Search Results for author:
Found 25 papers, 4 papers with code
Pandemic Literature Search: Finding Information on COVID-19
Finding information related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually.
Combining Shallow and Deep Representations for Text-Pair Classification
Contemporary methods use fine-tuned transformer encoder semantic representations of the classification token in the text-pair sequence from the transformer’s final layer for class prediction.
Cross-Domain Language Modeling: An Empirical Investigation
Transformer encoder models exhibit strong performance in single-domain applications.
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.
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.
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 prompt engineering.
Towards Real Smart Apps: Investigating Human-AI Interactions in Smartphone On-Device AI Apps
We further implemented our findings into a multi-faceted search-enabled gallery.
Enhancing Virtual Assistant Intelligence: Precise Area Targeting for Instance-level User Intents beyond Metadata
Although their capabilities of processing user intents have been developed rapidly, virtual assistants in most platforms are only capable of handling pre-defined high-level tasks supported by extra manual efforts of developers.
Source Code Data Augmentation for Deep Learning: A Survey
This paper fills this gap by conducting a comprehensive and integrative survey of data augmentation for source code, wherein we systematically compile and encapsulate existing literature to provide a comprehensive overview of the field.
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.
Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity
We believe that our findings may give light on future efforts to determine and mitigate the ethical hazards posed by machines in LLM applications.
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.
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.
Brain-inspired Search Engine Assistant based on Knowledge Graph
However, when a query is complex, developers need to repeatedly refine the search keywords and open a large number of web pages to find and summarize answers.
Holistic Combination of Structural and Textual Code Information for Context based API Recommendation
APIRec-CST is a deep learning model that combines the API usage with the text information in the source code based on an API Context Graph Network and a Code Token Network that simultaneously learn structural and textual features for API recommendation.
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.
Searching Scientific Literature for Answers on COVID-19 Questions
Finding answers related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually.
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.
ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge
We report on our system for textual inference and question entailment in the medical domain for the ACL BioNLP 2019 Shared Task, MEDIQA.
