OpenRaiser/Intern-Atlas huggingface dataset:
Text Classification using the Concept of Association Rule of Data Mining
Venue: arXiv cs.LG 2010
Year: 2010

As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. In this paper we will discuss a procedure of classifying text using the concept of association rule of data mining. Association rule mining technique has been used to derive feature set from pre-classified text documents. Naive Bayes classifier is then used on derived features for final classification.
Efficient Computation of Optimal Trading Strategies
Venue: arXiv cs.CE 2010
Year: 2010

Given the return series for a set of instruments, a \emph{trading strategy} is a switching function that transfers wealth from one instrument to another at specified times. We present efficient algorithms for constructing (ex-post) trading strategies that are optimal with respect to the total return, the Sterling ratio and the Sharpe ratio. Such ex-post optimal strategies are useful analysis tools. They can be used to analyze the "profitability of a market" in terms of optimal trading; to develop benchmarks against which real trading can be compared; and, within an inductive framework, the optimal trades can be used to to teach learning systems (predictors) which are then used to identify future trading opportunities.
Efficient L1/Lq Norm Regularization
Venue: arXiv cs.LG 2010
Year: 2010

Sparse learning has recently received increasing attention in many areas including machine learning, statistics, and applied mathematics. The mixed-norm regularization based on the L1/Lq norm with q > 1 is attractive in many applications of regression and classification in that it facilitates group sparsity in the model. The resulting optimization problem is, however, challenging to solve due to the structure of the L1/Lq -regularization. Existing work deals with special cases including q = 2,infinity, and they cannot be easily extended to the general case. In this paper, we propose an efficient algorithm based on the accelerated gradient method for solving the L1/Lq -regularized problem, which is applicable for all values of q larger than 1, thus significantly extending existing work. One key building block of the proposed algorithm is the L1/Lq -regularized Euclidean projection (EP1q). Our theoretical analysis reveals the key properties of EP1q and illustrates why EP1q for the general q is significantly more challenging to solve than the special cases. Based on our theoretical analysis, we develop an efficient algorithm for EP1q by solving two zero finding problems. Experimental results demonstrate the efficiency of the proposed algorithm.
Dynamic scheduling of virtual machines running hpc workloads in scientific grids
Venue: arXiv cs.DC 2010
Year: 2010

The primary motivation for uptake of virtualization has been resource isolation, capacity management and resource customization allowing resource providers to consolidate their resources in virtual machines. Various approaches have been taken to integrate virtualization in to scientific Grids especially in the arena of High Performance Computing (HPC) to run grid jobs in virtual machines, thus enabling better provisioning of the underlying resources and customization of the execution environment on runtime. Despite the gains, virtualization layer also incur a performance penalty and its not very well understood that how such an overhead will impact the performance of systems where jobs are scheduled with tight deadlines. Since this overhead varies the types of workload whether they are memory intensive, CPU intensive or network I/O bound, and could lead to unpredictable deadline estimation for the running jobs in the system. In our study, we have attempted to tackle this problem by developing an intelligent scheduling technique for virtual machines which monitors the workload types and deadlines, and calculate the system over head in real time to maximize number of jobs finishing within their agreed deadlines.
Deadline aware virtual machine scheduler for scientific grids and cloud computing
Venue: arXiv cs.DC 2010
Year: 2010

Virtualization technology has enabled applications to be decoupled from the underlying hardware providing the benefits of portability, better control over execution environment and isolation. It has been widely adopted in scientific grids and commercial clouds. Since virtualization, despite its benefits incurs a performance penalty, which could be significant for systems dealing with uncertainty such as High Performance Computing (HPC) applications where jobs have tight deadlines and have dependencies on other jobs before they could run. The major obstacle lies in bridging the gap between performance requirements of a job and performance offered by the virtualization technology if the jobs were to be executed in virtual machines. In this paper, we present a novel approach to optimize job deadlines when run in virtual machines by developing a deadline-aware algorithm that responds to job execution delays in real time, and dynamically optimizes jobs to meet their deadline obligations. Our approaches borrowed concepts both from signal processing and statistical techniques, and their comparative performance results are presented later in the paper including the impact on utilization rate of the hardware resources.
An Efficient Technique for Text Compression
Venue: arXiv cs.IT 2010
Year: 2010

For storing a word or the whole text segment, we need a huge storage space. Typically a character requires 1 Byte for storing it in memory. Compression of the memory is very important for data management. In case of memory requirement compression for text data, lossless memory compression is needed. We are suggesting a lossless memory requirement compression method for text data compression. The proposed compression method will compress the text segment or the text file based on two level approaches firstly reduction and secondly compression. Reduction will be done using a word lookup table not using traditional indexing system, then compression will be done using currently available compression methods. The word lookup table will be a part of the operating system and the reduction will be done by the operating system. According to this method each word will be replaced by an address value. This method can quite effectively reduce the size of persistent memory required for text data. At the end of the first level compression with the use of word lookup table, a binary file containing the addresses will be generated. Since the proposed method does not use any compression algorithm in the first level so this file can be compressed using the popular compression algorithms and finally will provide a great deal of data compression on purely English text data.
Pattern Classification using Simplified Neural Networks
Venue: arXiv cs.NE 2010
Year: 2010

In recent years, many neural network models have been proposed for pattern classification, function approximation and regression problems. This paper presents an approach for classifying patterns from simplified NNs. Although the predictive accuracy of ANNs is often higher than that of other methods or human experts, it is often said that ANNs are practically "black boxes", due to the complexity of the networks. In this paper, we have an attempted to open up these black boxes by reducing the complexity of the network. The factor makes this possible is the pruning algorithm. By eliminating redundant weights, redundant input and hidden units are identified and removed from the network. Using the pruning algorithm, we have been able to prune networks such that only a few input units, hidden units and connections left yield a simplified network. Experimental results on several benchmarks problems in neural networks show the effectiveness of the proposed approach with good generalization ability.
Text Classification using Data Mining
Venue: arXiv cs.IR 2010
Year: 2010

Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification using data mining that requires fewer documents for training. Instead of using words, word relation i.e. association rules from these words is used to derive feature set from pre-classified text documents. The concept of Naive Bayes classifier is then used on derived features and finally only a single concept of Genetic Algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental results show that the proposed system works as a successful text classifier.
Text Categorization using Association Rule and Naive Bayes Classifier
Venue: arXiv cs.IR 2010
Year: 2010

As the amount of online text increases, the demand for text categorization to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic categorization of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. Text categorization using Association Rule and Naïve Bayes Classifier is proposed here. Instead of using words word relation i.e association rules from these words is used to derive feature set from pre-classified text documents. Naive Bayes Classifier is then used on derived features for final categorization.
On the complexity of the multiple stack TSP, kSTSP
Venue: arXiv cs.CC 2010
Year: 2010

The multiple Stack Travelling Salesman Problem, STSP, deals with the collect and the deliverance of n commodities in two distinct cities. The two cities are represented by means of two edge-valued graphs (G1,d2) and (G2,d2). During the pick-up tour, the commodities are stored into a container whose rows are subject to LIFO constraints. As a generalisation of standard TSP, the problem obviously is NP-hard; nevertheless, one could wonder about what combinatorial structure of STSP does the most impact its complexity: the arrangement of the commodities into the container, or the tours themselves? The answer is not clear. First, given a pair (T1,T2) of pick-up and delivery tours, it is polynomial to decide whether these tours are or not compatible. Second, for a given arrangement of the commodities into the k rows of the container, the optimum pick-up and delivery tours w.r.t. this arrangement can be computed within a time that is polynomial in n, but exponential in k. Finally, we provide instances on which a tour that is optimum for one of three distances d1, d2 or d1+d2 lead to solutions of STSP that are arbitrarily far to the optimum STSP.
Testing of Bridging Faults in AND-EXOR based Reversible Logic Circuits
Venue: arXiv cs.OH 2010
Year: 2010

Reversible circuits find applications in many areas of Computer Science including Quantum Computation. This paper examines the testability of an important subclass of reversible logic circuits that are composed of k-wire controlled NOT (k-CNOT with k >/- 1) gates. A reversible k-CNOT gate can be implemented using an irreversible k-input AND gate and an EXOR gate. A reversible k-CNOT circuit where each k-CNOT gate is realized using irreversible k-input AND and EXOR gate, has been considered. One of the most commonly used Single Bridging Fault model (both wired-AND and wired-OR) has been assumed to be type of fault for such circuits. It has been shown that an (n+p)-input AND-EXOR based reversible logic circuit with p observable outputs, can be tested for single bridging faults (SBF) using (3n + \lefthalfcap log2p \righthalfcap + 2) tests.
Relay Selection with Network Coding in Two-Way Relay Channels
Venue: arXiv cs.IT 2010
Year: 2010

In this paper, we consider the design of joint network coding (NC)and relay selection (RS) in two-way relay channels. In the proposed schemes, two users first sequentially broadcast their respective information to all the relays. We propose two RS schemes, a single relay selection with NC and a dual relay selection with NC. For both schemes, the selected relay(s) perform NC on the received signals sent from the two users and forward them to both users. The proposed schemes are analyzed and the exact bit error rate (BER) expressions are derived and verified through Monte Carlo simulations. It is shown that the dual relay selection with NC outperforms other considered relay selection schemes in two-way relay channels. The results also reveal that the proposed NC relay selection schemes provide a selection gain compared to a NC scheme with no relay selection, and a network coding gain relative to a conventional relay selection scheme with no NC.
Robust Linear Precoder Design for Multi-cell Downlink Transmission
Venue: arXiv cs.IT 2010
Year: 2010

Coordinated information processing by the base stations of multi-cell wireless networks enhances the overall quality of communication in the network. Such coordinations for optimizing any desired network-wide quality of service (QoS) necessitate the base stations to acquire and share some channel state information (CSI). With perfect knowledge of channel states, the base stations can adjust their transmissions for achieving a network-wise QoS optimality. In practice, however, the CSI can be obtained only imperfectly. As a result, due to the uncertainties involved, the network is not guaranteed to benefit from a globally optimal QoS. Nevertheless, if the channel estimation perturbations are confined within bounded regions, the QoS measure will also lie within a bounded region. Therefore, by exploiting the notion of robustness in the worst-case sense some worst-case QoS guarantees for the network can be asserted. We adopt a popular model for noisy channel estimates that assumes that estimation noise terms lie within known hyper-spheres. We aim to design linear transceivers that optimize a worst-case QoS measure in downlink transmissions. In particular, we focus on maximizing the worst-case weighted sum-rate of the network and the minimum worst-case rate of the network. For obtaining such transceiver designs, we offer several centralized (fully cooperative) and distributed (limited cooperation) algorithms which entail different levels of complexity and information exchange among the base stations.
Towards an incremental maintenance of cyclic association rules
Venue: arXiv cs.DB 2010
Year: 2010

Recently, the cyclic association rules have been introduced in order to discover rules from items characterized by their regular variation over time. In real life situations, temporal databases are often appended or updated. Rescanning the whole database every time is highly expensive while existing incremental mining techniques can efficiently solve such a problem. In this paper, we propose an incremental algorithm for cyclic association rules maintenance. The carried out experiments of our proposal stress on its efficiency and performance.
A Framework for an Ego-centered and Time-aware Visualization of Relations in Arbitrary Data Repositories
Venue: arXiv cs.GR 2010
Year: 2010

Understanding constellations in large data collections has become a common task. One obstacle a user has to overcome is the internal complexity of these repositories. For example, extracting connected data from a normalized relational database requires knowledge of the table structure which might not be available for the casual user. In this paper we present a visualization framework which presents the collection as a set of entities and relations (on the data level). Using rating functions, we divide large relation networks into small graphs which resemble ego-centered networks. These graphs are connected so the user can browse from one to another. To further assist the user, we present two views which embed information on the evolution of the relations into the graphs. Each view emphasizes another aspect of temporal development. The framework can be adapted to any repository by a flexible data interface and a graph configuration file. We present some first web-based applications including a visualization of the DBLP data set. We use the DBLP visualization to evaluate our approach.
The Straight-Line RAC Drawing Problem is NP-Hard
Venue: arXiv cs.DS 2010
Year: 2010

Recent cognitive experiments have shown that the negative impact of an edge crossing on the human understanding of a graph drawing, tends to be eliminated in the case where the crossing angles are greater than 70 degrees. This motivated the study of RAC drawings, in which every pair of crossing edges intersects at right angle. In this work, we demonstrate a class of graphs with unique RAC combinatorial embedding and we employ members of this class in order to show that it is NP-hard to decide whether a graph admits a straight-line RAC drawing.
Vast Educational Mobile Content Broadcasting using ARMrayan Multimedia Mobile CMS
Venue: arXiv cs.CY 2010
Year: 2010

The huge information flow currently available to young generation makes it difficult for educational centers to train them as needed. Most of these information flows occur in transportation time or while on public areas. Competing with commercial information streams is far out of educational centers time and budget. For creating enough mobile applications for vast educational mobile content broadcasting that can match young spirits as well, we designed and developed the ARMrayan Multimedia Mobile CMS as the software that helps communities, educational, cultural or marketing centers in a way that ordinary operators be able to create a variety of fully functional multimedia mobile applications such as tutorials, catalogues, books, and guides in minutes without writing even a line of code. In this paper, we present the role of our developed software in our proposed vast educational content broadcasting system using kiosks and Bluetooth advertising, which will lead to a great leap in M-commerce marketing and public education solutions. Related experiences are described and diagrams are used to illustrate the solution. Upon release of the software, it achieved two titles and prizes in different festivals and various cultural and commercial centers became its customers.
Defending the future: An MSc module in End User Computing Risk Management
Venue: arXiv cs.SE 2010
Year: 2010

This paper describes the rationale, curriculum and subject matter of a new MSc module being taught on an MSc Finance and Information Management course at the University of Wales Institute in Cardiff. Academic research on spreadsheet risks now has some penetration in academic literature and there is a growing body of knowledge on the subjects of spreadsheet error, human factors, spreadsheet engineering, "best practice", spreadsheet risk management and various techniques used to mitigate spreadsheet errors. This new MSc module in End User Computing Risk Management is an attempt to pull all of this research and practitioner experience together to arm the next generation of finance spreadsheet champions with the relevant knowledge, techniques and critical perspective on an emerging discipline.
Spreadsheets - the Good, the Bad and the Downright Ugly
Venue: arXiv cs.SE 2010
Year: 2010

Spreadsheets are ubiquitous, heavily relied on throughout vast swathes of finance, commerce, industry, academia and Government. They are also acknowledged to be extraordinarily and unacceptably prone to error. If these two points are accepted, it has to follow that their uncontrolled use has the potential to inflict considerable damage. One approach to controlling such error should be to define as "good practice" a set of characteristics that a spreadsheet must possess and as "bad practice" another set that it must avoid. Defining such characteristics should, in principle, perfectly do-able. However, being able to say with authority at a definite moment that any particular spreadsheet complies with these characteristics is very much more difficult. The author asserts that the use of automated spreadsheet development could markedly help in ensuring and demonstrating such compliance.
On the Accuracy of the Wyner Model in Cellular Networks
Venue: arXiv cs.IT 2010
Year: 2010

The Wyner model has been widely used to model and analyze cellular networks due to its simplicity and analytical tractability. Its key aspects include fixed user locations and the deterministic and homogeneous interference intensity. While clearly a significant simplification of a real cellular system, which has random user locations and interference levels that vary by several orders of magnitude over a cell, a common presumption by theorists is that the Wyner model nevertheless captures the essential aspects of cellular interactions. But is this true? To answer this question, we consider both uplink and downlink transmissions, and both outage-based and average-based metrics. For the uplink, for both metrics, we conclude that the Wyner model is in fact quite accurate for systems with a sufficient number of simultaneous users, e.g. CDMA. Conversely, it is broadly inaccurate otherwise. With multicell processing, intracell TDMA is shown to be suboptimal in terms of average throughput, in sharp contrast to predictions using the Wyner model. Turning to the downlink, the Wyner model is highly inaccurate for outage since it depends largely on the user locations. However, for average or sum throughput, the Wyner model serves as an acceptable simplification in certain special cases if the interference parameter is set appropriately.
The Attentive Perceptron
Venue: arXiv cs.LG 2010
Year: 2010

We propose a focus of attention mechanism to speed up the Perceptron algorithm. Focus of attention speeds up the Perceptron algorithm by lowering the number of features evaluated throughout training and prediction. Whereas the traditional Perceptron evaluates all the features of each example, the Attentive Perceptron evaluates less features for easy to classify examples, thereby achieving significant speedups and small losses in prediction accuracy. Focus of attention allows the Attentive Perceptron to stop the evaluation of features at any interim point and filter the example. This creates an attentive filter which concentrates computation at examples that are hard to classify, and quickly filters examples that are easy to classify.
Information-Theoretic Analysis of an Energy Harvesting Communication System
Venue: arXiv cs.IT 2010
Year: 2010

In energy harvesting communication systems, an exogenous recharge process supplies energy for the data transmission and arriving energy can be buffered in a battery before consumption. Transmission is interrupted if there is not sufficient energy. We address communication with such random energy arrivals in an information-theoretic setting. Based on the classical additive white Gaussian noise (AWGN) channel model, we study the coding problem with random energy arrivals at the transmitter. We show that the capacity of the AWGN channel with stochastic energy arrivals is equal to the capacity with an average power constraint equal to the average recharge rate. We provide two different capacity achieving schemes: {\it save-and-transmit} and {\it best-effort-transmit}. Next, we consider the case where energy arrivals have time-varying average in a larger time scale. We derive the optimal offline power allocation for maximum average throughput and provide an algorithm that finds the optimal power allocation.