Papers

Goal-oriented dialogue agents can comfortably utilize the conversational context and understand its users' goals. However, in visually driven user experiences, these conversational agents are also required to make sense of the screen context in order to provide a proper interactive experience. In this paper, we propose a novel multimodal conversational framework where the dialogue agent's next action and their arguments are derived jointly conditioned both on the conversational and the visual context. We demonstrate the proposed approach via a prototypical furniture shopping experience for a multimodal virtual assistant.

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We present PantheonRL, a multiagent reinforcement learning software package for dynamic training interactions such as round-robin, adaptive, and ad-hoc training. Our package is designed around flexible agent objects that can be easily configured to support different training interactions, and handles fully general multiagent environments with mixed rewards and n agents. Built on top of StableBaselines3, our package works directly with existing powerful deep RL algorithms. Finally, PantheonRL comes with an intuitive yet functional web user interface for configuring experiments and launching multiple asynchronous jobs. Our package can be found at https://github.com/Stanford-ILIAD/PantheonRL.

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In this paper, we propose on-device voice command assistants for mobile games to increase user experiences even in hands-busy situations such as driving and cooking. Since most of the current mobile games cost large memory (e.g. more than 1GB memory), so it is necessary to reduce memory usage further to integrate voice commands systems on mobile clients. Therefore a need to design an on-device automatic speech recognition system that costs minimal memory and CPU resources rises. To this end, we apply cross layer parameter sharing to Conformer, named MONICA2 which results in lower memory usage for on-device speech recognition. MONICA2 reduces the number of parameters of deep neural network by 58%, with minimal recognition accuracy degradation measured in word error rate on Librispeech benchmark. As an on-device voice command user interface, MONICA2 costs only 12.8MB mobile memory and the average inference time for 3-seconds voice command is about 30ms, which is profiled in Samsung Galaxy S9. As far as we know, MONICA2 is the most memory efficient yet accurate on-device speech recognition which could be applied to various applications such as mobile games, IoT devices, etc.

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Fashion is the form we express ourselves to the world and has grown into one of the largest industries in the world. Despite the significant evolvement of the fashion industry over the past decades, it is still a great challenge to respond to the diverse preferences of a large number of different consumers in time and accurately. To deal with the problem, we present an innovative demonstration of a trend-driven fashion design system using deep generative modeling, which enables automatic fashion design and editing based on trend reports. Our system consists of three components, including trend-driven fashion design, interactive fashion editing, and popularity estimation. The system offers a unified framework for the mass production of fashion designs that conform to the trend, which helps businesses better respond to market demands.

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This demonstration paper introduces the Smart Out-of-Home Advertising Platform (SOAP), which leverages Geographic Information Systems (GIS) data and state-of-the-art Artificial Intelligence (AI) approaches to provide: (i) a documented, data-informed pricing model for billboards, which can be used to justify billboard prices to advertisers; and (ii) a set of non-dominated solutions (each corresponding to a different allocation of billboards to a given campaign) that explores the trade-offs between multiple conflicting objectives (e.g., cost and coverage). To the best of our knowledge, SOAP is the first to tackle such challenges in the context of Multi Objective Optimization (MOO).

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Traditional Chinese Medicine (TCM) constitution is a fundamental concept in TCM theory. It is determined by multimodal TCM clinical features which, in turn, are obtained from TCM clinical information of image (face, tongue, etc.), audio (pulse and voice), and text (inquiry) modality. The auto assessment of TCM constitution is faced with two major challenges: (1) learning discriminative TCM clinical feature representations; (2) jointly processing the features using multimodal fusion techniques. The TCM Constitution Assessment System (TCM-CAS) is proposed to provide an end-to-end solution to this task, along with auxiliary functions to aid TCM researchers. To improve the results of TCM constitution prediction, the system combines multiple machine learning algorithms such as facial landmark detection, image segmentation, graph neural networks and multimodal fusion. Extensive experiments are conducted on a four-category multimodal TCM constitution dataset, and the proposed method achieves state-of-the-art accuracy. Provided with datasets containing annotations of diseases, the system can also perform automatic disease diagnosis from a TCM perspective.

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Reliable and faithful research is the cornerstone of breakthrough advancements and disruptive innovations. Assessing the credibility of scientific findings and claims in research publications has long been a time-consuming and challenging task for researchers and decision-makers. In this paper, we introduce RES - an intelligent system that assists humans in analyzing the credibility of scientific findings and claims in research publications in the field of social and behavioral sciences by estimating their replicability. The pipeline of RES consists of four major modules that perform feature extraction, replicability estimation, result explanation, and sentiment analysis respectively. Our evaluation based on human experts' assessments suggests that the RES has achieved adequate performance. The RES is also built with a Graphical User Interface (GUI) that is publicly accessible at https://tamu-infolab.github.io/RES/.

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In recent years, the automation of machine learning and data science (AutoML) has attracted significant attention. One under-explored dimension of AutoML is being able to automatically utilize domain knowledge (such as semantic concepts and relationships) located in historical code or literature from the problem's domain. In this paper, we demonstrate Semantic Feature Discovery, which enables users to interactively explore features semantically discovered from existing data science code and external knowledge. It does so by detecting semantic concepts for a given dataset, and then using these concepts to determine relevant feature engineering operations from historical code and knowledge.

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Pre-trained multilingual language models are gaining popularity due to their cross-lingual zero-shot transfer ability, but these models do not perform equally well in all languages. Evaluating task-specific performance of a model in a large number of languages is often a challenge due to lack of labeled data, as is targeting improvements in low performing languages through few-shot learning. We present a tool - LITMUS Predictor - that can make reliable performance projections for a fine-tuned task-specific model in a set of languages without test and training data, and help strategize data labeling efforts to optimize performance and fairness objectives.

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This paper tries to resolve long waiting time to find a matching person in player versus player mode of online sports games, such as baseball, soccer and basketball. In player versus player mode, game playing AI which is instead of player needs to be not just smart as human but also show variety to improve user experience against AI. Therefore a need to design game playing AI agents with diverse personalized styles rises. To this end, we propose a personalized game AI which encodes user style vectors and card style vectors with a general DNN, named UCSM-DNN. Extensive experiments show that UCSM-DNN shows improved performance in terms of personalized styles, which enrich user experiences. UCSM-DNN has already been integrated into popular mobile baseball game: MaguMagu 2021 as personalized game AI.

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As an independent social and economic entity, game servers plays a dominant role in building a stable, living, and attractive virtual world in massive multi-player online role-playing games (MMORPGs). We propose and implement a novel intelligent decision support system for server merge (SM) for maintaining the game ecology at the macro level. The services provided by this system include server health diagnosis, server merge assessment, and combination strategy recommendation. Specifically, we design an effective time series prediction algorithm to diagnose the health status of one server (e.g., user activity, online time, daily revenue) based on real game scenarios, and then select the servers with poor status from all servers. Moreover, to dig out the inherent development laws of servers from the historical merge records, we leverage a correlation measurement algorithm to find the historical merged servers that are similar to the servers to be merged and then evaluate the potential trend after merging, which can assist experts to make reasonable decisions. We deploy our system into practice for multiple MMORPGs and achieve sound online performance endorsed by the game operation team.

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Lexical Semantic Change (LSC) detection, also known as Semantic Shift, is the process of identifying and characterizing variations in language usage across different scenarios such as time and domain. It allows us to track the evolution of word senses, as well as to understand the difference between the language used in distinct communities. LSC detection is often done by applying a distance measure over vectors of two aligned word embedding matrices. In this demonstration, we present SenSE, an interactive semantic shift exploration toolkit that provides visualization and explanation of lexical semantic change for an input pair of text sources. Our system focuses on showing how the different alignment strategies may affect the output of an LSC model as well as on explaining semantic change based on the neighbors of a chosen target word, while also extracting examples of sentences where these semantic deviations appear. The system runs as a web application (available at http://sense.mgruppi.me), allowing the audience to interact by configuring the alignment strategies while visualizing the results in a web browser.

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In this work, we present SenTag, a lightweight web-based tool focused on semantic annotation of textual documents. The platform allows multiple users to work on a corpus of documents. The tool enables to tag a corpus of documents through an intuitive and easy-to-use user interface that adopts the Extensible Markup Language (XML) as output format. The main goal of the application is two-fold: facilitating the tagging process and reducing or avoiding errors in the output documents. It allows also to identify arguments and other entities that are used to build an arguments graph. It is also possible to assess the level of agreement of annotators working on a corpus of text.

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Web applications and services are increasingly important in a distributed internet filled with diverse cloud services and applications, each of which enable the completion of narrowly defined tasks. Given the explosion in the scale and diversity of such services, their composition and integration for achieving complex user goals remains a challenging task for end-users and requires a lot of development effort when specified by hand. We present a demonstration of the Goal Oriented Flow Assistant (GOFA) system, which provides a natural language solution to generate workflows for application integration. Our tool is built on a three-step pipeline: it first uses Abstract Meaning Representation (AMR) to parse utterances; it then uses a knowledge graph to validate candidates; and finally uses an AI planner to compose the candidate flow. We provide a video demonstration of the deployed system as part of our submission.

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This demo paper presents a design and implementation of a system AnomalyKiTS for detecting anomalies from time series data for the purpose of offering a broad range of algorithms to the end user, with special focus on unsupervised/semi-supervised learning. Given an input time series, AnomalyKiTS provides four categories of model building capabilities followed by an enrichment module that helps to label anomaly. AnomalyKiTS also supports a wide range of execution engines to meet the diverse need of anomaly workloads such as Serveless for CPU intensive work, GPU for deep-learning model training, etc.

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Deep learning excels at learning task information from large amounts of data, but struggles with learning from declarative high-level knowledge that can be more succinctly expressed directly. In this work, we introduce PYLON, a neuro-symbolic training framework that builds on PyTorch to augment procedurally trained models with declaratively specified knowledge. PYLON lets users programmatically specify constraints as Python functions and compiles them into a differentiable loss, thus training predictive models that fit the data whilst satisfying the specified constraints. PYLON includes both exact as well as approximate compilers to efficiently compute the loss, employing fuzzy logic, sampling methods, and circuits, ensuring scalability even to complex models and constraints. Crucially, a guiding principle in designing PYLON is the ease with which any existing deep learning codebase can be extended to learn from constraints in a few lines code: a function that expresses the constraint, and a single line to compile it into a loss. Our demo comprises of models in NLP, computer vision, logical games, and knowledge graphs that can be interactively trained using constraints as supervision.

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Evaluating interactive AI (IAI) systems is a challenging task, as their output highly depends on the performed user actions. As a result, developers often depend on limited and mostly qualitative data derived from user testing to improve their systems. In this paper, we present InteractEva; a systematic evaluation framework for IAI systems. InteractEva employs (a) a user simulation backend to test the system against different use cases and user interactions at scale with (b) an interactive frontend allowing developers to perform important quantitative evaluation tasks, including acquiring a performance overview, performing error analysis, and conducting what-if studies. The framework has supported the evaluation and improvement of an industrial IAI text extraction system, results of which will be presented during our demonstration.

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Modern artificial intelligence (AI) methods have been used to solve problems that many humans struggle to solve. This opens up new opportunities for knowledge discovery and education. We demonstrate ALLURE, an educational AI system for learning to solve the Rubik’s cube that is designed to help students improve their problem solving skills. ALLURE can both find and explain its own strategies for solving the Rubik’s cube as well as build on user-provided strategies. Collaboration between AI and user happens using visual and natural language modalities.

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While Math Word Problem (MWP) solving has emerged as a popular field of study and made great progress in recent years, most existing methods are benchmarked solely on one or two datasets and implemented with different configurations. In this paper, we introduce the first open-source library for solving MWPs called MWPToolkit, which provides a unified, comprehensive, and extensible framework for the research purpose. Specifically, we deploy 17 deep learning-based MWP solvers and 6 MWP datasets in our toolkit. These MWP solvers are advanced models for MWP solving, covering the categories of Seq2seq, Seq2Tree, Graph2Tree, and Pre-trained Language Models. And these MWP datasets are popular datasets that are commonly used as benchmarks in existing work. Our toolkit is featured with highly modularized and reusable components, which can help researchers quickly get started and develop their own models. We have released the code and documentation of MWPToolkit in https://github.com/LYH-YF/MWPToolkit.

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Sound event detection aims to identify the sound events in the audio recordings, whose applications seem to be evident in our daily life, such as the surveillance and monitoring applications. In this paper, we present a novel framework for the detection task, by combining using several improvements. To compress the model efficiently while retaining the detection accuracy, the self-distillation paradigm is employed to improve offline training. To empower the machines with the ability of uncertainty estimation, the Monte Carlo dropout is used in our framework. Moreover, the inference data augmentation strategy is utilized to improve the robustness of the detection task. Lastly, we present an interactive interface, which can be used to visualize the detection and the uncertainty for the prediction. We hope our tool can be helpful for practical machine listening.

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This paper presents RealTIS, a Real-time emergent Topic Identification System for user-generated content on the web via social networking services such as Twitter, Weibo, and Facebook. Without user intervention, our proposed RealTIS system can efficiently collect necessary social media posts, construct a quality topic summarization from the vast sea of data, and then automatically identify whether the emerging topics will be out-breaking or just fading into silence. RealTIS uses a time-sliding window to compute the statistics about the basic structure (motifs) variation of the propagation network for a specific topic. These statistics are then used to predict unusual shifts in correlations, make early warning and detect outbreak. Besides, this work also illustrates the mechanism by which our proposed system makes early warning happen.

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Cloud Causality Analyzer (CCA) is an ML-based analytical pipeline to automate the tedious process of Root Cause Analysis (RCA) of Cloud IT events. The 3-stage pipeline is composed of 9 functional modules, including dimensionality reduction (feature engineering, selection and compression), embedded anomaly detection, and an ensemble of 3 custom explainability and causality models for Cloud Key Performance Indicators (KPI). Our challenge is: How to apply a reduced (sub)set of judiciously selected KPIs to detect Cloud performance anomalies, and their respective root causal culprits, all without compromising accuracy?

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Amid data privacy concerns, Federated Learning (FL) has emerged as a promising machine learning paradigm that enables privacy-preserving collaborative model training. However, there exists a need for a platform that matches data owners (supply) with model requesters (demand). In this paper, we present CrowdFL, a platform to facilitate the crowdsourcing of FL model training. It coordinates client selection, model training, and reputation management, which are essential steps for the FL crowdsourcing operations. By implementing model training on actual mobile devices, we demonstrate that the platform improves model performance and training efficiency. To the best of our knowledge, it is the first platform to support crowdsourcing-based FL on edge devices.

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Every year in the US, millions of animals are run over by vehicles making wildlife vehicle collisions a real danger to both animals and human. In addition, road networks be-come abiotic barriers to wildlife migration between regions creating ripple effects on ecosystems. In this paper, a smart wildlife warning sign system (SWWS) is demonstrated, utilizing the technologies of Internet of Things, image recognition, data processing and visualization. This smart sign system is intended to prevent roadkill by warning drivers to slow down once sensors are triggered and simultaneously capture animal images via infrared cam-era. Data collection is conducted through local neural network model identification of wildlife images and saved along with metadata based on animal activity occurrence. Wildlife activity data can be exported wirelessly to cloud database to assist ecologists and government road agencies to investigate and analyze the wildlife activity and migration patterns over time.

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The conversational recommender systems (CRSs) have received extensive attention in recent years. However, most of the existing works focus on various deep learning models, which are largely limited by the requirement of large-scale human-annotated datasets. Such methods are not able to deal with the cold-start scenarios in industrial products. To alleviate the problem, we propose FORCE, a Framework Of Rule-based Conversational rEcommender system that helps developers to quickly build CRS bots by simple configuration. We conduct experiments on two datasets in different languages and domains to verify its effectiveness and usability.

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Posters are broadly used to present the important points of academic papers and can be seen as a special form of document summarization. However, the problem of automatic poster generation is under-investigated. In this paper, we present PosterBot, an automatic poster generation system for academic papers. Given a scholarly paper, PosterBot takes three steps to generate the poster. It first selects the most important sections, and then generates corresponding panels from them. Finally, all panels are integrated to get the complete poster. The demonstration shows the efficacy of our proposed system.

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We present a demonstration of the interactive task learning agent Rosie, where it learns the task of patrolling a simulated barracks environment through situated natural language instruction. In doing so, it builds a sizable task hierarchy composed of both innate and learned tasks, tasks formulated as achieving a goal or following a procedure, tasks with conditional branches and loops, and involving communicative and mental actions. Rosie is implemented in the Soar cognitive architecture, and represents tasks using a declarative task network which it compiles into procedural rules through chunking. This is key to allowing it to learn from a single training episode and generalize quickly.

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We demonstrate an AI assisted data labeling system which applies unsupervised and semi-supervised machine learning to facilitate accurate and efficient labeling of large data sets. Our system (1) applies representative data sampling and active learning in order to seed and maintain a semi-supervised learner that assists the human labeler (2) provides visual labeling assistance and optimizes labeling mechanics using predicted labels (3) seamlessly updates and learns from ongoing human labeling activity (4) captures and presents metrics that indicate the quality of labeling assistance, and (5) provides an interactive auto labeling interface to group, review and apply predicted labels in a scalable manner.

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Explainably estimating confidence in published scholarly work offers opportunity for faster and more robust scientific progress. We develop a synthetic prediction market to assess the credibility of published claims in the social and behavioral sciences literature. We demonstrate our system and detail our findings using a collection of known replication projects. We suggest that this work lays the foundation for a research agenda that creatively uses AI for peer review.

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As countries enter the endemic phase of COVID-19, people's risk of exposure to the virus is greater than ever. There is a need to make more informed decisions in our daily lives on avoiding crowded places. Crowd monitoring systems typically require costly infrastructure. We propose a crowd-sourced crowd monitoring platform which leverages user inputs to generate crowd counts and forecast location crowdedness. A key challenge for crowd-sourcing is a lack of incentive for users to contribute. We propose a Reinforcement Learning based dynamic incentive mechanism to optimally allocate rewards to encourage user participation.

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