Reviewerly/exHarmony huggingface dataset:
Constrained state estimation using noisy destination information

Tracking performance can be improved by the incorporation of a destination constraint. However, directly using available noise-corrupted destination information in the existing destination constrained filtering methods may lead to performance degradation. To address this limitation, in this paper, the true destination is also treated as a state to be estimated along with the target state. Then, the destination constraint information is leveraged by using the relationship between the state components and a newly constructed pseudo-measurement. An uncertain destination constrained augmented state filter (UDC-ASF) with its state being augmented by destination, in which the unscented Kalman filter (UKF) is used to deal with the strong nonlinearity of the measurements, is proposed to produce both constrained target state and destination estimates. Moreover, the unknown slope and the intercept of the straightline representing the destination constraint can also be augmented into the state vector and two pseudo-measurements are constructed in the process. The corresponding UDC-ASF with slope and intercept (UDC-ASF-SI) is derived. The a priori noisy destination is used to initialize the two proposed filters. An extension of the UDC-ASF to track multiple targets heading to the same destination is also discussed. Simulation results demonstrate the effectiveness of the proposed methods.
An analysis of dimensionality reduction techniques for visualizing evolution

We consider the problem of visualizing the population dynamics along an evolutionary run using a dimensionality reduction technique for mapping individuals from the original search space to a 2-D space. We quantitatively assess four of these techniques in terms of their ability to preserve useful information about (a) population movements and (b) exploration-exploitation trade-off. We propose two compact visualizations aimed at highlighting these two aspects of population dynamics and evaluate them qualitatively. The results are very promising as the proposed framework is indeed able to represent crucial properties of population dynamics in a way that is both highly informative and simple to understand.
Virtual reality enhanced roller coasters and the future of entertainment – audience expectations

Roller coasters are acknowledged as icons of leisure with their towering structures forming an integral part of any theme or amusement parks' skyline. As with any infrastructure, roller coasters come at a high cost, but inevitably also become outdated. By leveraging Virtual Reality (VR) technology, there is an opportunity to integrate new VR experience overlays with existing roller coasters, generating new interest in older ride infrastructure. While VR additions to roller coasters are a recent introduction (as of 2015), the adoption rate is high. Despite this, very little research has been conducted pertaining to the actual VR experience – and even less so from an end user's perspective. This research examines existing literature (elements of best-practice VR experiences) and original data gathered from VR roller coaster audiences (pertaining to their reactions to current VR enhanced roller coaster experiences). From our findings we present a model, Burt's VR Entertainment Primer, which identifies critical categories and supplemental elements to consider in order to develop a successful VR enhanced roller coaster experience overlay. The generic, user-centric nature of the model extends itself to the VR amusement and entertainment industries within a broader context, supporting the innovative application and assessment of VR in entertainment overall.
From Social Media to Expert Reports: The Impact of Source Selection on Automatically Validating Complex Conceptual Models of Obesity

Models are predominantly developed using either quantitative data (e.g., for structured equation models) or qualitative data obtained through questionnaires designed by researchers (e.g., for fuzzy cognitive maps). The wide availability of social media data and advances in natural language processing raise the possibility of developing models from qualitative data naturally produced by users. This is of particular interest for public health surveillance and policymaking, as social media provide the opinions of constituents. In this paper, we contrast a model produced by social media with one produced via expert reports. We use the same process to derive a model in each case, thus focusing our analysis on the impact of source selection. We found that three expert reports were sufficient to touch on more aspects of a complex problem (measured by the number of relationships) than several million tweets. Consequently, developing a model exclusively from social media may lead to oversimplifying a problem. This may be avoided by complementing social media with expert reports. Alternatively, future research should explore whether a much larger volume of tweets would be needed, which also calls for improvements in scalable methods to transform qualitative data into models.
Propagation and attenuation of guided waves in stressed viscoelastic waveguides

The motion of guided waves in viscoelastic waveguides is generally accompanied by energy transport and dissipation. Envisioned applications demand an accurate understanding of both propagating and evanescent of waves related to dispersive properties. This paper uses the isogeometric analysis to consider wave characteristics in stressed viscoelastic waveguides based on the Floquet’s principle. Parameterized wave equation considering the acoustoelasticity and viscoelasticity is obtained from the virtual power principle and the updated Lagrangian format. In addition, the energy velocity of a guided wave mode is derived in the linear incremental theory. Error estimations of cutoff frequencies for flexural wave modes are shown for the comparison between the wave isogeometric analysis method and the classic wave finite element method. The results indicate the former can provide smaller error compared with the latter. Then, phase velocity, energy velocity, and wave attenuation are presented for two kinds of viscoelastic structures (double cylindrical rods, functionally graded material plates) under different loaded cases. The effects of stress and viscoelasticity are also demonstrated in detail.
Camera Geometric Calibration Using Dynamic Single-Pixel Illumination With Deep Learning Networks

Traditional methods of geometric camera calibration (GCC) are based on angle measurements (AM) or diffractive optical elements (DOE). However, the AM-based approach has the low accuracy and reliability because the vibration of rotating mechanisms, i.e., turntables and angle measuring accuracy easily influence the calibration accuracy. The DOE failure easily occurs in the calibration process when some micro-apertures are blocked by dust particles. In this paper, a new method for the GCC by means of single pixel illumination in a deep neural network (DNN) is presented. A closed-loop calibration link is composed of a single stimulus input produced by a single pixel generator and a collimator, an uncalibrated camera, and a DNN. The dynamic single-pixel illumination forms the different stimulus input of the DNN at different stimulus time. The synaptic weights (the camera interior parameters) of multilayer perceptron are adjusted until the cost function is minimized. This method is able to avoid the aforementioned shortcoming of conventional calibration methods. This method can be especially used for on-ground calibration of remote sensing cameras but in principle also suitable for on-orbit GCC and other cameras.
A Deep Learning Wireless Transceiver with Fully Learned Modulation and Synchronization

In this paper, we present a deep learning based wireless transceiver. We describe in detail the corresponding artificial neural network architecture, the training process, and report on excessive over-the-air measurement results. We employ the end-to-end training approach with an autoencoder model that includes a channel model in the middle layers as previously proposed in the literature. In contrast to other state-of-the-art results, our architecture supports learning time synchronization without any manually designed signal processing operations. Moreover, the neural transceiver has been tested over the air with an implementation in software defined radio. Our experimental results for the implemented single antenna system demonstrate a raw bit-rate of 0.5 million bits per second. This exceeds results from comparable systems presented in the literature and suggests the feasibility of high throughput deep learning transceivers.
Image analysis using modified exponent-Fourier moments

Classic exponent-Fourier moments (EFMs) have been popularly used for image reconstruction and invariant classification. However, EFMs lack natively the translation and scaling-invariant; in addition, they exhibit two types of drawbacks, namely numerical instability and reconstruction error, which in turn influence their reconstruction capability and image classification accuracy. This study considers the challenge of defining modified EFMs (MEFMs), which are based on modified exponent polynomials. In our methods, the basis function of traditional EFMs is appropriately modified, and these modified basis functions are used to replace the original ones. The basis function of the proposed moments is composed of piecewise modified exponent polynomials modulated by a variable parameter exponential envelope. Various types of optimal-order moments can be established by slightly adjusting the bandwidth of the modified basis functions. Finally, we extend the rotation-invariant feature of previous works and propose a new method of scaling and rotation-invariant image recognition using the proposed moments in a log-polar coordinate domain. The translation invariance can then be achieved by an image projection operation, which is substituted for the traditional approach based on the calculation of image geometric moments. The experimental results demonstrate that the MEFMs perform better than traditional EFMs and other classic orthogonal moments including the latest image moments in terms of the image reconstruction capability and the invariant recognition accuracy of smoothing filters, in both noise-free and noisy conditions.
Database of Gesture Attributes: Zero Shot Learning for Gesture Recognition

Existing gesture classification techniques assign a categorical label to each gesture instance and learn to recognize only a predetermined set of gesture classes. These techniques lack adaptability to new or unseen gestures which is the premise of zero shot learning (ZSL). Hence we propose to identify the properties of gestures and thereby infer the categorical label instead of recognizing the class label directly. ZSL for gesture recognition has hardly been studied in the pattern recognition research. The reason is partly due to the lack of benchmarks and specialized datasets consisting of annotations for gesture attributes. In this regard, this paper presents the first annotated database of attributes for the gestures present in ChaLearn 2013 and MSRC - 12 datasets. This was achieved as follows; First, we identified a finite set of 64 discriminative and representative high level attributes of gestures from the literature. Further, we performed crowdsourced human studies using Amazon Mechanical Turk to obtain attribute annotations for 28 gesture classes. Next, we used our dataset to train existing ZSL classifiers to predict attribute labels. Finally, we provide benchmarks for unseen gesture class prediction on CGD2013 and MSRC-12. We have made this dataset publicly available to encourage researchers to further investigate this problem.
Augmented Reality for Human-Robot Teaming in Field Environments

For teams of humans and mobile robots to work together, several challenges must be overcome, including understanding each others’ position, merging map information, sharing recognition of salient features of the environment, and establishing contextually-grounded communication. These challenges are further compounded for teams operating in field environments, which are unconstrained, uninstrumented, and unknown. While most modern studies that use augmented reality (AR) in human-robot teaming side-step these challenges by focusing on problems addressable in instrumented environments, we argue that current AR technology combined with novel approaches can enable successful teaming in such challenging, real-world settings. To support this, we present a set of prototypes that combine AR with an intelligent, autonomous robot to enable better human-robot teaming in field environments.
Similarity Analysis of Criminals on Social Networks: An Example on Twitter

Terrorist Networks (TNs) and Organized Crime (OC) are nowadays an increasing threat in the modern society. Due to the strong adoption of the IT technology, an emergent phenomenon is represented by the exploitation of social media, such as Twitter, Facebook, YouTube to disseminate and promote illegal activities, recruit terrorists and establish collaborations. The traditional approaches and countermeasures against cyber-crimes result inadequate in the cyber-space. In this context, the paper proposes an engineering method, centered on three main phases, to support the analysis of suspicious users on social media related to OC and TNs. It is based on the exploitation and extension of social network analysis approaches combined with well-known clustering techniques and association rules. It aims to identify similarities as well as groups of users associated to specific illegal activities such as drugs, weapons and human trafficking. Moreover, it supports the identification process of leaders in groups and mediators between them. A software, which enables the automatic execution of the proposed method, is developed and experimented on the Twitter social media. The results show both the identification of groups of users related to OC and TNs along with their intra-group activities as well as inter-group relationships through potential mediators.
Medical Data Sharing Model Based on Blockchain

Abstract In the traditional medical system, individual medical data is managed by hospitals rather than individual patients. It is difficult to exchange effectively with fragmented storage, and large amounts of data are difficult to realize their potential value. With the rapid development of medical informatization, centralized storage of fragmented medical data has been unable to meet the relevant needs of the medical industry. To solve the difficulty of sharing and the complexity of confirming rights in the medical system, this paper proposes a medical data sharing model based on blockchain. The model provides reliable storage with IPFS file system, uses Proxy re-encryption to realize data sharing and ensure data proprietary rights, and uses Token economic system to measure the contribution in the sharing process, which stimulates the enthusiasm of sharing. At last, based on the existing sharing problem of medical data, the paper shows the potential solution.
Recurrent Convolutional Neural Networks for AMR Steganalysis Based on Pulse Position

With the rapid development of stream multimedia, the adaptive multi-rate (AMR) audio steganography are emerging recently. However, the traditional steganalysis methods face great challenges in detecting short time speech at low embedding rates. To address this problem, we propose a steganalytic scheme by combining Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), SRCNet. AMR fixed codebook (FCB) steganography embed messages by modifying the pulse positions, which would destroy the FCB correlation. Firstly we analyzed the FCB correlations at different distances, and summarized these correlations into four categories. Furthermore, we utilizes RNN to extract higher level contextual representations of FCBs and CNN to fuse spatial-temporal features for the steganalysis. The proposed approach was evaluated on a public data-set. The experiment results validate that the proposed framework greatly outperforms the existing state-of-the-art methods. The correct detection rate of SRCNet has been improved above at least 10% when the sample is as short as 100ms at the 20% embedding rate. In particular, the network achieves the significant improvements for detecting the STCs based adaptive AMR steganography.
An analytical approach for estimating the global horizontal from the global tilted irradiance

Knowledge of horizontal solar irradiance is crucial for the nowcasting and forecasting of generated photovoltaic (PV) power. High quality irradiance measurement devices, however, are typically not collocated with PV systems. The lack of measurements can be compensated by numerical weather models or satellite-derived products, but they provide only limited temporal and spatial resolutions. Another possibility is to directly use PV systems as irradiance sensors, since the measured PV power is a good indicator of incoming solar irradiance. The challenging part in this procedure is the computation of the global horizontal irradiance Gh from the global tilted irradiance Gc, because a combination of decomposition and transposition models is not analytically invertible. Hence, the majority of existing solutions to this problem are numerical procedures. In this paper, an analytical approach to compute Gh from Gc is presented. The comparison of the proposed approach with one of the existing iterative (numerical) approaches shows promising results. When applied to 1-min data at four different locations, the new approach outperforms the iterative procedure by up to 9% in terms of the relative root mean square error (rRMSE) for east/west module's orientations, and performs slightly better with the south orientation. Moreover, the new approach provides results in less than 1 s, whereas the iterative procedure requires more than 20 min for a one year of data. An open source R-script of the new approach is also publicly available and provided as supplementary material.
Exploring mobile tool integration: Design activities carefully or students may not learn

This investigation explores the integration of a mobile language learning application into a high school English course. It uses detailed behavioral data, assessments of student vocabulary and language knowledge, student work, and teacher observations to identify how students use the mobile application; measure student learning; determine which aspects of learning activity design supported or failed to support application integration; and determine how application use related to changes in student vocabulary knowledge. Students’ application use and learning suggest certain aspects of the noticing hypothesis (i.e., logging) do not apply in formal mobile learning environments and microlearning is rarely realized in these contexts when there is evidence of learning. From a theoretical perspective, the data suggest mobile tools can be used to encourage spaced repetition and deep processing when the accompanying learning activities are appropriately designed. However, the desired depth of processing is unlikely to be observed when application usage patterns that are consistent with microlearning are seen.
DLBooster

In recent years, deep learning (DL) has prospered again due to improvements in both computing and learning theory. Emerging studies mostly focus on the acceleration of refining DL models but ignore data preprocessing issues. However, data preprocessing can significantly affect the overall performance of end-to-end DL workflows. Our studies on several image DL workloads show that existing preprocessing backends are quite inefficient: they either perform poorly in throughput (30% degradation) or burn too many (>10) CPU cores. Based on these observations, we propose DLBooster, a high-performance data preprocessing pipeline that selectively offloads key workloads to FPGAs, to fit the stringent demands on data preprocessing for cutting-edge DL applications. Our testbed experiments show that, compared with the existing baselines, DLBooster can achieve 1.35×~2.4× image processing throughput in several DL workloads, but consumes only 1/10 CPU cores. Besides, it also reduces the latency by 1/3 in online image inference.
Pioneer Study on Near-Range Sensing with 4D MIMO-FMCW Automotive Radars

This paper explores the feasibility to detect and classify static or moving targets with weak radar cross sections i.e. pedestrians with a 4D MIMO-FMCW automotive radar. A straightforward classification using data integration over time for pattern recognition is not possible due to the low point cloud density. Hence a particle filter is employed, where the probability density function of the points are used for object classification instead. Measured results of an adult person walking and a child dummy on the ground classified with a particle filter has been shown to be promising. Their locations relative to the radar are tracked and the pedestrians' heights can also be also estimated.
Tetrapartite entanglement measures of W-class in noninertial frames*

We present the entanglement measures of a tetrapartite W-class entangled system in a noninertial frame, where the transformation between Minkowski and Rindler coordinates is applied. Two cases are considered. First, when one qubit has uniform acceleration whilst the other three remain stationary. Second, when two qubits have nonuniform accelerations and the others stay inertial. The 1–1 tangle, 1–3 tangle, and whole entanglement measurements <?CDATA ${\pi }_{4}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>π</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:math> and <?CDATA ${{\rm{\Pi }}}_{4}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">Π</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:math> , are studied and illustrated with graphics through their dependence on the acceleration parameter r d for the first case and r c and r d for the second case. It is found that the negativities (1–1 tangle and 1–3 tangle) and π -tangle decrease when the acceleration parameter r d or in the second case r c and r d increase, remaining a nonzero entanglement in the majority of the results. This means that the system will be always entangled except for special cases. It is shown that only the 1–1 tangle for the first case vanishes at infinite accelerations, but for the second case the 1–1 tangle disappears completely when <?CDATA $r\gt 0.472473$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>r</mml:mi> <mml:mo>&gt;</mml:mo> <mml:mn>0.472473</mml:mn> </mml:math> . An analytical expression for the von Neumann information entropy of the system is found and we note that it increases with the acceleration parameter.