OpenRaiser/Intern-Atlas huggingface dataset:
Matrix Factorization-Based Clustering Of Image Features For Bandwidth-Constrained Information Retrieval
Venue: arXiv cs.CV 2016
Year: 2016

We consider the problem of accurately and efficiently querying a remote server to retrieve information about images captured by a mobile device. In addition to reduced transmission overhead and computational complexity, the retrieval protocol should be robust to variations in the image acquisition process, such as translation, rotation, scaling, and sensor-related differences. We propose to extract scale-invariant image features and then perform clustering to reduce the number of features needed for image matching. Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) are investigated as candidate clustering approaches. The image matching complexity at the database server is quadratic in the (small) number of clusters, not in the (very large) number of image features. We employ an image-dependent information content metric to approximate the model order, i.e., the number of clusters, needed for accurate matching, which is preferable to setting the model order using trial and error. We show how to combine the hypotheses provided by PCA and NMF factor loadings, thereby obtaining more accurate retrieval than using either approach alone. In experiments on a database of urban images, we obtain a top-1 retrieval accuracy of 89% and a top-3 accuracy of 92.5%.
Belief Merging by Source Reliability Assessment
Venue: arXiv cs.AI 2016
Year: 2016

Merging beliefs requires the plausibility of the sources of the information to be merged. They are typically assumed equally reliable in lack of hints indicating otherwise; yet, a recent line of research spun from the idea of deriving this information from the revision process itself. In particular, the history of previous revisions and previous merging examples provide information for performing subsequent mergings. Yet, no examples or previous revisions may be available. In spite of the apparent lack of information, something can still be inferred by a try-and-check approach: a relative reliability ordering is assumed, the merging process is performed based on it, and the result is compared with the original information. The outcome of this check may be incoherent with the initial assumption, like when a completely reliable source is rejected some of the information it provided. In such cases, the reliability ordering assumed in the first place can be excluded from consideration. The first theorem of this article proves that such a scenario is indeed possible. Other results are obtained under various definition of reliability and merging.
Fast Bilateral Filtering of Vector-Valued Images
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we consider a natural extension of the edge-preserving bilateral filter for vector-valued images. The direct computation of this non-linear filter is slow in practice. We demonstrate how a fast algorithm can be obtained by first approximating the Gaussian kernel of the bilateral filter using raised-cosines, and then using Monte Carlo sampling. We present simulation results on color images to demonstrate the accuracy of the algorithm and the speedup over the direct implementation.
Subgraph Isomorphism in Temporal Networks
Venue: arXiv cs.SI 2016
Year: 2016

Temporal information is increasingly available as part of large network data sets. This information reveals sequences of link activations between network entities, which can expose underlying processes in the data. Examples include the dissemination of information through a social network, the propagation of musical ideas in a music sampling network, and the spread of a disease via contacts between infected and susceptible individuals. The search for these more meaningful patterns may be formulated as a time-respecting subgraph isomorphism problem. Our set of query graphs include an enumeration of small random graphs and fan-out-fan-in structures, all composed of time-respecting paths. We explore three methods of solving the problem, which differ in how they exploit temporal and topological information. One approach extracts all subgraphs that have the temporal properties we require and then performs subgraph isomorphism testing on each subgraph. Another approach performs subgraph isomorphism testing first with temporal post-filtering, while the other is a hybrid approach that uses temporal information during the search. We empirically demonstrate the hybrid approach to be more efficient than the others, over a range of network data sets. These data come from communication and social networks, up to interactions in size.
Asymptotics of Input-Constrained Erasure Channel Capacity
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we examine an input-constrained erasure channel and we characterize the asymptotics of its capacity when the erasure rate is low. More specifically, for a general memoryless erasure channel with its input supported on an irreducible finite-type constraint, we derive partial asymptotics of its capacity, using some series expansion type formulas of its mutual information rate; and for a binary erasure channel with its first-order Markovian input supported on the $(1, \infty)$-RLL constraint, based on the concavity of its mutual information rate with respect to some parameterization of the input, we numerically evaluate its first-order Markov capacity and further derive its full asymptotics. The asymptotics obtained in this paper, when compared with the recently derived feedback capacity for a binary erasure channel with the same input constraint, enable us to draw the conclusion that feedback may increase the capacity of an input-constrained channel, even if the channel is memoryless.
Stateless Model Checking for POWER
Venue: arXiv cs.LO 2016
Year: 2016

We present the first framework for efficient application of stateless model checking (SMC) to programs running under the relaxed memory model of POWER. The framework combines several contributions. The first contribution is that we develop a scheme for systematically deriving operational execution models from existing axiomatic ones. The scheme is such that the derived execution models are well suited for efficient SMC. We apply our scheme to an axiomatic model of POWER. Our main contribution is a technique for efficient SMC, called Relaxed Stateless Model Checking (RSMC), which systematically explores the possible inequivalent executions of a program. RSMC is suitable for execution models obtained using our scheme. We prove that RSMC is sound and optimal for the POWER memory model, in the sense that each complete program behavior is explored exactly once. We show the feasibility of our technique by providing an implementation for programs written in C/pthreads.
Opportunistic Spectrum Sharing in Dynamic Access Networks: Deployment Challenges, Optimizations, Solutions, and Open Issues
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we investigate the issue of spectrum assignment in CRNs and examine various opportunistic spectrum access approaches proposed in the literature. We provide insight into the efficiency of such approaches and their ability to attain their design objectives. We discuss the factors that impact the selection of the appropriate operating channel(s), including the important interaction between the cognitive linkquality conditions and the time-varying nature of PRNs. Protocols that consider such interaction are described. We argue that using best quality channels does not achieve the maximum possible throughput in CRNs (does not provide the best spectrum utilization). The impact of guard bands on the design of opportunistic spectrum access protocols is also investigated. Various complementary techniques and optimization methods are underlined and discussed, including the utilization of variablewidth spectrum assignment, resource virtualization, full-duplex capability, cross-layer design, beamforming and MIMO technology, cooperative communication, network coding, discontinuousOFDM technology, and software defined radios. Finally, we highlight several directions for future research in this field.
On Image segmentation using Fractional Gradients-Learning Model Parameters using Approximate Marginal Inference
Venue: arXiv cs.CV 2016
Year: 2016

Estimates of image gradients play a ubiquitous role in image segmentation and classification problems since gradients directly relate to the boundaries or the edges of a scene. This paper proposes an unified approach to gradient estimation based on fractional calculus that is computationally cheap and readily applicable to any existing algorithm that relies on image gradients. We show experiments on edge detection and image segmentation on the Stanford Backgrounds Dataset where these improved local gradients outperforms state of the art, achieving a performance of 79.2% average accuracy.
Real-time collision detection method for deformable bodies
Venue: arXiv cs.CG 2016
Year: 2016

This paper presents a real-time solution for collision detection between objects based on the physics properties. Traditional approaches on collision detection often rely on the geometric relationships that computing the intersections between polygons. Such technique is very computationally expensive when applied for deformable objects. As an alternative, we approximate the 3D mesh in an spherical surface implicitly. This allows us to perform a coarse-level collision detection at extremely fast speed. Then a dynamic programming based procedure is applied to identify the collision in fine details. Our method demonstrates better prevention to collision tunnelling and works more efficiently than the state-of-the-arts.
Desiree: a Refinement Calculus for Requirements Problems
Venue: arXiv cs.SE 2016
Year: 2016

The requirements elicited from stakeholders are typically informal, incomplete, ambiguous, and inconsistent. It is the task of Requirements Engineering to transform them into an eligible (formal, sufficiently complete, unambiguous, consistent, modifiable and traceable) requirements specification of functions and qualities that the system-to-be needs to operationalize. To address this requirements problem, we have proposed Desiree, a requirements calculus for systematically transforming stakeholder requirements into an eligible specification. In this paper, we define the semantics of the concepts used to model requirements, and that of the operators used to refine and operationalize requirements. We present a graphical modeling tool that supports the entire framework, including the nine concepts, eight operators and the transformation methodology. We use a Meeting Scheduler example to illustrate the kinds of reasoning tasks that we can perform based on the given semantics.
Predicting Performance on MOOC Assessments using Multi-Regression Models
Venue: arXiv cs.CY 2016
Year: 2016

The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a stu- dent may achieve on a given grade-related assessment based on information, considered as prior performance or prior ac- tivity in the course. We develop a personalized linear mul- tiple regression (PLMR) model to predict the grade for a student, prior to attempting the assessment activity. The developed model is real-time and tracks the participation of a student within a MOOC (via click-stream server logs) and predicts the performance of a student on the next as- sessment within the course offering. We perform a com- prehensive set of experiments on data obtained from three openEdX MOOCs via a Stanford University initiative. Our experimental results show the promise of the proposed ap- proach in comparison to baseline approaches and also helps in identification of key features that are associated with the study habits and learning behaviors of students.
Synchronous Hybrid Message-Adversary
Venue: arXiv cs.DC 2016
Year: 2016

The theory of distributed computing, lagging in its development behind practice, has been biased in its modelling by employing mechanisms within the model mimicking reality. Reality means, processors can fail. But theory is about predicting consequences of reality, hence if we capture reality by "artificial models," but those nevertheless make analysis simpler, we should pursue the artificial models. Recently the idea was advocated to analyze distributed systems and view processors as infallible. It is the message delivery substrate that causes problems. This view not only can effectively emulate reality, but above all seems to allow to view any past models as \emph{synchronous} models. Synchronous models are easier to analyze than asynchronous ones. Furthermore, it gives rise to models we haven't contemplated in the past. One such model, presented here, is the Hybrid Message-Adversary. We motivate this model through the need to analyze Byzantine faults. The Hybrid model exhibits a phenomenon not seen in the past.
Bayesian Overlapping Community Detection in Dynamic Networks
Venue: arXiv cs.SI 2016
Year: 2016

Detecting community structures in social networks has gained considerable attention in recent years. However, lack of prior knowledge about the number of communities, and their overlapping nature have made community detection a challenging problem. Moreover, many of the existing methods only consider static networks, while most of real world networks are dynamic and evolve over time. Hence, finding consistent overlapping communities in dynamic networks without any prior knowledge about the number of communities is still an interesting open research problem. In this paper, we present an overlapping community detection method for dynamic networks called Dynamic Bayesian Overlapping Community Detector (DBOCD). DBOCD assumes that in every snapshot of network, overlapping parts of communities are dense areas and utilizes link communities instead of common node communities. Using Recurrent Chinese Restaurant Process and community structure of the network in the last snapshot, DBOCD simultaneously extracts the number of communities and soft community memberships of nodes while maintaining the consistency of communities over time. We evaluated DBOCD on both synthetic and real dynamic data-sets to assess its ability to find overlapping communities in different types of network evolution. The results show that DBOCD outperforms the recent state of the art dynamic community detection methods.
New Constructions of SD and MR Codes over Small Finite Fields
Venue: arXiv cs.IT 2016
Year: 2016

Data storage applications require erasure-correcting codes with prescribed sets of dependencies between data symbols and redundant symbols. The most common arrangement is to have $k$ data symbols and $h$ redundant symbols (that each depends on all data symbols) be partitioned into a number of disjoint groups, where for each group one allocates an additional (local) redundant symbol storing the parity of all symbols in the group. A code as above is maximally recoverable, if it corrects all erasure patterns that are information theoretically correctable given the dependency constraints. A slightly weaker guarantee is provided by SD codes. One key consideration in the design of MR and SD codes is the size of the finite field underlying the code as using small finite fields facilitates encoding and decoding operations. In this paper we present new explicit constructions of SD and MR codes over small finite fields.
On Secrecy Rates and Outage in Multi-User Multi-Eavesdroppers MISO Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we study the secrecy rate and outage probability in Multiple-Input-Single-Output (MISO) Gaussian wiretap channels at the limit of a large number of legitimate users and eavesdroppers. In particular, we analyze the asymptotic achievable secrecy rates and outage, when only statistical knowledge on the wiretap channels is available to the transmitter. The analysis provides exact expressions for the reduction in the secrecy rate as the number of eavesdroppers grows, compared to the boost in the secrecy rate as the number of legitimate users grows.
Approximate Gaussian Elimination for Laplacians: Fast, Sparse, and Simple
Venue: arXiv cs.DS 2016
Year: 2016

We show how to perform sparse approximate Gaussian elimination for Laplacian matrices. We present a simple, nearly linear time algorithm that approximates a Laplacian by a matrix with a sparse Cholesky factorization, the version of Gaussian elimination for symmetric matrices. This is the first nearly linear time solver for Laplacian systems that is based purely on random sampling, and does not use any graph theoretic constructions such as low-stretch trees, sparsifiers, or expanders. The crux of our analysis is a novel concentration bound for matrix martingales where the differences are sums of conditionally independent variables.
Unconditionally secure credit/debit card chip scheme and physical unclonable function
Venue: arXiv cs.ET 2016
Year: 2016

The statistical-physics-based Kirchhoff-law-Johnson-noise (KLJN) key exchange offers a new and simple unclonable system for credit/debit card chip authentication and payment. The key exchange, the authentication and the communication are unconditionally secure so that neither mathematics- nor statistics-based attacks are able to crack the scheme. The ohmic connection and the short wiring lengths between the chips in the card and the terminal constitute an ideal setting for the KLJN protocol, and even its simplest versions offer unprecedented security and privacy for credit/debit card chips and applications of physical unclonable functions.
The elephant in the room: The problem of quantifying productivity in evaluative scientometrics
Venue: arXiv cs.DL 2016
Year: 2016

In a critical and provocative paper, Abramo and D'Angelo claim that commonly used scientometric indicators such as the mean normalized citation score (MNCS) are completely inappropriate as indicators of scientific performance. Abramo and D'Angelo argue that scientific performance should be quantified using indicators that take into account the productivity of a research unit. We provide a response to Abramo and D'Angelo, indicating where we believe they raise important issues, but also pointing out where we believe their claims to be too extreme.
Energy Efficiency in Wireless Sensor Networks
Venue: arXiv cs.NI 2016
Year: 2016

Unlike most of the current research that focuses on a single aspect of WSNs, we present an Energy Driven Architecture (EDA) as a new architecture for minimising the total energy consumption of WSNs. EDA as a constituent-based architecture is used to deploy WSNs according to energy dissipation through their constituents. This view of overall energy consumption in WSNs can be applied to optimising and balancing energy consumption and increasing the network lifetime. Refer back to the architecture, we introduce a single overall model and propose a feasible formulation to express the overall energy consumption of a generic wireless sensor network application in terms of its energy constituents. The formulation offers a concrete expression for evaluating the performance of WSN application, optimising its constituents operations, and designing more energy-efficient applications. The ultimate aim is to produce an energy map architecture of a generic WSN application that comprises essential and definable energy constituents and the relationships between these constituents to explore strategies for minimising the overall energy consumption of the application. Later, parameters affecting energy in WSNs are extracted. The dependency between these parameters and the average energy consumption of an application is then investigated. A few statistical tools are applied for parameter reduction followed by random forest regression to model energy consumption per delivered packet with and without parameter reduction to determine the reduction in accuracy due to reduction. Finally, an energy-efficient dynamic topology management algorithm is proposed based on the EDA model and the prevalent parameters. The performance of the new topology management algorithm, which employs Dijkstra to find energy-efficient lowest cost paths among nodes, is compared to similar topology management algorithms.
Randomized Kaczmarz for Rank Aggregation from Pairwise Comparisons
Venue: arXiv cs.LG 2016
Year: 2016

We revisit the problem of inferring the overall ranking among entities in the framework of Bradley-Terry-Luce (BTL) model, based on available empirical data on pairwise preferences. By a simple transformation, we can cast the problem as that of solving a noisy linear system, for which a ready algorithm is available in the form of the randomized Kaczmarz method. This scheme is provably convergent, has excellent empirical performance, and is amenable to on-line, distributed and asynchronous variants. Convergence, convergence rate, and error analysis of the proposed algorithm are presented and several numerical experiments are conducted whose results validate our theoretical findings.
Efficiency Evaluation of Character-level RNN Training Schedules
Venue: arXiv cs.NE 2016
Year: 2016

We present four training and prediction schedules from the same character-level recurrent neural network. The efficiency of these schedules is tested in terms of model effectiveness as a function of training time and amount of training data seen. We show that the choice of training and prediction schedule potentially has a considerable impact on the prediction effectiveness for a given training budget.
Process Information Model for Sheet Metal Operations
Venue: arXiv cs.OH 2016
Year: 2016

The paper extracts the process parameters from a sheet metal part model (B-Rep). These process parameters can be used in sheet metal manufacturing to control the manufacturing operations. By extracting these process parameters required for manufacturing, CAM program can be generated automatically using the part model and resource information. A Product model is generated in modeling software and converted into STEP file which is used for extracting B-Rep which interned is used to classify and extract feature by using sheet metal feature recognition module. The feature edges are classified as CEEs, IEEs, CIEs and IIEs based on topological properties. Database is created for material properties of the sheet metal and machine tools required to manufacture features in a part model. The extracted feature, feature's edge information and resource information are then used to compute process parameters and values required to control manufacturing operations. The extracted feature, feature's edge information, resource information and process parameters are the integral components of the proposed process information model for sheet metal operations.