OpenRaiser/Intern-Atlas huggingface dataset:
Fast ABC-Boost for Multi-Class Classification
Venue: arXiv cs.LG 2010
Year: 2010

Abc-boost is a new line of boosting algorithms for multi-class classification, by utilizing the commonly used sum-to-zero constraint. To implement abc-boost, a base class must be identified at each boosting step. Prior studies used a very expensive procedure based on exhaustive search for determining the base class at each boosting step. Good testing performances of abc-boost (implemented as abc-mart and abc-logitboost) on a variety of datasets were reported. For large datasets, however, the exhaustive search strategy adopted in prior abc-boost algorithms can be too prohibitive. To overcome this serious limitation, this paper suggests a heuristic by introducing Gaps when computing the base class during training. That is, we update the choice of the base class only for every $G$ boosting steps (i.e., G=1 in prior studies). We test this idea on large datasets (Covertype and Poker) as well as datasets of moderate sizes. Our preliminary results are very encouraging. On the large datasets, even with G=100 (or larger), there is essentially no loss of test accuracy. On the moderate datasets, no obvious loss of test accuracy is observed when G<= 20~50. Therefore, aided by this heuristic, it is promising that abc-boost will be a practical tool for accurate multi-class classification.
Capacity Planning for Vertical Search Engines
Venue: arXiv cs.IR 2010
Year: 2010

Vertical search engines focus on specific slices of content, such as the Web of a single country or the document collection of a large corporation. Despite this, like general open web search engines, they are expensive to maintain, expensive to operate, and hard to design. Because of this, predicting the response time of a vertical search engine is usually done empirically through experimentation, requiring a costly setup. An alternative is to develop a model of the search engine for predicting performance. However, this alternative is of interest only if its predictions are accurate. In this paper we propose a methodology for analyzing the performance of vertical search engines. Applying the proposed methodology, we present a capacity planning model based on a queueing network for search engines with a scale typically suitable for the needs of large corporations. The model is simple and yet reasonably accurate and, in contrast to previous work, considers the imbalance in query service times among homogeneous index servers. We discuss how we tune up the model and how we apply it to predict the impact on the query response time when parameters such as CPU and disk capacities are changed. This allows a manager of a vertical search engine to determine a priori whether a new configuration of the system might keep the query response under specified performance constraints.
Automatic Probabilistic Program Verification through Random Variable Abstraction
Venue: arXiv cs.LO 2010
Year: 2010

The weakest pre-expectation calculus has been proved to be a mature theory to analyze quantitative properties of probabilistic and nondeterministic programs. We present an automatic method for proving quantitative linear properties on any denumerable state space using iterative backwards fixed point calculation in the general framework of abstract interpretation. In order to accomplish this task we present the technique of random variable abstraction (RVA) and we also postulate a sufficient condition to achieve exact fixed point computation in the abstract domain. The feasibility of our approach is shown with two examples, one obtaining the expected running time of a probabilistic program, and the other the expected gain of a gambling strategy. Our method works on general guarded probabilistic and nondeterministic transition systems instead of plain pGCL programs, allowing us to easily model a wide range of systems including distributed ones and unstructured programs. We present the operational and weakest precondition semantics for this programs and prove its equivalence.
Stochastic Calculus of Wrapped Compartments
Venue: arXiv cs.CE 2010
Year: 2010

The Calculus of Wrapped Compartments (CWC) is a variant of the Calculus of Looping Sequences (CLS). While keeping the same expressiveness, CWC strongly simplifies the development of automatic tools for the analysis of biological systems. The main simplification consists in the removal of the sequencing operator, thus lightening the formal treatment of the patterns to be matched in a term (whose complexity in CLS is strongly affected by the variables matching in the sequences). We define a stochastic semantics for this new calculus. As an application we model the interaction between macrophages and apoptotic neutrophils and a mechanism of gene regulation in E.Coli.
Technical Report: MIMO B-MAC Interference Network Optimization under Rate Constraints by Polite Water-filling and Duality
Venue: arXiv cs.IT 2010
Year: 2010

We take two new approaches to design efficient algorithms for transmitter optimization under rate constraints to guarantee the Quality of Service in general MIMO interference networks, named B-MAC Networks, which is a combination of multiple interfering broadcast channels (BC) and multiaccess channels (MAC). Two related optimization problems, maximizing the minimum of weighted rates under a sum-power constraint and minimizing the sum-power under rate constraints, are considered. The first approach takes advantage of existing efficient algorithms for SINR problems by building a bridge between rate and SINR through the design of optimal mappings between them so that the problems can be converted to SINR constraint problems. The approach can be applied to other optimization problems as well. The second approach employs polite water-filling, which is the optimal network version of water-filling that we recently found. It replaces almost all generic optimization algorithms currently used for networks and reduces the complexity while demonstrating superior performance even in non-convex cases. Both centralized and distributed algorithms are designed and the performance is analyzed in addition to numeric examples.
Reasoning Support for Risk Prediction and Prevention in Independent Living
Venue: arXiv cs.AI 2010
Year: 2010

In recent years there has been growing interest in solutions for the delivery of clinical care for the elderly, due to the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but, to be useful, this activity must not be too invasive for patients and a burden for caregivers. We prototyped a system called SINDI (Secure and INDependent lIving), focused on i) collecting a limited amount of data about the person and the environment through Wireless Sensor Networks (WSN), and ii) inferring from these data enough information to support caregivers in understanding patients' well being and in predicting possible evolutions of their health. Our hierarchical logic-based model of health combines data from different sources, sensor data, tests results, common-sense knowledge and patient's clinical profile at the lower level, and correlation rules between health conditions across upper levels. The logical formalization and the reasoning process are based on Answer Set Programming. The expressive power of this logic programming paradigm makes it possible to reason about health evolution even when the available information is incomplete and potentially incoherent, while declarativity simplifies rules specification by caregivers and allows automatic encoding of knowledge. This paper describes how these issues have been targeted in the application scenario of the SINDI system.
Geometric Approximations of Some Aloha-like Stability Regions
Venue: arXiv cs.IT 2010
Year: 2010

Most bounds on the stability region of Aloha give necessary and sufficient conditions for the stability of an arrival rate vector under a specific contention probability (control) vector. But such results do not yield easy-to-check bounds on the overall Aloha stability region because they potentially require checking membership in an uncountably infinite number of sets parameterized by each possible control vector. In this paper we consider an important specific inner bound on Aloha that has this property of difficulty to check membership in the set. We provide ellipsoids (for which membership is easy-to-check) that we conjecture are inner and outer bounds on this set. We also study the set of controls that stabilize a fixed arrival rate vector; this set is shown to be a convex set.
Automated co-evolution of GMF editor models
Venue: arXiv cs.SE 2010
Year: 2010

The Eclipse Graphical Modeling (GMF) Framework provides the major approach for implementing visual languages on top of the Eclipse platform. GMF relies on a family of modeling languages to describe different aspects of the visual language and its implementation in an editor. GMF uses a model-driven approach to map the different GMF models to Java code. The framework, as it stands, provides very little support for evolution. In particular, there is no support for propagating changes from say the domain model (i.e., the abstract syntax of the visual language) to other models. We analyze the resulting co-evolution challenge, and we provide a transformation-based solution, say GMF model adapters, that serve the propagation of abstract-syntax changes based on the interpretation of difference models.
Report on the XBase Project
Venue: arXiv cs.DB 2010
Year: 2010

This project addressed the conceptual fundamentals of data storage, investigating techniques for provision of highly generic storage facilities that can be tailored to produce various individually customised storage infrastructures, compliant to the needs of particular applications. This requires the separation of mechanism and policy wherever possible. Aspirations include: actors, whether users or individual processes, should be able to bind to, update and manipulate data and programs transparently with respect to their respective locations; programs should be expressed independently of the storage and network technology involved in their execution; storage facilities should be structure-neutral so that actors can impose multiple interpretations over information, simultaneously and safely; information should not be discarded so that arbitrary historical views are supported; raw stored information should be open to all; where security restrictions on its use are required this should be achieved using cryptographic techniques. The key advances of the research were: 1) the identification of a candidate set of minimal storage system building blocks, which are sufficiently simple to avoid encapsulating policy where it cannot be customised by applications, and composable to build highly flexible storage architectures 2) insight into the nature of append-only storage components, and the issues arising from their application to common storage use-cases.
Online Event Segmentation in Active Perception using Adaptive Strong Anticipation
Venue: arXiv cs.RO 2010
Year: 2010

Most cognitive architectures rely on discrete representation, both in space (e.g., objects) and in time (e.g., events). However, a robot interaction with the world is inherently continuous, both in space and in time. The segmentation of the stream of perceptual inputs a robot receives into discrete and meaningful events poses as a challenge in bridging the gap between internal cognitive representations, and the external world. Event Segmentation Theory, recently proposed in the context of cognitive systems research, sustains that humans segment time into events based on matching perceptual input with predictions. In this work we propose a framework for online event segmentation, targeting robots endowed with active perception. Moreover, sensory processing systems have an intrinsic latency, resulting from many factors such as sampling rate, and computational processing, and which is seldom accounted for. This framework is founded on the theory of dynamical systems synchronization, where the system considered includes both the robot and the world coupled (strong anticipation). An adaption rule is used to perform simultaneous system identification and synchronization, and anticipating synchronization is employed to predict the short-term system evolution. This prediction allows for an appropriate control of the robot actuation. Event boundaries are detected once synchronization is lost (sudden increase of the prediction error). An experimental proof of concept of the proposed framework is presented, together with some preliminary results corroborating the approach.
Dynamic and Transparent Analysis of Commodity Production Systems
Venue: arXiv cs.OS 2010
Year: 2010

We propose a framework that provides a programming interface to perform complex dynamic system-level analyses of deployed production systems. By leveraging hardware support for virtualization available nowadays on all commodity machines, our framework is completely transparent to the system under analysis and it guarantees isolation of the analysis tools running on its top. Thus, the internals of the kernel of the running system needs not to be modified and the whole platform runs unaware of the framework. Moreover, errors in the analysis tools do not affect the running system and the framework. This is accomplished by installing a minimalistic virtual machine monitor and migrating the system, as it runs, into a virtual machine. In order to demonstrate the potentials of our framework we developed an interactive kernel debugger, nicknamed HyperDbg. HyperDbg can be used to debug any critical kernel component, and even to single step the execution of exception and interrupt handlers.
Secure Transmission with Multiple Antennas II: The MIMOME Wiretap Channel
Venue: arXiv cs.IT 2010
Year: 2010

The capacity of the Gaussian wiretap channel model is analyzed when there are multiple antennas at the sender, intended receiver and eavesdropper. The associated channel matrices are fixed and known to all the terminals. A computable characterization of the secrecy capacity is established as the saddle point solution to a minimax problem. The converse is based on a Sato-type argument used in other broadcast settings, and the coding theorem is based on Gaussian wiretap codebooks. At high signal-to-noise ratio (SNR), the secrecy capacity is shown to be attained by simultaneously diagonalizing the channel matrices via the generalized singular value decomposition, and independently coding across the resulting parallel channels. The associated capacity is expressed in terms of the corresponding generalized singular values. It is shown that a semi-blind "masked" multi-input multi-output (MIMO) transmission strategy that sends information along directions in which there is gain to the intended receiver, and synthetic noise along directions in which there is not, can be arbitrarily far from capacity in this regime. Necessary and sufficient conditions for the secrecy capacity to be zero are provided, which simplify in the limit of many antennas when the entries of the channel matrices are independent and identically distributed. The resulting scaling laws establish that to prevent secure communication, the eavesdropper needs 3 times as many antennas as the sender and intended receiver have jointly, and that the optimimum division of antennas between sender and intended receiver is in the ratio of 2:1.
Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition
Venue: arXiv cs.CV 2010
Year: 2010

Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM), multiple classifier combination, etc. In this paper, we discuss the characteristics of the some classification methods that have been successfully applied to handwritten Devnagari character recognition and results of SVM and ANNs classification method, applied on Handwritten Devnagari characters. After preprocessing the character image, we extracted shadow features, chain code histogram features, view based features and longest run features. These features are then fed to Neural classifier and in support vector machine for classification. In neural classifier, we explored three ways of combining decisions of four MLP's designed for four different features.
Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance
Venue: arXiv cs.CV 2010
Year: 2010

This paper deals with a new method for recognition of offline Handwritten non-compound Devnagari Characters in two stages. It uses two well known and established pattern recognition techniques: one using neural networks and the other one using minimum edit distance. Each of these techniques is applied on different sets of characters for recognition. In the first stage, two sets of features are computed and two classifiers are applied to get higher recognition accuracy. Two MLP's are used separately to recognize the characters. For one of the MLP's the characters are represented with their shadow features and for the other chain code histogram feature is used. The decision of both MLP's is combined using weighted majority scheme. Top three results produced by combined MLP's in the first stage are used to calculate the relative difference values. In the second stage, based on these relative differences character set is divided into two. First set consists of the characters with distinct shapes and second set consists of confused characters, which appear very similar in shapes. Characters of distinct shapes of first set are classified using MLP. Confused characters in second set are classified using minimum edit distance method. Method of minimum edit distance makes use of corner detected in a character image using modified Harris corner detection technique. Experiment on this method is carried out on a database of 7154 samples. The overall recognition is found to be 90.74%.
Multiple Classifier Combination for Off-line Handwritten Devnagari Character Recognition
Venue: arXiv cs.CV 2010
Year: 2010

This work presents the application of weighted majority voting technique for combination of classification decision obtained from three Multi_Layer Perceptron(MLP) based classifiers for Recognition of Handwritten Devnagari characters using three different feature sets. The features used are intersection, shadow feature and chain code histogram features. Shadow features are computed globally for character image while intersection features and chain code histogram features are computed by dividing the character image into different segments. On experimentation with a dataset of 4900 samples the overall recognition rate observed is 92.16% as we considered top five choices results. This method is compared with other recent methods for Handwritten Devnagari Character Recognition and it has been observed that this approach has better success rate than other methods.
Fuzzy Classification of Facial Component Parameters
Venue: arXiv cs.CV 2010
Year: 2010

This paper presents a novel type-2 Fuzzy logic System to define the Shape of a facial component with the crisp output. This work is the part of our main research effort to design a system (called FASY) which offers a novel face construction approach based on the textual description and also extracts and analyzes the facial components from a face image by an efficient technique. The Fuzzy model, designed in this paper, takes crisp value of width and height of a facial component and produces the crisp value of Shape for different facial components. This method is designed using Matlab 6.5 and Visual Basic 6.0 and tested with the facial components extracted from 200 male and female face images of different ages from different face databases.
A Novel Approach towards Cost Effective Region-Based Group Key Agreement Protocol for Secure Group Communication
Venue: arXiv cs.CR 2010
Year: 2010

This paper addresses an interesting security problem in wireless ad hoc networks: the Dynamic Group Key Agreement key establishment. For secure group communication in an Ad hoc network, a group key shared by all group members is required. This group key should be updated when there are membership changes (when the new member joins or current member leaves) in the group. In this paper, We propose a novel, secure, scalable and efficient Region-Based Group Key Agreement protocol (RBGKA) for ad-hoc networks. This is implemented by a two-level structure and a new scheme of group key update. The idea is to divide the group into subgroups, each maintaining its subgroup keys using Group Diffie-Hellman (GDH) Protocol and links with other subgroups in a Tree structure using Tree-based Group Diffie-Hellman (TGDH) protocol. By introducing region-based approach, messages and key updates will be limited within subgroup and outer group; hence computation load is distributed among many hosts. Both theoretical analysis and experimental results show that this Region-based key agreement protocol performs better for the key establishment problem in ad-hoc network in terms of memory cost, computation cost and communication cost.
Adaptive and occupancy-based channel selection for unreliable cognitive radio networks
Venue: arXiv cs.NI 2010
Year: 2010

In this paper, we propose an adaptive and occupancy-based channel selection for unreliable cognitive radio networks.
A Cognitive Radio Based Internet Access Framework for Disaster Response Network Deployment
Venue: arXiv cs.NI 2010
Year: 2010

In this paper, we propose a cognitive radio based Internet access framework for disaster response network deployment in challenged environments. The proposed architectural framework is designed to help the existent but partially damaged networks to restore their connectivity and to connect them to the global Internet. This architectural framework provides the basis to develop algorithms and protocols for the future cognitive radio network deployments in challenged environments.
Detecting Inconsistencies in Large Biological Networks with Answer Set Programming
Venue: arXiv cs.LO 2010
Year: 2010

We introduce an approach to detecting inconsistencies in large biological networks by using Answer Set Programming (ASP). To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on ASP to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions.
Regular Labelings and Geometric Structures
Venue: arXiv cs.CG 2010
Year: 2010

Three types of geometric structure---grid triangulations, rectangular subdivisions, and orthogonal polyhedra---can each be described combinatorially by a regular labeling: an assignment of colors and orientations to the edges of an associated maximal or near-maximal planar graph. We briefly survey the connections and analogies between these three kinds of labelings, and their uses in designing efficient geometric algorithms.