OpenRaiser/Intern-Atlas huggingface dataset:
Robot Location Estimation in the Situation Calculus
Venue: arXiv cs.AI 2014
Year: 2014

Location estimation is a fundamental sensing task in robotic applications, where the world is uncertain, and sensors and effectors are noisy. Most systems make various assumptions about the dependencies between state variables, and especially about how these dependencies change as a result of actions. Building on a general framework by Bacchus, Halpern and Levesque for reasoning about degrees of belief in the situation calculus, and a recent extension to it for continuous domains, in this paper we illustrate location estimation in the presence of a rich theory of actions using an example. We also show that while actions might affect prior distributions in nonstandard ways, suitable posterior beliefs are nonetheless entailed as a side-effect of the overall specification.
Dynamic Uplink-Downlink Optimization in TDD-based Small Cell Networks
Venue: arXiv cs.NI 2014
Year: 2014

Dynamic Time-division duplex (TDD) can provide efficient and flexible splitting of the common wireless cellular resources between uplink (UL) and downlink (DL) users. In this paper, the UL/DL optimization problem is formulated as a noncooperative game among the small cell base stations (SCBSs) in which each base station aims at minimizing its total UL and DL flow delays. To solve this game, a self-organizing UL/DL resource configuration scheme for TDD-based small cell networks is proposed. Using the proposed scheme, an SCBS is able to estimate and learn the UL and DL loads autonomously while optimizing its UL/DL configuration accordingly. Simulations results show that the proposed algorithm achieves significant gains in terms of packet throughput in case of asymmetric UL and DL traffic loads. This gain increases as the traffic asymmetry increases, reaching up to 97% and 200% gains relative to random and fixed duplexing schemes respectively. Our results also show that the proposed algorithm is well- adapted to dynamic traffic conditions and different network sizes, and operates efficiently in case of severe cross-link interference in which neighboring cells transmit in opposite directions.
Geometrical approach to modeling of nonlinear systems from experimental data
Venue: arXiv cs.CE 2014
Year: 2014

This monograph presents a geometric modeling method nonlinear dynamical systems from experimental data . basis method is a qualitative approach to the analysis of linear models and construction of the symmetry groups of attractors of dynamical systems with controls . A theoretical study including the central theorem manifold defining conditions of existence of the class in question models in the local area , taking into account the group properties , estimation algorithms invariant characteristics , methods of constructing models and identifiable description of the results obtained using the method for simulation -driven engineering processes . included two application is the development of the proposed approach : identification of groups symmetries on the phase portraits of dynamical systems and the method of constructing neural network predictive models
A Novel approach as Multi-place Watermarking for Security in Database
Venue: arXiv cs.DB 2014
Year: 2014

Digital multimedia watermarking technology had suggested in the last decade to embed copyright information in digital objects such as images, audio and video. However, the increasing use of relational database systems in many real-life applications created an ever-increasing need for watermarking database systems. As a result, watermarking relational database system is now emerging as a research area that deals with the legal issue of copyright protection of database systems. The main goal of database watermarking is to generate robust and impersistent watermark for database. In this paper we propose a method, based on image as watermark and this watermark is embedded over the database at two different attribute of tuple, one in the numeric attribute of tuple and another in the date attribute's time (seconds) field. Our approach can be applied for numerical and categorical database.
Phase Retrieval with Application to Optical Imaging
Venue: arXiv cs.IT 2014
Year: 2014

This review article provides a contemporary overview of phase retrieval in optical imaging, linking the relevant optical physics to the information processing methods and algorithms. Its purpose is to describe the current state of the art in this area, identify challenges, and suggest vision and areas where signal processing methods can have a large impact on optical imaging and on the world of imaging at large, with applications in a variety of fields ranging from biology and chemistry to physics and engineering.
Second-Order Consensus of Networked Mechanical Systems With Communication Delays
Venue: arXiv cs.SY 2014
Year: 2014

In this paper, we consider the second-order consensus problem for networked mechanical systems subjected to nonuniform communication delays, and the mechanical systems are assumed to interact on a general directed topology. We propose an adaptive controller plus a distributed velocity observer to realize the objective of second-order consensus. It is shown that both the positions and velocities of the mechanical agents synchronize, and furthermore, the velocities of the mechanical agents converge to the scaled weighted average value of their initial ones. We further demonstrate that the proposed second-order consensus scheme can be used to solve the leader-follower synchronization problem with a constant-velocity leader and under constant communication delays. Simulation results are provided to illustrate the performance of the proposed adaptive controllers.
Temporal Image Fusion
Venue: arXiv cs.CV 2014
Year: 2014

This paper introduces temporal image fusion. The proposed technique builds upon previous research in exposure fusion and expands it to deal with the limited Temporal Dynamic Range of existing sensors and camera technologies. In particular, temporal image fusion enables the rendering of long-exposure effects on full frame-rate video, as well as the generation of arbitrarily long exposures from a sequence of images of the same scene taken over time. We explore the problem of temporal under-exposure, and show how it can be addressed by selectively enhancing dynamic structure. Finally, we show that the use of temporal image fusion together with content-selective image filters can produce a range of striking visual effects on a given input sequence.
A Solution to Bargaining Problem on Divisible Goods
Venue: arXiv cs.GT 2014
Year: 2014

Two-person bargaining problem is considered as to allocate a number of goods between two players. This paper suggests that any non-trivial division of goods cause a non-zero change on the solution of bargaining. So, a axiom of sharing division is presented, as an alternative axiom to Nash axiom of independence of irrelevant alternatives and Kalai-Smorodinsky axiom of monotonicity. This solution is targeted at the partialities of Nash and Kalai-Smorodinsky solution on some specific issues, but not to say it is better than others.
Additive Spanners: A Simple Construction
Venue: arXiv cs.DS 2014
Year: 2014

We consider additive spanners of unweighted undirected graphs. Let $G$ be a graph and $H$ a subgraph of $G$. The most naïve way to construct an additive $k$-spanner of $G$ is the following: As long as $H$ is not an additive $k$-spanner repeat: Find a pair $(u,v) \in H$ that violates the spanner-condition and a shortest path from $u$ to $v$ in $G$. Add the edges of this path to $H$. We show that, with a very simple initial graph $H$, this naïve method gives additive $6$- and $2$-spanners of sizes matching the best known upper bounds. For additive $2$-spanners we start with $H=\emptyset$ and end with $O(n^{3/2})$ edges in the spanner. For additive $6$-spanners we start with $H$ containing $\lfloor n^{1/3} \rfloor$ arbitrary edges incident to each node and end with a spanner of size $O(n^{4/3})$.
An Explicit Trust Model Towards Better System Security
Venue: arXiv cs.CR 2014
Year: 2014

Trust is an absolute necessity for digital communications; but is often viewed as an implicit singular entity. The use of the internet as the primary vehicle for information exchange has made accountability and verifiability of system code almost obsolete. This paper proposes a novel approach towards enforcing system security by requiring the explicit definition of trust for all operating code. By identifying the various classes and levels of trust required within a computing system; trust is defined as a combination of individual characteristics. Trust is then represented as a calculable metric obtained through the collective enforcement of each of these characteristics to varying degrees. System Security is achieved by facilitating trust to be a constantly evolving aspect for each operating code segment capable of getting stronger or weaker over time.
Matching Image Sets via Adaptive Multi Convex Hull
Venue: arXiv cs.CV 2014
Year: 2014

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.
Ergodic Sum-Rate Maximization for Fading Cognitive Multiple Access Channels without Successive Interference Cancellation
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, the ergodic sum-rate of a fading cognitive multiple access channel (C-MAC) is studied, where a secondary network (SN) with multiple secondary users (SUs) transmitting to a secondary base station (SBS) shares the spectrum band with a primary user (PU). An interference power constraint (IPC) is imposed on the SN to protect the PU. Under such a constraint and the individual transmit power constraint (TPC) imposed on each SU, we investigate the power allocation strategies to maximize the ergodic sum-rate of a fading C-MAC without successive interference cancellation (SIC). In particular, this paper considers two types of constraints: (1) average TPC and average IPC, (2) peak TPC and peak IPC. For the first case, it is proved that the optimal power allocation is dynamic time-division multiple-access (D-TDMA), which is exactly the same as the optimal power allocation to maximize the ergodic sum-rate of the fading C-MAC with SIC under the same constraints. For the second case, it is proved that the optimal solution must be at the extreme points of the feasible region. It is shown that D-TDMA is optimal with high probability when the number of SUs is large. Besides, we show that, when the SUs can be sorted in a certain order, an algorithm with linear complexity can be used to find the optimal power allocation.
Support Vector Machine Model for Currency Crisis Discrimination
Venue: arXiv cs.LG 2014
Year: 2014

Support Vector Machine (SVM) is powerful classification technique based on the idea of structural risk minimization. Use of kernel function enables curse of dimensionality to be addressed. However, proper kernel function for certain problem is dependent on specific dataset and as such there is no good method on choice of kernel function. In this paper, SVM is used to build empirical models of currency crisis in Argentina. An estimation technique is developed by training model on real life data set which provides reasonably accurate model outputs and helps policy makers to identify situations in which currency crisis may happen. The third and fourth order polynomial kernel is generally best choice to achieve high generalization of classifier performance. SVM has high level of maturity with algorithms that are simple, easy to implement, tolerates curse of dimensionality and good empirical performance. The satisfactory results show that currency crisis situation is properly emulated using only small fraction of database and could be used as an evaluation tool as well as an early warning system. To the best of knowledge this is the first work on SVM approach for currency crisis evaluation of Argentina.
Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis
Venue: arXiv cs.AI 2014
Year: 2014

The thyroid, an endocrine gland that secretes hormones in the blood, circulates its products to all tissues of the body, where they control vital functions in every cell. Normal levels of thyroid hormone help the brain, heart, intestines, muscles and reproductive system function normally. Thyroid hormones control the metabolism of the body. Abnormalities of thyroid function are usually related to production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism). Therefore, the correct diagnosis of these diseases is very important topic. In this study, Linguistic Hedges Neural-Fuzzy Classifier with Selected Features (LHNFCSF) is presented for diagnosis of thyroid diseases. The performance evaluation of this system is estimated by using classification accuracy and k-fold cross-validation. The results indicated that the classification accuracy without feature selection was 98.6047% and 97.6744% during training and testing phases, respectively with RMSE of 0.02335. After applying feature selection algorithm, LHNFCSF achieved 100% for all cluster sizes during training phase. However, in the testing phase LHNFCSF achieved 88.3721% using one cluster for each class, 90.6977% using two clusters, 91.8605% using three clusters and 97.6744% using four clusters for each class and 12 fuzzy rules. The obtained classification accuracy was very promising with regard to the other classification applications in literature for this problem.
The Enhancement of Communication Technologies and Networks for Smart Grid Applications
Venue: arXiv cs.NI 2014
Year: 2014

The current electrical grid is perhaps the greatest engineering achievement of the 20th century. However, it is increasingly outdated and overburdened, leading to costly blackouts and burnouts. For this and various other reasons,transformation efforts are underway to make the current electrical grid smarter. A reliable, universal and secure communication infrastructure is mandatory for the implementation and deployment of the future smart grid. A special interest is given to the design of efficient and robust network architecture capable of managing operation and control of the next generation power grid. For this purpose new wired and wireless technologies are emerging in addition to the formerly applied to help upgrade the current power grid. In this paper we will give an overview of smart grid reference model, and a comprehensive survey of the available networks for the smart grid and a critical review of the progress of wired and wireless communication technologies for smart grid communication infrastructure. And we propose end to end communication architecture for Home Area Networks (HANs), Neighborhood Area Networks (NANs) and Wide Area Networks (WANs) for smart grid applications. We believe that this work will provide appreciated insights for the novices who would like to follow related research in the SG domain.
Representing, reasoning and answering questions about biological pathways - various applications
Venue: arXiv cs.AI 2014
Year: 2014

Biological organisms are composed of numerous interconnected biochemical processes. Diseases occur when normal functionality of these processes is disrupted. Thus, understanding these biochemical processes and their interrelationships is a primary task in biomedical research and a prerequisite for diagnosing diseases, and drug development. Scientists studying these processes have identified various pathways responsible for drug metabolism, and signal transduction, etc. Newer techniques and speed improvements have resulted in deeper knowledge about these pathways, resulting in refined models that tend to be large and complex, making it difficult for a person to remember all aspects of it. Thus, computer models are needed to analyze them. We want to build such a system that allows modeling of biological systems and pathways in such a way that we can answer questions about them. Many existing models focus on structural and/or factoid questions, using surface-level knowledge that does not require understanding the underlying model. We believe these are not the kind of questions that a biologist may ask someone to test their understanding of the biological processes. We want our system to answer the kind of questions a biologist may ask. Such questions appear in early college level text books. Thus the main goal of our thesis is to develop a system that allows us to encode knowledge about biological pathways and answer such questions about them demonstrating understanding of the pathway. To that end, we develop a language that will allow posing such questions and illustrate the utility of our framework with various applications in the biological domain. We use some existing tools with modifications to accomplish our goal. Finally, we apply our system to real world applications by extracting pathway knowledge from text and answering questions related to drug development.
The Structurally Smoothed Graphlet Kernel
Venue: arXiv cs.LG 2014
Year: 2014

A commonly used paradigm for representing graphs is to use a vector that contains normalized frequencies of occurrence of certain motifs or sub-graphs. This vector representation can be used in a variety of applications, such as, for computing similarity between graphs. The graphlet kernel of Shervashidze et al. [32] uses induced sub-graphs of k nodes (christened as graphlets by Przulj [28]) as motifs in the vector representation, and computes the kernel via a dot product between these vectors. One can easily show that this is a valid kernel between graphs. However, such a vector representation suffers from a few drawbacks. As k becomes larger we encounter the sparsity problem; most higher order graphlets will not occur in a given graph. This leads to diagonal dominance, that is, a given graph is similar to itself but not to any other graph in the dataset. On the other hand, since lower order graphlets tend to be more numerous, using lower values of k does not provide enough discrimination ability. We propose a smoothing technique to tackle the above problems. Our method is based on a novel extension of Kneser-Ney and Pitman-Yor smoothing techniques from natural language processing to graphs. We use the relationships between lower order and higher order graphlets in order to derive our method. Consequently, our smoothing algorithm not only respects the dependency between sub-graphs but also tackles the diagonal dominance problem by distributing the probability mass across graphlets. In our experiments, the smoothed graphlet kernel outperforms graph kernels based on raw frequency counts.
Proof identity for mere mortals
Venue: arXiv cs.LO 2014
Year: 2014

The proof identity problem asks: When are two proofs the same? The question naturally occurs when one reflects on mathematical practice. The problem understandably can be seen as a challenge for mathematical logic, and indeed various perspectives on the problem can be found in the proof theory literature. From the proof theory perspective, the challenge is met by laying down new calculi that eliminate ``bureaucracy''; techniques such as normalization and cut-elimination, as well as proof compression, are employed. In this note a new perspective on the proof identity problem is outlined. The new approach employs the concepts and tools of automated theorem proving and complements the rather more theoretical perspectives coming from pure proof theory. The practical approach is illustrated with experiments coming from the TPTP Problem Library.
Random Projections on Manifolds of Symmetric Positive Definite Matrices for Image Classification
Venue: arXiv cs.CV 2014
Year: 2014

Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance. Taking into account manifold geometry is typically done via (1) embedding the manifolds in tangent spaces, or (2) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information. The RKHS approach retains more of the manifold structure, but may require non-trivial effort to kernelise Euclidean-based learning algorithms. In contrast to the above approaches, in this paper we offer a novel solution that allows SPD matrices to be used with unmodified Euclidean-based learning algorithms, with the true manifold shape well-preserved. Specifically, we propose to project SPD matrices using a set of random projection hyperplanes over RKHS into a random projection space, which leads to representing each matrix as a vector of projection coefficients. Experiments on face recognition, person re-identification and texture classification show that the proposed approach outperforms several recent methods, such as Tensor Sparse Coding, Histogram Plus Epitome, Riemannian Locality Preserving Projection and Relational Divergence Classification.