pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal Story Generation on Plural Images
Authors: Jing Jiang
Date: 2020-01-16 00:00:00
Tasks: Story Generation, Text Generation

Traditionally, text generation models take in a sequence of text as input, and iteratively generate the next most probable word using pre-trained parameters. In this work, we propose the architecture to use images instead of text as the input of the text generation model, called StoryGen. In the architecture, we design a Relational Text Data Generator algorithm that relates different features from multiple images. The output samples from the model demonstrate the ability to generate meaningful paragraphs of text containing the extracted features from the input images. This is an undergraduate project report. Completed Dec. 2019 at the Cooper Union.
Exit Wavefunction Reconstruction from Single Transmisson Electron Micrographs with Deep Learning
Authors: Jeffrey Ede, Jonathan J. P. Peters, Jeremy Sloan, Richard Beanland
Date: 2020-01-29 00:00:00
Tasks: Deep Learning

Half of wavefunction information is undetected by conventional transmission electron microscopy (CTEM) as only the intensity, and not the phase, of an image is recorded. Following successful applications of deep learning to optical hologram phase recovery, we have developed neural networks to recover phases from CTEM intensities for new datasets containing 98340 exit wavefunctions. Wavefunctions were simulated with clTEM multislice propagation for 12789 materials from the Crystallography Open Database. Our networks can recover 224x224 wavefunctions in ~25 ms for a large range of physical hyperparameters and materials, and we demonstrate that performance improves as the distribution of wavefunctions is restricted. Phase recovery with deep learning overcomes the limitations of traditional methods: it is live, not susceptible to distortions, does not require microscope modification or multiple images, and can be applied to any imaging regime. This paper introduces multiple approaches to CTEM phase recovery with deep learning, and is intended to establish starting points to be improved upon by future research. Source code and links to our new datasets and pre-trained models are available at https://github.com/Jeffrey-Ede/one-shot
Predicting Personalized Academic and Career Roads: First Steps Toward a Multi-Uses Recommender System
Authors: Alexandre Nadjem, Juan-Manuel Torres-Moreno, Marc El-Bèze, Guillaume Marrel, Benoît Bonte
Date: 2020-01-03 00:00:00
Tasks: Recommendation Systems

Nobody knows what one's do in the future and everyone will have had a different answer to the question : how do you see yourself in five years after your current job/diploma? In this paper we introduce concepts, large categories of fields of studies or job domains in order to represent the vision of the future of the user's trajectory. Then, we show how they can influence the prediction when proposing him a set of next steps to take.
Business Negotiation Definition Language
Authors: Rustam Tagiew
Date: 2020-01-04 00:00:00
Tasks: software testing

The target of this paper is to present an industry-ready prototype software for general game playing. This software can also be used as the central element for experimental economics research, interfacing of game-theoretic libraries, AI-driven software testing, algorithmic trade, human behavior mining and simulation of (strategic) interactions. The software is based on a domain-specific language for electronic business to business negotiations -- SIDL3.0. The paper also contains many examples to prove the power of this language.
RecovDB: accurate and efficient missing blocks recovery for large time series
Authors: Ines Arous, Mourad Khayati, Philippe Cudré-Mauroux, Ying Zhang, Martin Kersten, Svetlin Stalinlov
Date: 2019-04-08 00:00:00
Proceeding: 35th IEEE International Conference on Data Engineering (ICDE) 2019 4
Tasks: Missing Values, Time Series, Time Series Analysis

With the emergence of the Internet of Things (IoT), time series data has become ubiquitous in our daily life. Making sense of time series is a topic of great interest in many domains. Existing time series analysis applications generally assume or even require perfect time series (i.e. regular time intervals without unknown values), but real-world time series are rarely so neat. They often contain “holes” of different sizes (i.e. single missing values, or blocks of consecutive missing values) due to some failures or irregular time intervals. Hence, missing value recovery is a prerequisite for many time series analysis applications.

In this demo, we present RECOVDB, a relational database system enhanced with advanced matrix decomposition technology for missing blocks recovery. This demo will show the main features of RECOVDB that are important for today’s time series analysis but are lacking in state-of-the-art technologies: i) recovering large missing blocks in multiple time series at once; ii) achieving high recovery accuracy by benefiting from different correlations across time series; iii) maintaining recovery accuracy under increasing size of missing blocks; iv) maintaining recovery efficiency with increasing time series’ lengths and the number of time series; and iv) supporting all these features while being parameter-free. In this paper, we also compare the efficiency and accuracy of RECOVDB against state-of-the-art recovery systems.
An evaluation metric for object detection algorithms in autonomous navigation systems and its application to a real-time alerting system
Authors: Harshitha Machiraju, Sumohana S. Channappayya
Date: 2018-10-10 00:00:00
Proceeding: 2018 25th IEEE International Conference on Image Processing (ICIP) 2018 10
Tasks: Autonomous Navigation, Object, object-detection, Object Detection

An autonomous navigation system relies on a number of sensors including radar, LIDAR and a visible light camera for its operation. We focus our attention on the visible light camera in this work. Object detection is the key first step to processing the video input from the camera. Specifically, we address the problem of assessing the performance of object detection algorithms in hazardous driving conditions that an autonomous navigation system is expected to encounter in a realistic scenario. To this end, we propose a novel metric for quantifying the degradation in performance of an object detection algorithm under different weather conditions. Additionally' we introduce a real-time method to detect extreme variations in performance of the algorithm that can be used to issue an alert. We evaluate the performance of our metric and alerting system and demonstrate its utility using the YOLOv2 object detection algorithm trained on the KITTI and virtual KITTI dataset.
Action Recognition Based on Optimal Joint Selection and Discriminative Depth Descriptor
Authors: Haomiao Ni, Hong Liu, Xiangdong Wang, Yueliang Qian
Date: 2016-11-27 00:00:00
Proceeding: The 13th Asian Conference on Computer Vision 2016 11
Tasks: Action Recognition, Dynamic Time Warping, Temporal Action Localization

This paper proposes a novel human action recognition using the decision-level fusion of both skeleton and depth sequence. Firstly, a state-of-the-art descriptor RBPL, relative body part locations, is adopted to represent skeleton. But the original RBPL employs all the available joints, which may introduce redundancy or noise. This paper proposes an adaptive optimal joint selection model based on the distance traveled by joints before RBPL for each different action, which can reduce redundant joints. Then we use dynamic time warping to handle temporal misalignment and adopt KELM, kernel-based extreme learning machine, for action recognition. Secondly, an efficient feature descriptor DMM-disLBP, depth motion maps-based discriminative local binary patterns, is constructed to describe depth sequences, and KELM is also used for classification. Finally, we present an effective decision fusion for action recognition based on the maximum sum of decision values from skeleton and depth maps. Comparing with the baseline methods, we improve the performance using either skeleton or depth information, and achieve the state-of-the-art average recognition accuracy on the public dataset MSR Action3D using proposed fusing strategy.
Towards a Kernel based Uncertainty Decomposition Framework for Data and Models
Authors: Rishabh Singh, Jose C. Principe
Date: 2020-01-30 00:00:00
Tasks: Time Series Analysis, Uncertainty Quantification

This paper introduces a new framework for quantifying predictive uncertainty for both data and models that relies on projecting the data into a Gaussian reproducing kernel Hilbert space (RKHS) and transforming the data probability density function (PDF) in a way that quantifies the flow of its gradient as a topological potential field quantified at all points in the sample space. This enables the decomposition of the PDF gradient flow by formulating it as a moment decomposition problem using operators from quantum physics, specifically the Schrodinger's formulation. We experimentally show that the higher order modes systematically cluster the different tail regions of the PDF, thereby providing unprecedented discriminative resolution of data regions having high epistemic uncertainty. In essence, this approach decomposes local realizations of the data PDF in terms of uncertainty moments. We apply this framework as a surrogate tool for predictive uncertainty quantification of point-prediction neural network models, overcoming various limitations of conventional Bayesian based uncertainty quantification methods. Experimental comparisons with some established methods illustrate performance advantages exhibited by our framework.
MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1 error rate. Ensembles overview and proposal
Authors: S. Tabik, R. F. Alvear-Sandoval, M. M. Ruiz, J. L. Sancho-Gómez, A. R. Figueiras-Vidal, F. Herrera
Date: 2020-01-30 00:00:00

Ensemble methods have been widely used for improving the results of the best single classificationmodel. A large body of works have achieved better performance mainly by applying one specific ensemble method. However, very few works have explored complex fusion schemes using het-erogeneous ensembles with new aggregation strategies. This paper is three-fold: 1) It provides an overview of the most popular ensemble methods, 2) analyzes several fusion schemes using MNIST as guiding thread and 3) introduces MNIST-NET10, a complex heterogeneous fusion architecture based on a degree of certainty aggregation approach; it combines two heterogeneous schemes from the perspective of data, model and fusion strategy. MNIST-NET10 reaches a new record in MNISTwith only 10 misclassified images. Our analysis shows that such complex heterogeneous fusionarchitectures based on the degree of certainty can be considered as a way of taking benefit fromdiversity.
Continuous speech separation: dataset and analysis
Authors: Zhuo Chen, Takuya Yoshioka, Liang Lu, Tianyan Zhou, Zhong Meng, Yi Luo, Jian Wu, Xiong Xiao, Jinyu Li
Date: 2020-01-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech Separation

This paper describes a dataset and protocols for evaluating continuous speech separation algorithms. Most prior studies on speech separation use pre-segmented signals of artificially mixed speech utterances which are mostly \emph{fully} overlapped, and the algorithms are evaluated based on signal-to-distortion ratio or similar performance metrics. However, in natural conversations, a speech signal is continuous, containing both overlapped and overlap-free components. In addition, the signal-based metrics have very weak correlations with automatic speech recognition (ASR) accuracy. We think that not only does this make it hard to assess the practical relevance of the tested algorithms, it also hinders researchers from developing systems that can be readily applied to real scenarios. In this paper, we define continuous speech separation (CSS) as a task of generating a set of non-overlapped speech signals from a \textit{continuous} audio stream that contains multiple utterances that are \emph{partially} overlapped by a varying degree. A new real recorded dataset, called LibriCSS, is derived from LibriSpeech by concatenating the corpus utterances to simulate a conversation and capturing the audio replays with far-field microphones. A Kaldi-based ASR evaluation protocol is also established by using a well-trained multi-conditional acoustic model. By using this dataset, several aspects of a recently proposed speaker-independent CSS algorithm are investigated. The dataset and evaluation scripts are available to facilitate the research in this direction.
Brand Intelligence Analytics
Authors: A. Fronzetti Colladon, F. Grippa
Date: 2020-01-30 00:00:00
Tasks: Articles

Leveraging the power of big data represents an opportunity for brand managers to reveal patterns and trends in consumer perceptions, while monitoring positive or negative associations of the brand with desired topics. This chapter describes the functionalities of the SBS Brand Intelligence App (SBS BI), which has been designed to assess brand importance and provides brand analytics through the analysis of (big) textual data. To better describe the SBS BI's functionalities, we present a case study focused on the 2020 US Democratic Presidential Primaries. We downloaded 50,000 online articles from the Event Registry database, which contains both mainstream and blog news collected from around the world. These online news articles were transformed into networks of co-occurring words and analyzed by combining methods and tools from social network analysis and text mining.
Transport Gaussian Processes for Regression
Authors: Gonzalo Rios
Date: 2020-01-30 00:00:00
Tasks: Bayesian Inference, Gaussian Processes, regression

Gaussian process (GP) priors are non-parametric generative models with appealing modelling properties for Bayesian inference: they can model non-linear relationships through noisy observations, have closed-form expressions for training and inference, and are governed by interpretable hyperparameters. However, GP models rely on Gaussianity, an assumption that does not hold in several real-world scenarios, e.g., when observations are bounded or have extreme-value dependencies, a natural phenomenon in physics, finance and social sciences. Although beyond-Gaussian stochastic processes have caught the attention of the GP community, a principled definition and rigorous treatment is still lacking. In this regard, we propose a methodology to construct stochastic processes, which include GPs, warped GPs, Student-t processes and several others under a single unified approach. We also provide formulas and algorithms for training and inference of the proposed models in the regression problem. Our approach is inspired by layers-based models, where each proposed layer changes a specific property over the generated stochastic process. That, in turn, allows us to push-forward a standard Gaussian white noise prior towards other more expressive stochastic processes, for which marginals and copulas need not be Gaussian, while retaining the appealing properties of GPs. We validate the proposed model through experiments with real-world data.
Semi-Automatic Generation of Tight Binary Masks and Non-Convex Isosurfaces for Quantitative Analysis of 3D Biological Samples
Authors: Sourabh Bhide, Ralf Mikut, Maria Leptin, Johannes Stegmaier
Date: 2020-01-30 00:00:00
Tasks: Cell Segmentation, Segmentation

Current in vivo microscopy allows us detailed spatiotemporal imaging (3D+t) of complete organisms and offers insights into their development on the cellular level. Even though the imaging speed and quality is steadily improving, fully-automated segmentation and analysis methods are often not accurate enough. This is particularly true while imaging large samples (100um - 1mm) and deep inside the specimen. Drosophila embryogenesis, widely used as a developmental paradigm, presents an example for such a challenge, especially where cell outlines need to imaged - a general challenge in other systems as well. To deal with the current bottleneck in analyzing quantitatively the 3D+t light-sheet microscopy images of Drosophila embryos, we developed a collection of semi-automatic open-source tools. The presented methods include a semi-automatic masking procedure, automatic projection of non-convex 3D isosurfaces to 2D representations as well as cell segmentation and tracking.
Fase-AL -- Adaptation of Fast Adaptive Stacking of Ensembles for Supporting Active Learning
Authors: Agustín Alejandro Ortiz-Díaz, Fabiano Baldo, Laura María Palomino Mariño, Alberto Verdecia Cabrera
Date: 2020-01-30 00:00:00
Tasks: Active Learning, Classification, General Classification

Classification algorithms to mine data stream have been extensively studied in recent years. However, a lot of these algorithms are designed for supervised learning which requires labeled instances. Nevertheless, the labeling of the data is costly and time-consuming. Because of this, alternative learning paradigms have been proposed to reduce the cost of the labeling process without significant loss of model performance. Active learning is one of these paradigms, whose main objective is to build classification models that request the lowest possible number of labeled examples achieving adequate levels of accuracy. Therefore, this work presents the FASE-AL algorithm which induces classification models with non-labeled instances using Active Learning. FASE-AL is based on the algorithm Fast Adaptive Stacking of Ensembles (FASE). FASE is an ensemble algorithm that detects and adapts the model when the input data stream has concept drift. FASE-AL was compared with four different strategies of active learning found in the literature. Real and synthetic databases were used in the experiments. The algorithm achieves promising results in terms of the percentage of correctly classified instances.
Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
Authors: Subendhu Rongali, Luca Soldaini, Emilio Monti, Wael Hamza
Date: 2020-01-30 00:00:00
Tasks: Semantic Parsing, slot-filling, Slot Filling

Virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistant often rely on a semantic parsing component to understand which action(s) to execute for an utterance spoken by its users. Traditionally, rule-based or statistical slot-filling systems have been used to parse "simple" queries; that is, queries that contain a single action and can be decomposed into a set of non-overlapping entities. More recently, shift-reduce parsers have been proposed to process more complex utterances. These methods, while powerful, impose specific limitations on the type of queries that can be parsed; namely, they require a query to be representable as a parse tree. In this work, we propose a unified architecture based on Sequence to Sequence models and Pointer Generator Network to handle both simple and complex queries. Unlike other works, our approach does not impose any restriction on the semantic parse schema. Furthermore, experiments show that it achieves state of the art performance on three publicly available datasets (ATIS, SNIPS, Facebook TOP), relatively improving between 3.3% and 7.7% in exact match accuracy over previous systems. Finally, we show the effectiveness of our approach on two internal datasets.
STRIPS Action Discovery
Authors: Alejandro Suárez-Hernández, Javier Segovia-Aguas, Carme Torras, Guillem Alenyà
Date: 2020-01-30 00:00:00

The problem of specifying high-level knowledge bases for planning becomes a hard task in realistic environments. This knowledge is usually handcrafted and is hard to keep updated, even for system experts. Recent approaches have shown the success of classical planning at synthesizing action models even when all intermediate states are missing. These approaches can synthesize action schemas in Planning Domain Definition Language (PDDL) from a set of execution traces each consisting, at least, of an initial and final state. In this paper, we propose a new algorithm to unsupervisedly synthesize STRIPS action models with a classical planner when action signatures are unknown. In addition, we contribute with a compilation to classical planning that mitigates the problem of learning static predicates in the action model preconditions, exploits the capabilities of SAT planners with parallel encodings to compute action schemas and validate all instances. Our system is flexible in that it supports the inclusion of partial input information that may speed up the search. We show through several experiments how learned action models generalize over unseen planning instances.
Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages
Authors: Edoardo M. Ponti, Ivan Vulić, Ryan Cotterell, Marinela Parovic, Roi Reichart, Anna Korhonen
Date: 2020-01-30 00:00:00
Tasks: Cross-Lingual Transfer, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging, Variational Inference, zero-shot-classification, Zero-Shot Cross-Lingual Transfer, Zero-Shot Learning

Most combinations of NLP tasks and language varieties lack in-domain examples for supervised training because of the paucity of annotated data. How can neural models make sample-efficient generalizations from task-language combinations with available data to low-resource ones? In this work, we propose a Bayesian generative model for the space of neural parameters. We assume that this space can be factorized into latent variables for each language and each task. We infer the posteriors over such latent variables based on data from seen task-language combinations through variational inference. This enables zero-shot classification on unseen combinations at prediction time. For instance, given training data for named entity recognition (NER) in Vietnamese and for part-of-speech (POS) tagging in Wolof, our model can perform accurate predictions for NER in Wolof. In particular, we experiment with a typologically diverse sample of 33 languages from 4 continents and 11 families, and show that our model yields comparable or better results than state-of-the-art, zero-shot cross-lingual transfer methods. Moreover, we demonstrate that approximate Bayesian model averaging results in smoother predictive distributions, whose entropy inversely correlates with accuracy. Hence, the proposed framework also offers robust estimates of prediction uncertainty. Our code is located at github.com/cambridgeltl/parameter-factorization