OpenRaiser/Intern-Atlas huggingface dataset:
Capsule Deep Neural Network for Recognition of Historical Graffiti Handwriting
Venue: arXiv cs.CV 2018
Year: 2018

Automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine) was demonstrated by means of capsule deep learning neural network. It was applied to the image dataset of the carved Glagolitic and Cyrillic letters (CGCL), which was assembled and pre-processed recently for recognition and prediction by machine learning methods (https://www.kaggle.com/yoctoman/graffiti-st-sophia-cathedral-kyiv). CGCL dataset contains >4000 images for glyphs of 34 letters which are hardly recognized by experts even in contrast to notMNIST dataset with the better images of 10 letters taken from different fonts. Despite the much worse quality of CGCL dataset and extremely low number of samples (in comparison to notMNIST dataset) the capsule network model demonstrated much better results than the previously used convolutional neural network (CNN). The validation accuracy (and validation loss) was higher (lower) for capsule network model than for CNN without data augmentation even. The area under curve (AUC) values for receiver operating characteristic (ROC) were also higher for the capsule network model than for CNN model: 0.88-0.93 (capsule network) and 0.50 (CNN) without data augmentation, 0.91-0.95 (capsule network) and 0.51 (CNN) with lossless data augmentation, and similar results of 0.91-0.93 (capsule network) and 0.9 (CNN) in the regime of lossless data augmentation only. The confusion matrixes were much better for capsule network than for CNN model and gave the much lower type I (false positive) and type II (false negative) values in all three regimes of data augmentation. These results supports the previous claims that capsule-like networks allow to reduce error rates not only on MNIST digit dataset, but on the other notMNIST letter dataset and the more complex CGCL handwriting graffiti letter dataset also.
Multiple Combined Constraints for Image Stitching
Venue: arXiv cs.CV 2018
Year: 2018

Several approaches to image stitching use different constraints to estimate the motion model between image pairs. These constraints can be roughly divided into two categories: geometric constraints and photometric constraints. In this paper, geometric and photometric constraints are combined to improve the alignment quality, which is based on the observation that these two kinds of constraints are complementary. On the one hand, geometric constraints (e.g., point and line correspondences) are usually spatially biased and are insufficient in some extreme scenes, while photometric constraints are always evenly and densely distributed. On the other hand, photometric constraints are sensitive to displacements and are not suitable for images with large parallaxes, while geometric constraints are usually imposed by feature matching and are more robust to handle parallaxes. The proposed method therefore combines them together in an efficient mesh-based image warping framework. It achieves better alignment quality than methods only with geometric constraints, and can handle larger parallax than photometric-constraint-based method. Experimental results on various images illustrate that the proposed method outperforms representative state-of-the-art image stitching methods reported in the literature.
PAIM: Platoon-based Autonomous Intersection Management
Venue: arXiv cs.SY 2018
Year: 2018

With the emergence of autonomous ground vehicles and the recent advancements in Intelligent Transportation Systems, Autonomous Traffic Management has garnered more and more attention. Autonomous Intersection Management (AIM), also known as Cooperative Intersection Management (CIM) is among the more challenging traffic problems that poses important questions related to safety and optimization in terms of delays, fuel consumption, emissions and reliability. Previously we introduced two stop-sign based policies for autonomous intersection management that were compatible with platoons of autonomous vehicles. These policies outperformed regular stop-sign policy both in terms of average delay per vehicle and variance in delay. This paper introduces a reservation-based policy that utilizes the cost functions from our previous work to derive optimal schedules for platoons of vehicles. The proposed policy guarantees safety by not allowing vehicles with conflicting turning movement to be in the conflict zone at the same time. Moreover, a greedy algorithm is designed to search through all possible schedules to pick the best that minimizes a cost function based on a trade-off between total delay and variance in delay. A simulator software is designed to compare the results of the proposed policy in terms of average delay per vehicle and variance in delay with that of a 4-phase traffic light.
A Study on Deep Learning Based Sauvegrain Method for Measurement of Puberty Bone Age
Venue: arXiv cs.CV 2018
Year: 2018

This study applies a technique to expand the number of images to a level that allows deep learning. And the applicability of the Sauvegrain method through deep learning with relatively few elbow X-rays is studied. The study was composed of processes similar to the physicians' bone age assessment procedures. The selected reference images were learned without being included in the evaluation data, and at the same time, the data was extended to accommodate the number of cases. In addition, we adjusted the X-ray images to better images using U-Net and selected the ROI with RPN + so as to be able to perform bone age estimation through CNN. The mean absolute error of the Sauvegrain method based on deep learning is 2.8 months and the Mean Absolute Percentage Error (MAPE) is 0.018. This result shows that X - ray analysis using the Sauvegrain method shows higher accuracy than that of the age group of puberty even in the deep learning base. This means that deep learning of the Suvegrain method can be measured at a level similar to that of an expert, based on the extended X-ray image with the image data extension technique. Finally, we applied the Sauvegrain method to deep learning for accurate measurement of bone age at puberty. As a result, the present study is based on deep learning, and compared with the evaluation results of experts, it is possible to overcome limitations of the method of measuring bone age based on machine learning which was in TW3 or Greulich & Pyle due to lack of X- I confirmed the fact. And we also presented the Sauvegrain method, which is applicable to adolescents as well.
Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise
Venue: arXiv cs.LG 2018
Year: 2018

This study presents the results of a series of simulation experiments that evaluate and compare four different manifold alignment methods under the influence of noise. The data was created by simulating the dynamics of two slightly different double pendulums in three-dimensional space. The method of semi-supervised feature-level manifold alignment using global distance resulted in the most convincing visualisations. However, the semi-supervised feature-level local alignment methods resulted in smaller alignment errors. These local alignment methods were also more robust to noise and faster than the other methods.
Smart False Data Injection attacks against State Estimation in Power Grid
Venue: arXiv cs.CR 2018
Year: 2018

In this paper a new class of cyber attacks against state estimation in the electric power grid is considered. This class of attacks is named false data injection attacks. We show that with the knowledge of the system configuration an attacker could successfully inject false data into certain state variable while bypassing existing techniques for bad data detection. In the preliminary section we consider the feasibility of such an attack and the necessary condition to successfully avoid detection. After that we show that with the knowledge of the system configuration, certain line flow measurements could be manipulated to lead to profitable misconduct. By controlling Regional Transmission Organizations (RTOs) view of system power flow and congestion, an attacker could manipulate the LMPs of targeted buses according to prior biddings. Also, in this paper we show the implementation of the false data injection attacks. The numerical example considered was applied to a malicious data detection algorithm that was designed on a microcontroller. The results demonstrated the effectiveness of injecting false data measurements into the state estimation of electric power grids.
Detect, anticipate and generate: Semi-supervised recurrent latent variable models for human activity modeling
Venue: arXiv cs.CV 2018
Year: 2018

Successful Human-Robot collaboration requires a predictive model of human behavior. The robot needs to be able to recognize current goals and actions and to predict future activities in a given context. However, the spatio-temporal sequence of human actions is difficult to model since latent factors such as intention, task, knowledge, intuition and preference determine the action choices of each individual. In this work we introduce semi-supervised variational recurrent neural networks which are able to a) model temporal distributions over latent factors and the observable feature space, b) incorporate discrete labels such as activity type when available, and c) generate possible future action sequences on both feature and label level. We evaluate our model on the Cornell Activity Dataset CAD-120 dataset. Our model outperforms state-of-the-art approaches in both activity and affordance detection and anticipation. Additionally, we show how samples of possible future action sequences are in line with past observations.
Chalktalk : A Visualization and Communication Language -- As a Tool in the Domain of Computer Science Education
Venue: arXiv cs.HC 2018
Year: 2018

In the context of a classroom lesson, concepts must be visualized and organized in many ways depending on the needs of the teacher and students. Traditional presentation media such as the blackboard or electronic whiteboard allow for static hand-drawn images, and slideshow software may be used to generate linear sequences of text and pre-animated images. However, none of these media support the creation of dynamic visualizations that can be manipulated, combined, or re-animated in real-time, and so demonstrating new concepts or adapting to changes in the requirements of a presentation is a challenge. Thus, we propose Chalktalk as a solution. Chalktalk is an open-source presentation and visualization tool in which the user's drawings are recognized as animated and interactive "sketches," which the user controls via mouse gestures. Sketches help users demonstrate and experiment with complex ideas (e.g. computer graphics, procedural animation, logic) during a live presentation without needing to create and structure all content ahead of time. Because sketches can interoperate and be programmed to represent underlying data in multiple ways, Chalktalk presents the opportunity to visualize key concepts in computer science: especially data structures, whose data and form change over time due to the variety of interactions within a computer system. To show Chalktalk's capabilities, we have prototyped sketch implementations for binary search tree (BST) and stack (LIFO) data structures, which take advantage of sketches' ability to interact and change at run-time. We discuss these prototypes and conclude with considerations for future research using the Chalktalk platform.
Exploring the Impact of Password Dataset Distribution on Guessing
Venue: arXiv cs.CR 2018
Year: 2018

Leaks from password datasets are a regular occurrence. An organization may defend a leak with reassurances that just a small subset of passwords were taken. In this paper we show that the leak of a relatively small number of text-based passwords from an organizations' stored dataset can lead to a further large collection of users being compromised. Taking a sample of passwords from a given dataset of passwords we exploit the knowledge we gain of the distribution to guess other samples from the same dataset. We show theoretically and empirically that the distribution of passwords in the sample follows the same distribution as the passwords in the whole dataset. We propose a function that measures the ability of one distribution to estimate another. Leveraging this we show that a sample of passwords leaked from a given dataset, will compromise the remaining passwords in that dataset better than a sample leaked from another source.
Unsupervised cross-lingual matching of product classifications
Venue: arXiv cs.CL 2018
Year: 2018

Unsupervised cross-lingual embeddings mapping has provided a unique tool for completely unsupervised translation even for languages with different scripts. In this work we use this method for the task of unsupervised cross-lingual matching of product classifications. Our work also investigates limitations of unsupervised vector alignment and we also suggest two other techniques for aligning product classifications based on their descriptions: using hierarchical information and translations.
Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

On-board estimation of the pose of an uncooperative target spacecraft is an essential task for future on-orbit servicing and close-proximity formation flying missions. However, two issues hinder reliable on-board monocular vision based pose estimation: robustness to illumination conditions due to a lack of reliable visual features and scarcity of image datasets required for training and benchmarking. To address these two issues, this work details the design and validation of a monocular vision based pose determination architecture for spaceborne applications. The primary contribution to the state-of-the-art of this work is the introduction of a novel pose determination method based on Convolutional Neural Networks (CNN) to provide an initial guess of the pose in real-time on-board. The method involves discretizing the pose space and training the CNN with images corresponding to the resulting pose labels. Since reliable training of the CNN requires massive image datasets and computational resources, the parameters of the CNN must be determined prior to the mission with synthetic imagery. Moreover, reliable training of the CNN requires datasets that appropriately account for noise, color, and illumination characteristics expected in orbit. Therefore, the secondary contribution of this work is the introduction of an image synthesis pipeline, which is tailored to generate high fidelity images of any spacecraft 3D model. The proposed technique is scalable to spacecraft of different structural and physical properties as well as robust to the dynamic illumination conditions of space. Through metrics measuring classification and pose accuracy, it is shown that the presented architecture has desirable robustness and scalable properties.
Towards Dialogue-based Navigation with Multivariate Adaptation driven by Intention and Politeness for Social Robots
Venue: arXiv cs.RO 2018
Year: 2018

Service robots need to show appropriate social behaviour in order to be deployed in social environments such as healthcare, education, retail, etc. Some of the main capabilities that robots should have are navigation and conversational skills. If the person is impatient, the person might want a robot to navigate faster and vice versa. Linguistic features that indicate politeness can provide social cues about a person's patient and impatient behaviour. The novelty presented in this paper is to dynamically incorporate politeness in robotic dialogue systems for navigation. Understanding the politeness in users' speech can be used to modulate the robot behaviour and responses. Therefore, we developed a dialogue system to navigate in an indoor environment, which produces different robot behaviours and responses based on users' intention and degree of politeness. We deploy and test our system with the Pepper robot that adapts to the changes in user's politeness.
Feedback Control of a Cassie Bipedal Robot: Walking, Standing, and Riding a Segway
Venue: arXiv cs.RO 2018
Year: 2018

The Cassie bipedal robot designed by Agility Robotics is providing academics a common platform for sharing and comparing algorithms for locomotion, perception, and navigation. This paper focuses on feedback control for standing and walking using the methods of virtual constraints and gait libraries. The designed controller was implemented six weeks after the robot arrived at the University of Michigan and allowed it to stand in place as well as walk over sidewalks, grass, snow, sand, and burning brush. The controller for standing also enables the robot to ride a Segway. A model of the Cassie robot has been placed on GitHub and the controller will also be made open source if the paper is accepted.
Accelerating Flash Calculation through Deep Learning Methods
Venue: arXiv cs.CE 2018
Year: 2018

In the past two decades, researchers have made remarkable progress in accelerating flash calculation, which is very useful in a variety of engineering processes. In this paper, general phase splitting problem statements and flash calculation procedures using the Successive Substitution Method are reviewed, while the main shortages are pointed out. Two acceleration methods, Newton's method and the Sparse Grids Method are presented afterwards as a comparison with the deep learning model proposed in this paper. A detailed introduction from artificial neural networks to deep learning methods is provided here with the authors' own remarks. Factors in the deep learning model are investigated to show their effect on the final result. A selected model based on that has been used in a flash calculation predictor with comparison with other methods mentioned above. It is shown that results from the optimized deep learning model meet the experimental data well with the shortest CPU time. More comparison with experimental data has been conducted to show the robustness of our model.
Tight Continuous-Time Reachtubes for Lagrangian Reachability
Venue: arXiv cs.NA 2018
Year: 2018

We introduce continuous Lagrangian reachability (CLRT), a new algorithm for the computation of a tight and continuous-time reachtube for the solution flows of a nonlinear, time-variant dynamical system. CLRT employs finite strain theory to determine the deformation of the solution set from time $t_i$ to time $t_{i+1}$. We have developed simple explicit analytic formulas for the optimal metric for this deformation; this is superior to prior work, which used semi-definite programming. CLRT also uses infinitesimal strain theory to derive an optimal time increment $h_i$ between $t_i$ and $t_{i+1}$, nonlinear optimization to minimally bloat (i.e., using a minimal radius) the state set at time $t_i$ such that it includes all the states of the solution flow in the interval $[t_i,t_{i+1}]$. We use $δ$-satisfiability to ensure the correctness of the bloating. Our results on a series of benchmarks show that CLRT performs favorably compared to state-of-the-art tools such as CAPD in terms of the continuous reachtube volumes they compute.
A Quantitative Evaluation of Natural Language Question Interpretation for Question Answering Systems
Venue: arXiv cs.CL 2018
Year: 2018

Systematic benchmark evaluation plays an important role in the process of improving technologies for Question Answering (QA) systems. While currently there are a number of existing evaluation methods for natural language (NL) QA systems, most of them consider only the final answers, limiting their utility within a black box style evaluation. Herein, we propose a subdivided evaluation approach to enable finer-grained evaluation of QA systems, and present an evaluation tool which targets the NL question (NLQ) interpretation step, an initial step of a QA pipeline. The results of experiments using two public benchmark datasets suggest that we can get a deeper insight about the performance of a QA system using the proposed approach, which should provide a better guidance for improving the systems, than using black box style approaches.
MASON: A Model AgnoStic ObjectNess Framework
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a simple, yet very effective method to localize dominant foreground objects in an image, to pixel-level precision. The proposed method 'MASON' (Model-AgnoStic ObjectNess) uses a deep convolutional network to generate category-independent and model-agnostic heat maps for any image. The network is not explicitly trained for the task, and hence, can be used off-the-shelf in tandem with any other network or task. We show that this framework scales to a wide variety of images, and illustrate the effectiveness of MASON in three varied application contexts.
RGBD2lux: Dense light intensity estimation with an RGBD sensor
Venue: arXiv cs.CV 2018
Year: 2018

Lighting design and modelling or industrial applications like luminaire planning and commissioning rely heavily on time consuming manual measurements or on physically coherent computational simulations. Regarding the latter,standard approaches are based on CAD modeling simulations and offline rendering, with long processing times and therefore inflexible workflows. Thus, in this paper we pro-pose a computer vision based system to measure lighting with just a single RGBD camera. The proposed method uses both depth data and images from the sensor to provide a dense measure of light intensity in the field of view of the camera. We evaluate our system on novel ground truth data and compare it to state-of-the-art commercial light-planning software. Our system provides improved performance, while being completely automated, given that the CAD model is extracted from the depth and the albedo estimated with the support of RGB images. To the best of our knowledge, this is the first automatic framework for the estimation of lighting in general indoor scenarios from RGBDinput.
Challenges for Toxic Comment Classification: An In-Depth Error Analysis
Venue: arXiv cs.CL 2018
Year: 2018

Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task's challenges others still remain unsolved and directions for further research are needed. To this end, we compare different deep learning and shallow approaches on a new, large comment dataset and propose an ensemble that outperforms all individual models. Further, we validate our findings on a second dataset. The results of the ensemble enable us to perform an extensive error analysis, which reveals open challenges for state-of-the-art methods and directions towards pending future research. These challenges include missing paradigmatic context and inconsistent dataset labels.