pwc-archive/papers-with-abstracts huggingface dataset:
Perfectly Parallel Fairness Certification of Neural Networks
Authors: Caterina Urban, Maria Christakis, Valentin Wüstholz, Fuyuan Zhang
Date: 2019-12-05 00:00:00
Tasks: Decision Making, Fairness

Recently, there is growing concern that machine-learning models, which currently assist or even automate decision making, reproduce, and in the worst case reinforce, bias of the training data. The development of tools and techniques for certifying fairness of these models or describing their biased behavior is, therefore, critical. In this paper, we propose a perfectly parallel static analysis for certifying causal fairness of feed-forward neural networks used for classification of tabular data. When certification succeeds, our approach provides definite guarantees, otherwise, it describes and quantifies the biased behavior. We design the analysis to be sound, in practice also exact, and configurable in terms of scalability and precision, thereby enabling pay-as-you-go certification. We implement our approach in an open-source tool and demonstrate its effectiveness on models trained with popular datasets.
Scalable and Secure Architecture for Distributed IoT Systems
Authors: Najmeddine Dhieb$, Hakim Ghazzai, Hichem Besbes, Yehia Massoud
Date: 2020-04-20 00:00:00
Tasks: Management

Internet-of-things (IoT) is perpetually revolutionizing our daily life and rapidly transforming physical objects into an ubiquitous connected ecosystem. Due to their massive deployment and moderate security levels, those devices face a lot of security, management, and control challenges. Their classical centralized architecture is still cloaking vulnerabilities and anomalies that can be exploited by hackers for spying, eavesdropping, and taking control of the network. In this paper, we propose to improve the IoT architecture with additional security features using Artificial Intelligence (AI) and blockchain technology. We propose a novel architecture based on permissioned blockchain technology in order to build a scalable and decentralized end-to-end secure IoT system. Furthermore, we enhance the IoT system security with an AI-component at the gateway level to detect and classify suspected activities, malware, and cyber-attacks using machine learning techniques. Simulations and practical implementation show that the proposed architecture delivers high performance against cyber-attacks.
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence
Authors: Hamidreza Eivazi, Luca Guastoni, Philipp Schlatter, Hossein Azizpour, Ricardo Vinuesa
Date: 2020-05-01 00:00:00
Tasks: Model Selection

The capabilities of recurrent neural networks and Koopman-based frameworks are assessed in the prediction of temporal dynamics of the low-order model of near-wall turbulence by Moehlis et al. (New J. Phys. 6, 56, 2004). Our results show that it is possible to obtain excellent reproductions of the long-term statistics and the dynamic behavior of the chaotic system with properly trained long-short-term memory (LSTM) networks, leading to relative errors in the mean and the fluctuations below $1\%$. Besides, a newly developed Koopman-based framework, called Koopman with nonlinear forcing (KNF), leads to the same level of accuracy in the statistics at a significantly lower computational expense. Furthermore, the KNF framework outperforms the LSTM network when it comes to short-term predictions. We also observe that using a loss function based only on the instantaneous predictions of the chaotic system can lead to suboptimal reproductions in terms of long-term statistics. Thus, we propose a model-selection criterion based on the computed statistics which allows to achieve excellent statistical reconstruction even on small datasets, with minimal loss of accuracy in the instantaneous predictions.
AI in society and culture: decision making and values
Authors: Katalin Feher, Asta Zelenkauskaite
Date: 2020-04-29 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Decision Making

With the increased expectation of artificial intelligence, academic research face complex questions of human-centred, responsible and trustworthy technology embedded into society and culture. Several academic debates, social consultations and impact studies are available to reveal the key aspects of the changing human-machine ecosystem. To contribute to these studies, hundreds of related academic sources are summarized below regarding AI-driven decisions and valuable AI. In details, sociocultural filters, taxonomy of human-machine decisions and perspectives of value-based AI are in the focus of this literature review. For better understanding, it is proposed to invite stakeholders in the prepared large-scale survey about the next generation AI that investigates issues that go beyond the technology.
A Large-scale Industrial and Professional Occupation Dataset
Authors: Junhua Liu, Yung Chuen Ng, Kwan Hui Lim
Date: 2020-04-25 00:00:00

There has been growing interest in utilizing occupational data mining and analysis. In today's job market, occupational data mining and analysis is growing in importance as it enables companies to predict employee turnover, model career trajectories, screen through resumes and perform other human resource tasks. A key requirement to facilitate these tasks is the need for an occupation-related dataset. However, most research use proprietary datasets or do not make their dataset publicly available, thus impeding development in this area. To solve this issue, we present the Industrial and Professional Occupation Dataset (IPOD), which comprises 192k job titles belonging to 56k LinkedIn users. In addition to making IPOD publicly available, we also: (i) manually annotate each job title with its associated level of seniority, domain of work and location; and (ii) provide embedding for job titles and discuss various use cases. This dataset is publicly available at https://github.com/junhua/ipod.
W2VV++: Fully Deep Learning for Ad-hoc Video Search
Authors: Xirong Li; Chaoxi Xu; Gang Yang; Zhineng Chen; Jianfeng Dong
Date: 2019-10-21 00:00:00
Proceeding: ACM Multimedia 2019 2019 10
Tasks: Ad-hoc video search, Deep Learning, Representation Learning, Retrieval, Sentence, Text Matching, Triplet

Ad-hoc video search (AVS) is an important yet challenging problem in multimedia retrieval. Different from previous concept-based methods, we propose an end-to-end deep learning method for query representation learning. The proposed method requires no concept modeling, matching and selection. The backbone of our method is the proposed W2VV++ model, a super version of Word2VisualVec (W2VV) previously developed for visual-to-text matching. W2VV++ is obtained by tweaking W2VV with a better sentence encoding strategy and an improved triplet ranking loss. With these simple changes, W2VV++ brings in a substantial improvement in performance. As our participation in the TRECVID 2018 AVS task and retrospective experiments on the TRECVID 2016 and 2017 data show, our best single model, with an overall inferred average precision (infAP) of 0.157, outperforms the state-of-the-art. The performance can be further boosted by model ensemble using late average fusion, reaching a higher infAP of 0.163. With W2VV++, we establish a new baseline for ad-hoc video search.
Optimization of phase retrieval in the Fresnel domain by the modified Gerchberg-Saxton algorithm
Authors: Soheil Mehrabkhani, Melvin Kuester
Date: 2017-11-02 00:00:00
Tasks: Retrieval

The modified Gerchberg-Saxton algorithm (MGSA) is one of the standard methods for phase retrieval. In this work we apply the MGSA in the paraxial domain. For three given physical parameters - i.e. wavelength, propagation distance and pixel size the computational width in the Fresnel-Transform is fixed. This width can be larger than the real dimension of the input or output images. Consequently, it can induce a padding around the real input and output without given amplitude (intensity) values. To solve this problem, we propose a very simple and efficient solution and compare it to other approaches. We demonstrate that the new modified GSA provides almost perfect results without losing the time efficiency of the simplest method.
Music Genre Classification with Paralleling Recurrent Convolutional Neural Network
Authors: Lin Feng, Shenlan Liu, Jianing Yao
Date: 2017-12-22 00:00:00
Tasks: Classification, General Classification, Genre classification, Music Genre Classification

Deep learning has been demonstrated its effectiveness and efficiency in music genre classification. However, the existing achievements still have several shortcomings which impair the performance of this classification task. In this paper, we propose a hybrid architecture which consists of the paralleling CNN and Bi-RNN blocks. They focus on spatial features and temporal frame orders extraction respectively. Then the two outputs are fused into one powerful representation of musical signals and fed into softmax function for classification. The paralleling network guarantees the extracting features robust enough to represent music. Moreover, the experiments prove our proposed architecture improve the music genre classification performance and the additional Bi-RNN block is a supplement for CNNs.
THC simulation of halite scaling in deep geothermal single well production
Authors: TOUGHREACT-Pitzer, THC modeling, Geothermal single well, Counterflow, GeneSys project, Scaling
Date: 2017-01-01 00:00:00
Proceeding: Geothermics 2017 1

Using abandoned wells from oil and gas industry or dry holes, geothermal single well applications enable the prevention of drilling costs and prospecting risks which are crucial inhibitions in geothermal development.

Recently, deep geothermal single wells were numerically investigated for their thermal and hydraulic performance. For the first time, we now include the chemistry of the produced brine in a fully coupled THC model for studies of scaling formation. As a case study, the 2011 circulation test of the GeneSys well Gt1 Groß-Buchholz (Hanover, Germany) was modeled successfully using the fully coupled THC code TOUGHREACT. High salinity requires application of the Pitzer ion interaction model which was verified for this particular chemical system using literature data.

Modeled wellhead temperature is in very good accordance with measurements. Also simulated depth of scaling formation and total scaling volume fit to onsite observations. Evaluation of initial reservoir brine composition reveals its large impact on the range of depth where scaling occurs. Furthermore, it is shown that the presence of CaCl2 reduces halite solubility considerably and favors scaling formation.

Results show that our modeling concept is capable of quantifying the complex coupled THC processes in single wells.
Edit wars in Wikipedia
Authors: Róbert Sumi, Taha Yasseri, András Rung, András Kornai, János Kertész
Date: 2011-07-19 00:00:00

We present a new, efficient method for automatically detecting severe conflicts `edit wars' in Wikipedia and evaluate this method on six different language WPs. We discuss how the number of edits, reverts, the length of discussions, the burstiness of edits and reverts deviate in such pages from those following the general workflow, and argue that earlier work has significantly over-estimated the contentiousness of the Wikipedia editing process.
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
Authors: John Duchi, Alekh Agarwal, Martin Wainwright
Date: 2010-05-12 00:00:00
Tasks: Distributed Optimization, Stochastic Optimization

The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly nonsmooth) convex functions using only local computation and communication. It arises in various application domains, including distributed tracking and localization, multi-agent co-ordination, estimation in sensor networks, and large-scale optimization in machine learning. We develop and analyze distributed algorithms based on dual averaging of subgradients, and we provide sharp bounds on their convergence rates as a function of the network size and topology. Our method of analysis allows for a clear separation between the convergence of the optimization algorithm itself and the effects of communication constraints arising from the network structure. In particular, we show that the number of iterations required by our algorithm scales inversely in the spectral gap of the network. The sharpness of this prediction is confirmed both by theoretical lower bounds and simulations for various networks. Our approach includes both the cases of deterministic optimization and communication, as well as problems with stochastic optimization and/or communication.
“Where is My Parcel?” Fast and Efficient Classifiers to Detect User Intent in Natural Language
Authors: Constantina Nicolaou, Amal Vaidya, Fabon Dzogang, David Wardrope, Nikos Konstantinidis
Date: 2019-12-16 00:00:00
Proceeding: SNAMS 2019 12
Tasks: English Conversational Speech Recognition, Intent Detection, Text Classification

We study the performance of customer intent classifiers designed to predict the most popular intent received through ASOS.com Customer Care Department, namely “Where is my order?”. These queries are characterised by the use of colloquialism, label noise and short message length. We conduct extensive experiments with two well established classification models: logistic regression via n-grams to account for sequences in the data and recurrent neural networks that perform the extraction of these sequential patterns automatically. Maintaining the embedding layer fixed to GloVe coordinates, a Mann-Whitney U test indicated that the F1 score on a held out set of messages was lower for recurrent neural network classifiers than for linear n-grams classifiers (M1=0.828, M2=0.815; U=1,196, P=1.46e-20), unless all layers were jointly trained with all other network parameters (M1=0.831, M2=0.828, U=4,280, P=8.24e-4). This plain neural network produced top performance on a denoised set of labels (0.887 F1) matching with Human annotators (0.889 F1) and superior to linear classifiers (0.865 F1). Calibrating these models to achieve precision levels above Human performance (0.93 Precision), our results indicate a small difference in Recall of 0.05 for the plain neural networks (training under 1hr), and 0.07 for the linear n-grams (training under 10min), revealing the latter as a judicious choice of model architecture in modern AI production systems.
AIDeveloper: deep learning image classification in life science and beyond
Authors: Martin Kräter, Shada Abuhattum, Despina Soteriou, Angela Jacobi, Thomas Krüger, Jochen Guck, Maik Herbig
Date: 2020-03-05 00:00:00
Proceeding: bioRxiv 2020 3
Tasks: Blood Cell Count, Classification, Cultural Vocal Bursts Intensity Prediction, Deep Learning, General Classification, image-classification, Image Classification

Publications on artificial intelligence (AI)-based image analysis have increased drastically in recent years. However, all applications use individual solutions highly specialized for a particular task. Here, we present an easy-to-use, adaptable, open source software, called AIDeveloper (AID) to train neural nets (NN) for image classification without the need for programming. The software provides a variety of NN-architectures that can be simply selected for training. AID allows the user to apply trained models on new data, obtain metrics for classification performance, and export final models to different formats. The working principles of AID are first illustrated by training a convolutional neural net (CNN) on a large dataset consisting of images of different objects (CIFAR-10). We further explore the potential of AID by training a model to distinguish areas of differentiated and non-differentiated mesenchymal stem cells (MSCs) in culture. Additionally, we compare a conventional clinical whole blood cell count with a whole blood cell count performed by an NN-trained, using a dataset of more than 1.2 million images obtained by real-time deformability cytometry, delivering comparable results. Finally, we demonstrate how AID can be used for label-free classification of B- and T-cells derived from human blood, which currently requires costly and time-consuming sample preparation. Thus, AID can empower anyone to develop, train, and apply NNs for image classification. Moreover, models can be generated by non-programmers, exported, and used on different devices, which allows for an interdisciplinary use.
Effective Handwritten Digit Recognition using Deep Convolution Neural Network
Authors: Yellapragada SS Bharadwaj, Rajaram P, Sriram V.P, Sudhakar S, Kolla Bhanu Prakash
Date: 2020-04-30 00:00:00
Proceeding: International Journal of Advanced Trends in Computer Science and Engineering 2020 4
Tasks: BIG-bench Machine Learning, Handwritten Digit Recognition

This  paper  proposed  a  simple  neural  network  approach towards  handwritten  digit  recognition  using  convolution. With  machine  learning  algorithms  like  KNN, SVM/SOM, recognizing digits is considered as one of the unsolvable tasks due to its distinctiveness in the style of writing. In this paper, Convolution  Neural  Networks  are  implemented  with an MNIST  dataset  of  70000  digits  with  250  distinct forms of writings. The proposed method achieved 98.51% accuracy for real-world handwritten digit prediction with less than 0.1 % loss on training with 60000 digits while 10000 under validation.
Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans
Authors: Ivan Drokin, Elena Ericheva
Date: 2020-05-07 00:00:00
Tasks: Data Augmentation, Medical Image Analysis

This paper focuses on a novel approach for false-positive reduction (FPR) of nodule candidates in Computer-aided detection (CADe) systems following the suspicious lesions detection stage. Contrary to typical decisions in medical image analysis, the proposed approach considers input data not as a 2D or 3D image, but rather as a point cloud, and uses deep learning models for point clouds. We discovered that point cloud models require less memory and are faster both in training and inference compared to traditional CNN 3D, they achieve better performance and do not impose restrictions on the size of the input image, i.e. no restrictions on the size of the nodule candidate. We propose an algorithm for transforming 3D CT scan data to point cloud. In some cases, the volume of the nodule candidate can be much smaller than the surrounding context, for example, in the case of subpleural localization of the nodule. Therefore, we developed an algorithm for sampling points from a point cloud constructed from a 3D image of the candidate region. The algorithm is able to guarantee the capture of both context and candidate information as part of the point cloud of the nodule candidate. We designed and set up an experiment in creating a dataset from an open LIDC-IDRI database for a feature of the FPR task, and is herein described in detail. Data augmentation was applied both to avoid overfitting and as an upsampling method. Experiments were conducted with PointNet, PointNet++, and DGCNN. We show that the proposed approach outperforms baseline CNN 3D models and resulted in 85.98 FROC versus 77.26 FROC for baseline models. We compare our algorithm with published SOTA and demonstrate that even without significant modifications it works at the appropriate performance level on LUNA2016 and shows SOTA on LIDC-IDRI.
Plan2Vec: Unsupervised Representation Learning by Latent Plans
Authors: Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra
Date: 2020-05-07 00:00:00
Tasks: Motion Planning, reinforcement-learning, Reinforcement Learning (RL), Representation Learning

In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning. Plan2vec constructs a weighted graph on an image dataset using near-neighbor distances, and then extrapolates this local metric to a global embedding by distilling path-integral over planned path. When applied to control, plan2vec offers a way to learn goal-conditioned value estimates that are accurate over long horizons that is both compute and sample efficient. We demonstrate the effectiveness of plan2vec on one simulated and two challenging real-world image datasets. Experimental results show that plan2vec successfully amortizes the planning cost, enabling reactive planning that is linear in memory and computation complexity rather than exhaustive over the entire state space.
XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification
Authors: Kevin Fauvel, Élisa Fromont, Véronique Masson, Philippe Faverdin, Alexandre Termier
Date: 2020-05-07 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Time Series, Time Series Analysis, Time Series Classification

We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. Our evaluation shows that XEM outperforms the state-of-the-art MTS classifiers on the public UEA datasets. Furthermore, XEM provides faithful explainability-by-design and manifests robust performance when faced with challenges arising from continuous data collection (different MTS length, missing data and noise).
On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation
Authors: Chaojun Wang, Rico Sennrich
Date: 2020-05-07 00:00:00
Proceeding: ACL 2020 6
Tasks: Hallucination, Machine Translation, NMT, Translation

The standard training algorithm in neural machine translation (NMT) suffers from exposure bias, and alternative algorithms have been proposed to mitigate this. However, the practical impact of exposure bias is under debate. In this paper, we link exposure bias to another well-known problem in NMT, namely the tendency to generate hallucinations under domain shift. In experiments on three datasets with multiple test domains, we show that exposure bias is partially to blame for hallucinations, and that training with Minimum Risk Training, which avoids exposure bias, can mitigate this. Our analysis explains why exposure bias is more problematic under domain shift, and also links exposure bias to the beam search problem, i.e. performance deterioration with increasing beam size. Our results provide a new justification for methods that reduce exposure bias: even if they do not increase performance on in-domain test sets, they can increase model robustness to domain shift.
Where is Linked Data in Question Answering over Linked Data?
Authors: Tommaso Soru, Edgard Marx, André Valdestilhas, Diego Moussallem, Gustavo Publio, Muhammad Saleem
Date: 2020-05-07 00:00:00
Tasks: Question Answering

We argue that "Question Answering with Knowledge Base" and "Question Answering over Linked Data" are currently two instances of the same problem, despite one explicitly declares to deal with Linked Data. We point out the lack of existing methods to evaluate question answering on datasets which exploit external links to the rest of the cloud or share common schema. To this end, we propose the creation of new evaluation settings to leverage the advantages of the Semantic Web to achieve AI-complete question answering.