OpenRaiser/Intern-Atlas huggingface dataset:
Filter Banks on Discrete Abelian Groups
Venue: arXiv cs.IT 2016
Year: 2016

In this work we provide polyphase, modulation, and frame theoretical analyses of a filter bank on a discrete abelian group. Thus, multidimensional or cyclic filter banks as well as filter banks for signals in $\ell^2(\mathbb{Z}^d\times \mathbb{Z}_s)$ or $\ell^2(\mathbb{Z}_r \times \mathbb{Z}_s)$ spaces are studied in a unified way. We obtain perfect reconstruction conditions and the corresponding frame bounds.
Personalized Persuasion for Social Interactions in Nursing Homes
Venue: arXiv cs.CY 2016
Year: 2016

This paper presents our preliminary investigation and approach towards a mixed physical-virtual technology for stimulating social interactions among and with older adults in nursing homes. We report on set of surveys, apps and focus groups aiming at understanding the different motivations and obstacles in promoting social interactions in institutionalised care. We then present our approach to address some of the key themes found, e.g., the technological disparity, lack of conversation topics and opportunities to interact
Channel Decorrelation For Stereo Acoustic Echo Cancellation In High-Quality Audio Communication
Venue: arXiv cs.SD 2016
Year: 2016

In this paper, we address an important problem in high-quality audio communication systems. Acoustic echo cancellation with stereo signals is generally an under-determined problem because of the generally important correlation that exists between the left and right channels. In this paper, we present a novel method of significantly reducing that correlation without affecting the audio quality. This method is perceptually motivated and combines a shaped comb-allpass (SCAL) filter with the injection of psychoacoustically masked noise. We show that the proposed method performs significantly better than other known methods for channel decorrelation.
Recovery of signals under the high order RIP condition via prior support information
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we study the recovery conditions of weighted $l_{1}$ minimization for signal reconstruction from incomplete linear measurements when partial prior support information is available. We obtain that a high order RIP condition can guarantee stable and robust recovery of signals in bounded $l_{2}$ and Dantzig selector noise settings. Meanwhile, we not only prove that the sufficient recovery condition of weighted $l_{1}$ minimization method is weaker than that of standard $l_{1}$ minimization method, but also prove that weighted $l_{1}$ minimization method provides better upper bounds on the reconstruction error in terms of the measurement noise and the compressibility of the signal, provided that the accuracy of prior support estimate is at least $50\%$. Furthermore, the condition is proved sharp.
Checkpointing to minimize completion time for Inter-dependent Parallel Processes on Volunteer Grids
Venue: arXiv cs.DC 2016
Year: 2016

Volunteer computing is being used successfully for large scale scientific computations. This research is in the context of Volpex, a programming framework that supports communicating parallel processes in a volunteer environment. Redundancy and checkpointing are combined to ensure consistent forward progress with Volpex in this unique execution environment characterized by heterogeneous failure prone nodes and interdependent replicated processes. An important parameter for optimizing performance with Volpex is the frequency of checkpointing. The paper presents a mathematical model to minimize the completion time for inter-dependent parallel processes running in a volunteer environment by finding a suitable checkpoint interval. Validation is performed with a sample real world application running on a pool of distributed volunteer nodes. The results indicate that the performance with our predicted checkpoint interval is fairly close to the best performance obtained empirically by varying the checkpoint interval.
A Vertical Channel Model of Molecular Communication based on Alcohol Molecules
Venue: arXiv cs.ET 2016
Year: 2016

The study of Molecular Communication(MC) is more and more prevalence, and channel model of MC plays an important role in the MC System. Since different propagation environment and modulation techniques produce different channel model, most of the research about MC are in horizontal direction,but in nature the communications between nano machines are in short range and some of the information transportation are in the vertical direction, such as transpiration of plants, biological pump in ocean, and blood transportation from heart to brain. Therefore, this paper we propose a vertical channel model which nano-machines communicate with each other in the vertical direction based on pure diffusion. We first propose a vertical molecular communication model, we mainly considered the gravity as the factor, though the channel model is also affected by other main factors, such as the flow of the medium, the distance between the transmitter and the receiver, the delay or sensitivity of the transmitter and the receiver. Secondly, we set up a test-bed for this vertical channel model, in order to verify the difference between the theory result and the experiment data. At last, we use the data we get from the experiment and the non-linear least squares method to get the parameters to make our channel model more accurate.
Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting
Venue: arXiv cs.LG 2016
Year: 2016

When a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order to minimize the number of calculations. The reduction in complexity is at least a constant and in the best case quadratic. We demonstrate that this method does indeed save significant computation time in a practical implementation, especially when the networks receives a large number of time-frames.
Subsampling for Graph Power Spectrum Estimation
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we focus on subsampling stationary random processes that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.
A Recursive Born Approach to Nonlinear Inverse Scattering
Venue: arXiv cs.LG 2016
Year: 2016

The Iterative Born Approximation (IBA) is a well-known method for describing waves scattered by semi-transparent objects. In this paper, we present a novel nonlinear inverse scattering method that combines IBA with an edge-preserving total variation (TV) regularizer. The proposed method is obtained by relating iterations of IBA to layers of a feedforward neural network and developing a corresponding error backpropagation algorithm for efficiently estimating the permittivity of the object. Simulations illustrate that, by accounting for multiple scattering, the method successfully recovers the permittivity distribution where the traditional linear inverse scattering fails.
Gold Standard for Expert Ranking: A Survey on the XWiki Dataset
Venue: arXiv cs.SE 2016
Year: 2016

We are designing an automated technique to find and recommend experts for helping in Requirements Engineering tasks, which can be done by ranking the available people by level of expertise. For evaluating the correctness of the rankings produced by the automated technique, we want to compare them to a gold standard. In this work, we ask external people to look at a set of discussions and to rank their participants, before to evaluate the reliability of these rankings to serve as a gold standard. We describe the setting and running of this survey, the method used to build the gold standard from the rankings of the subjects, and the analysis of the results to obtain and validate this gold standard. Through the analysis of the results, we conclude that we obtained a reasonable gold standard although we lack evidences to support its total correctness. We also made the interesting observation that the most reliable subjects build the least ordered rankings (i.e. has few ranks with several people per rank), which goes against the usual assumptions of Information Retrieval measures.
Queue Theory based Response Time Analyses for Geo-Information Processing Chain
Venue: arXiv cs.DC 2016
Year: 2016

Typical characteristics of remote sensing applications are concurrent tasks, such as those found in disaster rapid response. The existing composition approach to geographical information processing service chain, searches for an optimisation solution and is what can be deemed a "selfish" way. This way leads to problems of conflict amongst concurrent tasks and decreases the performance of all service chains. In this study, a non-cooperative game-based mathematical model to analyse the competitive relationships between tasks, is proposed. A best response function is used, to assure each task maintains utility optimisation by considering composition strategies of other tasks and quantifying conflicts between tasks. Based on this, an iterative algorithm that converges to Nash equilibrium is presented, the aim being to provide good convergence and maximise the utilisation of all tasks under concurrent task conditions. Theoretical analyses and experiments showed that the newly proposed method, when compared to existing service composition methods, has better practical utility in all tasks.
Performance Evaluation of Unified Parallel C for Molecular Dynamics
Venue: arXiv cs.DC 2016
Year: 2016

Partitioned Global Address Space (PGAS) integrates the concepts of shared memory programming and the control of data distribution and locality provided by message passing into a single parallel programming model. The purpose of allying distributed data with shared memory is to cultivate a locality-aware shared memory paradigm. PGAS is comprised of a single shared address space, which is partitioned among threads. Each thread has a portion of the shared address space in local memory and therefore it can exploit data locality by mainly doing computation on local data. Unified Parallel C (UPC) is a parallel extension of ISO C and an implementation of the PGAS model. In this paper, we evaluate the performance of UPC based on a real-world scenario from Molecular Dynamics.
Using Twitter to Understand Public Interest in Climate Change: The case of Qatar
Venue: arXiv cs.SI 2016
Year: 2016

Climate change has received an extensive attention from public opinion in the last couple of years, after being considered for decades as an exclusive scientific debate. Governments and world-wide organizations such as the United Nations are working more than ever on raising and maintaining public awareness toward this global issue. In the present study, we examine and analyze Climate Change conversations in Qatar's Twittersphere, and sense public awareness towards this global and shared problem in general, and its various related topics in particular. Such topics include but are not limited to politics, economy, disasters, energy and sandstorms. To address this concern, we collect and analyze a large dataset of 109 million tweets posted by 98K distinct users living in Qatar -- one of the largest emitters of CO2 worldwide. We use a taxonomy of climate change topics created as part of the United Nations Pulse project to capture the climate change discourse in more than 36K tweets. We also examine which topics people refer to when they discuss climate change, and perform different analysis to understand the temporal dynamics of public interest toward these topics.
Learning zeroth class dictionary for human action recognition
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, a discriminative two-phase dictionary learning framework is proposed for classifying human action by sparse shape representations, in which the first-phase dictionary is learned on the selected discriminative frames and the second-phase dictionary is built for recognition using reconstruction errors of the first-phase dictionary as input features. We propose a "zeroth class" trick for detecting undiscriminating frames of the test video and eliminating them before voting on the action categories. Experimental results on benchmarks demonstrate the effectiveness of our method.
Image and Depth from a Single Defocused Image Using Coded Aperture Photography
Venue: arXiv cs.CV 2016
Year: 2016

Depth from defocus and defocus deblurring from a single image are two challenging problems that are derived from the finite depth of field in conventional cameras. Coded aperture imaging is one of the techniques that is used for improving the results of these two problems. Up to now, different methods have been proposed for improving the results of either defocus deblurring or depth estimation. In this paper, a multi-objective function is proposed for evaluating and designing aperture patterns with the aim of improving the results of both depth from defocus and defocus deblurring. Pattern evaluation is performed by considering the scene illumination condition and camera system specification. Based on the proposed criteria, a single asymmetric pattern is designed that is used for restoring a sharp image and a depth map from a single input. Since the designed pattern is asymmetric, defocus objects on the two sides of the focal plane can be distinguished. Depth estimation is performed by using a new algorithm, which is based on image quality assessment criteria and can distinguish between blurred objects lying in front or behind the focal plane. Extensive simulations as well as experiments on a variety of real scenes are conducted to compare our aperture with previously proposed ones.
Saliency Detection for Improving Object Proposals
Venue: arXiv cs.CV 2016
Year: 2016

Object proposals greatly benefit object detection task in recent state-of-the-art works. However, the existing object proposals usually have low localization accuracy at high intersection over union threshold. To address it, we apply saliency detection to each bounding box to improve their quality in this paper. We first present a geodesic saliency detection method in contour, which is designed to find closed contours. Then, we apply it to each candidate box with multi-sizes, and refined boxes can be easily produced in the obtained saliency maps which are further used to calculate saliency scores for proposal ranking. Experiments on PASCAL VOC 2007 test dataset demonstrate the proposed refinement approach can greatly improve existing models.
Regression-based Hypergraph Learning for Image Clustering and Classification
Venue: arXiv cs.CV 2016
Year: 2016

Inspired by the recently remarkable successes of Sparse Representation (SR), Collaborative Representation (CR) and sparse graph, we present a novel hypergraph model named Regression-based Hypergraph (RH) which utilizes the regression models to construct the high quality hypergraphs. Moreover, we plug RH into two conventional hypergraph learning frameworks, namely hypergraph spectral clustering and hypergraph transduction, to present Regression-based Hypergraph Spectral Clustering (RHSC) and Regression-based Hypergraph Transduction (RHT) models for addressing the image clustering and classification issues. Sparse Representation and Collaborative Representation are employed to instantiate two RH instances and their RHSC and RHT algorithms. The experimental results on six popular image databases demonstrate that the proposed RH learning algorithms achieve promising image clustering and classification performances, and also validate that RH can inherit the desirable properties from both hypergraph models and regression models.
Secrecy Sum Rate Maximization in Non-Orthogonal Multiple Access
Venue: arXiv cs.IT 2016
Year: 2016

Non-orthogonal multiple access (NOMA) has been recognized as a promising technique for providing high data rates in 5G systems. This letter is to study physical layer security in a single-input single-output (SISO) NOMA system consisting of a transmitter, multiple legitimate users and an eavesdropper. The aim of this letter is to maximize the secrecy sum rate (SSR) of the NOMA system subject to the users' quality of service (QoS) requirements. We firstly identify the feasible region of the transmit power for satisfying all users' QoS requirements. Then we derive the closed-form expression of an optimal power allocation policy that maximizes the SSR. Numerical results are provided to show a significant SSR improvement by NOMA compared with conventional orthogonal multiple access (OMA).
Fast Scalable Construction of (Minimal Perfect Hash) Functions
Venue: arXiv cs.DS 2016
Year: 2016

Recent advances in random linear systems on finite fields have paved the way for the construction of constant-time data structures representing static functions and minimal perfect hash functions using less space with respect to existing techniques. The main obstruction for any practical application of these results is the cubic-time Gaussian elimination required to solve these linear systems: despite they can be made very small, the computation is still too slow to be feasible. In this paper we describe in detail a number of heuristics and programming techniques to speed up the resolution of these systems by several orders of magnitude, making the overall construction competitive with the standard and widely used MWHC technique, which is based on hypergraph peeling. In particular, we introduce broadword programming techniques for fast equation manipulation and a lazy Gaussian elimination algorithm. We also describe a number of technical improvements to the data structure which further reduce space usage and improve lookup speed. Our implementation of these techniques yields a minimal perfect hash function data structure occupying 2.24 bits per element, compared to 2.68 for MWHC-based ones, and a static function data structure which reduces the multiplicative overhead from 1.23 to 1.03.
Input graph: the hidden geometry in controlling complex networks
Venue: arXiv cs.SY 2016
Year: 2016

The ability to control a complex network towards a desired behavior relies on our understanding of the complex nature of these social and technological networks. The existence of numerous control schemes in a network promotes us to wonder: what is the underlying relationship of all possible input nodes? Here we introduce input graph, a simple geometry that reveals the complex relationship between all control schemes and input nodes. We prove that the node adjacent to an input node in the input graph will appear in another control scheme, and the connected nodes in input graph have the same type in control, which they are either all possible input nodes or not. Furthermore, we find that the giant components emerge in the input graphs of many real networks, which provides a clear topological explanation of bifurcation phenomenon emerging in dense networks and promotes us to design an efficient method to alter the node type in control. The findings provide an insight into control principles of complex networks and offer a general mechanism to design a suitable control scheme for different purposes.
NetMemex: Providing Full-Fidelity Traffic Archival
Venue: arXiv cs.NI 2016
Year: 2016

NetMemex explores efficient network traffic archival without any loss of information. Unlike NetFlow-like aggregation, NetMemex allows retrieving the entire packet data including full payload, which makes it useful in forensic analysis, networked and distributed system research, and network administration. Different from packet trace dumps, NetMemex performs sophisticated data compression for small storage space use and optimizes the data layout for fast query processing. NetMemex takes advantage of high-speed random access of flash drives and inexpensive storage space of hard disk drives. These efforts lead to a cost-effective yet high-performance full traffic archival system. We demonstrate that NetMemex can record full-fidelity traffic at near-Gbps rates using a single commodity machine, handling common queries at up to 90.1 K queries/second, at a low storage cost comparable to conventional hard disk-only traffic archival solutions.
Internet Computing: Using Reputation to Select Workers from a Pool
Venue: arXiv cs.GT 2016
Year: 2016

The assignment and execution of tasks over the Internet is an inexpensive solution in contrast with supercomputers. We consider an Internet-based Master-Worker task computing approach, such as SETI@home. A master process sends tasks, across the Internet, to worker processors. Workers execute, and report back a result. Unfortunately, the disadvantage of this approach is the unreliable nature of the worker processes. Through different studies, workers have been categorized as either malicious (always report an incorrect result), altruistic (always report a correct result), or rational (report whatever result maximizes their benefit). We develop a reputation-based mechanism that guarantees that, eventually, the master will always be receiving the correct task result. We model the behavior of the rational workers through reinforcement learning, and we present three different reputation types to choose, for each computational round, the most reputable from a pool of workers. As workers are not always available, we enhance our reputation scheme to select the most responsive workers. We prove sufficient conditions for eventual correctness under the different reputation types. Our analysis is complemented by simulations exploring various scenarios. Our simulation results expose interesting trade-offs among the different reputation types, workers availability, and cost.
Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications
Venue: arXiv cs.IT 2016
Year: 2016

This paper compares three candidate large-scale propagation path loss models for use over the entire microwave and millimeter-wave (mmWave) radio spectrum: the alpha-beta-gamma (ABG) model, the close-in (CI) free space reference distance model, and the CI model with a frequency-weighted path loss exponent (CIF). Each of these models have been recently studied for use in standards bodies such as 3GPP, and for use in the design of fifth generation (5G) wireless systems in urban macrocell, urban microcell, and indoor office and shopping mall scenarios. Here we compare the accuracy and sensitivity of these models using measured data from 30 propagation measurement datasets from 2 GHz to 73 GHz over distances ranging from 4 m to 1238 m. A series of sensitivity analyses of the three models show that the physically-based two-parameter CI model and three-parameter CIF model offer computational simplicity, have very similar goodness of fit (i.e., the shadow fading standard deviation), exhibit more stable model parameter behavior across frequencies and distances, and yield smaller prediction error in sensitivity testing across distances and frequencies, when compared to the four-parameter ABG model. Results show the CI model with a 1 m close-in reference distance is suitable for outdoor environments, while the CIF model is more appropriate for indoor modeling. The CI and CIF models are easily implemented in existing 3GPP models by making a very subtle modification -- by replacing a floating non-physically based constant with a frequency-dependent constant that represents free space path loss in the first meter of propagation.