OpenRaiser/Intern-Atlas huggingface dataset:
Satisfaction of the Condition of Order Preservation: A Simulation Study
Venue: arXiv cs.DM 2019
Year: 2019

We examine satisfaction of the condition of order preservation (COP) with respect to different levels of inconsistency for randomly generated multiplicative pairwise comparison matrices (MPCMs) of the order n = {3,4,...,9}, where a priority vector is derived both by the eigenvalue method (EV) and the geometric mean (GM) method. Our results suggest the GM method and the EV method preserve the COP condition almost identically, both for the less inconsistent matrices (with Saaty's consistency index CI <0.10), and the more inconsistent matrices (with CI >= 0.10). Further, we find that the frequency of the COP violations grows (almost linearly) with increasing inconsistency of MPCMs measured by Koczkodaj's inconsistency index and Saaty's consistency index respectively, and we provide graphs to illustrate these relationships for MPCMs of the order n ={4, 7, 9}.
Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context
Venue: arXiv cs.CL 2019
Year: 2019

Making sense of words often requires to simultaneously examine the surrounding context of a term as well as the global themes characterizing the overall corpus. Several topic models have already exploited word embeddings to recognize local context, however, it has been weakly combined with the global context during the topic inference. This paper proposes to extract topical phrases corroborating the word embedding information with the global context detected by Latent Semantic Analysis, and then combine them by means of the Pólya urn model. To highlight the effectiveness of this combined approach the model was assessed analyzing clinical reports, a challenging scenario characterized by technical jargon and a limited word statistics available. Results show it outperforms the state-of-the-art approaches in terms of both topic coherence and computational cost.
Importance Sampling of Many Lights with Reinforcement Lightcuts Learning
Venue: arXiv cs.GR 2019
Year: 2019

In this manuscript, we introduce a novel technique for sampling and integrating direct illumination in the presence of many lights. Unlike previous work, the presented technique importance samples the product distribution of radiance and visibility while using bounded memory footprint and very low sampling overhead. This is achieved by learning a compact approximation of the target distributions over both space and time, allowing to reuse and adapt the learnt distributions both spatially, within a frame, and temporally, across multiple frames. Finally, the technique is amenable to massive parallelization on GPUs and suitable for both offline and real-time rendering.
Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core
Venue: arXiv cs.CV 2019
Year: 2019

We present a novel method to track 3D models in color and depth data. To this end, we introduce approximations that accelerate the state-of-the-art in region-based tracking by an order of magnitude while retaining similar accuracy. Furthermore, we show how the method can be made more robust in the presence of depth data and consequently formulate a new joint contour and ICP tracking energy. We present better results than the state-of-the-art while being much faster then most other methods and achieving all of the above on a single CPU core.
An Algorithm for Strong Stability in the Student-Project Allocation Problem with Ties
Venue: arXiv cs.DS 2019
Year: 2019

We study a variant of the Student-Project Allocation problem with lecturer preferences over Students where ties are allowed in the preference lists of students and lecturers (SPA-ST). We investigate the concept of strong stability in this context. Informally, a matching is strongly stable if there is no student and lecturer $l$ such that if they decide to form a private arrangement outside of the matching via one of $l$'s proposed projects, then neither party would be worse off and at least one of them would strictly improve. We describe the first polynomial-time algorithm to find a strongly stable matching or to report that no such matching exists, given an instance of SPA-ST. Our algorithm runs in $O(m^2)$ time, where $m$ is the total length of the students' preference lists.
Training Modern Deep Neural Networks for Memory-Fault Robustness
Venue: arXiv cs.LG 2019
Year: 2019

Because deep neural networks (DNNs) rely on a large number of parameters and computations, their implementation in energy-constrained systems is challenging. In this paper, we investigate the solution of reducing the supply voltage of the memories used in the system, which results in bit-cell faults. We explore the robustness of state-of-the-art DNN architectures towards such defects and propose a regularizer meant to mitigate their effects on accuracy. Our experiments clearly demonstrate the interest of operating the system in a faulty regime to save energy without reducing accuracy.
Scalable sim-to-real transfer of soft robot designs
Venue: arXiv cs.RO 2019
Year: 2019

The manual design of soft robots and their controllers is notoriously challenging, but it could be augmented---or, in some cases, entirely replaced---by automated design tools. Machine learning algorithms can automatically propose, test, and refine designs in simulation, and the most promising ones can then be manufactured in reality (sim2real). However, it is currently not known how to guarantee that behavior generated in simulation can be preserved when deployed in reality. Although many previous studies have devised training protocols that facilitate sim2real transfer of control polices, little to no work has investigated the simulation-reality gap as a function of morphology. This is due in part to an overall lack of tools capable of systematically designing and rapidly manufacturing robots. Here we introduce a low cost, open source, and modular soft robot design and construction kit, and use it to simulate, fabricate, and measure the simulation-reality gap of minimally complex yet soft, locomoting machines. We prove the scalability of this approach by transferring an order of magnitude more robot designs from simulation to reality than any other method. The kit and its instructions can be found here: https://github.com/skriegman/sim2real4designs
Representing Software Project Vision by Means of Video: A Quality Model for Vision Videos
Venue: arXiv cs.SE 2019
Year: 2019

Establishing a shared software project vision is a key challenge in Requirements Engineering (RE). Several approaches use videos to represent visions. However, these approaches omit how to produce a good video. This missing guidance is one crucial reason why videos are not established in RE. We propose a quality model for videos representing a vision, so-called vision videos. Based on two literature reviews, we elaborate ten quality characteristics of videos and five quality characteristics of visions which together form a quality model for vision videos that includes all 15 quality characteristics. We provide two representations of the quality model: (a) A hierarchical decomposition of vision video quality into the quality characteristics and (b) A mapping of these characteristics to the video production and use process. While the hierarchical decomposition supports the evaluation of vision videos, the mapping provides guidance for video production. In an evaluation with 139 students, we investigated whether the 15 characteristics are related to the overall quality of vision videos perceived by the subjects from a developer's the point of view. Six characteristics (video length, focus, prior knowledge, clarity, pleasure, and stability) correlated significantly with the likelihood that the subjects perceived a vision video as good. These relationships substantiate a fundamental relevance of the proposed quality model. Therefore, we conclude that the quality model is a sound basis for future refinements and extensions.
Empowering Artists, Songwriters & Musicians in a Data Cooperative through Blockchains and Smart Contracts
Venue: arXiv cs.CR 2019
Year: 2019

Over the last decade there has been a continuing decline in social trust on the part of individuals with regards to the handling and fair use of personal data, digital assets and other related rights in general. At the same time, there has been a change in the employment patterns for many people through the emergence of the gig economy. These gig workers include artists, songwriters and musicians in the music industry. We discuss the notion of the data cooperative with fiduciary responsibilities to its members, which is similar in purpose to credit unions in the financial sector. A data cooperative for artists and musicians allows the community to share IT resources, such as data storage, analytics processing, blockchains and distributed ledgers. A cooperative can also employ smart contracts to remedy the various challenges currently faced by the music industry with regards to the license tracking management.
ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition
Venue: arXiv cs.CL 2019
Year: 2019

Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks. In this work we present ScienceExamCER, a densely-labeled semantic classification corpus of 133k mentions in the science exam domain where nearly all (96%) of content words have been annotated with one or more fine-grained semantic class labels including taxonomic groups, meronym groups, verb/action groups, properties and values, and synonyms. Semantic class labels are drawn from a manually-constructed fine-grained typology of 601 classes generated through a data-driven analysis of 4,239 science exam questions. We show an off-the-shelf BERT-based named entity recognition model modified for multi-label classification achieves an accuracy of 0.85 F1 on this task, suggesting strong utility for downstream tasks in science domain question answering requiring densely-labeled semantic classification.
Neural Storyboard Artist: Visualizing Stories with Coherent Image Sequences
Venue: arXiv cs.LG 2019
Year: 2019

A storyboard is a sequence of images to illustrate a story containing multiple sentences, which has been a key process to create different story products. In this paper, we tackle a new multimedia task of automatic storyboard creation to facilitate this process and inspire human artists. Inspired by the fact that our understanding of languages is based on our past experience, we propose a novel inspire-and-create framework with a story-to-image retriever that selects relevant cinematic images for inspiration and a storyboard creator that further refines and renders images to improve the relevancy and visual consistency. The proposed retriever dynamically employs contextual information in the story with hierarchical attentions and applies dense visual-semantic matching to accurately retrieve and ground images. The creator then employs three rendering steps to increase the flexibility of retrieved images, which include erasing irrelevant regions, unifying styles of images and substituting consistent characters. We carry out extensive experiments on both in-domain and out-of-domain visual story datasets. The proposed model achieves better quantitative performance than the state-of-the-art baselines for storyboard creation. Qualitative visualizations and user studies further verify that our approach can create high-quality storyboards even for stories in the wild.
ContractGuard: Defend Ethereum Smart Contracts with Embedded Intrusion Detection
Venue: arXiv cs.SE 2019
Year: 2019

Ethereum smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. Smart contracts handle and transfer assets of values, offering strong incentives for malicious attacks. Intrusion attacks are a popular type of malicious attacks. In this paper, we propose ContractGuard, the first intrusion detection system (IDS) to defend Ethereum smart contracts against such attacks. Like IDSs for conventional programs, ContractGuard detects intrusion attempts as abnormal control flow. However, existing IDS techniques/tools are inapplicable to Ethereum smart contracts due to Ethereum's decentralized nature and its highly restrictive execution environment. To address these issues, we design ContractGuard by embedding it in the contracts to profile context-tagged acyclic paths, and optimizing it under the Ethereum gas-oriented performance model. The main goal is to minimize the overheads, to which the users will be extremely sensitive since the cost needs to be paid upfront in digital concurrency. Empirical investigation using real-life contracts deployed in the Ethereum mainnet shows that on average, ContractGuard only adds to 36.14% of the deployment overhead and 28.27% of the runtime overhead. Furthermore, we conducted controlled experiments and show that ContractGuard successfully guard against attacks on all real-world vulnerabilities and 83% of the seeded vulnerabilities.
A survey of of blockchain-based solutions for Energy Industry
Venue: arXiv cs.CY 2019
Year: 2019

The energy industry needs to shift to a new paradigm from its classical model of energy generation, distribution, and management. This shift is necessary to handle digitization, increased renewable energy generation, and to achieve goals of environmental sustainability. This shift has several challenges on its way and has been seen through research and development that blockchain which is one of the budding technology in this era could be suitable for addressing those challenges. This paper is aimed at the survey of all the research and development related to blockchain in the energy industry and uses a software engineering approach to categories all the existing work in several clusters such as challenges addressed, quality attribute promoted, the maturity level of the solutions, etc. This survey provides researchers in this field a well-defined categorization and insight into the existing work in this field from 3 different perspectives (challenges, quality attributes, maturity).
Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction
Venue: arXiv cs.LG 2019
Year: 2019

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of SHARE outperforms seven state-of-the-art baselines.
Latent space conditioning for improved classification and anomaly detection
Venue: arXiv cs.LG 2019
Year: 2019

We propose a new type of variational autoencoder to perform improved pre-processing for clustering and anomaly detection on data with a given label. Anomalies however are not known or labeled. We call our method conditional latent space variational autonencoder since it separates the latent space by conditioning on information within the data. The method fits one prior distribution to each class in the dataset, effectively expanding the prior distribution to include a Gaussian mixture model. Our approach is compared against the capabilities of a typical variational autoencoder by measuring their V-score during cluster formation with respect to the k-means and EM algorithms. For anomaly detection, we use a new metric composed of the mass-volume and excess-mass curves which can work in an unsupervised setting. We compare the results between established methods such as as isolation forest, local outlier factor and one-class support vector machine.
I Send, Therefore I Leak: Information Leakage in Low-Power Wide Area Networks
Venue: arXiv cs.CR 2019
Year: 2019

Low-power wide area networks (LPWANs), such as LoRa, are fast emerging as the preferred networking technology for large-scale Internet of Things deployments (e.g., smart cities). Due to long communication range and ultra low power consumption, LPWAN-enabled sensors are today being deployed in a variety of application scenarios where sensitive information is wirelessly transmitted. In this work, we study the privacy guarantees of LPWANs, in particular LoRa. We show that, although the event-based duty cycling of radio communication, i.e., transmission of radio signals only when an event occurs, saves power, it inherently leaks information. This information leakage is independent of the implemented crypto primitives. We identify two types of information leakage and show that it is hard to completely prevent leakage without incurring significant additional communication and computation costs.
Reducing the Human Effort in Developing PET-CT Registration
Venue: arXiv cs.CV 2019
Year: 2019

We aim to reduce the tedious nature of developing and evaluating methods for aligning PET-CT scans from multiple patient visits. Current methods for registration rely on correspondences that are created manually by medical experts with 3D manipulation, or assisted alignments done by utilizing mutual information across CT scans that may not be consistent when transferred to the PET images. Instead, we propose to label multiple key points across several 2D slices, which we then fit a key curve to. This removes the need for creating manual alignments in 3D and makes the labelling process easier. We use these key curves to define an error metric for the alignments that can be computed efficiently. While our metric is non-differentiable, we further show that we can utilize it during the training of our deep model via a novel method. Specifically, instead of relying on detailed geometric labels -- e.g., manual 3D alignments -- we use synthetically generated deformations of real data. To incorporate robustness to changes that occur between visits other than geometric changes, we enforce consistency across visits in the deep network's internal representations. We demonstrate the potential of our method via qualitative and quantitative experiments.
Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer
Venue: arXiv cs.CL 2019
Year: 2019

Conversation structure is useful for both understanding the nature of conversation dynamics and for providing features for many downstream applications such as summarization of conversations. In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to. Previous work usually took a pair of utterances to decide whether one utterance is the parent of the other. We believe the entire ancestral history is a very important information source to make accurate prediction. Therefore, we design a novel masking mechanism to guide the ancestor flow, and leverage the transformer model to aggregate all ancestors to predict parent utterances. Our experiments are performed on the Reddit dataset (Zhang, Culbertson, and Paritosh 2017) and the Ubuntu IRC dataset (Kummerfeld et al. 2019). In addition, we also report experiments on a new larger corpus from the Reddit platform and release this dataset. We show that the proposed model, that takes into account the ancestral history of the conversation, significantly outperforms several strong baselines including the BERT model on all datasets
Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning
Venue: arXiv cs.CV 2019
Year: 2019

Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly. More facts indicate racial bias indeed degrades the fairness of recognition system and the error rates on non-Caucasians are usually much higher than Caucasians. To encourage fairness, we introduce the idea of adaptive margin to learn balanced performance for different races based on large margin losses. A reinforcement learning based race balance network (RL-RBN) is proposed. We formulate the process of finding the optimal margins for non-Caucasians as a Markov decision process and employ deep Q-learning to learn policies for an agent to select appropriate margin by approximating the Q-value function. Guided by the agent, the skewness of feature scatter between races can be reduced. Besides, we provide two ethnicity aware training datasets, called BUPT-Globalface and BUPT-Balancedface dataset, which can be utilized to study racial bias from both data and algorithm aspects. Extensive experiments on RFW database show that RL-RBN successfully mitigates racial bias and learns more balanced performance for different races.
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images
Venue: arXiv cs.CV 2019
Year: 2019

The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the low-resolution image is a common and challenging case. In this paper, we propose the cascaded networks for document image super-resolution. Our model is composed by the Detail-Preserving Networks with small magnification. The loss function with perceptual terms is designed to simultaneously preserve the original patterns and enhance the edge of the characters. These networks are trained with the same architecture and different parameters and then assembled into a pipeline model with a larger magnification. The low-resolution images can upscale gradually by passing through each Detail-Preserving Network until the final high-resolution images. Through extensive experiments on two scanning document image datasets, we demonstrate that the proposed approach outperforms recent state-of-the-art image super-resolution methods, and combining it with standard OCR system lead to signification improvements on the recognition results.