Miracle-dz/UniFAR-FTUs huggingface dataset:
Integration of Security Modules in Software Development Lifecycle Phases

Information protection is becoming a focal point for designing, creating and implementing software applications within highly integrated technology environments. The use of a safe coding technique in the software development process is required by many industrial IT security standards and policies. Despite current cyber protection measures and best practices, vulnerabilities still remain strong and become a huge threat to every developed software. It is crucial to understand the position of secure software development for security management, which is affected by causes such as human security-related factors. Although developers are often held accountable for security vulnerabilities, in reality, many problems often grow from a lack of organizational support during development tasks to handle security. While abstract safe coding guidelines are generally recognized, there are limited low-level secure coding guidelines for various programming languages. A good technique is required to standardize these guidelines for software developers. The goal of this paper is to address this gap by providing software designers and developers with direction by identifying a set of secure software development guidelines. Additionally, an overview of criteria for selection of safe coding guidelines is performed along with investigation of appropriate awareness methods for secure coding.
Direct multimodal few-shot learning of speech and images

We propose direct multimodal few-shot models that learn a shared embedding space of spoken words and images from only a few paired examples. Imagine an agent is shown an image along with a spoken word describing the object in the picture, e.g. pen, book and eraser. After observing a few paired examples of each class, the model is asked to identify the "book" in a set of unseen pictures. Previous work used a two-step indirect approach relying on learned unimodal representations: speech-speech and image-image comparisons are performed across the support set of given speech-image pairs. We propose two direct models which instead learn a single multimodal space where inputs from different modalities are directly comparable: a multimodal triplet network (MTriplet) and a multimodal correspondence autoencoder (MCAE). To train these direct models, we mine speech-image pairs: the support set is used to pair up unlabelled in-domain speech and images. In a speech-to-image digit matching task, direct models outperform indirect models, with the MTriplet achieving the best multimodal five-shot accuracy. We show that the improvements are due to the combination of unsupervised and transfer learning in the direct models, and the absence of two-step compounding errors.
GAN Steerability without optimization

Recent research has shown remarkable success in revealing "steering" directions in the latent spaces of pre-trained GANs. These directions correspond to semantically meaningful image transformations e.g., shift, zoom, color manipulations), and have similar interpretable effects across all categories that the GAN can generate. Some methods focus on user-specified transformations, while others discover transformations in an unsupervised manner. However, all existing techniques rely on an optimization procedure to expose those directions, and offer no control over the degree of allowed interaction between different transformations. In this paper, we show that "steering" trajectories can be computed in closed form directly from the generator's weights without any form of training or optimization. This applies to user-prescribed geometric transformations, as well as to unsupervised discovery of more complex effects. Our approach allows determining both linear and nonlinear trajectories, and has many advantages over previous methods. In particular, we can control whether one transformation is allowed to come on the expense of another (e.g. zoom-in with or without allowing translation to keep the object centered). Moreover, we can determine the natural end-point of the trajectory, which corresponds to the largest extent to which a transformation can be applied without incurring degradation. Finally, we show how transferring attributes between images can be achieved without optimization, even across different categories.
Radial Density Profile and Stability of Meter-Scale Capillary Discharge Plasma Waveguides

We measured the parameter reproducibility and radial electron density profile of capillary discharge waveguides with diameters of 650um to 2mm and lengths of 9 to 40cm. To our knowledge, 40 cm is the longest discharge capillary plasma waveguide to date. This length is important for >=10GeV electron energy gain in a single laser driven plasma wakefield acceleration (LPA) stage. Evaluation of waveguide parameter variations showed that their focusing strength was stable and reproducible to <0.2% and their average on-axis plasma electron density to <1%. These variations explain only small fraction of LPA electron bunch variations observed in experiments to date. Measurements of laser pulse centroid oscillations revealed that the radial channel profile rises faster than parabolic and are in excellent agreement with magneto-hydro-dynamic simulation results. We show that the effects of non-parabolic contributions on Gaussian pulse propagation were negligible when the pulse was approximately matched to the channel. However, they affected pulse propagation for a non-matched configuration in which the waveguide was used as a plasma telescope to change the focused laser pulse spot size.
Local limits of bipartite maps with prescribed face degrees in high genus

We study the local limits of uniform high genus bipartite maps with prescribed face degrees. We prove the convergence towards a family of infinite maps of the plane, the q-IBPMs, which exhibit both a spatial Markov property and a hyperbolic behaviour. Therefore, we observe a similar local behaviour for a wide class of models of random high genus maps, which can be seen as a result of universality. Our results cover all the regimes where the expected degree of the root face remains finite in the limit. This follows a work by the same authors on high genus triangulations arXiv:1902.00492.
Asymptotic study of stochastic adaptive algorithm in non-convex landscape

This paper studies some asymptotic properties of adaptive algorithms widely used in optimization and machine learning, and among them Adagrad and Rmsprop, which are involved in most of the blackbox deep learning algorithms. Our setup is the non-convex landscape optimization point of view, we consider a one time scale parametrization and we consider the situation where these algorithms may be used or not with mini-batches. We adopt the point of view of stochastic algorithms and establish the almost sure convergence of these methods when using a decreasing step-size point of view towards the set of critical points of the target function. With a mild extra assumption on the noise, we also obtain the convergence towards the set of minimizer of the function. Along our study, we also obtain a "convergence rate" of the methods, in the vein of the works of \cite{GhadimiLan}.
Investigating Bias in Image Classification using Model Explanations

We evaluated whether model explanations could efficiently detect bias in image classification by highlighting discriminating features, thereby removing the reliance on sensitive attributes for fairness calculations. To this end, we formulated important characteristics for bias detection and observed how explanations change as the degree of bias in models change. The paper identifies strengths and best practices for detecting bias using explanations, as well as three main weaknesses: explanations poorly estimate the degree of bias, could potentially introduce additional bias into the analysis, and are sometimes inefficient in terms of human effort involved.
Internet of Buoys: An Internet of Things Implementation at Sea

Internet of Things (IoT) applications are emerging in many different areas, including maritime environments. One of the applications in this area is the monitoring of buoys at sea. To realize wireless tracking of buoys, an accurate prediction of the path loss in an open-sea environment is essential. So far, channel measurements at sea have mainly been conducted with antennas placed a couple of meters above the sea surface, which is higher than the buoys themselves. Therefore, we investigated the validity of the published channel models at sea by means of path loss measurements using a LoRa link with a transmitter antenna height of 0.35 m and a base station antenna height of 2.65 m and 5.2 m. Our results show that the round earth loss model is not accurate at these antenna heights. The ITU-R P.2001-3 model and a model by Bullington show a better agreement with our measurements. However, the difference between our two measurement campaigns shows that more investigation is needed on the dependence of the path loss on the sea state. Additionally, the availability of Sigfox, Narrowband Internet of Things and The Things has been explored. We found that that these can be used for IoT applications in the tested area at low antenna heights.
Thompson Sampling for CVaR Bandits

Risk awareness is an important feature to formulate a variety of real world problems. In this paper we study a multi-arm bandit problem in which the quality of each arm is measured by the Conditional Value at Risk (CVaR) at some level {\alpha} of the reward distribution. While existing works in this setting mainly focus on Upper Confidence Bound algorithms, we introduce the first Thompson Sampling approaches for CVaR bandits. Building on a recent work by Riou and Honda (2020), we propose {\alpha}-NPTS for bounded rewards and {\alpha}-Multinomial-TS for multinomial distributions. We provide a novel lower bound on the CVaR regret which extends the concept of asymptotic optimality to CVaR bandits and prove that {\alpha}-Multinomial-TS is the first algorithm to achieve this lower bound. Finally, we demonstrate empirically the benefit of Thompson Sampling approaches over their UCB counterparts.
Full Matching on Low Resolution for Disparity Estimation

A Multistage Full Matching disparity estimation scheme (MFM) is proposed in this work. We demonstrate that decouple all similarity scores directly from the low-resolution 4D volume step by step instead of estimating low-resolution 3D cost volume through focusing on optimizing the low-resolution 4D volume iteratively leads to more accurate disparity. To this end, we first propose to decompose the full matching task into multiple stages of the cost aggregation module. Specifically, we decompose the high-resolution predicted results into multiple groups, and every stage of the newly designed cost aggregation module learns only to estimate the results for a group of points. This alleviates the problem of feature internal competitive when learning similarity scores of all candidates from one low-resolution 4D volume output from one stage. Then, we propose the strategy of \emph{Stages Mutual Aid}, which takes advantage of the relationship of multiple stages to boost similarity scores estimation of each stage, to solve the unbalanced prediction of multiple stages caused by serial multistage framework. Experiment results demonstrate that the proposed method achieves more accurate disparity estimation results and outperforms state-of-the-art methods on Scene Flow, KITTI 2012 and KITTI 2015 datasets.
The SYZ mirror symmetry conjecture for del Pezzo surfaces and rational elliptic surfaces.

We prove the Strominger-Yau-Zaslow mirror symmetry conjecture for non-compact Calabi-Yau surfaces arising from, on the one hand, pairs $(\check{Y},\check{D})$ of a del Pezzo surface $\check{Y}$ and $\check{D}$ a smooth anti-canonical divisor and, on the other hand, pairs $(Y,D)$ of a rational elliptic surface $Y$, and $D$ a singular fiber of Kodaira type $I_k$. Three main results are established concerning the latter pairs $(Y,D)$. First, adapting work of Hein, we prove the existence of a complete Calabi-Yau metric on $Y\setminus D$ asymptotic to a (generically non-standard) semi-flat metric in every K\"ahler class. Secondly, we prove an optimal uniqueness theorem to the effect that, modulo automorphisms, every K\"ahler class on $Y\setminus D$ admits a unique asymptotically semi-flat Calabi-Yau metric. This result yields a finite dimensional K\"ahler moduli space of Calabi-Yau metrics on $Y\setminus D$. Further, this result answers a question of Tian-Yau and settles a folklore conjecture of Yau in this setting. Thirdly, we prove that $Y\setminus D$ equipped with an asymptotically semi-flat Calabi-Yau metric $\omega_{CY}$ admits a special Lagrangian fibration whenever the de Rham cohomology class of $\omega_{CY}$ is not topologically obstructed. Combining these results we define a mirror map from the moduli space of del Pezzo pairs $(\check{Y}, \check{D})$ to the complexified K\"ahler moduli of $(Y,D)$ and prove that the special Lagrangian fibration on $(Y,D)$ is $T$-dual to the special Lagrangian fibration on $(\check{Y}, \check{D})$ previously constructed by the authors. We give some applications of these results, including to the study of automorphisms of del Pezzo surfaces fixing an anti-canonical divisor.
Are Fewer Labels Possible for Few-shot Learning?

Few-shot learning is challenging due to its very limited data and labels. Recent studies in big transfer (BiT) show that few-shot learning can greatly benefit from pretraining on large scale labeled dataset in a different domain. This paper asks a more challenging question: "can we use as few as possible labels for few-shot learning in both pretraining (with no labels) and fine-tuning (with fewer labels)? ". Our key insight is that the clustering of target samples in the feature space is all we need for few-shot finetuning. It explains why the vanilla unsupervised pretraining (poor clustering) is worse than the supervised one. In this paper, we propose transductive unsupervised pretraining that achieves a better clustering by involving target data even though its amount is very limited. The improved clustering result is of great value for identifying the most representative samples ("eigen-samples") for users to label, and in return, continued finetuning with the labeled eigen-samples further improves the clustering. Thus, we propose eigen-finetuning to enable fewer shot learning by leveraging the co-evolution of clustering and eigen-samples in the finetuning. We conduct experiments on 10 different few-shot target datasets, and our average few-shot performance outperforms both vanilla inductive unsupervised transfer and supervised transfer by a large margin. For instance, when each target category only has 10 labeled samples, the mean accuracy gain over the above two baselines is 9.2% and 3.42 respectively.
Control Analysis and Synthesis of Data-Driven Learning: A Kalman State-Space Approach

This paper aims to deal with the control analysis and synthesis problem of data-driven learning, regardless of unknown plant models and iteration-varying uncertainties. For the tracking of any desired target, a Kalman state-space approach is presented to transform it into two robust stability problems, which bridges a connection between data-driven control and model-based control. This approach also makes it possible to employ the extended state observer (ESO) in the design of data-driven learning to overcome the effect of iteration-varying uncertainties. It is shown that ESO-based data-driven learning ensures model-free systems to achieve the tracking of any desired target. In particular, our results apply to iterative learning control, which is verified by an example.
Denitrification and ozone loss in the Arctic stratosphere

This thesis investigates the mechanism of denitrification o f the Arctic lower stratosphere and the impact o f denitrification on ozone loss using the SLIMCAT chemical transport model. The development of a new microphysical model for the simulation of growth and sedimentation of large nitric acid trihydrate particles is also described. Model simulations of Arctic denitrification were carried out using thermodynamic equilibrium schemes based on the sedimentation of either nitric acid trihydrate or ice using different meteorological analyses. The severity and extent of denitrification in ice-based model runs was found to be highly sensitive to the meteorological analyses used whereas nitric acid trihydrate denitrification schemes exhibited considerably less sensitivity. The response of thermodynamic equilibrium and microphysical NAT-based denitrification to meteorological conditions has been studied in a series of short idealised simulations. It was found that microphysical denitrification was considerably more sensitive to the relative orientation of the polar vortex flow and the region of cold temperatures. A concentric vortex and cold region are required to promote the long particle growth times required for strong denitrification in the microphysical model. Reduced rates of denitrification were evident in the microphysical model at the highest altitudes. Results from the microphyical denitrification scheme were compared with in-situ and remote observations of denitrification for two recent cold Arctic winters. There was remarkable agreement between model and observations of both the magnitude and location of denitrification despite the simple volume-averaged nucleation rate used in the model. The limited range of observations did not allow further constraints to be placed on the microphysical model. Denitrification was found to enhance Arctic ozone loss by up to 30% during 1999/2000. Sensitivity studies o f the impact of denitrification on Arctic ozone loss were performed using thermodynamic nitric acid trihydrate denitrification schemes. Cumulative ozone depletion was found to increase non-linearly with increasing denitrification. Enhanced recovery of chlorine radicals to hydrogen chloride in strongly denitrified model runs offset reduced recovery to chlorine nitrate, limiting the impact of denitrification to the equivalent of 20 days additional ozone loss
Implicit-explicit multistep formulations for finite element discretisations using continuous interior penalty

We consider a finite element method with symmetric stabilisation for the discretisation of the transient convection--diffusion equation. For the time-discretisation we consider either the second order backwards differentiation formula or the Crank-Nicolson method. Both the convection term and the associated stabilisation are treated explicitly using an extrapolated approximate solution. We prove stability of the method and the $\tau^2 + h^{p+{\frac12}}$ error estimates for the $L^2$-norm under either the standard hyperbolic CFL condition, when piecewise affine ($p=1$) approximation is used, or in the case of finite element approximation of order $p \ge 1$, a stronger, so-called $4/3$-CFL, i.e. $\tau \leq C h^{4/3}$. The theory is illustrated with some numerical examples.
Accelerated Randomized Methods for Receiver Design in Extra-Large Scale MIMO Arrays

Massive multiple-input-multiple-output (M-MIMO) features a capability for spatial multiplexing of large number of users. This number becomes even more extreme in extra-large (XL-MIMO), a variant of M-MIMO where the antenna array is of very large size. Yet, the problem of signal processing complexity in M-MIMO is further exacerbated by the XL size of the array. The basic processing problem boils down to a sparse system of linear equations that can be addressed by the randomized Kaczmarz (RK) algorithm. This algorithm has recently been applied to devise low-complexity M-MIMO receivers; however, it is limited by the fact that certain configurations of the linear equations may significantly deteriorate the performance of the RK algorithm. In this article, we embrace the interest in accelerated RK algorithms and introduce three new RK-based low-complexity receiver designs. In our experiments, our methods are not only able to overcome the previous scheme, but they are more robust against inter-user interference (IUI) and sparse channel matrices arising in the XL-MIMO regime. In addition, we show that the RK-based schemes use a mechanism similar to that used by successive interference cancellation (SIC) receivers to approximate the regularized zero-forcing (RZF) scheme.
Bew: Towards Answering Business-Entity-Related Web Questions

We present BewQA, a system specifically designed to answer a class of questions that we call Bew questions. Bew questions are related to businesses/services such as restaurants, hotels, and movie theaters; for example, "Until what time is happy hour? ". These questions are challenging to answer because the answers are found in open-domain Web, are present in short sentences without surrounding context, and are dynamic since the webpage information can be updated frequently. Under these conditions, existing QA systems perform poorly. We present a practical approach, called BewQA, that can answer Bew queries by mining a template of the business-related webpages and using the template to guide the search. We show how we can extract the template automatically by leveraging aggregator websites that aggregate information about business entities in a domain (e.g., restaurants). We answer a given question by identifying the section from the extracted template that is most likely to contain the answer. By doing so we can extract the answers even when the answer span does not have sufficient context. Importantly, BewQA does not require any training. We crowdsource a new dataset of 1066 Bew questions and ground-truth answers in the restaurant domain. Compared to state-of-the-art QA models, BewQA has a 27 percent point improvement in F1 score. Compared to a commercial search engine, BewQA answered correctly 29% more Bew questions.
GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification

Extreme multi-label text classification (XMTC) aims to tag a text instance with the most relevant subset of labels from an extremely large label set. XMTC has attracted much recent attention due to massive label sets yielded by modern applications, such as news annotation and product recommendation. The main challenges of XMTC are the data scalability and sparsity, thereby leading to two issues: i) the intractability to scale to the extreme label setting, ii) the presence of long-tailed label distribution, implying that a large fraction of labels have few positive training instances. To overcome these problems, we propose GNN-XML, a scalable graph neural network framework tailored for XMTC problems. Specifically, we exploit label correlations via mining their co-occurrence patterns and build a label graph based on the correlation matrix. We then conduct the attributed graph clustering by performing graph convolution with a low-pass graph filter to jointly model label dependencies and label features, which induces semantic label clusters. We further propose a bilateral-branch graph isomorphism network to decouple representation learning and classifier learning for better modeling tail labels. Experimental results on multiple benchmark datasets show that GNN-XML significantly outperforms state-of-the-art methods while maintaining comparable prediction efficiency and model size.