Reviewerly/exHarmony huggingface dataset:
HOOVER: Distributed, Flexible, and Scalable Streaming Graph Processing on OpenSHMEM

Many problems can benefit from being phrased as a graph processing or graph analytics problem: infectious disease modeling, insider threat detection, fraud prevention, social network analyis, and more. These problems all share a common property: the relationships between entitites in these systems are crucial to understanding the overall behavior of the systems themselves. However, relations are rarely if ever static. As our ability to collect information on those relations improve (e.g. on financial transactions in fraud prevention), the value added by large-scale, high-performance, dynamic/streaming (rather than static) graph analysis becomes significant. This paper introduces HOOVER, a distributed software framework for large-scale, dynamic graph modeling and analyis. HOOVER sits on top of OpenSHMEM, a PGAS programming system, and enables users to plug in application-specific logic while handling all runtime coordination of computation and communication. HOOVER has demonstrated scaling out to 24,576 cores, and is flexible enough to support a wide range of graph-based applications, including infectious disease modeling and anomaly detection.
A Mixture Likelihood Model of the Anisotropic Gaussian and Uniform Distributions for Accurate Oblique Image Point Matching

In this letter, we propose a mixture likelihood model for accurate oblique image point matching. The basic prior assumption is that the noises are anisotropic with zero mean and different covariances in x- and y -directions for inliers, while the outliers have uniform distribution, which is more suitable for tilted scenes or viewpoint changes. Furthermore, the oblique image point matching problem is formulated as an improved maximum a posteriori (IMAP) estimation of a Bayesian model. In this model, based on the vector field interpolation framework, we combined the mixture likelihood model and our previous adaptive image mismatch removal method, where a two-order term of the regularization coefficient is introduced into the regularized risk function, and a parameter self-adaptive Gaussian kernel function is imposed to construct the regularization term. Subsequently, the expectation-maximization algorithm is utilized to solve the IMAP estimation, in which all the latent variances are able to obtain excellent estimation. Experimental results on real data sets verified that our method was superior to some similar methods in terms of precision and also had better self-adaptability characteristic than some hypothesis-and-verify methods. More experiments on viewpoint changes demonstrated our method's effectiveness without loss of precision-recall tradeoffs, besides significant efficiency improvement.
The Value and Place for Empathy in Designing for Older People

It may be argued that developing empathy for the people we design for is a prerequisite to more effective design outcomes. Although we can intellectually appreciate aging and disability, only when we ‘experience another person’s experience’ can we begin to appreciate the lived experience we are trying to enhance. It is difficult for student designers to gain a perspective of the lived experience of ‘the other’. However, this is the very skill that is needed when they move into professional practice. With an ageing and more discerning population, future designers need to understand both the needs and aspirations of older populations who may experience age related decline in mobility, and life changing illnesses and diseased if they are to design for the whole person. This chapter describes the development of Discrete Learning Interventions (DLIs) to increase the empathic horizon of student designers and engineers, who may not have access to elderly populations. The DLIs used low-level simulations and the GERT (gerontology) aging suit, to complete activities of daily living while experiencing a range of physical impairments. While a relatively short immersion into disability and aging could be considered superficial, the authors believe that low level experiential simulations, together with the GERT suit could be valuable in training Industrial Design students and lead to more age sensitive, inclusive design.
The Warning System for Speed Cameras on the Road by Deep Learning

In order to reduce traffic accidents, government authorities would install speed cameras for identifying vehicles travelling over the legal speed limit. However, drivers often ignored warnings and were handed tickets, and meanwhile it did not achieve the purpose of setting up the cameras. This paper took the road in Taiwan as an example. By installing a camera in front of the vehicle, it could capture the image of the road ahead. This system was implemented on the NVIDIA Jetson TX2 and used YOLO V3 as the architecture for CNN classification. The images were divided into six categories: “police patrol car”, “warning sign for front speed camera”, “warning slogan for front speed camera”, “front view of radar speed camera”, “back view of radar speed camera”, “traffic sign of speed limit”. When the first five categories were detected, the system would issue a warning, and when “the traffic sign of speed limit” was detected, the number in the middle part would be identified to obtain and update the speed limit. The result of the experiment on the YOLO V3 when outputting 6 categories was that Mean AP was 81.25%, and Average Recall was 85.5%. The result of the digital recognition for speed limit had a precision of 83.5% and a recall of 80.2%.
A Deep Q-Network with Experience Optimization (DQN-EO) for Atari’s Space Invaders

Since Deep Q-Learning was introduced in 2013, a lot of academia and industry researchers, tried to use it to solve their problems, in different fields. However, Deep Q-Learning does not consist of a unified method for solving certain problems. In contrary, every problem requires specific settings and parameters. In this paper, we propose a Deep Q-Network with Experience Optimization, for Atari's "Space Invaders" environment. Training and Testing results show that, while using our proposed network, there is a strong correlation between the key action (shoot) and scoring.
Neural Networks Architecture for Amazigh POS Tagging

Morphosyntactic processing of natural languages is mainly restricted by the lack of labelled data sets. Deep Learning methods proved their efficiency in domains such as imaging or acoustic process. Part-of-speech tagging is an important preprocessing step in many natural language processing applications. Despite much work already carried out in this field, there is still room for improvement, especially in Amazigh language. We propose here architectures based on neural networks and word embeddings, and that has achieved promising results in English. Furthermore, instead of extracting from the sentence a rich set of hand-crafted features which are the fed to a standard classification algorithm, we drew our inspiration from recent papers about the automatic extraction of word embeddings from large unlabelled data sets. On such embeddings, we expect to benefit from linearity and compositionality properties to improve our Amazigh POS Tagging system performances.
All Together Now! The Benefits of Adaptively Fusing Pre-trained Deep Representations

Pre-trained deep neural networks, powerful models trained on large datasets, have become a popular tool in computer vision for transfer learning. However, the standard approach of using a single network potentially misses out on valuable information contained in other readily available models. In this work, we study the Mixture of Experts (MoE) approach for adaptively fusing multiple pre-trained models for each individual input image. In particular, we explore how far we can get by combining diverse pre-trained representations in a customized way that maximizes their potential in a lightweight framework. Our approach is motivated by an empirical study of the predictions made by popular pre-trained nets across various datasets, finding that both performance and agreement between models vary across datasets. We further propose a miniature CNN gating mechanism operating on a thumbnail version of the input image, and show this is enough to guide a good fusion. Finally, we explore a multi-modal blend of visual and natural-language representations, using a label-space embedding to inject pre-trained word-vectors. Across multiple datasets, we demonstrate that an adaptive fusion of pre-trained models can obtain favorable results.
AR Pottery Wheel-Throwing by Attaching Omnidirectional Cameras to the Center of a User's Palms

This research describes our system for AR pottery wheel-throwing employing an HMD and omnidirectional cameras each of which is attached to the center of a user's palm. The omnidirectional cameras enable the user's finger postures and the three-dimensional relative position and orientation between the user's hands and virtual clay model on the wheel to be estimated. Our system detects a marker on the desk and the wheel is set on its coordinate system along with the finger posture estimation in real time. The system then simulates the collision between the virtual clay model and the left/right hand model based on the above information. Pottery wheel-throwing is reproduced in Unity software environment by deforming the clay model by contact with hand models in this simulation.
On differentiability of common image processing algorithms

We present differentiable implementations of several common image processing algorithms: Canny edge detector, Niblack thresholding and Harris corner detector. The implementations are presented in the form of fully convolutional networks and explicitly arranged exactly to the original algorithms. Usage of such form of the algorithms allows to tune their parameters with a gradient descent. We performed parameter tuning in the edge detection problem and it shows that our implementation enables us to obtain better results on the BSDS-500 dataset. As a part of implementations of algorithms, we introduce a generalization of pooling method, which allows using arbitrary structure element. We also analyze the given neural network architectures and show the connections with contemporary approaches.
Substantia nigra segmentation on neuromelanin-sensitive MRI

Efficient segmentation of the substantia nigra (SN) in midbrain cerebral images is a prerequisite for reliable quantification and evaluation of severity of Parkinson’s disease (PD). General-purpose edge-detection techniques aren’t sufficient to for accurate segmentation due to inconsistent shape and fuzzy boundaries. Additionally, the regional properties (such as grey level) of the SN and other cerebral structures are significantly similar, and thus misclassification of segmented regions is also expected. This paper presents an algorithm for localization and segmentation of the SN in neuromelanin-sensitive magnetic resonance imaging (MRI) of the midbrain. The localization is performed using a cross-correlation template matching model in which multiple templates were used to find a match with Cerebral Peduncle, a collective structure of the SN and cerebral crus in the midbrain. We adopted a new approach that uses the parametric equation of cardioid plane curve (a curve that resembles the general structure of cerebral peduncle) to generate multiple deformable templates for localization algorithm. The segmentation of the SN is then performed using the freeform active contour segmentation model in the localized region. A total of 60 slices (10 training, 50 testing), obtained from 19 scans of 10 healthy volunteers and 9 patients with PD, were acquired using a 3T MRI system. The localization algorithm succeeded in 99.8% of the cases, while the segmentation method outperformed with an average sensitivity= 0.83, specificity = 0.97, and Dice-score = 0.73.
Stratified Nomic Truth Approximation

Assuming that the target of theory oriented empirical science in general and of nomic truth approximation in particular is to characterize the boundary or demarcation between nomic possibilities and nomic impossibilities, I have presented in Chap. 4 (“Models, postulates, and generalized nomic truth approximation”) the ‘basic’ version of generalized nomic truth approximation, starting from ‘two-sided’ theories. Its main claim is that nomic truth approximation can perfectly be achieved by combining two prima facie opposing views on theories: (1) the traditional (Popperian) view: theories are (sets of models of) postulates that exclude certain possibilities from being realizable, enabling explanation and prediction and (2) the model view: theories are sets of models that claim to (approximately) represent certain realizable possibilities. Nomic truth approximation, i.e., increasing truth-content and decreasing falsity-content, becomes in this way revising theories by revising their models and/or their postulates in the face of increasing evidence. The basic version of generalized nomic truth approximation is in many respects as simple as possible. Among other things, the basic version does not make a (theory-relative) distinction between an observational and a theoretical level. This raises the question to what extent in a stratified set-up (nomic) truth approximation on a theoretical level entails truth approximation on the observational level. In other words, the question whether ‘closer to the truth’ can be projected from the theoretical to the observational level. Of course, ‘empirical success’ is now explicated in observational terms, and ‘closer to the observational truth’ and ‘more successfulness’ are now related in the same way as in the unstratified case. The chapter closes with quantitative and refined versions of stratified nomic truth approximation in line with Chaps. 5 and 6 , respectively. (Referential truth approximation is dealt with in Chap. 11 )
A probabilistic approach for the registration of images with missing correspondences

The registration of two medical images is usually based on the assumption that corresponding regions exist in both images. If this assumption is violated by e. g. pathologies, most approaches encounter problems. The here proposed registration method is based on the use of probabilistic correspondences between sparse image representations, leading to a robust handling of potentially missing correspondences. A maximum-a-posteriori framework is used to derive the optimization criterion with respect to deformation parameters that aim to compensate not only spatial differences between the images but also appearance differences. A multi-resolution scheme speeds-up the optimization and increases the robustness. The approach is compared to a state-of-theart intensity-based variational registration method using MR brain images. The comprehensive quantitative evaluation using images with simulated stroke lesions shows a significantly higher accuracy and robustness of the proposed approach.
Experimental modeling the flow of character recognition results in video stream for document recognition

This paper considers problems regarding the development of stochastic models consistent with the results of character image recognition in video stream. Assumptions about their structure and properties are formulated for the constructed models. The description of the model components defines the Dirichlet distribution and its generalizations. The parameters of these distributions are determined using statistical estimation methods. The Akaike information criterion is used to rank models. The verification of the agreement of the proposed theoretical distributions to the sample data is carried out.
Exploring the two-stage approach in neural network compression for object detection

Recently, convolutional neural network (CNN) has been widely implemented in the compute vision, nature language processing and automatic driving. However, it makes much difficulties to employ the neural network in the embedded system because of the limit of memory storage and the computation bandwidth. To address those limitations, we explore a two-stage approach in neural network compression for the scene, object detection. In this paper, we first propose an effective pruning approach on a trained neural network, and achieve total 81.86%-91.54% sparse rate with the accuracy losing 1-3%. Then we explore the quantization method to apply on the pruned neural network, and propose an adaptive codebook to store the quantized weight parameters and the index of the weight parameters. We utilize the two-stage model compression approach, model pruning and weights quantization, to implement on tiny-YOLO, the state-of-art object detection model, achieving total 41.9-62.7X compression rate with the accuracy loss less than 3.3%.
UAS-based object tracking via Deep Learning

Unmanned Aerial System-based object tracking is a challenging new task in the computer vision community. In addition, existing benchmark and research focus on short sequences that are less than a minute long. In this work, we show the limitations of state-of-the art trackers in front of long-term aerial tracking. We propose a novel long-term, real-time, intelligent system for unmanned aerial system -based vehicle tracking utilizing deep learning techniques. We integrate a fast and accurate correlation filter with the expressiveness of a convolutional neural network embedding. A re-initialization policy based on a real-time anomaly detection on correlation map combined with a one-shot detector ensure that our system is impervious to drift and occlusion.
Behavioural Biometric Continuous User Authentication Using Multivariate Keystroke Streams in the Spectral Domain

Continuous authentication is significant with respect to many online applications where it is desirable to monitor a user’s identity throughout an entire session; not just at the beginning of the session. One example application domain, where this is a requirement, is in relation to Massive Open Online Courses (MOOCs) when users wish to obtain some kind of certification as evidence that they have successfully competed a course. Such continuous authentication can best be realised using some forms of biometric checking; traditional user credential checking methods, for example username and password checking, only provide for “entry” authentication. In this paper, we introduce a novel method for the continuous authentication of computer users founded on keystroke dynamics (keyboard behaviour patterns); a form of behavioural biometric. The proposed method conceptualises keyboard dynamics in terms of a Multivariate-Keystroke Time Series which in turn can be transformed into the spectral domain. The time series can then be monitored dynamically for typing patterns that are indicative of a claimed user. Two transforms are considered, the Discrete Fourier Transform and the Discrete Wavelet Transform. The proposed method is fully described and evaluated, in the context of impersonation detection, using real keystroke datasets. The reported results indicate that the proposed time series mechanism produced an excellent performance, outperforming the comparator approaches by a significant margin.
Quantitative and qualitative methods for efficient evaluation of multiple 3D organ segmentations

Quantitative comparison of automatic results for multi-organ segmentation by means of Dice scores often does not yield satisfactory results. It is especially challenging, when reference contours may be prone to errors. We developed a novel approach that analyzes regions of high mismatch between automatic and reference segmentations. We extract various metrics characterizing these mismatch clusters and compare them to other metrics derived from volume overlap and surface distance histograms by correlating them with qualitative ratings from clinical experts. We show that some novel features based on the mismatch sets or surface distance histograms performed better than the Dice score. We also show how the mismatch clusters can be used to generate visualizations to reduce the workload for visual inspection of segmentation results. The visualizations directly compare reference to automatic result at locations of high mismatch in orthogonal 2D views and 3D scenes zoomed to the appropriate positions. This can make it easier to detect systematic problems of an algorithm or to compare recurrent error patterns for different variants of segmentation algorithms, such as differently parameterized or trained CNN models.
Toward New Ecologies of Cyberphysical Representational Forms, Scales, and Interaction Modalities

Research on tangible user interfaces commonly focuses on tangible interfaces acting alone or in comparison with multi-touch or graphical interfaces. In contrast, hybrid approaches can be seen as the norm for established "mainstream'' interaction paradigms. In my work, I propose interfaces that support complementary interaction modalities, representational forms and scales, toward hybrid systems which are more legible and actionable than any strategy considered separately. I describe systems involving dial-like tangibles, which are passive and active, and systems combining interaction modalities such as tangible and multi-touch, and tangible and VR interaction. I briefly describe some of the planned and performed evaluations, and draw lessons from an already completed study involving a computationally-mediated scientific poster platform with content developed by undergraduate students.
A Pattern-based Algorithm for Transistor-level Combinational Circuits Netlists Visualization

Many areas of IC design are still in need of a program to present transistor-level netlists graphically. The complexity of the graphical representation of schemes using graphs is that each transistor circuit has its standard drawing view. This makes it impossible to implement standard graph visualization algorithms for circuit graphs visualization. This article presents a pattern-based transistor-level graph visualization algorithm. It allows visualizing netlist graph of a given flat netlist circuit correctly according to generally accepted rules for the elements placement.
Analysing the Efficiencies of Partially Entangled Three-Qubit States for Quantum Information Processing Under Real Conditions

Abstract In this article, we revisit the question of analysing the efficiencies of partially entangled states in three-qubit classes under real conditions. Our results show some interesting observations regarding the efficiencies and correlations of partially entangled states. Surprisingly, we find that the efficiencies of many three-qubit partially entangled states exceed that of maximally entangled three-qubit states under real noisy conditions and applications of weak measurements. Our analysis, therefore, suggests that the efficiencies of partially entangled states are much more robust to noise than those of maximally entangled states at least for the GHZ (Greenberger–Horne–Zeilinger) class states, for certain protocols; i.e. less correlations in the initially prepared state may also lead to better efficiency and hence one need not always consider starting with a maximally entangled state with maximum correlations between the qubits. For a set of partially entangled states, we find that the efficiency is optimal, independent of the decoherence and state parameters, if the value of weak measurement parameter is very large. For other values of the weak measurement parameter, the robustness of the states depends on the decoherence and state parameters. Moreover, we further show that one can achieve higher efficiencies in a protocol by using non-optimal weak measurement strengths instead of optimal weak measurement strengths.