OpenRaiser/Intern-Atlas huggingface dataset:
Towards a Realistic Model for Failure Propagation in Interdependent Networks
Venue: arXiv cs.NI 2015
Year: 2015

Modern networks are becoming increasingly interdependent. As a prominent example, the smart grid is an electrical grid controlled through a communications network, which in turn is powered by the electrical grid. Such interdependencies create new vulnerabilities and make these networks more susceptible to failures. In particular, failures can easily spread across these networks due to their interdependencies, possibly causing cascade effects with a devastating impact on their functionalities. In this paper we focus on the interdependence between the power grid and the communications network, and propose a novel realistic model, HINT (Heterogeneous Interdependent NeTworks), to study the evolution of cascading failures. Our model takes into account the heterogeneity of such networks as well as their complex interdependencies. We compare HINT with previously proposed models both on synthetic and real network topologies. Experimental results show that existing models oversimplify the failure evolution and network functionality requirements, resulting in severe underestimations of the cascading failures.
Toward Long Distance, Sub-diffraction Imaging Using Coherent Camera Arrays
Venue: arXiv cs.CV 2015
Year: 2015

In this work, we propose using camera arrays coupled with coherent illumination as an effective method of improving spatial resolution in long distance images by a factor of ten and beyond. Recent advances in ptychography have demonstrated that one can image beyond the diffraction limit of the objective lens in a microscope. We demonstrate a similar imaging system to image beyond the diffraction limit in long range imaging. We emulate a camera array with a single camera attached to an X-Y translation stage. We show that an appropriate phase retrieval based reconstruction algorithm can be used to effectively recover the lost high resolution details from the multiple low resolution acquired images. We analyze the effects of noise, required degree of image overlap, and the effect of increasing synthetic aperture size on the reconstructed image quality. We show that coherent camera arrays have the potential to greatly improve imaging performance. Our simulations show resolution gains of 10x and more are achievable. Furthermore, experimental results from our proof-of-concept systems show resolution gains of 4x-7x for real scenes. Finally, we introduce and analyze in simulation a new strategy to capture macroscopic Fourier Ptychography images in a single snapshot, albeit using a camera array.
Klout Score: Measuring Influence Across Multiple Social Networks
Venue: arXiv cs.SI 2015
Year: 2015

In this work, we present the Klout Score, an influence scoring system that assigns scores to 750 million users across 9 different social networks on a daily basis. We propose a hierarchical framework for generating an influence score for each user, by incorporating information for the user from multiple networks and communities. Over 3600 features that capture signals of influential interactions are aggregated across multiple dimensions for each user. The features are scalably generated by processing over 45 billion interactions from social networks every day, as well as by incorporating factors that indicate real world influence. Supervised models trained from labeled data determine the weights for features, and the final Klout Score is obtained by hierarchically combining communities and networks. We validate the correctness of the score by showing that users with higher scores are able to spread information more effectively in a network. Finally, we use several comparisons to other ranking systems to show that highly influential and recognizable users across different domains have high Klout scores.
Algorithmic Analysis of Qualitative and Quantitative Termination Problems for Affine Probabilistic Programs
Venue: arXiv cs.LO 2015
Year: 2015

In this paper, we consider termination of probabilistic programs with real-valued variables. The questions concerned are: 1. qualitative ones that ask (i) whether the program terminates with probability 1 (almost-sure termination) and (ii) whether the expected termination time is finite (finite termination); 2. quantitative ones that ask (i) to approximate the expected termination time (expectation problem) and (ii) to compute a bound B such that the probability to terminate after B steps decreases exponentially (concentration problem). To solve these questions, we utilize the notion of ranking supermartingales which is a powerful approach for proving termination of probabilistic programs. In detail, we focus on algorithmic synthesis of linear ranking-supermartingales over affine probabilistic programs (APP's) with both angelic and demonic non-determinism. An important subclass of APP's is LRAPP which is defined as the class of all APP's over which a linear ranking-supermartingale exists. Our main contributions are as follows. Firstly, we show that the membership problem of LRAPP (i) can be decided in polynomial time for APP's with at most demonic non-determinism, and (ii) is NP-hard and in PSPACE for APP's with angelic non-determinism; moreover, the NP-hardness result holds already for APP's without probability and demonic non-determinism. Secondly, we show that the concentration problem over LRAPP can be solved in the same complexity as for the membership problem of LRAPP. Finally, we show that the expectation problem over LRAPP can be solved in 2EXPTIME and is PSPACE-hard even for APP's without probability and non-determinism (i.e., deterministic programs). Our experimental results demonstrate the effectiveness of our approach to answer the qualitative and quantitative questions over APP's with at most demonic non-determinism.
Automatic Synthesis of Geometry Problems for an Intelligent Tutoring System
Venue: arXiv cs.AI 2015
Year: 2015

This paper presents an intelligent tutoring system, GeoTutor, for Euclidean Geometry that is automatically able to synthesize proof problems and their respective solutions given a geometric figure together with a set of properties true of it. GeoTutor can provide personalized practice problems that address student deficiencies in the subject matter.
SAID: A Control Protocol for Scalable and Adaptive Information Dissemination in ICN
Venue: arXiv cs.NI 2015
Year: 2015

Information dissemination applications (video, news, social media, etc.) with large number of receivers need to be efficient but also have limited loss tolerance. The new Information-Centric Networks (ICN) paradigm offers an alternative approach for reliably delivering data by naming content and exploiting data available at any intermediate point (e.g., caches). However, receivers are often heterogeneous, with widely varying receive rates. When using existing ICN congestion control mechanisms with in-sequence delivery, a particularly thorny problem of receivers going out-of-sync results in inefficiency and unfairness with heterogeneous receivers. We argue that separating reliability from congestion control leads to more scalable, efficient and fair data dissemination, and propose SAID, a Control Protocol for Scalable and Adaptive Information Dissemination in ICN. To maximize the amount of data transmitted at the first attempt, receivers request any next packet (ANP) of a flow instead of next-in-sequence packet, independent of the provider's transmit rate. This allows providers to transmit at an application-efficient rate, without being limited by the slower receivers. SAID ensures reliable delivery to all receivers eventually, by cooperative repair, while preserving privacy without unduly trusting other receivers.
How good is good enough? Re-evaluating the bar for energy disaggregation
Venue: arXiv cs.LG 2015
Year: 2015

Since the early 1980s, the research community has developed ever more sophisticated algorithms for the problem of energy disaggregation, but despite decades of research, there is still a dearth of applications with demonstrated value. In this work, we explore a question that is highly pertinent to this research community: how good does energy disaggregation need to be in order to infer characteristics of a household? We present novel techniques that use unsupervised energy disaggregation to predict both household occupancy and static properties of the household such as size of the home and number of occupants. Results show that basic disaggregation approaches performs up to 30% better at occupancy estimation than using aggregate power data alone, and are up to 10% better at estimating static household characteristics. These results show that even rudimentary energy disaggregation techniques are sufficient for improved inference of household characteristics. To conclude, we re-evaluate the bar set by the community for energy disaggregation accuracy and try to answer the question "how good is good enough?"
The influence of time and discipline on the magnitude of correlations between citation counts and quality scores
Venue: arXiv cs.DL 2015
Year: 2015

Although various citation-based indicators are commonly used to help research evaluations, there are ongoing controversies about their value. In response, they are often correlated with quality ratings or with other quantitative indicators in order to partly assess their validity. When correlations are calculated for sets of publications from multiple disciplines or years, however, the magnitude of the correlation coefficient may be reduced, masking the strength of the underlying correlation. In response, this article uses simulations to systematically investigate the extent to which mixing years or disciplines reduces correlations. The results show that mixing two sets of articles with different correlation strengths can reduce the correlation for the combined set to substantially below the average of the two. Moreover, even mixing two sets of articles with the same correlation strength but different mean citation counts can substantially reduce the correlation for the combined set. The extent of the reduction in correlation also depends upon whether the articles assessed have been pre-selected for being high quality and whether the relationship between the quality ratings and citation counts is linear or exponential. The results underline the importance of using homogeneous data sets but also help to interpret correlation coefficients when this is impossible.
A Deep Siamese Network for Scene Detection in Broadcast Videos
Venue: arXiv cs.CV 2015
Year: 2015

We present a model that automatically divides broadcast videos into coherent scenes by learning a distance measure between shots. Experiments are performed to demonstrate the effectiveness of our approach by comparing our algorithm against recent proposals for automatic scene segmentation. We also propose an improved performance measure that aims to reduce the gap between numerical evaluation and expected results, and propose and release a new benchmark dataset.
Generating Domain-Specific Transformation Languages for Component & Connector Architecture Descriptions
Venue: arXiv cs.SE 2015
Year: 2015

Component-based software engineering (CBSE) decomposes complex systems into reusable components. Model-driven engineering (MDE) aims to abstract from complexities by lifting abstract models to primary development artifacts. Component and connector architecture description languages (ADLs) combine CBSE and MDE to describe software systems as hierarchies of component models. Using models as development artifacts is accompanied with the need to evolve, maintain and refactor those models, which can be achieved by model transformations. Domain-specific transformation languages (DSTLs) are tailored to a specific modeling language as the modeling language's concrete syntax is used to describe transformations. To automate the development of DSTLs for ADLs, we present a framework to systematically derive such languages from domain-specific C&C language grammars. These DSTLs enable to describe such model transformations concisely in vocabulary of the underlying ADL. These domain-specific transformations are better comprehensible to ADL experts than generic transformations.
Maximum lifetime problem in sensor networks with limited channel capacity
Venue: arXiv cs.NI 2015
Year: 2015

In the paper we analyze the maximum lifetime problem in sensor networks with limited channel capacity for multipoint-to-multipoint and broadcast data transmission services. We show, that in order to achieve an optimal data transmission regarding the maximum lifetime problem we cannot allow for any interference of signals. We propose a new Signal to Interference plus Noise Ratio function and used is to modify the Shannon-Hartley channel capacity formula. For the modified channel capacity formula we solve the maximum lifetime problem in one dimensional regular sensor network $L_N$ for discussed data transmission services.
Computational Optimal Control of the Saint-Venant PDE Model Using the Time-scaling Technique
Venue: arXiv cs.SY 2015
Year: 2015

This paper proposes a new time-scaling approach for computational optimal control of a distributed parameter system governed by the Saint-Venant PDEs. We propose the time-scaling approach, which can change a uniform time partition to a nonuniform one. We also derive the gradient formulas by using the variational method. Then the method of lines (MOL) is applied to compute the Saint-Venant PDEs after implementing the time-scaling transformation and the associate costate PDEs. Finally, we compare the optimization results using the proposed time-scaling approach with the one not using it. The simulation result demonstrates the effectiveness of the proposed time-scaling method.
Simulating the Dynamic Behavior of Shear Thickening Fluids
Venue: arXiv cs.GR 2015
Year: 2015

While significant research has been dedicated to the simulation of fluids, not much attention has been given to exploring new interesting behavior that can be generated with the different types of non-Newtonian fluids with non-constant viscosity. Going in this direction, this paper introduces a computational model for simulating the interesting phenomena observed in non-Newtonian shear thickening fluids, which are fluids where the viscosity increases with increased stress. These fluids have unique and unconventional behavior, and they often appear in real world scenarios such as when sinking in quicksand or when experimenting with popular cornstarch and water mixtures. While interesting behavior of shear thickening fluids can be easily observed in the real world, the most interesting phenomena of these fluids have not been simulated before in computer graphics. The fluid exhibits unique phase changes between solid and liquid states, great impact resistance in its solid state and strong hysteresis effects. Our proposed approach builds on existing non-Newtonian fluid models in computer graphics and introduces an efficient history-based stiffness term that is essential to produce the most interesting shear thickening phenomena. The history-based stiffness is formulated through the use of fractional derivatives, leveraging the fractional calculus ability to depict both the viscoelastic behavior and the history effects of history-dependent systems. Simulations produced by our method are compared against real experiments and the results demonstrate that the proposed model successfully captures key phenomena observed in shear thickening fluids.
Accurate Vision-based Vehicle Localization using Satellite Imagery
Venue: arXiv cs.RO 2015
Year: 2015

We propose a method for accurately localizing ground vehicles with the aid of satellite imagery. Our approach takes a ground image as input, and outputs the location from which it was taken on a georeferenced satellite image. We perform visual localization by estimating the co-occurrence probabilities between the ground and satellite images based on a ground-satellite feature dictionary. The method is able to estimate likelihoods over arbitrary locations without the need for a dense ground image database. We present a ranking-loss based algorithm that learns location-discriminative feature projection matrices that result in further improvements in accuracy. We evaluate our method on the Malaga and KITTI public datasets and demonstrate significant improvements over a baseline that performs exhaustive search.
Sparsity-based Algorithm for Detecting Faults in Rotating Machines
Venue: arXiv cs.SD 2015
Year: 2015

This paper addresses the detection of periodic transients in vibration signals for detecting faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is given for its solution. A simulated signal is formulated to verify the performance of the proposed approach for periodic feature extraction. The detection performance of comparative methods is compared with that of the proposed approach via RMSE values and receiver operating characteristic (ROC) curves. Finally, the proposed approach is applied to compound faults diagnosis of motor bearings. The non-stationary vibration data were acquired from a SpectraQuest's machinery fault simulator. The processed results show the proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect.
A new chaos-based watermarking algorithm
Venue: arXiv cs.MM 2015
Year: 2015

This paper introduces a new watermarking algorithm based on discrete chaotic iterations. After defining some coefficients deduced from the description of the carrier medium, chaotic discrete iterations are used to mix the watermark and to embed it in the carrier medium. It can be proved that this procedure generates topological chaos, which ensures that desired properties of a watermarking algorithm are satisfied.
Computing the minimum distance of nonbinary LDPC codes using block Korkin-Zolotarev method
Venue: arXiv cs.IT 2015
Year: 2015

In article present measure code distance algorithm of binary and ternary linear block code using block Korkin-Zolotarev (BKZ). Proved the upper bound on scaling constant for measure code distance of non-systematic linear block code using BKZ-method for different value of the block size. Introduced method show linear decrease of runtime from number of threads and work especially good under not dense lattices of LDPC-code. These properties allow use this algorithm to measure the minimal distance of code with length several thousand. The algorithm can further improve by transform into probabilistic algorithm using lattice enumerating pruning techniques
A survey on measuring indirect discrimination in machine learning
Venue: arXiv cs.CY 2015
Year: 2015

Nowadays, many decisions are made using predictive models built on historical data.Predictive models may systematically discriminate groups of people even if the computing process is fair and well-intentioned. Discrimination-aware data mining studies how to make predictive models free from discrimination, when historical data, on which they are built, may be biased, incomplete, or even contain past discriminatory decisions. Discrimination refers to disadvantageous treatment of a person based on belonging to a category rather than on individual merit. In this survey we review and organize various discrimination measures that have been used for measuring discrimination in data, as well as in evaluating performance of discrimination-aware predictive models. We also discuss related measures from other disciplines, which have not been used for measuring discrimination, but potentially could be suitable for this purpose. We computationally analyze properties of selected measures. We also review and discuss measuring procedures, and present recommendations for practitioners. The primary target audience is data mining, machine learning, pattern recognition, statistical modeling researchers developing new methods for non-discriminatory predictive modeling. In addition, practitioners and policy makers would use the survey for diagnosing potential discrimination by predictive models.
Robust Temporal Logic Model Predictive Control
Venue: arXiv cs.SY 2015
Year: 2015

Control synthesis from temporal logic specifications has gained popularity in recent years. In this paper, we use a model predictive approach to control discrete time linear systems with additive bounded disturbances subject to constraints given as formulas of signal temporal logic (STL). We introduce a (conservative) computationally efficient framework to synthesize control strategies based on mixed integer programs. The designed controllers satisfy the temporal logic requirements, are robust to all possible realizations of the disturbances, and optimal with respect to a cost function. In case the temporal logic constraint is infeasible, the controller satisfies a relaxed, minimally violating constraint. An illustrative case study is included.
Evaluating Model Checking Approaches to Verify Stability of Control Systems in Simulink
Venue: arXiv cs.SY 2015
Year: 2015

This paper examines the verification of stability, a control requirement, over discrete control systems represented as Simulink diagrams, using different model checking approaches and tools. Model checking comprises the (exhaustive) exploration of a model of a system, to determine if a requirement is satisfied. If that is not the case, examples of the requirement's violation within the system's model are provided, as witnesses. These examples are potentially complementary to previous work on automatic theorem proving, when a system is not proven to be stable, but no proof of instability can be provided. We experimentally evaluated the suitability of four model checking approaches to verify stability on a set of benchmarks including linear and nonlinear, controlled and uncontrolled, discrete systems, via Lyapunov's second method or Lyapunov's direct method. Our study included symbolic, bounded, statistical and hybrid model checking, through the open-source tools NuSMV, UCLID, S-TaLiRo and SpaceEx, respectively. Our experiments and results provide an insight on the strengths and limitations of these model checking approaches for the verification of control requirements for discrete systems at Simulink level. We found that statistical model checking with S-TaLiRo is the most suitable option to complement our previous work on automatic theorem proving.
Saturation Power based Simple Energy Efficiency Maximization Schemes for MU-MISO Systems
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we investigate an energy efficiency (EE) maximization problem in multi-user multiple input single output downlink channels. The optimization problem in this system model is difficult to solve in general, since it is in non-convex fractional form. Hence, conventional algorithms have addressed the problem in an iterative manner for each channel realization, which leads to high computational complexity. To tackle this complexity issue, we propose a new simple method by utilizing the fact that the EE maximization is identical to the spectral efficiency (SE) maximization for the region of the power below a certain transmit power referred to as saturation power. In order to calculate the saturation power, we first introduce upper and lower bounds of the EE performance by adopting a maximal ratio transmission beamforming strategy. Then, we propose an efficient way to compute the saturation power for the EE maximization problem. Once we determine the saturation power corresponding to the maximum EE in advance, we can solve the EE maximization problem with SE maximization schemes with low complexity. The derived saturation power is parameterized by employing random matrix theory, which relies only on the second order channel statistics. Hence, this approach requires much lower computational complexity compared to a conventional scheme which exploits instantaneous channel state information, and provides insight on the saturation power. Numerical results validate that the proposed algorithm achieves near optimal EE performance with significantly reduced complexity.