sector, ISO 9000, implementation barriers, local government
User Experience challenges for designing and evaluating Computer-Based Assessments for children

Computer-Based Assessment (CBA), i.e., the use of computers instead of paper & pencil for testing purposes is now increasingly used, both in education and in the workforce. Along with this trend, several issues regarding the usage of computers in assessment can be raised. With respect to CBA, test validity and acceptance appear at stake during interacting with a complex assessment system. For instance, individual differences in computer literacy (i.e. ability to handle technology) might cause different outcomes that are not related to the problem-solving task. Prior investigation has shown that there is a scarcity of research on the User Experience (UX) in the context of CBA, also due to a focus on adult users. This doctoral thesis aims to adapt and develop new evaluation methods from the Human-Computer Interaction (HCI) field, applied in the context of CBA. The contributions will result in the development of best practices guidelines for both research and practitioners by adopting design and evaluation methods drawn from the field of Child-computer Interaction (CCI).
Bargmann-Fock percolation is noise sensitive

We show that planar Bargmann-Fock percolation is noise sensitive under the Ornstein-Ulhenbeck process. The proof is based on the randomized algorithm approach introduced by Schramm and Steif and gives quantitative polynomial bounds on the noise sensitivity of crossing events for Bargmann-Fock. A rather counter-intuitive consequence is as follows. Let $F$ be a Bargmann-Fock Gaussian field in $\mathbb{R}^3$ and consider two horizontal planes $P_1,P_2$ at small distance $\varepsilon$ from each other. Even though $F$ is a.s. analytic, the above noise sensitivity statement implies that the full restriction of $F$ to $P_1$ (i.e. $F_{| P_1}$) gives almost no information on the percolation configuration induced by $F_{|P_2}$. As an application of this noise sensitivity analysis, we provide a Schramm-Steif based proof that the near-critical window of level line percolation around $\ell_c=0$ is polynomially small. This new approach extends earlier sharp threshold results to a larger family of planar Gaussian fields.
Equilibrium thermodynamic susceptibilities for a dense degenerate Dirac field

Parity preserving relativistic fluids in four spacetime dimensions admit seven independent thermodynamic susceptibilities at the second order in the hydrodynamic derivative expansion. We compute all parity-even second order thermodynamic susceptibilities for a free massive Dirac field at zero temperature and a nonzero chemical potential, based on the Kubo formulas reported in P. Kovtun and A. Shukla, Kubo formulas for thermodynamic transport coefficients, J. High Energy Phys. 10 (2018) 007. We also compute the second order constitutive relations for the energy-momentum tensor and the conserved current in the absence of external gauge fields.
When Does Label Smoothing Help?

The generalization and learning speed of a multi-class neural network can often be significantly improved by using soft targets that are a weighted average of the hard targets and the uniform distribution over labels. Smoothing the labels in this way prevents the network from becoming over-confident and label smoothing has been used in many state-of-the-art models, including image classification, language translation and speech recognition. Despite its widespread use, label smoothing is still poorly understood. Here we show empirically that in addition to improving generalization, label smoothing improves model calibration which can significantly improve beam-search. However, we also observe that if a teacher network is trained with label smoothing, knowledge distillation into a student network is much less effective. To explain these observations, we visualize how label smoothing changes the representations learned by the penultimate layer of the network. We show that label smoothing encourages the representations of training examples from the same class to group in tight clusters. This results in loss of information in the logits about resemblances between instances of different classes, which is necessary for distillation, but does not hurt generalization or calibration of the model's predictions.
Synthesis of control Lyapunov functions and stabilizing feedback strategies using exit‐time optimal control Part I: Theory

This work studies the problem of constructing control Lyapunov functions (CLFs) and feedback stabilization strategies for deterministic nonlinear control systems described by ordinary differential equations. Many numerical methods for solving the Hamilton–Jacobi–Bellman partial differential equations specifying CLFs typically require dense state space discretizations and consequently suffer from the curse of dimensionality. A relevant direction of attenuating the curse of dimensionality concerns reducing the computation of the values of CLFs and associated feedbacks at any selected states to finite‐dimensional nonlinear programming problems. We propose to use exit‐time optimal control for that purpose. This article is the first part of a two‐part work. First, we state an exit‐time optimal control problem with respect to a sublevel set of an appropriate local CLF and establish that, under a number of reasonable conditions, the concatenation of the corresponding value function and the local CLF is a global CLF in the whole domain of asymptotic null‐controllability. We also investigate the formulated optimal control problem. A modification of these constructions for the case when one does not find a suitable local CLF is provided as well. Our developments serve as a theoretical basis for a curse‐of‐dimensionality‐free approach to feedback stabilization, that is presented in the second part Yegorov et al. (2021) of this work together with supporting numerical simulation results.
Towards certified separate compilation for concurrent programs

Certified separate compilation is important for establishing end-to-end guarantees for certified systems consisting of multiple program modules. There has been much work building certified compilers for sequential programs. In this paper, we propose a language-independent framework consisting of the key semantics components and lemmas that bridge the verification gap between the compilers for sequential programs and those for (race-free) concurrent programs, so that the existing verification work for the former can be reused. One of the key contributions of the framework is a novel footprint-preserving compositional simulation as the compilation correctness criterion. The framework also provides a new mechanism to support confined benign races which are usually found in efficient implementations of synchronization primitives. With our framework, we develop CASCompCert, which extends CompCert for certified separate compilation of race-free concurrent Clight programs. It also allows linking of concurrent Clight modules with x86-TSO implementations of synchronization primitives containing benign races. All our work has been implemented in the Coq proof assistant.
Simple Online Fault Detecting Scheme for Short-Circuited Turn in a PMSM Through Current Harmonic Monitoring

To detect a stator winding fault caused by a short-circuited turn in a permanent magnet synchronous motor, a simple online fault detecting scheme is presented. The proposed scheme is based on the monitoring of the second-order harmonic components in the q-axis current through harmonic analysis. To verify the effectiveness of the proposed scheme, a test motor which allows an interturn short in the stator winding has been built, and the entire control system has been implemented using the DSP TMS320F28335.
Parenting stress and external stressors as predictors of maternal ratings of child adjustment.

This study sought to disentangle the effects of different kinds of stress on maternal ratings of child externalizing and internalizing problems, social inhibition, and social competence, with a primary focus on parenting stress. The relations were explored in a sample consisting of mothers of 436 children (Mage = 7 years) in Sweden. Half the sample had had early clinical contacts during infancy due to child regulation problems, and the rest were mothers without known such early contacts. Demographic factors, family stressors, and parenting stress were examined in stress - adjustment models. Family stressors were clinical contact during infancy, current child and parent health problems, recent negative life events, and insufficient social support. Parenting stress as a mediator of the effect of other stressors on rated child adjustment was tested as was social support as a moderator of the effect of parenting stress on adjustment. The results showed that a higher parenting stress level was associated with maternal ratings of more externalizing and internalizing behaviors, more social inhibition, and lower social competence. Other family stressors and background variables were also found to be of importance, mainly for externalizing and internalizing problems and to some extent for social competence. Social inhibition had a unique relation to parenting stress only. Parenting stress mediated effects of other stressors in twelve models, whereas social support had no moderating effect on the link between parenting stress and child adjustment. Thus, parenting stress seems to be an important overarching construct. Clinical implications are proposed.
Von Hippel‐Lindau tumor suppressor and HIF‐1α: new targets of NSAID inhibition of hypoxia‐induced angiogenesis

Nonsteroidal anti‐inflammatory drugs (NSAIDs) block prostaglandin synthesis and impair healing of gastrointestinal ulcers and growth of colonic tumors, in part, by inhibiting angiogenesis. The mechanisms of this inhibition are incompletely explained. Here we demonstrate that both nonselective (indomethacin) and COX‐2‐selective (NS‐398) NSAIDs inhibit hypoxia‐induced in vitro angiogenesis in gastric microvascular endothelial cells via coordinated sequential events: 1) increased expression of the von Hippel‐Lindau (VHL) tumor suppressor, which targets proteins for ubiquitination leading to 2) reduced accumulation of hypoxia‐inducible factor‐1α (HIF‐1α) and, as a result, 3) reduced expression of vascular endothelial growth factor (VEGF) and its specific receptor Flt‐1. Because HIF‐1α is the major trigger for hypoxia‐induced activation of the VEGF and Flt‐1 genes, this could explain how NSAIDs inhibit hypoxia‐induced angiogenesis. Exogenous VEGF and, to a lesser extent, exogenous prostaglandins partly reversed the NSAIDs inhibition of hypoxia‐induced angiogenesis. Taken together, these results indicate that NSAIDs inhibit hypoxia‐induced angiogenesis in endothelial cells by inhibiting VEGF and Flt‐1 expression through increased VHL expression and the resulting ubiquitination and degradation of HIF‐1α. This action of NSAIDs has both prostaglandin‐dependent and prostaglandin‐independent components.
The Evolution and Origin of Ionized Gas Velocity Dispersion from z ∼ 2.6 to z ∼ 0.6 with KMOS3D

We present the 0.6 < z < 2.6 evolution of the ionized gas velocity dispersion in 175 star-forming disk galaxies based on data from the full integral field spectroscopic survey. In a forward-modeling Bayesian framework including instrumental effects and beam-smearing, we fit simultaneously the observed galaxy velocity and velocity dispersion along the kinematic major axis to derive the intrinsic velocity dispersion σ0. We find a reduction of the average intrinsic velocity dispersion of disk galaxies as a function of cosmic time, from σ0 ∼ 45 km s−1 at z ∼ 2.3 to σ0 ∼ 30 km s−1 at z ∼ 0.9. There is substantial intrinsic scatter ( ) around the best-fit σ0–z relation beyond what can be accounted for from the typical measurement uncertainties (δσ0 ≈ 12 km s−1), independent of other identifiable galaxy parameters. This potentially suggests a dynamic mechanism such as minor mergers or variation in accretion being responsible for the scatter. Putting our data into the broader literature context, we find that ionized and atomic+molecular velocity dispersions evolve similarly with redshift, with the ionized gas dispersion being ∼10–15 km s−1 higher on average. We investigate the physical driver of the on average elevated velocity dispersions at higher redshift and find that our galaxies are at most marginally Toomre-stable, suggesting that their turbulent velocities are powered by gravitational instabilities, while stellar feedback as a driver alone is insufficient. This picture is supported through comparison with a state-of-the-art analytical model of galaxy evolution.
CHARACTERISTIC OUTFLOW BOUNDARY CONDITIONS FOR SIMULATIONS OF ONE-DIMENSIONAL HEMODYNAMICS

The Windkessel outflow models, describing the relation between blood pressure and flow, are commonly used for modeling the truncated downstream vasculature bed in the arterial system. By means of a characteristic approach, a unified formalism of characteristic boundary conditions was proposed in the present work. A novel defined parameter, termed the reflection coefficient, is convenient to quantify and analyze the incoming waves occurring at the boundary of the computational domain. Numerical examples using different forms of outflow models including one-, two-, and three-element Windkessel models were studied and discussed. The results indicate that when the 1-D terminal ends are coupled with the three-element Windkessel outflow model, the system is able to capture the physiological features. Keywords— Windkessel models, reflection coefficient, hyperbolic system, distributed arterial network, nonreflecting boundary condition.