GenAI4ELab/papercli-papers huggingface dataset:
nBIIG: A Neural BI Insights Generation System for Table Reporting
Authors: Yotam Perlitz; Dafna Sheinwald; Noam Slonim; Michal Shmueli-Scheuer
Venue: AAAI 2023 (main)

Abstract: We present nBIIG, a neural Business Intelligence (BI) Insights Generation system. Given a table, our system applies various analyses to create corresponding RDF representations, and then uses a neural model to generate fluent textual insights out of these representations. The generated insights can be used by an analyst, via a human-in-the-loop paradigm, to enhance the task of creating compelling table reports. The underlying generative neural model is trained over large and carefully distilled data, curated from multiple BI domains. Thus, the system can generate faithful and fluent insights over open-domain tables, making it practical and useful.
Prototyping Logic-Based AI Services with LogicUS
Authors: Víctor Ramos-González; Joaquín Borrego-Díaz; Fernando Sancho-Caparrini
Venue: AAAI 2023 (main)

Abstract: Currently, there is renewed interest in logic-related solutions for AI and Computer Science. The availability of software tools to support the realization of such studies (both as powerful and versatile prototyping tools and as teaching tools) has become a necessity. Intending to contribute to this field, we present a tool that allows the unification of different logic tasks, focused on Computer Logic but adaptable to the treatment in several subfields, contexts, and abstraction levels (LogicUS-LIB, LogicUS-NB, LogicUS-GUI).  

The tool provides a sound framework for two activity fields. On the one hand, in the topic of logic-based systems research, prototyping is facilitated in a relatively fast, simple, and highly adaptable way. On the other hand, in Education, by allowing the student to abstract from low-level execution of algorithms whilst preserving the conceptual structures and procedural methodologies underlying the logical foundations.
KnowGL: Knowledge Generation and Linking from Text
Authors: Gaetano Rossiello; Md. Faisal Mahbub Chowdhury; Nandana Mihindukulasooriya; Owen Cornec; Alfio Massimiliano Gliozzo
Venue: AAAI 2023 (main)

Abstract: We propose KnowGL, a tool that allows converting text into structured relational data represented as a set of ABox assertions compliant with the TBox of a given Knowledge Graph (KG), such as Wikidata. 
We address this problem as a sequence generation task by leveraging pre-trained sequence-to-sequence language models, e.g. BART.
Given a sentence, we fine-tune such models to detect pairs of entity mentions and jointly generate a set of facts consisting of the full set of semantic annotations for a KG, such as entity labels, entity types, and their relationships.
To showcase the capabilities of our tool, we build a web application consisting of a set of UI widgets that help users to navigate through the semantic data extracted from a given input text. We make the KnowGL model available at https://huggingface.co/ibm/knowgl-large.
Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case
Authors: Kaushik Roy; Vedant Khandelwal; Raxit Goswami; Nathan Dolbir; Jinendra Malekar; Amit Sheth
Venue: AAAI 2023 (main)

Abstract: After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve:
(a) Personalized patient understanding, (b) Safety-constrained and medically validated chatbot patient interactions, and (c) Support for continued feedback-based refinements in design using chatbot-patient interactions. 
We propose Alleviate, a chatbot designed to assist patients suffering from mental health challenges with personalized care and assist clinicians with understanding their patients better. Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed decisions. In addition, Alleviate's modular design and explainable decision-making lends itself to robust and continued feedback-based refinements to its design. In this paper, we explain the different modules of Alleviate and submit a short video demonstrating Alleviate's capabilities to help patients and clinicians understand each other better to facilitate optimal care strategies.
Task2KB: A Public Task-Oriented Knowledge Base
Authors: Procheta Sen; Xi Wang; Ruiqing Xu; Emine Yilmaz
Venue: AAAI 2023 (main)

Abstract: Search engines and conversational assistants are commonly used to help users complete their every day tasks such as booking travel, cooking, etc. While there are some existing datasets that can be used for this purpose, their coverage is limited to very few domains. In this paper, we propose a novel knowledge base, ‘Task2KB’, which is constructed using data crawled from WikiHow, an online knowledge resource offering instructional articles on a wide range of tasks. Task2KB encapsulates various types of task-related information and attributes, such as requirements, detailed step description, and available methods to complete tasks. Due to its higher coverage compared to existing related knowledge graphs, Task2KB can be highly useful in the development of general purpose task completion assistants.
CodeStylist: A System for Performing Code Style Transfer Using Neural Networks
Authors: Chih-Kai Ting; Karl Munson; Serenity Wade; Anish Savla; Kiran Kate; Kavitha Srinivas
Venue: AAAI 2023 (main)

Abstract: Code style refers to attributes of computer programs that affect their readability, maintainability, and performance. Enterprises consider code style as important and enforce style requirements during code commits. Tools that assist in coding style compliance and transformations are highly valuable. However, many key aspects of programming style transfer are difficult to automate, as it can be challenging to specify the patterns required to perform the transfer algorithmically.  In this paper, we describe a system called CodeStylist which uses neural methods to perform style transfer on code.
AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series
Authors: Patara Trirat; Youngeun Nam; Taeyoon Kim; Jae-Gil Lee
Venue: AAAI 2023 (main)

Abstract: This paper presents AnoViz, a novel visualization tool of anomalies in multivariate time series, to support domain experts and data scientists in understanding anomalous instances in their systems. AnoViz provides an overall summary of time series as well as detailed visualizations of relevant detected anomalies in both query and stream modes, rendering near real-time visual analysis available. Here, we show that AnoViz streamlines the process of finding a potential cause of an anomaly with a deeper analysis of anomalous instances, giving explainability to any anomaly detector.
CLUE-AD: A Context-Based Method for Labeling Unobserved Entities in Autonomous Driving Data
Authors: Ruwan Wickramarachchi; Cory Henson; Amit Sheth
Venue: AAAI 2023 (main)

Abstract: Generating high-quality annotations for object detection and recognition is a challenging and important task, especially in relation to safety-critical applications such as autonomous driving (AD). Due to the difficulty of perception in challenging situations such as occlusion, degraded weather, and sensor failure, objects can go unobserved and unlabeled. In this paper, we present CLUE-AD, a general-purpose method for detecting and labeling unobserved entities by leveraging the object continuity assumption within the context of a scene. This method is dataset-agnostic, supporting any existing and future AD datasets. Using a real-world dataset representing complex urban driving scenes, we demonstrate the applicability of CLUE-AD for detecting unobserved entities and augmenting the scene data with new labels.
An Online Presentation Slide Assessment System Using Visual and Semantic Segmentation Features
Authors: Shengzhou Yi; Junichiro Matsugami; Hiroshi Yumoto; Toshihiko Yamasaki
Venue: AAAI 2023 (main)

Abstract: In this study, we present a new presentation slide assessment system that can extract the structural features from any slide file formats. Our previous work used a neural network to identify novice vs. well-designed presentation slides based on visual and structural features. However, the structural feature extraction was only applicable to PowerPoint files. To solve this problem, we extract the semantic segmentation from the slide images as a new format of structural features. The proposed multi-modal Transformer extracts the features from the original images and semantic segmentation results to assess the slide design. The prediction targets are the top-10 checkpoints pointed out by the professional consultants. Class-imbalanced learning and multi-task learning methods are also applied to improve the accuracy. The proposed model only requiring the slide images achieved an average accuracy of 81.67% that is comparative to the performance of the previous work requiring the PowerPoint files.
Retraction Note to: McOmet: Multimodal Fusion Transformer for Physical Audiovisual Commonsense Reasoning
Venue: AAAI 2023 (main)

Abstract: This Retraction Note refers to:RETRACTED: McOmet: Multimodal Fusion Transformer for Physical Audiovisual Commonsense Reasoning.The referenced article, published in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023), has been retracted by agreement between the authors and the journal, as described in the PDF file for this Retraction Note.
Erratum to: 3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation
Venue: AAAI 2023 (main)

Abstract: TheOriginal Articlewas published on 26 June 2023.
A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation
Authors: Yongkang  Wang; Xuan Liu; Feng Huang; Zhankun Xiong; Wen Zhang
Venue: AAAI 2024 (main)

Abstract: Therapeutic peptides represent a unique class of pharmaceutical agents crucial for the treatment of human diseases. Recently, deep generative models have exhibited remarkable potential for generating therapeutic peptides, but they only utilize sequence or structure information alone, which hinders the performance in generation. In this study, we propose a Multi-Modal Contrastive Diffusion model (MMCD), fusing both sequence and structure modalities in a diffusion framework to co-generate novel peptide sequences and structures. Specifically, MMCD constructs the sequence-modal and structure-modal diffusion models, respectively, and devises a multi-modal contrastive learning strategy with inter-contrastive and intra-contrastive in each diffusion timestep, aiming to capture the consistency between two modalities and boost model performance. The inter-contrastive aligns sequences and structures of peptides by maximizing the agreement of their embeddings, while the intra-contrastive differentiates therapeutic and non-therapeutic peptides by maximizing the disagreement of their sequence/structure embeddings simultaneously. The extensive experiments demonstrate that MMCD performs better than other state-of-the-art deep generative methods in generating therapeutic peptides across various metrics, including antimicrobial/anticancer score, diversity, and peptide-docking.
Towards Automated RISC-V Microarchitecture Design with Reinforcement Learning
Authors: Chen Bai; Jianwang Zhai; Yuzhe Ma; Bei Yu; Martin D. F. Wong
Venue: AAAI 2024 (main)

Abstract: Microarchitecture determines the implementation of a microprocessor. Designing a microarchitecture to achieve better performance, power, and area (PPA) trade-off has been increasingly difficult. Previous data-driven methodologies hold inappropriate assumptions and lack more tightly coupling with expert knowledge. This paper proposes a novel reinforcement learning-based (RL) solution that addresses these limitations. With the integration of microarchitecture scaling graph, PPA preference space embedding, and proposed lightweight environment in RL, experiments using commercial electronic design automation (EDA) tools show that our method achieves an average PPA trade-off improvement of 16.03% than previous state-of-the-art approaches with 4.07× higher efficiency. The solution qualities outperform human implementations by at most 2.03× in the PPA trade-off.
Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback
Authors: Shreyas Bhat Brahmavar; Ashwin Srinivasan; Tirtharaj Dash; Sowmya Ramaswamy Krishnan; Lovekesh Vig; Arijit Roy; Raviprasad Aduri
Venue: AAAI 2024 (main)

Abstract: Large Language Models (LLMs) can be used as repositories of biological and chemical information to generate pharmacological lead compounds. However, for LLMs to focus on specific drug targets typically requires experimentation with progressively more refined prompts. Results thus become dependent not just on what is known about the target, but also on what is known about the prompt- engineering. In this paper, we separate the prompt into domain-constraints that can be written in a standard logical form and a simple text-based query. We investigate whether LLMs can be guided, not by refining prompts manually, but by refining the logical component automatically, keeping the query unchanged. We describe an iterative procedure LMLF (“Language Model with Logical Feedback”) in which the constraints are progressively refined using a logical notion of generalisation. On any iteration, newly generated instances are verified against the constraint, providing "logical-feedback" for the next iteration's refinement of the constraints. We evaluate LMLF using two well-known  targets (inhibition of the Janus Kinase 2; and Dopamine Receptor D2); and two different LLMs (GPT-3 and PaLM). We show that LMLF, starting with the same logical constraints and query text, can be used to guide both LLMs to generate potential leads. We find: (a)  Binding affinities of LMLF-generated molecules are skewed towards higher binding affinities than those from existing baselines; (b) LMLF results in generating molecules that are skewed towards higher binding affinities than without logical feedback; (c) Assessment by a computational chemist suggests that LMLF generated compounds may be novel inhibitors. These findings suggest that LLMs with logical feedback may provide a mechanism for generating new leads without requiring the domain-specialist to acquire sophisticated skills in prompt-engineering.
SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter
Authors: Ying-Ying Chang; Wei-Yao Wang; Wen-Chih Peng
Venue: AAAI 2024 (main)

Abstract: In the dynamic and rapidly evolving world of social media, detecting anomalous users has become a crucial task to address malicious activities such as misinformation and cyberbullying. As the increasing number of anomalous users improves the ability to mimic normal users and evade detection, existing methods only focusing on bot detection are ineffective in terms of capturing subtle distinctions between users. To address these challenges, we proposed SeGA, preference-aware self-contrastive learning for anomalous user detection, which leverages heterogeneous entities and their relations in the Twittersphere to detect anomalous users with different malicious strategies. SeGA utilizes the knowledge of large language models to summarize user preferences via posts. In addition, integrating user preferences with prompts as pseudo-labels for preference-aware self-contrastive learning enables the model to learn multifaceted aspects for describing the behaviors of users. Extensive experiments on the proposed TwBNT benchmark demonstrate that SeGA significantly outperforms the state-of-the-art methods (+3.5% ∼ 27.6%) and empirically validate the effectiveness of the model design and pre-training strategies. Our code and data are publicly available at https://github.com/ying0409/SeGA.
Neural Embeddings for kNN Search in Biological Sequence
Authors: Zhihao Chang; Linzhu Yu; Yanchao Xu; Wentao Hu
Venue: AAAI 2024 (main)

Abstract: Biological sequence nearest neighbor search plays a fundamental role in bioinformatics. To alleviate the pain of quadratic complexity for conventional distance computation, neural distance embeddings, which project sequences into geometric space, have been recognized as a promising paradigm. To maintain the distance order between sequences, these models all deploy triplet loss and use intuitive methods to select a subset of triplets for training from a vast selection space. However, we observed that such training often enables models to distinguish only a fraction of distance orders, leaving others unrecognized. Moreover, naively selecting more triplets for training under the state-of-the-art network not only adds costs but also hampers model performance.
    In this paper, we introduce Bio-kNN: a kNN search framework for biological sequences. It includes a systematic triplet selection method and a multi-head network, enhancing the discernment of all distance orders without increasing training expenses. Initially, we propose a clustering-based approach to partition all triplets into several clusters with similar properties, and then select triplets from these clusters using an innovative strategy. Meanwhile, we noticed that simultaneously training different types of triplets in the same network cannot achieve the expected performance, thus we propose a multi-head network to tackle this. Our network employs a convolutional neural network(CNN) to extract local features shared by all clusters, and then learns a multi-layer perception(MLP) head for each cluster separately. Besides, we treat CNN as a special head, thereby integrating crucial local features which are neglected in previous models into our model for similarity recognition. Extensive experiments show that our Bio-kNN significantly outperforms the state-of-the-art methods on two large-scale datasets without increasing the training cost.
i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance
Authors: Haoyang Chen; Peiyan Sun; Qiyuan Song; Wanyuan Wang; Weiwei Wu; Wencan Zhang; Guanyu Gao; Yan Lyu
Venue: AAAI 2024 (main)

Abstract: Ride-hailing platforms have been facing the challenge of balancing demand and supply. Existing vehicle reposition techniques often treat drivers as homogeneous agents and relocate them deterministically, assuming compliance with the reposition. In this paper, we consider a more realistic and driver-centric scenario where drivers have unique cruising preferences and can decide whether to take the recommendation or not on their own. We propose i-Rebalance, a personalized vehicle reposition technique with deep reinforcement learning (DRL). i-Rebalance estimates drivers' decisions on accepting reposition recommendations through an on-field user study involving 99 real drivers. To optimize supply-demand balance and enhance preference satisfaction simultaneously, i-Rebalance has a sequential reposition strategy with dual DRL agents: Grid Agent to determine the reposition order of idle vehicles, and Vehicle Agent to provide personalized recommendations to each vehicle in the pre-defined order. This sequential learning strategy facilitates more effective policy training within a smaller action space compared to traditional joint-action methods. Evaluation of real-world trajectory data shows that i-Rebalance improves driver acceptance rate by 38.07% and total driver income by 9.97%.
GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion
Authors: Le Cheng; Peican Zhu; Keke Tang; Chao Gao; Zhen Wang
Venue: AAAI 2024 (main)

Abstract: Source detection in graphs has demonstrated robust efficacy in the domain of rumor source identification. Although recent solutions have enhanced performance by leveraging deep neural networks, they often require complete user data. In this paper, we address a more challenging task, rumor source detection with incomplete user data, and propose a novel framework, i.e., Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion (GIN-SD), to tackle this challenge. Specifically, our approach utilizes a positional embedding module to distinguish nodes that are incomplete and employs a self-attention mechanism to focus on nodes with greater information transmission capacity. To mitigate the prediction bias caused by the significant disparity between the numbers of source and non-source nodes, we also introduce a class-balancing mechanism. Extensive experiments validate the effectiveness of GIN-SD and its superiority to state-of-the-art methods.
Deep Quantum Error Correction
Authors: Yoni Choukroun; Lior Wolf
Venue: AAAI 2024 (main)

Abstract: Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. QECC, as its classical counterpart (ECC), enables the reduction of error rates, by distributing quantum logical information across redundant physical qubits, such that errors can be detected and corrected. In this work, we efficiently train novel end-to-end deep quantum error decoders. We resolve the quantum measurement collapse by augmenting syndrome decoding to predict an initial estimate of the system noise, which is then refined iteratively through a deep neural network. The logical error rates calculated over finite fields are directly optimized via a differentiable objective, enabling efficient decoding under the constraints imposed by the code. Finally, our architecture is extended to support faulty syndrome measurement, by efficient decoding of repeated syndrome sampling. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy, outperforming for small distance topological codes, the existing end-to-end neural and classical decoders, which are often computationally prohibitive.
Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection
Authors: Chaoqun Cui; Caiyan Jia
Venue: AAAI 2024 (main)

Abstract: Rumor detection on social media has become increasingly important. Most existing graph-based models presume rumor propagation trees (RPTs) have deep structures and learn sequential stance features along branches. However, through statistical analysis on real-world datasets, we find RPTs exhibit wide structures, with most nodes being shallow 1-level replies. To focus learning on intensive substructures, we propose Rumor Adaptive Graph Contrastive Learning (RAGCL) method with adaptive view augmentation guided by node centralities. We summarize three principles for RPT augmentation: 1) exempt root nodes, 2) retain deep reply nodes, 3) preserve lower-level nodes in deep sections. We employ node dropping, attribute masking and edge dropping with probabilities from centrality-based importance scores to generate views. A graph contrastive objective then learns robust rumor representations. Extensive experiments on four benchmark datasets demonstrate RAGCL outperforms state-of-the-art methods. Our work reveals the wide-structure nature of RPTs and contributes an effective graph contrastive learning approach tailored for rumor detection through principled adaptive augmentation. The proposed principles and augmentation techniques can potentially benefit other applications involving tree-structured graphs.