OpenRaiser/Intern-Atlas huggingface dataset:
Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment
Venue: arXiv cs.IT 2017
Year: 2017

Efficient beam alignment is a crucial component in millimeter wave systems with analog beamforming, especially in fast-changing vehicular settings. This paper proposes a position-aided approach where the vehicle's position (e.g., available via GPS) is used to query the multipath fingerprint database, which provides prior knowledge of potential pointing directions for reliable beam alignment. The approach is the inverse of fingerprinting localization, where the measured multipath signature is compared to the fingerprint database to retrieve the most likely position. The power loss probability is introduced as a metric to quantify misalignment accuracy and is used for optimizing candidate beam selection. Two candidate beam selection methods are developed, where one is a heuristic while the other minimizes the misalignment probability. The proposed beam alignment is evaluated using realistic channels generated from a commercial ray-tracing simulator. Using the generated channels, an extensive investigation is provided, which includes the required measurement sample size to build an effective fingerprint, the impact of measurement noise, the sensitivity to changes in traffic density, and beam alignment overhead comparison with IEEE 802.11ad as the baseline. Using the concept of beam coherence time, which is the duration between two consecutive beam alignments, and parameters of IEEE 802.11ad, the overhead is compared in the mobility context. The results show that while the proposed approach provides increasing rates with larger antenna arrays, IEEE 802.11ad has decreasing rates due to the larger beam training overhead that eats up a large portion of the beam coherence time, which becomes shorter with increasing mobility.
Convergence Results on Pulse Coupled Oscillator Protocols in Locally Connected Networks
Venue: arXiv cs.NI 2017
Year: 2017

This work provides new insights on the convergence of a locally connected network of pulse coupled oscillator (PCOs) (i.e., a bio-inspired model for communication networks) to synchronous and desynchronous states, and their implication in terms of the decentralized synchronization and scheduling in communication networks. Bio-inspired techniques have been advocated by many as fault-tolerant and scalable alternatives to produce self-organization in communication networks. The PCO dynamics in particular have been the source of inspiration for many network synchronization and scheduling protocols. However, their convergence properties, especially in locally connected networks, have not been fully understood, prohibiting the migration into mainstream standards. This work provides further results on the convergence of PCOs in locally connected networks and the achievable convergence accuracy under propagation delays. For synchronization, almost sure convergence is proved for $3$ nodes and accuracy results are obtained for general locally connected networks whereas, for scheduling (or desynchronization), results are derived for locally connected networks with mild conditions on the overlapping set of maximal cliques. These issues have not been fully addressed before in the literature.
REMIX: Automated Exploration for Interactive Outlier Detection
Venue: arXiv cs.AI 2017
Year: 2017

Outlier detection is the identification of points in a dataset that do not conform to the norm. Outlier detection is highly sensitive to the choice of the detection algorithm and the feature subspace used by the algorithm. Extracting domain-relevant insights from outliers needs systematic exploration of these choices since diverse outlier sets could lead to complementary insights. This challenge is especially acute in an interactive setting, where the choices must be explored in a time-constrained manner. In this work, we present REMIX, the first system to address the problem of outlier detection in an interactive setting. REMIX uses a novel mixed integer programming (MIP) formulation for automatically selecting and executing a diverse set of outlier detectors within a time limit. This formulation incorporates multiple aspects such as (i) an upper limit on the total execution time of detectors (ii) diversity in the space of algorithms and features, and (iii) meta-learning for evaluating the cost and utility of detectors. REMIX provides two distinct ways for the analyst to consume its results: (i) a partitioning of the detectors explored by REMIX into perspectives through low-rank non-negative matrix factorization; each perspective can be easily visualized as an intuitive heatmap of experiments versus outliers, and (ii) an ensembled set of outliers which combines outlier scores from all detectors. We demonstrate the benefits of REMIX through extensive empirical validation on real-world data.
Impact on the Usage of Wireless Sensor Networks in Healthcare Sector
Venue: arXiv cs.CY 2017
Year: 2017

Recent advancement in the wireless sensor networks has provided a platform to numerous applications in healthcare sector. It has become an active research area due to its large scale potential. This research focuses on the application areas of wireless sensor networks specifically in the healthcare sector. In this work, we have tried to explain the different challenges faced by the WSNs in order to implement them. The different pros and cons of the WSNs in healthcare sector are also discussed. Some important parameters which can be used to evaluate the performance of the wireless sensor networks are also presented in this work. Wireless sensor networks have a tremendous future and it should be taken at its earliest because of the significant importance of the healthcare issues.
Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps
Venue: arXiv cs.CV 2017
Year: 2017

In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images. Deep neural networks have been used in the past for remote sensing data classification from various sensors, including multispectral, hyperspectral, SAR and LiDAR data. While OpenStreetMap has already been used as ground truth data for training such networks, this abundant data source remains rarely exploited as an input information layer. In this paper, we study different use cases and deep network architectures to leverage OpenStreetMap data for semantic labeling of aerial and satellite images. Especially , we look into fusion based architectures and coarse-to-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks. We illustrate how these methods can be successfully used on two public datasets: ISPRS Potsdam and DFC2017. We show that OpenStreetMap data can efficiently be integrated into the vision-based deep learning models and that it significantly improves both the accuracy performance and the convergence speed of the networks.
Scratch iridescence: Wave-optical rendering of diffractive surface structure
Venue: arXiv cs.GR 2017
Year: 2017

The surface of metal, glass and plastic objects is often characterized by microscopic scratches caused by manufacturing and/or wear. A closer look onto such scratches reveals iridescent colors with a complex dependency on viewing and lighting conditions. The physics behind this phenomenon is well understood; it is caused by diffraction of the incident light by surface features on the order of the optical wavelength. Existing analytic models are able to reproduce spatially unresolved microstructure such as the iridescent appearance of compact disks and similar materials. Spatially resolved scratches, on the other hand, have proven elusive due to the highly complex wave-optical light transport simulations needed to account for their appearance. In this paper, we propose a wave-optical shading model based on non-paraxial scalar diffraction theory to render this class of effects. Our model expresses surface roughness as a collection of line segments. To shade a point on the surface, the individual diffraction patterns for contributing scratch segments are computed analytically and superimposed coherently. This provides natural transitions from localized glint-like iridescence to smooth BRDFs representing the superposition of many reflections at large viewing distances. We demonstrate that our model is capable of recreating the overall appearance as well as characteristic detail effects observed on real-world examples.
Robust Registration of Gaussian Mixtures for Colour Transfer
Venue: arXiv cs.CV 2017
Year: 2017

We present a flexible approach to colour transfer inspired by techniques recently proposed for shape registration. Colour distributions of the palette and target images are modelled with Gaussian Mixture Models (GMMs) that are robustly registered to infer a non linear parametric transfer function. We show experimentally that our approach compares well to current techniques both quantitatively and qualitatively. Moreover, our technique is computationally the fastest and can take efficient advantage of parallel processing architectures for recolouring images and videos. Our transfer function is parametric and hence can be stored in memory for later usage and also combined with other computed transfer functions to create interesting visual effects. Overall this paper provides a fast user friendly approach to recolouring of image and video materials.
Robust Sum Secrecy Rate Optimization for MIMO Two-way Full Duplex Systems
Venue: arXiv cs.IT 2017
Year: 2017

This paper considers multiple-input multiple-output (MIMO) full-duplex (FD) two-way secrecy systems. Specifically, both multi-antenna FD legitimate nodes exchange their own confidential message in the presence of an eavesdropper. Taking into account the imperfect channel state information (CSI) of the eavesdropper, we formulate a robust sum secrecy rate maximization (RSSRM) problem subject to the outage probability constraint of the achievable sum secrecy rate and the transmit power constraint. Unlike other existing channel uncertainty models, e.g., norm-bounded and Gaussian-distribution, we exploit a moment-based random distributed CSI channel uncertainty model to recast our formulate RSSRM problem into the convex optimization frameworks based on a Markov's inequality and robust conic reformulation, i.e., semidefinite programming (SDP). In addition, difference-of-concave (DC) approximation is employed to iteratively tackle the transmit covariance matrices of these legitimate nodes. Simulation results are provided to validate our proposed FD approaches.
Stability and Performance of Coalitions of Prosumers Through Diversification in the Smart Grid
Venue: arXiv cs.MA 2017
Year: 2017

Achieving a successful energetic transition through a smarter and greener electricity grid is a major goal for the 21st century. It is assumed that such smart grids will be characterized by bidirectional electricity flows coupled with the use of small renewable generators and a proper efficient information system. All these bricks might enable end users to take part in the grid stability by injecting power, or by shaping their consumption against financial compensation. In this paper, we propose an algorithm that forms coalitions of agents, called prosumers, that both produce and consume. It is designed to be used by aggregators that aim at selling the aggregated surplus of production of the prosumers they control. We rely on real weather data sampled across stations of a given territory in order to simulate realistic production and consumption patterns for each prosumer. This approach enables us to capture geographical correlations among the agents while preserving the diversity due to different behaviors. As aggregators are bound to the grid operator by a contract, they seek to maximize their offer while minimizing their risk. The proposed graph based algorithm takes the underlying correlation structure of the agents into account and outputs coalitions with both high productivity and low variability. We show then that the resulting diversified coalitions are able to generate higher benefits on a constrained energy market, and are more resilient to random failures of the agents.
Design, analysis and implementation of electronic test for knowledge evaluation in the course of Information Technologies for pharmaceutical students
Venue: arXiv cs.CY 2017
Year: 2017

The increased usage of the information technologies in everyday life and especially in education leads to demands for new forms of teaching, studying and appropriate examination and evaluation of acquired knowledge and skills of the students. Modern electronic educational systems use only those technologies that improve the learning process and make it more effective. The interactive education provides an opportunity to develop skills for independent literature research and activation of the cognitive activity. In this work, it is shown how a modern electronic education is implemented in the curriculum of English language pharmaceutical students at the Medical University - Plovdiv in the course of Information Technologies. It is developed a methodological approach of a hybrid system, i.e. a compulsory attendance at lectures in combination with two different types of conduction of the final test for comparison - a paper-based test and a remote web-based one. The results received from the parallel tests are processed and analysed and the conclusions are used to enhance the quality of the developed test and the type of implementation. Moreover, the examined students fill in an anonymous poll to show the authors their thoughts for this type of hybrid educational system.
Bayer Demosaicking Using Optimized Mean Curvature over RGB channels
Venue: arXiv cs.CV 2017
Year: 2017

Color artifacts of demosaicked images are often found at contours due to interpolation across edges and cross-channel aliasing. To tackle this problem, we propose a novel demosaicking method to reliably reconstruct color channels of a Bayer image based on two different optimized mean-curvature (MC) models. The missing pixel values in green (G) channel are first estimated by minimizing a variational MC model. The curvatures of restored G-image surface are approximated as a linear MC model which guides the initial reconstruction of red (R) and blue (B) channels. Then a refinement process is performed to interpolate accurate full-resolution R and B images. Experiments on benchmark images have testified to the superiority of the proposed method in terms of both the objective and subjective quality.
An exact upper bound on the size of minimal clique covers
Venue: arXiv cs.DM 2017
Year: 2017

Indeterminate strings have received considerable attention in the recent past; see for example Christodoulakis et al 2015 and Helling et al 2017. This attention is due to their applicability in bioinformatics, and to the natural correspondence with undirected graphs. One aspect of this correspondence is the fact that the minimal alphabet size of indeterminates representing any given undirected graph corresponds to the size of the minimal clique cover of this graph. This paper solves a related problem proposed in Helling et al 2017: compute $Θ_n(m)$, which is the size of the largest possible minimal clique cover (i.e., an exact upper bound), and hence alphabet size of the corresponding indeterminate, of any graph on $n$ vertices and $m$ edges.
Distributed Vector Representation Of Shopping Items, The Customer And Shopping Cart To Build A Three Fold Recommendation System
Venue: arXiv cs.IR 2017
Year: 2017

The main idea of this paper is to represent shopping items through vectors because these vectors act as the base for building em- beddings for customers and shopping carts. Also, these vectors are input to the mathematical models that act as either a recommendation engine or help in targeting potential customers. We have used exponential family embeddings as the tool to construct two basic vectors - product embeddings and context vectors. Using the basic vectors, we build combined embeddings, trip embeddings and customer embeddings. Combined embeddings mix linguistic properties of product names with their shopping patterns. The customer embeddings establish an understand- ing of the buying pattern of customers in a group and help in building customer profile. For example a customer profile can represent customers frequently buying pet-food. Identifying such profiles can help us bring out offers and discounts. Similarly, trip embeddings are used to build trip profiles. People happen to buy similar set of products in a trip and hence their trip embeddings can be used to predict the next product they would like to buy. This is a novel technique and the first of its kind to make recommendation using product, trip and customer embeddings.
An Overview of Data Mining Applications in Oil and Gas Exploration: Structural Geology and Reservoir Property-Issues
Venue: arXiv cs.OH 2017
Year: 2017

Low oil prices have motivated energy executives to look into cost reduction in their supply chains more seriously. To this end, a new technology that is experimentally considered in hydrocarbon exploration is data mining. There are two major categories of geoscientific problems in which data mining is applied: structural geology and reservoir property-issues. This research overviews these categories by considering a variety of interesting works in each of them. The result is an understanding of the specific geoscientific problems studied in the literature, along with the relative data mining methods. This way, this work tries to lay the ground for a mutual understanding on oil and gas exploration between the data miners and the geoscientists.
Wireless Information and Power Transfer over an AWGN channel: Nonlinearity and Asymmetric Gaussian Signaling
Venue: arXiv cs.IT 2017
Year: 2017

Simultaneous transmission of information and power over a point-to-point flat-fading complex Additive White Gaussian Noise (AWGN) channel is studied. In contrast with the literature that relies on an inaccurate linear model of the energy harvester, an experimentally-validated nonlinear model is considered. A general form of the delivered Direct Current (DC) power in terms of system baseband parameters is derived, which demonstrates the dependency of the delivered DC power on higher order statistics of the channel input distribution. The optimization problem of maximizing Rate-Power (R-P) region is studied. Assuming that the Channel gain is available at both the receiver and the transmitter, and constraining to independent and identically distributed (i.i.d.) channel inputs determined only by their first and second moment statistics, an inner bound for the general problem is obtained. Notably, as a consequence of the harvester nonlinearity, the studied inner bound exhibits a tradeoff between the delivered power and the rate of received information. It is shown that the tradeoff-characterizing input distribution is with mean zero and with asymmetric power allocations to the real and imaginary dimensions.
The Weight Distribution of Quasi-quadratic Residue Codes
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we begin by reviewing some of the known properties of QQR codes and proved that $PSL_2(p)$ acts on the extended QQR code when $p \equiv 3 \pmod 4$. Using this discovery, we then showed their weight polynomials satisfy a strong divisibility condition, namely that they are divisible by $(x^2 + y^2)^{d-1}$, where $d$ is the corresponding minimum distance. Using this result, we were able to construct an efficient algorithm to compute weight polynomials for QQR codes and correct errors in existing results on quadratic residue codes. In the second half, we use the relation between the weight of codewords and the number of points on hyperelliptic curves to prove that the symmetrized distribution of a set of hyperelliptic curves is asymptotically normal.
A systematic mapping study on cross-project defect prediction
Venue: arXiv cs.SE 2017
Year: 2017

Cross-Project-Defect Prediction as a sub-topic of defect prediction in general has become a popular topic in research. In this article, we present a systematic mapping study with the focus on CPDP, for which we found 50 publications. We summarize the approaches presented by each publication and discuss the case study setups and results. We discovered a great amount of heterogeneity in the way case studies are conducted, because of differences in the data sets, classifiers, performance metrics, and baseline comparisons used. Due to this, we could not compare the results of our review on a qualitative basis, i.e., determine which approaches perform best for CPDP.
Elastic and Secure Energy Forecasting in Cloud Environments
Venue: arXiv cs.DC 2017
Year: 2017

Although cloud computing offers many advantages with regards to adaption of resources, we witness either a strong resistance or a very slow adoption to those new offerings. One reason for the resistance is that (i) many technologies such as stream processing systems still lack of appropriate mechanisms for elasticity in order to fully harness the power of the cloud, and (ii) do not provide mechanisms for secure processing of privacy sensitive data such as when analyzing energy consumption data provided through smart plugs in the context of smart grids. In this white paper, we present our vision and approach for elastic and secure processing of streaming data. Our approach is based on StreamMine3G, an elastic event stream processing system and Intel's SGX technology that provides secure processing using enclaves. We highlight the key aspects of our approach and research challenges when using Intel's SGX technology.
Detecting Cyber-Physical Attacks in Additive Manufacturing using Digital Audio Signing
Venue: arXiv cs.CR 2017
Year: 2017

Additive Manufacturing (AM, or 3D printing) is a novel manufacturing technology that is being adopted in industrial and consumer settings. However, the reliance of this technology on computerization has raised various security concerns. In this paper we address sabotage via tampering with the 3D printing process. We present an object verification system using side-channel emanations: sound generated by onboard stepper motors. The contributions of this paper are following. We present two algorithms: one which generates a master audio fingerprint for the unmodified printing process, and one which computes the similarity between other print recordings and the master audio fingerprint. We then evaluate the deviation due to tampering, focusing on the detection of minimal tampering primitives. By detecting the deviation at the time of its occurrence, we can stop the printing process for compromised objects, thus save time and prevent material waste. We discuss impacts on the method by aspects like background noise, or different audio recorder positions. We further outline our vision with use cases incorporating our approach.
Exemplar or Matching: Modeling DCJ Problems with Unequal Content Genome Data
Venue: arXiv cs.DS 2017
Year: 2017

The edit distance under the DCJ model can be computed in linear time for genomes with equal content or with Indels. But it becomes NP-Hard in the presence of duplications, a problem largely unsolved especially when Indels are considered. In this paper, we compare two mainstream methods to deal with duplications and associate them with Indels: one by deletion, namely DCJ-Indel-Exemplar distance; versus the other by gene matching, namely DCJ-Indel-Matching distance. We design branch-and-bound algorithms with set of optimization methods to compute exact distances for both. Furthermore, median problems are discussed in alignment with both of these distance methods, which are to find a median genome that minimizes distances between itself and three given genomes. Lin-Kernighan (LK) heuristic is leveraged and powered up by sub-graph decomposition and search space reduction technologies to handle median computation. A wide range of experiments are conducted on synthetic data sets and real data sets to show pros and cons of these two distance metrics per se, as well as putting them in the median computation scenario.