OpenRaiser/Intern-Atlas huggingface dataset:
A Review of Temporal Aspects of Hand Gesture Analysis Applied to Discourse Analysis and Natural Conversation
Venue: arXiv cs.HC 2013
Year: 2013

Lately, there has been an increasing interest in hand gesture analysis systems. Recent works have employed pattern recognition techniques and have focused on the development of systems with more natural user interfaces. These systems may use gestures to control interfaces or recognize sign language gestures, which can provide systems with multimodal interaction; or consist in multimodal tools to help psycholinguists to understand new aspects of discourse analysis and to automate laborious tasks. Gestures are characterized by several aspects, mainly by movements and sequence of postures. Since data referring to movements or sequences carry temporal information, this paper presents a literature review about temporal aspects of hand gesture analysis, focusing on applications related to natural conversation and psycholinguistic analysis, using Systematic Literature Review methodology. In our results, we organized works according to type of analysis, methods, highlighting the use of Machine Learning techniques, and applications.
Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
Venue: arXiv cs.CV 2013
Year: 2013

Image retrieval refers to finding relevant images from an image database for a query, which is considered difficult for the gap between low-level representation of images and high-level representation of queries. Recently further developed Deep Neural Network sheds light on automatically learning high-level image representation from raw pixels. In this paper, we proposed a multi-task DNN learned for image retrieval, which contains two parts, i.e., query-sharing layers for image representation computation and query-specific layers for relevance estimation. The weights of multi-task DNN are learned on clickthrough data by Ring Training. Experimental results on both simulated and real dataset show the effectiveness of the proposed method.
Co-Sparse Textural Similarity for Image Segmentation
Venue: arXiv cs.CV 2013
Year: 2013

We propose an algorithm for segmenting natural images based on texture and color information, which leverages the co-sparse analysis model for image segmentation within a convex multilabel optimization framework. As a key ingredient of this method, we introduce a novel textural similarity measure, which builds upon the co-sparse representation of image patches. We propose a Bayesian approach to merge textural similarity with information about color and location. Combined with recently developed convex multilabel optimization methods this leads to an efficient algorithm for both supervised and unsupervised segmentation, which is easily parallelized on graphics hardware. The approach provides competitive results in unsupervised segmentation and outperforms state-of-the-art interactive segmentation methods on the Graz Benchmark.
New Approach to Optimize the Time of Association Rules Extraction
Venue: arXiv cs.DB 2013
Year: 2013

The knowledge discovery algorithms have become ineffective at the abundance of data and the need for fast algorithms or optimizing methods is required. To address this limitation, the objective of this work is to adapt a new method for optimizing the time of association rules extractions from large databases. Indeed, given a relational database (one relation) represented as a set of tuples, also called set of attributes, we transform the original database as a binary table (Bitmap table) containing binary numbers. Then, we use this Bitmap table to construct a data structure called Peano Tree stored as a binary file on which we apply a new algorithm called BF-ARM (extension of the well known Apriori algorithm). Since the database is loaded into a binary file, our proposed algorithm will traverse this file, and the processes of association rules extractions will be based on the file stored on disk. The BF-ARM algorithm is implemented and compared with Apriori, Apriori+ and RS-Rules+ algorithms. The evaluation process is based on three benchmarks (Mushroom, Car Evaluation and Adult). Our preliminary experimental results showed that our algorithm produces association rules with a minimum time compared to other algorithms.
A Statistical Peek into Average Case Complexity
Venue: arXiv cs.DS 2013
Year: 2013

The present paper gives a statistical adventure towards exploring the average case complexity behavior of computer algorithms. Rather than following the traditional count based analytical (pen and paper) approach, we instead talk in terms of the weight based analysis that permits mixing of distinct operations into a conceptual bound called the statistical bound and its empirical estimate, the so called "empirical O". Based on careful analysis of the results obtained, we have introduced two new conjectures in the domain of algorithmic analysis. The analytical way of average case analysis falls flat when it comes to a data model for which the expectation does not exist (e.g. Cauchy distribution for continuous input data and certain discrete distribution inputs as those studied in the paper). The empirical side of our approach, with a thrust in computer experiments and applied statistics in its paradigm, lends a helping hand by complimenting and supplementing its theoretical counterpart. Computer science is or at least has aspects of an experimental science as well, and hence hopefully, our statistical findings will be equally recognized among theoretical scientists as well.
Array Convolutional Low-Density Parity-Check Codes
Venue: arXiv cs.IT 2013
Year: 2013

This paper presents a design technique for obtaining regular time-invariant low-density parity-check convolutional (RTI-LDPCC) codes with low complexity and good performance. We start from previous approaches which unwrap a low-density parity-check (LDPC) block code into an RTI-LDPCC code, and we obtain a new method to design RTI-LDPCC codes with better performance and shorter constraint length. Differently from previous techniques, we start the design from an array LDPC block code. We show that, for codes with high rate, a performance gain and a reduction in the constraint length are achieved with respect to previous proposals. Additionally, an increase in the minimum distance is observed.
Typing linear algebra: A biproduct-oriented approach
Venue: arXiv cs.SE 2013
Year: 2013

Interested in formalizing the generation of fast running code for linear algebra applications, the authors show how an index-free, calculational approach to matrix algebra can be developed by regarding matrices as morphisms of a category with biproducts. This shifts the traditional view of matrices as indexed structures to a type-level perspective analogous to that of the pointfree algebra of programming. The derivation of fusion, cancellation and abide laws from the biproduct equations makes it easy to calculate algorithms implementing matrix multiplication, the central operation of matrix algebra, ranging from its divide-and-conquer version to its vectorization implementation. From errant attempts to learn how particular products and coproducts emerge from biproducts, not only blocked matrix algebra is rediscovered but also a way of extending other operations (e.g. Gaussian elimination) blockwise, in a calculational style, is found. The prospect of building biproduct-based type checkers for computer algebra systems such as MatlabTM is also considered.
A simple sequent calculus for nominal logic
Venue: arXiv cs.LO 2013
Year: 2013

Nominal logic is a variant of first-order logic that provides support for reasoning about bound names in abstract syntax. A key feature of nominal logic is the new-quantifier, which quantifies over fresh names (names not appearing in any values considered so far). Previous attempts have been made to develop convenient rules for reasoning with the new-quantifier, but we argue that none of these attempts is completely satisfactory. In this article we develop a new sequent calculus for nominal logic in which the rules for the new- quantifier are much simpler than in previous attempts. We also prove several structural and metatheoretic properties, including cut-elimination, consistency, and equivalence to Pitts' axiomatization of nominal logic.
Rio: A System Solution for Sharing I/O between Mobile Systems
Venue: arXiv cs.OS 2013
Year: 2013

Mobile systems are equipped with a diverse collection of I/O devices, including cameras, microphones, sensors, and modems. There exist many novel use cases for allowing an application on one mobile system to utilize I/O devices from another. This paper presents Rio, an I/O sharing solution that supports unmodified applications and exposes all the functionality of an I/O device for sharing. Rio's design is common to many classes of I/O devices, thus significantly reducing the engineering effort to support new I/O devices. Our implementation of Rio on Android consists of 6700 total lines of code and supports four I/O classes with fewer than 450 class-specific lines of code. Rio also supports I/O sharing between mobile systems of different form factors, including smartphones and tablets. We show that Rio achieves performance close to that of local I/O for audio, sensors, and modems, but suffers noticeable performance degradation for camera due to network throughput limitations between the two systems, which is likely to be alleviated by emerging wireless standards.
Application of Remote Sensing, GIS and GPS for efficient Urban Management Plan, A case study of part of Hyderabad city
Venue: arXiv cs.CY 2013
Year: 2013

Role of urban planning and management in Hyderabad is becoming more and more crucial due to the dramatic increase in urban population and allied urban problems. Hyderabad is experiencing a rapid urbanization rate. Urbanization contributes many advantages in terms of economics, but if uncontrolled, would produce negative consequences to the physical, social and natural environment. With the advancement of GIS, which considerably influenced the dynamic nature of urban and regional planning, incorporation of GIS becomes imperative for better and improved decision-making in urban planning and management. It offers a solution to the urban problems and decision-making, which is more reliant to the real-time spatial modelling. The integration of Geographical Information System (GIS) and Remote Sensing has provided a tool, which can contribute to much clearer understanding of real planning problems as well as prescriptive planning scenarios to enhance the quality of urban planning and management.
Evaluation of Plane Detection with RANSAC According to Density of 3D Point Clouds
Venue: arXiv cs.RO 2013
Year: 2013

We have implemented a method that detects planar regions from 3D scan data using Random Sample Consensus (RANSAC) algorithm to address the issue of a trade-off between the scanning speed and the point density of 3D scanning. However, the limitation of the implemented method has not been clear yet. In this paper, we conducted an additional experiment to evaluate the implemented method by changing its parameter and environments in both high and low point density data. As a result, the number of detected planes in high point density data was different from that in low point density data with the same parameter value.
Wideband Parameters Analysis and Validation for Indoor radio Channel at 60/70/80GHz for Gigabit Wireless Communication employing Isotropic, Horn and Omni directional Antenna
Venue: arXiv cs.NI 2013
Year: 2013

Recently, applications of millimeter (mm) waves for high-speed broadband wireless local area network communication systems in indoor environment are increasingly gaining recognition as it provides gigabit-speed wireless communications with carrier-class performances over distances of a mile or more due to spectrum availability and wider bandwidth requirements. Collectively referred to as E-Band, the millimeter wave wireless technology present the potential to offer bandwidth delivery comparable to that of fiber optic, but without the financial and logistic challenges of deploying fiber. This paper investigates the wideband parameters using the ray tracing technique for indoor propagation systems with rms delay spread for Omnidirectional and Horn Antennas for Bent Tunnel at 80GHz. The results obtained were 2.03 and 1.95 respectively, besides, the normalized received power with 0:55fix10^8 excess delay at 70GHz for Isotropic Antenna was at 0.97.
Long Time No See: The Probability of Reusing Tags as a Function of Frequency and Recency
Venue: arXiv cs.IR 2013
Year: 2013

In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory. This approach uses the frequency and recency of previous tag assignments to estimate the probability of reusing a particular tag. Using three real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike and Flickr, we show how adding a time-dependent component outperforms conventional "most popular tags" approaches and another existing and very effective but less theory-driven, time-dependent recommendation mechanism. By combining our approach with a simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as FolkRank, Pairwise Interaction Tensor Factorization and Collaborative Filtering. We conclude that our approach provides an accurate and computationally efficient model of a user's temporal tagging behavior. We show how effective principles for information retrieval can be designed and implemented if human memory processes are taken into account.
A Design Blueprint for Virtual Organizations in a Service Oriented Landscape
Venue: arXiv cs.CY 2013
Year: 2013

"United we stand, divided we fall" is a well known saying. We are living in the era of virtual collaborations. Advancement on conceptual and technological level has enhanced the way people communicate. Everything-as-a-Service once a dream, now becoming a reality. Problem nature has also been changed over the time. Today, e-Collaborations are applied to all the domains possible. Extensive data and computing resources are in need and assistance from human experts is also becoming essential. This puts a great responsibility on Information Technology (IT) researchers and developers to provide generic platforms where user can easily communicate and solve their problems. To realize this concept, distributed computing has offered many paradigms, e.g. cluster, grid, cloud computing. Virtual Organization (VO) is a logical orchestration of globally dispersed resources to achieve common goals. Existing paradigms and technology are used to form Virtual Organization, but lack of standards remained a critical issue for last two decades. Our research endeavor focuses on developing a design blueprint for Virtual Organization building process. The proposed standardization process is a two phase activity. First phase provides requirement analysis and the second phase presents a Reference Architecture for Virtual Organization (RAVO). This form of standardization is chosen to accommodate both technological and paradigm shift. We categorize our efforts in two parts. First part consists of a pattern to identify the requirements and components of a Virtual Organization. Second part details a generic framework based on the concept of Everything-as-a-Service.
New Repair strategy of Hadamard Minimum Storage Regenerating Code for Distributed Storage System
Venue: arXiv cs.IT 2013
Year: 2013

The newly presented $(k+2,k)$ Hadamard minimum storage regenerating (MSR) code is the first class of high rate storage code with optimal repair property for all single node failures. In this paper, we propose a new simple repair strategy, which can considerably reduces the computation load of the node repair in contrast to the original one.
Cyclostationary Spectrum Sensing in Cognitive Radios Using FRESH Filters
Venue: arXiv cs.OH 2013
Year: 2013

This paper deals with spectrum sensing in Cognitive Radios to enable unlicensed secondary users to opportunistically access a licensed band. The ability to detect the presence of a primary user at a low signal to noise ratio (SNR) is a challenging prerequisite to spectrum sensing and earlier proposed techniques like energy detection and cyclostationary detection have only been partially successful. This paper proposes the use of FRESH (FREquency SHift) filters [1] to enable spectrum sensing at low SNR by optimally estimating a cyclostationary signal using its spectral coherence properties. We establish the mean square error convergence of the adaptive FRESH filter through simulation. Subsequently, we formulate a cyclostationarity based binary hypothesis test on the filtered signal and observe the resultant detection performance. Simulation results show that the proposed approach performs better than energy detection and cyclostationary detection techniques for spectrum sensing.
Generative NeuroEvolution for Deep Learning
Venue: arXiv cs.NE 2013
Year: 2013

An important goal for the machine learning (ML) community is to create approaches that can learn solutions with human-level capability. One domain where humans have held a significant advantage is visual processing. A significant approach to addressing this gap has been machine learning approaches that are inspired from the natural systems, such as artificial neural networks (ANNs), evolutionary computation (EC), and generative and developmental systems (GDS). Research into deep learning has demonstrated that such architectures can achieve performance competitive with humans on some visual tasks; however, these systems have been primarily trained through supervised and unsupervised learning algorithms. Alternatively, research is showing that evolution may have a significant role in the development of visual systems. Thus this paper investigates the role neuro-evolution (NE) can take in deep learning. In particular, the Hypercube-based NeuroEvolution of Augmenting Topologies is a NE approach that can effectively learn large neural structures by training an indirect encoding that compresses the ANN weight pattern as a function of geometry. The results show that HyperNEAT struggles with performing image classification by itself, but can be effective in training a feature extractor that other ML approaches can learn from. Thus NeuroEvolution combined with other ML methods provides an intriguing area of research that can replicate the processes in nature.
Some Improvements on Deep Convolutional Neural Network Based Image Classification
Venue: arXiv cs.CV 2013
Year: 2013

We investigate multiple techniques to improve upon the current state of the art deep convolutional neural network based image classification pipeline. The techiques include adding more image transformations to training data, adding more transformations to generate additional predictions at test time and using complementary models applied to higher resolution images. This paper summarizes our entry in the Imagenet Large Scale Visual Recognition Challenge 2013. Our system achieved a top 5 classification error rate of 13.55% using no external data which is over a 20% relative improvement on the previous year's winner.
Enriched Performance on Wireless Sensor Network using Fuzzy based Clustering Technique
Venue: arXiv cs.NI 2013
Year: 2013

The wireless sensor networks combines sensing, computation, and communication into a single small device. These devices depend on battery power and may be placed in hostile environments replacing them becomes a tedious task. Thus improving the energy of these networks becomes important. Clustering in wireless sensor network looks several challenges such as selection of an optimal group of sensor nodes as cluster, optimum selection of cluster head, energy balanced optimal strategy for rotating the role of cluster head in a cluster, maintaining intra and inter cluster connectivity and optimal data routing in the network. In this paper, we study a protocol supporting an energy efficient clustering, cluster head selection and data routing method to extend the lifetime of sensor network. Simulation results demonstrate that the proposed protocol prolongs network lifetime due to the use of efficient clustering, cluster head selection and data routing. The results of simulation show that at the end of some certain part of running the EECS and Fuzzy based clustering algorithm increases the number of alive nodes comparing with the LEACH and HEED methods and this can lead to an increase in sensor network lifetime. By using the EECS method the total number of messages received at base station is increased when compared with LEACH and HEED methods. The Fuzzy based clustering method compared with the K-Means Clustering by means of iteration count and time taken to die first node in wireless sensor network, as the result shows that the fuzzy based clustering method perform well than kmeans clustering methods.
An Adaptive Dictionary Learning Approach for Modeling Dynamical Textures
Venue: arXiv cs.CV 2013
Year: 2013

Video representation is an important and challenging task in the computer vision community. In this paper, we assume that image frames of a moving scene can be modeled as a Linear Dynamical System. We propose a sparse coding framework, named adaptive video dictionary learning (AVDL), to model a video adaptively. The developed framework is able to capture the dynamics of a moving scene by exploring both sparse properties and the temporal correlations of consecutive video frames. The proposed method is compared with state of the art video processing methods on several benchmark data sequences, which exhibit appearance changes and heavy occlusions.
Quantum Private Comparison: A Review
Venue: arXiv cs.CR 2013
Year: 2013

As an important branch of quantum secure multiparty computation, quantum private comparison (QPC) has attracted more and more attention recently. In this paper, according to the quantum implementation mechanism that these protocols used, we divide these protocols into three categories: The quantum cryptography QPC, the superdense coding QPC, and the entanglement swapping QPC. And then, a more in-depth analysis on the research progress, design idea, and substantive characteristics of corresponding QPC categories is carried out, respectively. Finally, the applications of QPC and quantum secure multi-party computation issues are discussed and, in addition, three possible research mainstream directions are pointed out.
Further results on strong edge-colourings in outerplanar graphs
Venue: arXiv cs.DM 2013
Year: 2013

An edge-colouring is {\em strong} if every colour class is an induced matching. In this work we give a formulae that determines either the optimal or the optimal plus one strong chromatic index of bipartite outerplanar graphs. Further, we give an improved upper bound for any outerplanar graph which is close to optimal. All our proofs yield efficient algorithms to construct such colourings.