OpenRaiser/Intern-Atlas huggingface dataset:
Supporting large-scale image recognition with out-of-domain samples
Venue: arXiv cs.CV 2020
Year: 2020

This article presents an efficient end-to-end method to perform instance-level recognition employed to the task of labeling and ranking landmark images. In a first step, we embed images in a high dimensional feature space using convolutional neural networks trained with an additive angular margin loss and classify images using visual similarity. We then efficiently re-rank predictions and filter noise utilizing similarity to out-of-domain images. Using this approach we achieved the 1st place in the 2020 edition of the Google Landmark Recognition challenge.
Interface Design for HCI Classroom: From Learners' Perspective
Venue: arXiv cs.HC 2020
Year: 2020

Having a good Human-Computer Interaction (HCI) design is challenging. Previous works have contributed significantly to fostering HCI, including design principle with report study from the instructor view. The questions of how and to what extent students perceive the design principles are still left open. To answer this question, this paper conducts a study of HCI adoption in the classroom. The studio-based learning method was adapted to teach 83 graduate and undergraduate students in 16 weeks long with four activities. A standalone presentation tool for instant online peer feedback during the presentation session was developed to help students justify and critique other's work. Our tool provides a sandbox, which supports multiple application types, including Web-applications, Object Detection, Web-based Virtual Reality (VR), and Augmented Reality (AR). After presenting one assignment and two projects, our results showed that students acquired a better understanding of the Golden Rules principle over time, which was demonstrated by the development of visual interface design. The Wordcloud reveals the primary focus was on the user interface and shed some light on students' interest in user experience. The inter-rater score indicates the agreement among students that they have the same level of understanding of the principles. The results show a high level of guideline compliance with HCI principles, in which we witnessed variations in visual cognitive styles. Regardless of diversity in visual preference, the students presented high consistency and a similar perspective on adopting HCI design principles. The results also elicited suggestions into the development of the HCI curriculum in the future.
Data-efficient Online Classification with Siamese Networks and Active Learning
Venue: arXiv cs.LG 2020
Year: 2020

An ever increasing volume of data is nowadays becoming available in a streaming manner in many application areas, such as, in critical infrastructure systems, finance and banking, security and crime and web analytics. To meet this new demand, predictive models need to be built online where learning occurs on-the-fly. Online learning poses important challenges that affect the deployment of online classification systems to real-life problems. In this paper we investigate learning from limited labelled, nonstationary and imbalanced data in online classification. We propose a learning method that synergistically combines siamese neural networks and active learning. The proposed method uses a multi-sliding window approach to store data, and maintains separate and balanced queues for each class. Our study shows that the proposed method is robust to data nonstationarity and imbalance, and significantly outperforms baselines and state-of-the-art algorithms in terms of both learning speed and performance. Importantly, it is effective even when only 1% of the labels of the arriving instances are available.
Learning Complete 3D Morphable Face Models from Images and Videos
Venue: arXiv cs.CV 2020
Year: 2020

Most 3D face reconstruction methods rely on 3D morphable models, which disentangle the space of facial deformations into identity geometry, expressions and skin reflectance. These models are typically learned from a limited number of 3D scans and thus do not generalize well across different identities and expressions. We present the first approach to learn complete 3D models of face identity geometry, albedo and expression just from images and videos. The virtually endless collection of such data, in combination with our self-supervised learning-based approach allows for learning face models that generalize beyond the span of existing approaches. Our network design and loss functions ensure a disentangled parameterization of not only identity and albedo, but also, for the first time, an expression basis. Our method also allows for in-the-wild monocular reconstruction at test time. We show that our learned models better generalize and lead to higher quality image-based reconstructions than existing approaches.
MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

In object detection with deep neural networks, the box-wise objectness score tends to be overconfident, sometimes even indicating high confidence in presence of inaccurate predictions. Hence, the reliability of the prediction and therefore reliable uncertainties are of highest interest. In this work, we present a post processing method that for any given neural network provides predictive uncertainty estimates and quality estimates. These estimates are learned by a post processing model that receives as input a hand-crafted set of transparent metrics in form of a structured dataset. Therefrom, we learn two tasks for predicted bounding boxes. We discriminate between true positives ($\mathit{IoU}\geq0.5$) and false positives ($\mathit{IoU} < 0.5$) which we term meta classification, and we predict $\mathit{IoU}$ values directly which we term meta regression. The probabilities of the meta classification model aim at learning the probabilities of success and failure and therefore provide a modelled predictive uncertainty estimate. On the other hand, meta regression gives rise to a quality estimate. In numerical experiments, we use the publicly available YOLOv3 network and the Faster-RCNN network and evaluate meta classification and regression performance on the Kitti, Pascal VOC and COCO datasets. We demonstrate that our metrics are indeed well correlated with the $\mathit{IoU}$. For meta classification we obtain classification accuracies of up to 98.92% and AUROCs of up to 99.93%. For meta regression we obtain an $R^2$ value of up to 91.78%. These results yield significant improvements compared to other network's objectness score and other baseline approaches. Therefore, we obtain more reliable uncertainty and quality estimates which is particularly interesting in the absence of ground truth.
A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise
Venue: arXiv cs.LG 2020
Year: 2020

We study the problem of PAC learning homogeneous halfspaces in the presence of Tsybakov noise. In the Tsybakov noise model, the label of every sample is independently flipped with an adversarially controlled probability that can be arbitrarily close to $1/2$ for a fraction of the samples. {\em We give the first polynomial-time algorithm for this fundamental learning problem.} Our algorithm learns the true halfspace within any desired accuracy $ε$ and succeeds under a broad family of well-behaved distributions including log-concave distributions. Prior to our work, the only previous algorithm for this problem required quasi-polynomial runtime in $1/ε$. Our algorithm employs a recently developed reduction \cite{DKTZ20b} from learning to certifying the non-optimality of a candidate halfspace. This prior work developed a quasi-polynomial time certificate algorithm based on polynomial regression. {\em The main technical contribution of the current paper is the first polynomial-time certificate algorithm.} Starting from a non-trivial warm-start, our algorithm performs a novel "win-win" iterative process which, at each step, either finds a valid certificate or improves the angle between the current halfspace and the true one. Our warm-start algorithm for isotropic log-concave distributions involves a number of analytic tools that may be of broader interest. These include a new efficient method for reweighting the distribution in order to recenter it and a novel characterization of the spectrum of the degree-$2$ Chow parameters.
Transformer-Based Neural Text Generation with Syntactic Guidance
Venue: arXiv cs.CL 2020
Year: 2020

We study the problem of using (partial) constituency parse trees as syntactic guidance for controlled text generation. Existing approaches to this problem use recurrent structures, which not only suffer from the long-term dependency problem but also falls short in modeling the tree structure of the syntactic guidance. We propose to leverage the parallelism of Transformer to better incorporate parse trees. Our method first expands a partial template constituency parse tree to a full-fledged parse tree tailored for the input source text, and then uses the expanded tree to guide text generation. The effectiveness of our model in this process hinges upon two new attention mechanisms: 1) a path attention mechanism that forces one node to attend to only other nodes located in its path in the syntax tree to better incorporate syntax guidance; 2) a multi-encoder attention mechanism that allows the decoder to dynamically attend to information from multiple encoders. Our experiments in the controlled paraphrasing task show that our method outperforms SOTA models both semantically and syntactically, improving the best baseline's BLEU score from 11.83 to 26.27.
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?
Venue: arXiv cs.LG 2020
Year: 2020

Task-agnostic forms of data augmentation have proven widely effective in computer vision, even on pretrained models. In NLP similar results are reported most commonly for low data regimes, non-pretrained models, or situationally for pretrained models. In this paper we ask how effective these techniques really are when applied to pretrained transformers. Using two popular varieties of task-agnostic data augmentation (not tailored to any particular task), Easy Data Augmentation (Wei and Zou, 2019) and Back-Translation (Sennrichet al., 2015), we conduct a systematic examination of their effects across 5 classification tasks, 6 datasets, and 3 variants of modern pretrained transformers, including BERT, XLNet, and RoBERTa. We observe a negative result, finding that techniques which previously reported strong improvements for non-pretrained models fail to consistently improve performance for pretrained transformers, even when training data is limited. We hope this empirical analysis helps inform practitioners where data augmentation techniques may confer improvements.
Resonant Energy Recycling SRAM Architecture
Venue: arXiv cs.ET 2020
Year: 2020

Although we may be at the end of Moore's law, lowering chip power consumption is still the primary driving force for the designers. To enable low-power operation, we propose a resonant energy recovery static random access memory (SRAM). We propose the first series resonance scheme to reduce the dynamic power consumption of the SRAM operation. Besides, we identified the requirement of supply boosting of the write buffers for proper resonant operation. We evaluated the resonant 144KB SRAM cache through SPICE and test chip using a commercial 28nm CMOS technology. The experimental results show that the resonant SRAM can save up to 30% dynamic power at 1GHz operating frequency compared to the state-of-the-art design.
Improving AMR Parsing with Sequence-to-Sequence Pre-training
Venue: arXiv cs.CL 2020
Year: 2020

In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance. To alleviate such data size restriction, pre-trained models have been drawing more and more attention in AMR parsing. However, previous pre-trained models, like BERT, are implemented for general purpose which may not work as expected for the specific task of AMR parsing. In this paper, we focus on sequence-to-sequence (seq2seq) AMR parsing and propose a seq2seq pre-training approach to build pre-trained models in both single and joint way on three relevant tasks, i.e., machine translation, syntactic parsing, and AMR parsing itself. Moreover, we extend the vanilla fine-tuning method to a multi-task learning fine-tuning method that optimizes for the performance of AMR parsing while endeavors to preserve the response of pre-trained models. Extensive experimental results on two English benchmark datasets show that both the single and joint pre-trained models significantly improve the performance (e.g., from 71.5 to 80.2 on AMR 2.0), which reaches the state of the art. The result is very encouraging since we achieve this with seq2seq models rather than complex models. We make our code and model available at https://github.com/xdqkid/S2S-AMR-Parser.
Quality of Service-aware Security Framework for Mobile Ad hoc Networks using Optimized Link State Routing Protocol
Venue: arXiv cs.NI 2020
Year: 2020

All networks must provide an acceptable and desirable level of Quality of Service (QoS) to ensure that applications are well supported. This becomes a challenge when it comes to Mobile ad-hoc networks (MANETs). This paper presents a security framework that is QoS-aware in MANETs using a network protocol called Optimized Link State Routing Protocol (OLSR). Security & QoS targets may not necessarily be similar but this framework seeks to bridge the gap for the provision of an optimal functioning MANET. This paper presents the various security challenges, attacks, and goals in MANETs and the existing architectures or mechanisms used to combat security attacks. Additionally, this framework includes a security keying system to ascertain QoS. The keying system is linked to the basic configuration of the protocol OLSR through its Multi-point Relays (MPRs) functionality. The proposed framework is one that optimizes the use of network resources and time.
Joint Pruning & Quantization for Extremely Sparse Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

We investigate pruning and quantization for deep neural networks. Our goal is to achieve extremely high sparsity for quantized networks to enable implementation on low cost and low power accelerator hardware. In a practical scenario, there are particularly many applications for dense prediction tasks, hence we choose stereo depth estimation as target. We propose a two stage pruning and quantization pipeline and introduce a Taylor Score alongside a new fine-tuning mode to achieve extreme sparsity without sacrificing performance. Our evaluation does not only show that pruning and quantization should be investigated jointly, but also shows that almost 99% of memory demand can be cut while hardware costs can be reduced up to 99.9%. In addition, to compare with other works, we demonstrate that our pruning stage alone beats the state-of-the-art when applied to ResNet on CIFAR10 and ImageNet.
Intelligent reflecting surface aided wireless networks-Harris Hawks optimization for beamforming design
Venue: arXiv cs.NI 2020
Year: 2020

Intelligent Reflecting Surface (IRS) is envisioned to be a promising green and cost-effective solution to enhance wireless network performance by smartly reconfiguring the signal propagation. In this paper, we study an IRS-aided multiple-input single-output wireless network where a multi-antenna Access Point (AP) services a single-antenna user assisted by an IRS. The goal is to maximize the received signal power by jointly optimizing the transmit beamforming at the AP and the reflection coefficient at the IRS. The formulated optimization problem is non-convex and subject to constraints. We adopt a novel nature-inspired optimization technique named Harris Hawks Optimizer (HHO) to tackle the problem. After transforming the constrained problem into an unconstrained problem using penalty method, the formulated problem is optimized by the HHO. To the best of our knowledge, this is the first time to use a meta-heuristic algorithm to solve the IRS-aided network optimization problem. Simulation is conducted to verify the feasibility of the HHO-based scheme. The results show that the HHO-based scheme could provide similar or even better optimization results compared with other optimization algorithms.
Multi-Loss Sub-Ensembles for Accurate Classification with Uncertainty Estimation
Venue: arXiv cs.LG 2020
Year: 2020

Deep neural networks (DNNs) have made a revolution in numerous fields during the last decade. However, in tasks with high safety requirements, such as medical or autonomous driving applications, providing an assessment of the models reliability can be vital. Uncertainty estimation for DNNs has been addressed using Bayesian methods, providing mathematically founded models for reliability assessment. These model are computationally expensive and generally impractical for many real-time use cases. Recently, non-Bayesian methods were proposed to tackle uncertainty estimation more efficiently. We propose an efficient method for uncertainty estimation in DNNs achieving high accuracy. We simulate the notion of multi-task learning on single-task problems by producing parallel predictions from similar models differing by their loss. This multi-loss approach allows one-phase training for single-task learning with uncertainty estimation. We keep our inference time relatively low by leveraging the advantage proposed by the Deep-Sub-Ensembles method. The novelty of this work resides in the proposed accurate variational inference with a simple and convenient training procedure, while remaining competitive in terms of computational time. We conduct experiments on SVHN, CIFAR10, CIFAR100 as well as Image-Net using different architectures. Our results show improved accuracy on the classification task and competitive results on several uncertainty measures.
Synchronizer-Free Digital Link Controller
Venue: arXiv cs.AR 2020
Year: 2020

This work presents a producer-consumer link between two independent clock domains. The link allows for metastability-free, low-latency, high-throughput communication by slight adjustments to the clock frequencies of the producer and consumer domains steered by a controller circuit. Any such controller cannot deterministically avoid, detect, nor resolve metastability. Typically, this is addressed by synchronizers, incurring a larger dead time in the control loop. We follow the approach of Friedrichs et al. (TC 2018) who proposed metastability-containing circuits. The result is a simple control circuit that may become metastable, yet deterministically avoids buffer underrun or overflow. More specifically, the controller output may become metastable, but this may only affect oscillator speeds within specific bounds. In contrast, communication is guaranteed to remain metastability-free. We formally prove correctness of the producer-consumer link and a possible implementation that has only small overhead. With SPICE simulations of the proposed implementation we further substantiate our claims. The simulation uses 65nm process running at roughly 2GHz.
A Simple Framework for Uncertainty in Contrastive Learning
Venue: arXiv cs.LG 2020
Year: 2020

Contrastive approaches to representation learning have recently shown great promise. In contrast to generative approaches, these contrastive models learn a deterministic encoder with no notion of uncertainty or confidence. In this paper, we introduce a simple approach based on "contrasting distributions" that learns to assign uncertainty for pretrained contrastive representations. In particular, we train a deep network from a representation to a distribution in representation space, whose variance can be used as a measure of confidence. In our experiments, we show that this deep uncertainty model can be used (1) to visually interpret model behavior, (2) to detect new noise in the input to deployed models, (3) to detect anomalies, where we outperform 10 baseline methods across 11 tasks with improvements of up to 14% absolute, and (4) to classify out-of-distribution examples where our fully unsupervised model is competitive with supervised methods.
A Discrete and Continuous Study of the Max-Chain-Formation Problem
Venue: arXiv cs.DC 2020
Year: 2020

Most existing robot formation problems seek a target formation of a certain \emph{minimal} and, thus, efficient structure. Examples include the Gathering and the Chain-Formation problem. In this work, we study formation problems that try to reach a \emph{maximal} structure, supporting for example an efficient coverage in exploration scenarios. A recent example is the NASA Shapeshifter project, which describes how the robots form a relay chain along which gathered data from extraterrestrial cave explorations may be sent to a home base. As a first step towards understanding such maximization tasks, we introduce and study the Max-Chain-Formation problem, where $n$ robots are ordered along a winding, potentially self-intersecting chain and must form a connected, straight line of maximal length connecting its two endpoints. We propose and analyze strategies in a discrete and in a continuous time model. In the discrete case, we give a complete analysis if all robots are initially collinear, showing that the worst-case time to reach an $\varepsilon$-approximation is upper bounded by $\mathcal{O}(n^2 \cdot \log (n/\varepsilon))$ and lower bounded by $Ω(n^2 \cdot~\log (1/\varepsilon))$. If one endpoint of the chain remains stationary, this result can be extended to the non-collinear case. If both endpoints move, we identify a family of instances whose runtime is unbounded. For the continuous model, we give a strategy with an optimal runtime bound of $Θ(n)$. Avoiding an unbounded runtime similar to the discrete case relies crucially on a counter-intuitive aspect of the strategy: slowing down the endpoints while all other robots move at full speed. Surprisingly, we can show that a similar trick does not work in the discrete model.
Modulated Fusion using Transformer for Linguistic-Acoustic Emotion Recognition
Venue: arXiv cs.CL 2020
Year: 2020

This paper aims to bring a new lightweight yet powerful solution for the task of Emotion Recognition and Sentiment Analysis. Our motivation is to propose two architectures based on Transformers and modulation that combine the linguistic and acoustic inputs from a wide range of datasets to challenge, and sometimes surpass, the state-of-the-art in the field. To demonstrate the efficiency of our models, we carefully evaluate their performances on the IEMOCAP, MOSI, MOSEI and MELD dataset. The experiments can be directly replicated and the code is fully open for future researches.
Specification of the Giskard Consensus Protocol
Venue: arXiv cs.DC 2020
Year: 2020

The Giskard consensus protocol is used to validate transactions and computations in the PlatON network. In this paper, we provide a rigorous specification of Giskard, suitable to serve as a reference in protocol implementation and in formal verification. Using our specification, we prove that the protocol guarantees several notable safety properties.