pwc-archive/papers-with-abstracts huggingface dataset:
Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices
Authors: Anirban Das, Thomas Brunschwiler
Date: 2019-11-11 00:00:00
Tasks: Federated Learning

Federated Learning enables training of a general model through edge devices without sending raw data to the cloud. Hence, this approach is attractive for digital health applications, where data is sourced through edge devices and users care about privacy. Here, we report on the feasibility to train deep neural networks on the Raspberry Pi4s as edge devices. A CNN, a LSTM and a MLP were successfully trained on the MNIST data-set. Further, federated learning is demonstrated experimentally on IID and non-IID samples in a parametric study, to benchmark the model convergence. The weight updates from the workers are shared with the cloud to train the general model through federated learning. With the CNN and the non-IID samples a test-accuracy of up to 85% could be achieved within a training time of 2 minutes, while exchanging less than $10$ MB data per device. In addition, we discuss federated learning from an use-case standpoint, elaborating on privacy risks and labeling requirements for the application of emotion detection from sound. Based on the experimental findings, we discuss possible research directions to improve model and system performance. Finally, we provide best practices for a practitioner, considering the implementation of federated learning.
Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine
Authors: Weisi Guo
Date: 2019-11-11 00:00:00
Tasks: Autonomous Driving, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

As the 5th Generation (5G) mobile networks are bringing about global societal benefits, the design phase for the 6th Generation (6G) has started. 6G will need to enable greater levels of autonomy, improve human machine interfacing, and achieve deep connectivity in more diverse environments. The need for increased explainability to enable trust is critical for 6G as it manages a wide range of mission critical services (e.g. autonomous driving) to safety critical tasks (e.g. remote surgery). As we migrate from traditional model-based optimisation to deep learning, the trust we have in our optimisation modules decrease. This loss of trust means we cannot understand the impact of: 1) poor/bias/malicious data, and 2) neural network design on decisions; nor can we explain to the engineer or the public the network's actions. In this review, we outline the core concepts of Explainable Artificial Intelligence (XAI) for 6G, including: public and legal motivations, definitions of explainability, performance vs. explainability trade-offs, methods to improve explainability, and frameworks to incorporate XAI into future wireless systems. Our review is grounded in cases studies for both PHY and MAC layer optimisation, and provide the community with an important research area to embark upon.
Understanding BERT performance in propaganda analysis
Authors: Yiqing Hua
Date: 2019-11-11 00:00:00
Proceeding: WS 2019 11
Tasks: Sentence

In this paper, we describe our system used in the shared task for fine-grained propaganda analysis at sentence level. Despite the challenging nature of the task, our pretrained BERT model (team YMJA) fine tuned on the training dataset provided by the shared task scored 0.62 F1 on the test set and ranked third among 25 teams who participated in the contest. We present a set of illustrative experiments to better understand the performance of our BERT model on this shared task. Further, we explore beyond the given dataset for false-positive cases that likely to be produced by our system. We show that despite the high performance on the given testset, our system may have the tendency of classifying opinion pieces as propaganda and cannot distinguish quotations of propaganda speech from actual usage of propaganda techniques.
A Simple Differentiable Programming Language
Authors: Martin Abadi, Gordon D. Plotkin
Date: 2019-11-11 00:00:00

Automatic differentiation plays a prominent role in scientific computing and in modern machine learning, often in the context of powerful programming systems. The relation of the various embodiments of automatic differentiation to the mathematical notion of derivative is not always entirely clear---discrepancies can arise, sometimes inadvertently. In order to study automatic differentiation in such programming contexts, we define a small but expressive programming language that includes a construct for reverse-mode differentiation. We give operational and denotational semantics for this language. The operational semantics employs popular implementation techniques, while the denotational semantics employs notions of differentiation familiar from real analysis. We establish that these semantics coincide.
Tool Substitution with Shape and Material Reasoning Using Dual Neural Networks
Authors: Nithin Shrivatsav, Lakshmi Nair, Sonia Chernova
Date: 2019-11-11 00:00:00

This paper explores the problem of tool substitution, namely, identifying substitute tools for performing a task from a given set of candidate tools. We introduce a novel approach to tool substitution, that unlike prior work in the area, combines both shape and material reasoning to effectively identify substitute tools. Our approach combines the use of visual and spectral reasoning using dual neural networks. It takes as input, the desired action to be performed, and outputs a ranking of the available candidate tools based on their suitability for performing the action. Our results on a test set of 30 real-world objects show that our approach is able to effectively match shape and material similarities, with improved tool substitution performance when combining both.
Making Good on LSTMs' Unfulfilled Promise
Authors: Daniel Philps, Artur d'Avila Garcez, Tillman Weyde
Date: 2019-11-11 00:00:00
Tasks: Continual Learning, Decision Making, Dynamic Time Warping, Fairness, Management, Time Series, Time Series Analysis

LSTMs promise much to financial time-series analysis, temporal and cross-sectional inference, but we find that they do not deliver in a real-world financial management task. We examine an alternative called Continual Learning (CL), a memory-augmented approach, which can provide transparent explanations, i.e. which memory did what and when. This work has implications for many financial applications including credit, time-varying fairness in decision making and more. We make three important new observations. Firstly, as well as being more explainable, time-series CL approaches outperform LSTMs as well as a simple sliding window learner using feed-forward neural networks (FFNN). Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM). Thirdly, we examine how real-world, time-series noise impacts several similarity approaches used in CL memory addressing. We provide these insights using an approach called Continual Learning Augmentation (CLA) tested on a complex real-world problem, emerging market equities investment decision making. CLA provides a test-bed as it can be based on different types of time-series learners, allowing testing of LSTM and FFNN learners side by side. CLA is also used to test several distance approaches used in a memory recall-gate: Euclidean distance (ED), dynamic time warping (DTW), auto-encoders (AE) and a novel hybrid approach, warp-AE. We find that ED under-performs DTW and AE but warp-AE shows the best overall performance in a real-world financial task.
On the Computational Complexity of Multi-Agent Pathfinding on Directed Graphs
Authors: Bernhard Nebel
Date: 2019-11-11 00:00:00

The determination of the computational complexity of multi-agent pathfinding on directed graphs has been an open problem for many years. For undirected graphs, solvability can be decided in polynomial time, as has been shown already in the eighties. Further, recently it has been shown that a special case on directed graphs is solvable in polynomial time. In this paper, we show that the problem is NP-hard in the general case. In addition, some upper bounds are proven.
Constructing Gradient Controllable Recurrent Neural Networks Using Hamiltonian Dynamics
Authors: Konstantin Rusch, John W. Pearson, Konstantinos C. Zygalakis
Date: 2019-11-11 00:00:00
Tasks: Hyperparameter Optimization

Recurrent neural networks (RNNs) have gained a great deal of attention in solving sequential learning problems. The learning of long-term dependencies, however, remains challenging due to the problem of a vanishing or exploding hidden states gradient. By exploring further the recently established connections between RNNs and dynamical systems we propose a novel RNN architecture, which we call a Hamiltonian recurrent neural network (Hamiltonian RNN), based on a symplectic discretization of an appropriately chosen Hamiltonian system. The key benefit of this approach is that the corresponding RNN inherits the favorable long time properties of the Hamiltonian system, which in turn allows us to control the hidden states gradient with a hyperparameter of the Hamiltonian RNN architecture. This enables us to handle sequential learning problems with arbitrary sequence lengths, since for a range of values of this hyperparameter the gradient neither vanishes nor explodes. Additionally, we provide a heuristic for the optimal choice of the hyperparameter, which we use in our numerical simulations to illustrate that the Hamiltonian RNN is able to outperform other state-of-the-art RNNs without the need of computationally intensive hyperparameter optimization.
A Computing Kernel for Network Binarization on PyTorch
Authors: Xianda Xu, Marco Pedersoli
Date: 2019-11-11 00:00:00
Tasks: Binarization, CPU, GPU, image-classification, Image Classification, Model Compression, object-detection, Object Detection

Deep Neural Networks have now achieved state-of-the-art results in a wide range of tasks including image classification, object detection and so on. However, they are both computation consuming and memory intensive, making them difficult to deploy on low-power devices. Network binarization is one of the existing effective techniques for model compression and acceleration, but there is no computing kernel yet to support it on PyTorch. In this paper we developed a computing kernel supporting 1-bit xnor and bitcount computation on PyTorch. Experimental results show that our kernel could accelerate the inference of the binarized neural network by 3 times in GPU and by 4.5 times in CPU compared with the control group.
TENER: Adapting Transformer Encoder for Named Entity Recognition
Authors: Hang Yan, Bocao Deng, Xiaonan Li, Xipeng Qiu
Date: 2019-11-10 00:00:00
Tasks: Chinese Named Entity Recognition, Named Entity Recognition, Named Entity Recognition (NER)

The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task. Recently, the Transformer is broadly adopted in various Natural Language Processing (NLP) tasks owing to its parallelism and advantageous performance. Nevertheless, the performance of the Transformer in NER is not as good as it is in other NLP tasks. In this paper, we propose TENER, a NER architecture adopting adapted Transformer Encoder to model the character-level features and word-level features. By incorporating the direction and relative distance aware attention and the un-scaled attention, we prove the Transformer-like encoder is just as effective for NER as other NLP tasks.
Combined analysis of coronary arteries and the left ventricular myocardium in cardiac CT angiography for detection of patients with functionally significant stenosis
Authors: Majd Zreik, Tim Leiner, Nadieh Khalili, Robbert W. van Hamersvelt, Jelmer M. Wolterink, Michiel Voskuil, Max A. Viergever, Ivana Išgum
Date: 2019-11-10 00:00:00
Tasks: Multiple Instance Learning

Treatment of patients with obstructive coronary artery disease is guided by the functional significance of a coronary artery stenosis. Fractional flow reserve (FFR), measured during invasive coronary angiography (ICA), is considered the gold standard to define the functional significance of a coronary stenosis. Here, we present a method for non-invasive detection of patients with functionally significant coronary artery stenosis, combining analysis of the coronary artery tree and the left ventricular (LV) myocardium in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 126 patients who underwent invasive FFR measurements, to determine the functional significance of coronary stenoses. We combine our previous works for the analysis of the complete coronary artery tree and the LV myocardium: Coronary arteries are encoded by two disjoint convolutional autoencoders (CAEs) and the LV myocardium is characterized by a convolutional neural network (CNN) and a CAE. Thereafter, using the extracted encodings of all coronary arteries and the LV myocardium, patients are classified according to the presence of functionally significant stenosis, as defined by the invasively measured FFR. To handle the varying number of coronary arteries in a patient, the classification is formulated as a multiple instance learning problem and is performed using an attention-based neural network. Cross-validation experiments resulted in an average area under the receiver operating characteristic curve of $0.74 \pm 0.01$, and showed that the proposed combined analysis outperformed the analysis of the coronary arteries or the LV myocardium only. The results demonstrate the feasibility of combining the analyses of the complete coronary artery tree and the LV myocardium in CCTA images for the detection of patients with functionally significant stenosis in coronary arteries.
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB
Authors: Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave, Armand Joulin
Date: 2019-11-10 00:00:00
Proceeding: ACL 2021 5
Tasks: NMT, Sentence, Translation, Vocal Bursts Intensity Prediction

We show that margin-based bitext mining in a multilingual sentence space can be applied to monolingual corpora of billions of sentences. We are using ten snapshots of a curated common crawl corpus (Wenzek et al., 2019) totalling 32.7 billion unique sentences. Using one unified approach for 38 languages, we were able to mine 4.5 billions parallel sentences, out of which 661 million are aligned with English. 20 language pairs have more then 30 million parallel sentences, 112 more then 10 million, and most more than one million, including direct alignments between many European or Asian languages. To evaluate the quality of the mined bitexts, we train NMT systems for most of the language pairs and evaluate them on TED, WMT and WAT test sets. Using our mined bitexts only and no human translated parallel data, we achieve a new state-of-the-art for a single system on the WMT'19 test set for translation between English and German, Russian and Chinese, as well as German/French. In particular, our English/German system outperforms the best single one by close to 4 BLEU points and is almost on pair with best WMT'19 evaluation system which uses system combination and back-translation. We also achieve excellent results for distant languages pairs like Russian/Japanese, outperforming the best submission at the 2019 workshop on Asian Translation (WAT).
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
Authors: Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, Matthew Richardson
Date: 2019-11-10 00:00:00
Proceeding: ACL 2020 6
Tasks: Relation, Semantic Parsing, Text to SQL, Text-To-SQL

When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query. We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging Spider dataset this framework boosts the exact match accuracy to 57.2%, surpassing its best counterparts by 8.7% absolute improvement. Further augmented with BERT, it achieves the new state-of-the-art performance of 65.6% on the Spider leaderboard. In addition, we observe qualitative improvements in the model's understanding of schema linking and alignment. Our implementation will be open-sourced at https://github.com/Microsoft/rat-sql.
Deep RAN: A Scalable Data-driven platform to Detect Anomalies in Live Cellular Network Using Recurrent Convolutional Neural Network
Authors: Mohammad Rasoul Tanhatalab, Hossein Yousefi, Hesam Mohammad Hosseini, Mostafa Mofarah Bonab, Vahid Fakharian, Hadis Abarghouei
Date: 2019-11-10 00:00:00
Tasks: Anomaly Detection

In this paper, we propose a novel algorithm to detect anomaly in terms of Key Parameter Indicators (KPI)s over live cellular networks based on the combination of Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), as Recurrent Convolutional Neural Networks (R-CNN). CNN models the spatial correlations and information, whereas, RNN models the temporal correlations and information. In this paper, the studied cellular network consists of 2G, 3G, 4G, and 4.5G technologies, and the KPIs include Voice and data traffic of the cells. The data and voice traffic are extremely important for the owner of wireless networks because it is directly related to the revenue and quality of service that users experience. These traffic changes happen due to a couple of reasons: the subscriber behavior changes due to special events, making neighbor sites on-air or down, or by shifting the traffic to the other technologies, e.g. shifting the traffic from 3G to 4G. Traditionally, in order to keep the network stable, the traffic should be observed layer by layer during each interval to detect major changes in KPIs, in large scale telecommunication networks it will be too time-consuming with the low accuracy of anomaly detection. However, the proposed algorithm is capable of detecting the abnormal KPIs for each element of the network in a time-efficient and accurate manner. It observes the traffic layer trends and classifies them into 8 traffic categories: Normal, Suddenly Increasing, Gradually Increasing, Suddenly Decreasing, Gradually Decreasing, Faulty Site, New Site, and Down Site. This classification task enables the vendors and operators to detect anomalies in their live networks in order to keep the KPIs in a normal trend. The algorithm is trained and tested on the real dataset over a cellular network with more than 25000 cells.
A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking
Authors: Ahmed Ali Hammam, Mona Soliman, Aboul Ella Hassanien
Date: 2019-11-09 00:00:00
Tasks: Action Detection, Action Localization, Object, object-detection, Object Detection, Object Tracking, Real-Time Object Detection

In recent years, artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest. DL is widely used today and has expanded into various interesting areas. It is becoming more popular in cross-subject research, such as studies of smart city systems, which combine computer science with engineering applications. Human action detection is one of these areas. Human action detection is an interesting challenge due to its stringent requirements in terms of computing speed and accuracy. High-accuracy real-time object tracking is also considered a significant challenge. This paper integrates the YOLO detection network, which is considered a state-of-the-art tool for real-time object detection, with motion vectors and the Coyote Optimization Algorithm (COA) to construct a real-time human action localization and tracking system. The proposed system starts with the extraction of motion information from a compressed video stream and the extraction of appearance information from RGB frames using an object detector. Then, a fusion step between the two streams is performed, and the results are fed into the proposed action tracking model. The COA is used in object tracking due to its accuracy and fast convergence. The basic foundation of the proposed model is the utilization of motion vectors, which already exist in a compressed video bit stream and provide sufficient information to improve the localization of the target action without requiring high consumption of computational resources compared with other popular methods of extracting motion information, such as optical flows. This advantage allows the proposed approach to be implemented in challenging environments where the computational resources are limited, such as Internet of Things (IoT) systems.
Semi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval
Authors: Jianjun Lei, Yuxin Song, Bo Peng, Zhanyu Ma, Ling Shao, Yi-Zhe Song
Date: 2019-11-10 00:00:00
Tasks: Image Retrieval, Retrieval, Sketch-Based Image Retrieval

Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain gap between sketches and natural images. How to align abstract sketches and natural images into a common high-level semantic space remains a key problem in SBIR. In this paper, we propose a novel semi-heterogeneous three-way joint embedding network (Semi3-Net), which integrates three branches (a sketch branch, a natural image branch, and an edgemap branch) to learn more discriminative cross-domain feature representations for the SBIR task. The key insight lies with how we cultivate the mutual and subtle relationships amongst the sketches, natural images, and edgemaps. A semi-heterogeneous feature mapping is designed to extract bottom features from each domain, where the sketch and edgemap branches are shared while the natural image branch is heterogeneous to the other branches. In addition, a joint semantic embedding is introduced to embed the features from different domains into a common high-level semantic space, where all of the three branches are shared. To further capture informative features common to both natural images and the corresponding edgemaps, a co-attention model is introduced to conduct common channel-wise feature recalibration between different domains. A hybrid-loss mechanism is designed to align the three branches, where an alignment loss and a sketch-edgemap contrastive loss are presented to encourage the network to learn invariant cross-domain representations. Experimental results on two widely used category-level datasets (Sketchy and TU-Berlin Extension) demonstrate that the proposed method outperforms state-of-the-art methods.
Geometry-Aware Neural Rendering
Authors: Josh Tobin, OpenAI Robotics, Pieter Abbeel
Date: 2019-10-28 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Neural Rendering

Understanding the 3-dimensional structure of the world is a core challenge in computer vision and robotics. Neural rendering approaches learn an implicit 3D model by predicting what a camera would see from an arbitrary viewpoint. We extend existing neural rendering to more complex, higher dimensional scenes than previously possible. We propose Epipolar Cross Attention (ECA), an attention mechanism that leverages the geometry of the scene to perform efficient non-local operations, requiring only $O(n)$ comparisons per spatial dimension instead of $O(n^2)$. We introduce three new simulated datasets inspired by real-world robotics and demonstrate that ECA significantly improves the quantitative and qualitative performance of Generative Query Networks (GQN).