January 26, 2020

3545 words 17 mins read

Paper Group ANR 1597

Temporal-Clustering Invariance in Irregular Healthcare Time Series. V2S attack: building DNN-based voice conversion from automatic speaker verification. Cost-sensitive Selection of Variables by Ensemble of Model Sequences. Automated Peer-to-peer Negotiation for Energy Contract Settlements in Residential Cooperatives. Benchmarking air-conditioning e …

Temporal-Clustering Invariance in Irregular Healthcare Time Series


Title	Temporal-Clustering Invariance in Irregular Healthcare Time Series
Authors	Mohammad Taha Bahadori, Zachary Chase Lipton
Abstract	Electronic records contain sequences of events, some of which take place all at once in a single visit, and others that are dispersed over multiple visits, each with a different timestamp. We postulate that fine temporal detail, e.g., whether a series of blood tests are completed at once or in rapid succession should not alter predictions based on this data. Motivated by this intuition, we propose models for analyzing sequences of multivariate clinical time series data that are invariant to this temporal clustering. We propose an efficient data augmentation technique that exploits the postulated temporal-clustering invariance to regularize deep neural networks optimized for several clinical prediction tasks. We introduce two techniques to temporally coarsen (downsample) irregular time series: (i) grouping the data points based on regularly-spaced timestamps; and (ii) clustering them, yielding irregularly-paced timestamps. Moreover, we propose a MultiResolution Ensemble (MRE) model, improving predictive accuracy by ensembling predictions based on inputs sequences transformed by different coarsening operators. Our experiments show that MRE improves the mAP on the benchmark mortality prediction task from 51.53% to 53.92%.
Tasks	Data Augmentation, Irregular Time Series, Mortality Prediction, Time Series
Published	2019-04-27
URL	http://arxiv.org/abs/1904.12206v1
PDF	http://arxiv.org/pdf/1904.12206v1.pdf
PWC	https://paperswithcode.com/paper/temporal-clustering-invariance-in-irregular
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V2S attack: building DNN-based voice conversion from automatic speaker verification


Title	V2S attack: building DNN-based voice conversion from automatic speaker verification
Authors	Taiki Nakamura, Yuki Saito, Shinnosuke Takamichi, Yusuke Ijima, Hiroshi Saruwatari
Abstract	This paper presents a new voice impersonation attack using voice conversion (VC). Enrolling personal voices for automatic speaker verification (ASV) offers natural and flexible biometric authentication systems. Basically, the ASV systems do not include the users’ voice data. However, if the ASV system is unexpectedly exposed and hacked by a malicious attacker, there is a risk that the attacker will use VC techniques to reproduce the enrolled user’s voices. We name this the ``verification-to-synthesis (V2S) attack’’ and propose VC training with the ASV and pre-trained automatic speech recognition (ASR) models and without the targeted speaker’s voice data. The VC model reproduces the targeted speaker’s individuality by deceiving the ASV model and restores phonetic property of an input voice by matching phonetic posteriorgrams predicted by the ASR model. The experimental evaluation compares converted voices between the proposed method that does not use the targeted speaker’s voice data and the standard VC that uses the data. The experimental results demonstrate that the proposed method performs comparably to the existing VC methods that trained using a very small amount of parallel voice data. \|
Tasks	Speaker Verification, Speech Recognition, Voice Conversion
Published	2019-08-05
URL	https://arxiv.org/abs/1908.01454v1
PDF	https://arxiv.org/pdf/1908.01454v1.pdf
PWC	https://paperswithcode.com/paper/v2s-attack-building-dnn-based-voice
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Cost-sensitive Selection of Variables by Ensemble of Model Sequences


Title	Cost-sensitive Selection of Variables by Ensemble of Model Sequences
Authors	Donghui Yan, Zhiwei Qin, Songxiang Gu, Haiping Xu, Ming Shao
Abstract	Many applications require the collection of data on different variables or measurements over many system performance metrics. We term those broadly as measures or variables. Often data collection along each measure incurs a cost, thus it is desirable to consider the cost of measures in modeling. This is a fairly new class of problems in the area of cost-sensitive learning. A few attempts have been made to incorporate costs in combining and selecting measures. However, existing studies either do not strictly enforce a budget constraint, or are not the `most' cost effective. With a focus on classification problem, we propose a computationally efficient approach that could find a near optimal model under a given budget by exploring the most` promising’ part of the solution space. Instead of outputting a single model, we produce a model schedule—a list of models, sorted by model costs and expected predictive accuracy. This could be used to choose the model with the best predictive accuracy under a given budget, or to trade off between the budget and the predictive accuracy. Experiments on some benchmark datasets show that our approach compares favorably to competing methods.
Tasks
Published	2019-01-02
URL	http://arxiv.org/abs/1901.00456v1
PDF	http://arxiv.org/pdf/1901.00456v1.pdf
PWC	https://paperswithcode.com/paper/cost-sensitive-selection-of-variables-by
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Automated Peer-to-peer Negotiation for Energy Contract Settlements in Residential Cooperatives


Title	Automated Peer-to-peer Negotiation for Energy Contract Settlements in Residential Cooperatives
Authors	Shantanu Chakraborty, Tim Baarslag, Michael Kaisers
Abstract	This paper presents an automated peer-to-peer negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative considering heterogeneity prosumer preferences. The heterogeneity arises from prosumers’ evaluation of energy contracts through multiple societal and environmental criteria and the prosumers’ private preferences over those criteria. The prosumers engage in bilateral negotiations with peers to mutually agree on periodical energy contracts/loans consisting of the energy volume to be exchanged at that period and the return time of the exchanged energy. The negotiating prosumers navigate through a common negotiation domain consisting of potential energy contracts and evaluate those contracts from their valuations on the entailed criteria against a utility function that is robust against generation and demand uncertainty. From the repeated interactions, a prosumer gradually learns about the compatibility of its peers in reaching energy contracts that are closer to Nash solutions. Empirical evaluation on real demand, generation and storage profiles – in multiple system scales – illustrates that the proposed negotiation based strategy can increase the system efficiency (measured by utilitarian social welfare) and fairness (measured by Nash social welfare) over a baseline strategy and an individual flexibility control strategy representing the status quo strategy. We thus elicit system benefits from peer-to-peer flexibility exchange already without any central coordination and market operator, providing a simple yet flexible and effective paradigm that complements existing markets.
Tasks
Published	2019-11-26
URL	https://arxiv.org/abs/1911.12303v1
PDF	https://arxiv.org/pdf/1911.12303v1.pdf
PWC	https://paperswithcode.com/paper/automated-peer-to-peer-negotiation-for-energy
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Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques


Title	Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques
Authors	Yuren Zhou, Clement Lork, Wen-Tai Li, Chau Yuen, Yeong Ming Keow
Abstract	Air conditioning (AC) accounts for a critical portion of the global energy consumption. To improve its energy performance, it is important to fairly benchmark its energy performance and provide the evaluation feedback to users. However, this task has not been well tackled in the residential sector. In this paper, we propose a data-driven approach to fairly benchmark the AC energy performance of residential rooms. First, regression model is built for each benchmarked room so that its power consumption can be predicted given different weather conditions and AC settings. Then, all the rooms are clustered based on their areas and usual AC temperature set points. Lastly, within each cluster, rooms are benchmarked based on their predicted power consumption under uniform weather conditions and AC settings. A real-world case study was conducted with data collected from 44 residential rooms. Results show that the constructed regression models have an average prediction accuracy of 85.1% in cross-validation tests, and support vector regression with Gaussian kernel is the overall most suitable model structure for building the regression model. In the clustering step, 44 rooms are successfully clustered into seven clusters. By comparing the benchmarking scores generated by the proposed approach with two sets of scores computed from historical power consumption data, we demonstrate that the proposed approach is able to eliminate the influences of room areas, weather conditions, and AC settings on the benchmarking results. Therefore, the proposed benchmarking approach is valid and fair. As a by-product, the approach is also shown to be useful to investigate how room areas, weather conditions, and AC settings affect the AC power consumption of rooms in real life.
Tasks
Published	2019-08-22
URL	https://arxiv.org/abs/1908.08176v2
PDF	https://arxiv.org/pdf/1908.08176v2.pdf
PWC	https://paperswithcode.com/paper/benchmarking-air-conditioning-energy
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PID: A New Benchmark Dataset to Classify and Densify Pavement Distresses


Title	PID: A New Benchmark Dataset to Classify and Densify Pavement Distresses
Authors	Hamed Majidifard, Peng Jin, Yaw Adu-Gyamfi, William G. Buttlar
Abstract	Automated pavement distresses detection using road images remains a challenging topic in the computer vision research community. Recent developments in deep learning has led to considerable research activity directed towards improving the efficacy of automated pavement distress identification and rating. Deep learning models require a large ground truth data set, which is often not readily available in the case of pavements. In this study, a labeled dataset approach is introduced as a first step towards a more robust, easy-to-deploy pavement condition assessment system. The technique is termed herein as the Pavement Image Dataset (PID) method. The dataset consists of images captured from two camera views of an identical pavement segment, i.e., a wide-view and a top-down view. The wide-view images were used to classify the distresses and to train the deep learning frameworks, while the top-down view images allowed calculation of distress density, which will be used in future studies aimed at automated pavement rating. For the wide view group dataset, 7,237 images were manually annotated and distresses classified into nine categories. Images were extracted using the Google Application Programming Interface (API), selecting street-view images using a python-based code developed for this project. The new dataset was evaluated using two mainstream deep learning frameworks: You Only Look Once (YOLO v2) and Faster Region Convolution Neural Network (Faster R-CNN). Accuracy scores using the F1 index were found to be 0.84 for YOLOv2 and 0.65 for the Faster R-CNN model runs; both quite acceptable considering the convenience of utilizing Google maps images.
Tasks
Published	2019-10-20
URL	https://arxiv.org/abs/1910.11123v1
PDF	https://arxiv.org/pdf/1910.11123v1.pdf
PWC	https://paperswithcode.com/paper/pid-a-new-benchmark-dataset-to-classify-and
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Hotels-50K: A Global Hotel Recognition Dataset


Title	Hotels-50K: A Global Hotel Recognition Dataset
Authors	Abby Stylianou, Hong Xuan, Maya Shende, Jonathan Brandt, Richard Souvenir, Robert Pless
Abstract	Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain.
Tasks	Data Augmentation
Published	2019-01-26
URL	http://arxiv.org/abs/1901.11397v1
PDF	http://arxiv.org/pdf/1901.11397v1.pdf
PWC	https://paperswithcode.com/paper/hotels-50k-a-global-hotel-recognition-dataset
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A Spiking Network for Inference of Relations Trained with Neuromorphic Backpropagation


Title	A Spiking Network for Inference of Relations Trained with Neuromorphic Backpropagation
Authors	Johannes C. Thiele, Olivier Bichler, Antoine Dupret, Sergio Solinas, Giacomo Indiveri
Abstract	The increasing need for intelligent sensors in a wide range of everyday objects requires the existence of low power information processing systems which can operate autonomously in their environment. In particular, merging and processing the outputs of different sensors efficiently is a necessary requirement for mobile agents with cognitive abilities. In this work, we present a multi-layer spiking neural network for inference of relations between stimuli patterns in dedicated neuromorphic systems. The system is trained with a new version of the backpropagation algorithm adapted to on-chip learning in neuromorphic hardware: Error gradients are encoded as spike signals which are propagated through symmetric synapses, using the same integrate-and-fire hardware infrastructure as used during forward propagation. We demonstrate the strength of the approach on an arithmetic relation inference task and on visual XOR on the MNIST dataset. Compared to previous, biologically-inspired implementations of networks for learning and inference of relations, our approach is able to achieve better performance with less neurons. Our architecture is the first spiking neural network architecture with on-chip learning capabilities, which is able to perform relational inference on complex visual stimuli. These features make our system interesting for sensor fusion applications and embedded learning in autonomous neuromorphic agents.
Tasks	Sensor Fusion
Published	2019-03-11
URL	http://arxiv.org/abs/1903.04341v1
PDF	http://arxiv.org/pdf/1903.04341v1.pdf
PWC	https://paperswithcode.com/paper/a-spiking-network-for-inference-of-relations
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Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text


Title	Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text
Authors	Ian Porada, Kaheer Suleman, Jackie Chi Kit Cheung
Abstract	Modeling semantic plausibility requires commonsense knowledge about the world and has been used as a testbed for exploring various knowledge representations. Previous work has focused specifically on modeling physical plausibility and shown that distributional methods fail when tested in a supervised setting. At the same time, distributional models, namely large pretrained language models, have led to improved results for many natural language understanding tasks. In this work, we show that these pretrained language models are in fact effective at modeling physical plausibility in the supervised setting. We therefore present the more difficult problem of learning to model physical plausibility directly from text. We create a training set by extracting attested events from a large corpus, and we provide a baseline for training on these attested events in a self-supervised manner and testing on a physical plausibility task. We believe results could be further improved by injecting explicit commonsense knowledge into a distributional model.
Tasks
Published	2019-11-13
URL	https://arxiv.org/abs/1911.05689v1
PDF	https://arxiv.org/pdf/1911.05689v1.pdf
PWC	https://paperswithcode.com/paper/can-a-gorilla-ride-a-camel-learning-semantic-1
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BERT has a Moral Compass: Improvements of ethical and moral values of machines


Title	BERT has a Moral Compass: Improvements of ethical and moral values of machines
Authors	Patrick Schramowski, Cigdem Turan, Sophie Jentzsch, Constantin Rothkopf, Kristian Kersting
Abstract	Allowing machines to choose whether to kill humans would be devastating for world peace and security. But how do we equip machines with the ability to learn ethical or even moral choices? Jentzsch et al.(2019) showed that applying machine learning to human texts can extract deontological ethical reasoning about “right” and “wrong” conduct by calculating a moral bias score on a sentence level using sentence embeddings. The machine learned that it is objectionable to kill living beings, but it is fine to kill time; It is essential to eat, yet one might not eat dirt; it is important to spread information, yet one should not spread misinformation. However, the evaluated moral bias was restricted to simple actions – one verb – and a ranking of actions with surrounding context. Recently BERT —and variants such as RoBERTa and SBERT— has set a new state-of-the-art performance for a wide range of NLP tasks. But has BERT also a better moral compass? In this paper, we discuss and show that this is indeed the case. Thus, recent improvements of language representations also improve the representation of the underlying ethical and moral values of the machine. We argue that through an advanced semantic representation of text, BERT allows one to get better insights of moral and ethical values implicitly represented in text. This enables the Moral Choice Machine (MCM) to extract more accurate imprints of moral choices and ethical values.
Tasks	Sentence Embeddings
Published	2019-12-11
URL	https://arxiv.org/abs/1912.05238v1
PDF	https://arxiv.org/pdf/1912.05238v1.pdf
PWC	https://paperswithcode.com/paper/bert-has-a-moral-compass-improvements-of
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Country-wide high-resolution vegetation height mapping with Sentinel-2


Title	Country-wide high-resolution vegetation height mapping with Sentinel-2
Authors	Nico Lang, Konrad Schindler, Jan Dirk Wegner
Abstract	Sentinel-2 multi-spectral images collected over periods of several months were used to estimate vegetation height for Gabon and Switzerland. A deep convolutional neural network (CNN) was trained to extract suitable spectral and textural features from reflectance images and to regress per-pixel vegetation height. In Gabon, reference heights for training and validation were derived from airborne LiDAR measurements. In Switzerland, reference heights were taken from an existing canopy height model derived via photogrammetric surface reconstruction. The resulting maps have a mean absolute error (MAE) of 1.7 m in Switzerland and 4.3 m in Gabon (a root mean square error (RMSE) of 3.4 m and 5.6 m, respectively), and correctly estimate vegetation heights up to >50 m. They also show good qualitative agreement with existing vegetation height maps. Our work demonstrates that, given a moderate amount of reference data (i.e., 2000 km$^2$ in Gabon and $\approx$5800 km$^2$ in Switzerland), high-resolution vegetation height maps with 10 m ground sampling distance (GSD) can be derived at country scale from Sentinel-2 imagery.
Tasks
Published	2019-04-30
URL	https://arxiv.org/abs/1904.13270v2
PDF	https://arxiv.org/pdf/1904.13270v2.pdf
PWC	https://paperswithcode.com/paper/country-wide-high-resolution-vegetation
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On Least Squares Estimation under Heteroscedastic and Heavy-Tailed Errors


Title	On Least Squares Estimation under Heteroscedastic and Heavy-Tailed Errors
Authors	Arun K. Kuchibhotla, Rohit K. Patra
Abstract	We consider least squares estimation in a general nonparametric regression model. The rate of convergence of the least squares estimator (LSE) for the unknown regression function is well studied when the errors are sub-Gaussian. We find upper bounds on the rates of convergence of the LSE when the errors have uniformly bounded conditional variance and have only finitely many moments. We show that the interplay between the moment assumptions on the error, the metric entropy of the class of functions involved, and the “local” structure of the function class around the truth drives the rate of convergence of the LSE. We find sufficient conditions on the errors under which the rate of the LSE matches the rate of the LSE under sub-Gaussian error. Our results are finite sample and allow for heteroscedastic and heavy-tailed errors.
Tasks
Published	2019-09-04
URL	https://arxiv.org/abs/1909.02088v2
PDF	https://arxiv.org/pdf/1909.02088v2.pdf
PWC	https://paperswithcode.com/paper/on-least-squares-estimation-under
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STC Antispoofing Systems for the ASVspoof2019 Challenge


Title	STC Antispoofing Systems for the ASVspoof2019 Challenge
Authors	Galina Lavrentyeva, Sergey Novoselov, Andzhukaev Tseren, Marina Volkova, Artem Gorlanov, Alexandr Kozlov
Abstract	This paper describes the Speech Technology Center (STC) antispoofing systems submitted to the ASVspoof 2019 challenge. The ASVspoof2019 is the extended version of the previous challenges and includes 2 evaluation conditions: logical access use-case scenario with speech synthesis and voice conversion attack types and physical access use-case scenario with replay attacks. During the challenge we developed anti-spoofing solutions for both scenarios. The proposed systems are implemented using deep learning approach and are based on different types of acoustic features. We enhanced Light CNN architecture previously considered by the authors for replay attacks detection and which performed high spoofing detection quality during the ASVspoof2017 challenge. In particular here we investigate the efficiency of angular margin based softmax activation for training robust deep Light CNN classifier to solve the mentioned-above tasks. Submitted systems achieved EER of 1.86% in logical access scenario and 0.54% in physical access scenario on the evaluation part of the Challenge corpora. High performance obtained for the unknown types of spoofing attacks demonstrates the stability of the offered approach in both evaluation conditions.
Tasks	Speech Synthesis, Voice Conversion
Published	2019-04-11
URL	http://arxiv.org/abs/1904.05576v1
PDF	http://arxiv.org/pdf/1904.05576v1.pdf
PWC	https://paperswithcode.com/paper/stc-antispoofing-systems-for-the-asvspoof2019
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A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection


Title	A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
Authors	Baihong Jin, Yuxin Chen, Dan Li, Kameshwar Poolla, Alberto Sangiovanni-Vincentelli
Abstract	It is important to identify the change point of a system’s health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. The approach uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data.
Tasks	Anomaly Detection, Calibration, Change Point Detection, Time Series
Published	2019-02-18
URL	http://arxiv.org/abs/1902.06361v1
PDF	http://arxiv.org/pdf/1902.06361v1.pdf
PWC	https://paperswithcode.com/paper/a-one-class-support-vector-machine
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Modelling Resistive and Phase Change Memory with Passive Selector Arrays – A Matlab Tool


Title	Modelling Resistive and Phase Change Memory with Passive Selector Arrays – A Matlab Tool
Authors	Yasir J Noori, C H, de Groot
Abstract	Memristor devices are crucial for developing neuromorphic computers and next-generation memory technologies. In this work, we provide a comprehensive modelling tool for simulating static DC reading operations of memristor crossbar arrays that use passive selectors with matrix algebra in MATLAB. The software tool was parallel coded and optimized to run with personal computers and distributed computer clusters with minimized CPU and memory consumption. Using the tool, we demonstrate the effect of changing the line resistance, array size, voltage selection scheme, selector diode’s ideality factor, reverse saturation current, temperature and sense resistance on the electrical behavior and expected sense margin of one-diode-one-resistor crossbar arrays. We then investigate the effect of single and dual side array biasing and grounding on the dissipated current throughout the array cells. The tool we offer to the memristor community and the studies we present enables the design of larger and more practical memristor arrays for application in data storage and neuromorphic computing.
Tasks
Published	2019-10-13
URL	https://arxiv.org/abs/1910.05836v1
PDF	https://arxiv.org/pdf/1910.05836v1.pdf
PWC	https://paperswithcode.com/paper/modelling-resistive-and-phase-change-memory
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