Reviewerly/exHarmony huggingface dataset:
PSGAN: Pose-Robust Spatial-Aware GAN for Customizable Makeup Transfer.

In this paper, we address the makeup transfer task, which aims to transfer the makeup from a reference image to a source image. Existing methods have achieved promising progress in constrained scenarios, but transferring between images with large pose and expression differences is still challenging. Besides, they cannot realize customizable transfer that allows a controllable shade of makeup or specifies the part to transfer, which limits their applications. To address these issues, we propose Pose and expression robust Spatial-aware GAN (PSGAN). It first utilizes Makeup Distill Network to disentangle the makeup of the reference image as two spatial-aware makeup matrices. Then, Attentive Makeup Morphing module is introduced to specify how the makeup of a pixel in the source image is morphed from the reference image. With the makeup matrices and the source image, Makeup Apply Network is used to perform makeup transfer. Our PSGAN not only achieves state-of-the-art results even when large pose and expression differences exist but also is able to perform partial and shade-controllable makeup transfer. We also collected a dataset containing facial images with various poses and expressions for evaluations.
Dendro: A FAIR, Open-Source Data Sharing Platform

Dendro, a research data management (RDM) platform developed at FEUP/INESC TEC since 2014, was initially targeted at collaborative data storage and description in preparation for deposit in any data repository (CKAN, Zenodo, ePrints or B2Share). We implemented our own data deposit and dataset search features, consolidating the whole RDM workflow in Dendro: dataset exporting, automatic DOI attribution, and a dataset faceted search, among other features. We discuss the challenges faced when implemented these features and how they make Dendro more FAIR.
Using Lexicon and Random Forest Classifier for Twitter Sentiment Analysis

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Incorporating intent propensities in personalized next best action recommendation

Next best action (NBA) is a technique that is widely considered as the best practice in modern personalized marketing. It takes users' unique characteristics into consideration and recommends next actions that help users progress towards business goals as quickly and smoothly as possible. Many NBA engines are built with rules handcrafted by marketers based on experience or gut feelings. It is not effective. In this proposal, we show our machine learning based approach for such a real-time recommendation engine, detail our design choices, and discuss evaluation techniques.
The DKU-SMIIP System for NIST 2018 Speaker Recognition Evaluation

In this paper, we present the system submission for the NIST 2018 Speaker Recognition Evaluation by DKU Speech and Multi-Modal Intelligent Information Processing (SMIIP) Lab. We explore various kinds of state-of-the-art front-end extractors as well as back-end modeling for text-independent speaker verifications. Our submitted primary systems employ multiple state-of-the-art front-end extractors, including the MFCC i-vector, the DNN tandem i-vector, the TDNN x-vector, and the deep ResNet. After speaker embedding is extracted, we exploit several kinds of back-end modeling to perform variability compensation and domain adaptation for mismatch training and testing conditions. The final submitted system on the fixed condition obtains actual detection cost of 0.392 and 0.494 on CMN2 and VAST evaluation data respectively. After the official evaluation, we further extend our experiments by investigating multiple encoding layer designs and loss functions for the deep ResNet system.
A New Model for Applying Extended Kalman Filtering to Extract Harmonic Signals from Noisy Measurements

The extended Kalman filter has been used to estimate a harmonic signal from noisy measurements. Most algorithms are based on the Cartesian model, which is a discretization in time of the continuous state space model associated with the differential equation that is satisfied by a sinusoidal signal with constant amplitude and frequency. In order to handle the more realistic case where both amplitude and frequency are changing, this basic model is modified by including ad hoc extensions. This paper starts by deriving a differential equation that explicitly includes time varying amplitude and frequency, and it is shown that this can be reduced to a Bessel's equation of order 1/2 that has a closed form solution. This is used to derive an explicit expression for a discrete-time model, which forms the basis of an extended Kalman filter. Simulation results show that this algorithm outperforms other approaches, particularly for harmonic signals where the frequency is changing rapidly.
WHAT IS ONTOLOGY? A DIALOGUE

This dialogue presents a substantive account of the nature and aim of ontology.
RL Extraction of Syntax-Based Chunks for Sentence Compression

Sentence compression involves selecting key information present in the input and rewriting this information into a short, coherent text. While dependency parses have often been used for this purpose, we propose to exploit such syntactic information within a modern reinforcement learning-based extraction model. Furthermore, compared to other approaches that include syntactic features into deep learning models, we design a model that has better explainability properties and is flexible enough to support various shallow syntactic parsing modules. More specifically, we linearize the syntactic tree into the form of overlapping text segments, which are then selected with reinforcement learning and regenerated into a compressed form. Hence, despite relying on extractive components, our model is also able to handle abstractive summarization. We explore different ways of selecting subtrees from the dependency structure of the input sentence and compare the results of various models on the Gigaword corpus.
Late Breaking Results: Analog Circuit Generator based on Deep Neural Network enhanced Combinatorial optimization

A deep neural network (DNN) based stochastic combinatorial optimization framework is presented that can find the optimal sizing of circuits in a sample-efficient manner. This sample efficiency allows us to unify this framework with generator-based tools like Berkeley Analog Generator (BAG) [1] to directly optimize layout, given the high level circuit specifications. We use this tool to design an optical link receiver layout, satisfying high-level design specifications, using post-layout simulations of only 348 design instances. Compared to an evolutionary algorithm without our DNN-based discriminator, our framework improves the sample efficiency and run time by more than 200x.
Target Recognition Based on SIFT Algorithm for a Train Uncoupling Robot

The train uncoupling robot is a 4-DOF robot which can complete the uncoupling work efficiently and accurately in the process of disassembly or marshalling of freight trains or passenger trains without the help of workers. The uncoupling work should be completed when the train and the uncoupling robot are both in motion. However, this increases the difficulty of recognizing the hook of trains and finishing the uncoupling work automatically. Therefore, the visual system of the robot plays a vital role during the uncoupling process so that it can be able to recognize the target hook by analyzing images and data from cameras and sensors. Furthermore, it should also have the ability to predict the motion planning of the object. In this paper, a visual control scheme is developed by using the Scale-invariant Feature Transform (SIFT) algorithm to recognize the hook of train in motion. It searches the extreme points as the key-points, and extracts the features such as position, scale and direction to match between the two images. The results indicate that the target hook can be recognized accurately and the recognition speed can meet the working requirements of the system by using the proposed visual control scheme even if the size of the target is varying or the target rotates moderately.
Use of Computer Vision for White Line Detection for Robotic Applications

Image processing and its use for object detection that allows to alter decisions based on what is being observed remains a challenge today. As participants in the Intelligent Ground Vehicle Competition (IGVC) 2018 and 2019, we were tasked to build an autonomous vehicle that is capable of maneuvering and traversing through a grassy course delineated with white lines and laid out with scattered obstacles. In order to remain within the white line boundaries, computer vision was used with the implementation of OpenCV and Python for image processing and white line detection. To accomplish the white line detection task, various image processing techniques such as change of color space, image filtering, blurring, histogram equalizing, and edge detection are utilized. Testing of the image processing techniques was performed using prerecorded videos from previous competitions as well as videos taken using the autonomous vehicle’s ZED camera. In both test cases there were challenges due to differences in lighting conditions, image clarity between cameras used for both video types, and the angle of the camera.
Commentary: Clinical Accuracy of Holographic Navigation Using Point-Based Registration on Augmented-Reality Glasses

Fredrickson, Vance L MD; Lin, Michelle MD; Catapano, Joshua S MD; Attenello, Frank J MD Author Information
An anti-occlusion tracking system for UAV imagery based on Discriminative Scale Space Tracker and Optical Flow

In this paper, an anti-occlusion tracking system is proposed for Unmanned Aerial Vehicle (UAV) images. Innovatively combining the efficient Discriminative Scale Space Tracker (DSST) and optical flow, the system can realize accurate and successive tracking in long-range UAV images, providing a foundation for unmanned autonomous system. The DSST contains two correlation filters, i.e., the scale filter and the translation filter to achieve satisfying tracking performance in terms of speed and accuracy when compared with the recently introduced tracking-by-detection strategy which relays on deep learning model of the convolutional neural network. Optical flow including the motion information is used to extract regions of interests (ROIs) which will be matched with a template to complete the anti-occlusion tracking. Field experiments in different backgrounds and various occlusions are conducted to verify that the proposed system integrating DSST and optical flow can track target robustly, especially for the occluded moving target.
Pedestrian Detection Method Based on SSD Model

Pedestrian detection has a wide range of applications in daily life, and many fields require pedestrians to conduct detection with high precision and speed, which is an urgent problem to be solved. The traditional pedestrian detection method improves the detection performance by improving the classification algorithm and extracting more effective features. In this paper, a pedestrian detection method is proposed based on single shot multibox detector (SSD) model, which replaces the basic network part of SSD model with inception network structure with smaller parameters, faster running speed and stronger nonlinear expression ability. A high-performance network model for pedestrian detection was based on improved SSD. The experimental results show that the proposed method is faster than the original model, and the average precision of pedestrian recognition and location is 89.6%, which is 2.6% higher than the original model.
Current Trends in Biomedical and Health Informatics

Biomedical and health informatics (BMHI) as an interdisciplinary field of research and practice is growing, and research topics and trends are evolving rapidly. The future of the field has been subject to scientific discussions and bibliometric analyses have been published regularly in different BMHI journals. A recent cluster analysis of scientific publications in BMHI during 2006 to 2017 revealed five different clusters whereof two, namely “Mobile Health” and “Clinical Informatics,” describe upcoming topics.[1] The remaining three clusters, namely “Organizational Aspects of Health Information Systems,” “Biomedical Data Analysis,” and “Electronic Health Records and Knowledge Representation” correspond to clusters that were found in a previous analysis of research publications by other authors for the time period 1993 to 2007.[2]
Real-Time Traffic Monitoring with Occlusion Handling

Traffic surveillance through vision systems is a highly demanded task. To solve it, it is necessary to combine detection and tracking in a way that meets the requirements of operating in real time while being robust against occlusions. This paper proposes a traffic monitoring system that meets these requirements. It is formed by a deep learning-based detector, tracking through a combination of Discriminative Correlation Filter and a Kalman Filter, and data association based on the Hungarian method. The viability of the system has been proved for roundabout input/output analysis with near 1,000 vehicles in real-life scenarios.
Demonstrating Quantum Advantage in Security and Efficiency with Practical Photonic Systems

We discuss the current landscape in quantum communication and cryptography, and focus in particular on recent photonic implementations, using encoding in discrete or continuous properties of light, of central quantum network protocols, enabling secret key distribution, verification of entangled resources and transactions of quantum money, with maximal security guarantees. We also describe current challenges in this field and our efforts towards the miniaturization of the developed photonic systems, their integration into telecommunication network infrastructures, including with satellite links, as well as the practical demonstration of novel protocols featuring a quantum advantage in communication efficiency for a wide range of useful tasks in a network environment. These advances enrich the resources and applications of the emerging quantum networks that will play a central role in the context of future quantum-safe communications.
Flexible Entanglement Distribution Based on WDM and Active Switching Technology

In future, the distribution of single or entangled photons inside optical networks will be a prerequisite for a general roll-out and adoption of quantum communication technologies. In particular, on-demand routing and active wavelength allocation will be needed to meet the demand of complex network architectures. In the last years several attempts have been made, based either on passive optical WDM technology or active switching of channels. Here we present a novel approach whereby we combine spectral slicing of the emission spectrum of photon-pair sources together with space-switches to generate a reconfigurable distribution node for entanglement. The increased switching complexity offered by our hybrid solution allows us to realise quantum reconfigurable optical add-drop multiplexers (ROADMs) with up to three degrees.
Robust Target Tracking with Discontinuous Signal for Passive Sensor Systems

This paper considers the tracking problem of a moving emitter, which discontinuously emits certain signals, in a passive sensor system. The aim is to estimate both the kinematic state of the emitter and the discontinuous property of the signal emission. To this end, we first build the signal emission model of emitter and the discontinuous measurement model of the passive sensor. Next, we formulate the tracking problem in the Bayesian estimation framework based on these models. We then propose a backward adaptive update time Kalman filter (BAUT-KF), which can update state estimation at arbitrary time, to solve this tracking problem. In order to better estimate the discontinuous property of the signal emission, BAUT-KF employs an adaptive strategy to adjust the state update time. We also propose a forward adaptive update time Kalman filter (FAUT-KF) which adopts a forward adaptive strategy based on BAUT-KF. The linear and Gaussian implementation of the proposed algorithms is also presented. Finally, both the simulation and real-world data from a passive sonar system demonstrate the robustness and effectiveness of the proposed methods.
Regular Expressions in Python

Regular Expression are a very powerful way of processing text while looking for recurring patterns; they are often used with data held in plain text files (such as log files), CSV files as well as Excel files. This chapter introduces regular expressions, discusses the syntax used to define a regular expression pattern and presents the Python re module and its use.
Using Multidimensional Scaling to Quantify Category Heterogeneity Effects in Visual Search

Recent categorical search work demonstrated that searching for an exemplar among items of the same category is faster for heterogenous categories relative to homogeneous categories (Hout et al., 2017). However, precisely quantifying heterogeneity is difficult. Multidimensional Scaling (MDS) is a method used to model similarity (Hout et al, 2015), quantified as the distance between items in a hypothetical “psychological space.” The more dissimilar two items are perceived to be, the larger the distance between them in space. We used MDS distances to quantify category heterogeneity. We selected stimuli from a large MDS image database (Hout et al., in prep) which provides similarity estimates for 1200 images across 20 distinct categories. To derive our measure of category heterogeneity from the database, we averaged the distances between each exemplar within each category (e.g., average distance between all cars). Therefore, categories with larger average distances could be described as more heterogeneous in appearance. We then correlated these values with performance in a visual search task in which participants searched for a single target item among non-targets from the same category. We expect RT’s to decrease with increased heterogeneity because target-distractor similarity of any given search display should be lower given the within category nature of each display. Consistent with prior work, average RTs to target categories were negatively correlated with the average MDS distance. These results indicate that as average distance between exemplars within a category increases (i.e., category heterogeneity increases), search RTs for those exemplars decrease. We conclude that using MDS-derived similarity ratings to quantify category heterogeneity can be a useful and valid method to select stimuli for vision research.
Enhancement of Representational Sparsity in Deep Neural Networks Can Improve Generalization

Sparse coding in the human visual system has been viewed as a fundamental mechanism to provide increased representational capacity and efficiency with discriminative features. For artificial networks, sparsity has been introduced in numerous ways, e.g., in the form of dropout during training, rectification of activation, or wholesale removal of network nodes or individual connections. However, the main goal of sparsifying artificial networks is to reduce overfitting and network size and the methods for enforcing sparsity are determined more by computational convenience than generalization improvement. We compared standard methods of introducing sparsity in deep learning models, such as dropout, with more human-like schemes like permanent, targeted removal of weights or nodes as might happen during synaptic pruning during normal human development. A series of simulations were systematically conducted using the handwritten digit dataset (MNIST) with a simple, fully-connected three-layer network. We show that introducing sparsity with human-like schemes can significantly improve generalizability in the form of far transfer to untrained datasets, such as digit images with added noise (either random Gaussian noise or extra digit parts in the background). These generalization tests are distinct from the typical ways of testing a network (i.e. with unseen exemplars that are similar to the training set). However, such far transfer tests more closely resemble the kind of generalization performed by the human visual system. Selective pruning for sparsity significantly increased recognition accuracy in the far transfer tasks by maximum 38%. A principal component analysis of the features encoded by the network showed that increased sparsity makes the digit representations more distinct. However, we also find that representational sparsity is bounded within a range: while insufficient sparsity reduces coding efficiency, over-sparsity could lead to reduction of generalization to untrained stimuli.
Eye Gaze Region Estimation via Multi-scale Sparse Dictionary Learning

Traditional model-based gaze estimation methods relying on pupil or iris detection is vulnerable under large head movement. To solve this problem, a novel head pose-free appearance-based eye gaze prediction method without gaze calibration is proposed, combining head pose information and multi-scale eye image sparse features to determine the gaze region. Multi-scale dictionaries are learned by sparse coding to represent eye appearance more effectively, in which whole eye images are employed as global scale while their corresponding distinct patch-based images as local scale. Supported by multiscale dictionaries, multi-scale eye image sparse features are the sparse coefficients encoding eye image by linear combinations of bases selected from the learned dictionaries. In order to handle gaze variation under large head movement, a multiclass classifier concatenates the known head pose information and multi-scale eye image sparse features to estimate the gaze region. The experimental results show that the proposed approach outperforms baseline methods on a public dataset.
4-Rule Harmony Search Algorithm for Solving Computationally Expensive Optimization Test Problems

This paper proposes an enhanced harmony search algorithm for solving computationally expensive benchmarks widely used in the literature. We explored the potential and applicability of the original harmony search (HS) algorithm through introducing an extended version of the algorithm integrated with a new dynamic search equation enabling the algorithm to take guided larger steps at the beginning of the search. In the 4-Rule Harmony Search (4RHS), an extra rule is added to the standard HS without adding any user parameters to existing initial parameters. The 4RHS algorithm is then tested through optimal solving of different well-known and well-used benchmarks from classical to so CEC’ 2015 series, where the results of the 4RHS are compared with simple and improved version of HS algorithms as well as other optimization techniques. The obtained optimization results show the attractiveness of the added rule into the standard HS.