OpenRaiser/Intern-Atlas huggingface dataset:
Combining Tools for Optimization and Analysis of Floating-Point Computations
Venue: arXiv cs.PL 2018
Year: 2018

Recent renewed interest in optimizing and analyzing floating-point programs has lead to a diverse array of new tools for numerical programs. These tools are often complementary, each focusing on a distinct aspect of numerical programming. Building reliable floating point applications typically requires addressing several of these aspects, which makes easy composition essential. This paper describes the composition of two recent floating-point tools: Herbie, which performs accuracy optimization, and Daisy, which performs accuracy verification. We find that the combination provides numerous benefits to users, such as being able to use Daisy to check whether Herbie's unsound optimizations improved the worst-case roundoff error, as well as benefits to tool authors, including uncovering a number of bugs in both tools. The combination also allowed us to compare the different program rewriting techniques implemented by these tools for the first time. The paper lays out a road map for combining other floating-point tools and for surmounting common challenges.
Continuous Terrain Guarding with Two-Sided Guards
Venue: arXiv cs.CG 2018
Year: 2018

Herein, we consider the continuous 1.5-dimensional(1.5D) terrain guarding problem with two-sided guarding. We provide an x-monotone chain T and determine the minimal number of vertex guards such that all points of T have been two-sided guarded. A point p is two-sided guarded if there exist two vertices vi (left of p) and (right of p) that both see p. A vertex vi sees a point p on T if the line segment connecting vi to p is on or above T. We demonstrate that the continuous 1.5D terrain guarding problem can be transformed to the discrete terrain guarding problem with a finite point set X and that if X is two-sided guarded, then T is also two-sided guarded. Through this transformation, we achieve an optimal algorithm that solves the continuous 1.5D terrain guarding problem under two-sided guarding.
GeneVis - An interactive visualization tool for combining cross-discipline datasets within genetics
Venue: arXiv cs.GR 2018
Year: 2018

GeneVis is a web-based tool to visualize complementary data sets of different disciplines within the field of genetics. It overlays gene-cluster information, gene-interaction data and gene-disease association data by means of web-based interactive graph visualizations. This allows an intuitive and quick assessment of possible relations between the different datasets. By starting from a high-level graph abstraction based on gene clusters, which can be selected for detailed inspection at the gene-interaction level in a separate window, GeneVis circumvents the common visual clutter problem when using gene datasets with a high number of gene entries.
Degrees of Freedom of the Bursty MIMO X Channel with Instantaneous Topological Information
Venue: arXiv cs.IT 2018
Year: 2018

We study the effects of instantaneous feedback of channel topology on the degrees of freedom (DoF) of the bursty MIMO X channel, where the four transmitter-receiver links are intermittently on-and-off, governed by four independent Bernoulli $(p)$ random sequences, and each transmitter and receiver are equipped with $M$ and $N$ antennas, respectively. We partially characterize this channel: The sum DoF is characterized when $p\le \frac{1}{2}$ or when $\frac{\min(M,N)}{\max(M,N)} \le \frac{2}{3}$. In the remaining regime, the lower bound is within $5.2\%$ of the upper bound. Strictly higher DoF is achieved by coding across channel topologies. In particular, codes over as many as $5$ topologies are proposed to achieve the sum DoF of the channel when $p\le \frac{1}{2}$. A transfer function view of the network is employed to simplify the code design and to elucidate the fact that these are space-time codes, obtained by interference alignment over space and time.
Near-drowning Early Prediction Technique Using Novel Equations (NEPTUNE) for Swimming Pools
Venue: arXiv cs.CV 2018
Year: 2018

Safety is a critical aspect in all swimming pools. This paper describes a near drowning early prediction technique using novel equations (NEPTUNE). NEPTUNE uses equations or rules that would be able to detect near drowning using at least 1 but not more than 5 seconds of video sequence with no false positives. The backbone of NEPTUNE encompasses a mix of statistical image processing to merge images for a video sequence followed by K means clustering to extract segments in the merged image and finally a revisit to statistical image processing to derive variables for every segment. These variables would be used by the equations to identify near-drowning. NEPTUNE has the potential to be integrated into a swimming pool camera system that would send an alarm to the lifeguards for early response so that the likelihood of recovery is high.
Trajectory Representation and Landmark Projection for Continuous-Time Structure from Motion
Venue: arXiv cs.CV 2018
Year: 2018

This paper revisits the problem of continuous-time structure from motion, and introduces a number of extensions that improve convergence and efficiency. The formulation with a $\mathcal{C}^2$-continuous spline for the trajectory naturally incorporates inertial measurements, as derivatives of the sought trajectory. We analyse the behaviour of split interpolation on $\mathbb{SO}(3)$ and on $\mathbb{R}^3$, and a joint interpolation on $\mathbb{SE}(3)$, and show that the latter implicitly couples the direction of translation and rotation. Such an assumption can make good sense for a camera mounted on a robot arm, but not for hand-held or body-mounted cameras. Our experiments show that split interpolation on $\mathbb{SO}(3)$ and on $\mathbb{R}^3$ is preferable over $\mathbb{SE}(3)$ interpolation in all tested cases. Finally, we investigate the problem of landmark reprojection on rolling shutter cameras, and show that the tested reprojection methods give similar quality, while their computational load varies by a factor of 2.
Computational Complexity of Space-Bounded Real Numbers
Venue: arXiv cs.CC 2018
Year: 2018

In this work we study the space complexity of computable real numbers represented by fast convergent Cauchy sequences. We show the existence of families of trascendental numbers which are logspace computable, as opposed to algebraic irrational numbers which seem to required linear space. We characterized the complexity of space-bounded real numbers by quantifying the space complexities of tally sets. The latter result introduces a technique to prove the space complexity of real numbers by studying its corresponding tally sets, which is arguably a more natural approach. Results of this work present a new approach to study real numbers whose transcendence is unknown.
Toward Human-in-the-Loop Supervisory Control for Cyber-Physical Networks
Venue: arXiv cs.SY 2018
Year: 2018

This work proposes a novel approach to include a model of making decision in human brain into the control loop. Employing the methodology developed in mathematical neuroscience, we construct a model that accounts for quality of human decision in supervisory tasks. We specifically focus on adaptive gain theory and the strategy selection problem. The proposed model is shown to be capable of explaining the change of a strategy from compensatory to heuristics in different conditions. We also propose a method to incorporate the effect of internal and external parameters such as stress level and emergencies in the decision model. The model is employed in a supervisory controller that dispatches the jobs between autonomy and a human supervisor in an efficient way.
On Redundant Observability: From Security Index to Attack Detection and Resilient State Estimation
Venue: arXiv cs.SY 2018
Year: 2018

The security of control systems under sensor attacks is investigated. Redundant observability is introduced, explaining existing security notions including the security index, attack detectability, and observability under attacks. Equivalent conditions between redundant observability and existing notions are presented. Based on a bank of partial observers utilizing Kalman decomposition and a decoder exploiting redundancy, an estimator design algorithm is proposed enhancing the resilience of control systems. This scheme substantially improves computational efficiency utilizing far less memory.
Improving Network Intrusion Detection Classifiers by Non-payload-Based Exploit-Independent Obfuscations: An Adversarial Approach
Venue: arXiv cs.CR 2018
Year: 2018

Machine-learning based intrusion detection classifiers are able to detect unknown attacks, but at the same time, they may be susceptible to evasion by obfuscation techniques. An adversary intruder which possesses a crucial knowledge about a protection system can easily bypass the detection module. The main objective of our work is to improve the performance capabilities of intrusion detection classifiers against such adversaries. To this end, we firstly propose several obfuscation techniques of remote attacks that are based on the modification of various properties of network connections; then we conduct a set of comprehensive experiments to evaluate the effectiveness of intrusion detection classifiers against obfuscated attacks. We instantiate our approach by means of a tool, based on NetEm and Metasploit, which implements our obfuscation operators on any TCP communication. This allows us to generate modified network traffic for machine learning experiments employing features for assessing network statistics and behavior of TCP connections. We perform the evaluation of five classifiers: Gaussian Naive Bayes, Gaussian Naive Bayes with kernel density estimation, Logistic Regression, Decision Tree, and Support Vector Machines. Our experiments confirm the assumption that it is possible to evade the intrusion detection capability of all classifiers trained without prior knowledge about obfuscated attacks, causing an exacerbation of the TPR ranging from 7.8% to 66.8%. Further, when widening the training knowledge of the classifiers by a subset of obfuscated attacks, we achieve a significant improvement of the TPR by 4.21% - 73.3%, while the FPR is deteriorated only slightly (0.1% - 1.48%). Finally, we test the capability of an obfuscations-aware classifier to detect unknown obfuscated attacks, where we achieve over 90% detection rate on average for most of the obfuscations.
Detecting Compressed Cleartext Traffic from Consumer Internet of Things Devices
Venue: arXiv cs.CR 2018
Year: 2018

Data encryption is the primary method of protecting the privacy of consumer device Internet communications from network observers. The ability to automatically detect unencrypted data in network traffic is therefore an essential tool for auditing Internet-connected devices. Existing methods identify network packets containing cleartext but cannot differentiate packets containing encrypted data from packets containing compressed unencrypted data, which can be easily recovered by reversing the compression algorithm. This makes it difficult for consumer protection advocates to identify devices that risk user privacy by sending sensitive data in a compressed unencrypted format. Here, we present the first technique to automatically distinguish encrypted from compressed unencrypted network transmissions on a per-packet basis. We apply three machine learning models and achieve a maximum 66.9% accuracy with a convolutional neural network trained on raw packet data. This result is a baseline for this previously unstudied machine learning problem, which we hope will motivate further attention and accuracy improvements. To facilitate continuing research on this topic, we have made our training and test datasets available to the public.
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots
Venue: arXiv cs.RO 2018
Year: 2018

Learning for control can acquire controllers for novel robotic tasks, paving the path for autonomous agents. Such controllers can be expert-designed policies, which typically require tuning of parameters for each task scenario. In this context, Bayesian optimization (BO) has emerged as a promising approach for automatically tuning controllers. However, when performing BO on hardware for high-dimensional policies, sample-efficiency can be an issue. Here, we develop an approach that utilizes simulation to map the original parameter space into a domain-informed space. During BO, similarity between controllers is now calculated in this transformed space. Experiments on the ATRIAS robot hardware and another bipedal robot simulation show that our approach succeeds at sample-efficiently learning controllers for multiple robots. Another question arises: What if the simulation significantly differs from hardware? To answer this, we create increasingly approximate simulators and study the effect of increasing simulation-hardware mismatch on the performance of Bayesian optimization. We also compare our approach to other approaches from literature, and find it to be more reliable, especially in cases of high mismatch. Our experiments show that our approach succeeds across different controller types, bipedal robot models and simulator fidelity levels, making it applicable to a wide range of bipedal locomotion problems.
Cutting Away the Confusion From Crowdtesting
Venue: arXiv cs.SE 2018
Year: 2018

Crowdtesting is effective especially when it comes to the feedback on GUI systems, or subjective opinions about features. Despite of this, we find crowdtesting reports are highly replicated, i.e., 82% of them are replicates of others. Hence automatically detecting replicate reports could help reduce triaging efforts. Most of the existing approaches mainly adopted textual information for replicate detection, and suffered from low accuracy because of the expression gap. Our observation on real industrial crowdtesting data found that when dealing with crowdtesting reports of GUI systems, the reports would accompanied with images, i.e., the screenshots of the app. We assume the screenshot to be valuable for replicate crowdtesting report detection because it reflects the real scenario of the failure and is not affected by the variety of natural languages. In this work, we propose a replicate detection approach, TSDetector, which combines information from the screenshots and the textual descriptions to detect replicate crowdtesting reports. We extract four types of features to characterize the screenshots and the textual descriptions, and design an algorithm to detect replicates based on four similarity scores derived from the four different features respectively. We investigate the effectiveness and advantage of TSDetector on 15 commercial projects with 4,172 reports from one of the Chinese largest crowdtesting platforms.Results show that TSDetector can outperform existing state-of-the-art approaches significantly. In addition, we also evaluate its usefulness using real-world case studies. The feedback from real-world testers demonstrates its practical value
3D printing of a leaf spring: A demonstration of closed-loop control in additive manufacturing
Venue: arXiv cs.SY 2018
Year: 2018

This paper presents the integration of a feedback control loop during the printing of a plastic object using additive manufacturing. The printed object is a leaf spring made of several parts of different infill density values, which are the control variables in this problem. In order to achieve a desired objective stiffness, measurements are taken after each part is completed and the infill density is adjusted accordingly in a closed-loop framework. The absolute error in the stiffness at the end of printing is reduced from 11.63% to 1.34% by using a closed-loop instead of an open-loop control. This experiments serves as a proof of concept to show the relevance of using feedback control in additive manufacturing. By considering the printing process and the measurements as stochastic processes, we show how stochastic optimal control and Kalman filtering can be used to improve the quality of objects manufactured with rudimentary printers.
Blockchain for the IoT: Opportunities and Challenges
Venue: arXiv cs.DC 2018
Year: 2018

Blockchain technology has been transforming the financial industry and has created a new crypto-economy in the last decade. The foundational concepts such as decentralized trust and distributed ledger are promising for distributed, and large-scale Internet of Things (IoT) applications. However, the applications of Blockchain beyond cryptocurrencies in this domain are few and far between because of the lack of understanding and inherent architectural challenges. In this paper, we describe the opportunities for applications of blockchain for the IoT and examine the challenges involved in architecting Blockchain-based IoT applications.
Tile2Vec: Unsupervised representation learning for spatially distributed data
Venue: arXiv cs.CV 2018
Year: 2018

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language -- words appearing in similar contexts tend to have similar meanings -- to spatially distributed data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations on three datasets. Our learned representations significantly improve performance in downstream classification tasks and, similar to word vectors, visual analogies can be obtained via simple arithmetic in the latent space.
Phoneme-to-viseme mappings: the good, the bad, and the ugly
Venue: arXiv cs.CV 2018
Year: 2018

Visemes are the visual equivalent of phonemes. Although not precisely defined, a working definition of a viseme is "a set of phonemes which have identical appearance on the lips". Therefore a phoneme falls into one viseme class but a viseme may represent many phonemes: a many to one mapping. This mapping introduces ambiguity between phonemes when using viseme classifiers. Not only is this ambiguity damaging to the performance of audio-visual classifiers operating on real expressive speech, there is also considerable choice between possible mappings. In this paper we explore the issue of this choice of viseme-to-phoneme map. We show that there is definite difference in performance between viseme-to-phoneme mappings and explore why some maps appear to work better than others. We also devise a new algorithm for constructing phoneme-to-viseme mappings from labeled speech data. These new visemes, `Bear' visemes, are shown to perform better than previously known units.
Improving Character-level Japanese-Chinese Neural Machine Translation with Radicals as an Additional Input Feature
Venue: arXiv cs.CL 2018
Year: 2018

In recent years, Neural Machine Translation (NMT) has been proven to get impressive results. While some additional linguistic features of input words improve word-level NMT, any additional character features have not been used to improve character-level NMT so far. In this paper, we show that the radicals of Chinese characters (or kanji), as a character feature information, can be easily provide further improvements in the character-level NMT. In experiments on WAT2016 Japanese-Chinese scientific paper excerpt corpus (ASPEC-JP), we find that the proposed method improves the translation quality according to two aspects: perplexity and BLEU. The character-level NMT with the radical input feature's model got a state-of-the-art result of 40.61 BLEU points in the test set, which is an improvement of about 8.6 BLEU points over the best system on the WAT2016 Japanese-to-Chinese translation subtask with ASPEC-JP. The improvements over the character-level NMT with no additional input feature are up to about 1.5 and 1.4 BLEU points in the development-test set and the test set of the corpus, respectively.
Parity to Safety in Polynomial Time for Pushdown and Collapsible Pushdown Systems
Venue: arXiv cs.LO 2018
Year: 2018

We give a direct polynomial-time reduction from parity games played over the configuration graphs of collapsible pushdown systems to safety games played over the same class of graphs. That a polynomial-time reduction would exist was known since both problems are complete for the same complexity class. Coming up with a direct reduction, however, has been an open problem. Our solution to the puzzle brings together a number of techniques for pushdown games and adds three new ones. This work contributes to a recent trend of liveness to safety reductions which allow the advanced state-of-the-art in safety checking to be used for more expressive specifications.
A Simple and Efficient Strategy for the Coin Weighing Problem with a Spring Scale
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers a generalized version of the coin weighing problem with a spring scale that lies at the intersection of group testing and compressed sensing problems. Given a collection of $n\geq 2$ coins of total weight $d$ (for a known integer $d$), where the weight of each coin is an unknown integer in the range of $\{0,1,\dots,k\}$ (for a known integer $k\geq 1$), the problem is to determine the weight of each coin by weighing subsets of coins in a spring scale. The goal is to minimize the average number of weighings over all possible weight configurations. For $d=k=1$, an adaptive bisecting weighing strategy is known to be optimal. However, even the case of $d=k=2$, which is the simplest non-trivial case of the problem, is still open. For this case, we propose and analyze a simple and effective adaptive weighing strategy. A numerical evaluation of the exact recursive formulas, derived for the analysis of the proposed strategy, shows that this strategy requires about ${1.365\log_2 n -0.5}$ weighings on average. To the best of our knowledge, this is the first non-trivial achievable upper bound on the minimum expected required number of weighings for the case of $d=k=2$. As $n$ grows unbounded, the proposed strategy, when compared to an optimal strategy within the commonly-used class of nested strategies, requires about $31.75\%$ less number of weighings on average; and in comparison with the information-theoretic lower bound, it requires at most about $8.16\%$ extra number of weighings on average.
Polite Dialogue Generation Without Parallel Data
Venue: arXiv cs.CL 2018
Year: 2018

Stylistic dialogue response generation, with valuable applications in personality-based conversational agents, is a challenging task because the response needs to be fluent, contextually-relevant, as well as paralinguistically accurate. Moreover, parallel datasets for regular-to-stylistic pairs are usually unavailable. We present three weakly-supervised models that can generate diverse polite (or rude) dialogue responses without parallel data. Our late fusion model (Fusion) merges the decoder of an encoder-attention-decoder dialogue model with a language model trained on stand-alone polite utterances. Our label-fine-tuning (LFT) model prepends to each source sequence a politeness-score scaled label (predicted by our state-of-the-art politeness classifier) during training, and at test time is able to generate polite, neutral, and rude responses by simply scaling the label embedding by the corresponding score. Our reinforcement learning model (Polite-RL) encourages politeness generation by assigning rewards proportional to the politeness classifier score of the sampled response. We also present two retrieval-based polite dialogue model baselines. Human evaluation validates that while the Fusion and the retrieval-based models achieve politeness with poorer context-relevance, the LFT and Polite-RL models can produce significantly more polite responses without sacrificing dialogue quality.