OpenRaiser/Intern-Atlas huggingface dataset:
Distributed Bayesian Quickest Change Detection in Sensor Networks via Two-layer Large Deviation Analysis
Venue: arXiv cs.IT 2015
Year: 2015

We propose a distributed Bayesian quickest change detection algorithm for sensor networks, based on a random gossip inter-sensor communication structure. Without a control or fusion center, each sensor executes its local change detection procedure in a parallel and distributed fashion, interacting with its neighbor sensors via random inter-sensor communications to propagate information. By modeling the information propagation dynamics in the network as a Markov process, two-layer large deviation analysis is presented to analyze the performance of the proposed algorithm. The first-layer analysis shows that the relation between the probability of false alarm and the conditional averaged detection delay satisfies the large deviation principle, implying that the probability of false alarm according to a rare event decays to zero at an exponentially fast rate when the conditional averaged detection decay increases, where the Kullback-Leibler information number is established as a crucial factor. The second-layer analysis shows that the probability of the rare event that not all observations are available at a sensor decays to zero at an exponentially fast rate when the averaged number of communications increases, where the large deviation upper and lower bounds for this rate are also derived, based on which we show that the performance of the distributed algorithm converges exponentially fast to that of the centralized one, by proving that the defined distributed Kullback-Leibler information number converges to the centralized Kullback-Leibler information number.
Is Hamming distance the only way for matching binary image feature descriptors?
Venue: arXiv cs.CV 2015
Year: 2015

Brute force matching of binary image feature descriptors is conventionally performed using the Hamming distance. This paper assesses the use of alternative metrics in order to see whether they can produce feature correspondences that yield more accurate homography matrices. Two statistical tests, namely ANOVA (Analysis of Variance) and McNemar's test were employed for evaluation. Results show that Jackard-Needham and Dice metrics can display better performance for some descriptors. Yet, these performance differences were not found to be statistically significant.
Towards the Application of Linear Programming Methods For Multi-Camera Pose Estimation
Venue: arXiv cs.CV 2015
Year: 2015

We presented a separation based optimization algorithm which, rather than optimization the entire variables altogether, This would allow us to employ: 1) a class of nonlinear functions with three variables and 2) a convex quadratic multivariable polynomial, for minimization of reprojection error. Neglecting the inversion required to minimize the nonlinear functions, in this paper we demonstrate how separation allows eradication of matrix inversion.
On the joint impact of bias and power control on downlink spectral efficiency in cellular networks
Venue: arXiv cs.IT 2015
Year: 2015

Cell biasing and downlink transmit power are two controls that may be used to improve the spectral efficiency of cellular networks. With cell biasing, each mobile user associates with the base station offering, say, the highest biased signal to interference plus noise ratio. Biasing affects the cell association decisions of mobile users, but not the received instantaneous downlink transmission rates. Adjusting the collection of downlink transmission powers can likewise affect the cell associations, but in contrast with biasing, it also directly affects the instantaneous rates. This paper investigates the joint use of both cell biasing and transmission power control and their (individual and joint) effects on the statistical properties of the collection of per-user spectral efficiencies. Our analytical results and numerical investigations demonstrate in some cases a significant performance improvement in the Pareto efficient frontiers of both a mean-variance and throughput-fairness tradeoff from using both bias and power controls over using either control alone.
Hunting for Spammers: Detecting Evolved Spammers on Twitter
Venue: arXiv cs.IR 2015
Year: 2015

Once an email problem, spam has nowadays branched into new territories with disruptive effects. In particular, spam has established itself over the recent years as a ubiquitous, annoying, and sometimes threatening aspect of online social networks. Due to its prevalent existence, many works have tackled spam on Twitter from different angles. Spam is, however, a moving target. The new generation of spammers on Twitter has evolved into online creatures that are not easily recognizable by old detection systems. With the strong tangled spamming community, automatic tweeting scripts, and the ability to massively create Twitter accounts with a negligible cost, spam on Twitter is becoming smarter, fuzzier and harder to detect. Our own analysis of spam content on Arabic trending hashtags in Saudi Arabia results in an estimate of about three quarters of the total generated content. This alarming rate makes the development of adaptive spam detection techniques a very real and pressing need. In this paper, we analyze the spam content of trending hashtags on Saudi Twitter, and assess the performance of previous spam detection systems on our recently gathered dataset. Due to the escalating manipulation that characterizes newer spamming accounts, simple manual labeling currently leads to inaccurate results. In order to get reliable ground-truth data, we propose an updated manual classification algorithm that avoids the deficiencies of older manual approaches. We also adapt the previously proposed features to respond to spammers evading techniques, and use these features to build a new data-driven detection system.
Suporte à Mobilidade em Redes Mesh Sem Fio: estratégias comuns versus SDN
Venue: arXiv cs.NI 2015
Year: 2015

Wireless mesh networks have been presented as a robust, scalable and low cost solution to provide connectivity in long distance areas. However, given its nature, routing strategies must support seamless mobility of nodes while enabling operation with a good performance and fast self-recovery from links fault. The routing approaches that meet these requirements are based on protocols usually used in ad-hoc networks (e.g. OLSR and B.A.T.M.A.N.) and, more recently, in the SDN paradigm (e.g. OpenWiMesh), each one of them having its own pros and cons. Therefore, it is important to evaluate the benefits and impacts of each approach, taking into account models and metrics inherent to the mobility. In this paper, we propose a simplified implementation of mobility support in OpenWiMesh and evaluate its performance compared to other protocols, using differents mobility and data traffic models. The chosen metrics are based on packet loss, occupation of the links with control traffic and delay. Simulated results show that the SDN approach perform better than the classic protocols, beyond flexibility and programmability of SDN itself.
'healthWISE' An Android Application For Personal Health And Nutrition Management
Venue: arXiv cs.CY 2015
Year: 2015

Now-a-days, people are getting more health conscious and tend to keep a check on nutritional gain from the packed food products they consume. This application christened 'healthWISE' helps a mobile user to scan/enter the bar-code on the packed food product, know the nutritional information, and upon entering the quantity to be consumed, it displays the energy the individual can consume. If the user has exceeded the stipulated amount of calories, then it suggests the exercise to burn the extra number of calories.
Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Venue: arXiv cs.CV 2015
Year: 2015

Given a food image, can a fine-grained object recognition engine tell "which restaurant which dish" the food belongs to? Such ultra-fine grained image recognition is the key for many applications like search by images, but it is very challenging because it needs to discern subtle difference between classes while dealing with the scarcity of training data. Fortunately, the ultra-fine granularity naturally brings rich relationships among object classes. This paper proposes a novel approach to exploit the rich relationships through bipartite-graph labels (BGL). We show how to model BGL in an overall convolutional neural networks and the resulting system can be optimized through back-propagation. We also show that it is computationally efficient in inference thanks to the bipartite structure. To facilitate the study, we construct a new food benchmark dataset, which consists of 37,885 food images collected from 6 restaurants and totally 975 menus. Experimental results on this new food and three other datasets demonstrates BGL advances previous works in fine-grained object recognition. An online demo is available at http://www.f-zhou.com/fg_demo/.
Incremental approaches to knowledge reduction of covering decision information systems with variations of coverings
Venue: arXiv cs.IT 2015
Year: 2015

In practical situations, calculating approximations of concepts is the central step for knowledge reduction of dynamic covering decision information system, which has received growing interests of researchers in recent years. In this paper, the second and sixth lower and upper approximations of sets in dynamic covering information systems with variations of coverings are computed from the perspective of matrix using incremental approaches. Especially, effective algorithms are designed for calculating the second and sixth lower and upper approximations of sets in dynamic covering information systems with the immigration of coverings. Experimental results demonstrate that the designed algorithms provide an efficient and effective method for constructing the second and sixth lower and upper approximations of sets in dynamic covering information systems. Two examples are explored to illustrate the process of knowledge reduction of dynamic covering decision information systems with the covering immigration.
Distributed Balanced Partitioning via Linear Embedding
Venue: arXiv cs.DC 2015
Year: 2015

Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems, e.g., in some cases, a big graph can be chopped into pieces that fit on one machine to be processed independently before stitching the results together. In other cases, links between different parts may show up in the running time and/or network communications cost. We study a distributed balanced partitioning problem where the goal is to partition the vertices of a given graph into k pieces so as to minimize the total cut size. Our algorithm is composed of a few steps that are easily implementable in distributed computation frameworks. The algorithm first embeds nodes of the graph onto a line, and then processes nodes in a distributed manner guided by the linear embedding order. We examine various ways to find the first embedding, e.g., via a hierarchical clustering or Hilbert curves. Then we apply four different techniques including local swaps, minimum cuts on the boundaries of partitions, as well as contraction and dynamic programming. As our empirical study, we compare the above techniques with each other, and also to previous work in distributed graph algorithms, e.g., a label propagation method, FENNEL and Spinner. We report our results both on a private map graph and several public social networks, and show that our results beat previous distributed algorithms: e.g., compared to the label propagation algorithm, we report an improvement of 15-25% in the cut value. We also observe that our algorithms allow for scalable distributed implementation for any number of partitions. Finally, we apply our techniques for the Google Maps Driving Directions to minimize the number of multi-shard queries with the goal of saving in CPU usage. During live experiments, we observe an ~40% drop in the number of multi-shard queries when comparing our method with a standard geography-based method.
Window-Object Relationship Guided Representation Learning for Generic Object Detections
Venue: arXiv cs.CV 2015
Year: 2015

In existing works that learn representation for object detection, the relationship between a candidate window and the ground truth bounding box of an object is simplified by thresholding their overlap. This paper shows information loss in this simplification and picks up the relative location/size information discarded by thresholding. We propose a representation learning pipeline to use the relationship as supervision for improving the learned representation in object detection. Such relationship is not limited to object of the target category, but also includes surrounding objects of other categories. We show that image regions with multiple contexts and multiple rotations are effective in capturing such relationship during the representation learning process and in handling the semantic and visual variation caused by different window-object configurations. Experimental results show that the representation learned by our approach can improve the object detection accuracy by 6.4% in mean average precision (mAP) on ILSVRC2014. On the challenging ILSVRC2014 test dataset, 48.6% mAP is achieved by our single model and it is the best among published results. On PASCAL VOC, it outperforms the state-of-the-art result of Fast RCNN by 3.3% in absolute mAP.
Using Symmetry to Schedule Classical Matrix Multiplication
Venue: arXiv cs.DC 2015
Year: 2015

Presented with a new machine with a specific interconnect topology, algorithm designers use intuition about the symmetry of the algorithm to design time and communication-efficient schedules that map the algorithm to the machine. Is there a systematic procedure for designing schedules? We present a new technique to design schedules for algorithms with no non-trivial dependencies, focusing on the classical matrix multiplication algorithm. We model the symmetry of algorithm with the set of instructions $X$ as the action of the group formed by the compositions of bijections from the set $X$ to itself. We model the machine as the action of the group $N\times Δ$, where $N$ and $Δ$ represent the interconnect topology and time increments respectively, on the set $P\times T$ of processors iterated over time steps. We model schedules as symmetry-preserving equivariant maps between the set $X$ and a subgroup of its symmetry and the set $P\times T$ with the symmetry $N\timesΔ$. Such equivariant maps are the solutions of a set of algebraic equations involving group homomorphisms. We associate time and communication costs with the solutions to these equations. We solve these equations for the classical matrix multiplication algorithm and show that equivariant maps correspond to time- and communication-efficient schedules for many topologies. We recover well known variants including the Cannon's algorithm and the communication-avoiding "2.5D" algorithm for toroidal interconnects, systolic computation for planar hexagonal VLSI arrays, recursive algorithms for fat-trees, the cache-oblivious algorithm for the ideal cache model, and the space-bounded schedule for the parallel memory hierarchy model. This suggests that the design of a schedule for a new class of machines can be motivated by solutions to algebraic equations.
Motion model transitions in GPS-IMU sensor fusion for user tracking in augmented reality
Venue: arXiv cs.OH 2015
Year: 2015

Finding the position of the user is an important processing step for augmented reality (AR) applications. This paper investigates the use of different motion models in order to choose the most suitable one, and eventually reduce the Kalman filter errors in sensor fusion for such applications where the accuracy of user tracking is crucial. A Deterministic Finite Automaton (DFA) was employed using the innovation parameters of the filter. Results show that the approach presented here reduces the filter error compared to a static model and prevents filter divergence. The approach was tested on a simple AR game in order to justify the accuracy and performance of the algorithm.
Latency Evaluation of a Virtualized MME
Venue: arXiv cs.NI 2015
Year: 2015

Network Virtualization is one of the key technologies for developing the future mobile networks. However, the performance of virtual mobile entities may not be sufficient for delivering the service required for future networks in terms of throughput or service time. In addition, to take advantage of the virtualization capabilities, a criterion to decide when to scale out the number of instances is a must. In this paper we propose an LTE virtualized Mobility Management Entity queue model to evaluate its service time for a given signaling workload. The estimation of this latency can serve to decide how many processing instances should be deployed to provide a target service. Additionally, we provide a compound data traffic model for the future mobile applications, and we predict theoretically the control workload that it will generate. Finally, we evaluate the virtualized Mobility Management Entity overall delay by simulation, providing insights for selecting the number of virtual instances for a given number of users.
5G Ultra-Dense Cellular Networks
Venue: arXiv cs.NI 2015
Year: 2015

Traditional ultra-dense wireless networks are recommended as a complement for cellular networks and are deployed in partial areas, such as hotspot and indoor scenarios. Based on the massive multiple-input multi-output (MIMO) antennas and the millimeter wavecommunication technologies, the 5G ultra-dense cellular network is proposed to deploy in overall cellular scenarios. Moreover, a distribution network architecture is presented for 5G ultra-dense cellular networks. Furthermore, the backhaul network capacity and the backhaul energy efficiency of ultra-dense cellular networks are investigated to answer an important question, i.e., how much densification can be deployed for 5G ultra-dense cellular networks. Simulation results reveal that there exist densification limits for 5G ultra-dense cellualr networks with backhaul network capacity and backhaul energy efficiency constraints.
Gated networks: an inventory
Venue: arXiv cs.LG 2015
Year: 2015

Gated networks are networks that contain gating connections, in which the outputs of at least two neurons are multiplied. Initially, gated networks were used to learn relationships between two input sources, such as pixels from two images. More recently, they have been applied to learning activity recognition or multi-modal representations. The aims of this paper are threefold: 1) to explain the basic computations in gated networks to the non-expert, while adopting a standpoint that insists on their symmetric nature. 2) to serve as a quick reference guide to the recent literature, by providing an inventory of applications of these networks, as well as recent extensions to the basic architecture. 3) to suggest future research directions and applications.
Toward more localized local algorithms: removing assumptions concerning global knowledge
Venue: arXiv cs.DC 2015
Year: 2015

Numerous sophisticated local algorithm were suggested in the literature for various fundamental problems. Notable examples are the MIS and $(Δ+1)$-coloring algorithms by Barenboim and Elkin [6], by Kuhn [22], and by Panconesi and Srinivasan [34], as well as the $O(Δ2)$-coloring algorithm by Linial [28]. Unfortunately, most known local algorithms (including, in particular, the aforementioned algorithms) are non-uniform, that is, local algorithms generally use good estimations of one or more global parameters of the network, e.g., the maximum degree $Δ$ or the number of nodes n. This paper provides a method for transforming a non-uniform local algorithm into a uniform one. Furthermore , the resulting algorithm enjoys the same asymp-totic running time as the original non-uniform algorithm. Our method applies to a wide family of both deterministic and randomized algorithms. Specifically, it applies to almost all state of the art non-uniform algorithms for MIS and Maximal Matching, as well as to many results concerning the coloring problem. (In particular, it applies to all aforementioned algorithms.) To obtain our transformations we introduce a new distributed tool called pruning algorithms, which we believe may be of independent interest.
Towards Massive Machine Type Cellular Communications
Venue: arXiv cs.IT 2015
Year: 2015

Cellular networks have been engineered and optimized to carrying ever-increasing amounts of mobile data, but over the last few years, a new class of applications based on machine-centric communications has begun to emerge. Automated devices such as sensors, tracking devices, and meters - often referred to as machine-to-machine (M2M) or machine-type communications (MTC) - introduce an attractive revenue stream for mobile network operators, if a massive number of them can be efficiently supported. The novel technical challenges posed by MTC applications include increased overhead and control signaling as well as diverse application-specific constraints such as ultra-low complexity, extreme energy efficiency, critical timing, and continuous data intensive uploading. This paper explains the new requirements and challenges that large-scale MTC applications introduce, and provides a survey on key techniques for overcoming them. We focus on the potential of 4.5G and 5G networks to serve both the high data rate needs of conventional human-type communications (HTC) subscribers and the forecasted billions of new MTC devices. We also opine on attractive economic models that will enable this new class of cellular subscribers to grow to its full potential.
Integral Concurrent Learning: Adaptive Control with Parameter Convergence without PE or State Derivatives
Venue: arXiv cs.SY 2015
Year: 2015

Concurrent learning is a recently developed adaptive update scheme that can be used to guarantee parameter convergence without requiring persistent excitation. However, this technique requires knowledge of state derivatives, which are usually not directly sensed and therefore must be estimated. A novel integral concurrent learning method is developed in this paper that removes the need to estimate state derivatives while maintaining parameter convergence properties. A Monte Carlo simulation illustrates improved robustness to noise compared to the traditional derivative formulation.
Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions
Venue: arXiv cs.NE 2015
Year: 2015

NM-landscapes have been recently introduced as a class of tunable rugged models. They are a subset of the general interaction models where all the interactions are of order less or equal $M$. The Boltzmann distribution has been extensively applied in single-objective evolutionary algorithms to implement selection and study the theoretical properties of model-building algorithms. In this paper we propose the combination of the multi-objective NM-landscape model and the Boltzmann distribution to obtain Pareto-front approximations. We investigate the joint effect of the parameters of the NM-landscapes and the probabilistic factorizations in the shape of the Pareto front approximations.