OpenRaiser/Intern-Atlas huggingface dataset:
Evolutionary Image Transition Based on Theoretical Insights of Random Processes
Venue: arXiv cs.NE 2016
Year: 2016

Evolutionary algorithms have been widely studied from a theoretical perspective. In particular, the area of runtime analysis has contributed significantly to a theoretical understanding and provided insights into the working behaviour of these algorithms. We study how these insights into evolutionary processes can be used for evolutionary art. We introduce the notion of evolutionary image transition which transfers a given starting image into a target image through an evolutionary process. Combining standard mutation effects known from the optimization of the classical benchmark function OneMax and different variants of random walks, we present ways of performing evolutionary image transition with different artistic effects.
Satisfiability-Based Methods for Reactive Synthesis from Safety Specifications
Venue: arXiv cs.LO 2016
Year: 2016

Existing approaches to synthesize reactive systems from declarative specifications mostly rely on Binary Decision Diagrams (BDDs), inheriting their scalability issues. We present novel algorithms for safety specifications that use decision procedures for propositional formulas (SAT solvers), Quantified Boolean Formulas (QBF solvers), or Effectively Propositional Logic (EPR). Our algorithms are based on query learning, templates, reduction to EPR, QBF certification, and interpolation. A parallelization combines multiple algorithms. Our optimizations expand quantifiers and utilize unreachable states and variable independencies. Our approach outperforms a simple BDD-based tool and is competitive with a highly optimized one. It won two medals in the SyntComp competition.
Evaluation of the Effect of Improper Segmentation on Word Spotting
Venue: arXiv cs.CV 2016
Year: 2016

Word spotting is an important recognition task in historical document analysis. In most cases methods are developed and evaluated assuming perfect word segmentations. In this paper we propose an experimental framework to quantify the effect of goodness of word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We apply the framework on the George Washington and Barcelona Marriage Dataset and on several established and state-of-the-art methods. The experiments allow for an estimate of the end-to-end performance of word spotting methods.
Incorporating Semantic Knowledge into Latent Matching Model in Search
Venue: arXiv cs.IR 2016
Year: 2016

The relevance between a query and a document in search can be represented as matching degree between the two objects. Latent space models have been proven to be effective for the task, which are often trained with click-through data. One technical challenge with the approach is that it is hard to train a model for tail queries and tail documents for which there are not enough clicks. In this paper, we propose to address the challenge by learning a latent matching model, using not only click-through data but also semantic knowledge. The semantic knowledge can be categories of queries and documents as well as synonyms of words, manually or automatically created. Specifically, we incorporate semantic knowledge into the objective function by including regularization terms. We develop two methods to solve the learning task on the basis of coordinate descent and gradient descent respectively, which can be employed in different settings. Experimental results on two datasets from an app search engine demonstrate that our model can make effective use of semantic knowledge, and thus can significantly enhance the accuracies of latent matching models, particularly for tail queries.
The data, they are a-changin'
Venue: arXiv cs.DB 2016
Year: 2016

The cost of deriving actionable knowledge from large datasets has been decreasing thanks to a convergence of positive factors: low cost data generation, inexpensively scalable storage and processing infrastructure (cloud), software frameworks and tools for massively distributed data processing, and parallelisable data analytics algorithms. One observation that is often overlooked, however, is that each of these elements is not immutable, rather they all evolve over time. This suggests that the value of such derivative knowledge may decay over time, unless it is preserved by reacting to those changes. Our broad research goal is to develop models, methods, and tools for selectively reacting to changes by balancing costs and benefits, i.e. through complete or partial re-computation of some of the underlying processes. In this paper we present an initial model for reasoning about change and re-computations, and show how analysis of detailed provenance of derived knowledge informs re-computation decisions. We illustrate the main ideas through a real-world case study in genomics, namely on the interpretation of human variants in support of genetic diagnosis.
On Stronger Calculi for QBFs
Venue: arXiv cs.LO 2016
Year: 2016

Quantified Boolean formulas (QBFs) generalize propositional formulas by admitting quantifications over propositional variables. QBFs can be viewed as (restricted) formulas of first-order predicate logic and easy translations of QBFs into first-order formulas exist. We analyze different translations and show that first-order resolution combined with such translations can polynomially simulate well-known deduction concepts for QBFs. Furthermore, we extend QBF calculi by the possibility to instantiate a universal variable by an existential variable of smaller level. Combining such an enhanced calculus with the propositional extension rule results in a calculus with a universal quantifier rule which essentially introduces propositional formulas for universal variables. In this way, one can mimic a very general quantifier rule known from sequent systems.
Humans and deep networks largely agree on which kinds of variation make object recognition harder
Venue: arXiv cs.CV 2016
Year: 2016

View-invariant object recognition is a challenging problem, which has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g. 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best algorithms for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition using the same images and controlling for both the kinds of transformation as well as their magnitude. We used four object categories and images were rendered from 3D computer models. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position. This suggests that humans recognize objects mainly through 2D template matching, rather than by constructing 3D object models, and that DCNNs are not too unreasonable models of human feed-forward vision. Also, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research.
Concurrent Regenerating Codes and Scalable Application in Network Storage
Venue: arXiv cs.IT 2016
Year: 2016

To recover simultaneous multiple failures in erasure coded storage systems, Patrick Lee et al introduce concurrent repair based minimal storage regenerating codes to reduce repair traffic. The architecture of this approach is simpler and more practical than that of the cooperative mechanism in non-fully distributed environment, hence this paper unifies such class of regenerating codes as concurrent regenerating codes and further studies its characteristics by analyzing cut-based information flow graph in the multiple-node recovery model. We present a general storage-bandwidth tradeoff and give closed-form expressions for the points on the curve, including concurrent repair mechanism based on minimal bandwidth regenerating codes. We show that the general concurrent regenerating codes can be constructed by reforming the existing single-node regenerating codes or multiplenode cooperative regenerating codes. Moreover, a connection to strong-MDS is also analyzed. On the other respect, the application of RGC is hardly limited to "repairing". It is of great significance for "scaling", a scenario where we need to increase(decrease) nodes to upgrade(degrade) redundancy and reliability. Thus, by clarifying the similarities and differences, we integrate them into a unified model to adjust to the dynamic storage network.
SweLL on the rise: Swedish Learner Language corpus for European Reference Level studies
Venue: arXiv cs.CL 2016
Year: 2016

We present a new resource for Swedish, SweLL, a corpus of Swedish Learner essays linked to learners' performance according to the Common European Framework of Reference (CEFR). SweLL consists of three subcorpora - SpIn, SW1203 and Tisus, collected from three different educational establishments. The common metadata for all subcorpora includes age, gender, native languages, time of residence in Sweden, type of written task. Depending on the subcorpus, learner texts may contain additional information, such as text genres, topics, grades. Five of the six CEFR levels are represented in the corpus: A1, A2, B1, B2 and C1 comprising in total 339 essays. C2 level is not included since courses at C2 level are not offered. The work flow consists of collection of essays and permits, essay digitization and registration, meta-data annotation, automatic linguistic annotation. Inter-rater agreement is presented on the basis of SW1203 subcorpus. The work on SweLL is still ongoing with more than 100 essays waiting in the pipeline. This article both describes the resource and the "how-to" behind the compilation of SweLL.
A Class of Nonconvex Penalties Preserving Overall Convexity in Optimization-Based Mean Filtering
Venue: arXiv cs.IT 2016
Year: 2016

$\ell_1$ mean filtering is a conventional, optimization-based method to estimate the positions of jumps in a piecewise constant signal perturbed by additive noise. In this method, the $\ell_1$ norm penalizes sparsity of the first-order derivative of the signal. Theoretical results, however, show that in some situations, which can occur frequently in practice, even when the jump amplitudes tend to $\infty$, the conventional method identifies false change points. This issue is referred to as stair-casing problem and restricts practical importance of $\ell_1$ mean filtering. In this paper, sparsity is penalized more tightly than the $\ell_1$ norm by exploiting a certain class of nonconvex functions, while the strict convexity of the consequent optimization problem is preserved. This results in a higher performance in detecting change points. To theoretically justify the performance improvements over $\ell_1$ mean filtering, deterministic and stochastic sufficient conditions for exact change point recovery are derived. In particular, theoretical results show that in the stair-casing problem, our approach might be able to exclude the false change points, while $\ell_1$ mean filtering may fail. A number of numerical simulations assist to show superiority of our method over $\ell_1$ mean filtering and another state-of-the-art algorithm that promotes sparsity tighter than the $\ell_1$ norm. Specifically, it is shown that our approach can consistently detect change points when the jump amplitudes become sufficiently large, while the two other competitors cannot.
Automatic verbal aggression detection for Russian and American imageboards
Venue: arXiv cs.CL 2016
Year: 2016

The problem of aggression for Internet communities is rampant. Anonymous forums usually called imageboards are notorious for their aggressive and deviant behaviour even in comparison with other Internet communities. This study is aimed at studying ways of automatic detection of verbal expression of aggression for the most popular American (4chan.org) and Russian (2ch.hk) imageboards. A set of 1,802,789 messages was used for this study. The machine learning algorithm word2vec was applied to detect the state of aggression. A decent result is obtained for English (88%), the results for Russian are yet to be improved.
Detecting state of aggression in sentences using CNN
Venue: arXiv cs.CL 2016
Year: 2016

In this article we study verbal expression of aggression and its detection using machine learning and neural networks methods. We test our results using our corpora of messages from anonymous imageboards. We also compare Random forest classifier with convolutional neural network for "Movie reviews with one sentence per review" corpus.
Evaluating Journal Quality: A Review of Journal Citation Indicators and Ranking in Business and Management
Venue: arXiv cs.DL 2016
Year: 2016

Evaluating the quality of academic journal is becoming increasing important within the context of research performance evaluation. Traditionally, journals have been ranked by peer review lists such as that of the Association of Business Schools (UK) or though their journal impact factor (JIF). However, several new indicators have been developed, such as the h-index, SJR, SNIP and the Eigenfactor which take into account different factors and therefore have their own particular biases. In this paper we evaluate these metrics both theoretically and also through an empirical study of a large set of business and management journals. We show that even though the indicators appear highly correlated in fact they lead to large differences in journal rankings. We contextualize our results in terms of the UK's large scale research assessment exercise (the RAE/REF) and particularly the ABS journal ranking list. We conclude that no one indicator is superior but that the h-index (which includes the productivity of a journal) and SNIP (which aims to normalize for field effects) may be the most effective at the moment.
Learning rotation invariant convolutional filters for texture classification
Venue: arXiv cs.CV 2016
Year: 2016

We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN). We encode rotation invariance directly in the model by tying the weights of groups of filters to several rotated versions of the canonical filter in the group. These filters can be used to extract rotation invariant features well-suited for image classification. We test this learning procedure on a texture classification benchmark, where the orientations of the training images differ from those of the test images. We obtain results comparable to the state-of-the-art. Compared to standard shallow CNNs, the proposed method obtains higher classification performance while reducing by an order of magnitude the number of parameters to be learned.
Generating Concurrency Checks Automatically
Venue: arXiv cs.LO 2016
Year: 2016

This article introduces ATAB, a tool that automatically generates pairwise reachability checks for action trees. Action trees can be used to study the behaviour of real-world concurrent programs. ATAB encodes pairwise reachability checks into alternating tree automata that determine whether an action tree has a schedule where any pair of given points in the program are simultaneously reachable. Because the pairwise reachability problem is undecidable in general ATAB operates under a restricted form of lock-based concurrency. ATAB produces alternating tree automata that are more compact and more efficiently checkable than those that have been previously used. The process is entirely automated, which simplifies the process of encoding checks for more complex action trees. The alternating tree automata produced are easier to scale to large numbers of locks than previous constructions.
Global Sensitivity Analysis with 2D Hydraulic Codes: Application on Uncertainties Related to High-Resolution Topographic Data
Venue: arXiv cs.CE 2016
Year: 2016

Technologies such as aerial photogrammetry allow production of 3D topographic data including complex environments such as urban areas. Therefore, it is possible to create High Resolution (HR) Digital Elevation Models (DEM) incorporating thin above ground elements influencing overland flow paths. Even though this category of big data has a high level of accuracy, there are still errors in measurements and hypothesis under DEM elaboration. Moreover, operators look for optimizing spatial discretization resolution in order to improve flood models computation time. Errors in measurement, errors in DEM generation, and operator choices for inclusion of this data within 2D hydraulic model, might influence results of flood models simulations. These errors and hypothesis may influence significantly flood modelling results variability. The purpose of this study is to investigate uncertainties related to (i) the own error of high resolution topographic data, and (ii) the modeller choices when including topographic data in hydraulic codes. The aim is to perform a Global Sensitivity Analysis (GSA) which goes through a Monte-Carlo uncertainty propagation, to quantify impact of uncertainties, followed by a Sobol' indices computation, to rank influence of identified parameters on result variability. A process using a coupling of an environment for parametric computation (Prom{é}th{é}e) and a code relying on 2D shallow water equations (FullSWOF 2D) has been developed (P-FS tool). The study has been performed over the lower part of the Var river valley using the estimated hydrograph of 1994 flood event. HR topographic data has been made available for the study area, which is 17.5 km 2 , by Nice municipality. Three uncertain parameters were studied: the measurement error (var. E), the level of details of above-ground element representation in DEM (buildings, sidewalks, etc.) (var. S), and the spatial discretization resolution (grid cell size for regular mesh) (var. R). Parameter var. E follows a probability density function, whereas parameters var. S and var. R. are discrete operator choices. Combining these parameters, a database of 2, 000 simulations has been produced using P-FS tool implemented on a high performance computing structure. In our study case, the output of interest is the maximal
Rank-Deficient Solutions for Optimal Signaling over Wiretap MIMO Channels
Venue: arXiv cs.IT 2016
Year: 2016

Capacity-achieving signaling strategies for the Gaussian wiretap MIMO channel are investigated without the degradedness assumption. In addition to known solutions, a number of new rank-deficient solutions for the optimal transmit covariance matrix are obtained. The case of a weak eavesdropper is considered in detail and the optimal covariance is established in an explicit, closed form with no extra assumptions. This provides lower and upper bounds to the secrecy capacity in the general case with a bounded gap, which are tight for a weak eavesdropper or/and low SNR. Closed form solutions are also obtained for isotropic and omnidirectional eavesdroppers, based on which lower and upper bounds to the secrecy capacity are established in the general case. Sufficient and necessary conditions for optimality of 3 popular transmission techniques, namely the zero-forcing (ZF), the standard water-filling (WF) over the channel eigenmodes and the isotropic signaling (IS), are established for the MIMO wiretap channel. These solutions are appealing due to their lower complexity. In particular, no wiretap codes are needed for the ZF transmission, and no precoding or feedback is needed for the isotropic signaling.
DisCSPs with Privacy Recast as Planning Problems for Utility-based Agents
Venue: arXiv cs.AI 2016
Year: 2016

Privacy has traditionally been a major motivation for decentralized problem solving. However, even though several metrics have been proposed to quantify it, none of them is easily integrated with common solvers. Constraint programming is a fundamental paradigm used to approach various families of problems. We introduce Utilitarian Distributed Constraint Satisfaction Problems (UDisCSP) where the utility of each state is estimated as the difference between the the expected rewards for agreements on assignments for shared variables, and the expected cost of privacy loss. Therefore, a traditional DisCSP with privacy requirements is viewed as a planning problem. The actions available to agents are: communication and local inference. Common decentralized solvers are evaluated here from the point of view of their interpretation as greedy planners. Further, we investigate some simple extensions where these solvers start taking into account the utility function. In these extensions we assume that the planning problem is further restricting the set of communication actions to only the communication primitives present in the corresponding solver protocols. The solvers obtained for the new type of problems propose the action (communication/inference) to be performed in each situation, defining thereby the policy.
Simulating LTE and Wi-Fi Coexistence in Unlicensed Spectrum with ns-3
Venue: arXiv cs.NI 2016
Year: 2016

The use of unlicensed spectrum for future LTE systems raises concerns about its impact on co-located Wi-Fi networks. We report herein on extensions to a popular network simulator, known as ns-3, to model such coexistence. To our knowledge, this simulator is novel in two important ways. First, it is presently the only freely available simulator for coexistence studies, and as open source software, it offers new opportunities for reproducible research and collaborative model development. Second, we are not aware of other simulators with similar wireless models that allow full system studies to be conducted, such as performance evaluation of upper-layer protocols in a coexistence environment. We illustrate the value of a system simulator by describing a simulation campaign for the indoor scenario designed by 3GPP RAN1 using new ns-3 models for Release 13 LTE Listen Before Talk (LBT) techniques, intended for fair coexistence with Wi-Fi. LBT models have been widely simulated at the link and physical layer by many companies in 3GPP RAN1. We first show that we are able to tune the scenario to reproduce similar performance metrics to those reported in the literature. We then extend the simulations by adding TCP to the file transfers and explore the different behaviour observed. Our initial study reveals that there remain many open questions about how LBT should be implemented to achieve good coexistence properties, and that a broader scope of implementation issues, traffic models, and fuller system models should be studied further before drawing final conclusions on the LBT coexistence performance.