Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Lexical Features for Malicious URL Detection - A Machine Learning Approach

Malicious websites are responsible for a majority of the cyber-attacks and scams today. Malicious URLs are delivered to unsuspecting users via email, text messages, pop-ups or advertisements. Clicking on or crawling such URLs can result in compromised email accounts, launching of phishing campaigns, download of malware, spyware and ransomware, as well as severe monetary losses. A machine learning based ensemble classification approach is proposed to detect malicious URLs in emails, which can be extended to other methods of delivery of malicious URLs. The approach uses static lexical features extracted from the URL string, with the assumption that these features are notably different for malicious and benign URLs. The use of such static features is safer and faster since it does not involve crawling the URLs or blacklist lookups which tend to introduce a significant amount of latency in producing verdicts. The goal of the classification was to achieve high sensitivity i.e. detect as many malicious URLs as possible. URL strings tend to be very unstructured and noisy. Hence, bagging algorithms were found to be a good fit for the task since they average out multiple learners trained on different parts of the training data, thus reducing variance. The classification model was tested on five different testing sets and produced an average False Negative Rate (FNR) of 0.1%, average accuracy of 92% and average AUC of 0.98. The model is presently being used in the FireEye Advanced URL Detection Engine (used to detect malicious URLs in emails), to generate fast real-time verdicts on URLs. The malicious URL detections from the engine have gone up by 22% since the deployment of the model into the engine workflow. The results obtained show noteworthy evidence that a purely lexical approach can be used to detect malicious URLs.
Microwave photonic fractional Hilbert transformer with an integrated optical soliton crystal micro-comb

We report a photonic microwave and RF fractional Hilbert transformer based on an integrated Kerr micro-comb source. The micro-comb source has a free spectral range (FSR) of 50GHz, generating a large number of comb lines that serve as a high-performance multiwavelength source for the transformer. By programming and shaping the comb lines according to calculated tap weights, we achieve both arbitrary fractional orders and a broad operation bandwidth. We experimentally characterize the RF amplitude and phase response for different fractional orders and perform system demonstrations of real-time fractional Hilbert transforms. We achieve a phase ripple of < 0.15 rad within the 3-dB pass-band, with bandwidths ranging from 5 to 9 octaves, depending on the order. The experimental results show good agreement with theory, confirming the effectiveness of our approach as a new way to implement high-performance fractional Hilbert transformers with broad processing bandwidth, high reconfigurability, and greatly reduced size and complexity.
Dynamic Attention Networks for Task Oriented Grounding

In order to successfully perform tasks specified by natural language instructions, an artificial agent operating in a visual world needs to map words, concepts, and actions from the instruction to visual elements in its environment. This association is termed as Task-Oriented Grounding. In this work, we propose a novel Dynamic Attention Network architecture for the efficient multi-modal fusion of text and visual representations which can generate a robust definition of state for the policy learner. Our model assumes no prior knowledge from visual and textual domains and is an end to end trainable. For a 3D visual world where the observation changes continuously, the attention on the visual elements tends to be highly co-related from a one-time step to the next. We term this as "Dynamic Attention". In this work, we show that Dynamic Attention helps in achieving grounding and also aids in the policy learning objective. Since most practical robotic applications take place in the real world where the observation space is continuous, our framework can be used as a generalized multi-modal fusion unit for robotic control through natural language. We show the effectiveness of using 1D convolution over Gated Attention Hadamard product on the rate of convergence of the network. We demonstrate that the cell-state of a Long Short Term Memory (LSTM) is a natural choice for modeling Dynamic Attention and shows through visualization that the generated attention is very close to how humans tend to focus on the environment.
Facial Emotion Recognition using Convolutional Neural Networks

Facial expression recognition is a topic of great interest in most fields from artificial intelligence and gaming to marketing and healthcare. The goal of this paper is to classify images of human faces into one of seven basic emotions. A number of different models were experimented with, including decision trees and neural networks before arriving at a final Convolutional Neural Network (CNN) model. CNNs work better for image recognition tasks since they are able to capture spacial features of the inputs due to their large number of filters. The proposed model consists of six convolutional layers, two max pooling layers and two fully connected layers. Upon tuning of the various hyperparameters, this model achieved a final accuracy of 0.60.
Quantitative estimates in stochastic homogenization for correlated coefficient fields

This paper is about the homogenization of linear elliptic operators in divergence form with stationary random coefficients that have only slowly decaying correlations. It deduces optimal estimates of the homogenization error from optimal growth estimates of the (extended) corrector. In line with the heuristics, there are transitions at dimension $d=2$, and for a correlation-decay exponent $\beta=2$; we capture the correct power of logarithms coming from these two sources of criticality. The decay of correlations is sharply encoded in terms of a multiscale logarithmic Sobolev inequality (LSI) for the ensemble under consideration --- the results would fail if correlation decay were encoded in terms of an $\alpha$-mixing condition. Among other ensembles popular in modelling of random media, this class includes coefficient fields that are local transformations of stationary Gaussian fields. The optimal growth of the corrector $\phi$ is derived from bounding the size of spatial averages $F=\int g\cdot\nabla\phi $ of its gradient. This in turn is done by a (deterministic) sensitivity estimate of $F$, that is, by estimating the functional derivative $\frac{\partial F}{\partial a}$ of $F$ w. ~r. ~t. ~the coefficient field $a$. Appealing to the LSI in form of concentration of measure yields a stochastic estimate on $F$. The sensitivity argument relies on a large-scale Schauder theory for the heterogeneous elliptic operator $-\nabla\cdot a\nabla$. The treatment allows for non-symmetric $a$ and for systems like linear elasticity.
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs

While a wide range of interpretable generative procedures for graphs exist, matching observed graph topologies with such procedures and choices for its parameters remains an open problem. Devising generative models that closely reproduce real-world graphs requires domain knowledge and time-consuming simulation. While existing deep learning approaches rely on less manual modelling, they offer little interpretability. This work approaches graph generation (decoding) as the inverse of graph compression (encoding). We show that in a disentanglement-focused deep autoencoding framework, specifically Beta-Variational Autoencoders (Beta-VAE), choices of generative procedures and their parameters arise naturally in the latent space. Our model is capable of learning disentangled, interpretable latent variables that represent the generative parameters of procedurally generated random graphs and real-world graphs. The degree of disentanglement is quantitatively measured using the Mutual Information Gap (MIG). When training our Beta-VAE model on ER random graphs, its latent variables have a near one-to-one mapping to the ER random graph parameters n and p. We deploy the model to analyse the correlation between graph topology and node attributes measuring their mutual dependence without handpicking topological properties.
Statistical Topology of Bond Networks with Applications to Silica

Whereas knowledge of a crystalline material's unit cell is fundamental to understanding the material's properties and behavior, there are no obvious analogs to unit cells for disordered materials despite the frequent existence of considerable medium-range order. This article views a material's structure as a collection of local atomic environments that are sampled from some underlying probability distribution of such environments, with the advantage of offering a unified description of both ordered and disordered materials. Crystalline materials can then be regarded as special cases where the underlying probability distribution is highly concentrated around the traditional unit cell. The H_{1} barcode is proposed as a descriptor of local atomic environments suitable for disordered bond networks and is applied with three other descriptors to molecular dynamics simulations of silica glasses. Each descriptor reliably distinguishes the structure of glasses produced at different cooling rates, with the H_{1} barcode and coordination profile providing the best separation. The approach is generally applicable to any system that can be represented as a sparse graph.
AdaWISH: Faster Discrete Integration via Adaptive Quantiles

Discrete integration in a high dimensional space of $n$ variables poses fundamental challenges. The WISH algorithm reduces the intractable discrete integration problem into $n$ optimization queries subject to randomized constraints, obtaining a constant approximation guarantee. The optimization queries are expensive, which limits the applicability of WISH. We propose AdaWISH, which is able to obtain the same guarantee, but accesses only a small subset of queries of WISH. For example, when the number of function values is bounded by a constant, AdaWISH issues only $O(\log n)$ queries. The key idea is to query adaptively, taking advantage of the shape of the weight function. In general, we prove that AdaWISH has a regret of no more than $O(\log n)$ relative to an oracle that issues queries at data-dependent optimal points. Experimentally, AdaWISH gives precise estimates for discrete integration problems, of the same quality as that of WISH and better than several competing approaches, on a variety of probabilistic inference benchmarks, while saving substantially on the number of optimization queries compared to WISH. For example, it saves $81.5\%$ of WISH queries while retaining the quality of results on a suite of UAI inference challenge benchmarks.
Multifractality in aperiodic quantum spin chains

Recently, it has been investigated that the ground-state wavefunction of the one dimensional quantum spin-1/2 chain models is multifractal in general with non-trivial fractal dimension. We are studying this phenomena for the quantum Ising chain with aperiodic perturbation. By performing a block real-space renormalization approach, we obtain the ground-state wave function and we extract the generalized multifractal dimension and the multifractal spectrum. For a spin chain with negative wandering exponent the multifractal quantities have the same behavior with the unperturbed chain while for a spin chain with a vanishing wandering exponent, are dependent on the coupling ratio. Finally, for a spin chain with a positive wandering exponent, the multifractal quantities present a different non-linear behavior.
Emergent properties of the local geometry of neural loss landscapes

The local geometry of high dimensional neural network loss landscapes can both challenge our cherished theoretical intuitions as well as dramatically impact the practical success of neural network training. Indeed recent works have observed 4 striking local properties of neural loss landscapes on classification tasks: (1) the landscape exhibits exactly $C$ directions of high positive curvature, where $C$ is the number of classes; (2) gradient directions are largely confined to this extremely low dimensional subspace of positive Hessian curvature, leaving the vast majority of directions in weight space unexplored; (3) gradient descent transiently explores intermediate regions of higher positive curvature before eventually finding flatter minima; (4) training can be successful even when confined to low dimensional {\it random} affine hyperplanes, as long as these hyperplanes intersect a Goldilocks zone of higher than average curvature. We develop a simple theoretical model of gradients and Hessians, justified by numerical experiments on architectures and datasets used in practice, that {\it simultaneously} accounts for all $4$ of these surprising and seemingly unrelated properties. Our unified model provides conceptual insights into the emergence of these properties and makes connections with diverse topics in neural networks, random matrix theory, and spin glasses, including the neural tangent kernel, BBP phase transitions, and Derrida's random energy model.
What is a Process Model Composed of? A Systematic Literature Review of Meta-Models in BPM

Business process modelling languages typically enable the representation of business process models by employing (graphical) symbols. These symbols can vary depending upon the verbosity of the language, the modelling paradigm, the focus of the language, and so on. To make explicit the different constructs and rules employed by a specific language, as well as bridge the gap across different languages, meta-models have been proposed in literature. These meta-models are a crucial source of knowledge on what state-of-the-art literature considers relevant to describe business processes. The goal of this work is to provide an extensive systematic literature review (SLR) of business process meta-models. This SLR aims at answering research questions concerning: (i) the kind of meta-models proposed in literature; (ii) the recurring constructs they contain; (iii) their purposes; and (iv) their evaluations.
Transversal polynomial of r-fold covers

We explore the interplay between algebraic combinatorics and algorithmic problems in graph theory by defining a polynomial with connections to correspondence colouring (also known as DP-colouring), a recent generalization of list-colouring, and the Unique Games Conjecture. Like the chromatic polynomial of a graph, we are able to evaluate this polynomial at a point, despite the complexity of computing this polynomial. We construct a cover of a graph $X$ by blowing up each vertex to a set of $r$ vertices and joining each pair of sets corresponding to adjacent vertices by a matching with $r$ edges. To each cover $Y$ of $X$ we associate a polynomial $\xi(Y,t)$, called the transversal polynomial. The coefficient $t^k$ of $\xi(Y,t)$ is the number of $k$-edge induced subgraphs of $Y$ whose vertex set is a transversal of the set system given by the blown-up vertices. We show that $\xi(Y,t)$ satisfies a contraction-deletion formula, and that if $n=|V_X|$ and the cover has index $r$, then $\xi(Y,-(r-1)) \equiv 0 \mod r^n$.
Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers

We study the convergence of gradient flows related to learning deep linear neural networks (where the activation function is the identity map) from data. In this case, the composition of the network layers amounts to simply multiplying the weight matrices of all layers together, resulting in an overparameterized problem. The gradient flow with respect to these factors can be re-interpreted as a Riemannian gradient flow on the manifold of rank-$r$ matrices endowed with a suitable Riemannian metric. We show that the flow always converges to a critical point of the underlying functional. Moreover, we establish that, for almost all initializations, the flow converges to a global minimum on the manifold of rank $k$ matrices for some $k\leq r$.
Two-sample Testing Using Deep Learning

We propose a two-sample testing procedure based on learned deep neural network representations. To this end, we define two test statistics that perform an asymptotic location test on data samples mapped onto a hidden layer. The tests are consistent and asymptotically control the type-1 error rate. Their test statistics can be evaluated in linear time (in the sample size). Suitable data representations are obtained in a data-driven way, by solving a supervised or unsupervised transfer-learning task on an auxiliary (potentially distinct) data set. If no auxiliary data is available, we split the data into two chunks: one for learning representations and one for computing the test statistic. In experiments on audio samples, natural images and three-dimensional neuroimaging data our tests yield significant decreases in type-2 error rate (up to 35 percentage points) compared to state-of-the-art two-sample tests such as kernel-methods and classifier two-sample tests.
Construcción de funciones de estimación múltiple protegidas en modelos con verosimilitud factorizada

En los ultimos anos, los metodos de estimacion doble protegidos dieron lugar al desarrollo de propuestas multiple protegidas. Diversos autores han presentado estimadores consistentes en mas escenarios que los contemplados por los estimadores doble protegidos. Estas propuestas de estimacion, lejos de construirse bajo una teoria general, son el resultado de procedimientos ad-hoc creados para cada modelo considerado. En este trabajo, desarrollamos un marco teorico que explica la existencia de estimadores multiple protegidos en varias de las propuestas de estimacion multiple protegidas ya existentes, en modelos donde la verosimilitud se factoriza. Dicha teoria provee condiciones suficientes bajo las cuales es posible la construccion de ecuaciones de estimacion que, bajo condiciones de regularidad, tienen soluciones que constituyen estimadores multiple protegidos. Presentamos tambien condiciones suficientes bajo las cuales estimar de manera multiple protegida ciertos parametros de ruido, cuya estimacion es necesaria para poder estimar de manera multiple protegida al parametro de interes. Ademas, aplicamos los metodos desarrollados para estimar en forma cuadruple protegida la componente lineal bajo un modelo de regresion parcialmente lineal con datos faltantes, ya que dicho modelo se inscribe en el marco de la teoria desarrollada. Incluimos tambien, los resultados de un estudio de Monte Carlo realizado bajo el modelo mencionado.
A new approach to assessing the dynamic uncertainty of measuring devices

A spectral method for estimating the dynamic uncertainty of measuring instruments based on a mathematical model of the frequency characteristic of a measuring instrument and a model of the spectral function of an input signal is presented. The model equation for estimating the amplitude value of the dynamic measurement uncertainty is obtained, which is caused by the limited properties of the measuring devices when a measuring signal passes through it in dynamic operation modes. A mathematical simulation of the characteristic of the dynamic uncertainty variation during the passage of a measuring signal through a measuring transducer is performed using the dynamic model of a vibration transducer as an example.
Bmc Medical Genetics Genome-wide Association and Linkage Analyses of Hemostatic Factors and Hematological Phenotypes in the Framingham Heart Study

Background: Increased circulating levels of hemostatic factors as well as anemia have been associated with increased risk of cardiovascular disease (CVD). Known associations between hemostatic factors and sequence variants at genes encoding these factors explain only a small proportion of total phenotypic variation. We sought to confirm known putative loci and identify novel loci that may influence either trait in genome-wide association and linkage analyses using the Affymetrix GeneChip 100K single nucleotide polymorphism (SNP) set.
Building Causal Graphs from Medical Literature and Electronic Medical Records

Large repositories of medical data, such as Electronic Medical Record (EMR) data, are recognized as promising sources for knowledge discovery. Effective analysis of such repositories often necessitate a thorough understanding of dependencies in the data. For example, if the patient age is ignored, then one might wrongly conclude a causal relationship between cataract and hypertension. Such confounding variables are often identified by causal graphs, where variables are connected by causal relationships. Current approaches to automatically building such graphs are based on text analysis over medical literature; yet, the result is typically a large graph of low precision. There are statistical methods for constructing causal graphs from observational data, but they are less suitable for dealing with a large number of covariates, which is the case in EMR data. Consequently, confounding variables are often identified by medical domain experts via a manual, expensive, and time-consuming process. We present a novel approach for automatically constructing causal graphs between medical conditions. The first part is a novel graph-based method to better capture causal relationships implied by medical literature, especially in the presence of multiple causal factors. Yet even after using these advanced text-analysis methods, the text data still contains many weak or uncertain causal connections. Therefore, we construct a second graph for these terms based on an EMR repository of over 1.5M patients. We combine the two graphs, leaving only edges that have both medical-text-based and observational evidence. We examine several strategies to carry out our approach, and compare the precision of the resulting graphs using medical experts. Our results show a significant improvement in the precision of any of our methods compared to the state of the art.
Holography and Kinect

Holographic images are often presented as static images. This project aims at solutions to depict a dynamic ‘holographic’ image that can be manipulated. First an illusion is created of a generic object floating in mid air by using a Mirascope, a mirror room that consists of two concave parabolic mirrors stacked on top of each other. The top mirror has an aperture in the center to allow viewing inside of the mirror room. The generic object that is placed inside the mirascope then appears to be floating in mid-air just above the top mirror. The object can be made dynamic by projecting images onto it, using 2 beamers (one beaming onto the frontal surface of the object and one beaming onto the backside of the object). Subsequently the Kinect, an input device developed by Microsoft, that recognizes movement and gestures, is used to manipulate the image beamed onto the object. Gestures will change the projections beamed onto the object, and therefore changing the illusion of the floating object above the mirascope.
Networked Microgrids: State-of-the-Art and Future Perspectives

The operation of multiple microgrids (MGs) in coordination with distribution system enables high penetration of locally available distributed energy resources (DERs). This approach enhances the reliability and resiliency of the power supply significantly. Also, the overall cost of energy gets reduced because of the integration of cost-free power from photovoltaic panels and wind turbines. The most effective utilization of DERs can be achieved through networked MGs. However, the implementation of the concepts of networked MGs requires extensive research. This paper presents a comprehensive literature review of the most important research works on networked MGs. Major benefits and challenges related to this new and highly exploring area have been analyzed. Also, some of the most important research areas related to networked MGs have been highlighted and discussed as the future perspectives.