OpenRaiser/Intern-Atlas huggingface dataset:
Outsmarting Network Security with SDN Teleportation
Venue: arXiv cs.NI 2018
Year: 2018

Software-defined networking is considered a promising new paradigm, enabling more reliable and formally verifiable communication networks. However, this paper shows that the separation of the control plane from the data plane, which lies at the heart of Software-Defined Networks (SDNs), introduces a new vulnerability which we call \emph{teleportation}. An attacker (e.g., a malicious switch in the data plane or a host connected to the network) can use teleportation to transmit information via the control plane and bypass critical network functions in the data plane (e.g., a firewall), and to violate security policies as well as logical and even physical separations. This paper characterizes the design space for teleportation attacks theoretically, and then identifies four different teleportation techniques. We demonstrate and discuss how these techniques can be exploited for different attacks (e.g., exfiltrating confidential data at high rates), and also initiate the discussion of possible countermeasures. Generally, and given today's trend toward more intent-based networking, we believe that our findings are relevant beyond the use cases considered in this paper.
Learning the Number of Neurons in Deep Networks
Venue: arXiv cs.CV 2018
Year: 2018

Nowadays, the number of layers and of neurons in each layer of a deep network are typically set manually. While very deep and wide networks have proven effective in general, they come at a high memory and computation cost, thus making them impractical for constrained platforms. These networks, however, are known to have many redundant parameters, and could thus, in principle, be replaced by more compact architectures. In this paper, we introduce an approach to automatically determining the number of neurons in each layer of a deep network during learning. To this end, we propose to make use of structured sparsity during learning. More precisely, we use a group sparsity regularizer on the parameters of the network, where each group is defined to act on a single neuron. Starting from an overcomplete network, we show that our approach can reduce the number of parameters by up to 80\% while retaining or even improving the network accuracy.
Fair Division via Social Comparison
Venue: arXiv cs.DS 2018
Year: 2018

In the classical cake cutting problem, a resource must be divided among agents with different utilities so that each agent believes they have received a fair share of the resource relative to the other agents. We introduce a variant of the problem in which we model an underlying social network on the agents with a graph, and agents only evaluate their shares relative to their neighbors' in the network. This formulation captures many situations in which it is unrealistic to assume a global view, and also exposes interesting phenomena in the original problem. Specifically, we say an allocation is locally envy-free if no agent envies a neighbor's allocation and locally proportional if each agent values her own allocation as much as the average value of her neighbor's allocations, with the former implying the latter. While global envy-freeness implies local envy-freeness, global proportionality does not imply local proportionality, or vice versa. A general result is that for any two distinct graphs on the same set of nodes and an allocation, there exists a set of valuation functions such that the allocation is locally proportional on one but not the other. We fully characterize the set of graphs for which an oblivious single-cutter protocol-- a protocol that uses a single agent to cut the cake into pieces --admits a bounded protocol with $O(n^2)$ query complexity for locally envy-free allocations in the Robertson-Webb model. We also consider the price of envy-freeness, which compares the total utility of an optimal allocation to the best utility of an allocation that is envy-free. We show that a lower bound of $Ω(\sqrt{n})$ on the price of envy-freeness for global allocations in fact holds for local envy-freeness in any connected undirected graph. Thus, sparse graphs surprisingly do not provide more flexibility with respect to the quality of envy-free allocations.
Degrees of Freedom of the Bursty MIMO X Channel without Feedback
Venue: arXiv cs.IT 2018
Year: 2018

We study the sum degrees of freedom (DoF) of the bursty MIMO X channel without feedback, where the four transmitter-receiver links are intermittently on-and-off, controlled by four Bernoulli random sequences which may be arbitrarily correlated, subject to a symmetry assumption: The two direct-links have the same level of burstiness, modeled by $\mathrm{Ber}(p_d)$, and so do the cross-links, modeled by $\mathrm{Ber}(p_c)$. The sum DoF is fully characterized in the regime where $\frac{p_c}{p_d}$ is small, i.e. below a certain threshold, and is partially characterized in the other regime where $\frac{p_c}{p_d}$ is above the threshold. The achievability is proved with a combination of Han-Kobayashi strategy and interference alignment, which can achieve strictly higher DoF than interference alignment alone. The converse proof employs a channel-state-sequence pairing technique. We highlight that burstiness of the channel disrupts the network topology, turning the MIMO X channel into a network with time-varying topology. This fundamental difference has striking ramifications. In particular, various interference alignment schemes that achieve the DoF of non-bursty X channels are found to be suboptimal when the channels become bursty. The reciprocity between the forward and the reverse links is lost, and the sum DoF does not saturate when the ratio of the transmitter and the receiver antennas exceeds $\frac{2}{3}$.
Interpolation-Based GR(1) Assumptions Refinement
Venue: arXiv cs.LO 2018
Year: 2018

This paper considers the problem of assumptions refinement in the context of unrealizable specifications for reactive systems. We propose a new counterstrategy-guided synthesis approach for GR(1) specifications based on Craig's interpolants. Our interpolation-based method identifies causes for unrealizability and computes assumptions that directly target unrealizable cores, without the need for user input. Thereby, we discuss how this property reduces the maximum number of steps needed to converge to realizability compared with other techniques. We describe properties of interpolants that yield helpful GR(1) assumptions and prove the soundness of the results. Finally, we demonstrate that our approach yields weaker assumptions than baseline techniques, and finds solutions in case studies that are unsolvable via existing techniques.
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Venue: arXiv cs.CL 2018
Year: 2018

We introduce a large scale MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset comprises of 1,010,916 anonymized questions---sampled from Bing's search query logs---each with a human generated answer and 182,669 completely human rewritten generated answers. In addition, the dataset contains 8,841,823 passages---extracted from 3,563,535 web documents retrieved by Bing---that provide the information necessary for curating the natural language answers. A question in the MS MARCO dataset may have multiple answers or no answers at all. Using this dataset, we propose three different tasks with varying levels of difficulty: (i) predict if a question is answerable given a set of context passages, and extract and synthesize the answer as a human would (ii) generate a well-formed answer (if possible) based on the context passages that can be understood with the question and passage context, and finally (iii) rank a set of retrieved passages given a question. The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering. We believe that the scale and the real-world nature of this dataset makes it attractive for benchmarking machine reading comprehension and question-answering models.
Optimal three-weight cubic codes
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we construct an infinite family of three-weight binary codes from linear codes over the ring $R=\mathbb{F}_2+v\mathbb{F}_2+v^2\mathbb{F}_2$, where $v^3=1.$ These codes are defined as trace codes. They have the algebraic structure of abelian codes. Their Lee weight distributions are computed by employing character sums. The three-weight binary linear codes which we construct are shown to be optimal when $m$ is odd and $m>1$. They are cubic, that is to say quasi-cyclic of co-index three. An application to secret sharing schemes is given.
An impossibility theorem for paired comparisons
Venue: arXiv cs.GT 2018
Year: 2018

In several decision-making problems, alternatives should be ranked on the basis of paired comparisons between them. We present an axiomatic approach for the universal ranking problem with arbitrary preference intensities, incomplete and multiple comparisons. In particular, two basic properties -- independence of irrelevant matches and self-consistency -- are considered. It is revealed that there exists no ranking method satisfying both requirements at the same time. The impossibility result holds under various restrictions on the set of ranking problems, however, it does not emerge in the case of round-robin tournaments. An interesting and more general possibility result is obtained by restricting the domain of independence of irrelevant matches through the concept of macrovertex.
New Ideas for Brain Modelling 3
Venue: arXiv cs.NE 2018
Year: 2018

This paper considers a process for the creation and subsequent firing of sequences of neuronal patterns, as might be found in the human brain. The scale is one of larger patterns emerging from an ensemble mass, possibly through some type of energy equation and a reduction procedure. The links between the patterns can be formed naturally, as a residual effect of the pattern creation itself. This paper follows-on closely from the earlier research, including two earlier papers in the series and uses the ideas of entropy and cohesion. With a small addition, it is possible to show how the inter-pattern links can be determined. A compact Grid form of an earlier Counting Mechanism is also demonstrated and may be a new clustering technique. It is possible to explain how a very basic repeating structure can form the arbitrary patterns and activation sequences between them, and a key question of how nodes synchronise may even be answerable.
Toward a Tractable Delay Analysis in Ultra-dense Networks
Venue: arXiv cs.IT 2018
Year: 2018

Meeting the diverse delay requirements of emerging wireless applications is one of the most critical goals for the design of ultradense networks. Though the delay of point-to-point communications has been well investigated using classical queueing theory, the delay of multi-point to multi-point communications, such as in ultradense networks, has not been explored in depth. The main technical difficulty lies in the interacting queues problem, in which the service rate is coupled with the statuses of other queues. In this article, we elaborate on the main challenges in the delay analysis in ultradense networks. Several promising approaches, such as introducing the dominant system and the simplified system, to bypass these difficulties are proposed and summarized to provide useful guidance.
Randomized Incremental Construction for the Hausdorff Voronoi Diagram of point clusters
Venue: arXiv cs.CG 2018
Year: 2018

This paper applies the randomized incremental construction (RIC) framework to computing the Hausdorff Voronoi diagram of a family of k clusters of points in the plane. The total number of points is n. The diagram is a generalization of Voronoi diagrams based on the Hausdorff distance function. The combinatorial complexity of the Hausdorff Voronoi diagram is O(n+m), where m is the total number of crossings between pairs of clusters. For non-crossing clusters (m=0), our algorithm works in expected O(n log n + k log n log k) time and deterministic O(n) space. For arbitrary clusters (m=O(n^2)), the algorithm runs in expected O((m+n log k) log n) time and O(m +n log k) space. When clusters cross, bisectors are disconnected curves resulting in disconnected Voronoi regions that challenge the incremental construction. This paper applies the RIC paradigm to a Voronoi diagram with disconnected regions and disconnected bisectors, for the first time.
Capacity Regions of Two-Receiver Broadcast Erasure Channels with Feedback and Memory
Venue: arXiv cs.IT 2018
Year: 2018

The two-receiver broadcast packet erasure channel with feedback and memory is studied. Memory is modeled using a finite-state Markov chain representing a channel state. Two scenarios are considered: (i) when the transmitter has causal knowledge of the channel state (i.e., the state is visible), and (ii) when the channel state is unknown at the transmitter, but observations of it are available at the transmitter through feedback (i.e., the state is hidden). In both scenarios, matching outer and inner bounds on the rates of communication are derived and the capacity region is determined. It is shown that similar results carry over to channels with memory and delayed feedback and memoryless compound channels with feedback. When the state is visible, the capacity region has a single-letter characterization and is in terms of a linear program. Two optimal coding schemes are devised that use feedback to keep track of the sent/received packets via a network of queues: a probabilistic scheme and a deterministic backpressure-like algorithm. The former bases its decisions solely on the past channel state information and the latter follows a max-weight queue-based policy. The performance of the algorithms are analyzed using the frameworks of rate stability in networks of queues, max-flow min-cut duality in networks, and finite-horizon Lyapunov drift analysis. When the state is hidden, the capacity region does not have a single-letter characterization and is, in this sense, uncomputable. Approximations of the capacity region are provided and two optimal coding algorithms are outlined. The first algorithm is a probabilistic coding scheme that bases its decisions on the past L acknowledgments and its achievable rate region approaches the capacity region exponentially fast in L. The second algorithm is a backpressure-like algorithm that performs optimally in the long run.
Transition-based versus State-based Reward Functions for MDPs with Value-at-Risk
Venue: arXiv cs.AI 2018
Year: 2018

In reinforcement learning, the reward function on current state and action is widely used. When the objective is about the expectation of the (discounted) total reward only, it works perfectly. However, if the objective involves the total reward distribution, the result will be wrong. This paper studies Value-at-Risk (VaR) problems in short- and long-horizon Markov decision processes (MDPs) with two reward functions, which share the same expectations. Firstly we show that with VaR objective, when the real reward function is transition-based (with respect to action and both current and next states), the simplified (state-based, with respect to action and current state only) reward function will change the VaR. Secondly, for long-horizon MDPs, we estimate the VaR function with the aid of spectral theory and the central limit theorem. Thirdly, since the estimation method is for a Markov reward process with the reward function on current state only, we present a transformation algorithm for the Markov reward process with the reward function on current and next states, in order to estimate the VaR function with an intact total reward distribution.
Covering many points with a small-area box
Venue: arXiv cs.CG 2018
Year: 2018

Let $P$ be a set of $n$ points in the plane. We show how to find, for a given integer $k>0$, the smallest-area axis-parallel rectangle that covers $k$ points of $P$ in $O(nk^2 \log n+ n\log^2 n)$ time. We also consider the problem of, given a value $α>0$, covering as many points of $P$ as possible with an axis-parallel rectangle of area at most $α$. For this problem we give a probabilistic $(1-\varepsilon)$-approximation that works in near-linear time: In $O((n/\varepsilon^4)\log^3 n \log (1/\varepsilon))$ time we find an axis-parallel rectangle of area at most $α$ that, with high probability, covers at least $(1-\varepsilon)\mathrm{κ^*}$ points, where $\mathrm{κ^*}$ is the maximum possible number of points that could be covered.
Consensus Based Medical Image Segmentation Using Semi-Supervised Learning And Graph Cuts
Venue: arXiv cs.CV 2018
Year: 2018

Medical image segmentation requires consensus ground truth segmentations to be derived from multiple expert annotations. A novel approach is proposed that obtains consensus segmentations from experts using graph cuts (GC) and semi supervised learning (SSL). Popular approaches use iterative Expectation Maximization (EM) to estimate the final annotation and quantify annotator's performance. Such techniques pose the risk of getting trapped in local minima. We propose a self consistency (SC) score to quantify annotator consistency using low level image features. SSL is used to predict missing annotations by considering global features and local image consistency. The SC score also serves as the penalty cost in a second order Markov random field (MRF) cost function optimized using graph cuts to derive the final consensus label. Graph cut obtains a global maximum without an iterative procedure. Experimental results on synthetic images, real data of Crohn's disease patients and retinal images show our final segmentation to be accurate and more consistent than competing methods.
Asynchronous approach in the plane: A deterministic polynomial algorithm
Venue: arXiv cs.DS 2018
Year: 2018

In this paper we study the task of approach of two mobile agents having the same limited range of vision and moving asynchronously in the plane. This task consists in getting them in finite time within each other's range of vision. The agents execute the same deterministic algorithm and are assumed to have a compass showing the cardinal directions as well as a unit measure. On the other hand, they do not share any global coordinates system (like GPS), cannot communicate and have distinct labels. Each agent knows its label but does not know the label of the other agent or the initial position of the other agent relative to its own. The route of an agent is a sequence of segments that are subsequently traversed in order to achieve approach. For each agent, the computation of its route depends only on its algorithm and its label. An adversary chooses the initial positions of both agents in the plane and controls the way each of them moves along every segment of the routes, in particular by arbitrarily varying the speeds of the agents. A deterministic approach algorithm is a deterministic algorithm that always allows two agents with any distinct labels to solve the task of approach regardless of the choices and the behavior of the adversary. The cost of a complete execution of an approach algorithm is the length of both parts of route travelled by the agents until approach is completed. Let $Δ$ and $l$ be the initial distance separating the agents and the length of the shortest label, respectively. Assuming that $Δ$ and $l$ are unknown to both agents, does there exist a deterministic approach algorithm always working at a cost that is polynomial in $Δ$ and $l$? In this paper, we provide a positive answer to the above question by designing such an algorithm.
Robust Multiplication-based Tests for Reed-Muller Codes
Venue: arXiv cs.CC 2018
Year: 2018

We consider the following multiplication-based tests to check if a given function $f: \mathbb{F}_q^n\to \mathbb{F}_q$ is a codeword of the Reed-Muller code of dimension $n$ and order $d$ over the finite field $\mathbb{F}_q$ for prime $q$ (i.e., $f$ is the evaluation of a degree-$d$ polynomial over $\mathbb{F}_q$ for $q$ prime). * $\mathrm{Test}_{e,k}$: Pick $P_1,\ldots,P_k$ independent random degree-$e$ polynomials and accept iff the function $fP_1\cdots P_k$ is the evaluation of a degree-$(d+ek)$ polynomial (i.e., is a codeword of the Reed-Muller code of dimension $n$ and order $(d+ek)$). We prove the robust soundness of the above tests for large values of $e$, answering a question of Dinur and Guruswami [Israel Journal of Mathematics, 209:611-649, 2015]. Previous soundness analyses of these tests were known only for the case when either $e=1$ or $k=1$. Even for the case $k=1$ and $e>1$, earlier soundness analyses were not robust. We also analyze a derandomized version of this test, where (for example) the polynomials $P_1,\dots,P_k$ can be the same random polynomial $P$. This generalizes a result of Guruswami et al. [SIAM J. Comput., 46(1):132-159, 2017]. One of the key ingredients that go into the proof of this robust soundness is an extension of the standard Schwartz-Zippel lemma over general finite fields $\mathbb{F}_q$, which may be of independent interest.
UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision
Venue: arXiv cs.CV 2018
Year: 2018

A reliable stereo algorithm is critical for many robotics applications. But textureless and specular regions can easily cause failure by making feature matching difficult. Understanding whether an algorithm is robust to these hazardous regions is important. Although many stereo benchmarks have been developed to evaluate performance, it is hard to quantify the effect of hazardous regions in real images because the location and severity of these regions are unknown. In this paper, we develop a synthetic image generation tool enabling to control hazardous factors, such as making objects more specular or transparent, to produce hazardous regions at different degrees. The densely controlled sampling strategy in virtual worlds enables to effectively stress test stereo algorithms by varying the types and degrees of the hazard. We generate a large synthetic image dataset with automatically computed hazardous regions and analyze algorithms on these regions. The observations from synthetic images are further validated by annotating hazardous regions in real-world datasets Middlebury and KITTI (which gives a sparse sampling of the hazards). Our synthetic image generation tool is based on a game engine Unreal Engine 4 and will be open-source along with the virtual scenes in our experiments. Many publicly available realistic game contents can be used by our tool to provide an enormous resource for development and evaluation of algorithms.
Graph Summarization Methods and Applications: A Survey
Venue: arXiv cs.IR 2018
Year: 2018

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind, and the challenges of, graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.