pwc-archive/papers-with-abstracts huggingface dataset:
Learning fashion compatibility across apparel categories for outfit recommendation
Authors: Luisa F. Polania, Satyajit Gupte
Date: 2019-05-01 00:00:00

This paper addresses the problem of generating recommendations for completing
the outfit given that a user is interested in a particular apparel item. The
proposed method is based on a siamese network used for feature extraction
followed by a fully-connected network used for learning a fashion compatibility
metric. The embeddings generated by the siamese network are augmented with
color histogram features motivated by the important role that color plays in
determining fashion compatibility. The training of the network is formulated as
a maximum a posteriori (MAP) problem where Laplacian distributions are assumed
for the filters of the siamese network to promote sparsity and matrix-variate
normal distributions are assumed for the weights of the metric network to
efficiently exploit correlations between the input units of each
fully-connected layer.
A note on 'A fully parallel 3D thinning algorithm and its applications'
Authors: Tao Wang, Anup Basu
Date: 2019-05-01 00:00:00

A 3D thinning algorithm erodes a 3D binary image layer by layer to extract
the skeletons. This paper presents a correction to Ma and Sonka's thinning
algorithm, A fully parallel 3D thinning algorithm and its applications, which
fails to preserve connectivity of 3D objects. We start with Ma and Sonka's
algorithm and examine its verification of connectivity preservation. Our
analysis leads to a group of different deleting templates, which can preserve
connectivity of 3D objects.
Bilinear discriminant feature line analysis for image feature extraction
Authors: Lijun Yan, Jun-Bao Li, Xiaorui Zhu, Jeng-Shyang Pan, Linlin Tang
Date: 2019-05-03 00:00:00
Tasks: General Classification, image-classification, Image Classification

A novel bilinear discriminant feature line analysis (BDFLA) is proposed for image feature extraction. The nearest feature line (NFL) is a powerful classifier. Some NFL-based subspace algorithms were introduced recently. In most of the classical NFL-based subspace learning approaches, the input samples are vectors. For image classification tasks, the image samples should be transformed to vectors first. This process induces a high computational complexity and may also lead to loss of the geometric feature of samples. The proposed BDFLA is a matrix-based algorithm. It aims to minimise the within-class scatter and maximise the between-class scatter based on a two-dimensional (2D) NFL. Experimental results on two-image databases confirm the effectiveness.
Fully Parallel Architecture for Semi-global Stereo Matching with Refined Rank Method
Authors: Yiwu Yao, Yuhua Cheng
Date: 2019-05-07 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

Fully parallel architecture at disparity-level for efficient semi-global matching (SGM) with refined rank method is presented. The improved SGM algorithm is implemented with the non-parametric unified rank model which is the combination of Rank filter/AD and Rank SAD. Rank SAD is a novel definition by introducing the constraints of local image structure into the rank method. As a result, the unified rank model with Rank SAD can make up for the defects of Rank filter/AD. Experimental results show both excellent subjective quality and objective performance of the refined SGM algorithm. The fully parallel construction for hardware implementation of SGM is architected with reasonable strategies at disparity-level. The parallelism of the data-stream allows proper throughput for specific applications with acceptable maximum frequency. The results of RTL emulation and synthesis ensure that the proposed parallel architecture is suitable for VLSI implementation.
Show, Price and Negotiate: A Negotiator with Online Value Look-Ahead
Authors: Amin Parvaneh, Ehsan Abbasnejad, Qi Wu, Javen Qinfeng Shi, Anton Van Den Hengel
Date: 2019-05-07 00:00:00

Negotiation, as an essential and complicated aspect of online shopping, is still challenging for an intelligent agent. To that end, we propose the Price Negotiator, a modular deep neural network that addresses the unsolved problems in recent studies by (1) considering images of the items as a crucial, though neglected, source of information in a negotiation, (2) heuristically finding the most similar items from an external online source to predict the potential value and an acceptable agreement price, (3) predicting a general price-based action at each turn which is fed into the language generator to output the supporting natural language, and (4) adjusting the prices based on the predicted actions. Empirically, we show that our model, that is trained in both supervised and reinforcement learning setting, significantly improves negotiation on the CraigslistBargain dataset, in terms of the agreement price, price consistency, and dialogue quality.
Learning Interpretable Features via Adversarially Robust Optimization
Authors: Ashkan Khakzar, Shadi Albarqouni, Nassir Navab
Date: 2019-05-09 00:00:00
Tasks: Decision Making

Neural networks are proven to be remarkably successful for classification and diagnosis in medical applications. However, the ambiguity in the decision-making process and the interpretability of the learned features is a matter of concern. In this work, we propose a method for improving the feature interpretability of neural network classifiers. Initially, we propose a baseline convolutional neural network with state of the art performance in terms of accuracy and weakly supervised localization. Subsequently, the loss is modified to integrate robustness to adversarial examples into the training process. In this work, feature interpretability is quantified via evaluating the weakly supervised localization using the ground truth bounding boxes. Interpretability is also visually assessed using class activation maps and saliency maps. The method is applied to NIH ChestX-ray14, the largest publicly available chest x-rays dataset. We demonstrate that the adversarially robust optimization paradigm improves feature interpretability both quantitatively and visually.
MAP Inference via L2-Sphere Linear Program Reformulation
Authors: Baoyuan Wu, Li Shen, Tong Zhang, Bernard Ghanem
Date: 2019-05-09 00:00:00
Tasks: valid

Maximum a posteriori (MAP) inference is an important task for graphical models. Due to complex dependencies among variables in realistic model, finding an exact solution for MAP inference is often intractable. Thus, many approximation methods have been developed, among which the linear programming (LP) relaxation based methods show promising performance. However, one major drawback of LP relaxation is that it is possible to give fractional solutions. Instead of presenting a tighter relaxation, in this work we propose a continuous but equivalent reformulation of the original MAP inference problem, called LS-LP. We add the L2-sphere constraint onto the original LP relaxation, leading to an intersected space with the local marginal polytope that is equivalent to the space of all valid integer label configurations. Thus, LS-LP is equivalent to the original MAP inference problem. We propose a perturbed alternating direction method of multipliers (ADMM) algorithm to optimize the LS-LP problem, by adding a sufficiently small perturbation epsilon onto the objective function and constraints. We prove that the perturbed ADMM algorithm globally converges to the epsilon-Karush-Kuhn-Tucker (epsilon-KKT) point of the LS-LP problem. The convergence rate will also be analyzed. Experiments on several benchmark datasets from Probabilistic Inference Challenge (PIC 2011) and OpenGM 2 show competitive performance of our proposed method against state-of-the-art MAP inference methods.
Targeted Sentiment Analysis: A Data-Driven Categorization
Authors: Jiaxin Pei, Aixin Sun, Chenliang Li
Date: 2019-05-09 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

Targeted sentiment analysis (TSA), also known as aspect based sentiment analysis (ABSA), aims at detecting fine-grained sentiment polarity towards targets in a given opinion document. Due to the lack of labeled datasets and effective technology, TSA had been intractable for many years. The newly released datasets and the rapid development of deep learning technologies are key enablers for the recent significant progress made in this area. However, the TSA tasks have been defined in various ways with different understandings towards basic concepts like `target' and `aspect'. In this paper, we categorize the different tasks and highlight the differences in the available datasets and their specific tasks. We then further discuss the challenges related to data collection and data annotation which are overlooked in many previous studies.
Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech
Authors: Tobias Menne, Ilya Sklyar, Ralf Schlüter, Hermann Ney
Date: 2019-05-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Clustering, Deep Clustering, speech-recognition, Speech Recognition

Significant performance degradation of automatic speech recognition (ASR) systems is observed when the audio signal contains cross-talk. One of the recently proposed approaches to solve the problem of multi-speaker ASR is the deep clustering (DPCL) approach. Combining DPCL with a state-of-the-art hybrid acoustic model, we obtain a word error rate (WER) of 16.5 % on the commonly used wsj0-2mix dataset, which is the best performance reported thus far to the best of our knowledge. The wsj0-2mix dataset contains simulated cross-talk where the speech of multiple speakers overlaps for almost the entire utterance. In a more realistic ASR scenario the audio signal contains significant portions of single-speaker speech and only part of the signal contains speech of multiple competing speakers. This paper investigates obstacles of applying DPCL as a preprocessing method for ASR in such a scenario of sparsely overlapping speech. To this end we present a data simulation approach, closely related to the wsj0-2mix dataset, generating sparsely overlapping speech datasets of arbitrary overlap ratio. The analysis of applying DPCL to sparsely overlapping speech is an important interim step between the fully overlapping datasets like wsj0-2mix and more realistic ASR datasets, such as CHiME-5 or AMI.
Compositional Coding for Collaborative Filtering
Authors: Chenghao Liu, Tao Lu, Xin Wang, Zhiyong Cheng, Jianling Sun, Steven C. H. Hoi
Date: 2019-05-09 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Efficiency is crucial to the online recommender systems. Representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computation in the Hamming space, in recent years, we have witnessed an emerging research effort in exploiting binary hashing techniques for CF methods. However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data. In this work, we attempt to improve the efficiency without hurting the model performance by utilizing both the accuracy of real-valued vectors and the efficiency of binary codes to represent users/items. In particular, we propose the Compositional Coding for Collaborative Filtering (CCCF) framework, which not only gains better recommendation efficiency than the state-of-the-art binarized CF approaches but also achieves even higher accuracy than the real-valued CF method. Specifically, CCCF innovatively represents each user/item with a set of binary vectors, which are associated with a sparse real-value weight vector. Each value of the weight vector encodes the importance of the corresponding binary vector to the user/item. The continuous weight vectors greatly enhances the representation capability of binary codes, and its sparsity guarantees the processing speed. Furthermore, an integer weight approximation scheme is proposed to further accelerate the speed. Based on the CCCF framework, we design an efficient discrete optimization algorithm to learn its parameters. Extensive experiments on three real-world datasets show that our method outperforms the state-of-the-art binarized CF methods (even achieves better performance than the real-valued CF method) by a large margin in terms of both recommendation accuracy and efficiency.
Automatic Design of Artificial Neural Networks for Gamma-Ray Detection
Authors: Filipe Assunção, João Correia, Rúben Conceição, Mário Pimenta, Bernardo Tomé, Nuno Lourenço, Penousal Machado
Date: 2019-05-09 00:00:00

The goal of this work is to investigate the possibility of improving current gamma/hadron discrimination based on their shower patterns recorded on the ground. To this end we propose the use of Convolutional Neural Networks (CNNs) for their ability to distinguish patterns based on automatically designed features. In order to promote the creation of CNNs that properly uncover the hidden patterns in the data, and at same time avoid the burden of hand-crafting the topology and learning hyper-parameters we resort to NeuroEvolution; in particular we use Fast-DENSER++, a variant of Deep Evolutionary Network Structured Representation. The results show that the best CNN generated by Fast-DENSER++ improves by a factor of 2 when compared with the results reported by classic statistical approaches. Additionally, we experiment ensembling the 10 best generated CNNs, one from each of the evolutionary runs; the ensemble leads to an improvement by a factor of 2.3. These results show that it is possible to improve the gamma/hadron discrimination based on CNNs that are automatically generated and are trained with instances of the ground impact patterns.
Classificação de espécies de peixe utilizando redes neurais convolucional
Authors: Andre G. C. Pacheco
Date: 2019-05-09 00:00:00
Tasks: General Classification

Data classification is present in different real problems, such as recognizing patterns in images, differentiating defective parts in a production line, classifying benign and malignant tumors, among many others. Many of these problems have data patterns that are hard to identify, which requires more advanced techniques for resolution. Recently, several works addressing different artificial neural network architectures have been applied to solve classification problems. When the classification problem must be obtained through images, currently, the standard methodology is the use of convolutional neural networks. Thus, in this report convolutional neural networks are used to classify fish species. Classifica\c{c}\~ao de dados est\'a presente em diversos problemas reais, tais como: reconhecer padr\~oes em imagens, diferenciar pe\c{c}as defeituosas em uma linha de produ\c{c}\~ao, classificar tumores benignos e malignos, dentre diversas outras. Muitos desses problemas possuem padr\~oes de dados dif\'iceis de serem identificados, o que requer, consequentemente, t\'ecnicas mais avan\c{c}adas para sua resolu\c{c}\~ao. Recentemente, diversos trabalhos abordando diferentes arquiteturas de redes neurais artificiais v\^em sendo aplicados para solucionar problemas de classifica\c{c}\~ao. Quando a classifica\c{c}\~ao do problema deve ser obtida por meio de imagens, atualmente a metodologia padr\~ao \'e uso de redes neurais convolucionais. Sendo assim, neste trabalho s\~ao utilizadas redes neurais convolucionais para classifica\c{c}\~ao de esp\'ecies de peixes.
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction
Authors: Yunhan Jia, Yantao Lu, Senem Velipasalar, Zhenyu Zhong, Tao Wei
Date: 2019-05-08 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection, Optical Character Recognition, Optical Character Recognition (OCR)

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they maintain their effectiveness even against other models. With great efforts delved into the transferability of adversarial examples, surprisingly, less attention has been paid to its impact on real-world deep learning deployment. In this paper, we investigate the transferability of adversarial examples across a wide range of real-world computer vision tasks, including image classification, explicit content detection, optical character recognition (OCR), and object detection. It represents the cybercriminal's situation where an ensemble of different detection mechanisms need to be evaded all at once. We propose practical attack that overcomes existing attacks' limitation of requiring task-specific loss functions by targeting on the `dispersion' of internal feature map. We report evaluation on four different computer vision tasks provided by Google Cloud Vision APIs to show how our approach outperforms existing attacks by degrading performance of multiple CV tasks by a large margin with only modest perturbations.
Path Design for Cellular-Connected UAV with Reinforcement Learning
Authors: Yong Zeng, Xiaoli Xu
Date: 2019-05-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper studies the path design problem for cellular-connected unmanned aerial vehicle (UAV), which aims to minimize its mission completion time while maintaining good connectivity with the cellular network. We first argue that the conventional path design approach via formulating and solving optimization problems faces several practical challenges, and then propose a new reinforcement learning-based UAV path design algorithm by applying \emph{temporal-difference} method to directly learn the \emph{state-value function} of the corresponding Markov Decision Process. The proposed algorithm is further extended by using linear function approximation with tile coding to deal with large state space. The proposed algorithms only require the raw measured or simulation-generated signal strength as the input and are suitable for both online and offline implementations. Numerical results show that the proposed path designs can successfully avoid the coverage holes of cellular networks even in the complex urban environment.
Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain
Authors: Chris Einar San Agustin
Date: 2019-05-09 00:00:00
Tasks: Adversarial Attack, Deep Reinforcement Learning, Reinforcement Learning, Self-Driving Cars

Deep learning models are known to solve classification and regression problems by employing a number of epoch and training samples on a large dataset with optimal accuracy. However, that doesn't mean they are attack-proof or unexposed to vulnerabilities. Newly deployed systems particularly on a public environment (i.e public networks) are vulnerable to attacks from various entities. Moreover, published research on deep learning systems (Goodfellow et al., 2014) have determined a significant number of attacks points and a wide array of attack surface that has evidence of exploitation from adversarial examples. Successful exploit on these systems could lead to critical real world repercussions. For instance, (1) an adversarial attack on a self-driving car running a deep reinforcement learning system yields a direct misclassification on humans causing untoward accidents.(2) a self-driving vehicle misreading a red light signal may cause the car to crash to another car (3) misclassification of a pedestrian lane as an intersection lane that could lead to car crashes. This is just the tip of the iceberg, computer vision deployment are not entirely focused on self-driving cars but on many other areas as well - that would have definitive impact on the real-world. These vulnerabilities must be mitigated at an early stage of development. It is imperative to develop and implement baseline security standards at a global level prior to real-world deployment.
Evaluation of Machine Learning Classifiers for Zero-Day Intrusion Detection -- An Analysis on CIC-AWS-2018 dataset
Authors: Qianru Zhou, Dimitrios Pezaros
Date: 2019-05-09 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Intrusion Detection, Zero-day intrusion detection

Detecting Zero-Day intrusions has been the goal of Cybersecurity, especially intrusion detection for a long time. Machine learning is believed to be the promising methodology to solve that problem, numerous models have been proposed but a practical solution is still yet to come, mainly due to the limitation caused by the out-of-date open datasets available. In this paper, we take a deep inspection of the flow-based statistical data generated by CICFlowMeter, with six most popular machine learning classification models for Zero-Day attacks detection. The training dataset CIC-AWS-2018 Dataset contains fourteen types of intrusions, while the testing datasets contains eight different types of attacks. The six classification models are evaluated and cross validated on CIC-AWS-2018 Dataset for their accuracy in terms of false-positive rate, true-positive rate, and time overhead. Testing dataset, including eight novel (or Zero-Day) real-life attacks and benign traffic flows collected in real research production network are used to test the performance of the chosen decision tree classifier. Promising results are received with the accuracy as high as 100% and reasonable time overhead. We argue that with the statistical data collected from CICFlowMeter, simple machine learning models such as the decision tree classification could be able to take charge in detecting Zero-Day attacks.
Coresets for Minimum Enclosing Balls over Sliding Windows
Authors: Yanhao Wang, Yuchen Li, Kian-Lee Tan
Date: 2019-05-09 00:00:00

\emph{Coresets} are important tools to generate concise summaries of massive datasets for approximate analysis. A coreset is a small subset of points extracted from the original point set such that certain geometric properties are preserved with provable guarantees. This paper investigates the problem of maintaining a coreset to preserve the minimum enclosing ball (MEB) for a sliding window of points that are continuously updated in a data stream. Although the problem has been extensively studied in batch and append-only streaming settings, no efficient sliding-window solution is available yet. In this work, we first introduce an algorithm, called AOMEB, to build a coreset for MEB in an append-only stream. AOMEB improves the practical performance of the state-of-the-art algorithm while having the same approximation ratio. Furthermore, using AOMEB as a building block, we propose two novel algorithms, namely SWMEB and SWMEB+, to maintain coresets for MEB over the sliding window with constant approximation ratios. The proposed algorithms also support coresets for MEB in a reproducing kernel Hilbert space (RKHS). Finally, extensive experiments on real-world and synthetic datasets demonstrate that SWMEB and SWMEB+ achieve speedups of up to four orders of magnitude over the state-of-the-art batch algorithm while providing coresets for MEB with rather small errors compared to the optimal ones.
Data-driven computing in elasticity via kernel regression
Authors: Yoshihiro Kanno
Date: 2018-12-12 00:00:00
Proceeding: Theoretical and Applied Mechanics Letters 2018 12
Tasks: regression, Stress-Strain Relation

This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set.