OpenRaiser/Intern-Atlas huggingface dataset:
Learning measures of semi-additive behaviour
Venue: arXiv cs.AI 2015
Year: 2015

In business analytics, measure values, such as sales numbers or volumes of cargo transported, are often summed along values of one or more corresponding categories, such as time or shipping container. However, not every measure should be added by default (e.g., one might more typically want a mean over the heights of a set of people); similarly, some measures should only be summed within certain constraints (e.g., population measures need not be summed over years). In systems such as Watson Analytics, the exact additive behaviour of a measure is often determined by a human expert. In this work, we propose a small set of features for this issue. We use these features in a case-based reasoning approach, where the system suggests an aggregation behaviour, with 86% accuracy in our collected dataset.
Hybrid MIMO Architectures for Millimeter Wave Communications: Phase Shifters or Switches?
Venue: arXiv cs.IT 2015
Year: 2015

Hybrid analog/digital MIMO architectures were recently proposed as an alternative for fully-digitalprecoding in millimeter wave (mmWave) wireless communication systems. This is motivated by the possible reduction in the number of RF chains and analog-to-digital converters. In these architectures, the analog processing network is usually based on variable phase shifters. In this paper, we propose hybrid architectures based on switching networks to reduce the complexity and the power consumption of the structures based on phase shifters. We define a power consumption model and use it to evaluate the energy efficiency of both structures. To estimate the complete MIMO channel, we propose an open loop compressive channel estimation technique which is independent of the hardware used in the analog processing stage. We analyze the performance of the new estimation algorithm for hybrid architectures based on phase shifters and switches. Using the estimated, we develop two algorithms for the design of the hybrid combiner based on switches and analyze the achieved spectral efficiency. Finally, we study the trade-offs between power consumption, hardware complexity, and spectral efficiency for hybrid architectures based on phase shifting networks and switching networks. Numerical results show that architectures based on switches obtain equal or better channel estimation performance to that obtained using phase shifters, while reducing hardware complexity and power consumption. For equal power consumption, all the hybrid architectures provide similar spectral efficiencies.
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
Venue: arXiv cs.CV 2015
Year: 2015

Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing. It aims to learn hierarchical representations of data by using deep architecture models. In a smart city, a lot of data (e.g. videos captured from many distributed sensors) need to be automatically processed and analyzed. In this paper, we review the deep learning algorithms applied to video analytics of smart city in terms of different research topics: object detection, object tracking, face recognition, image classification and scene labeling.
5G green cellular networks considering power allocation schemes
Venue: arXiv cs.NI 2015
Year: 2015

It is important to assess the effect of transmit power allocation schemes on the energy consumption on random cellular networks. The energy efficiency of 5G green cellular networks with average and water-filling power allocation schemes is studied in this paper. Based on the proposed interference and achievable rate model, an energy efficiency model is proposed for MIMO random cellular networks. Furthermore, the energy efficiency with average and water-filling power allocation schemes are presented, respectively. Numerical results indicate that the maximum limits of energy efficiency are always there for MIMO random cellular networks with different intensity ratios of mobile stations (MSs) to base stations (BSs) and channel conditions. Compared with the average power allocation scheme, the water-filling scheme is shown to improve the energy efficiency of MIMO random cellular networks when channel state information (CSI) is attainable for both transmitters and receivers.
Semantic Boolean Arabic Information Retrieval
Venue: arXiv cs.IR 2015
Year: 2015

Arabic language is one of the most widely spoken languages. This language has a complex morphological structure and is considered as one of the most prolific languages in terms of article linguistic. Therefore, Arabic Information Retrieval (AIR) models need specific techniques to deal with this complex morphological structure. This paper aims to develop an integrate AIR frameworks. It lists and analysis the different Information Retrieval (IR) methods and techniques such as query processing, stemming and indexing which are used in AIR systems. We conclude that AIR frameworks have a weakness to deal with semantic in term of indexing, Boolean model, Latent Semantic Analysis (LSA), Latent Semantic Index (LSI) and semantic ranking. Therefore, semantic Boolean IR framework is proposed in this paper. This model is implemented and the precision, recall and run time are measured and compared with the traditional IR model.
Deductive Way of Reasoning about the Internet AS Level Topology
Venue: arXiv cs.NI 2015
Year: 2015

Our current understanding about the AS level topology of the Internet is based on measurements and inductive-type models which set up rules describing the behavior (node and edge dynamics) of the individual ASes and generalize the consequences of these individual actions for the complete AS ecosystem using induction. In this paper we suggest a third, deductive approach in which we have premises for the whole AS system and the consequences of these premises are determined through deductive reasoning. We show that such a deductive approach can give complementary insights into the topological properties of the AS graph. While inductive models can mostly reflect high level statistics (e.g. degree distribution, clustering, diameter), deductive reasoning can identify omnipresent subgraphs and peering likelihood. We also propose a model, called YEAS, incorporating our deductive analytical findings that produces topologies contain both traditional and novel metrics for the AS level Internet.
Graph-theoretic autofill
Venue: arXiv cs.HC 2015
Year: 2015

Imagine a website that asks the user to fill in a web form and -- based on the input values -- derives a relevant figure, for instance an expected salary, a medical diagnosis or the market value of a house. How to deal with missing input values at run-time? Besides using fixed defaults, a more sophisticated approach is to use predefined dependencies (logical or correlational) between different fields to autofill missing values in an iterative way. Directed loopless graphs (in which cycles are allowed) are the ideal mathematical model to formalize these dependencies. We present two new graph-theoretic approaches to filling missing values at run-time.
Mapping the Region of Entropic Vectors with Support Enumeration & Information Geometry
Venue: arXiv cs.IT 2015
Year: 2015

The region of entropic vectors is a convex cone that has been shown to be at the core of many fundamental limits for problems in multiterminal data compression, network coding, and multimedia transmission. This cone has been shown to be non-polyhedral for four or more random variables, however its boundary remains unknown for four or more discrete random variables. Methods for specifying probability distributions that are in faces and on the boundary of the convex cone are derived, then utilized to map optimized inner bounds to the unknown part of the entropy region. The first method utilizes tools and algorithms from abstract algebra to efficiently determine those supports for the joint probability mass functions for four or more random variables that can, for some appropriate set of non-zero probabilities, yield entropic vectors in the gap between the best known inner and outer bounds. These supports are utilized, together with numerical optimization over non-zero probabilities, to provide inner bounds to the unknown part of the entropy region. Next, information geometry is utilized to parameterize and study the structure of probability distributions on these supports yielding entropic vectors in the faces of entropy and in the unknown part of the entropy region.
Analyzing the Impact of Access Point Density on the Performance of Finite-Area Networks
Venue: arXiv cs.IT 2015
Year: 2015

Assuming a network of infinite extent, several researchers have analyzed small-cell networks using a Poisson point process (PPP) location model, leading to simple analytic expressions. The general assumption has been that these results apply to finite-area networks as well. However, do the results of infinite-area networks apply to finite-area networks? In this paper, we answer this question by obtaining an accurate approximation for the achievable signal-to-interference-plus-noise ratio (SINR) and user capacity in the downlink of a \textit{finite-area} network with \textit{a fixed number of} access points (APs). The APs are uniformly distributed within the area of interest. Our analysis shows that, crucially, the results of infinite-area networks are very different from those for finite-area networks of low-to-medium AP density. Comprehensive simulations are used to illustrate the accuracy of our analysis. For practical values of signal transmit powers and AP densities, the analytic expressions capture the behavior of the system well. As an added benefit, the formulations developed here can be used in parametric studies for network design. Here, the analysis is used to obtain the required number of APs to guarantee a desired target capacity in a finite-area network.
Tighter Bounds on the Inefficiency Ratio of Stable Equilibria in Load Balancing Games
Venue: arXiv cs.GT 2015
Year: 2015

In this paper we study the inefficiency ratio of stable equilibria in load balancing games introduced by Asadpour and Saberi [3]. We prove tighter lower and upper bounds of 7/6 and 4/3, respectively. This improves over the best known bounds in problem (19/18 and 3/2, respectively). Equivalently, the results apply to the question of how well the optimum for the $L_2$ -norm can approximate the $L_{\infty}$-norm (makespan) in identical machines scheduling.
Highly Dynamic Spectrum Management within Licensed Shared Access Regulatory Framework
Venue: arXiv cs.NI 2015
Year: 2015

Historical fragmentation in spectrum access models accentuates the need for novel concepts that allow for efficient sharing of already available but underutilized spectrum. The emerging Licensed Shared Access (LSA) regulatory framework is expected to enable more advanced spectrum sharing between a limited number of users while guaranteeing their much needed interference protection. However, the ultimate benefits of LSA may in practice be constrained by space-time availability of the LSA bands. Hence, more dynamic LSA spectrum management is required to leverage such real-time variability and sustain reliability when e.g., the original spectrum user suddenly revokes the previously granted frequency bands as they are required again. In this article, we maintain the vision of highly dynamic LSA architecture and rigorously study its future potential: from reviewing market opportunities and discussing available technology implementations to conducting performance evaluation of LSA dynamics and outlining the standardization landscape. Our investigations are based on a comprehensive system-level evaluation framework, which has been specifically designed to assess highly dynamic LSA deployments.
Scalable Package Queries in Relational Database Systems
Venue: arXiv cs.DB 2015
Year: 2015

Traditional database queries follow a simple model: they define constraints that each tuple in the result must satisfy. This model is computationally efficient, as the database system can evaluate the query conditions on each tuple individually. However, many practical, real-world problems require a collection of result tuples to satisfy constraints collectively, rather than individually. In this paper, we present package queries, a new query model that extends traditional database queries to handle complex constraints and preferences over answer sets. We develop a full-fledged package query system, implemented on top of a traditional database engine. Our work makes several contributions. First, we design PaQL, a SQL-based query language that supports the declarative specification of package queries. We prove that PaQL is as least as expressive as integer linear programming, and therefore, evaluation of package queries is in general NP-hard. Second, we present a fundamental evaluation strategy that combines the capabilities of databases and constraint optimization solvers to derive solutions to package queries. The core of our approach is a set of translation rules that transform a package query to an integer linear program. Third, we introduce an offline data partitioning strategy allowing query evaluation to scale to large data sizes. Fourth, we introduce SketchRefine, a scalable algorithm for package evaluation, with strong approximation guarantees ($(1 \pmε)^6$-factor approximation). Finally, we present extensive experiments over real-world and benchmark data. The results demonstrate that SketchRefine is effective at deriving high-quality package results, and achieves runtime performance that is an order of magnitude faster than directly using ILP solvers over large datasets.
Estimation of Radio Channel Parameters in Case of an Unknown Transmitter
Venue: arXiv cs.IT 2015
Year: 2015

This paper investigates the estimation of radio channel parameters from receiver data, whereby the transmitter is fully unknown. We use a multipath model to describe the radio channel between transmitter and receiver. According to this model, we discuss the accessibility of parameters for estimation. Based on the Maximum-Likelihood principle, we derive a cost function. A second cost function is derived from the cross relation between the receiver channels. To estimate the parameters, we seek for the minimum of these cost functions. The performance of the presented cost functions are compared in simulations.
Deep Feature Learning with Relative Distance Comparison for Person Re-identification
Venue: arXiv cs.CV 2015
Year: 2015

Identifying the same individual across different scenes is an important yet difficult task in intelligent video surveillance. Its main difficulty lies in how to preserve similarity of the same person against large appearance and structure variation while discriminating different individuals. In this paper, we present a scalable distance driven feature learning framework based on the deep neural network for person re-identification, and demonstrate its effectiveness to handle the existing challenges. Specifically, given the training images with the class labels (person IDs), we first produce a large number of triplet units, each of which contains three images, i.e. one person with a matched reference and a mismatched reference. Treating the units as the input, we build the convolutional neural network to generate the layered representations, and follow with the $L2$ distance metric. By means of parameter optimization, our framework tends to maximize the relative distance between the matched pair and the mismatched pair for each triplet unit. Moreover, a nontrivial issue arising with the framework is that the triplet organization cubically enlarges the number of training triplets, as one image can be involved into several triplet units. To overcome this problem, we develop an effective triplet generation scheme and an optimized gradient descent algorithm, making the computational load mainly depends on the number of original images instead of the number of triplets. On several challenging databases, our approach achieves very promising results and outperforms other state-of-the-art approaches.
A New Approach of Gray Images Binarization with Threshold Methods
Venue: arXiv cs.CV 2015
Year: 2015

The paper presents some aspects of the (gray level) image binarization methods used in artificial vision systems. It is introduced a new approach of gray level image binarization for artificial vision systems dedicated to industrial automation temporal thresholding. In the first part of the paper are extracted some limitations of using the global optimum thresholding in gray level image binarization. In the second part of this paper are presented some aspects of the dynamic optimum thresholding method for gray level image binarization. Starting from classic methods of global and dynamic optimal thresholding of the gray level images in the next section are introduced the concepts of temporal histogram and temporal thresholding. In the final section are presented some practical aspects of the temporal thresholding method in artificial vision applications form the moving scene in robotic automation class; pointing out the influence of the acquisition frequency on the methods results.
Mathematics of Domains
Venue: arXiv cs.LO 2015
Year: 2015

Two groups of naturally arising questions in the mathematical theory of domains for denotational semantics are addressed. Domains are equipped with Scott topology and represent data types. Scott continuous functions represent computable functions and form the most popular continuous model of computations. Covariant Logic of Domains: Domains are represented as sets of theories, and Scott continuous functions are represented as input-output inference engines. The questions addressed are: A. What constitutes a subdomain? Do subdomains of a given domain $A$ form a domain? B. Which retractions are finitary? C. What is the essence of generalizations of information systems based on non-reflexive logics? Are these generalizations restricted to continuous domains? Analysis on Domains: D. How to describe Scott topologies via generalized distance functions satisfying the requirement of Scott continuity ("abstract computability")? The answer is that the axiom $ρ(x, x) = 0$ is incompatible with Scott continuity of distance functions. The resulting \bf relaxed metrics are studied. E. Is it possible to obtain Scott continuous relaxed metrics via measures of domain subsets representing positive and negative information about domain elements? The positive answer is obtained via the discovery of the novel class of co-continuous valuations on the systems of Scott open sets. Some of these natural questions were studied earlier. However, in each case a novel approach is presented, and the answers are supplied with much more compelling and clear justifications, than were known before.
RNN Fisher Vectors for Action Recognition and Image Annotation
Venue: arXiv cs.CV 2015
Year: 2015

Recurrent Neural Networks (RNNs) have had considerable success in classifying and predicting sequences. We demonstrate that RNNs can be effectively used in order to encode sequences and provide effective representations. The methodology we use is based on Fisher Vectors, where the RNNs are the generative probabilistic models and the partial derivatives are computed using backpropagation. State of the art results are obtained in two central but distant tasks, which both rely on sequences: video action recognition and image annotation. We also show a surprising transfer learning result from the task of image annotation to the task of video action recognition.
Action Recognition with Image Based CNN Features
Venue: arXiv cs.CV 2015
Year: 2015

Most of human actions consist of complex temporal compositions of more simple actions. Action recognition tasks usually relies on complex handcrafted structures as features to represent the human action model. Convolutional Neural Nets (CNN) have shown to be a powerful tool that eliminate the need for designing handcrafted features. Usually, the output of the last layer in CNN (a layer before the classification layer -known as fc7) is used as a generic feature for images. In this paper, we show that fc7 features, per se, can not get a good performance for the task of action recognition, when the network is trained only on images. We present a feature structure on top of fc7 features, which can capture the temporal variation in a video. To represent the temporal components, which is needed to capture motion information, we introduced a hierarchical structure. The hierarchical model enables to capture sub-actions from a complex action. At the higher levels of the hierarchy, it represents a coarse capture of action sequence and lower levels represent fine action elements. Furthermore, we introduce a method for extracting key-frames using binary coding of each frame in a video, which helps to improve the performance of our hierarchical model. We experimented our method on several action datasets and show that our method achieves superior results compared to other state-of-the-arts methods.
Comparing Weakest Precondition and Weakest Liberal Precondition
Venue: arXiv cs.PL 2015
Year: 2015

In this article we investigate the relationships between the classical notions of weakest precondition and weakest liberal precondition, and provide several results, namely that in general, weakest liberal precondition is neither stronger nor weaker than weakest precondition, however, given a deterministic and terminating sequential while program and a postcondition, they are equivalent. Hence, in such situation, it does not matter which definition is used.
Performance Analysis of CSMA/CA based Medium Access in Full Duplex Wireless Communications
Venue: arXiv cs.NI 2015
Year: 2015

Full duplex communication promises a paradigm shift in wireless networks by allowing simultaneous packet transmission and reception within the same channel. While recent prototypes indicate the feasibility of this concept, there is a lack of rigorous theoretical development on how full duplex impacts medium access control (MAC) protocols in practical wireless networks. In this paper, we formulate the first analytical model of a CSMA/CA based full duplex MAC protocol for a wireless LAN network composed of an access point serving mobile clients. There are two major contributions of our work: First, our Markov chain-based approach results in closed form expressions of throughput for both the access point and the clients for this new class of networks. Second, our study provides quantitative insights on how much of the classical hidden terminal problem can be mitigated through full duplex. We specifically demonstrate that the improvement in the network throughput is up to 35-40\% over the half duplex case. Our analytical models are verified through packet level simulations in ns-2. Our results also reveal the benefit of full duplex under varying network configuration parameters, such as number of hidden terminals, client density, and contention window size.
Policy Gradient Methods for Off-policy Control
Venue: arXiv cs.AI 2015
Year: 2015

Off-policy learning refers to the problem of learning the value function of a way of behaving, or policy, while following a different policy. Gradient-based off-policy learning algorithms, such as GTD and TDC/GQ, converge even when using function approximation and incremental updates. However, they have been developed for the case of a fixed behavior policy. In control problems, one would like to adapt the behavior policy over time to become more greedy with respect to the existing value function. In this paper, we present the first gradient-based learning algorithms for this problem, which rely on the framework of policy gradient in order to modify the behavior policy. We present derivations of the algorithms, a convergence theorem, and empirical evidence showing that they compare favorably to existing approaches.