pwc-archive/papers-with-abstracts huggingface dataset:
Estimating indoor occupancy through low-cost BLE devices
Authors: Florenc Demrozi, Cristian Turetta, Fabio Chiarani, Philipp H. Kindt, Graziano Pravadelli
Date: 2021-01-30 00:00:00

Detecting the presence of persons and estimating their quantity in an indoor environment has grown in importance recently. For example, the information if a room is unoccupied can be used for automatically switching off the light, air conditioning, and ventilation, thereby saving significant amounts of energy in public buildings. Most existing solutions rely on dedicated hardware installations, which involve presence sensors, video cameras, and carbon dioxide sensors. Unfortunately, such approaches are costly, are subject to privacy concerns, have high computational requirements, and lack ubiquitousness. The work presented in this article addresses these limitations by proposing a low-cost occupancy detection system. Our approach builds upon detecting variations in Bluetooth Low Energy (BLE) signals related to the presence of humans. The effectiveness of this approach is evaluated by performing comprehensive tests on five different datasets. We apply several pattern recognition models and compare our methodology with systems building upon IEEE 802.11 (WiFi). On average, in multifarious environments, we can correctly classify the occupancy with an accuracy of 97.97%. When estimating the number of people in a room, on average, the estimated number of subjects differs from the actual one by 0.32 persons. We conclude that our system's performance is comparable to that of existing ones based on WiFi, while significantly reducing cost and installation effort. Hence, our approach makes occupancy detection practical for real-world deployments.
Temporal convolutional networks and transformers for classifying the sleep stage in awake or asleep using pulse oximetry signals
Authors: Ramiro Casal, Leandro E. Di Persia, Gastón Schlotthauer
Date: 2021-01-29 00:00:00
Tasks: Diagnostic, Specificity

Sleep disorders are very widespread in the world population and suffer from a generalized underdiagnosis, given the complexity of their diagnostic methods. Therefore, there is an increasing interest in developing simpler screening methods. A pulse oximeter is an ideal device for sleep disorder screenings since it is a portable, low-cost and accessible technology. This device can provide an estimation of the heart rate (HR), which can be useful to obtain information regarding the sleep stage. In this work, we developed a network architecture with the aim of classifying the sleep stage in awake or asleep using only HR signals from a pulse oximeter. The proposed architecture has two fundamental parts. The first part has the objective of obtaining a representation of the HR by using temporal convolutional networks. Then, the obtained representation is used to feed the second part, which is based on transformers, a model built solely with attention mechanisms. Transformers are able to model the sequence, learning the transition rules between sleep stages. The performance of the proposed method was evaluated on Sleep Heart Health Study dataset, composed of 5000 healthy and pathological subjects. The dataset was split into three subsets: 2500 for training, $1250$ for validating, and 1250 for testing. The overall accuracy, specificity, sensibility, and Cohen's Kappa coefficient were 90.0%, 94.9%, 78.1%, and 0.73.
Cross-domain Activity Recognition via Substructural Optimal Transport
Authors: Wang Lu, Yiqiang Chen, Jindong Wang, Xin Qin
Date: 2021-01-29 00:00:00
Tasks: Activity Recognition, Clustering, Cross-Domain Activity Recognition, Domain Adaptation, Human Activity Recognition, Transfer Learning

It is expensive and time-consuming to collect sufficient labeled data for human activity recognition (HAR). Domain adaptation is a promising approach for cross-domain activity recognition. Existing methods mainly focus on adapting cross-domain representations via domain-level, class-level, or sample-level distribution matching. However, they might fail to capture the fine-grained locality information in activity data. The domain- and class-level matching are too coarse that may result in under-adaptation, while sample-level matching may be affected by the noise seriously and eventually cause over-adaptation. In this paper, we propose substructure-level matching for domain adaptation (SSDA) to better utilize the locality information of activity data for accurate and efficient knowledge transfer. Based on SSDA, we propose an optimal transport-based implementation, Substructural Optimal Transport (SOT), for cross-domain HAR. We obtain the substructures of activities via clustering methods and seeks the coupling of the weighted substructures between different domains. We conduct comprehensive experiments on four public activity recognition datasets (i.e. UCI-DSADS, UCI-HAR, USC-HAD, PAMAP2), which demonstrates that SOT significantly outperforms other state-of-the-art methods w.r.t classification accuracy (9%+ improvement). In addition, our mehtod is 5x faster than traditional OT-based DA methods with the same hyper-parameters.
Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning
Authors: Francis Ogoke, Amir Barati Farimani
Date: 2021-01-29 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Powder-based additive manufacturing techniques provide tools to construct intricate structures that are difficult to manufacture using conventional methods. In Laser Powder Bed Fusion, components are built by selectively melting specific areas of the powder bed, to form the two-dimensional cross-section of the specific part. However, the high occurrence of defects impacts the adoption of this method for precision applications. Therefore, a control policy for dynamically altering process parameters to avoid phenomena that lead to defect occurrences is necessary. A Deep Reinforcement Learning (DRL) framework that derives a versatile control strategy for minimizing the likelihood of these defects is presented. The generated control policy alters the velocity of the laser during the melting process to ensure the consistency of the melt pool and reduce overheating in the generated product. The control policy is trained and validated on efficient simulations of the continuum temperature distribution of the powder bed layer under various laser trajectories.
Artificial Intelligence based Sensor Data Analytics Framework for Remote Electricity Network Condition Monitoring
Authors: Tharmakulasingam Sirojan
Date: 2021-01-21 00:00:00
Tasks: Edge-computing, Management

Rural electrification demands the use of inexpensive technologies such as single wire earth return (SWER) networks. There is a steadily growing energy demand from remote consumers, and the capacity of existing lines may become inadequate soon. Furthermore, high impedance arcing faults (HIF) from SWER lines can cause catastrophic bushfires such as the 2009 Black Saturday event. As a solution, reliable remote electricity networks can be established through breaking the existing systems down into microgrids, and existing SWER lines can be utilised to interconnect those microgrids. The development of such reliable networks with better energy demand management will rely on having an integrated network-wide condition monitoring system. As the first contribution of this thesis, a distributed online monitoring platform is developed that incorporates power quality monitoring, real-time HIF identification and transient classification in SWER network. Artificial Intelligence (AI) based techniques are developed to classify faults and transients. The proposed approach demonstrates higher HIF detection accuracy (98.67%) and reduced detection latency (115.2 ms). Secondly, a remote consumer load identification methodology is developed to detect the load type from its transients. An edge computing-based architecture is proposed to facilitate the high-frequency analysis for load identification. The proposed approach is evaluated in real-time, and it achieves an average accuracy of 98% in identifying different loads. Finally, a deep neural network-based energy disaggregation framework is developed to separate the load specific energy usage from an aggregated signal. The proposed framework is evaluated using a real-world data set. It improves the signal aggregate error by 44% and mean aggregate error by 19% in comparison with the state-of-the-art techniques.
Machine Learning for Electronic Design Automation: A Survey
Authors: Guyue Huang, Jingbo Hu, Yifan He, Jialong Liu, Mingyuan Ma, Zhaoyang Shen, Juejian Wu, Yuanfan Xu, Hengrui Zhang, Kai Zhong, Xuefei Ning, Yuzhe ma, HaoYu Yang, Bei Yu, Huazhong Yang, Yu Wang
Date: 2021-01-10 00:00:00
Tasks: BIG-bench Machine Learning, Survey

With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in electronic design automation (EDA) can trace its history back to the 90s, the recent breakthrough of ML and the increasing complexity of EDA tasks have aroused more interests in incorporating ML to solve EDA tasks. In this paper, we present a comprehensive review of existing ML for EDA studies, organized following the EDA hierarchy.
Phase-aware Single-stage Speech Denoising and Dereverberation with U-Net
Date: 2020-06-01 00:00:00
Proceeding: Interspeech 2020 6
Tasks: Denoising, Speech Denoising, Speech Enhancement

In this work, we tackle a denoising and dereverberation problem with a single-stage framework. Although denoising and dereverberation may be considered two separate challenging tasks, and thus, two modules are typically required for each task, we show that a single deep network can be shared to solve the two problems. To this end, we propose a new masking method called phase-aware beta-sigmoid mask (PHM), which reuses the estimated magnitude values to estimate the clean phase by respecting the triangle inequality in the complex domain between three signal components such as mixture, source and the rest. Two PHMs are used to deal with direct and reverberant source, which allows controlling the proportion of reverberation in the enhanced speech at inference time. In addition, to improve the speech enhancement performance, we propose a new time-domain loss function and show a reasonable performance gain compared to MSE loss in the complex domain. Finally, to achieve a real-time inference, an optimization strategy for U-Net is proposed which significantly reduces the computational overhead up to 88.9% compared to the na\"ive version.
Cobiveco: Consistent biventricular coordinates for precise and intuitive description of position in the heart -- with MATLAB implementation
Authors: Steffen Schuler, Nicolas Pilia, Danila Potyagaylo, Axel Loewe
Date: 2021-02-04 00:00:00
Tasks: Position

Ventricular coordinates are widely used as a versatile tool for various applications that benefit from a description of local position within the heart. However, the practical usefulness of ventricular coordinates is determined by their ability to meet application-specific requirements. For regression-based estimation of biventricular position, for example, a symmetric definition of coordinate directions in both ventricles is important. For the transfer of data between different hearts as another use case, the consistency of coordinate values across different geometries is particularly relevant. To meet these requirements, we compare different approaches to compute coordinates and present Cobiveco, a symmetric, consistent and intuitive biventricular coordinate system that builds upon existing coordinate systems, but overcomes some of their limitations. A novel one-way transfer error is introduced to assess the consistency of the coordinates. Normalized distances along bijective trajectories between two boundaries were found to be superior to solutions of Laplace's equation for defining coordinate values, as they show better linearity in space. Evaluation of transfer and linearity errors on 36 patient geometries revealed a more than 4-fold improvement compared to a state-of-the-art method. Finally, we show two application examples underlining the relevance for cardiac data processing. Cobiveco MATLAB code is available under a permissive open-source license.
Explicit 3D Change Detection using Ray-Tracing in Spherical Coordinates
Authors: JP Underwood, D Gillsjö, T Bailey, V Vlaskine
Date: 2013-05-01 00:00:00
Tasks: Change Detection

Change detection is important for autonomous perception systems that operate in dynamic environments. Mapping and tracking components commonly handle two ends of the dynamic spectrum: stationarity and rapid motion. This paper presents a fast algorithm for 3D change detection from LIDAR or equivalent optical range sensors, that can operate from arbitrary viewpoints and can detect fast and slow dynamics. 
Distinct from prior work, the method explicitly detects changes in the world, and suppresses apparent changes in the data due to exploration at frontiers or behind occlusions. Comprehensive experimentation is performed to assess the performance in several application domains. Sample data and source code are provided.
CoNFET: An English Sentence to Emojis Translation Algorithm
Authors: Alex Day, Chris Mankos, Soo Kim, Jody Strausser
Date: 2021-01-06 00:00:00
Tasks: Natural Language Understanding, Sentence, Sentence Summarization, Translation

Emojis are a collection of emoticons that have been standardized by the Unicode Consortium. Currently, there are
over 3,000 emojis in the Unicode standard. These small pictographs can represent an object as vague as a laughter (🤣)
to something as specific as a passport control (🛂). Due to
their high information density and the sheer amount, emojis have become prevalent in common communication media
such as SMS and Twitter. There is a need to increase natural language understanding in the emoji domain. To this end,
we present the CoNFET (Composition of N-grams for Emoji
Translation) algorithm to translate an English sentence into
a sequence of emojis. This translation algorithm consists of
three main parts: the n-gram sequence generation, the n-gram
to emoji translation, and the translation scoring. First, the input sentence is split into its constituent n-grams either in an
exhaustive manner or using dependency relations. Second,
the n-grams of the sentence are translated into emojis using
the nearest neighbor in a vectorized linguistic space. Finally,
these translations are scored using either a simple average or
an average weighted by the Term Frequency-Inverse Document Frequency (TF-IDF) score of the n-gram. As the result,
the sequence of emojis with the highest score is selected as
an output of the sentence summarization.
Robust Single-step Adversarial Training with Regularizer
Authors: Lehui Xie, Yaopeng Wang, Jia-Li Yin, Ximeng Liu
Date: 2021-02-05 00:00:00

High cost of training time caused by multi-step adversarial example generation is a major challenge in adversarial training. Previous methods try to reduce the computational burden of adversarial training using single-step adversarial example generation schemes, which can effectively improve the efficiency but also introduce the problem of catastrophic overfitting, where the robust accuracy against Fast Gradient Sign Method (FGSM) can achieve nearby 100\% whereas the robust accuracy against Projected Gradient Descent (PGD) suddenly drops to 0\% over a single epoch. To address this problem, we propose a novel Fast Gradient Sign Method with PGD Regularization (FGSMPR) to boost the efficiency of adversarial training without catastrophic overfitting. Our core idea is that single-step adversarial training can not learn robust internal representations of FGSM and PGD adversarial examples. Therefore, we design a PGD regularization term to encourage similar embeddings of FGSM and PGD adversarial examples. The experiments demonstrate that our proposed method can train a robust deep network for L$_\infty$-perturbations with FGSM adversarial training and reduce the gap to multi-step adversarial training.
An Unbiased Estimator of the Full-sky CMB Angular Power Spectrum at Large Scales using Neural Networks
Authors: Pallav Chanda, Rajib Saha
Date: 2021-02-08 00:00:00

Accurate estimation of the Cosmic Microwave Background (CMB) angular power spectrum is enticing due to the prospect for precision cosmology it presents. Galactic foreground emissions, however, contaminate the CMB signal and need to be subtracted reliably in order to lessen systematic errors on the CMB temperature estimates. Typically bright foregrounds in a region lead to further uncertainty in temperature estimates in the area even after some foreground removal technique is performed and hence determining the underlying full-sky angular power spectrum poses a challenge. We explore the feasibility of utilizing artificial neural networks to predict the angular power spectrum of the full sky CMB temperature maps from the observed angular power spectrum of the partial sky in which CMB temperatures in some bright foreground regions are masked. We present our analysis at large angular scales with two different masks. We produce unbiased predictions of the full-sky angular power spectrum and recover the underlying theoretical power spectrum using neural networks. Our predictions are also uncorrelated to a large extent. We further show that the multipole-space covariances of the predictions of full-sky spectra made by the ANNs are much smaller than those of the estimates obtained using the pseudo-$C_\ell$ method.
MudrockNet: Semantic Segmentation of Mudrock SEM Images through Deep Learning
Authors: Abhishek Bihani, Hugh Daigle, Javier E. Santos, Christopher Landry, Masa Prodanovic, Kitty Milliken
Date: 2021-02-05 00:00:00
Tasks: Deep Learning, Image Segmentation, Segmentation, Semantic Segmentation

Segmentation and analysis of individual pores and grains of mudrocks from scanning electron microscope images is non-trivial because of noise, imaging artifacts, variation in pixel grayscale values across images, and overlaps in grayscale values among different physical features such as silt grains, clay grains, and pores in an image, which make their identification difficult. Moreover, because grains and pores often have overlapping grayscale values, direct application of threshold-based segmentation techniques is not sufficient. Recent advances in the field of computer vision have made it easier and faster to segment images and identify multiple occurrences of such features in an image, provided that ground-truth data for training the algorithm is available. Here, we propose a deep learning SEM image segmentation model, MudrockNet based on Google's DeepLab-v3+ architecture implemented with the TensorFlow library. The ground-truth data was obtained from an image-processing workflow applied to scanning electron microscope images of uncemented muds from the Kumano Basin offshore Japan at depths < 1.1 km. The trained deep learning model obtained a pixel-accuracy about 90%, and predictions for the test data obtained a mean intersection over union (IoU) of 0.6591 for silt grains and 0.6642 for pores. We also compared our model with the random forest classifier using trainable Weka segmentation in ImageJ, and it was observed that MudrockNet gave better predictions for both silt grains and pores. The size, concentration, and spatial arrangement of the silt and clay grains can affect the petrophysical properties of a mudrock, and an automated method to accurately identify the different grains and pores in mudrocks can help improve reservoir and seal characterization for petroleum exploration and anthropogenic waste sequestration.
Understanding the Interaction of Adversarial Training with Noisy Labels
Authors: Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan Kankanhalli, Masashi Sugiyama
Date: 2021-02-06 00:00:00

Noisy labels (NL) and adversarial examples both undermine trained models, but interestingly they have hitherto been studied independently. A recent adversarial training (AT) study showed that the number of projected gradient descent (PGD) steps to successfully attack a point (i.e., find an adversarial example in its proximity) is an effective measure of the robustness of this point. Given that natural data are clean, this measure reveals an intrinsic geometric property -- how far a point is from its class boundary. Based on this breakthrough, in this paper, we figure out how AT would interact with NL. Firstly, we find if a point is too close to its noisy-class boundary (e.g., one step is enough to attack it), this point is likely to be mislabeled, which suggests to adopt the number of PGD steps as a new criterion for sample selection for correcting NL. Secondly, we confirm AT with strong smoothing effects suffers less from NL (without NL corrections) than standard training (ST), which suggests AT itself is an NL correction. Hence, AT with NL is helpful for improving even the natural accuracy, which again illustrates the superiority of AT as a general-purpose robust learning criterion.
Functional Space Analysis of Local GAN Convergence
Authors: Valentin Khrulkov, Artem Babenko, Ivan Oseledets
Date: 2021-02-08 00:00:00
Tasks: Data Augmentation

Recent work demonstrated the benefits of studying continuous-time dynamics governing the GAN training. However, this dynamics is analyzed in the model parameter space, which results in finite-dimensional dynamical systems. We propose a novel perspective where we study the local dynamics of adversarial training in the general functional space and show how it can be represented as a system of partial differential equations. Thus, the convergence properties can be inferred from the eigenvalues of the resulting differential operator. We show that these eigenvalues can be efficiently estimated from the target dataset before training. Our perspective reveals several insights on the practical tricks commonly used to stabilize GANs, such as gradient penalty, data augmentation, and advanced integration schemes. As an immediate practical benefit, we demonstrate how one can a priori select an optimal data augmentation strategy for a particular generation task.
Applying the Affective Aware Pseudo Association Method to Enhance the Top-N Recommendations Distribution to Users in Group Emotion Recommender Systems
Authors: John Kalung Leung, Igor Griva, William G. Kennedy
Date: 2021-02-08 00:00:00
Tasks: Decision Making, Information Retrieval, Recommendation Systems, Retrieval

Recommender Systems are a subclass of information retrieval systems, or more succinctly, a class of information filtering systems that seeks to predict how close is the match of the user's preference to a recommended item. A common approach for making recommendations for a user group is to extend Personalized Recommender Systems' capability. This approach gives the impression that group recommendations are retrofits of the Personalized Recommender Systems. Moreover, such an approach not taken the dynamics of group emotion and individual emotion into the consideration in making top_N recommendations. Recommending items to a group of two or more users has certainly raised unique challenges in group behaviors that influence group decision-making that researchers only partially understand. This study applies the Affective Aware Pseudo Association Method in studying group formation and dynamics in group decision-making. The method shows its adaptability to group's moods change when making recommendations.