OpenRaiser/Intern-Atlas huggingface dataset:
PPLS: A Privacy-Preserving Location-Sharing Scheme in Vehicular Social Networks
Venue: arXiv cs.CR 2018
Year: 2018

Recent advances in Socially Aware Networks (SANs) have allowed its use in many domains, out of which social Internet of vehicles (SIOV) is of prime importance. SANs can provide a promising routing and forwarding paradigm for SIOV by using interest-based communication. Though able to improve the forwarding performance, existing interest-based schemes fail to consider the important issue of protecting users' interest information. In this paper, we propose a PRivacy-preserving Interest-based Forwarding scheme (PRIF) for SIOV, which not only protects the interest information, but also improves the forwarding performance. We propose a privacy-preserving authentication protocol to recognize communities among mobile nodes. During data routing and forwarding, a node can know others' interests only if they are affiliated with the same community. Moreover, to improve forwarding performance, a new metric {\em community energy} is introduced to indicate vehicular social proximity. Community energy is generated when two nodes encounter one another and information is shared among them. PRIF considers this energy metric to select forwarders towards the destination node or the destination community. Security analysis indicates PRIF can protect nodes' interest information. In addition, extensive simulations have been conducted to demonstrate that PRIF outperforms the existing algorithms including the BEEINFO, Epidemic, and PRoPHET.
Symbolic Reasoning for Automatic Signal Placement (Extended Version)
Venue: arXiv cs.PL 2018
Year: 2018

Explicit signaling between threads is a perennial cause of bugs in concurrent programs. While there are several run-time techniques to automatically notify threads upon the availability of some shared resource, such techniques are not widely-adopted due to their run-time overhead. This paper proposes a new solution based on static analysis for automatically generating a performant explicit-signal program from its corresponding implicit-signal implementation. The key idea is to generate verification conditions that allow us to minimize the number of required signals and unnecessary context switches, while guaranteeing semantic equivalence between the source and target programs. We have implemented our method in a tool called Expresso and evaluate it on challenging benchmarks from prior papers and open-source software. Expresso-generated code significantly outperforms past automatic signaling mechanisms (avg. 1.56x speedup) and closely matches the performance of hand-optimized explicit-signal code.
$\varepsilon$-Coresets for Clustering (with Outliers) in Doubling Metrics
Venue: arXiv cs.DS 2018
Year: 2018

We study the problem of constructing $\varepsilon$-coresets for the $(k, z)$-clustering problem in a doubling metric $M(X, d)$. An $\varepsilon$-coreset is a weighted subset $S\subseteq X$ with weight function $w : S \rightarrow \mathbb{R}_{\geq 0}$, such that for any $k$-subset $C \in [X]^k$, it holds that $\sum_{x \in S}{w(x) \cdot d^z(x, C)} \in (1 \pm \varepsilon) \cdot \sum_{x \in X}{d^z(x, C)}$. We present an efficient algorithm that constructs an $\varepsilon$-coreset for the $(k, z)$-clustering problem in $M(X, d)$, where the size of the coreset only depends on the parameters $k, z, \varepsilon$ and the doubling dimension $\mathsf{ddim}(M)$. To the best of our knowledge, this is the first efficient $\varepsilon$-coreset construction of size independent of $|X|$ for general clustering problems in doubling metrics. To this end, we establish the first relation between the doubling dimension of $M(X, d)$ and the shattering dimension (or VC-dimension) of the range space induced by the distance $d$. Such a relation was not known before, since one can easily construct instances in which neither one can be bounded by (some function of) the other. Surprisingly, we show that if we allow a small $(1\pmε)$-distortion of the distance function $d$, and consider the notion of $τ$-error probabilistic shattering dimension, we can prove an upper bound of $O( \mathsf{ddim}(M)\cdot \log(1/\varepsilon) +\log\log{\frac{1}τ} )$ for the probabilistic shattering dimension for even weighted doubling metrics. We believe this new relation is of independent interest and may find other applications. We also study the robust coresets and centroid sets in doubling metrics. Our robust coreset construction leads to new results in clustering and property testing, and the centroid sets can be used to accelerate the local search algorithms for clustering problems.
Penetrating a Social Network: The Follow-back Problem
Venue: arXiv cs.SI 2018
Year: 2018

Modern threats have emerged from the prevalence of social networks. Hostile actors, such as extremist groups or foreign governments, utilize these networks to run propaganda campaigns with different aims. For extremists, these campaigns are designed for recruiting new members or inciting violence. For foreign governments, the aim may be to create instability in rival nations. Proper social network counter-measures are needed to combat these threats. Here we present one important counter-measure: penetrating social networks. This means making target users connect with or follow agents deployed in the social network. Once such connections are established with the targets, the agents can influence them by sharing content which counters the influence campaign. In this work we study how to penetrate a social network, which we call the follow-back problem. The goal here is to find a policy that maximizes the number of targets that follow the agent. We conduct an empirical study to understand what behavioral and network features affect the probability of a target following an agent. We find that the degree of the target and the size of the mutual neighborhood of the agent and target in the network affect this probability. Based on our empirical findings, we then propose a model for targets following an agent. Using this model, we solve the follow-back problem exactly on directed acyclic graphs and derive a closed form expression for the expected number of follows an agent receives under the optimal policy. We then formulate the follow-back problem on an arbitrary graph as an integer program. To evaluate our integer program based policies, we conduct simulations on real social network topologies in Twitter. We find that our polices result in more effective network penetration, with significant increases in the expected number of targets that follow the agent.
Module-less Synthesis on Cyberphysical Digital Microfluidic Biochip Ensuring Error Detection and Routing Performance Optimization
Venue: arXiv cs.ET 2018
Year: 2018

Digital Microfluidic Biochips consist of Two Dimensional microarrays that are integrated with different healthcare related cyberphysical systems and expected to be used extensively in near future. Thus, faster and error-free synthesis techniques need to be implemented on such chips. Various Bio protocols are performed based on different mixing modules present on the chip until now. In this work, the concept of such dedicated virtual modules has been eliminated and a novel Module Less Synthesis method is proposed for accomplishing bioassays for cyberphysical DMFBs. However, path congestion problem and operational errors are inevitable in MLS approach.
Meta Distribution of the SIR in Large-Scale Uplink and Downlink NOMA Networks
Venue: arXiv cs.IT 2018
Year: 2018

We develop an analytical framework to derive the meta distribution and moments of the conditional success probability (CSP), which is defined as {success probability for a given realization of the transmitters}, in large-scale co-channel uplink and downlink non-orthogonal multiple access (NOMA) networks with one NOMA cluster per cell. The moments of CSP translate to various network performance metrics such as the standard success or signal-to-interference ratio (SIR) coverage probability (which is the $1$-st moment), the mean local delay (which is the $-1$-st moment in a static network setting), and the meta distribution (which is the complementary cumulative distribution function of the conditional success probability and can be approximated by using the $1$-st and $2$-nd moments). For uplink NOMA, to make the framework tractable, we propose two point process models for the spatial locations of the interferers by utilizing the base station (BS)/user pair correlation function. We validate the proposed models by comparing the second moment measure of each model with that of the actual point process for the inter-cluster (or inter-cell) interferers obtained via simulations. For downlink NOMA, we derive closed-form solutions for the moments of the CSP, success (or coverage) probability, average local delay, and meta distribution for the users. As an application of the developed analytical framework, we use the closed-form expressions to optimize the power allocations for downlink NOMA users in order to maximize the success probability of a given NOMA user with and without latency constraints. Closed-form optimal solutions for the transmit powers are obtained for two-user NOMA scenario. We note that maximizing the success probability with latency constraints can significantly impact the optimal power solutions for low SIR thresholds and favour orthogonal multiple access (OMA).
A Novel Geometry-based Stochastic Double Directional Analytical Model for Millimeter Wave Outdoor NLOS Channels
Venue: arXiv cs.IT 2018
Year: 2018

Millimeter wave (mmWave) communications which essentially employ directional antennas find applications spanning from indoor short range wireless personal area networks to outdoor cellular networks. A thorough understanding of mmWave signal propagation through the wireless channel provides valuable insights for the design of such networks which in turn dictates the achievable performance limits. High path loss, penetration loss, and negligible signal scattering are certain distinctive features of the mmWave channel which necessitates the development of new mathematical models. Importantly, the impact of directional antennas which spatially filter multi-path components needs to be embodied as an integral part of the channel model. In this paper, we model outdoor directional non-line-of-sight mmWave channels using a combination of stochastic geometry and image theory by expressing channel parameters as joint functions of transmitter-receiver separation distance, antenna half power beamwidth, and antenna beam pointing direction. By approximating the signal propagation path due to first and second order reflections from buildings, closed form analytical expressions for average number of first order reflection components, path loss, and root-mean square delay spread of channel are derived. The accuracy of the model is verified by comparing numerically obtained results with experimental data reported by various urban outdoor peer-to-peer mmWave measurements, thus demonstrating the usefulness of the proposed analytical model for performance evaluation of mmWave communication networks.
Information Sources and Needs in the Obesity and Diabetes Twitter Discourse
Venue: arXiv cs.SI 2018
Year: 2018

Obesity and diabetes epidemics are affecting about a third and tenth of US population, respectively, capturing the attention of the nation and its institutions. Social media provides an open forum for communication between individuals and health organizations, a forum which is easily joined by parties seeking to gain profit from it. In this paper we examine 1.5 million tweets mentioning obesity and diabetes in order to assess (1) the quality of information circulating in this conversation, as well as (2) the behavior and information needs of the users engaged in it. The analysis of top cited domains shows a strong presence of health information sources which are not affiliated with a governmental or academic institution at 41% in obesity and 50% diabetes samples, and that tweets containing these domains are retweeted more than those containing domains of reputable sources. On the user side, we estimate over a quarter of non-informational obesity discourse to contain fat-shaming -- a practice of humiliating and criticizing overweight individuals -- with some self-directed toward the writers themselves. We also find a great diversity in questions asked in these datasets, spanning definition of obesity as a disease, social norms, and governmental policies. Our results indicate a need for addressing the quality control of health information on social media, as well as a need to engage in a topically diverse, psychologically charged discourse around these diseases.
Distribution-Aware Binarization of Neural Networks for Sketch Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Deep neural networks are highly effective at a range of computational tasks. However, they tend to be computationally expensive, especially in vision-related problems, and also have large memory requirements. One of the most effective methods to achieve significant improvements in computational/spatial efficiency is to binarize the weights and activations in a network. However, naive binarization results in accuracy drops when applied to networks for most tasks. In this work, we present a highly generalized, distribution-aware approach to binarizing deep networks that allows us to retain the advantages of a binarized network, while reducing accuracy drops. We also develop efficient implementations for our proposed approach across different architectures. We present a theoretical analysis of the technique to show the effective representational power of the resulting layers, and explore the forms of data they model best. Experiments on popular datasets show that our technique offers better accuracies than naive binarization, while retaining the same benefits that binarization provides - with respect to run-time compression, reduction of computational costs, and power consumption.
Definite Sums as Solutions of Linear Recurrences With Polynomial Coefficients
Venue: arXiv cs.SC 2018
Year: 2018

We present an algorithm which, given a linear recurrence operator $L$ with polynomial coefficients, $m \in \mathbb{N}\setminus\{0\}$, $a_1,a_2,\ldots,a_m \in \mathbb{N}\setminus\{0\}$ and $b_1,b_2,\ldots,b_m \in \mathbb{K}$, returns a linear recurrence operator $L'$ with rational coefficients such that for every sequence $h$, \[ L\left(\sum_{k=0}^\infty \prod_{i=1}^m \binom{a_i n + b_i}{k} h_k\right) = 0 \] if and only if $L' h = 0$.
Second-Guessing in Tracing Tasks Considered Harmful?
Venue: arXiv cs.SE 2018
Year: 2018

[Context and motivation] Trace matrices are lynch pins for the development of mission- and safety-critical software systems and are useful for all software systems, yet automated methods for recovering trace links are far from perfect. This limitation makes the job of human analysts who must vet recovered trace links more difficult. [Question/Problem] Earlier studies suggested that certain analyst behaviors when performing trace recovery tasks lead to decreased accuracy of recovered trace relationships. We propose a three-step experimental study to: (a) determine if there really are behaviors that lead to errors of judgment for analysts, (b) enhance the requirements tracing software to curtail such behaviors, and (c) determine if curtailing such behaviors results in increased accuracy. [Principal ideas/results] We report on a preliminary study we undertook in which we modified the user interface of RETRO.NET to curtail two behaviors indicated by the earlier work. We report on observed results. [Contributions] We describe and discuss a major study of potentially unwanted analyst behaviors and present results of a preliminary study toward determining if curbing these behaviors with enhancements to tracing software leads to fewer human errors.
Automated Discovery of Internet Censorship by Web Crawling
Venue: arXiv cs.CY 2018
Year: 2018

Censorship of the Internet is widespread around the world. As access to the web becomes increasingly ubiquitous, filtering of this resource becomes more pervasive. Transparency about specific content that citizens are denied access to is atypical. To counter this, numerous techniques for maintaining URL filter lists have been proposed by various individuals and organisations that aim to empirical data on censorship for benefit of the public and wider censorship research community. We present a new approach for discovering filtered domains in different countries. This method is fully automated and requires no human interaction. The system uses web crawling techniques to traverse between filtered sites and implements a robust method for determining if a domain is filtered. We demonstrate the effectiveness of the approach by running experiments to search for filtered content in four different censorship regimes. Our results show that we perform better than the current state of the art and have built domain filter lists an order of magnitude larger than the most widely available public lists as of Jan 2018. Further, we build a dataset mapping the interlinking nature of blocked content between domains and exhibit the tightly networked nature of censored web resources.
Computational identification of the lowest space-wise dependent coefficient of a parabolic equation
Venue: arXiv cs.NA 2018
Year: 2018

In the present work, we consider a nonlinear inverse problem of identifying the lowest coefficient of a parabolic equation. The desired coefficient depends on spatial variables only. Additional information about the solution is given at the final time moment, i.e., we consider the final redefinition. An iterative process is used to evaluate the lowest coefficient, where at each iteration we solve the standard initial-boundary value problem for the parabolic equation. On the basis of the maximum principle for the solution of the differential problem, the monotonicity of the iterative process is established along with the fact that the coefficient approaches from above. The possibilities of the proposed computational algorithm are illustrated by numerical examples for a model two-dimensional problem.
Simulation-Based Reachability Analysis for High-Index Large Linear Differential Algebraic Equations
Venue: arXiv cs.SC 2018
Year: 2018

Reachability analysis is a fundamental problem for safety verification and falsification of Cyber-Physical Systems (CPS) whose dynamics follow physical laws usually represented as differential equations. In the last two decades, numerous reachability analysis methods and tools have been proposed for a common class of dynamics in CPS known as ordinary differential equations (ODE). However, there is lack of methods dealing with differential algebraic equations (DAE) which is a more general class of dynamics that is widely used to describe a variety of problems from engineering and science such as multibody mechanics, electrical cicuit design, incompressible fluids, molecular dynamics and chemcial process control. Reachability analysis for DAE systems is more complex than ODE systems, especially for high-index DAEs because they contain both a differential part (i.e., ODE) and algebraic constraints (AC). In this paper, we extend the recent scalable simulation-based reachability analysis in combination with decoupling techniques for a class of high-index large linear DAEs. In particular, a high-index linear DAE is first decoupled into one ODE and one or several AC subsystems based on the well-known Marz decoupling method ultilizing admissible projectors. Then, the discrete reachable set of the DAE, represented as a list of star-sets, is computed using simulation. Unlike ODE reachability analysis where the initial condition is freely defined by a user, in DAE cases, the consistency of the inititial condition is an essential requirement to guarantee a feasible solution. Therefore, a thorough check for the consistency is invoked before computing the discrete reachable set. Our approach sucessfully verifies (or falsifies) a wide range of practical, high-index linear DAE systems in which the number of state variables varies from several to thousands.
ModelFactory: A Matlab/Octave based toolbox to create human body models
Venue: arXiv cs.RO 2018
Year: 2018

Background: Model-based analysis of movements can help better understand human motor control. Here, the models represent the human body as an articulated multi-body system that reflects the characteristics of the human being studied. Results: We present an open-source toolbox that allows for the creation of human models with easy-to-setup, customizable configurations. The toolbox scripts are written in Matlab/Octave and provide a command-based interface as well as a graphical interface to construct, visualize and export models. Built-in software modules provide functionalities such as automatic scaling of models based on subject height and weight, custom scaling of segment lengths, mass and inertia, addition of body landmarks, and addition of motion capture markers. Users can set up custom definitions of joints, segments and other body properties using the many included examples as templates. In addition to the human, any number of objects (e.g. exoskeletons, orthoses, prostheses, boxes) can be added to the modeling environment. Conclusions: The ModelFactory toolbox is published as open-source software under the permissive zLib license. The toolbox fulfills an important function by making it easier to create human models, and should be of interest to human movement researchers. This document is the author's version of this article.
Scrum2Kanban: Integrating Kanban and Scrum in a University Software Engineering Capstone Course
Venue: arXiv cs.SE 2018
Year: 2018

Using university capstone courses to teach agile software development methodologies has become commonplace, as agile methods have gained support in professional software development. This usually means students are introduced to and work with the currently most popular agile methodology: Scrum. However, as the agile methods employed in the industry change and are adapted to different contexts, university courses must follow suit. A prime example of this is the Kanban method, which has recently gathered attention in the industry. In this paper, we describe a capstone course design, which adds the hands-on learning of the lean principles advocated by Kanban into a capstone project run with Scrum. This both ensures that students are aware of recent process frameworks and ideas as well as gain a more thorough overview of how agile methods can be employed in practice. We describe the details of the course and analyze the participating students' perceptions as well as our observations. We analyze the development artifacts, created by students during the course in respect to the two different development methodologies. We further present a summary of the lessons learned as well as recommendations for future similar courses. The survey conducted at the end of the course revealed an overwhelmingly positive attitude of students towards the integration of Kanban into the course.