Reviewerly/exHarmony huggingface dataset:
A Differential Evolution Algorithm with Q-Learning for Solving Engineering Design Problems

In this paper, a differential evolution algorithm with Q-Learning (DE-QL) for solving engineering Design Problems (EDPs) is presented. As well known, the performance of a DE algorithm depends on the mutation strategy and its control parameters, namely, crossover and mutation rates. For this reason, the proposed DE-QL generates the trial population by using the QL method in such a way that the QL guides the selection of the mutation strategy amongst four distinct strategies as well as crossover and mutation rates from the Q table. The DE-QL algorithm is well equipped with the epsilon constraint handling method to balance the search between feasible regions and infeasible regions during the evolutionary process. Furthermore, a new mutation operator, namely DE/Best to current/l, is proposed in the DE-QL algorithm. In this paper, 57 EDPs provided in “Problem Definitions and Evaluation Criteria for the CEC 2020 Competition and Special Session on A Test-suite of Non-Convex Constrained optimization Problems from the Real-World and Some Baseline Results” are tested by the DE-QL. We provide our results in Appendixes and will be evaluated with other competitors in the competition.
Dyadic Mirror

A parent's capacity to understand the mental states of both him/herself and the child is considered to play a significant role in various aspects of parent-child relationship-e.g., lowering parental stress and supporting cognitive development of the child. We propose Dyadic Mirror, a wearable smart mirror which is designed to foster the aforementioned parental capacity in everyday parent-child interaction. Its key feature is to provide a parent with a second-person live-view from the child, i.e., the parent's own face as seen by the child, during their face-to-face interaction. Dyadic Mirror serves as a straightforward cue that helps the parent be aware of (1) his/her emotional state, and (2) the way he/she would be now being seen by the child, thereby facilitate the parent to infer the child's mental state. To evaluate Dyadic Mirror under unconstrained parent-child interactions in real-life, we implemented the working prototype of Dyadic Mirror and deployed it to 6 families over 4 weeks. The participating parents reported extensive experiences with Dyadic Mirror, supporting that Dyadic Mirror has helped them be aware of their recurring but unconscious behaviors, understand their children's feelings, reason with the children's behaviors, and find self-driven momenta to better their attitude and expressions towards their children.
Killing EM Side-Channel Leakage at its Source

Side-channel analysis (SCA) is a big threat to the security of connected embedded devices. Over the last few years, physical non-invasive SCA attacks utilizing the electromagnetic (EM) radiation (EM side-channel `leakage') from a crypto IC has gained huge momentum owing to the availability of the low-cost EM probes and development of the deep-learning (DL) based profiling attacks. In this paper, our goal is to understand the source of the EM leakage by analyzing a white-box modeling of the EM leakage from the crypto IC, leading towards a low-overhead generic countermeasure. To kill this EM leakage from its source, the solution utilizes a signature attenuation hardware (SAH) encapsulating the crypto core locally within the lower metal layers such that the critical correlated crypto current signature is significantly attenuated before it passes through the higher metal layers to connect to the external pin. The protection circuit utilizing AES256 as the crypto core is fabricated in 65nm process and shows for the first time the effects of metal routing on the EM leakage. The > 350× signature attenuation of the SAH together with the local lower metal routing ensured that the protected AES remains secure even after 1B measurements for both EM and power SCA, which is an 100× improvement over the state-of-the-art with comparable overheads. Overall, with the combination of the 2 techniques - signature suppression and local lower metal routing, we are able to kill the EM side-channel leakage at its source such that the correlated signature is not passed through the top-level metals, MIM capacitors, or on-board inductors, which are the primary sources of EM leakage, thereby preventing EM SCA attacks.
3D automatic levels propagation approach to breast MRI tumor segmentation

Magnetic Resonance Imaging MRI is a relevant tool for breast cancer screening. Moreover, an accurate 3D segmentation of breast tumors from MRI scans plays a key role in the analysis of the disease. In this manuscript, we propose a novel 3D automatic method for segmenting MRI breast tumors, called 3D Automatic Levels Propagation Approach (3D-ALPA). The proposed method performs the segmentation automatically in two steps: in the first step, the entire MRI volume to process is segmented slice by slice. Specifically, using a new automatic approach called 2D Automatic Levels Propagation Approach (2D-ALPA) which is an improved version of a previous semi-automatic approach, named 2D Levels Propagation Approach (2D-LPA). In the second step, the partial segmentations obtained after the application of 2D-ALPA are recombined to rebuild the complete volume(s) of tumor(s). 3D-ALPA has many characteristics, mainly: it is an automatic method which can take into consideration multi-tumor segmentation, and it has the property to be easily applicable according to the Axial, Coronal, as well as Sagittal planes. Therefore, it offers a multi-view representation of the segmented tumor(s). To validate the new 3D-ALPA method, we have firstly performed tests on a 2D private dataset composed of eighteen patients to estimate the accuracy of the new 2D-ALPA in comparison to the previous 2D-LPA. The obtained results have been in favor of the proposed 2D-ALPA, showing hence an improvement in accuracy after integrating the automatization in the 2D-ALPA approach. Then, we have evaluated the complete 3D-ALPA method on a 3D private dataset constituted of MRI exams of twenty-two patients having real breast tumors of different types, and on the public RIDER dataset. Essentially, 3D-ALPA has been evaluated regarding two main features: segmentation accuracy and running time, by considering two kinds of breast tumors: non-enhanced and enhanced tumors. The experimental studies have shown that 3D-ALPA has produced better results for the both kinds of tumors than a recent and concurrent method in the literature that addresses the same problematic.
DEGAN: Decentralized generative adversarial networks

We propose a distributed and decentralized Generative Adversarial Networks (GANs) framework without the exchange of the training data. Each node contains local dataset, a discriminator and a generator, from which only the generator gradients are shared with other nodes. In this paper, we introduce a novel, distributed technique in which workers communicate directly with each other, having no central nodes. Our experimental results on the benchmark datasets demonstrate almost the same performance and accuracy compared with existing centralized GAN frameworks. The proposed framework addresses the lack of decentralized learning for GANs.
An Automatic Merge Technique to Improve the Clustering Quality Performed by LAMDA

Clustering is a research challenge focused on discovering knowledge from data samples whose goal is to build good quality partitions. In this paper is proposed an approach based on LAMDA (Learning Algorithm for Multivariable Data Analysis), whose most important features are: a) it is a non-iterative fuzzy algorithm that can work with online data streams, b) it does not require the number of clusters, c) it can generate new partitions with objects that do not have enough similarity with the preexisting clusters (incremental-learning). However, in some applications, the number of created partitions does not correspond with the number of desired clusters, which can be excessive or impractical for the expert. Therefore, our contribution is the formalization of an automatic merge technique to update the cluster partition performed by LAMDA to improve the quality of the clusters, and a new methodology to compute the Marginal Adequacy Degree that enhances the individual-cluster assignment. The proposal, called LAMDA-RD, is applied to several benchmarks, comparing the results against the original LAMDA and other clustering algorithms, to evaluate the performance based on different metrics. Finally, LAMDA-RD is validated in a real case study related to the identification of production states in a gas-lift well, with data stream. The results have shown that LAMDA-RD achieves a competitive performance with respect to the other well-known algorithms, especially in unbalanced benchmarks and benchmarks with an overlapping of around 9%. In these cases, our algorithm is the best, reaching a Rand Index (RI) >98%. Besides, it is consistently among the best for all metrics considered (Silhouette coefficient, modification of the Silhouette coefficient, WB-index, Performance Coefficient, among others) in all case studies analyzed in this paper. Finally, in the real case study, it is better in all the metrics.
Part alignment network for vehicle re-identification

Vehicle re-identification (re-ID) has numerous applications in real life, such as video surveillance, information retrieval, and public security. However, it suffers from the misalignment of vehicles, which is a critical problem caused by inaccurate detection and different views. This paper proposes a novel network named Part Alignment Network (PAN) which can properly handle the misalignment of vehicles. It is therefore adapted to the vehicle re-ID task, avoiding the use of any additional pre-processing steps such as the annotation of vehicle key points and part segmentation by hand. In PAN, cross-correlation is adopted to the alignment of vehicle parts. Then, an effective network architecture is designed to extract the discriminative aligned features. By combining complementary aligned features and original features, more robust feature representations are learned. To show the effectiveness of PAN, this paper conducts experiments on three vehicle re-ID databases (VD1, VD2, and VehicleID), on which it improves the current state-of-the-art performance.
Displacement-free stereoscopic phase measuring deflectometry based on phase difference minimization

In this paper, we propose a phase difference minimization algorithm to measure the specular surface shape in a displacement-free stereoscopic phase measuring deflectometry (PMD) system. The presented system is capable of solving the height-normal ambiguity appearing in a PMD system without moving any system component. Both the surface normal and the absolute height are simultaneously obtained by implementing phase difference minimization between the phase distributions in the LCD screen and the camera image plane. In particular, phase difference minimization is performed by using a second order polynomial fitting iteration method. Bi-cubic sub-pixel interpolation combined with 2D Fourier integration is used to reconstruct the surface. Finally, the performance of the proposed stereoscopic PMD system is verified by measuring the surface shapes of different mirrors and performing repeatability tests.
Kernel center adaptation in the reproducing kernel Hilbert space embedding method

Summary The performance of adaptive estimators that employ embedding in reproducing kernel Hilbert spaces (RKHS) depends on the choice of the location of basis kernel centers. Parameter convergence and error approximation rates depend on where and how the kernel centers are distributed in the state‐space. In this article, we develop the theory that relates parameter convergence and approximation rates to the position of kernel centers. We develop criteria for choosing kernel centers in a specific class of systems by exploiting the fact that the state trajectory regularly visits the neighborhood of the positive limit set. Two algorithms, based on centroidal Voronoi tessellations and Kohonen self‐organizing maps, are derived to choose kernel centers in the RKHS embedding method. Finally, we implement these methods on two practical examples and test their effectiveness.
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings

We propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings. In particular, we assume that global latent topics are shared across documents, a word is generated by a hidden semantic vector encoding its contextual semantic meaning, and its context words are generated conditional on both the hidden semantic vector and global latent topics. Topics are trained jointly with the word embeddings. The trained model maps words to topic-dependent embeddings, which naturally addresses the issue of word polysemy. Experimental results show that the proposed model outperforms the word-level embedding methods in both word similarity evaluation and word sense disambiguation. Furthermore, the model also extracts more coherent topics compared with existing neural topic models or other models for joint learning of topics and word embeddings. Finally, the model can be easily integrated with existing deep contextualized word embedding learning methods to further improve the performance of downstream tasks such as sentiment classification.
Continual Learning Approach for Remote Sensing Scene Classification

In this letter, we propose a continual learning approach for a set of sequential scene classification tasks, where each task contains a group of land-cover classes. Our aim is to learn new tasks in a continual way without significantly degrading the performances of the old ones, due to the tricky catastrophic forgetting problem inherent to neural networks. To this end, we propose a neural architecture composed of two trainable modules. The first module learns its weights by discriminating between the land-cover classes within the new task while keeping trace of the old ones. On the other side, the second module tries to maximize the separation between the tasks by learning on task-prototypes stored in a linear memory (one prototype per task). The experimental results on two scene data sets (Merced and Optimal31) confirm the promising capability of the proposed method.
The robots are here, but are nurse educators prepared?

<h2>ABSTRACT</h2><h3>Background</h3> As robotics continues to be applied to medical and nursing practice, nurse educators need to be aware of innovations and discuss practice and challenges with students. <h3>Objectives</h3> This paper provides a literature review of the application of robotics in nursing and the implications for nurse education. <h3>Review methods</h3> The methodology adopted for this review was informed by the definition of service robots. The review focused on how clinical service robotics are being discussed in nursing and was undertaken using the Cochrane Library, ScienceDirect and ProQuest databases, using the truncated search terms: nurs* <i>and</i> robot*, education, students and future. Research articles were limited to those that were published in peer reviewed journals, between 2010 and 2019, since this covers the time when robotic science has been flourishing. The field of robotics is vast, thus the search yielded over 400 articles, but most are not specifically relevant to nursing. Twenty-six relevant papers were included. <h3>Results</h3> Thematic review of the literature found four main developments affecting nursing and mixed perceptions and ideas among health care staff and the public across varied themes. <h3>Conclusion</h3> Health care and nursing work are being transformed through robotics. New technologies, tasks and technician roles that currently do not exist will soon be part of the health workforce, and the nature of nursing work will inevitably change. In the light of such change, there is a need to redefine the nature and practice of nursing and to re-prioritise caring as fundamental to the nursing profession. These changes have implications for the education of future nursing professionals.
Multiobject operational tasks for convex quantum resource theories of state-measurement pairs

The prevalent modus operandi within the framework of quantum resource theories has been to characterise and harness the resources within single objects, in what we can call \emph{single-object} quantum resource theories. One can wonder however, whether the resources contained within multiple different types of objects, now in a \emph{multi-object} quantum resource theory, can simultaneously be exploited for the benefit of an operational task. In this work, we introduce examples of such multi-object operational tasks in the form of subchannel discrimination and subchannel exclusion games, in which the player harnesses the resources contained within a state-measurement pair. We prove that for any state-measurement pair in which either of them is resourceful, there exist discrimination and exclusion games for which such a pair outperforms any possible free state-measurement pair. These results hold for arbitrary convex resources of states, and arbitrary convex resources of measurements for which classical post-processing is a free operation. Furthermore, we prove that the advantage in these multi-object operational tasks is determined, in a multiplicative manner, by the resource quantifiers of: \emph{generalised robustness of resource} of both state and measurement for discrimination games and \emph{weight of resource} of both state and measurement for exclusion games.
BITPNet: Unsupervised Bio-Inspired Two-Path Network for Nighttime Traffic Image Enhancement

Due to the low luminance in nighttime traffic images, image features are not salient, making tasks in intelligent transportation systems such as nighttime vehicle detection challenging. Recently, convolutional neural network based methods have been developed for low-light image enhancement. Most of these methods are supervised and require high-light reference images at the same scenes. However, reference images are difficult to be obtained in nighttime traffic scenes because vehicles always move. In the early visual system the input signals are processed by two parallel visual paths in the retina: one path has small receptive fields (RFs) to process the high frequency information and another path has large RFs to deal with the low frequency information. Inspired by this, we design a novel bio-inspired two-path convolutional neural network (BITPNet) for nighttime traffic image enhancement. The high-frequency path with small convolution kernel size is designed to suppress noises and preserve the details. The low-frequency path with large convolution kernel size is used to enhance the luminance of images. Each path includes an encoder-to-decoder network followed by a new multi-level attention module to combine features of levels with different RFs. The outputs of the two paths are summed by learnt weights for generating the final image enhancement result. Several no-reference image quality metrics are utilized to design a new loss function, resulting in an unsupervised approach. The proposed BITPNet is trained on one nighttime traffic image dataset and evaluated on another nighttime dataset. Experimental results demonstrate that the proposed BITPNet outperforms several state-of-the-art low-light image enhancement methods in terms of visual quality and three no-reference image quality metrics. In addition, when the proposed BITPNet is used as pre-processing for the nighttime multi-class vehicle detection task, it achieves higher detection rate (97.18%) than other methods.
Anisotropic Diffusion Based Unsharp Masking and Crispening for Denoising and Enhancement of MRI Images

1. Abstract-Unsharp masking and crispening of Magnetic Resonance Images (MRIs) is a vital factor for good visual inspection, accurate parameter estimation, and for further image processes like feature extraction and classification. To accomplish this objective, a method is proposed that is adapted from anisotropic diffusion filter(ADF). It utilizes ADF regularization as an intermediate step for smoothening of MRI image. It is an expanded and improved version of “Perona and Malik” Diffusion which gives a better image quality assessment when compares to other methods. The image quality assessment criterions like Peak signal-to-noise ratio (PSNR), mean square error (MSE) and structural similarity index (SSIM) are determined with respect to full reference image. Some no reference metric like Perception based Image Quality Evaluator (PIQE) and Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) are also determined. The corresponding results of full reference image for kaggle data-set. are estimated as 39.13dB, 7.21 and 0.99 respectively. The average result regarding for no reference metric are given as 48.84 and 37.13 respectively. The following outline has been executed in MATLAB R2015b which is used for MRI images of various resolutions, shape and size. The achieved outcomes expressed that overall image is deniosed & enhanced by the proposed method.
Micro-Expression Recognition Based on 2D-3D CNN

As a special facial expression, micro expressions have subtle muscle movements, which occur at a very short duration. There are many promising applications of micro expression recognition, such as criminal investigation and public security. Due to the facial motion features of micro expression are difficult to capture, it is difficult to perceive and interpret the micro expression. In this paper, a new 2D-3D CNN for micro expression recognition is proposed, which consist of Net-A and Net-B.Net-A is used to extract spatio-temporal features, and consists of multi-scale 1D temporal convolution and 2D spatial convolution. Multi-scale 1D temporal convolution is used to extract the dynamic information in micro expression video, and 2D spatial convolution is used to extract the spatial information of single frame image. Then the temporal and spatial features are fused multiple times and get the short-term spatio-temporal features. The Net-B, consists of residual blocks, is used to process differential information image and obtains the long-term spatio-temporal feature. Micro expression video contains too much temporal redundancy information, so a differential information image is proposed. Redundant features can be removed by using Net-B to extract features from differential information images. Then, the differential information feature map is fused with the features extracted by Net-A to enhance the motion features in the micro expression video. The effectiveness of the proposed 2D-3D CNN is evaluated on two open datasets: CASME and SAMM. The experimental results show that our method can achieve a better performance than the existing state-of-art methods.
Efficient Video Frame Interpolation Using Generative Adversarial Networks

Frame interpolation, which generates an intermediate frame given adjacent ones, finds various applications such as frame rate up-conversion, video compression, and video streaming. Instead of using complex network models and additional data involved in the state-of-the-art frame interpolation methods, this paper proposes an approach based on an end-to-end generative adversarial network. A combined loss function is employed, which jointly considers the adversarial loss (difference between data models), reconstruction loss, and motion blur degradation. The objective image quality metric values reach a PSNR of 29.22 dB and SSIM of 0.835 on the UCF101 dataset, similar to those of the state-of-the-art approach. The good visual quality is notably achieved by approximately one-fifth computational time, which entails possible real-time frame rate up-conversion. The interpolated output can be further improved by a GAN based refinement network that better maintains motion and color by image-to-image translation.