pwc-archive/papers-with-abstracts huggingface dataset:
Critical data analysis of COVID-19 spreading in Indonesia to measure the readiness of new-normal policy
Authors: Muhammad Ariful Furqon, Nina Fadilah Najwa, Endah Septa Sintiya, Erista Maya Safitri, Iqbal Ramadhani Mukhlis
Date: 2020-11-16 00:00:00

COVID-19 pandemic has become a global issue nowadays. Various efforts have been made to break the chain of the spread of the COVID-19. Indonesia's government issued a large-scale social restrictions policy to prevent the spread of the COVID-19. However, large-scale social restrictions policy impacted the economy of the Indonesian. After several considerations, the Indonesian government implemented a new-normal policy, which regulates the activities outside the home with strict health protocols. This study's objective is to measure Indonesia's readiness level after the large-scale social restrictions period towards the new-normal period. To specify the readiness level, the measurement parameters required in the form of statistical analysis and forecasting modeling. Based on the results of statistical analysis and forecasting, over the past month, new confirmed cases increased more than two times. Besides, the growth rate of new confirmed cases dramatically increased rapidly compared to the prediction results. Therefore, the government must review the new-normal policy again and emphasize economic factors and think about health factors
IIT_kgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports
Authors: Arka Mitra, Harshvardhan Srivastava, Yugam Tiwari
Date: 2020-11-16 00:00:00
Tasks: regression, Sentence, Sentence Embeddings

The paper describes the work that the team submitted to FinCausal 2020 Shared Task. This work is associated with the first sub-task of identifying causality in sentences. The various models used in the experiments tried to obtain a latent space representation for each of the sentences. Linear regression was performed on these representations to classify whether the sentence is causal or not. The experiments have shown BERT (Large) performed the best, giving a F1 score of 0.958, in the task of detecting the causality of sentences in financial texts and reports. The class imbalance was dealt with a modified loss function to give a better metric score for the evaluation.
Analog Circuit Design with Dyna-Style Reinforcement Learning
Authors: Wook Lee, Frans A. Oliehoek
Date: 2020-11-16 00:00:00
Tasks: Layout Design, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work, we present a learning based approach to analog circuit design, where the goal is to optimize circuit performance subject to certain design constraints. One of the aspects that makes this problem challenging to optimize, is that measuring the performance of candidate configurations with simulation can be computationally expensive, particularly in the post-layout design. Additionally, the large number of design constraints and the interaction between the relevant quantities makes the problem complex. Therefore, to better facilitate supporting the human designers, it is desirable to gain knowledge about the whole space of feasible solutions. In order to tackle these challenges, we take inspiration from model-based reinforcement learning and propose a method with two key properties. First, it learns a reward model, i.e., surrogate model of the performance approximated by neural networks, to reduce the required number of simulation. Second, it uses a stochastic policy generator to explore the diverse solution space satisfying constraints. Together we combine these in a Dyna-style optimization framework, which we call DynaOpt, and empirically evaluate the performance on a circuit benchmark of a two-stage operational amplifier. The results show that, compared to the model-free method applied with 20,000 circuit simulations to train the policy, DynaOpt achieves even much better performance by learning from scratch with only 500 simulations.
hyper-sinh: An Accurate and Reliable Function from Shallow to Deep Learning in TensorFlow and Keras
Authors: Luca Parisi, Renfei Ma, Narrendar RaviChandran, Matteo Lanzillotta
Date: 2020-11-15 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

This paper presents the 'hyper-sinh', a variation of the m-arcsinh activation function suitable for Deep Learning (DL)-based algorithms for supervised learning, such as Convolutional Neural Networks (CNN). hyper-sinh, developed in the open source Python libraries TensorFlow and Keras, is thus described and validated as an accurate and reliable activation function for both shallow and deep neural networks. Improvements in accuracy and reliability in image and text classification tasks on five (N = 5) benchmark data sets available from Keras are discussed. Experimental results demonstrate the overall competitive classification performance of both shallow and deep neural networks, obtained via this novel function. This function is evaluated with respect to gold standard activation functions, demonstrating its overall competitive accuracy and reliability for both image and text classification.
ArraMon: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments
Authors: Hyounghun Kim, Abhay Zala, Graham Burri, Hao Tan, Mohit Bansal
Date: 2020-11-15 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Referring Expression, Referring Expression Comprehension, Vision and Language Navigation

For embodied agents, navigation is an important ability but not an isolated goal. Agents are also expected to perform specific tasks after reaching the target location, such as picking up objects and assembling them into a particular arrangement. We combine Vision-and-Language Navigation, assembling of collected objects, and object referring expression comprehension, to create a novel joint navigation-and-assembly task, named ArraMon. During this task, the agent (similar to a PokeMON GO player) is asked to find and collect different target objects one-by-one by navigating based on natural language instructions in a complex, realistic outdoor environment, but then also ARRAnge the collected objects part-by-part in an egocentric grid-layout environment. To support this task, we implement a 3D dynamic environment simulator and collect a dataset (in English; and also extended to Hindi) with human-written navigation and assembling instructions, and the corresponding ground truth trajectories. We also filter the collected instructions via a verification stage, leading to a total of 7.7K task instances (30.8K instructions and paths). We present results for several baseline models (integrated and biased) and metrics (nDTW, CTC, rPOD, and PTC), and the large model-human performance gap demonstrates that our task is challenging and presents a wide scope for future work. Our dataset, simulator, and code are publicly available at: https://arramonunc.github.io
Deep multi-modal networks for book genre classification based on its cover
Authors: Chandra Kundu, Lukun Zheng
Date: 2020-11-15 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, General Classification, Genre classification, Retrieval

Book covers are usually the very first impression to its readers and they often convey important information about the content of the book. Book genre classification based on its cover would be utterly beneficial to many modern retrieval systems, considering that the complete digitization of books is an extremely expensive task. At the same time, it is also an extremely challenging task due to the following reasons: First, there exists a wide variety of book genres, many of which are not concretely defined. Second, book covers, as graphic designs, vary in many different ways such as colors, styles, textual information, etc, even for books of the same genre. Third, book cover designs may vary due to many external factors such as country, culture, target reader populations, etc. With the growing competitiveness in the book industry, the book cover designers and typographers push the cover designs to its limit in the hope of attracting sales. The cover-based book classification systems become a particularly exciting research topic in recent years. In this paper, we propose a multi-modal deep learning framework to solve this problem. The contribution of this paper is four-fold. First, our method adds an extra modality by extracting texts automatically from the book covers. Second, image-based and text-based, state-of-the-art models are evaluated thoroughly for the task of book cover classification. Third, we develop an efficient and salable multi-modal framework based on the images and texts shown on the covers only. Fourth, a thorough analysis of the experimental results is given and future works to improve the performance is suggested. The results show that the multi-modal framework significantly outperforms the current state-of-the-art image-based models. However, more efforts and resources are needed for this classification task in order to reach a satisfactory level.
Predicting Human Strategies in Simulated Search and Rescue Task
Authors: Vidhi Jain, Rohit Jena, Huao Li, Tejus Gupta, Dana Hughes, Michael Lewis, Katia Sycara
Date: 2020-11-15 00:00:00
Tasks: Minecraft

In a search and rescue scenario, rescuers may have different knowledge of the environment and strategies for exploration. Understanding what is inside a rescuer's mind will enable an observer agent to proactively assist them with critical information that can help them perform their task efficiently. To this end, we propose to build models of the rescuers based on their trajectory observations to predict their strategies. In our efforts to model the rescuer's mind, we begin with a simple simulated search and rescue task in Minecraft with human participants. We formulate neural sequence models to predict the triage strategy and the next location of the rescuer. As the neural networks are data-driven, we design a diverse set of artificial "faux human" agents for training, to test them with limited human rescuer trajectory data. To evaluate the agents, we compare it to an evidence accumulation method that explicitly incorporates all available background knowledge and provides an intended upper bound for the expected performance. Further, we perform experiments where the observer/predictor is human. We show results in terms of prediction accuracy of our computational approaches as compared with that of human observers.
Good proctor or "Big Brother"? AI Ethics and Online Exam Supervision Technologies
Authors: Simon Coghlan, Tim Miller, Jeannie Paterson
Date: 2020-11-15 00:00:00
Tasks: Ethics, Fairness

This article philosophically analyzes online exam supervision technologies, which have been thrust into the public spotlight due to campus lockdowns during the COVID-19 pandemic and the growing demand for online courses. Online exam proctoring technologies purport to provide effective oversight of students sitting online exams, using artificial intelligence (AI) systems and human invigilators to supplement and review those systems. Such technologies have alarmed some students who see them as `Big Brother-like', yet some universities defend their judicious use. Critical ethical appraisal of online proctoring technologies is overdue. This article philosophically analyzes these technologies, focusing on the ethical concepts of academic integrity, fairness, non-maleficence, transparency, privacy, respect for autonomy, liberty, and trust. Most of these concepts are prominent in the new field of AI ethics and all are relevant to the education context. The essay provides ethical considerations that educational institutions will need to carefully review before electing to deploy and govern specific online proctoring technologies.
Advances in the training, pruning and enforcement of shape constraints of Morphological Neural Networks using Tropical Algebra
Authors: Nikolaos Dimitriadis, Petros Maragos
Date: 2020-11-15 00:00:00

In this paper we study an emerging class of neural networks based on the morphological operators of dilation and erosion. We explore these networks mathematically from a tropical geometry perspective as well as mathematical morphology. Our contributions are threefold. First, we examine the training of morphological networks via Difference-of-Convex programming methods and extend a binary morphological classifier to multiclass tasks. Second, we focus on the sparsity of dense morphological networks trained via gradient descent algorithms and compare their performance to their linear counterparts under heavy pruning, showing that the morphological networks cope far better and are characterized with superior compression capabilities. Our approach incorporates the effect of the training optimizer used and offers quantitative and qualitative explanations. Finally, we study how the architectural structure of a morphological network can affect shape constraints, focusing on monotonicity. Via Maslov Dequantization, we obtain a softened version of a known architecture and show how this approach can improve training convergence and performance.
Stein Variational Model Predictive Control
Authors: Alexander Lambert, Adam Fishman, Dieter Fox, Byron Boots, Fabio Ramos
Date: 2020-11-15 00:00:00
Tasks: Bayesian Inference, Decision Making, Decision Making Under Uncertainty, model, Model Predictive Control

Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex probability distributions. In this paper, we propose a generalization of MPC that represents a multitude of solutions as posterior distributions. By casting MPC as a Bayesian inference problem, we employ variational methods for posterior computation, naturally encoding the complexity and multi-modality of the decision making problem. We present a Stein variational gradient descent method to estimate the posterior directly over control parameters, given a cost function and observed state trajectories. We show that this framework leads to successful planning in challenging, non-convex optimal control problems.
Precision-Recall Curve (PRC) Classification Trees
Authors: Jiaju Miao, Wei Zhu
Date: 2020-11-15 00:00:00
Tasks: Classification, Fraud Detection, General Classification, Variable Selection

The classification of imbalanced data has presented a significant challenge for most well-known classification algorithms that were often designed for data with relatively balanced class distributions. Nevertheless skewed class distribution is a common feature in real world problems. It is especially prevalent in certain application domains with great need for machine learning and better predictive analysis such as disease diagnosis, fraud detection, bankruptcy prediction, and suspect identification. In this paper, we propose a novel tree-based algorithm based on the area under the precision-recall curve (AUPRC) for variable selection in the classification context. Our algorithm, named as the "Precision-Recall Curve classification tree", or simply the "PRC classification tree" modifies two crucial stages in tree building. The first stage is to maximize the area under the precision-recall curve in node variable selection. The second stage is to maximize the harmonic mean of recall and precision (F-measure) for threshold selection. We found the proposed PRC classification tree, and its subsequent extension, the PRC random forest, work well especially for class-imbalanced data sets. We have demonstrated that our methods outperform their classic counterparts, the usual CART and random forest for both synthetic and real data. Furthermore, the ROC classification tree proposed by our group previously has shown good performance in imbalanced data. The combination of them, the PRC-ROC tree, also shows great promise in identifying the minority class.
Open4Business(O4B): An Open Access Dataset for Summarizing Business Documents
Authors: Amanpreet Singh, Niranjan Balasubramanian
Date: 2020-11-15 00:00:00
Tasks: Articles

A major challenge in fine-tuning deep learning models for automatic summarization is the need for large domain specific datasets. One of the barriers to curating such data from resources like online publications is navigating the license regulations applicable to their re-use, especially for commercial purposes. As a result, despite the availability of several business journals there are no large scale datasets for summarizing business documents. In this work, we introduce Open4Business(O4B),a dataset of 17,458 open access business articles and their reference summaries. The dataset introduces a new challenge for summarization in the business domain, requiring highly abstractive and more concise summaries as compared to other existing datasets. Additionally, we evaluate existing models on it and consequently show that models trained on O4B and a 7x larger non-open access dataset achieve comparable performance on summarization. We release the dataset, along with the code which can be leveraged to similarly gather data for multiple domains.
DORB: Dynamically Optimizing Multiple Rewards with Bandits
Authors: Ramakanth Pasunuru, Han Guo, Mohit Bansal
Date: 2020-11-15 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Data-to-Text Generation, Question Generation, Question-Generation, Text Generation

Policy gradients-based reinforcement learning has proven to be a promising approach for directly optimizing non-differentiable evaluation metrics for language generation tasks. However, optimizing for a specific metric reward leads to improvements in mostly that metric only, suggesting that the model is gaming the formulation of that metric in a particular way without often achieving real qualitative improvements. Hence, it is more beneficial to make the model optimize multiple diverse metric rewards jointly. While appealing, this is challenging because one needs to manually decide the importance and scaling weights of these metric rewards. Further, it is important to consider using a dynamic combination and curriculum of metric rewards that flexibly changes over time. Considering the above aspects, in our work, we automate the optimization of multiple metric rewards simultaneously via a multi-armed bandit approach (DORB), where at each round, the bandit chooses which metric reward to optimize next, based on expected arm gains. We use the Exp3 algorithm for bandits and formulate two approaches for bandit rewards: (1) Single Multi-reward Bandit (SM-Bandit); (2) Hierarchical Multi-reward Bandit (HM-Bandit). We empirically show the effectiveness of our approaches via various automatic metrics and human evaluation on two important NLG tasks: question generation and data-to-text generation, including on an unseen-test transfer setup. Finally, we present interpretable analyses of the learned bandit curriculum over the optimized rewards.
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
Authors: Jinyuan Jia, Binghui Wang, Xiaoyu Cao, Hongbin Liu, Neil Zhenqiang Gong
Date: 2020-11-15 00:00:00
Proceeding: ICLR 2022 4
Tasks: Recommendation Systems

Top-k predictions are used in many real-world applications such as machine learning as a service, recommender systems, and web searches. $\ell_0$-norm adversarial perturbation characterizes an attack that arbitrarily modifies some features of an input such that a classifier makes an incorrect prediction for the perturbed input. $\ell_0$-norm adversarial perturbation is easy to interpret and can be implemented in the physical world. Therefore, certifying robustness of top-$k$ predictions against $\ell_0$-norm adversarial perturbation is important. However, existing studies either focused on certifying $\ell_0$-norm robustness of top-$1$ predictions or $\ell_2$-norm robustness of top-$k$ predictions. In this work, we aim to bridge the gap. Our approach is based on randomized smoothing, which builds a provably robust classifier from an arbitrary classifier via randomizing an input. Our major theoretical contribution is an almost tight $\ell_0$-norm certified robustness guarantee for top-$k$ predictions. We empirically evaluate our method on CIFAR10 and ImageNet. For instance, our method can build a classifier that achieves a certified top-3 accuracy of 69.2\% on ImageNet when an attacker can arbitrarily perturb 5 pixels of a testing image.
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
Authors: Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Håvard D. Johansen, Dag D. Johansen, Jens Rittscher, Michael A. Riegler, Pål Halvorsen
Date: 2020-11-15 00:00:00
Tasks: Benchmarking, Colorectal Polyps Characterization, Medical Image Segmentation, Medical Object Detection, Real-Time Object Detection, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation

Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images for polyp detection, localisation, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that the proposed ColonSegNet achieved a better trade-off between an average precision of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per second for the detection and localisation task. Likewise, the proposed ColonSegNet achieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimise miss-detection rates.
Safety Synthesis Sans Specification
Authors: Roderick Bloem, Hana Chockler, Masoud Ebrahimi, Dana Fisman, Heinz Riener
Date: 2020-11-15 00:00:00

We define the problem of learning a transducer ${S}$ from a target language $U$ containing possibly conflicting transducers, using membership queries and conjecture queries. The requirement is that the language of ${S}$ be a subset of $U$. We argue that this is a natural question in many situations in hardware and software verification. We devise a learning algorithm for this problem and show that its time and query complexity is polynomial with respect to the rank of the target language, its incompatibility measure, and the maximal length of a given counterexample. We report on experiments conducted with a prototype implementation.