OpenRaiser/Intern-Atlas huggingface dataset:
To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation
Venue: arXiv cs.CV 2017
Year: 2017

It is important for robots to be able to decide whether they can go through a space or not, as they navigate through a dynamic environment. This capability can help them avoid injury or serious damage, e.g., as a result of running into people and obstacles, getting stuck, or falling off an edge. To this end, we propose an unsupervised and a near-unsupervised method based on Generative Adversarial Networks (GAN) to classify scenarios as traversable or not based on visual data. Our method is inspired by the recent success of data-driven approaches on computer vision problems and anomaly detection, and reduces the need for vast amounts of negative examples at training time. Collecting negative data indicating that a robot should not go through a space is typically hard and dangerous because of collisions, whereas collecting positive data can be automated and done safely based on the robot's own traveling experience. We verify the generality and effectiveness of the proposed approach on a test dataset collected in a previously unseen environment with a mobile robot. Furthermore, we show that our method can be used to build costmaps (we call as "GoNoGo" costmaps) for robot path planning using visual data only.
Channel Access Method Classification For Cognitive Radio Applications
Venue: arXiv cs.NI 2017
Year: 2017

Motivated by improved detection and prediction of temporal holes, we propose a two stage algorithm to classify the channel access method used by a primary network. The first stage extends an existing fourth-order cumulant-based modulation classifier to distinguish between TDMA, OFDMA, and CDMA. The second stage proposes a novel collision detector using the sample variance of the same cumulant to detect contention-based channel access methods. Our proposed method is blind and independent of the received SNR. Simulations show that our classification of TDMA, OFDMA, and CDMA is robust to network load while detection of contention outperforms existing methods.
Performance Analysis of FSO System with Spatial Diversity and Relays for M-QAM over Log-Normal Channel
Venue: arXiv cs.IT 2017
Year: 2017

The performance analysis of free space optical communication (FSO) systems using relays and spatial diversity at the transmitter end is presented in this paper. The impact of atmospheric turbulence and attenuation caused by different weather conditions and geometric losses has also been taken into account. The effect of turbulence is modeled over a log-normal probability density function. We present the exact closed form expressions of bit-error rate (BER) for M-ary quadrature amplitude modulation (M-QAM). The FSO system link performance is compared for on-off keying, M-ary pulse amplitude modulation and M-QAM modulation techniques. For relay based free space optical communication systems, M-QAM is proved to be superior than other systems considering the same spectral efficiency for each system. A significant performance enhancement in terms of BER analysis and SNR gains is shown for multi-hop MISO FSO system.
Challenges and potentials for visible light communications: State of the art
Venue: arXiv cs.IT 2017
Year: 2017

Visible Light Communication is the emerging field in the area of Indoor Optical Wireless Communication which uses white light LEDs for transmitting data and light simultaneously. LEDs can be modulated at very high speeds which increases its efficiency and enabling it for the dual purposes of data communication and illumination simultaneously. Radio Frequency have some limitations which is not at par with the current demand of bandwidth but using visible light, it is possible to achieve higher data rates per user. In this paper, we discuss some challenges, potentials and possible future applications for this new technology. Basically, visible light communication is for indoor application capable of multiuser access. We also design a very basic illumination pattern inside a room using uniform power distribution.
Performance analysis of dual-hop optical wireless communication systems over k-distribution turbulence channel with pointing error
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we investigate the performance of the dual-hop free space optical (FSO) communication systems under the effect of strong atmospheric turbulence together with misalignment effects (pointing error). We consider a relay assisted link using decode and forward (DF) relaying protocol between source and destination with the assumption that Channel State Information is available at both transmitting and receiving terminals. The atmospheric turbulence channels are modeled by k-distribution with pointing error impairment. The exact closed form expression is derived for outage probability and bit error rate and illustrated through numerical plots. Further BER results are compared for the different modulation schemes.
Explicit Solution for Constrained Stochastic Linear-Quadratic Control with Multiplicative Noise
Venue: arXiv cs.SY 2017
Year: 2017

We study in this paper a class of constrained linear-quadratic (LQ) optimal control problem formulations for the scalar-state stochastic system with multiplicative noise, which has various applications, especially in the financial risk management. The linear constraint on both the control and state variables considered in our model destroys the elegant structure of the conventional LQ formulation and has blocked the derivation of an explicit control policy so far in the literature. We successfully derive in this paper the analytical control policy for such a class of problems by utilizing the state separation property induced from its structure. We reveal that the optimal control policy is a piece-wise affine function of the state and can be computed off-line efficiently by solving two coupled Riccati equations. Under some mild conditions, we also obtain the stationary control policy for infinite time horizon. We demonstrate the implementation of our method via some illustrative examples and show how to calibrate our model to solve dynamic constrained portfolio optimization problems.
Design, Development and Evaluation of a UAV to Study Air Quality in Qatar
Venue: arXiv cs.RO 2017
Year: 2017

Measuring gases for air quality monitoring is a challenging task that claims a lot of time of observation and large numbers of sensors. The aim of this project is to develop a partially autonomous unmanned aerial vehicle (UAV) equipped with sensors, in order to monitor and collect air quality real time data in designated areas and send it to the ground base. This project is designed and implemented by a multidisciplinary team from electrical and computer engineering departments. The electrical engineering team responsible for implementing air quality sensors for detecting real time data and transmit it from the plane to the ground. On the other hand, the computer engineering team is in charge of Interface sensors and provide platform to view and visualize air quality data and live video streaming. The proposed project contains several sensors to measure Temperature, Humidity, Dust, CO, CO2 and O3. The collected data is transmitted to a server over a wireless internet connection and the server will store, and supply these data to any party who has permission to access it through android phone or website in semi-real time. The developed UAV has carried several field tests in Al Shamal airport in Qatar, with interesting results and proof of concept outcomes.
Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery
Venue: arXiv cs.CV 2017
Year: 2017

Computer vision and robotics are being increasingly applied in medical interventions. Especially in interventions where extreme precision is required they could make a difference. One such application is robot-assisted retinal microsurgery. In recent works, such interventions are conducted under a stereo-microscope, and with a robot-controlled surgical tool. The complementarity of computer vision and robotics has however not yet been fully exploited. In order to improve the robot control we are interested in 3D reconstruction of the anatomy and in automatic tool localization using a stereo microscope. In this paper, we solve this problem for the first time using a single pipeline, starting from uncalibrated cameras to reach metric 3D reconstruction and registration, in retinal microsurgery. The key ingredients of our method are: (a) surgical tool landmark detection, and (b) 3D reconstruction with the stereo microscope, using the detected landmarks. To address the former, we propose a novel deep learning method that detects and recognizes keypoints in high definition images at higher than real-time speed. We use the detected 2D keypoints along with their corresponding 3D coordinates obtained from the robot sensors to calibrate the stereo microscope using an affine projection model. We design an online 3D reconstruction pipeline that makes use of smoothness constraints and performs robot-to-camera registration. The entire pipeline is extensively validated on open-sky porcine eye sequences. Quantitative and qualitative results are presented for all steps.
On Inductive Abilities of Latent Factor Models for Relational Learning
Venue: arXiv cs.LG 2017
Year: 2017

Latent factor models are increasingly popular for modeling multi-relational knowledge graphs. By their vectorial nature, it is not only hard to interpret why this class of models works so well, but also to understand where they fail and how they might be improved. We conduct an experimental survey of state-of-the-art models, not towards a purely comparative end, but as a means to get insight about their inductive abilities. To assess the strengths and weaknesses of each model, we create simple tasks that exhibit first, atomic properties of binary relations, and then, common inter-relational inference through synthetic genealogies. Based on these experimental results, we propose new research directions to improve on existing models.
Organizing Multimedia Data in Video Surveillance Systems Based on Face Verification with Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we propose the two-stage approach of organizing information in video surveillance systems. At first, the faces are detected in each frame and a video stream is split into sequences of frames with face region of one person. Secondly, these sequences (tracks) that contain identical faces are grouped using face verification algorithms and hierarchical agglomerative clustering. Gender and age are estimated for each cluster (person) in order to facilitate the usage of the organized video collection. The particular attention is focused on the aggregation of features extracted from each frame with the deep convolutional neural networks. The experimental results of the proposed approach using YTF and IJB-A datasets demonstrated that the most accurate and fast solution is achieved for matching of normalized average of feature vectors of all frames in a track.
Flexible Computing Services for Comparisons and Analyses of Classical Chinese Poetry
Venue: arXiv cs.CL 2017
Year: 2017

We collect nine corpora of representative Chinese poetry for the time span of 1046 BCE and 1644 CE for studying the history of Chinese words, collocations, and patterns. By flexibly integrating our own tools, we are able to provide new perspectives for approaching our goals. We illustrate the ideas with two examples. The first example show a new way to compare word preferences of poets, and the second example demonstrates how we can utilize our corpora in historical studies of the Chinese words. We show the viability of the tools for academic research, and we wish to make it helpful for enriching existing Chinese dictionary as well.
Neural network-based arithmetic coding of intra prediction modes in HEVC
Venue: arXiv cs.MM 2017
Year: 2017

In both H.264 and HEVC, context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding method. CABAC relies on manually designed binarization processes as well as handcrafted context models, which may restrict the compression efficiency. In this paper, we propose an arithmetic coding strategy by training neural networks, and make preliminary studies on coding of the intra prediction modes in HEVC. Instead of binarization, we propose to directly estimate the probability distribution of the 35 intra prediction modes with the adoption of a multi-level arithmetic codec. Instead of handcrafted context models, we utilize convolutional neural network (CNN) to perform the probability estimation. Simulation results show that our proposed arithmetic coding leads to as high as 9.9% bits saving compared with CABAC.
Indistinguishability and Energy Sensitivity of Asymptotically Gaussian Compressed Encryption
Venue: arXiv cs.IT 2017
Year: 2017

The principle of compressed sensing (CS) can be applied in a cryptosystem by providing the notion of security. In information-theoretic sense, it is known that a CS-based cryptosystem can be perfectly secure if it employs a random Gaussian sensing matrix updated at each encryption and its plaintext has constant energy. In this paper, we propose a new CS-based cryptosystem that employs a secret bipolar keystream and a public unitary matrix, which can be suitable for practical implementation by generating and renewing the keystream in a fast and efficient manner. We demonstrate that the sensing matrix is asymptotically Gaussian for a sufficiently large plaintext length, which guarantees a reliable CS decryption for a legitimate recipient. By means of probability metrics, we also show that the new CS-based cryptosystem can have the indistinguishability against an adversary, as long as the keystream is updated at each encryption and each plaintext has constant energy. Finally, we investigate how much the security of the new CS-based cryptosystem is sensitive to energy variation of plaintexts.
Bug or Not? Bug Report Classification Using N-Gram IDF
Venue: arXiv cs.SE 2017
Year: 2017

Previous studies have found that a significant number of bug reports are misclassified between bugs and non-bugs, and that manually classifying bug reports is a time-consuming task. To address this problem, we propose a bug reports classification model with N-gram IDF, a theoretical extension of Inverse Document Frequency (IDF) for handling words and phrases of any length. N-gram IDF enables us to extract key terms of any length from texts, these key terms can be used as the features to classify bug reports. We build classification models with logistic regression and random forest using features from N-gram IDF and topic modeling, which is widely used in various software engineering tasks. With a publicly available dataset, our results show that our N-gram IDF-based models have a superior performance than the topic-based models on all of the evaluated cases. Our models show promising results and have a potential to be extended to other software engineering tasks.
Direction-Aware Semi-Dense SLAM
Venue: arXiv cs.CV 2017
Year: 2017

To aide simultaneous localization and mapping (SLAM), future perception systems will incorporate forms of scene understanding. In a step towards fully integrated probabilistic geometric scene understanding, localization and mapping we propose the first direction-aware semi-dense SLAM system. It jointly infers the directional Stata Center World (SCW) segmentation and a surfel-based semi-dense map while performing real-time camera tracking. The joint SCW map model connects a scene-wide Bayesian nonparametric Dirichlet Process von-Mises-Fisher mixture model (DP-vMF) prior on surfel orientations with the local surfel locations via a conditional random field (CRF). Camera tracking leverages the SCW segmentation to improve efficiency via guided observation selection. Results demonstrate improved SLAM accuracy and tracking efficiency at state of the art performance.
Social Style Characterization from Egocentric Photo-streams
Venue: arXiv cs.CV 2017
Year: 2017

This paper proposes a system for automatic social pattern characterization using a wearable photo-camera. The proposed pipeline consists of three major steps. First, detection of people with whom the camera wearer interacts and, second, categorization of the detected social interactions into formal and informal. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task, and a LSTM network is employed for time-series classification. In the last step, recurrences of the same person across the whole set of social interactions are clustered to achieve a comprehensive understanding of the diversity and frequency of the social relations of the user. Experiments over a dataset acquired by a user wearing a photo-camera during a month show promising results on the task of social pattern characterization from egocentric photo-streams.
Non-Linear Editor for Text-Based Screencast
Venue: arXiv cs.HC 2017
Year: 2017

Screencasts, where computer screen is broadcast to a large audience on the web, are becoming popular as an online educational tool. Among various types of screencast content, popular are the contents that involve text editing, including computer programming. There are emerging platforms that support such text-based screencasts by recording every character insertion/deletion from the creator and reconstructing its playback on the viewer's screen. However, these platforms lack rich support for creating and editing the screencast itself, mainly due to the difficulty of manipulating recorded text changes; the changes are tightly coupled in sequence, thus modifying arbitrary part of the sequence is not trivial. We present a non-linear editing tool for text-based screencasts. With the proposed selective history rewrite process, our editor allows users to substitute an arbitrary part of a text-based screencast while preserving overall consistency of the rest of the text-based screencast.
Rapid Fading Due to Human Blockage in Pedestrian Crowds at 5G Millimeter-Wave Frequencies
Venue: arXiv cs.IT 2017
Year: 2017

Rapidly fading channels caused by pedestrians in dense urban environments will have a significant impact on millimeter-wave (mmWave) communications systems that employ electrically-steerable and narrow beamwidth antenna arrays. A peer-to-peer (P2P) measurement campaign was conducted with 7-degree, 15-degree, and 60-degree half-power beamwidth (HPBW) antenna pairs at 73.5 GHz and with 1 GHz of RF null-to-null bandwidth in a heavily populated open square scenario in Brooklyn, New York, to study blockage events caused by typical pedestrian traffic. Antenna beamwidths that range approximately an order of magnitude were selected to gain knowledge of fading events for antennas with different beamwidths since antenna patterns for mmWave systems will be electronically-adjustable. Two simple modeling approaches in the literature are introduced to characterize the blockage events by either a two-state Markov model or a four-state piecewise linear modeling approach. Transition probability rates are determined from the measurements and it is shown that average fade durations with a -5 dB threshold are 299.0 ms for 7-degree HPBW antennas and 260.2 ms for 60-degree HPBW antennas. The four-state piecewise linear modeling approach shows that signal strength decay and rise times are asymmetric for blockage events and that mean signal attenuations (average fade depths) are inversely proportional to antenna HPBW, where 7-degree and 60-degree HPBW antennas resulted in mean signal fades of 15.8 dB and 11.5 dB, respectively. The models presented herein are valuable for extending statistical channel models at mmWave to accurately simulate real-world pedestrian blockage events when designing fifth-generation (5G) wireless systems.
A Generalized Framework for Kullback-Leibler Markov Aggregation
Venue: arXiv cs.SY 2017
Year: 2017

This paper proposes an information-theoretic cost function for aggregating a Markov chain via a (possibly stochastic) mapping. The cost function is motivated by two objectives: 1) The process obtained by observing the Markov chain through the mapping should be close to a Markov chain, and 2) the aggregated Markov chain should retain as much of the temporal dependence structure of the original Markov chain as possible. We discuss properties of this parameterized cost function and show that it contains the cost functions previously proposed by Deng et al., Xu et al., and Geiger et al. as special cases. We moreover discuss these special cases providing a better understanding and highlighting potential shortcomings: For example, the cost function proposed by Geiger et al. is tightly connected to approximate probabilistic bisimulation, but leads to trivial solutions if optimized without regularization. We furthermore propose a simple heuristic to optimize our cost function for deterministic aggregations and illustrate its performance on a set of synthetic examples.
Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video
Venue: arXiv cs.CV 2017
Year: 2017

Object detection is considered one of the most challenging problems in this field of computer vision, as it involves the combination of object classification and object localization within a scene. Recently, deep neural networks (DNNs) have been demonstrated to achieve superior object detection performance compared to other approaches, with YOLOv2 (an improved You Only Look Once model) being one of the state-of-the-art in DNN-based object detection methods in terms of both speed and accuracy. Although YOLOv2 can achieve real-time performance on a powerful GPU, it still remains very challenging for leveraging this approach for real-time object detection in video on embedded computing devices with limited computational power and limited memory. In this paper, we propose a new framework called Fast YOLO, a fast You Only Look Once framework which accelerates YOLOv2 to be able to perform object detection in video on embedded devices in a real-time manner. First, we leverage the evolutionary deep intelligence framework to evolve the YOLOv2 network architecture and produce an optimized architecture (referred to as O-YOLOv2 here) that has 2.8X fewer parameters with just a ~2% IOU drop. To further reduce power consumption on embedded devices while maintaining performance, a motion-adaptive inference method is introduced into the proposed Fast YOLO framework to reduce the frequency of deep inference with O-YOLOv2 based on temporal motion characteristics. Experimental results show that the proposed Fast YOLO framework can reduce the number of deep inferences by an average of 38.13%, and an average speedup of ~3.3X for objection detection in video compared to the original YOLOv2, leading Fast YOLO to run an average of ~18FPS on a Nvidia Jetson TX1 embedded system.