OpenRaiser/Intern-Atlas huggingface dataset:
Natural Language Inference for Arabic Using Extended Tree Edit Distance with Subtrees
Venue: arXiv cs.CL 2014
Year: 2014

Many natural language processing (NLP) applications require the computation of similarities between pairs of syntactic or semantic trees. Many researchers have used tree edit distance for this task, but this technique suffers from the drawback that it deals with single node operations only. We have extended the standard tree edit distance algorithm to deal with subtree transformation operations as well as single nodes. The extended algorithm with subtree operations, TED+ST, is more effective and flexible than the standard algorithm, especially for applications that pay attention to relations among nodes (e.g. in linguistic trees, deleting a modifier subtree should be cheaper than the sum of deleting its components individually). We describe the use of TED+ST for checking entailment between two Arabic text snippets. The preliminary results of using TED+ST were encouraging when compared with two string-based approaches and with the standard algorithm.
Qualitative Order of Magnitude Energy-Flow-Based Failure Modes and Effects Analysis
Venue: arXiv cs.AI 2014
Year: 2014

This paper presents a structured power and energy-flow-based qualitative modelling approach that is applicable to a variety of system types including electrical and fluid flow. The modelling is split into two parts. Power flow is a global phenomenon and is therefore naturally represented and analysed by a network comprised of the relevant structural elements from the components of a system. The power flow analysis is a platform for higher-level behaviour prediction of energy related aspects using local component behaviour models to capture a state-based representation with a global time. The primary application is Failure Modes and Effects Analysis (FMEA) and a form of exaggeration reasoning is used, combined with an order of magnitude representation to derive the worst case failure modes. The novel aspects of the work are an order of magnitude(OM) qualitative network analyser to represent any power domain and topology, including multiple power sources, a feature that was not required for earlier specialised electrical versions of the approach. Secondly, the representation of generalised energy related behaviour as state-based local models is presented as a modelling strategy that can be more vivid and intuitive for a range of topologically complex applications than qualitative equation-based representations.The two-level modelling strategy allows the broad system behaviour coverage of qualitative simulation to be exploited for the FMEA task, while limiting the difficulties of qualitative ambiguity explanation that can arise from abstracted numerical models. We have used the method to support an automated FMEA system with examples of an aircraft fuel system and domestic a heating system discussed in this paper.
AI Methods in Algorithmic Composition: A Comprehensive Survey
Venue: arXiv cs.AI 2014
Year: 2014

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.
Protecting Privacy through Distributed Computation in Multi-agent Decision Making
Venue: arXiv cs.AI 2014
Year: 2014

As large-scale theft of data from corporate servers is becoming increasingly common, it becomes interesting to examine alternatives to the paradigm of centralizing sensitive data into large databases. Instead, one could use cryptography and distributed computation so that sensitive data can be supplied and processed in encrypted form, and only the final result is made known. In this paper, we examine how such a paradigm can be used to implement constraint satisfaction, a technique that can solve a broad class of AI problems such as resource allocation, planning, scheduling, and diagnosis. Most previous work on privacy in constraint satisfaction only attempted to protect specific types of information, in particular the feasibility of particular combinations of decisions. We formalize and extend these restricted notions of privacy by introducing four types of private information, including the feasibility of decisions and the final decisions made, but also the identities of the participants and the topology of the problem. We present distributed algorithms that allow computing solutions to constraint satisfaction problems while maintaining these four types of privacy. We formally prove the privacy properties of these algorithms, and show experiments that compare their respective performance on benchmark problems.
Scene Labeling with Contextual Hierarchical Models
Venue: arXiv cs.CV 2014
Year: 2014

Scene labeling is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in scene labeling frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which learns contextual information in a hierarchical framework for scene labeling. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM outperforms state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500).
The Complexity of Synchronous Notions of Information Flow Security
Venue: arXiv cs.CR 2014
Year: 2014

The paper considers the complexity of verifying that a finite state system satisfies a number of definitions of information flow security. The systems model considered is one in which agents operate synchronously with awareness of the global clock. This enables timing based attacks to be captured, whereas previous work on this topic has dealt primarily with asynchronous systems. Versions of the notions of nondeducibility on inputs, nondeducibility on strategies, and an unwinding based notion are formulated for this model. All three notions are shown to be decidable, and their computational complexity is characterised.
User Friendly Implementation for Efficiently Conducting Hammersmith Infant Neurological Examination
Venue: arXiv cs.CY 2014
Year: 2014

The aim of this work is to design a semi-automatic application that can be used as an aid by the doctors for smoothly conducting Hammersmith Infant Neurological Examination (IDNE). A simplified version of the examination which provides a quantitative neurological assessment is used to design the application. The application includes a methodology of conducting IDNE examination suited to inexperienced staff, applicable to both neonatal and post-neonatal infants. It also provides a facility to go through the previous records of a patient that can help in diagnosing patients with high risk of neurological disorder. A semi-automatic approach is proposed for skeleton generation. The application has been installed in hospitals and currently in operation. It is expected to increase the efficiency of conducting HINE using the proposed application.
Model performance indicators ERP systems
Venue: arXiv cs.SE 2014
Year: 2014

Implementation process ERP is complex and expensive process. Typically always be faced with many failures. Successfully implemented in an organization has many challenges. Organizations in the deployment and success of the system depends on several factors.One of the key factors in the successful deployment of systems methodology is the implementation process. Methodology has several indicators for successful implementation of ERP systems, we have examined. And indicators for each of the methodologies have identified. The proposed method is also an important indicator of the success of security controls and indicators to be monitored and controlled.
A Game Theoretic Analysis of Collaboration in Wikipedia
Venue: arXiv cs.GT 2014
Year: 2014

Peer production projects such as Wikipedia or open-source software development allow volunteers to collectively create knowledge based products. The inclusive nature of such projects poses difficult challenges for ensuring trustworthiness and combating vandalism. Prior studies in the area deal with descriptive aspects of peer production, failing to capture the idea that while contributors collaborate, they also compete for status in the community and for imposing their views on the product. In this paper we investigate collaborative authoring in Wikipedia where contributors append and overwrite previous contributions to a page. We assume that a contributors goal is to maximize ownership of content sections such that content owned (or originated) by her survived the most recent revision of the page. We model contributors interactions to increase their content ownership as a noncooperative game where a players utility is associated with content owned and cost is a function of effort expended. Our results capture several real life aspects of contributors interactions within peer production projects. We show that at the Nash equilibrium there is an inverse relationship between the effort required to make a contribution and the survival of a contributors content. In other words majority of the content that survives is necessarily contributed by experts who expend relatively less effort than non experts. An empirical analysis of Wikipedia articles provides support for our models predictions. Implications for research and practice are discussed in the context of trustworthy collaboration as well as vandalism.
Sequential Model-Based Ensemble Optimization
Venue: arXiv cs.LG 2014
Year: 2014

One of the most tedious tasks in the application of machine learning is model selection, i.e. hyperparameter selection. Fortunately, recent progress has been made in the automation of this process, through the use of sequential model-based optimization (SMBO) methods. This can be used to optimize a cross-validation performance of a learning algorithm over the value of its hyperparameters. However, it is well known that ensembles of learned models almost consistently outperform a single model, even if properly selected. In this paper, we thus propose an extension of SMBO methods that automatically constructs such ensembles. This method builds on a recently proposed ensemble construction paradigm known as agnostic Bayesian learning. In experiments on 22 regression and 39 classification data sets, we confirm the success of this proposed approach, which is able to outperform model selection with SMBO.
Interval scheduling and colorful independent sets
Venue: arXiv cs.DM 2014
Year: 2014

Numerous applications in scheduling, such as resource allocation or steel manufacturing, can be modeled using the NP-hard Independent Set problem (given an undirected graph and an integer k, find a set of at least k pairwise non-adjacent vertices). Here, one encounters special graph classes like 2-union graphs (edge-wise unions of two interval graphs) and strip graphs (edge-wise unions of an interval graph and a cluster graph), on which Independent Set remains NP-hard but admits constant-ratio approximations in polynomial time. We study the parameterized complexity of Independent Set on 2-union graphs and on subclasses like strip graphs. Our investigations significantly benefit from a new structural "compactness" parameter of interval graphs and novel problem formulations using vertex-colored interval graphs. Our main contributions are: 1. We show a complexity dichotomy: restricted to graph classes closed under induced subgraphs and disjoint unions, Independent Set is polynomial-time solvable if both input interval graphs are cluster graphs, and is NP-hard otherwise. 2. We chart the possibilities and limits of effective polynomial-time preprocessing (also known as kernelization). 3. We extend Halldórsson and Karlsson (2006)'s fixed-parameter algorithm for Independent Set on strip graphs parameterized by the structural parameter "maximum number of live jobs" to show that the problem (also known as Job Interval Selection) is fixed-parameter tractable with respect to the parameter k and generalize their algorithm from strip graphs to 2-union graphs. Preliminary experiments with random data indicate that Job Interval Selection with up to fifteen jobs and 5*10^5 intervals can be solved optimally in less than five minutes.
Understanding Individual Differences: Towards Effective Mobile Interface Design and Adaptation for the Blind
Venue: arXiv cs.HC 2014
Year: 2014

No two people are alike. We usually ignore this diversity as we have the capability to adapt and, without noticing, become experts in interfaces that were probably misadjusted to begin with. This adaptation is not always at the user's reach. One neglected group is the blind. Spatial ability, memory, and tactile sensitivity are some characteristics that diverge between users. Regardless, all are presented with the same methods ignoring their capabilities and needs. Interaction with mobile devices is highly visually demanding which widens the gap between blind people. Our research goal is to identify the individual attributes that influence mobile interaction, considering the blind, and match them with mobile interaction modalities in a comprehensive and extensible design space. We aim to provide knowledge both for device design, device prescription and interface adaptation.
Image Acquisition in an Underwater Vision System with NIR and VIS Illumination
Venue: arXiv cs.CV 2014
Year: 2014

The paper describes the image acquisition system able to capture images in two separated bands of light, used to underwater autonomous navigation. The channels are: the visible light spectrum and near infrared spectrum. The characteristics of natural, underwater environment were also described together with the process of the underwater image creation. The results of an experiment with comparison of selected images acquired in these channels are discussed.
Equilibria Interchangeability in Cellular Games
Venue: arXiv cs.GT 2014
Year: 2014

The notion of interchangeability has been introduced by John Nash in one of his original papers on equilibria. This paper studies properties of Nash equilibria interchangeability in cellular games that model behavior of infinite chain of homogeneous economic agents. The paper shows that there are games in which strategy of any given player is interchangeable with strategies of players in an arbitrary large neighborhood of the given player, but is not interchangeable with the strategy of a remote player outside of the neighborhood. The main technical result is a sound and complete logical system describing universal properties of interchangeability common to all cellular games.
Localized epidemic detection in networks with overwhelming noise
Venue: arXiv cs.SI 2014
Year: 2014

We consider the problem of detecting an epidemic in a population where individual diagnoses are extremely noisy. The motivation for this problem is the plethora of examples (influenza strains in humans, or computer viruses in smartphones, etc.) where reliable diagnoses are scarce, but noisy data plentiful. In flu/phone-viruses, exceedingly few infected people/phones are professionally diagnosed (only a small fraction go to a doctor) but less reliable secondary signatures (e.g., people staying home, or greater-than-typical upload activity) are more readily available. These secondary data are often plagued by unreliability: many people with the flu do not stay home, and many people that stay home do not have the flu. This paper identifies the precise regime where knowledge of the contact network enables finding the needle in the haystack: we provide a distributed, efficient and robust algorithm that can correctly identify the existence of a spreading epidemic from highly unreliable local data. Our algorithm requires only local-neighbor knowledge of this graph, and in a broad array of settings that we describe, succeeds even when false negatives and false positives make up an overwhelming fraction of the data available. Our results show it succeeds in the presence of partial information about the contact network, and also when there is not a single "patient zero", but rather many (hundreds, in our examples) of initial patient-zeroes, spread across the graph.
Vers une interface pour l enrichissement des requetes en arabe dans un systeme de recherche d information
Venue: arXiv cs.IR 2014
Year: 2014

This presentation focuses on the automatic expansion of Arabic request using morphological analyzer and Arabic Wordnet. The expanded request is sent to Google.
Study the function of building blocks in SHA Family
Venue: arXiv cs.CR 2014
Year: 2014

In this paper we analyse the role of some of the building blocks in SHA-256. We show that the disturbance correction strategy is applicable to the SHA-256 architecture and we prove that functions $Σ$, $σ$ are vital for the security of SHA-256 by showing that for a variant without them it is possible to find collisions with complexity 2 64 hash operations. As a step towards an analysis of the full function, we present the results of our experiments on Hamming weights of expanded messages for different variants of the message expansion and show that there exist low-weight expanded messages for XOR-linearised variants.
Real-time Pedestrian Surveillance with Top View Cumulative Grids
Venue: arXiv cs.CV 2014
Year: 2014

This manuscript presents an efficient approach to map pedestrian surveillance footage to an aerial view for global assessment of features. The analysis of the footages relies on low level computer vision and enable real-time surveillance. While we neglect object tracking, we introduce cumulative grids on top view scene flow visualization to highlight situations of interest in the footage. Our approach is tested on multiview footage both from RGB cameras and, for the first time in the field, on RGB-D-sensors.
Variational Free Energies for Compressed Sensing
Venue: arXiv cs.IT 2014
Year: 2014

We consider the variational free energy approach for compressed sensing. We first show that the naïve mean field approach performs remarkably well when coupled with a noise learning procedure. We also notice that it leads to the same equations as those used for iterative thresholding. We then discuss the Bethe free energy and how it corresponds to the fixed points of the approximate message passing algorithm. In both cases, we test numerically the direct optimization of the free energies as a converging sparse-estimationalgorithm.
Deployment of an Innovative Resource Choice Method for Process Planning
Venue: arXiv cs.OH 2014
Year: 2014

Designers, process planners and manufacturers naturally consider different concepts for a same object. The stiffness of production means and the design specification requirements mark out process planners as responsible of the coherent integration of all constraints. First, this paper details an innovative solution of resource choice, applied for aircraft manufacturing parts. In a second part, key concepts are instanced for the considered industrial domain. Finally, a digital mock up validates the solution viability and demonstrates the possibility of an in-process knowledge capitalisation and use. Formalising the link between Design and Manufacturing allows to hope enhancements of simultaneous Product / Process developments.
On the Dynamics of Social Media Popularity: A YouTube Case Study
Venue: arXiv cs.SI 2014
Year: 2014

Understanding the factors that impact the popularity dynamics of social media can drive the design of effective information services, besides providing valuable insights to content generators and online advertisers. Taking YouTube as case study, we analyze how video popularity evolves since upload, extracting popularity trends that characterize groups of videos. We also analyze the referrers that lead users to videos, correlating them, features of the video and early popularity measures with the popularity trend and total observed popularity the video will experience. Our findings provide fundamental knowledge about popularity dynamics and its implications for services such as advertising and search.
A Natural Generalization of Bounded Tree-Width and Bounded Clique-Width
Venue: arXiv cs.DS 2014
Year: 2014

We investigate a new width parameter, the fusion-width of a graph. It is a natural generalization of the tree-width, yet strong enough that not only graphs of bounded tree-width, but also graphs of bounded clique-width, trivially have bounded fusion-width. In particular, there is no exponential growth between tree-width and fusion-width, as is the case between tree-width and clique-width. The new parameter gives a good intuition about the relationship between tree-width and clique-width.
An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning
Venue: arXiv cs.LG 2014
Year: 2014

From concentration inequalities for the suprema of Gaussian or Rademacher processes an inequality is derived. It is applied to sharpen existing and to derive novel bounds on the empirical Rademacher complexities of unit balls in various norms appearing in the context of structured sparsity and multitask dictionary learning or matrix factorization. A key role is played by the largest eigenvalue of the data covariance matrix.
MCA Learning Algorithm for Incident Signals Estimation: A Review
Venue: arXiv cs.NE 2014
Year: 2014

Recently there has been many works on adaptive subspace filtering in the signal processing literature. Most of them are concerned with tracking the signal subspace spanned by the eigenvectors corresponding to the eigenvalues of the covariance matrix of the signal plus noise data. Minor Component Analysis (MCA) is important tool and has a wide application in telecommunications, antenna array processing, statistical parametric estimation, etc. As an important feature extraction technique, MCA is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will present a MCA learning algorithm to extract minor component from input signals, and the learning rate parameter is also presented, which ensures fast convergence of the algorithm, because it has direct effect on the convergence of the weight vector and the error level is affected by this value. MCA is performed to determine the estimated DOA. Simulation results will be furnished to illustrate the theoretical results achieved.