pwc-archive/papers-with-abstracts huggingface dataset:
Context-Dependent Implicit Authentication for Wearable Device User
Authors: William Cheung, Sudip Vhaduri
Date: 2020-08-25 00:00:00

As market wearables are becoming popular with a range of services, including making financial transactions, accessing cars, etc. that they provide based on various private information of a user, security of this information is becoming very important. However, users are often flooded with PINs and passwords in this internet of things (IoT) world. Additionally, hard-biometric, such as facial or finger recognition, based authentications are not adaptable for market wearables due to their limited sensing and computation capabilities. Therefore, it is a time demand to develop a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from the market wearables. In this work, we present a context-dependent soft-biometric-based wearable authentication system utilizing the heart rate, gait, and breathing audio signals. From our detailed analysis, we find that a binary support vector machine (SVM) with radial basis function (RBF) kernel can achieve an average accuracy of $0.94 \pm 0.07$, $F_1$ score of $0.93 \pm 0.08$, an equal error rate (EER) of about $0.06$ at a lower confidence threshold of 0.52, which shows the promise of this work.
Avoiding Negative Side Effects due to Incomplete Knowledge of AI Systems
Authors: Sandhya Saisubramanian, Shlomo Zilberstein, Ece Kamar
Date: 2020-08-24 00:00:00

Autonomous agents acting in the real-world often operate based on models that ignore certain aspects of the environment. The incompleteness of any given model -- handcrafted or machine acquired -- is inevitable due to practical limitations of any modeling technique for complex real-world settings. Due to the limited fidelity of its model, an agent's actions may have unexpected, undesirable consequences during execution. Learning to recognize and avoid such negative side effects of an agent's actions is critical to improve the safety and reliability of autonomous systems. Mitigating negative side effects is an emerging research topic that is attracting increased attention due to the rapid growth in the deployment of AI systems and their broad societal impacts. This article provides a comprehensive overview of different forms of negative side effects and the recent research efforts to address them. We identify key characteristics of negative side effects, highlight the challenges in avoiding negative side effects, and discuss recently developed approaches, contrasting their benefits and limitations. The article concludes with a discussion of open questions and suggestions for future research directions.
Local error quantification for Neural Network Differential Equation solvers
Authors: Akshunna S. Dogra, William T Redman
Date: 2020-08-24 00:00:00

Neural networks have been identified as powerful tools for the study of complex systems. A noteworthy example is the neural network differential equation (NN DE) solver, which can provide functional approximations to the solutions of a wide variety of differential equations. Such solvers produce robust functional expressions, are well suited for further manipulations on the quantities of interest (for example, taking derivatives), and capable of leveraging the modern advances in parallelization and computing power. However, there is a lack of work on the role precise error quantification can play in their predictions: usually, the focus is on ambiguous and/or global measures of performance like the loss function and/or obtaining global bounds on the errors associated with the predictions. Precise, local error quantification is seldom possible without external means or outright knowledge of the true solution. We address these concerns in the context of dynamical system NN DE solvers, leveraging learnt information within the NN DE solvers to develop methods that allow them to be more accurate and efficient, while still pursuing an unsupervised approach that does not rely on external tools or data. We achieve this via methods that can precisely estimate NN DE solver prediction errors point-wise, thus allowing the user the capacity for efficient and targeted error correction. We exemplify the utility of our methods by testing them on a nonlinear and a chaotic system each.
PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions
Authors: Seokhyun Moon, Wonho Zhung, Soojung Yang, Jaechang Lim, Woo Youn Kim
Date: 2020-08-22 00:00:00
Tasks: Drug Discovery

Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of in-silico drug discovery. We propose two key strategies to enhance generalization in the DTI model. The first is to predict the atom-atom pairwise interactions via physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein-ligand complex as their sum. We further improved the model generalization by augmenting a broader range of binding poses and ligands to training data. We validated our model, PIGNet, in the comparative assessment of scoring functions (CASF) 2016, demonstrating the outperforming docking and screening powers than previous methods. Our physics-informing strategy also enables the interpretation of predicted affinities by visualizing the contribution of ligand substructures, providing insights for further ligand optimization.
Competence-Based Student Modelling with Dynamic Bayesian Networks
Authors: Rafael Morales-Gamboa, L. Enrique Sucar
Date: 2020-08-21 00:00:00

We present a general method for using a competences map, created by defining generalization/specialization and inclusion/part-of relationships between competences, in order to build an overlay student model in the form of a dynamic Bayesian network in which conditional probability distributions are defined per relationship type. We have created a competences map for a subset of the transversal competences defined as educational goals for the Mexican high school system, then we have built a dynamic Bayesian student model as said before, and we have use it to trace the development of the corresponding competences by some hypothetical students exhibiting representative performances along an online course (low to medium performance, medium to high performance but with low final score, and two terms medium to high performance). The results obtained suggest that the proposed way for constructing dynamic Bayesian student models on the basis of competences maps could be useful to monitor competence development by real students in online course.
Biomechanic Posture Stabilisation via Iterative Training of Multi-policy Deep Reinforcement Learning Agents
Authors: Mohammed Hossny, Julie Iskander
Date: 2020-08-21 00:00:00
Tasks: AI Agent, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

It is not until we become senior citizens do we recognise how much we took maintaining a simple standing posture for granted. It is truly fascinating to observe the magnitude of control the human brain exercises, in real time, to activate and deactivate the lower body muscles and solve a multi-link 3D inverted pendulum problem in order to maintain a stable standing posture. This realisation is even more apparent when training an artificial intelligence (AI) agent to maintain a standing posture of a digital musculoskeletal avatar due to the error propagation problem. In this work we address the error propagation problem by introducing an iterative training procedure for deep reinforcement learning which allows the agent to learn a finite set of actions and how to coordinate between them in order to achieve a stable standing posture. The proposed training approach allowed the agent to increase standing duration from 4 seconds using the traditional training method to 348 seconds using the proposed method. The proposed training method allowed the agent to generalise and accommodate perception and actuation noise for almost 108 seconds.
Learning to Profile: User Meta-Profile Network for Few-Shot Learning
Authors: Hao Gong, Qifang Zhao, Tianyu Li, Derek Cho, DuyKhuong Nguyen
Date: 2020-08-21 00:00:00
Tasks: Few-Shot Learning, Meta-Learning, Multi-Task Learning, Out-of-Distribution Detection, Relation Network, Representation Learning, Transfer Learning

Meta-learning approaches have shown great success in vision and language domains. However, few studies discuss the practice of meta-learning for large-scale industrial applications. Although e-commerce companies have spent many efforts on learning representations to provide a better user experience, we argue that such efforts cannot be stopped at this step. In addition to learning a strong profile, the challenging question about how to effectively transfer the learned representation is raised simultaneously. This paper introduces the contributions that we made to address these challenges from three aspects. 1) Meta-learning model: In the context of representation learning with e-commerce user behavior data, we propose a meta-learning framework called the Meta-Profile Network, which extends the ideas of matching network and relation network for knowledge transfer and fast adaptation; 2) Encoding strategy: To keep high fidelity of large-scale long-term sequential behavior data, we propose a time-heatmap encoding strategy that allows the model to encode data effectively; 3) Deep network architecture: A multi-modal model combined with multi-task learning architecture is utilized to address the cross-domain knowledge learning and insufficient label problems. Moreover, we argue that an industrial model should not only have good performance in terms of accuracy, but also have better robustness and uncertainty performance under extreme conditions. We evaluate the performance of our model with extensive control experiments in various extreme scenarios, i.e. out-of-distribution detection, data insufficiency and class imbalance scenarios. The Meta-Profile Network shows significant improvement in the model performance when compared to baseline models.
Automatic Generation of Chatbots for Conversational Web Browsing
Authors: Pietro Chittò, Marcos Baez, Florian Daniel, Boualem Benatallah
Date: 2020-08-19 00:00:00
Tasks: Chatbot

In this paper, we describe the foundations for generating a chatbot out of a website equipped with simple, bot-specific HTML annotations. The approach is part of what we call conversational web browsing, i.e., a dialog-based, natural language interaction with websites. The goal is to enable users to use content and functionality accessible through rendered UIs by "talking to websites" instead of by operating the graphical UI using keyboard and mouse. The chatbot mediates between the user and the website, operates its graphical UI on behalf of the user, and informs the user about the state of interaction. We describe the conceptual vocabulary and annotation format, the supporting conversational middleware and techniques, and the implementation of a demo able to deliver conversational web browsing experiences through Amazon Alexa.
Deep Inverse Reinforcement Learning for Structural Evolution of Small Molecules
Authors: Brighter Agyemang, Wei-Ping Wu, Daniel Addo, Michael Y. Kpiebaareh, Ebenezer Nanor, Charles Roland Haruna
Date: 2020-07-24 00:00:00
Tasks: Drug Discovery, Generative Adversarial Network, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The size and quality of chemical libraries to the drug discovery pipeline are crucial for developing new drugs or repurposing existing drugs. Existing techniques such as combinatorial organic synthesis and High-Throughput Screening usually make the process extraordinarily tough and complicated since the search space of synthetically feasible drugs is exorbitantly huge. While reinforcement learning has been mostly exploited in the literature for generating novel compounds, the requirement of designing a reward function that succinctly represents the learning objective could prove daunting in certain complex domains. Generative Adversarial Network-based methods also mostly discard the discriminator after training and could be hard to train. In this study, we propose a framework for training a compound generator and learning a transferable reward function based on the entropy maximization inverse reinforcement learning paradigm. We show from our experiments that the inverse reinforcement learning route offers a rational alternative for generating chemical compounds in domains where reward function engineering may be less appealing or impossible while data exhibiting the desired objective is readily available.
Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion
Authors: Roland Hafner, Tim Hertweck, Philipp Klöppner, Michael Bloesch, Michael Neunert, Markus Wulfmeier, Saran Tunyasuvunakool, Nicolas Heess, Martin Riedmiller
Date: 2020-08-06 00:00:00
Tasks: Reinforcement Learning (RL)

Modern Reinforcement Learning (RL) algorithms promise to solve difficult motor control problems directly from raw sensory inputs. Their attraction is due in part to the fact that they can represent a general class of methods that allow to learn a solution with a reasonably set reward and minimal prior knowledge, even in situations where it is difficult or expensive for a human expert. For RL to truly make good on this promise, however, we need algorithms and learning setups that can work across a broad range of problems with minimal problem specific adjustments or engineering. In this paper, we study this idea of generality in the locomotion domain. We develop a learning framework that can learn sophisticated locomotion behavior for a wide spectrum of legged robots, such as bipeds, tripeds, quadrupeds and hexapods, including wheeled variants. Our learning framework relies on a data-efficient, off-policy multi-task RL algorithm and a small set of reward functions that are semantically identical across robots. To underline the general applicability of the method, we keep the hyper-parameter settings and reward definitions constant across experiments and rely exclusively on on-board sensing. For nine different types of robots, including a real-world quadruped robot, we demonstrate that the same algorithm can rapidly learn diverse and reusable locomotion skills without any platform specific adjustments or additional instrumentation of the learning setup.
New Normal: Cooperative Paradigm for Covid-19 Timely Detection and Containment using Internet of Things and Deep Learning
Authors: Farooque Hassan Kumbhar, Syed Ali Hassan, Soo Young Shin
Date: 2020-08-15 00:00:00
Tasks: object-detection, Object Detection

The spread of the novel coronavirus (COVID-19) has caused trillions of dollars in damages to the governments and health authorities by affecting the global economies. The purpose of this study is to introduce a connected smart paradigm that not only detects the possible spread of viruses but also helps to restart businesses/economies, and resume social life. We are proposing a connected Internet of Things ( IoT) based paradigm that makes use of object detection based on convolution neural networks (CNN), smart wearable and connected e-health to avoid current and future outbreaks. First, connected surveillance cameras feed continuous video stream to the server where we detect the inter-object distance to identify any social distancing violations. A violation activates area-based monitoring of active smartphone users and their current state of illness. In case a confirmed patient or a person with high symptoms is present, the system tracks exposed and infected people and appropriate measures are put into actions. We evaluated the proposed scheme for social distancing violation detection using YOLO (you only look once) v2 and v3, and for infection spread tracing using Python simulation.
A Survey on Reinforcement Learning for Combinatorial Optimization
Authors: Yunhao Yang, Andrew Whinston
Date: 2020-08-17 00:00:00
Tasks: Combinatorial Optimization, Deep Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey, Traveling Salesman Problem

This paper gives a detailed review of reinforcement learning (RL) in combinatorial optimization, introduces the history of combinatorial optimization starting in the 1950s, and compares it with the RL algorithms of recent years. This paper explicitly looks at a famous combinatorial problem-traveling salesperson problem (TSP). It compares the approach of modern RL algorithms for the TSP with an approach published in the 1970s. By comparing the similarities and variances between these methodologies, the paper demonstrates how RL algorithms are optimized due to the evolution of machine learning techniques and computing power. The paper then briefly introduces the deep learning approach to the TSP named deep RL, which is an extension of the traditional mathematical framework. In deep RL, attention and feature encoding mechanisms are introduced to generate near-optimal solutions. The survey shows that integrating the deep learning mechanism, such as attention with RL, can effectively approximate the TSP. The paper also argues that deep learning could be a generic approach that can be integrated with any traditional RL algorithm to enhance the outcomes of the TSP.
Bracketing brackets with bras and kets
Authors: Emily Clark, Angelie Vincent, J. Nathan Kutz, Steven L. Brunton
Date: 2020-07-31 00:00:00
Tasks: Clustering

Brackets are an essential component in aircraft manufacture and design, joining parts together, supporting weight, holding wires, and strengthening joints. Hundreds or thousands of unique brackets are used in every aircraft, but manufacturing a large number of distinct brackets is inefficient and expensive. Fortunately, many so-called "different" brackets are in fact very similar or even identical to each other. In this manuscript, we present a data-driven framework for constructing a comparatively small group of representative brackets from a large catalog of current brackets, based on hierarchical clustering of bracket data. We find that for a modern commercial aircraft, the full set of brackets can be reduced by 30\% while still describing half of the test set sufficiently accurately. This approach is based on designing an inner product that quantifies a multi-objective similarity between two brackets, which are the "bra" and the "ket" of the inner product. Although we demonstrate this algorithm to reduce the number of brackets in aerospace manufacturing, it may be generally applied to any large-scale component standardization effort.
You Only Train Once: Loss-Conditional Training of Deep Networks
Authors: Alexey Dosovitskiy, Josip Djolonga
Date: 2020-05-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Image Compression, Style Transfer

In many machine learning problems, loss functions are weighted sums of several terms. A typical approach to dealing with these is to train multiple separate models with different selections of weights and then either choose the best one according to some criterion or keep multiple models if it is desirable to maintain a diverse set of solutions. This is inefficient both at training and at inference time. We propose a method that allows replacing multiple models trained on one loss function each by a single model trained on a distribution of losses. At test time a model trained this way can be conditioned to generate outputs corresponding to any loss from the training distribution of losses. We demonstrate this approach on three tasks with parametrized losses: beta-VAE, learned image compression, and fast style transfer.
Domain Adaptive Multibranch Networks
Authors: Róger Bermúdez-Chacón, Mathieu Salzmann, Pascal Fua
Date: 2020-05-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

We tackle unsupervised domain adaptation by accounting for the fact that different domains may need to be processed differently to arrive to a common feature representation effective for recognition. To this end, we introduce a deep learning framework where each domain undergoes a different sequence of operations, allowing some, possibly more complex, domains to go through more computations than others.
This contrasts with  state-of-the-art domain adaptation techniques that force all domains to be processed with the same series of operations, even when using multi-stream architectures whose parameters are not shared.
As evidenced by our experiments, the greater flexibility of our method translates to higher accuracy. Furthermore, it allows us to handle any number of domains simultaneously.
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings
Authors: Daniel Ruffinelli, Samuel Broscheit, Rainer Gemulla
Date: 2020-05-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Graph Embedding, Hyperparameter Optimization, Knowledge Graph Embedding, Knowledge Graph Embeddings, Link Prediction

Knowledge graph embedding (KGE) models learn algebraic representations of the entities and relations in a knowledge graph. A vast number of KGE techniques for multi-relational link prediction have been proposed in the recent literature, often with state-of-the-art performance. These approaches differ along a number of dimensions, including different model architectures, different training strategies, and different approaches to hyperparameter optimization. In this paper, we take a step back and aim to summarize and quantify empirically the impact of each of these dimensions on model performance. We report on the results of an extensive experimental study with popular model architectures and training strategies across a wide range of hyperparameter settings. We found that when trained appropriately, the relative performance differences between various model architectures often shrinks and sometimes even reverses when compared to prior results. For example, RESCAL~\citep{nickel2011three}, one of the first KGE models, showed strong performance when trained with state-of-the-art techniques; it was competitive to or outperformed more recent architectures. We also found that good (and often superior to prior studies) model configurations can be found by exploring relatively few random samples from a large hyperparameter space. Our results suggest that many of the more advanced architectures and techniques proposed in the literature should be revisited to reassess their individual benefits. To foster further reproducible research, we provide all our implementations and experimental results as part of the open source LibKGE framework.