Reviewerly/exHarmony huggingface dataset:
Synchronous full-field measurement of temperature and deformation based on separated radiation and reflected light

Optical method has been playing an important role in measuring temperature and deformation at elevated temperatures. Here we propose a simple and effective iterative algorithm to realize accurate and simultaneous measurement of the full-field temperature and deformation of specimens subjected to flame heating at temperatures up to 1000 °C. The algorithm separates the optical images of specimen surface into radiant and reflected parts. The radiant part is used to calculate the full-field temperature by an improved two-color method, and the reflected part is used to obtain the deformation of the specimen surface based on high-temperature digital image correlation method. Experimental validation shows that the proposed method can effectively eliminate the mutual interference between the radiation and reflected light, displaying great potential for synchronous measurement of temperature and deformation with sufficient accuracy by using one camera.
Patch match networks: Improved two-channel and Siamese networks for image patch matching

Descriptor and metric learning using deep convolutional neural networks (CNNs) have drawn attention of researchers in the domain of computer vision due to their remarkable performance over traditional methods. Different networks like two-channel, Siamese and triplet, etc., have been proposed recently with the aim to learn a metric or a low dimensional embedding from image patches and have outperformed traditional local descriptors like scale-invariant feature transform (SIFT). Plain CNNs resulting from stacking of several convolutional layers are employed in recent works. In this article, we have followed a recent approach called Deep Compare for metric and descriptor learning and have proposed improved architecture for two-channel and Siamese networks. Our proposed modification is inspired from the novel dense convolutional neural network known as DenseNet architecture. The proposed two-channel and Siamese networks employ dense convolutional layers which reuse feature maps from preceding layers. Our networks, trained with pairs of patches, outperform Deep Compare networks by a significant margin justifying the proposed architecture and obtain results comparable to triplet networks on UBC benchmark dataset. Moreover, we have obtained promising results on patch verification, image matching and patch retrieval tasks on large scale HPatches benchmark dataset using the descriptors from our Siamese network.
Generalized rewrite theories, coherence completion, and symbolic methods

A new notion of generalized rewrite theory suitable for symbolic reasoning and generalizing the standard notion in [19] is motivated and defined. Also, new requirements for symbolic executability of generalized rewrite theories that extend those in [33] for standard rewrite theories, including a generalized notion of coherence, are given. Symbolic executability, including coherence, is both ensured and made available for a wide class of such theories by automatable theory transformations. Using these foundations, several symbolic reasoning methods using generalized rewrite theories are studied, including: (i) symbolic description of sets of terms by pattern predicates; (ii) reasoning about universal reachability properties by generalized rewriting; (iii) reasoning about existential reachability properties by constrained narrowing; and (iv) symbolic verification of safety properties such as invariants and stability properties.
An effective and conceptually simple feature representation for off-line text-independent writer identification

Feature engineering forms an important component of machine learning and pattern recognition. It is a fundamental process for off-line writer identification of handwritten documents, which continues to be an interesting subject of research in various forensic and authentication areas. In this work, we propose an efficient, yet computationally and conceptually simple framework for off-line text independent writer identification using local textural features in characterizing the writing style of each writer. These include Local Binary Patterns (LBP), Local Ternary Patterns (LTP), and Local Phase Quantization (LPQ). Our approach focuses on exploiting the writing images at small observation regions where a set of connected component sub-images are cropped and extracted from each handwriting sample (document or set of word/text line images). These connected components are seen as texture images where each one of them is subjected to feature extraction using LBP, LPQ, or LTP. Then, a histogram sequence concatenation is applied to the feature image after dimensionality reduction followed by image subdivision into a number of non-overlapping regions. For classification, the 1-NN (Nearest Neighbor) classifier is used to identify the writer of the questioned samples based on the dissimilarity of feature vectors computed from all components in the writing. Experiments on IFN/ENIT (411 writers/Arabic), AHTID/MW (53 writers/Arabic), CVL (309 writers/English), and IAM (657 writers/English) databases demonstrate that our proposed system outperforms old and recent state-of-the-art writer identification systems on Arabic script, and demonstrates a competitive performance on English ones.
A Fast and Power-Efficient Hardware Architecture for Non-Maximum Suppression

Non-maximum suppression (NMS) is an indispensable post-processing step in face detection. The vast majority of face detection methods need NMS to merge the candidate detected face boxes that belong to the same face. However, the standard NMS is a greedy and local optimization technique which suffers from several shortcomings, such as high complexity (O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )), high latency, and large power consumption. This brief alleviates these problems and presents an efficient hardware architecture for NMS, meanwhile, carries out the optimization for the calculation unit to achieve the reduction of area accordingly. Based on the multi-thread computing, this brief utilizes sliding window to obtain parallelism and uses position-based bit table technique for the enhancement of data accessing and data reusing, which greatly decreases the cost of memory access and power consumption. The proposed hardware architecture is implemented in TSMC 28-nm technology. Experiments show that the power consumption is 6.142 mW and the latency is 12.79 μs to cluster 1000 candidate boxes, whose energy efficiency is higher than those state-of-the-art methods by 3798× and 358×, respectively.
Research on ARM TrustZone

ARM TrustZone [1] is a hardware-based security feature that can provide software with a high-privilege and isolated execution environment. Such isolation is ensured by hardware, which is usually considered as more trustworthy than software. Thus the execution environment is also known as trusted execution environment (TEE). TrustZone technology was proposed in 2002, but did not get widely used until 2009, when Apple released iPhone 5s. In iPhone 5s, Apple leveraged TrustZone to protect its Touch ID, which ensures that even if the iOS is fully compromised, the user's fingerprint data can still be safe. In 2017, Google made TEE a mandatory requirement on any Android devices with a fingerprint scanner. Nowadays, almost all mobile phones and tablets have TEE deployed. Meanwhile, ARM integrates TrustZone in ARM64 and ARMv8-M to support a broader range of platforms including servers and IoT devices.
Variational Inference for Stochastic Differential Equations

Abstract The statistical inference of the state variable and the drift function of stochastic differential equations (SDE) from sparsely sampled observations are discussed herein. A variational approach is used to approximate the distribution over the unknown path of the SDE conditioned on the observations. This approach also provides approximations for the intractable likelihood of the drift. The method is combined with a nonparametric Bayesian approach which is based on a Gaussian process prior over drift functions.
Hack for Hire: Exploring the Emerging Market for Account Hijacking

Email accounts represent an enticing target for attackers, both for the information they contain and the root of trust they provide to other connected web services. While defense-in-depth approaches such as phishing detection, risk analysis, and two-factor authentication help to stem large-scale hijackings, targeted attacks remain a potent threat due to the customization and effort involved. In this paper, we study a segment of targeted attackers known as "hack for hire" services to understand the playbook that attackers use to gain access to victim accounts. Posing as buyers, we interacted with 27 English, Russian, and Chinese blackmarket services, only five of which succeeded in attacking synthetic (though realistic) identities we controlled. Attackers primarily relied on tailored phishing messages, with enough sophistication to bypass SMS two-factor authentication. However, despite the ability to successfully deliver account access, the market exhibited low volume, poor customer service, and had multiple scammers. As such, we surmise that retail email hijacking has yet to mature to the level of other criminal market segments.
Task-Aware TCP in Data Center Networks

In modern data centers, many flow-based and task-based schemes have been proposed to speed up the data transmission in order to provide fast, reliable services for millions of users. However, the existing flow-based schemes treat all flows in isolation, contributing less to or even hurting user experience due to the stalled flows. Other prevalent task-based approaches, such as centralized and decentralized scheduling, are sophisticated or unable to share task information. In this work, we first reveal that the relinquishing bandwidth of leading flows to the stalled ones effectively reduces the task completion time. We further present the design and implementation of a general supporting scheme that shares the flow-tardiness information through a receiver-driven coordination. Our scheme can be flexible and widely integrated with the state-of-the-art TCP protocols designed for data centers in either single stage or multiple stage scenario, while making no modification on switches. Through the testbed experiments and simulations of typical data center applications, we show that in single stage scenario, our scheme reduces the task completion time by 70% and 50% compared with the flow-based protocols (e.g., DCTCP, L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DCT) and task-based scheduling (e.g., Baraat), respectively. Moreover, our scheme also outperforms other approaches by 18%~25% in prevalent topologies of the data center. For multiple stage scenario, our scheme also has up to 50% improvement compared to other schemes.
Evaluating Digital Cultural Heritage ‘In the Wild’

Digital heritage interpretation is often untethered from traditional museological techniques and environments. As museums and heritage sites explore the potential of locative technologies and ever more sophisticated content-triggering mechanisms for use outdoors, the kinds of questions that digital heritage researchers are able to explore have become increasingly more complex. Researchers now find themselves in the realm of the immersive, the experiential, and the performative. Working closely with their research participants, they navigate ambiguous terrain, including the often unpredictable affective resonances that are the direct consequences of interaction. This article creates a dialogue between two case studies which, taken together, help to unpack some key methodological and ethical questions emerging from these developments. First, we introduce With New Eyes I See, an itinerant and immersive digital heritage encounter which collapsed boundaries between physical/digital, fact/fiction and past/present. Second, we detail Rock Art on Mobile Phones , a set of dialogic web apps that aimed to explore the potential of mobile devices in delivering heritage interpretation in the rural outdoors. Looking outward from these case studies, we reflect on how traditional evaluation frameworks are being stretched and strained given the kinds of questions that digital heritage researchers are now exploring. Drawing on vignettes from experience-oriented qualitative studies with participants, we articulate specific common evaluative challenges related to the embodied, multimodal, and transmedial nature of the digital heritage experiences under investigation. In doing so, we make the case for reflexivity as a central and more collaborative feature of research design within this field going forward — paying attention to and advocating the reciprocal relationship between researchers and the heritage experiences that we study.
Application of WorldView-3 imagery and ASTER TIR data to map alteration minerals associated with the Rodalquilar gold deposits, southeast Spain

The Rodalquilar epithermal quartz-alunite gold deposits that occur within the Rodalquilar caldera complex in southeast Spain, are associated with a pronounced hydrothermal alteration of the country rocks. The hydrothermal alteration zones that are exposed on the surface consist of the vuggy silica zone, the advanced argillic alteration zone, the intermediate argillic alteration zone, the propylitic alteration zone, and a second stage supergene acid sulfate alteration. High spatial resolution multispectral imagery recorded by the WorldView-3 satellite was used in this study to map the spatial distribution of the main alteration minerals in the Rodalquilar caldera complex. Thermal infrared (TIR) data of the ASTER satellite were used to detect the quartz-rich zones. The analysis of the Rodalquilar WorldView-3 data was based on the Adaptive Coherence Estimator (ACE), a partial unmixing algorithm. The ACE processing accurately mapped the spatial distribution of alunite, kaolinite, illite and goethite. Alunite is abundant in the vuggy silica and advanced argillic alteration zones, and in the second stage supergene acid sulfate alteration. Kaolinite is predominant in the intermediate argillic alteration zone. Illite is abundant in the outer parts of the intermediate argillic alteration zone. Goethite image maps gossans that mainly occur in the vuggy silica and advanced argillic alteration zones, and in the areas characterized by the second stage supergene acid sulfate alteration. The detection of quartz-rich zones from the ASTER TIR data complemented the WorldView-3 mapping results. The study shows the efficiency of high spatial resolution multispectral remote sensing imagery recorded by the WorldView-3 satellite for district-level mineral exploration studies.
End-to-End 6-DoF Object Pose Estimation Through Differentiable Rasterization

Here we introduce an approximated differentiable renderer to refine a 6-DoF pose prediction using only 2D alignment information. To this end, a two-branched convolutional encoder network is employed to jointly estimate the object class and its 6-DoF pose in the scene. We then propose a new formulation of an approximated differentiable renderer to re-project the 3D object on the image according to its predicted pose; in this way the alignment error between the observed and the re-projected object silhouette can be measured. Since the renderer is differentiable, it is possible to back-propagate through it to correct the estimated pose at test time in an online learning fashion. Eventually we show how to leverage the classification branch to profitably re-project a representative model of the predicted class (i.e. a medoid) instead. Each object in the scene is processed independently and novel viewpoints in which both objects arrangement and mutual pose are preserved can be rendered. Differentiable renderer code is available at: https://github.com/ndrplz/tensorflow-mesh-renderer .
Learning a Unified Blind Image Quality Metric via On-Line and Off-Line Big Training Instances

In this work, we resolve a big challenge that most current image quality metrics (IQMs) are unavailable across different image contents, especially simultaneously coping with natural scene (NS) images or screen content (SC) images. By comparison with existing works, this paper deploys on-line and off-line data for proposing a unified no-reference (NR) IQM, not only applied to different distortion types and intensities but also to various image contents including classical NS images and prevailing SC images. Our proposed NR IQM is developed with two data-driven learning processes following feature extraction, which is based on scene statistic models, free-energy brain principle, and human visual system (HVS) characteristics. In the first process, the scene statistic models and an image retrieve technique are combined, based on on-line and off-line training instances, to derive a novel loose classifier for retrieving clean images and helping to infer the image content. In the second process, the features extracted by incorporating the inferred image content, free-energy and low-level perceptual characteristics of the HVS are learned by utilizing off-line training samples to analyze the distortion types and intensities and thereby to predict the image quality. The two processes mentioned above depend on a gigantic quantity of training data, much exceeding the number of images applied to performance validation, and thus make our model's performance more reliable. Through extensive experiments, it has been validated that the proposed blind IQM is capable of simultaneously inferring the quality of NS and SC images, and it has attained superior performance as compared with popular and state-of-the-art IQMs on the subjective NS and SC image quality databases. The source code of our model will be released with the publication of the paper at https://kegu.netlify.com.
Improved Wavelet Threshold for Image De-noising

With the development of communication technology and network technology, as well as the rising popularity of digital electronic products, an image has become an important carrier of access to outside information. However, images are vulnerable to noise interference during collection, transmission and storage, thereby decreasing image quality. Therefore, image noise reduction processing is necessary to obtain higher-quality images. For the characteristics of its multi-analysis, relativity removal, low entropy, and flexible bases, the wavelet transform has become a powerful tool in the field of image de-noising. The wavelet transform in application mathematics has a rapid development. De-noising methods based on wavelet transform is proposed and achieved with good results, but shortcomings still remain. Traditional threshold functions have some deficiencies in image de-noising. A hard threshold function is discontinuous, whereas a soft threshold function causes constant deviation. To address these shortcomings, a method for removing image noise is proposed in this paper. First, the method decomposes the noise image to determine the wavelet coefficients. Second, the wavelet coefficient is applied on the high-frequency part of the threshold processing by using the improved threshold function. Finally, the de-noised images are obtained to rebuild the images in accordance with the estimation in the wavelet-based conditions. Experiment results show that this method, discussed in this paper, is better than traditional hard threshold de-noising and soft threshold de-noising methods, in terms of objective effects and subjective visual effects.
Augmented Reality in the Health Domain: Projecting Spatial Augmented Reality Visualizations on a Perceiver's Body for Health Communication Effects

An experiment is reported that studied the effects of spatial embodiment in augmented reality on medical attitudes about the self. College students (N = 90) viewed public service announcements (PSAs) with overlaid virtual fetuses and X-rayed images of lungs on various interfaces representing embodiment-a two-dimensional screen, a three-dimensional (3D) mannequin, and the participants' bodies (3D). Results indicated that PSA messages with richer embodied interfaces increase the sense of "being there," also known as spatial presence (SP), in sequential order; this leads to increased negative emotion regarding smoking cigarettes and an increased willingness to engage with a cigarette cessation campaign. When the SP mediates the dual model process, only affective attitudes increase the behavioral intention to engage with the campaign.
A new calibration method for line-structured light vision sensors based on concentric circle feature

Determining the relative ubiety between the camera and the laser projector in a line-structured light vision sensor is a classical yet important task. Typical calibration methods often confront problems, such as difficulty of producing the target precisely and introduction of perspective projection errors. In this work, a new calibration method based on a concentric circle feature is introduced. The proposed method is based on geometrical properties and can reduce the perspective projection error. In our method, the vanishing line of the light plane is firstly deduced from the imaged concentric circles. Then the normal vector of the light plane is determined. Consequently, the complete expression can be confirmed from the principle of the intersecting planes. The proposed method is simple and robustness as the basic theory is geometrical properties. Accuracy evaluation experiment shows that the accuracy of the calibration method can reach 0.07 mm within the view field of about 200 × 200 mm. This accuracy is comparable to the commonly used calibration method with a checkerboard planar target, whereas our target is simple to produce.
Deep Patch Representations with Shared Codebook for Scene Classification

Scene classification is a challenging problem. Compared with object images, scene images are more abstract, as they are composed of objects. Object and scene images have different characteristics with different scales and composition structures. How to effectively integrate the local mid-level semantic representations including both object and scene concepts needs to be investigated, which is an important aspect for scene classification. In this article, the idea of a sharing codebook is introduced by organically integrating deep learning, concept feature, and local feature encoding techniques. More specifically, the shared local feature codebook is generated from the combined ImageNet1K and Places365 concepts (Mixed1365) using convolutional neural networks. As the Mixed1365 features cover all the semantic information including both object and scene concepts, we can extract a shared codebook from the Mixed1365 features, which only contain a subset of the whole 1,365 concepts with the same codebook size. The shared codebook can not only provide complementary representations without additional codebook training but also be adaptively extracted toward different scene classification tasks. A method of fusing the encoded features with both the original codebook and the shared codebook is proposed for scene classification. In this way, more comprehensive and representative image features can be generated for classification. Extensive experimentations conducted on two public datasets validate the effectiveness of the proposed method. Besides, some useful observations are also revealed to show the advantage of shared codebook.