OpenRaiser/Intern-Atlas huggingface dataset:
Multi-task Learning based Pre-trained Language Model for Code Completion
Venue: arXiv cs.SE 2020
Year: 2020

Code completion is one of the most useful features in the Integrated Development Environments (IDEs), which can accelerate software development by suggesting the next probable token based on the contextual code in real-time. Recent studies have shown that statistical language modeling techniques can improve the performance of code completion tools through learning from large-scale software repositories. However, these models suffer from two major drawbacks: a) Existing research uses static embeddings, which map a word to the same vector regardless of its context. The differences in the meaning of a token in varying contexts are lost when each token is associated with a single representation; b) Existing language model based code completion models perform poor on completing identifiers, and the type information of the identifiers is ignored in most of these models. To address these challenges, in this paper, we develop a multi-task learning based pre-trained language model for code understanding and code generation with a Transformer-based neural architecture. We pre-train it with hybrid objective functions that incorporate both code understanding and code generation tasks. Then we fine-tune the pre-trained model on code completion. During the completion, our model does not directly predict the next token. Instead, we adopt multi-task learning to predict the token and its type jointly and utilize the predicted type to assist the token prediction. Experiments results on two real-world datasets demonstrate the effectiveness of our model when compared with state-of-the-art methods.
Chasing the Tail in Monocular 3D Human Reconstruction with Prototype Memory
Venue: arXiv cs.CV 2020
Year: 2020

Deep neural networks have achieved great progress in single-image 3D human reconstruction. However, existing methods still fall short in predicting rare poses. The reason is that most of the current models perform regression based on a single human prototype, which is similar to common poses while far from the rare poses. In this work, we 1) identify and analyze this learning obstacle and 2) propose a prototype memory-augmented network, PM-Net, that effectively improves performances of predicting rare poses. The core of our framework is a memory module that learns and stores a set of 3D human prototypes capturing local distributions for either common poses or rare poses. With this formulation, the regression starts from a better initialization, which is relatively easier to converge. Extensive experiments on several widely employed datasets demonstrate the proposed framework's effectiveness compared to other state-of-the-art methods. Notably, our approach significantly improves the models' performances on rare poses while generating comparable results on other samples.
Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity
Venue: arXiv cs.LG 2020
Year: 2020

Despite the latest prevailing success of deep neural networks (DNNs), several concerns have been raised against their usage, including the lack of intepretability the gap between DNNs and other well-established machine learning models, and the growingly expensive computational costs. A number of recent works [1], [2], [3] explored the alternative to sequentially stacking decision tree/random forest building blocks in a purely feed-forward way, with no need of back propagation. Since decision trees enjoy inherent reasoning transparency, such deep forest models can also facilitate the understanding of the internaldecision making process. This paper further extends the deep forest idea in several important aspects. Firstly, we employ a probabilistic tree whose nodes make probabilistic routing decisions, a.k.a., soft routing, rather than hard binary decisions.Besides enhancing the flexibility, it also enables non-greedy optimization for each tree. Second, we propose an innovative topology learning strategy: every node in the ree now maintains a new learnable hyperparameter indicating the probability that it will be a leaf node. In that way, the tree will jointly optimize both its parameters and the tree topology during training. Experiments on the MNIST dataset demonstrate that our empowered deep forests can achieve better or comparable performance than [1],[3] , with dramatically reduced model complexity. For example,our model with only 1 layer of 15 trees can perform comparably with the model in [3] with 2 layers of 2000 trees each.
A Number Theoretic Approach for Fast Discovery of Single-Hop Wireless Networks
Venue: arXiv cs.IT 2020
Year: 2020

Interference management has become a key factor in regulating transmissions in wireless communication networks. To support effective interference management schemes, it can be essential to have prior knowledge about the network topology. In this paper, we build on existing results in the literature on the simulation of the message passing model, and present an efficient strategy for fast discovery of the network topology during a pilot communication phase. More precisely, we investigate the minimum number of communication rounds that is needed to discover an arbitrary network topology with a maximum number of links per receiver, while assuming a single-hop network that is restricted to interference-avoidance based schemes in its pilot phase. We first ignore any interference cancellation strategy such that no receiver can recognize, and cancel transmissions of, previously discovered transmitters, and then capture the gains obtained through interference cancellation during the pilot phase. Our results evince how the required number of rounds scale in an approximately logarithmic fashion with practical values of the total number of users in the network, having a slope proportional to the number of interfering transmitters per receiver.
Towards Fair Deep Anomaly Detection
Venue: arXiv cs.LG 2020
Year: 2020

Anomaly detection aims to find instances that are considered unusual and is a fundamental problem of data science. Recently, deep anomaly detection methods were shown to achieve superior results particularly in complex data such as images. Our work focuses on deep one-class classification for anomaly detection which learns a mapping only from the normal samples. However, the non-linear transformation performed by deep learning can potentially find patterns associated with social bias. The challenge with adding fairness to deep anomaly detection is to ensure both making fair and correct anomaly predictions simultaneously. In this paper, we propose a new architecture for the fair anomaly detection approach (Deep Fair SVDD) and train it using an adversarial network to de-correlate the relationships between the sensitive attributes and the learned representations. This differs from how fairness is typically added namely as a regularizer or a constraint. Further, we propose two effective fairness measures and empirically demonstrate that existing deep anomaly detection methods are unfair. We show that our proposed approach can remove the unfairness largely with minimal loss on the anomaly detection performance. Lastly, we conduct an in-depth analysis to show the strength and limitations of our proposed model, including parameter analysis, feature visualization, and run-time analysis.
Few-Shot Named Entity Recognition: A Comprehensive Study
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents a comprehensive study to efficiently build named entity recognition (NER) systems when a small number of in-domain labeled data is available. Based upon recent Transformer-based self-supervised pre-trained language models (PLMs), we investigate three orthogonal schemes to improve the model generalization ability for few-shot settings: (1) meta-learning to construct prototypes for different entity types, (2) supervised pre-training on noisy web data to extract entity-related generic representations and (3) self-training to leverage unlabeled in-domain data. Different combinations of these schemes are also considered. We perform extensive empirical comparisons on 10 public NER datasets with various proportions of labeled data, suggesting useful insights for future research. Our experiments show that (i) in the few-shot learning setting, the proposed NER schemes significantly improve or outperform the commonly used baseline, a PLM-based linear classifier fine-tuned on domain labels; (ii) We create new state-of-the-art results on both few-shot and training-free settings compared with existing methods. We will release our code and pre-trained models for reproducible research.
TCP D*: A Low Latency First Congestion Control Algorithm
Venue: arXiv cs.NI 2020
Year: 2020

The choice of feedback mechanism between delay and packet loss has long been a point of contention in TCP congestion control. This has partly been resolved, as it has become increasingly evident that delay based methods are needed to facilitate modern interactive web applications. However, what has not been resolved is what control should be used, with the two candidates being the congestion window and the pacing rate. BBR is a new delay based congestion control algorithm that uses a pacing rate as its primary control and the congestion window as a secondary control. We propose that a congestion window first algorithm might give more desirable performance characteristics in situations where latency must be minimized even at the expense of some loss in throughput. To evaluate this hypothesis we introduce a new congestion control algorithm called TCP D*, which is a congestion window first algorithm that adopts BBR's approach of maximizing delivery rate while minimizing latency. In this paper, we discuss the key features of this algorithm, discuss the differences and similarity to BBR, and present some preliminary results based on a real implementation.
Multi-view Temporal Alignment for Non-parallel Articulatory-to-Acoustic Speech Synthesis
Venue: arXiv cs.SD 2020
Year: 2020

Articulatory-to-acoustic (A2A) synthesis refers to the generation of audible speech from captured movement of the speech articulators. This technique has numerous applications, such as restoring oral communication to people who cannot longer speak due to illness or injury. Most successful techniques so far adopt a supervised learning framework, in which time-synchronous articulatory-and-speech recordings are used to train a supervised machine learning algorithm that can be used later to map articulator movements to speech. This, however, prevents the application of A2A techniques in cases where parallel data is unavailable, e.g., a person has already lost her/his voice and only articulatory data can be captured. In this work, we propose a solution to this problem based on the theory of multi-view learning. The proposed algorithm attempts to find an optimal temporal alignment between pairs of non-aligned articulatory-and-acoustic sequences with the same phonetic content by projecting them into a common latent space where both views are maximally correlated and then applying dynamic time warping. Several variants of this idea are discussed and explored. We show that the quality of speech generated in the non-aligned scenario is comparable to that obtained in the parallel scenario.
The Challenges of Crowd Workers in Rural and Urban America
Venue: arXiv cs.HC 2020
Year: 2020

Crowd work has the potential of helping the financial recovery of regions traditionally plagued by a lack of economic opportunities, e.g., rural areas. However, we currently have limited information about the challenges facing crowd work-ers from rural and super rural areas as they struggle to make a living through crowd work sites. This paper examines the challenges and advantages of rural and super rural AmazonMechanical Turk (MTurk) crowd workers and contrasts them with those of workers from urban areas. Based on a survey of421 crowd workers from differing geographic regions in theU.S., we identified how across regions, people struggled with being onboarded into crowd work. We uncovered that despite the inequalities and barriers, rural workers tended to be striving more in micro-tasking than their urban counterparts. We also identified cultural traits, relating to time dimension and individualism, that offer us an insight into crowd workers and the necessary qualities for them to succeed on gig platforms. We finish by providing design implications based on our findings to create more inclusive crowd work platforms and tools
Similarity Classification of Public Transit Stations
Venue: arXiv cs.DB 2020
Year: 2020

We study the following problem: given two public transit station identifiers A and B, each with a label and a geographic coordinate, decide whether A and B describe the same station. For example, for "St Pancras International" at (51.5306, -0.1253) and "London St Pancras" at (51.5319, -0.1269), the answer would be "Yes". This problem frequently arises in areas where public transit data is used, for example in geographic information systems, schedule merging, route planning, or map matching. We consider several baseline methods based on geographic distance and simple string similarity measures. We also experiment with more elaborate string similarity measures and manually created normalization rules. Our experiments show that these baseline methods produce good, but not fully satisfactory results. We therefore develop an approach based on a random forest classifier which is trained on matching trigrams between two stations, their distance, and their position on an interwoven grid. All approaches are evaluated on extensive ground truth datasets we generated from OpenStreetMap (OSM) data: (1) The union of Great Britain and Ireland and (2) the union of Germany, Switzerland, and Austria. On all datasets, our learning-based approach achieves an F1 score of over 99%, while even the most elaborate baseline approach (based on TFIDF scores and the geographic distance) achieves an F1 score of at most 94%, and a naive approach of using a geographical distance threshold achieves an F1 score of only 75%. Both our training and testing datasets are publicly available.
ConfigFix: Interactive Configuration Conflict Resolution for the Linux Kernel
Venue: arXiv cs.SE 2020
Year: 2020

Highly configurable systems are highly complex systems, with the Linux kernel arguably being one of the most well-known ones. Since 2007, it has been a frequent target of the research community, conducting empirical studies and building dedicated methods and tools for analyzing, configuring, testing, optimizing, and maintaining the kernel in the light of its vast configuration space. However, despite a large body of work, mainly bug fixes that were the result of such research made it back into the kernel's source tree. Unfortunately, Linux users still struggle with kernel configuration and resolving configuration conflicts, since the kernel largely lacks automated support. Additionally, there are technical and community requirements for supporting automated conflict resolution in the kernel, such as, for example, using a pure C-based solution that uses only compatible third-party libraries (if any). With the aim of contributing back to the Linux community, we present CONFIGFIX, a tooling that we integrated with the kernel configurator, that is purely implemented in C, and that is finally a working solution able to produce fixes for configuration conflicts. In this experience report, we describe our experiences ranging over a decade of building upon the large body of work from research on the Linux kernel configuration mechanisms as well as how we designed and realized CONFIGFIX while adhering to the Linux kernel's community requirements and standards. While CONFIGFIX helps Linux kernel users obtaining their desired configuration, the sound semantic abstraction we implement provides the basis for many of the above techniques supporting kernel configuration, helping researchers and kernel developers.
A Decentralized Dynamic PKI based on Blockchain
Venue: arXiv cs.CR 2020
Year: 2020

The central role of the certificate authority (CA) in traditional public key infrastructure (PKI) makes it fragile and prone to compromises and operational failures. Maintaining CAs and revocation lists is demanding especially in loosely-connected and large systems. Log-based PKIs have been proposed as a remedy but they do not solve the problem effectively. We provide a general model and a solution for decentralized and dynamic PKI based on a blockchain and web of trust model where the traditional CA and digital certificates are removed and instead, everything is registered on the blockchain. Registration, revocation, and update of public keys are based on a consensus mechanism between a certain number of entities that are already part of the system. Any node which is part of the system can be an auditor and initiate the revocation procedure once it finds out malicious activities. Revocation lists are no longer required as any node can efficiently verify the public keys through witnesses.
Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings
Venue: arXiv cs.CL 2020
Year: 2020

Models based on the transformer architecture, such as BERT, have marked a crucial step forward in the field of Natural Language Processing. Importantly, they allow the creation of word embeddings that capture important semantic information about words in context. However, as single entities, these embeddings are difficult to interpret and the models used to create them have been described as opaque. Binder and colleagues proposed an intuitive embedding space where each dimension is based on one of 65 core semantic features. Unfortunately, the space only exists for a small dataset of 535 words, limiting its uses. Previous work (Utsumi, 2018, 2020, Turton, Vinson & Smith, 2020) has shown that Binder features can be derived from static embeddings and successfully extrapolated to a large new vocabulary. Taking the next step, this paper demonstrates that Binder features can be derived from the BERT embedding space. This provides contextualised Binder embeddings, which can aid in understanding semantic differences between words in context. It additionally provides insights into how semantic features are represented across the different layers of the BERT model.
OSTeC: One-Shot Texture Completion
Venue: arXiv cs.CV 2020
Year: 2020

The last few years have witnessed the great success of non-linear generative models in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction and pose manipulation from a single image approaches still rely on large and clean face datasets to train image-to-image Generative Adversarial Networks (GANs). Yet the collection of such a large scale high-resolution 3D texture dataset is still very costly and difficult to maintain age/ethnicity balance. Moreover, regression-based approaches suffer from generalization to the in-the-wild conditions and are unable to fine-tune to a target-image. In this work, we propose an unsupervised approach for one-shot 3D facial texture completion that does not require large-scale texture datasets, but rather harnesses the knowledge stored in 2D face generators. The proposed approach rotates an input image in 3D and fill-in the unseen regions by reconstructing the rotated image in a 2D face generator, based on the visible parts. Finally, we stitch the most visible textures at different angles in the UV image-plane. Further, we frontalize the target image by projecting the completed texture into the generator. The qualitative and quantitative experiments demonstrate that the completed UV textures and frontalized images are of high quality, resembles the original identity, can be used to train a texture GAN model for 3DMM fitting and improve pose-invariant face recognition.
Beating Attackers At Their Own Games: Adversarial Example Detection Using Adversarial Gradient Directions
Venue: arXiv cs.CV 2020
Year: 2020

Adversarial examples are input examples that are specifically crafted to deceive machine learning classifiers. State-of-the-art adversarial example detection methods characterize an input example as adversarial either by quantifying the magnitude of feature variations under multiple perturbations or by measuring its distance from estimated benign example distribution. Instead of using such metrics, the proposed method is based on the observation that the directions of adversarial gradients when crafting (new) adversarial examples play a key role in characterizing the adversarial space. Compared to detection methods that use multiple perturbations, the proposed method is efficient as it only applies a single random perturbation on the input example. Experiments conducted on two different databases, CIFAR-10 and ImageNet, show that the proposed detection method achieves, respectively, 97.9% and 98.6% AUC-ROC (on average) on five different adversarial attacks, and outperforms multiple state-of-the-art detection methods. Results demonstrate the effectiveness of using adversarial gradient directions for adversarial example detection.
Audio-Visual Floorplan Reconstruction
Venue: arXiv cs.CV 2020
Year: 2020

Given only a few glimpses of an environment, how much can we infer about its entire floorplan? Existing methods can map only what is visible or immediately apparent from context, and thus require substantial movements through a space to fully map it. We explore how both audio and visual sensing together can provide rapid floorplan reconstruction from limited viewpoints. Audio not only helps sense geometry outside the camera's field of view, but it also reveals the existence of distant freespace (e.g., a dog barking in another room) and suggests the presence of rooms not visible to the camera (e.g., a dishwasher humming in what must be the kitchen to the left). We introduce AV-Map, a novel multi-modal encoder-decoder framework that reasons jointly about audio and vision to reconstruct a floorplan from a short input video sequence. We train our model to predict both the interior structure of the environment and the associated rooms' semantic labels. Our results on 85 large real-world environments show the impact: with just a few glimpses spanning 26% of an area, we can estimate the whole area with 66% accuracy -- substantially better than the state of the art approach for extrapolating visual maps.
Multiple Plans are Better than One: Diverse Stochastic Planning
Venue: arXiv cs.RO 2020
Year: 2020

In planning problems, it is often challenging to fully model the desired specifications. In particular, in human-robot interaction, such difficulty may arise due to human's preferences that are either private or complex to model. Consequently, the resulting objective function can only partially capture the specifications and optimizing that may lead to poor performance with respect to the true specifications. Motivated by this challenge, we formulate a problem, called diverse stochastic planning, that aims to generate a set of representative -- small and diverse -- behaviors that are near-optimal with respect to the known objective. In particular, the problem aims to compute a set of diverse and near-optimal policies for systems modeled by a Markov decision process. We cast the problem as a constrained nonlinear optimization for which we propose a solution relying on the Frank-Wolfe method. We then prove that the proposed solution converges to a stationary point and demonstrate its efficacy in several planning problems.