OpenRaiser/Intern-Atlas huggingface dataset:
Lake symbols for island parsing
Venue: arXiv cs.PL 2020
Year: 2020

Context: An island parser reads an input text and builds the parse (or abstract syntax) tree of only the programming constructs of interest in the text. These constructs are called islands and the rest of the text is called water, which the parser ignores and skips over. Since an island parser does not have to parse all the details of the input, it is often easy to develop but still useful enough for a number of software engineering tools. When a parser generator is used, the developer can implement an island parser by just describing a small number of grammar rules, for example, in Parsing Expression Grammar (PEG). Inquiry: In practice, however, the grammar rules are often complicated since the developer must define the water inside the island; otherwise, the island parsing will not reduce the total number of grammar rules. When describing the grammar rules for such water, the developer must consider other rules and enumerate a set of symbols, which we call alternative symbols. Due to this difficulty, island parsing seems to be not widely used today despite its usefulness in many applications. Approach: This paper proposes the lake symbols for addressing this difficulty in developing an island parser. It also presents an extension to PEG for supporting the lake symbols. The lake symbols automate the enumeration of the alternative symbols for the water inside an island. The paper proposes an algorithm for translating the extended PEG to the normal PEG, which can be given to an existing parser generator based on PEG. Knowledge: The user can use lake symbols to define water without specifying each alternative symbol. Our algorithms can calculate all alternative symbols for a lake symbol, based on where the lake symbol is used in the grammar. Grounding: We implemented a parser generator accepting our extended PEG and implemented 36 island parsers for Java and 20 island parsers for Python. Our experiments show that the lake symbols reduce 42 % of grammar rules for Java and 89 % of rules for Python on average, excluding the case where islands are expressions. Importance: This work eases the use of island parsing. Lake symbols enable the user to define the water inside the island simpler than before. Defining water inside the island is essential to apply island parsing for practical programming languages.
Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning
Venue: arXiv cs.CV 2020
Year: 2020

In this work, we present a robust and efficient solution for counting and identifying train wagons using computer vision and deep learning. The proposed solution is cost-effective and can easily replace solutions based on radiofrequency identification (RFID), which are known to have high installation and maintenance costs. According to our experiments, our two-stage methodology achieves impressive results on real-world scenarios, i.e., 100% accuracy in the counting stage and 99.7% recognition rate in the identification one. Moreover, the system is able to automatically reject some of the train wagons successfully counted, as they have damaged identification codes. The results achieved were surprising considering that the proposed system requires low processing power (i.e., it can run in low-end setups) and that we used a relatively small number of images to train our Convolutional Neural Network (CNN) for character recognition. The proposed method is registered, under number BR512020000808-9, with the National Institute of Industrial Property (Brazil).
Leveraging Extracted Model Adversaries for Improved Black Box Attacks
Venue: arXiv cs.LG 2020
Year: 2020

We present a method for adversarial input generation against black box models for reading comprehension based question answering. Our approach is composed of two steps. First, we approximate a victim black box model via model extraction (Krishna et al., 2020). Second, we use our own white box method to generate input perturbations that cause the approximate model to fail. These perturbed inputs are used against the victim. In experiments we find that our method improves on the efficacy of the AddAny---a white box attack---performed on the approximate model by 25% F1, and the AddSent attack---a black box attack---by 11% F1 (Jia and Liang, 2017).
Towards making formal methods normal: meeting developers where they are
Venue: arXiv cs.LO 2020
Year: 2020

Formal verification of software is a bit of a niche activity: it is only applied to the most safety-critical or security-critical software and it is typically only performed by specialized verification engineers. This paper considers whether it would be possible to increase adoption of formal methods by integrating formal methods with developers' existing practices and workflows. We do not believe that widespread adoption will follow from making the prevailing formal methods argument that correctness is more important than engineering teams realize. Instead, our focus is on what we would need to do to enable programmers to make effective use of formal verification tools and techniques. We do this by considering how we might make verification tooling that both serves developers' needs and fits into their existing development lifecycle. We propose a target of two orders of magnitude increase in adoption within a decade driven by ensuring a positive `weekly cost-benefit' ratio for developer time invested.
A Cross-lingual Natural Language Processing Framework for Infodemic Management
Venue: arXiv cs.CL 2020
Year: 2020

The COVID-19 pandemic has put immense pressure on health systems which are further strained due to the misinformation surrounding it. Under such a situation, providing the right information at the right time is crucial. There is a growing demand for the management of information spread using Artificial Intelligence. Hence, we have exploited the potential of Natural Language Processing for identifying relevant information that needs to be disseminated amongst the masses. In this work, we present a novel Cross-lingual Natural Language Processing framework to provide relevant information by matching daily news with trusted guidelines from the World Health Organization. The proposed pipeline deploys various techniques of NLP such as summarizers, word embeddings, and similarity metrics to provide users with news articles along with a corresponding healthcare guideline. A total of 36 models were evaluated and a combination of LexRank based summarizer on Word2Vec embedding with Word Mover distance metric outperformed all other models. This novel open-source approach can be used as a template for proactive dissemination of relevant healthcare information in the midst of misinformation spread associated with epidemics.
Towards Preference Learning for Autonomous Ground Robot Navigation Tasks
Venue: arXiv cs.RO 2020
Year: 2020

We are interested in the design of autonomous robot behaviors that learn the preferences of users over continued interactions, with the goal of efficiently executing navigation behaviors in a way that the user expects. In this paper, we discuss our work in progress to modify a general model for robot navigation behaviors in an exploration task on a per-user basis using preference-based reinforcement learning. The novel contribution of this approach is that it combines reinforcement learning, motion planning, and natural language processing to allow an autonomous agent to learn from sustained dialogue with a human teammate as opposed to one-off instructions.
Online Edge Coloring Algorithms via the Nibble Method
Venue: arXiv cs.DS 2020
Year: 2020

Nearly thirty years ago, Bar-Noy, Motwani and Naor [IPL'92] conjectured that an online $(1+o(1))Δ$-edge-coloring algorithm exists for $n$-node graphs of maximum degree $Δ=ω(\log n)$. This conjecture remains open in general, though it was recently proven for bipartite graphs under \emph{one-sided vertex arrivals} by Cohen et al.~[FOCS'19]. In a similar vein, we study edge coloring under widely-studied relaxations of the online model. Our main result is in the \emph{random-order} online model. For this model, known results fall short of the Bar-Noy et al.~conjecture, either in the degree bound [Aggarwal et al.~FOCS'03], or number of colors used [Bahmani et al.~SODA'10]. We achieve the best of both worlds, thus resolving the Bar-Noy et al.~conjecture in the affirmative for this model. Our second result is in the adversarial online (and dynamic) model with \emph{recourse}. A recent algorithm of Duan et al.~[SODA'19] yields a $(1+ε)Δ$-edge-coloring with poly$(\log n/ε)$ recourse. We achieve the same with poly$(1/ε)$ recourse, thus removing all dependence on $n$. Underlying our results is one common offline algorithm, which we show how to implement in these two online models. Our algorithm, based on the Rödl Nibble Method, is an adaptation of the distributed algorithm of Dubhashi et al.~[TCS'98]. The Nibble Method has proven successful for distributed edge coloring. We display its usefulness in the context of online algorithms.
Optimal Prediction of Synchronization-Preserving Races
Venue: arXiv cs.PL 2020
Year: 2020

Concurrent programs are notoriously hard to write correctly, as scheduling nondeterminism introduces subtle errors that are both hard to detect and to reproduce. The most common concurrency errors are (data) races, which occur when memory-conflicting actions are executed concurrently. Consequently, considerable effort has been made towards developing efficient techniques for race detection. The most common approach is dynamic race prediction: given an observed, race-free trace $σ$ of a concurrent program, the task is to decide whether events of $σ$ can be correctly reordered to a trace $σ^*$ that witnesses a race hidden in $σ$. In this work we introduce the notion of sync(hronization)-preserving races. A sync-preserving race occurs in $σ$ when there is a witness $σ^*$ in which synchronization operations (e.g., acquisition and release of locks) appear in the same order as in $σ$. This is a broad definition that strictly subsumes the famous notion of happens-before races. Our main results are as follows. First, we develop a sound and complete algorithm for predicting sync-preserving races. For moderate values of parameters like the number of threads, the algorithm runs in $\widetilde{O}(\mathcal{N})$ time and space, where $\mathcal{N}$ is the length of the trace $σ$. Second, we show that the problem has a $Ω(\mathcal{N}/\log^2 \mathcal{N})$ space lower bound, and thus our algorithm is essentially time and space optimal. Third, we show that predicting races with even just a single reversal of two sync operations is $\operatorname{NP}$-complete and even $\operatorname{W}[1]$-hard when parameterized by the number of threads. Thus, sync-preservation characterizes exactly the tractability boundary of race prediction, and our algorithm is nearly optimal for the tractable side.
Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding Loss
Venue: arXiv cs.CV 2020
Year: 2020

We present our winning submission to the First International Workshop on Bodily Expressed Emotion Understanding (BEEU) challenge. Based on recent literature on the effect of context/environment on emotion, as well as visual representations with semantic meaning using word embeddings, we extend the framework of Temporal Segment Network to accommodate these. Our method is verified on the validation set of the Body Language Dataset (BoLD) and achieves 0.26235 Emotion Recognition Score on the test set, surpassing the previous best result of 0.2530.
Goal directed molecule generation using Monte Carlo Tree Search
Venue: arXiv cs.LG 2020
Year: 2020

One challenging and essential task in biochemistry is the generation of novel molecules with desired properties. Novel molecule generation remains a challenge since the molecule space is difficult to navigate through, and the generated molecules should obey the rules of chemical valency. Through this work, we propose a novel method, which we call unitMCTS, to perform molecule generation by making a unit change to the molecule at every step using Monte Carlo Tree Search. We show that this method outperforms the recently published techniques on benchmark molecular optimization tasks such as QED and penalized logP. We also demonstrate the usefulness of this method in improving molecule properties while being similar to the starting molecule. Given that there is no learning involved, our method finds desired molecules within a shorter amount of time.
Mere account mein kitna balance hai? -- On building voice enabled Banking Services for Multilingual Communities
Venue: arXiv cs.CL 2020
Year: 2020

Tremendous progress in speech and language processing has brought language technologies closer to daily human life. Voice technology has the potential to act as a horizontal enabling layer across all aspects of digitization. It is especially beneficial to rural communities in scenarios like a pandemic. In this work we present our initial exploratory work towards one such direction -- building voice enabled banking services for multilingual societies. Speech interaction for typical banking transactions in multilingual communities involves the presence of filled pauses and is characterized by Code Mixing. Code Mixing is a phenomenon where lexical items from one language are embedded in the utterance of another. Therefore speech systems deployed for banking applications should be able to process such content. In our work we investigate various training strategies for building speech based intent recognition systems. We present our results using a Naive Bayes classifier on approximate acoustic phone units using the Allosaurus library.
MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing
Venue: arXiv cs.CV 2020
Year: 2020

Despite the recent success of face image generation with GANs, conditional hair editing remains challenging due to the under-explored complexity of its geometry and appearance. In this paper, we present MichiGAN (Multi-Input-Conditioned Hair Image GAN), a novel conditional image generation method for interactive portrait hair manipulation. To provide user control over every major hair visual factor, we explicitly disentangle hair into four orthogonal attributes, including shape, structure, appearance, and background. For each of them, we design a corresponding condition module to represent, process, and convert user inputs, and modulate the image generation pipeline in ways that respect the natures of different visual attributes. All these condition modules are integrated with the backbone generator to form the final end-to-end network, which allows fully-conditioned hair generation from multiple user inputs. Upon it, we also build an interactive portrait hair editing system that enables straightforward manipulation of hair by projecting intuitive and high-level user inputs such as painted masks, guiding strokes, or reference photos to well-defined condition representations. Through extensive experiments and evaluations, we demonstrate the superiority of our method regarding both result quality and user controllability. The code is available at https://github.com/tzt101/MichiGAN.
A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification
Venue: arXiv cs.CL 2020
Year: 2020

Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. Our model outperforms the prior work by 9.53% F1-Score.
Differentially Private ADMM Algorithms for Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we study efficient differentially private alternating direction methods of multipliers (ADMM) via gradient perturbation for many machine learning problems. For smooth convex loss functions with (non)-smooth regularization, we propose the first differentially private ADMM (DP-ADMM) algorithm with performance guarantee of $(ε,δ)$-differential privacy ($(ε,δ)$-DP). From the viewpoint of theoretical analysis, we use the Gaussian mechanism and the conversion relationship between Rényi Differential Privacy (RDP) and DP to perform a comprehensive privacy analysis for our algorithm. Then we establish a new criterion to prove the convergence of the proposed algorithms including DP-ADMM. We also give the utility analysis of our DP-ADMM. Moreover, we propose an accelerated DP-ADMM (DP-AccADMM) with the Nesterov's acceleration technique. Finally, we conduct numerical experiments on many real-world datasets to show the privacy-utility tradeoff of the two proposed algorithms, and all the comparative analysis shows that DP-AccADMM converges faster and has a better utility than DP-ADMM, when the privacy budget $ε$ is larger than a threshold.
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis
Venue: arXiv cs.CL 2020
Year: 2020

This paper analyzes the pre-trained hidden representations learned from reviews on BERT for tasks in aspect-based sentiment analysis (ABSA). Our work is motivated by the recent progress in BERT-based language models for ABSA. However, it is not clear how the general proxy task of (masked) language model trained on unlabeled corpus without annotations of aspects or opinions can provide important features for downstream tasks in ABSA. By leveraging the annotated datasets in ABSA, we investigate both the attentions and the learned representations of BERT pre-trained on reviews. We found that BERT uses very few self-attention heads to encode context words (such as prepositions or pronouns that indicating an aspect) and opinion words for an aspect. Most features in the representation of an aspect are dedicated to the fine-grained semantics of the domain (or product category) and the aspect itself, instead of carrying summarized opinions from its context. We hope this investigation can help future research in improving self-supervised learning, unsupervised learning and fine-tuning for ABSA. The pre-trained model and code can be found at https://github.com/howardhsu/BERT-for-RRC-ABSA.
Reliability of Power System Frequency on Times-Stamping Digital Recordings
Venue: arXiv cs.CR 2020
Year: 2020

Power system frequency could be captured by digital recordings and extracted to compare with a reference database for forensic time-stamp verification. It is known as the electric network frequency (ENF) criterion, enabled by the properties of random fluctuation and intra-grid consistency. In essence, this is a task of matching a short random sequence within a long reference, and the reliability of this criterion is mainly concerned with whether this match could be unique and correct. In this paper, we comprehensively analyze the factors affecting the reliability of ENF matching, including length of test recording, length of reference, temporal resolution, and signal-to-noise ratio (SNR). For synthetic analysis, we incorporate the first-order autoregressive (AR) ENF model and propose an efficient time-frequency domain (TFD) noisy ENF synthesis method. Then, the reliability analysis schemes for both synthetic and real-world data are respectively proposed. Through a comprehensive study we reveal that while the SNR is an important external factor to determine whether time-stamp verification is viable, the length of test recording is the most important inherent factor, followed by the length of reference. However, the temporal resolution has little impact on the matching process.
An analytic performance model for overlapping execution of memory-bound loop kernels on multicore CPUs
Venue: arXiv cs.DC 2020
Year: 2020

Complex applications running on multicore processors show a rich performance phenomenology. The growing number of cores per ccNUMA domain complicates performance analysis of memory-bound code since system noise, load imbalance, or task-based programming models can lead to thread desynchronization. Hence, the simplifying assumption that all cores execute the same loop can not be upheld. Motivated by observations on plain and modified versions of the HPCG benchmark, we construct a performance model of execution of memory-bound loop kernels. It can predict the memory bandwidth share per kernel on a memory contention domain depending on the number of active cores and which other workload the kernel is paired with. The only code features required are the single-thread cache line access frequency per kernel, which is directly related to the single-thread memory bandwidth, and its saturated bandwidth. It can either be measured directly or predicted using the Execution-Cache-Memory (ECM) performance model. The computational intensity of the kernels and the detailed structure of the code is of no significance. We validate our model on Intel Broadwell, Intel Cascade Lake, and AMD Rome processors pairing various streaming and stencil kernels. The error in predicting the bandwidth share per kernel is less than 8%.
Evaluating Bias In Dutch Word Embeddings
Venue: arXiv cs.CL 2020
Year: 2020

Recent research in Natural Language Processing has revealed that word embeddings can encode social biases present in the training data which can affect minorities in real world applications. This paper explores the gender bias implicit in Dutch embeddings while investigating whether English language based approaches can also be used in Dutch. We implement the Word Embeddings Association Test (WEAT), Clustering and Sentence Embeddings Association Test (SEAT) methods to quantify the gender bias in Dutch word embeddings, then we proceed to reduce the bias with Hard-Debias and Sent-Debias mitigation methods and finally we evaluate the performance of the debiased embeddings in downstream tasks. The results suggest that, among others, gender bias is present in traditional and contextualized Dutch word embeddings. We highlight how techniques used to measure and reduce bias created for English can be used in Dutch embeddings by adequately translating the data and taking into account the unique characteristics of the language. Furthermore, we analyze the effect of the debiasing techniques on downstream tasks which show a negligible impact on traditional embeddings and a 2% decrease in performance in contextualized embeddings. Finally, we release the translated Dutch datasets to the public along with the traditional embeddings with mitigated bias.
Efficient Arabic emotion recognition using deep neural networks
Venue: arXiv cs.CL 2020
Year: 2020

Emotion recognition from speech signal based on deep learning is an active research area. Convolutional neural networks (CNNs) may be the dominant method in this area. In this paper, we implement two neural architectures to address this problem. The first architecture is an attention-based CNN-LSTM-DNN model. In this novel architecture, the convolutional layers extract salient features and the bi-directional long short-term memory (BLSTM) layers handle the sequential phenomena of the speech signal. This is followed by an attention layer, which extracts a summary vector that is fed to the fully connected dense layer (DNN), which finally connects to a softmax output layer. The second architecture is based on a deep CNN model. The results on an Arabic speech emotion recognition task show that our innovative approach can lead to significant improvements (2.2% absolute improvements) over a strong deep CNN baseline system. On the other hand, the deep CNN models are significantly faster than the attention based CNN-LSTM-DNN models in training and classification.