OpenRaiser/Intern-Atlas huggingface dataset:
Hybrid technique for effective knowledge representation & a comparative study
Venue: arXiv cs.AI 2012
Year: 2012

Knowledge representation (KR) and inference mechanism are most desirable thing to make the system intelligent. System is known to an intelligent if its intelligence is equivalent to the intelligence of human being for a particular domain or general. Because of incomplete ambiguous and uncertain information the task of making intelligent system is very difficult. The objective of this paper is to present the hybrid KR technique for making the system effective & Optimistic. The requirement for (effective & optimistic) is because the system must be able to reply the answer with a confidence of some factor. This paper also presents the comparison between various hybrid KR techniques with the proposed one.
Content Management in Ruby on Rails
Venue: arXiv cs.SE 2012
Year: 2012

Web development is currently driven by model-view-controller (MVC) frameworks. How has content management adapted to this scenario? This paper reviews content management features in Ruby on Rails framework and its most popular plug-ins. These features are distributed among the different layers of the MVC architecture.
On morphological hierarchical representations for image processing and spatial data clustering
Venue: arXiv cs.DM 2012
Year: 2012

Hierarchical data representations in the context of classi cation and data clustering were put forward during the fties. Recently, hierarchical image representations have gained renewed interest for segmentation purposes. In this paper, we briefly survey fundamental results on hierarchical clustering and then detail recent paradigms developed for the hierarchical representation of images in the framework of mathematical morphology: constrained connectivity and ultrametric watersheds. Constrained connectivity can be viewed as a way to constrain an initial hierarchy in such a way that a set of desired constraints are satis ed. The framework of ultrametric watersheds provides a generic scheme for computing any hierarchical connected clustering, in particular when such a hierarchy is constrained. The suitability of this framework for solving practical problems is illustrated with applications in remote sensing.
Formal Concept Analysis Based Association Rules Extraction
Venue: arXiv cs.DB 2012
Year: 2012

Generating a huge number of association rules reduces their utility in the decision making process, done by domain experts. In this context, based on the theory of Formal Concept Analysis, we propose to extend the notion of Formal Concept through the generalization of the notion of itemset in order to consider the itemset as an intent, its support as the cardinality of the extent and its relevance which is related to the confidence of rule. Accordingly, we propose a new approach to extract interesting itemsets through the concept coverage. This approach uses a new quality-criteria of a rule: the relevance bringing a semantic added value to formal concept analysis approach to discover association rules.
PaxosLease: Diskless Paxos for Leases
Venue: arXiv cs.DC 2012
Year: 2012

This paper describes PaxosLease, a distributed algorithm for lease negotiation. PaxosLease is based on Paxos, but does not require disk writes or clock synchrony. PaxosLease is used for master lease negotation in the open-source Keyspace and ScalienDB replicated key-value stores.
An Energy-Efficient Power Allocation Game with Selfish Channel State Reporting in Cellular Networks
Venue: arXiv cs.NI 2012
Year: 2012

With energy-efficient resource allocation, mobile users and base station have different objectives. While the base station strives for an energy-efficient operation of the complete cell, each user aims to maximize its own data rate. To obtain this individual benefit, users may selfishly adjust their Channel State Information (CSI) reports, reducing the cell's energy efficiency. To analyze this conflict of interest, we formalize energy-efficient power allocation as a utility maximization problem and present a simple algorithm that performs close to the optimum. By formulating selfish CSI reporting as a game, we prove the existence of an unique equilibrium and characterize energy efficiency with true and selfish CSI in closed form. Our numerical results show that, surprisingly, energy-efficient power allocation in small cells is more robust against selfish CSI than cells with large transmit powers. This and further design rules show that our paper provides valuable theoretical insight to energy-efficient networks when CSI reports cannot be trusted.
Communication-Efficient and Exact Clustering Distributed Streaming Data
Venue: arXiv cs.DB 2012
Year: 2012

A widely used approach to clustering a single data stream is the two-phased approach in which the online phase creates and maintains micro-clusters while the off-line phase generates the macro-clustering from the micro-clusters. We use this approach to propose a distributed framework for clustering streaming data. Our proposed framework consists of fundamen- tal processes: one coordinator-site process and many remote-site processes. Remote-site processes can directly communicate with the coordinator-process but cannot communicate the other remote site processes. Every remote-site process generates and maintains micro- clusters that represent cluster information summary, from its local data stream. Remote sites send the local micro-clusterings to the coordinator by the serialization technique, or the coordinator invokes the remote methods in order to get the local micro-clusterings from the remote sites. After the coordinator receives all the local micro-clusterings from the remote sites, it generates the global clustering by the macro-clustering method. Our theoretical and empirical results show that, the global clustering generated by our distributed framework is similar to the clustering generated by the underlying centralized algorithm on the same data set. By using the local micro-clustering approach, our framework achieves high scalability, and communication-efficiency.
Cognitive Bias for Universal Algorithmic Intelligence
Venue: arXiv cs.AI 2012
Year: 2012

Existing theoretical universal algorithmic intelligence models are not practically realizable. More pragmatic approach to artificial general intelligence is based on cognitive architectures, which are, however, non-universal in sense that they can construct and use models of the environment only from Turing-incomplete model spaces. We believe that the way to the real AGI consists in bridging the gap between these two approaches. This is possible if one considers cognitive functions as a "cognitive bias" (priors and search heuristics) that should be incorporated into the models of universal algorithmic intelligence without violating their universality. Earlier reported results suiting this approach and its overall feasibility are discussed on the example of perception, planning, knowledge representation, attention, theory of mind, language, and some others.
Strongly Convex Programming for Principal Component Pursuit
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, we address strongly convex programming for princi- pal component pursuit with reduced linear measurements, which decomposes a superposition of a low-rank matrix and a sparse matrix from a small set of linear measurements. We first provide sufficient conditions under which the strongly convex models lead to the exact low-rank and sparse matrix recov- ery; Second, we also give suggestions on how to choose suitable parameters in practical algorithms.
Frames in Outdoor 802.11 WLANs Provide a Hybrid Binary-Symmetric/Packet-Erasure Channel
Venue: arXiv cs.NI 2012
Year: 2012

Corrupted frames with CRC errors potentially provide a useful channel through which we can transmit information. Using measurements taken in an outdoor environment, we demonstrate that for 802.11 wireless links the channel provided by corrupted frames alone (i.e. ignoring frames with PHY errors and frames received correctly) can be accurately modelled as a binary symmetric channel (BSC) provided appropriate pre- and post- processing is carried out. Also, the channel provided by corrupted frames and other frames combined can be accurately modelled as a hybrid binary symmetric/packet erasure channel. Importantly, we find that this hybrid channel offers capacity increases of more than 100% compared to a conventional packet erasure channel over a wide range of RSSIs. This is a striking observation as it indicates that the potential exists for significant network throughput gains if the information contained in 802.11 corrupted packets is exploited.
Sorting distinct integers using improved in-place associative sort
Venue: arXiv cs.DS 2012
Year: 2012

In-place associative integer sorting technique was proposed for integer lists which requires only constant amount of additional memory replacing bucket sort, distribution counting sort and address calculation sort family of algorithms. Afterwards, the technique was further improved and an in-place sorting algorithm is proposed where n integers S[0...n-1] each in the range [0, n-1] are sorted exactly in O(n) time while the complexity of the former technique was the recursion T(n) = T(n/2) + O(n) yielding T(n) = O(n). The technique was specialized with two variants one for read-only distinct integer keys and the other for modifiable distinct integers, as well. Assuming w is the fixed word length, the variant for modifiable distinct integers was capable of sorting n distinct integers S[0...n-1] each in the range [0, m-1] in exactly O(n) time if m < (w-logn)n. Otherwise, it sort in O(n + m/(w-logn)) time for the worst, O(m/(w-logn)) time for the average (uniformly distributed keys) and O(n) time for the best case using only O(1) extra space. In this study, the variant for modifiable distinct integers is improved and an algorithm is obtained that sorts n distinct integers S[0...n-1] each in the range [0, m-1] in exactly O(n) time if m < (w-1)n. Otherwise, it sort in O(n + m/(w-1)) time for the worst, O(m/(w-1)) time for the average (uniformly distributed keys) and O(n) time for the best case using only O(1) extra space.
Algorithms of Fast Search of Center, Radius and Diameter on Weighted Graphs
Venue: arXiv cs.DS 2012
Year: 2012

Two problems in the search of metric characteristics on weighted undirected graphs with non-negative edge weights are being considered. The first problem: a weighted undirected graph with non-negative edge weight is given. The radius, diameter and at least one center and one pair of peripheral vertices of the graph are to be found. In the second problem we have additionally calculated the distances matrix. For the problems being considered, we proposed fast search algorithms which use only small fraction of graph's vertices for the search of the metric characteristics. The proposed algorithms have been compared to other popular methods of solving problems considered on various inputs.
Performance Analysis of Error Control Coding Techniques for Peak-to-Average Power Ratio Reduction of Multicarrier Signals
Venue: arXiv cs.IT 2012
Year: 2012

Increasing demands on high data rate mobile communications services will inevitably drive future broadband mobile communication systems toward achieving data transmission rates in excess of 100 Mbps. One of the promising technologies which can satisfy this demand on high data rate mobile communications services is the Orthogonal Frequency Division Multiplexing (OFDM) transmission technology which falls under the general category of multicarrier modulation systems. OFDM is a spectrally efficient modulation technique that can achieve high speed data transmission over multipath fading channels without the need for powerful equalization techniques. However the price paid for this high spectral efficiency and less intensive equalization is low power efficiency. OFDM signals are very sensitive to non-linear effects due to the high peak-to-average power ratio (PAPR), which leads to the power inefficiency in the RF section of the transmitter. This paper analyzes the relation between aperiodic autocorrelation of OFDM symbols and PAPR. The paper also gives a comparative study of PAPR reduction performance of various channel coding techniques for the OFDM signals. For our study we have considered Hamming codes, cyclic codes, convolution codes, Golay and Reed-Muller codes. The results show that each of the channel coding technique has a different PAPR reduction performance. Coding technique with the highest value of PAPR reduction has been identified along with an illustration on PAPR reduction performances with respect to each code.
A Unified Relay Framework with both D-F and C-F Relay Nodes
Venue: arXiv cs.IT 2012
Year: 2012

Decode-and-forward (D-F) and compress-and-forward (C-F) are two fundamentally different relay strategies proposed by (Cover and El Gamal, 1979). Individually, either of them has been successfully generalized to multi-relay channels. In this paper, to allow each relay node the freedom of choosing either of the two strategies, we propose a unified framework, where both the D-F and C-F strategies can be employed simultaneously in the network. It turns out that, to fully incorporate the advantages of both the best known D-F and C-F strategies into a unified framework, the major challenge arises as follows: For the D-F relay nodes to fully utilize the help of the C-F relay nodes, decoding at the D-F relay nodes should not be conducted until all the blocks have been finished; However, in the multi-level D-F strategy, the upstream nodes have to decode prior to the downstream nodes in order to help, which makes simultaneous decoding at all the D-F relay nodes after all the blocks have been finished inapplicable. To tackle this problem, nested blocks combined with backward decoding are used in our framework, so that the D-F relay nodes at different levels can perform backward decoding at different frequencies. As such, the upstream D-F relay nodes can decode before the downstream D-F relay nodes, and the use of backward decoding at each D-F relay node ensures the full exploitation of the help of both the other D-F relay nodes and the C-F relay nodes. The achievable rates under our unified relay framework are found to combine both the best known D-F and C-F achievable rates and include them as special cases.
A Neuro-Fuzzy Technique for Implementing the Half-Adder Circuit Using the CANFIS Model
Venue: arXiv cs.NE 2012
Year: 2012

A Neural Network, in general, is not considered to be a good solver of mathematical and binary arithmetic problems. However, networks have been developed for such problems as the XOR circuit. This paper presents a technique for the implementation of the Half-adder circuit using the CoActive Neuro-Fuzzy Inference System (CANFIS) Model and attempts to solve the problem using the NeuroSolutions 5 Simulator. The paper gives the experimental results along with the interpretations and possible applications of the technique.
Covering matroid
Venue: arXiv cs.AI 2012
Year: 2012

In this paper, we propose a new type of matroids, namely covering matroids, and investigate the connections with the second type of covering-based rough sets and some existing special matroids. Firstly, as an extension of partitions, coverings are more natural combinatorial objects and can sometimes be more efficient to deal with problems in the real world. Through extending partitions to coverings, we propose a new type of matroids called covering matroids and prove them to be an extension of partition matroids. Secondly, since some researchers have successfully applied partition matroids to classical rough sets, we study the relationships between covering matroids and covering-based rough sets which are an extension of classical rough sets. Thirdly, in matroid theory, there are many special matroids, such as transversal matroids, partition matroids, 2-circuit matroid and partition-circuit matroids. The relationships among several special matroids and covering matroids are studied.
Using multimodal speech production data to evaluate articulatory animation for audiovisual speech synthesis
Venue: arXiv cs.HC 2012
Year: 2012

The importance of modeling speech articulation for high-quality audiovisual (AV) speech synthesis is widely acknowledged. Nevertheless, while state-of-the-art, data-driven approaches to facial animation can make use of sophisticated motion capture techniques, the animation of the intraoral articulators (viz. the tongue, jaw, and velum) typically makes use of simple rules or viseme morphing, in stark contrast to the otherwise high quality of facial modeling. Using appropriate speech production data could significantly improve the quality of articulatory animation for AV synthesis.
Image Classification and Optimized Image Reproduction
Venue: arXiv cs.CV 2012
Year: 2012

By taking into account the properties and limitations of the human visual system, images can be more efficiently compressed, colors more accurately reproduced, prints better rendered. To show all these advantages in this paper new adapted color charts have been created based on technical and visual image category analysis. A number of tests have been carried out using extreme images with their key information strictly in dark and light areas. It was shown that the image categorization using the adapted color charts improves the analysis of relevant image information with regard to both the image gradation and the detail reproduction. The images with key information in hi-key areas were also test printed using the adapted color charts.
Complexity Reduction for Parameter-Dependent Linear Systems
Venue: arXiv cs.SY 2012
Year: 2012

We present a complexity reduction algorithm for a family of parameter-dependent linear systems when the system parameters belong to a compact semi-algebraic set. This algorithm potentially describes the underlying dynamical system with fewer parameters or state variables. To do so, it minimizes the distance (i.e., H-infinity-norm of the difference) between the original system and its reduced version. We present a sub-optimal solution to this problem using sum-of-squares optimization methods. We present the results for both continuous-time and discrete-time systems. Lastly, we illustrate the applicability of our proposed algorithm on numerical examples.
A Note on the Space Complexity of Fast D-Finite Function Evaluation
Venue: arXiv cs.SC 2012
Year: 2012

We state and analyze a generalization of the "truncation trick" suggested by Gourdon and Sebah to improve the performance of power series evaluation by binary splitting. It follows from our analysis that the values of D-finite functions (i.e., functions described as solutions of linear differential equations with polynomial coefficients) may be computed with error bounded by 2^(-p) in time O(p*(lg p)^(3+o(1))) and space O(p). The standard fast algorithm for this task, due to Chudnovsky and Chudnovsky, achieves the same time complexity bound but requires Θ(p*lg p) bits of memory.
Julia: A Fast Dynamic Language for Technical Computing
Venue: arXiv cs.PL 2012
Year: 2012

Dynamic languages have become popular for scientific computing. They are generally considered highly productive, but lacking in performance. This paper presents Julia, a new dynamic language for technical computing, designed for performance from the beginning by adapting and extending modern programming language techniques. A design based on generic functions and a rich type system simultaneously enables an expressive programming model and successful type inference, leading to good performance for a wide range of programs. This makes it possible for much of the Julia library to be written in Julia itself, while also incorporating best-of-breed C and Fortran libraries.
Spike Timing Dependent Competitive Learning in Recurrent Self Organizing Pulsed Neural Networks Case Study: Phoneme and Word Recognition
Venue: arXiv cs.CV 2012
Year: 2012

Synaptic plasticity seems to be a capital aspect of the dynamics of neural networks. It is about the physiological modifications of the synapse, which have like consequence a variation of the value of the synaptic weight. The information encoding is based on the precise timing of single spike events that is based on the relative timing of the pre- and post-synaptic spikes, local synapse competitions within a single neuron and global competition via lateral connections. In order to classify temporal sequences, we present in this paper how to use a local hebbian learning, spike-timing dependent plasticity for unsupervised competitive learning, preserving self-organizing maps of spiking neurons. In fact we present three variants of self-organizing maps (SOM) with spike-timing dependent Hebbian learning rule, the Leaky Integrators Neurons (LIN), the Spiking_SOM and the recurrent Spiking_SOM (RSSOM) models. The case study of the proposed SOM variants is phoneme classification and word recognition in continuous speech and speaker independent.
A Graphical Tool for Testing Timed Systems based on Meta- Modeling and Graph Grammars
Venue: arXiv cs.SE 2012
Year: 2012

The test is one of the approaches commonly used for validating systems to ensure qualitative and quantitative implementation requirements. In this paper, we interest in formal testing using graph transformation, thus we propose an approach for translating a Durational Actions Timed Automata model (DATA*) with a high number of states into a timed refusals region graph (TRRG) for creating a canonical tester and generating test cases using graph transformation. Though, our approach allows to generate automatically a visual modeling tool for DATA*, TRRG and the canonical tester. The cost of building a visual modeling tool from scratch is prohibitive. Meta- modeling approach is useful to deal with this problem since it allows the modeling of the formalisms themselves, by means of graph grammars. The meta-modeling tool AToM3 is used.
Mining Social Data to Extract Intellectual Knowledge
Venue: arXiv cs.AI 2012
Year: 2012

Social data mining is an interesting phe-nomenon which colligates different sources of social data to extract information. This information can be used in relationship prediction, decision making, pat-tern recognition, social mapping, responsibility distri-bution and many other applications. This paper presents a systematical data mining architecture to mine intellectual knowledge from social data. In this research, we use social networking site facebook as primary data source. We collect different attributes such as about me, comments, wall post and age from facebook as raw data and use advanced data mining approaches to excavate intellectual knowledge. We also analyze our mined knowledge with comparison for possible usages like as human behavior prediction, pattern recognition, job responsibility distribution, decision making and product promoting.
P2P Appliance Calculation Method For Trust between Nodes
Venue: arXiv cs.CY 2012
Year: 2012

Modern ways of communications, such as in a web services environment, also influences trust relationships between organisations. This concept of web-based (way towards semantic web services) trust is new and has as yet not been resolved. We hope that some of the trust properties mentioned above can be successfully employed to improve the understanding of trust between machines. So, trust is a vital ingredient of any successful interaction between individuals, among organizations and/or in society at large. In this paper, we suggested a trust model using fuzzy logic in semantic network of nodes. Trust is an aggregation of consensus given a set of past interaction among nodes (semantic network based on machines, agents etc.). We applied our suggested model to semantic networks in order to show how trust mechanisms are involved in communicating algorithm to choose the proper path from source to destination. Authors use the terms untrust and distrust as synonyms for the condition opposite to the trust.