OpenRaiser/Intern-Atlas huggingface dataset:
Handwritten and Printed Text Separation in Real Document
Venue: arXiv cs.CV 2013
Year: 2013

The aim of the paper is to separate handwritten and printed text from a real document embedded with noise, graphics including annotations. Relying on run-length smoothing algorithm (RLSA), the extracted pseudo-lines and pseudo-words are used as basic blocks for classification. To handle this, a multi-class support vector machine (SVM) with Gaussian kernel performs a first labelling of each pseudo-word including the study of local neighbourhood. It then propagates the context between neighbours so that we can correct possible labelling errors. Considering running time complexity issue, we propose linear complexity methods where we use k-NN with constraint. When using a kd-tree, it is almost linearly proportional to the number of pseudo-words. The performance of our system is close to 90%, even when very small learning dataset where samples are basically composed of complex administrative documents.
On Improving Energy Efficiency within Green Femtocell Networks: A Hierarchical Reinforcement Learning Approach
Venue: arXiv cs.LG 2013
Year: 2013

One of the efficient solutions of improving coverage and increasing capacity in cellular networks is the deployment of femtocells. As the cellular networks are becoming more complex, energy consumption of whole network infrastructure is becoming important in terms of both operational costs and environmental impacts. This paper investigates energy efficiency of two-tier femtocell networks through combining game theory and stochastic learning. With the Stackelberg game formulation, a hierarchical reinforcement learning framework is applied for studying the joint expected utility maximization of macrocells and femtocells subject to the minimum signal-to-interference-plus-noise-ratio requirements. In the learning procedure, the macrocells act as leaders and the femtocells are followers. At each time step, the leaders commit to dynamic strategies based on the best responses of the followers, while the followers compete against each other with no further information but the leaders' transmission parameters. In this paper, we propose two reinforcement learning based intelligent algorithms to schedule each cell's stochastic power levels. Numerical experiments are presented to validate the investigations. The results show that the two learning algorithms substantially improve the energy efficiency of the femtocell networks.
Discovering link communities in complex networks by exploiting link dynamics
Venue: arXiv cs.SI 2013
Year: 2013

Discovery of communities in complex networks is a fundamental data analysis problem with applications in various domains. Most of the existing approaches have focused on discovering communities of nodes, while recent studies have shown great advantages and utilities of the knowledge of communities of links in networks. From this new perspective, we propose a link dynamics based algorithm, called UELC, for identifying link communities of networks. In UELC, the stochastic process of a link-node-link random walk is employed to unfold an embedded bipartition structure of links in a network. The local mixing properties of the Markov chain underlying the random walk are then utilized to extract two emerged link communities. Further, the random walk and the bipartitioning processes are wrapped in an iterative subdivision strategy to recursively identify link partitions that segregate the network links into multiple subdivisions. We evaluate the performance of the new method on synthetic benchmarks and demonstrate its utility on real-world networks. Our experimental results show that our method is highly effective for discovering link communities in complex networks. As a comparison, we also extend UELC to extracting communities of node, and show that it is effective for node community identification.
IEC 61499 vs. 61131: A Comparison Based on Misperceptions
Venue: arXiv cs.SE 2013
Year: 2013

IEC 61131 has been widely accepted in the industrial automation domain. However, it is claimed that the standard does not address today the new requirements of complex industrial systems, which include among others, portability, interoperability, increased reusability and distribution. To address these restrictions, IEC has initiated the task of developing IEC 61499, which is presented as a mature technology to enable intelligent automation in various domains. This standard was not accepted by industry even though it is highly promoted by the academic community. In this paper, a comparison between the two standards is presented. We argue that IEC 61499 has been promoted by academy based on unsubstantiated claims on its main features, i.e., reusability, portability, interoperability, event-driven execution. A number of misperceptions are presented and discussed. Based on this, it is claimed that IEC 61499 does not provide a solid framework for the next generation of industrial automation systems.
A Survey of Appearance Models in Visual Object Tracking
Venue: arXiv cs.CV 2013
Year: 2013

Visual object tracking is a significant computer vision task which can be applied to many domains such as visual surveillance, human computer interaction, and video compression. In the literature, researchers have proposed a variety of 2D appearance models. To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic. The contributions of this survey are four-fold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source code and video datasets) are examined in this survey.
Poseidon: Mitigating Interest Flooding DDoS Attacks in Named Data Networking
Venue: arXiv cs.NI 2013
Year: 2013

Content-Centric Networking (CCN) is an emerging networking paradigm being considered as a possible replacement for the current IP-based host-centric Internet infrastructure. In CCN, named content becomes a first-class entity. CCN focuses on content distribution, which dominates current Internet traffic and is arguably not well served by IP. Named-Data Networking (NDN) is an example of CCN. NDN is also an active research project under the NSF Future Internet Architectures (FIA) program. FIA emphasizes security and privacy from the outset and by design. To be a viable Internet architecture, NDN must be resilient against current and emerging threats. This paper focuses on distributed denial-of-service (DDoS) attacks; in particular we address interest flooding, an attack that exploits key architectural features of NDN. We show that an adversary with limited resources can implement such attack, having a significant impact on network performance. We then introduce Poseidon: a framework for detecting and mitigating interest flooding attacks. Finally, we report on results of extensive simulations assessing proposed countermeasure.
Universal Numerical Encoder and Profiler Reduces Computing's Memory Wall with Software, FPGA, and SoC Implementations
Venue: arXiv cs.OH 2013
Year: 2013

In the multicore era, the time to computational results is increasingly determined by how quickly operands are accessed by cores, rather than by the speed of computation per operand. From high-performance computing (HPC) to mobile application processors, low multicore utilization rates result from the slowness of accessing off-chip operands, i.e. the memory wall. The APplication AXcelerator (APAX) universal numerical encoder reduces computing's memory wall by compressing numerical operands (integers and floats), thereby decreasing CPU access time by 3:1 to 10:1 as operands stream between memory and cores. APAX encodes numbers using a low-complexity algorithm designed both for time series sensor data and for multi-dimensional data, including images. APAX encoding parameters are determined by a profiler that quantifies the uncertainty inherent in numerical datasets and recommends encoding parameters reflecting this uncertainty. Compatible software, FPGA, and systemon-chip (SoC) implementations efficiently support encoding rates between 150 MByte/sec and 1.5 GByte/sec at low power. On 25 integer and floating-point datasets, we achieved encoding rates between 3:1 and 10:1, with average correlation of 0.999959, while accelerating computational "time to results."
Model Based Framework for Estimating Mutation Rate of Hepatitis C Virus in Egypt
Venue: arXiv cs.AI 2013
Year: 2013

Hepatitis C virus (HCV) is a widely spread disease all over the world. HCV has very high mutation rate that makes it resistant to antibodies. Modeling HCV to identify the virus mutation process is essential to its detection and predicting its evolution. This paper presents a model based framework for estimating mutation rate of HCV in two steps. Firstly profile hidden Markov model (PHMM) architecture was builder to select the sequences which represents sequence per year. Secondly mutation rate was calculated by using pair-wise distance method between sequences. A pilot study is conducted on NS5B zone of HCV dataset of genotype 4 subtype a (HCV4a) in Egypt.
Energy Consumption Rate based Stable Election Protocol (ECRSEP) for WSNs
Venue: arXiv cs.NI 2013
Year: 2013

In recent few yearsWireless Sensor Networks (WSNs) have seen an increased interest in various applications like border field security, disaster management and medical applications. So large number of sensor nodes are deployed for such applications, which can work autonomously. Due to small power batteries in WSNs, efficient utilization of battery power is an important factor. Clustering is an efficient technique to extend life time of sensor networks by reducing the energy consumption. In this paper, we propose a new protocol; Energy Consumption Rate based Stable Election Protocol (ECRSEP). Our CH selection scheme is based on the weighted election probabilities of each node according to the Energy Consumption Rate (ECR) of each node. We compare results of our proposed protocol with Low Energy Adaptive Clustering Hierarchy (LEACH), Distributed Energy Efficient Clustering (DEEC), Stable Election Protocol (SEP), and Enhanced SEP(ESEP). Our simulation results show that our proposed protocol, ECRSEP outperforms all these protocols in terms of network stability and network lifetime.
BEENISH: Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol for Wireless Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

In past years there has been increasing interest in field of Wireless Sensor Networks (WSNs). One of the major issue of WSNs is development of energy efficient routing protocols. Clustering is an effective way to increase energy efficiency. Mostly, heterogenous protocols consider two or three energy level of nodes. In reality, heterogonous WSNs contain large range of energy levels. By analyzing communication energy consumption of the clusters and large range of energy levels in heterogenous WSN, we propose BEENISH (Balanced Energy Efficient Network Integrated Super Heterogenous) Protocol. It assumes WSN containing four energy levels of nodes. Here, Cluster Heads (CHs) are elected on the bases of residual energy level of nodes. Simulation results show that it performs better than existing clustering protocols in heterogeneous WSNs. Our protocol achieve longer stability, lifetime and more effective messages than Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC) and Enhanced DEEC (EDEEC).
Error Performance and Diversity Analysis of Multi-Source Multi-Relay Wireless Networks with Binary Network Coding and Cooperative MRC
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we contribute to the theoretical understanding, the design, and the performance evaluation of multi-source multi-relay network-coded cooperative diversity protocols. These protocols are useful to counteract the spectral inefficiency of repetition-based cooperation. We provide a general analytical framework for analysis and design of wireless networks using the Demodulate-and-Forward (DemF) protocol with binary Network Coding (NC) at the relays and Cooperative Maximal Ratio Combining (C-MRC) at the destination. Our system model encompasses an arbitrary number of relays which offer two cooperation levels: i) full-cooperative relays, which postpone the transmission of their own data frames to help the transmission of the sources via DemF relaying and binary NC; and ii) partial-cooperative relays, which exploit NC to transmit their own data frames along with the packets received from the sources. The relays can apply NC on different subsets of sources, which is shown to provide the sources with unequal diversity orders. Guidelines to choose the packets to be combined, i.e., the network code, to achieve the desired diversity order are given. Our study shows that partial-cooperative relays provide no contribution to the diversity order of the sources. Theoretical findings and design guidelines are validated through extensive Monte Carlo simulations.
Z-SEP: Zonal-Stable Election Protocol for Wireless Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

Wireless Sensor Networks (WSNs) are comprised of thousands of sensor nodes, with restricted energy, that co-operate to accomplish a sensing task. Various routing Protocols are designed for transmission in WSNs. In this paper, we proposed a hybrid routing protocol: Zonal-Stable Election Protocol (Z-SEP) for heterogeneous WSNs. In this protocol, some nodes transmit data directly to base station while some use clustering technique to send data to base station as in SEP. We implemented Z-SEP and compared it with traditional Low Energy adaptive clustering hierarchy (LEACH) and SEP. Simulation results showed that Z-SEP enhanced the stability period and throughput than existing protocols like LEACH and SEP.
Improving Network Efficiency by Removing Energy Holes in WSNs
Venue: arXiv cs.NI 2013
Year: 2013

Cluster based Wireless Sensor Networks (WSNs) have been widely used for better performance in terms of energy efficiency. Efficient use of energy is challenging task of designing these protocols. Energy holedare created due to quickly drain the energy of a few nodes due to non-uniform distribution in the network. Normally, energy holes make the data routing failure when nodes transmit data back to the base station. We proposedEnergy-efficient HOleRemoving Mechanism (E-HORM) technique to remove energy holes. In this technique, we use sleep and awake mechanism for sensor nodes to save energy. This approach finds the maximum distance node to calculate the maximum energy for data transmission. We considered it as a threshold energy Eth. Every node first checks its energy level for data transmission. If the energy level is less than Eth, it cannot transmit data. We also explain mathematically the energy consumption and average energy saving of sensor nodes in each round. Extensive simulations showed that when use this approach for WSNs significantly helps to extend the network lifetime and stability period.
RES - a Relative Method for Evidential Reasoning
Venue: arXiv cs.AI 2013
Year: 2013

In this paper we describe a novel method for evidential reasoning [1]. It involves modelling the process of evidential reasoning in three steps, namely, evidence structure construction, evidence accumulation, and decision making. The proposed method, called RES, is novel in that evidence strength is associated with an evidential support relationship (an argument) between a pair of statements and such strength is carried by comparison between arguments. This is in contrast to the onventional approaches, where evidence strength is represented numerically and is associated with a statement.
Optimizing Causal Orderings for Generating DAGs from Data
Venue: arXiv cs.AI 2013
Year: 2013

An algorithm for generating the structure of a directed acyclic graph from data using the notion of causal input lists is presented. The algorithm manipulates the ordering of the variables with operations which very much resemble arc reversal. Operations are only applied if the DAG after the operation represents at least the independencies represented by the DAG before the operation until no more arcs can be removed from the DAG. The resulting DAG is a minimal l-map.
Reformulating Inference Problems Through Selective Conditioning
Venue: arXiv cs.AI 2013
Year: 2013

We describe how we selectively reformulate portions of a belief network that pose difficulties for solution with a stochastic-simulation algorithm. With employ the selective conditioning approach to target specific nodes in a belief network for decomposition, based on the contribution the nodes make to the tractability of stochastic simulation. We review previous work on BNRAS algorithms- randomized approximation algorithms for probabilistic inference. We show how selective conditioning can be employed to reformulate a single BNRAS problem into multiple tractable BNRAS simulation problems. We discuss how we can use another simulation algorithm-logic sampling-to solve a component of the inference problem that provides a means for knitting the solutions of individual subproblems into a final result. Finally, we analyze tradeoffs among the computational subtasks associated with the selective conditioning approach to reformulation.
Possibilistic Assumption based Truth Maintenance System, Validation in a Data Fusion Application
Venue: arXiv cs.AI 2013
Year: 2013

Data fusion allows the elaboration and the evaluation of a situation synthesized from low level informations provided by different kinds of sensors. The fusion of the collected data will result in fewer and higher level informations more easily assessed by a human operator and that will assist him effectively in his decision process. In this paper we present the suitability and the advantages of using a Possibilistic Assumption based Truth Maintenance System (n-ATMS) in a data fusion military application. We first describe the problem, the needed knowledge representation formalisms and problem solving paradigms. Then we remind the reader of the basic concepts of ATMSs, Possibilistic Logic and 11-ATMSs. Finally we detail the solution to the given data fusion problem and conclude with the results and comparison with a non-possibilistic solution.
An Entropy-based Learning Algorithm of Bayesian Conditional Trees
Venue: arXiv cs.LG 2013
Year: 2013

This article offers a modification of Chow and Liu's learning algorithm in the context of handwritten digit recognition. The modified algorithm directs the user to group digits into several classes consisting of digits that are hard to distinguish and then constructing an optimal conditional tree representation for each class of digits instead of for each single digit as done by Chow and Liu (1968). Advantages and extensions of the new method are discussed. Related works of Wong and Wang (1977) and Wong and Poon (1989) which offer a different entropy-based learning algorithm are shown to rest on inappropriate assumptions.
The Dynamic of Belief in the Transferable Belief Model and Specialization-Generalization Matrices
Venue: arXiv cs.AI 2013
Year: 2013

The fundamental updating process in the transferable belief model is related to the concept of specialization and can be described by a specialization matrix. The degree of belief in the truth of a proposition is a degree of justified support. The Principle of Minimal Commitment implies that one should never give more support to the truth of a proposition than justified. We show that Dempster's rule of conditioning corresponds essentially to the least committed specialization, and that Dempster's rule of combination results essentially from commutativity requirements. The concept of generalization, dual to thc concept of specialization, is described.
A Note on the Measure of Discord
Venue: arXiv cs.AI 2013
Year: 2013

A new entropy-like measure as well as a new measure of total uncertainty pertaining to the Dempster-Shafer theory are introduced. It is argued that these measures are better justified than any of the previously proposed candidates.
Semantics for Probabilistic Inference
Venue: arXiv cs.AI 2013
Year: 2013

A number of writers(Joseph Halpern and Fahiem Bacchus among them) have offered semantics for formal languages in which inferences concerning probabilities can be made. Our concern is different. This paper provides a formalization of nonmonotonic inferences in which the conclusion is supported only to a certain degree. Such inferences are clearly 'invalid' since they must allow the falsity of a conclusion even when the premises are true. Nevertheless, such inferences can be characterized both syntactically and semantically. The 'premises' of probabilistic arguments are sets of statements (as in a database or knowledge base), the conclusions categorical statements in the language. We provide standards for both this form of inference, for which high probability is required, and for an inference in which the conclusion is qualified by an intermediate interval of support.
A Probabilistic Network of Predicates
Venue: arXiv cs.AI 2013
Year: 2013

Bayesian networks are directed acyclic graphs representing independence relationships among a set of random variables. A random variable can be regarded as a set of exhaustive and mutually exclusive propositions. We argue that there are several drawbacks resulting from the propositional nature and acyclic structure of Bayesian networks. To remedy these shortcomings, we propose a probabilistic network where nodes represent unary predicates and which may contain directed cycles. The proposed representation allows us to represent domain knowledge in a single static network even though we cannot determine the instantiations of the predicates before hand. The ability to deal with cycles also enables us to handle cyclic causal tendencies and to recognize recursive plans.
A Fuzzy Logic Approach to Target Tracking
Venue: arXiv cs.AI 2013
Year: 2013

This paper discusses a target tracking problem in which no dynamic mathematical model is explicitly assumed. A nonlinear filter based on the fuzzy If-then rules is developed. A comparison with a Kalman filter is made, and empirical results show that the performance of the fuzzy filter is better. Intensive simulations suggest that theoretical justification of the empirical results is possible.
An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation
Venue: arXiv cs.AI 2013
Year: 2013

In a previous paper [Pearl and Verma, 1991] we presented an algorithm for extracting causal influences from independence information, where a causal influence was defined as the existence of a directed arc in all minimal causal models consistent with the data. In this paper we address the question of deciding whether there exists a causal model that explains ALL the observed dependencies and independencies. Formally, given a list M of conditional independence statements, it is required to decide whether there exists a directed acyclic graph (dag) D that is perfectly consistent with M, namely, every statement in M, and no other, is reflected via dseparation in D. We present and analyze an effective algorithm that tests for the existence of such a day, and produces one, if it exists.