GenAI4ELab/papercli-papers huggingface dataset:
MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways
Authors: Mingyu Derek Ma; Alexander K. Taylor; Nuan Wen; Yanchen Liu; Po-Nien Kung; Wenna Qin; Shicheng Wen; Azure Zhou; Diyi Yang; Xuezhe Ma; Nanyun Peng; Wei Wang
Venue: AAAI 2024 (main)

Abstract: We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by comprehensive insights including user/community susceptibility level, as well as events and popular opinions raised by the crowd while propagating the information. Besides discovering information flow patterns among users, we construct communities among users and develop the propagation forecasting capability, enabling tracing and understanding of how information is disseminated at a higher level. A demo video and more are available at https://info-pathways.github.io.
ESG Accountability Made Easy: DocQA at Your Service
Authors: Lokesh Mishra; Cesar Berrospi; Kasper Dinkla; Diego Antognini; Francesco Fusco; Benedikt Bothur; Maksym Lysak; Nikolaos Livathinos; Ahmed Nassar; Panagiotis Vagenas; Lucas Morin; Christoph Auer; Michele Dolfi; Peter Staar
Venue: AAAI 2024 (main)

Abstract: We present Deep Search DocQA. This application enables information extraction from documents via a question-answering conversational assistant. The system integrates several technologies from different AI disciplines consisting of document conversion to machine-readable format (via computer vision), finding relevant data (via natural language processing), and formulating an eloquent response (via large language models). Users can explore over 10,000 Environmental, Social, and Governance (ESG) disclosure reports from over 2000 corporations. The Deep Search platform can be accessed at: https://ds4sd.github.io.
CHICOT: A Developer-Assistance Toolkit for Code Search with High-Level Contextual Information
Authors: Terufumi Morishita; Yuta Koreeda; Atsuki Yamaguchi; Gaku Morio; Osamu Imaichi; Yasuhiro Sogawa
Venue: AAAI 2024 (main)

Abstract: We propose a source code search system named CHICOT (Code search with HIgh level COnText) to assist developers in reusing existing code.
While previous studies have examined code search on the basis of code-level, fine-grained specifications such as functionality, logic, or implementation, CHICOT addresses a unique mission: code search with high-level contextual information, such as the purpose or domain of a developer's project.
It achieves this feature by first extracting the context information from codebases and then considering this context during the search.
It provides a VSCode plugin for daily coding assistance, and the built-in crawler ensures up-to-date code suggestions.
The case study attests to the utility of CHICOT in real-world scenarios.
Expressive and Flexible Simulation of Information Spread Strategies in Social Networks Using Planning
Authors: Bharath Muppasani; Vignesh Narayanan; Biplav Srivastava; Michael N. Huhns
Venue: AAAI 2024 (main)

Abstract: In the digital age, understanding the dynamics of information spread and opinion formation within networks is paramount. This research introduces an innovative framework that combines the principles of opinion dynamics with the strategic capabilities of Automated Planning. We have developed, to the best of our knowledge, the first-ever numeric PDDL tailored for opinion dynamics. Our tool empowers users to visualize intricate networks, simulate the evolution of opinions, and strategically influence that evolution to achieve specific outcomes. By harnessing Automated Planning techniques, our framework offers a nuanced approach to devise sequences of actions tailored to transition a network from its current opinion landscape to a desired state. This holistic approach provides insights into the intricate interplay of individual nodes within a network and paves the way for targeted interventions. Furthermore, the tool facilitates human-AI collaboration, enabling users to not only understand information spread but also devise practical strategies to mitigate potential harmful outcomes arising from it. Demo Video link - https://tinyurl.com/3k7bp99h
GEAR-Up: Generative AI and External Knowledge-Based Retrieval: Upgrading Scholarly Article Searches for Systematic Reviews
Authors: Kaushik Roy; Vedant Khandelwal; Valerie Vera; Harshul Surana; Heather Heckman; Amit Sheth
Venue: AAAI 2024 (main)

Abstract: This paper addresses the time-intensive nature of systematic reviews (SRs) and proposes a solution leveraging advancements in Generative AI (e.g., ChatGPT) and external knowledge augmentation (e.g., Retrieval-Augmented Generation). The proposed system, GEAR-Up, automates query development and translation in SRs, enhancing efficiency by enriching user queries with context from language models and knowledge graphs. Collaborating with librarians, qualitative evaluations demonstrate improved reproducibility and search strategy quality. Access the demo at https://youtu.be/zMdP56GJ9mU.
SciSpace Copilot: Empowering Researchers through Intelligent Reading Assistance
Authors: Trinita Roy; Asheesh Kumar; Daksh Raghuvanshi; Siddhant Jain; Goutham Vignesh; Kartik Shinde; Rohan Tondulkar
Venue: AAAI 2024 (main)

Abstract: We introduce SciSpace Copilot, an AI research assistant that helps in understanding and reading research papers faster by providing a plethora of features. Answering questions from a document has recently become popular using the Retrieval Augmented Generation (RAG) approach. Our tool uses an advanced question-answering pipeline to get accurate answers and also provide exact citations for the same. We provide many more valuable features on scientific text, including generating explanations, generating summaries, adding notes and highlights, and finding related papers from our 200 million corpus. Our tool supports 100+ languages, making research more accessible across language barriers. Thousands of users use SciSpace Copilot on a daily basis by uploading their articles to understand research faster and better. Our tool can be accessed at this link: https://typeset.io.
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning
Authors: Soumyendu Sarkar; Ashwin Ramesh Babu; Sajad Mousavi; Vineet Gundecha; Avisek Naug; Sahand Ghorbanpour
Venue: AAAI 2024 (main)

Abstract: We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classification (2D), and video classification (3D). The framework focuses on identifying sensitive regions and inducing misclassifications with minimal distortions and various distortion types. The novel RL method outperforms state-of-the-art methods for all three applications, proving its efficiency. Our RL approach produces superior localization masks, enhancing interpretability for image classification and ECG analysis models. For applications such as ECG analysis, our platform highlights critical ECG segments for clinicians while ensuring resilience against prevalent distortions. This comprehensive tool aims to bolster both resilience with adversarial training and transparency across varied applications and data types.
Sustainability of Data Center Digital Twins with Reinforcement Learning
Authors: Soumyendu Sarkar; Avisek Naug; Antonio Guillen; Ricardo Luna; Vineet Gundecha; Ashwin Ramesh Babu; Sajad Mousavi
Venue: AAAI 2024 (main)

Abstract: The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address this issue and reduce carbon emissions, intelligent design and control of DC components such as IT servers, cabinets, HVAC cooling, flexible load shifting, and battery energy storage are essential. However, the complexity of designing and controlling them in tandem presents a significant challenge. While some individual components like CFD-based design and Reinforcement Learning (RL) based HVAC control have been researched, there's a gap in the holistic design and optimization covering all elements simultaneously. To tackle this, we've developed DCRL-Green, a multi-agent RL environment that empowers the ML community to design data centers and research, develop, and refine RL controllers for carbon footprint reduction in DCs. It is a flexible, modular, scalable, and configurable platform that can handle large High Performance Computing (HPC) clusters. Furthermore, in its default setup, DCRL-Green provides a benchmark for evaluating single as well as multi-agent RL algorithms. It easily allows users to subclass the default implementations and design their own control approaches, encouraging community development for sustainable data centers. Open Source Link: https://github.com/HewlettPackard/dc-rl
EmFORE: Learning Email Folder Classification Rules by Demonstration
Authors: Mukul Singh; Gust Verbruggen; José Cambronero; Vu Le; Sumit Gulwani
Venue: AAAI 2024 (main)

Abstract: Tools that help with email folder management are limited, as users have to manually write rules to assign emails to folders. We present EMFORE, an iterative learning system that automatically learns and updates such rules from observations. EMFORE is fast enough to suggest and update rules in real time and suppresses mails with low confidence to reduce the number of false positives. EMFORE can use different rule grammars, and thus be adapted to different clients, without changing the user experience. Previous methods do not learn rules, require complete retraining or multiple new examples after making a mistake, and do not distinguish between inbox and other folders. EMFORE learns rules incrementally and can make the neutral decision of leaving emails in the inbox, making it an ideal candidate for integration in email clients.
Interactive Human-Centric Bias Mitigation
Authors: Inge Vejsbjerg; Elizabeth M. Daly; Rahul Nair; Svetoslav Nizhnichenkov
Venue: AAAI 2024 (main)

Abstract: Bias mitigation algorithms differ in their definition of bias and how they go about achieving that objective. Bias mitigation algorithms impact different cohorts differently and allowing end users and data scientists to understand the impact of these differences in order to make informed choices is a relatively unexplored domain. This demonstration presents an interactive bias mitigation pipeline that allows users to understand the cohorts impacted by their algorithm choice and provide feedback in order to provide a bias mitigated pipeline that most aligns with their goals.
Visual Language – Let the Product Say What You Want
Authors: Jiaying Wang; Shuailing Hao; Jing Shan; Xiaoxu Song
Venue: AAAI 2024 (main)

Abstract: Visual Language is a multitasking on-line system focusing on e-commerce, which involves in generating accurate product descriptions for sellers and providing convenient product retrieval service for customers. To achieve this goal, the system adopts image description technology and multi-modal retrieval technology. 
By utilizing cross-modal generation technique, we could help sellers on rapid uploading products and customers on rapid retrieval, which could improve the experience of both sellers and customers.
The CoachAI Badminton Environment: Bridging the Gap between a Reinforcement Learning Environment and Real-World Badminton Games
Authors: Kuang-Da Wang; Yu-Tse Chen; Yu-Heng Lin; Wei-Yao Wang; Wen-Chih Peng
Venue: AAAI 2024 (main)

Abstract: We present the CoachAI Badminton Environment, a reinforcement learning (RL) environment tailored for AI-driven sports analytics. In contrast to traditional environments using rule-based opponents or simplistic physics-based randomness, our environment integrates authentic opponent AIs and realistic randomness derived from real-world matches data to bridge the performance gap encountered in real-game deployments. This novel feature enables RL agents to seamlessly adapt to genuine scenarios. The CoachAI Badminton Environment empowers researchers to validate strategies in intricate real-world settings, offering: i) Realistic opponent simulation for RL training; ii) Visualizations for evaluation; and iii) Performance benchmarks for assessing agent capabilities. By bridging the RL environment with actual badminton games, our environment is able to advance the discovery of winning strategies for players. Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/Strategic%20Environment.
Virtual Try-On: Real-Time Interactive Hybrid Network with High-Fidelity
Authors: Umer Waqas; Yunwan Jeon; Donghun Lee
Venue: AAAI 2024 (main)

Abstract: A significant upsurge in the fashion e-commerce industry in recent years has brought considerable attention to image-based virtual fitting. This image-based technology allows users to try on clothes virtually without physically touching them. However, the current techniques have notable limitations in terms of real-world scenarios, noisy results, partial clothing categories and computational cost, thus limiting the real-world applications. To address these critical limitations, we propose a hybrid interactive network that allows actual users to interact with the system to try on clothes virtually. The network is composed of state of art keypoint extraction, appearance flow alteration and wrapping modules. The pro-posed network facilitates real-time application with high-quality noise-free results, a variety of clothing categories and efficient computational cost.
Generation of Visual Representations for Multi-Modal Mathematical Knowledge
Authors: Lianlong Wu; Seewon Choi; Daniel Raggi; Aaron Stockdill; Grecia Garcia Garcia; Fiorenzo Colarusso; Peter C.H. Cheng; Mateja Jamnik
Venue: AAAI 2024 (main)

Abstract: In this paper we introduce MaRE, a tool designed to generate representations in multiple modalities for a given mathematical problem while ensuring the correctness and interpretability of the transformations between different representations. The theoretical foundation for this tool is Representational Systems Theory (RST), a mathematical framework for studying the structure and transformations of representations. In MaRE’s web front-end user interface, a set of probability equations in Bayesian Notation can be rigorously transformed into Area Diagrams, Contingency Tables, and Probability Trees with just one click, utilising a back-end engine based on RST. A table of cognitive costs, based on the cognitive Representational Interpretive Structure Theory (RIST), that a representation places on a particular profile of user is produced at the same time. MaRE is general and domain independent, applicable to other representations encoded in RST. It may enhance mathematical education and research, facilitating multi-modal knowledge representation and discovery.
EasyTS: The Express Lane to Long Time Series Forecasting
Authors: Tiancheng Zhang; Shaoyuan Huang; Cheng Zhang; Xiaofei Wang; Wenyu Wang
Venue: AAAI 2024 (main)

Abstract: Responding to the escalating interest in long-term forecasting within the industry, we introduce EasyTS, a comprehensive toolkit engineered to streamline data collection, analysis, and model creation procedures. EasyTS acts as a unified solution, driving progress in long-term time series forecasting. The platform provides effortless access to various time series datasets, including a newly open-sourced multi-scenario dataset in the electricity domain. Integrated visualization and analysis tools help unveil inherent data features and relationships. EasyTS facilitates a user-friendly model validation approach with versatile evaluation criteria. This toolkit allows researchers to compare their models proficiently against renowned benchmarks. With our ongoing commitment to expanding our dataset collection and enhancing toolkit functionalities, we aspire to contribute significantly to the time series forecasting domain. Code is available at this repository: https://github.com/EdgeBigBang/EasyTS.git.
RecWizard: A Toolkit for Conversational Recommendation with Modular, Portable Models and Interactive User Interface
Authors: Zeyuan Zhang; Tanmay Laud; Zihang He; Xiaojie Chen; Xinshuang Liu; Zhouhang Xie; Julian McAuley; Zhankui He
Venue: AAAI 2024 (main)

Abstract: We present a new Python toolkit called RecWizard for Conversational Recommender Systems (CRS). RecWizard offers support for development of models and interactive user interface, drawing from the best practices of the Huggingface ecosystems. CRS with RecWizard are modular, portable, interactive and Large Language Models (LLMs)-friendly, to streamline the learning process and reduce the additional effort for CRS research. For more comprehensive information about RecWizard, please check our GitHub https://github.com/McAuley-Lab/RecWizard.
NarrativePlay: An Automated System for Crafting Visual Worlds in Novels for Role-Playing
Authors: Runcong Zhao; Wenjia Zhang; Jiazheng Li; Lixing Zhu; Yanran Li; Yulan He; Lin Gui
Venue: AAAI 2024 (main)

Abstract: In this demo, we present NarrativePlay -- an innovative system enabling users to role-play a fictional character and interact with dynamically generated narrative environments. Unlike existing predefined sandbox approaches, NarrativePlay centres around the main storyline events extracted from the narrative, allowing users to experience the story from the perspective of a character they chose. To design versatile AI agents for diverse scenarios, we employ a framework built on a Large Language Models (LLMs) to extract detailed character traits from text. We also incorporate automatically generated visual displays of narrative settings, character portraits, and character speech, greatly enhancing the overall user experience.
Real-Time Calibration Model for Low-Cost Sensor in Fine-Grained Time Series
Authors: Seokho Ahn; Hyungjin Kim; Sungbok Shin; Young-Duk Seo
Venue: AAAI 2025 (main)

Abstract: Precise measurements from sensors are crucial, but data is usually collected from low-cost, low-tech systems, which are often inaccurate. Thus, they require further calibrations. To that end, we first identify three requirements for effective calibration under practical low-tech sensor conditions. Based on the requirements, we develop a model called TESLA, Transformer for effective sensor calibration utilizing logarithmic-binned attention. TESLA uses a high-performance deep learning model, Transformers, to calibrate and capture non-linear components. At its core, it employs logarithmic binning, to minimize attention complexity. TESLA achieves consistent real-time calibration, even with longer sequences and finer-grained time series in hardware-constrained systems. Experiments show that TESLA outperforms existing novel deep learning and newly crafted linear models in accuracy, calibration speed, and energy efficiency.
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale
Authors: Randy Ardywibowo; Rakesh Sunki; Shin Tsz Lucy Kuo; Sankalp Nayak
Venue: AAAI 2025 (main)

Abstract: Information Retrieval (IR) systems used in search and recommendation platforms frequently employ Learning-to-Rank (LTR) models to rank items in response to user queries. These models heavily rely on features derived from user interactions, such as clicks and engagement data. This dependence introduces cold start issues for items lacking user engagement and poses challenges in adapting to non-stationary shifts in user behavior over time. We address both challenges holistically as an online learning problem and propose BayesCNS, a Bayesian approach designed to handle cold start and non-stationary distribution shifts in search systems at scale. BayesCNS achieves this by estimating prior distributions for user-item interactions, which are continuously updated with new user interactions gathered online. This online learning procedure is guided by a ranker model, enabling efficient exploration of relevant items using contextual information provided by the ranker. We successfully deployed BayesCNS in a large-scale search system and demonstrated its efficacy through comprehensive offline and online experiments. Notably, an online A/B experiment showed a 10.60% increase in new item interactions and a 1.05% improvement in overall success metrics over the existing production baseline.
Scalable Surrogate Verification of Image-Based Neural Network Control Systems Using Composition and Unrolling
Authors: Feiyang Cai; Chuchu Fan; Stanley Bak
Venue: AAAI 2025 (main)

Abstract: Verifying safety of neural network control systems that use images as input is a difficult problem because, from a given system state, there is no known way to mathematically model what images are possible in the real-world. We build upon recent work that considers a surrogate verification approach,  training a conditional generative adversarial network (cGAN) as an image generator in place of the real world. This setup enables set-based formal analysis of the closed-loop system, providing analysis beyond simulation and testing. While existing work is effective on small examples, excessive overapproximation both within a single control period (one-step error) and across multiple periods (multi-step error) limits its scalability. We propose approaches to overcome these errors. First, we address one-step error by composing the system's dynamics along with the cGAN and neural network controller, without losing the dependencies between input states and the control outputs as in the monotonic analysis of the system dynamics. Second, we reduce multi-step error by repeating the single-step composition, essentially unrolling multiple steps of the control loop into a large neural network. We then leverage existing network verification algorithms to compute accurate reachable sets for multiple steps, avoiding the accumulation of abstraction error at each step.We demonstrate the effectiveness of our approach in terms of both accuracy and scalability using two case studies. On the aircraft taxiing system, the converged reachable set is  175% larger using the prior baseline method compared with our proposed approach. On the emergency braking system, with 24x the number of image output variables from the cGAN, the baseline method fails to prove any states are safe, whereas our improvements enable set-based safety analysis.
External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection
Authors: Biwei Cao; Qihang Wu; Jiuxin Cao; Bo Liu; Jie Gui
Venue: AAAI 2025 (main)

Abstract: With the rapid development of the Internet, the information dissemination paradigm has changed and the efficiency has been improved greatly. While this also brings the quick spread of fake news and leads to negative impacts on cyberspace. Currently, the information presentation formats have evolved gradually, with the news formats shifting from texts to multimodal contents. As a result, detecting multimodal fake news has become one of the research hotspots. However, multimodal fake news detection research field still faces two main challenges: the inability to fully and effectively utilize multimodal information for detection, and the low credibility or static nature of the introduced external information, which limits dynamic updates. To bridge the gaps, we propose ERIC-FND, an external reliable information-enhanced multimodal contrastive learning framework for fake news detection. ERIC-FND strengthens the representation of news contents by entity-enriched external information enhancement method. It also enriches the multimodal news information via multimodal semantic interaction method where the multimodal constrative learning is employed to make different modality representations learn from each other. Moreover, an adaptive fusion method is taken to integrate the news representations from different dimensions for the eventual classification. Experiments are done on two commonly used datasets in different languages, X (Twitter) and Weibo. Experiment results demonstrate that our proposed model ERIC-FND outperforms existing state-of-the-art fake news detection methods under the same settings.