Open Access. Powered by Scholars. Published by Universities.®
Artificial Intelligence and Robotics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Databases and Information Systems (344)
- Engineering (291)
- Operations Research, Systems Engineering and Industrial Engineering (255)
- Graphics and Human Computer Interfaces (171)
- Software Engineering (129)
-
- Business (125)
- Numerical Analysis and Scientific Computing (111)
- Social and Behavioral Sciences (101)
- Theory and Algorithms (97)
- Public Affairs, Public Policy and Public Administration (66)
- Transportation (61)
- Programming Languages and Compilers (57)
- Information Security (40)
- OS and Networks (37)
- Medicine and Health Sciences (35)
- Computer Engineering (31)
- Education (24)
- Health Information Technology (24)
- Asian Studies (23)
- International and Area Studies (23)
- Finance and Financial Management (14)
- Technology and Innovation (13)
- Communication (12)
- Operations and Supply Chain Management (12)
- Social Media (12)
- Higher Education (11)
- Computer and Systems Architecture (7)
- Keyword
-
- Artificial intelligence (49)
- Machine learning (46)
- Reinforcement learning (39)
- Deep learning (37)
- Large Language Models (26)
-
- Large Language Model (22)
- Large language models (21)
- Computer vision (18)
- Optimization (18)
- Scheduling (18)
- Anomaly detection (17)
- Generative AI (17)
- Large language model (17)
- Singapore (17)
- ChatGPT (16)
- Deep reinforcement learning (16)
- Reinforcement Learning (16)
- LLMs (15)
- Vehicle routing problem (15)
- Deep Learning (14)
- Natural language processing (14)
- Artificial Intelligence (13)
- Neural networks (13)
- Uncertainty (13)
- Machine Learning (12)
- Graph neural networks (11)
- Metaverse (10)
- Multi-agent systems (10)
- Representation learning (10)
- Semantics (10)
- Publication Year
- File Type
Articles 541 - 570 of 1664
Full-Text Articles in Artificial Intelligence and Robotics
Empathyear : An Open-Source Avatar Multimodal Empathetic Chatbot, Hao Fei, Han Zhang, Bin Wang, Lizi Liao, Qian Liu, Erik Cambria
Empathyear : An Open-Source Avatar Multimodal Empathetic Chatbot, Hao Fei, Han Zhang, Bin Wang, Lizi Liao, Qian Liu, Erik Cambria
Research Collection School Of Computing and Information Systems
This paper introduces EmpathyEar, a pioneering open-source, avatar-based multimodal empathetic chatbot, to fill the gap in traditional text-only empathetic response generation (ERG) systems. Leveraging the advancements of a large language model, combined with multimodal encoders and generators, EmpathyEar supports user inputs in any combination of text, sound, and vision, and produces multimodal empathetic responses, offering users, not just textual responses but also digital avatars with talking faces and synchronized speeches. A series of emotion-aware instruction-tuning is performed for comprehensive emotional understanding and generation capabilities. In this way, EmpathyEar provides users with responses that achieve a deeper emotional resonance, closely emulating …
Planning Like Human : A Dual-Process Framework For Dialogue Planning, Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Ming Liu, Zerui Chen, Bing Qin
Planning Like Human : A Dual-Process Framework For Dialogue Planning, Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Ming Liu, Zerui Chen, Bing Qin
Research Collection School Of Computing and Information Systems
In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typically struggle due to their reactive nature. Traditional approaches to enhance dialogue planning in LLMs, ranging from elaborate prompt engineering to the integration of policy networks, either face efficiency issues or deliver suboptimal performance. Inspired by the dual-process theory in psychology, which identifies two distinct modes of thinking—intuitive (fast) and analytical (slow), we propose the Dual-Process Dialogue Planning (DPDP) framework. DPDP embodies this theory through two complementary planning systems: an instinctive policy model for familiar …
Synergizing Large Language Models And Pre-Trained Smaller Models For Conversational Intent Discovery, Jinggui Liang, Lizi Liao, Hao Fei, Jing Jiang
Synergizing Large Language Models And Pre-Trained Smaller Models For Conversational Intent Discovery, Jinggui Liang, Lizi Liao, Hao Fei, Jing Jiang
Research Collection School Of Computing and Information Systems
In Conversational Intent Discovery (CID), Small Language Models (SLMs) struggle with overfitting to familiar intents and fail to label newly discovered ones. This issue stems from their limited grasp of semantic nuances and their intrinsically discriminative framework. Therefore, we propose Synergizing Large Language Models (LLMs) with pre-trained SLMs for CID (SynCID). It harnesses the profound semantic comprehension of LLMs alongside the operational agility of SLMs. By utilizing LLMs to refine both utterances and existing intent labels, SynCID significantly enhances the semantic depth, subsequently realigning these enriched descriptors within the SLMs’ feature space to correct cluster distortion and promote robust learning …
Unifying Global-Local Representations In Salient Object Detection With Transformers, Sucheng Ren, Nanxuan Zhao, Qiang Wen, Guoqiang Han, Shengfeng He
Unifying Global-Local Representations In Salient Object Detection With Transformers, Sucheng Ren, Nanxuan Zhao, Qiang Wen, Guoqiang Han, Shengfeng He
Research Collection School Of Computing and Information Systems
The fully convolutional network (FCN) has dominated salient object detection for a long period. However, the locality of CNN requires the model deep enough to have a global receptive field and such a deep model always leads to the loss of local details. In this paper, we introduce a new attention-based encoder, vision transformer, into salient object detection to ensure the globalization of the representations from shallow to deep layers. With the global view in very shallow layers, the transformer encoder preserves more local representations to recover the spatial details in final saliency maps. Besides, as each layer can capture …
Speaker Verification In Agent-Generated Conversations, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Ee-Peng Lim
Speaker Verification In Agent-Generated Conversations, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
The recent success of large language models (LLMs) has attracted widespread interest to develop role-playing conversational agents personalized to the characteristics and styles of different speakers to enhance their abilities to perform both general and special purpose dialogue tasks. However, the ability to personalize the generated utterances to speakers, whether conducted by human or LLM, has not been well studied. To bridge this gap, our study introduces a novel evaluation challenge: speaker verification in agent-generated conversations, which aimed to verify whether two sets of utterances originate from the same speaker. To this end, we assemble a large dataset collection encompassing …
Self-Adaptive Psro : Towards An Automatic Population-Based Game Solver, Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang, Xiao Huang, Hau Chan, Bo An
Self-Adaptive Psro : Towards An Automatic Population-Based Game Solver, Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang, Xiao Huang, Hau Chan, Bo An
Research Collection School Of Computing and Information Systems
Policy-Space Response Oracles (PSRO) as a general algorithmic framework has achieved state-of-the-art performance in learning equilibrium policies of two-player zero-sum games. However, the hand-crafted hyperparameter value selection in most of the existing works requires extensive domain knowledge, forming the main barrier to applying PSRO to different games. In this work, we make the first attempt to investigate the possibility of self-adaptively determining the optimal hyperparameter values in the PSRO framework. Our contributions are three-fold: (1) Using several hyperparameters, we propose a parametric PSRO that unifies the gradient descent ascent (GDA) and different PSRO variants. (2) We propose the self-adaptive PSRO …
A Multimodal Foundation Agent For Financial Trading : Tool-Augmented, Diversified, And Generalist, Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, Longtao Zheng, Xinrun Wang, Bo An
A Multimodal Foundation Agent For Financial Trading : Tool-Augmented, Diversified, And Generalist, Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, Longtao Zheng, Xinrun Wang, Bo An
Research Collection School Of Computing and Information Systems
Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in finance, their application in financial trading tasks often faces challenges due to inadequate handling of multimodal data and limited generalizability across various tasks. To address these challenges, we present FinAgent, a multimodal foundational agent with tool augmentation for financial trading. FinAgent's market intelligence module processes a diverse range of data-numerical, textual, …
Macrohft : Memory Augmented Context-Aware Reinforcement Learning On High Frequency Trading, Chuqiao Zong, Chaojie Wang, Molei Qin, Lei Feng, Xinrun Wang, Xinrun Wang
Macrohft : Memory Augmented Context-Aware Reinforcement Learning On High Frequency Trading, Chuqiao Zong, Chaojie Wang, Molei Qin, Lei Feng, Xinrun Wang, Xinrun Wang
Research Collection School Of Computing and Information Systems
High-frequency trading (HFT) that executes algorithmic trading in short time scales, has recently occupied the majority of cryptocurrency market. Besides traditional quantitative trading methods, reinforcement learning (RL) has become another appealing approach for HFT due to its terrific ability of handling high-dimensional financial data and solving sophisticated sequential decision-making problems, e.g., hierarchical reinforcement learning (HRL) has shown its promising performance on second-level HFT by training a router to select only one sub-agent from the agent pool to execute the current transaction. However, existing RL methods for HFT still have some defects: 1) standard RL-based trading agents suffer from the overfitting …
Topic Modeling On Document Networks With Dirichlet Optimal Transport Barycenter, Ce Zhang, Hady Wirawan Lauw
Topic Modeling On Document Networks With Dirichlet Optimal Transport Barycenter, Ce Zhang, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Text documents are often interconnected in a network structure, e.g., academic papers via citations, Web pages via hyperlinks. On the one hand, though Graph Neural Networks (GNNs) have shown promising ability to derive effective embeddings for such networked documents, they do not assume a latent topic structure and result in uninterpretable embeddings. On the other hand, topic models can infer semantically interpretable topic distributions for documents by associating each topic with a group of understandable key words. However, most topic models mainly focus on plain text within documents and fail to leverage network structure across documents. Network connectivity reveals topic …
Reinforcement Nash Equilibrium Solver, Xinrun Wang, Chang Yang, Shuxin Li, Pengdeng Li, Xiao Huang, Hau Chan, Bo An
Reinforcement Nash Equilibrium Solver, Xinrun Wang, Chang Yang, Shuxin Li, Pengdeng Li, Xiao Huang, Hau Chan, Bo An
Research Collection School Of Computing and Information Systems
Nash Equilibrium (NE) is the canonical solution concept of game theory, which provides an elegant tool to understand the rationalities. Though mixed strategy NE exists in any game with finite players and actions, computing NE in two- or multi-player general-sum games is PPAD-Complete. Various alternative solutions, e.g., Correlated Equilibrium (CE), and learning methods, e.g., fictitious play (FP), are proposed to approximate NE. For convenience, we call these methods as "inexact solvers", or "solvers" for short. However, the alternative solutions differ from NE and the learning methods generally fail to converge to NE. Therefore, in this work, we propose REinforcement Nash …
Exploring A Multimodal Fusion-Based Deep Learning Network For Detecting Facial Palsy, Heng Yim Nicole Oo, Min Hun Lee, J. H. Lim
Exploring A Multimodal Fusion-Based Deep Learning Network For Detecting Facial Palsy, Heng Yim Nicole Oo, Min Hun Lee, J. H. Lim
Research Collection School Of Computing and Information Systems
Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessment by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes unstructured data (i.e. an image frame with facial line segments) and structured data (i.e. features of facial expressions) to detect facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of 21 facial palsy patients. Our experimental results show that among various data modalities (i.e. unstructured data - RGB images …
Towards Gradient-Based Time-Series Explanations Through A Spatiotemporal Attention Network, Min Hun Lee
Towards Gradient-Based Time-Series Explanations Through A Spatiotemporal Attention Network, Min Hun Lee
Research Collection School Of Computing and Information Systems
In this paper, we explore the feasibility of using a transformer-based, spatiotemporal attention network (STAN) for gradient-based time-series explanations. First, we trained the STAN model for video classifications using the global and local views of data and weakly supervised labels on time-series data (i.e. the type of an activity). We then leveraged a gradient-based XAI technique (e.g. saliency map) to identify salient frames of time-series data. According to the experiments using the datasets of four medically relevant activities, the STAN model demonstrated its potential to identify important frames of videos.
Enabling Sustainable Freight Forwarding Network Via Collaborative Games, Pang Jin Tan, Shih-Fen Cheng, Richard Chen
Enabling Sustainable Freight Forwarding Network Via Collaborative Games, Pang Jin Tan, Shih-Fen Cheng, Richard Chen
Research Collection School Of Computing and Information Systems
Freight forwarding plays a crucial role in facilitating global trade and logistics. However, as the freight forwarding market is extremely fragmented, freight forwarders often face the issue of not being able to fill the available shipping capacity. This recurrent issue motivates the creation of various freight forwarding networks that aim at exchanging capacities and demands so that the resource utilization of individual freight forwarders can be maximized. In this paper, we focus on how to design such a collaborative network based on collaborative game theory, with the Shapley value representing a fair scheme for profit sharing. Noting that the exact …
Is Aggregation The Only Choice? Federated Learning Via Layer-Wise Model Recombination, Ming Hu, Zhihao Yue, Xiaofei Xie, Cheng Chen Chen
Is Aggregation The Only Choice? Federated Learning Via Layer-Wise Model Recombination, Ming Hu, Zhihao Yue, Xiaofei Xie, Cheng Chen Chen
Research Collection School Of Computing and Information Systems
Although Federated Learning (FL) enables global model training Xiaofei Xie [email protected] Singapore Management University Singapore, Singapore Xian Wei [email protected] East China Normal University Shanghai, China Mingsong Chen∗ [email protected] East China Normal University Shanghai, China • Computing methodologies → Distributed artificial intelligence. across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance. Specifically, different data distributions among clients lead to various optimization directions of local models. Aggregating local models usually results in a low-generalized global model, which performs worse on most of the …
Contrastive General Graph Matching With Adaptive Augmentation Sampling, Jianyuan Bo, Yuan Fang
Contrastive General Graph Matching With Adaptive Augmentation Sampling, Jianyuan Bo, Yuan Fang
Research Collection School Of Computing and Information Systems
Graph matching has important applications in pattern recognition and beyond. Current approaches predominantly adopt supervised learning, demanding extensive labeled data which can be limited or costly. Meanwhile, self-supervised learning methods for graph matching often require additional side information such as extra categorical information and input features, limiting their application to the general case. Moreover, designing the optimal graph augmentations for self-supervised graph matching presents another challenge to ensure robustness and effcacy. To address these issues, we introduce a novel Graph-centric Contrastive framework for Graph Matching (GCGM), capitalizing on a vast pool of graph augmentations for contrastive learning, yet without needing …
A Learned Generalized Geodesic Distance Function-Based Approach For Node Feature Augmentation On Graphs, Amitoz Azad, Yuan Fang
A Learned Generalized Geodesic Distance Function-Based Approach For Node Feature Augmentation On Graphs, Amitoz Azad, Yuan Fang
Research Collection School Of Computing and Information Systems
Geodesic distances on manifolds have numerous applications in image processing, computer graphics and computer vision. In this work, we introduce an approach called 'LGGD' (Learned Generalized Geodesic Distances). This method involves generating node features by learning a generalized geodesic distance function through a training pipeline that incorporates training data, graph topology and the node content features. The strength of this method lies in the proven robustness of the generalized geodesic distances to noise and outliers. Our contributions encompass improved performance in node classification tasks, competitive results with state-of-the-art methods on real-world graph datasets, the demonstration of the learnability of parameters …
Sibo : A Simple Booster For Parameter-Efficient Fine-Tuning, Zhihao Wen, Jie Zhang, Yuan Fang
Sibo : A Simple Booster For Parameter-Efficient Fine-Tuning, Zhihao Wen, Jie Zhang, Yuan Fang
Research Collection School Of Computing and Information Systems
Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustments to only a minor fraction of the parameters of these LLMs. Concurrently, it has been noted that the issue of over-smoothing diminishes the effectiveness of these Transformer-based LLMs, resulting in suboptimal performances in downstream tasks. In this paper, we present SIBO, which is a SImple BOoster to enhance PEFT, by injecting an initial residual. SIBO is straightforward and readily extensible to a range of state-of-the-art PEFT techniques to alleviate …
Heterogeneous Graph Transformer With Poly-Tokenization, Zhiyuan Lu, Yuan Fang, Cheng Yang, Chuan Shi
Heterogeneous Graph Transformer With Poly-Tokenization, Zhiyuan Lu, Yuan Fang, Cheng Yang, Chuan Shi
Research Collection School Of Computing and Information Systems
Graph neural networks have shown widespread success for learning on graphs, but they still face fundamental drawbacks, such as limited expressive power, over-smoothing, and over-squashing. Meanwhile, the transformer architecture offers a potential solution to these issues. However, existing graph transformers primarily cater to homogeneous graphs and are unable to model the intricate semantics of heterogeneous graphs. Moreover, unlike small molecular graphs where the entire graph can be considered as the receptive field in graph transformers, real-world heterogeneous graphs comprise a significantly larger number of nodes and cannot be entirely treated as such. Consequently, existing graph transformers struggle to capture the …
Cross-Problem Learning For Solving Vehicle Routing Problems, Zhuoyi Lin, Yaoxin Wu, Bangjian Zhou, Zhiguang Cao, Wen Song, Yingqian Zhang, Senthilnath Jayavelu
Cross-Problem Learning For Solving Vehicle Routing Problems, Zhuoyi Lin, Yaoxin Wu, Bangjian Zhou, Zhiguang Cao, Wen Song, Yingqian Zhang, Senthilnath Jayavelu
Research Collection School Of Computing and Information Systems
Existing neural heuristics often train a deep architecture from scratch for each specific vehicle routing problem (VRP), ignoring the transferable knowledge across different VRP variants. This paper proposes the cross-problem learning to assist heuristics training for different downstream VRP variants. Particularly, we modularize neural architectures for complex VRPs into 1) the backbone Transformer for tackling the travelling salesman problem (TSP), and 2) the additional lightweight modules for processing problem-specific features in complex VRPs. Accordingly, we propose to pre-train the backbone Transformer for TSP, and then apply it in the process of fine-tuning the Transformer models for each target VRP variant. …
Certified Policy Verification And Synthesis For Mdps Under Distributional Reach-Avoidance Properties, S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Dorde Zikelic
Certified Policy Verification And Synthesis For Mdps Under Distributional Reach-Avoidance Properties, S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Dorde Zikelic
Research Collection School Of Computing and Information Systems
Markov Decision Processes (MDPs) are a classical model for decision making in the presence of uncertainty. Often they are viewed as state transformers with planning objectives defined with respect to paths over MDP states. An increasingly popular alternative is to view them as distribution transformers, giving rise to a sequence of probability distributions over MDP states. For instance, reachability and safety properties in modeling robot swarms or chemical reaction networks are naturally defined in terms of probability distributions over states. Verifying such distributional properties is known to be hard and often beyond the reach of classical state-based verification techniques. In …
Solving Long-Run Average Reward Robust Mdps Via Stochastic Games, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Petr Novotný, Dorde Zikelic
Solving Long-Run Average Reward Robust Mdps Via Stochastic Games, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Petr Novotný, Dorde Zikelic
Research Collection School Of Computing and Information Systems
Markov decision processes (MDPs) provide a standard framework for sequential decision making under uncertainty. However, MDPs do not take uncertainty in transition probabilities into account. Robust Markov decision processes (RMDPs) address this shortcoming of MDPs by assigning to each transition an uncertainty set rather than a single probability value. In this work, we consider polytopic RMDPs in which all uncertainty sets are polytopes and study the problem of solving long-run average reward polytopic RMDPs. We present a novel perspective on this problem and show that it can be reduced to solving long-run average reward turn-based stochastic games with finite state …
Task Scheduling Strategy For 3dpcp Considering Multidynamic Information Perturbation In Green Scene, Jianjia He, Jian Wu, Keng Siau
Task Scheduling Strategy For 3dpcp Considering Multidynamic Information Perturbation In Green Scene, Jianjia He, Jian Wu, Keng Siau
Research Collection School Of Computing and Information Systems
The 3D printing cloud platform (3DPCP) plays a pivotal role in breaking down the information silos between supply and demand, effectively reducing waste through information integration and intelligent production. However, due to the complexity of 3DPCP scheduling in green scenes and the multidynamic information perturbations, unveils problems in traditional task scheduling methods in 3DPCP. These issues manifest as incomplete considerations, subpar green performance, and weak adaptability to dynamic changes. There is an urgent need to design practical methods to realize the multidynamic information perturbations in green scenes within 3DPCP. Therefore, this article first defines the 3DPCP task scheduling problem for …
Fuel-Saving Route Planning With Data-Driven And Learning-Based Approaches: A Systematic Solution For Harbor Tugs, Shengming Wang, Xiaocai Zhang, Jing Li, Xiaoyang Wei, Hoong Chuin Lau, Bing Tian Dai, Binbin Huang Huang, Zhe Xiao, Xiuju Fu, Zheng Qin
Fuel-Saving Route Planning With Data-Driven And Learning-Based Approaches: A Systematic Solution For Harbor Tugs, Shengming Wang, Xiaocai Zhang, Jing Li, Xiaoyang Wei, Hoong Chuin Lau, Bing Tian Dai, Binbin Huang Huang, Zhe Xiao, Xiuju Fu, Zheng Qin
Research Collection School Of Computing and Information Systems
In recent years, there are trends toward cleaner port environments through enforcement by imposed legislation. Transit optimisation of fuel-based port service boats like harbour tugs has emerged as a critical task to reduce fuel consumption and carbon emission. In this paper, an innovative learning-based method, comprising a Reinforcement Learning (RL) model together with a fuel consumption prediction model, was proposed to formulate fuel-saving transit routes. Firstly, an ensemble model is established by combining a Long Short-Term Memory (LSTM) model with a Multilayer Perceptron (MLP) model, predicting fuel use based on tugboat movement and environment factors. Subsequently, an innovative RL based …
Optimization Of Customer Service And Driver Dispatch Areas For On-Demand Food Delivery, Jingfeng Yang, Hoong Chuin Lau, Hai Wang
Optimization Of Customer Service And Driver Dispatch Areas For On-Demand Food Delivery, Jingfeng Yang, Hoong Chuin Lau, Hai Wang
Research Collection School Of Computing and Information Systems
With the rapid development and popularization of mobile and wireless communication technologies, on-demand food delivery (OFD) platforms have been able to connect restaurants, customers, and drivers in real time, drastically changing dining and food delivery services. Motivated by the critical need for supply and demand management in the on-demand food delivery market, we focus on the optimization of customer service area and driver dispatch area for on-demand food delivery services. Specifically, for each restaurant, the platform needs to decide the (1) customer service area (CSA), i.e., the surrounding area within which customers can see the restaurant’s information and order food …
How People Prompt Generative Ai To Create Interactive Vr Scenes, Setareh Aghel Manesh, Tianyi Zhang, Yuki Onishi, Kotaro Hara, Scott Bateman, Jiannan Li, Anthony Tang
How People Prompt Generative Ai To Create Interactive Vr Scenes, Setareh Aghel Manesh, Tianyi Zhang, Yuki Onishi, Kotaro Hara, Scott Bateman, Jiannan Li, Anthony Tang
Research Collection School Of Computing and Information Systems
Generative AI tools can provide people with the ability to create virtual environments and scenes with natural language prompts. Yet, how people will formulate such prompts is unclear---particularly when they inhabit the environment that they are designing. For instance, it is likely that a person might say, "Put a chair here,'' while pointing at a location. If such linguistic and embodied features are common to people's prompts, we need to tune models to accommodate them. In this work, we present a Wizard of Oz elicitation study with 22 participants, where we studied people's implicit expectations when verbally prompting such programming …
A Deep Learning Method To Predict Bacterial Adp-Ribosyltransferase Toxins, Dandan Zheng, Siyu Zhou, Lihong Chen, Guansong Pang, Jian Yang
A Deep Learning Method To Predict Bacterial Adp-Ribosyltransferase Toxins, Dandan Zheng, Siyu Zhou, Lihong Chen, Guansong Pang, Jian Yang
Research Collection School Of Computing and Information Systems
Motivation: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. Results: We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity …
Broadening The View: Demonstration-Augmented Prompt Learning For Conversational Recommendation, Quang Huy Dao, Yang Deng, Dung D. Le, Lizi Liao
Broadening The View: Demonstration-Augmented Prompt Learning For Conversational Recommendation, Quang Huy Dao, Yang Deng, Dung D. Le, Lizi Liao
Research Collection School Of Computing and Information Systems
Conversational Recommender Systems (CRSs) leverage natural language dialogues to provide tailored recommendations. Traditional methods in this field primarily focus on extracting user preferences from isolated dialogues. It often yields responses with a limited perspective, confined to the scope of individual conversations. Recognizing the potential in collective dialogue examples, our research proposes an expanded approach for CRS models, utilizing selective analogues from dialogue histories and responses to enrich both generation and recommendation processes. This introduces significant research challenges, including: (1) How to secure high-quality collections of recommendation dialogue exemplars? (2) How to effectively leverage these exemplars to enhance CRS models?To tackle …
Comparative Analysis Of Hate Speech Detection: Traditional Vs. Deep Learning Approaches, Haibo Pen, Nicole Anne Huiying Teo, Zhaoxia Wang
Comparative Analysis Of Hate Speech Detection: Traditional Vs. Deep Learning Approaches, Haibo Pen, Nicole Anne Huiying Teo, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
Detecting hate speech on social media poses a significant challenge, especially in distinguishing it from offensive language, as learning-based models often struggle due to nuanced differences between them, which leads to frequent misclassifications of hate speech instances, with most research focusing on refining hate speech detection methods. Thus, this paper seeks to know if traditional learning-based methods should still be used, considering the perceived advantages of deep learning in this domain. This is done by investigating advancements in hate speech detection. It involves the utilization of deep learning-based models for detailed hate speech detection tasks and compares the results with …
Performance Analysis Of Llama 2 Among Other Llms, Donghao Huang, Zhenda Hu, Zhaoxia Wang
Performance Analysis Of Llama 2 Among Other Llms, Donghao Huang, Zhenda Hu, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
Llama 2, an open-source large language model developed by Meta, offers a versatile and high-performance solution for natural language processing, boasting a broad scale, competitive dialogue capabilities, and open accessibility for research and development, thus driving innovation in AI applications. Despite these advancements, there remains a limited understanding of the underlying principles and performance of Llama 2 compared with other LLMs. To address this gap, this paper presents a comprehensive evaluation of Llama 2, focusing on its application in in-context learning — an AI design pattern that harnesses pre-trained LLMs for processing confidential and sensitive data. Through a rigorous comparative …
Generative Ai For Pull Request Descriptions: Adoption, Impact, And Developer Interventions, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto
Generative Ai For Pull Request Descriptions: Adoption, Impact, And Developer Interventions, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to automate various developer tasks related to PRs, such as generating summaries of changes or providing complete walkthroughs with links to the relevant code. As this innovative technology gains traction in the Open Source Software (OSS) community, it is crucial to examine its early adoption and its impact on the development process. Additionally, it offers a unique opportunity to observe how developers respond when they disagree with the generated content. In our study, we employ a mixed-methods approach, blending quantitative analysis with qualitative insights, to examine 18,256 PRs in …