Reviewerly/exHarmony huggingface dataset:
Rapid 3D measurement technique for colorful objects employing RGB color light projection

Three-dimensional (3D) measurement of colorful objects is challenging. As different colors can absorb different wavelengths of projected light, the brightness and contrast of the captured fringe are not uniform when employing single-color light projection, which will lead to measurement error. In this paper, we present a rapid 3D measurement technique for colorful objects employing red, green, and blue (RGB) light projection. According to the research in this paper, for common colors, the pixel with the largest brightness and contrast can be extracted from the three fringes projected by RGB light. Furthermore, we introduce the selection method of exposure time, and then combine the high-speed projection technique with the optimal pixel-extraction algorithm to get the optimal set of fringes for phase calculation. Experiments show that the proposed method improves the measurement accuracy and efficiency.
Heuristic tree searching for pose-independent 3D/2D rigid registration of vessel structures

The 3D/2D registration of pre-operative computed tomography angiography (CTA) and intra-operative x-ray angiography (XRA) images in vascular intervention is imperative for guiding surgical instruments and reducing the dosage of toxic contrast agents. In this study, 3D/2D vascular registration is formulated as a search tree problem on the basis of the topological continuity of vessels and the fact that matching can be decomposed into continuous states. In each node of the tree, a closed-solution of 3D/2D transformation is used to obtain the registration results based on the dense correspondences of vessel points, and the results of matching and registration are calculated and recorded. Then, a hand-crafted score that quantifies the qualities of matching and registration of vessels is used, and the remaining problem focuses on finding the highest score in the search tree. An improved heuristic tree search strategy is also proposed to find the best registration. The proposed method is evaluated and compared with four state-of-the-art methods. Experiments on simulated data demonstrate that our method is insensitive to initial pose and robust to noise and deformation. It outperforms other methods in terms of registering real model data and clinical coronary data. In the 3D/2D registration of uninitialized and initialized coronary arteries, the average registration errors are 1.85 and 1.79 mm, respectively. Given that the proposed method is independent of the initial pose, it can be used to navigate vascular intervention for clinical practice.
Modulation Recognition using Wavelet Transform based on AlexNet

In this paper, a modulation recognition method is presented to implement the complex modulation recognition in non-cooperation communication systems by using an AlexNet-based deep learning technique. In the image preprocessing step, we convert the time-domain diagrams of different complex modulated signals to the spectrogram images by using wavelet transform (WT). In the training step, with the feature extraction of AlexNet model to the input spectrogram images, we can attain the training model, which can be used to classify the different modulations of signals. In this work, we select the eight modulated signals (2ASK, 4ASK, 2PSK, 4PSK, 2FSK, 4FSK, 16QAM, 64QAM) for recognition. The simulation result shows that the classification accuracy of eight signals is almost 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> at higher signal-to-noise ratio (SNR). Besides, the experimental result also illustrates the similarity between the spectrogram images of 16QAM, 4PSK and 4ASK. Compared with using initial time-domain diagrams without wavelet transform as input, we confirm that the superiority of the presented method whose classification accuracy is significantly improved.
Robust Particle Filtering With Time-Varying Model Uncertainty and Inaccurate Noise Covariance Matrix

This article proposes a robust particle filtering (PF) approach for a generic class of nonlinear systems with both additive time-varying uncertainty (ATVU) in the state transition equation and inaccurate process noise covariance matrices. To avoid sampling efficiency degradation of the PF approach caused by ATVU, we employ the disturbance observer-based PF (DOBPF) approach where the effect of ATVU is compensated in the particle generation stage. Different from the existing DOBPF method where disturbance estimation is achieved via the Kalman filter, the disturbance observer adopted in this article is in the form of variational Bayesian adaptive Kalman filter (VBAKF) which deals with the inaccurate process noise covariance matrices in both the dynamic models of the state and the ATVU. Compared with conventional PF approaches, the proposed method, named VBAKF-PF, exhibits enhanced robustness against both the ATVU in the state transition equation and the uncertainties of process noise covariance matrices. The simulation results demonstrate the superiority of VBAKF-PF over both the VBAKF and DOBPF methods.
A trustworthy network or a technologically disguised scam

Purpose The initiation of cryptocurrency such as bitcoin has invoked a general curiosity in the international financial regime. Bitcoin works in a peer-to-peer framework with no third party acting as a monitoring agency. This brings in both positive and negative spirit to the table. Though generic understanding is available, the studies done on bitcoin and blockchain are far and few. The purpose of this paper is to decipher the level of understanding the scientific world has on bitcoin and the underlying blockchain and thereby to find out the pertinent research gaps existing in this field. Design/methodology/approach A biblio-morphological analysis is undertaken which underscores a comprehensive framework characterizing the bitcoin and blockchain literature in terms of dimensions and options. This was developed as a result of extensive scanning of the relevant literature. Findings The biblio-morphological analysis broadly outlines the present status of the research on the topic. The morphological analysis brings out the 169 research gaps that exist in the area of bitcoin and underlying blockchain technology. The cross-consistency matrix underscores the fact that this novel technology bears large research potential. Research limitations/implications The biggest contribution of this paper would be to underline the present status of the literature on bitcoin and blockchain technology. This study attempts to make this document a vade mecum for both the industry and the academia in the area of bitcoin and blockchain technology. The blockchain technology and its effects and influences on other disciplines can effectively be explored by taking cues from the gaps identified by this biblio-morphological analysis. Such unique combinations can open up new avenues of research in the broader realm of business and economics. Originality/value To the best of the authors’ knowledge, this is a first comprehensive biblio-morphological analysis on bitcoin and underlying blockchain technology, which characterizes blockchain literature and thereby offers to reduce redundant research by delineating the possible avenues in the area of blockchain technology.
Complete grid pattern decoding method for a one-shot structured light system

Structured light 3D reconstruction methods using a De Bruijn sequence-based color grid pattern have an impressive advantage of fast and accurate decoding, which leads to fast 3D reconstruction. They are especially suitable for capturing moving objects. However, the drawback of these methods is their high false decoding rate while dealing with feature points at the object’s boundaries, and objects can be prone to becoming deformed by the uneven structure of the dynamic scene. To solve this problem, we present an efficient opened-grid-point detector and a complete grid pattern decoding method. Specifically, a new, to the best of our knowledge, color grid pattern is designed to reduce the influence of color noise and increase the density of 3D cloud points. In addition, a LCD screen projected with the proposed pattern is utilized to calibrate the camera-projector system. The experiments, conducted in a laboratory without a light curtain, demonstrate that the proposed method can fully satisfy the requirements of real applications.
Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models

Today's proliferation of powerful facial recognition systems poses a real threat to personal privacy. As this http URL demonstrated, anyone can canvas the Internet for data and train highly accurate facial recognition models of individuals without their knowledge. We need tools to protect ourselves from potential misuses of unauthorized facial recognition systems. Unfortunately, no practical or effective solutions exist. In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models. Fawkes achieves this by helping users add imperceptible pixel-level changes (we call them cloaks) to their own photos before releasing them. When used to train facial recognition models, these cloaked images produce functional models that consistently cause normal images of the user to be misidentified. We experimentally demonstrate that Fawkes provides 95+% protection against user recognition regardless of how trackers train their models. Even when clean, uncloaked images are leaked to the tracker and used for training, Fawkes can still maintain an 80+% protection success rate. We achieve 100% success in experiments against today's state-of-the-art facial recognition services. Finally, we show that Fawkes is robust against a variety of countermeasures that try to detect or disrupt image cloaks.
Assessment of Expert-Level Automated Detection of <i>Plasmodium falciparum</i> in Digitized Thin Blood Smear Images

Decades of effort have been devoted to establishing an automated microscopic diagnosis of malaria, but there are challenges in achieving expert-level performance in real-world clinical settings because publicly available annotated data for benchmark and validation are required.To assess an expert-level malaria detection algorithm using a publicly available benchmark image data set.In this diagnostic study, clinically validated malaria image data sets, the Taiwan Images for Malaria Eradication (TIME), were created by digitizing thin blood smears acquired from patients with malaria selected from the biobank of the Taiwan Centers for Disease Control from January 1, 2003, to December 31, 2018. These smear images were annotated by 4 clinical laboratory scientists who worked in medical centers in Taiwan and trained for malaria microscopic diagnosis at the national reference laboratory of the Taiwan Centers for Disease Control. With TIME, a convolutional neural network-based object detection algorithm was developed for identification of malaria-infected red blood cells. A diagnostic challenge using another independent data set within TIME was performed to compare the algorithm performance against that of human experts as clinical validation.Performance on detecting Plasmodium falciparum-infected blood cells was measured by average precision, and performance on detecting P falciparum infection at the image level was measured using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).The TIME data sets contained 8145 images of 36 blood smears from patients with suspected malaria (30 P falciparum-positive and 6 P falciparum-negative smears) that had reliable annotations. For clinical validation, the average precision was 0.885 for detecting P falciparum-infected blood cells and 0.838 for ring form. For detecting P falciparum infection on blood smear images, the algorithm had expert-level performance (sensitivity, 0.995; specificity, 0.900; AUC, 0.997 [95% CI, 0.993-0.999]), especially in detecting ring form (sensitivity, 0.968; specificity, 0.960; AUC, 0.995 [95% CI, 0.990-0.998]) compared with experienced microscopists (mean sensitivity, 0.995 [95% CI, 0.993-0.998]; mean specificity, 0.955 [95% CI, 0.885-1.000]).The findings suggest that a clinically validated expert-level malaria detection algorithm can be developed by using reliable data sets.
A min-max regret approach to maximum likelihood inference under incomplete data

Various methods have been proposed to express and solve maximum likelihood problems with incomplete data. In some of these approaches, the idea is that incompleteness makes the likelihood function imprecise. Two proposals can be found to cope with this situation: the maximax approach that maximizes the greatest likelihood value induced by precise data sets compatible with the incomplete observations, and the maximin approach that maximizes the least such likelihood value. These approaches prove to display extreme behaviors in some contexts, the maximax approach having a tendency to disambiguate the data, while the maximin approach favors uniform distributions. In this paper, we propose an alternative approach consisting in minimizing a relative regret criterion with respect to maximal likelihood solutions obtained for all precise data sets compatible with the coarse data. In contrast with the maximax and the maximin methods, the min-max-regret method relies on comparing relative likelihoods and obtains results that achieve a trade-off between results of the two other methods. The methods are compared on toy examples and also on simulated random data as well as a supervised classification problem.
Improving Syntactical Clone Detection Methods through the Use of an Intermediate Representation

Detection of type-3 and type-4 clones remains a difficult task. Current methods are complex, both on a conceptual and computational level. Similarly, their usage requires substantial implementation efforts. Instead of creating yet another method, it might be more productive to combine the simplicity of syntactic approaches with the abstractions granted by intermediate representations (IR). To this end, we devised a c-like IR based on LLVM and ran NiCad on it (LLNiCad). To establish whether the clone detection capabilities of syntactic approaches can be improved through an IR, we compared NiCad and LLNiCad on three open source projects taken from Krutz's benchmark and a subset of Google code jam solutions. In our results, the f1-score of LLNiCad consistently outperforms NiCad. Indeed, for all clone types in Krutz's benchmark, LLNiCad has a f1-score that is 37% higher than NiCad; with both better precision and recall. For type-4 clones in our GCJ benchmark, the f1-score of LLNiCad also outperforms CCCD (a semantic clone detector) by 44%. These findings suggest that IRs are beneficial for improving clone detection and that they have a larger impact on type-3 and type-4 clones.
Learning Three-dimensional Skeleton Data from Sign Language Video

Data for sign language research is often difficult and costly to acquire. We therefore present a novel pipeline able to generate motion three-dimensional (3D) skeleton data from single-camera sign language videos only. First, three recurrent neural networks are learned to infer the three-dimensional position data of body, face, and finger joints for a high resolution of the signer’s skeleton. Subsequently, the angular displacements of all joints over time are estimated using inverse kinematics and mapped to a virtual sign avatar for animation. Last, the generated data are evaluated in detail, including a sign language recognition and sign language synthesis scenario. Utilizing a neural word classifier trained on real motion capture data, we reliably classify word segments built from our newly generated position data with similar accuracy as motion capture data (absolute difference 3.8%). Furthermore, qualitative evaluation of sign animations shows that the avatar performs natural movements that are comprehensible and resemble animations created with original motion capture data.
An Evaluation of Feature Matchers for Fundamental Matrix Estimation.

Matching two images while estimating their relative geometry is a key step in many computer vision applications. For decades, a well-established pipeline, consisting of SIFT, RANSAC, and 8-point algorithm, has been used for this task. Recently, many new approaches were proposed and shown to outperform previous alternatives on standard benchmarks, including the learned features, correspondence pruning algorithms, and robust estimators. However, whether it is beneficial to incorporate them into the classic pipeline is less-investigated. To this end, we are interested in i) evaluating the performance of these recent algorithms in the context of image matching and epipolar geometry estimation, and ii) leveraging them to design more practical registration systems. The experiments are conducted in four large-scale datasets using strictly defined evaluation metrics, and the promising results provide insight into which algorithms suit which scenarios. According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator. They show remarkable performances and have potentials to a large part of computer vision tasks. To facilitate future research, the full evaluation pipeline and the proposed methods are made publicly available.
One-decoy state reference-frame-independent quantum key distribution*

Reference-frame-independent quantum key distribution (RFI-QKD) has been proven to be very useful and practical under realistic environment. Here, we present a scheme for one-decoy state RFI-QKD based on the work of Rusca et al. [ Appl. Phys. Lett . 112 , 171104 (2018)], and carry out investigation on its performance under realistic experimental conditions. Numerical simulation results show that the one-decoy state RFI-QKD can achieve comparable performance in terms of secret key rate and transmission distance as the two-decoy state correspondence under practical experimental conditions. On contrast, it does not need to prepare the vacuum state in the former case, substantially reducing the experimental complexity and random number consumptions. Therefore, our present proposal seems very promising in practical implementations of RFI-QKD.
Airborne Sensor Data-Based Unsupervised Recursive Identification for UAV Flight Phases

With the widespread application of Unmanned Aerial Vehicle (UAV), more and more attention has been paid to the analysis of flight data, which can realize the condition monitoring and help improve operational safety. The UAV flight phase is dynamically switched, and flight phase identification is the vital basis for accurate flight data analysis. However, UAV flight data do not always contain a parameter that can be used for the direct division of flight phases, and it is difficult to give uniform thresholds for the flight phase identification. To realize the automatic identification of UAV flight phases based on airborne sensor data, an unsupervised Gaussian Mixture Model Recursive Clustering (GMMRC) method is proposed. GMMRC divides a complex flight phase identification task into simple subtasks which are corresponding to UAV operations. It can not only improve the performance of identification, but also enhance the interpretability of the results. In the recursive method, GMM is used as an unsupervised identification operator to obtain the desired identification results in each subtask. To further improve the identification effect, an interval detection method is used to eliminate false results. Experiments based on simulated flight data and real small fixed-wing UAV flight data demonstrate the potential of this method for accurate and robust flight phase identification.
Path planning for UAV/UGV collaborative systems in intelligent manufacturing

Due to the development needs of the intelligent manufacturing industry, the use of drones is expanding indefinitely, and at the same time, extremely high requirements are imposed on the autonomous navigation of drones to reduce human intervention. This paper presents an UAV/ UGV scheduling problem in which the UAV needs to be recharged by the UGV in order to working in persistent tasks, and meanwhile, the UGV need to visits UGV working depots. Two different problem are presented: fixed charging sets problem (FCSP) and discrete charging sets problem (DCSP). In FCSP, charging sets are fixed, and a two-stage travelling salesman problem method is proposed to solve the problem. DCSP is a modification of FCSP while charging segments are discrete into serval segments, an graph transformation approach was proposed to transform DCSP into GTSP, so DCSP can be resolved by using GTSP solvers. Simulation results shows that both DCSP and FCSP can ensure UAV/UGV work in persistent tasks, and the graph transformation algorithm can efficiently transform DCSP into GTSP.
Mixed Reality System for Virtual Chemistry Lab

This paper presents a new method for improving teaching aids with the design and development of a chemistry education platform -the Mixed Reality (MR) Chemistry Lab.Our system provides a new educational experience in which students can simulate a chemistry experiment in a virtual lab and interact with objects using Oculus Helmet-Mounted Displays (HMD) and hand controller devices.The proposed system aims to familiarize students with experimental procedures and safety knowledge before conducting actual experiments.Moreover, students will be able to observe microscopic atomic structure models in three dimensions.Our research also includes a five-part, quantitative evaluation system user tests perform using a quantitative questionnaire consisting of five items, including hardware equipment, immersion, education, interaction sense of control, and degree of difficulty.The evaluation results confirm that this system will be helpful to the educational experience of conducting chemistry experiments with scientific evaluation methods, and the proposed system is also expected to have a broad range of applications in many other subjects.
Modelling High-Level Mathematical Reasoning in Mechanised Declarative Proofs

Mathematical proofs can be mechanised using proof assistants to eliminate gaps and errors. However, mechanisation still requires intensive labour. To promote automation, it is essential to capture high-level human mathematical reasoning, which we address as the problem of generating suitable propositions. We build a non-synthetic dataset from the largest repository of mechanised proofs and propose a task on causal reasoning, where a model is required to fill in a missing intermediate proposition given a causal context. Our experiments (using various neural sequence-to-sequence models) reveal that while the task is challenging, neural models can indeed capture non-trivial mathematical reasoning. We further propose a hierarchical transformer model that outperforms the transformer baseline.
Learning Efficient Dialogue Policy from Demonstrations through Shaping

Training a task-oriented dialogue agent with reinforcement learning is prohibitively expensive since it requires a large volume of interactions with users.Human demonstrations can be used to accelerate learning progress.However, how to effectively leverage demonstrations to learn dialogue policy remains less explored.In this paper, we present that efficiently learns dialogue policy from demonstrations through policy shaping and reward shaping.We use an imitation model to distill knowledge from demonstrations, based on which policy shaping estimates feedback on how the agent should act in policy space.Reward shaping is then incorporated to bonus state-actions similar to demonstrations explicitly in value space encouraging better exploration.The effectiveness of the proposed S 2 Agent is demonstrated in three dialogue domains and a challenging domain adaptation task with both user simulator evaluation and human evaluation.