Reviewerly/exHarmony huggingface dataset:
Carotid artery segmentation using level set method with double adaptive threshold (DATLS) on TOF-MRA images

This study aimed to propose a method to semi-automatically segment lumen of carotid artery on TOF-MRA images with well performance on images with weak boundary. The proposed method modified traditional level set method with double adaptive threshold (DATLS). Two thresholds were calculated from probability density function of initial region of interest. Threshold Th1 was used to generate initial contour and threshold Th2 was used to control evolution by modifying energy function of the level set. The performance of the proposed method was tested on TOF-MRA images from CARE II study via using manual delineation as reference. Sixty cases were randomly selected to compare the performance of traditional level set with DATLS, all 283 cases were used to test the robustness of DATLS and 20 cases were used to compare the intra- and inter-operator reproducibility of manual delineation with DATLS. The Dice Similarity Coefficient, Mean Contour Distance and Hausdorff Distance between the proposed method and the manual segmentation were reported. Traditional level set failed to segment carotid artery on 13 cases because of over convergence. Compared to traditional level set, DATLS showed a higher DSC (0.88 ± 0.07 vs. 0.75 ± 0.15), lower MCD (0.48 ± 0.37 mm vs. 1.89 ± 2.24 mm) and HD (1.41 ± 1.11 mm vs. 4.59 ± 4.59 mm) in the remaining 47 cases. The average DSC, MCD and HD of DATLS on 283 cases were 0.87 ± 0.09, 0.64 ± 0.87 mm and 1.76 ± 2.23 mm, respectively. Compared to manual delineation, DATLS had better reproducibility on DSC (intra-operator: 0.97 ± 0.09 and inter-operator: 0.97 ± 0.09 vs. intra-operator: 0.91 ± 0.04 and inter-operator: 0.91 ± 0.04), as well as MCD (intra-operator: 0.08 ± 0.18 mm and inter-operator: 0.13 ± 0.52 mm vs. intra-operator: 0.35 ± 0.20 mm and inter-operator: 0.37 ± 0.19 mm) and HD (intra-operator: 0.30 ± 0.66 mm and inter-operator: 0.48 ± 1.38 mm vs. intra-operator: 0.99 ± 0.79 mm and inter-operator: 0.90 ± 0.85 mm). Compared to traditional level set, DATLS out-performed on computing time, robustness and accuracy of segmentation on TOF-MRA. The proposed method using modified level set with double adaptive threshold might be a promising tool for lumen segmentation of carotid artery on TOF-MRA.
Geometry-driven Deterministic Sampling for Nonlinear Bingham Filtering

We propose a geometry-driven deterministic sampling method for Bingham distributions in arbitrary dimensions. With flexibly adjustable sampling sizes, the novel scheme can generate equally weighted samples that satisfy requirements of the unscented transform and approximate higher-order shape information of the Bingham distribution. By leveraging retraction techniques from Riemannian geometry, the sigma points are constrained to preserve the second-order moment. Meanwhile, samples in each principal direction are located in a way that minimizes a distance measure between the on-tangent-plane Dirac mixtures and the underlying on-manifold density. For that, the modified Cramér-von Mises distance based on the localized cumulative distribution (LCD) is employed. We further integrate the proposed approach into a quaternion-based orientation estimation framework. Compared to the existing unscented sampling approach drawing only fixed and limited numbers of sigma points, simulation results show that the proposed scheme enables better accuracy and robustness for nonlinear Bingham filtering.
Modeling the Perceptual Quality of Viewport Adaptive Omnidirectional Video Streaming

Instead of streaming the entire OmniDirectional Videos (ODVs) that are often sampled at ultra high definition and high frame rate, a viewport adaptive streaming is preferred in practice. We usually stream the High-Quality (HQ) content within current viewport, while Low-Quality (LQ) elsewhere to save the network bandwidth consumption. Such scheme would lead to a quality refinement after user adapts his/her focus to a new viewport. In this paper, we thus model the perceptual impact of the quality variations (through adjusting the Quantization Stepsize (QS or q) and Spatial Resolution (SR or s)) with respect to the Refinement Duration (RD or τ) when performing the refinement from an arbitrary LQ scale to an arbitrary HQ one. A number of quality variations are studied to cover sufficient use cases in practice, resulting in a unified analytical model, as a product of separable exponential functions that measure the QS and SR induced perceptual impacts in terms of the RD, and a perceptual index measuring the subjective quality of corresponding viewport video after refinement. This model is first validated in a managed lab environment via independent subjective assessments by constraining user's navigation to avoid unexpected noise, where both Pearson Correlation Coefficient (PCC) and Spearman's Rank Correlation Coefficient (SRCC) are around 0.97. We then extend the validations in a real-life viewport-dependent streaming system, still yielding PCC and SRCC about 0.96 when comparing collected subjective scores with model predictions.
Contact–based Navigation Path Planning for Aerial Robots

In this paper the problem of contact-based navigation path planning for aerial robots is considered with the goal of enabling the autonomous in-contact operation on surfaces that can be highly anomalous. Such a capacity can prove critical in inspection through contact missions, as well as when a flying robot is tasked to operate in very narrow environments rendering safe free-flight impossible. To achieve this objective, beyond sliding in contact, a new locomotion primitive is introduced, namely that of azimuth rotations perpendicular to the surface under consideration. This new navigation mode, called flying cartwheel mode, offers navigation resourcefulness and resilience when the system is tasked to move in contact with surfaces that are otherwise non-traversable. The designed path planning method exploits both navigation modalities and a traversability metric to decide when to switch from sliding to flying cartwheel mode, and overall provides cost-optimal trajectories for in-contact navigation. The proposed approach is verified both in simulation, as well as experimentally using a surface presenting complex anomalies. It is highlighted that the proposed method does not assume any specialized contact mechanism or a control law tailored to physical interaction tasks, and hence is applicable to almost any micro aerial vehicle integrating protective shrouds around its propellers.
Committee of the Combined RBF-SGTM Neural-Like Structures for Prediction Tasks

The paper describes the committee of non-iterative artificial intelligence tools for solving the regression task. It is based on the use of high-speed neural-like structures with extended inputs. Such an extension involves the combined use of primary inputs and extended inputs, via RBF. The resulting combination of inputs allows increasing the extrapolation properties of each element of the committee. This ensures a decreasing of the prediction errors for the solution of the regression tasks in cases of large volumes of data processing. The developed committee is used to solve the task of prediction of insurance costs. It is experimentally found that the proposed committee decreases training and test errors compared with the use of one neural-like structure of this type. The comparison of the committee’s effectiveness with existing iterative and non-iterative computational intelligence methods has confirmed the highest accuracy of its work with a small increase of the time of the training procedure. The developed committee in software or hardware variants can be used to solve regression and classification tasks in the condition of large volumes of data for different application areas.
Building Community and Tools for Analyzing Web Archives Through Datathons

Starting in March 2016, the Archives Unleashed team and our collaborators have brought together social scientists, humanists, archivists, librarians, computer scientists, and other stakeholders to explore web archives as research objects. Three objectives motivated our team to develop and organize these events: facilitating scholarly access, community building, and skills training. We believe that we have been successful on all three fronts. For each event, over the course of two to three days, participants formed interdisciplinary teams and explored web archives using a variety of methods and tools. This paper details our experiences in designing these "datathons", with an intent to share lessons learned, highlight interdisciplinary approaches to research and education on web archives, and describe future opportunities.
Autonomous Human Activity Classification From Wearable Multi-Modal Sensors

There has been significant amount of research work on human activity classification relying either on Inertial Measurement Unit (IMU) data or data from static cameras providing a third-person view. There has been relatively less work using wearable cameras, providing first-person or egocentric view, and even fewer approaches combining egocentric video with IMU data. Using only IMU data limits the variety and complexity of the activities that can be detected. For instance, the sitting activity can be detected by IMU data, but it cannot be determined whether the subject has sat on a chair or a sofa, or where the subject is. To perform fine-grained activity classification, and to distinguish between activities that cannot be differentiated by only IMU data, we present an autonomous and robust method using data from both wearable cameras and IMUs. In contrast to convolutional neural network-based approaches, we propose to employ capsule networks to obtain features from egocentric video data. Moreover, Convolutional Long Short Term Memory framework is employed both on egocentric videos and IMU data to capture the temporal aspect of actions. We also propose a genetic algorithm-based approach to autonomously and systematically set various network parameters, rather than using manual settings. Experiments have been conducted to perform 9- and 26-label activity classification, and the proposed method, using autonomously set network parameters, has provided very promising results, achieving overall accuracies of 86.6% and 77.2%, respectively. The proposed approach, combining both modalities, also provides increased accuracy compared to using only egovision data and only IMU data.
The Evolution of Cranfield

Evaluating search system effectiveness is a foundational hallmark of information retrieval research. Doing so requires infrastructure appropriate for the task at hand, which generally follows the Cranfield paradigm: test collections and associated evaluation measures. A primary purpose of Information Retrieval (IR) evaluation campaigns such as Text REtrieval Conference (TREC) and Conference and Labs of the Evaluation Forum (CLEF) is to build this infrastructure. The first TREC collections targeted the same task as the original Cranfield tests and used measures that were familiar to test collection users of the time. But as evaluation tasks have multiplied and diversified, test collection construction techniques and evaluation measure definitions have also been forced to evolve. This chapter examines how the Cranfield paradigm has been adapted to meet the changing requirements for search systems enabling it to continue to support a vibrant research community.
Continuous Occupancy Map Fusion with Fast Bayesian Hilbert Maps

Mapping the occupancy of an environment is central for robot autonomy. Traditional occupancy grid maps discretise the environment into independent cells, neglecting important spatial correlations, and are unable to capture the continuous nature of the real world. With these drawbacks of grid maps in mind, Hilbert Maps (HM) and more recently Bayesian Hilbert Maps (BHMs), were introduced as a continuous representation of the environment. In this paper we propose a method to merge Bayesian Hilbert Maps built by a team of robots in a decentralised manner. The training of BHMs requires the inversion of a large covariance matrix, incurring cubic complexity. We introduce an approximation, Fast Bayesian Hilbert Maps (Fast-BHM), which reduces the time complexity to below quadratic. Such speed-ups allow the building and merging of Bayesian Hilbert Map models to be practical, opening the door for multi-robot Hilbert Map systems which can be much faster and more robust than an individual robot. By merging several individual Fast-BHMs in a decentralised manner we obtain a unified model of the environment which is itself a Fast-BHM. We conduct experiments to show that global Fast-BHM models do not deteriorate after repeated merging and training. We then empirically demonstrate, due to its the compact representation, fused Fast-BHMs outperform fusion methods involving discretising continuous representations, when the amount of information communicated is limited.
Sequential patterns in SMS and WhatsApp dialogues: Practices for coordinating actions and managing topics

In computer-mediated communication, users cannot ensure that responsive postings are placed in a directly adjacent position. Yet, paired actions are discernible in which a first pair part (FPP) makes a second pair part (SPP) conditionally relevant. While previous studies of short messaging service (SMS) communication show that users usually send clusters of FPPs and that SPPs are ordered in the same chronology, little is known about sequential practices of dealing with multiple FPPs in text-based WhatsApp communication. This article shows that in German WhatsApp dialogues, users apply a chronological as well as a reversed ordering of SPPs. It is argued that this result can only be partly attributed to the affordances of the mobile messenger. Rather, users arrange SPPs in order to foreground particular topics in extended, chat-like dialogues.
Rapid Prototyping of Deep Learning Models on Radiation Hardened CPUs

Interest is increasing in the use of neural networks and deep-learning for on-board processing tasks in the space industry [1]. However development has lagged behind terrestrial applications for several reasons: space qualified computers have significantly less processing power than their terrestrial equivalents, reliability requirements are more stringent than the majority of applications deep-learning is being used for. The long requirements, design and qualification cycles in much of the space industry slows adoption of recent developments. GPUs are the first hardware choice for implementing neural networks on terrestrial computers, however no radiation hardened equivalent parts are currently available. Field Programmable Gate Array devices are capable of efficiently implementing neural networks and radiation hardened parts are available, however the process to deploy and validate an inference network is non-trivial and robust tools that automate the process are not available. We present an open source tool chain that can automatically deploy a trained inference network from the TensorFlow framework directly to the LEON 3, and an industrial case study of the design process used to train and optimise a deep-learning model for this processor. This does not directly change the three challenges described above however it greatly accelerates prototyping and analysis of neural network solutions, allowing these options to be more easily considered than is currently possible. Future improvements to the tools are identified along with a summary of some of the obstacles to using neural networks and potential solutions to these in the future.
Information Security Behavior: A Cross-Cultural Comparison of Irish and US Employees

This study explores how aspects of perceived national culture affect the information security attitudes and behavior of employees. Data was collected using 19 semi-structured interviews in Ireland and the United States of America (US). The main findings are that US employees in the observed organizations are more inclined to adopt formalized information security policies and procedures than Irish employees, and are also more likely to have higher levels of compliance and lower levels of non-compliance.
Bayesian pairwise learning to rank via one-class collaborative filtering

With the ever-growing scale of social websites and online transactions, in past decade, Recommender System (RS) has become a crucial tool to overcome information overload, due to its powerful capability in information filtering and retrieval. Traditional rating prediction based RS could learn user’s preference according to the explicit feedback, however, such numerical user-item ratings are always unavailable in real life. By contrast, pairwise learning algorithms could directly optimize for ranking and provide personalized recommendation from implicit feedback, although suffering from such data sparsity and slow convergence problems. Motivated by these, in this article, a novel collaborative pairwise learning to rank method referred to as BPLR is proposed, which aims to improve the performance of personalized ranking from implicit feedback. To this end, BPLR tries to partition items into positive feedback, potential feedback and negative feedback, and takes account of the neighborhood relationship between users as well as the item similarity while deriving the potential candidates, moreover, a dynamic sampling strategy is designed to reduce the computational complexity and speed up model training. Empirical experiments over four real world datasets certificate the effectiveness and efficiency of BPLR, which could speed up convergence, and outperform state-of-the-art algorithms significantly in personalized top-N recommendation.
Mitigation of GPS Chirp Jammer Using a Transversal FIR Filter and LMS Algorithm

In this study, we present an experimental result of least mean squares (LMS)-based method for GPS chirp jammer mitigation. Because the chirp jamming signal has a time-varying frequency, the weight vector is calculated by iterative update rather than by solving the Wiener-Hopf equation. The computed weight vector is applied to the transversal finite impulse response (FIR) filter as filter coefficients. The experimental result demonstrates the anti-jamming performance of our approach with live GPS signals and synthetic jamming signals.
Introducing Dead Drops to Network Steganography using ARP-Caches and SNMP-Walks

Network covert channels enable various secret data exchange scenarios among two or more secret parties via a communication network. The diversity of the existing network covert channel techniques has rapidly increased due to research during the last couple of years and most of them share the same characteristics, i.e., they require a direct communication between the participating partners. However, it is sometimes simply not possible or it can raise suspicions to communicate directly. That is why, in this paper we introduce a new concept we call "dead drop", i.e., a covert network storage which does not depend on the direct network traffic exchange between covert communication sides. Instead, the covert sender stores secret information in the ARP (Address Resolution Protocol) cache of an unaware host that is not involved in the hidden data exchange. Thus, the ARP cache is used as a covert network storage and the accumulated information can then be extracted by the covert receiver using SNMP (Simple Network Management Protocol).
Deep learning concepts and datasets for image recognition: overview 2019

We present basics of a deep learning concept and an overview of well-known deep learning concepts as general Convolutional Neural Networks, R-CNN family, Single Shot Multibox Detector, You Only Look Once architecture and the RetinaNet in the first part of this paper. The all mentioned architectures are described to quickly compare to each other regarding their suitability for given general task. Several selected datasets often used in deep learning competitions are listed in the subsequent chapters in more details. The most known of practically used and listed datasets are COCO, KITTI, PascalVOC and CityShapes. The overview serves as a comparison of the state-of-the-art deep learning methods.
Mask-off: Synthesizing Face Images in the Presence of Head-mounted Displays

Wearable VR/AR devices provide users with fully immersive experience in a virtual environment, enabling possibilities to reshape the forms of entertainment and telepresence. While the body language is a crucial element in effective communication, wearing a head-mounted display (HMD) could severely hinder the eye contact and block facial expressions. We present a novel headset removal technique that enables high-quality occlusion-free communication in virtual environment. In particular, our solution synthesizes photoreal faces in the occluded region with faithful reconstruction of facial expressions and eye movements. Towards this goal, we develop a novel capture setup that consists of two near-infrared (NIR) cameras inside the HMD for eye capturing and one external RGB camera for recording visible face regions. To enable realistic face synthesis with consistent illuminations, we propose a data-driven approach to fuse the narrow-field-of-view NIR images with the RGB image captured from the external camera. In addition, to generate pho-torealistic eyes, a dedicated algorithm is proposed to colorize the NIR eye images and further rectify the color distortion caused by the non-linear mapping of IR light sensitivity. Experimental results demonstrate that our framework is capable to synthesize high-fidelity unoccluded facial images with accurate tracking of head motion, facial expression and eye movement.
Person Finding: An Autonomous Robot Search Method for Finding Multiple Dynamic Users in Human-Centered Environments

Robot search for multiple dynamic users within a multi-room environment is important for social robots to find and engage in various human-robot interaction scenarios with these users. In this paper, we present a novel autonomous person search technique for a robot finding a group of dynamic users before a deadline. The uniqueness of our approach is that unlike existing robot search methods, we consider activity information to predict where, when, and for how long a user will be in a specific room. This allows for the generation of search plans without any assumption on the frequency of user movements. We represent our search problem as an extension of the orienteering problem (OP), which we define herein as the robot person search OP (PSOP). User activity information is represented as spatial-temporal user activity probability density functions (APDFs). We solve the PSOP using APDFs to generate a search plan to maximize the expected number of users found before the deadline. The solution of the PSOP is obtained in two steps. First, by solving a variant of the multiperiod knapsack problem to determine which rooms should be searched and for how long these rooms should be searched. Then, we solve the traveling salesman problem to obtain the order in which to search these rooms. Experiments were conducted to validate the performance of our robot search method in finding different numbers of multiple dynamic users for varying environment sizes and search durations. We also compared our method with two coverage planners and a Markov decision process planner. On average, our planner found more users than the other planners for a variety of scenarios. Finally, we performed experiments that introduced uncertainty into both the APDFs as well as during the search to validate the robustness of our overall approach.