bluuebunny/crossref_metadata_2025 huggingface dataset:
Early Safety Warnings for Long-Distance Pipelines: A Distributed Optical Fiber Sensor Machine Learning Approach
Authors: Yang Yiyuan , Li Yi , Zhang Taojia , Zhou Yan , Zhang Haifeng
Published: September 2022

Automated pipeline safety early warning (PSEW) systems are designed to automatically identify and locate third-party damage events on oil and gas pipelines. They are intended to replace traditional, inefficient manual inspection methods. However, current PSEW methods cannot achieve universality for various complex environments because they are sensitive to the spatiotemporal stability of the signal obtained by its distributed sensors at various locations and times. Our research aimed to improve the accuracy of long-distance oil–gas PSEW systems through machine learning. In this paper, we propose a novel real-time action recognition method for long-distance PSEW systems based on a coherent Rayleigh scattering distributed optical fiber sensor. More specifically, we put forward two complementary feature calculation methods to describe signals and build a new action recognition deep learning network based on those features. Encouraging empirical results on the data collected at a real location confirm that the features can effectively describe signals in an environment with strong noise and weak signals, and the entire approach can identify and locate third-party damage events quickly under various hardware conditions with accuracies of 99.26% (500 Hz) and 97.20% (100 Hz). More generically, our method can be applied to other fields as well.
Joint Incentive Optimization of Customer and Merchant in Mobile Payment Marketing
Authors: Yu Li , Wu Zhengwei , Cai Tianchi , Liu Ziqi , Zhang Zhiqiang , Gu Lihong , Zeng Xiaodong , Gu Jinjie
Published: September 2022

In the mobile Internet era, mobile payment service becomes the foundation of inclusive finance, which brings convenience and security to people.
Various marketing strategies are designed to encourage mobile payment activities by allocating incentives such as coupons or commissions to customers or merchants.
We summary two significant issues.
First, there is a phenomenon of mutual influence between merchants and customers, i.e., bipartite influence issue, thus making the independent optimization of customers and merchants non-optimal.
Second, the redemptions of coupons are partially observed, as we can only observe that the customer redeems the coupon or not at a specific incentive value, but cannot observe that at other incentive value, i.e., data censorship issue.
In this paper, we propose a novel joint incentive optimization framework to address the above two issues.
We propose to use a graph neural network to represent customers and merchants jointly by modeling the underlying bipartite influences.
We then formulate the response model under the hazard regression setting and model the hazard rate with a piecewise nonlinear function to capture the changes of responses to different incentive values.
Finally, we propose a linear programming method to allocate approximated optimal incentive values to customers and merchants in real-time.
Extensive offline and online experimental results demonstrate the effectiveness of our proposed approach.
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Authors: Zhang Xiyue , Huang Chao , Xu Yong , Xia Lianghao , Dai Peng , Bo Liefeng , Zhang Junbo , Zheng Yu
Published: September 2022

Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment. While previous work has made significant efforts to learn traffic temporal dynamics and spatial dependencies, two key limitations exist in current models. First, only the neighboring spatial correlations among adjacent regions are considered in most existing methods, and the global interregion dependency is ignored. Additionally, these methods fail to encode the complex traffic transition regularities exhibited with time-dependent and multi-resolution in nature. To tackle these challenges, we develop a new traffic prediction framework–Spatial-Temporal Graph Diffusion Network (ST-GDN). In particular, ST-GDN is a hierarchically structured graph neural architecture which learns not only the local region-wise geographical dependencies, but also the spatial semantics from a global perspective. Furthermore, a multi-scale attention network is developed to empower ST-GDN with the capability of capturing multi-level temporal dynamics. Experiments on four real-life traffic datasets demonstrate that ST-GDN outperforms different types of state-of-the-art baselines.
Land Deformation Prediction via Slope-Aware Graph Neural Networks
Authors: Zhou Fan , Li Rongfan , Trajcevski Goce , Zhang Kunpeng
Published: September 2022

We introduce a slope-aware graph neural network (SA-GNN) to leverage continuously monitored data and predict the land displacement. Unlike general GNNs tackling tasks in the plain graphs, our method is capable of generalizing 3D spatial knowledge from InSAR point clouds. Specifically, we structure of the land surface, while preserving the spatial correlations among adjacent points. The point cloud can then be efficiently converted to a near-neighbor graph where general GNN methods can be applied to predict the displacement of the slope surface. We conducted experiments on real-world datasets and the results demonstrate that SA-GNN outperforms existing 3D CNN and point GNN methods.
Thinking Fast and Slow in AI
Authors: Booch Grady , Fabiano Francesco , Horesh Lior , Kate Kiran , Lenchner Jonathan , Linck Nick , Loreggia Andreas , Murgesan Keerthiram , Mattei Nicholas , Rossi Francesca , Srivastava Biplav
Published: September 2022

This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities in an AI system by embedding these causal components. We hope that the high-level description of our vision included in this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks, and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence.
Lifelong and Continual Learning Dialogue Systems: Learning during Conversation
Authors: Liu Bing , Mazumder Sahisnu
Published: September 2022

Dialogue systems, also called chatbots, are now used in a wide range of applications. However, they still have some major weaknesses. One key weakness is that they are typically trained from manually-labeled data and/or written with handcrafted rules, and their knowledge bases (KBs) are also compiled by human experts. Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs. Thus, the level of user satisfactory is often low. In this paper, we propose to dramatically improve the situation by endowing the chatbots the ability to continually learn (1) new world knowledge, (2) new language expressions to ground them to actions, and (3) new conversational skills, during conversation by themselves so that as they chat more and more with users, they become more and more knowledgeable and are better and better able to understand diverse natural language expressions and to improve their conversational skills.
EeLISA: Combating Global Warming Through the Rapid Analysis of Eelgrass Wasting Disease
Authors: Rappazzo Brendan H. , Eisenlord Morgan E. , Graham Olivia J. , Aoki Lillian R. , Dawkins Phoebe D. , Harvell Drew , Gomes Carla
Published: September 2022

Global warming is the greatest threat facing our planet, and is causing environmental disturbance at an unprecedented scale. We are strongly positioned to leverage the advancements of Artificial Intelligence (AI) and Machine Learning (ML) which provide humanity, for the first time in history, an analysis and decision making tool at massive scale. Strong evidence supports that global warming is contributing to marine ecosystem decline, including eelgrass habitat. Eelgrass is affected by an opportunistic marine pathogen and infections are likely exacerbated by rising ocean temperatures. The necessary disease analysis required to inform conservation priorities is incredibly laborious, and acts as a significant bottleneck for research. To this end, we developed EeLISA (Eelgrass Lesion Image Segmentation Application). EeLISA enables ecologist experts to train a segmentation module to perform this crucial analysis at human level accuracy, while minimizing their labeling time and integrating into their existing workflow. EeLISA has been deployed for over 16 months, and has facilitated the preparation of four manuscripts including a critical eelgrass study ranging from Southern California to Alaska. These studies, utilizing EeLISA, have led to scientific insight and discovery in marine disease ecology.
Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change
Authors: Choi Taeyeong , Pyenson Benjamin , Liebig Juergen , Pavlic Theodore P.
Published: September 2022

Biology is both an important application area and a source of motivation for development of advanced machine learning techniques. Although much attention has been paid to large and complex data sets resulting from high-throughput sequencing, advances in high-quality video recording technology have begun to generate similarly rich data sets requiring sophisticated techniques from both computer vision and time-series analysis. Moreover, just as studying gene expression patterns in one organism can reveal general principles that apply to other organisms, the study of complex social interactions in an experimentally tractable model system, such as a laboratory ant colony, can provide general principles about the dynamics of other social groups. Here, we focus on one such example from the study of reproductive regulation in small laboratory colonies of more than 50 Harpegnathos ants. These ants can be artificially induced to begin a ~20 day process of hierarchy reformation. Although the conclusion of this process is conspicuous to a human observer, it remains unclear which behaviors during the transient period are contributing to the process. To address this issue, we explore the potential application of One-class Classification (OC) to the detection of abnormal states in ant colonies for which behavioral data is only available for the normal societal conditions during training. Specifically, we build upon the Deep Support Vector Data Description (DSVDD) and introduce the Inner-Outlier Generator (IO-GEN) that synthesizes fake “inner outlier” observations during training that are near the center of the DSVDD data description.We show that IO-GEN increases the reliability of the final OC classifier relative to other DSVDD baselines. This method can be used to screen video frames for which additional human observation is needed. Although we focus on an application with laboratory colonies of social insects, this approach may be applied to video data from other social systems to either better understand the causal factors behind social phase transitions or even to predict the onset of future transitions.
Spatiotemporal Graph Neural Network for Performance Prediction of Photovoltaic Power Systems
Authors: Karimi Ahmad Maroof , Wu Yinghui , Koyuturk Mehmet , French Roger H.
Published: September 2022

In recent years, a large number of photovoltaic (PV) systems have been added to the electrical grid as well as installed as off-grid systems. The trend suggests that the deployment of PV systems will continue to rise in the future. Thus, accurate forecasting of PV performance is critical for the reliability of PV systems. Due to the complex non-linear variability in power output of the PV systems, forecasting PV power is a non-trivial task. This variability affects the stability and planning of a power system network, and accurate forecasting of the performance of the PV system can reduce the uncertainty caused during PV operation. In this work, we leverage spatial and temporal coherence among the power plants for PV power forecasting. Our approach is motivated by the observation that power plants in a region undergo similar environmental exposure. Thus, one power plant’s performance can help improve the forecast of other power plants' power values in the region. We utilize the relationship between PV plants to build a spatiotemporal graph neural network (st-GNN) and train machine learning models to forecast the PV power. The computational experiments on large-scale data from a network of 316 systems show that spatiotemporal forecasting of PV power performs significantly better than a model that only applies temporal convolution to isolated systems or nodes. Furthermore, the longer the future forecast time, the difference between the spatiotemporal forecasting and the isolated system forecast when only temporal convolution is applied increases further.
A Reciprocal Embedding Framework For Modelling Mutual Preferences
Authors: Ramanathan R. , Shinada Nicolas K. , Shimatani Michinobu , Yamaguchi Yuhei , Tanaka Junichi , Iizuka Yuta , Palaniappan Sucheendra K.
Published: September 2022

Understanding the mutual preferences between potential dating partners is core to the success of modern web-scale personalized recommendation systems that power online dating platforms. In contrast to classical user-item recommendation systems which model the unidirectional preferences of users to items, understanding the bidirectional preferences between people in a reciprocal recommendation system is more complex and challenging given the dynamic nature of interactions. In this paper, we describe a reciprocal recommendation system we built for one of the leading online dating applications in Japan. We also discuss the lessons learnt from designing, developing and deploying the reciprocal recommendation system in production.
Predicting Parking Availability from Mobile Payment Transactions with Positive Unlabeled Learning
Authors: Sonntag Jonas , Engel Michael , Schmidt-Thieme Lars
Published: September 2022

Cruising for parking in city centers is a problem for many motorists and for communities that need to reduce emissions. A widespread provision of parking assistance to address this problem requires a scalable system to generate availability information.
Existing approaches to estimate the availability of parking spaces use supervised learning and depend on ground-truth labeling processes involving sensors or manual data collection. This dependency constraints the widespread roll-out and operation of such systems as the ground-truth data collection for model training, monitoring and retraining is prohibitively expensive.
We describe a parking availability prediction system for paid on-street parking zones that does not depend on costly ground-truth labeling. The new approach uses solely data from parking ticket bookings via a mobile phone app. Every parking transaction serves as an implicit signal for the availability of one parking spot shortly before the booking. The system leverages this weak supervision signal by applying algorithms and metrics for positive-unlabeled learning (PU-learning). This approach enables the deployment in diverse regions, as well as the scalable monitoring and retraining of models. We evaluate our framework on a public dataset from Seattle.
HetSeq: Distributed GPU Training on Heterogeneous Infrastructure
Authors: Ding Yifan , Botzer Nicholas , Weninger Tim
Published: September 2022

Modern deep learning systems like PyTorch and Tensorflow are able to train enormous models with billions (or trillions) of parameters on a distributed infrastructure. These systems require that the internal nodes have the same memory capacity and compute performance. Unfortunately, most organizations, especially universities, have a piecemeal approach to purchasing computer systems resulting in a heterogeneous infrastructure, which cannot be used to compute large models. The present work describes HetSeq, a software package adapted from the popular PyTorch package that provides the capability to train large neural network models on heterogeneous infrastructure. Experiments with language translation, text and image classification shows that HetSeq scales over heterogeneous systems. Additional information, support documents, source code are publicly available at https://github.com/yifding/hetseq.
Empirical Best Practices On Using Product-Specific Schema.org
Authors: Kejriwal Mayank , Selvam Ravi Kiran , Ni Chien-Chun , Torzec Nicolas
Published: September 2022

Schema.org has experienced high growth in recent years. Structured descriptions of products embedded in HTML pages are now not uncommon, especially on e-commerce websites. The Web Data Commons (WDC) project has extracted schema.org data at scale from webpages in the Common Crawl and made it available as an RDF `knowledge graph' at scale. The portion of this data that specifically describes products offers a golden opportunity for researchers and small companies to leverage it for analytics and downstream applications. Yet, because of the broad and expansive scope of this data, it is not evident whether the data is usable in its raw form. In this paper, we do a detailed empirical study on the product-specific schema.org data made available by WDC. Rather than simple analysis, the goal of our study is to devise an empirically grounded set of best practices for using and consuming WDC product-specific schema.org data. Our studies reveal five best practices, each of which is justified by experimental data and analysis.
Visualizing NLP in Undergraduate Students' Learning about Natural Language
Authors: Alm Cecilia O. , Hedges Alex
Published: September 2022

We report on the use of open-source natural language processing capabilities in a web-based interface to allow undergraduate students to apply what they have learned about formal natural language structures. The learning activities encourage students to interpret data in new ways, think originally about natural language, and critique the back-end NLP models and algorithms visualized on the user front end. This work is of relevance to AI resources developed for education by focusing on inclusivity of students from many disciplinary backgrounds. Specifically, we comprehensively extended a web-based system with new resources. To test the students' reactions to NLP analyses that offer insights into both the strengths and limitations of AI systems, we incorporated a range of automated analyses focused on language-independent processing or meaning representations which still represent challenges for NLP. We conducted a survey-based evaluation with students in open-ended case-based assignments in undergraduate coursework. Responses indicated that the students reinforced their knowledge, applied critical thinking about language and NLP applications, and used the application not to solve the assignment for them, but as a tool in their own effort to address the task. We further discuss how using interpretable visualizations of system decisions is an opportunity to learn about ethical issues in NLP, and how making AI systems interpretable may broaden multidisciplinary interest in AI in early educational experiences.
Evaluating Gin Rummy Hands Using Opponent Modeling and Myopic Meld Distance
Authors: Goldman Phoebe , Knutson Corey R. , Mahtab Ryan , Maloney Jack , Mueller Joseph B. , Freedman Richard G.
Published: September 2022

Gin Rummy is a popular two-player card game involving choices to draw and discard cards to form sets of matching cards. Unlike other popular games such as Chess, Poker, and Go, there is little formal artificial intelligence research about how to make good decisions when playing Gin Rummy. In this paper, we develop an agent that plays Gin Rummy through a combination of known and expected card values, modeling the opponent to predict their cards of interest, and a conservative approach to assessing when to end the hand. In addition to discussing our observations about Gin Rummy that inspired our agent's design and how the agent works, we evaluate the relative importance of various features employed by our agent by competing agents which implement various subsets of those features.
Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems
Authors: Grenander Matt , Belfer Robert , Kochmar Ekaterina , Serban Iulian V. , St-Hilaire François , Cheung Jackie C. K.
Published: September 2022

We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect concepts in student answers in order to achieve better student learning gains. Although automatic methods for providing personalized feedback exist, they do not explicitly inform students about which concepts in their answers are correct or incorrect. Our approach involves decomposing students answers using neural discourse segmentation and classification techniques. This decomposition yields a relational graph over all discourse units covered by the reference solutions and student answers. We use this inferred relational graph structure and a neural classifier to match student answers with reference solutions and generate personalized feedback. Although the process is completely automated and data-driven, the personalized feedback generated is highly contextual, domain-aware and effectively targets each student's misconceptions and knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that our approach results in high-quality feedback and significantly improved student learning gains.
Knocking in the Game of Gin Rummy
Authors: Maravich Ryzeson C. , Neller Taylor C. , Neller Todd W.
Published: September 2022

We perform an empirical study of Gin Rummy knocking strategies, drawing insight from a population of AI players that vary in both discarding and knocking strategies. For our best performing player, simple linear regression yielded a knocking strategy that both affirmed the features expert players give attention to in making knock decisions, and yet called into question the way such features are conventionally used.
Educational Question Mining At Scale: Prediction, Analysis and Personalization
Authors: Wang Zichao , Tschiatschek Sebastian , Woodhead Simon , Hernández-Lobato José Miguel , Peyton Jones Simon , Baraniuk Richard G. , Zhang Cheng
Published: September 2022

Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students. With large volumes of available questions, it is important to have an automated way to quantify their properties and intelligently select them for students, enabling effective and personalized learning experiences. In this work, we propose a framework for mining insights from educational questions at scale. We utilize the state-of-the-art Bayesian deep learning method, in particular partial variational auto-encoders (p-VAE), to analyze real students' answers to a large collection of questions. Based on p-VAE, we propose two novel metrics that quantify question quality and difficulty, respectively, and a personalized strategy to adaptively select questions for students. We apply our proposed framework to a real-world dataset with tens of thousands of questions and tens of millions of answers from an online education platform. Our framework not only demonstrates promising results in terms of statistical metrics but also obtains highly consistent results with domain experts' evaluation.
A Computational Approach to Sign Language Understanding
Authors: Ananthanarayana Tejaswini
Published: September 2022

Sign language is the primary mode of communication in the Deaf and Hard-of-Hearing (DHH) communities. Unfortunately, sign language is not as well understood among the non-signing hearing population leading to limited access and services to the DHH community, and also acts as a barrier between non-signing and DHH people. In my Ph.D. thesis, I am working on improving the sequence modeling for sign language translation and understanding by considering different types of sequence models, various input features, and by understanding the semantic relation between the words and the signs. Currently, my research focuses on a popular publicly available German Sign Language dataset.
Effective Clustering of scRNA-seq Data to Identify Biomarkers without User Input
Authors: Chowdhury Hussain A.
Published: September 2022

Clustering unleashes the power of scRNA-seq through identification of appropriate cell groups. It is considered a pre-requisite to performing differential expression analysis, followed by functional profiling to identify potential biomarkers from scRNA-seq data. Most existing clustering methods either integrate cluster validity indices or need user assistance to identify clusters of arbitrary shape. We develop two clustering methods 1) UIFDBC to identify clusters of arbitrary shapes, 2) UIPBC to cluster scRNA-seq data. Neither method integrates a cluster validity index nor takes any user input. However, specialised approaches are used to benchmark the parameters. Both approaches outperform state-of-the-art methods.
Artificial Intelligence and Machine Learning for Autonomous Agents that Learn to Plan and Operate in Unpredictable Dynamic Environments
Authors: Lamanna Leonardo
Published: September 2022

My research activity focuses on the integration of acting, learning and planning. The main objective is to build a system that is capable to learn how to plan and act in an unknown, dynamic and complex environment. The only knowledge the agent has about the environment is provided by a set of sensor observations which returns continuous measures on the environment.
On the learning side, I’m interested in developing algorithms that allow an artificial agent to learn an abstract model of the dynamics of the environment (either an explicit model like a deterministic finite state machine or a model described in a language to express planning domains). The type of abstract model is specified by means of discrete state variables rather than continuous variables representing agent observations. In addition to learning the abstract model, I’m interested in learning probabilistic (generative) models that connects the abstract model with the perceptions of the agents. On the acting and planning side, the artificial agent does not rely on a prior set of execution traces, it rather decides online how to act by means of state-of-art planners. With its own experience, it enriches the planner knowledge, as well as the learned model of the environment. On the learning part, the agent applies techniques for dynamic probabilistic clustering of perceptions in a set of abstract states, neural network for learning transition models, and inductive reasoning for learning action model descriptions.
Notice that this is different from Reinforcement Learning where the focus is to learn a policy for achieving a goal, we are interested in learning an abstract model of the environment. We do not have a reward function that encodes the goal to be reached. Indeed in this work an agent does not necessarily need to reach a goal.
Robots that Help Humans Build Better Mental Models of Robots
Authors: Ramaraj Preeti
Published: September 2022

Interactive Task Learning (ITL) is an approach to teaching robots new tasks through language and demonstration. It relies on the fact that people have experience teaching each other. However, this can be challenging if the human instructor does not have an accurate mental model of a robot. This mental model consists of the robot’s knowledge, capabilities, shortcomings, goals, and intentions. The research question that I investigate is “How can the robot help the human build a better mental model of the robot?”
How Human Centered AI Will Contribute Towards Intelligent Gaming Systems
Authors: Zeng Yilei
Published: September 2022

A paradigm shift towards human-centered intelligent gaming systems is gradually setting in. Such intelligent gaming systems with embedded machine learning algorithms would explain player motivations, help design more personalized single and collaborative player experiences, transfer and generalize the learning from game to game. The multi-modal user behavior trajectories, both in-game and across various platforms, incorporate heterogeneous information and graph structures. These gaming modalities range from text, audios, video demos, activity replays, and social networks to psychological questionnaires. Identifying decision-making patterns and strategies by observing in-game behavior actions and mining heterogeneous sources could construct a more holistic representation of the gaming community. Human priors publicly available on the World Wide Web would inspire the modeling for human-like non-player characters, adaptive recommendation systems, automatic game design, testing, and human-AI collaborations. My doctoral research goal is to mine, represent, and learn from human priors existing in the interactive entertainment community's heterogeneous sources and introduce ways to model single and multi-agent interactive behavior patterns.
Clustering Partial Lexicographic Preference Trees (Student Abstract)
Authors: Allen Joseph , Liu Xudong , Umapathy Karthikeyan , Reddivari Sandeep
Published: September 2022

In this work, we consider distance-based clustering of partial lexicographic
preference trees (PLP-trees), intuitive and compact graphical representations
of user preferences over multi-valued attributes. To compute distances between
PLP-trees, we propose a polynomial time algorithm that computes Kendall's
Tau distance directly from the trees and show its efficacy compared to the
brute-force algorithm. To this end, we implement several clustering methods
(i.e., spectral clustering, affinity propagation, and agglomerative nesting)
augmented by our distance algorithm, experiment with clustering of up to 10,000
PLP-trees, and show the effectiveness of the clustering methods and visualizations
of their results.
Responsible Prediction Making of COVID-19 Mortality (Student Abstract)
Authors: Baniecki Hubert , Biecek Przemyslaw
Published: September 2022

For high-stakes prediction making, the Responsible Artificial Intelligence (RAI) is more important than ever. It builds upon Explainable Artificial Intelligence (XAI) to advance the efforts in providing fairness, model explainability, and accountability to the AI systems. During the literature review of COVID-19 related prognosis and diagnosis, we found out that most of the predictive models are not faithful to the RAI principles, which can lead to biassed results and wrong reasoning. To solve this problem, we show how novel XAI techniques boost transparency, reproducibility and quality of models.
Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement (Student Abstract)
Authors: Garg Prateek , Mandal Murari , Narang Pratik
Published: September 2022

Low light conditions in aerial images adversely affect the performance of several vision based applications. There is a need for methods that can efficiently remove the low light attributes and assist in the performance of key vision tasks. In this work, we propose a new method that is capable of enhancing the low light image in a self-supervised fashion, and sequentially apply detection and segmentation tasks in an end-to-end manner. The proposed method occupies a very small overhead in terms of memory and computational power over the original algorithm and delivers superior results. Additionally, we propose the generation of a new low light aerial dataset using GANs, which can be used to evaluate vision based networks for similar adverse conditions.
Global Fusion Attention for Vision and Language Understanding (Student Abstract)
Authors: Guo Zixin , Liang Chen , Wan Ziyu , Bai Yang
Published: September 2022

We extend the popular transformer architecture to a multi-modal model, processing both visual and textual inputs. We propose a new attention mechanism on Transformer-based architecture for the joint vision and language understanding tasks. Our model fuses multi-level comprehension between images and texts in a weighted manner, which could better curve the internal relationships. Experiments on benchmark VQA dataset CLEVR demonstrate the effectiveness of the proposed attention mechanism. We also observe the improvements in sample efficiency of reinforcement learning through the experiments on grounded language understanding tasks of BabyAI platform.
Comparing Symbolic Models of Language via Bayesian Inference (Student Abstract)
Authors: Heuser Annika , Tsvilodub Polina
Published: September 2022

Given recurring interest in structured representations in computational cognitive models, we extend a Bayesian scoring procedure for comparing symbolic models of language grammar. We conduct a case-study of modeling syntactic principles in German, providing preliminary results consistent with linguistic theory. We also note that dataset and part-of-speech (POS) tagger quality should not be taken for granted.
HetSAGE: Heterogenous Graph Neural Network for Relational Learning (Student Abstract)
Authors: Jankovics Vince , Garcia Ortiz Michael , Alonso Eduardo
Published: September 2022

This paper aims to bridge this gap between neuro-symbolic learning (NSL) and
graph neural networks (GNN) approaches and provide a comparative study. We
argue that the natural evolution of NSL leads to GNNs, while the logic
programming foundations of NSL can bring powerful tools to improve the way
information is represented and pre-processed for the GNN. In order to make
this comparison, we propose HetSAGE, a GNN architecture that can efficiently
deal with the resulting heterogeneous graphs that represent typical NSL
learning problems. We show that our approach
outperforms the state-of-the-art on 3 NSL tasks: CORA, MUTA188 and MovieLens.
Towards Sample Efficient Agents through Algorithmic Alignment (Student Abstract)
Authors: Li Mingxuan , Littman Michael L.
Published: September 2022

In this work, we propose and explore Deep Graph Value Network (DeepGV) as a promising method to work around sample complexity in deep reinforcement-learning agents using a message-passing mechanism. The main idea is that the agent should be guided by structured non-neural-network algorithms like dynamic programming. According to recent advances in algorithmic alignment, neural networks with structured computation procedures can be trained efficiently. We demonstrate the potential of graph neural network in supporting sample efficient learning by showing that Deep Graph Value Network can outperform unstructured baselines by a large margin in solving Markov Decision Process (MDP). We believe this would open up a new avenue for structured agents design. See https://github.com/drmeerkat/Deep-Graph-Value-Network for the code.
Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions (Student Abstract)
Authors: Martinez Jonathan
Published: September 2022

AI-systems that model and interact with their users can update their models over time to reflect new information and changes in the environment. Although these updates may improve the overall performance of the AI-system, they may actually hurt the performance with respect to individual users. Prior work has studied the tradeoff between improving the system's performance following an update and the compatibility of the updated system with prior user experience. The more the model is forced to be compatible with a prior version, the higher loss in performance it will incur. This paper shows that that by personalizing the loss function to specific users, it is possible to increase the prediction performance of the AI-system while sacrificing less compatibility for these users following an update to improve the system's performance. Our approach updates the sample weights to reflect their contribution to the compatibility of the model for a particular user following the update. We construct a portfolio of different models that vary in how they personalize the loss function for a target user and then select the best model to use based on a validation set. We use a model selection algorithm to choose the best model from the portfolio for each user given a set of features that reflect the users' characteristics and performance of the different models on a training set. We apply this approach to three supervised learning tasks commonly used in the human-computer decision-making literature. We show that using our approach leads to significant improvements in the performance-compatibility tradeoff over the non-personalized approach of Bansal et al., achieving up to 300% improvement for certain users.
Detection of Digital Manipulation in Facial Images (Student Abstract)
Authors: Mehra Aman , Agarwal Akshay , Vatsa Mayank , Singh Richa
Published: September 2022

Advances in deep learning have enabled the creation of photo-realistic DeepFakes by switching the identity or expression of individuals. Such technology in the wrong hands can seed chaos through blackmail, extortion, and forging false statements of influential individuals. This work proposes a novel approach to detect forged videos by magnifying their temporal inconsistencies. A study is also conducted to understand role of ethnicity bias due to skewed datasets on deepfake detection. A new dataset comprising forged videos of Indian ethnicity individuals is presented to facilitate this study.
Improving Label Noise Robustness with Data Augmentation and Semi-Supervised Learning (Student Abstract)
Authors: Nishi Kento , Ding Yi , Rich Alex , Höllerer Tobias
Published: September 2022

Modern machine learning algorithms typically require large amounts of labeled training data to fit a reliable model. To minimize the cost of data collection, researchers often employ techniques such as crowdsourcing and web scraping. However, web data and human annotations are known to exhibit high margins of error, resulting in sizable amounts of incorrect labels. Poorly labeled training data can cause models to overfit to the noise distribution, crippling performance in real-world applications. In this work, we investigate the viability of using data augmentation in conjunction with semi-supervised learning to improve the label noise robustness of image classification models. We conduct several experiments using noisy variants of the CIFAR-10 image classification dataset to benchmark our method against existing algorithms. Experimental results show that our augmentative SSL approach improves upon the state-of-the-art.
SSA2D: Single Shot Actor-Action Detection in Videos (Student Abstract)
Authors: Rana Aayush Jung , Rawat Yogesh S
Published: September 2022

We propose a single-shot approach for actor-action detection in videos. The existing approaches use a two-step process, which rely on Region Proposal Network (RPN), where the action is estimated based on the detected proposals followed by post-processing such as non-maximal suppression. While effective in terms of performance, these methods pose limitations in scalability for dense video scenes with a high memory requirement for thousand of proposals, which leads to slow processing time. We propose SSA2D, a unified end-to-end deep network, which performs joint actor-action detection in a single-shot without the need of any proposals and post-processing, making it memory as well as time efficient.
Remember More by Recalling Less: Investigating the Role of Batch Size in Continual Learning with Experience Replay (Student Abstract)
Authors: Wołczyk Maciej , Krutsylo Andrii
Published: September 2022

Experience replay is a simple and well-performing strategy for continual learning problems, often used as a basis for more advanced methods. However, the dynamics of experience replay are not yet well understood. To showcase this, we focus on a single component of this problem, namely choosing the batch size of the buffer samples. We find that small batches perform much better at stopping forgetting than larger batches, contrary to the intuitive assumption that it is better to recall more samples from the past to avoid forgetting. We show that this phenomenon does not disappear under learning rate tuning and we propose possible directions for further analysis.
Contextual Bandits with Delayed Feedback and Semi-supervised Learning (Student Abstract)
Authors: Yang Luting , Yang Jianyi , Ren Shaolei
Published: September 2022

Contextual multi-armed bandit (MAB) is a classic online learning problem, where a learner/agent selects actions (i.e., arms) given contextual information and discovers optimal actions based on reward feedback. Applications of contextual bandit have been increasingly expanding, including advertisement, personalization, resource allocation in wireless networks, among others. Nonetheless, the reward feedback is delayed in many applications (e.g., a user may only provide service ratings after a period of time), creating challenges for contextual bandits. In this paper, we address delayed feedback in contextual bandits by using semi-supervised learning — incorporate estimates of delayed rewards to improve the estimation of future rewards. Concretely, the reward feedback for an arm selected at the beginning of a round is only observed by the agent/learner with some observation noise and provided to the agent after some a priori unknown but bounded delays. Motivated by semi-supervised learning that produces pseudo labels for unlabeled data to further improve the model performance, we generate fictitious estimates of rewards that are delayed and have yet to arrive based on already-learnt reward functions. Thus, by combining semi-supervised learning with online contextual bandit learning, we propose a novel extension and design two algorithms, which estimate the values for currently unavailable reward feedbacks to minimize the maximum estimation error and average estimation error, respectively.
State-Wise Adaptive Discounting from Experience (SADE): A Novel Discounting Scheme for Reinforcement Learning (Student Abstract)
Authors: Zinzuvadiya Milan , Behzadan Vahid
Published: September 2022

In Markov Decision Process (MDP) models of sequential decision-making, it is common practice to account for temporal discounting by incorporating a constant discount factor. While the effectiveness of fixed-rate discounting in various Reinforcement Learning (RL) settings is well-established, the efficiency of this scheme has been questioned in recent studies. Another notable shortcoming of fixed-rate discounting stems from abstracting away the experiential information of the agent, which is shown to be a significant component of delay discounting in human cognition. To address this issue, we propose State-wise Adaptive Discounting from Experience (SADE) as a novel adaptive discounting scheme for RL agents. SADE leverages the experiential observations of state values in episodic trajectories to iteratively adjust state-specific discount rates. We report experimental evaluations of SADE in Q-learning agents, which demonstrate significant enhancement of sample complexity and convergence rate compared to fixed-rate discounting.
Evolving Spiking Circuit Motifs Using Weight Agnostic Neural Networks
Authors: Anwar Abrar
Published: September 2022

Neural architecture search (NAS) has emerged as an algorithmic method of developing neural network architectures. Weight Agnostic Neural Networks (WANNs) are an evolutionary-based NAS approach. Fundamentally, WANNs find network structures that are relatively insensitive to shifts in weight values and are typically much smaller than an equivalent performance dense network. Here, we extend the WANN framework to search for spiking circuits and in doing so investigate whether these circuit motifs can also yield task performance that is weight agnostic. We analyze properties such as the complexity of the solution, as well as performance. Our results successfully show the performance of spiking WANNs on several exemplar tasks.
Analyzing Games with a Variable Number of Players
Authors: Gatchel Madelyn
Published: September 2022

We introduce a novel technique that uses a multi-headed neural network to analyze symmetric games with a variable number of players, where the number of participants falls in a specified range. We hypothesize that the payoffs in a game with x players are similar or related to the same game with x + 1 players, given a large value of x. With this hypothesis, we generalize prior work to analyze games with a large, variable number of players.
Demonstration of the EMPATHIC Framework for Task Learning from Implicit Human Feedback
Authors: Cui Yuchen , Zhang Qiping , Jain Sahil , Allievi Alessandro , Stone Peter , Niekum Scott , Knox W. Bradley
Published: September 2022

Reactions such as gestures, facial expressions, and vocalizations are an abundant, naturally occurring channel of information that humans provide during interactions.
An agent could leverage an understanding of such implicit human feedback to improve its task performance at no cost to the human. This approach contrasts with common agent teaching methods based on demonstrations, critiques, or other guidance that need to be attentively and intentionally provided.
In this work, we demonstrate a novel data-driven framework for learning from implicit human feedback, EMPATHIC. This two-stage method consists of (1) mapping implicit human feedback to relevant task statistics such as reward, optimality, and advantage;
and (2) using such a mapping to learn a task.
We instantiate the first stage and three second-stage evaluations of the learned mapping. To do so, we collect a dataset of human facial reactions while participants observe an agent execute a sub-optimal policy for a prescribed training task.
We train a deep neural network on this data and demonstrate its ability to (1) infer relative reward ranking of events in the training task from prerecorded human facial reactions; (2) improve the policy of an agent in the training task using live human facial reactions; and (3) transfer to a novel domain in which it evaluates robot manipulation trajectories. In the video, we focus on demonstrating the online learning capability of our instantiation of EMPATHIC.
Juice: A Julia Package for Logic and Probabilistic Circuits
Authors: Dang Meihua , Khosravi Pasha , Liang Yitao , Vergari Antonio , Van den Broeck Guy
Published: September 2022

Juice is an open-source Julia package providing tools for logic and probabilistic reasoning and learning based on logic circuits (LCs) and probabilistic circuits (PCs). It provides a range of efficient algorithms for probabilistic inference queries, such as computing marginal probabilities (MAR), as well as many more advanced queries. Certain structural circuit properties are needed to achieve this tractability, which Juice helps validate. Additionally, it supports several parameter and structure learning algorithms proposed in the recent literature. By leveraging parallelism (on both CPU and GPU), Juice provides a fast implementation of circuit-based algorithms, which makes it suitable for tackling large-scale datasets and models.
DeepRobust: a Platform for Adversarial Attacks and Defenses
Authors: Li Yaxin , Jin Wei , Xu Han , Tang Jiliang
Published: September 2022

DeepRobust is a PyTorch platform for generating adversarial examples and building robust machine learning models for different data domains. Users can easily evaluate the attack performance against different defense methods with DeepRobust and get performance analyzing visualization. In this paper, we introduce the functions of DeepRobust with detailed instructions. We believe that DeepRobust is a useful tool to measure deep learning model robustness and to find the suitable countermeasures against adversarial attacks. The platform is kept updated and can be found at https://github.com/DSE-MSU/DeepRobust. More details of instruction can be found in the documentation at https://deeprobust.readthedocs.io/en/latest/.