pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating Explanation Methods for Deep Learning in Security
Authors: Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck
Date: 2019-06-05 00:00:00
Tasks: Computer Security, Deep Learning, Malware Detection, Open-Ended Question Answering

Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machine learning community has started to address this problem by developing methods for explaining the predictions of neural networks. While several of these approaches have been successfully applied in the area of computer vision, their application in security has received little attention so far. It is an open question which explanation methods are appropriate for computer security and what requirements they need to satisfy. In this paper, we introduce criteria for comparing and evaluating explanation methods in the context of computer security. These cover general properties, such as the accuracy of explanations, as well as security-focused aspects, such as the completeness, efficiency, and robustness. Based on our criteria, we investigate six popular explanation methods and assess their utility in security systems for malware detection and vulnerability discovery. We observe significant differences between the methods and build on these to derive general recommendations for selecting and applying explanation methods in computer security.
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization
Authors: Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng, Roeland Nusselder
Date: 2019-06-05 00:00:00
Proceeding: NeurIPS 2019 12

Optimization of Binarized Neural Networks (BNNs) currently relies on real-valued latent weights to accumulate small update steps. In this paper, we argue that these latent weights cannot be treated analogously to weights in real-valued networks. Instead their main role is to provide inertia during training. We interpret current methods in terms of inertia and provide novel insights into the optimization of BNNs. We subsequently introduce the first optimizer specifically designed for BNNs, Binary Optimizer (Bop), and demonstrate its performance on CIFAR-10 and ImageNet. Together, the redefinition of latent weights as inertia and the introduction of Bop enable a better understanding of BNN optimization and open up the way for further improvements in training methodologies for BNNs. Code is available at: https://github.com/plumerai/rethinking-bnn-optimization
Unbiased estimators for the variance of MMD estimators
Authors: Danica J. Sutherland, Namrata Deka
Date: 2019-06-05 00:00:00
Tasks: Two-sample testing

The maximum mean discrepancy (MMD) is a kernel-based distance between probability distributions useful in many applications (Gretton et al. 2012), bearing a simple estimator with pleasing computational and statistical properties. Being able to efficiently estimate the variance of this estimator is very helpful to various problems in two-sample testing. Towards this end, Bounliphone et al. (2016) used the theory of U-statistics to derive estimators for the variance of an MMD estimator, and differences between two such estimators. Their estimator, however, drops lower-order terms, and is unnecessarily biased. We show in this note - extending and correcting work of Sutherland et al. (2017) - that we can find a truly unbiased estimator for the actual variance of both the squared MMD estimator and the difference of two correlated squared MMD estimators, at essentially no additional computational cost.
Diameter-based Interactive Structure Discovery
Authors: Christopher Tosh, Daniel Hsu
Date: 2019-06-05 00:00:00
Tasks: Active Learning, Drug Discovery

We introduce interactive structure discovery, a generic framework that encompasses many interactive learning settings, including active learning, top-k item identification, interactive drug discovery, and others. We adapt a recently developed active learning algorithm of Tosh and Dasgupta (2017) for interactive structure discovery, and show that the new algorithm can be made noise-tolerant and enjoys favorable query complexity bounds.
Artificial Intelligence in Clinical Health Care Applications: Viewpoint
Authors: Michael van Hartskamp, Sergio Consoli, Wim Verhaegh, Milan Petković, Anja van de Stolpe
Date: 2019-06-05 00:00:00

The idea of Artificial Intelligence (AI) has a long history. It turned out, however, that reaching intelligence at human levels is more complicated than originally anticipated. Currently we are experiencing a renewed interest in AI, fueled by an enormous increase in computing power and an even larger increase in data, in combination with improved AI technologies like deep learning. Healthcare is considered the next domain to be revolutionized by Artificial Intelligence. While AI approaches are excellently suited to develop certain algorithms, for biomedical applications there are specific challenges. We propose recommendations to improve AI projects in the biomedical space and especially clinical healthcare.
GOT: An Optimal Transport framework for Graph comparison
Authors: Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard
Date: 2019-06-05 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Graph Classification

We present a novel framework based on optimal transport for the challenging problem of comparing graphs. Specifically, we exploit the probabilistic distribution of smooth graph signals defined with respect to the graph topology. This allows us to derive an explicit expression of the Wasserstein distance between graph signal distributions in terms of the graph Laplacian matrices. This leads to a structurally meaningful measure for comparing graphs, which is able to take into account the global structure of graphs, while most other measures merely observe local changes independently. Our measure is then used for formulating a new graph alignment problem, whose objective is to estimate the permutation that minimizes the distance between two graphs. We further propose an efficient stochastic algorithm based on Bayesian exploration to accommodate for the non-convexity of the graph alignment problem. We finally demonstrate the performance of our novel framework on different tasks like graph alignment, graph classification and graph signal prediction, and we show that our method leads to significant improvement with respect to the-state-of-art algorithms.
A Passage-Based Approach to Learning to Rank Documents
Authors: Eilon Sheetrit, Anna Shtok, Oren Kurland
Date: 2019-06-05 00:00:00
Tasks: Document Ranking, Learning-To-Rank, Passage Ranking, Retrieval

According to common relevance-judgments regimes, such as TREC's, a document can be deemed relevant to a query even if it contains a very short passage of text with pertinent information. This fact has motivated work on passage-based document retrieval: document ranking methods that induce information from the document's passages. However, the main source of passage-based information utilized was passage-query similarities. We address the challenge of utilizing richer sources of passage-based information to improve document retrieval effectiveness. Specifically, we devise a suite of learning-to-rank-based document retrieval methods that utilize an effective ranking of passages produced in response to the query; the passage ranking is also induced using a learning-to-rank approach. Some of the methods quantify the ranking of the passages of a document. Others utilize the feature-based representation of passages used for learning a passage ranker. Empirical evaluation attests to the clear merits of our methods with respect to highly effective baselines. Our best performing method is based on learning a document ranking function using document-query features and passage-query features of the document's passage most highly ranked.
Learning to Rank for Plausible Plausibility
Authors: Zhongyang Li, Tongfei Chen, Benjamin Van Durme
Date: 2019-06-05 00:00:00
Proceeding: ACL 2019 7
Tasks: Learning-To-Rank, Natural Language Understanding

Researchers illustrate improvements in contextual encoding strategies via resultant performance on a battery of shared Natural Language Understanding (NLU) tasks. Many of these tasks are of a categorical prediction variety: given a conditioning context (e.g., an NLI premise), provide a label based on an associated prompt (e.g., an NLI hypothesis). The categorical nature of these tasks has led to common use of a cross entropy log-loss objective during training. We suggest this loss is intuitively wrong when applied to plausibility tasks, where the prompt by design is neither categorically entailed nor contradictory given the context. Log-loss naturally drives models to assign scores near 0.0 or 1.0, in contrast to our proposed use of a margin-based loss. Following a discussion of our intuition, we describe a confirmation study based on an extreme, synthetically curated task derived from MultiNLI. We find that a margin-based loss leads to a more plausible model of plausibility. Finally, we illustrate improvements on the Choice Of Plausible Alternative (COPA) task through this change in loss.
On the use of Pairwise Distance Learning for Brain Signal Classification with Limited Observations
Authors: David Calhas, Enrique Romero, Rui Henriques
Date: 2019-06-05 00:00:00
Tasks: Electroencephalogram (EEG), General Classification

The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neuronal diseases. This work proposes a pairwise distance learning approach for Schizophrenia classification relying on the spectral properties of the signal. Given the limited number of observations (i.e. the case and/or control individuals) in clinical trials, we propose a Siamese neural network architecture to learn a discriminative feature space from pairwise combinations of observations per channel. In this way, the multivariate order of the signal is used as a form of data augmentation, further supporting the network generalization ability. Convolutional layers with parameters learned under a cosine contrastive loss are proposed to adequately explore spectral images derived from the brain signal. Results on a case-control population show that the features extracted using the proposed neural network lead to an improved Schizophrenia diagnosis (+10pp in accuracy and sensitivity) against spectral features, thus suggesting the existence of non-trivial, discriminative electrophysiological brain patterns.
An Effective Algorithm for Learning Single Occurrence Regular Expressions with Interleaving
Authors: Yeting Li, Haiming Chen, Xiaolan Zhang, Lingqi Zhang
Date: 2019-06-05 00:00:00
Tasks: valid

The advantages offered by the presence of a schema are numerous. However, many XML documents in practice are not accompanied by a (valid) schema, making schema inference an attractive research problem. The fundamental task in XML schema learning is inferring restricted subclasses of regular expressions. Most previous work either lacks support for interleaving or only has limited support for interleaving. In this paper, we first propose a new subclass Single Occurrence Regular Expressions with Interleaving (SOIRE), which has unrestricted support for interleaving. Then, based on single occurrence automaton and maximum independent set, we propose an algorithm iSOIRE to infer SOIREs. Finally, we further conduct a series of experiments on real datasets to evaluate the effectiveness of our work, comparing with both ongoing learning algorithms in academia and industrial tools in real-world. The results reveal the practicability of SOIRE and the effectiveness of iSOIRE, showing the high preciseness and conciseness of our work.
Automated Activity Recognition of Construction Equipment Using a Data Fusion Approach
Authors: Behnam Sherafat, Abbas Rashidi, Yong-Cheol Lee, Changbum R. Ahn
Date: 2019-06-05 00:00:00
Tasks: Activity Recognition

Automated monitoring of construction operations, especially operations of equipment and machines, is an essential step toward cost-estimating, and planning of construction projects. In recent years, a number of methods were suggested for recognizing activities of construction equipment. These methods are based on processing single types of data (audio, visual, or kinematic data). Considering the complexity of construction jobsites, using one source of data is not reliable enough to cover all conditions and scenarios. To address the issue, we utilized a data fusion approach: This approach is based on collecting audio and kinematic data, and includes the following steps: 1) recording audio and kinematic data generated by machines, 2) preprocessing data, 3) extracting time- and frequency-domain-features, 4) feature-fusion, and 5) categorizing activities using a machine-learning algorithm. The proposed approach was implemented on multiple machines and the experiments show that it is possible to get up to 25% more-accurate results compared to cases of using single-data-sources.
Architectural Middleware that Supports Building High-performance, Scalable, Ubiquitous, Intelligent Personal Assistants
Authors: Oscar J. Romero
Date: 2019-06-05 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Intelligent Personal Assistants (IPAs) are software agents that can perform tasks on behalf of individuals and assist them on many of their daily activities. IPAs capabilities are expanding rapidly due to the recent advances on areas such as natural language processing, machine learning, artificial cognition, and ubiquitous computing, which equip the agents with competences to understand what users say, collect information from everyday ubiquitous devices (e.g., smartphones, wearables, tablets, laptops, cars, household appliances, etc.), learn user preferences, deliver data-driven search results, and make decisions based on user's context. Apart from the inherent complexity of building such IPAs, developers and researchers have to address many critical architectural challenges (e.g., low-latency, scalability, concurrency, ubiquity, code mobility, interoperability, support to cognitive services and reasoning, to name a few.), thereby diverting them from their main goal: building IPAs. Thus, our contribution in this paper is twofold: 1) we propose an architecture for a platform-agnostic, high-performance, ubiquitous, and distributed middleware that alleviates the burdensome task of dealing with low-level implementation details when building IPAs by adding multiple abstraction layers that hide the underlying complexity; and 2) we present an implementation of the middleware that concretizes the aforementioned architecture and allows the development of high-level capabilities while scaling the system up to hundreds of thousands of IPAs with no extra effort. We demonstrate the powerfulness of our middleware by analyzing software metrics for complexity, effort, performance, cohesion and coupling when developing a conversational IPA.
Neural Legal Judgment Prediction in English
Authors: Ilias Chalkidis, Ion Androutsopoulos, Nikolaos Aletras
Date: 2019-06-05 00:00:00
Proceeding: ACL 2019 7
Tasks: Binary text classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Prediction

Legal judgment prediction is the task of automatically predicting the outcome of a court case, given a text describing the case's facts. Previous work on using neural models for this task has focused on Chinese; only feature-based models (e.g., using bags of words and topics) have been considered in English. We release a new English legal judgment prediction dataset, containing cases from the European Court of Human Rights. We evaluate a broad variety of neural models on the new dataset, establishing strong baselines that surpass previous feature-based models in three tasks: (1) binary violation classification; (2) multi-label classification; (3) case importance prediction. We also explore if models are biased towards demographic information via data anonymization. As a side-product, we propose a hierarchical version of BERT, which bypasses BERT's length limitation.
The Language of Dialogue Is Complex
Authors: Alexander Robertson, Luca Maria Aiello, Daniele Quercia
Date: 2019-06-05 00:00:00

Integrative Complexity (IC) is a psychometric that measures the ability of a person to recognize multiple perspectives and connect them, thus identifying paths for conflict resolution. IC has been linked to a wide variety of political, social and personal outcomes but evaluating it is a time-consuming process requiring skilled professionals to manually score texts, a fact which accounts for the limited exploration of IC at scale on social media.We combine natural language processing and machine learning to train an IC classification model that achieves state-of-the-art performance on unseen data and more closely adheres to the established structure of the IC coding process than previous automated approaches. When applied to the content of 400k+ comments from online fora about depression and knowledge exchange, our model was capable of replicating key findings of prior work, thus providing the first example of using IC tools for large-scale social media analytics.
KAS-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning
Authors: Nikola Ljubešić, Darja Fišer, Tomaž Erjavec
Date: 2019-06-05 00:00:00
Tasks: BIG-bench Machine Learning, Term Extraction

This paper presents a dataset and supervised learning experiments for term extraction from Slovene academic texts. Term candidates in the dataset were extracted via morphosyntactic patterns and annotated for their termness by four annotators. Experiments on the dataset show that most co-occurrence statistics, applied after morphosyntactic patterns and a frequency threshold, perform close to random and that the results can be significantly improved by combining, with supervised machine learning, all the seven statistic measures included in the dataset. On multi-word terms the model using all statistics obtains an AUC of 0.736 while the best single statistic produces only AUC 0.590. Among many additional candidate features, only adding multi-word morphosyntactic pattern information and length of the single-word term candidates achieves further improvements of the results.
The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English
Authors: Nikola Ljubešić, Darja Fišer, Tomaž Erjavec
Date: 2019-06-05 00:00:00

In this paper we present datasets of Facebook comment threads to mainstream media posts in Slovene and English developed inside the Slovene national project FRENK which cover two topics, migrants and LGBT, and are manually annotated for different types of socially unacceptable discourse (SUD). The main advantages of these datasets compared to the existing ones are identical sampling procedures, producing comparable data across languages and an annotation schema that takes into account six types of SUD and five targets at which SUD is directed. We describe the sampling and annotation procedures, and analyze the annotation distributions and inter-annotator agreements. We consider this dataset to be an important milestone in understanding and combating SUD for both languages.
Single-Camera Basketball Tracker through Pose and Semantic Feature Fusion
Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro
Date: 2019-06-05 00:00:00
Tasks: Deep Learning

Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.
Imitation Learning for Non-Autoregressive Neural Machine Translation
Authors: Bingzhen Wei, Mingxuan Wang, Hao Zhou, Junyang Lin, Jun Xie, Xu sun
Date: 2019-06-05 00:00:00
Proceeding: ACL 2019 7
Tasks: Imitation Learning, Machine Translation, Sentence, Translation

Non-autoregressive translation models (NAT) have achieved impressive inference speedup. A potential issue of the existing NAT algorithms, however, is that the decoding is conducted in parallel, without directly considering previous context. In this paper, we propose an imitation learning framework for non-autoregressive machine translation, which still enjoys the fast translation speed but gives comparable translation performance compared to its auto-regressive counterpart. We conduct experiments on the IWSLT16, WMT14 and WMT16 datasets. Our proposed model achieves a significant speedup over the autoregressive models, while keeping the translation quality comparable to the autoregressive models. By sampling sentence length in parallel at inference time, we achieve the performance of 31.85 BLEU on WMT16 Ro$\rightarrow$En and 30.68 BLEU on IWSLT16 En$\rightarrow$De.
A GLCM Embedded CNN Strategy for Computer-aided Diagnosis in Intracerebral Hemorrhage
Authors: Yifan Hu, Yefeng Zheng
Date: 2019-06-05 00:00:00
Tasks: Computed Tomography (CT), General Classification

Computer-aided diagnosis (CADx) systems have been shown to assist radiologists by providing classifications of all kinds of medical images like Computed tomography (CT) and Magnetic resonance (MR). Currently, convolutional neural networks play an important role in CADx. However, since CNN model should have a square-like input, it is usually difficult to directly apply the CNN algorithms on the irregular segmentation region of interests (ROIs) where the radiologists are interested in. In this paper, we propose a new approach to construct the model by extracting and converting the information of the irregular region into a fixed-size Gray-Level Co-Occurrence Matrix (GLCM) and then utilize the GLCM as one input of our CNN model. In this way, as an useful implementary to the original CNN, a couple of GLCM-based features are also extracted by CNN. Meanwhile, the network will pay more attention to the important lesion area and achieve a higher accuracy in classification. Experiments are performed on three classification databases: Hemorrhage, BraTS18 and Cervix to validate the universality of our innovative model. In conclusion, the proposed framework outperforms the corresponding state-of-art algorithms on each database with both test losses and classification accuracy as the evaluation criteria.
Multi-way Encoding for Robustness
Authors: Donghyun Kim, Sarah Adel Bargal, Jianming Zhang, Stan Sclaroff
Date: 2019-06-05 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection

Deep models are state-of-the-art for many computer vision tasks including image classification and object detection. However, it has been shown that deep models are vulnerable to adversarial examples. We highlight how one-hot encoding directly contributes to this vulnerability and propose breaking away from this widely-used, but highly-vulnerable mapping. We demonstrate that by leveraging a different output encoding, multi-way encoding, we decorrelate source and target models, making target models more secure. Our approach makes it more difficult for adversaries to find useful gradients for generating adversarial attacks. We present robustness for black-box and white-box attacks on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN. The strength of our approach is also presented in the form of an attack for model watermarking, raising challenges in detecting stolen models.