Reviewerly/exHarmony huggingface dataset:
An Anisotropic Fast Marching Method Applied to Path Planning for Mars Rovers

This paper presents the application of the Anisotropic Fast Marching Method to the path planning problem of mobile robots moving in outdoors environments, such as Mars. Considering that at any point on a 3D surface there are two main slopes: the maximum, which is the slope of the gradient, and the minimum, the height map of a terrain can be considered as a tensor filed. Using the Anisotropic Fast Marching Method, the resulting trajectory of the path planning takes the tensor field into account so that the slopes in the trajectory are minimized. Numerical simulations are presented to show the advantage of this method over its isotropic version. Besides, the influence of the anisotropic index and the traversability of the resultant paths are analyzed.
Abnormal Event Detection and Localization in Visual Surveillance

In this paper, we propose a framework for abnormal event detection and analysis in the field of visual surveillance based on the state-of-the-art deep learning techniques. We train a pair of conditional generative adversarial networks (cGANs) using the normal behavior samples, where one cGAN takes video frames as inputs and generates the corresponding optical flow features. While on the other hand, the other cGANs take optical flow features as inputs and generate the corresponding video frames. By analyzing the differences between the generated frames/optical flow features and the realistic samples, abnormal events can be detected and localized effectively. Moreover, for suspected regions, we adopt the faster RCNN to analyze the abnormal events. Experimental results demonstrate that the proposed framework can detect the abnormal events accurately and efficiently.
Reorient the Gazed Scene Towards the Center: Novel Virtual Turning Using Head and Gaze Motions and Blink

Stationary subjects exposed to compelling scene movements in virtual reality environments often experience symptoms of visually induced motion sickness (cybersickness). This study specifically focuses on virtual turning and evaluates user experience in the task of searching and collecting objects appeared around the player in the first-person view. Typical conventional control schemes for turning includes the smooth and snap turns. The smooth turn is continuous turning performed by moving a scene in the horizontal direction. In this case, reflexive eye movement occurs even in the absence of physical motion, which often causes eye strain and nausea. The snap turn achieves a prompt direction change by omitting intervening turning, which often damages the sense of reality. To address these negative effects, we propose the novel turning method inspired by the particular human behavior, which achieves to reorient the gazed view towards the center. Prompt reorientation during rapid head motion and blinking performed unnoticeable scene switching that achieved the seamless user experience, especially for the wide-angle turning. Whereas, continuous narrow-angle turning by horizontally rotating the virtual world corresponding to the face orientation achieved enhanced the sense of reality. The proposal comprises a hybrid of these two turning schemes. Experiments using simulator sickness and presence questionnaires revealed that our methods achieved comparable or lower sickness scores and higher presence scores than either of conventional turning schemes.
Real-time Gait Speed Evaluation at Home

In the last decades, aging population has raised substantial awareness by the scientific communities. An important part of these populations experience cognitive decline, without any method to threat it. However, the gait speed has been revealed as a great predictor of Mild Cognitive Impairments, making its monitoring very interesting for early detection. Since it would require a lot of human resources to do it at this scale, an ambient sensor would be a perfect solution for this task. It would be necessary to be able to associate the gait speed evaluated with the user who walked at this speed. This paper deals with our new conceptualized prototype to monitor gait speed in a smart home and automatically associate it with the right person. The method induced promising results.
Real-time Underwater Caustics for Mixed Reality 360° Videos

We present a novel mixed reality (MR) rendering solution that illuminates and blends virtual objects into underwater 360° video with real-time underwater caustic effects. Image-based lighting is used in conjunction with underwater caustics to provide automatic ambient and high frequency underwater lighting. This ensures that the caustics and virtual objects are lit and blend into each frame of the video semi-automatically and in real-time. We provide an interactive interface with intuitive parameter controls to fine tune caustics to match with the background video.
Discovering Emotional Logic Rules From Physiological Data of Individuals

This paper discusses our work on discovering a set of emotional logic rules, derived from physiological data of individuals from a wearable technology perspective. We concentrated the analysis on physiological data such as plethysmography, respiration, galvanic skin response, and temperature that can be detected by wearable sensors. We sourced our data from the DEAP dataset, which is a popular labelled Affective Computing dataset. Our approach implemented a fusion of preprocessing and data mining techniques, to discover logic rules relating to the valence and arousal emotional dimensions. Our findings indicate that while there are similar changes in heart rates or galvanic skin response across individuals during emotional stimuli, every individual has a unique and quantifiable physiological reaction.
Vacuum freeze-drying of sediment cores: an optimised method for preserving archaeostratigraphic archives

Abstract
Robust Non-blind Video Watermarking Using DWT and QR Decomposition

In this article, a robust and non-blind watermarking technique is presented for video data. In the embedding step, a gray-scale image is embedded into the key frames using DWT and QR decomposition. Before embedding, watermark is scrambled using Fibonacci-Lucas transform to enhance the security level of the watermark. The result of the experiment established that the proposed method is robust againstMaiti, Chinmay different attacks like salt-and-pepper noise, rotation, Gaussian noise, blurring, cropping, frame averaging, compression, etc. The performance of the proposed technique is also comparedDhara, Bibhas Chandra with existing methods and it is established that current methods perform better than the existing methods.
Target Aware Visual Object Tracking

We propose a visual object tracker that improves accuracy while significantly decreasing false alarm rate. This is achieved by a late fusion scheme that integrates the motion model of particle sampling with the region proposal network of Mask R-CNN during inference. The qualified bounding boxes selected by the late fusion are fed into the Mask R-CNN’s head layer for the detection of the tracked object. We refer the introduced scheme, TAVOT, as target aware visual object tracker since it is capable of minimizing false detections with the guidance of variable rate particle sampling initialized by the target region of interest. It is shown that TAVOT is capable of modeling temporal video content with a simple motion model thus constitutes a promising video object tracker. Performance evaluation performed on VOT2016 video sequences demonstrates that TAVOT 22% increases the success rate, while 73% decreasing the false alarm rate compared to the baseline Mask R-CNN. Compared to the top tracker of VOT2016 around 5% increase at the success rate is reported where intersection over union is greater than 0.5.
On the Exploitation of Online SMS Receiving Services to Forge ID Verification

Communication service providers (e.g., Whatsapp) enable users to connect with people around the world. These services have been widely adopted and used by millions of users, and such services have emerged as a replacement of the transitional calling and messaging. Unfortunately, these communication services have also been used to commit illegal activities and serious crimes. Therefore, service providers ask for user's phone/mobile number to verify the user's identity and to prevent misuses.
Prescriptive Cluster-Dependent Support Vector Machines with an Application to Reducing Hospital Readmissions

We augment linear Support Vector Machine (SVM) classifiers by adding three important features: (i) we introduce a regularization constraint to induce a sparse classifier; (ii) we devise a method that partitions the positive class into clusters and selects a sparse SVM classifier for each cluster; and (iii) we develop a method to optimize the values of controllable variables in order to reduce the number of data points which are predicted to have an undesirable outcome, which, in our setting, coincides with being in the positive class. The latter feature leads to personalized prescriptions/recommendations. We apply our methods to the problem of predicting and preventing hospital readmissions within 30-days from discharge for patients that underwent a general surgical procedure. To that end, we leverage a large dataset containing over 2.28 million patients who had surgeries in the period 2011–2014 in the U.S. The dataset has been collected as part of the American College of Surgeons National Surgical Quality Improvement Program (NSOIP).
Ship Detection Based on Faster R-CNN Network in Optical Remote Sensing Images

A ship detection model based on Faster R-CNN is proposed for ship detection tasks in optical remote sensing images. Deep convolutional neural network could replace traditional manual design feature to extract ship features automatically and quickly from makes the detection performance of ship no longer relying on the design of artificial features. This paper proposes a strategy that combines the model with two different size of convolution neural networks respectively. Experiments on datasets HRSC16 verify the models detection capabilities and the mean average precision can achieve 78.2%. For the problem of low recall rate in the detection of adjacent vessels, this paper adopts the Soft-NMS method. Compared with the traditional NMS, the Soft-NMS method can electively improve the model detection performance to 80.1%. At the same time, it also shows that the model we proposed is a robust model and has a certain degree of generalization ability.
Classification of blood cancer images using a convolutional neural networks ensemble

This paper presents the method of our submission to the ISBI 2019 Challenge for the task of classification of normal versus malignant cells in B-ALL white blood cancer microscopic images. We aimed to combine convolutional neural networks with several state-of-the-art techniques. Specifically, we fine-tuned pretrained deep learning networks including ResNet and DenseNet for this task. Overfitting is one of the major problems for this challenge. We solve overfitting by using the gradient norm clipping and the cosine annealing learning rate schedule with restarts, which have a significant impact on the performance of our deep neural network. More importantly, adaptive pooling layer is used in our models. With this modification, models are able to adapt to images of any size. An ensemble of deep models achieved a 0.8570 weighted-f1 score on the preliminary test set reported by the test server.
Online Recognition via a Finite Mixture of Multivariate Generalized Gaussian Distributions

The huge amount of data expanding day by day entail creating powerful real-time algorithms. Such algorithms allow a reactive processing between the input multimedia data and the system. In particular, we are mainly concerned with active learning and clustering images and videos for the purpose of pattern recognition. In this paper, we propose a novel online recognition algorithm based on multivariate generalized Gaussian distributions. We estimate at first the generative model’s parameters within a discriminative framework (fixed-point, Riemannian averaged fixed-point, and Fisher scoring). Then, we propose an online recognition algorithm in accordance with those algorithms. Finally, we applied our proposed framework on three challenging problems, namely: human action recognition, facial expression recognition, and pedestrian detection from infrared images. Experiments demonstrate the robustness of our approach by comparing with the state-of-the art algorithms and offline learning techniques.
Fine-tuning of Distributed Network Covert Channels Parameters and Their Impact on Undetectability

Currently the usage of various information hiding techniques for nefarious purposes becomes a major issue. Especially when the attackers, in order to stay under the radar, utilize increasingly sophisticated ways to conceal data. One of such advanced techniques is the use of distributed network covert channels (DNCC) where the secrets are spread among a number of available covert channels and transmitted in parallel. Taking above into consideration, in this paper we investigate how the data hiding techniques forming a DNCC can be configured to achieve an improved undetectability and how this impacts the DNCC performance and its detection susceptibility. Based on the presented results it can be concluded that if the utilized steganographic techniques forming a DNCC are used in a balanced manner then this may pose difficulties from the detection perspective although this typically also means significant limitation of the overall secret data rate.
Learning Software Security in Context

Learning software security has become a complex and difficult task today than it was even a decade ago. With the increased complexity of computer systems and a variety of applications, it is hard for software developers to master the expertise required to deal with the variety of security concepts, methods, and technologies that are required in software projects. Although a large number of security learning materials are widely available in books, open literature or on the Internet, they are difficult for learners to understand the rationale of security topics and correlate the concepts with real software scenarios. We argue that the traditional approach, which usually organizes knowledge content topically, with security-centric, is not suitable to motivate learners and stimulate learners' interest. To tackle this learning issue, our research is focused on forging a contextualized learning environment for software security where learners can explore security knowledge and relate it to the context that they are familiar with. This learning system is developed base on our proposed context-based learning approach and based on ontological technologies. In this paper, we present our evaluation study in the open source software (OSS) development environment. Our results demonstrate that contextualized learning can help OSS developers identify their necessary security information, improve learning efficiency and make security knowledge more meaningful for their software development tasks
Deep Learning with Semi-Synthetic Training Images for Detection of Non-Cooperative UAVs

This paper presents a method to generate a dataset for training a deep convolutional network to detect a non cooperative unmanned aerial vehicle in video data. Deep convolutional network have shown a great potential for tasks like object detection and have been continuously improved in the last years. Still, the amount of training data is large and their generation can be complex and time consuming, especially if the appearance of the detected object is not clearly specified. The concept presented here is to train a deep convolutional neural network just with a few two dimensional images of unmanned aerial vehicle to simplify the process of generating training data. Performance of the trained network is evaluated with data from real experimental flights and compared with hand-labeled ground truth data to validate the correctness. To cover situations when the classifier fails at the detection, the output is integrated in a image processing pipeline for object tracking in order to establish a continuous tracking.
Effectiveness of bilateral and multilateral concessional debts on economic growth in Africa

Purpose The purpose of this paper is to assess the relative effectiveness of bilateral and multilateral concessional debts on economic growth in 32 sub-Saharan African (SSA) countries over the period 1985–2016. Design/methodology/approach The recently developed dynamic panel autoregressive distributed lag models which comprise three different estimators, the mean group, pooled mean group (PMG) estimator and dynamic fixed effect, were applied to estimate the model. Following these estimators, the Hausman test was employed to determine the efficient and consistent estimator. Findings The results showed that bilateral concessional debts had a negative impact on growth. From the findings, a 1 percent increase in bilateral concessional debts induced economic growth to decline by 38.1 percent points in the short run, and by 7.1 percent points in the long run; convergence to long-run equilibrium adjusted at the speed of 90 percent on an annual basis. Multilateral concessional debts were found to have a positive impact on growth both in the short and long run. The coefficient of the error term was negatively signed and indicates that deviations from the long-run equilibrium path were being corrected at the speed of 89.4 percent annually. Originality/value To the authors’ best knowledge, empirical studies that specifically seek to examine how bilateral and multilateral concessional debts impacted on growth are yet to attract the attention of researchers. As a result, this study will complement related extant growth studies, especially in the case of SSA.
Modified Selection and Search in Learning Automata Based Artificial Bee Colony in Noisy Environment

The paper proposes two novel extensions of the learning automata induced noisy bee colony (LANBC) to handle the presence of stochastic noise in the fitness landscape. The reward/penalty based reinforcement learning scheme of the stochastic learning automata (SLA) of LANBC helps a solution to prudently select a sample size for its periodic fitness evaluations based on the fitness variance in its local neighborhood. However, the LANBC suffers from two stalemates, including weak search dynamic in the presence of noise and the deterministic parental selection scheme leading to dismissal of quality solutions and loss of population diversity. The paper overcomes these impasses by employing two novel strategies. First, the search dynamic is amended with an aim to generate promising offspring solutions by appropriately tuning the control parameter based on the contamination effect of noise on other population members. Second, a modified probabilistic crowding induced niching behavior is introduced to promote quality solutions to the next generation to ensure both the population quality and the population diversity. Computer simulations undertaken on the noisy versions of a set of 28 benchmark functions reveal that the proposed algorithm outperforms its contenders with respect to function error value.
Deep Color Image Demosaicking with Feature Pyramid Channel Attention

Image demosaicking is the most crucial preprocessing step in the current color digital camera pipeline. Efficiency and high quality are of importance to demosaicking methods at the request of practical applications. Recently, convolutional neural network (CNN) has demonstrated its superior performance on image demosaicking. However, most existed CNN-based demosaicking methods fail to take full advantage of the self-similarity and redundancy in natural image, and interpolation artifacts (e.g. zippering and color moire) easily occur when local geometry cannot be inferred correctly from neighboring pixels. To solve these problems, we propose a fully convolutional feature pyramid network to exploit image self-similarity and redundant information as much as possible for image demosaicking. Furthermore, we add a compact channel attention module to the proposed network to flexibly rescale channel-wise features by modeling interdependencies among channels. Our experimental results on three datasets show that our method obviously outperforms state-of-the-art methods on both quantitative and visual quality assessments, and maintains competitive running time in the inference stage.
IO-Trust

Modern malware is complex, stealthy, and employ anti-forensics techniques to evade detection. In order to detect malware, data must be collected, such, allows further analyses of the malware's behaviour. However, when both the malware and the detecting system run on the same domain (the CPU) it's questionable whether the data acquired by the acquisition method is not tampered with. Hardware based techniques, such as acquiring data out-of-band using a PCIe device allow for data acquisition that is deemed trusted when the acquisition method does not rely on any data present on the host memory. Unfortunately, in Input-Output Memory Management Unit (IOMMU) based systems, peripheral devices access to host memory go through a stage of translation by the IOMMU. The translation tables which reside in the host's memory are subject to malware control, hence are not trustworthy. In this paper we present a method that allows acquiring the data reliably without dependant on data residing in host memory, even when IOMMU is being used to restrict devices. We show how accessing host physical memory is achieved and discuss why this is not a vulnerability in some platforms, but rather a powerful tool for securing data acquisition when the host is not trusted to perform the acquisition.
Measured accuracy improvement method of velocity and displacement based on adaptive Kalman filter

Purpose This paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm. Design/methodology/approach A novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function. Findings The simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement. Originality/value The adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.
A Comparison of Relic-toolkit and ELiPS Libraries for a Pairing-based Homomorphic Encryption

In recent years, the number of companies are adopting cloud computing service for outsourcing is increasing. On the other hand, there are threats that are like falsification or leakage to data to be kept secret. Therefore, the technologies for managing encrypted data are the attractive topics in the cryptographic field. Among them, functional encryption which is constructed with pairing-based cryptography is closing to the practical stage because pairing operation is getting faster by the recent study of pairing. In addition, there are some pairing libraries which allow a user to introduce the pairing-based cryptography to their system. Nevertheless, they have some differences in several operations. In this paper, we reconstruct pairing-based homomorphic encryption by focusing on features of libraries and examine the performance evaluations of them.