Reviewerly/exHarmony huggingface dataset:
An Improved Adaptive Unscented Kalman Filter With Application in the Deeply Integrated BDS/INS Navigation System

The integration of the BeiDou Navigation Satellite System(BDS) and the Inertial Navigation System(INS) can provide a more reliable and accurate navigation service than either system alone. The deeply coupled architecture for BDS and INS integration has more superior performance than the loosely coupled or the tightly coupled. Owing to the complicated dynamic scenario and the nonlinear system's noise uncertainty, the adaptive Kalman filter(AKF) algorithm is often adopted in the deep integration(DI) system. The adaptive Sage window methods including innovation-based adaptive estimation(IAE) and residual-based adaptive estimation(RAE) are widely applied in AKF algorithms, but they have several limitations. We propose an improved adaptive unscented Kalman filter(AUKF) based on forgetting-factor-weight smoothing and multi-factor adaptation to overcome these limitations. Compared with the Sage window methods, the improved AUKF algorithm is immune to the quantity change of the satellites concerning integration and more sensitive to present dynamic. Furthermore, it can reduce the computation and storage burden in implementation. A simulation test based on a software platform and the deeply integrated BDS/INS navigation system is carried out to evaluate the performance of the improved AUKF. Simulation results show that the improved AUKF algorithm outperforms the extended Kalman filter(EKF) and has a similar performance with the RAE-AUKF.
An Adaptive Turn Rate Estimation for Tracking a Maneuvering Target

Tracking maneuvering targets accurately is one of the most challenging tasks in the design of aircraft tracking systems. For efficient tracking performance, the target motion predicted by the target motion model needs to match the target's actual motion during the maneuver. For tracking a maneuvering target, a combination of the constant velocity (CV) model and the coordinated turn (CT) model with a known turn rate are incorporated in the interacting multiple model (IMM) algorithm. However, in such a scheme when a target performs an unexpected maneuver, the tracking performance deteriorates, or the scheme may even fail to track the target. To overcome this problem, there exists a scheme in the literature, in which instead of using an a priori knowledge of the target turn rate, it is estimated adaptively using the target acceleration and speed. However, this algorithm uses a three-dimensional model to estimate the turn rate in two-dimensional space, which may result in an inaccurate estimation of the target acceleration, and thus may lead to in inaccurate turn rate value. In this paper, an adaptive algorithm to track a maneuvering target in an IMM framework is proposed. Estimating the turn rate is based on the speed of the target and the radius of the turn, where the latter is computed by a simple method using the previous three successive measurements. Further, a detailed study to select an appropriate transition probability matrix for the proposed algorithm is carried out. Simulation results demonstrate that the proposed tracking algorithm outperforms the other algorithms in terms of its tracking accuracy and consistency, particularly in the realistic situation when neither an a priori knowledge about the target turn rate nor about the range rate information is available to the tracking algorithm.
Coherence measures with respect to general quantum measurements

Quantum coherence with respect to orthonormal bases has been studied extensively in the past few years. Recently, Bischof, et al. [Phys. Rev. Lett. 123, 110402 (2019)] generalized it to the case of general positive operator-valued measure (POVM) measurements. Such POVM-based coherence, including the block coherence as a special case, have significant operational interpretations in quantifying the advantage of quantum states in quantum information processing. In this work we first establish an alternative framework for quantifying the block coherence and provide several block coherence measures. We then present several coherence measures with respect to POVM measurements, and prove a conjecture on the $l_{1}$-norm related POVM coherence measure.
Capacity-approaching quantum repeaters for quantum communications

In present-day quantum communications, one of the main problems is the lack of a quantum repeater design that can simultaneously secure high rates and long distances. Recent literature has established the end-to-end capacities that are achievable by the most general protocols for quantum and private communication within a quantum network, encompassing the case of a quantum repeater chain. However, whether or not a physical design exists to approach such capacities remains a challenging objective. Driven by this motivation, in this work, we put forward a design for continuous-variable quantum repeaters and show that it can actually achieve the feat. We also show that even in a noisy regime our rates surpass the Pirandola-Laurenza-Ottaviani-Banchi (PLOB) bound. Our repeater setup is developed upon using noiseless linear amplifiers, quantum memories, and continuous-variable Bell measurements. We, furthermore, propose a non-ideal model for continuous-variable quantum memories that we make use of in our design. We then show that potential quantum communications rates would deviate from the theoretical capacities, as one would expect, if the quantum link is too noisy and/or low-quality quantum memories and amplifiers are employed.
Numeric Optimization for Configurable, Parallel, Error‐Robust Entangling Gates in Large Ion Registers

Abstract A class of entangling gates for trapped atomic ions is studied and the use of numeric optimization techniques to create a wide range of fast, error‐robust gate constructions is demonstrated. A numeric optimization framework is introduced targeting maximally‐ and partially‐entangling operations on ion pairs, multi‐ion registers, multi‐ion subsets of large registers, and parallel operations within a single register. Ions are assumed to be individually addressed, permitting optimization over amplitude‐ and phase‐modulated controls. Calculations and simulations demonstrate that the inclusion of modulation of the difference phase for the bichromatic drive used in the Mølmer–Sørensen gate permits approximately time‐optimal control across a range of gate configurations, and when suitably combined with analytic constraints can also provide robustness against key experimental sources of error. The impact of experimental constraints such as bounds on coupling rates or modulation band‐limits on achievable performance is further demonstrated. Using a custom optimization engine based on TensorFlow, for optimizations on ion registers up to 20 ions, time‐to‐solution of order tens of minutes using a local‐instance laptop is also demonstrated, allowing computational access to system‐scales relevant to near‐term trapped‐ion devices.
A framework based on deep neural networks to extract anatomy of mosquitoes from images

We design a framework based on Mask Region-based Convolutional Neural Network to automatically detect and separately extract anatomical components of mosquitoes-thorax, wings, abdomen and legs from images. Our training dataset consisted of 1500 smartphone images of nine mosquito species trapped in Florida. In the proposed technique, the first step is to detect anatomical components within a mosquito image. Then, we localize and classify the extracted anatomical components, while simultaneously adding a branch in the neural network architecture to segment pixels containing only the anatomical components. Evaluation results are favorable. To evaluate generality, we test our architecture trained only with mosquito images on bumblebee images. We again reveal favorable results, particularly in extracting wings. Our techniques in this paper have practical applications in public health, taxonomy and citizen-science efforts.
Next word prediction based on the N-gram model for Kurdish Sorani and Kurmanji

Next word prediction is an input technology that simplifies the process of typing by suggesting the next word to a user to select, as typing in a conversation consumes time. A few previous studies have focused on the Kurdish language, including the use of next word prediction. However, the lack of a Kurdish text corpus presents a challenge. Moreover, the lack of a sufficient number of N-grams for the Kurdish language, for instance, five-grams, is the reason for the rare use of next Kurdish word prediction. Furthermore, the improper display of several Kurdish letters in the RStudio software is another problem. This paper provides a Kurdish corpus, creates five, and presents a unique research work on next word prediction for Kurdish Sorani and Kurmanji. The N-gram model has been used for next word prediction to reduce the amount of time while typing in the Kurdish language. In addition, little work has been conducted on next Kurdish word prediction; thus, the N-gram model is utilized to suggest text accurately. To do so, R programming and RStudio are used to build the application. The model is 96.3% accurate.
Privacy-Preserving Data Publishing in Process Mining

Process mining aims to provide insights into the actual processes based on event data. These data are often recorded by information systems and are widely available. However, they often contain sensitive private information that should be analyzed responsibly. Therefore, privacy issues in process mining are recently receiving more attention. Privacy preservation techniques obviously need to modify the original data, yet, at the same time, they are supposed to preserve the data utility. Privacy-preserving transformations of the data may lead to incorrect or misleading analysis results. Hence, new infrastructures need to be designed for publishing the privacy-aware event data whose aim is to provide metadata regarding the privacy-related transformations on event data without revealing details of privacy preservation techniques or the protected information. In this paper, we provide formal definitions for the main anonymization operations, used by privacy models in process mining. These are used to create an infrastructure for recording the privacy metadata. We advocate the proposed privacy metadata in practice by designing a privacy extension for the XES standard and a general data structure for event data which are not in the form of standard event logs.
Augmented reality in neurosurgical navigation: A survey

Abstract Background Neurosurgery has exceptionally high requirements for minimally invasive and safety. This survey attempts to analyse the practical application of AR in neurosurgical navigation. Also, this survey describes future trends in augmented reality neurosurgical navigation systems. Methods In this survey, we searched related keywords ‘augmented reality’, ‘virtual reality’, ‘neurosurgery’, ‘surgical simulation’, ‘brain tumour surgery’, ‘neurovascular surgery’, ‘temporal bone surgery’ and ‘spinal surgery’ through Google Scholar, World Neurosurgery, PubMed and Science Direct. We collected 85 articles published over the past 5 years in areas related to this survey. Results Detailed study has been conducted on the application of AR in neurosurgery and found that AR is constantly improving the overall efficiency of doctor training and treatment, which can help neurosurgeons learn and practice surgical procedures with zero risks. Conclusions Neurosurgical navigation is essential in neurosurgery. Despite certain technical limitations, it is still a necessary tool for the pursuit of maximum security and minimal intrusiveness.
Sum of the masses of the Milky Way and M31: A likelihood-free inference approach

We use Density Estimation Likelihood-Free Inference, $\Lambda$ Cold Dark Matter simulations of $\sim 2M$ galaxy pairs, and data from Gaia and the Hubble Space Telescope to infer the sum of the masses of the Milky Way and Andromeda (M31) galaxies, the two main components of the Local Group. This method overcomes most of the approximations of the traditional timing argument, makes the writing of a theoretical likelihood unnecessary, and allows the non-linear modelling of observational errors that take into account correlations in the data and non-Gaussian distributions. We obtain an $M_{200}$ mass estimate $M_{\rm MW+M31} = 4.6^{+2.3}_{-1.8} \times 10^{12} M_{\odot}$ ($68 \%$ C.L.), in agreement with previous estimates both for the sum of the two masses and for the individual masses. This result is not only one of the most reliable estimates of the sum of the two masses to date, but is also an illustration of likelihood-free inference in a problem with only one parameter and only three data points.
The Role of Sensory Changes in Everyday Technology use by People with Mild to Moderate Dementia

Technology design for dementia primarily focuses on cognitive needs. This includes providing task support, accommodating memory changes, and simplifying interfaces by reducing complexity. However, research has demonstrated that dementia affects not only the cognitive abilities of people with dementia, but also their sensory and motor abilities. This work provides a first step towards understanding the interaction between sensory changes and technology use by people with dementia through interviews with people with mild to moderate dementia and practitioners. Our analysis yields an understanding of strategies to use technology to overcome sensory changes associated with dementia as well as barriers to using certain technologies. We present new directions for the design of technologies for people with mild to moderate dementia, including intentional sensory stimulation to facilitate comprehension, as well as opportunities to leverage advances in technology design from other disabilities for dementia.
PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset

Energy systems around the world are undergoing substantial changes, with an increasing penetration of Renewable Energy Sources. For this reason, the availability of a pool of suitable forecasting models specific for the needed time horizon and task is becoming crucial in the grid operation. In addition, nowcasting techniques aiming at provideing the power forecast for the immediate future, are more often investigated due to the spread of micro-grids and the need of facing changing electrical market environments. In this paper a novel comprehensive methodology aiming at computing the PV power forecast on different time horizons and resolutions is introduced. Moving from the 24-hours ahead prediction provided by the Physical Hybrid Artificial Neural Network (PHANN), a technique to refine the power forecast for the following 3 hours with an hourly granularity is analyzed, leveraging on newer information available during the operations. Moreover, in order to provide the power forecast for the following 30 minutes on a minutely basis, an innovative modification of a statistical technique is proposed, the robust persistence. The proposed comprehensive approach allowed to greatly reduce the overall error committed when compared with the benchmark models. Finally, the proposed methodology is validated and tested on a freely available database consisting on different parameters recorded at both the meteorological and photovoltaic test facility at SolarTech <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LAB</sup> , Politecnico di Milano, Milan.
Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment

Despite the growing number of unmanned aerial vehicles (UAVs) and aerial videos, there is a paucity of studies focusing on the aesthetics of aerial videos that can provide valuable information for improving the aesthetic quality of aerial photography. In this article, we present a method of deep multimodality learning for UAV video aesthetic quality assessment. More specifically, a multistream framework is designed to exploit aesthetic attributes from multiple modalities, including spatial appearance, drone camera motion, and scene structure. A novel specially designed motion stream network is proposed for this new multistream framework. We construct a dataset with 6,000 UAV video shots captured by drone cameras. Our model can judge whether a UAV video was shot by professional photographers or amateurs together with the scene type classification. The experimental results reveal that our method outperforms the video classification methods and traditional SVM-based methods for video aesthetics. In addition, we present three application examples of UAV video grading, professional segment detection and aesthetic-based UAV path planning using the proposed method.
Truncated random measures

Completely random measures (CRMs) and their normalizations are a rich source of Bayesian nonparametric priors.Examples include the beta, gamma, and Dirichlet processes.In this paper, we detail two major classes of sequential CRM representations-series representations and superposition representations-within which we organize both novel and existing sequential representations that can be used for simulation and posterior inference.These two classes and their constituent representations subsume existing ones that have previously been developed in an ad hoc manner for specific processes.Since a complete infinite-dimensional CRM cannot be used explicitly for computation, sequential representations are often truncated for tractability.We provide truncation error analyses for each type of sequential representation, as well as their normalized versions, thereby generalizing and improving upon existing truncation error bounds in the literature.We analyze the computational complexity of the sequential representations, which in conjunction with our error bounds allows us to directly compare representations and discuss their relative efficiency.We include numerous applications of our theoretical results to commonly-used (normalized) CRMs, demonstrating that our results enable a straightforward representation and analysis of CRMs that has not previously been available in a Bayesian nonparametric context.
Personal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming

To date, the privacy-protection intended pixelation tasks are still labor-intensive and yet to be studied. With the prevailing of video live streaming, establishing an online face pixelation mechanism during streaming is an urgency. In this paper, we develop a new method called Face Pixelation in Video Live Streaming (FPVLS) to generate automatic personal privacy filtering during unconstrained streaming activities. Simply applying multi-face trackers will encounter problems in target drifting, computing efficiency, and over-pixelation. Therefore, for fast and accurate pixelation of irrelevant people's faces, FPVLS is organized in a frame-to-video structure of two core stages. On individual frames, FPVLS utilizes image-based face detection and embedding networks to yield face vectors. In the raw trajectories generation stage, the proposed Positioned Incremental Affinity Propagation (PIAP) clustering algorithm leverages face vectors and positioned information to quickly associate the same person's faces across frames. Such frame-wise accumulated raw trajectories are likely to be intermittent and unreliable on video level. Hence, we further introduce the trajectory refinement stage that merges a proposal network with the two-sample test based on the Empirical Likelihood Ratio (ELR) statistic to refine the raw trajectories. A Gaussian filter is laid on the refined trajectories for final pixelation. On the video live streaming dataset we collected, FPVLS obtains satisfying accuracy, real-time efficiency, and contains the over-pixelation problems.
A network-ready random-access qubits memory

Abstract Photonic qubits memories are essential ingredients of numerous quantum networking protocols. The ideal situation features quantum computing nodes that are efficiently connected to quantum communication channels via quantum interfaces. The nodes contain a set of long-lived matter qubits, the channels support the propagation of light qubits, and the interface couples light and matter qubits. Toward this vision, we here demonstrate a random-access multi-qubit write-read memory for photons using two rubidium atoms coupled to the same mode of an optical cavity, a setup that is known to feature quantum computing capabilities. We test the memory with more than ten independent photonic qubits, observe no noticeable cross-talk, and find no need for re-initialization even after ten write-read attempts. The combined write-read efficiency is 26% and the coherence time approaches 1 ms. With these features, the node constitutes a promising building block for a quantum repeater and ultimately a quantum internet.
Facial geometric feature extraction based emotional expression classification using machine learning algorithms

Emotion plays a significant role in interpersonal communication and also improving social life. In recent years, facial emotion recognition is highly adopted in developing human-computer interfaces (HCI) and humanoid robots. In this work, a triangulation method for extracting a novel set of geometric features is proposed to classify six emotional expressions (sadness, anger, fear, surprise, disgust, and happiness) using computer-generated markers. The subject’s face is recognized by using Haar-like features. A mathematical model has been applied to positions of eight virtual markers in a defined location on the subject’s face in an automated way. Five triangles are formed by manipulating eight markers’ positions as an edge of each triangle. Later, these eight markers are uninterruptedly tracked by Lucas- Kanade optical flow algorithm while subjects’ articulating facial expressions. The movement of the markers during facial expression directly changes the property of each triangle. The area of the triangle (AoT), Inscribed circle circumference (ICC), and the Inscribed circle area of a triangle (ICAT) are extracted as features to classify the facial emotions. These features are used to distinguish six different facial emotions using various types of machine learning algorithms. The inscribed circle area of the triangle (ICAT) feature gives a maximum mean classification rate of 98.17% using a Random Forest (RF) classifier compared to other features and classifiers in distinguishing emotional expressions.
Security Analysis of a Color Image Encryption Algorithm Using a Fractional-Order Chaos

Fractional-order chaos has complex dynamic behavior characteristics, so its application in secure communication has attracted much attention. Compared with the design of fractional-order chaos-based cipher, there are fewer researches on security analysis. This paper conducts a comprehensive security analysis of a color image encryption algorithm using a fractional-order hyperchaotic system (CIEA-FOHS). Experimental simulation based on excellent numerical statistical results supported that CIEA-FOHS is cryptographically secure. Yet, from the perspective of cryptanalysis, this paper found that CIEA-FOHS can be broken by a chosen-plaintext attack method owing to its some inherent security defects. Firstly, the diffusion part can be eliminated by choosing some special images with all the same pixel values. Secondly, the permutation-only part can be deciphered by some chosen plain images and the corresponding cipher images. Finally, using the equivalent diffusion and permutation keys obtained in the previous two steps, the original plain image can be recovered from a target cipher image. Theoretical analysis and experimental simulations show that the attack method is both effective and efficient. To enhance the security, some suggestions for improvement are given. The reported results would help the designers of chaotic cryptography pay more attention to the gap of complex chaotic system and secure cryptosystem.