Reviewerly/exHarmony huggingface dataset:
Machine Learning of Air Traffic Controller Command Extraction Models for Speech Recognition Applications

Increasing digitization and automation is a widely accepted method to cope with the challenges of constantly increasing air traffic. The analogue communication of air traffic controllers (ATCo) to pilots has been excluded so far from the digitization process. However, the content of this communication is of decisive importance for various automation systems. Although Assistant Based Speech Recognition (ABSR) has recently significantly improved the recognition performance and, therefore, enables the digitization of ATCo-pilot-communication, its adaptation to other airports is a critical and costly process, This is even more important, if ATCos tend to deviate from the published ICAO phraseology: “start reducing to two fifty” instead of “reduce two five zero knots” is just an example. User acceptance requires that these deviations are also correctly recognized. Therefore, this paper presents an approach, which automatically learns a so-called Command Extraction Model from labelled controller utterances. The initial Command Extraction Model without learning only covers 60% of the commands, whereas the automatically learned Command Extraction Model covers more than 98%. With just six hours of training data we could achieve 94%.
Proposing a Secure Self-Fining Vehicle Using Blockchain and Vehicular Edge Computing

In recent years driving violations have increased in big and crowded cities specially in developing countries. This has resulted in the escalation of traffic congestions and number of accidents, making them difficult to handle by a central managing authority. New technologies can be used in order to mitigate this problem. Hence, in this article, we propose a novel concept, called self-fining vehicle, using blockchain infrastructure on top of the vehicular edge computing. Self-fining vehicle is a vehicle that detects and records driving violations, issues tickets, and pays fines without the need for a centralized management system. Violations can be coded into on-board units and detected by vehicular communications. The conceptual architecture of secure self-fining vehicle is proposed and discussed in this article. The proposed architecture improves violation data security, processing speed, and communication delay using encryption, authentication, blockchain, and edge computing (EC), enhancing quality of service and consumers’ quality of experience. The performance analysis of the proposed architecture proves its efficiency in terms of latency in service delivery.
Towards a standard procedure for the measurement of the multi-photon component in a CW telecom heralded single-photon source

Single-photon sources are set to be a fundamental tool for metrological applications as well as for quantum information related technologies. Because of their upcoming widespread dissemination, the need for their characterization and standardization is becoming of the utmost importance. Here, we illustrate a strategy to provide a quantitative estimate of the multi-photon component of a single-photon source, showing the results achieved in a pilot study for the measurement of the second-order autocorrelation function g(2)(0) of a low-noise CW heralded single photon source prototype (operating at telecom wavelength nm) realized in INRiM. The results of this pilot study, involving INRiM, NPL and PTB, will help to build a robust and unambiguous procedure for the characterization of the emission of a single-photon source.
Recommendations for capturing signatures digitally to optimize their suitability for forensic handwriting examination

Abstract The use of digitally captured signatures in everyday course of business increases annually and, compared to pen and paper signatures, provides various advantages concerning the administration of documents. These signatures may also become subjects of a forensic handwriting examination and, therefore, in order to optimize their suitability for this purpose, they should satisfy several requirements. This paper presents recommendations drawn up by forensic handwriting examiners associated with ENFHEX (ENFSI) in a project aimed at defining best practices in forensic examination of digitally captured signatures. The paper is dedicated mainly to hardware and software developers, providers, and user institutions of digitally captured signature technologies to improve their practice to a level optimized for forensic handwriting examination. The most important requirements outlined in this paper concern digitally captured signature data, hardware, and software used to acquire these data, as well as optimized signing conditions. Following these requirements ensures the suitability of signature data for forensic handwriting examination and, consequently, increases the reliability of the associated electronic documents. In spite of rapidly evolving technology, they can serve as a solid basis for understanding and consideration of the optimal use of digitally captured signatures for signing electronic documents.
Modeling Fashion Influence From Photos

The evolution of clothing styles and their migration across the world is intriguing, yet difficult to describe quantitatively. We propose to discover and quantify fashion influences from catalog and social media photos. We explore fashion influence along two channels: geolocation and fashion brands. We introduce an approach that detects which of these entities influence which other entities in terms of propagating their styles. We then leverage the discovered influence patterns to inform a novel forecasting model that predicts the future popularity of any given style within any given city or brand. To demonstrate our idea, we leverage public large-scale datasets of 7.7M Instagram photos from 44 major world cities (where styles are worn with variable frequency) as well as 41K Amazon product photos (where styles are purchased with variable frequency). Our model learns directly from the image data how styles move between locations and how certain brands affect each other's designs in a predictable way. The discovered influence relationships reveal how both cities and brands exert and receive fashion influence for an array of visual styles inferred from the images. Furthermore, the proposed forecasting model achieves state-of-the-art results for challenging style forecasting tasks. Our results indicate the advantage of grounding visual style evolution both spatially and temporally, and for the first time, they quantify the propagation of inter-brand and inter-city influences. Project page: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.cs.utexas.edu/~ziad/influence_from_photos.html</uri>
NABU – Multilingual Graph-Based Neural RDF Verbalizer

The RDF-to-text task has recently gained substantial attention due to continuous growth of Linked Data. In contrast to traditional pipeline models, recent studies have focused on neural models, which are now able to convert a set of RDF triples into text in an end-to-end style with promising results. However, English is the only language widely targeted. We address this research gap by presenting NABU, a multilingual graph-based neural model that verbalizes RDF data to German, Russian, and English. NABU is based on an encoder-decoder architecture, uses an encoder inspired by Graph Attention Networks and a Transformer as decoder. Our approach relies on the fact that knowledge graphs are language-agnostic and they hence can be used to generate multilingual text. We evaluate NABU in monolingual and multilingual settings on standard benchmarking WebNLG datasets. Our results show that NABU outperforms state-of-the-art approaches on English with 66.21 BLEU, and achieves consistent results across all languages on the multilingual scenario with 56.04 BLEU.
Sentiment Analysis for Airline Tweets Utilizing Machine Learning Techniques

The airline industry is a very competitive market. In the past two decades, the industry has expanded and still expanding its routes domestically and internationally with the web’s improvement and providing better services driven by competition. The Twitter boom has boosted investigation in this area owing to its potential applications, and survey gatherings offer a rich wellspring of feeling information for sentimental data mining. It is necessary to see the data and use mechanism that examination of such information and the territory of sentiment analysis has been developed. The essential assessment is to discover the extremity of the content and order it into positive, negative or neutral sentiment tweets. Therefore, the focal point of this research paper is to perform estimation investigation using machine learning (ML) methods for sentiment analysis. This work endeavoured to use three different ML techniques for the undertaking of estimation examination. The ML tests are performed, while utilizing US airline Twitter informational index is caught from Kaggle. The efficacies of three ML grouping strategies like decision tree, SVM and neural networks are evaluated, and there is consideration about accurate information. The neural network-based approach has perceived a most noteworthy accuracy (75.99%).
Capture of 3D Human Motion Pose in Virtual Reality Based on Video Recognition

Motion pose capture technology can effectively solve the problem of difficulty in defining character motion in the process of 3D animation production and greatly reduce the workload of character motion control, thereby improving the efficiency of animation development and the fidelity of character motion. Motion gesture capture technology is widely used in virtual reality systems, virtual training grounds, and real-time tracking of the motion trajectories of general objects. This paper proposes an attitude estimation algorithm adapted to be embedded. The previous centralized Kalman filter is divided into two-step Kalman filtering. According to the different characteristics of the sensors, they are processed separately to isolate the cross-influence between sensors. An adaptive adjustment method based on fuzzy logic is proposed. The acceleration, angular velocity, and geomagnetic field strength of the environment are used as the input of fuzzy logic to judge the motion state of the carrier and then adjust the covariance matrix of the filter. The adaptive adjustment of the sensor is converted to the recognition of the motion state. For the study of human motion posture capture, this paper designs a verification experiment based on the existing robotic arm in the laboratory. The experiment shows that the studied motion posture capture method has better performance. The human body motion gesture is designed for capturing experiments, and the capture results show that the obtained pose angle information can better restore the human body motion. A visual model of human motion posture capture was established, and after comparing and analyzing with the real situation, it was found that the simulation approach reproduced the motion process of human motion well. For the research of human motion recognition, this paper designs a two-classification model and human daily behaviors for experiments. Experiments show that the accuracy of the two-category human motion gesture capture and recognition has achieved good results. The experimental effect of SVC on the recognition of two classifications is excellent. In the case of using all optimization algorithms, the accuracy rate is higher than 90%, and the final recognition accuracy rate is also higher than 90%. In terms of recognition time, the time required for human motion gesture capture and recognition is less than 2 s.
On the Low-Latency Region of Best-Effort Links for Delay-Sensitive Streaming Traffic

This letter analyzes the Low-latency Region (LLR) of a best-effort link (i.e., no traffic differentiation, and first come first serve scheduling) carrying both delay-sensitive (DS) streaming and non-delay-sensitive (NDS) background traffic. Moreover, inside the LLR, we show it exists a proportional fair arrival rate allocation for both the DS and NDS traffic streams. This optimal operating point results from maximizing a simple throughput-delay trade-off that considers the NDS traffic load, and the mean delay of the DS packets. To show how the presented trade-off could be used to allocate NDS traffic in a realistic scenario, we use Google Stadia traffic traces to generate the DS flow. Results from this use-case confirm that the throughput-delay trade-off also works regardless the distribution of the packet arrival and packet service times.
SAR Ship Target Detection for SSDv2 under Complex Backgrounds

With the launch of space-borne satellites, more synthetic aperture radar (SAR) images are available than ever before, thus making dynamic ship monitoring possible. Object detectors in deep learning achieve top performance, for example, Faster R-CNN, ResNet, FPN, YoLo and SSD Net. Compared with YoLo, SSD algorithm performs better in accuracy and speed, but it is not good at small targets detection like SAR images. To solve this problem, we proposed an improved method. We added a deconvolution module and prediction module on the basis of SSD. The deconvolution module is mainly used to integrate the high-level semantic information into the feature information of the low-level network so as to improve the detection accuracy. The prediction module, which is composed of residual network, can extract depth features and input them into regression task and classification task., we call it SSDv2. We test our model in the SAR-Ship-Dataset. SAR-Ship-Dataset was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. It consists of 43,819 ship chips of 512 pixels in both range and azimuth. These ships mainly have distinct scales and backgrounds. The result shows that our SSDv2-512*512 achieves 91.07% mAP, outperforming a state-of-the-art method SSD.
Single Image Dehazing Algorithm Analysis with Hyperspectral Images in the Visible Range

In foggy or hazy conditions, images are degraded due to the scattering and attenuation of atmospheric particles, reducing the contrast and visibility and changing the color. This degradation depends on the distance, the density of the atmospheric particles and the wavelength. We have tested and applied five single image dehazing algorithms, originally developed to work on RGB images and not requiring user interaction and/or prior knowledge about the images, on a spectral hazy image database in the visible range. We have made the evaluation using two strategies: the first is based on the analysis of eleven state-of-the-art metrics and the second is two psychophysical experiments with 126 subjects. Our results suggest that the higher the wavelength within the visible range is, the higher the quality of the dehazed images. The quality increases for low haze/fog levels. The choice of the best performing algorithm depends on the criterion prioritized by the metric design strategy. The psychophysical experiment results show that the level of agreement between observers and metrics depends on the criterion set for the observers' task.
Directional Neighborhood Topologies Based Multi-Scale Quinary Pattern for Texture Classification

This paper ideates a new computationally simple and effective local image feature descriptor, referred to as Directional Neighborhood Topologies based Multi-scale Quinary Pattern (DNT-MQP) for texture description and classification. The essence of DNT-MQP is to encode the structure of local neighborhood by analyzing the differential excitation and directional information using various directional neighborhood topologies and new pattern encoding scheme. We first designed four different versions of single scale DNT-QP features based on four directional neighborhood topologies based sampling sets which are then combined together to build the effective multiscale DNT-MQP model. Unlike some existing parametric methods that employ static thresholds to perform thresholding, the construction process of DNT-MQP includes an automatic mechanism for dynamic thresholds estimation. Thanks to a richer local description ensured by exploiting complementary information resulting from single scale DNT-QP operators' combination, DNT-MQP descriptor has high capability to elicit stable and discriminative feature representation than other local feature descriptors. In addition, DNT-QP has the advantages of computational simplicity in feature extraction and low-dimensionality in feature representation. The effectiveness of DNT-QP is evaluated on sixteen challenging texture datasets and it is found that it maintains a high level of performance stability where the achieved performances are competitive or better than several recent most promising state-of-the-art texture descriptors as well as deep learning-based feature extraction approaches. Impressively, DNT-MQP showed good tolerance to rotation as well as illumination, scale and viewpoint changes against certain descriptors which are originally conceived to deal with these challenges. Furthermore, statistical hypothesis testing through Wilcoxon signed rank test is applied to prove the statistical significance of the accuracy improvement obtained in all the datasets.
Neural Enhancement in Content Delivery Systems

Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360° videos to video-conferencing and live streaming. However, robustly delivering visual content under fluctuating networking conditions on devices of diverse capabilities remains an open problem. In recent years, advances in the field of deep learning on tasks such as superresolution and image enhancement have led to unprecedented performance in generating high-quality images from low-quality ones, a process we refer to as neural enhancement. In this paper, we survey state-of-the-art content delivery systems that employ neural enhancement as a key component in achieving both fast response time and high visual quality. We first present the deployment challenges of neural enhancement models. We then cover systems targeting diverse use-cases and analyze their design decisions in overcoming technical challenges. Moreover, we present promising directions based on the latest insights from deep learning research to further boost the quality of experience of these systems.
Skeleton Based Dynamic Hand Gesture Recognition using LSTM and CNN

Dynamic Hand Gestures offer a natural, non-verbal way of communication that can substitute other communication modalities like verbal speech and script writing. Not only for the voice command, hand gestures also play significant role in Augmented Reality (AR), Virtual Reality (VR) and games. There are some factors like computational cost, flexibility and recognition accuracy that can impact the incorporation of hand gestures in these fields. In this paper, a Dynamic Hand Gesture Recognition (DHGR) approach is propose that is based on Convolutional Neural Network (CNN) and long-short term memory (LSTM). This system is trained to execute the sequence of 3D input data along with the velocities and positions information learned from Leap Motion Controller (LMC).When evaluated and compared with state-of-art DHGR methods, this architecture shows relative high accuracy of 98%.
Recursive Bayesian Filtering for Multiple Human Pose Tracking from Multiple Cameras

Markerless motion capture allows the extraction of multiple 3D human poses from natural scenes, without the need for a controlled but artificial studio environment or expensive hardware. In this work we present a novel tracking algorithm which utilizes recent advancements in 2D human pose estimation as well as 3D human motion anticipation. During the prediction step we utilize an RNN to forecast a set of plausible future poses while we utilize a 2D multiple human pose estimation model during the update step to incorporate observations. Casting the problem of estimating multiple persons from multiple cameras as a tracking problem rather than an association problem results in a linear relationship between runtime and the number of tracked persons. Furthermore, tracking enables our method to overcome temporary occlusions by relying on the prediction model. Our approach achieves state-of-the-art results on popular benchmarks for 3D human pose estimation and tracking.
Virtual reality rendering methods for training deep learning, analysing landscapes, and preventing virtual reality sickness

Virtual reality (VR) has been proposed for various purposes such as design studies, presentation, simulation and communication in the field of computer-aided architectural design. This paper explores new roles for VR; in particular, we propose rendering methods that consist of post-processing rendering, segmentation rendering and shadow-casting rendering for more-versatile approaches in the use of data. We focus on the creation of a dataset of annotated images, composed of paired foreground-background and semantic-relevant images, in addition to traditional immersive rendering for training deep learning neural networks and analysing landscapes. We also develop a camera velocity rendering method using a customised segmentation rendering technique that calculates the linear and angular velocities of the virtual camera within the VR space at each frame and overlays a colour on the screen according to the velocity value. Using this velocity information, developers of VR applications can improve the animation path within the VR space and prevent VR sickness. We successfully applied the developed methods to urban design and a design project for a building complex. In conclusion, the proposed method was evaluated to be both feasible and effective.
Mixed Reinforcement Learning for Efficient Policy Optimization in Stochastic Environments

Reinforcement learning has the potential to control stochastic nonlinear systems in optimal manners successfully. We propose a mixed reinforcement learning (mixed RL) algorithm by simultaneously using dual representations of environmental dynamics to search the optimal policy. The dual representation includes an empirical dynamic model and a set of state-action data. The former can embed the designer’s knowledge and reduce the difficulty of learning, and the latter can be used to compensate the model inaccuracy since it reflects the real system dynamics accurately. Such a design has the capability of improving both learning accuracy and training speed. In the mixed RL framework, the additive uncertainty of stochastic model is compensated by using explored state-action data via iterative Bayesian estimator (IBE). The optimal policy is then computed in an iterative way by alternating between policy evaluation (PEV) and policy improvement (PIM). The effectiveness of mixed RL is demonstrated by a typical optimal control problem of stochastic non-affine nonlinear systems (i.e., double lane change task with an automated vehicle).