OpenRaiser/Intern-Atlas huggingface dataset:
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior
Venue: arXiv cs.SI 2018
Year: 2018

In recent years, offensive, abusive and hateful language, sexism, racism and other types of aggressive and cyberbullying behavior have been manifesting with increased frequency, and in many online social media platforms. In fact, past scientific work focused on studying these forms in popular media, such as Facebook and Twitter. Building on such work, we present an 8-month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior, at the same time. We propose an incremental and iterative methodology, that utilizes the power of crowdsourcing to annotate a large scale collection of tweets with a set of abuse-related labels. In fact, by applying our methodology including statistical analysis for label merging or elimination, we identify a reduced but robust set of labels. Finally, we offer a first overview and findings of our collected and annotated dataset of 100 thousand tweets, which we make publicly available for further scientific exploration.
Analysis of WDM-PON for Next-Generation Back- and Fronthaul
Venue: arXiv cs.NI 2018
Year: 2018

An analysis of next-generation infrastructure for mobile back- and fronthaul is presented. The same infrastructure can be used for wireline backhaul and dedicated business access. Possible coexistence with FTTH residential access based on NG-PON2 TWDM is also analyzed. Further, different locations for pools of base-band units in fronthaul scenarios are compared with regard to resulting cost. It turns out that fronthaul with highly concentrated base-band unit pools can be cost-efficient even if fronthaul bit rates reach 10 Gb/s.
Dense 3D Object Reconstruction from a Single Depth View
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of 256^3 by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.
Deep-Temporal LSTM for Daily Living Action Recognition
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose to improve the traditional use of RNNs by employing a many to many model for video classification. We analyze the importance of modeling spatial layout and temporal encoding for daily living action recognition. Many RGB methods focus only on short term temporal information obtained from optical flow. Skeleton based methods on the other hand show that modeling long term skeleton evolution improves action recognition accuracy. In this work, we propose a deep-temporal LSTM architecture which extends standard LSTM and allows better encoding of temporal information. In addition, we propose to fuse 3D skeleton geometry with deep static appearance. We validate our approach on public available CAD60, MSRDailyActivity3D and NTU-RGB+D, achieving competitive performance as compared to the state-of-the art.
Coded Status Updates in an Energy Harvesting Erasure Channel
Venue: arXiv cs.IT 2018
Year: 2018

We consider an energy harvesting transmitter sending status updates to a receiver over an erasure channel, where each status update is of length $k$ symbols. The energy arrivals and the channel erasures are independent and identically distributed (i.i.d.) and Bernoulli distributed in each slot. In order to combat the effects of the erasures in the channel and the uncertainty in the energy arrivals, we use channel coding to encode the status update symbols. We consider two types of channel coding: maximum distance separable (MDS) codes and rateless erasure codes. For each of these models, we study two achievable schemes: best-effort and save-and-transmit. In the best-effort scheme, the transmitter starts transmission right away, and sends a symbol if it has energy. In the save-and-transmit scheme, the transmitter remains silent in the beginning in order to save some energy to minimize energy outages in future slots. We analyze the average age of information (AoI) under each of these policies. We show through numerical results that as the average recharge rate decreases, MDS coding with save-and-transmit outperforms all best-effort schemes. We show that rateless coding with save-and-transmit outperforms all the other schemes.
On a fractional version of Haemers' bound
Venue: arXiv cs.IT 2018
Year: 2018

In this note, we present a fractional version of Haemers' bound on the Shannon capacity of a graph, which is originally due to Blasiak. This bound is a common strengthening of both Haemers' bound and the fractional chromatic number of a graph. We show that this fractional version outperforms any bound on the Shannon capacity that could be attained through Haemers' bound. We show also that this bound is multiplicative, unlike Haemers' bound.
A New Registration Approach for Dynamic Analysis of Calcium Signals in Organs
Venue: arXiv cs.CV 2018
Year: 2018

Wing disc pouches of fruit flies are a powerful genetic model for studying physiological intercellular calcium ($Ca^{2+}$) signals for dynamic analysis of cell signaling in organ development and disease studies. A key to analyzing spatial-temporal patterns of $Ca^{2+}$ signal waves is to accurately align the pouches across image sequences. However, pouches in different image frames may exhibit extensive intensity oscillations due to $Ca^{2+}$ signaling dynamics, and commonly used multimodal non-rigid registration methods may fail to achieve satisfactory results. In this paper, we develop a new two-phase non-rigid registration approach to register pouches in image sequences. First, we conduct segmentation of the region of interest. (i.e., pouches) using a deep neural network model. Second, we obtain an optimal transformation and align pouches across the image sequences. Evaluated using both synthetic data and real pouch data, our method considerably outperforms the state-of-the-art non-rigid registration methods.
Snort Intrusion Detection System with Intel Software Guard Extension (Intel SGX)
Venue: arXiv cs.NI 2018
Year: 2018

Network Function Virtualization (NFV) promises the benefits of reduced infrastructure, personnel, and management costs by outsourcing network middleboxes to the public or private cloud. Unfortunately, running network functions in the cloud entails security challenges, especially for complex stateful services. In this paper, we describe our experiences with hardening the king of middleboxes - Intrusion Detection Systems (IDS) - using Intel Software Guard Extensions (Intel SGX) technology. Our IDS secured using Intel SGX, called SEC-IDS, is an unmodified Snort 3 with a DPDK network layer that achieves 10Gbps line rate. SEC-IDS guarantees computational integrity by running all Snort code inside an Intel SGX enclave. At the same time, SEC-IDS achieves near-native performance, with throughput close to 100 percent of vanilla Snort 3, by retaining network I/O outside of the enclave. Our experiments indicate that performance is only constrained by the modest Enclave Page Cache size available on current Intel SGX Skylake based E3 Xeon platforms. Finally, we kept the porting effort minimal by using the Graphene-SGX library OS. Only 27 Lines of Code (LoC) were modified in Snort and 178 LoC in Graphene-SGX itself.
Toward Optimal Coupon Allocation in Social Networks: An Approximate Submodular Optimization Approach
Venue: arXiv cs.SI 2018
Year: 2018

CMO Council reports that 71\% of internet users in the U.S. were influenced by coupons and discounts when making their purchase decisions. It has also been shown that offering coupons to a small fraction of users (called seed users) may affect the purchase decisions of many other users in a social network. This motivates us to study the optimal coupon allocation problem, and our objective is to allocate coupons to a set of users so as to maximize the expected cascade. Different from existing studies on influence maximizaton (IM), our framework allows a general utility function and a more complex set of constraints. In particular, we formulate our problem as an approximate submodular maximization problem subject to matroid and knapsack constraints. Existing techniques relying on the submodularity of the utility function, such as greedy algorithm, can not work directly on a non-submodular function. We use $ε$ to measure the difference between our function and its closest submodular function and propose a novel approximate algorithm with approximation ratio $β(ε)$ with $\lim_{ε\rightarrow 0}β(ε)=1-1/e$. This is the best approximation guarantee for approximate submodular maximization subject to a partition matroid and knapsack constraints, our results apply to a broad range of optimization problems that can be formulated as an approximate submodular maximization problem.
ExpNet: Landmark-Free, Deep, 3D Facial Expressions
Venue: arXiv cs.CV 2018
Year: 2018

We describe a deep learning based method for estimating 3D facial expression coefficients. Unlike previous work, our process does not relay on facial landmark detection methods as a proxy step. Recent methods have shown that a CNN can be trained to regress accurate and discriminative 3D morphable model (3DMM) representations, directly from image intensities. By foregoing facial landmark detection, these methods were able to estimate shapes for occluded faces appearing in unprecedented in-the-wild viewing conditions. We build on those methods by showing that facial expressions can also be estimated by a robust, deep, landmark-free approach. Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients. We propose a unique method for collecting data to train this network, leveraging on the robustness of deep networks to training label noise. We further offer a novel means of evaluating the accuracy of estimated expression coefficients: by measuring how well they capture facial emotions on the CK+ and EmotiW-17 emotion recognition benchmarks. We show that our ExpNet produces expression coefficients which better discriminate between facial emotions than those obtained using state of the art, facial landmark detection techniques. Moreover, this advantage grows as image scales drop, demonstrating that our ExpNet is more robust to scale changes than landmark detection methods. Finally, at the same level of accuracy, our ExpNet is orders of magnitude faster than its alternatives.
Real Time Collision Detection and Identification for Robotic Manipulators
Venue: arXiv cs.RO 2018
Year: 2018

The majority of everyday tasks involve interacting with unstructured environments. This implies that, in order for robots to be truly useful they must be able to handle contacts. This paper explores how a particle filter can be used to localize a contact point and estimate the external force. We demonstrate the capability of the particle filter on a simulated 4DoF planar robotic arm, and compare it to a well-established analytical approach.
Unlabelled Sensing: A Sparse Bayesian Learning Approach
Venue: arXiv cs.IT 2018
Year: 2018

We address the recovery of sparse vectors in an overcomplete, linear and noisy multiple measurement framework, where the measurement matrix is known upto a permutation of its rows. We derive sparse Bayesian learning (SBL) based updates for joint recovery of the unknown sparse vector and the sensing order, represented using a permutation matrix. We model the sparse matrix using multiple uncorrelated and correlated vectors, and in particular, we use the first order AR model for the correlated sparse vectors. We propose the Permutation-MSBL and a Kalman filtering based Permutation-KSBL algorithm for low-complex joint recovery of the uncorrelated and correlated sparse vectors, jointly with the permutation matrix. The novelty of this work emerges in providing a simple update step for the permutation matrix using the rearrangement inequality. We demonstrate the mean square error and the permutation recovery performance of the proposed algorithms using Monte Carlo simulations.
Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach
Venue: arXiv cs.SI 2018
Year: 2018

We propose a generalized framework for influence maximization in large-scale, time evolving networks. Many real-life influence graphs such as social networks, telephone networks, and IP traffic data exhibit dynamic characteristics, e.g., the underlying structure and communication patterns evolve with time. Correspondingly, we develop a dynamic framework for the influence maximization problem, where we perform effective local updates to quickly adjust the top-k influencers, as the structure and communication patterns in the network change. We design a novel N-Family approach (N=1, 2, 3, ...) based on the maximum influence arborescence (MIA) propagation model with approximation guarantee of (1-1/e). We then develop heuristic algorithms by extending the N-Family approach to other information propagation models (e.g., independent cascade, linear threshold) and influence maximization algorithms (e.g., CELF, reverse reachable sketch). Based on a detailed empirical analysis over several real-world, dynamic, and large-scale networks, we find that our proposed solution, N-Family improves the updating time of the top-k influencers by 1-2 orders of magnitude, compared to state-of-the-art algorithms, while ensuring similar memory usage and influence spreads.
On the complexity of the outer-connected bondage and the outer-connected reinforcement problems
Venue: arXiv cs.DM 2018
Year: 2018

Let $G=(V,E)$ be a graph. A subset $S \subseteq V$ is a dominating set of $G$ if every vertex not in $S$ is adjacent to a vertex in $S$. A set $\tilde{D} \subseteq V$ of a graph $G=(V,E) $ is called an outer-connected dominating set for $G$ if (1) $\tilde{D}$ is a dominating set for $G$, and (2) $G [V \setminus \tilde{D}]$, the induced subgraph of $G$ by $V \setminus \tilde{D}$, is connected. The minimum size among all outer-connected dominating sets of $G$ is called the outer-connected domination number of $G$ and is denoted by $\tildeγ_c(G)$. We define the outer-connected bondage number of a graph $G$ as the minimum number of edges whose removal from $G$ results in a graph with an outer-connected domination number larger than the one for $G$. Also, the outer-connected reinforcement number of a graph $G$ is defined as the minimum number of edges whose addition to $G$ results in a graph with an outer-connected domination number, which is smaller than the one for $G$. This paper shows that the decision problems for the outer-connected bondage and the outer-connected reinforcement numbers are $\mathbf{NP}$-hard. Also, the exact values of the bondage number are determined for several classes of graphs.
On the Complexity of the Cayley Semigroup Membership Problem
Venue: arXiv cs.CC 2018
Year: 2018

We investigate the complexity of deciding, given a multiplication table representing a semigroup S, a subset X of S and an element t of S, whether t can be expressed as a product of elements of X. It is well-known that this problem is NL-complete and that the more general Cayley groupoid membership problem, where the multiplication table is not required to be associative, is P-complete. For groups, the problem can be solved in deterministic log-space which raised the question of determining the exact complexity of this variant. Barrington, Kadau, Lange and McKenzie showed that for Abelian groups and for certain solvable groups, the problem is contained in the complexity class FOLL and they concluded that these variants are not hard for any complexity class containing PARITY. The more general case of arbitrary groups remained open. In this work, we show that for both groups and for commutative semigroups, the problem is solvable in qAC^0 (quasi-polynomial size circuits of constant depth with unbounded fan-in) and conclude that these variants are also not hard for any class containing PARITY. Moreover, we prove that NL-completeness already holds for the classes of 0-simple semigroups and nilpotent semigroups. Together with our results on groups and commutative semigroups, we prove the existence of a natural class of finite semigroups which generates a variety of finite semigroups with NL-complete Cayley semigroup membership, while the Cayley semigroup membership problem for the class itself is not NL-hard. We also discuss applications of our technique to FOLL.
A Simple Object that Spans the Whole Consensus Hierarchy
Venue: arXiv cs.DC 2018
Year: 2018

This paper presents a simple generalization of the basic atomic read/write register object, whose genericity parameter spans the whole set of integers and is such that its k-parameterized instance has exactly consensus number k. This object, whose definition is pretty natural, is a sliding window register of size k. Its interest lies in its simplicity and its genericity dimension which provides a global view capturing the whole consensus hierarchy. Hence, this short article must be seen as a simple pedagogical introduction to Herlihy's consensus hierarchy.
TikZ-FeynHand: Basic User Guide
Venue: arXiv cs.OH 2018
Year: 2018

This is a userguide for the LaTex package Tikz-FeynHand at https://ctan.org/pkg/tikz-feynhand which let's you draw Feynman diagrams using TikZ. It contains many examples and a 5-minute introduction to TikZ. The package is a low-end modification of the package TikZ-Feynman at https://ctan.org/pkg/tikz-feynman, one of whose principal advantages is the automatic generation of diagrams, for which it needs LuaTex. FeynHand only provides the manual mode and hence runs in LaTex without any reference to LuaTex. In addition it provides some NEW STYLES for vertices and propagators, alternative SHORTER KEYWORDS in addition to TikZ-Feynman's longer ones, some shortcut commands for QUICKLY CUSTOMIZING the diagrams' look, and the new feature to put one propagator "ON TOP" of another.
Zero-Cost Coercions for Program and Proof Reuse
Venue: arXiv cs.PL 2018
Year: 2018

We introduce the notion of identity coercions between non-indexed and indexed variants of inductive datatypes, such as lists and vectors. An identity coercion translates one type to another such that the coercion function definitionally reduces to the identity function. This allows us to reuse vector programs to derive list programs (and vice versa), without any runtime cost. This also allows us to reuse vector proofs to derive list proofs (and vice versa), without the cost of equational reasoning proof obligations. Our work is formalized in Cedille, a dependently typed programming language based on a type-annotated Curry-style type the- ory with implicit (or, erased) products (or, dependent functions), and relies crucially on erasure to introduce definitional equalities between underlying untyped terms.
Full- & Reduced-Order State-Space Modeling of Wind Turbine Systems with Permanent-Magnet Synchronous Generator
Venue: arXiv cs.SY 2018
Year: 2018

Wind energy is an integral part of nowadays energy supply and one of the fastest growing sources of electricity in the world today. Accurate models for wind energy conversion systems (WECSs) are of key interest for the analysis and control design of present and future energy systems. Existing control-oriented WECSs models are subject to unstructured simplifications, which have not been discussed in literature so far. Thus, this technical note presents are thorough derivation of a physical state-space model for permanent magnet synchronous generator WECSs. The physical model considers all dynamic effects that significantly influence the system's power output, including the switching of the power electronics. Alternatively, the model is formulated in the $(a,b,c)$- and $(d,q)$-reference frame. Secondly, a complete control and operation management system for the wind regimes II and III and the transition between the regimes is presented. The control takes practical effects such as input saturation and integral windup into account. Thirdly, by a structured model reduction procedure, two state-space models of WECS with reduced complexity are derived: a non-switching model and a non-switching reduced-order model. The validity of the models is illustrated and compared through a numerical simulation study.
Stochastic simulation of pattern formation in growing tissue: a multilevel approach
Venue: arXiv cs.CE 2018
Year: 2018

We take up the challenge of designing realistic computational models of large interacting cell populations. The goal is essentially to bring Gillespie's celebrated stochastic methodology to the level of an interacting population of cells. Specifically, we are interested in how the gold standard of single cell computational modeling, here taken to be spatial stochastic reaction-diffusion models, may be efficiently coupled with a similar approach at the cell population level. Concretely, we target a recently proposed set of pathways for pattern formation involving Notch-Delta signaling mechanisms. These involve cell-to-cell communication as mediated both via direct membrane contact sites as well as via cellular protrusions. We explain how to simulate the process in growing tissue using a multilevel approach and we discuss implications for future development of the associated computational methods.
Software Fault Isolation for Robust Compilation
Venue: arXiv cs.CR 2018
Year: 2018

Memory corruption vulnerabilities are endemic to unsafe languages, such as C, and they can even be found in safe languages that themselves are implemented in unsafe languages or linked with libraries implemented in unsafe languages. Robust compilation mitigates the threat of linking with memory-unsafe libraries. The source language is a C-like language, enriched with a notion of a component which encapsulates data and code, exposing functionality through well-defined interfaces. Robust compilation defines what security properties a component still has, even, if one or more components are compromised. The main contribution of this work is to demonstrate that the compartmentalization necessary for a compiler that has the robust compilation property can be realized on a basic RISC processor using software fault isolation.
Smooth $q$-Gram, and Its Applications to Detection of Overlaps among Long, Error-Prone Sequencing Reads
Venue: arXiv cs.DS 2018
Year: 2018

We propose smooth $q$-gram, the first variant of $q$-gram that captures $q$-gram pair within a small edit distance. We apply smooth $q$-gram to the problem of detecting overlapping pairs of error-prone reads produced by single molecule real time sequencing (SMRT), which is the first and most critical step of the de novo fragment assembly of SMRT reads. We have implemented and tested our algorithm on a set of real world benchmarks. Our empirical results demonstrated the significant superiority of our algorithm over the existing $q$-gram based algorithms in accuracy.