OpenRaiser/Intern-Atlas huggingface dataset:
Using data mining techniques for diagnosis and prognosis of cancer disease
Venue: arXiv cs.DB 2012
Year: 2012

Breast cancer is one of the leading cancers for women in developed countries including India. It is the second most common cause of cancer death in women. The high incidence of breast cancer in women has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various review and technical articles on breast cancer diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
Mixed-mode implementation of PETSc for scalable linear algebra on multi-core processors
Venue: arXiv cs.DC 2012
Year: 2012

With multi-core processors a ubiquitous building block of modern supercomputers, it is now past time to enable applications to embrace these developments in processor design. To achieve exascale performance, applications will need ways of exploiting the new levels of parallelism that are exposed in modern high-performance computers. A typical approach to this is to use shared-memory programming techniques to best exploit multi-core nodes along with inter-node message passing. In this paper, we describe the addition of OpenMP threaded functionality to the PETSc library. We highlight some issues that hinder good performance of threaded applications on modern processors and describe how to negate them. The OpenMP branch of PETSc was benchmarked using matrices extracted from Fluidity, a CFD application code, which uses the library as its linear solver engine. The overall performance of the mixed-mode implementation is shown to be superior to that of the pure-MPI version.
Identifying And Weighting Integration Hypotheses On Open Data Platforms
Venue: arXiv cs.DB 2012
Year: 2012

Open data platforms such as data.gov or opendata.socrata. com provide a huge amount of valuable information. Their free-for-all nature, the lack of publishing standards and the multitude of domains and authors represented on these platforms lead to new integration and standardization problems. At the same time, crowd-based data integration techniques are emerging as new way of dealing with these problems. However, these methods still require input in form of specific questions or tasks that can be passed to the crowd. This paper discusses integration problems on Open Data Platforms, and proposes a method for identifying and ranking integration hypotheses in this context. We will evaluate our findings by conducting a comprehensive evaluation using on one of the largest Open Data platforms.
Open Data Visualization: Keeping Traces of the Exploration Process
Venue: arXiv cs.HC 2012
Year: 2012

This paper describes a system to support the visual exploration of Open Data. During his/her interactive experience with the graphics, the user can easily store the current complete state of the visualization application (called a viewpoint). Next, he/she can compose sequences of these viewpoints (called scenarios) that can easily be reloaded. This feature allows to keep traces of a former exploration process, which can be useful in single user (to support investigation carried out in multiple sessions) as well as in collaborative setting (to share points of interest identified in the data set).
Edge-clique graphs of cocktail parties have unbounded rankwidth
Venue: arXiv cs.DM 2012
Year: 2012

In an attempt to find a polynomial-time algorithm for the edge-clique cover problem on cographs we tried to prove that the edge-clique graphs of cographs have bounded rankwidth. However, this is not the case. In this note we show that the edge-clique graphs of cocktail party graphs have unbounded rankwidth.
Refining Inductive Types
Venue: arXiv cs.LO 2012
Year: 2012

Dependently typed programming languages allow sophisticated properties of data to be expressed within the type system. Of particular use in dependently typed programming are indexed types that refine data by computationally useful information. For example, the N-indexed type of vectors refines lists by their lengths. Other data types may be refined in similar ways, but programmers must produce purpose-specific refinements on an ad hoc basis, developers must anticipate which refinements to include in libraries, and implementations must often store redundant information about data and their refinements. In this paper we show how to generically derive inductive characterisations of refinements of inductive types, and argue that these characterisations can alleviate some of the aforementioned difficulties associated with ad hoc refinements. Our characterisations also ensure that standard techniques for programming with and reasoning about inductive types are applicable to refinements, and that refinements can themselves be further refined.
The Entire Quantile Path of a Risk-Agnostic SVM Classifier
Venue: arXiv cs.LG 2012
Year: 2012

A quantile binary classifier uses the rule: Classify x as +1 if P(Y = 1|X = x) >= t, and as -1 otherwise, for a fixed quantile parameter t {[0, 1]. It has been shown that Support Vector Machines (SVMs) in the limit are quantile classifiers with t = 1/2 . In this paper, we show that by using asymmetric cost of misclassification SVMs can be appropriately extended to recover, in the limit, the quantile binary classifier for any t. We then present a principled algorithm to solve the extended SVM classifier for all values of t simultaneously. This has two implications: First, one can recover the entire conditional distribution P(Y = 1|X = x) = t for t {[0, 1]. Second, we can build a risk-agnostic SVM classifier where the cost of misclassification need not be known apriori. Preliminary numerical experiments show the effectiveness of the proposed algorithm.
Temporal-Difference Networks for Dynamical Systems with Continuous Observations and Actions
Venue: arXiv cs.LG 2012
Year: 2012

Temporal-difference (TD) networks are a class of predictive state representations that use well-established TD methods to learn models of partially observable dynamical systems. Previous research with TD networks has dealt only with dynamical systems with finite sets of observations and actions. We present an algorithm for learning TD network representations of dynamical systems with continuous observations and actions. Our results show that the algorithm is capable of learning accurate and robust models of several noisy continuous dynamical systems. The algorithm presented here is the first fully incremental method for learning a predictive representation of a continuous dynamical system.
Measuring Inconsistency in Probabilistic Knowledge Bases
Venue: arXiv cs.AI 2012
Year: 2012

This paper develops an inconsistency measure on conditional probabilistic knowledge bases. The measure is based on fundamental principles for inconsistency measures and thus provides a solid theoretical framework for the treatment of inconsistencies in probabilistic expert systems. We illustrate its usefulness and immediate application on several examples and present some formal results. Building on this measure we use the Shapley value-a well-known solution for coalition games-to define a sophisticated indicator that is not only able to measure inconsistencies but to reveal the causes of inconsistencies in the knowledge base. Altogether these tools guide the knowledge engineer in his aim to restore consistency and therefore enable him to build a consistent and usable knowledge base that can be employed in probabilistic expert systems.
Domain Knowledge Uncertainty and Probabilistic Parameter Constraints
Venue: arXiv cs.LG 2012
Year: 2012

Incorporating domain knowledge into the modeling process is an effective way to improve learning accuracy. However, as it is provided by humans, domain knowledge can only be specified with some degree of uncertainty. We propose to explicitly model such uncertainty through probabilistic constraints over the parameter space. In contrast to hard parameter constraints, our approach is effective also when the domain knowledge is inaccurate and generally results in superior modeling accuracy. We focus on generative and conditional modeling where the parameters are assigned a Dirichlet or Gaussian prior and demonstrate the framework with experiments on both synthetic and real-world data.
Multiple Source Adaptation and the Renyi Divergence
Venue: arXiv cs.LG 2012
Year: 2012

This paper presents a novel theoretical study of the general problem of multiple source adaptation using the notion of Renyi divergence. Our results build on our previous work [12], but significantly broaden the scope of that work in several directions. We extend previous multiple source loss guarantees based on distribution weighted combinations to arbitrary target distributions P, not necessarily mixtures of the source distributions, analyze both known and unknown target distribution cases, and prove a lower bound. We further extend our bounds to deal with the case where the learner receives an approximate distribution for each source instead of the exact one, and show that similar loss guarantees can be achieved depending on the divergence between the approximate and true distributions. We also analyze the case where the labeling functions of the source domains are somewhat different. Finally, we report the results of experiments with both an artificial data set and a sentiment analysis task, showing the performance benefits of the distribution weighted combinations and the quality of our bounds based on the Renyi divergence.
Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization
Venue: arXiv cs.LG 2012
Year: 2012

The problem of joint feature selection across a group of related tasks has applications in many areas including biomedical informatics and computer vision. We consider the l2,1-norm regularized regression model for joint feature selection from multiple tasks, which can be derived in the probabilistic framework by assuming a suitable prior from the exponential family. One appealing feature of the l2,1-norm regularization is that it encourages multiple predictors to share similar sparsity patterns. However, the resulting optimization problem is challenging to solve due to the non-smoothness of the l2,1-norm regularization. In this paper, we propose to accelerate the computation by reformulating it as two equivalent smooth convex optimization problems which are then solved via the Nesterov's method-an optimal first-order black-box method for smooth convex optimization. A key building block in solving the reformulations is the Euclidean projection. We show that the Euclidean projection for the first reformulation can be analytically computed, while the Euclidean projection for the second one can be computed in linear time. Empirical evaluations on several data sets verify the efficiency of the proposed algorithms.
MAP Estimation, Message Passing, and Perfect Graphs
Venue: arXiv cs.AI 2012
Year: 2012

Efficiently finding the maximum a posteriori (MAP) configuration of a graphical model is an important problem which is often implemented using message passing algorithms. The optimality of such algorithms is only well established for singly-connected graphs and other limited settings. This article extends the set of graphs where MAP estimation is in P and where message passing recovers the exact solution to so-called perfect graphs. This result leverages recent progress in defining perfect graphs (the strong perfect graph theorem), linear programming relaxations of MAP estimation and recent convergent message passing schemes. The article converts graphical models into nand Markov random fields which are straightforward to relax into linear programs. Therein, integrality can be established in general by testing for graph perfection. This perfection test is performed efficiently using a polynomial time algorithm. Alternatively, known decomposition tools from perfect graph theory may be used to prove perfection for certain families of graphs. Thus, a general graph framework is provided for determining when MAP estimation in any graphical model is in P, has an integral linear programming relaxation and is exactly recoverable by message passing.
Convex Coding
Venue: arXiv cs.LG 2012
Year: 2012

Inspired by recent work on convex formulations of clustering (Lashkari & Golland, 2008; Nowozin & Bakir, 2008) we investigate a new formulation of the Sparse Coding Problem (Olshausen & Field, 1997). In sparse coding we attempt to simultaneously represent a sequence of data-vectors sparsely (i.e. sparse approximation (Tropp et al., 2006)) in terms of a 'code' defined by a set of basis elements, while also finding a code that enables such an approximation. As existing alternating optimization procedures for sparse coding are theoretically prone to severe local minima problems, we propose a convex relaxation of the sparse coding problem and derive a boosting-style algorithm, that (Nowozin & Bakir, 2008) serves as a convex 'master problem' which calls a (potentially non-convex) sub-problem to identify the next code element to add. Finally, we demonstrate the properties of our boosted coding algorithm on an image denoising task.
Alternating Projections for Learning with Expectation Constraints
Venue: arXiv cs.LG 2012
Year: 2012

We present an objective function for learning with unlabeled data that utilizes auxiliary expectation constraints. We optimize this objective function using a procedure that alternates between information and moment projections. Our method provides an alternate interpretation of the posterior regularization framework (Graca et al., 2008), maintains uncertainty during optimization unlike constraint-driven learning (Chang et al., 2007), and is more efficient than generalized expectation criteria (Mann & McCallum, 2008). Applications of this framework include minimally supervised learning, semisupervised learning, and learning with constraints that are more expressive than the underlying model. In experiments, we demonstrate comparable accuracy to generalized expectation criteria for minimally supervised learning, and use expressive structural constraints to guide semi-supervised learning, providing a 3%-6% improvement over stateof-the-art constraint-driven learning.
Non-Interactive Differential Privacy: a Survey
Venue: arXiv cs.DB 2012
Year: 2012

OpenData movement around the globe is demanding more access to information which lies locked in public or private servers. As recently reported by a McKinsey publication, this data has significant economic value, yet its release has potential to blatantly conflict with people privacy. Recent UK government inquires have shown concern from various parties about publication of anonymized databases, as there is concrete possibility of user identification by means of linkage attacks. Differential privacy stands out as a model that provides strong formal guarantees about the anonymity of the participants in a sanitized database. Only recent results demonstrated its applicability on real-life datasets, though. This paper covers such breakthrough discoveries, by reviewing applications of differential privacy for non-interactive publication of anonymized real-life datasets. Theory, utility and a data-aware comparison are discussed on a variety of principles and concrete applications.
Perceptual Evaluation Of Playout Buffer Algorithm For Enhancing Perceived Quality Of Voice Transmission Over Ip Network
Venue: arXiv cs.NI 2012
Year: 2012

Voice over Internet Protocol (VoIP) is a technology that allows you to make voice calls using a broadband Internet connection instead of a regular (or analog) phone line. Voice over Internet Protocol (VoIP) has led human speech to a new level, where conversation across continents can be much cheaper & faster. However, as IP networks are not designed for real-time applications, the network impairments such as packet loss, jitter and delay have a severe impact on speech quality. The playout buffer at the receiver side is used to compensate jitter at a trade-off of delay and loss. We found the characteristics of delay and loss are dependent on IP network and sudden variable delay (spike) often performs both regular and irregular characteristics. Different playout buffer algorithms can have different impacts on the achievement speech quality. It is important to design a playout buffer algorithm which can help achieve an optimum speech quality. In this paper, we investigate to the understanding how network impairments and existing adaptive buffer algorithms affect the speech quality and further to design a modified buffer algorithm to obtain an optimized voice quality. We conduct experiments to existing algorithms and compared their performance under different network conditions with high and low network delay variations. Preliminary results show that the new algorithm can enhance the perceived speech quality in most network conditions and it is more efficient and suitable for real buffer mechanism.
Promotional effect on cold start problem and diversity in a data characteristic based recommendation method
Venue: arXiv cs.IR 2012
Year: 2012

Pure methods generally perform excellently in either recommendation accuracy or diversity, whereas hybrid methods generally outperform pure cases in both recommendation accuracy and diversity, but encounter the dilemma of optimal hybridization parameter selection for different recommendation focuses. In this article, based on a user-item bipartite network, we propose a data characteristic based algorithm, by relating the hybridization parameter to the data characteristic. Different from previous hybrid methods, the present algorithm adaptively assign the optimal parameter specifically for each individual items according to the correlation between the algorithm and the item degrees. Compared with a highly accurate pure method, and a hybrid method which is outstanding in both the recommendation accuracy and the diversity, our method shows a remarkably promotional effect on the long-standing challenging problem of the cold start, as well as the recommendation diversity, while simultaneously keeps a high overall recommendation accuracy. Even compared with an improved hybrid method which is highly efficient on the cold start problem, the proposed method not only further improves the recommendation accuracy of the cold items, but also enhances the recommendation diversity. Our work might provide a promising way to better solving the personal recommendation from the perspective of relating algorithms with dataset properties.
Effective performance of information retrieval on web by using web crawling
Venue: arXiv cs.IR 2012
Year: 2012

World Wide Web consists of more than 50 billion pages online. It is highly dynamic i.e. the web continuously introduces new capabilities and attracts many people. Due to this explosion in size, the effective information retrieval system or search engine can be used to access the information. In this paper we have proposed the EPOW (Effective Performance of WebCrawler) architecture. It is a software agent whose main objective is to minimize the overload of a user locating needed information. We have designed the web crawler by considering the parallelization policy. Since our EPOW crawler has a highly optimized system it can download a large number of pages per second while being robust against crashes. We have also proposed to use the data structure concepts for implementation of scheduler & circular Queue to improve the performance of our web crawler.
b-Bit Minwise Hashing in Practice: Large-Scale Batch and Online Learning and Using GPUs for Fast Preprocessing with Simple Hash Functions
Venue: arXiv cs.IR 2012
Year: 2012

In this paper, we study several critical issues which must be tackled before one can apply b-bit minwise hashing to the volumes of data often used industrial applications, especially in the context of search. 1. (b-bit) Minwise hashing requires an expensive preprocessing step that computes k (e.g., 500) minimal values after applying the corresponding permutations for each data vector. We developed a parallelization scheme using GPUs and observed that the preprocessing time can be reduced by a factor of 20-80 and becomes substantially smaller than the data loading time. 2. One major advantage of b-bit minwise hashing is that it can substantially reduce the amount of memory required for batch learning. However, as online algorithms become increasingly popular for large-scale learning in the context of search, it is not clear if b-bit minwise yields significant improvements for them. This paper demonstrates that $b$-bit minwise hashing provides an effective data size/dimension reduction scheme and hence it can dramatically reduce the data loading time for each epoch of the online training process. This is significant because online learning often requires many (e.g., 10 to 100) epochs to reach a sufficient accuracy. 3. Another critical issue is that for very large data sets it becomes impossible to store a (fully) random permutation matrix, due to its space requirements. Our paper is the first study to demonstrate that $b$-bit minwise hashing implemented using simple hash functions, e.g., the 2-universal (2U) and 4-universal (4U) hash families, can produce very similar learning results as using fully random permutations. Experiments on datasets of up to 200GB are presented.
A Tight Lower Bound on the Controllability of Networks with Multiple Leaders
Venue: arXiv cs.SY 2012
Year: 2012

In this paper we study the controllability of networked systems with static network topologies using tools from algebraic graph theory. Each agent in the network acts in a decentralized fashion by updating its state in accordance with a nearest-neighbor averaging rule, known as the consensus dynamics. In order to control the system, external control inputs are injected into the so called leader nodes, and the influence is propagated throughout the network. Our main result is a tight topological lower bound on the rank of the controllability matrix for such systems with arbitrary network topologies and possibly multiple leaders.
Malware Detection Module using Machine Learning Algorithms to Assist in Centralized Security in Enterprise Networks
Venue: arXiv cs.CR 2012
Year: 2012

Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats from various sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can cause systems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form of viruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. Typically, anti-virus software is based on a signature definition system which keeps updating from the internet and thus keeping track of known viruses. While this may be sufficient for home-users, a security risk from a new virus could threaten an entire enterprise network. This paper proposes a new and more sophisticated antivirus engine that can not only scan files, but also build knowledge and detect files as potential viruses. This is done by extracting system API calls made by various normal and harmful executable, and using machine learning algorithms to classify and hence, rank files on a scale of security risk. While such a system is processor heavy, it is very effective when used centrally to protect an enterprise network which maybe more prone to such threats.