OpenRaiser/Intern-Atlas huggingface dataset:
Color-Phase Analysis for Sinusoidal Structured Light in Rapid Range Imaging
Venue: arXiv cs.CV 2015
Year: 2015

Active range sensing using structured-light is the most accurate and reliable method for obtaining 3D information. However, most of the work has been limited to range sensing of static objects, and range sensing of dynamic (moving or deforming) objects has been investigated recently only by a few researchers. Sinusoidal structured-light is one of the well-known optical methods for 3D measurement. In this paper, we present a novel method for rapid high-resolution range imaging using color sinusoidal pattern. We consider the real-world problem of nonlinearity and color-band crosstalk in the color light projector and color camera, and present methods for accurate recovery of color-phase. For high-resolution ranging, we use high-frequency patterns and describe new unwrapping algorithms for reliable range recovery. The experimental results demonstrate the effectiveness of our methods.
Exploiting Spatial Correlation in Energy Constrained Distributed Detection
Venue: arXiv cs.IT 2015
Year: 2015

We consider the detection of a correlated random process immersed in noise in a wireless sensor network. Each node has an individual energy constraint and the communication with the processing central units are affected by the path loss propagation effect. Guided by energy efficiency concerns, we consider the partition of the whole network into clusters, each one with a coordination node or \emph{cluster head}. Thus, the nodes transmit their measurements to the corresponding cluster heads, which after some processing, communicate a summary of the received information to the fusion center, which takes the final decision about the state of the nature. As the network has a fixed size, communication within smaller clusters will be less affected by the path loss effect, reducing energy consumption in the information exchange process between nodes and cluster heads. However, this limits the capability of the network of beneficially exploiting the spatial correlation of the process, specially when the spatial correlation coherence of the process is of the same scale as the clusters size. Therefore, a trade-off is established between the energy efficiency and the beneficial use of spatial correlation. The study of this trade-off is the main goal of this paper. We derive tight approximations of the false alarm and miss-detection error probabilities under the Neyman-Pearson framework for the above scenario. We also consider the application of these results to a particular network and correlation model obtaining closed form expressions. Finally, we validate the results for more general network and correlation models through numerical simulations.
A Practioner's Guide to Evaluating Entity Resolution Results
Venue: arXiv cs.DB 2015
Year: 2015

Entity resolution (ER) is the task of identifying records belonging to the same entity (e.g. individual, group) across one or multiple databases. Ironically, it has multiple names: deduplication and record linkage, among others. In this paper we survey metrics used to evaluate ER results in order to iteratively improve performance and guarantee sufficient quality prior to deployment. Some of these metrics are borrowed from multi-class classification and clustering domains, though some key differences exist differentiating entity resolution from general clustering. Menestrina et al. empirically showed rankings from these metrics often conflict with each other, thus our primary motivation for studying them. This paper provides practitioners the basic knowledge to begin evaluating their entity resolution results.
The adaptive patched cubature filter and its implementation
Venue: arXiv cs.IT 2015
Year: 2015

There are numerous contexts where one wishes to describe the state of a randomly evolving system. Effective solutions combine models that quantify the underlying uncertainty with available observational data to form scientifically reasonable estimates for the uncertainty in the system state. Stochastic differential equations are often used to mathematically model the underlying system. The Kusuoka-Lyons-Victoir (KLV) approach is a higher order particle method for approximating the weak solution of a stochastic differential equation that uses a weighted set of scenarios to approximate the evolving probability distribution to a high order of accuracy. The algorithm can be performed by integrating along a number of carefully selected bounded variation paths. The iterated application of the KLV method has a tendency for the number of particles to increase. This can be addressed and, together with local dynamic recombination, which simplifies the support of discrete measure without harming the accuracy of the approximation, the KLV method becomes eligible to solve the filtering problem in contexts where one desires to maintain an accurate description of the ever-evolving conditioned measure. In addition to the alternate application of the KLV method and recombination, we make use of the smooth nature of the likelihood function and high order accuracy of the approximations to lead some of the particles immediately to the next observation time and to build into the algorithm a form of automatic high order adaptive importance sampling.
Dual-Layer Video Encryption using RSA Algorithm
Venue: arXiv cs.CR 2015
Year: 2015

This paper proposes a video encryption algorithm using RSA and Pseudo Noise (PN) sequence, aimed at applications requiring sensitive video information transfers. The system is primarily designed to work with files encoded using the Audio Video Interleaved (AVI) codec, although it can be easily ported for use with Moving Picture Experts Group (MPEG) encoded files. The audio and video components of the source separately undergo two layers of encryption to ensure a reasonable level of security. Encryption of the video component involves applying the RSA algorithm followed by the PN-based encryption. Similarly, the audio component is first encrypted using PN and further subjected to encryption using the Discrete Cosine Transform. Combining these techniques, an efficient system, invulnerable to security breaches and attacks with favorable values of parameters such as encryption/decryption speed, encryption/decryption ratio and visual degradation; has been put forth. For applications requiring encryption of sensitive data wherein stringent security requirements are of prime concern, the system is found to yield negligible similarities in visual perception between the original and the encrypted video sequence. For applications wherein visual similarity is not of major concern, we limit the encryption task to a single level of encryption which is accomplished by using RSA, thereby quickening the encryption process. Although some similarity between the original and encrypted video is observed in this case, it is not enough to comprehend the happenings in the video.
Dependency length minimization: Puzzles and Promises
Venue: arXiv cs.CL 2015
Year: 2015

In the recent issue of PNAS, Futrell et al. claims that their study of 37 languages gives the first large scale cross-language evidence for Dependency Length Minimization, which is an overstatement that ignores similar previous researches. In addition,this study seems to pay no attention to factors like the uniformity of genres,which weakens the validity of the argument that DLM is universal. Another problem is that this study sets the baseline random language as projective, which fails to truly uncover the difference between natural language and random language, since projectivity is an important feature of many natural languages. Finally, the paper contends an "apparent relationship between head finality and dependency length" despite the lack of an explicit statistical comparison, which renders this conclusion rather hasty and improper.
Efficient Kernel Fusion Techniques for Massive Video Data Analysis on GPGPUs
Venue: arXiv cs.DC 2015
Year: 2015

Kernels are executable code segments and kernel fusion is a technique for combing the segments in a coherent manner to improve execution time. For the first time, we have developed a technique to fuse image processing kernels to be executed on GPGPUs for improving execution time and total throughput (amount of data processed in unit time). We have applied our techniques for feature tracking on video images captured by a high speed digital video camera where the number of frames captured varies between 600-1000 frames per second. Image processing kernels are composed of multiple simple kernels, which executes on the input image in a given sequence. A set of kernels that can be fused together forms a partition (or fused kernel). Given a set of Kernels and the data dependencies between them, it is difficult to determine the partitions of kernels such that the total performance is maximized (execution time and throughput). We have developed and implemented an optimization model to find such a partition. We also developed an algorithm to fuse multiple kernels based on their data dependencies. Additionally, to further improve performance on GPGPU systems, we have provided methods to distribute data and threads to processors. Our model was able to reduce data traffic, which resulted better performance.The performance (both execution time and throughput) of the proposed method for kernel fusing and its subsequent execution is shown to be 2 to 3 times higher than executing kernels in sequence. We have demonstrated our technique for facial feature tracking with applications to Neuroscience.
Splitting Compounds by Semantic Analogy
Venue: arXiv cs.CL 2015
Year: 2015

Compounding is a highly productive word-formation process in some languages that is often problematic for natural language processing applications. In this paper, we investigate whether distributional semantics in the form of word embeddings can enable a deeper, i.e., more knowledge-rich, processing of compounds than the standard string-based methods. We present an unsupervised approach that exploits regularities in the semantic vector space (based on analogies such as "bookshop is to shop as bookshelf is to shelf") to produce compound analyses of high quality. A subsequent compound splitting algorithm based on these analyses is highly effective, particularly for ambiguous compounds. German to English machine translation experiments show that this semantic analogy-based compound splitter leads to better translations than a commonly used frequency-based method.
Perpetual Codes for Network Coding
Venue: arXiv cs.NI 2015
Year: 2015

Random Linear Network Coding (RLNC) provides a theoretically efficient method for coding. Some of its practical drawbacks are the complexity of decoding and the overhead due to the coding vectors. For computationally weak and battery-driven platforms, these challenges are particular important. In this work, we consider the coding variant Perpetual codes which are sparse, non-uniform and the coding vectors have a compact representation. The sparsity allows for fast encoding and decoding, and the non-uniform protection of symbols enables recoding where the produced symbols are indistinguishable from those encoded at the source. The presented results show that the approach can provide a coding overhead arbitrarily close to that of RLNC, but at reduced computational load. The achieved gain over RLNC grows with the generation size, and both encoding and decoding throughput is approximately one order of magnitude higher compared to RLNC at a generation size of 2048. Additionally, the approach allows for easy adjustment between coding throughput and code overhead, which makes it suitable for a broad range of platforms and applications.
Integration of Heterogeneous Modeling Languages via Extensible and Composable Language Components
Venue: arXiv cs.SE 2015
Year: 2015

Effective model-driven engineering of complex systems requires to appropriately describe different specific system aspects. To this end, efficient integration of different heterogeneous modeling languages is essential. Modeling language integaration is onerous and requires in-depth conceptual and technical knowledge and ef- fort. Traditional modeling lanugage integration approches require language engineers to compose monolithic language aggregates for a specific task or project. Adapting these aggregates cannot be to different contexts requires vast effort and makes these hardly reusable. This contribution presents a method for the engineering of grammar-based language components that can be independently developed, are syntactically composable, and ultimately reusable. To this end, it introduces the concepts of language aggregation, language embed- ding, and language inheritance, as well as their realization in the language workbench MontiCore. The result is a generalizable, systematic, and efficient syntax-oriented composition of languages that allows the agile employment of modeling languages efficiently tailored for individual software projects.
Improvements in Google Scholar Citations are for the summer: creating an institutional affiliation link feature
Venue: arXiv cs.DL 2015
Year: 2015

This report describes the feature introduced by Google to provide standardized access to institutional affiliations within Google Scholar Citations. First, this new tool is described, pointing out its main characteristics and functioning. Next, the coverage and precision of the tool are evaluated. Two special cases (Google Inc. and Spanish Universities) are briefly treated with the purpose of illustrating some aspects about the accuracy of the tool for the task of gathering authors within their appropriate institution. Finally, some inconsistencies, errors and malfunctioning are identified, categorized and described. The report finishes by providing some suggestions to improve the feature. The general conclusion is that the standardized institutional affiliation link provided by Google Scholar Citations, despite working pretty well for a large number of institutions (especially Anglo-Saxon universities) still has a number of shortcomings and pitfalls which need to be addressed in order to make this authority control tool fully useful worldwide, both for searching purposes and for metric tasks
Ranking Entities in the Age of Two Webs, an Application to Semantic Snippets
Venue: arXiv cs.IR 2015
Year: 2015

The advances of the Linked Open Data (LOD) initiative are giving rise to a more structured Web of data. Indeed, a few datasets act as hubs (e.g., DBpedia) connecting many other datasets. They also made possible new Web services for entity detection inside plain text (e.g., DBpedia Spotlight), thus allowing for new applications that can benefit from a combination of the Web of documents and the Web of data. To ease the emergence of these new applications, we propose a query-biased algorithm (LDRANK) for the ranking of web of data resources with associated textual data. Our algorithm combines link analysis with dimensionality reduction. We use crowdsourcing for building a publicly available and reusable dataset for the evaluation of query-biased ranking of Web of data resources detected in Web pages. We show that, on this dataset, LDRANK outperforms the state of the art. Finally, we use this algorithm for the construction of semantic snippets of which we evaluate the usefulness with a crowdsourcing-based approach.
On the Secrecy Capacity of a MIMO Gaussian Wiretap Channel with a Cooperative Jammer
Venue: arXiv cs.IT 2015
Year: 2015

We study the secrecy capacity of a helper-assisted Gaussian wiretap channel with a source, a legitimate receiver, an eavesdropper and an external helper, where each terminal is equipped with multiple antennas. Determining the secrecy capacity in this scenario generally requires solving a nonconvex secrecy rate maximization (SRM) problem. To deal with this issue, we first reformulate the original SRM problem into a sequence of convex subproblems. For the special case of single-antenna legitimate receiver, we obtain the secrecy capacity via a combination of convex optimization and one-dimensional search, while for the general case of multi-antenna legitimate receiver, we propose an iterative solution. To gain more insight into how the secrecy capacity of a helper-assisted Gaussian wiretap channel behaves, we examine the achievable secure degrees of freedom (s.d.o.f.) and obtain the maximal achievable s.d.o.f. in closed-form. We also derive a closed-form solution to the original SRM problem which achieves the maximal s.d.o.f.. Numerical results are presented to illustrate the efficacy of the proposed schemes.
Forecasting Method for Grouped Time Series with the Use of k-Means Algorithm
Venue: arXiv cs.LG 2015
Year: 2015

The paper is focused on the forecasting method for time series groups with the use of algorithms for cluster analysis. $K$-means algorithm is suggested to be a basic one for clustering. The coordinates of the centers of clusters have been put in correspondence with summarizing time series data the centroids of the clusters. A description of time series, the centroids of the clusters, is implemented with the use of forecasting models. They are based on strict binary trees and a modified clonal selection algorithm. With the help of such forecasting models, the possibility of forming analytic dependences is shown. It is suggested to use a common forecasting model, which is constructed for time series the centroid of the cluster, in forecasting the private (individual) time series in the cluster. The promising application of the suggested method for grouped time series forecasting is demonstrated.
Integrating Research Data Management into Geographical Information Systems
Venue: arXiv cs.DL 2015
Year: 2015

Ocean modelling requires the production of high-fidelity computational meshes upon which to solve the equations of motion. The production of such meshes by hand is often infeasible, considering the complexity of the bathymetry and coastlines. The use of Geographical Information Systems (GIS) is therefore a key component to discretising the region of interest and producing a mesh appropriate to resolve the dynamics. However, all data associated with the production of a mesh must be provided in order to contribute to the overall recomputability of the subsequent simulation. This work presents the integration of research data management in QMesh, a tool for generating meshes using GIS. The tool uses the PyRDM library to provide a quick and easy way for scientists to publish meshes, and all data required to regenerate them, to persistent online repositories. These repositories are assigned unique identifiers to enable proper citation of the meshes in journal articles.
Validation of daylighting model in CODYRUN building simulation code
Venue: arXiv cs.OH 2015
Year: 2015

CODYRUN is a multi-zone software integrating thermal building simulation, airflow, and pollutant transfer. A first question thus arose as to the integration of indoor lighting conditions into the simulation, leading to a new model calculating natural and artificial lighting. The results of this new daylighting module were then compared with results of other simulation codes and experimental cases both in artificial and natural environments. Excellent agreements were obtained, such as the values for luminous efficiencies in a tropical and humid climate. In this paper, a comparison of the model output with detailed measures is presented using a dedicated test cell in Reunion Island (French overseas territory in the Indian Ocean), thus confirming the interest for thermal and daylighting designs in low-energy buildings. Introduction Several software packages are available for thermal and airflow simulation in buildings. The most frequently used are ENERGY+ [1], ESP-r [2], and TRNSYS [3]. These applications allow an increasing number of models to be integrated, such as airflow, pollutant transport, and daylighting. In the latter category, we may note ENERGY+, ESP-r and ECOTECT [4] software. After more than 20 years of developing a specific code named CODYRUN, we decided to add a lighting module to our software. This paper therefore provides some details on this evolution and elements of validation. The CODYRUN initial software and its validation Developed by the Physics and Mathematical Engineering Laboratory for Energy and Environment at the University of Reunion Island, CODYRUN [5-14] is a multi-zone software program integrating ventilation and moisture transport transfer in buildings. The software employs a zone approach based on nodal analysis and resolves a coupled system describing thermal and airflow phenomena. Numerous validation tests of the CODYRUN code were successfully applied to the software. Apart from the daylighting model, the majority applied the BESTEST procedure [15]. The International Energy Agency (IEA) sponsors a number of programs to improve the use and associated technologies of energy. The National Renewable Energy Laboratory (NREL) developed BESTEST, which is a method based on comparative testing of building simulation programs, on the IEA's behalf. The procedure consists of a series of test cases buildings that are designed to isolate individual aspects of building energy and test the extremes of a program. As the modelling approach is very different between codes, the test cases are specified so that input equivalency can be defined thus allowing the different cases to be modelled by most of codes. The basis for comparison is a range of results from a number of programs considered to be a state-of-art in United States and Europe. Associated with other specific comparisons, a very confident level of validation was obtained for the CODYRUN initial software [8].
A Framework for Fine Robotic Assembly
Venue: arXiv cs.RO 2015
Year: 2015

Fine robotic assembly, in which the parts to be assembled are small and fragile and lie in an unstructured environment, is still out of reach of today's industrial robots. The main difficulties arise in the precise localization of the parts in an unstructured environment and the control of contact interactions. Our contribution in this paper is twofold. First, we propose a taxonomy of the manipulation primitives that are specifically involved in fine assembly. Such a taxonomy is crucial for designing a scalable robotic system (both hardware and software) given the complexity of real-world assembly tasks. Second, we present a hardware and software architecture where we have addressed, in an integrated way, a number of issues arising in fine assembly, such as workspace optimization, external wrench compensation, position-based force control, etc. Finally, we show the above taxonomy and architecture in action on a highly dexterous task -- bimanual pin insertion -- which is one of the key steps in our long term project, the autonomous assembly of an IKEA chair.
DenseBox: Unifying Landmark Localization with End to End Object Detection
Venue: arXiv cs.CV 2015
Year: 2015

How can a single fully convolutional neural network (FCN) perform on object detection? We introduce DenseBox, a unified end-to-end FCN framework that directly predicts bounding boxes and object class confidences through all locations and scales of an image. Our contribution is two-fold. First, we show that a single FCN, if designed and optimized carefully, can detect multiple different objects extremely accurately and efficiently. Second, we show that when incorporating with landmark localization during multi-task learning, DenseBox further improves object detection accuray. We present experimental results on public benchmark datasets including MALF face detection and KITTI car detection, that indicate our DenseBox is the state-of-the-art system for detecting challenging objects such as faces and cars.