OpenRaiser/Intern-Atlas huggingface dataset:
Making Convolutions Resilient via Algorithm-Based Error Detection Techniques
Venue: arXiv cs.DC 2020
Year: 2020

The ability of Convolutional Neural Networks (CNNs) to accurately process real-time telemetry has boosted their use in safety-critical and high-performance computing systems. As such systems require high levels of resilience to errors, CNNs must execute correctly in the presence of hardware faults. Full duplication provides the needed assurance but incurs a prohibitive 100% overhead. Algorithmic techniques are known to offer low-cost solutions, but the practical feasibility and performance of such techniques have never been studied for CNN deployment platforms (e.g., TensorFlow or TensorRT on GPUs). In this paper, we focus on algorithmically verifying Convolutions, which are the most resource-demanding operations in CNNs. We use checksums to verify convolutions, adding a small amount of redundancy, far less than full-duplication. We first identify the challenges that arise in employing Algorithm-Based Error Detection (ABED) for Convolutions in optimized inference platforms that fuse multiple network layers and use reduced-precision operations, and demonstrate how to overcome them. We propose and evaluate variations of ABED techniques that offer implementation complexity, runtime overhead, and coverage trade-offs. Results show that ABED can detect all transient hardware errors that might otherwise corrupt output and does so while incurring low runtime overheads (6-23%), offering at least 1.6X throughput to workloads compared to full duplication.
The Hitchhiker's Guide to the All-Interval 12-Tone Rows
Venue: arXiv cs.SD 2020
Year: 2020

This article revisits the generation, classification and categorization of all-intervals 12-tone series (AIS). Inspired by the seminal work of Morris and Starr in 1974 (Morris and Starr, The Structure of All-Interval Series 1974), it expands their analysis using complex network theory and provides composers and theorists with the re-ordering scheme that links all AISs together by chains of relations.
RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking
Venue: arXiv cs.CV 2020
Year: 2020

Augmented reality devices require multiple sensors to perform various tasks such as localization and tracking. Currently, popular cameras are mostly frame-based (e.g. RGB and Depth) which impose a high data bandwidth and power usage. With the necessity for low power and more responsive augmented reality systems, using solely frame-based sensors imposes limits to the various algorithms that needs high frequency data from the environement. As such, event-based sensors have become increasingly popular due to their low power, bandwidth and latency, as well as their very high frequency data acquisition capabilities. In this paper, we propose, for the first time, to use an event-based camera to increase the speed of 3D object tracking in 6 degrees of freedom. This application requires handling very high object speed to convey compelling AR experiences. To this end, we propose a new system which combines a recent RGB-D sensor (Kinect Azure) with an event camera (DAVIS346). We develop a deep learning approach, which combines an existing RGB-D network along with a novel event-based network in a cascade fashion, and demonstrate that our approach significantly improves the robustness of a state-of-the-art frame-based 6-DOF object tracker using our RGB-D-E pipeline.
Single Image Deraining via Scale-space Invariant Attention Neural Network
Venue: arXiv cs.CV 2020
Year: 2020

Image enhancement from degradation of rainy artifacts plays a critical role in outdoor visual computing systems. In this paper, we tackle the notion of scale that deals with visual changes in appearance of rain steaks with respect to the camera. Specifically, we revisit multi-scale representation by scale-space theory, and propose to represent the multi-scale correlation in convolutional feature domain, which is more compact and robust than that in pixel domain. Moreover, to improve the modeling ability of the network, we do not treat the extracted multi-scale features equally, but design a novel scale-space invariant attention mechanism to help the network focus on parts of the features. In this way, we summarize the most activated presence of feature maps as the salient features. Extensive experiments results on synthetic and real rainy scenes demonstrate the superior performance of our scheme over the state-of-the-arts.
Spatial Firewalls: Quarantining Malware Epidemics in Large Scale Massive Wireless Networks
Venue: arXiv cs.CR 2020
Year: 2020

Billions of wireless devices are foreseen to participate in big data aggregation and smart automation in order to interface the cyber and physical worlds. Such large-scale ultra-dense wireless connectivity is vulnerable to malicious software (malware) epidemics. Malware worms can exploit multi-hop wireless connectivity to stealthily diffuse throughout the wireless network without being noticed to security servers at the core network. Compromised devices can then be used by adversaries to remotely launch cyber attacks that cause large-scale critical physical damage and threaten public safety. This article overviews the types, threats, and propagation models for malware epidemics in large-scale wireless networks (LSWN). Then, the article proposes a novel and cost efficient countermeasure against malware epidemics in LSWN, denoted as spatial firewalls. It is shown that equipping a strategically selected small portion (i.e., less than 10\%) of the devices with state-of-the-art security mechanisms is sufficient to create spatially secured zones that quarantine malware epidemics. Quarantined infected devices are then cured by on-demand localized software patching. To this end, several firewall deployment strategies are discussed and compared.
Stable Prediction via Leveraging Seed Variable
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we focus on the problem of stable prediction across unknown test data, where the test distribution is agnostic and might be totally different from the training one. In such a case, previous machine learning methods might exploit subtly spurious correlations in training data induced by non-causal variables for prediction. Those spurious correlations are changeable across data, leading to instability of prediction across data. By assuming the relationships between causal variables and response variable are invariant across data, to address this problem, we propose a conditional independence test based algorithm to separate those causal variables with a seed variable as priori, and adopt them for stable prediction. By assuming the independence between causal and non-causal variables, we show, both theoretically and with empirical experiments, that our algorithm can precisely separate causal and non-causal variables for stable prediction across test data. Extensive experiments on both synthetic and real-world datasets demonstrate that our algorithm outperforms state-of-the-art methods for stable prediction.
Pattern Atlas
Venue: arXiv cs.SE 2020
Year: 2020

Pattern languages are well-established in the software architecture community. Many different aspects of creating a software architecture are addressed by such languages. Thus, several pattern languages have to be considered when building a particular architecture. But these pattern languages are isolated, i.e. it is hard to determine the relevant patterns to be applied from the different pattern languages. Moreover, the sum of patterns from different languages may be huge, i.e. restriction to relevant patterns is desirable. In this contribution we envision an encompassing tool, the pattern atlas, that supports building complex systems based on pattern languages. The analogy to cartography motivates the name of the tool.
ConfNet2Seq: Full Length Answer Generation from Spoken Questions
Venue: arXiv cs.CL 2020
Year: 2020

Conversational and task-oriented dialogue systems aim to interact with the user using natural responses through multi-modal interfaces, such as text or speech. These desired responses are in the form of full-length natural answers generated over facts retrieved from a knowledge source. While the task of generating natural answers to questions from an answer span has been widely studied, there has been little research on natural sentence generation over spoken content. We propose a novel system to generate full length natural language answers from spoken questions and factoid answers. The spoken sequence is compactly represented as a confusion network extracted from a pre-trained Automatic Speech Recognizer. This is the first attempt towards generating full-length natural answers from a graph input(confusion network) to the best of our knowledge. We release a large-scale dataset of 259,788 samples of spoken questions, their factoid answers and corresponding full-length textual answers. Following our proposed approach, we achieve comparable performance with best ASR hypothesis.
Automated Design Space Exploration for optimised Deployment of DNN on Arm Cortex-A CPUs
Venue: arXiv cs.LG 2020
Year: 2020

The spread of deep learning on embedded devices has prompted the development of numerous methods to optimise the deployment of deep neural networks (DNN). Works have mainly focused on: i) efficient DNN architectures, ii) network optimisation techniques such as pruning and quantisation, iii) optimised algorithms to speed up the execution of the most computational intensive layers and, iv) dedicated hardware to accelerate the data flow and computation. However, there is a lack of research on cross-level optimisation as the space of approaches becomes too large to test and obtain a globally optimised solution. Thus, leading to suboptimal deployment in terms of latency, accuracy, and memory. In this work, we first detail and analyse the methods to improve the deployment of DNNs across the different levels of software optimisation. Building on this knowledge, we present an automated exploration framework to ease the deployment of DNNs. The framework relies on a Reinforcement Learning search that, combined with a deep learning inference framework, automatically explores the design space and learns an optimised solution that speeds up the performance and reduces the memory on embedded CPU platforms. Thus, we present a set of results for state-of-the-art DNNs on a range of Arm Cortex-A CPU platforms achieving up to 4x improvement in performance and over 2x reduction in memory with negligible loss in accuracy with respect to the BLAS floating-point implementation.
Parking Packet Payload with P4
Venue: arXiv cs.NI 2020
Year: 2020

Network Function (NF) deployments suffer from poor link goodput, because popular NFs such as firewalls process only packet headers while receiving and transmitting complete packets. As a result, unnecessary packet payloads needlessly consume link bandwidth. We introduce PayloadPark, which improves goodput by temporarily parking packet payloads in the stateful memory of dataplane programmable switches. PayloadPark forwards only packet headers to NF servers, thereby saving bandwidth between the switch and the NF server. PayloadPark is a transparent in-network optimization that complements existing approaches for optimizing NF performance on end-hosts. We prototyped PayloadPark on a Barefoot Tofino ASIC using the P4 language. Our prototype, when deployed on a top-of-rack switch, can service up to 8 NF servers using less than 40% of the on-chip memory resources. The prototype improves goodput by 10- 36% for Firewall and NAT NFs and by 10-26% for a Firewall -> NAT NF chain without harming latency. The prototype also reduces PCIe bus load by 2-58% on the NF server thanks to the reduced data transmission between the switch and the NF server. With workloads that have datacenter network traffic characteristics, PayloadPark provides a 13% goodput gain with the Firewall -> NAT -> LB NF chain without latency penalty. In the same setup, we can further increase the goodput gain to 28% by using packet recirculation.
An Efficient Accelerator Design Methodology for Deformable Convolutional Networks
Venue: arXiv cs.DC 2020
Year: 2020

Deformable convolutional networks have demonstrated outstanding performance in object recognition tasks with an effective feature extraction. Unlike standard convolution, the deformable convolution decides the receptive field size using dynamically generated offsets, which leads to an irregular memory access. Especially, the memory access pattern varies both spatially and temporally, making static optimization ineffective. Thus, a naive implementation would lead to an excessive memory footprint. In this paper, we present a novel approach to accelerate deformable convolution on FPGA. First, we propose a novel training method to reduce the size of the receptive field in the deformable convolutional layer without compromising accuracy. By optimizing the receptive field, we can compress the maximum size of the receptive field by 12.6 times. Second, we propose an efficient systolic architecture to maximize its efficiency. We then implement our design on FPGA to support the optimized dataflow. Experimental results show that our accelerator achieves up to 17.25 times speedup over the state-of-the-art accelerator.
New Fusion Algorithm provides an alternative approach to Robotic Path planning
Venue: arXiv cs.RO 2020
Year: 2020

For rapid growth in technology and automation, human tasks are being taken over by robots as robots have proven to be better with both speed and precision. One of the major and widespread usages of these robots is in the industrial businesses, where they are employed to carry massive loads in and around work areas. As these working environments might not be completely localized and could be dynamically changing, new approaches must be evaluated to guarantee a crash-free way of performing duties. This paper presents a new and efficient fusion algorithm for solving the path planning problem in a custom 2D environment. This fusion algorithm integrates an improved and optimized version of both, A* algorithm and the Artificial potential field method. Firstly, an initial or preliminary path is planned in the environmental model by adopting the A* algorithm. The heuristic function of this A* algorithm is optimized and improved according to the environmental model. This is followed by selecting and saving the key nodes in the initial path. Lastly, on the basis of these saved key nodes, path smoothing is done by artificial potential field method. Our simulation results carried out using Python viz. libraries indicate that the new fusion algorithm is feasible and superior in smoothness performance and can satisfy as a time-efficient and cheaper alternative to conventional A* strategies of path planning.
Non-iterative Modal Logics are Coalgebraic
Venue: arXiv cs.LO 2020
Year: 2020

A modal logic is \emph{non-iterative} if it can be defined by axioms that do not nest modal operators, and \emph{rank-1} if additionally all propositional variables in axioms are in scope of a modal operator. It is known that every syntactically defined rank-1 modal logic can be equipped with a canonical coalgebraic semantics, ensuring soundness and strong completeness. In the present work, we extend this result to non-iterative modal logics, showing that every non-iterative modal logic can be equipped with a canonical coalgebraic semantics defined in terms of a copointed functor, again ensuring soundness and strong completeness via a canonical model construction. Like in the rank-1 case, the canonical coalgebraic semantics is equivalent to a neighbourhood semantics with suitable frame conditions, so the known strong completeness of non-iterative modal logics over neighbourhood semantics is implied. As an illustration of these results, we discuss deontic logics with factual detachment, which is captured by axioms that are non-iterative but not rank~1.
Distributed Learning on Heterogeneous Resource-Constrained Devices
Venue: arXiv cs.LG 2020
Year: 2020

We consider a distributed system, consisting of a heterogeneous set of devices, ranging from low-end to high-end. These devices have different profiles, e.g., different energy budgets, or different hardware specifications, determining their capabilities on performing certain learning tasks. We propose the first approach that enables distributed learning in such a heterogeneous system. Applying our approach, each device employs a neural network (NN) with a topology that fits its capabilities; however, part of these NNs share the same topology, so that their parameters can be jointly learned. This differs from current approaches, such as federated learning, which require all devices to employ the same NN, enforcing a trade-off between achievable accuracy and computational overhead of training. We evaluate heterogeneous distributed learning for reinforcement learning (RL) and observe that it greatly improves the achievable reward on more powerful devices, compared to current approaches, while still maintaining a high reward on the weaker devices. We also explore supervised learning, observing similar gains.
Dialog Policy Learning for Joint Clarification and Active Learning Queries
Venue: arXiv cs.CV 2020
Year: 2020

Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training. Dialog interaction can enable this by the use of clarifications for correction and resolving uncertainty, and active learning queries to learn new concepts encountered during operation. Prior work on dialog systems has either focused on exclusively learning how to perform clarification/ information seeking, or to perform active learning. In this work, we train a hierarchical dialog policy to jointly perform both clarification and active learning in the context of an interactive language-based image retrieval task motivated by an online shopping application, and demonstrate that jointly learning dialog policies for clarification and active learning is more effective than the use of static dialog policies for one or both of these functions.
Tactile Roughness Perception of Virtual Gratings by Electrovibration
Venue: arXiv cs.HC 2020
Year: 2020

Realistic display of tactile textures on touch screens is a big step forward for haptic technology to reach a wide range of consumers utilizing electronic devices on a daily basis. Since the texture topography cannot be rendered explicitly by electrovibration on touch screens, it is important to understand how we perceive the virtual textures displayed by friction modulation via electrovibration. We investigated the roughness perception of real gratings made of plexiglass and virtual gratings displayed by electrovibration through a touch screen for comparison. In particular, we conducted two psychophysical experiments with 10 participants to investigate the effect of spatial period and the normal force applied by finger on roughness perception of real and virtual gratings in macro size. We also recorded the contact forces acting on the participants' finger during the experiments. The results showed that the roughness perception of real and virtual gratings are different. We argue that this difference can be explained by the amount of fingerpad penetration into the gratings. For real gratings, penetration increased tangential forces acting on the finger, whereas for virtual ones where skin penetration is absent, tangential forces decreased with spatial period. Supporting our claim, we also found that increasing normal force increases the perceived roughness of real gratings while it causes an opposite effect for the virtual gratings. These results are consistent with the tangential force profiles recorded for both real and virtual gratings. In particular, the rate of change in tangential force ($dF_t/dt$) as a function of spatial period and normal force followed trends similar to those obtained for the roughness estimates of real and virtual gratings, suggesting that it is a better indicator of the perceived roughness than the tangential force magnitude.
Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction
Venue: arXiv cs.IR 2020
Year: 2020

Accurate parsing of citation reference strings is crucial to automatically construct scholarly databases such as Google Scholar or Semantic Scholar. Citation field extraction (CFE) is precisely this task---given a reference label which tokens refer to the authors, venue, title, editor, journal, pages, etc. Most methods for CFE are supervised and rely on training from labeled datasets that are quite small compared to the great variety of reference formats. BibTeX, the widely used reference management tool, provides a natural method to automatically generate and label training data for CFE. In this paper, we describe a technique for using BibTeX to generate, automatically, a large-scale 41M labeled strings), labeled dataset, that is four orders of magnitude larger than the current largest CFE dataset, namely the UMass Citation Field Extraction dataset [Anzaroot and McCallum, 2013]. We experimentally demonstrate how our dataset can be used to improve the performance of the UMass CFE using a RoBERTa-based [Liu et al., 2019] model. In comparison to previous SoTA, we achieve a 24.48% relative error reduction, achieving span level F1-scores of 96.3%.
CNN-Based Semantic Change Detection in Satellite Imagery
Venue: arXiv cs.CV 2020
Year: 2020

Timely disaster risk management requires accurate road maps and prompt damage assessment. Currently, this is done by volunteers manually marking satellite imagery of affected areas but this process is slow and often error-prone. Segmentation algorithms can be applied to satellite images to detect road networks. However, existing methods are unsuitable for disaster-struck areas as they make assumptions about the road network topology which may no longer be valid in these scenarios. Herein, we propose a CNN-based framework for identifying accessible roads in post-disaster imagery by detecting changes from pre-disaster imagery. Graph theory is combined with the CNN output for detecting semantic changes in road networks with OpenStreetMap data. Our results are validated with data of a tsunami-affected region in Palu, Indonesia acquired from DigitalGlobe.
Fitted Q-Learning for Relational Domains
Venue: arXiv cs.LG 2020
Year: 2020

We consider the problem of Approximate Dynamic Programming in relational domains. Inspired by the success of fitted Q-learning methods in propositional settings, we develop the first relational fitted Q-learning algorithms by representing the value function and Bellman residuals. When we fit the Q-functions, we show how the two steps of Bellman operator; application and projection steps can be performed using a gradient-boosting technique. Our proposed framework performs reasonably well on standard domains without using domain models and using fewer training trajectories.