pwc-archive/papers-with-abstracts huggingface dataset:
Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks
Authors: Metin Ozturk, Attai Ibrahim Abubakar, Rao Naveed Bin Rais, Mona Jaber, Sajjad Hussain, Muhammad Ali Imran
Date: 2020-04-30 00:00:00

A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response-time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm, and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response-time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multi-objective optimisation criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.
Continuous sign language recognition from wearable IMUs using deep capsule networks and game theory
Authors: Karush Suri, Rinki Gupta
Date: 2020-04-27 00:00:00
Tasks: Sign Language Recognition

Sign Language is used by the deaf community all over world. The work presented here proposes a novel one-dimensional deep capsule network (CapsNet) architecture for continuous Indian Sign Language recognition by means of signals obtained from a custom designed wearable IMU system. The performance of the proposed CapsNet architecture is assessed by altering dynamic routing between capsule layers. The proposed CapsNet yields improved accuracy values of 94% for 3 routings and 92.50% for 5 routings in comparison with the convolutional neural network (CNN) that yields an accuracy of 87.99%. Improved learning of the proposed architecture is also validated by spatial activations depicting excited units at the predictive layer. Finally, a novel non-cooperative pick-and-predict competition is designed between CapsNet and CNN. Higher value of Nash equilibrium for CapsNet as compared to CNN indicates the suitability of the proposed approach.
Classification of Hand Gestures from Wearable IMUs using Deep Neural Network
Authors: Karush Suri, Rinki Gupta
Date: 2020-04-27 00:00:00
Tasks: Classification, General Classification

IMUs are gaining significant importance in the field of hand gesture analysis, trajectory detection and kinematic functional study. An Inertial Measurement Unit (IMU) consists of tri-axial accelerometers and gyroscopes which can together be used for formation analysis. The paper presents a novel classification approach using a Deep Neural Network (DNN) for classifying hand gestures obtained from wearable IMU sensors. An optimization objective is set for the classifier in order to reduce correlation between the activities and fit the signal-set with best performance parameters. Training of the network is carried out by feed-forward computation of the input features followed by the back-propagation of errors. The predicted outputs are analyzed in the form of classification accuracies which are then compared to the conventional classification schemes of SVM and kNN. A 3-5% improvement in accuracies is observed in the case of DNN classification. Results are presented for the recorded accelerometer and gyroscope signals and the considered classification schemes.
On the Merging of Domain-Specific Heterogeneous Ontologies using Wordnet and Web Pattern-based Queries
Authors: M. Maree, M. Belkhatir
Date: 2020-04-30 00:00:00
Tasks: Information Retrieval, Retrieval

Ontologies form the basic interest in various computer science disciplines such as semantic web, information retrieval, database design, etc. They aim at providing a formal, explicit and shared conceptualization and understanding of common domains between different communities. In addition, they allow for concepts and their constraints of a specific domain to be explicitly defined. However, the distributed nature of ontology development and the differences in viewpoints of the ontology engineers have resulted in the so called "semantic heterogeneity" between ontologies. Semantic heterogeneity constitutes the major obstacle against achieving interoperability between ontologies. To overcome this obstacle, we present a multi-purpose framework which exploits the WordNet generic knowledge base for: i) Discovering and correcting the incorrect semantic relations between the concepts of the ontology in a specific domain. This step is a primary step of ontology merging. ii) Merging domain-specific ontologies through computing semantic relations between their concepts. iii) Handling the issue of missing concepts in WordNet through the acquisition of statistical information on the Web. And iv) Enriching WordNet with these missing concepts. An experimental instantiation of the framework and comparisons with state-of-the-art syntactic and semantic-based systems validate our proposal.
Getting Insights from a Large Corpus of Scientific Papers on Specialisted Comprehensive Topics -- the Case of COVID-19
Authors: Bernard Dousset, Josiane Mothe
Date: 2020-04-30 00:00:00

COVID-19 is one of the most important topic these days, specifically on search engines and news. While fake news are easily shared, scientific papers are reliable sources where information can be extracted. With about 24,000 scientific publications on COVID-19 and related research on PUBMED, automatic computer-assisted analysis is required. In this paper, we develop two methodologies to get insights on specific sub-topics of interest and latest research sub-topics. They rely on natural language processing and graph-based visualizations. We run these methodologies on two cases: the virus origin and the uses of existing drugs.
Lemna: Explaining deep learning based security applications
Authors: Wenbo Guo, Dongliang Mu5, Jun Xu4, Purui Su6, Gang Wang3, Xinyu Xing
Date: 2018-01-15 00:00:00
Proceeding: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security 2018 1
Tasks: Deep Learning

While deep learning has shown a great potential in various domains,the lack of transparency has limited its application in security orsafety-critical areas. Existing research has attempted to developexplanation techniques to provide interpretable explanations foreach classication decision. Unfortunately, current methods areoptimized for non-security tasks (e.g., image analysis). Their keyassumptions are often violated in security applications, leading toa poor explanation delity.In this paper, we proposeLEMNA, a high-delity explanationmethod dedicated for security applications. Given an input datasample,LEMNAgenerates a small set of interpretable features to ex-plain how the input sample is classied. The core idea is to approx-imate a local area of the complex deep learning decision boundaryusing a simple interpretable model. The local interpretable modelis specially designed to (1) handle feature dependency to betterwork with security applications (e.g., binary code analysis); and(2) handle nonlinear local boundaries to boost explanation delity.We evaluate our system using two popular deep learning applica-tions in security (a malware classier, and a function start detectorfor binary reverse-engineering). Extensive evaluations show thatLEMNA’s explanation has a much higher delity level compared toexisting methods. In addition, we demonstrate practical use casesofLEMNAto help machine learning developers to validate model be-havior, troubleshoot classication errors, and automatically patchthe errors of the target models.
Transforming and Projecting Images into Class-conditional Generative Networks
Authors: Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, Aaron Hertzmann
Date: 2020-05-04 00:00:00
Tasks: Generative Adversarial Network, Translation

We present a method for projecting an input image into the space of a class-conditional generative neural network. We propose a method that optimizes for transformation to counteract the model biases in generative neural networks. Specifically, we demonstrate that one can solve for image translation, scale, and global color transformation, during the projection optimization to address the object-center bias and color bias of a Generative Adversarial Network. This projection process poses a difficult optimization problem, and purely gradient-based optimizations fail to find good solutions. We describe a hybrid optimization strategy that finds good projections by estimating transformations and class parameters. We show the effectiveness of our method on real images and further demonstrate how the corresponding projections lead to better editability of these images.
Depth-2 Neural Networks Under a Data-Poisoning Attack
Authors: Sayar Karmakar, Anirbit Mukherjee, Theodore Papamarkou
Date: 2020-05-04 00:00:00
Tasks: Adversarial Attack, Data Poisoning

In this work, we study the possibility of defending against data-poisoning attacks while training a shallow neural network in a regression setup. We focus on doing supervised learning for a class of depth-2 finite-width neural networks, which includes single-filter convolutional networks. In this class of networks, we attempt to learn the network weights in the presence of a malicious oracle doing stochastic, bounded and additive adversarial distortions on the true output during training. For the non-gradient stochastic algorithm that we construct, we prove worst-case near-optimal trade-offs among the magnitude of the adversarial attack, the weight approximation accuracy, and the confidence achieved by the proposed algorithm. As our algorithm uses mini-batching, we analyze how the mini-batch size affects convergence. We also show how to utilize the scaling of the outer layer weights to counter output-poisoning attacks depending on the probability of attack. Lastly, we give experimental evidence demonstrating how our algorithm outperforms stochastic gradient descent under different input data distributions, including instances of heavy-tailed distributions.
How to Train Your Energy-Based Model for Regression
Authors: Fredrik K. Gustafsson, Martin Danelljan, Radu Timofte, Thomas B. Schön
Date: 2020-05-04 00:00:00
Tasks: object-detection, Object Detection, regression, Visual Object Tracking, Visual Tracking

Energy-based models (EBMs) have become increasingly popular within computer vision in recent years. While they are commonly employed for generative image modeling, recent work has applied EBMs also for regression tasks, achieving state-of-the-art performance on object detection and visual tracking. Training EBMs is however known to be challenging. While a variety of different techniques have been explored for generative modeling, the application of EBMs to regression is not a well-studied problem. How EBMs should be trained for best possible regression performance is thus currently unclear. We therefore accept the task of providing the first detailed study of this problem. To that end, we propose a simple yet highly effective extension of noise contrastive estimation, and carefully compare its performance to six popular methods from literature on the tasks of 1D regression and object detection. The results of this comparison suggest that our training method should be considered the go-to approach. We also apply our method to the visual tracking task, achieving state-of-the-art performance on five datasets. Notably, our tracker achieves 63.7% AUC on LaSOT and 78.7% Success on TrackingNet. Code is available at https://github.com/fregu856/ebms_regression.
Setting up experimental Bell test with reinforcement learning
Authors: Alexey A. Melnikov, Pavel Sekatski, Nicolas Sangouard
Date: 2020-05-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Finding optical setups producing measurement results with a targeted probability distribution is hard as a priori the number of possible experimental implementations grows exponentially with the number of modes and the number of devices. To tackle this complexity, we introduce a method combining reinforcement learning and simulated annealing enabling the automated design of optical experiments producing results with the desired probability distributions. We illustrate the relevance of our method by applying it to a probability distribution favouring high violations of the Bell-CHSH inequality. As a result, we propose new unintuitive experiments leading to higher Bell-CHSH inequality violations than the best currently known setups. Our method might positively impact the usefulness of photonic experiments for device-independent quantum information processing.
Learning Geo-Contextual Embeddings for Commuting Flow Prediction
Authors: Zhicheng Liu, Fabio Miranda, Weiting Xiong, Junyan Yang, Qiao Wang, Claudio T. Silva
Date: 2020-05-04 00:00:00
Tasks: Graph Attention, Graph Embedding, Prediction

Predicting commuting flows based on infrastructure and land-use information is critical for urban planning and public policy development. However, it is a challenging task given the complex patterns of commuting flows. Conventional models, such as gravity model, are mainly derived from physics principles and limited by their predictive power in real-world scenarios where many factors need to be considered. Meanwhile, most existing machine learning-based methods ignore the spatial correlations and fail to model the influence of nearby regions. To address these issues, we propose Geo-contextual Multitask Embedding Learner (GMEL), a model that captures the spatial correlations from geographic contextual information for commuting flow prediction. Specifically, we first construct a geo-adjacency network containing the geographic contextual information. Then, an attention mechanism is proposed based on the framework of graph attention network (GAT) to capture the spatial correlations and encode geographic contextual information to embedding space. Two separate GATs are used to model supply and demand characteristics. A multitask learning framework is used to introduce stronger restrictions and enhance the effectiveness of the embedding representation. Finally, a gradient boosting machine is trained based on the learned embeddings to predict commuting flows. We evaluate our model using real-world datasets from New York City and the experimental results demonstrate the effectiveness of our proposal against the state of the art.
Neural Networks and Value at Risk
Authors: Alexander Arimond, Damian Borth, Andreas Hoepner, Michael Klawunn, Stefan Weisheit
Date: 2020-05-04 00:00:00

Utilizing a generative regime switching framework, we perform Monte-Carlo simulations of asset returns for Value at Risk threshold estimation. Using equity markets and long term bonds as test assets in the global, US, Euro area and UK setting over an up to 1,250 weeks sample horizon ending in August 2018, we investigate neural networks along three design steps relating (i) to the initialization of the neural network, (ii) its incentive function according to which it has been trained and (iii) the amount of data we feed. First, we compare neural networks with random seeding with networks that are initialized via estimations from the best-established model (i.e. the Hidden Markov). We find latter to outperform in terms of the frequency of VaR breaches (i.e. the realized return falling short of the estimated VaR threshold). Second, we balance the incentive structure of the loss function of our networks by adding a second objective to the training instructions so that the neural networks optimize for accuracy while also aiming to stay in empirically realistic regime distributions (i.e. bull vs. bear market frequencies). In particular this design feature enables the balanced incentive recurrent neural network (RNN) to outperform the single incentive RNN as well as any other neural network or established approach by statistically and economically significant levels. Third, we half our training data set of 2,000 days. We find our networks when fed with substantially less data (i.e. 1,000 days) to perform significantly worse which highlights a crucial weakness of neural networks in their dependence on very large data sets ...
Group Equivariant Generative Adversarial Networks
Authors: Neel Dey, Antong Chen, Soheil Ghafurian
Date: 2020-05-04 00:00:00
Proceeding: ICLR 2021 1

Recent improvements in generative adversarial visual synthesis incorporate real and fake image transformation in a self-supervised setting, leading to increased stability and perceptual fidelity. However, these approaches typically involve image augmentations via additional regularizers in the GAN objective and thus spend valuable network capacity towards approximating transformation equivariance instead of their desired task. In this work, we explicitly incorporate inductive symmetry priors into the network architectures via group-equivariant convolutional networks. Group-convolutions have higher expressive power with fewer samples and lead to better gradient feedback between generator and discriminator. We show that group-equivariance integrates seamlessly with recent techniques for GAN training across regularizers, architectures, and loss functions. We demonstrate the utility of our methods for conditional synthesis by improving generation in the limited data regime across symmetric imaging datasets and even find benefits for natural images with preferred orientation.
What is Learned in Visually Grounded Neural Syntax Acquisition
Authors: Noriyuki Kojima, Hadar Averbuch-Elor, Alexander M. Rush, Yoav Artzi
Date: 2020-05-04 00:00:00
Proceeding: ACL 2020 6

Visual features are a promising signal for learning bootstrap textual models. However, blackbox learning models make it difficult to isolate the specific contribution of visual components. In this analysis, we consider the case study of the Visually Grounded Neural Syntax Learner (Shi et al., 2019), a recent approach for learning syntax from a visual training signal. By constructing simplified versions of the model, we isolate the core factors that yield the model's strong performance. Contrary to what the model might be capable of learning, we find significantly less expressive versions produce similar predictions and perform just as well, or even better. We also find that a simple lexical signal of noun concreteness plays the main role in the model's predictions as opposed to more complex syntactic reasoning.
Fast and Robust Unsupervised Contextual Biasing for Speech Recognition
Authors: Young Mo Kang, Yingbo Zhou
Date: 2020-05-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Small Language Model, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) system is becoming a ubiquitous technology. Although its accuracy is closing the gap with that of human level under certain settings, one area that can further improve is to incorporate user-specific information or context to bias its prediction. A common framework is to dynamically construct a small language model from the provided contextual mini corpus and interpolate its score with the main language model during the decoding process. Here we propose an alternative approach that does not entail explicit contextual language model. Instead, we derive the bias score for every word in the system vocabulary from the training corpus. The method is unique in that 1) it does not require meta-data or class-label annotation for the context or the training corpus. 2) The bias score is proportional to the word's log-probability, thus not only would it bias the provided context, but also robust against irrelevant context (e.g. user mis-specified or in case where it is hard to quantify a tight scope). 3) The bias score for the entire vocabulary is pre-determined during the training stage, thereby eliminating computationally expensive language model construction during inference. We show significant improvement in recognition accuracy when the relevant context is available. Additionally, we also demonstrate that the proposed method exhibits high tolerance to false-triggering errors in the presence of irrelevant context.
Evaluating Explanation Methods for Neural Machine Translation
Authors: Jierui Li, Lemao Liu, Huayang Li, Guanlin Li, Guoping Huang, Shuming Shi
Date: 2020-05-04 00:00:00
Proceeding: ACL 2020 6
Tasks: Machine Translation, NMT, Translation, Word Alignment

Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods. Word Alignment Error Rate can be used as such a metric that matches human understanding, however, it can not measure explanation methods on those target words that are not aligned to any source word. This paper thereby makes an initial attempt to evaluate explanation methods from an alternative viewpoint. To this end, it proposes a principled metric based on fidelity in regard to the predictive behavior of the NMT model. As the exact computation for this metric is intractable, we employ an efficient approach as its approximation. On six standard translation tasks, we quantitatively evaluate several explanation methods in terms of the proposed metric and we reveal some valuable findings for these explanation methods in our experiments.
PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks
Authors: Nabil Ibtehaz, Sakib Mahmud, Muhammad E. H. Chowdhury, Amith Khandakar, Mohamed Arselene Ayari, Anas Tahir, M. Sohel Rahman
Date: 2020-05-04 00:00:00

Cardiovascular diseases are one of the most severe causes of mortality, taking a heavy toll of lives annually throughout the world. The continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, bringing about several layers of complexities. This motivates us to develop a method to predict the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using photoplethysmogram (PPG) signals. In addition we explore the advantage of deep learning as it would free us from sticking to ideally shaped PPG signals only, by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present, PPG2ABP, a deep learning based method, that manages to predict the continuous ABP waveform from the input PPG signal, with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of DBP, MAP and SBP from the predicted ABP waveform outperforms the existing works under several metrics, despite that PPG2ABP is not explicitly trained to do so.