OpenRaiser/Intern-Atlas huggingface dataset:
Classification of the relative positions between an ellipsoid and an elliptic paraboloid
Venue: arXiv cs.CG 2018
Year: 2018

We classify all the relative positions between an ellipsoid and an elliptic paraboloid when the ellipsoid is small in comparison with the paraboloid ({\it small} meaning that the ellipsoid cannot be tangent to the paraboloid at two points simultaneously). This provides an easy way to detect contact between the two surfaces by a direct analysis of the coefficients of a fourth degree polynomial.
Frequency domain variants of velvet noise and their application to speech processing and synthesis: with appendices
Venue: arXiv cs.SD 2018
Year: 2018

We propose a new excitation source signal for VOCODERs and an all-pass impulse response for post-processing of synthetic sounds and pre-processing of natural sounds for data-augmentation. The proposed signals are variants of velvet noise, which is a sparse discrete signal consisting of a few non-zero (1 or -1) elements and sounds smoother than Gaussian white noise. One of the proposed variants, FVN (Frequency domain Velvet Noise) applies the procedure to generate a velvet noise on the cyclic frequency domain of DFT (Discrete Fourier Transform). Then, by smoothing the generated signal to design the phase of an all-pass filter followed by inverse Fourier transform yields the proposed FVN. Temporally variable frequency weighted mixing of FVN generated by frozen and shuffled random number provides a unified excitation signal which can span from random noise to a repetitive pulse train. The other variant, which is an all-pass impulse response, significantly reduces "buzzy" impression of VOCODER output by filtering. Finally, we will discuss applications of the proposed signal for watermarking and psychoacoustic research.
Automated Bridge Component Recognition using Video Data
Venue: arXiv cs.CV 2018
Year: 2018

This paper investigates the automated recognition of structural bridge components using video data. Although understanding video data for structural inspections is straightforward for human inspectors, the implementation of the same task using machine learning methods has not been fully realized. In particular, single-frame image processing techniques, such as convolutional neural networks (CNNs), are not expected to identify structural components accurately when the image is a close-up view, lacking contextual information regarding where on the structure the image originates. Inspired by the significant progress in video processing techniques, this study investigates automated bridge component recognition using video data, where the information from the past frames is used to augment the understanding of the current frame. A new simulated video dataset is created to train the machine learning algorithms. Then, convolutional Neural Networks (CNNs) with recurrent architectures are designed and applied to implement the automated bridge component recognition task. Results are presented for simulated video data, as well as video collected in the field.
Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images
Venue: arXiv cs.CV 2018
Year: 2018

Increasing demand for high field magnetic resonance (MR) scanner indicates the need for high-quality MR images for accurate medical diagnosis. However, cost constraints, instead, motivate a need for algorithms to enhance images from low field scanners. We propose an approach to process the given low field (3T) MR image slices to reconstruct the corresponding high field (7T-like) slices. Our framework involves a novel architecture of a merged convolutional autoencoder with a single encoder and multiple decoders. Specifically, we employ three decoders with random initializations, and the proposed training approach involves selection of a particular decoder in each weight-update iteration for back propagation. We demonstrate that the proposed algorithm outperforms some related contemporary methods in terms of performance and reconstruction time.
Is social capital associated with synchronization in human communication? An analysis of Italian call records and measures of civic engagement
Venue: arXiv cs.CY 2018
Year: 2018

Social capital has been studied in economics, sociology and political science as one of the key elements that promote the development of modern societies. It can be defined as the source of capital that facilitates cooperation through shared social norms. In this work, we investigate whether and to what extent synchronization aspects of mobile communication patterns are associated with social capital metrics. Interestingly, our results show that our synchronization-based approach well correlates with existing social capital metrics (i.e., Referendum turnout, Blood donations, and Association density), being also able to characterize the different role played by high synchronization within a close proximity-based community and high synchronization among different communities. Hence, the proposed approach can provide timely, effective analysis at a limited cost over a large territory.
Deep Neural Decision Trees
Venue: arXiv cs.LG 2018
Year: 2018

Deep neural networks have been proven powerful at processing perceptual data, such as images and audio. However for tabular data, tree-based models are more popular. A nice property of tree-based models is their natural interpretability. In this work, we present Deep Neural Decision Trees (DNDT) -- tree models realised by neural networks. A DNDT is intrinsically interpretable, as it is a tree. Yet as it is also a neural network (NN), it can be easily implemented in NN toolkits, and trained with gradient descent rather than greedy splitting. We evaluate DNDT on several tabular datasets, verify its efficacy, and investigate similarities and differences between DNDT and vanilla decision trees. Interestingly, DNDT self-prunes at both split and feature-level.
Capacitor Based Activity Sensing for Kinetic Powered Wearable IoTs
Venue: arXiv cs.HC 2018
Year: 2018

We propose a novel use of the conventional energy storage component, i.e., capacitor, in kinetic-powered wearable IoTs as a sensor to detect human activities. Since different activities accumulate energies in the capacitor at different rates, these activities can be detected directly by observing the charging rate of the capacitor. The key advantage of the proposed capacitor based activity sensing mechanism, called CapSense, is that it obviates the need for sampling the motion signal during the activity detection period thus significantly saving power consumption of the wearable device. A challenge we face is that capacitors are inherently non-linear energy accumulators, which, even for the same activity, leads to significant variations in charging rates at different times depending on the current charge level of the capacitor. We solve this problem by jointly configuring the parameters of the capacitor and the associated energy harvesting circuits, which allows us to operate on charging cycles that are approximately linear. We design and implement a kinetic-powered shoe sole and conduct experiments with 10 subjects. Our results show that CapSense can classify five different daily activities with 95% accuracy while consuming 73% less system power compared to conventional motion signal based activity detection.
A model-driven approach for a new generation of adaptive libraries
Venue: arXiv cs.PF 2018
Year: 2018

Efficient high-performance libraries often expose multiple tunable parameters to provide highly optimized routines. These can range from simple loop unroll factors or vector sizes all the way to algorithmic changes, given that some implementations can be more suitable for certain devices by exploiting hardware characteristics such as local memories and vector units. Traditionally, such parameters and algorithmic choices are tuned and then hard-coded for a specific architecture and for certain characteristics of the inputs. However, emerging applications are often data-driven, thus traditional approaches are not effective across the wide range of inputs and architectures used in practice. In this paper, we present a new adaptive framework for data-driven applications which uses a predictive model to select the optimal algorithmic parameters by training with synthetic and real datasets. We demonstrate the effectiveness of a BLAS library and specifically on its matrix multiplication routine. We present experimental results for two GPU architectures and show significant performance gains of up to 3x (on a high-end NVIDIA Pascal GPU) and 2.5x (on an embedded ARM Mali GPU) when compared to a traditionally optimized library.
Transfer Learning with Human Corneal Tissues: An Analysis of Optimal Cut-Off Layer
Venue: arXiv cs.NE 2018
Year: 2018

Transfer learning is a powerful tool to adapt trained neural networks to new tasks. Depending on the similarity of the original task to the new task, the selection of the cut-off layer is critical. For medical applications like tissue classification, the last layers of an object classification network might not be optimal. We found that on real data of human corneal tissues the best feature representation can be found in the middle layers of the Inception-v3 and in the rear layers of the VGG-19 architecture.
A Graph Transduction Game for Multi-target Tracking
Venue: arXiv cs.CV 2018
Year: 2018

Semi-supervised learning is a popular class of techniques to learn from labeled and unlabeled data. The paper proposes an application of a recently proposed approach of graph transduction that exploits game theoretic notions to the problem of multiple people tracking. Within the proposed framework, targets are considered as players of a multi-player non-cooperative game. The equilibria of the game is considered as a consistent labeling solution and thus an estimation of the target association in the sequence of frames. Patches of persons are extracted from the video frames using a HOG based detector and their similarity is modeled using distances among their covariance matrices. The solution we propose achieves satisfactory results on video surveillance datasets. The experiments show the robustness of the method even with a heavy unbalance between the number of labeled and unlabeled input patches.
Unsupervised Deep Multi-focus Image Fusion
Venue: arXiv cs.CV 2018
Year: 2018

Convolutional neural networks have recently been used for multi-focus image fusion. However, due to the lack of labeled data for supervised training of such networks, existing methods have resorted to adding Gaussian blur in focused images to simulate defocus and generate synthetic training data with ground-truth for supervised learning. Moreover, they classify pixels as focused or defocused and leverage the results to construct the fusion weight maps which then necessitates a series of post-processing steps. In this paper, we present unsupervised end-to-end learning for directly predicting the fully focused output image from multi-focus input image pairs. The proposed approach uses a novel CNN architecture trained to perform fusion without the need for ground truth fused images and exploits the image structural similarity (SSIM) to calculate the loss; a metric that is widely accepted for fused image quality evaluation. Consequently, we are able to utilize {\em real} benchmark datasets, instead of simulated ones, to train our network. The model is a feed-forward, fully convolutional neural network that can process images of variable sizes during test time. Extensive evaluations on benchmark datasets show that our method outperforms existing state-of-the-art in terms of visual quality and objective evaluations.
Upward confluence in the interaction calculus
Venue: arXiv cs.LO 2018
Year: 2018

The lambda calculus is not upward confluent, one of counterexamples known thanks to Plotkin. This paper investigates upward confluence in the interaction calculus. Can an interaction system have this property? We positively answer this question and also provide a necessary and sufficient condition for stronger one-step upward confluence which happens to be very restrictive. However, the provided condition is not necessary for upward confluence as we prove that the interaction system of the linear lambda calculus is upward confluent.
NFV and SDN - Key Technology Enablers for 5G Networks
Venue: arXiv cs.NI 2018
Year: 2018

Communication networks are undergoing their next evolutionary step towards 5G. The 5G networks are envisioned to provide a flexible, scalable, agile and programmable network platform over which different services with varying requirements can be deployed and managed within strict performance bounds. In order to address these challenges a paradigm shift is taking place in the technologies that drive the networks, and thus their architecture. Innovative concepts and techniques are being developed to power the next generation mobile networks. At the heart of this development lie Network Function Virtualization and Software Defined Networking technologies, which are now recognized as being two of the key technology enablers for realizing 5G networks, and which have introduced a major change in the way network services are deployed and operated. For interested readers that are new to the field of SDN and NFV this paper provides an overview of both these technologies with reference to the 5G networks. Most importantly it describes how the two technologies complement each other and how they are expected to drive the networks of near future.
Multi-agent Gaussian Process Motion Planning via Probabilistic Inference
Venue: arXiv cs.RO 2018
Year: 2018

This paper deals with motion planning for multiple agents by representing the problem as a simultaneous optimization of every agent's trajectory. Each trajectory is considered as a sample from a one-dimensional continuous-time Gaussian process (GP) generated by a linear time-varying stochastic differential equation driven by white noise. By formulating the planning problem as probabilistic inference on a factor graph, the structure of the pertaining GP can be exploited to find the solution efficiently using numerical optimization. In contrast to planning each agent's trajectory individually, where only the current poses of other agents are taken into account, we propose simultaneous planning of multiple trajectories that works in a predictive manner. It takes into account the information about each agent's whereabouts at every future time instant, since full trajectories of each agent are found jointly during a single optimization procedure. We compare the proposed method to an individual trajectory planning approach, demonstrating significant improvement in both success rate and computational efficiency.
Opportunistic Scheduling in Underlay Cognitive Radio based Systems: User Selection Probability Analysis
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, an underlay cognitive radio (CR) system is considered with multiple cognitive or secondary users contending to transmit their information to the cognitive destination (e.g., eNodeB) using the spectral resource of a primary user. The novel closed-form expressions are derived for the selection probabilities of cognitive users with opportunistic scheduling wherein an optimal metric is employed for opportunistic transmission. The analytical results corroborated by the Monte Carlo simulations, can be used to demonstrate the fairness achieved in opportunistic scheduling. It is shown that the fairness in terms of equal chance for transmission amongst all cognitive users can only be seen for the scenarios when the fraction of distances between the cognitive transmitter and cognitive receiver, and cognitive transmitter and primary receiver is identical for each of the cognitive transmitters.
DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

Terramechanics plays a critical role in the areas of ground vehicles and ground mobile robots since understanding and estimating the variables influencing the vehicle-terrain interaction may mean the success or the failure of an entire mission. This research applies state-of-the-art algorithms in deep learning to two key problems: estimating wheel slip and classifying the terrain being traversed by a ground robot. Three data sets collected by ground robotic platforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot Fitorobot) are employed in order to compare the performance of traditional machine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer Perceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). This work also shows the impact that certain tuning parameters and the network architecture (MLP, DNN and CNN) play on the performance of those methods. This paper also contributes a deep discussion with the lessons learned in the implementation of DNNs and CNNs and how these methods can be extended to solve other problems.
Augmenting Source Code Lines with Sample Variable Values
Venue: arXiv cs.SE 2018
Year: 2018

Source code is inherently abstract, which makes it difficult to understand. Activities such as debugging can reveal concrete runtime details, including the values of variables. However, they require that a developer explicitly requests these data for a specific execution moment. We present a simple approach, RuntimeSamp, which collects sample variable values during normal executions of a program by a programmer. These values are then displayed in an ambient way at the end of each line in the source code editor. We discuss questions which should be answered for this approach to be usable in practice, such as how to efficiently record the values and when to display them. We provide partial answers to these questions and suggest future research directions.
CommunityWatch: The Swiss-Army Knife of BGP Anomaly Detection
Venue: arXiv cs.NI 2018
Year: 2018

We present CommunityWatch, an open-source system that enables timely and accurate detection of BGP routing anomalies. CommunityWatch leverages meta-data encoded by AS operators on their advertised routes through the BGP Communities attribute. The BGP Communities values lack standardized semantics, offering the flexibility to attach a wide range of information, including AS relationships, location data, and route redistribution policies. Therefore, parsing and correlating Community values and their dynamics enables the detection and tracking of a variety of routing anomalies. We exhibit the efficacy of CommunityWatch through the detection of three different types of anomalies: infrastructure outages, route leaks, and traffic blackholing.
LazyFP: Leaking FPU Register State using Microarchitectural Side-Channels
Venue: arXiv cs.OS 2018
Year: 2018

Modern processors utilize an increasingly large register set to facilitate efficient floating point and SIMD computation. This large register set is a burden for operating systems, as its content needs to be saved and restored when the operating system context switches between tasks. As an optimization, the operating system can defer the context switch of the FPU and SIMD register set until the first instruction is executed that needs access to these registers. Meanwhile, the old content is left in place with the hope that the current task might not use these registers at all. This optimization is commonly called lazy FPU context switching. To make it possible, a processor offers the ability to toggle the availability of instructions utilizing floating point and SIMD registers. If the instructions are turned off, any attempt of executing them will generate a fault. In this paper, we present an attack that exploits lazy FPU context switching and allows an adversary to recover the FPU and SIMD register set of arbitrary processes or VMs. The attack works on processors that transiently execute FPU or SIMD instructions that follow an instruction generating the fault indicating the first use of FPU or SIMD instructions. On operating systems using lazy FPU context switching, the FPU and SIMD register content of other processes or virtual machines can then be reconstructed via cache side effects. With SIMD registers not only being used for cryptographic computation, but also increasingly for simple operations, such as copying memory, we argue that lazy FPU context switching is a dangerous optimization that needs to be turned off in all operating systems, if there is a chance that they run on affected processors.
Myocardial Segmentation of Contrast Echocardiograms Using Random Forests Guided by Shape Model
Venue: arXiv cs.CV 2018
Year: 2018

Myocardial Contrast Echocardiography (MCE) with micro-bubble contrast agent enables myocardial perfusion quantification which is invaluable for the early detection of coronary artery diseases. In this paper, we proposed a new segmentation method called Shape Model guided Random Forests (SMRF) for the analysis of MCE data. The proposed method utilizes a statistical shape model of the myocardium to guide the Random Forest (RF) segmentation in two ways. First, we introduce a novel Shape Model (SM) feature which captures the global structure and shape of the myocardium to produce a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to further refine and constrain the final segmentation to plausible myocardial shapes. Evaluated on clinical MCE images from 15 patients, our method obtained promising results (Dice=0.81, Jaccard=0.70, MAD=1.68 mm, HD=6.53 mm) and showed a notable improvement in segmentation accuracy over the classic RF and its variants.
On the Learning of Deep Local Features for Robust Face Spoofing Detection
Venue: arXiv cs.CV 2018
Year: 2018

Biometrics emerged as a robust solution for security systems. However, given the dissemination of biometric applications, criminals are developing techniques to circumvent them by simulating physical or behavioral traits of legal users (spoofing attacks). Despite face being a promising characteristic due to its universality, acceptability and presence of cameras almost everywhere, face recognition systems are extremely vulnerable to such frauds since they can be easily fooled with common printed facial photographs. State-of-the-art approaches, based on Convolutional Neural Networks (CNNs), present good results in face spoofing detection. However, these methods do not consider the importance of learning deep local features from each facial region, even though it is known from face recognition that each facial region presents different visual aspects, which can also be exploited for face spoofing detection. In this work we propose a novel CNN architecture trained in two steps for such task. Initially, each part of the neural network learns features from a given facial region. Afterwards, the whole model is fine-tuned on the whole facial images. Results show that such pre-training step allows the CNN to learn different local spoofing cues, improving the performance and the convergence speed of the final model, outperforming the state-of-the-art approaches.
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification
Venue: arXiv cs.SD 2018
Year: 2018

In the past, Acoustic Scene Classification systems have been based on hand crafting audio features that are input to a classifier. Nowadays, the common trend is to adopt data driven techniques, e.g., deep learning, where audio representations are learned from data. In this paper, we propose a system that consists of a simple fusion of two methods of the aforementioned types: a deep learning approach where log-scaled mel-spectrograms are input to a convolutional neural network, and a feature engineering approach, where a collection of hand-crafted features is input to a gradient boosting machine. We first show that both methods provide complementary information to some extent. Then, we use a simple late fusion strategy to combine both methods. We report classification accuracy of each method individually and the combined system on the TUT Acoustic Scenes 2017 dataset. The proposed fused system outperforms each of the individual methods and attains a classification accuracy of 72.8% on the evaluation set, improving the baseline system by 11.8%.