OpenRaiser/Intern-Atlas huggingface dataset:
Transfer Learning for Unseen Robot Detection and Joint Estimation on a Multi-Objective Convolutional Neural Network
Venue: arXiv cs.RO 2018
Year: 2018

A significant problem of using deep learning techniques is the limited amount of data available for training. There are some datasets available for the popular problems like item recognition and classification or self-driving cars, however, it is very limited for the industrial robotics field. In previous work, we have trained a multi-objective Convolutional Neural Network (CNN) to identify the robot body in the image and estimate 3D positions of the joints by using just a 2D image, but it was limited to a range of robots produced by Universal Robots (UR). In this work, we extend our method to work with a new robot arm - Kuka LBR iiwa, which has a significantly different appearance and an additional joint. However, instead of collecting large datasets once again, we collect a number of smaller datasets containing a few hundred frames each and use transfer learning techniques on the CNN trained on UR robots to adapt it to a new robot having different shapes and visual features. We have proven that transfer learning is not only applicable in this field, but it requires smaller well-prepared training datasets, trains significantly faster and reaches similar accuracy compared to the original method, even improving it on some aspects.
Emptiness of Stack Automata is NEXPTIME-complete: A Correction
Venue: arXiv cs.FL 2018
Year: 2018

A saturation algorithm for collapsible pushdown systems was published in ICALP 2012. This work introduced a class of stack automata used to recognised regular sets of collapsible pushdown configurations. It was shown that these automata form an effective boolean algebra, have a linear time membership problem, and are equivalent to an alternative automata representation appearing in LICS 2010. It was also claimed that the emptiness problem for stack automata is PSPACE-complete. Unfortunately, this claim is not true. We show that the problem is in fact NEXPTIME-complete when the stacks being accepted are collapsible pushdown stacks, rather than the annotated stacks used in ICALP 2012.
Predictive Performance Modeling for Distributed Computing using Black-Box Monitoring and Machine Learning
Venue: arXiv cs.DC 2018
Year: 2018

In many domains, the previous decade was characterized by increasing data volumes and growing complexity of computational workloads, creating new demands for highly data-parallel computing in distributed systems. Effective operation of these systems is challenging when facing uncertainties about the performance of jobs and tasks under varying resource configurations, e.g., for scheduling and resource allocation. We survey predictive performance modeling (PPM) approaches to estimate performance metrics such as execution duration, required memory or wait times of future jobs and tasks based on past performance observations. We focus on non-intrusive methods, i.e., methods that can be applied to any workload without modification, since the workload is usually a black-box from the perspective of the systems managing the computational infrastructure. We classify and compare sources of performance variation, predicted performance metrics, required training data, use cases, and the underlying prediction techniques. We conclude by identifying several open problems and pressing research needs in the field.
Propagating Confidences through CNNs for Sparse Data Regression
Venue: arXiv cs.CV 2018
Year: 2018

In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open problem with numerous applications in autonomous driving, robotics, and surveillance. To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. Furthermore, we propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. Comprehensive experiments are performed on the KITTI depth benchmark and the results clearly demonstrate that the proposed approach achieves superior performance while requiring three times fewer parameters than the state-of-the-art methods. Moreover, our approach produces a continuous pixel-wise confidence map enabling information fusion, state inference, and decision support.
l0-norm Based Centers Selection for Training Fault Tolerant RBF Networks and Selecting Centers
Venue: arXiv cs.LG 2018
Year: 2018

The aim of this paper is to train an RBF neural network and select centers under concurrent faults. It is well known that fault tolerance is a very attractive property for neural networks. And center selection is an important procedure during the training process of an RBF neural network. In this paper, we devise two novel algorithms to address these two issues simultaneously. Both of them are based on the ADMM framework. In the first method, the minimax concave penalty (MCP) function is introduced to select centers. In the second method, an l0-norm term is directly used, and the hard threshold (HT) is utilized to address the l0-norm term. Under several mild conditions, we can prove that both methods can globally converge to a unique limit point. Simulation results show that, under concurrent fault, the proposed algorithms are superior to many existing methods.
Beam Discovery Using Linear Block Codes for Millimeter Wave Communication Networks
Venue: arXiv cs.IT 2018
Year: 2018

The surge in mobile broadband data demands is expected to surpass the available spectrum capacity below $6$ GHz. This expectation has prompted the exploration of millimeter wave (mm-wave) frequency bands as a candidate technology for next generation wireless networks. However, numerous challenges to deploying mm-wave communication systems, including channel estimation, need to be met before practical deployments are possible. This work addresses the mm-wave channel estimation problem and treats it as a beam discovery problem in which locating beams with strong path reflectors is analogous to locating errors in linear block codes. We show that a significantly small number of measurements (compared to the original dimensions of the channel matrix) is sufficient to reliably estimate the channel. We also show that this can be achieved using a simple and energy-efficient transceiver architecture.
Privacy Aware Offloading of Deep Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Deep neural networks require large amounts of resources which makes them hard to use on resource constrained devices such as Internet-of-things devices. Offloading the computations to the cloud can circumvent these constraints but introduces a privacy risk since the operator of the cloud is not necessarily trustworthy. We propose a technique that obfuscates the data before sending it to the remote computation node. The obfuscated data is unintelligible for a human eavesdropper but can still be classified with a high accuracy by a neural network trained on unobfuscated images.
Graph Sparsification, Spectral Sketches, and Faster Resistance Computation, via Short Cycle Decompositions
Venue: arXiv cs.DS 2018
Year: 2018

We develop a framework for graph sparsification and sketching, based on a new tool, short cycle decomposition -- a decomposition of an unweighted graph into an edge-disjoint collection of short cycles, plus few extra edges. A simple observation gives that every graph G on n vertices with m edges can be decomposed in $O(mn)$ time into cycles of length at most $2\log n$, and at most $2n$ extra edges. We give an $m^{1+o(1)}$ time algorithm for constructing a short cycle decomposition, with cycles of length $n^{o(1)}$, and $n^{1+o(1)}$ extra edges. These decompositions enable us to make progress on several open questions: * We give an algorithm to find $(1\pmε)$-approximations to effective resistances of all edges in time $m^{1+o(1)}ε^{-1.5}$, improving over the previous best of $\tilde{O}(\min\{mε^{-2},n^2 ε^{-1}\})$. This gives an algorithm to approximate the determinant of a Laplacian up to $(1\pmε)$ in $m^{1 + o(1)} + n^{15/8+o(1)}ε^{-7/4}$ time. * We show existence and efficient algorithms for constructing graphical spectral sketches -- a distribution over sparse graphs H such that for a fixed vector $x$, we have w.h.p. $x'L_Hx=(1\pmε)x'L_Gx$ and $x'L_H^+x=(1\pmε)x'L_G^+x$. This implies the existence of resistance-sparsifiers with about $nε^{-1}$ edges that preserve the effective resistances between every pair of vertices up to $(1\pmε).$ * By combining short cycle decompositions with known tools in graph sparsification, we show the existence of nearly-linear sized degree-preserving spectral sparsifiers, as well as significantly sparser approximations of directed graphs. The latter is critical to recent breakthroughs on faster algorithms for solving linear systems in directed Laplacians. Improved algorithms for constructing short cycle decompositions will lead to improvements for each of the above results.
A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer
Venue: arXiv cs.CV 2018
Year: 2018

Predicting TNM stage is the major determinant of breast cancer prognosis and treatment. The essential part of TNM stage classification is whether the cancer has metastasized to the regional lymph nodes (N-stage). Pathologic N-stage (pN-stage) is commonly performed by pathologists detecting metastasis in histological slides. However, this diagnostic procedure is prone to misinterpretation and would normally require extensive time by pathologists because of the sheer volume of data that needs a thorough review. Automated detection of lymph node metastasis and pN-stage prediction has a great potential to reduce their workload and help the pathologist. Recent advances in convolutional neural networks (CNN) have shown significant improvements in histological slide analysis, but accuracy is not optimized because of the difficulty in the handling of gigapixel images. In this paper, we propose a robust method for metastasis detection and pN-stage classification in breast cancer from multiple gigapixel pathology images in an effective way. pN-stage is predicted by combining patch-level CNN based metastasis detector and slide-level lymph node classifier. The proposed framework achieves a state-of-the-art quadratic weighted kappa score of 0.9203 on the Camelyon17 dataset, outperforming the previous winning method of the Camelyon17 challenge.
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network
Venue: arXiv cs.CV 2018
Year: 2018

Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.
Marian: Cost-effective High-Quality Neural Machine Translation in C++
Venue: arXiv cs.CL 2018
Year: 2018

This paper describes the submissions of the "Marian" team to the WNMT 2018 shared task. We investigate combinations of teacher-student training, low-precision matrix products, auto-tuning and other methods to optimize the Transformer model on GPU and CPU. By further integrating these methods with the new averaging attention networks, a recently introduced faster Transformer variant, we create a number of high-quality, high-performance models on the GPU and CPU, dominating the Pareto frontier for this shared task.
Monodromy Solver: Sequential and Parallel
Venue: arXiv cs.SC 2018
Year: 2018

We describe, study, and experiment with an algorithm for finding all solutions of systems of polynomial equations using homotopy continuation and monodromy. This algorithm follows a framework developed in previous work and can operate in the presence of a large number of failures of the homotopy continuation subroutine. We give special attention to parallelization and probabilistic analysis of a model adapted to parallelization and failures. Apart from theoretical results, we developed a simulator that allows us to run a large number of experiments without recomputing the outcomes of the continuation subroutine.
Blockchain for Access Control in e-Health Scenarios
Venue: arXiv cs.CR 2018
Year: 2018

Access control is a crucial part of a system's security, restricting what actions users can perform on resources. Therefore, access control is a core component when dealing with e-Health data and resources, discriminating which is available for a certain party. We consider that current systems that attempt to assure the share of policies between facilities are prone to system's and network's faults and do not assure the integrity of policies lifecycle. By approaching this problem with the use of a distributed ledger, namely a consortium blockchain, where the operations are stored as transactions, we ensure that the different facilities have knowledge about all the parties that can act over the e-Health resources while maintaining integrity, auditability, authenticity, and scalability.
FPGA-based Acceleration of FT Convolution for Pulsar Search Using OpenCL
Venue: arXiv cs.DC 2018
Year: 2018

The Square Kilometre Array (SKA) project will be the world largest radio telescope array. With its large number of antennas, the number of signals that need to be processed is dramatic. One important element of the SKA's Central Signal Processor package is pulsar search. This paper focuses on the FPGA-based acceleration of the Frequency-Domain Acceleration Search module, which is a part of SKA pulsar search engine. In this module, the frequency-domain input signals have to be processed by 85 Finite Impulse response (FIR) filters within a short period of limitation and for thousands of input arrays. Because of the large scale of the input length and FIR filter size, even high-end FPGA devices cannot parallelise the task completely. We start by investigating both time-domain FIR filter (TDFIR) and frequency-domain FIR filter (FDFIR) to tackle this task. We applied the overlap-add algorithm to split the coefficient array of TDFIR and the overlap-save algorithm to split the input signals of FDFIR. To achieve fast prototyping design, we employed OpenCL, which is a high-level FPGA development technique. The performance and power consumption are evaluated using multiple FPGA devices simultaneously and compared with GPU results, which is achieved by porting FPGA-based OpenCL kernels. The experimental evaluation shows that the FDFIR solution is very competitive in terms of performance, with a clear energy consumption advantage over the GPU solution.
Start Late or Finish Early: A Distributed Graph Processing System with Redundancy Reduction
Venue: arXiv cs.DC 2018
Year: 2018

Graph processing systems are important in the big data domain. However, processing graphs in parallel often introduces redundant computations in existing algorithms and models. Prior work has proposed techniques to optimize redundancies for the out-of-core graph systems, rather than the distributed graph systems. In this paper, we study various state-of-the-art distributed graph systems and observe root causes for these pervasively existing redundancies. To reduce redundancies without sacrificing parallelism, we further propose SLFE, a distributed graph processing system, designed with the principle of "start late or finish early". SLFE employs a novel preprocessing stage to obtain a graph's topological knowledge with negligible overhead. SLFE's redundancy-aware vertex-centric computation model can then utilize such knowledge to reduce the redundant computations at runtime. SLFE also provides a set of APIs to improve the programmability. Our experiments on an 8-node high-performance cluster show that SLFE outperforms all well-known distributed graph processing systems on real-world graphs (yielding up to 74.8x speedup). SLFE's redundancy-reduction schemes are generally applicable to other vertex-centric graph processing systems.
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
Venue: arXiv cs.LG 2018
Year: 2018

We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks. We demonstrate the effectiveness of these methods using both quantitative metrics and human evaluation on various state-of-the-art models for text classification, including a word-based CNN, a character-based CNN and an LSTM. As as example of our results, we show that the accuracy of character-based convolutional networks drops to the level of random selection by modifying only five characters through Greedy Attack.
QuickIM: Efficient, Accurate and Robust Influence Maximization Algorithm on Billion-Scale Networks
Venue: arXiv cs.SI 2018
Year: 2018

The Influence Maximization (IM) problem aims at finding k seed vertices in a network, starting from which influence can be spread in the network to the maximum extent. In this paper, we propose QuickIM, the first versatile IM algorithm that attains all the desirable properties of a practically applicable IM algorithm at the same time, namely high time efficiency, good result quality, low memory footprint, and high robustness. On real-world social networks, QuickIM achieves the $Ω(n + m)$ lower bound on time complexity and $Ω(n)$ space complexity, where $n$ and $m$ are the number of vertices and edges in the network, respectively. Our experimental evaluation verifies the superiority of QuickIM. Firstly, QuickIM runs 1-3 orders of magnitude faster than the state-of-the-art IM algorithms. Secondly, except EasyIM, QuickIM requires 1-2 orders of magnitude less memory than the state-of-the-art algorithms. Thirdly, QuickIM always produces as good quality results as the state-of-the-art algorithms. Lastly, the time and the memory performance of QuickIM is independent of influence probabilities. On the largest network used in the experiments that contains more than 3.6 billion edges, QuickIM is able to find hundreds of influential seeds in less than 4 minutes, while all the state-of-the-art algorithms fail to terminate in an hour.
Crowdsourcing for Reminiscence Chatbot Design
Venue: arXiv cs.HC 2018
Year: 2018

In this work-in-progress paper we discuss the challenges in identifying effective and scalable crowd-based strategies for designing content, conversation logic, and meaningful metrics for a reminiscence chatbot targeted at older adults. We formalize the problem and outline the main research questions that drive the research agenda in chatbot design for reminiscence and for relational agents for older adults in general.
Light Field Denoising via Anisotropic Parallax Analysis in a CNN Framework
Venue: arXiv cs.CV 2018
Year: 2018

Light field (LF) cameras provide perspective information of scenes by taking directional measurements of the focusing light rays. The raw outputs are usually dark with additive camera noise, which impedes subsequent processing and applications. We propose a novel LF denoising framework based on anisotropic parallax analysis (APA). Two convolutional neural networks are jointly designed for the task: first, the structural parallax synthesis network predicts the parallax details for the entire LF based on a set of anisotropic parallax features. These novel features can efficiently capture the high frequency perspective components of a LF from noisy observations. Second, the view-dependent detail compensation network restores non-Lambertian variation to each LF view by involving view-specific spatial energies. Extensive experiments show that the proposed APA LF denoiser provides a much better denoising performance than state-of-the-art methods in terms of visual quality and in preservation of parallax details.
The Types, Roles, and Practices of Documentation in Data Analytics Open Source Software Libraries: A Collaborative Ethnography of Documentation Work
Venue: arXiv cs.CY 2018
Year: 2018

Computational research and data analytics increasingly relies on complex ecosystems of open source software (OSS) "libraries" -- curated collections of reusable code that programmers import to perform a specific task. Software documentation for these libraries is crucial in helping programmers/analysts know what libraries are available and how to use them. Yet documentation for open source software libraries is widely considered low-quality. This article is a collaboration between CSCW researchers and contributors to data analytics OSS libraries, based on ethnographic fieldwork and qualitative interviews. We examine several issues around the formats, practices, and challenges around documentation in these largely volunteer-based projects. There are many different kinds and formats of documentation that exist around such libraries, which play a variety of educational, promotional, and organizational roles. The work behind documentation is similarly multifaceted, including writing, reviewing, maintaining, and organizing documentation. Different aspects of documentation work require contributors to have different sets of skills and overcome various social and technical barriers. Finally, most of our interviewees do not report high levels of intrinsic enjoyment for doing documentation work (compared to writing code). Their motivation is affected by personal and project-specific factors, such as the perceived level of credit for doing documentation work versus more "technical" tasks like adding new features or fixing bugs. In studying documentation work for data analytics OSS libraries, we gain a new window into the changing practices of data-intensive research, as well as help practitioners better understand how to support this often invisible and infrastructural work in their projects.