Reviewerly/exHarmony huggingface dataset:
Texture Representation Through Overlapped Multi-Oriented Tri-Scale Local Binary Pattern

This paper ideates a novel texture descriptor that retains its classification accuracy under varying conditions of image orientation, scale, and illumination. The proposed Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP) texture descriptor also remains insensitive to additive white Gaussian noise. The wavelet decomposition stage of the OMTLBP provides robustness to photometric variations, while the two subsequent stages - overlapped multi-oriented fusion and multi-scale fusion - provide resilience against geometric transformations within an image. Isolated encoding of constituent pixels along each scale in the joint histogram enables the proposed descriptor to capture both micro and macro structures within the texture. Performance of the OMTLBP is evaluated by classifying a variety of textured images belonging to Outex, KTH-TIPS, Brodatz, CUReT, and UIUC datasets. The experimental results validate the superiority of the proposed method in terms of classification accuracy when compared with the state-of-the-art texture descriptors for noisy images.
Spatial phase-shift digital shearography for simultaneous measurements in three shearing directions based on adjustable aperture multiplexing

Because the sensitivity of shearography is determined by its shearing direction, aeolotropic defects could be difficult to detect in nondestructive testing (NDT) using a digital shearography system with a single shearing direction. We report an adjustable aperture multiplexing spatial phase-shift digital shearography system for simultaneous measurement of displacement derivatives in three directions. By setting the aperture parameters properly, three spatial carrier frequencies are produced within the speckle pattern, and the information of three interferograms can be separated in the frequency domain. Phase maps of the three shearograms can be obtained by applying a windowed inverse Fourier transform, which enables simultaneous measurement of the displacement derivative. The system is suitable for NDT with a dynamic load. The capability of the triple-directional spatial phase-shift digital shearography system is described by theoretical discussions as well as experiments.
Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates

Understanding the structures of political debates (which actors make what claims) is essential for understanding democratic political decision-making.The vision of computational construction of such discourse networks from newspaper reports brings together political science and natural language processing.This paper presents three contributions towards this goal: (a) a requirements analysis, linking the task to knowledge base population; (b) a first release of an annotated corpus of claims on the topic of migration, based on German newspaper reports; (c) initial modeling results.
Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection

Response selection plays an important role in fully automated dialogue systems. Given the dialogue context, the goal of response selection is to identify the best-matched next utterance (i.e., response) from multiple candidates. Despite the efforts of many previous useful models, this task remains challenging due to the huge semantic gap and also the large size of candidate set. To address these issues, we propose a Spatio-Temporal Matching network (STM) for response selection. In detail, soft alignment is first used to obtain the local relevance between the context and the response. And then, we construct spatio-temporal features by aggregating attention images in time dimension and make use of 3D convolution and pooling operations to extract matching information. Evaluation on two large-scale multi-turn response selection tasks has demonstrated that our proposed model significantly outperforms the state-of-the-art model. Particularly, visualization analysis shows that the spatio-temporal features enables matching information in segment pairs and time sequences, and have good interpretability for multi-turn text matching.
Calibration method for hand-eye system with rotation and translation couplings

This paper develops a novel hand-eye calibration method for hand-eye systems with rotation and translation coupling terms. First, a nonlinear camera model with distortion terms and a model of a hand-eye system with rotation and translation coupling terms are established. Based on a non-linear optimization method and a reverse projection method, a decoupling calibration method for a lower-degree-of-freedom hand-eye system is proposed. Then the path planning for the calibration process is carried out. Based on the analysis of coupling constraints and hand-eye system motion constraints, three types of hand-eye calibration paths with high efficiency and easy operation are developed. In addition, the influence of key parameters on hand-eye calibration accuracy is analyzed. Finally, calibration experiments and parametric influence experiments are carried out. The results demonstrate that the proposed method is effective and practical for calibrating the hand-eye system.
Inception: Virtual Space in Memory Space in Real Space – Memory Forensics of Immersive Virtual Reality with the HTC Vive

Virtual Reality (VR) has become a reality. With the technology's increased use cases, comes its misuse. Malware affecting the Virtual Environment (VE) may prevent an investigator from ascertaining virtual information from a physical scene, or from traditional "dead" analysis. Following the trend of anti-forensics, evidence of an attack may only be found in memory, along with many other volatile data points. Our work provides the primary account for the memory forensics of Immersive VR systems, and in specific the HTC Vive. Our approach is capable of reconstituting artifacts from memory that are relevant to the VE, and is also capable of reconstructing a visualization of the room setup a VR player was immersed into. In specific, we demonstrate that the VE, location, state and class of VR devices can be extracted from memory. Our work resulted in the first open source VR memory forensics plugin for the Volatility Framework. We discuss our findings, and our replicable approach that may be used in future memory forensics research.
Learning Local Metrics and Influential Regions for Classification

The performance of distance-based classifiers heavily depends on the underlying distance metric, so it is valuable to learn a suitable metric from the data. To address the problem of multimodality, it is desirable to learn local metrics. In this short paper, we define a new intuitive distance with local metrics and influential regions, and subsequently propose a novel local metric learning algorithm called LMLIR for distance-based classification. Our key intuition is to partition the metric space into influential regions and a background region, and then regulate the effectiveness of each local metric to be within the related influential regions. We learn multiple local metrics and influential regions to reduce the empirical hinge loss, and regularize the parameters on the basis of a resultant learning bound. Encouraging experimental results are obtained from various public and popular data sets.
Event-triggered sequential fusion estimation with correlated noises

This paper focuses on the event-triggered sequential fusion estimation for the multi-sensor systems with correlated noises. An event-triggered communication mechanism is introduced to reduce unnecessary energy waste. Considering that measurement noise of different sensors is correlated with each other and also correlated with the system noise of the previous step, an event-triggered sequential fusion estimation algorithm is proposed in the sense of linear minimum covariance. The standard values of the correlation parameters are defined to ensure convergence of the designed fusion algorithm and an upper bound of the estimation error covariance is given. A numerical example is used to illustrate the effectiveness of the presented fusion algorithm.
Proprioceptive Precision and Degree of Visuo-Proprioceptive Discrepancy Do Not Influence the Strength of the Rubber Hand Illusion

Rubber hand illusion is caused by spatiotemporally congruent visuotactile stimulation which induces a sense of ownership towards a fake limb. We tested two predictions of the Bayesian bottom-up model; namely, that the strength of the illusion is inversely proportional to (a) the distance separating hands and (b) the precision of proprioceptive signals. To manipulate distance, we displaced participants’ hands to either a position close to (8 cm) or far from (24 cm) the rubber hand. Before manipulation, we assessed proprioceptive abilities in a task requiring active reproduction of one’s arm’s position. Proprioceptive precision was operationalised as inversely related to the variance of the estimations. Multiple regression showed that both for subjective and physiological measures neither distance, nor proprioceptive precision, nor their interaction were predictors of illusion strength. Bayes factor analyses provided evidence for null effects. Our findings suggest the limited relevance of proprioception for the strength of visuo-haptically induced rubber hand illusion.
Detection of edge defects by embedded eigenvalues of quantum walks

We consider a position-dependent quantum walk on $$\mathbf{Z}$$ . In particular, we derive a detection method for edge defects by embedded eigenvalues of its time evolution operator. In the present paper, an edge defect is a set $$ \{ y-1 ,y\} $$ for $$y\in \mathbf{Z}$$ , in which the coin operator is an anti-diagonal matrix. In fact, under some suitable assumptions, the existence of a finite number of edge defects is equivalent to the existence of embedded eigenvalues of the time evolution operator. In view of applications, by checking the spectrum, we can detect the existence of disconnecting edge (in the sense of edge defects above) on the line without directly watching it.
Linear capabilities for fully abstract compilation of separation-logic-verified code

Separation logic is a powerful program logic for the static modular verification of imperative programs. However, dynamic checking of separation logic contracts on the boundaries between verified and untrusted modules is hard, because it requires one to enforce (among other things) that outcalls from a verified to an untrusted module do not access memory resources currently owned by the verified module. This paper proposes an approach to dynamic contract checking by relying on support for capabilities, a well-studied form of unforgeable memory pointers that enables fine-grained, efficient memory access control. More specifically, we rely on a form of capabilities called linear capabilities for which the hardware enforces that they cannot be copied. We formalize our approach as a fully abstract compiler from a statically verified source language to an unverified target language with support for linear capabilities. The key insight behind our compiler is that memory resources described by spatial separation logic predicates can be represented at run time by linear capabilities. The compiler is separation-logic-proof-directed: it uses the separation logic proof of the source program to determine how memory accesses in the source program should be compiled to linear capability accesses in the target program. The full abstraction property of the compiler essentially guarantees that compiled verified modules can interact with untrusted target language modules as if they were compiled from verified code as well.
Crack propagation in a PVA dual-crosslink hydrogel: Crack tip fields measured using digital image correlation

As a non-contact, full field deformation measurement technique, 2D digital image correlation (DIC) is a good candidate for the measurement of the crack tip fields of gels. We apply 2D DIC to study the crack propagation of a Poly(vinyl) alcohol (PVA) hydrogel specimen immersed in mineral oil. Practical issues such as the quality of the speckle pattern and the possible optical distortion are discussed. The accuracy of the setup in measuring the high-gradient crack tip strain field is carefully validated. A hydrogel specimen with an existing crack is loaded to fracture and the strain field near the tip of the propagating crack is measured using DIC. We find that the crack propagates under steady state conditions. The moving crack induces high strain rates at material points ahead of the crack.
Whole volume brain extraction for multi-centre, multi-disease FLAIR MRI datasets

Automatic segmentation of the brain from magnetic resonance images (MRI) is a fundamental step in many neuroimaging processing frameworks. There are mature technologies for this task for T1- and T2-weighted MRI; however, a widely-accepted brain extraction method for Fluid-Attenuated Inversion Recovery (FLAIR) MRI has yet to be established. FLAIR MRI are becoming increasingly important for the analysis of neurodegenerative diseases and tools developed for this sequence would have clinical value. To maximize translation opportunities and for large scale research studies, algorithms for brain extraction in FLAIR MRI should generalize to multi-centre (MC) data. To this end, this work proposes a fully automated, whole volume brain extraction methodology for MC FLAIR MRI datasets. The framework is built using a novel standardization framework which reduces acquisition artifacts, standardizes the intensities of tissues and normalizes the spatial coordinates of brain tissue across MC datasets. Using the standardized datasets, an intuitive set of features based on intensity, spatial location and gradients are extracted and classified using a random forest (RF) classifier to segment the brain tissue class. A series of experiments were conducted to optimize classifier parameters, and to determine segmentation accuracy for standardized and unstandardized (original) data, as a function of scanner vendor, feature type and disease type. The models are trained, tested and validated on 156 image volumes (∼8000 image slices) from two multi-centre, multi-disease datasets, acquired with varying imaging parameters from 30 centres and three scanner vendors. The image datasets, denoted as CAIN and ADNI for vascular and dementia disease, respectively, represent a diverse collection of MC data to test the generalization capabilities of the proposed design. Results demonstrate the importance of standardization for segmentation of MC data, as models trained on standardized data yielded a drastic improvement in brain extraction accuracy compared to the original, unstandardized data (CAIN: DSC = 91% and ADNI: DSC = 86% vs. CAIN: 78% and ADNI: 65%). It was also found that models created from one scanner vendor based on unstandardized data yielded poor segmentation results in data acquired from other scanner vendors, which was improved through standardization. These results demonstrate that to create consistency in segmentations from multi-institutional datasets it is paramount that MC variability be mitigated to improve stability and to ensure generalization of machine learning algorithms for MRI.
Informing Unsupervised Pretraining with External Linguistic Knowledge

Unsupervised pretraining models have been shown to facilitate a wide range of downstream applications. These models, however, still encode only the distributional knowledge, incorporated through language modeling objectives. In this work, we complement the encoded distributional knowledge with external lexical knowledge. We generalize the recently proposed (state-of-the-art) unsupervised pretraining model BERT to a multi-task learning setting: we couple BERT's masked language modeling and next sentence prediction objectives with the auxiliary binary word relation classification, through which we inject clean linguistic knowledge into the model. Our initial experiments suggest that our linguistically-informed BERT (LIBERT) yields performance gains over the linguistically-blind vanilla BERT on several language understanding tasks.
Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation

The photo-response non-uniformity (PRNU) is a distinctive image sensor characteristic, and an imaging device inadvertently introduces its sensor's PRNU into all media it captures. Therefore, the PRNU can be regarded as a camera fingerprint and used for source attribution. The imaging pipeline in a camera, however, involves various processing steps that are detrimental to PRNU estimation. In the context of photographic images, these challenges are successfully addressed and the method for estimating a sensor's PRNU pattern is well established. However, various additional challenges related to generation of videos remain largely untackled. With this perspective, this work introduces methods to mitigate disruptive effects of widely deployed H.264 and H.265 video compression standards on PRNU estimation. Our approach involves an intervention in the decoding process to eliminate a filtering procedure applied at the decoder to reduce blockiness. It also utilizes decoding parameters to develop a weighting scheme and adjust the contribution of video frames at the macroblock level to PRNU estimation process. Results obtained on videos captured by 28 cameras show that our approach increases the PRNU matching metric up to more than five times over the conventional estimation method tailored for photos.
Automatic Left Ventricle Recognition, Segmentation and Tracking in Cardiac Ultrasound Image Sequences

In this study, we propose a novel method incorporating faster region-based convolutional neural network and active shape model to automatically recognize, segment, and track the left ventricle in cardiac ultrasound image sequences, respectively. Ultrasound images typically contain noise and artifacts. The conventional filters cannot preserve the edges of image contours, and thus blurry images are often obtained. In this study, we propose an improved adaptive anisotropic diffusion filter to effectively reduce noise and reinforce image contours. In addition, because of the shape and appearance of the left ventricle vary considerably between adjacent images, conventional methods cannot automatically identify the position of the left ventricle or accurately segment them. A novel method that combines the faster region-based convolutional neural network with the active shape model is proposed to automatically recognize, segment, and track the left ventricle in cardiac ultrasound image sequences. Compared with four state-of-the-art approaches, the method proposed in this study can be applied to accurately segment and track the left ventricle in cardiac ultrasound image sequences. The proposed method produces the most satisfactory results in terms of visual presentation and segmentation quality based on four criteria.
Fine Tuning U-Net for Ultrasound Image Segmentation: Which Layers?

Fine-tuning a network which has been trained on a large dataset is an alternative to full training in order to overcome the problem of scarce and expensive data in medical applications. While the shallow layers of the network are usually kept unchanged, deeper layers are modified according to the new dataset. This approach may not work for ultrasound images due to their drastically different appearance. In this study, we investigated the effect of fine-tuning different layers of a U-Net which was trained on segmentation of natural images in breast ultrasound image segmentation. Tuning the contracting part and fixing the expanding part resulted in substantially better results compared to fixing the contracting part and tuning the expanding part. Furthermore, we showed that starting to fine-tune the U-Net from the shallow layers and gradually including more layers will lead to a better performance compared to fine-tuning the network from the deep layers moving back to shallow layers. We did not observe the same results on segmentation of X-ray images, which have different salient features compared to ultrasound, it may therefore be more appropriate to fine-tune the shallow layers rather than deep layers. Shallow layers learn lower level features (including speckle pattern, and probably the noise and artifact properties) which are critical in automatic segmentation in this modality.
Intelligent Image Synthesis to Attack a Segmentation CNN Using Adversarial Learning

Deep learning approaches based on convolutional neural networks (CNNs) have been successful in solving a number of problems in medical imaging, including image segmentation. In recent years, it has been shown that CNNs are vulnerable to attacks in which the input image is perturbed by relatively small amounts of noise so that the CNN is no longer able to perform a segmentation of the perturbed image with sufficient accuracy. Therefore, exploring methods on how to attack CNN-based models as well as how to defend models against attacks have become a popular topic as this also provides insights into the performance and generalization abilities of CNNs. However, most of the existing work assumes unrealistic attack models, i.e. the resulting attacks were specified in advance. In this paper, we propose a novel approach for generating adversarial examples to attack CNN-based segmentation models for medical images. Our approach has three key features: (1) The generated adversarial examples exhibit anatomical variations (in form of deformations) as well as appearance perturbations; (2) The adversarial examples attack segmentation models so that the Dice scores decrease by a pre-specified amount; (3) The attack is not required to be specified beforehand. We have evaluated our approach on CNN-based approaches for the multi-organ segmentation problem in 2D CT images. We show that the proposed approach can be used to attack different CNN-based segmentation models.