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Found 43 papers, 9 papers with code
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.
Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models
In this work, we find that the insertion of even a short prefix to the prompt leads to the generation of outputs far away from factually correct answers.
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features.
Navigating Privacy and Copyright Challenges Across the Data Lifecycle of Generative AI
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.
Local Differential Privacy for Smart Meter Data Sharing
In this paper, we propose a novel LDP approach (named LDP-SmartEnergy) that utilizes randomized response techniques with sliding windows to facilitate the sharing of appliance-level energy consumption data over time while not revealing individual users' appliance usage patterns.
Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?
The correctness and unambiguity of API usage among these code models are crucial for achieving desirable program functionalities, requiring them to learn various API fully qualified names structurally and semantically.
Decentralised Governance-Driven Architecture for Designing Foundation Model based Systems: Exploring the Role of Blockchain in Responsible AI
Foundation models including large language models (LLMs) are increasingly attracting interest worldwide for their distinguished capabilities and potential to perform a wide variety of tasks.
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.
Towards Responsible AI in the Era of Generative AI: A Reference Architecture for Designing Foundation Model based Systems
The release of ChatGPT 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.
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.
Decision Models for Selecting Federated Learning Architecture Patterns
Federated machine learning is growing fast in academia and industries as a solution to solve data hungriness and privacy issues in machine learning.
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.
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum
Next, a bottom-up traversal of the tree trains the RL agent from easier sub-tasks with denser rewards on bottom layers to harder ones on top layers and collects its cost on each sub-task train the planner in the next episode.
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).
Vote for Nearest Neighbors Meta-Pruning of Self-Supervised Networks
Can we find a better initialization for a new task, e. g., a much smaller network closer to the final pruned model, by exploiting its similar tasks?
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.
FLRA: A Reference Architecture for Federated Learning Systems
The proposed FLRA reference architecture is based on an extensive review of existing patterns of federated learning systems found in the literature and existing industrial implementation.
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.
Attn-HybridNet: Improving Discriminability of Hybrid Features with Attention Fusion
The principal component analysis network (PCANet) is an unsupervised parsimonious deep network, utilizing principal components as filters in its convolution layers.
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.
A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective
Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates.
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.
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations.
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.
Interpreting Shared Deep Learning Models via Explicable Boundary Trees
In this paper, we propose a method to disclose a small set of training data that is just sufficient for users to get the insight of a complicated model.
