OpenRaiser/Intern-Atlas huggingface dataset:
Hand Gesture Recognition Based on Karhunen-Loeve Transform
Venue: arXiv cs.CV 2013
Year: 2013

In this paper, we have proposed a system based on K-L Transform to recognize different hand gestures. The system consists of five steps: skin filtering, palm cropping, edge detection, feature extraction, and classification. Firstly the hand is detected using skin filtering and palm cropping was performed to extract out only the palm portion of the hand. The extracted image was then processed using the Canny Edge Detection technique to extract the outline images of palm. After palm extraction, the features of hand were extracted using K-L Transform technique and finally the input gesture was recognized using proper classifier. In our system, we have tested for 10 different hand gestures, and recognizing rate obtained was 96%. Hence we propose an easy approach to recognize different hand gestures.
Precisely Verifying the Null Space Conditions in Compressed Sensing: A Sandwiching Algorithm
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we propose new efficient algorithms to verify the null space condition in compressed sensing (CS). Given an $(n-m) \times n$ ($m>0$) CS matrix $A$ and a positive $k$, we are interested in computing $\displaystyle α_k = \max_{\{z: Az=0,z\neq 0\}}\max_{\{K: |K|\leq k\}}$ ${\|z_K \|_{1}}{\|z\|_{1}}$, where $K$ represents subsets of $\{1,2,...,n\}$, and $|K|$ is the cardinality of $K$. In particular, we are interested in finding the maximum $k$ such that $α_k < {1}{2}$. However, computing $α_k$ is known to be extremely challenging. In this paper, we first propose a series of new polynomial-time algorithms to compute upper bounds on $α_k$. Based on these new polynomial-time algorithms, we further design a new sandwiching algorithm, to compute the \emph{exact} $α_k$ with greatly reduced complexity. When needed, this new sandwiching algorithm also achieves a smooth tradeoff between computational complexity and result accuracy. Empirical results show the performance improvements of our algorithm over existing known methods; and our algorithm outputs precise values of $α_k$, with much lower complexity than exhaustive search.
Understanding Dropout: Training Multi-Layer Perceptrons with Auxiliary Independent Stochastic Neurons
Venue: arXiv cs.NE 2013
Year: 2013

In this paper, a simple, general method of adding auxiliary stochastic neurons to a multi-layer perceptron is proposed. It is shown that the proposed method is a generalization of recently successful methods of dropout (Hinton et al., 2012), explicit noise injection (Vincent et al., 2010; Bishop, 1995) and semantic hashing (Salakhutdinov & Hinton, 2009). Under the proposed framework, an extension of dropout which allows using separate dropping probabilities for different hidden neurons, or layers, is found to be available. The use of different dropping probabilities for hidden layers separately is empirically investigated.
A New Graphical Password Scheme Resistant to Shoulder-Surfing
Venue: arXiv cs.CR 2013
Year: 2013

Shoulder-surfing is a known risk where an attacker can capture a password by direct observation or by recording the authentication session. Due to the visual interface, this problem has become exacerbated in graphical passwords. There have been some graphical schemes resistant or immune to shoulder-surfing, but they have significant usability drawbacks, usually in the time and effort to log in. In this paper, we propose and evaluate a new shoulder-surfing resistant scheme which has a desirable usability for PDAs. Our inspiration comes from the drawing input method in DAS and the association mnemonics in Story for sequence retrieval. The new scheme requires users to draw a curve across their password images orderly rather than click directly on them. The drawing input trick along with the complementary measures, such as erasing the drawing trace, displaying degraded images, and starting and ending with randomly designated images provide a good resistance to shouldersurfing. A preliminary user study showed that users were able to enter their passwords accurately and to remember them over time.
Pseudorandomness for Regular Branching Programs via Fourier Analysis
Venue: arXiv cs.CC 2013
Year: 2013

We present an explicit pseudorandom generator for oblivious, read-once, permutation branching programs of constant width that can read their input bits in any order. The seed length is $O(\log^2 n)$, where $n$ is the length of the branching program. The previous best seed length known for this model was $n^{1/2+o(1)}$, which follows as a special case of a generator due to Impagliazzo, Meka, and Zuckerman (FOCS 2012) (which gives a seed length of $s^{1/2+o(1)}$ for arbitrary branching programs of size $s$). Our techniques also give seed length $n^{1/2+o(1)}$ for general oblivious, read-once branching programs of width $2^{n^{o(1)}}$, which is incomparable to the results of Impagliazzo et al.Our pseudorandom generator is similar to the one used by Gopalan et al. (FOCS 2012) for read-once CNFs, but the analysis is quite different; ours is based on Fourier analysis of branching programs. In particular, we show that an oblivious, read-once, regular branching program of width $w$ has Fourier mass at most $(2w^2)^k$ at level $k$, independent of the length of the program.
A Face-like Structure Detection on Planet and Satellite Surfaces using Image Processing
Venue: arXiv cs.CV 2013
Year: 2013

This paper demonstrates that face-like structures are everywhere, and can be de-tected automatically even with computers. Huge amount of satellite images of the Earth, the Moon, the Mars are explored and many interesting face-like structure are detected. Throughout this fact, we believe that science and technologies can alert people not to easily become an occultist.
Modal Interface Automata
Venue: arXiv cs.LO 2013
Year: 2013

De Alfaro and Henzinger's Interface Automata (IA) and Nyman et al.'s recent combination IOMTS of IA and Larsen's Modal Transition Systems (MTS) are established frameworks for specifying interfaces of system components. However, neither IA nor IOMTS consider conjunction that is needed in practice when a component shall satisfy multiple interfaces, while Larsen's MTS-conjunction is not closed and Beneš et al.'s conjunction on disjunctive MTS does not treat internal transitions. In addition, IOMTS-parallel composition exhibits a compositionality defect. This article defines conjunction (and also disjunction) on IA and disjunctive MTS and proves the operators to be 'correct', i.e., the greatest lower bounds (least upper bounds) wrt. IA- and resp. MTS-refinement. As its main contribution, a novel interface theory called Modal Interface Automata (MIA) is introduced: MIA is a rich subset of IOMTS featuring explicit output-must-transitions while input-transitions are always allowed implicitly, is equipped with compositional parallel, conjunction and disjunction operators, and allows a simpler embedding of IA than Nyman's. Thus, it fixes the shortcomings of related work, without restricting designers to deterministic interfaces as Raclet et al.'s modal interface theory does.
Can background baroque music help to improve the memorability of graphical passwords?
Venue: arXiv cs.CR 2013
Year: 2013

Graphical passwords have been proposed as an alternative to alphanumeric passwords with their advantages in usability and security. However, they still tend to follow predictable patterns that are easier for attackers to exploit, probably due to users' memory limitations. Various literatures show that baroque music has positive effects on human learning and memorizing. To alleviate users' memory burden, we investigate the novel idea of introducing baroque music to graphical password schemes (specifically DAS, PassPoints and Story) and conduct a laboratory study to see whether it is helpful. In a ten minutes short-term recall, we found that participants in all conditions had high recall success rates that were not statistically different from each other. After one week, the music group coped PassPoints passwords significantly better than the group without music. But there was no statistical difference between two groups in recalling DAS passwords or Story passwords. Further more, we found that the music group tended to set significantly more complicated PassPoints passwords but less complicated DAS passwords.
Segmentation et Interprétation de Nuages de Points pour la Modélisation d'Environnements Urbains
Venue: arXiv cs.CV 2013
Year: 2013

Dans cet article, nous présentons une méthode pour la détection et la classification d'artefacts au niveau du sol, comme phase de filtrage préalable à la modélisation d'environnements urbains. La méthode de détection est réalisée sur l'image profondeur, une projection de nuage de points sur un plan image où la valeur du pixel correspond à la distance du point au plan. En faisant l'hypothèse que les artefacts sont situés au sol, ils sont détectés par une transformation de chapeau haut de forme par remplissage de trous sur l'image de profondeur. Les composantes connexes ainsi obtenues, sont ensuite caractérisées et une analyse des variables est utilisée pour la sélection des caractéristiques les plus discriminantes. Les composantes connexes sont donc classifiées en quatre catégories (lampadaires, piétons, voitures et "Reste") à l'aide d'un algorithme d'apprentissage supervisé. La méthode a été testée sur des nuages de points de la ville de Paris, en montrant de bons résultats de détection et de classification dans l'ensemble de données.---In this article, we present a method for detection and classification of artifacts at the street level, in order to filter cloud point, facilitating the urban modeling process. Our approach exploits 3D information by using range image, a projection of 3D points onto an image plane where the pixel intensity is a function of the measured distance between 3D points and the plane. By assuming that the artifacts are on the ground, they are detected using a Top-Hat of the hole filling algorithm of range images. Then, several features are extracted from the detected connected components and a stepwise forward variable/model selection by using the Wilk's Lambda criterion is performed. Afterward, CCs are classified in four categories (lampposts, pedestrians, cars and others) by using a supervised machine learning method. The proposed method was tested on cloud points of Paris, and have shown satisfactory results on the whole dataset.
Guaranteed Classification via Regularized Similarity Learning
Venue: arXiv cs.LG 2013
Year: 2013

Learning an appropriate (dis)similarity function from the available data is a central problem in machine learning, since the success of many machine learning algorithms critically depends on the choice of a similarity function to compare examples. Despite many approaches for similarity metric learning have been proposed, there is little theoretical study on the links between similarity met- ric learning and the classification performance of the result classifier. In this paper, we propose a regularized similarity learning formulation associated with general matrix-norms, and establish their generalization bounds. We show that the generalization error of the resulting linear separator can be bounded by the derived generalization bound of similarity learning. This shows that a good gen- eralization of the learnt similarity function guarantees a good classification of the resulting linear classifier. Our results extend and improve those obtained by Bellet at al. [3]. Due to the techniques dependent on the notion of uniform stability [6], the bound obtained there holds true only for the Frobenius matrix- norm regularization. Our techniques using the Rademacher complexity [5] and its related Khinchin-type inequality enable us to establish bounds for regularized similarity learning formulations associated with general matrix-norms including sparse L 1 -norm and mixed (2,1)-norm.
Words with unbounded periodicity complexity
Venue: arXiv cs.FL 2013
Year: 2013

If an infinite non-periodic word is uniformly recurrent or is of bounded repetition, then the limit of its periodicity complexity is infinity. Moreover, there are uniformly recurrent words with the periodicity complexity arbitrarily high at infinitely many positions.
A Universal Machine for Biform Theory Graphs
Venue: arXiv cs.LO 2013
Year: 2013

Broadly speaking, there are two kinds of semantics-aware assistant systems for mathematics: proof assistants express the semantic in logic and emphasize deduction, and computer algebra systems express the semantics in programming languages and emphasize computation. Combining the complementary strengths of both approaches while mending their complementary weaknesses has been an important goal of the mechanized mathematics community for some time. We pick up on the idea of biform theories and interpret it in the MMTt/OMDoc framework which introduced the foundations-as-theories approach, and can thus represent both logics and programming languages as theories. This yields a formal, modular framework of biform theory graphs which mixes specifications and implementations sharing the module system and typing information. We present automated knowledge management work flows that interface to existing specification/programming tools and enable an OpenMath Machine, that operationalizes biform theories, evaluating expressions by exhaustively applying the implementations of the respective operators. We evaluate the new biform framework by adding implementations to the OpenMath standard content dictionaries.
M2M Communications for E-Health and Smart Grid: An Industry and Standard Perspective
Venue: arXiv cs.CY 2013
Year: 2013

An overview of several standardization activities for machine-to-machine (M2M) communications is presented, analyzing some of the enabling technologies and applications of M2M in industry sectors such as Smart Grid and e-Health. This summary and overview of the ongoing work in M2M from the industrial and standardization perspective complements the prevalent academic perspective of such publications to date in this field.
Comparison of OFDM and SC-DFE Capacities Without Channel Knowledge at the Transmitter
Venue: arXiv cs.IT 2013
Year: 2013

This letter provides a capacity analysis between OFDM and the ideal SC-DFE when no channel knowledge is available at the transmitter. Through some algebraic manipulation of the OFDM and SC-DFE capacities and using the concavity property of the manipulated capacity function and Jensen's inequality, we are able to prove that the SC-DFE capacity is always superior to that of an OFDM scheme for 4- and 16-QAM for any given channel. For higher-order modulations, however, the results indicate that OFDM may only surpass the ideal SC-DFE capacity by a small amount in some specific scenarios.
Encoding Petri Nets in Answer Set Programming for Simulation Based Reasoning
Venue: arXiv cs.AI 2013
Year: 2013

One of our long term research goals is to develop systems to answer realistic questions (e.g., some mentioned in textbooks) about biological pathways that a biologist may ask. To answer such questions we need formalisms that can model pathways, simulate their execution, model intervention to those pathways, and compare simulations under different circumstances. We found Petri Nets to be the starting point of a suitable formalism for the modeling and simulation needs. However, we need to make extensions to the Petri Net model and also reason with multiple simulation runs and parallel state evolutions. Towards that end Answer Set Programming (ASP) implementation of Petri Nets would allow us to do both. In this paper we show how ASP can be used to encode basic Petri Nets in an intuitive manner. We then show how we can modify this encoding to model several Petri Net extensions by making small changes. We then highlight some of the reasoning capabilities that we will use to accomplish our ultimate research goal.
Encoding Higher Level Extensions of Petri Nets in Answer Set Programming
Venue: arXiv cs.AI 2013
Year: 2013

Answering realistic questions about biological systems and pathways similar to the ones used by text books to test understanding of students about biological systems is one of our long term research goals. Often these questions require simulation based reasoning. To answer such questions, we need formalisms to build pathway models, add extensions, simulate, and reason with them. We chose Petri Nets and Answer Set Programming (ASP) as suitable formalisms, since Petri Net models are similar to biological pathway diagrams; and ASP provides easy extension and strong reasoning abilities. We found that certain aspects of biological pathways, such as locations and substance types, cannot be represented succinctly using regular Petri Nets. As a result, we need higher level constructs like colored tokens. In this paper, we show how Petri Nets with colored tokens can be encoded in ASP in an intuitive manner, how additional Petri Net extensions can be added by making small code changes, and how this work furthers our long term research goals. Our approach can be adapted to other domains with similar modeling needs.
Recurrent Convolutional Neural Networks for Discourse Compositionality
Venue: arXiv cs.CL 2013
Year: 2013

The compositionality of meaning extends beyond the single sentence. Just as words combine to form the meaning of sentences, so do sentences combine to form the meaning of paragraphs, dialogues and general discourse. We introduce both a sentence model and a discourse model corresponding to the two levels of compositionality. The sentence model adopts convolution as the central operation for composing semantic vectors and is based on a novel hierarchical convolutional neural network. The discourse model extends the sentence model and is based on a recurrent neural network that is conditioned in a novel way both on the current sentence and on the current speaker. The discourse model is able to capture both the sequentiality of sentences and the interaction between different speakers. Without feature engineering or pretraining and with simple greedy decoding, the discourse model coupled to the sentence model obtains state of the art performance on a dialogue act classification experiment.
Thresholds of Spatially Coupled Systems via Lyapunov's Method
Venue: arXiv cs.IT 2013
Year: 2013

The threshold, or saturation phenomenon of spatially coupled systems is revisited in the light of Lyapunov's theory of dynamical systems. It is shown that an application of Lyapunov's direct method can be used to quantitatively describe the threshold phenomenon, prove convergence, and compute threshold values. This provides a general proof methodology for the various systems recently studied. Examples of spatially coupled systems are given and their thresholds are computed.
An open diachronic corpus of historical Spanish: annotation criteria and automatic modernisation of spelling
Venue: arXiv cs.CL 2013
Year: 2013

The IMPACT-es diachronic corpus of historical Spanish compiles over one hundred books --containing approximately 8 million words-- in addition to a complementary lexicon which links more than 10 thousand lemmas with attestations of the different variants found in the documents. This textual corpus and the accompanying lexicon have been released under an open license (Creative Commons by-nc-sa) in order to permit their intensive exploitation in linguistic research. Approximately 7% of the words in the corpus (a selection aimed at enhancing the coverage of the most frequent word forms) have been annotated with their lemma, part of speech, and modern equivalent. This paper describes the annotation criteria followed and the standards, based on the Text Encoding Initiative recommendations, used to the represent the texts in digital form. As an illustration of the possible synergies between diachronic textual resources and linguistic research, we describe the application of statistical machine translation techniques to infer probabilistic context-sensitive rules for the automatic modernisation of spelling. The automatic modernisation with this type of statistical methods leads to very low character error rates when the output is compared with the supervised modern version of the text.
Symmetric Two-User MIMO BC and IC with Evolving Feedback
Venue: arXiv cs.IT 2013
Year: 2013

Extending recent findings on the two-user MISO broadcast channel (BC) with imperfect and delayed channel state information at the transmitter (CSIT), the work here explores the performance of the two user MIMO BC and the two user MIMO interference channel (MIMO IC), in the presence of feedback with evolving quality and timeliness. Under standard assumptions, and in the presence of M antennas per transmitter and N antennas per receiver, the work derives the DoF region, which is optimal for a large regime of sufficiently good (but potentially imperfect) delayed CSIT. This region concisely captures the effect of having predicted, current and delayed-CSIT, as well as concisely captures the effect of the quality of CSIT offered at any time, about any channel. In addition to the progress towards describing the limits of using such imperfect and delayed feedback in MIMO settings, the work offers different insights that include the fact that, an increasing number of receive antennas can allow for reduced quality feedback, as well as that no CSIT is needed for the direct links in the IC.
Performance Analysis for Physical Layer Security in Multi-Antenna Downlink Networks with Limited CSI Feedback
Venue: arXiv cs.IT 2013
Year: 2013

Channel state information (CSI) at the transmitter is of importance to the performance of physical layer security based on multi-antenna networks. Specifically, CSI is not only beneficial to improve the capacity of the legitimate channel, but also can be used to degrade the performance of the eavesdropper channel. Thus, the secrecy rate increases accordingly. This letter focuses on the quantitative analysis of the ergodic secrecy sum-rate in terms of feedback amount of the CSI from the legitimate users in multiuser multi-antenna downlink networks. Furthermore, the asymptotic characteristics of the ergodic secrecy sum-rate in two extreme cases is investigated in some detail. Finally, our theoretical claims are confirmed by the numerical results.
Automatic functions, linear time and learning
Venue: arXiv cs.FL 2013
Year: 2013

The present work determines the exact nature of {\em linear time computable} notions which characterise automatic functions (those whose graphs are recognised by a finite automaton). The paper also determines which type of linear time notions permit full learnability for learning in the limit of automatic classes (families of languages which are uniformly recognised by a finite automaton). In particular it is shown that a function is automatic iff there is a one-tape Turing machine with a left end which computes the function in linear time where the input before the computation and the output after the computation both start at the left end. It is known that learners realised as automatic update functions are restrictive for learning. In the present work it is shown that one can overcome the problem by providing work tapes additional to a resource-bounded base tape while keeping the update-time to be linear in the length of the largest datum seen so far. In this model, one additional such work tape provides additional learning power over the automatic learner model and two additional work tapes give full learning power. Furthermore, one can also consider additional queues or additional stacks in place of additional work tapes and for these devices, one queue or two stacks are sufficient for full learning power while one stack is insufficient.