OpenRaiser/Intern-Atlas huggingface dataset:
Minimum Violation Control Synthesis on Cyber-Physical Systems under Attacks
Venue: arXiv cs.SY 2018
Year: 2018

Cyber-physical systems are conducting increasingly complex tasks, which are often modeled using formal languages such as temporal logic. The system's ability to perform the required tasks can be curtailed by malicious adversaries that mount intelligent attacks. At present, however, synthesis in the presence of such attacks has received limited research attention. In particular, the problem of synthesizing a controller when the required specifications cannot be satisfied completely due to adversarial attacks has not been studied. In this paper, we focus on the minimum violation control synthesis problem under linear temporal logic constraints of a stochastic finite state discrete-time system with the presence of an adversary. A minimum violation control strategy is one that satisfies the most important tasks defined by the user while violating the less important ones. We model the interaction between the controller and adversary using a concurrent Stackelberg game and present a nonlinear programming problem to formulate and solve for the optimal control policy. To reduce the computation effort, we develop a heuristic algorithm that solves the problem efficiently and demonstrate our proposed approach using a numerical case study.
JuncNet: A Deep Neural Network for Road Junction Disambiguation for Autonomous Vehicles
Venue: arXiv cs.CV 2018
Year: 2018

With a great amount of research going on in the field of autonomous vehicles or self-driving cars, there has been considerable progress in road detection and tracking algorithms. Most of these algorithms use GPS to handle road junctions and its subsequent decisions. However, there are places in the urban environment where it becomes difficult to get GPS fixes which render the junction decision handling erroneous or possibly risky. Vision-based junction detection, however, does not have such problems. This paper proposes a novel deep convolutional neural network architecture for disambiguation of junctions from roads with a high degree of accuracy. This network is benchmarked against other well known classifying network architectures like AlexNet and VGGnet. Further, we discuss a potential road navigation methodology which uses the proposed network model. We conclude by performing an experimental validation of the trained network and the navigational method on the roads of the Indian Institute of Science (IISc).
An automatic tool for checking multi-party contracts
Venue: arXiv cs.LO 2018
Year: 2018

Contracts play an important role in business where relationships among different parties are dictated by legal rules. The notion of electronic contracts has emerged mostly due to technological advances and the electronic trading among companies and customers. Thereby new challenges have arisen to guarantee reliability among the stakeholders in electronic negotiations. In this scenery, the automatic verification of electronic contracts appeared as the solution but as a new challenge at the same time. An important task on verifying contracts is concerned of detecting conflicts in multi-party contracts. The problem of checking contracts has been largely addressed in the literature, but we are not aware about any method and tool that deals with multi-party contracts and conflict detection using a contract language. This work presents an automatic checker, so-called RECALL, for finding conflicts on multi-party contracts modeled by an extension of a contract language. We developed an automatic checking tool and also applied it to a a well-known case study of selling products that is characterized by multi-party aspects of the contracts. We also performed some experiments in order to show the tool performance w.r.t. the size of contracts.
Challenges of capturing engagement on Facebook for Altmetrics
Venue: arXiv cs.SI 2018
Year: 2018

Previous research shows that, despite its popularity, Facebook is less frequently used to share academic content. In order to investigate this discrepancy we set out to explore engagement numbers through their Graph API by querying the Facebook API with multiple URLs for a random set of 103,539 articles from the Web of Science. We identified two major challenge areas: mapping articles to URLs and the mapping URLs to objects inside Facebook. We then explored three problem cases within our dataset: (1) identifying a landing page for any given URL, (2) instances where equivalent URLs are mapped to different Facebook objects, and (3) instances of different articles being mapped onto the same Facebook object. We found that the engagement numbers for 11.8% of all articles that have been shared on Facebook at least once are not reliable because of these problems. Moreover, we were unable to identify the URL for 11.6% of the articles in our data. Taken together, the three problem cases constitute 12.3% of the 103,539 tested articles for which engagement numbers cannot be relied upon. Given that we only tested a small number of problem cases and URL variants, our results point to large challenges facing those wishing to collect Facebook metrics programatically through the available API.
SOS lower bounds with hard constraints: think global, act local
Venue: arXiv cs.DS 2018
Year: 2018

Many previous Sum-of-Squares (SOS) lower bounds for CSPs had two deficiencies related to global constraints. First, they were not able to support a "cardinality constraint", as in, say, the Min-Bisection problem. Second, while the pseudoexpectation of the objective function was shown to have some value $β$, it did not necessarily actually "satisfy" the constraint "objective = $β$". In this paper we show how to remedy both deficiencies in the case of random CSPs, by translating \emph{global} constraints into \emph{local} constraints. Using these ideas, we also show that degree-$Ω(\sqrt{n})$ SOS does not provide a $(\frac{4}{3} - ε)$-approximation for Min-Bisection, and degree-$Ω(n)$ SOS does not provide a $(\frac{11}{12} + ε)$-approximation for Max-Bisection or a $(\frac{5}{4} - ε)$-approximation for Min-Bisection. No prior SOS lower bounds for these problems were known.
Vulcan: A Monte Carlo Algorithm for Large Chance Constrained MDPs with Risk Bounding Functions
Venue: arXiv cs.AI 2018
Year: 2018

Chance Constrained Markov Decision Processes maximize reward subject to a bounded probability of failure, and have been frequently applied for planning with potentially dangerous outcomes or unknown environments. Solution algorithms have required strong heuristics or have been limited to relatively small problems with up to millions of states, because the optimal action to take from a given state depends on the probability of failure in the rest of the policy, leading to a coupled problem that is difficult to solve. In this paper we examine a generalization of a CCMDP that trades off probability of failure against reward through a functional relationship. We derive a constraint that can be applied to each state history in a policy individually, and which guarantees that the chance constraint will be satisfied. The approach decouples states in the CCMDP, so that large problems can be solved efficiently. We then introduce Vulcan, which uses our constraint in order to apply Monte Carlo Tree Search to CCMDPs. Vulcan can be applied to problems where it is unfeasible to generate the entire state space, and policies must be returned in an anytime manner. We show that Vulcan and its variants run tens to hundreds of times faster than linear programming methods, and over ten times faster than heuristic based methods, all without the need for a heuristic, and returning solutions with a mean suboptimality on the order of a few percent. Finally, we use Vulcan to solve for a chance constrained policy in a CCMDP with over $10^{13}$ states in 3 minutes.
Developing a Purely Visual Based Obstacle Detection using Inverse Perspective Mapping
Venue: arXiv cs.RO 2018
Year: 2018

Our solution is implemented in and for the frame of Duckietown. The goal of Duckietown is to provide a relatively simple platform to explore, tackle and solve many problems linked to autonomous driving. "Duckietown" is simple in the basics, but an infinitely expandable environment. From controlling single driving Duckiebots until complete fleet management, every scenario is possible and can be put into practice. So far, none of the existing modules was capable of reliably detecting obstacles and reacting to them in real time. We faced the general problem of detecting obstacles given images from a monocular RGB camera mounted at the front of our Duckiebot and reacting to them properly without crashing or erroneously stopping the Duckiebot. Both, the detection as well as the reaction have to be implemented and have to run on a Raspberry Pi in real time. Due to the strong hardware limitations, we decided to not use any learning algorithms for the obstacle detection part. As it later transpired, a working "hard coded" software needs thorough analysis and understanding of the given problem. In layman's terms, we simply seek to make Duckietown a safer place.
BPE and CharCNNs for Translation of Morphology: A Cross-Lingual Comparison and Analysis
Venue: arXiv cs.CL 2018
Year: 2018

Neural Machine Translation (NMT) in low-resource settings and of morphologically rich languages is made difficult in part by data sparsity of vocabulary words. Several methods have been used to help reduce this sparsity, notably Byte-Pair Encoding (BPE) and a character-based CNN layer (charCNN). However, the charCNN has largely been neglected, possibly because it has only been compared to BPE rather than combined with it. We argue for a reconsideration of the charCNN, based on cross-lingual improvements on low-resource data. We translate from 8 languages into English, using a multi-way parallel collection of TED transcripts. We find that in most cases, using both BPE and a charCNN performs best, while in Hebrew, using a charCNN over words is best.
Randomized Incremental Construction of Net-Trees
Venue: arXiv cs.CG 2018
Year: 2018

Net-trees are a general purpose data structure for metric data that have been used to solve a wide range of algorithmic problems. We give a simple randomized algorithm to construct net-trees on doubling metrics using $O(n\log n)$ time in expectation. Along the way, we define a new, linear-size net-tree variant that simplifies the analyses and algorithms. We show a connection between these trees and approximate Voronoi diagrams and use this to simplify the point location necessary in net-tree construction. Our analysis uses a novel backwards analysis that may be of independent interest.
FlipTracker: Understanding Natural Error Resilience in HPC Applications
Venue: arXiv cs.DC 2018
Year: 2018

As high-performance computing systems scale in size and computational power, the danger of silent errors, i.e., errors that can bypass hardware detection mechanisms and impact application state, grows dramatically. Consequently, applications running on HPC systems need to exhibit resilience to such errors. Previous work has found that, for certain codes, this resilience can come for free, i.e., some applications are naturally resilient, but few studies have shown the code patterns---combinations or sequences of computations---that make an application naturally resilient. In this paper, we present FlipTracker, a framework designed to extract these patterns using fine-grained tracking of error propagation and resilience properties, and we use it to present a set of computation patterns that are responsible for making representative HPC applications naturally resilient to errors. This not only enables a deeper understanding of resilience properties of these codes, but also can guide future application designs towards patterns with natural resilience.
Generating Highly Realistic Images of Skin Lesions with GANs
Venue: arXiv cs.CV 2018
Year: 2018

As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses problems for the training of robust and well-generalizing models. The ability to synthesize realistic looking images of skin lesions could act as a reliever for the aforementioned problems. Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking medical images, however limited to low resolution, whereas machine learning models for challenging tasks such as skin lesion segmentation or classification benefit from much higher resolution data. In this work, we successfully synthesize realistically looking images of skin lesions with GANs at such high resolution. Therefore, we utilize the concept of progressive growing, which we both quantitatively and qualitatively compare to other GAN architectures such as the DCGAN and the LAPGAN. Our results show that with the help of progressive growing, we can synthesize highly realistic dermoscopic images of skin lesions that even expert dermatologists find hard to distinguish from real ones.
Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design
Venue: arXiv cs.IT 2018
Year: 2018

Intelligent reflecting surface (IRS) is envisioned to have abundant applications in future wireless networks by smartly reconfiguring the signal propagation for performance enhancement. Specifically, an IRS consists of a large number of low-cost passive elements each reflecting the incident signal with a certain phase shift to collaboratively achieve beamforming and suppress interference at one or more designated receivers. In this paper, we study an IRS-enhanced point-to-point multiple-input single-output (MISO) wireless system where one IRS is deployed to assist in the communication from a multi-antenna access point (AP) to a single-antenna user. As a result, the user simultaneously receives the signal sent directly from the AP as well as that reflected by the IRS. We aim to maximize the total received signal power at the user by jointly optimizing the (active) transmit beamforming at the AP and (passive) reflect beamforming by the phase shifters at the IRS. We first propose a centralized algorithm based on the technique of semidefinite relaxation (SDR) by assuming the global channel state information (CSI) available at the IRS. Since the centralized implementation requires excessive channel estimation and signal exchange overheads, we further propose a low-complexity distributed algorithm where the AP and IRS independently adjust the transmit beamforming and the phase shifts in an alternating manner until the convergence is reached. Simulation results show that significant performance gains can be achieved by the proposed algorithms as compared to benchmark schemes. Moreover, it is verified that the IRS is able to drastically enhance the link quality and/or coverage over the conventional setup without the IRS.
Stellar Cluster Detection using GMM with Deep Variational Autoencoder
Venue: arXiv cs.LG 2018
Year: 2018

Detecting stellar clusters have always been an important research problem in Astronomy. Although images do not convey very detailed information in detecting stellar density enhancements, we attempt to understand if new machine learning techniques can reveal patterns that would assist in drawing better inferences from the available image data. This paper describes an unsupervised approach in detecting star clusters using Deep Variational Autoencoder combined with a Gaussian Mixture Model. We show that our method works significantly well in comparison with state-of-the-art detection algorithm in recognizing a variety of star clusters even in the presence of noise and distortion.
Data Augmentation for Skin Lesion Analysis
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning models show remarkable results in automated skin lesion analysis. However, these models demand considerable amounts of data, while the availability of annotated skin lesion images is often limited. Data augmentation can expand the training dataset by transforming input images. In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet). Scenarios include traditional color and geometric transforms, and more unusual augmentations such as elastic transforms, random erasing and a novel augmentation that mixes different lesions. We also explore the use of data augmentation at test-time and the impact of data augmentation on various dataset sizes. Our results confirm the importance of data augmentation in both training and testing and show that it can lead to more performance gains than obtaining new images. The best scenario results in an AUC of 0.882 for melanoma classification without using external data, outperforming the top-ranked submission (0.874) for the ISIC Challenge 2017, which was trained with additional data.
Appendix - Recommended Statistical Significance Tests for NLP Tasks
Venue: arXiv cs.CL 2018
Year: 2018

Statistical significance testing plays an important role when drawing conclusions from experimental results in NLP papers. Particularly, it is a valuable tool when one would like to establish the superiority of one algorithm over another. This appendix complements the guide for testing statistical significance in NLP presented in \cite{dror2018hitchhiker} by proposing valid statistical tests for the common tasks and evaluation measures in the field.
Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection
Venue: arXiv cs.CL 2018
Year: 2018

In this paper, we present the system we have used for the Implicit WASSA 2018 Implicit Emotion Shared Task. The task is to predict the emotion of a tweet of which the explicit mentions of emotion terms have been removed. The idea is to come up with a model which has the ability to implicitly identify the emotion expressed given the context words. We have used a Gated Recurrent Neural Network (GRU) and a Capsule Network based model for the task. Pre-trained word embeddings have been utilized to incorporate contextual knowledge about words into the model. GRU layer learns latent representations using the input word embeddings. Subsequent Capsule Network layer learns high-level features from that hidden representation. The proposed model managed to achieve a macro-F1 score of 0.692.
Blur-Countering Keypoint Detection via Eigenvalue Asymmetry
Venue: arXiv cs.CV 2018
Year: 2018

Well-known corner or local extrema feature based detectors such as FAST and DoG have achieved noticeable successes. However, detecting keypoints in the presence of blur has remained to be an unresolved issue. As a matter of fact, various kinds of blur (e.g., motion blur, out-of-focus, and space-variant) remarkably increase challenges for keypoint detection. As a result, those methods have limited performance. To settle this issue, we propose a blur-countering method for detecting valid keypoints for various types and degrees of blurred images. Specifically, we first present a distance metric for derivative distributions, which preserves the distinctiveness of patch pairs well under blur. We then model the asymmetry by utilizing the difference of squared eigenvalues based on the distance metric. To make it scale-robust, we also extend it to scale space. The proposed detector is efficient as the main computational cost is the square of derivatives at each pixel. Extensive visual and quantitative results show that our method outperforms current approaches under different types and degrees of blur. Without any parallelization, our implementation\footnote{We will make our code publicly available upon the acceptance.} achieves real-time performance for low-resolution images (e.g., $320\times240$ pixel).
A Supervised Learning Approach For Heading Detection
Venue: arXiv cs.IR 2018
Year: 2018

As the Portable Document Format (PDF) file format increases in popularity, research in analysing its structure for text extraction and analysis is necessary. Detecting headings can be a crucial component of classifying and extracting meaningful data. This research involves training a supervised learning model to detect headings with features carefully selected through recursive feature elimination. The best performing classifier had an accuracy of 96.95%, sensitivity of 0.986 and a specificity of 0.953. This research into heading detection contributes to the field of PDF based text extraction and can be applied to the automation of large scale PDF text analysis in a variety of professional and policy based contexts.
Modeling human intuitions about liquid flow with particle-based simulation
Venue: arXiv cs.AI 2018
Year: 2018

Humans can easily describe, imagine, and, crucially, predict a wide variety of behaviors of liquids--splashing, squirting, gushing, sloshing, soaking, dripping, draining, trickling, pooling, and pouring--despite tremendous variability in their material and dynamical properties. Here we propose and test a computational model of how people perceive and predict these liquid dynamics, based on coarse approximate simulations of fluids as collections of interacting particles. Our model is analogous to a "game engine in the head", drawing on techniques for interactive simulations (as in video games) that optimize for efficiency and natural appearance rather than physical accuracy. In two behavioral experiments, we found that the model accurately captured people's predictions about how liquids flow among complex solid obstacles, and was significantly better than two alternatives based on simple heuristics and deep neural networks. Our model was also able to explain how people's predictions varied as a function of the liquids' properties (e.g., viscosity and stickiness). Together, the model and empirical results extend the recent proposal that human physical scene understanding for the dynamics of rigid, solid objects can be supported by approximate probabilistic simulation, to the more complex and unexplored domain of fluid dynamics.
Dynamically Context-Sensitive Time-Decay Attention for Dialogue Modeling
Venue: arXiv cs.CL 2018
Year: 2018

Spoken language understanding (SLU) is an essential component in conversational systems. Considering that contexts provide informative cues for better understanding, history can be leveraged for contextual SLU. However, most prior work only paid attention to the related content in history utterances and ignored the temporal information. In dialogues, it is intuitive that the most recent utterances are more important than the least recent ones, and time-aware attention should be in a decaying manner. Therefore, this paper allows the model to automatically learn a time-decay attention function where the attentional weights can be dynamically decided based on the content of each role's contexts, which effectively integrates both content-aware and time-aware perspectives and demonstrates remarkable flexibility to complex dialogue contexts. The experiments on the benchmark Dialogue State Tracking Challenge (DSTC4) dataset show that the proposed dynamically context-sensitive time-decay attention mechanisms significantly improve the state-of-the-art model for contextual understanding performance.
Stance Prediction for Russian: Data and Analysis
Venue: arXiv cs.CL 2018
Year: 2018

Stance detection is a critical component of rumour and fake news identification. It involves the extraction of the stance a particular author takes related to a given claim, both expressed in text. This paper investigates stance classification for Russian. It introduces a new dataset, RuStance, of Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language. As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language.