Tootfinder

Leveraging swarm capabilities to assist other systems
Miquel Kegeleirs, David Garz\'on Ramos, Guillermo Legarda Herranz, Ilyes Gharbi, Jeanne Szpirer, Ken Hasselmann, Lorenzo Garattoni, Gianpiero Francesca, Mauro Birattari
https://arxiv.org/abs/2405.04079

Leveraging swarm capabilities to assist other systems
Most studies in swarm robotics treat the swarm as an isolated system of interest. We argue that the prevailing view of swarms as self-sufficient, independent systems limits the scope of potential applications for swarm robotics. A robot swarm could act as a support in an heterogeneous system comprising other robots and/or human operators, in particular by quickly providing access to a large amount of data acquired in large unknown environments. Tasks such as target identification & tracking, sc…

In Situ AI Prototyping: Infusing Multimodal Prompts into Mobile Settings with MobileMaker
Savvas Petridis, Michael Xieyang Liu, Alexander J. Fiannaca, Vivian Tsai, Michael Terry, Carrie J. Cai
https://arxiv.org/abs/2405.03806

In Situ AI Prototyping: Infusing Multimodal Prompts into Mobile Settings with MobileMaker
Recent advances in multimodal large language models (LLMs) have lowered the barriers to rapidly prototyping AI-powered features via prompting, especially for mobile-intended use cases. Despite the value of situated user feedback, the process of soliciting early, mobile-situated user feedback on AI prototypes remains challenging. The broad scope and flexibility of LLMs means that, for a given use-case-specific prototype, there is a crucial need to understand the wide range of in-the-wild input l…

We are now offering online, 6-week intensive summer courses in Elementary Biblical Hebrew and Elementary New Testament Greek, open to the public.
Course tuition is free for YDS students. For all others it is: $3,673 
For more info and to apply: https://divi…

Automated User Story Generation with Test Case Specification Using Large Language Model
Tajmilur Rahman, Yuecai Zhu
https://arxiv.org/abs/2404.01558 https:…

Automated User Story Generation with Test Case Specification Using Large Language Model
Modern Software Engineering era is moving fast with the assistance of artificial intelligence (AI), especially Large Language Models (LLM). Researchers have already started automating many parts of the software development workflow. Requirements Engineering (RE) is a crucial phase that begins the software development cycle through multiple discussions on a proposed scope of work documented in different forms. RE phase ends with a list of user-stories for each unit task identified through discus…

The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights
Wenhao Zhu, Shujian Huang, Fei Yuan, Cheng Chen, Jiajun Chen, Alexandra Birch
https://arxiv.org/abs/2405.01345

The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights
Bridging the significant gap between large language model's English and non-English performance presents a great challenge. While some previous studies attempt to mitigate this gap with translated training data, the recently proposed question alignment approach leverages the model's English expertise to improve multilingual performance with minimum usage of expensive, error-prone translation. In this paper, we explore how broadly this method can be applied by examining its effects in reasoning …

This https://arxiv.org/abs/2310.02424 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

AXNav: Replaying Accessibility Tests from Natural Language
Developers and quality assurance testers often rely on manual testing to test accessibility features throughout the product lifecycle. Unfortunately, manual testing can be tedious, often has an overwhelming scope, and can be difficult to schedule amongst other development milestones. Recently, Large Language Models (LLMs) have been used for a variety of tasks including automation of UIs, however to our knowledge no one has yet explored their use in controlling assistive technologies for the pur…

A study of Galactic Plane Planck Galactic Cold Clumps observed by SCOPE and the JCMT Plane Survey
D. J. Eden, Tie Liu, T. J. T. Moore, J. Di Francesco, G. Fuller, Kee-Tae Kim, Di Li, S. -Y. Liu, R. Plume, Ken'ichi Tatematsu, M. A. Thompson, Y. Wu, L. Bronfman, H. M. Butner, M. J. Currie, G. Garay, P. F. Goldsmith, N. Hirano, D. Johnstone, M. Juvela, S. -P. Lai, C. W. Lee, E. E. Mannfors, F. Olguin, K. Pattle, Geumsook Park, D. Polychroni, M. Rawlings, A. J. Rigby, P. Sanhueza, A. T…

A study of Galactic Plane Planck Galactic Cold Clumps observed by SCOPE and the JCMT Plane Survey
We have investigated the physical properties of Planck Galactic Cold Clumps (PGCCs) located in the Galactic Plane, using the JCMT Plane Survey (JPS) and the SCUBA-2 Continuum Observations of Pre-protostellar Evolution (SCOPE) survey. By utilising a suite of molecular-line surveys, velocities and distances were assigned to the compact sources within the PGCCs, placing them in a Galactic context. The properties of these compact sources show no large-scale variations with Galactic environment. Inv…

A Path Towards Legal Autonomy: An interoperable and explainable approach to extracting, transforming, loading and computing legal information using large language models, expert systems and Bayesian networks
Axel Constant, Hannes Westermann, Bryan Wilson, Alex Kiefer, Ines Hipolito, Sylvain Pronovost, Steven Swanson, Mahault Albarracin, Maxwell J. D. Ramstead

A Path Towards Legal Autonomy: An interoperable and explainable approach to extracting, transforming, loading and computing legal information using large language models, expert systems and Bayesian networks
Legal autonomy - the lawful activity of artificial intelligence agents - can be achieved in one of two ways. It can be achieved either by imposing constraints on AI actors such as developers, deployers and users, and on AI resources such as data, or by imposing constraints on the range and scope of the impact that AI agents can have on the environment. The latter approach involves encoding extant rules concerning AI driven devices into the software of AI agents controlling those devices (e.g., …

Graphics Processing Unit/Artificial Neural Network-accelerated large-eddy simulation of turbulent combustion: Application to swirling premixed flames
Min Zhang, Runze Mao, Han Li, Zhenhua An, Zhi X. Chen
https://arxiv.org/abs/2402.18858

Graphics Processing Unit/Artificial Neural Network-accelerated large-eddy simulation of turbulent combustion: Application to swirling premixed flames
Within the scope of reacting flow simulations, the real-time direct integration (DI) of stiff ordinary differential equations (ODE) for the computation of chemical kinetics stands as the primary demand on computational resources. Meanwhile, as the number of transport equations that need to be solved increases, the computational cost grows more substantially, particularly for those combustion models involving direct coupling of chemistry and flow such as the transported probability density funct…

This https://arxiv.org/abs/2403.14280 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Large Language Models for Blockchain Security: A Systematic Literature Review
Large Language Models (LLMs) have emerged as powerful tools in various domains involving blockchain security (BS). Several recent studies are exploring LLMs applied to BS. However, there remains a gap in our understanding regarding the full scope of applications, impacts, and potential constraints of LLMs on blockchain security. To fill this gap, we conduct a literature review on LLM4BS. As the first review of LLM's application on blockchain security, our study aims to comprehensively analyze…

MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
Hanlei Zhang, Xin Wang, Hua Xu, Qianrui Zhou, Kai Gao, Jianhua Su, jinyue Zhao, Wenrui Li, Yanting Chen
https://arxiv.org/abs/2403.10943

MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. Existing benchmark datasets are limited in scale and suffer from difficulties in handling out-of-scope samples that arise in multi-turn conversational interactions. We introduce MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 dialogu…

Not getting into an argument over a #RandomPedantry point, but as a matter of objective fact, we are leaving our kids cleaner air on a global basis than we inherited with the exception of CO2 levels. Sulfur, fine particulates, NOx, etc. are all *down* in a multi-decadal scope.
Which is part of why we seeing a tipping point in temperatures. Fuel oil used in large ships has gotten much c…

A profile of Chinese private hacking contractor I-Soon, as leaked documents show shady business practices, worker disgruntlement, poor security protocols, more (Associated Press)
https://apnews.com/article/chinese-hack…

Behind the doors of a Chinese hacking company, a sordid culture fueled by influence, alcohol and sex
China’s hacking industry, leaked internal documents reveal, is vast in size and scope but also suffers from shady business practices, disgruntlement over pay and work quality, and poor security protocols. China’s state security agencies have contracted large amounts of hacking and espionage work to private contractors to slash costs and expand their reach, allowing Beijing to direct a huge number of hackers to infiltrate systems overseas. But leaked documents from one contractor, I-Soon, sh…

This https://arxiv.org/abs/2401.16277 has been replaced.
link: https://scholar.google.com/scholar?q=a

SECOMP: Formally Secure Compilation of Compartmentalized C Programs
Undefined behavior in C often causes devastating security vulnerabilities. One practical mitigation is compartmentalization, which allows developers to structure large programs into mutually distrustful compartments with clearly specified privileges and interactions. In this paper we introduce SECOMP, a compiler for compartmentalized C code that comes with machine-checked proofs guaranteeing that the scope of undefined behavior is restricted to the compartments that encounter it and become dyna…

AgentFL: Scaling LLM-based Fault Localization to Project-Level Context
Yihao Qin, Shangwen Wang, Yiling Lou, Jinhao Dong, Kaixin Wang, Xiaoling Li, Xiaoguang Mao
https://arxiv.org/abs/2403.16362

AgentFL: Scaling LLM-based Fault Localization to Project-Level Context
Fault Localization (FL) is an essential step during the debugging process. With the strong capabilities of code comprehension, the recent Large Language Models (LLMs) have demonstrated promising performance in diagnosing bugs in the code. Nevertheless, due to LLMs' limited performance in handling long contexts, existing LLM-based fault localization remains on localizing bugs within a small code scope (i.e., a method or a class), which struggles to diagnose bugs for a large code scope (i.e., an …

Optimal Layout Synthesis for Deep Quantum Circuits on NISQ Processors with 100 Qubits
Irfansha Shaik, Jaco van de Pol
https://arxiv.org/abs/2403.11598 htt…

Optimal Layout Synthesis for Deep Quantum Circuits on NISQ Processors with 100+ Qubits
Layout synthesis is mapping a quantum circuit to a quantum processor. SWAP gate insertions are needed for scheduling 2-qubit gates only on connected physical qubits. With the ever-increasing number of qubits in NISQ processors, scalable layout synthesis is of utmost importance. With large optimality gaps observed in heuristic approaches, scalable exact methods are needed. While recent exact and near-optimal approaches scale to moderate circuits, large deep circuits are still out of scope. In …

Cumulative Incidence Function Estimation Based on Population-Based Biobank Data
Malka Gorfine, David M. Zucker, Shoval Shoham
https://arxiv.org/abs/2403.18464

Cumulative Incidence Function Estimation Based on Population-Based Biobank Data
Many countries have established population-based biobanks, which are being used increasingly in epidemiolgical and clinical research. These biobanks offer opportunities for large-scale studies addressing questions beyond the scope of traditional clinical trials or cohort studies. However, using biobank data poses new challenges. Typically, biobank data is collected from a study cohort recruited over a defined calendar period, with subjects entering the study at various ages falling between $c_L…

This https://arxiv.org/abs/2403.14280 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Large Language Models for Blockchain Security: A Systematic Literature Review
Large Language Models (LLMs) have emerged as powerful tools in various domains involving blockchain security (BS). Several recent studies are exploring LLMs applied to BS. However, there remains a gap in our understanding regarding the full scope of applications, impacts, and potential constraints of LLMs on blockchain security. To fill this gap, we conduct a literature review on LLM4BS. As the first review of LLM's application on blockchain security, our study aims to comprehensively analyze…

MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
Hanlei Zhang, Xin Wang, Hua Xu, Qianrui Zhou, Kai Gao, Jianhua Su, jinyue Zhao, Wenrui Li, Yanting Chen
https://arxiv.org/abs/2403.10943

MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. Existing benchmark datasets are limited in scale and suffer from difficulties in handling out-of-scope samples that arise in multi-turn conversational interactions. We introduce MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 dialogu…

This https://arxiv.org/abs/2401.07348 has been replaced.
link: https://scholar.google.com/scholar?q=a

Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity
The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper delves into the legal and regulatory implications of Generative AI and LLMs in the European Union…

Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories
Ning Yang, Shuo Chen, Haijun Zhang, Randall Berry
https://arxiv.org/abs/2404.14238

Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories
Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices. This expansion facilitates the implementation of large-scale "connected things" within edge networks. The advent of applications necessitating real-time, high-quality service presents several challenges, such as low latency, high data rate, reliability, efficiency, and security, all of which demand resolution. The incorporation of reinforcement lea…

How Far Can We Go with Practical Function-Level Program Repair?
Jiahong Xiang, Xiaoyang Xu, Fanchu Kong, Mingyuan Wu, Haotian Zhang, Yuqun Zhang
https://arxiv.org/abs/2404.12833 <…

How Far Can We Go with Practical Function-Level Program Repair?
Recently, multiple Automated Program Repair (APR) techniques based on Large Language Models (LLMs) have been proposed to enhance the repair performance. While these techniques mainly focus on the single-line or hunk-level repair, they face significant challenges in real-world application due to the limited repair task scope and costly statement-level fault localization. However, the more practical function-level APR, which broadens the scope of APR task to fix entire buggy functions and require…

This https://arxiv.org/abs/2401.16277 has been replaced.
link: https://scholar.google.com/scholar?q=a

SECOMP: Formally Secure Compilation of Compartmentalized C Programs
Undefined behavior in C often causes devastating security vulnerabilities. One practical mitigation is compartmentalization, which allows developers to structure large programs into mutually distrustful compartments with clearly specified privileges and interactions. In this paper we introduce SECOMP, a compiler for compartmentalized C code that comes with machine-checked proofs guaranteeing that the scope of undefined behavior is restricted to the compartments that encounter it and become dyna…

This https://arxiv.org/abs/2403.10943 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMM_…

MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. Existing benchmark datasets are limited in scale and suffer from difficulties in handling out-of-scope samples that arise in multi-turn conversational interactions. We introduce MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 dialogu…

This https://arxiv.org/abs/2401.07348 has been replaced.
link: https://scholar.google.com/scholar?q=a

Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity
The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper delves into the legal and regulatory implications of Generative AI and LLMs in the European Union…

Graph Neural Networks for Predicting Solubility in Diverse Solvents using MolMerger incorporating Solute-solvent Interactions
Vansh Ramani, Tarak Karmakar
https://arxiv.org/abs/2402.11340

Graph Neural Networks for Predicting Solubility in Diverse Solvents using MolMerger incorporating Solute-solvent Interactions
Prediction of solubility has been a complex and challenging physiochemical problem that has tremendous implications in the chemical and pharmaceutical industry. Recent advancements in machine learning methods have provided great scope for predicting the reliable solubility of a large number of molecular systems. However, most of these methods rely on using physical properties obtained from experiments and or expensive quantum chemical calculations. Here, we developed a method that utilizes a gr…

A Systematic Overview of Single-Cell Transcriptomics Databases, their Use cases, and Limitations
Mahnoor N. Gondal, Saad Ur Rehman Shah, Arul M. Chinnaiyan, Marcin Cieslik
https://arxiv.org/abs/2404.10545

A Systematic Overview of Single-Cell Transcriptomics Databases, their Use cases, and Limitations
Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of genomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the tec…

This https://arxiv.org/abs/2401.01269 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

LLbezpeky: Leveraging Large Language Models for Vulnerability Detection
Despite the continued research and progress in building secure systems, Android applications continue to be ridden with vulnerabilities, necessitating effective detection methods. Current strategies involving static and dynamic analysis tools come with limitations like overwhelming number of false positives and limited scope of analysis which make either difficult to adopt. Over the past years, machine learning based approaches have been extensively explored for vulnerability detection, but its…

This https://arxiv.org/abs/2401.07348 has been replaced.
link: https://scholar.google.com/scholar?q=a

Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity
The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper delves into the legal and regulatory implications of Generative AI and LLMs in the European Union…