OpenRaiser/Intern-Atlas huggingface dataset:
Large Multi-modality Model Assisted AI-Generated Image Quality Assessment
Venue: arXiv cs.CV 2024
Year: 2024

Traditional deep neural network (DNN)-based image quality assessment (IQA) models leverage convolutional neural networks (CNN) or Transformer to learn the quality-aware feature representation, achieving commendable performance on natural scene images. However, when applied to AI-Generated images (AGIs), these DNN-based IQA models exhibit subpar performance. This situation is largely due to the semantic inaccuracies inherent in certain AGIs caused by uncontrollable nature of the generation process. Thus, the capability to discern semantic content becomes crucial for assessing the quality of AGIs. Traditional DNN-based IQA models, constrained by limited parameter complexity and training data, struggle to capture complex fine-grained semantic features, making it challenging to grasp the existence and coherence of semantic content of the entire image. To address the shortfall in semantic content perception of current IQA models, we introduce a large Multi-modality model Assisted AI-Generated Image Quality Assessment (MA-AGIQA) model, which utilizes semantically informed guidance to sense semantic information and extract semantic vectors through carefully designed text prompts. Moreover, it employs a mixture of experts (MoE) structure to dynamically integrate the semantic information with the quality-aware features extracted by traditional DNN-based IQA models. Comprehensive experiments conducted on two AI-generated content datasets, AIGCQA-20k and AGIQA-3k show that MA-AGIQA achieves state-of-the-art performance, and demonstrate its superior generalization capabilities on assessing the quality of AGIs. Code is available at https://github.com/wangpuyi/MA-AGIQA.
Limits of Sequential Local Algorithms on the Random $k$-XORSAT Problem
Venue: arXiv cs.CC 2024
Year: 2024

The random $k$-XORSAT problem is a random constraint satisfaction problem of $n$ Boolean variables and $m=rn$ clauses, which a random instance can be expressed as a $G\mathbb{F}(2)$ linear system of the form $Ax=b$, where $A$ is a random $m \times n$ matrix with $k$ ones per row, and $b$ is a random vector. It is known that there exist two distinct thresholds $r_{core}(k) < r_{sat}(k)$ such that as $n \rightarrow \infty$ for $r < r_{sat}(k)$ the random instance has solutions with high probability, while for $r_{core} < r < r_{sat}(k)$ the solution space shatters into an exponential number of clusters. Sequential local algorithms are a natural class of algorithms which assign values to variables one by one iteratively. In each iteration, the algorithm runs some heuristics, called local rules, to decide the value assigned, based on the local neighborhood of the selected variables under the factor graph representation of the instance. We prove that for any $r > r_{core}(k)$ the sequential local algorithms with certain local rules fail to solve the random $k$-XORSAT with high probability. They include (1) the algorithm using the Unit Clause Propagation as local rule for $k \ge 9$, and (2) the algorithms using any local rule that can calculate the exact marginal probabilities of variables in instances with factor graphs that are trees, for $k\ge 13$. The well-known Belief Propagation and Survey Propagation are included in (2). Meanwhile, the best known linear-time algorithm succeeds with high probability for $r < r_{core}(k)$. Our results support the intuition that $r_{core}(k)$ is the sharp threshold for the existence of a linear-time algorithm for random $k$-XORSAT.
MRScore: Evaluating Radiology Report Generation with LLM-based Reward System
Venue: arXiv cs.CL 2024
Year: 2024

In recent years, automated radiology report generation has experienced significant growth. This paper introduces MRScore, an automatic evaluation metric tailored for radiology report generation by leveraging Large Language Models (LLMs). Conventional NLG (natural language generation) metrics like BLEU are inadequate for accurately assessing the generated radiology reports, as systematically demonstrated by our observations within this paper. To address this challenge, we collaborated with radiologists to develop a framework that guides LLMs for radiology report evaluation, ensuring alignment with human analysis. Our framework includes two key components: i) utilizing GPT to generate large amounts of training data, i.e., reports with different qualities, and ii) pairing GPT-generated reports as accepted and rejected samples and training LLMs to produce MRScore as the model reward. Our experiments demonstrate MRScore's higher correlation with human judgments and superior performance in model selection compared to traditional metrics. Our code and datasets will be available on GitHub.
Automatic Build Repair for Test Cases using Incompatible Java Versions
Venue: arXiv cs.SE 2024
Year: 2024

Context: Bug bisection is a common technique used to identify a revision that introduces a bug or indirectly fixes a bug, and often involves executing multiple revisions of a project to determine whether the bug is present within the revision. However, many legacy revisions often cannot be successfully compiled due to changes in the programming language or tools used in the compilation process, adding complexity and preventing automation in the bisection process. Objective: In this paper, we introduce an approach to repair test cases of Java projects by performing dependency minimization. Our approach aims to remove classes and methods that are not required for the execution of one or more test cases. Unlike existing state-of-the-art techniques, our approach performs minimization at source-level, which allows compile-time errors to be fixed. Method: A standalone Java tool implementing our technique was developed, and we evaluated our technique using subjects from Defects4J retargeted against Java 8 and 17. Results: Our evaluation showed that a majority of subjects can be repaired solely by performing minimization, including replicating the test results of the original version. Furthermore, our technique is also shown to achieve accurate minimized results, while only adding a small overhead to the bisection process. Conclusion: Our proposed technique is shown to be effective for repairing build failures with minimal overhead, making it suitable for use in automated bug bisection. Our tool can also be adapted for use cases such as bug corpus creation and refactoring.
Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation
Venue: arXiv cs.RO 2024
Year: 2024

Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human-like cognition and cost evaluation for off-road driving. First, we construct a multi-layer map describing different features of off-road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a CNN-LSTM network to learn the trajectories planned by human drivers in various off-road scenarios. Then, based on human-like generated trajectories in different environments, we design a primitive-based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off-road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off-road environment with complex terrain and varying road conditions. The experimental results show that the proposed human-like motion planner has excellent adaptability to different off-road conditions. It shows real-time operation, greater stability, and more human-like planning ability in diverse and challenging scenarios.
GenAI Distortion: The Effect of GenAI Fluency and Positive Affect
Venue: arXiv cs.HC 2024
Year: 2024

The introduction of generative artificial intelligence (GenAI) into educational practices has been transformative, yet it brings a crucial concern about the potential distortion of users' beliefs. Given the prevalence of GenAI among college students, examining the psychological mechanisms that lead to GenAI distortion from both technological factors and the individual's psychological processes is a critical priority. A mixed-methods approach is employed to test the proposed hypotheses. Study 1 (N = 10) revealed through qualitative analysis that GenAI's fluent outputs significantly engaged college students, eliciting positive emotional responses during an interaction. GenAI's tendency to conflate fact with fiction often led to presentations of unrealistic and exaggerated information, potentially distorting users' perceptions of reality-a phenomenon termed GenAI distortion. Following these insights, Study 2 (cross-sectional survey, N = 999) and Study 3 (experimental manipulation, N = 175) explored how GenAI fluency affects college students' GenAI distortion and examined the mediating effect of positive affect. The results indicated that GenAI fluency predicts GenAI distortion via the mediating role of positive affect. Our findings provide theoretical foundations and practical implications for understanding GenAI distortion among college students.
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum
Venue: arXiv cs.CL 2024
Year: 2024

Efficiently capturing consistent and complementary semantic features in a multimodal conversation context is crucial for Multimodal Emotion Recognition in Conversation (MERC). Existing methods mainly use graph structures to model dialogue context semantic dependencies and employ Graph Neural Networks (GNN) to capture multimodal semantic features for emotion recognition. However, these methods are limited by some inherent characteristics of GNN, such as over-smoothing and low-pass filtering, resulting in the inability to learn long-distance consistency information and complementary information efficiently. Since consistency and complementarity information correspond to low-frequency and high-frequency information, respectively, this paper revisits the problem of multimodal emotion recognition in conversation from the perspective of the graph spectrum. Specifically, we propose a Graph-Spectrum-based Multimodal Consistency and Complementary collaborative learning framework GS-MCC. First, GS-MCC uses a sliding window to construct a multimodal interaction graph to model conversational relationships and uses efficient Fourier graph operators to extract long-distance high-frequency and low-frequency information, respectively. Then, GS-MCC uses contrastive learning to construct self-supervised signals that reflect complementarity and consistent semantic collaboration with high and low-frequency signals, thereby improving the ability of high and low-frequency information to reflect real emotions. Finally, GS-MCC inputs the collaborative high and low-frequency information into the MLP network and softmax function for emotion prediction. Extensive experiments have proven the superiority of the GS-MCC architecture proposed in this paper on two benchmark data sets.
Generalizing Roberts' characterization of unit interval graphs
Venue: arXiv cs.DM 2024
Year: 2024

For any natural number $d$, a graph $G$ is a (disjoint) $d$-interval graph if it is the intersection graph of (disjoint) $d$-intervals, the union of $d$ (disjoint) intervals on the real line. Two important subclasses of $d$-interval graphs are unit and balanced $d$-interval graphs (where every interval has unit length or all the intervals associated to a same vertex have the same length, respectively). A celebrated result by Roberts gives a simple characterization of unit interval graphs being exactly claw-free interval graphs. Here, we study the generalization of this characterization for $d$-interval graphs. In particular, we prove that for any $d \geq 2$, if $G$ is a $K_{1,2d+1}$-free interval graph, then $G$ is a unit $d$-interval graph. However, somehow surprisingly, under the same assumptions, $G$ is not always a \emph{disjoint} unit $d$-interval graph. This implies that the class of disjoint unit $d$-interval graphs is strictly included in the class of unit $d$-interval graphs. Finally, we study the relationships between the classes obtained under disjoint and non-disjoint $d$-intervals in the balanced case and show that the classes of disjoint balanced 2-intervals and balanced 2-intervals coincide, but this is no longer true for $d>2$.
PromptCL: Improving Event Representation via Prompt Template and Contrastive Learning
Venue: arXiv cs.CL 2024
Year: 2024

The representation of events in text plays a significant role in various NLP tasks. Recent research demonstrates that contrastive learning has the ability to improve event comprehension capabilities of Pre-trained Language Models (PLMs) and enhance the performance of event representation learning. However, the efficacy of event representation learning based on contrastive learning and PLMs is limited by the short length of event texts. The length of event texts differs significantly from the text length used in the pre-training of PLMs. As a result, there is inconsistency in the distribution of text length between pre-training and event representation learning, which may undermine the learning process of event representation based on PLMs. In this study, we present PromptCL, a novel framework for event representation learning that effectively elicits the capabilities of PLMs to comprehensively capture the semantics of short event texts. PromptCL utilizes a Prompt template borrowed from prompt learning to expand the input text during Contrastive Learning. This helps in enhancing the event representation learning by providing a structured outline of the event components. Moreover, we propose Subject-Predicate-Object (SPO) word order and Event-oriented Masked Language Modeling (EventMLM) to train PLMs to understand the relationships between event components. Our experimental results demonstrate that PromptCL outperforms state-of-the-art baselines on event related tasks. Additionally, we conduct a thorough analysis and demonstrate that using a prompt results in improved generalization capabilities for event representations. Our code will be available at https://github.com/YuboFeng2023/PromptCL.
Directed Isoperimetry and Monotonicity Testing: A Dynamical Approach
Venue: arXiv cs.DS 2024
Year: 2024

This paper explores the connection between classical isoperimetric inequalities, their directed analogues, and monotonicity testing. We study the setting of real-valued functions $f : [0,1]^d \to \mathbb{R}$ on the solid unit cube, where the goal is to test with respect to the $L^p$ distance. Our goals are twofold: to further understand the relationship between classical and directed isoperimetry, and to give a monotonicity tester with sublinear query complexity in this setting. Our main results are 1) an $L^2$ monotonicity tester for $M$-Lipschitz functions with query complexity $\widetilde O(\sqrt{d} M^2 / ε^2)$ and, behind this result, 2) the directed Poincaré inequality $\mathsf{dist}^{\mathsf{mono}}_2(f)^2 \le C \mathbb{E}[|\nabla^- f|^2]$, where the "directed gradient" operator $\nabla^-$ measures the local violations of monotonicity of $f$. To prove the second result, we introduce a partial differential equation (PDE), the directed heat equation, which takes a one-dimensional function $f$ into a monotone function $f^*$ over time and enjoys many desirable analytic properties. We obtain the directed Poincaré inequality by combining convergence aspects of this PDE with the theory of optimal transport. Crucially for our conceptual motivation, this proof is in complete analogy with the mathematical physics perspective on the classical Poincaré inequality, namely as characterizing the convergence of the standard heat equation toward equilibrium.
Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection
Venue: arXiv cs.CV 2024
Year: 2024

Semi-supervised 3D object detection can benefit from the promising pseudo-labeling technique when labeled data is limited. However, recent approaches have overlooked the impact of noisy pseudo-labels during training, despite efforts to enhance pseudo-label quality through confidence-based filtering. In this paper, we examine the impact of noisy pseudo-labels on IoU-based target assignment and propose the Reliable Student framework, which incorporates two complementary approaches to mitigate errors. First, it involves a class-aware target assignment strategy that reduces false negative assignments in difficult classes. Second, it includes a reliability weighting strategy that suppresses false positive assignment errors while also addressing remaining false negatives from the first step. The reliability weights are determined by querying the teacher network for confidence scores of the student-generated proposals. Our work surpasses the previous state-of-the-art on KITTI 3D object detection benchmark on point clouds in the semi-supervised setting. On 1% labeled data, our approach achieves a 6.2% AP improvement for the pedestrian class, despite having only 37 labeled samples available. The improvements become significant for the 2% setting, achieving 6.0% AP and 5.7% AP improvements for the pedestrian and cyclist classes, respectively.
SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Radiology Report Generation (R2Gen) demonstrates how Multi-modal Large Language Models (MLLMs) can automate the creation of accurate and coherent radiological reports. Existing methods often hallucinate details in text-based reports that don't accurately reflect the image content. To mitigate this, we introduce a novel strategy, SERPENT-VLM (SElf Refining Radiology RePort GENeraTion using Vision Language Models), which improves the R2Gen task by integrating a self-refining mechanism into the MLLM framework. We employ a unique self-supervised loss that leverages similarity between pooled image representations and the contextual representations of the generated radiological text, alongside the standard Causal Language Modeling objective, to refine image-text representations. This allows the model to scrutinize and align the generated text through dynamic interaction between a given image and the generated text, therefore reducing hallucination and continuously enhancing nuanced report generation. SERPENT-VLM outperforms existing baselines such as LLaVA-Med, BiomedGPT, etc., achieving SoTA performance on the IU X-ray and Radiology Objects in COntext (ROCO) datasets, and also proves to be robust against noisy images. A qualitative case study emphasizes the significant advancements towards more sophisticated MLLM frameworks for R2Gen, opening paths for further research into self-supervised refinement in the medical imaging domain.
I Have an Attention Bridge to Sell You: Generalization Capabilities of Modular Translation Architectures
Venue: arXiv cs.CL 2024
Year: 2024

Modularity is a paradigm of machine translation with the potential of bringing forth models that are large at training time and small during inference. Within this field of study, modular approaches, and in particular attention bridges, have been argued to improve the generalization capabilities of models by fostering language-independent representations. In the present paper, we study whether modularity affects translation quality; as well as how well modular architectures generalize across different evaluation scenarios. For a given computational budget, we find non-modular architectures to be always comparable or preferable to all modular designs we study.
Approximation and FPT Algorithms for Finding DM-Irreducible Spanning Subgraphs
Venue: arXiv cs.DS 2024
Year: 2024

Finding a minimum-weight strongly connected spanning subgraph of an edge-weighted directed graph is equivalent to the weighted version of the well-known strong connectivity augmentation problem. This problem is NP-hard, and a simple $2$-approximation algorithm was proposed by Frederickson and Jájá (1981); surprisingly, it still achieves the best known approximation ratio in general. Also, Bang-Jensen and Yeo (2008) showed that the unweighted problem is FPT (fixed-parameter tractable) parameterized by the difference from a trivial upper bound of the optimal value. In this paper, we consider a generalization related to the Dulmage--Mendelsohn decompositions of bipartite graphs instead of the strong connectivity of directed graphs, and extend these approximation and FPT results to the generalized setting.
A Survey of Third-Party Library Security Research in Application Software
Venue: arXiv cs.SE 2024
Year: 2024

In the current software development environment, third-party libraries play a crucial role. They provide developers with rich functionality and convenient solutions, speeding up the pace and efficiency of software development. However, with the widespread use of third-party libraries, associated security risks and potential vulnerabilities are increasingly apparent. Malicious attackers can exploit these vulnerabilities to infiltrate systems, execute unauthorized operations, or steal sensitive information, posing a severe threat to software security. Research on third-party libraries in software becomes paramount to address this growing security challenge. Numerous research findings exist regarding third-party libraries' usage, ecosystem, detection, and fortification defenses. Understanding the usage and ecosystem of third-party libraries helps developers comprehend the potential risks they bring and select trustworthy libraries. Third-party library detection tools aid developers in automatically discovering third-party libraries in software, facilitating their management. In addition to detection, fortification defenses are also indispensable. This article profoundly investigates and analyzes this literature, summarizing current research achievements and future development directions. It aims to provide practical and valuable insights for developers and researchers, jointly promoting the healthy development of software ecosystems and better-protecting software from security threats.