OpenRaiser/Intern-Atlas huggingface dataset:
MCPA: Program Analysis as Machine Learning
Venue: arXiv cs.SE 2019
Year: 2019

Static program analysis today takes an analytical approach which is quite suitable for a well-scoped system. Data- and control-flow is taken into account. Special cases such as pointers, procedures, and undefined behavior must be handled. A program is analyzed precisely on the statement level. However, the analytical approach is ill-equiped to handle implementations of complex, large-scale, heterogeneous software systems we see in the real world. Existing static analysis techniques that scale, trade correctness (i.e., soundness or completeness) for scalability and build on strong assumptions (e.g., language-specificity). Scalable static analysis are well-known to report errors that do *not* exist (false positives) or fail to report errors that *do* exist (false negatives). Then, how do we know the degree to which the analysis outcome is correct? In this paper, we propose an approach to scale-oblivious greybox program analysis with bounded error which applies efficient approximation schemes (FPRAS) from the foundations of machine learning: PAC learnability. Given two parameters $δ$ and $ε$, with probability at least $(1-δ)$, our Monte Carlo Program Analysis (MCPA) approach produces an outcome that has an average error at most $ε$. The parameters $δ>0$ and $ε>0$ can be chosen arbitrarily close to zero (0) such that the program analysis outcome is said to be probably-approximately correct (PAC). We demonstrate the pertinent concepts of MCPA using three applications: $(ε,δ)$-approximate quantitative analysis, $(ε,δ)$-approximate software verification, and $(ε,δ)$-approximate patch verification.
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling
Venue: arXiv cs.LG 2019
Year: 2019

Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of data. However, most mainstream models suffer from catastrophic forgetting and insufficient robustness issues, thereby failing to fully retain or exploit long-term knowledge while being prone to cause severe error accumulation. In this paper, we propose a novel Continual Meta-Learning approach with Bayesian Graph Neural Networks (CML-BGNN) that mathematically formulates meta-learning as continual learning of a sequence of tasks. With each task forming as a graph, the intra- and inter-task correlations can be well preserved via message-passing and history transition. To remedy topological uncertainty from graph initialization, we utilize Bayes by Backprop strategy that approximates the posterior distribution of task-specific parameters with amortized inference networks, which are seamlessly integrated into the end-to-end edge learning. Extensive experiments conducted on the miniImageNet and tieredImageNet datasets demonstrate the effectiveness and efficiency of the proposed method, improving the performance by 42.8% compared with state-of-the-art on the miniImageNet 5-way 1-shot classification task.
Debugging Crashes using Continuous Contrast Set Mining
Venue: arXiv cs.SE 2019
Year: 2019

Facebook operates a family of services used by over two billion people daily on a huge variety of mobile devices. Many devices are configured to upload crash reports should the app crash for any reason. Engineers monitor and triage millions of crash reports logged each day to check for bugs, regressions, and any other quality problems. Debugging groups of crashes is a manually intensive process that requires deep domain expertise and close inspection of traces and code, often under time constraints. We use contrast set mining, a form of discriminative pattern mining, to learn what distinguishes one group of crashes from another. Prior works focus on discretization to apply contrast mining to continuous data. We propose the first direct application of contrast learning to continuous data, without the need for discretization. We also define a weighted anomaly score that unifies continuous and categorical contrast sets while mitigating bias, as well as uncertainty measures that communicate confidence to developers. We demonstrate the value of our novel statistical improvements by applying it on a challenging dataset from Facebook production logs, where we achieve 40x speedup over baseline approaches using discretization.
Artist and style exposure bias in collaborative filtering based music recommendations
Venue: arXiv cs.IR 2019
Year: 2019

Algorithms have an increasing influence on the music that we consume and understanding their behavior is fundamental to make sure they give a fair exposure to all artists across different styles. In this on-going work we contribute to this research direction analyzing the impact of collaborative filtering recommendations from the perspective of artist and music style exposure given by the system. We first analyze the distribution of the recommendations considering the exposure of different styles or genres and compare it to the users' listening behavior. This comparison suggests that the system is reinforcing the popularity of the items. Then, we simulate the effect of the system in the long term with a feedback loop. From this simulation we can see how the system gives less opportunity to the majority of artists, concentrating the users on fewer items. The results of our analysis demonstrate the need for a better evaluation methodology for current music recommendation algorithms, not only limited to user-focused relevance metrics.
Generating an Explainable ECG Beat Space With Variational Auto-Encoders
Venue: arXiv cs.LG 2019
Year: 2019

Electrocardiogram signals are omnipresent in medicine. A vital aspect in the analysis of this data is the identification and classification of heart beat types which is often done through automated algorithms. Advancements in neural networks and deep learning have led to a high classification accuracy. However, the final adoption of these models into clinical practice is limited due to the black-box nature of the methods. In this work, we explore the use of variational auto-encoders based on linear dense networks to learn human interpretable beat embeddings in time-series data. We demonstrate that using this method, an interpretable and explainable ECG beat space can be generated, set up by characteristic base beats.
O Peer, Where Art Thou? Uncovering Remote Peering Interconnections at IXPs
Venue: arXiv cs.NI 2019
Year: 2019

Internet eXchange Points (IXPs) are Internet hubs that mainly provide the switching infrastructure to interconnect networks and exchange traffic. While the initial goal of IXPs was to bring together networks residing in the same city or country, and thus keep local traffic local, this model is gradually shifting. Many networks connect to IXPs without having physical presence at their switching infrastructure. This practice, called Remote Peering, is changing the Internet topology and economy, and has become the subject of a contentious debate within the network operators' community. However, despite the increasing attention it attracts, the understanding of the characteristics and impact of remote peering is limited. In this work, we introduce and validate a heuristic methodology for discovering remote peers at IXPs. We (i) identify critical remote peering inference challenges, (ii) infer remote peers with high accuracy (>95%) and coverage (93%) per IXP, and (iii) characterize different aspects of the remote peering ecosystem by applying our methodology to 30 large IXPs. We observe that remote peering is a significantly common practice in all the studied IXPs; for the largest IXPs, remote peers account for 40% of their member base. We also show that today, IXP growth is mainly driven by remote peering, which contributes two times more than local peering.
Fairness-Aware Neural Réyni Minimization for Continuous Features
Venue: arXiv cs.LG 2019
Year: 2019

The past few years have seen a dramatic rise of academic and societal interest in fair machine learning. While plenty of fair algorithms have been proposed recently to tackle this challenge for discrete variables, only a few ideas exist for continuous ones. The objective in this paper is to ensure some independence level between the outputs of regression models and any given continuous sensitive variables. For this purpose, we use the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation coefficient as a fairness metric. We propose two approaches to minimize the HGR coefficient. First, by reducing an upper bound of the HGR with a neural network estimation of the $χ^{2}$ divergence. Second, by minimizing the HGR directly with an adversarial neural network architecture. The idea is to predict the output Y while minimizing the ability of an adversarial neural network to find the estimated transformations which are required to predict the HGR coefficient. We empirically assess and compare our approaches and demonstrate significant improvements on previously presented work in the field.
Exploiting Clinically Available Delineations for CNN-based Segmentation in Radiotherapy Treatment Planning
Venue: arXiv cs.LG 2019
Year: 2019

Convolutional neural networks (CNNs) have been widely and successfully used for medical image segmentation. However, CNNs are typically considered to require large numbers of dedicated expert-segmented training volumes, which may be limiting in practice. This work investigates whether clinically obtained segmentations which are readily available in picture archiving and communication systems (PACS) could provide a possible source of data to train a CNN for segmentation of organs-at-risk (OARs) in radiotherapy treatment planning. In such data, delineations of structures deemed irrelevant to the target clinical use may be lacking. To overcome this issue, we use multi-label instead of multi-class segmentation. We empirically assess how many clinical delineations would be sufficient to train a CNN for the segmentation of OARs and find that increasing the training set size beyond a limited number of images leads to sharply diminishing returns. Moreover, we find that by using multi-label segmentation, missing structures in the reference standard do not have a negative effect on overall segmentation accuracy. These results indicate that segmentations obtained in a clinical workflow can be used to train an accurate OAR segmentation model.
Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials
Venue: arXiv cs.LG 2019
Year: 2019

A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge neutral and electronegativity balanced) samples out of all generated ones reaches 84.5% by our GAN when trained with materials from ICSD even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules. Our algorithm could be used to speed up inverse design or computational screening of inorganic materials.
Learning Sparse Sharing Architectures for Multiple Tasks
Venue: arXiv cs.CL 2019
Year: 2019

Most existing deep multi-task learning models are based on parameter sharing, such as hard sharing, hierarchical sharing, and soft sharing. How choosing a suitable sharing mechanism depends on the relations among the tasks, which is not easy since it is difficult to understand the underlying shared factors among these tasks. In this paper, we propose a novel parameter sharing mechanism, named \emph{Sparse Sharing}. Given multiple tasks, our approach automatically finds a sparse sharing structure. We start with an over-parameterized base network, from which each task extracts a subnetwork. The subnetworks of multiple tasks are partially overlapped and trained in parallel. We show that both hard sharing and hierarchical sharing can be formulated as particular instances of the sparse sharing framework. We conduct extensive experiments on three sequence labeling tasks. Compared with single-task models and three typical multi-task learning baselines, our proposed approach achieves consistent improvement while requiring fewer parameters.
KPsec: Secure End-to-End Communications for Multi-Hop Wireless Networks
Venue: arXiv cs.NI 2019
Year: 2019

The security of cyber-physical systems, from self-driving cars to medical devices, depends on their underlying multi-hop wireless networks. Yet, the lack of trusted central infrastructures and limited nodes' resources make securing these networks challenging. Recent works on key pre-distribution schemes, where nodes communicate over encrypted overlay paths, provide an appealing solution because of their distributed, computationally light-weight nature. Alas, these schemes share a glaring security vulnerability: the two ends of every overlay link can decrypt---and potentially modify and alter---the message. Plus, the longer overlay paths impose traffic overhead and increase latency. We present a novel routing mechanism, KPsec, to address these issues. KPsec deploys multiple disjoint paths and an initial key-exchange phase to secure end-to-end communications. After the initial key-exchange phase, traffic in KPsec follows the shortest paths and, in contrast to key pre-distribution schemes, intermediate nodes cannot decrypt it. We measure the security and performance of KPsec as well as three state-of-the-art key pre-distribution schemes using a real 10-node testbed and large-scale simulations. Our experiments show that, in addition to its security benefits, KPsec results in $5-15\%$ improvement in network throughput, up to $75\%$ reduction in latency, and an order of magnitude reduction in energy consumption.
Improving Robustness of Task Oriented Dialog Systems
Venue: arXiv cs.CL 2019
Year: 2019

Task oriented language understanding in dialog systems is often modeled using intents (task of a query) and slots (parameters for that task). Intent detection and slot tagging are, in turn, modeled using sentence classification and word tagging techniques respectively. Similar to adversarial attack problems with computer vision models discussed in existing literature, these intent-slot tagging models are often over-sensitive to small variations in input -- predicting different and often incorrect labels when small changes are made to a query, thus reducing their accuracy and reliability. However, evaluating a model's robustness to these changes is harder for language since words are discrete and an automated change (e.g. adding `noise') to a query sometimes changes the meaning and thus labels of a query. In this paper, we first describe how to create an adversarial test set to measure the robustness of these models. Furthermore, we introduce and adapt adversarial training methods as well as data augmentation using back-translation to mitigate these issues. Our experiments show that both techniques improve the robustness of the system substantially and can be combined to yield the best results.
Classifying Relevant Social Media Posts During Disasters Using Ensemble of Domain-agnostic and Domain-specific Word Embeddings
Venue: arXiv cs.SI 2019
Year: 2019

The use of social media as a means of communication has significantly increased over recent years. There is a plethora of information flow over the different topics of discussion, which is widespread across different domains. The ease of information sharing has increased noisy data being induced along with the relevant data stream. Finding such relevant data is important, especially when we are dealing with a time-critical domain like disasters. It is also more important to filter the relevant data in a real-time setting to timely process and leverage the information for decision support. However, the short text and sometimes ungrammatical nature of social media data challenge the extraction of contextual information cues, which could help differentiate relevant vs. non-relevant information. This paper presents a novel method to classify relevant social media posts during disaster events by ensembling the features of both domain-specific word embeddings as well as more generic domain-agnostic word embeddings. Therefore, we develop and evaluate a hybrid feature engineering framework for integrating diverse semantic representations using a combination of word embeddings to efficiently classify a relevant social media post. The application of the proposed classification framework could help in filtering public posts at large scale, given the growing usage of social media posts in recent years.
TCT: A Cross-supervised Learning Method for Multimodal Sequence Representation
Venue: arXiv cs.CV 2019
Year: 2019

Multimodalities provide promising performance than unimodality in most tasks. However, learning the semantic of the representations from multimodalities efficiently is extremely challenging. To tackle this, we propose the Transformer based Cross-modal Translator (TCT) to learn unimodal sequence representations by translating from other related multimodal sequences on a supervised learning method. Combined TCT with Multimodal Transformer Network (MTN), we evaluate MTN-TCT on the video-grounded dialogue which uses multimodality. The proposed method reports new state-of-the-art performance on video-grounded dialogue which indicates representations learned by TCT are more semantics compared to directly use unimodality.
On the Constructions of MDS Self-dual Codes via Cyclotomy
Venue: arXiv cs.IT 2019
Year: 2019

MDS self-dual codes over finite fields have attracted a lot of attention in recent years by their theoretical interests in coding theory and applications in cryptography and combinatorics. In this paper we present a series of MDS self-dual codes with new length by using generalized Reed-Solomon codes and extended generalized Reed-Solomon codes as the candidates of MDS codes and taking their evaluation sets as an union of cyclotomic classes. The conditions on such MDS codes being self-dual are expressed in terms of cyclotomic numbers.
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation
Venue: arXiv cs.CV 2019
Year: 2019

Despite the recent works on knowledge distillation (KD) have achieved a further improvement through elaborately modeling the decision boundary as the posterior knowledge, their performance is still dependent on the hypothesis that the target network has a powerful capacity (representation ability). In this paper, we propose a knowledge representing (KR) framework mainly focusing on modeling the parameters distribution as prior knowledge. Firstly, we suggest a knowledge aggregation scheme in order to answer how to represent the prior knowledge from teacher network. Through aggregating the parameters distribution from teacher network into more abstract level, the scheme is able to alleviate the phenomenon of residual accumulation in the deeper layers. Secondly, as the critical issue of what the most important prior knowledge is for better distilling, we design a sparse recoding penalty for constraining the student network to learn with the penalized gradients. With the proposed penalty, the student network can effectively avoid the over-regularization during knowledge distilling and converge faster. The quantitative experiments exhibit that the proposed framework achieves the state-ofthe-arts performance, even though the target network does not have the expected capacity. Moreover, the framework is flexible enough for combining with other KD methods based on the posterior knowledge.
Searching for Anomalies over Composite Hypotheses
Venue: arXiv cs.IT 2019
Year: 2019

The problem of detecting anomalies in multiple processes is considered. We consider a composite hypothesis case, in which the measurements drawn when observing a process follow a common distribution with an unknown parameter (vector), whose value lies in normal or abnormal parameter spaces, depending on its state. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. We develop a deterministic search algorithm with the following desired properties. First, when no additional side information on the process states is known, the proposed algorithm is asymptotically optimal in terms of minimizing the detection delay as the error probability approaches zero. Second, when the parameter value under the null hypothesis is known and equal for all normal processes, the proposed algorithm is asymptotically optimal as well, with better detection time determined by the true null state. Third, when the parameter value under the null hypothesis is unknown, but is known to be equal for all normal processes, the proposed algorithm is consistent in terms of achieving error probability that decays to zero with the detection delay. Finally, an explicit upper bound on the error probability under the proposed algorithm is established for the finite sample regime. Extensive experiments on synthetic dataset and DARPA intrusion detection dataset are conducted, demonstrating strong performance of the proposed algorithm over existing methods.
Crowd Video Captioning
Venue: arXiv cs.CV 2019
Year: 2019

Describing a video automatically with natural language is a challenging task in the area of computer vision. In most cases, the on-site situation of great events is reported in news, but the situation of the off-site spectators in the entrance and exit is neglected which also arouses people's interest. Since the deployment of reporters in the entrance and exit costs lots of manpower, how to automatically describe the behavior of a crowd of off-site spectators is significant and remains a problem. To tackle this problem, we propose a new task called crowd video captioning (CVC) which aims to describe the crowd of spectators. We also provide baseline methods for this task and evaluate them on the dataset WorldExpo'10. Our experimental results show that captioning models have a fairly deep understanding of the crowd in video and perform satisfactorily in the CVC task.
Multi-MotifGAN (MMGAN): Motif-targeted Graph Generation and Prediction
Venue: arXiv cs.SI 2019
Year: 2019

Generative graph models create instances of graphs that mimic the properties of real-world networks. Generative models are successful at retaining pairwise associations in the underlying networks but often fail to capture higher-order connectivity patterns known as network motifs. Different types of graphs contain different network motifs, an example of which are triangles that often arise in social and biological networks. It is hence vital to capture these higher-order structures to simulate real-world networks accurately. We propose Multi-MotifGAN (MMGAN), a motif-targeted Generative Adversarial Network (GAN) that generalizes the benchmark NetGAN approach. The generalization consists of combining multiple biased random walks, each of which captures a different motif structure. MMGAN outperforms NetGAN at creating new graphs that accurately reflect the network motif statistics of input graphs such as Citeseer, Cora and Facebook.