Reviewerly/exHarmony huggingface dataset:
One leg at a time

Custom made textile sensors encounter design and manufacturing challenges that differ from conventional printed circuit board-based sensors. The field of e-textiles commonly deploys such sensors on the human body, meaning that overcoming these challenges are crucial for reliable sensor performance. In this paper, we present and evaluate the design of trousers with embedded fabric sensors. Two iterative prototypes were manufactured and tested in two user studies, focusing on mechanical aspects of the design for applications in capturing body movement within social interaction. We report on failures and risks of the design, and furthermore propose solutions for a more robust, yet soft wearable sensing system.
Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion

This paper presents an approach to voice conversion, whichdoes neither require parallel data nor speaker or phone labels fortraining. It can convert between speakers which are not in thetraining set by employing the previously proposed concept of afactorized hierarchical variational autoencoder. Here, linguisticand speaker induced variations are separated upon the notionthat content induced variations change at a much shorter timescale, i.e., at the segment level, than speaker induced variations,which vary at the longer utterance level. In this contribution wepropose to employ convolutional instead of recurrent networklayers in the encoder and decoder blocks, which is shown toachieve better phone recognition accuracy on the latent segmentvariables at frame-level due to their better temporal resolution.For voice conversion the mean of the utterance variables is re-placed with the respective estimated mean of the target speaker.The resulting log-mel spectra of the decoder output are used aslocal conditions of a WaveNet which is utilized for synthesis ofthe speech waveforms. Experiments show both good disentan-glement properties of the latent space variables, and good voiceconversion performance.
Approaches to the construction of nonlinear models in fuzzy environment

Abstract The Bayesian methods to the problems of statistical estimation when building nonlinear models are considered. Bayesian methods can be used to construct linear and nonlinear regression models with non-Gaussian laws for the distribution of probabilities of random observation errors. We consider continuous and discrete processes that can be described by statistical models. For this purpose, a description of computational Bayesian procedures and recommendations for the construction of nonlinear regression models are given introduction.
Beginning with Flask

Flask is a BSD licensed, Python microframework based on Werkzeug and Jinja2. Being a microframework doesn't make it any less functional; Flask is a very simple yet highly extensible framework. This gives developers the power to choose the configuration they want, thereby making writing applications or plugins easy. Flask was originally created by Pocoo, a team of open source developers in 2010, and it is now developed and maintained by The Pallets Project who power all the components behind Flask. Flask is supported by an active and helpful developer community including an active IRC channel and a mailing list.
Effectiveness Evaluation of UUV Cooperative Combat Based on GAPSO-BP Neural Network

Underwater unmanned vehicle (UUV) cooperative combat is an important pattern of underwater warfare.. Aiming at the problem of effectiveness evaluation, this paper presents an effectiveness evaluation model based on GAPSO-BP neural network, which can acquire the information of the expert by training, and calculate the effectiveness value fast and continuously. Firstly, according to the indicator system of UUV Cooperative Combat Effectiveness Evaluation, the evaluation system combined with AHP method improved by intuitionistic fuzzy and BP neural network is given. Secondly, by analyzing GA and PSO method, and combing their advantages, the GAPSO-BP neural network is constructed. Finally, simulation experiments are performed using simulation examples and the simulation results show that the model proposed can evaluate the UUV cooperative combat effectiveness accurately and effectively, and the iterative convergence of GAPSO optimization algorithm is superior to GA, PSO algorithm, and its Evaluation accuracy is the best.
Customizable Dynamic Hand Gesture recognition System for Motor Impaired people using Siamese neural network

Every year, between 250000 and 500000 people across the world suffer a major spinal cord injury, many of which result in motor impairment. A set of physical gestures can comprise an entire language which makes it a powerful form of communication for people who suffer from motor impairment. Most gesture recognition systems require the user to learn specific gestures prescribed by the system. This turns out to be a major disadvantage of these systems as most motor impaired individuals are heavily constrained in terms of movement. Hence they may not be able to accomplish certain gestures defined by the system. So in this paper we propose a realtime system which can be customized to user-specific dynamic hand movements. This system can be used to carry out certain tasks. Haar Cascade detection algorithm was used in order to track the movements of the hand and trace the path. Siamese neural network was used for the purpose of customization and recognition of the gestures.
V2S attack: building DNN-based voice conversion from automatic speaker verification

This paper presents a new voice impersonation attack using voice conversion (VC). Enrolling personal voices for automatic speaker verification (ASV) offers natural and flexible biometric authentication systems. Basically, the ASV systems do not include the users' voice data. However, if the ASV system is unexpectedly exposed and hacked by a malicious attacker, there is a risk that the attacker will use VC techniques to reproduce the enrolled user's voices. We name this the ``verification-to-synthesis (V2S) attack'' and propose VC training with the ASV and pre-trained automatic speech recognition (ASR) models and without the targeted speaker's voice data. The VC model reproduces the targeted speaker's individuality by deceiving the ASV model and restores phonetic property of an input voice by matching phonetic posteriorgrams predicted by the ASR model. The experimental evaluation compares converted voices between the proposed method that does not use the targeted speaker's voice data and the standard VC that uses the data. The experimental results demonstrate that the proposed method performs comparably to the existing VC methods that trained using a very small amount of parallel voice data.
Stochastic Primal-Dual Q-Learning Algorithm For Discounted MDPs

In this work, we present a new model-free and off-policy reinforcement learning (RL) algorithm, that is capable of finding a near-optimal policy with state-action observations from arbitrary behavior policies. Our algorithm, called the stochastic primal-dual Q-learning (SPD Q-learning), hinges upon a new linear programming formulation and a dual perspective of the standard Q-learning. In contrast to previous primal-dual RL algorithms, SPD-Q learning includes a Q-function estimation step, thus allowing to recover an approximate policy from the primal solution as well as the dual solution. We prove a first-of-its-kind result that the SPD Q-learning guarantees a certain convergence rate, even when the state-action distribution under a given behavior policy is time-varying but sub-linearly converges to a stationary distribution.
Reversible decryption of covert nanometer-thick patterns in modular metamaterials

Continuous development of security features is mandatory for the fight against forgery of valuable documents and products, the most notable example being banknotes. Such features demonstrate specific properties under certain stimuli such as fluorescent patterns glowing under ultraviolet light. These security features should also be hard to copy by unlicensed people and be interrogated by anyone using easily accessible tools. To this end, this Letter demonstrates the development of an ideal security feature enabled by the realization of modular metamaterials based on metal&#x2013;dielectric&#x2013;metal cavities that consist of two separate parts: metal nanoparticles on an elastomeric substrate and a bottom mirror coated with a thin dielectric. Patterns generated by creating nanometer-thick changes in the dielectric layer are invisible (encrypted) and can only be detected (decrypted) by sticking the elastomeric patch on. The observed optical effects such as visibility and colors can only be produced with the correct combination of materials and film thicknesses, making the proposed structures a strong alternative to compromised security features.
Towards Using Context-Dependent Symbols in CTC Without State-Tying Decision Trees

Deep neural acoustic models benefit from context-dependent (CD) modeling of output symbols. We consider direct training of CTC networks with CD outputs, and identify two issues. The first one is frame-level normalization of probabilities in CTC, which induces strong language modeling behavior that leads to overfitting and interference with external language models. The second one is poor generalization in the presence of numerous lexical units like triphones or tri-chars. We mitigate the former with utterance-level normalization of probabilities. The latter typically requires reducing the CD symbol inventory with state-tying decision trees, which have to be transferred from classical GMM-HMM systems. We replace the trees with a CD symbol embedding network, which saves parameters and ensures generalization to unseen and undersampled CD symbols. The embedding network is trained together with the rest of the acoustic model and removes one of the last cases in which neural systems have to be bootstrapped from GMM-HMM ones.
Lorentz boosts of bispinor Bell-like states

Abstract We describe in this paper the effects of Lorentz boost on the quantum entanglement encoded in two-particle Dirac bispinor Bell-like states. Each particle composing the system described in this formalism has three degrees of freedom: spin, chirality, and momentum, and the joint state can be interpreted as a 6 qubit state. Given the transformation law of bispinor under boosts, we compute the change of the Meyer-Wallach global measure of quantum entanglement due to the frame transformation and study its equivalence to the results obtained for the relativistic spin 1/2 Bell-like states, constructed in the framework of the irreducible representations of the Lorentz group. We verify that the monotonic increase of the global entanglement under boosts for ultra-relativistic states is solely due to an increasing of the entanglement associated with the spins subsystems. For such ultra-relativistic states, the entanglement related to the chirality degrees of freedom is invariant, and the variation of the global entanglement of bispinor states is the same as the one calculated for relativistic spin 1/2 states. We also show that the particle-particle entanglement is invariant under boosts for any Bell-like state.
Searching Document Repositories using 3D Model Reconstruction

A common representation of a three dimensional object is a multi-view collection of two dimensional images showing the object from multiple angles. This technique is often used with document repositories such as collections of engineering drawings and governmental repositories of design patents and 3D trademarks. It is rare for the original physical artifact to be available. When the original physical artifact is modeled as a set of images, the resulting multi-view collection of images may be indexed and retrieved using traditional image retrieval techniques. Consequently, massive repositories of multi-view collections exist. While these repositories are in use and easy to construct, the conversion of a physical object into multi-view images results in a degraded representation of both the original three dimensional artifact and the resulting document repository. We propose an alternative approach where the archived multi-view representation of the physical artifact is used to reconstruct the 3D model, and the reconstructed model is used for retrieval against a database of 3D models. We demonstrate that document retrieval using the reconstructed 3D model achieves higher accuracy than document retrieval using a document image against a collection of degraded multi-view images. The Princeton Shape Benchmark 3D model database and the ShapeNet Core 3D model database are used as ground truth for the 3D image collection. Traditional indexing and retrieval is simulated using the multi-view images generated from the 3D models. A more accurate 3D model search is then considered using a reconstruction of the original 3D models from the multi-view archive, and this model is searched against the 3D model database.
Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learning

Modern problems in AI or in numerical analysis require nonsmooth approaches with a flexible calculus. We introduce generalized derivatives called conservative fields for which we develop a calculus and provide representation formulas. Functions having a conservative field are called path differentiable: convex, concave, Clarke regular and any semialgebraic Lipschitz continuous functions are path differentiable. Using Whitney stratification techniques for semialgebraic and definable sets, our model provides variational formulas for nonsmooth automatic differentiation oracles, as for instance the famous backpropagation algorithm in deep learning. Our differential model is applied to establish the convergence in values of nonsmooth stochastic gradient methods as they are implemented in practice.
A Hybrid Approach for Aspect Extraction from Customer Reviews

Aspect Extraction from consumer reviews has become an essential factor for successful Aspect Based Sentiment Analysis. Typical user trends to mention his opinion against several aspects in a single review; therefore, aspect extraction has been tackled as a multi-label classification task. Due to its complexity and the variety across different domains, yet, no single system has been able to achieve comparable accuracy levels to the human-accuracy. However, novel neural network architectures and hybrid approaches have shown promising results for aspect extraction. (Support Vector Machines) SVMs and (Convolutional Neural Networks) CNNs pose a viable solution to the multi-label text classification task and has been successfully applied to identify aspects in reviews. In this paper, we first define an improved CNN architecture for aspect extraction which achieves comparable results against the current state-of-the-art systems. Then we propose a mixture of classifiers for aspect extraction, combining the proposed improved CNN with an SVM that uses the state-of-the-art manually engineered features. The combined system outperforms the results of individual systems while showing a significant improvement over the state-of-the-art aspect extraction systems that employ complex neural architectures such as MTNA.
Robust longitudinal motion planning using vehicle model inversion

Trajectory planning for autonomous vehicles has significantly increased as more and more ADAS are included in modern cars. It A vehicle definitely needs to globally plan a trajectory, taking all the driving factors into account. For an autonomous terrestrial vehicle, this article proposes using an optimal trajectory on a specific range as a reference in a tracking loop. In previous studies, the optimization has been made using a Genetic Algorithm (GA), and the obtained trajectory has been injected into a Potential Field (PF) so as to be reactive to unforeseen events. Here, the previously developed GA-PF method is inserted in a new global planning and tracking method for longitudinal feedback control. An optimal trajectory is used as an input reference and a tracking schema is developed using an inverted bicycle model as an efficient feedforward control, and a robust controller to take the vehicle parameter variations into account. Autonomous car simulation results are given.
Neural Attentive Cross-Domain Recommendation

Nowadays, users open multiple accounts on social media platforms and e-commerce sites, expressing their personal preferences on different domains. However, users' behaviors change across domains, depending on the content that users interact with, such as movies, music, clothing and retail products. The main challenge is how to capture users' complex preferences when generating cross-domain recommendations, that is exploiting users' preferences from source domains to generate recommendations in a target domain. In this study, we propose a Neural Attentive Cross-domain model, namely NAC. We design a neural architecture, to carefully transfer the knowledge of user preferences across domains by taking into account the cross-domain latent effects of multiple source domains on users' selections in a target domain. In addition, we introduce a cross-domain behavioral attention mechanism to adaptively perform the weighting of users' preferences from the source domains, and consequently generate accurate cross-domain recommendations. Our experiments on ten cross-domain recommendation tasks show that the proposed NAC model achieves higher recommendation accuracy than other state-of-the-art methods for both ordinary and cold-start users. Furthermore, we study the effect of the proposed cross-domain behavioral attention mechanism and show that it is a key factor to our model's performance.
Feature-Based Deformable Registration Using Minimal Spanning Tree for Prostate MR Segmentation

Automatic and accurate segmentation of the prostate is still a challenging task due to intensity inhomogeneity and complicated deformation of MR images. To tackle these problems with multi-atlas segmentation, in this paper, we propose a new metric for image registration and new descriptor for label fusion. First, to reduce the amount of edges in entropic graph, a modified α-mutual information (α-MI) based on fast minimal spanning tree (MST) is implemented for deformable registration. Second, localized α-MI allowing for the spatial information is proposed with the stochastic gradient optimization, and the feature space is encoded by a sparse auto-encoder. Finally, a multi-scale descriptor utilizing local self-similarity is integrated into the patch-based label fusion to obtain final segmentation. Experiments were performed on two subsets of totally 46 T2-weighted prostate MR images from 46 patients. Compared to α-MI based on k-nearest neighbor graph, the registration time of α-MI based on fast MST can be reduced by almost half. The median Dice overlap of registration using localized α-MI on one subset is shown to improve significantly from 0.725 to 0.764 (p = 1.14 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> ), compared to using α-MI without the spatial information. The median Dice overlap of prostate segmentation using the proposed method on 20 testing images of another subset is 0.871, and the median Hausdorff distance is 8.013 mm, which demonstrate a comparable accuracy to state-of-the-art methods.
Effective Soft-Adaptation for Neural Machine Translation

Domain mismatch between training data and test data often degrades translation quality. It is necessary to make domain adaptation for machine translation tasks. In this paper, we propose a novel method to tackle Neural Machine Translation (NMT) domain adaptation issue, where a soft-domain adapter (SDA) is added in the encoder-decoder NMT framework. Our SDA automatically learns domain representations from the training corpus, and dynamically compute domain-aware context for inputs which can guide the decoder to generate domain-aware translations. Our method can softly leverage domain information to translate source sentences, which can not only improve the translation quality on specific domain but also be robust and scalable on different domains. Experiments on Chinese-English and English-French tasks show that our proposed method can significantly improve the translation quality of in-domain test sets, without performance sacrifice of out-of-domain/general-domain data sets.
Text Mining and Subject Analysis for Fiction; or, Using Machine Learning and Information Extraction to Assign Subject Headings to Dime Novels

This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.
Feature Weighting and Boosting for Few-Shot Segmentation.

This paper is about few-shot segmentation of foreground objects in images. We train a CNN on small subsets of training images, each mimicking the few-shot setting. In each subset, one image serves as the query and the other(s) as support image(s) with ground-truth segmentation. The CNN first extracts feature maps from the query and support images. Then, a class feature vector is computed as an average of the support's feature maps over the known foreground. Finally, the target object is segmented in the query image by using a cosine similarity between the class feature vector and the query's feature map. We make two contributions by: (1) Improving discriminativeness of features so their activations are high on the foreground and low elsewhere; and (2) Boosting inference with an ensemble of experts guided with the gradient of loss incurred when segmenting the support images in testing. Our evaluations on the PASCAL-$5^i$ and COCO-$20^i$ datasets demonstrate that we significantly outperform existing approaches.