OpenRaiser/Intern-Atlas huggingface dataset:
Tales of Two Cities: Using Social Media to Understand Idiosyncratic Lifestyles in Distinctive Metropolitan Areas
Venue: arXiv cs.SI 2017
Year: 2017

Lifestyles are a valuable model for understanding individuals' physical and mental lives, comparing social groups, and making recommendations for improving people's lives. In this paper, we examine and compare lifestyle behaviors of people living in cities of different sizes, utilizing freely available social media data as a large-scale, low-cost alternative to traditional survey methods. We use the Greater New York City area as a representative for large cities, and the Greater Rochester area as a representative for smaller cities in the United States. We employed matrix factor analysis as an unsupervised method to extract salient mobility and work-rest patterns for a large population of users within each metropolitan area. We discovered interesting human behavior patterns at both a larger scale and a finer granularity than is present in previous literature, some of which allow us to quantitatively compare the behaviors of individuals of living in big cities to those living in small cities. We believe that our social media-based approach to lifestyle analysis represents a powerful tool for social computing in the big data age.
Criteria for Finite Difference Groebner Bases of Normal Binomial Difference Ideals
Venue: arXiv cs.SC 2017
Year: 2017

In this paper, we give decision criteria for normal binomial difference polynomial ideals in the univariate difference polynomial ring F{y} to have finite difference Groebner bases and an algorithm to compute the finite difference Groebner bases if these criteria are satisfied. The novelty of these criteria lies in the fact that complicated properties about difference polynomial ideals are reduced to elementary properties of univariate polynomials in Z[x].
Person Re-Identification via Recurrent Feature Aggregation
Venue: arXiv cs.CV 2017
Year: 2017

We address the person re-identification problem by effectively exploiting a globally discriminative feature representation from a sequence of tracked human regions/patches. This is in contrast to previous person re-id works, which rely on either single frame based person to person patch matching, or graph based sequence to sequence matching. We show that a progressive/sequential fusion framework based on long short term memory (LSTM) network aggregates the frame-wise human region representation at each time stamp and yields a sequence level human feature representation. Since LSTM nodes can remember and propagate previously accumulated good features and forget newly input inferior ones, even with simple hand-crafted features, the proposed recurrent feature aggregation network (RFA-Net) is effective in generating highly discriminative sequence level human representations. Extensive experimental results on two person re-identification benchmarks demonstrate that the proposed method performs favorably against state-of-the-art person re-identification methods.
The Reliability Function for the Additive White Gaussian Noise Channel at Rates above the Capacity
Venue: arXiv cs.IT 2017
Year: 2017

We consider the additive white Gaussian noise channels. We prove that the error probability of decoding tends to one exponentially for rates above the capacity and derive the optimal exponent function. We shall demonstrate that the information spectrum approach is quite useful for investigating this problem.
A Participatory Sensing Framework for Environment Pollution Monitoring and Management
Venue: arXiv cs.CY 2017
Year: 2017

Effective monitoring and management of environment pollution is key to the development of modern metropolitan cities. To sustain and to cope with the exponential growth of the cities with high industrialization, expert decision making is very essential in this process. A good governance system must be supported by an actively participating population. In participatory sensing, individuals and groups engages in the data collection actively and the helps the city governance to make proper decisions. In this paper, we propose a participatory sensing based three-tier framework to fight environment pollution in urban areas of Bangladesh. The framework includes an android application named `My City, My Environment', a server for storage and computation and also a web server for the authority to monitor and maintain environmental issues through expert decision making. We have already developed a prototype system and deployed it to a small scale and demonstrated the effectiveness of this framework.
An Upper Bound of the Minimal Dispersion via Delta Covers
Venue: arXiv cs.CG 2017
Year: 2017

For a point set of $n$ elements in the $d$-dimensional unit cube and a class of test sets we are interested in the largest volume of a test set which does not contain any point. For all natural numbers $n$, $d$ and under the assumption of a $delta$-cover with cardinality $\vert Γ_δ\vert$ we prove that there is a point set, such that the largest volume of such a test set without any point is bounded by $\frac{\log \vert Γ_δ\vert}{n} + δ$. For axis-parallel boxes on the unit cube this leads to a volume of at most $\frac{4d}{n}\log(\frac{9n}{d})$ and on the torus to $\frac{4d}{n}\log (2n)$.
Individual and Social Requirement Aspects of Sustainable eLearning Systems
Venue: arXiv cs.CY 2017
Year: 2017

Internationalization of the higher education has created the so-called borderless university, which provides better opportunities for learning and increases the human and social sustainability. eLearning systems are a special kind of software systems, developed to provide a platform for accessible teaching and learning, including also online access to learning materials and online support for learning and teaching. The aim of our current work is to extract, analyse, and combine the results from multiple studies in order to develop an RE framework for sustainable eLearning systems. We call a system sustainable, if it has a positive effect on and whose direct and indirect negative impacts resulting from its development, deployment, and usage are minimal. Sustainability has various dimensions. We classify sustainability requirements of eLearning system to five dimensions: individual (human), social, technical, environmental, and economic. In this paper, we focus on human and social aspects (i.e., individual needs the relationship of people within society), as the eLearning systems have a very strong impact on human dimension of sustainability, where their impact on environmental dimension is rather small. This also provides us a basis to identify the corresponding requirements for sustainable eLearning systems. These requirements include collaboration, learner-centred features, leadership development and the reuse of the learning materials. As a result, achieving individual and social requirements for eLearning systems would provide higher quality of leaning and teaching, as well as better opportunities for learning and increasing the human and social sustainability.
Performance Limits of Solutions to Network Utility Maximization Problems
Venue: arXiv cs.IT 2017
Year: 2017

We study performance limits of solutions to utility maximization problems (e.g., max-min problems) in wireless networks as a function of the power budget $\bar{p}$ available to transmitters. Special focus is devoted to the utility and the transmit energy efficiency (i.e., utility over transmit power) of the solution. Briefly, we show tight bounds for the general class of network utility optimization problems that can be solved by computing conditional eigenvalues of standard interference mappings. The proposed bounds, which are based on the concept of asymptotic functions, are simple to compute, provide us with good estimates of the performance of networks for any value of $\bar{p}$ in many real-world applications, and enable us to determine points in which networks move from a noise limited regime to an interference limited regime. Furthermore, they also show that the utility and the transmit energy efficiency scales as $Θ(1)$ and $Θ(1/\bar{p})$, respectively, as $\bar{p}\to\infty$.
ENIGMA: Efficient Learning-based Inference Guiding Machine
Venue: arXiv cs.LO 2017
Year: 2017

ENIGMA is a learning-based method for guiding given clause selection in saturation-based theorem provers. Clauses from many proof searches are classified as positive and negative based on their participation in the proofs. An efficient classification model is trained on this data, using fast feature-based characterization of the clauses . The learned model is then tightly linked with the core prover and used as a basis of a new parameterized evaluation heuristic that provides fast ranking of all generated clauses. The approach is evaluated on the E prover and the CASC 2016 AIM benchmark, showing a large increase of E's performance.
Decoding of Interleaved Reed-Solomon Codes Using Improved Power Decoding
Venue: arXiv cs.IT 2017
Year: 2017

We propose a new partial decoding algorithm for $m$-interleaved Reed--Solomon (IRS) codes that can decode, with high probability, a random error of relative weight $1-R^{\frac{m}{m+1}}$ at all code rates $R$, in time polynomial in the code length $n$. For $m>2$, this is an asymptotic improvement over the previous state-of-the-art for all rates, and the first improvement for $R>1/3$ in the last $20$ years. The method combines collaborative decoding of IRS codes with power decoding up to the Johnson radius.
The Augustin Center and The Sphere Packing Bound For Memoryless Channels
Venue: arXiv cs.IT 2017
Year: 2017

For any channel with a convex constraint set and finite Augustin capacity, existence of a unique Augustin center and associated Erven-Harremoes bound are established. Augustin-Legendre capacity, center, and radius are introduced and proved to be equal to the corresponding Renyi-Gallager entities. Sphere packing bounds with polynomial prefactors are derived for codes on two families of channels: (possibly non-stationary) memoryless channels with multiple additive cost constraints and stationary memoryless channels with convex constraints on the empirical distribution of the input codewords.
Block-Diagonal Coding for Distributed Computing With Straggling Servers
Venue: arXiv cs.IT 2017
Year: 2017

We consider the distributed computing problem of multiplying a set of vectors with a matrix. For this scenario, Li et al. recently presented a unified coding framework and showed a fundamental tradeoff between computational delay and communication load. This coding framework is based on maximum distance separable (MDS) codes of code length proportional to the number of rows of the matrix, which can be very large. We propose a block-diagonal coding scheme consisting of partitioning the matrix into submatrices and encoding each submatrix using a shorter MDS code. We show that the assignment of coded matrix rows to servers to minimize the communication load can be formulated as an integer program with a nonlinear cost function, and propose an algorithm to solve it. We further prove that, up to a level of partitioning, the proposed scheme does not incur any loss in terms of computational delay (as defined by Li et al.) and communication load compared to the scheme by Li et al.. We also show numerically that, when the decoding time is also taken into account, the proposed scheme significantly lowers the overall computational delay with respect to the scheme by Li et al.. For heavy partitioning, this is achieved at the expense of a slight increase in communication load.
Perceptually Optimized Image Rendering
Venue: arXiv cs.CV 2017
Year: 2017

We develop a framework for rendering photographic images, taking into account display limitations, so as to optimize perceptual similarity between the rendered image and the original scene. We formulate this as a constrained optimization problem, in which we minimize a measure of perceptual dissimilarity, the Normalized Laplacian Pyramid Distance (NLPD), which mimics the early stage transformations of the human visual system. When rendering images acquired with higher dynamic range than that of the display, we find that the optimized solution boosts the contrast of low-contrast features without introducing significant artifacts, yielding results of comparable visual quality to current state-of-the art methods with no manual intervention or parameter settings. We also examine a variety of other display constraints, including limitations on minimum luminance (black point), mean luminance (as a proxy for energy consumption), and quantized luminance levels (halftoning). Finally, we show that the method may be used to enhance details and contrast of images degraded by optical scattering (e.g. fog).
Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification
Venue: arXiv cs.CV 2017
Year: 2017

In the recent years there have been a number of studies that applied deep learning algorithms to neuroimaging data. Pipelines used in those studies mostly require multiple processing steps for feature extraction, although modern advancements in deep learning for image classification can provide a powerful framework for automatic feature generation and more straightforward analysis. In this paper, we show how similar performance can be achieved skipping these feature extraction steps with the residual and plain 3D convolutional neural network architectures. We demonstrate the performance of the proposed approach for classification of Alzheimer's disease versus mild cognitive impairment and normal controls on the Alzheimer's Disease National Initiative (ADNI) dataset of 3D structural MRI brain scans.
DSSD : Deconvolutional Single Shot Detector
Venue: arXiv cs.CV 2017
Year: 2017

The main contribution of this paper is an approach for introducing additional context into state-of-the-art general object detection. To achieve this we first combine a state-of-the-art classifier (Residual-101[14]) with a fast detection framework (SSD[18]). We then augment SSD+Residual-101 with deconvolution layers to introduce additional large-scale context in object detection and improve accuracy, especially for small objects, calling our resulting system DSSD for deconvolutional single shot detector. While these two contributions are easily described at a high-level, a naive implementation does not succeed. Instead we show that carefully adding additional stages of learned transformations, specifically a module for feed-forward connections in deconvolution and a new output module, enables this new approach and forms a potential way forward for further detection research. Results are shown on both PASCAL VOC and COCO detection. Our DSSD with $513 \times 513$ input achieves 81.5% mAP on VOC2007 test, 80.0% mAP on VOC2012 test, and 33.2% mAP on COCO, outperforming a state-of-the-art method R-FCN[3] on each dataset.
Decentralized Coded Caching in Wireless Networks: Trade-off between Storage and Latency
Venue: arXiv cs.IT 2017
Year: 2017

This paper studies the decentralized coded caching for a Fog Radio Access Network (F-RAN), whereby two edge-nodes (ENs) connected to a cloud server via fronthaul links with limited capacity are serving the requests of $K_r$ users. We consider all ENs and users are equipped with caches. A decentralized content placement is proposed to independently store contents at each network node during the off-peak hours. After that, we design a coded delivery scheme in order to deliver the user demands during the peak-hours under the objective of minimizing the normalized delivery time (NDT), which refers to the worst case delivery latency. An information-theoretic lower bound on the minimum NDT is derived for arbitrary number of ENs and users. We evaluate numerically the performance of the decentralized scheme. Additionally, we prove the approximate optimality of the decentralized scheme for a special case when the caches are only available at the ENs.
A Contextual Bandit Approach for Stream-Based Active Learning
Venue: arXiv cs.LG 2017
Year: 2017

Contextual bandit algorithms -- a class of multi-armed bandit algorithms that exploit the contextual information -- have been shown to be effective in solving sequential decision making problems under uncertainty. A common assumption adopted in the literature is that the realized (ground truth) reward by taking the selected action is observed by the learner at no cost, which, however, is not realistic in many practical scenarios. When observing the ground truth reward is costly, a key challenge for the learner is how to judiciously acquire the ground truth by assessing the benefits and costs in order to balance learning efficiency and learning cost. From the information theoretic perspective, a perhaps even more interesting question is how much efficiency might be lost due to this cost. In this paper, we design a novel contextual bandit-based learning algorithm and endow it with the active learning capability. The key feature of our algorithm is that in addition to sending a query to an annotator for the ground truth, prior information about the ground truth learned by the learner is sent together, thereby reducing the query cost. We prove that by carefully choosing the algorithm parameters, the learning regret of the proposed algorithm achieves the same order as that of conventional contextual bandit algorithms in cost-free scenarios, implying that, surprisingly, cost due to acquiring the ground truth does not increase the learning regret in the long-run. Our analysis shows that prior information about the ground truth plays a critical role in improving the system performance in scenarios where active learning is necessary.
Instantaneous Decentralized Poker
Venue: arXiv cs.CR 2017
Year: 2017

We present efficient protocols for amortized secure multiparty computation with penalties and secure cash distribution, of which poker is a prime example. Our protocols have an initial phase where the parties interact with a cryptocurrency network, that then enables them to interact only among themselves over the course of playing many poker games in which money changes hands. The high efficiency of our protocols is achieved by harnessing the power of stateful contracts. Compared to the limited expressive power of Bitcoin scripts, stateful contracts enable richer forms of interaction between standard secure computation and a cryptocurrency. We formalize the stateful contract model and the security notions that our protocols accomplish, and provide proofs using the simulation paradigm. Moreover, we provide a reference implementation in Ethereum/Solidity for the stateful contracts that our protocols are based on. We also adopt our off-chain cash distribution protocols to the special case of stateful duplex micropayment channels, which are of independent interest. In comparison to Bitcoin based payment channels, our duplex channel implementation is more efficient and has additional features.
Linear-Time Data Dissemination in Dynamic Networks
Venue: arXiv cs.DC 2017
Year: 2017

Broadcasting and convergecasting are pivotal services in distributed systems, in particular, in wireless ad-hoc and sensor networks, which are characterized by time- varying communication graphs. We study the question of whether it is possible to disseminate data available locally at some process to all n processes in sparsely connected synchronous dynamic networks with directed links in linear time. Recently, Charron-Bost, Függer and Nowak proved an upper bound of O(n log n) rounds for the case where every communication graph is an arbitrary directed rooted tree. We present a new formalism, which not only facilitates a concise proof of this result, but also allows us to prove that O(n) data dissemination is possible when the number of leaves of the rooted trees are bounded by a constant. In the special case of rooted chains, only (n-1) rounds are needed. Our approach can also be adapted for undirected networks, where only (n-1)/2 rounds in the case of arbitrary chain graphs are needed.
One evaluation of model-based testing and its automation
Venue: arXiv cs.SE 2017
Year: 2017

Model-based testing relies on behavior models for the generation of model traces: input and expected output---test cases---for an implementation. We use the case study of an automotive network controller to assess different test suites in terms of error detection, model coverage, and implementation coverage. Some of these suites were generated automatically with and without models, purely at random, and with dedicated functional test selection criteria. Other suites were derived manually, with and without the model at hand. Both automatically and manually derived model-based test suites detected significantly more requirements errors than hand-crafted test suites that were directly derived from the requirements. The number of detected programming errors did not depend on the use of models. Automatically generated model-based test suites detected as many errors as hand-crafted model-based suites with the same number of tests. A sixfold increase in the number of model-based tests led to an 11% increase in detected errors.
If you are not paying for it, you are the product: How much do advertisers pay to reach you?
Venue: arXiv cs.GT 2017
Year: 2017

Online advertising is progressively moving towards a programmatic model in which ads are matched to actual interests of individuals collected as they browse the web. Letting the huge debate around privacy aside, a very important question in this area, for which little is known, is: How much do advertisers pay to reach an individual? In this study, we develop a first of its kind methodology for computing exactly that -- the price paid for a web user by the ad ecosystem -- and we do that in real time. Our approach is based on tapping on the Real Time Bidding (RTB) protocol to collect cleartext and encrypted prices for winning bids paid by advertisers in order to place targeted ads. Our main technical contribution is a method for tallying winning bids even when they are encrypted. We achieve this by training a model using as ground truth prices obtained by running our own "probe" ad-campaigns. We design our methodology through a browser extension and a back-end server that provides it with fresh models for encrypted bids. We validate our methodology using a one year long trace of 1600 mobile users and demonstrate that it can estimate a user's advertising worth with more than 82% accuracy.
Stable explicit schemes for simulation of nonlinear moisture transfer in porous materials
Venue: arXiv cs.CE 2017
Year: 2017

Implicit schemes have been extensively used in building physics to compute the solution of moisture diffusion problems in porous materials for improving stability conditions. Nevertheless, these schemes require important sub-iterations when treating non-linear problems. To overcome this disadvantage, this paper explores the use of improved explicit schemes, such as Dufort-Frankel, Crank-Nicolson and hyperbolisation approaches. A first case study has been considered with the hypothesis of linear transfer. The Dufort-Frankel, Crank-Nicolson and hyperbolisation schemes were compared to the classical Euler explicit scheme and to a reference solution. Results have shown that the hyperbolisation scheme has a stability condition higher than the standard Courant-Friedrichs-Lewy (CFL) condition. The error of this schemes depends on the parameter τrepresenting the hyperbolicity magnitude added into the equation. The Dufort-Frankel scheme has the advantages of being unconditionally stable and is preferable for non-linear transfer, which is the second case study. Results have shown the error is proportional to O(Δt). A modified Crank-Nicolson scheme has been proposed in order to avoid sub-iterations to treat the non-linearities at each time step. The main advantages of the Dufort-Frankel scheme are (i) to be twice faster than the Crank-Nicolson approach; (ii) to compute explicitly the solution at each time step; (iii) to be unconditionally stable and (iv) easier to parallelise on high-performance computer systems. Although the approach is unconditionally stable, the choice of the time discretisation $Δt$ remains an important issue to accurately represent the physical phenomena.