Reviewerly/exHarmony huggingface dataset:
Monte Carlo sampling for error propagation in linear regression and applications in isochron geochronology

Geochronology is essential for understanding Earth’s history. The availability of precise and accurate isotopic data is increasing; hence it is crucial to develop transparent and accessible data reduction techniques and tools to transform raw mass spectrometry data into robust chronological data. Here we present a Monte Carlo sampling approach to fully propagate uncertainties from linear regressions for isochron dating. Our new approach makes no prior assumption about the causes of variability in the derived chronological results and propagates uncertainties from both experimental measurements (analytical uncertainties) and underlying assumptions (model uncertainties) into the final age determination. Using synthetic examples, we find that although the estimates of the slope and y-intercept (hence age and initial isotopic ratios) are comparable between the Monte Carlo method and the benchmark “Isoplot” algorithm, uncertainties from the later could be underestimated by up to 60%, which are likely due to an incomplete propagation of model uncertainties. An additional advantage of the new method is its ability to integrate with geological information to yield refined chronological constraints. The new method presented here is specifically designed to fully propagate errors in geochronological applications involves linear regressions such as Rb-Sr, Sm-Nd, Re-Os, Pt-Os, Lu-Hf, U-Pb (with discordant points), Pb-Pb and Ar-Ar.
Presentation Attacks in Signature Biometrics: Types and Introduction to Attack Detection

Authentication applications based on the use of biometric methods have received a lot of interest during the last years due to the breathtaking results obtained using personal traits such as face or fingerprint. However, it is important not to forget that these biometric systems have to withstand different types of possible attacks. This work carries out an analysis of different Presentation Attack (PA) scenarios for on-line handwritten signature verification. The main contributions of the present work are: (1) short overview of representative methods for Presentation Attack Detection (PAD) in signature biometrics; (2) to describe the different levels of PAs existing in on-line signature verification regarding the amount of information available to the attacker, as well as the training, effort and ability to perform the forgeries; and (3) to report an evaluation of the system performance in signature biometrics under different PAs and writing tools considering freely available signature databases. Results obtained for both BiosecurID and e-BioSign databases show the high impact on the system performance regarding not only the level of information that the attacker has but also the training and effort performing the signature. This work is in line with recent efforts in the Common Criteria standardization community towards security evaluation of biometric systems, where attacks are rated depending on, among other factors, time spent, effort and expertise of the attacker, as well as the information available and used from the target being attacked.
CAMPS: Efficient and privacy-preserving medical primary diagnosis over outsourced cloud

With the flourishing of ubiquitous healthcare and cloud computing technologies, medical primary diagnosis system, which forms a critical capability to link big data analysis technologies with medical knowledge, has shown great potential in improving the quality of healthcare services. However, it still faces many severe challenges on both users’ medical privacy and intellectual property of healthcare service providers, which deters the wide adoption of medical primary diagnosis system. In this paper, we propose an efficient and privacy-preserving medical primary diagnosis framework (CAMPS). Within CAMPS framework, the precise diagnosis models are outsourced to the cloud server in an encrypted manner, and users can access accurate medical primary diagnosis service timely without divulging their medical data. Specifically, based on partially decryption and secure comparison techniques, a special fast secure two-party vector dominance scheme over ciphertext is proposed, with which CAMPS achieves privacy preservation of user’s query and the diagnosis result, as well as the confidentiality of diagnosis models in the outsourced cloud server. Through extensive analysis, we show that CAMPS can ensure that users’ medical data and healthcare service provider’s diagnosis model are kept confidential, and has significantly reduce computation and communication overhead. In addition, performance evaluations via implementing CAMPS demonstrate its effectiveness in term of the real environment.
Deep infrared pedestrian classification based on automatic image matting

Infrared pedestrian classification plays an important role in advanced driver assistance systems. However, it encounters great difficulties when the pedestrian images are superimposed on a cluttered background. Many researchers design very deep neural networks to classify pedestrian from cluttered background. However, a very deep neural network associated with a high computational cost. The suppression of cluttered background can boost the performance of deep neural networks without increasing their depth, while it has received little attention in the past. This study presents an automatic image matting approach for infrared pedestrians that suppresses the cluttered background and provides consistent input to deep learning. The domain expertise in pedestrian classification is applied to automatically and softly extract foreground objects from images with cluttered backgrounds. This study generates trimaps, which must be generated manually in conventional approaches, according to the estimated positions of pedestrian's head and upper body without the need for any user interaction. We implement image matting by adopting the global matting approach and taking the generated trimap as an input. The representation of pedestrian is discovered by a deep learning approach from the resulting alpha mattes in which cluttered background is suppressed, and foreground is enhanced. The experimental results show that the proposed approach improves the infrared pedestrian classification performance of the state-of-the-art deep learning approaches at a negligible computational cost.
Gesture Prediction Using Wearable Sensing Systems with Neural Networks for Temporal Data Analysis

A human gesture prediction system can be used to estimate human gestures in advance of the actual action to reduce delays in interactive systems. Hand gestures are particularly necessary for human–computer interaction. Therefore, the gesture prediction system must be able to capture hand movements that are both complex and quick. We have already reported a method that allows strain sensors and wearable devices to be fabricated in a simple and easy manner using pyrolytic graphite sheets (PGSs). The wearable electronics could detect various types of human gestures with high sensitivity, high durability, and fast response. In this study, we demonstrated hand gesture prediction by artificial neural networks (ANNs) using gesture data obtained from data gloves based on PGSs. Our experiments entailed measuring the hand gestures of subjects for learning purposes and we used these data to create four-layered ANNs, which enabled the proposed system to successfully predict hand gestures in real time. A comparison of the proposed method with other algorithms using temporal data analysis suggested that the hand gesture prediction system using ANNs would be able to forecast various types of hand gestures using resistance data obtained from wearable devices based on PGSs.
Accelerating adiabatic protocols for entangling two qubits in circuit QED

We introduce a method to speed up adiabatic protocols for creating entanglement between two qubits dispersively coupled to a transmission line, while keeping fidelities high and maintaining robustness to control errors. The method takes genuinely adiabatic sweeps, ranging from a simple Landau-Zener drive to boundary cancellation methods and local adiabatic drivings, and adds fast oscillations to speed up the protocol while canceling unwanted transitions. We compare our protocol with existing adiabatic methods in a state-of-the-art parameter range and show substantial gains. Numerical simulations emphasize that this strategy is efficient also beyond the rotating-wave approximation and that the method is robust against random static biases in the control parameters and with respect to damping and decoherence effects.
A Privacy-Preserving Edge Computation-Based Face Verification System for User Authentication

The face recognition has become a common means of identity authentication because of the advantages of uniqueness, non-invasive and not easy to be stolen. The outsourcing of face recognition to the service provider is a typical manner nowadays. However, it raises vital concerns about the privacy of outsourcing server due to the sensitivity of face data. Therefore, a frame of identity authentication based on the technology of privacy-preserving face recognition is presented in this paper. The convolutional neural network is used for face feature extraction. To overcome the issue of privacy leaked, a method of secure nearest neighbor that can compute the cosine similarity over encrypted feature vectors is proposed. What's more, the edge computing is introduced in our frame to increase the authentication efficiency by removing some operations from the cloud to the edge of the Internet. Moreover, we also propose a secret sharing homomorphism technology which is used for distributed computing to improve the fault-tolerance of our identity authentication system. The experimental results show that the proposed schemes are secure and effective.
Local distinguishability of orthogonal quantum states with no more than one ebit of entanglement

In this paper, we study the local distinguishability of mutually orthogonal product quantum states with entanglement as a valuable resource. We present protocols in which entanglement is used more efficiently than in the previous protocols presented by Zhang et al. [Phys. Rev. A 97, 022334 (2018)]. Specifically, the main technical contributions are as follows: First, we present a method to locally distinguish a set of orthogonal product quantum states in $d\ensuremath{\bigotimes}d$ ($d$ is odd) by using only one ancillary two-qubit maximally entangled state. Then we present a similar method to locally distinguish multipartite orthogonal product states by using entanglement efficiently. Moreover, we propose a method to study the local distinguishability of this kind of orthogonal product state with less than one ebit of entanglement, and analyze the relationship between the probability of error and the quantity of entanglement, which reveals the phenomenon of less nonlocality with more entanglement.
A Joint Generative Model for Zero-Shot Learning

Zero-shot learning (ZSL) is a challenging task due to the lack of data from unseen classes during training. Existing methods tend to have the strong bias towards seen classes, which is also known as the domain shift problem. To mitigate the gap between seen and unseen class data, we propose a joint generative model to synthesize features as the replacement for unseen data. Based on the generated features, the conventional ZSL problem can be tackled in a supervised way. Specifically, our framework integrates Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) conditioned on class-level semantic attributes for feature generation based on element-wise and holistic reconstruction. A categorization network acts as the additional guide to generate features beneficial for the subsequent classification task. Moreover, we propose a perceptual reconstruction loss to preserve semantic similarities. Experimental results on five benchmarks show the superiority of our framework over the state-of-the-art approaches in terms of both conventional ZSL and generalized ZSL settings.
Adaptive Nonlocal Random Walks for Image Superpixel Segmentation

In this paper, we propose a novel superpixel segmentation method using an adaptive nonlocal random walk (ANRW) algorithm. There are three main steps in our image superpixel segmentation algorithm. Our method is based on the random walk model, in which the seed points are produced to generate the initial superpixels by a gradient-based method in the first step. In the second step, the ANRW is proposed to get the initial superpixels by adjusting the NRW to obtain a better image and superpixel segmentation. In the last step, these small superpixels are merged to get the final regular and compact superpixels. The experimental results demonstrate that our method achieves a better superpixel performance than the state-of-the-art methods. Our source code will be available at: http://github.com/shenjianbing/ANRW.
Algorithm Research of Known-plaintext Attack on Double Random Phase Mask Based on WSNs

With the development and popularization of computer and Internet, information communication plays an irreplaceable role in the modern society. Security problems of the information exchange processing is put on the agenda. While the image as one of the most effective information carrier, has been widely used in various fields, such as residents living, national security, etc. How to ensure the safety of image transmission in the processing of nature was widespread attention, has become an important research direction in the field of information security the security of image. At present, has proposed a series of image encryption algorithm, the image encryption algorithm based on Fourier transform is a hot research topic, attracted a lot of attention. We studies and implements the image encryption algorithm based on double random phase encoding (DRPE) in this paper, implement the known plaintext attack method based on the understanding of the algorithm. In the encryption process, the first step is generating two random phase matrix, and modulating the image as a cipher image using the random phase function as the key. But because of the linear properties of the algorithm itself, the algorithm is not robust to the attacks, the simulation results also show this point.
Coevolution Pigeon-Inspired Optimization with Cooperation-Competition Mechanism for Multi-UAV Cooperative Region Search

In this paper, a dynamic two-stage closed search (DTSCS) scheme for the unmanned aerial vehicle (UAV) cooperative region search is designed, which satisfies the range constraint (RC) and orientation constraint (OC). The closed trajectory is composed of two coupling stages, the search stage and the return stage. The position and orientation at the end of the search stage are the starting cell and orientation of the return stage. In the first stage, a coevolution pigeon-inspired optimization (CPIO) algorithm based on the cooperation-competition mechanism is proposed for multi-UAV cooperative search. In the return stage, inspired by region searching and trajectory tracking, a search tracking (ST) approach is presented to obtain the lowest-cost path under OC. The simulation results show that: (i) N p = 5 is the best prediction time step. (ii) CPIO algorithm performs better than the compared intelligent algorithms in region searching. (iii) ST has high tracking performance than other algorithms. (iv) The DTSCS scheme enables every UAV to make the best use of its fuel to cover more region and return to the airport within the RC, and the average range utilization of UAVs is 97% under the 3OC.
Efficient Projection and Coding Tools for 360° Video

In this paper, an efficient cubemap projection format and 360° specific coding tools are proposed. The proposed hybrid equi-angular cubemap projection applies efficient mapping functions to achieve more uniform sampling and reduce the boundary artifacts across different faces. We propose several 360° specific coding tools that respect the spherical geometry in the 2D coding domain. Our tools appropriately process the reference information across the frame boundary and the discontinuous edge in inter prediction, intra prediction, and in-loop filters. As compared to the padded equirectangular projection integrated in 360Lib-5.0 and HM-16.16, the experimental results indicate that the average BD-rate reduction provided by the proposed algorithm achieves 35.5%/69.5%/71.6% in terms of the end-to-end WS-PSNR-Y/U/V, respectively. For subjective quality comparison, the seam artifacts caused by the discontinuous edge and frame boundary are also noticeably reduced by the proposed 360° specific coding tools.
Unlexicalized Transition-based Discontinuous Constituency Parsing

Abstract Lexicalized parsing models are based on the assumptions that (i) constituents are organized around a lexical head and (ii) bilexical statistics are crucial to solve ambiguities. In this paper, we introduce an unlexicalized transition-based parser for discontinuous constituency structures, based on a structure-label transition system and a bi-LSTM scoring system. We compare it with lexicalized parsing models in order to address the question of lexicalization in the context of discontinuous constituency parsing. Our experiments show that unlexicalized models systematically achieve higher results than lexicalized models, and provide additional empirical evidence that lexicalization is not necessary to achieve strong parsing results. Our best unlexicalized model sets a new state of the art on English and German discontinuous constituency treebanks. We further provide a per-phenomenon analysis of its errors on discontinuous constituents.
Pixel-Level Discrete Multiobjective Sampling for Image Matting

In sampling-based matting methods, the alpha is estimated by choosing the best pair of foreground and background color samples. The lack of true samples is the major obstacle in obtaining high-quality alpha mattes. Regrettably, several proposed approaches did not address the conflicts among multiple sampling criteria and the effects of incomplete sample spaces. To address this issue, we propose a pixel-level discrete multiobjective sampling (PDMS) method. The color sampling process at each unknown pixel is formalized as a multiobjective optimization problem (MOP). The strength of PDMS includes its ability to minimize both color difference and spatial distance between unknown and known pixels, and its capacity to adaptively make trade-offs among conflicting sampling criteria. To mitigate the effects of incomplete sample spaces, the sample space is extended to complete known regions in PDMS, which means that the colors of all known pixels can be sampled, instead of mean colors of superpixels. Our experimental results show that PDMS collects a small set of samples while achieving smaller minimum absolute difference in alpha estimation. Moreover, PDMS implements pixel-level sampling by using the proposed multiobjective optimization algorithm to efficiently solve sampling MOPs. The PDMS-based matting method provides high-quality alpha mattes with sharp boundaries and thus outperforms those prior image matting methods in terms of gradient error.
High capacity reversible data hiding with interpolation and adaptive embedding

A new Interpolation based Reversible Data Hiding (IRDH) scheme is reported in this paper. For different applications of an IRDH scheme to the digital image, video, multimedia, big-data and biological data, the embedding capacity requirement usually varies. Disregarding this important consideration, existing IRDH schemes do not offer a better embedding rate-distortion performance for varying size payloads. To attain this varying capacity requirement with our proposed adaptive embedding, we formulate a capacity control parameter and propose to utilize it to determine a minimum set of embeddable bits in a pixel. Additionally, we use a logical (or bit-wise) correlation between the embeddable pixel and estimated versions of an embedded pixel. Thereby, while a higher range between an upper and lower limit of the embedding capacity is maintained, a given capacity requirement within that limit is also attained with a better-embedded image quality. Computational modeling of all new processes of the scheme is presented, and performance of the scheme is evaluated with a set of popular test-images. Experimental results of our proposed scheme compared to the prominent IRDH schemes have recorded a significantly better-embedding rate-distortion performance.
Investigation on the diffusive surface modeling detail in geometrical acoustics based simulations

This study shows an investigation on the modeling level of detail of diffusive surfaces, which strongly influences the efficiency of geometrical acoustic based software. One diffusive condition of the lateral wall of a shoebox hall has been modeled as (a) flat surfaces with surface scattering coefficient and (b) three-dimensional relief including edge diffraction. The analysis has been performed by comparing the conventional acoustic parameters, which showed no significant differences, and by subjectively investigating the sensitivity of listeners to both modeling approaches, which highlighted that perceptual differences related to reverberance and spaciousness are clearer when using the modeling approach (b).
Ontology Based Global and Collective Motion Patterns for Event Classification in Basketball Videos

In multi-person videos, especially team sport videos, a semantic event is usually represented as a confrontation between two teams of players, which can be represented as collective motion. In broadcast basketball videos, specific camera motions are used to present specific events. Therefore, a semantic event in broadcast basketball videos is closely related to both the global motion (camera motion) and the collective motion. A semantic event in basketball videos can be generally divided into three stages: pre-event, event occurrence (event-occ), and post-event. By analyzing the influence of different stages of video segments to semantic events discrimination, it is observed that the pre-event and event-occ segments are effective for classification, while the post-events are effective for event success/failure classification. In this paper, we propose an ontology-based global and collective motion pattern (On_GCMP) algorithm for the basketball event classification. First, a two-stage GCMP-based event classification scheme is proposed. The GCMP is extracted using the optical flow. The two-stage scheme progressively combines a five-class event classification algorithm on event-occs and a two-class event classification algorithm on pre-events. Both algorithms utilize the sequential convolutional neural networks (CNNs) and the long short-term memory (LSTM) networks to extract the spatial and temporal features of GCMP for event classification. Second, we utilize the post-event segments to predict success/failure using deep features of images in the video frames (RGB_DF_VF)-based algorithms. Finally, the event classification results and success/failure classification results are integrated to obtain the final results. To evaluate the proposed scheme, we collected a new dataset called NCAA+, which is automatically obtained from the NCAA dataset by extending the fixed length of video clips forward and backward of the corresponding semantic events. The experimental results demonstrate that the proposed scheme achieves the mean average precision of 58.10% on NCAA+. It is higher by 6.50% than the state of the art on NCAA.