OpenRaiser/Intern-Atlas huggingface dataset:
A Study of Using Multimodal LLMs for Non-Crash Functional Bug Detection in Android Apps
Venue: arXiv cs.SE 2024
Year: 2024

Numerous approaches employing various strategies have been developed to test the graphical user interfaces (GUIs) of mobile apps. However, traditional GUI testing techniques, such as random and model-based testing, primarily focus on generating test sequences that excel in achieving high code coverage but often fail to act as effective test oracles for non-crash functional (NCF) bug detection. To tackle these limitations, this study empirically investigates the capability of leveraging large language models (LLMs) to be test oracles to detect NCF bugs in Android apps. Our intuition is that the training corpora of LLMs, encompassing extensive mobile app usage and bug report descriptions, enable them with the domain knowledge relevant to NCF bug detection. We conducted a comprehensive empirical study to explore the effectiveness of LLMs as test oracles for detecting NCF bugs in Android apps on 71 well-documented NCF bugs. The results demonstrated that LLMs achieve a 49% bug detection rate, outperforming existing tools for detecting NCF bugs in Android apps. Additionally, by leveraging LLMs to be test oracles, we successfully detected 24 previously unknown NCF bugs in 64 Android apps, with four of these bugs being confirmed or fixed. However, we also identified limitations of LLMs, primarily related to performance degradation, inherent randomness, and false positives. Our study highlights the potential of leveraging LLMs as test oracles for Android NCF bug detection and suggests directions for future research.
A Recipe for Success? Exploring Strategies for Improving Non-Visual Access to Cooking Instructions
Venue: arXiv cs.HC 2024
Year: 2024

Cooking is an essential activity that enhances quality of life by enabling individuals to prepare their own meals. However, cooking often requires multitasking between cooking tasks and following instructions, which can be challenging to cooks with vision impairments if recipes or other instructions are inaccessible. To explore the practices and challenges of recipe access while cooking, we conducted semi-structured interviews with 20 people with vision impairments who have cooking experience and four cooking instructors at a vision rehabilitation center. We also asked participants to edit and give feedback on existing recipes. We revealed unique practices and challenges to accessing recipe information at different cooking stages, such as the heavy burden of hand-washing to interact with recipe readers. We also presented the preferred information representation and structure of recipes. We then highlighted design features of technological supports that could facilitate the development of more accessible kitchen technologies for recipe access. Our work contributes nuanced insights and design guidelines to enhance recipe accessibility for people with vision impairments.
Optimizing Checkpoint-Restart Mechanisms for HPC with DMTCP in Containers at NERSC
Venue: arXiv cs.DC 2024
Year: 2024

This paper presents an in-depth examination of checkpoint-restart mechanisms in High-Performance Computing (HPC). It focuses on the use of Distributed MultiThreaded CheckPointing (DMTCP) in various computational settings, including both within and outside of containers. The study is grounded in real-world applications running on NERSC Perlmutter, a state-of-the-art supercomputing system. We discuss the advantages of checkpoint-restart (C/R) in managing complex and lengthy computations in HPC, highlighting its efficiency and reliability in such environments. The role of DMTCP in enhancing these workflows, especially in multi-threaded and distributed applications, is thoroughly explored. Additionally, the paper delves into the use of HPC containers, such as Shifter and Podman-HPC, which aid in the management of computational tasks, ensuring uniform performance across different environments. The methods, results, and potential future directions of this research, including its application in various scientific domains, are also covered, showcasing the critical advancements made in computational methodologies through this study.
Few-Shot Medical Image Segmentation with Large Kernel Attention
Venue: arXiv cs.CV 2024
Year: 2024

Medical image segmentation has witnessed significant advancements with the emergence of deep learning. However, the reliance of most neural network models on a substantial amount of annotated data remains a challenge for medical image segmentation. To address this issue, few-shot segmentation methods based on meta-learning have been employed. Presently, the methods primarily focus on aligning the support set and query set to enhance performance, but this approach hinders further improvement of the model's effectiveness. In this paper, our objective is to propose a few-shot medical segmentation model that acquire comprehensive feature representation capabilities, which will boost segmentation accuracy by capturing both local and long-range features. To achieve this, we introduce a plug-and-play attention module that dynamically enhances both query and support features, thereby improving the representativeness of the extracted features. Our model comprises four key modules: a dual-path feature extractor, an attention module, an adaptive prototype prediction module, and a multi-scale prediction fusion module. Specifically, the dual-path feature extractor acquires multi-scale features by obtaining features of 32{\times}32 size and 64{\times}64 size. The attention module follows the feature extractor and captures local and long-range information. The adaptive prototype prediction module automatically adjusts the anomaly score threshold to predict prototypes, while the multi-scale fusion prediction module integrates prediction masks of various scales to produce the final segmentation result. We conducted experiments on publicly available MRI datasets, namely CHAOS and CMR, and compared our method with other advanced techniques. The results demonstrate that our method achieves state-of-the-art performance.
Rendezvous and Merging for Two Metamorphic Robotic Systems without Global Compass
Venue: arXiv cs.RO 2024
Year: 2024

A metamorphic robotic system (MRS) consists of anonymous modules, each of which autonomously moves in the 2D square grid by sliding and rotation with keeping connectivity among the modules. Existing literature considers distributed coordination among modules so that they collectively form a single MRS. In this paper, we consider distributed coordination for two MRSs. We first present a rendezvous algorithm that makes the two MRSs gather so that each module can observe all the other modules. Then, we present a merge algorithm that makes the two MRSs assemble and establish connectivity after rendezvous is finished. These two algorithms assume that each MRS consists of five modules, that do not have a common coordinate system. Finally, we show that five modules for each MRS is necessary to solve the rendezvous problem. To the best of our knowledge, our result is the first result on distributed coordination of multiple MRSs.
Efficiently improving key weather variables forecasting by performing the guided iterative prediction in latent space
Venue: arXiv cs.LG 2024
Year: 2024

Weather forecasting refers to learning evolutionary patterns of some key upper-air and surface variables which is of great significance. Recently, deep learning-based methods have been increasingly applied in the field of weather forecasting due to their powerful feature learning capabilities. However, prediction methods based on the original space iteration struggle to effectively and efficiently utilize large number of weather variables. Therefore, we propose an 'encoding-prediction-decoding' prediction network. This network can efficiently benefit to more related input variables with key variables, that is, it can adaptively extract key variable-related low-dimensional latent feature from much more input atmospheric variables for iterative prediction. And we construct a loss function to guide the iteration of latent feature by utilizing multiple atmospheric variables in corresponding lead times. The obtained latent features through iterative prediction are then decoded to obtain the predicted values of key variables in multiple lead times. In addition, we improve the HTA algorithm in \cite{bi2023accurate} by inputting more time steps to enhance the temporal correlation between the prediction results and input variables. Both qualitative and quantitative prediction results on ERA5 dataset validate the superiority of our method over other methods. (The code will be available at https://github.com/rs-lsl/Kvp-lsi)
A Construction of the Lie Algebra of a Lie Group in Isabelle/HOL
Venue: arXiv cs.LO 2024
Year: 2024

This paper describes a formal theory of smooth vector fields, Lie groups and the Lie algebra of a Lie group in the theorem prover Isabelle. Lie groups are abstract structures that are composable, invertible and differentiable. They are pervasive as models of continuous transformations and symmetries in areas from theoretical particle physics, where they underpin gauge theories such as the Standard Model, to the study of differential equations and robotics. Formalisation of mathematics in an interactive theorem prover, such as Isabelle, provides strong correctness guarantees by expressing definitions and theorems in a logic that can be checked by a computer. Many libraries of formalised mathematics lack significant development of textbook material beyond undergraduate level, and this contribution to mathematics in Isabelle aims to reduce that gap, particularly in differential geometry. We comment on representational choices and challenges faced when integrating complex formalisations, such as smoothness of vector fields, with the restrictions of the simple type theory of HOL. This contribution paves the way for extensions both in advanced mathematics, and in formalisations in natural science.
Comprehensive Survey of Complex-Valued Neural Networks: Insights into Backpropagation and Activation Functions
Venue: arXiv cs.LG 2024
Year: 2024

Artificial neural networks (ANNs), particularly those employing deep learning models, have found widespread application in fields such as computer vision, signal processing, and wireless communications, where complex numbers are crucial. Despite the prevailing use of real-number implementations in current ANN frameworks, there is a growing interest in developing ANNs that utilize complex numbers. This paper presents a comprehensive survey of recent advancements in complex-valued neural networks (CVNNs), focusing on their activation functions (AFs) and learning algorithms. We delve into the extension of the backpropagation algorithm to the complex domain, which enables the training of neural networks with complex-valued inputs, weights, AFs, and outputs. This survey considers three complex backpropagation algorithms: the complex derivative approach, the partial derivatives approach, and algorithms incorporating the Cauchy-Riemann equations. A significant challenge in CVNN design is the identification of suitable nonlinear Complex Valued Activation Functions (CVAFs), due to the conflict between boundedness and differentiability over the entire complex plane as stated by Liouville theorem. We examine both fully complex AFs, which strive for boundedness and differentiability, and split AFs, which offer a practical compromise despite not preserving analyticity. This review provides an in-depth analysis of various CVAFs essential for constructing effective CVNNs. Moreover, this survey not only offers a comprehensive overview of the current state of CVNNs but also contributes to ongoing research and development by introducing a new set of CVAFs (fully complex, split and complex amplitude-phase AFs).
Evaluating Large Language Models in Detecting Test Smells
Venue: arXiv cs.SE 2024
Year: 2024

Test smells are coding issues that typically arise from inadequate practices, a lack of knowledge about effective testing, or deadline pressures to complete projects. The presence of test smells can negatively impact the maintainability and reliability of software. While there are tools that use advanced static analysis or machine learning techniques to detect test smells, these tools often require effort to be used. This study aims to evaluate the capability of Large Language Models (LLMs) in automatically detecting test smells. We evaluated ChatGPT-4, Mistral Large, and Gemini Advanced using 30 types of test smells across codebases in seven different programming languages collected from the literature. ChatGPT-4 identified 21 types of test smells. Gemini Advanced identified 17 types, while Mistral Large detected 15 types of test smells. Conclusion: The LLMs demonstrated potential as a valuable tool in identifying test smells.
Interactive Learning in Computer Science Education Supported by a Discord Chatbot
Venue: arXiv cs.AI 2024
Year: 2024

Enhancing interaction and feedback collection in a first-semester computer science course poses a significant challenge due to students' diverse needs and engagement levels. To address this issue, we created and integrated a command-based chatbot on the course communication server on Discord. The DiscordBot enables students to provide feedback on course activities through short surveys, such as exercises, quizzes, and lectures, facilitating stress-free communication with instructors. It also supports attendance tracking and introduces lectures before they start. The research demonstrates the effectiveness of the DiscordBot as a communication tool. The ongoing feedback allowed course instructors to dynamically adjust and improve the difficulty level of upcoming activities and promote discussion in subsequent tutor sessions. The data collected reveal that students can accurately perceive the activities' difficulty and expected results, providing insights not possible through traditional end-of-semester surveys. Students reported that interaction with the DiscordBot was easy and expressed a desire to continue using it in future semesters. This responsive approach ensures the course meets the evolving needs of students, thereby enhancing their overall learning experience.
Smart Contracts, Smarter Payments: Innovating Cross Border Payments and Reporting Transactions
Venue: arXiv cs.CE 2024
Year: 2024

The global financial landscape is experiencing significant transformation driven by technological advancements and evolving market dynamics. Moreover, blockchain technology has become a pivotal platform with widespread applications, especially in finance. Cross-border payments have emerged as a key area of interest, with blockchain offering inherent benefits such as enhanced security, transparency, and efficiency compared to traditional banking systems. This paper presents a novel framework leveraging blockchain technology and smart contracts to emulate cross-border payments, ensuring interoperability and compliance with international standards such as ISO20022. Key contributions of this paper include a novel prototype framework for implementing smart contracts and web clients for streamlined transactions and a mechanism to translate ISO20022 standard messages. Our framework can provide a practical solution for secure, efficient, and transparent cross-border transactions, contributing to the ongoing evolution of global finance and the emerging landscape of decentralized finance.
Map-Matching Queries under Fréchet Distance on Low-Density Spanners
Venue: arXiv cs.CG 2024
Year: 2024

Map matching is a common task when analysing GPS tracks, such as vehicle trajectories. The goal is to match a recorded noisy polygonal curve to a path on the map, usually represented as a geometric graph. The Fréchet distance is a commonly used metric for curves, making it a natural fit. The map-matching problem is well-studied, yet until recently no-one tackled the data structure question: preprocess a given graph so that one can query the minimum Fréchet distance between all graph paths and a polygonal curve. Recently, Gudmundsson, Seybold, and Wong [SODA 2023, arXiv:2211.02951] studied this problem for arbitrary query polygonal curves and $c$-packed graphs. In this paper, we instead require the graphs to be $λ$-low-density $t$-spanners, which is significantly more representative of real-world networks. We also show how to report a path that minimises the distance efficiently rather than only returning the minimal distance, which was stated as an open problem in their paper.
ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

Grasp generation aims to create complex hand-object interactions with a specified object. While traditional approaches for hand generation have primarily focused on visibility and diversity under scene constraints, they tend to overlook the fine-grained hand-object interactions such as contacts, resulting in inaccurate and undesired grasps. To address these challenges, we propose a controllable grasp generation task and introduce ClickDiff, a controllable conditional generation model that leverages a fine-grained Semantic Contact Map (SCM). Particularly when synthesizing interactive grasps, the method enables the precise control of grasp synthesis through either user-specified or algorithmically predicted Semantic Contact Map. Specifically, to optimally utilize contact supervision constraints and to accurately model the complex physical structure of hands, we propose a Dual Generation Framework. Within this framework, the Semantic Conditional Module generates reasonable contact maps based on fine-grained contact information, while the Contact Conditional Module utilizes contact maps alongside object point clouds to generate realistic grasps. We evaluate the evaluation criteria applicable to controllable grasp generation. Both unimanual and bimanual generation experiments on GRAB and ARCTIC datasets verify the validity of our proposed method, demonstrating the efficacy and robustness of ClickDiff, even with previously unseen objects. Our code is available at https://github.com/adventurer-w/ClickDiff.
IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of applications. However, the graph data used for training may contain sensitive personal information of the involved individuals. Once trained, GNNs typically encode such information in their learnable parameters. As a consequence, privacy leakage may happen when the trained GNNs are deployed and exposed to potential attackers. Facing such a threat, machine unlearning for GNNs has become an emerging technique that aims to remove certain personal information from a trained GNN. Among these techniques, certified unlearning stands out, as it provides a solid theoretical guarantee of the information removal effectiveness. Nevertheless, most of the existing certified unlearning methods for GNNs are only designed to handle node and edge unlearning requests. Meanwhile, these approaches are usually tailored for either a specific design of GNN or a specially designed training objective. These disadvantages significantly jeopardize their flexibility. In this paper, we propose a principled framework named IDEA to achieve flexible and certified unlearning for GNNs. Specifically, we first instantiate four types of unlearning requests on graphs, and then we propose an approximation approach to flexibly handle these unlearning requests over diverse GNNs. We further provide theoretical guarantee of the effectiveness for the proposed approach as a certification. Different from existing alternatives, IDEA is not designed for any specific GNNs or optimization objectives to perform certified unlearning, and thus can be easily generalized. Extensive experiments on real-world datasets demonstrate the superiority of IDEA in multiple key perspectives.
NVC-1B: A Large Neural Video Coding Model
Venue: arXiv cs.CV 2024
Year: 2024

The emerging large models have achieved notable progress in the fields of natural language processing and computer vision. However, large models for neural video coding are still unexplored. In this paper, we try to explore how to build a large neural video coding model. Based on a small baseline model, we gradually scale up the model sizes of its different coding parts, including the motion encoder-decoder, motion entropy model, contextual encoder-decoder, contextual entropy model, and temporal context mining module, and analyze the influence of model sizes on video compression performance. Then, we explore to use different architectures, including CNN, mixed CNN-Transformer, and Transformer architectures, to implement the neural video coding model and analyze the influence of model architectures on video compression performance. Based on our exploration results, we design the first neural video coding model with more than 1 billion parameters -- NVC-1B. Experimental results show that our proposed large model achieves a significant video compression performance improvement over the small baseline model, and represents the state-of-the-art compression efficiency. We anticipate large models may bring up the video coding technologies to the next level.
Identity-Driven Hierarchical Role-Playing Agents
Venue: arXiv cs.AI 2024
Year: 2024

Utilizing large language models (LLMs) to achieve role-playing has gained great attention recently. The primary implementation methods include leveraging refined prompts and fine-tuning on role-specific datasets. However, these methods suffer from insufficient precision and limited flexibility respectively. To achieve a balance between flexibility and precision, we construct a Hierarchical Identity Role-Playing Framework (HIRPF) based on identity theory, constructing complex characters using multiple identity combinations. We develop an identity dialogue dataset for this framework and propose an evaluation benchmark including scale evaluation and open situation evaluation. Empirical results indicate the remarkable efficacy of our framework in modeling identity-level role simulation, and reveal its potential for application in social simulation.
A Generic Review of Integrating Artificial Intelligence in Cognitive Behavioral Therapy
Venue: arXiv cs.AI 2024
Year: 2024

Cognitive Behavioral Therapy (CBT) is a well-established intervention for mitigating psychological issues by modifying maladaptive cognitive and behavioral patterns. However, delivery of CBT is often constrained by resource limitations and barriers to access. Advancements in artificial intelligence (AI) have provided technical support for the digital transformation of CBT. Particularly, the emergence of pre-training models (PTMs) and large language models (LLMs) holds immense potential to support, augment, optimize and automate CBT delivery. This paper reviews the literature on integrating AI into CBT interventions. We begin with an overview of CBT. Then, we introduce the integration of AI into CBT across various stages: pre-treatment, therapeutic process, and post-treatment. Next, we summarized the datasets relevant to some CBT-related tasks. Finally, we discuss the benefits and current limitations of applying AI to CBT. We suggest key areas for future research, highlighting the need for further exploration and validation of the long-term efficacy and clinical utility of AI-enhanced CBT. The transformative potential of AI in reshaping the practice of CBT heralds a new era of more accessible, efficient, and personalized mental health interventions.