Reviewerly/exHarmony huggingface dataset:
Boundary detection using unbiased sparseness‐constrained colour‐opponent response and superpixel contrast

Boundaries play a crucial role in various image-based tasks, but many existing non-learning-based boundary detection methods underperform in recognising authentic boundaries from a complex background. In this study, the authors address this problem using the sparseness-constrained colour-opponent response and the superpixel contrast. First, building on the biologically inspired colour-opponency mechanism, the authors elaborate a method to compute the unbiased sparseness-constrained colour-opponent response. In this procedure, locations showing colour variations are enhanced, while the textural locations are preliminarily suppressed by the cue of local sparseness measure. Second, with the help of superpixel segmentation, the authors present an effective approach to obtain the superpixel contrast map. This approach helps to exploit the object shape information in suppressing textures. Consequently, the authors propose a non-learning-based method to detect boundaries in images, combining the unbiased sparseness-constrained colour-opponent response and the overall superpixel contrast map. Experiment results on widely adopted datasets manifest that the authors method outperforms most of the competing methods. In particular, compared with the state-of-the-art surround-modulation method, the proposed method obtains a comparable performance while consuming much less runtime.
Innovative approach to training industry 4.0 experts

Abstract The research concerns the changing requirements to a new generation of experts demanded by Industry 4.0. Rapid technological development of the enterprises, digitalization of their processes galvanize the universities to adapt their approaches to both students, training technology and quality control management. Moreover, we compared manufacturing process with the educational one, analyzing the possibilities of the currently developed teaching technology to comply with the enterprises’ demands.
Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints

Creating plausible virtual actors from images of real actors remains one of the key challenges in computer vision and computer graphics. Marker-less human motion estimation and shape modeling from images in the wild bring this challenge to the fore. Although the recent advances on view synthesis and image-to-image translation, currently available formulations are limited to transfer solely style and do not take into account the character's motion and shape, which are by nature intermingled to produce plausible human forms. In this paper, we propose a unifying formulation for transferring appearance and retargeting human motion from monocular videos that regards all these aspects. Our method synthesizes new videos of people in a different context where they were initially recorded. Differently from recent appearance transferring methods, our approach takes into account body shape, appearance, and motion constraints. The evaluation is performed with several experiments using publicly available real videos containing hard conditions. Our method is able to transfer both human motion and appearance outperforming state-of-the-art methods, while preserving specific features of the motion that must be maintained (e.g., feet touching the floor, hands touching a particular object) and holding the best visual quality and appearance metrics such as Structural Similarity (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS).
On Loss Functions and Regret Bounds for Multi-Category Classification

We develop new approaches in multi-class settings for constructing loss functions and establishing corresponding regret bounds with respect to the zero-one or cost-weighted classification loss. We provide new general representations of losses by deriving inverse mappings from a concave generalized entropy to a loss through a convex dissimilarity function related to the multi-distribution <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> -divergence. This approach is then applied to study both hinge-like losses and proper scoring rules. In the first case, we derive new hinge-like convex losses, which are tighter extensions outside the probability simplex than related hinge-like losses and geometrically simpler with fewer non-differentiable edges. We also establish a classification regret bound in general for all losses with the same generalized entropy as the zero-one loss, thereby substantially extending and improving existing results. In the second case, we identify new sets of multi-class proper scoring rules through different types of dissimilarity functions and reveal interesting relationships between various composite losses currently in use. We also establish new classification regret bounds in general for multi-class proper scoring rules and, as applications, provide simple meaningful regret bounds for two specific sets of proper scoring rules. These results generalize, for the first time, previous two-class regret bounds to multi-class settings.
Design of a New Game for Teaching Assembly Process

Assembly practice is regarded as a great way to deal with the training needs of the increasing number of mechanical companies as students can be equipped with hands-on skills and working experience from assembly training. However, the current assembly-related courses with hands-on activities have a series of problems including cost and safety. This paper analyzes the possibility of applying serious games to assembly training and introduces a novel serious game to help mechanical engineering students participate the assembly process in a fun environment. The game is developed by Unity3D and based on BCN3D MOVEO robotic arm. Learners can get familiar with the assembly process from the game and instructors can customize the assembly process for their students to learn. The benefits this game can have on mechanical engineering education are examined
Obstacle Detection Using Faster R-CNN Oriented to an Autonomous Feeding Assistance System

Obstacle detection has been a relevant issue for the implementation of autonomous robotic systems, within which increasingly robust algorithms have begun to be applied, especially Deep Learning techniques. However, these have not been widely used for the detection of obstacles in static robotic agents, contrary to what happens with mobile agents. For this reason, this work explores the use of one of these techniques, which is a neural network based on the Faster R-CNN, focused on detecting a specific obstacle (hands) in an application environment for a food assistance robot. For this purpose, a database containing 6205 training images and 1350 validation images was prepared, where 31 users perform different movements with their hands. To verify the capacity of the network, 3 architectures of different depths were implemented, which were evaluated and compared, resulting in the network of greater depth obtained the highest accuracy, of 77.4%, taking into account that the hands are not only still but also in movement, generating distortion in them and greater difficulty for their detection. Also, the internal behavior of the network was visualized through activations, to verify what it had learned, showing that it managed to focus on the hands, with some activations located in parts of the user's body such as face and arm.
Performance Analysis of Keystroke Dynamics Using Classification Algorithms

Authentication is the process of verifying the identity of a user. Biometric authentication assures user identity by identifying users physiological or behavioral traits. Keystroke dynamics is a behavioral biometric based on users typing pattern. It can be used to authenticate legitimate users based on their unique typing style on the keyboard. From a pattern recognition point of view, user authentication using keystroke dynamics is a challenging task. It can be accomplished by using classification algorithms - two-class and one-class classification algorithms. In this paper, we study and evaluate the effectiveness of using the one-class classification algorithms over the two-class classification algorithms for keystroke dynamics authentication system. We implemented and evaluated 18 classification algorithms (both two-class and one-class) from the literature of keystroke dynamics and pattern recognition. The result of our experiments is evaluated using 28 subjects with the total of 378 unique comparisons for each classifier. Our results show that the top-performing classifiers of one-class are not very different from two-class classifiers and can be considered to use in the real-world authentication systems.
Heterogeneous Information Network Embedding for Mention Recommendation

Mention recommendation is the task of recommending the right candidate users in a message. Many works have been conducted on the problem of whom to mention. However, due to the sparsity and heterogeneous of mention data, none of them well solve the problem. The recent advances in network embedding representation learning provide an effective approach to model the sparsity and heterogeneous simultaneously in heterogeneous information network. To this end, we propose a novel Network Embedding Mention (NEM) recommendation model to recommend the right users in a message. NEM constructs a heterogeneous mention network based on different relationships among different entities. Then NEM learns a unified low dimensional embedding vector using random walk for users and messages by considering network structure and vertex content information. Finally, whom to mention is ranked by calculating the relevance scores from heterogeneous user and message embeddings. To evaluate the proposed method, we construct a large dataset and their corresponding social networks from a real-world social media platform. Through extensive experiments on real-world mention collection, we demonstrate that our proposed model outperforms the previous state-of-the-art methods in term of recommendation task.
The Plausibility Paradox For Scaled-Down Users In Virtual Environments

This paper identifies a new phenomenon: when users interact with simulated objects in a virtual environment where the user is much smaller than usual, there is a mismatch between the object physics that they expect and the object physics that would be correct at that scale. We report the findings of our study investigating the relationship between perceived realism and a physically accurate approximation of reality in a virtual reality experience in which the user has been scaled down by a factor of ten. We conducted a within-subjects experiment in which 44 subjects performed a simple interaction task with objects under two different physics simulation conditions. In one condition, the objects, when dropped and thrown, behaved accurately according to the physics that would be correct at that reduced scale in the real world, our true physics condition. In the other condition, the movie physics condition, the objects behaved in a similar manner as they would if no scaling of the user had occurred. We found that a significant majority of the users considered the latter condition to be the more realistic one. We argue that our findings have implications for many virtual reality and telepresence applications involving operation with simulated or physical objects in small scales.
“What did you say?”: Extracting unintentional secrets from predictive text learning systems

As a primary form of communication, text is used widely for online communications, including e-mail conversations, mobile text messaging, chatroom and forum discussions. Modern systems include facilities such as predictive text, recently implemented using deep learning algorithms, to estimate the next word to be written based on previous historical entries. However, we often enter sensitive information such as passwords using the same input devices - namely, smartphone soft keyboards. In this paper, we explore the problem of deep learning models which memorise sensitive training data, and how secrets can be extracted from predictive text models. We propose a general black-box attack algorithm to accomplish this for all kinds of memorised sequences, discuss mitigations and countermeasures, and explore how this attack vector could be deployed on an Android or iOS mobile device platforms as part of target reconnaissance.
Food Transaction Scheme Based on Blockchain Technology

In the traditional food transaction, there are many problems, such as lack of trust between customers and businesses, and the information of food trade is easy to be tampered with. Therefore, this paper applies the blockchain technology to the traditional food transaction and proposes a food transaction scheme based on blockchain technology, combining with the problems existing in the actual transaction. This scheme includes two stages, namely, user information collection stage and the food transaction stage. This scheme also uses the orderly aggregation signature to ensure the authenticity and reliability of the transaction. First of all, in the user information collection stage, the food safety system will process the user information with cryptography, so that the information can be stored in the blockchain safely and efficiently. Next, in the food transaction stage, the initiator and the receiver of the food transaction will sign the transaction information released by themselves; then, the information will be verified to be effective enough that can be uploaded to the blockchain network. Finally, in terms of security, the scheme can resist password attacks, distributed attacks, and other network attacks.
mpiPython: A Robust Python MPI Binding

For the last two decades, Python has become one of the most popular programming languages and been used to develop and analyze data-intensive scientific and engineering applications and in the areas such as Bigdata Analytics, Social Media, Data Science, Physics, Psychology, Healthcare, Political Science, etc. Moreover, demand of supporting Python data-parallel applications for those areas is rapidly growing. There have been international efforts to produce a message passing interface for Python bindings to support parallel computing, but specific challenges still remain to improve Python bindings. The main purpose of this paper is to introduce our MPI Python binding, called mpiPython, with the MPI standard communication API. In this paper, we first will discuss the design issues of the mpiPython API, associated with its development. In the second part of the paper, we will discuss node/parallel performance to compare mpiPython to other MPI bindings on a Linux cluster and can expect mpiPython achieves quite acceptable performance.
Study on Operation Strategy of Solar-Powered UAV Based on Longest Endurance

Aiming at the problem of energy collection and consumption of solar-powered UAV under different maneuvers, an energy collection model combining solar-powered UAV attitude and solar radiation was established. Firstly, the ASHRAE model was used as the solar radiation estimation model in the flight environment. Secondly, the energy harvesting model of the solar-powered UAV is deduced by the relationship between the attitude of the UAV, the relative position of the sun and the earth. The experimental data was used to establish the energy consumption model of solar-powered UAV in different flight states. Finally, the flight time of the same mission path under different operating modes is obtained through simulation. Simulation results show that when the solar-powered UAV is hovering at a height of 3 000 meters and a radius of 50 meters, the flight time of aileron operation is 7.19% longer than power differential operation; when hovering at a radius of 170 meters, the flight time of the two operations is almost equal; when hovering at a radius of 600 meters and flying in a line, the flight time of the power differential operation is about 1.08% longer than that of the aileron operation.
3D Human Reconstruction from an Image for Mobile Telepresence Systems

In remote collaboration using a telepresence system, viewing a 3D-reconstructed workspace can help a person understand the workspace situation. This is because, by viewing the workspace from birds-eye view, it is possible to both understand the positional relationship between the workspace and the worker and see the movements of workers. However, when reconstructing the workspace in 3D using depth cameras mounted on a telepresence robot or worker, the back of workers or objects in front of the depth cameras becomes a blind spot and cannot be captured. Therefore, when a person in a remote location views the 3D-reconstructed workspace, the person cannot see workers or objects in the blind spot. This makes it difficult to know the positional relationship between the workspace and the worker and to see the movement of the workers. Here, we propose a method to reduce the blind spots of a 3D-reconstructed person by reconstructing a 3D model of the person from an RGB-D image taken with a depth camera and changing the pose of the 3D model according to the movement of the persons body.
Color Composition Similarity and Its Application in Fine-grained Similarity

Assessing visual similarity in-the-wild, a core ability of the human visual system, is a challenging problem for computer vision methods because of its subjective nature and limited annotated datasets. We make a stride forward, showing that visual similarity can be better studied by isolating its components. We identify color composition similarity as an important aspect and study its interaction with category-level similarity. Color composition similarity considers the distribution of colors and their layout in images. We create predictive models accounting for the global similarity that is beyond pixel-based and patch-based, or histogram level information. Using an active learning approach, we build a large-scale color composition similarity dataset with subjective ratings via crowd-sourcing, the first of its kind. We train a Siamese network using the dataset to create a color similarity metric and descriptors which outperform existing color descriptors. We also provide a benchmark for global color descriptors for perceptual color similarity. Finally, we combine color similarity and category level features for fine-grained visual similarity. Our proposed model surpasses the state-of-the-art performance while using three orders of magnitude less training data. The results suggest that our proposal to study visual similarity by isolating its components, modeling and combining them is a promising paradigm for further development.
Representation Learning for Improved Generalization of Adversarial Domain Adaptation with Text Classification

Domain Adaptation techniques remain limited in accuracy and robustness due to data sparsity. In this paper, we present a new approach called Domain Adversarial network with Representation Learning (DARL), to improve domain adaptation by introducing an encoding layer as part of DARL model learning. We integrate a Stacked Denoising Autoencoder and Adversarial learning for the domain adaptation process. The advantage of the proposed method is that it can extract descriptive features under noisy conditions while still learning task discriminative features. The encoding under noisy reconstruction ensures both higher accuracy and increased robustness in the learning process. We evaluate DARL using Amazon review data set and the results showed superior accuracy and robustness compared to Domain Adversarial Neural Networks (DANN).
Adaptive switching interpolation filter for restoring impulse corrupted digital images

The study introduces an adaptive switching interpolation filter (ASIF) for restoring images contaminated with salt & pepper impulse noise. The new method restores noisy pixels by applying a new interpolation scheme that combines Shepard's inverse distance weighting and Simpson's natural neighbour interpolation techniques. The new interpolation scheme is designed based on the percentage of the overlapping area and the distance between the sites created by noisy pixels with the sites of uncorrupted pixels in the Voronoi tessellation. Since natural neighbour-based techniques do not support extrapolation, the proposed algorithm performs Shepard's inverse distance weighting-based restoration when the noisy pixels fall outside the convex hull formed by uncorrupted pixels. As the proposed method combines the advantages of Simpson's natural neighbour and Shepard's inverse distance weighting interpolation methods, it better preserves the natural intensity variations in images and provides smoother approximation than other methods used in the comparative study. Visual and quantitative experimental analysis performed on various images with peak signal to noise ratio, mean structural similarity index measure, image enhancement factor, and feature-similarity index measure demarcates the improved capability of ASIF over other comparative filters.
Development of augmented reality application to detect the 3D marker through POSIT algorithm

Performance of any system can be enhanced by factors such as accuracy and latency. In past six decades, many papers have been published that compares the detection accuracy algorithms and method but very few information is presented about the latency and the methods with which it can be reduced. Dynamic 2d, 3d marker detection through camera has attracted much attention nowadays. This paper presents a fast 2D, 3D marker recognition at least time slot (latency) for augmented reality applications. The proposed algorithm is stimulated by the A forge.net framework and designed against the challenging occlusion and illuminous condition of marker at least delay time. To achieve the sharp edges detection used Otsu thresholding approach. Position of dynamic marker with respect to camera is calibrated through Pose estimation approach. Numerous experiments have been piloted on recognition of 2D, 3D markers. The result analysis computes the performance of the system through enhancing accuracy & latency.
Exploring Effect Of Different External Stimuli On Body Association In VR

Body association in VR is the extent to which users perceive a virtual body as their own. Prior research has studied the effect of tactile, visual and visuomotor stimuli on body association. Additionally, studies have been conducted to test the effect of olfactory stimuli on immersion, but how it affects body association hasn’t been explored. Through a systematic study, we compare the effect of tactile, visual, visuomotor and olfactory stimuli on body association in VR. This work paves the way towards understanding olfactory sensations and how they might affect experiences in VR.
Real-time Depth Estimation for Aerial Panoramas in Virtual Reality

With the emergence of consumer-level 360° panoramic cameras, omnidirectional RGB images and videos are now easy to be captured either in the hand-held mode or from a drone. Although previous works achieve plausible results in room-sized panoramic datasets, they are limited in indoor scenes with little rotations. In this paper, we present a real-time depth estimation method for more challenging aerial panoramas, where the viewing angle is changing rapidly and the lighting condition is more complicated. Our graph convolutional network(GCN)-based framework makes full use of the global connection information of the omnidirectional images, and is trained with extensive outdoor data. Experiments show that our method is robust to estimate the depth of outdoor aerial panoramas captured from various angles accurately.