OpenRaiser/Intern-Atlas huggingface dataset:
Optimizing B-spline surfaces for developability and paneling architectural freeform surfaces
Venue: arXiv cs.GR 2019
Year: 2019

Motivated by applications in architecture and design, we present a novel method for increasing the developability of a B-spline surface. We use the property that the Gauss image of a developable surface is 1-dimensional and can be locally well approximated by circles. This is cast into an algorithm for thinning the Gauss image by increasing the planarity of the Gauss images of appropriate neighborhoods. A variation of the main method allows us to tackle the problem of paneling a freeform architectural surface with developable panels, in particular enforcing rotational cylindrical, rotational conical and planar panels, which are the main preferred types of developable panels in architecture due to the reduced cost of manufacturing.
Deep Portrait Image Completion and Extrapolation
Venue: arXiv cs.GR 2019
Year: 2019

General image completion and extrapolation methods often fail on portrait images where parts of the human body need to be recovered - a task that requires accurate human body structure and appearance synthesis. We present a two-stage deep learning framework for tacking this problem. In the first stage, given a portrait image with an incomplete human body, we extract a complete, coherent human body structure through a human parsing network, which focuses on structure recovery inside the unknown region with the help of pose estimation. In the second stage, we use an image completion network to fill the unknown region, guided by the structure map recovered in the first stage. For realistic synthesis the completion network is trained with both perceptual loss and conditional adversarial loss. We evaluate our method on public portrait image datasets, and show that it outperforms other state-of-art general image completion methods. Our method enables new portrait image editing applications such as occlusion removal and portrait extrapolation. We further show that the proposed general learning framework can be applied to other types of images, e.g. animal images.
Secure Relaying in Non-Orthogonal Multiple Access: Trusted and Untrusted Scenarios
Venue: arXiv cs.IT 2019
Year: 2019

A downlink single-input single-output non-orthogonal multiple access setting is considered, in which a base station (BS) is communicating with two legitimate users in two possible scenarios of unsecure environments: existence of an external eavesdropper and communicating through an untrusted relay. For the first scenario, a number of trusted cooperative half-duplex relays is employed to assist with the BS's transmission and secure its signals from the external eavesdropper. Various relaying schemes are proposed and analyzed for that matter: cooperative jamming, decode-and-forward, and amplify-and-forward. For each scheme, secure beamforming signals are devised at the relays to maximize the achievable secrecy rate regions. For the second scenario, with the untrusted relay, achievable secrecy rate regions are derived for two different relaying schemes: compress-and-forward and amplify-and-forward, under two different modes of operation. In the first mode, coined passive user mode, the users receive signals from both the BS and the untrusted relay, and combine them to decode their messages. In the second mode, coined active user mode, the users transmit a cooperative jamming signal simultaneously with the BS's transmission to further confuse the relay. Focusing on half-duplex nodes, the users cannot receive the BS's signal while jamming the relay, i.e., while being active, and rely only on the signals forwarded to them by the relay. It is shown that the best relaying scheme highly depends on the system parameters, in particular distances between the nodes, and also on which part of the secrecy rate region the system is to operate at.
SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems
Venue: arXiv cs.RO 2019
Year: 2019

The recent drive towards achieving greater autonomy and intelligence in robotics has led to high levels of complexity. Autonomous robots increasingly depend on third party off-the-shelf components and complex machine-learning techniques. This trend makes it challenging to provide strong design-time certification of correct operation. To address these challenges, we present SOTER, a robotics programming framework with two key components: (1) a programming language for implementing and testing high-level reactive robotics software and (2) an integrated runtime assurance (RTA) system that helps enable the use of uncertified components, while still providing safety guarantees. SOTER provides language primitives to declaratively construct a RTA module consisting of an advanced, high-performance controller (uncertified), a safe, lower-performance controller (certified), and the desired safety specification. The framework provides a formal guarantee that a well-formed RTA module always satisfies the safety specification, without completely sacrificing performance by using higher performance uncertified components whenever safe. SOTER allows the complex robotics software stack to be constructed as a composition of RTA modules, where each uncertified component is protected using a RTA module. To demonstrate the efficacy of our framework, we consider a real-world case-study of building a safe drone surveillance system. Our experiments both in simulation and on actual drones show that the SOTER-enabled RTA ensures the safety of the system, including when untrusted third-party components have bugs or deviate from the desired behavior.
Runtime verification in Erlang by using contracts
Venue: arXiv cs.PL 2019
Year: 2019

During its lifetime, a program suffers several changes that seek to improve or to augment some parts of its functionality. However, these modifications usually also introduce errors that affect the already-working code. There are several approaches and tools that help to spot and find the source of these errors. However, most of these errors could be avoided beforehand by using some of the knowledge that the programmers had when they were writing the code. This is the idea behind the design-by-contract approach, where users can define contracts that can be checked during runtime. In this paper, we apply the principles of this approach to Erlang, enabling, in this way, a runtime verification system in this language. We define two types of contracts. One of them can be used in any Erlang program, while the second type is intended to be used only in concurrent programs. We provide the details of the implementation of both types of contracts. Moreover, we provide an extensive explanation of each contract as well as examples of their usage. All the ideas presented in this paper have been implemented in a contract-based runtime verification system named EDBC. Its source code is available at GitHub as an open-source and free project.
From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities
Venue: arXiv cs.CV 2019
Year: 2019

Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through internal/external communication systems, have resulted in a recent surge of significant interest in "Radiomics". Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models, and is expected to become a critical component for integration of image-derived information for personalized treatment in the near future. The conventional Radiomics workflow is typically based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest. Nevertheless, recent advancements in deep learning have caused trends towards deep learning-based Radiomics (also referred to as discovery Radiomics). Considering the advantages of these two approaches, there are also hybrid solutions developed to exploit the potentials of multiple data sources. Considering the variety of approaches to Radiomics, further improvements require a comprehensive and integrated sketch, which is the goal of this article. This manuscript provides a unique interdisciplinary perspective on Radiomics by discussing state-of-the-art signal processing solutions in the context of Radiomics.
A dynamical approach to privacy preserving average consensus
Venue: arXiv cs.SY 2019
Year: 2019

In this paper we propose a novel method for achieving average consensus in a continuous-time multiagent network while avoiding to disclose the initial states of the individual agents. In order to achieve privacy protection of the state variables, we introduce maps, called output masks, which alter the value of the states before transmitting them. These output masks are local (i.e., implemented independently by each agent), deterministic, time-varying and converging asymptotically to the true state. The resulting masked system is also time-varying and has the original (unmasked) system as its limit system. It is shown in the paper that the masked system has the original average consensus value as a global attractor. However, in order to preserve privacy, it cannot share an equilibrium point with the unmasked system, meaning that in the masked system the global attractor cannot be also stable.
Reference Type Logic Variables in Constraint-logic Object-oriented Programming
Venue: arXiv cs.PL 2019
Year: 2019

Constraint-logic object-oriented programming, for example using Muli, facilitates the integrated development of business software that occasionally involves finding solutions to constraint-logic problems. The availability of object-oriented features calls for the option to use objects as logic variables as well, as opposed to being limited to primitive type logic variables. The present work contributes a concept for reference type logic variables in constraint-logic object-oriented programming that takes arbitrary class hierarchies of programs written in object-oriented languages into account. The concept discusses interactions between constraint-logic object-oriented programs and reference type logic variables, particularly invocations on and access to logic variables, type operations, and equality. Furthermore, it proposes approaches as to how these interactions can be handled by a corresponding execution environment.
Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and Multicast Transmission: Spectral and Energy Efficiency Analysis
Venue: arXiv cs.IT 2019
Year: 2019

In a Non-Orthogonal Unicast and Multicast (NOUM) transmission system, a multicast stream intended to all the receivers is superimposed in the power domain on the unicast streams. One layer of Successive Interference Cancellation (SIC) is required at each receiver to remove the multicast stream before decoding its intended unicast stream. In this paper, we first show that a linearly-precoded 1-layer Rate-Splitting (RS) strategy at the transmitter can efficiently exploit this existing SIC receiver architecture. We further propose multi-layer transmission strategies based on the generalized RS and power-domain Non-Orthogonal Multiple Access (NOMA). Two different objectives are studied for the design of the precoders, namely, maximizing the Weighted Sum Rate (WSR) of the unicast messages and maximizing the system Energy Efficiency (EE), both subject to Quality of Service (QoS) rate requirements of all the messages and a sum power constraint. A Weighted Minimum Mean Square Error (WMMSE)-based algorithm and a Successive Convex Approximation (SCA)-based algorithm are proposed to solve the WSR and EE problems, respectively. Numerical results show that the proposed RS-assisted NOUM transmission strategies are more spectrally and energy efficient than the conventional Multi-User Linear-Precoding (MU-LP), Orthogonal Multiple Access (OMA) and power-domain NOMA in a wide range of user deployments (with a diversity of channel directions, channel strengths and qualities of channel state information at the transmitter) and network loads (underloaded and overloaded regimes). It is superior for the downlink multi-antenna NOUM transmission.
Online Human Activity Recognition using Low-Power Wearable Devices
Venue: arXiv cs.CV 2019
Year: 2019

Human activity recognition~(HAR) has attracted significant research interest due to its applications in health monitoring and patient rehabilitation. Recent research on HAR focuses on using smartphones due to their widespread use. However, this leads to inconvenient use, limited choice of sensors and inefficient use of resources, since smartphones are not designed for HAR. This paper presents the first HAR framework that can perform both online training and inference. The proposed framework starts with a novel technique that generates features using the fast Fourier and discrete wavelet transforms of a textile-based stretch sensor and accelerometer. Using these features, we design an artificial neural network classifier which is trained online using the policy gradient algorithm. Experiments on a low power IoT device (TI-CC2650 MCU) with nine users show 97.7% accuracy in identifying six activities and their transitions with less than 12.5 mW power consumption.
AutoAlias: Automatic Variable-Precision Alias Analysis for Object-Oriented Programs
Venue: arXiv cs.SE 2019
Year: 2019

The aliasing question (can two reference expressions point, during an execution, to the same object?) is both one of the most critical in practice, for applications ranging from compiler optimization to programmer verification, and one of the most heavily researched, with many hundreds of publications over several decades. One might then expect that good off-the-shelf solutions are widely available, ready to be plugged into a compiler or verifier. This is not the case. In practice, efficient and precise alias analysis remains an open problem. We present a practical tool, AutoAlias, which can be used to perform automatic alias analysis for object-oriented programs. Based on the theory of "duality semantics", an application of Abstract Interpretation ideas, it is directed at object-oriented languages and has been implemented for Eiffel as an addition to the EiffelStudio environment. It offers variable-precision analysis, controllable through the choice of a constant that governs the number of fix point iterations: a higher number means better precision and higher computation time. All the source code of AutoAlias, as well as detailed results of analyses reported in this article, are publicly available. Practical applications so far have covered a library of data structures and algorithms and a library for GUI creation. For the former, AutoAlias achieves a precision appropriate for practical purposes and execution times in the order of 25 seconds for about 8000 lines of intricate code. For the GUI library, AutoAlias produces the alias analysis in around 232 seconds for about 150000 lines of intricate code.
Coincidental Correctness in the Defects4J Benchmark
Venue: arXiv cs.SE 2019
Year: 2019

Coincidental correctness (CC) arises when a defective program produces the correct output despite the fact that the defect within was exercised. Researchers have recognized the negative impact of coincidental correctness, and the authors have previously conducted a study demonstrating its prevalence in test suites. However, that study was limited to system tests and small subjects seeded with artificial defects. In this paper, we conduct a wider scope study of CC that addresses the following research questions in the context of the Defects4J benchmark: RQ1: Is CC prevalent in Defects4J? RQ2: Is CC affected by the testing levels in Defects4J? RQ3: Do CC tests induce peculiar infection paths in Defects4J? RQ4: Are the infections likely to be nullified within or outside the buggy method? ....
Creating a surrogate commuter network from Australian Bureau of Statistics census data
Venue: arXiv cs.DB 2019
Year: 2019

Between the 2011 and 2016 national censuses, the Australian Bureau of Statistics changed its anonymity policy compliance system for the distribution of census data. The new method has resulted in dramatic inconsistencies when comparing low-resolution data to aggregated high-resolution data. Hence, aggregated totals do not match true totals, and the mismatch gets worse as the data resolution gets finer. Here, we address several aspects of this inconsistency with respect to the 2016 usual-residence to place-of-work travel data. We introduce a re-sampling system that rectifies many of the artifacts introduced by the new ABS protocol, ensuring a higher level of consistency across partition sizes. We offer a surrogate high-resolution 2016 commuter dataset that reduces the difference between aggregated and true commuter totals from ~34% to only ~7%, which is on the order of the discrepancy across partition resolutions in data from earlier years.
Xu: An Automated Query Expansion and Optimization Tool
Venue: arXiv cs.CL 2019
Year: 2019

The exponential growth of information on the Internet is a big challenge for information retrieval systems towards generating relevant results. Novel approaches are required to reformat or expand user queries to generate a satisfactory response and increase recall and precision. Query expansion (QE) is a technique to broaden users' queries by introducing additional tokens or phrases based on some semantic similarity metrics. The tradeoff is the added computational complexity to find semantically similar words and a possible increase in noise in information retrieval. Despite several research efforts on this topic, QE has not yet been explored enough and more work is needed on similarity matching and composition of query terms with an objective to retrieve a small set of most appropriate responses. QE should be scalable, fast, and robust in handling complex queries with a good response time and noise ceiling. In this paper, we propose Xu, an automated QE technique, using high dimensional clustering of word vectors and Datamuse API, an open source query engine to find semantically similar words. We implemented Xu as a command line tool and evaluated its performances using datasets containing news articles and human-generated QEs. The evaluation results show that Xu was better than Datamuse by achieving about 88% accuracy with reference to the human-generated QE.
Gibbs Phenomenon of Framelet Expansions and Quasi-projection Approximation
Venue: arXiv cs.IT 2019
Year: 2019

The Gibbs phenomenon is widely known for Fourier expansions of periodic functions and refers to the phenomenon that the $n$th Fourier partial sums overshoot a target function at jump discontinuities in such a way that such overshoots do not die out as $n$ goes to infinity. The Gibbs phenomenon for wavelet expansions using (bi)orthogonal wavelets has been studied in the literature. Framelets (also called wavelet frames) generalize (bi)orthogonal wavelets. Approximation by quasi-projection operators are intrinsically linked to approximation by truncated wavelet and framelet expansions. In this paper we shall establish a key identity for quasi-projection operators and then we use it to study the Gibbs phenomenon of framelet expansions and approximation by general quasi-projection operators. We shall also study and characterize the Gibbs phenomenon at an arbitrary point for approximation by quasi-projection operators. As a consequence, we show that the Gibbs phenomenon appears at all points for every tight or dual framelet having at least two vanishing moments and for quasi-projection operators having at least three accuracy orders. Our results not only improve current results in the literature on the Gibbs phenomenon for (bi)orthogonal wavelet expansions but also are new for framelet expansions and approximation by quasi-projection operators.
Lipschitz regularized Deep Neural Networks generalize and are adversarially robust
Venue: arXiv cs.LG 2019
Year: 2019

In this work we study input gradient regularization of deep neural networks, and demonstrate that such regularization leads to generalization proofs and improved adversarial robustness. The proof of generalization does not overcome the curse of dimensionality, but it is independent of the number of layers in the networks. The adversarial robustness regularization combines adversarial training, which we show to be equivalent to Total Variation regularization, with Lipschitz regularization. We demonstrate empirically that the regularized models are more robust, and that gradient norms of images can be used for attack detection.
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
Venue: arXiv cs.CL 2019
Year: 2019

Question categorization and expert retrieval methods have been crucial for information organization and accessibility in community question & answering (CQA) platforms. Research in this area, however, has dealt with only the text modality. With the increasing multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images. Specifically, we leverage the success of representation learning for text and images in the visual question answering (VQA) domain, and adapt the underlying concept and architecture for automated category classification and expert retrieval on image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of Yahoo! Answers. To the best of our knowledge, this is the first work to tackle the multimodality challenge in CQA, and to adapt VQA models for tasks on a more ecologically valid source of visual questions. Our analysis of the differences between visual QA and community QA data drives our proposal of novel augmentations of an attention method tailored for CQA, and use of auxiliary tasks for learning better grounding features. Our final model markedly outperforms the text-only and VQA model baselines for both tasks of classification and expert retrieval on real-world multimodal CQA data.
Approximate Exploration through State Abstraction
Venue: arXiv cs.LG 2019
Year: 2019

Although exploration in reinforcement learning is well understood from a theoretical point of view, provably correct methods remain impractical. In this paper we study the interplay between exploration and approximation, what we call approximate exploration. Our main goal is to further our theoretical understanding of pseudo-count based exploration bonuses (Bellemare et al., 2016), a practical exploration scheme based on density modelling. As a warm-up, we quantify the performance of an exploration algorithm, MBIE-EB (Strehl and Littman, 2008), when explicitly combined with state aggregation. This allows us to confirm that, as might be expected, approximation allows the agent to trade off between learning speed and quality of the learned policy. Next, we show how a given density model can be related to an abstraction and that the corresponding pseudo-count bonus can act as a substitute in MBIE-EB combined with this abstraction, but may lead to either under- or over-exploration. Then, we show that a given density model also defines an implicit abstraction, and find a surprising mismatch between pseudo-counts derived either implicitly or explicitly. Finally we derive a new pseudo-count bonus alleviating this issue.