Reviewerly/exHarmony huggingface dataset:
Dual-graph regularized discriminative transfer sparse coding for facial expression recognition

Facial expression recognition has recently received an increasing attention due to its great potentiality in real world applications. Conventional facial expression recognition is often conducted on the assumption that training data and testing data are obtained from the same dataset. However, in reality, the data are often collected from different devices or environments, which will severely degrade the recognition performance. To tackle this problem, in this paper, we investigate the cross-dataset facial expression recognition problem, and propose a novel dual-graph regularized transfer sparse coding method (DGTSC). Specifically, aiming to reduce the distribution divergence of different databases while preserving the geometrical structures, we construct a dual-graph, by defining the inter-domain and intra-domain similarity, to measure the distance between different databases. Moreover, we further present a dual-graph regularized discriminative transfer sparse coding method (DGDTSC), which exploits the label information, to make our model has more discriminative power. Extensive experimental results and analysis on several facial expression datasets show the feasibility and effectiveness of the proposed methods.
Enhanced Arnold’s Cat Map-AES Encryption Technique for Medical Images

Human's health information is considered momentous information, which is represented in medical systems. The amount of medical image information available for analysis is increasing with the modern medical image devices and biomedical image processing techniques. To prevent data modification from unauthorized persons from an insecure network, medical images should be encrypted efficiently. In this paper, a novel chaotic-based medical image encryption technique is proposed. This technique uses first a Butterworth High Pass Filter (BHPF) to enhance the medical image's details to avoid any possible loss of medical details during the encryption-decryption process. The proposed technique is then developed by modifying Arnold's cat map technique combined with the well-known Advanced Encryption Standard (AES) algorithm. By modifying Arnold's cat map technique, three bits are formulated and added to the regular AES encryption key to increase the overall encryption robustness. A comparative study is conducted to compare first the efficiency of the proposed technique concerning Arnold's Cat Map with AES (Cat-AES) and AES in its standard form. Then, the proposed encryption technique is also compared to the state-of-the-art chaotic-based medical image encryption techniques. It is shown from the comparative study that the proposed approach is capable of increasing both the strength of the encryption/decryption process and the quality of medical images with a reduction of the overall computational cost.
Breaking the chain of transmission within a tertiary health service: An approach to contact tracing during the COVID-19 pandemic

<h2>Abstract</h2><h3>Background</h3> Tertiary referral health service. <h3>Intervention(s)</h3> An approach to hospital based contact tracing is described along with tools employed to streamline the process and including the development of an outbreak management team (OMT) for each contact trace. <h3>Results</h3> Forty-one OMTs occurred, involving 23 HCW and 18 patient index cases. The total furloughed HCWs arising from these contact traces was 383, with individual contact traces furloughing a mean (range) of 10 (0–80) HCWs. Importantly, 15 furloughed HCWs subsequently became COVID-19 positive during their 14-day isolation period, showing the importance of the contact tracing process and the ability to remove workers from the workplace before they become infectious. <h3>Conclusions</h3> A standardised, streamlined contact tracing procedure in healthcare settings ensures any impacts of COVID-19 positive cases are consistently managed. This response framework may be of use to other health services and help reduce the transmission of COVID-19 in the workplace.
Untangling mechanized proofs

Proof assistants like Coq, Lean, or HOL4 rely heavily on stateful meta-programs called scripts to assemble proofs. Unlike pen-and-paper proofs, proof scripts only describe the steps to take (induct on x, apply a theorem, …), not the states that these steps lead to; as a result, plain proof scripts are essentially incomprehensible without the assistance of an interactive user interface able to run the script and show the corresponding proof states.
Review of DL-Based Generation Techniques of Augmented Images using Portraits Specification

Artificial Intelligence (AI) reached various domains in our daily problems, among them is aiding law enforcement in identifying suspects by generating face images or retrieving their images from existing databases, based on the description of witnesses. The existing methods like hand-drawn sketches take time and absorb human resources; in addition to that rendering images from the software fails most of the time to sketch a real image and does not get the ideal scenario. In this paper, we present a survey of the recent progress concerning generating images from description and sketch face recognition, we analyze the difference between several algorithms on the problem based on evaluation metrics like accuracy and correlation similarity. We also give an overview of datasets used for generations or recognition.
Optical Flow Filtering-Based Micro-Expression Recognition Method

The recognition accuracy of micro-expressions in the field of facial expressions is still understudied, as current research methods mainly focus on feature extraction and classification. Based on optical flow and decision thinking theory, we propose a novel micro-expression recognition method, which can filter low-quality micro-expression video clips. Determined by preset thresholds, we develop two optical flow filtering mechanisms: one based on two-branch decisions (OFF2BD) and the other based on three-way decisions (OFF3WD). In OFF2BD, which use the classical binary logic to classify images, and divide the images into positive or negative domain for further filtering. Differ from the OFF2BD, OFF3WD added boundary domain to delay to judge the motion quality of the images. In this way, the video clips with low degree of morphological change can be eliminated, so as to directly improve the quality of micro-expression features and recognition rate. From the experimental results, we verify the recognition accuracy of 61.57%, and 65.41% for CASMEII, and SMIC datasets, respectively. Through the comparative analysis, it shows that the scheme can effectively improve the recognition performance.
Pragmatic inferences are QUD-sensitive: an experimental study

Implicatures serve as an important testing ground for examining the process of integrating semantic and pragmatic information. Starting with Bott &amp; Noveck (2004), several studies have found that implicature computation is costly. More recently, attention has shifted toward identifying contextual cues that modulate this processing cost. Specifically, it has been hypothesized that calculation rate and processing cost are a function of whether the Question Under Discussion (QUD) supports generating the implicature (Degen 2013; Degen &amp; Tanenhaus 2015). In this paper, we present a novel elicitation task establishing what the relevant QUDs are for a given context (Experiment 1). In Experiment 2, a sentence-picture verification study, we extend earlier findings about the effect of QUDs on scalar inference to a different kind of pragmatic inference: it -cleft exhaustivity. For both inferences, we find that under QUDs that bias toward calculation, there is no increase in reaction times, but under QUDs that bias against calculating the inference we observe longer reaction times. These results are most compatible with a constraint-based account of implicature, where QUD is one of many cues. Additionally, we explore whether our findings can be informative in narrowing down precisely what aspect of the inferential process incurs a cost.
Robust Handwriting Recognition with Limited and Noisy Data

Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved. Most existing approaches focus on handwriting datasets that have clearly written text and carefully segmented labels. In this paper, we instead focus on learning handwritten characters from maintenance logs, a constrained setting where data is very limited and noisy. We break the problem into two consecutive stages of word segmentation and word recognition respectively, and utilize data augmentation techniques to train both stages. Extensive comparisons with popular baselines for scene-text detection and word recognition show that our system achieves a lower error rate and is more suited to handle noisy and difficult documents.
Brief Survey of Model-Based Reinforcement Learning Techniques

Model-free reinforcement learning (MFRL) usually has better asymptotic performance than the model-based reinforcement (MBRL) learning algorithms, especially in complex environments. But MBRL algorithms are very often much more sample-efficient, and sometimes are able to learn control tasks in just a handful of trials. In addition, in some domains, the MBRL algorithms can reach the MFRL performance with better sample efficiency. In recent years, MBRL research has increased in various application domains, such as robot control tasks, or game environments with complex observations. In this paper, we review the most popular techniques used in MBRL and look at some useful classification of algorithms in this area.
A Scalable Optimization Mechanism for Pairwise Based Discrete Hashing

Maintaining the pairwise relationship among originally high-dimensional data into a low-dimensional binary space is a popular strategy to learn binary codes. One simple and intuitive method is to utilize two identical code matrices produced by hash functions to approximate a pairwise real label matrix. However, the resulting quartic problem in term of hash functions is difficult to directly solve due to the non-convex and non-smooth nature of the objective. In this paper, unlike previous optimization methods using various relaxation strategies, we aim to directly solve the original quartic problem using a novel alternative optimization mechanism to linearize the quartic problem by introducing a linear regression model. Additionally, we find that gradually learning each batch of binary codes in a sequential mode, i.e. batch by batch, is greatly beneficial to the convergence of binary code learning. Based on this significant discovery and the proposed strategy, we introduce a scalable symmetric discrete hashing algorithm that gradually and smoothly updates each batch of binary codes. To further improve the smoothness, we also propose a greedy symmetric discrete hashing algorithm to update each bit of batch binary codes. Moreover, we extend the proposed optimization mechanism to solve the non-convex optimization problems for binary code learning in many other pairwise based hashing algorithms. Extensive experiments on benchmark single-label and multi-label databases demonstrate the superior performance of the proposed mechanism over recent state-of-the-art methods on two kinds of retrieval tasks: similarity and ranking order. The source codes are available on https://github.com/xsshi2015/Scalable-Pairwise-based-Discrete-Hashing.
CEFS

Iterative Synchronous Applications (ISApps) are popular in today's data centers, represented by distributed deep learning (DL) training. In ISApps, multiple nodes carry out the computing task iteratively, with globally synchronizing the results in each iteration. To increase the scaling efficiency of ISApps, in this paper we propose a new flow scheduling approach, called CEFS. CEFS saves the waiting time of computing nodes from two aspects. For a single node, flows with data which can trigger earlier computation at the node are assigned with higher priority; among nodes, flows towards slower nodes are assigned with higher priority.
Vertex Reordering for Real-World Graphs and Applications: An Empirical Evaluation

Vertex reordering is a way to improve locality in graph computations. Given an input (or “natural”) order, reordering aims to compute an alternate permutation of the vertices that is aimed at maximizing a locality-based objective. Given decades of research on this topic, there are tens of graph reordering schemes, and there are also several linear arrangement “gap” measures for treatment as objectives. However, a comprehensive empirical analysis of the efficacy of the ordering schemes against the different gap measures, and against real-world applications is currently lacking. In this study, we present an extensive empirical evaluation of up to 11 ordering schemes, taken from different classes of approaches, on a set of 34 real-world graphs emerging from different application domains. Our study is presented in two parts: a) a thorough comparative evaluation of the different ordering schemes on their effectiveness to optimize different linear arrangement gap measures, relevant to preserving locality; and b) extensive evaluation of the impact of the ordering schemes on two real-world, parallel graph applications, namely, community detection and influence maximization. Our studies show a significant divergence among the ordering schemes (up to 40x between the best and the poor) in their effectiveness to reduce the gap measures; and a wide ranging impact of the ordering schemes on various aspects including application runtime (up to 4x), memory and cache use, load balancing, and parallel work and efficiency. The comparative study also helps in revealing the nuances of a parallel environment (compared to serial) on the ordering schemes and their role in optimizing applications.
An Adaptive Fuzzy Symbiotic Organisms Search Algorithm and Its Applications

This paper discusses the development of a Symbiotic Organisms Search Algorithm (SOS) variant, called Adaptive Fuzzy SOS (FSOS).Like SOS, FSOS exploits three types of symbiosis operators namely mutualism, commensalism, and parasitism in order to undertake the search process.Unlike SOS, FSOS is able to adaptively select a single or any combination of mutualism, commensalism, and parasitism update operator(s) as the search progresses based on the current search status controlled by their individual probabilities via the fuzzy decision-making.To validate its performance, we have evaluated FSOS to solve 23 benchmark functions and take a t-way test generation as our case study.Experimental results demonstrate that FSOS exhibits competitive performance against its predecessor (SOS) and other competing metaheuristic algorithms.
Impacts of Covid-19 on Company and Efforts to Support Organization Adaptable

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On Sparse Linear Regression in the Local Differential Privacy Model

In this paper, we study the sparse linear regression problem under the Local Differential Privacy (LDP) model. We first show that polynomial dependency on the dimensionality <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> of the space is unavoidable for the estimation error in both non-interactive and sequential interactive local models, if the privacy of the whole dataset needs to be preserved. Similar limitations also exist for other types of error measurements and in the relaxed local models. This indicates that differential privacy in high dimensional space is unlikely achievable for the problem. With the understanding of this limitation, we then present two algorithmic results. The first one is a sequential interactive LDP algorithm for the low dimensional sparse case, called Locally Differentially Private Iterative Hard Thresholding (LDP-IHT), which achieves a near optimal upper bound. This algorithm is actually rather general and can be used to solve quite a few other problems, such as (Local) DP-ERM with sparsity constraints and sparse regression with non-linear measurements. The second one is for the restricted (high dimensional) case where only the privacy of the responses (labels) needs to be preserved. For this case, we show that the optimal rate of the error estimation can be made logarithmically dependent on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> (i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\log p$ </tex-math></inline-formula> ) in the local model, where an upper bound is obtained by a label-privacy version of LDP-IHT. Experiments on real world and synthetic datasets confirm our theoretical analysis.
DCBT-Net: Training Deep Convolutional Neural Networks With Extremely Noisy Labels

Obtaining data with correct labels is crucial to attain the state-of-the-art performance of Convolutional Neural Network (CNN) models. However, labeling datasets is significantly time-consuming and expensive process because it requires expert knowledge in a particular domain. Therefore, real-life datasets often exhibit incorrect labels due to the involvement of nonexperts in the data-labeling process. Consequently, there are many cases of incorrectly labeled data in the wild. Although the issue of poorly labeled datasets has been studied, the existing methods are complex and difficult to reproduce. Thus, in this study, we proposed a simpler algorithm called “Deep Clean Before Training Net” (DCBT-Net) that is based on cleaning wrongly labeled data points using the information from eigenvalues of the Laplacian matrix obtained from similarities between the data samples. The cleaned data were trained using deep CNN (DCNN) to attain the state-of-the-art results. This system achieved better performance than the existing approaches. In conducted experiments, the performance of the DCBT-Net was tested on three commercially available datasets, namely, Modified National Institute of Standards and Technology (MNIST) database of handwritten digits, Canadian Institute for Advanced Research (CIFAR) and WebVision1000 datasets. The proposed method achieved better results when assessed using several evaluation metrics compared with the existing state-of-the-art methods. Specifically, the DCBT-Net attained an average 15%, 20%, and 3% increase in accuracy score using MNIST database, CIFAR-10 dataset, and WebVision dataset, respectively. Also, the proposed approach demonstrated better results in specificity, sensitivity, positive predictive value, and negative predictive value evaluation metrics.
Retrieving geological units with unsupervised clustering of gamma-ray spectrometry data

Airborne gamma-ray spectrometry (AGRS) provides valuable insights for geological mapping, mineral exploration, geomorphological studies, and lithological differentiation. The interpretation of AGRS data is usually performed by skilled interpreters, requires visual acuity and is time-consuming. In this study, we used unsupervised clustering and object-based classification on the primary variables potassium (K), uranium (eU), and thorium (eTh) to automatically retrieve geological units in Mara Rosa Magmatic Arc – Goiás, Brazil, a well-studied area known by a gold district. We first cluster a color image composition (Red = K; Green = eTh; Blue = eU) using gaussian mixture models. We estimate the ideal number of classes to partition the image with the bayesian information criterion. Then, we performed a class separability analysis to identify and merge classes with low statistical separability. Finally, we used an object-based approach to reduce inconsistencies in the unsupervised classification map. To assess our approach, we compute the structural similarity index between the classification map and a reference geological map of the study area. The results show that our approach retrieved geologically meaningful regions in the AGRS data, reaching 0.94 of overall structural similarity with the geological map. Combining unsupervised clustering and AGRS data allows a better interpretation of the geological units into the same Complex or Groups.
An Abstract Machine for Strong Call by Value

We present an abstract machine that implements a full-reducing (a.k.a. strong) call-by-value strategy for pure $$\lambda $$ -calculus. It is derived using Danvy et al.’s functional correspondence from Crégut’s KN by: (1) deconstructing KN to a call-by-name normalization-by-evaluation function akin to Filinski and Rohde’s, (2) modifying the resulting normalizer so that it implements the right-to-left call-by-value function application, and (3) constructing the functionally corresponding abstract machine. This new machine implements a reduction strategy that subsumes the fireball-calculus variant of call by value studied by Accattoli et al. We describe the strong strategy of the machine in terms of a reduction semantics and prove the correctness of the machine using a method based on Biernacka et al.’s generalized refocusing. As a byproduct, we present an example application of the machine to checking term convertibility by discriminating on the basis of their partially normalized forms.
Polynomial scaling of the quantum approximate optimization algorithm for ground-state preparation of the fully connected <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>p</mml:mi></mml:math> -spin ferromagnet in a transverse field

We show that the quantum approximate optimization algorithm (QAOA) can construct, with polynomially scaling resources, the ground state of the fully connected $p$-spin Ising ferromagnet, a problem that notoriously poses severe difficulties to a vanilla quantum annealing (QA) approach due to the exponentially small gaps encountered at first-order phase transition for $p\ensuremath{\ge}3$. For a target ground state at arbitrary transverse field, we find that an appropriate QAOA parameter initialization is necessary to achieve good performance of the algorithm when the number of variational parameters $2P$ is much smaller than the system size $N$ because of the large number of suboptimal local minima. Instead, when $P$ exceeds a critical value ${P}_{N}^{*}\ensuremath{\propto}N$, the structure of the parameter space simplifies, as all minima become degenerate. This allows achieving the ground state with perfect fidelity with a number of parameters scaling extensively with $N$ and with resources scaling polynomially with $N$.
Bernoulli filter for tracking maritime targets using point measurements with amplitude

Statistical performance of the Bernoulli Track-Before-Detect (TBD) algorithm for maritime radar is superior to that of the Bernoulli tracking filter which processes, in a conventional manner, the point measurements (PM) produced by a detector. However, the Bernoulli TBD is orders of magnitude computationally more intensive. In this paper we develop a hybrid algorithm that takes advantage of the amplitude information (exploited by the TBD algorithm), but operates in the framework of conventional point-measurement Bayesian tracking. The resulting filter is formulated using the Rao-Blackwell decomposition: a particle filter estimates the target amplitude, while the target position and velocity, given the amplitude, are estimated using a Gaussian mixture filter. Numerical results show that the statistical and computational efficiency of the new hybrid filter is between the two extremes, namely, the Bernoulli TBD and the Bernoulli PM tracker.