Tootfinder

GUing: A Mobile GUI Search Engine using a Vision-Language Model
Jialiang Wei, Anne-Lise Courbis, Thomas Lambolais, Binbin Xu, Pierre Louis Bernard, G\'erard Dray, Walid Maalej
https://arxiv.org/abs/2405.00145

GUing: A Mobile GUI Search Engine using a Vision-Language Model
App developers use the Graphical User Interface (GUI) of other apps as an important source of inspiration to design and improve their own apps. In recent years, research suggested various approaches to retrieve GUI designs that fit a certain text query from screenshot datasets acquired through automated GUI exploration. However, such text-to-GUI retrieval approaches only leverage the textual information of the GUI elements in the screenshots, neglecting visual information such as icons or backg…

Efficient ultra-broadband low-resolution astrophotonic spectrographs
Pradip Gatkine, Greg Sercel, Nemanja Jovanovic, Ronald Broeke, Katarzyna Lawniczuk, Marco Passoni, Ashok Balakrishnan, Serge Bidnyk, Jielong Yin, Jeffrey Jewell, J. Kent Wallace, Dimitri Mawet
https://arxiv.org/abs/2404.02376

Efficient ultra-broadband low-resolution astrophotonic spectrographs
Broadband low-resolution near-infrared spectrographs in a compact form are crucial for ground- and space-based astronomy and other fields of sensing. Astronomical spectroscopy poses stringent requirements including high efficiency, broad band operation ($>$ 300 nm), and in some cases, polarization insensitivity. We present and compare experimental results from the design, fabrication, and characterization of broadband (1200 - 1650 nm) arrayed waveguide grating (AWG) spectrographs built using th…

Efficient ultra-broadband low-resolution astrophotonic spectrographs
Pradip Gatkine, Greg Sercel, Nemanja Jovanovic, Ronald Broeke, Katarzyna Lawniczuk, Marco Passoni, Ashok Balakrishnan, Serge Bidnyk, Jielong Yin, Jeffrey Jewell, J. Kent Wallace, Dimitri Mawet
https://arxiv.org/abs/2404.02376

Efficient ultra-broadband low-resolution astrophotonic spectrographs
Broadband low-resolution near-infrared spectrographs in a compact form are crucial for ground- and space-based astronomy and other fields of sensing. Astronomical spectroscopy poses stringent requirements including high efficiency, broad band operation ($>$ 300 nm), and in some cases, polarization insensitivity. We present and compare experimental results from the design, fabrication, and characterization of broadband (1200 - 1650 nm) arrayed waveguide grating (AWG) spectrographs built using th…

Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
Jiliang Li, Yifan Zhang, Zachary Karas, Collin McMillan, Kevin Leach, Yu Huang
https://arxiv.org/abs/2402.14182

Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
Recent language models have demonstrated proficiency in summarizing source code. However, as in many other domains of machine learning, language models of code lack sufficient explainability. Informally, we lack a formulaic or intuitive understanding of what and how models learn from code. Explainability of language models can be partially provided if, as the models learn to produce higher-quality code summaries, they also align in deeming the same code parts important as those identified by hu…

Are Large Language Models (LLMs) Good Social Predictors?
Kaiqi Yang, Hang Li, Hongzhi Wen, Tai-Quan Peng, Jiliang Tang, Hui Liu
https://arxiv.org/abs/2402.12620

Are Large Language Models (LLMs) Good Social Predictors?
The prediction has served as a crucial scientific method in modern social studies. With the recent advancement of Large Language Models (LLMs), efforts have been made to leverage LLMs to predict the human features in social life, such as presidential voting. These works suggest that LLMs are capable of generating human-like responses. However, we find that the promising performance achieved by previous studies is because of the existence of input shortcut features to the response. In fact, by r…

A Survey on Deep Learning for Theorem Proving
Zhaoyu Li, Jialiang Sun, Logan Murphy, Qidong Su, Zenan Li, Xian Zhang, Kaiyu Yang, Xujie Si
https://arxiv.org/abs/2404.09939

A Survey on Deep Learning for Theorem Proving
Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in mathematical language to rigorous derivations in formal systems. In recent years, the advancement of deep learning, especially the emergence of large language models, has sparked a notable surge of research exploring these techniques to enhance the process of theorem proving. This paper presents a pioneering comprehensive survey of deep learning for theorem proving by offering i) a thorough review of exi…

Bringing Generative AI to Adaptive Learning in Education
Hang Li, Tianlong Xu, Chaoli Zhang, Eason Chen, Jing Liang, Xing Fan, Haoyang Li, Jiliang Tang, Qingsong Wen
https://arxiv.org/abs/2402.14601

Bringing Generative AI to Adaptive Learning in Education
The recent surge in generative AI technologies, such as large language models and diffusion models, have boosted the development of AI applications in various domains, including science, finance, and education. Concurrently, adaptive learning, a concept that has gained substantial interest in the educational sphere, has proven its efficacy in enhancing students' learning efficiency. In this position paper, we aim to shed light on the intersectional studies of these two methods, which combine ge…

Language-Codec: Reducing the Gaps Between Discrete Codec Representation and Speech Language Models
Shengpeng Ji, Minghui Fang, Ziyue Jiang, Rongjie Huang, Jialung Zuo, Shulei Wang, Zhou Zhao
https://arxiv.org/abs/2402.12208

EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Yifan Zhang, Jiliang Li, Zachary Karas, Aakash Bansal, Toby Jia-Jun Li, Collin McMillan, Kevin Leach, Yu Huang
https://arxiv.org/abs/2402.14096

EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led to extensive use of attention during model design. While existing work has primarily and almost exclusively focused on static properties of source code and related structural representations like the Abstract Syntax Tree (AST), few studies have considered human attention, that is, where programmers focus while examini…

Multi-role Consensus through LLMs Discussions for Vulnerability Detection
Zhenyu Mao, Jialong Li, Munan Li, Kenji Tei
https://arxiv.org/abs/2403.14274 http…

Multi-role Consensus through LLMs Discussions for Vulnerability Detection
Recent advancements in large language models (LLMs) have highlighted the potential for vulnerability detection, a crucial component of software quality assurance. Despite this progress, most studies have been limited to the perspective of a single role, usually testers, lacking diverse viewpoints from different roles in a typical software development life-cycle, including both developers and testers. To this end, this paper introduces an approach to employ LLMs to act as different roles to simu…