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Found 43 papers, 9 papers with code
Unlocking Multi-Modal Potentials for Dynamic Text-Attributed Graph Representation
Dynamic Text-Attributed Graphs (DyTAGs) are a novel graph paradigm that captures evolving temporal edges alongside rich textual attributes.
FactFlow: Automatic Fact Sheet Generation and Customization from Tabular Dataset via AI Chain Design & Implementation
With the proliferation of data across various domains, there is a critical demand for tools that enable non-experts to derive meaningful insights without deep data analysis skills.
Foundation Models for Anomaly Detection: Vision and Challenges
As data continues to grow in volume and complexity across domains such as finance, manufacturing, and healthcare, effective anomaly detection is essential for identifying irregular patterns that may signal critical issues.
On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing
Missing data imputation, which aims to impute the missing values in the raw datasets to achieve the completeness of datasets, is crucial for modern data-driven models like large language models (LLMs) and has attracted increasing interest over the past decades.
From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps
While some studies targeted at automated detection, they are constrained to static patterns and still necessitate manual app exploration.
A Layered Architecture for Developing and Enhancing Capabilities in Large Language Model-based Software Systems
Significant efforts has been made to expand the use of Large Language Models (LLMs) beyond basic language tasks.
Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning
In order to improve the efficiency, PoG prunes irrelevant information from the graph exploration first and introduces efficient three-step pruning techniques that incorporate graph structures, LLM prompting, and a pre-trained language model (e. g., SBERT) to effectively narrow down the explored candidate paths.
Ranked #1 on
Knowledge Base Question Answering
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A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model
The rapid advancement of AI technology has led to widespread applications of agent systems across various domains.
Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agents
Foundation model-enabled generative artificial intelligence facilitates the development and implementation of agents, which can leverage distinguished reasoning and language processing capabilities to takes a proactive, autonomous role to pursue users' goals.
Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems
Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks.
Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective
The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns.
Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents
Foundation models, such as large language models (LLMs), have been widely recognised as transformative AI technologies due to their capabilities to understand and generate content, including plans with reasoning capabilities.
Test-takers have a say: understanding the implications of the use of AI in language tests
To the best of our knowledge, this is the first empirical study aimed at identifying the implications of AI adoption in language tests from a test-taker perspective.
Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions
In this paper, we explore these challenges and provide our insights on how to implement technical solutions for the RTBF, including the use of differential privacy, machine unlearning, model editing, and guardrails.
Query2GMM: Learning Representation with Gaussian Mixture Model for Reasoning over Knowledge Graphs
Research along this line suggests that using multi-modal distribution to represent answer entities is more suitable than uni-modal distribution, as a single query may contain multiple disjoint answer subsets due to the compositional nature of multi-hop queries and the varying latent semantics of relations.
Distributed Trust Through the Lens of Software Architecture
Distributed trust is a nebulous concept that has evolved from different perspectives in recent years.
A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture
The recent release of large language model (LLM) based chatbots, such as ChatGPT, has attracted huge interest in foundation models.
A Reference Architecture for Designing Foundation Model based Systems
The release of ChatGPT, Gemini, and other large language model has drawn huge interests on foundations models.
Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges
Second, we focus on the convergence of blockchain and data sharing to give a clear picture of this landscape and propose a reference architecture for blockchain-based data sharing.
Emerging Synergies in Causality and Deep Generative Models: A Survey
In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance.
IronForge: An Open, Secure, Fair, Decentralized Federated Learning
Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner.
Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible AI Engineering Approach
In this article, we first summarise the major challenges in operationalising responsible AI at scale and introduce how we use the Responsible AI Pattern Catalogue to address those challenges.
Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering
Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle.
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems
Recognizing that confounders may be elusive, we propose a contrastive self-supervised learning to minimize exposure bias, employing inverse propensity scores and expanding the positive sample set.
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects
We regularly consider answering counterfactual questions in practice, such as "Would people with diabetes take a turn for the better had they choose another medication?".
Psychologically-Inspired, Unsupervised Inference of Perceptual Groups of GUI Widgets from GUI Images
These principles are domain-independent and have been widely adopted by practitioners to structure content on GUIs to improve aesthetic pleasant and usability.
Towards a Roadmap on Software Engineering for Responsible AI
Although AI is transforming the world, there are serious concerns about its ability to behave and make decisions responsibly.
Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems
In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis.
Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns
These patterns provide concrete, operationalised guidance that facilitate the development of responsible AI systems.
Cycle-Balanced Representation Learning For Counterfactual Inference
With the widespread accumulation of observational data, researchers obtain a new direction to learn counterfactual effects in many domains (e. g., health care and computational advertising) without Randomized Controlled Trials(RCTs).
Blockchain-based Trustworthy Federated Learning Architecture
To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture.
AI and Ethics -- Operationalising Responsible AI
In the last few years, AI continues demonstrating its positive impact on society while sometimes with ethically questionable consequences.
Architectural Patterns for the Design of Federated Learning Systems
Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems.
Generating Informative CVE Description From ExploitDB Posts by Extractive Summarization
The extracted aspects from an ExploitDB post are then composed into a CVE description according to the suggested CVE description templates, which is must-provided information for requesting new CVEs.
Meta Gradient Boosting Neural Networks
A key challenge for meta-optimization based approaches is to determine whether an initialization condition can be generalized to tasks with diverse distributions to accelerate learning.
Generative Inverse Deep Reinforcement Learning for Online Recommendation
Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process.
Blockchain-based Federated Learning for Failure Detection in Industrial IoT
Therefore, in this paper, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT.
Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?
We conduct the first large-scale empirical study of seven representative GUI element detection methods on over 50k GUI images to understand the capabilities, limitations and effective designs of these methods.
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users.
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.
Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning
However, the prerequisite of using screen readers is that developers have to add natural-language labels to the image-based components when they are developing the app.
Metric Factorization: Recommendation beyond Matrix Factorization
In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations.
