OpenRaiser/Intern-Atlas huggingface dataset:
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Venue: arXiv cs.LG 2014
Year: 2014

The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been traditionally addressed through the textual query. In other words, information retrieval techniques will be exploited based on the textual similarity between queries and textual representation of software artifacts, which is generated by collecting words from comments, identifiers, and descriptions of programs. However, in addition to these semantic information, there are rich information embedded in source codes themselves. These source codes, if analyzed properly, can be a rich source for enhancing the efforts of software artifact retrieval. To this end, in this paper, we develop a feature extraction method on source codes. Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes. Moreover, we design a heterogeneous metric learning approach, which allows to integrate code features and text features into the same latent semantic space. This, in turn, can help to measure the artifact similarity by exploiting the joint power of both code and text features. Finally, extensive experiments on real-world data show that the proposed method can help to improve the performances of software artifact retrieval with a significant margin.
Exemplary and Complete Object Interaction Descriptions
Venue: arXiv cs.SE 2014
Year: 2014

In this paper we present a variant of message sequence diagrams called EETs Extended Event Traces We provide the graphical notation discuss the methodological use of EETs to describe behavior of object oriented business information systems and sketch their semantics Special emphasis is put on the different implications of using EETs for exemplary and complete interaction descriptions. The possibility to describe interactions between single objects as well as composite objects with EETs makes them particularly suitable to describe the behavior of large systems.
Optimal decremental connectivity in planar graphs
Venue: arXiv cs.DS 2014
Year: 2014

We show an algorithm for dynamic maintenance of connectivity information in an undirected planar graph subject to edge deletions. Our algorithm may answer connectivity queries of the form `Are vertices $u$ and $v$ connected with a path?' in constant time. The queries can be intermixed with any sequence of edge deletions, and the algorithm handles all updates in $O(n)$ time. This results improves over previously known $O(n \log n)$ time algorithm.
Event Handling in ET++ - A Case Study in the Algebraic Specification of Object-Oriented Application Frameworks
Venue: arXiv cs.SE 2014
Year: 2014

In this report we investigate the suitability of algebraic specication techniques for the modular speci cation of complex object oriented systems As an example part of the event handling mechanism of the application framework ET is speci ed using a variant of the algebraic specication language Spectrum.
The Multiple Facets of Software Diversity: Recent Developments in Year 2000 and Beyond
Venue: arXiv cs.SE 2014
Year: 2014

Early experiments with software diversity in the mid 1970's investigated N-version programming and recovery blocks to increase the reliability of embedded systems. Four decades later, the literature about software diversity has expanded in multiple directions: goals (fault-tolerance, security, software engineering); means (managed or automated diversity) and analytical studies (quantification of diversity and its impact). Our paper contributes to the field of software diversity as the first paper that adopts an inclusive vision of the area, with an emphasis on the most recent advances in the field. This survey includes classical work about design and data diversity for fault tolerance, as well as the cybersecurity literature that investigates randomization at different system levels. It broadens this standard scope of diversity, to include the study and exploitation of natural diversity and the management of diverse software products. Our survey includes the most recent works, with an emphasis from 2000 to present. The targeted audience is researchers and practitioners in one of the surveyed fields, who miss the big picture of software diversity. Assembling the multiple facets of this fascinating topic sheds a new light on the field.
Autoencoder Trees
Venue: arXiv cs.LG 2014
Year: 2014

We discuss an autoencoder model in which the encoding and decoding functions are implemented by decision trees. We use the soft decision tree where internal nodes realize soft multivariate splits given by a gating function and the overall output is the average of all leaves weighted by the gating values on their path. The encoder tree takes the input and generates a lower dimensional representation in the leaves and the decoder tree takes this and reconstructs the original input. Exploiting the continuity of the trees, autoencoder trees are trained with stochastic gradient descent. On handwritten digit and news data, we see that the autoencoder trees yield good reconstruction error compared to traditional autoencoder perceptrons. We also see that the autoencoder tree captures hierarchical representations at different granularities of the data on its different levels and the leaves capture the localities in the input space.
Algorithms for Stochastic Games on Interference Channels
Venue: arXiv cs.IT 2014
Year: 2014

We consider a wireless channel shared by multiple transmitter-receiver pairs. Their transmissions interfere with each other. Each transmitter-receiver pair aims to maximize its long-term average transmission rate subject to an average power constraint. This scenario is modeled as a stochastic game. We provide sufficient conditions for existence and uniqueness of a Nash equilibrium (NE). We then formulate the problem of finding NE as a variational inequality (VI) problem and present an algorithm to solve the VI using regularization. We also provide distributed algorithms to compute Pareto optimal solutions for the proposed game.
Generating Conceptual Metaphors from Proposition Stores
Venue: arXiv cs.CL 2014
Year: 2014

Contemporary research on computational processing of linguistic metaphors is divided into two main branches: metaphor recognition and metaphor interpretation. We take a different line of research and present an automated method for generating conceptual metaphors from linguistic data. Given the generated conceptual metaphors, we find corresponding linguistic metaphors in corpora. In this paper, we describe our approach and its evaluation using English and Russian data.
Verifying Isolation Properties in the Presence of Middleboxes
Venue: arXiv cs.NI 2014
Year: 2014

Great progress has been made recently in verifying the correctness of router forwarding tables. However, these approaches do not work for networks containing middleboxes such as caches and firewalls whose forwarding behavior depends on previously observed traffic. We explore how to verify isolation properties in networks that include such "dynamic datapath" elements using model checking. Our work leverages recent advances in SMT solvers, and the main challenge lies in scaling the approach to handle large and complicated networks. While the straightforward application of model checking to this problem can only handle very small networks (if at all), our approach can verify simple realistic invariants on networks containing 30,000 middleboxes in a few minutes.
Combating Corrupt Messages in Sparse Clustered Associative Memories
Venue: arXiv cs.NE 2014
Year: 2014

In this paper we analyze and extend the neural network based associative memory proposed by Gripon and Berrou. This associative memory resembles the celebrated Willshaw model with an added partite cluster structure. In the literature, two retrieving schemes have been proposed for the network dynamics, namely sum-of-sum and sum-of-max. They both offer considerably better performance than Willshaw and Hopfield networks, when comparable retrieval scenarios are considered. Former discussions and experiments concentrate on the erasure scenario, where a partial message is used as a probe to the network, in the hope of retrieving the full message. In this regard, sum-of-max outperforms sum-of-sum in terms of retrieval rate by a large margin. However, we observe that when noise and errors are present and the network is queried by a corrupt probe, sum-of-max faces a severe limitation as its stringent activation rule prevents a neuron from reviving back into play once deactivated. In this manuscript, we categorize and analyze different error scenarios so that both the erasure and the corrupt scenarios can be treated consistently. We make an amendment to the network structure to improve the retrieval rate, at the cost of an extra scalar per neuron. Afterwards, five different approaches are proposed to deal with corrupt probes. As a result, we extend the network capability, and also increase the robustness of the retrieving procedure. We then experimentally compare all these proposals and discuss pros and cons of each approach under different types of errors. Simulation results show that if carefully designed, the network is able to preserve both a high retrieval rate and a low running time simultaneously, even when queried by a corrupt probe.
Resource-Constrained Adaptive Search for Sparse Multi-Class Targets with Varying Importance
Venue: arXiv cs.IT 2014
Year: 2014

In sparse target inference problems it has been shown that significant gains can be achieved by adaptive sensing using convex criteria. We generalize previous work on adaptive sensing to (a) include multiple classes of targets with different levels of importance and (b) accommodate multiple sensor models. New optimization policies are developed to allocate a limited resource budget to simultaneously locate, classify and estimate a sparse number of targets embedded in a large space. Upper and lower bounds on the performance of the proposed policies are derived by analyzing a baseline policy, which allocates resources uniformly across the scene, and an oracle policy which has a priori knowledge of the target locations/classes. These bounds quantify analytically the potential benefit of adaptive sensing as a function of target frequency and importance. Numerical results indicate that the proposed policies perform close to the oracle bound (<3dB) when signal quality is sufficiently high (e.g.~performance within 3 dB for SNR above 15 dB). Moreover, the proposed policies improve on previous policies in terms of reducing estimation error, reducing misclassification probability, and increasing expected return. To account for sensors with different levels of agility, three sensor models are considered: global adaptive (GA), which can allocate different amounts of resource to each location in the space; global uniform (GU), which can allocate resources uniformly across the scene; and local adaptive (LA), which can allocate fixed units to a subset of locations. Policies that use a mixture of GU and LA sensors are shown to perform similarly to those that use GA sensors while being more easily implementable.
Exploring the Impact of Wind Penetration on Power System Equilibrium Using a Numerical Continuation Approach
Venue: arXiv cs.SY 2014
Year: 2014

In this paper we investigate how the equilibrium characteristics of conventional power systems may change with an increase in wind penetration. We first derive a differential-algebraic model of a power system network consisting of synchronous generators, loads and a wind power plant modeled by a wind turbine and a doubly-fed induction generator (DFIG). The models of these three components are coupled via nonlinear power flow equations. In contrast to the traditional approach for solving the power flows via iterative methods that often lead to only local solutions, we apply a recently developed parameter-homotopy based numerical continuation algorithm to compute all possible solutions. The method solves the power flow equations over multiple values of the wind penetration level with far less computational effort instead of solving them at each value individually. We observe that depending on the penetration limit and the setpoint value for the magnitude of the wind bus voltage, the system may exhibit several undesired or even unstable equilibria. We illustrate these results through a detailed simulation of a 5-machine power system model with wind injection, and highlight how the solutions may be helpful for small-signal stability assessment.
An Improved Node Ranking for Label Propagation and Modularity based Clustering
Venue: arXiv cs.SI 2014
Year: 2014

In this paper I'll speak about non-spectral clustering techniques and see how a node ordering based on centrality measures can improve the quality of communities detected. I'll also discuss an improvement to existing techniques, which further improves modularity.
Lazier Than Lazy Greedy
Venue: arXiv cs.LG 2014
Year: 2014

Is it possible to maximize a monotone submodular function faster than the widely used lazy greedy algorithm (also known as accelerated greedy), both in theory and practice? In this paper, we develop the first linear-time algorithm for maximizing a general monotone submodular function subject to a cardinality constraint. We show that our randomized algorithm, STOCHASTIC-GREEDY, can achieve a $(1-1/e-\varepsilon)$ approximation guarantee, in expectation, to the optimum solution in time linear in the size of the data and independent of the cardinality constraint. We empirically demonstrate the effectiveness of our algorithm on submodular functions arising in data summarization, including training large-scale kernel methods, exemplar-based clustering, and sensor placement. We observe that STOCHASTIC-GREEDY practically achieves the same utility value as lazy greedy but runs much faster. More surprisingly, we observe that in many practical scenarios STOCHASTIC-GREEDY does not evaluate the whole fraction of data points even once and still achieves indistinguishable results compared to lazy greedy.
The Utility of Text: The Case of Amicus Briefs and the Supreme Court
Venue: arXiv cs.CL 2014
Year: 2014

We explore the idea that authoring a piece of text is an act of maximizing one's expected utility. To make this idea concrete, we consider the societally important decisions of the Supreme Court of the United States. Extensive past work in quantitative political science provides a framework for empirically modeling the decisions of justices and how they relate to text. We incorporate into such a model texts authored by amici curiae ("friends of the court" separate from the litigants) who seek to weigh in on the decision, then explicitly model their goals in a random utility model. We demonstrate the benefits of this approach in improved vote prediction and the ability to perform counterfactual analysis.
CRF-based Named Entity Recognition @ICON 2013
Venue: arXiv cs.CL 2014
Year: 2014

This paper describes performance of CRF based systems for Named Entity Recognition (NER) in Indian language as a part of ICON 2013 shared task. In this task we have considered a set of language independent features for all the languages. Only for English a language specific feature, i.e. capitalization, has been added. Next the use of gazetteer is explored for Bengali, Hindi and English. The gazetteers are built from Wikipedia and other sources. Test results show that the system achieves the highest F measure of 88% for English and the lowest F measure of 69% for both Tamil and Telugu. Note that for the least performing two languages no gazetteer was used. NER in Bengali and Hindi finds accuracy (F measure) of 87% and 79%, respectively.
An ECG-on-Chip with 535-nW/Channel Integrated Lossless Data Compressor for Wireless Sensors
Venue: arXiv cs.AR 2014
Year: 2014

This paper presents a low-power ECG recording system-on-chip (SoC) with on-chip low-complexity lossless ECG compression for data reduction in wireless/ambulatory ECG sensor devices. The chip uses a linear slope predictor for data compression, and incorporates a novel low-complexity dynamic coding-packaging scheme to frame the prediction error into fixed-length 16-bit format. The proposed technique achieves an average compression ratio of 2.25x on MIT/BIH ECG database. Implemented in a standard 0.35 um process, the compressor uses 0.565K gates/channel occupying 0.4 mm2 for four channels, and consumes 535 nW/channel at 2.4 V for ECG sampled at 512 Hz. Small size and ultra-low power consumption makes the proposed technique suitable for wearable ECG sensor applications.
Medical diagnosis as pattern recognition in a framework of information compression by multiple alignment, unification and search
Venue: arXiv cs.AI 2014
Year: 2014

This paper describes a novel approach to medical diagnosis based on the SP theory of computing and cognition. The main attractions of this approach are: a format for representing diseases that is simple and intuitive; an ability to cope with errors and uncertainties in diagnostic information; the simplicity of storing statistical information as frequencies of occurrence of diseases; a method for evaluating alternative diagnostic hypotheses that yields true probabilities; and a framework that should facilitate unsupervised learning of medical knowledge and the integration of medical diagnosis with other AI applications.
From High-Level Modeling Towards Efficient and Trustworthy Circuits
Venue: arXiv cs.SE 2014
Year: 2014

Behavior-Interaction-Priority (BIP) is a layered embedded system design and verification framework that provides separation of functionality, synchronization, and priority concerns to simplify system design and to establish correctness by construction. The framework comes with a runtime engine and a suite of verification tools that uses D-Finder and NuSMV as model checkers. In this paper we provide a method and a supporting tool that takes a BIP system and a set of invariants and computes a reduced sequential circuit with a system-specific scheduler and with a designated output that is true when the invariants hold. Our method uses ABC, a sequential circuit synthesis and verification framework to (1) generate an efficient FPGA implementation of the system, and to (2) verify the system and debug it in case a counterexample was found. Moreover we generate a concurrent C implementation of the circuit that can be directly used as a simulator. We evaluated our method with two large systems and our results outperform those possible with existing techniques.
$μ$-diff: an open-source Matlab toolbox for computing multiple scattering problems by disks
Venue: arXiv cs.MS 2014
Year: 2014

The aim of this paper is to describe a Matlab toolbox, called $μ$-diff, for modeling and numerically solving two-dimensional complex multiple scattering by a large collection of circular cylinders. The approximation methods in $μ$-diff are based on the Fourier series expansions of the four basic integral operators arising in scattering theory. Based on these expressions, an efficient spectrally accurate finite-dimensional solution of multiple scattering problems can be simply obtained for complex media even when many scatterers are considered as well as large frequencies. The solution of the global linear system to solve can use either direct solvers or preconditioned iterative Krylov subspace solvers for block Toeplitz matrices. Based on this approach, this paper explains how the code is built and organized. Some complete numerical examples of applications (direct and inverse scattering) are provided to show that $μ$-diff is a flexible, efficient and robust toolbox for solving some complex multiple scattering problems.
A Neural Networks Committee for the Contextual Bandit Problem
Venue: arXiv cs.NE 2014
Year: 2014

This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.
Kaczmarz Algorithm and Frames
Venue: arXiv cs.IT 2014
Year: 2014

Sequences of unit vectors for which the Kaczmarz algorithm always converges in Hilbert space can be characterized in frame theory by tight frames with constant 1. We generalize this result to the context of frames and bases. In particular, we show that the only effective sequences which are Riesz bases are orthonormal bases. Moreover, we consider the infinite system of linear algebraic equations $A x = b$ and characterize the (bounded) matrices $A$ for which the Kaczmarz algorithm always converges to a solution.
Indonesian's presidential social media campaigns
Venue: arXiv cs.CY 2014
Year: 2014

Social media has been used for political campaigns of presidential candidates in a number of democratic countries, including Indonesia.The candidates for president and vice president have been following the latest trends in the virtual world by using social media such as: 1) Facebook, and 2) Twitter.The author's continuing research with a focus on the popularity of candidates for president and vice president of the Indonesian presidential election in July 9, 2014.The authors found that Facebook remains a social media most widely used in Indonesia for presidential campaigns. Prabowo secured the most number of fans on Facebook. While Joko Widodo secured the most number of followers on Twitter.Facebook users is dominated by the user in the age range 18-24 years, and most cities are using Facebook is Jakarta.
Interference Effects in Quantum Belief Networks
Venue: arXiv cs.AI 2014
Year: 2014

Probabilistic graphical models such as Bayesian Networks are one of the most powerful structures known by the Computer Science community for deriving probabilistic inferences. However, modern cognitive psychology has revealed that human decisions could not follow the rules of classical probability theory, because humans cannot process large amounts of data in order to make judgements. Consequently, the inferences performed are based on limited data coupled with several heuristics, leading to violations of the law of total probability. This means that probabilistic graphical models based on classical probability theory are too limited to fully simulate and explain various aspects of human decision making. Quantum probability theory was developed in order to accommodate the paradoxical findings that the classical theory could not explain. Recent findings in cognitive psychology revealed that quantum probability can fully describe human decisions in an elegant framework. Their findings suggest that, before taking a decision, human thoughts are seen as superposed waves that can interfere with each other, influencing the final decision. In this work, we propose a new Bayesian Network based on the psychological findings of cognitive scientists. We made experiments with two very well known Bayesian Networks from the literature. The results obtained revealed that the quantum like Bayesian Network can affect drastically the probabilistic inferences, specially when the levels of uncertainty of the network are very high (no pieces of evidence observed). When the levels of uncertainty are very low, then the proposed quantum like network collapses to its classical counterpart.