pwc-archive/papers-with-abstracts huggingface dataset:
A.I. based Embedded Speech to Text Using Deepspeech
Authors: Muhammad Hafidh Firmansyah, Anand Paul, Deblina Bhattacharya, Gul Malik Urfa
Date: 2020-02-25 00:00:00
Tasks: Raspberry Pi 3, speech-recognition, Speech Recognition, Speech-to-Text

Deepspeech was very useful for development IoT devices that need voice recognition. One of the voice recognition systems is deepspeech from Mozilla. Deepspeech is an open-source voice recognition that was using a neural network to convert speech spectrogram into a text transcript. This paper shows the implementation process of speech recognition on a low-end computational device. Development of English-language speech recognition that has many datasets become a good point for starting. The model that used results from pre-trained model that provide by each version of deepspeech, without change of the model that already released, furthermore the benefit of using raspberry pi as a media end-to-end speech recognition device become a good thing, user can change and modify of the speech recognition, and also deepspeech can be standalone device without need continuously internet connection to process speech recognition, and even this paper show the power of Tensorflow Lite can make a significant difference on inference by deepspeech rather than using Tensorflow non-Lite.This paper shows the experiment using Deepspeech version 0.1.0, 0.1.1, and 0.6.0, and there is some improvement on Deepspeech version 0.6.0, faster while processing speech-to-text on old hardware raspberry pi 3 b+.
A Model of Inductive Bias Learning
Authors: J. Baxter
Date: 2011-06-01 00:00:00
Tasks: Inductive Bias, model

A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem being learnt, yet small enough to ensure reliable generalization from reasonably-sized training sets. Typically such bias is supplied by hand through the skill and insights of experts. In this paper a model for automatically learning bias is investigated. The central assumption of the model is that the learner is embedded within an environment of related learning tasks. Within such an environment the learner can sample from multiple tasks, and hence it can search for a hypothesis space that contains good solutions to many of the problems in the environment. Under certain restrictions on the set of all hypothesis spaces available to the learner, we show that a hypothesis space that performs well on a sufficiently large number of training tasks will also perform well when learning novel tasks in the same environment. Explicit bounds are also derived demonstrating that learning multiple tasks within an environment of related tasks can potentially give much better generalization than learning a single task.
BMI: A Behavior Measurement Indicator for Fuel Poverty Using Aggregated Load Readings from Smart Meters
Authors: P. Fergus, C. Chalmers
Date: 2020-02-16 00:00:00

Fuel poverty affects between 50 and 125 million households in Europe and is a significant issue for both developed and developing countries globally. This means that fuel poor residents are unable to adequately warm their home and run the necessary energy services needed for lighting, cooking, hot water, and electrical appliances. The problem is complex but is typically caused by three factors; low income, high energy costs, and energy inefficient homes. In the United Kingdom (UK), 4 million families are currently living in fuel poverty. Those in series financial difficulty are either forced to self-disconnect or have their services terminated by energy providers. Fuel poverty contributed to 10,000 reported deaths in England in the winter of 2016-2107 due to homes being cold. While it is recognized by governments as a social, public health and environmental policy issue, the European Union (EU) has failed to provide a common definition of fuel poverty or a conventional set of indicators to measure it. This chapter discusses current fuel poverty strategies across the EU and proposes a new and foundational behavior measurement indicator designed to directly assess and monitor fuel poverty risks in households using smart meters, Consumer Access Device (CAD) data and machine learning. By detecting Activities of Daily Living (ADLS) through household appliance usage, it is possible to spot the early signs of financial difficulty and identify when support packages are required.
Effective End-to-End Learning Framework for Economic Dispatch
Authors: Chenbei Lu, Kui Wang, Chenye Wu
Date: 2020-02-22 00:00:00
Tasks: BIG-bench Machine Learning, Load Forecasting

Conventional wisdom to improve the effectiveness of economic dispatch is to design the load forecasting method as accurately as possible. However, this approach can be problematic due to the temporal and spatial correlations between system cost and load prediction errors. This motivates us to adopt the notion of end-to-end machine learning and to propose a task-specific learning criteria to conduct economic dispatch. Specifically, to maximize the data utilization, we design an efficient optimization kernel for the learning process. We provide both theoretical analysis and empirical insights to highlight the effectiveness and efficiency of the proposed learning framework.
Speech Synthesis using EEG
Authors: Gautam Krishna, Co Tran, Yan Han, Mason Carnahan
Date: 2020-02-22 00:00:00
Tasks: EEG, Electroencephalogram (EEG), regression, Speech Synthesis

In this paper we demonstrate speech synthesis using different electroencephalography (EEG) feature sets recently introduced in [1]. We make use of a recurrent neural network (RNN) regression model to predict acoustic features directly from EEG features. We demonstrate our results using EEG features recorded in parallel with spoken speech as well as using EEG recorded in parallel with listening utterances. We provide EEG based speech synthesis results for four subjects in this paper and our results demonstrate the feasibility of synthesizing speech directly from EEG features.
A Novel Decision Tree for Depression Recognition in Speech
Authors: Zhenyu Liu, Dongyu Wang, Lan Zhang, Bin Hu
Date: 2020-02-22 00:00:00

Depression is a common mental disorder worldwide which causes a range of serious outcomes. The diagnosis of depression relies on patient-reported scales and psychiatrist interview which may lead to subjective bias. In recent years, more and more researchers are devoted to depression recognition in speech , which may be an effective and objective indicator. This study proposes a new speech segment fusion method based on decision tree to improve the depression recognition accuracy and conducts a validation on a sample of 52 subjects (23 depressed patients and 29 healthy controls). The recognition accuracy are 75.8% and 68.5% for male and female respectively on gender-dependent models. It can be concluded from the data that the proposed decision tree model can improve the depression classification performance.
DIHARD II is Still Hard: Experimental Results and Discussions from the DKU-LENOVO Team
Authors: Qingjian Lin, Weicheng Cai, Lin Yang, Jun-Jie Wang, Jun Zhang, Ming Li
Date: 2020-02-23 00:00:00
Tasks: Action Detection, Activity Detection, Clustering

In this paper, we present the submitted system for the second DIHARD Speech Diarization Challenge from the DKULENOVO team. Our diarization system includes multiple modules, namely voice activity detection (VAD), segmentation, speaker embedding extraction, similarity scoring, clustering, resegmentation and overlap detection. For each module, we explore different techniques to enhance performance. Our final submission employs the ResNet-LSTM based VAD, the Deep ResNet based speaker embedding, the LSTM based similarity scoring and spectral clustering. Variational Bayes (VB) diarization is applied in the resegmentation stage and overlap detection also brings slight improvement. Our proposed system achieves 18.84% DER in Track1 and 27.90% DER in Track2. Although our systems have reduced the DERs by 27.5% and 31.7% relatively against the official baselines, we believe that the diarization task is still very difficult.
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
Authors: Shuo Wang, Yan-ran Li, Jiang Zhang, Qingye Meng, Lingwei Meng, Fei Gao
Date: 2020-02-10 00:00:00
Proceeding: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2020 11
Tasks: Graph Neural Network

When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service.
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples
Authors: Shehzeen Hussain, Paarth Neekhara, Malhar Jere, Farinaz Koushanfar, Julian McAuley
Date: 2020-02-09 00:00:00
Tasks: DeepFake Detection, Face Swapping, Video Compression

Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce trust in media. Therefore detection of fake videos has garnered immense interest in academia and industry. Recently developed Deepfake detection methods rely on deep neural networks (DNNs) to distinguish AI-generated fake videos from real videos. In this work, we demonstrate that it is possible to bypass such detectors by adversarially modifying fake videos synthesized using existing Deepfake generation methods. We further demonstrate that our adversarial perturbations are robust to image and video compression codecs, making them a real-world threat. We present pipelines in both white-box and black-box attack scenarios that can fool DNN based Deepfake detectors into classifying fake videos as real.
A spatio-temporalisation of ALC(D) and its translation into alternating automata augmented with spatial constraints
Authors: Amar Isli
Date: 2020-02-22 00:00:00
Tasks: Translation

The aim of this work is to provide a family of qualitative theories for spatial change in general, and for motion of spatial scenes in particular. To achieve this, we consider a spatio-temporalisation MTALC(Dx), of the well-known ALC(D) family of Description Logics (DLs) with a concrete domain: the MTALC(Dx) concepts are interpreted over infinite k-ary Sigma-trees, with the nodes standing for time points, and Sigma including, additionally to its uses in classical k-ary Sigma-trees, the description of the snapshot of an n-object spatial scene of interest; the roles split into m+n immediate-successor (accessibility) relations, which are serial, irreflexive and antisymmetric, and of which m are general, not necessarily functional, the other n functional; the concrete domain Dx is generated by an RCC8-like spatial Relation Algebra (RA) x, and is used to guide the change by imposing spatial constraints on objects of the "followed" spatial scene, eventually at different time points of the input trees. In order to capture the expressiveness of most modal temporal logics encountered in the literature, we introduce weakly cyclic Terminological Boxes (TBoxes) of MTALC(Dx), whose axioms capture the decreasing property of modal temporal operators. We show the important result that satisfiability of an MTALC(Dx) concept with respect to a weakly cyclic TBox can be reduced to the emptiness problem of a Buchi weak alternating automaton augmented with spatial constraints. In another work, complementary to this one, also submitted to this conference, we thoroughly investigate Buchi automata augmented with spatial constraints, and provide, in particular, a translation of an alternating into a nondeterministic, and an effective decision procedure for the emptiness problem of the latter.
A Framework for Software Defect Prediction and Metric Selection
Authors: SHAMSUL HUDA 1, SULTAN ALYAHYA2, MD MOHSIN ALI3, SHAFIQ AHMAD 4, JEMAL ABAWAJY1, HMOOD AL-DOSSARI2, AND JOHN YEARWOOD1
Date: 2017-12-27 00:00:00
Proceeding: 2169-3536   2017 IEEE. Translations and content mining are permitted for academic research only. 2017 12
Tasks: Prediction

ABSTRACT Automated software defect prediction is an important and fundamental activity in the domain of
software development. However, modern software systems are inherently large and complex with numerous
correlated metrics that capture different aspects of the software components. This large number of correlated
metrics makes building a software defect prediction model very complex. Thus, identifying and selecting a
subset of metrics that enhance the software defect prediction method’s performance are an important but
challenging problem that has received little attention in the literature. The main objective of this paper
is to identify significant software metrics, to build and evaluate an automated software defect prediction
model. We propose two novel hybrid software defect prediction models to identify the significant attributes
(metrics) using a combination of wrapper and filter techniques. The novelty of our approach is that it
embeds the metric selection and training processes of software defect prediction as a single process while
reducing the measurement overhead significantly. Different wrapper approaches were combined, including
SVM and ANN, with a maximum relevance filter approach to find the significant metrics. A filter score
was injected into the wrapper selection process in the proposed approaches to direct the search process
efficiently to identify significant metrics. Experimental results with real defect-prone software data sets show
that the proposed hybrid approaches achieve significantly compact metrics (i.e., selecting the most significant
metrics) with high prediction accuracy compared with conventional wrapper or filter approaches. The
performance of the proposed framework has also been verified using a statistical multivariate quality control
process using multivariate exponentially weighted moving average. The proposed framework demonstrates
that the hybrid heuristic can guide the metric selection process in a computationally efficient way by
integrating the intrinsic characteristics from the filters into the wrapper and using the advantages of both
the filter and wrapper approaches.
DeepCalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view cameras
Authors: Oleksandr Bogdan, Viktor Eckstein, Francois Rameau, Jean-Charles Bazin
Date: 2018-12-15 00:00:00
Proceeding: CVMP 2018: Proceedings of the 15th ACM SIGGRAPH European Conference on Visual Media Production 2018 12
Tasks: 3D Reconstruction, Motion Estimation

Calibration of wide field-of-view cameras is a fundamental step for numerous visual media production applications, such as 3D reconstruction, image undistortion, augmented reality and camera motion estimation. However, existing calibration methods require multiple images of a calibration pattern (typically a checkerboard), assume the presence of lines, require manual interaction and/or need an image sequence. In contrast, we present a novel fully automatic deep learning-based approach that overcomes all these limitations and works with a single image of general scenes. Our approach builds upon the recent developments in deep Convolutional Neural Networks (CNN): our network automatically estimates the intrinsic parameters of the camera (focal length and distortion parameter) from a single input image. In order to train the CNN, we leverage the great amount of omnidirectional images available on the Internet to automatically generate a large-scale dataset composed of millions of wide field-of-view images with ground truth intrinsic parameters. Experiments successfully demonstrated the quality of our results, both quantitatively and qualitatively.
Learning Nonparametric Human Mesh Reconstruction from a Single Image without Ground Truth Meshes
Authors: Kevin Lin, Lijuan Wang, Ying Jin, Zicheng Liu, Ming-Ting Sun
Date: 2020-02-28 00:00:00
Tasks: Segmentation

Nonparametric approaches have shown promising results on reconstructing 3D human mesh from a single monocular image. Unlike previous approaches that use a parametric human model like skinned multi-person linear model (SMPL), and attempt to regress the model parameters, nonparametric approaches relax the heavy reliance on the parametric space. However, existing nonparametric methods require ground truth meshes as their regression target for each vertex, and obtaining ground truth mesh labels is very expensive. In this paper, we propose a novel approach to learn human mesh reconstruction without any ground truth meshes. This is made possible by introducing two new terms into the loss function of a graph convolutional neural network (Graph CNN). The first term is the Laplacian prior that acts as a regularizer on the reconstructed mesh. The second term is the part segmentation loss that forces the projected region of the reconstructed mesh to match the part segmentation. Experimental results on multiple public datasets show that without using 3D ground truth meshes, the proposed approach outperforms the previous state-of-the-art approaches that require ground truth meshes for training.
Bio-Inspired Modality Fusion for Active Speaker Detection
Authors: Gustavo Assunção, Nuno Gonçalves, Paulo Menezes
Date: 2020-02-28 00:00:00
Tasks: Active Speaker Detection

Human beings have developed fantastic abilities to integrate information from various sensory sources exploring their inherent complementarity. Perceptual capabilities are therefore heightened, enabling, for instance, the well-known "cocktail party" and McGurk effects, i.e., speech disambiguation from a panoply of sound signals. This fusion ability is also key in refining the perception of sound source location, as in distinguishing whose voice is being heard in a group conversation. Furthermore, neuroscience has successfully identified the superior colliculus region in the brain as the one responsible for this modality fusion, with a handful of biological models having been proposed to approach its underlying neurophysiological process. Deriving inspiration from one of these models, this paper presents a methodology for effectively fusing correlated auditory and visual information for active speaker detection. Such an ability can have a wide range of applications, from teleconferencing systems to social robotics. The detection approach initially routes auditory and visual information through two specialized neural network structures. The resulting embeddings are fused via a novel layer based on the superior colliculus, whose topological structure emulates spatial neuron cross-mapping of unimodal perceptual fields. The validation process employed two publicly available datasets, with achieved results confirming and greatly surpassing initial expectations.
Transferring Dense Pose to Proximal Animal Classes
Authors: Artsiom Sanakoyeu, Vasil Khalidov, Maureen S. McCarthy, Andrea Vedaldi, Natalia Neverova
Date: 2020-02-28 00:00:00
Proceeding: CVPR 2020 6
Tasks: Transfer Learning

Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail. In principle, the same approach could be extended to any animal class, but the effort required for collecting new annotations for each case makes this strategy impractical, despite important applications in natural conservation, science and business. We show that, at least for proximal animal classes such as chimpanzees, it is possible to transfer the knowledge existing in dense pose recognition for humans, as well as in more general object detectors and segmenters, to the problem of dense pose recognition in other classes. We do this by (1) establishing a DensePose model for the new animal which is also geometrically aligned to humans (2) introducing a multi-head R-CNN architecture that facilitates transfer of multiple recognition tasks between classes, (3) finding which combination of known classes can be transferred most effectively to the new animal and (4) using self-calibrated uncertainty heads to generate pseudo-labels graded by quality for training a model for this class. We also introduce two benchmark datasets labelled in the manner of DensePose for the class chimpanzee and use them to evaluate our approach, showing excellent transfer learning performance.
Self-supervised Representation Learning for Ultrasound Video
Authors: Jianbo Jiao, Richard Droste, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble
Date: 2020-02-28 00:00:00
Tasks: Anatomy, Medical Image Analysis, Representation Learning, Saliency Prediction, Self-Supervised Learning

Recent advances in deep learning have achieved promising performance for medical image analysis, while in most cases ground-truth annotations from human experts are necessary to train the deep model. In practice, such annotations are expensive to collect and can be scarce for medical imaging applications. Therefore, there is significant interest in learning representations from unlabelled raw data. In this paper, we propose a self-supervised learning approach to learn meaningful and transferable representations from medical imaging video without any type of human annotation. We assume that in order to learn such a representation, the model should identify anatomical structures from the unlabelled data. Therefore we force the model to address anatomy-aware tasks with free supervision from the data itself. Specifically, the model is designed to correct the order of a reshuffled video clip and at the same time predict the geometric transformation applied to the video clip. Experiments on fetal ultrasound video show that the proposed approach can effectively learn meaningful and strong representations, which transfer well to downstream tasks like standard plane detection and saliency prediction.
Towards Using Count-level Weak Supervision for Crowd Counting
Authors: Yinjie Lei, Yan Liu, Pingping Zhang, Lingqiao Liu
Date: 2020-02-29 00:00:00
Tasks: Crowd Counting

Most existing crowd counting methods require object location-level annotation, i.e., placing a dot at the center of an object. While being simpler than the bounding-box or pixel-level annotation, obtaining this annotation is still labor-intensive and time-consuming especially for images with highly crowded scenes. On the other hand, weaker annotations that only know the total count of objects can be almost effortless in many practical scenarios. Thus, it is desirable to develop a learning method that can effectively train models from count-level annotations. To this end, this paper studies the problem of weakly-supervised crowd counting which learns a model from only a small amount of location-level annotations (fully-supervised) but a large amount of count-level annotations (weakly-supervised). To perform effective training in this scenario, we observe that the direct solution of regressing the integral of density map to the object count is not sufficient and it is beneficial to introduce stronger regularizations on the predicted density map of weakly-annotated images. We devise a simple-yet-effective training strategy, namely Multiple Auxiliary Tasks Training (MATT), to construct regularizes for restricting the freedom of the generated density maps. Through extensive experiments on existing datasets and a newly proposed dataset, we validate the effectiveness of the proposed weakly-supervised method and demonstrate its superior performance over existing solutions.