Reviewerly/exHarmony huggingface dataset:
A Study of Feature Selection and Dimensionality Reduction Methods for Classification-Based Phishing Detection System

Phishing was introduced in 1996, and now phishing is the biggest cybercrime challenge. Phishing is an abstract way to deceive users over the internet. Purpose of phishers is to extract the sensitive information of the user. Researchers have been working on solutions of phishing problem, but the parallel evolution of cybercrime techniques have made it a tough nut to crack. Recently, machine learning-based solutions are widely adopted to tackle the menace of phishing. This survey paper studies various feature selection method and dimensionality reduction methods and sees how they perform with machine learning-based classifier. The selection of features is vital for developing a good performance machine learning model. This work is comparing three broad categories of feature selection methods, namely filter, wrapper, and embedded feature selection methods, to reduce the dimensionality of data. The effectiveness of these methods has been assessed on several machine learning classifiers using k-fold cross-validation score, accuracy, precision, recall, and time.
Structured Convolutions for Efficient Neural Network Design

In this work, we tackle model efficiency by exploiting redundancy in the \textit{implicit structure} of the building blocks of convolutional neural networks. We start our analysis by introducing a general definition of Composite Kernel structures that enable the execution of convolution operations in the form of efficient, scaled, sum-pooling components. As its special case, we propose \textit{Structured Convolutions} and show that these allow decomposition of the convolution operation into a sum-pooling operation followed by a convolution with significantly lower complexity and fewer weights. We show how this decomposition can be applied to 2D and 3D kernels as well as the fully-connected layers. Furthermore, we present a Structural Regularization loss that promotes neural network layers to leverage on this desired structure in a way that, after training, they can be decomposed with negligible performance loss. By applying our method to a wide range of CNN architectures, we demonstrate versions of the ResNets that are up to 2$\times$ smaller and a new Structured-MobileNetV2 that is more efficient while staying within an accuracy loss of 1% on ImageNet and CIFAR-10 datasets. We also show similar structured versions of EfficientNet on ImageNet and HRNet architecture for semantic segmentation on the Cityscapes dataset. Our method performs equally well or superior in terms of the complexity reduction in comparison to the existing tensor decomposition and channel pruning methods.
Learning efficient structured dictionary for image classification

Recent years have witnessed the success of dictionary learning (DL)-based approaches in the domain of pattern classification. We present an efficient structured dictionary learning (ESDL) method that takes both the diversity and label information of training samples into account. Specifically, ESDL introduces alternative training samples into the process of DL. To increase the discriminative capability of representation coefficients for classification, an ideal regularization term is incorporated into the objective function of ESDL. Moreover, in contrast with conventional DL approaches, which impose a computationally expensive ℓ1-norm constraint on the coefficient matrix, ESDL employs an ℓ2-norm regularization term. Experimental results on benchmark databases (including four face databases and one scene dataset) demonstrate that ESDL outperforms previous DL approaches. More importantly, ESDL can be applied in a wide range of pattern classification tasks.
768 Machine Learning Prediction Tools for All-Cause Readmissions in Patients Hospitalised for Heart Failure Using Routinely Collected Medical Record Data

Heart failure (HF) is a leading cause of hospitalisation in Australia. Accurately identifying individuals at high risk of future HF hospitalisation is of critical importance. We compared several machine learning (ML) models in their ability to predict 30-day, 90-day and 12-month HF readmissions.
Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling

Semantic hashing is a powerful paradigm for representing texts as compact binary hash codes. The explosion of short text data has spurred the demand of few-bits hashing. However, the performance of existing semantic hashing methods cannot be guaranteed when applied to few-bits hashing because of severe information loss. In this paper, we present a simple but effective unsupervised neural generative semantic hashing method with a focus on few-bits hashing. Our model is built upon variational autoencoder and represents each hash bit as a Bernoulli variable, which allows the model to be end-to-end trainable. To address the issue of information loss, we introduce a set of auxiliary implicit topic vectors. With the aid of these topic vectors, the generated hash codes are not only low-dimensional representations of the original texts but also capture their implicit topics. We conduct comprehensive experiments on four datasets. The results demonstrate that our approach achieves significant improvements over state-of-the-art semantic hashing methods in few-bits hashing.
Refinement Type Contracts for Verification of Scientific Investigative Software

Our scientific knowledge is increasingly built on software output. User code which defines data analysis pipelines and computational models is essential for research in the natural and social sciences, but little is known about how to ensure its correctness. The structure of this code and the development process used to build it limit the utility of traditional testing methodology. Formal methods for software verification have seen great success in ensuring code correctness but generally require more specialized training, development time, and funding than is available in the natural and social sciences. Here, we present a Python library which uses lightweight formal methods to provide correctness guarantees without the need for specialized knowledge or substantial time investment. Our package provides runtime verification of function entry and exit condition contracts using refinement types. It allows checking hyperproperties within contracts and offers automated test case generation to supplement online checking. We co-developed our tool with a medium-sized ($$\approx $$3000 LOC) software package which simulates decision-making in cognitive neuroscience. In addition to helping us locate trivial bugs earlier on in the development cycle, our tool was able to locate four bugs which may have been difficult to find using traditional testing methods. It was also able to find bugs in user code which did not contain contracts or refinement type annotations. This demonstrates how formal methods can be used to verify the correctness of scientific software which is difficult to test with mainstream approaches.
A Tractable Logic for Molecular Biology

We introduce a logic for knowledge representation and reasoning on protein-protein interactions.Modulo a theory, formulas describe protein structures and dynamic changes.They can be composed in order to add or remove static and dynamic observations.A second-order circumscription operator then enables nonmonotonic reasoning on the changes implied by a formula.We introduce deduction rules that produce formulas which are, up to equivalence, in a first-order fragment with decidable satisfiability and validity.Importantly, the rules can produce circumscribed formulas.
Substructural Observed Communication Semantics

Session-types specify communication protocols for communicating processes, and session-typed languages are often specified using substructural operational semantics given by multiset rewriting systems.We give an observed communication semantics [2] for a session-typed language with recursion, where a process's observation is given by its external communications.To do so, we introduce fair executions for multiset rewriting systems, and extract observed communications from fair process executions.This semantics induces an intuitively reasonable notion of observational equivalence that we conjecture coincides with semantic equivalences induced by denotational semantics [15], bisimulations [13], and barbed congruences [16,27] for these languages.
Image Denoising Based on Bivariate Distribution

The literature has shown that the performance of the de-noising algorithm was greatly influenced by the dependencies between wavelet coefficients. In this paper, the bivariate probability density function (PDF) was proposed which was symmetric, and the dependencies between the coefficients were considered. The bivariate Cauchy distribution and the bivariate Student’s distribution are special cases of the proposed bivariate PDF. One of the parameters in the probability density function gave the estimation method, and the other parameter can take any real number greater than 2. The algorithm adopted a maximum a posteriori estimator employing the dual-tree complex wavelet transform (DTCWT). Compared with the existing best results, the method is faster and more efficient than the previous numerical integration techniques. The bivariate shrinkage function of the proposed algorithm can be expressed explicitly. The proposed method is simple to implement.
A CONVEX SURFACE WITH FRACTAL CURVATURE

We construct a surface that is obtained from the octahedron by pushing out four of the faces so that the curvature is supported in a copy of the Sierpinski gasket (SG) in each of them, and is essentially the self similar measure on SG. We then compute the bottom of the spectrum of the associated Laplacian using the finite element method on polyhedral approximations of our surface, and speculate on the behavior of the entire spectrum.
Effects for efficiency: asymptotic speedup with first-class control

We study the fundamental efficiency of delimited control. Specifically, we show that effect handlers enable an asymptotic improvement in runtime complexity for a certain class of functions. We consider the generic count problem using a pure PCF-like base language $\lambda_b$ and its extension with effect handlers $\lambda_h$. We show that $\lambda_h$ admits an asymptotically more efficient implementation of generic count than any $\lambda_b$ implementation. We also show that this efficiency gap remains when $\lambda_b$ is extended with mutable state. To our knowledge this result is the first of its kind for control operators.
Optical amplification of single-photon polarization qubit using weak measurement

Abstract Single-photon amplification can be used to overcome the photon loss in long-distance quantum communication. However, the trade-off of success probability and amplification gain should be further improved. We propose a single-photon polarization qubit amplification using weak measurement. The polarization qubit can be amplified by the pre- and post-selection of the single-photon meter state weakly coupling with the input state. The success probability does not decrease asymptotically to 0 with increasing amplification gain in the presented proposal, and is higher than that in entanglement-based schemes, without coincidence detection required. Our proposal is feasible in current technology, and helpful for near-future quantum communication.
Cybersecurity Education and Formal Methods

Formal methods have been largely thought of in the context of safety-critical systems, where they have achieved major acceptance. Tens of millions of people trust their lives every day to such systems, based on formal proofs rather than “we haven’t found a bug” (yet!); but why is “we haven’t found a bug” an acceptable basis for systems trusted with hundreds of millions of people’s personal data? This paper looks at some of these issues in cybersecurity, and the extent to which formal methods, ranging from “fully verified” to better tool support, could help. More importantly, recent policy reports and curricula initiatives appear to recommended formal methods in the limited context of “safety critical applications”; we suggest this is too limited in scope and ambition. Not only are formal methods needed in cybersecurity, the repeated and very public weaknesses of the cybersecurity industry provide a powerful motivation for formal methods.
Research on Entity Coreference Resolution Technology Oriented to Military Knowledge Graph

Abstract Data processing in military field is in the stage of fusion disambiguation. The way of addressing the same thing is complex and difficult to integrate. Entity coreference resolution can effectively solve this problem. At the same time, entity coreference resolution is also an important part of knowledge fusion stage in the process of building military knowledge graph. This paper studies and implements Chinese coreference resolution from two aspects: Named Entity Recognition and coreference resolution. Firstly, use the BiLSTM+CRF model of neural network to realize NER in military field. By mining Wikipedia corpus, construct a pattern base, and iteratively find the coreference relationship in text based on pattern, and finally establish a model. To complete the rapid and effective construction of a total of 220,000 thesaurus, covering the military field of aircraft and ships two types of objectives, to achieve the military field entity coreference resolution, to provide strong support for the construction of military knowledge graph.
Time-Series Information and Unsupervised Learning of Representations

Numerous control and learning problems face the situation where sequences of high-dimensional highly dependent data are available but no or little feedback is provided to the learner, which makes any inference rather challenging. To address this challenge, we formulate the following problem. Given a series of observations X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , ... , X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> coming from a large (highdimensional) space X , find a representation function f mapping X to a finite space Y such that the series f (X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ), .. . , f (X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) preserves as much information as possible about the original time-series dependence in X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , ... , X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> . We show that, for stationary time series, the function f can be selected as the one maximizing a certain information criterion that we call time-series information. Some properties of this functions are investigated, including its uniqueness and consistency of its empirical estimates. Implications for the problem of optimal control are presented.
Quantum Fingerprinting Over AWGN Channels With Power-Limited Optical Signals

Quantum fingerprinting reduces communication complexity of determination whether two n-bit long inputs are equal or different in the simultaneous message passing model. Here we quantify the advantage of quantum fingerprinting over classical protocols when communication is carried out using optical signals with limited power and unrestricted bandwidth propagating over additive white Gaussian noise (AWGN) channels with power spectral density (PSD) much less than one photon per unit time and unit bandwidth. We identify a noise parameter whose order of magnitude separates near-noiseless quantum fingerprinting, with signal duration effectively independent of n, from a regime where the impact of AWGN is significant. In the latter case the signal duration is found to scale as O(√n), analogously to classical fingerprinting. However, the dependence of the signal duration on the AWGN PSD is starkly distinct, leading to quantum advantage in the form of a reduced multiplicative factor in O(√n) scaling.
Recovering Localized Adversarial Attacks

Deep convolutional neural networks have achieved great successes over recent years, particularly in the domain of computer vision. They are fast, convenient, and -- thanks to mature frameworks -- relatively easy to implement and deploy. However, their reasoning is hidden inside a black box, in spite of a number of proposed approaches that try to provide human-understandable explanations for the predictions of neural networks. It is still a matter of debate which of these explainers are best suited for which situations, and how to quantitatively evaluate and compare them. In this contribution, we focus on the capabilities of explainers for convolutional deep neural networks in an extreme situation: a setting in which humans and networks fundamentally disagree. Deep neural networks are susceptible to adversarial attacks that deliberately modify input samples to mislead a neural network's classification, without affecting how a human observer interprets the input. Our goal with this contribution is to evaluate explainers by investigating whether they can identify adversarially attacked regions of an image. In particular, we quantitatively and qualitatively investigate the capability of three popular explainers of classifications -- classic salience, guided backpropagation, and LIME -- with respect to their ability to identify regions of attack as the explanatory regions for the (incorrect) prediction in representative examples from image classification. We find that LIME outperforms the other explainers.
Decompounding discrete distributions: A nonparametric Bayesian approach

Abstract Suppose that a compound Poisson process is observed discretely in time and assume that its jump distribution is supported on the set of natural numbers. In this paper we propose a nonparametric Bayesian approach to estimate the intensity of the underlying Poisson process and the distribution of the jumps. We provide a Markov chain Monte Carlo scheme for obtaining samples from the posterior. We apply our method on both simulated and real data examples, and compare its performance with the frequentist plug‐in estimator proposed by Buchmann and Grübel. On a theoretical side, we study the posterior from the frequentist point of view and prove that as the sample size n → ∞ , it contracts around the “true,” data‐generating parameters at rate , up to a factor.
Optically pumped Floquet states of magnetization in ferromagnets

Floquet states have been the subject of great research interest since Zel'dovich's pioneering work on the quasienergy of a quantum system influenced by a temporally periodic action. Nowadays, periodic modulation of the system Hamiltonian is achieved mostly by microwaves, leading to novel exciting phenomena in condensed matter physics. On the other hand, nonthermal optical control of magnetization at picosecond time scales is currently a highly appealing research topic for potential applications in magnetic data storage. Here we combine these two concepts to theoretically investigate Floquet states in the system of exchange-coupled spins in a ferromagnet. Periodic perturbation of the magnetization of an iron-garnet film by circularly polarized femtosecond laser pulses is shown to establish the magnetization dynamics behaving like Floquet states. An external magnetic field allows tuning of the Floquet states, leading to pronounced increase in the precession amplitude by one order of magnitude at the center of the Brillouin zone, i.e., when the precession frequency is a multiple of the laser pulse repetition rate. Floquet states might potentially allow for parametric generation of magnetic oscillations. The observed phenomena expand the capabilities of coherent ultrafast optical control of magnetization and pave the way for their application in quantum computation or data processing.
Domain-Independent Cost-Optimal Planning in ASP

We investigate the problem of cost-optimal planning in ASP. Current ASP planners can be trivially extended to a cost-optimal one by adding weak constraints, but only for a given makespan (number of steps). It is desirable to have a planner that guarantees global optimality. In this paper, we present two approaches to addressing this problem. First, we show how to engineer a cost-optimal planner composed of two ASP programs running in parallel. Using lessons learned from this, we then develop an entirely new approach to cost-optimal planning, stepless planning, which is completely free of makespan. Experiments to compare the two approaches with the only known cost-optimal planner in SAT reveal good potentials for stepless planning in ASP. The paper is under consideration for acceptance in TPLP.
DeepMimic: Mentor-Student Unlabeled Data Based Training

In this paper, we present a deep neural network (DNN) training approach called the "DeepMimic" training method. Enormous amounts of data are available nowadays for training usage. Yet, only a tiny portion of these data is manually labeled, whereas almost all of the data are unlabeled. The training approach presented utilizes, in a most simplified manner, the unlabeled data to the fullest, in order to achieve remarkable (classification) results. Our DeepMimic method uses a small portion of labeled data and a large amount of unlabeled data for the training process, as expected in a real-world scenario. It consists of a mentor model and a student model. Employing a mentor model trained on a small portion of the labeled data and then feeding it only with unlabeled data, we show how to obtain a (simplified) student model that reaches the same accuracy and loss as the mentor model, on the same test set, without using any of the original data labels in the training of the student model. Our experiments demonstrate that even on challenging classification tasks the student network architecture can be simplified significantly with a minor influence on the performance, i.e., we need not even know the original network architecture of the mentor. In addition, the time required for training the student model to reach the mentor's performance level is shorter, as a result of a simplified architecture and more available data. The proposed method highlights the disadvantages of regular supervised training and demonstrates the benefits of a less traditional training approach.
Trotter errors in digital adiabatic quantum simulation of quantum ℤ2 lattice gauge theory

Trotter decomposition is the basis of the digital quantum simulation. Asymmetric and symmetric decompositions are used in our GPU demonstration of the digital adiabatic quantum simulations of $2+1$ dimensional quantum $\mathbb{Z}_2$ lattice gauge theory. The actual errors in Trotter decompositions are investigated as functions of the coupling parameter and the number of Trotter substeps in each step of the variation of coupling parameter. The relative error of energy is shown to be closely related to the Trotter error usually defined defined in terms of the evolution operators. They are much smaller than the order-of-magnitude estimation. The error in the symmetric decomposition is much smaller than that in the asymmetric decomposition. The features of the Trotter errors obtained here are useful in the experimental implementation of digital quantum simulation and its numerical demonstration.