Reviewerly/exHarmony huggingface dataset:
An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability

Motion capture (Mocap) data are widely used as time series to study human movement. Indeed, animation movies, video games, and biomechanical systems for rehabilitation are significant applications related to Mocap data. However, classifying multi-channel time series from Mocap requires coding the intrinsic dependencies (even nonlinear relationships) between human body joints. Furthermore, the same human action may have variations because the individual alters their movement and therefore the inter/intraclass variability. Here, we introduce an enhanced Hilbert embedding-based approach from a cross-covariance operator, termed EHECCO, to map the input Mocap time series to a tensor space built from both 3D skeletal joints and a principal component analysis-based projection. Obtained results demonstrate how EHECCO represents and discriminates joint probability distributions as kernel-based evaluation of input time series within a tensor reproducing kernel Hilbert space (RKHS). Our approach achieves competitive classification results for style/subject and action recognition tasks on well-known publicly available databases. Moreover, EHECCO favors the interpretation of relevant anthropometric variables correlated with players’ expertise and acted movement on a Tennis-Mocap database (also publicly available with this work). Thereby, our EHECCO-based framework provides a unified representation (through the tensor RKHS) of the Mocap time series to compute linear correlations between a coded metric from joint distributions and player properties, i.e., age, body measurements, and sport movement (action class).
Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training

In this paper, we propose Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better.In contrast to standard adversarial training algorithms, IAT encourages the model to be sensitive to the perturbation in the dialogue history and therefore learning from perturbations.By giving higher rewards for responses whose output probability reduces more significantly when dialogue history is perturbed, the model is encouraged to generate more diverse and consistent responses.By penalizing the model when generating the same response given perturbed dialogue history, the model is forced to better capture dialogue history and generate more informative responses.Experimental results on two benchmark datasets show that our approach can better model dialogue history and generate more diverse and consistent responses.In addition, we point out a problem of the widely used maximum mutual information (MMI) based methods for improving the diversity of dialogue response generation models and demonstrate it empirically.
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning

Unsupervised machine translation, which utilizes unpaired monolingual corpora as training data, has achieved comparable performance against supervised machine translation.However, it still suffers from data-scarce domains.To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data.We assume that domain-general knowledge is a significant factor in handling datascarce domains.Hence, we extend the metalearning algorithm, which utilizes knowledge learned from high-resource domains, to boost the performance of low-resource UNMT.Our model surpasses a transfer learning-based approach by up to 2-3 BLEU scores.Extensive experimental results show that our proposed algorithm is pertinent for fast adaptation and consistently outperforms other baselines.
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder

Recent work has proposed several efficient approaches for generating gradient-based adversarial perturbations on embeddings and proved that the model's performance and robustness can be improved when they are trained with these contaminated embeddings.While they paid little attention to how to help the model to learn these adversarial samples more efficiently.In this work, we focus on enhancing the model's ability to defend gradient-based adversarial attack during the model's training process and propose two novel adversarial training approaches: (1) CARL narrows the original sample and its adversarial sample in the representation space while enlarging their distance from different labeled samples.(2) RAR forces the model to reconstruct the original sample from its adversarial representation.Experiments show that the proposed two approaches outperform strong baselines on various text classification datasets.Analysis experiments find that when using our approaches, the semantic representation of the input sentence won't be significantly affected by adversarial perturbations, and the model's performance drops less under adversarial attack.That is to say, our approaches can effectively improve the robustness of the model.Besides, RAR can also be used to generate text-form adversarial samples.
Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning

This paper studies decision-making in two-player scenarios where the type (e.g. adversary, neutral, or teammate) of the other agent (opponent) is uncertain to the decision-making agent (protagonist), which is an abstraction of security-domain applications. In these settings, the reward for the protagonist agent depends on the type of the opponent, but this is private information known only to the opponent itself, and thus hidden from the protagonist. In contrast, as is often the case, the type of the protagonist agent is assumed to be known to the opponent, and this information-asymmetry significantly complicates the protagonist's decision-making. In particular, to determine the best actions to take, the protagonist agent must infer the opponent type from the observations and agent modeling. To address this problem, this paper presents an opponent-type deduction module based on Bayes' rule. This inference module takes as input the imagined opponent's decision-making rule (opponent model) as well as the observed opponent's history of actions and states, and outputs a belief over the opponent's hidden type. A multiagent reinforcement learning approach is used to develop this game-theoretic opponent model through self-play, which avoids the expensive data collection step that requires interaction with a real opponent. Besides, this multiagent approach also captures the strategy interaction and reasoning between agents. In addition, we apply ensemble training to avoid over-fitting to a single opponent model during the training. As a result, the learned protagonist policy is also effective against unseen opponents. Experimental results show that the proposed game-theoretic modeling, explicit opponent type inference and the ensemble training significantly improves the decision-making performance over baseline approaches, and generalizes well against adversaries that have not been seen during the training.
Model-based evidence of the dominance of the guitar brace design over material and climatic variability for dynamic behaviors

It is generally alleged that the design choices of acoustic guitar bracing patterns lead to a specific sound of the instrument. However, in the presence of strong uncertainties due to variability of material properties and climatic conditions, the robustness of the soundboard dynamics has yet to be investigated. In this study, three types of bracing patterns are studied using physics-based models and stochastic analyses are performed to account for material and climatic uncertainties. It is shown that the choice of a brace design leads, at least in the low frequency domain, to a dynamic behaviour that is not stackable with another design, even in the presence of strong aleatory uncertainties. This assessment supports the conjecture that guitar brace design choices have a greater impact than material variability where guitar soundboard dynamics are concerned. More generally, these results illustrate the usefulness of detailed physics-based models in the understanding, design and making of guitars.
Characterizing the spatial entanglement from laser modes analogous to quantum wave functions

The Schmidt decomposition is exploited to study the spatial entanglement of laser transverse modes analogous to quantum Lissajous states. Based on the inverse Fourier transform, the stationary Lissajous state can be analytically derived as a coherent superposition of degenerate Hermite–Gaussian eigenmodes. With the derived stationary state, the Schmidt modes and the participation number <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> can be employed to evaluate the spatial localization and the quantum entanglement. The larger the participation number, the more localized is the stationary coherent state on the Lissajous figure. Moreover, the larger the participation number, the higher is the spatial entanglement.
Exploring System Thinking Leadership Approaches to the Healthcare Cybersecurity Environment

A Florida-based obstetrics and gynecology facility reported in February 2019 that they lost data because of a ransomware attack. In November 2017, 107,000 healthcare records were exposed from data breaches, and 340,000 records were exposed in December 2017. In 2019, 23,000 patient records at Critical Care, Pulmonary &amp; Sleep Associates were compromised when a hacker gained access to an employee's email account and sent out phishing emails to the other employees, eventually exposing the patient data. On January 11, 2018, Adams Memorial Hospital and Hancock Regional Hospital, both in Indiana, experienced independent ransomware attacks, with Hancock Regional Hospital paying $50,000 in ransom. These incidents point to significant and complex cybersecurity risks for all healthcare organizations. Effectively managing these risks requires healthcare managers to develop system thinking and adaptive leadership skills. This paper explores the nuances and complexities around systems thinking in the healthcare cybersecurity environment.
Resource allocation optimization in cloud computing using the whale optimization algorithm

Cloud computing is a massively distributed system in which existing resources interact with user-requested tasks to meet their requests. In such a system, the problem of optimizing Resource Allocation and Scheduling (RAS) is vital, because recourse allocation and scheduling deals with the mapping between recourses and user requests and also is responsible for optimal allocating of tasks to available resources. In the cloud environment, a user may face hundreds of computational resources to do his work. Therefore, manually recourse allocation and scheduling are impossible, and having a schedule between user requests and available recourses seems logical. In this paper, we used Whale Optimization Algorithm (WOA) to solve resource allocation and task scheduling problem in cloud computing to have optimal resource allocation and reduce the total runtime of requested services by users. The proposed algorithm is compared with the other existed algorithms. Results indicate the proper performance of the proposed algorithm than other ones.
Image Encryption with Fusion of Two Maps

In this paper, we propose a fusion technique for image encryption combining two maps and generate a new optimized map by fusing Hartley and Duffing chaotic oscillators. In order to measure and quantify the amount of chaos present in data sequences, the resultant map is tested by a new method of nonlinear time series analysis. This test ensures its robustness during its use to construct an encrypted image when combined with some mathematical function that we have designed. The cryptosystem was tested on images such as Lena, Barbara, Mandrill, and Man. The results were compared to those in the literature and proved satisfactory.
Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.
Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients

Abstract Within the tumor microenvironment, specifically aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Tumor-associated collagen signatures (TACS) have been linked to breast cancer patient outcome. Robust and affordable methods for assessing biological information contained in collagen architecture need to be developed. We have developed a novel artificial neural network (ANN) based approach for tumor collagen segmentation from bright-field histology images and have tested it on a set of tissue microarray sections from early hormone receptor-positive invasive ductal breast carcinoma stained with Sirius Red (1 core per patient, n = 92). We designed and trained ANNs on sets of differently annotated image patches to segment collagen fibers and extracted 37 features of collagen fiber morphometry, density, orientation, texture, and fractal characteristics in the entire cohort. Independent instances of ANN models trained on highly differing annotations produced reasonably concordant collagen segmentation masks and allowed reliable prognostic Cox regression models (with likelihood ratios 14.11–22.99, at p -value &lt; 0.05) superior to conventional clinical parameters (size of the primary tumor (T), regional lymph node status (N), histological grade (G), and patient age). Additionally, we noted statistically significant differences of collagen features between tumor grade groups, and the factor analysis revealed features resembling the TACS concept. Our proposed method offers collagen framework segmentation from bright-field histology images and provides novel image-based features for better breast cancer patient prognostication.
A night low‐illumination image enhancement model based on small probability area filtering and lossless mapping enhancement

A novel night-time image enhancement approach was proposed in this paper to address the problems of low contrast and poor details of low-illumination images captured at night. To begin with, the luminance component V was extracted that was irrelevant to the colour information of the image upon converting the image to the HSV space from the RGB space. Then, by converting the luminance component V of the image into the probability space, the image was divided into a small-probability grey-scale area and a normal area based on the theory of probability. Moreover, pixels were transferred from the small probability area to the normal area of the image according to the nearest attribution principle that was established. Lastly, the contrast enhancement of the image was realized thanks to lossless mapping functions without losing the number of grey levels of the image. As can be observed from experimental results, the proposed method is superior to the most advanced algorithm in visual quality and quantitative measurement.
Neural-Network-Based Cross-Channel Intra Prediction

To reduce the redundancy among different color channels, e.g., YUV, previous methods usually adopt a linear model that tends to be oversimple for complex image content. We propose a neural-network-based method for cross-channel prediction in intra frame coding. The proposed network utilizes twofold cues, i.e., the neighboring reconstructed samples with all channels, and the co-located reconstructed samples with partial channels. Specifically, for YUV video coding, the neighboring samples with YUV are processed by several fully connected layers; the co-located samples with Y are processed by convolutional layers; and the proposed network fuses the twofold cues. We observe that the integration of twofold information is crucial to the performance of intra prediction of the chroma components. We have designed the network architecture to achieve a good balance between compression performance and computational efficiency. Moreover, we propose a transform domain loss for the training of the network. The transform domain loss helps obtain more compact representations of residues in the transform domain, leading to higher compression efficiency. The proposed method is plugged into HEVC and VVC test models to evaluate its effectiveness. Experimental results show that our method provides more accurate cross-channel intra prediction compared with previous methods. On top of HEVC, our method achieves on average 1.3%, 5.4%, and 3.8% BD-rate reductions for Y, Cb, and Cr on common test sequences, and on average 3.8%, 11.3%, and 9.0% BD-rate reductions for Y, Cb, and Cr on ultra-high-definition test sequences. On top of VVC, our method achieves on average 0.5%, 1.7%, and 1.3% BD-rate reductions for Y, Cb, and Cr on common test sequences.
Anomaly Detection in Invasively Recorded Neuronal Signals Using Deep Neural Network: Effect of Sampling Frequency

Abnormality detection has advanced in recent years with the help of machine learning, in particular with deep learning models, which can predict accurately across many types of signals and applications. In the case of neuronal signals, abnormalities can present themselves as artefacts or manifestations of neurological diseases. Among the diverse neuronal pathologies, we chose to look at the detection of seizures, as they manifest as a brief anomaly in contrast to normal brain activity in the majority portion of the data during a prolonged recording. Epileptic patients benefit from portable systems, which are dependant on efficient energy consumption, and the sampling frequency of the signal is of vital importance element to its battery lifespan. In this article, the impact of the sampling rate on a deep learning-based multi-class classification model is explored via the use of an open-source seizure dataset.
A novel filtering kernel based on difference of derivative Gaussians with applications to dynamic texture representation

Efficiently representing spatio-temporal features of dynamic textures (DTs) in videos has been restricted due to negative impacts of the well-known issues of environmental changes, illumination, and noise. In order to mitigate those, this paper proposes a new approach for an efficient DT representation by addressing the following novel concepts. Firstly, a novel filtering kernel, called Difference of Derivative Gaussians (DoDG), is introduced for the first time based on high-order derivative of a Gaussian kernel. It allows to point out DoDG-based filtered outcomes which are prominently resistant to noise for DT representation compared to exploiting the conventional Difference of Gaussians (DoG). A new framework in low computational complexity is then presented to take DoDG into account video denoising as an effective preprocessing of DT encoding. Finally, a simple variant of Local Binary Patterns (LBPs) is addressed to extract local features from these DoDG-filtered outcomes for constructing discriminative DoDG-based descriptors in small dimension, expected as one of appreciated solutions for mobile applications. Experimental results for DT recognition have verified that our proposal significantly performs well compared to all non-deep-learning methods, while being very close to deep-learning approaches. Also, ours are eminently better than those based on the traditional DoG.
Does Face Recognition Error Echo Gender Classification Error?

This paper is the first to explore the question of whether images that are classified incorrectly by a face analytics algorithm (e.g., gender classification) are any more or less likely to participate in an image pair that results in a face recognition error. We analyze results from three different gender classification algorithms (one open-source and two commercial), and two face recognition algorithms (one open-source and one commercial), on image sets representing four demographic groups (African-American female and male, Caucasian female and male). For impostor image pairs, our results show that pairs in which one image has a gender classification error have a better impostor distribution than pairs in which both images have correct gender classification, and so are less likely to generate a false match error. For genuine image pairs, our results show that individuals whose images have a mix of correct and incorrect gender classification have a worse genuine distribution (increased false non-match rate) compared to individuals whose images consistently have correct gender classification. Thus, compared to images that generate correct gender classification, images with gender classification error have a lower false match rate and a higher false non-match rate.