OpenRaiser/Intern-Atlas huggingface dataset:
Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease
Venue: arXiv cs.AI 2016
Year: 2016

Background: Lung cancer was known as primary cancers and the survival rate of cancer is about 15%. Early detection of lung cancer is the leading factor in survival rate. All symptoms (features) of lung cancer do not appear until the cancer spreads to other areas. It needs an accurate early detection of lung cancer, for increasing the survival rate. For accurate detection, it need characterizes efficient features and delete redundancy features among all features. Feature selection is the problem of selecting informative features among all features. Materials and Methods: Lung cancer database consist of 32 patient records with 57 features. This database collected by Hong and Youngand indexed in the University of California Irvine repository. Experimental contents include the extracted from the clinical data and X-ray data, etc. The data described 3 types of pathological lung cancers and all features are taking an integer value 0-3. In our study, new method is proposed for identify efficient features of lung cancer. It is based on Hyper-Heuristic. Results: We obtained an accuracy of 80.63% using reduced 11 feature set. The proposed method compare to the accuracy of 5 machine learning feature selections. The accuracy of these 5 methods are 60.94, 57.81, 68.75, 60.94 and 68.75. Conclusions: The proposed method has better performance with the highest level of accuracy. Therefore, the proposed model is recommended for identifying an efficient symptom of Disease. These finding are very important in health research, particularly in allocation of medical resources for patients who predicted as high-risks
On Deep Representation Learning from Noisy Web Images
Venue: arXiv cs.CV 2016
Year: 2016

The keep-growing content of Web images may be the next important data source to scale up deep neural networks, which recently obtained a great success in the ImageNet classification challenge and related tasks. This prospect, however, has not been validated on convolutional networks (convnet) -- one of best performing deep models -- because of their supervised regime. While unsupervised alternatives are not so good as convnet in generalizing the learned model to new domains, we use convnet to leverage semi-supervised representation learning. Our approach is to use massive amounts of unlabeled and noisy Web images to train convnets as general feature detectors despite challenges coming from data such as high level of mislabeled data, outliers, and data biases. Extensive experiments are conducted at several data scales, different network architectures, and data reranking techniques. The learned representations are evaluated on nine public datasets of various topics. The best results obtained by our convnets, trained on 3.14 million Web images, outperform AlexNet trained on 1.2 million clean images of ILSVRC 2012 and is closing the gap with VGG-16. These prominent results suggest a budget solution to use deep learning in practice and motivate more research in semi-supervised representation learning.
Data Driven Resource Allocation for Distributed Learning
Venue: arXiv cs.LG 2016
Year: 2016

In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points often belong to the same or similar classes, and more generally, classification rules of high accuracy tend to be "locally simple but globally complex" (Vapnik & Bottou 1993), we propose data dependent dispatching that takes advantage of such structure. We present an in-depth analysis of this model, providing new algorithms with provable worst-case guarantees, analysis proving existing scalable heuristics perform well in natural non worst-case conditions, and techniques for extending a dispatching rule from a small sample to the entire distribution. We overcome novel technical challenges to satisfy important conditions for accurate distributed learning, including fault tolerance and balancedness. We empirically compare our approach with baselines based on random partitioning, balanced partition trees, and locality sensitive hashing, showing that we achieve significantly higher accuracy on both synthetic and real world image and advertising datasets. We also demonstrate that our technique strongly scales with the available computing power.
A Light Touch for Heavily Constrained SGD
Venue: arXiv cs.LG 2016
Year: 2016

Minimizing empirical risk subject to a set of constraints can be a useful strategy for learning restricted classes of functions, such as monotonic functions, submodular functions, classifiers that guarantee a certain class label for some subset of examples, etc. However, these restrictions may result in a very large number of constraints. Projected stochastic gradient descent (SGD) is often the default choice for large-scale optimization in machine learning, but requires a projection after each update. For heavily-constrained objectives, we propose an efficient extension of SGD that stays close to the feasible region while only applying constraints probabilistically at each iteration. Theoretical analysis shows a compelling trade-off between per-iteration work and the number of iterations needed on problems with a large number of constraints.
Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning
Venue: arXiv cs.CL 2016
Year: 2016

Morpho-syntactic lexicons provide information about the morphological and syntactic roles of words in a language. Such lexicons are not available for all languages and even when available, their coverage can be limited. We present a graph-based semi-supervised learning method that uses the morphological, syntactic and semantic relations between words to automatically construct wide coverage lexicons from small seed sets. Our method is language-independent, and we show that we can expand a 1000 word seed lexicon to more than 100 times its size with high quality for 11 languages. In addition, the automatically created lexicons provide features that improve performance in two downstream tasks: morphological tagging and dependency parsing.
Small Model $2$-Complexes in $4$-space and Applications
Venue: arXiv cs.CG 2016
Year: 2016

We consider computational complexity of problems related to the fundamental group and the first homology group of (embeddable) $2$-complexes. We show, as an extension of an earlier work, that computing first homology of $2$-complexes is equivalent in computational complexity to matrix diagonalization. That is, the usual procedures for computing homology cannot be improved other than by matrix methods. This is true even if the complex is in the euclidean $4$-space. For this purpose, we use $2$-complexes built in a standard way from group presentations, called model $2$-complexes. Model complexes have fundamental group isomorphic with the group defined by the presentation. We show that there are model complexes of size in the order of the bit-complexity of the presentation that can be realized linearly in $4$-space. We further derive some applications of this result regarding embeddability problems in the euclidean $4$-space.
Two improved normalized subband adaptive filter algorithms with good robustness against impulsive interferences
Venue: arXiv cs.OH 2016
Year: 2016

To improve the robustness of subband adaptive filter (SAF) against impulsive interferences, we propose two modified SAF algorithms with an individual scale function for each subband, which are derived by maximizing correntropy-based cost function and minimizing logarithm-based cost function, respectively, called MCC-SAF and LC-SAF. Whenever the impulsive interference happens, the subband scale functions can sharply drop the step size, which eliminate the influence of outliers on the tap-weight vector update. Therefore, the proposed algorithms are robust against impulsive interferences, and exhibit the faster convergence rate and better tracking capability than the sign SAF (SSAF) algorithm. Besides, in impulse-free interference environments, the proposed algorithms achieve similar convergence performance as the normalized SAF (NSAF) algorithm. Simulation results have demonstrated the performance of our proposed algorithms.
Large Scale Business Discovery from Street Level Imagery
Venue: arXiv cs.CV 2016
Year: 2016

Search with local intent is becoming increasingly useful due to the popularity of the mobile device. The creation and maintenance of accurate listings of local businesses worldwide is time consuming and expensive. In this paper, we propose an approach to automatically discover businesses that are visible on street level imagery. Precise business store front detection enables accurate geo-location of businesses, and further provides input for business categorization, listing generation, etc. The large variety of business categories in different countries makes this a very challenging problem. Moreover, manual annotation is prohibitive due to the scale of this problem. We propose the use of a MultiBox based approach that takes input image pixels and directly outputs store front bounding boxes. This end-to-end learning approach instead preempts the need for hand modeling either the proposal generation phase or the post-processing phase, leveraging large labelled training datasets. We demonstrate our approach outperforms the state of the art detection techniques with a large margin in terms of performance and run-time efficiency. In the evaluation, we show this approach achieves human accuracy in the low-recall settings. We also provide an end-to-end evaluation of business discovery in the real world.
Parameter-Free Probabilistic API Mining across GitHub
Venue: arXiv cs.SE 2016
Year: 2016

Existing API mining algorithms can be difficult to use as they require expensive parameter tuning and the returned set of API calls can be large, highly redundant and difficult to understand. To address this, we present PAM (Probabilistic API Miner), a near parameter-free probabilistic algorithm for mining the most interesting API call patterns. We show that PAM significantly outperforms both MAPO and UPMiner, achieving 69% test-set precision, at retrieving relevant API call sequences from GitHub. Moreover, we focus on libraries for which the developers have explicitly provided code examples, yielding over 300,000 LOC of hand-written API example code from the 967 client projects in the data set. This evaluation suggests that the hand-written examples actually have limited coverage of real API usages.
Proof complexity of intuitionistic implicational formulas
Venue: arXiv cs.LO 2016
Year: 2016

We study implicational formulas in the context of proof complexity of intuitionistic propositional logic (IPC). On the one hand, we give an efficient transformation of tautologies to implicational tautologies that preserves the lengths of intuitionistic extended Frege (EF) or substitution Frege (SF) proofs up to a polynomial. On the other hand, EF proofs in the implicational fragment of IPC polynomially simulate full intuitionistic logic for implicational tautologies. The results also apply to other fragments of other superintuitionistic logics under certain conditions. In particular, the exponential lower bounds on the length of intuitionistic EF proofs by Hrubeš \cite{hru:lbint}, generalized to exponential separation between EF and SF systems in superintuitionistic logics of unbounded branching by Jeřábek \cite{ej:sfef}, can be realized by implicational tautologies.
Lower Bounds for the Domination Numbers of Connected Graphs without Short Cycles
Venue: arXiv cs.DM 2016
Year: 2016

In this paper, we obtain lower bounds for the domination numbers of connected graphs with girth at least $7$. We show that the domination number of a connected graph with girth at least $7$ is either $1$ or at least $\frac{1}{2}(3+\sqrt{8(m-n)+9})$, where $n$ is the number of vertices in the graph and $m$ is the number of edges in the graph. For graphs with minimum degree $2$ and girth at least $7$, the lower bound can be improved to $\max{\{\sqrt{n}, \sqrt{\frac{2m}{3}}\}}$, where $n$ and $m$ are the numbers of vertices and edges in the graph respectively. In cases where the graph is of minimum degree $2$ and its girth $g$ is at least $12$, the lower bound can be further improved to $\max{\{\sqrt{n}, \sqrt{\frac{\lfloor \frac{g}{3} \rfloor-1}{3}m}\}}$.
Effective Keyword Search in Graphs
Venue: arXiv cs.DB 2016
Year: 2016

In a node-labeled graph, keyword search finds subtrees of the graph whose nodes contain all of the query keywords. This provides a way to query graph databases that neither requires mastery of a query language such as SPARQL, nor a deep knowledge of the database schema. Previous work ranks answer trees using combinations of structural and content-based metrics, such as path lengths between keywords or relevance of the labels in the answer tree to the query keywords. We propose two new ways to rank keyword search results over graphs. The first takes node importance into account while the second is a bi-objective optimization of edge weights and node importance. Since both of these problems are NP-hard, we propose greedy algorithms to solve them, and experimentally verify their effectiveness and efficiency on a real dataset.
Solving $k$-SUM using few linear queries
Venue: arXiv cs.DS 2016
Year: 2016

The $k$-SUM problem is given $n$ input real numbers to determine whether any $k$ of them sum to zero. The problem is of tremendous importance in the emerging field of complexity theory within $P$, and it is in particular open whether it admits an algorithm of complexity $O(n^c)$ with $c<\lceil \frac{k}{2} \rceil$. Inspired by an algorithm due to Meiser (1993), we show that there exist linear decision trees and algebraic computation trees of depth $O(n^3\log^3 n)$ solving $k$-SUM. Furthermore, we show that there exists a randomized algorithm that runs in $\tilde{O}(n^{\lceil \frac{k}{2} \rceil+8})$ time, and performs $O(n^3\log^3 n)$ linear queries on the input. Thus, we show that it is possible to have an algorithm with a runtime almost identical (up to the $+8$) to the best known algorithm but for the first time also with the number of queries on the input a polynomial that is independent of $k$. The $O(n^3\log^3 n)$ bound on the number of linear queries is also a tighter bound than any known algorithm solving $k$-SUM, even allowing unlimited total time outside of the queries. By simultaneously achieving few queries to the input without significantly sacrificing runtime vis-à-vis known algorithms, we deepen the understanding of this canonical problem which is a cornerstone of complexity-within-$P$. We also consider a range of tradeoffs between the number of terms involved in the queries and the depth of the decision tree. In particular, we prove that there exist $o(n)$-linear decision trees of depth $o(n^4)$.
Utility-based Link Recommendation for Online Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

Link recommendation, which suggests links to connect currently unlinked users, is a key functionality offered by major online social networks. Salient examples of link recommendation include "People You May Know" on Facebook and LinkedIn as well as "You May Know" on Google+. The main stakeholders of an online social network include users (e.g., Facebook users) who use the network to socialize with other users and an operator (e.g., Facebook Inc.) that establishes and operates the network for its own benefit (e.g., revenue). Existing link recommendation methods recommend links that are likely to be established by users but overlook the benefit a recommended link could bring to an operator. To address this gap, we define the utility of recommending a link and formulate a new research problem - the utility-based link recommendation problem. We then propose a novel utility-based link recommendation method that recommends links based on the value, cost, and linkage likelihood of a link, in contrast to existing link recommendation methods which focus solely on linkage likelihood. Specifically, our method models the dependency relationship between value, cost, linkage likelihood and utility-based link recommendation decision using a Bayesian network, predicts the probability of recommending a link with the Bayesian network, and recommends links with the highest probabilities. Using data obtained from a major U.S. online social network, we demonstrate significant performance improvement achieved by our method compared to prevalent link recommendation methods from representative prior research.
The Bi-Objective Workflow Satisfiability Problem and Workflow Resiliency
Venue: arXiv cs.CR 2016
Year: 2016

A computerized workflow management system may enforce a security policy, specified in terms of authorized actions and constraints, thereby restricting which users can perform particular steps in a workflow. The existence of a security policy may mean it is impossible to find a valid plan (an assignment of steps to authorized users such that all constraints are satisfied). Work in the literature focuses on the workflow satisfiability problem, a \emph{decision} problem that outputs a valid plan if the instance is satisfiable (and a negative result otherwise). In this paper, we introduce the \textsc{Bi-Objective Workflow Satisfiability Problem} (\BOWSP), which enables us to solve \emph{optimization} problems related to workflows and security policies. In particular, we are able to compute a "least bad" plan when some components of the security policy may be violated. In general, \BOWSP is intractable from both the classical and parameterized complexity point of view. We prove there exists an fixed-parameter tractable (FPT) algorithm to compute a Pareto front for \BOWSP if we restrict our attention to user-independent constraints. We also present a second algorithm to compute a Pareto front which uses mixed integer programming (MIP). We compare the performance of both our algorithms on synthetic instances, and show that the FPT algorithm outperforms the MIP-based one by several orders of magnitude on most of the instances. Finally, we study the important question of workflow resiliency and prove new results establishing that known decision problems are fixed-parameter tractable when restricted to user-independent constraints. We then propose a new way of modeling the availability of users and demonstrate that many questions related to resiliency in the context of this new model may be reduced to instances of \BOWSP.
Distributed Phasers
Venue: arXiv cs.DC 2016
Year: 2016

A phaser is an expressive synchronization construct that unifies collective and point-to-point coordination with dynamic task parallelism. Each task can participate in a phaser as a signaler, a waiter, or both. The participants in a phaser may change over time as dynamic tasks are added and deleted. In this poster, we present a highly concurrent and scalable design of phasers for a distributed memory environment that is suitable for use with asynchronous partitioned global address space programming models. Our design for a distributed phaser employs a pair of skip lists augmented with the ability to collect and propagate synchronization signals. To enable a high degree of concurrency, addition and deletion of participant tasks are performed in two phases: a "fast single-link-modify" step followed by multiple hand-overhand "lazy multi-link-modify" steps. We show that the cost of synchronization and structural operations on a distributed phaser scales logarithmically, even in the presence of concurrent structural modifications. To verify the correctness of our design for distributed phasers, we employ the SPIN model checker. To address this issue of state space explosion, we describe how we decompose the state space to separately verify correct handling for different kinds of messages, which enables complete model checking of our phaser design.
Interacting Behavior and Emerging Complexity
Venue: arXiv cs.NE 2016
Year: 2016

Can we quantify the change of complexity throughout evolutionary processes? We attempt to address this question through an empirical approach. In very general terms, we simulate two simple organisms on a computer that compete over limited available resources. We implement Global Rules that determine the interaction between two Elementary Cellular Automata on the same grid. Global Rules change the complexity of the state evolution output which suggests that some complexity is intrinsic to the interaction rules themselves. The largest increases in complexity occurred when the interacting elementary rules had very little complexity, suggesting that they are able to accept complexity through interaction only. We also found that some Class 3 or 4 CA rules are more fragile than others to Global Rules, while others are more robust, hence suggesting some intrinsic properties of the rules independent of the Global Rule choice. We provide statistical mappings of Elementary Cellular Automata exposed to Global Rules and different initial conditions onto different complexity classes.
Linear-Time Fitting of a $k$-Step Function
Venue: arXiv cs.CG 2016
Year: 2016

Given a set of $n$ weighted points on the $x$-$y$ plane, we want to find a step function consisting of $k$ horizontal steps such that the maximum vertical weighted distance from any point to a step is minimized. We solve this problem in $O(n)$ time when $k$ is a constant. Our approach relies on the prune-and-search technique, and can be adapted to design similar linear time algorithms to solve the line-constrained k-center problem and the size-$k$ histogram construction problem as well.
Cooperative Localization for Mobile Networks: A Distributed Belief Propagation - Mean Field Message Passing Algorithm
Venue: arXiv cs.SY 2016
Year: 2016

We propose a hybrid message passing method for distributed cooperative localization and tracking of mobile agents. Belief propagation and mean field message passing are employed for, respectively, the motion-related and measurement-related part of the factor graph. Using a Gaussian belief approximation, only three real values per message passing iteration have to be broadcast to neighboring agents. Despite these very low communication requirements, the estimation accuracy can be comparable to that of particle-based belief propagation.