OpenRaiser/Intern-Atlas huggingface dataset:
Performance Analysis Of Neural Network Models For Oxazolines And Oxazoles Derivatives Descriptor Dataset
Venue: arXiv cs.CE 2013
Year: 2013

Neural networks have been used successfully to a broad range of areas such as business, data mining, drug discovery and biology. In medicine, neural networks have been applied widely in medical diagnosis, detection and evaluation of new drugs and treatment cost estimation. In addition, neural networks have begin practice in data mining strategies for the aim of prediction, knowledge discovery. This paper will present the application of neural networks for the prediction and analysis of antitubercular activity of Oxazolines and Oxazoles derivatives. This study presents techniques based on the development of Single hidden layer neural network (SHLFFNN), Gradient Descent Back propagation neural network (GDBPNN), Gradient Descent Back propagation with momentum neural network (GDBPMNN), Back propagation with Weight decay neural network (BPWDNN) and Quantile regression neural network (QRNN) of artificial neural network (ANN) models Here, we comparatively evaluate the performance of five neural network techniques. The evaluation of the efficiency of each model by ways of benchmark experiments is an accepted application. Cross-validation and resampling techniques are commonly used to derive point estimates of the performances which are compared to identify methods with good properties. Predictive accuracy was evaluated using the root mean squared error (RMSE), Coefficient determination(???), mean absolute error(MAE), mean percentage error(MPE) and relative square error(RSE). We found that all five neural network models were able to produce feasible models. QRNN model is outperforms with all statistical tests amongst other four models.
Identification Of Outliers In Oxazolines AND Oxazoles High Dimension Molecular Descriptor Dataset Using Principal Component Outlier Detection Algorithm And Comparative Numerical Study Of Other Robust Estimators
Venue: arXiv cs.CE 2013
Year: 2013

From the past decade outlier detection has been in use. Detection of outliers is an emerging topic and is having robust applications in medical sciences and pharmaceutical sciences. Outlier detection is used to detect anomalous behaviour of data. Typical problems in Bioinformatics can be addressed by outlier detection. A computationally fast method for detecting outliers is shown, that is particularly effective in high dimensions. PrCmpOut algorithm make use of simple properties of principal components to detect outliers in the transformed space, leading to significant computational advantages for high dimensional data. This procedure requires considerably less computational time than existing methods for outlier detection. The properties of this estimator (Outlier error rate (FN), Non-Outlier error rate(FP) and computational costs) are analyzed and compared with those of other robust estimators described in the literature through simulation studies. Numerical evidence based Oxazolines and Oxazoles molecular descriptor dataset shows that the proposed method performs well in a variety of situations of practical interest. It is thus a valuable companion to the existing outlier detection methods.
Representing Knowledge Base into Database for WAP and Web-based Expert System
Venue: arXiv cs.AI 2013
Year: 2013

Expert System is developed as consulting service for users spread or public requires affordable access. The Internet has become a medium for such services, but presence of mobile devices make the access becomes more widespread by utilizing mobile web and WAP (Wireless Application Protocol). Applying expert systems applications over the web and WAP requires a knowledge base representation that can be accessed simultaneously. This paper proposes single database to accommodate the knowledge representation with decision tree mapping approach. Because of the database exist, consulting application through both web and WAP can access it to provide expert system services options for more affordable for public.
Fast Approximate $K$-Means via Cluster Closures
Venue: arXiv cs.CV 2013
Year: 2013

$K$-means, a simple and effective clustering algorithm, is one of the most widely used algorithms in multimedia and computer vision community. Traditional $k$-means is an iterative algorithm---in each iteration new cluster centers are computed and each data point is re-assigned to its nearest center. The cluster re-assignment step becomes prohibitively expensive when the number of data points and cluster centers are large. In this paper, we propose a novel approximate $k$-means algorithm to greatly reduce the computational complexity in the assignment step. Our approach is motivated by the observation that most active points changing their cluster assignments at each iteration are located on or near cluster boundaries. The idea is to efficiently identify those active points by pre-assembling the data into groups of neighboring points using multiple random spatial partition trees, and to use the neighborhood information to construct a closure for each cluster, in such a way only a small number of cluster candidates need to be considered when assigning a data point to its nearest cluster. Using complexity analysis, image data clustering, and applications to image retrieval, we show that our approach out-performs state-of-the-art approximate $k$-means algorithms in terms of clustering quality and efficiency.
How have the Eastern European countries of the former Warsaw Pact developed since 1990? A bibliometric study
Venue: arXiv cs.DL 2013
Year: 2013

Did the demise of the Soviet Union in 1991 influence the scientific performance of the researchers in Eastern European countries? Did this historical event affect international collaboration by researchers from the Eastern European countries with those of Western countries? Did it also change international collaboration among researchers from the Eastern European countries? Trying to answer these questions, this study aims to shed light on international collaboration by researchers from the Eastern European countries (Russia, Ukraine, Belarus, Moldova, Bulgaria, the Czech Republic, Hungary, Poland, Romania and Slovakia). The number of publications and normalized citation impact values are compared for these countries based on InCites (Thomson Reuters), from 1981 up to 2011. The international collaboration by researchers affiliated to institutions in Eastern European countries at the time points of 1990, 2000 and 2011 was studied with the help of Pajek and VOSviewer software, based on data from the Science Citation Index (Thomson Reuters). Our results show that the breakdown of the communist regime did not lead, on average, to a huge improvement in the publication performance of the Eastern European countries and that the increase in international co-authorship relations by the researchers affiliated to institutions in these countries was smaller than expected. Most of the Eastern European countries are still subject to changes and are still awaiting their boost in scientific development.
Approximate Least Squares
Venue: arXiv cs.DS 2013
Year: 2013

We present a novel iterative algorithm for approximating the linear least squares solution with low complexity. After a motivation of the algorithm we discuss the algorithm's properties including its complexity, and we present theoretical results as well as simulation based performance results. We describe the analysis of its convergence behavior and show that in the noise free case the algorithm converges to the least squares solution.
Iterated group products and leakage resilience against NC^1
Venue: arXiv cs.CC 2013
Year: 2013

We show that if NC$^1 \neq$ L, then for every element $α$ of the alternating group $A_t$, circuits of depth $O(\log t)$ cannot distinguish between a uniform vector over $(A_t)^t$ with product $= α$ and one with product $=$ identity. Combined with a recent construction by the author and Viola in the setting of leakage-resilient cryptography [STOC '13], this gives a compiler that produces circuits withstanding leakage from NC$^1$ (assuming NC$^1 \neq$ L). For context, leakage from NC$^1$ breaks nearly all previous constructions, and security against leakage from P is impossible. %In the multi-query setting, circuits produced by this compiler use a simple secure hardware component. We build on work by Cook and McKenzie [J.\ Algorithms '87] establishing the relationship between L $=$ logarithmic space and the symmetric group $S_t$. Our techniques include a novel algorithmic use of commutators to manipulate the cycle structure of permutations in $A_t$.
How to deal with malleability of BitCoin transactions
Venue: arXiv cs.CR 2013
Year: 2013

BitCoin transactions are malleable in a sense that given a transaction an adversary can easily construct an equivalent transaction which has a different hash. This can pose a serious problem in some BitCoin distributed contracts in which changing a transaction's hash may result in the protocol disruption and a financial loss. The problem mostly concerns protocols, which use a "refund" transaction to withdraw a deposit in a case of the protocol interruption. In this short note, we show a general technique for creating malleability-resilient "refund" transactions, which does not require any modification of the BitCoin protocol. Applying our technique to our previous paper "Fair Two-Party Computations via the BitCoin Deposits" (Cryptology ePrint Archive, 2013) allows to achieve fairness in any Two-Party Computation using the BitCoin protocol in its current version.
Numerical Reproducibility and Parallel Computations: Issues for Interval Algorithms
Venue: arXiv cs.NA 2013
Year: 2013

What is called "numerical reproducibility" is the problem of getting the same result when the scientific computation is run several times, either on the same machine or on different machines, with different types and numbers of processing units, execution environments, computational loads etc. This problem is especially stringent for HPC numerical simulations. In what follows, the focus is on parallel implementations of interval arithmetic using floating-point arithmetic. For interval computations, numerical reproducibility is of course an issue for testing and debugging purposes. However, as long as the computed result encloses the exact and unknown result, the inclusion property, which is the main property of interval arithmetic, is satisfied and getting bit for bit identical results may not be crucial. Still, implementation issues may invalidate the inclusion property. Several ways to preserve the inclusion property are presented, on the example of the product of matrices with interval coefficients.
New Codes on Graphs Constructed by Connecting Spatially Coupled Chains
Venue: arXiv cs.IT 2013
Year: 2013

A novel code construction based on spatially coupled low-density parity-check (SC-LDPC) codes is presented. The proposed code ensembles are described by protographs, comprised of several protograph-based chains characterizing individual SC-LDPC codes. We demonstrate that code ensembles obtained by connecting appropriately chosen SC-LDPC code chains at specific points have improved iterative decoding thresholds compared to those of single SC-LDPC coupled chains. In addition, it is shown that the improved decoding properties of the connected ensembles result in reduced decoding complexity required to achieve a specific bit error probability. The constructed ensembles are also asymptotically good, in the sense that the minimum distance grows linearly with the block length. Finally, we show that the improved asymptotic properties of the connected chain ensembles also translate into improved finite length performance.
On RIC bounds of Compressed Sensing Matrices for Approximating Sparse Solutions Using $\ell_q$ Quasi Norms
Venue: arXiv cs.IT 2013
Year: 2013

This paper follows the recent discussion on the sparse solution recovery with quasi-norms $\ell_q,~q\in(0,1)$ when the sensing matrix possesses a Restricted Isometry Constant $δ_{2k}$ (RIC). Our key tool is an improvement on a version of "the converse of a generalized Cauchy-Schwarz inequality" extended to the setting of quasi-norm. We show that, if $δ_{2k}\le 1/2$, any minimizer of the $l_q$ minimization, at least for those $q\in(0,0.9181]$, is the sparse solution of the corresponding underdetermined linear system. Moreover, if $δ_{2k}\le0.4931$, the sparse solution can be recovered by any $l_q, q\in(0,1)$ minimization. The values $0.9181$ and $0.4931$ improves those reported previously in the literature.
Group Key Agreement Protocol for MANETs Based on HSK Scheme
Venue: arXiv cs.CR 2013
Year: 2013

In this paper, we first provide a spanning tree (ST)-based centralized group key agreement protocol for unbalanced mobile Ad Hoc networks (MANETs). Based on the centralized solution, a local spanning tree (LST)-based distributed protocol for general MANETs is subsequently presented. Both protocols follow the basic features of the HSK scheme: 1) H means that a hybrid approach, which is the combination of key agreement and key distribution via symmetric encryption, is exploited; 2) S indicates that a ST or LSTs are adopted to form a connected network topology; and 3) K implies that the extended Kruskal algorithm is employed to handle dynamic events. It is shown that the HSK scheme is a uniform approach to handle the initial key establishment process as well as all kinds of dynamic events in group key agreement protocol for MANETs. Additionally, the extended Kruskal algorithm enables to realize the reusability of the precomputed secure links to reduce the overhead. Moreover, some other aspects, such as the network topology connectivity and security, are well analyzed.
Unsupervised learning of depth and motion
Venue: arXiv cs.CV 2013
Year: 2013

We present a model for the joint estimation of disparity and motion. The model is based on learning about the interrelations between images from multiple cameras, multiple frames in a video, or the combination of both. We show that learning depth and motion cues, as well as their combinations, from data is possible within a single type of architecture and a single type of learning algorithm, by using biologically inspired "complex cell" like units, which encode correlations between the pixels across image pairs. Our experimental results show that the learning of depth and motion makes it possible to achieve state-of-the-art performance in 3-D activity analysis, and to outperform existing hand-engineered 3-D motion features by a very large margin.
Smooth Orthogonal Drawings of Planar Graphs
Venue: arXiv cs.CG 2013
Year: 2013

In \emph{smooth orthogonal layouts} of planar graphs, every edge is an alternating sequence of axis-aligned segments and circular arcs with common axis-aligned tangents. In this paper, we study the problem of finding smooth orthogonal layouts of low \emph{edge complexity}, that is, with few segments per edge. We say that a graph has \emph{smooth complexity} k---for short, an SC_k-layout---if it admits a smooth orthogonal drawing of edge complexity at most $k$. Our main result is that every 4-planar graph has an SC_2-layout. While our drawings may have super-polynomial area, we show that, for 3-planar graphs, cubic area suffices. Further, we show that every biconnected 4-outerplane graph admits an SC_1-layout. On the negative side, we demonstrate an infinite family of biconnected 4-planar graphs that requires exponential area for an SC_1-layout. Finally, we present an infinite family of biconnected 4-planar graphs that does not admit an SC_1-layout.
Optimal Distributed Control for Networked Control Systems with Delays
Venue: arXiv cs.SY 2013
Year: 2013

In networked control systems (NCS), sensing and control signals between the plant and controllers are typically transmitted wirelessly. Thus, the time delay plays an important role for the stability of NCS, especially with distributed controllers. In this paper, the optimal control strategy is derived for distributed control networks with time delays. In particular, we form the optimal control problem as a non-cooperative linear quadratic game (LQG). Then, the optimal control strategy of each controller is obtained that is based on the current state and the last control strategies. The proposed optimal distributed controller reduces to some known controllers under certain conditions. Moreover, we illustrate the application of the proposed distributed controller to load frequency control in power grid systems.
Partially Overlapping Tones for Uncoordinated Networks
Venue: arXiv cs.IT 2013
Year: 2013

In an uncoordinated network, the link performance between the devices might degrade significantly due to the interference from other links in the network sharing the same spectrum. As a solution, in this study, the concept of partially overlapping tones (POT) is introduced. The interference energy observed at the victim receiver is mitigated by partially overlapping the individual subcarriers via an intentional carrier frequency offset between the links. Also, it is shown that while orthogonal transformations at the receiver cannot mitigate the other-user interference without losing spectral efficiency, non-orthogonal transformations are able to mitigate the other-user interference without any spectral efficiency loss at the expense of self-interference. Using spatial Poisson point process, a tractable bit error rate analysis is provided to demonstrate potential benefits emerging from POT.
Secure Beamforming for MIMO Two-Way Communications with an Untrusted Relay
Venue: arXiv cs.IT 2013
Year: 2013

This paper studies the secure beamforming design in a multiple-antenna three-node system where two source nodes exchange messages with the help of an untrusted relay node. The relay acts as both an essential signal forwarder and a potential eavesdropper. Both two-phase and three-phase two-way relay strategies are considered. Our goal is to jointly optimize the source and relay beamformers for maximizing the secrecy sum rate of the two-way communications. We first derive the optimal relay beamformer structures. Then, iterative algorithms are proposed to find source and relay beamformers jointly based on alternating optimization. Furthermore, we conduct asymptotic analysis on the maximum secrecy sum-rate. Our analysis shows that when all transmit powers approach infinity, the two-phase two-way relay scheme achieves the maximum secrecy sum rate if the source beamformers are designed such that the received signals at the relay align in the same direction. This reveals an important advantage of signal alignment technique in against eavesdropping. It is also shown that if the source powers approach zero the three-phase scheme performs the best while the two-phase scheme is even worse than direct transmission. Simulation results have verified the efficiency of the secure beamforming algorithms as well as the analytical findings.
Path Based Mapping Technique for Robots
Venue: arXiv cs.RO 2013
Year: 2013

The purpose of this paper is to explore a new way of autonomous mapping. Current systems using perception techniques like LAZER or SONAR use probabilistic methods and have a drawback of allowing considerable uncertainty in the mapping process. Our approach is to break down the environment, specifically indoor, into reachable areas and objects, separated by boundaries, and identifying their shape, to render various navigable paths around them. This is a novel method to do away with uncertainties, as far as possible, at the cost of temporal efficiency. Also this system demands only minimum and cheap hardware, as it relies on only Infra-Red sensors to do the job.
Computational impact of hydrophobicity in protein stability
Venue: arXiv cs.CE 2013
Year: 2013

Among the various features of amino acids, the hydrophobic property has most visible impact on stability of a sequence folding. This is mentioned in many protein folding related work, in this paper we more elaborately discuss the computational impact of the well defined hydrophobic aspect in determining stability, approach with the help of a developed free energy computing algorithm covering various aspects preprocessing of an amino acid sequence, generating the folding and calculating free energy. Later discussing its use in protein structure related research work.
Strong Turing Degrees for Additive BSS RAM's
Venue: arXiv cs.LO 2013
Year: 2013

For the additive real BSS machines using only constants 0 and 1 and order tests we consider the corresponding Turing reducibility and characterize some semi-decidable decision problems over the reals. In order to refine, step-by-step, a linear hierarchy of Turing degrees with respect to this model, we define several halting problems for classes of additive machines with different abilities and construct further suitable decision problems. In the construction we use methods of the classical recursion theory as well as techniques for proving bounds resulting from algebraic properties. In this way we extend a known hierarchy of problems below the halting problem for the additive machines using only equality tests and we present a further subhierarchy of semi-decidable problems between the halting problems for the additive machines using only equality tests and using order tests, respectively.
Classifiers With a Reject Option for Early Time-Series Classification
Venue: arXiv cs.CV 2013
Year: 2013

Early classification of time-series data in a dynamic environment is a challenging problem of great importance in signal processing. This paper proposes a classifier architecture with a reject option capable of online decision making without the need to wait for the entire time series signal to be present. The main idea is to classify an odor/gas signal with an acceptable accuracy as early as possible. Instead of using posterior probability of a classifier, the proposed method uses the "agreement" of an ensemble to decide whether to accept or reject the candidate label. The introduced algorithm is applied to the bio-chemistry problem of odor classification to build a novel Electronic-Nose called Forefront-Nose. Experimental results on wind tunnel test-bed facility confirms the robustness of the forefront-nose compared to the standard classifiers from both earliness and recognition perspectives.
Oblivious Query Processing
Venue: arXiv cs.DB 2013
Year: 2013

Motivated by cloud security concerns, there is an increasing interest in database systems that can store and support queries over encrypted data. A common architecture for such systems is to use a trusted component such as a cryptographic co-processor for query processing that is used to securely decrypt data and perform computations in plaintext. The trusted component has limited memory, so most of the (input and intermediate) data is kept encrypted in an untrusted storage and moved to the trusted component on ``demand.'' In this setting, even with strong encryption, the data access pattern from untrusted storage has the potential to reveal sensitive information; indeed, all existing systems that use a trusted component for query processing over encrypted data have this vulnerability. In this paper, we undertake the first formal study of secure query processing, where an adversary having full knowledge of the query (text) and observing the query execution learns nothing about the underlying database other than the result size of the query on the database. We introduce a simpler notion, oblivious query processing, and show formally that a query admits secure query processing iff it admits oblivious query processing. We present oblivious query processing algorithms for a rich class of database queries involving selections, joins, grouping and aggregation. For queries not handled by our algorithms, we provide some initial evidence that designing oblivious (and therefore secure) algorithms would be hard via reductions from two simple, well-studied problems that are generally believed to be hard. Our study of oblivious query processing also reveals interesting connections to database join theory.