OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Loss Decay based Robust Oriented Object Detection on Remote Sensing Images with Noisy Labels
Venue: arXiv cs.CV 2024
Year: 2024

The ambiguous appearance, tiny scale, and fine-grained classes of objects in remote sensing imagery inevitably lead to the noisy annotations in category labels of detection dataset. However, the effects and treatments of the label noises are underexplored in modern oriented remote sensing object detectors. To address this issue, we propose a robust oriented remote sensing object detection method through dynamic loss decay (DLD) mechanism, inspired by the two phase ``early-learning'' and ``memorization'' learning dynamics of deep neural networks on clean and noisy samples. To be specific, we first observe the end point of early learning phase termed as EL, after which the models begin to memorize the false labels that significantly degrade the detection accuracy. Secondly, under the guidance of the training indicator, the losses of each sample are ranked in descending order, and we adaptively decay the losses of the top K largest ones (bad samples) in the following epochs. Because these large losses are of high confidence to be calculated with wrong labels. Experimental results show that the method achieves excellent noise resistance performance tested on multiple public datasets such as HRSC2016 and DOTA-v1.0/v2.0 with synthetic category label noise. Our solution also has won the 2st place in the "fine-grained object detection based on sub-meter remote sensing imagery" track with noisy labels of 2023 National Big Data and Computing Intelligence Challenge.
3D Shape Augmentation with Content-Aware Shape Resizing
Venue: arXiv cs.CV 2024
Year: 2024

Recent advancements in deep learning for 3D models have propelled breakthroughs in generation, detection, and scene understanding. However, the effectiveness of these algorithms hinges on large training datasets. We address the challenge by introducing Efficient 3D Seam Carving (E3SC), a novel 3D model augmentation method based on seam carving, which progressively deforms only part of the input model while ensuring the overall semantics are unchanged. Experiments show that our approach is capable of producing diverse and high-quality augmented 3D shapes across various types and styles of input models, achieving considerable improvements over previous methods. Quantitative evaluations demonstrate that our method effectively enhances the novelty and quality of shapes generated by other subsequent 3D generation algorithms.
A safety realignment framework via subspace-oriented model fusion for large language models
Venue: arXiv cs.CL 2024
Year: 2024

The current safeguard mechanisms for large language models (LLMs) are indeed susceptible to jailbreak attacks, making them inherently fragile. Even the process of fine-tuning on apparently benign data for downstream tasks can jeopardize safety. One potential solution is to conduct safety fine-tuning subsequent to downstream fine-tuning. However, there's a risk of catastrophic forgetting during safety fine-tuning, where LLMs may regain safety measures but lose the task-specific knowledge acquired during downstream fine-tuning. In this paper, we introduce a safety realignment framework through subspace-oriented model fusion (SOMF), aiming to combine the safeguard capabilities of initially aligned model and the current fine-tuned model into a realigned model. Our approach begins by disentangling all task vectors from the weights of each fine-tuned model. We then identify safety-related regions within these vectors by subspace masking techniques. Finally, we explore the fusion of the initial safely aligned LLM with all task vectors based on the identified safety subspace. We validate that our safety realignment framework satisfies the safety requirements of a single fine-tuned model as well as multiple models during their fusion. Our findings confirm that SOMF preserves safety without notably compromising performance on downstream tasks, including instruction following in Chinese, English, and Hindi, as well as problem-solving capabilities in Code and Math.
CTS: A Consistency-Based Medical Image Segmentation Model
Venue: arXiv cs.CV 2024
Year: 2024

In medical image segmentation tasks, diffusion models have shown significant potential. However, mainstream diffusion models suffer from drawbacks such as multiple sampling times and slow prediction results. Recently, consistency models, as a standalone generative network, have resolved this issue. Compared to diffusion models, consistency models can reduce the sampling times to once, not only achieving similar generative effects but also significantly speeding up training and prediction. However, they are not suitable for image segmentation tasks, and their application in the medical imaging field has not yet been explored. Therefore, this paper applies the consistency model to medical image segmentation tasks, designing multi-scale feature signal supervision modes and loss function guidance to achieve model convergence. Experiments have verified that the CTS model can obtain better medical image segmentation results with a single sampling during the test phase.
See to Believe: Using Visualization To Motivate Updating Third-party Dependencies
Venue: arXiv cs.SE 2024
Year: 2024

Security vulnerabilities introduced by applications using third-party dependencies are on the increase, caused by the emergence of large ecosystems of libraries such as the NPM packages for JavaScript. Nowadays, libraries depend on each other. Relying on these large ecosystems thus means that vulnerable dependencies are not only direct but also indirect (transitive) dependencies. There are automated tool supports to manage these complex dependencies but recent work still shows that developers are wary of library updates, even to fix vulnerabilities, citing that being unaware, or that the migration effort to update outweighs the decision. In this paper, we hypothesize that the dependency graph visualization (DGV) approach will motivate developers to update, especially when convincing developers. To test this hypothesis, we performed a user study involving 20 participants divided equally into experimental and control groups, comparing the state-of-the-art tools with the tasks of reviewing vulnerabilities with complexities and vulnerabilities with indirect dependencies. We find that 70% of the participants who saw the visualization did re-prioritize their updates in both tasks. This is higher than the 30% and 60% of the participants who used the npm audit tool in both tasks, respectively.
Explainable AI for Ship Collision Avoidance: Decoding Decision-Making Processes and Behavioral Intentions
Venue: arXiv cs.RO 2024
Year: 2024

This study developed an explainable AI for ship collision avoidance. Initially, a critic network composed of sub-task critic networks was proposed to individually evaluate each sub-task in collision avoidance to clarify the AI decision-making processes involved. Additionally, an attempt was made to discern behavioral intentions through a Q-value analysis and an Attention mechanism. The former focused on interpreting intentions by examining the increment of the Q-value resulting from AI actions, while the latter incorporated the significance of other ships in the decision-making process for collision avoidance into the learning objective. AI's behavioral intentions in collision avoidance were visualized by combining the perceived collision danger with the degree of attention to other ships. The proposed method was evaluated through a numerical experiment. The developed AI was confirmed to be able to safely avoid collisions under various congestion levels, and AI's decision-making process was rendered comprehensible to humans. The proposed method not only facilitates the understanding of DRL-based controllers/systems in the ship collision avoidance task but also extends to any task comprising sub-tasks.
RobustMVS: Single Domain Generalized Deep Multi-view Stereo
Venue: arXiv cs.CV 2024
Year: 2024

Despite the impressive performance of Multi-view Stereo (MVS) approaches given plenty of training samples, the performance degradation when generalizing to unseen domains has not been clearly explored yet. In this work, we focus on the domain generalization problem in MVS. To evaluate the generalization results, we build a novel MVS domain generalization benchmark including synthetic and real-world datasets. In contrast to conventional domain generalization benchmarks, we consider a more realistic but challenging scenario, where only one source domain is available for training. The MVS problem can be analogized back to the feature matching task, and maintaining robust feature consistency among views is an important factor for improving generalization performance. To address the domain generalization problem in MVS, we propose a novel MVS framework, namely RobustMVS. A DepthClustering-guided Whitening (DCW) loss is further introduced to preserve the feature consistency among different views, which decorrelates multi-view features from viewpoint-specific style information based on geometric priors from depth maps. The experimental results further show that our method achieves superior performance on the domain generalization benchmark.
An Empirical Study of Token-based Micro Commits
Venue: arXiv cs.SE 2024
Year: 2024

In software development, developers frequently apply maintenance activities to the source code that change a few lines by a single commit. A good understanding of the characteristics of such small changes can support quality assurance approaches (e.g., automated program repair), as it is likely that small changes are addressing deficiencies in other changes; thus, understanding the reasons for creating small changes can help understand the types of errors introduced. Eventually, these reasons and the types of errors can be used to enhance quality assurance approaches for improving code quality. While prior studies used code churns to characterize and investigate the small changes, such a definition has a critical limitation. Specifically, it loses the information of changed tokens in a line. For example, this definition fails to distinguish the following two one-line changes: (1) changing a string literal to fix a displayed message and (2) changing a function call and adding a new parameter. These are definitely maintenance activities, but we deduce that researchers and practitioners are interested in supporting the latter change. To address this limitation, in this paper, we define micro commits, a type of small change based on changed tokens. Our goal is to quantify small changes using changed tokens. Changed tokens allow us to identify small changes more precisely. In fact, this token-level definition can distinguish the above example. We investigate defined micro commits in four OSS projects and understand their characteristics as the first empirical study on token-based micro commits. We find that micro commits mainly replace a single name or literal token, and micro commits are more likely used to fix bugs. Additionally, we propose the use of token-based information to support software engineering approaches in which very small changes significantly affect their effectiveness.
Influence Maximization in Hypergraphs Using A Genetic Algorithm with New Initialization and Evaluation Methods
Venue: arXiv cs.SI 2024
Year: 2024

Influence maximization (IM) is a crucial optimization task related to analyzing complex networks in the real world, such as social networks, disease propagation networks, and marketing networks. Publications to date about the IM problem focus mainly on graphs, which fail to capture high-order interaction relationships from the real world. Therefore, the use of hypergraphs for addressing the IM problem has been receiving increasing attention. However, identifying the most influential nodes in hypergraphs remains challenging, mainly because nodes and hyperedges are often strongly coupled and correlated. In this paper, to effectively identify the most influential nodes, we first propose a novel hypergraph-independent cascade model that integrates the influences of both node and hyperedge failures. Afterward, we introduce genetic algorithms (GA) to identify the most influential nodes that leverage hypergraph collective influences. In the GA-based method, the hypergraph collective influence is effectively used to initialize the population, thereby enhancing the quality of initial candidate solutions. The designed fitness function considers the joint influences of both nodes and hyperedges. This ensures the optimal set of nodes with the best influence on both nodes and hyperedges to be evaluated accurately. Moreover, a new mutation operator is designed by introducing factors, i.e., the collective influence and overlapping effects of nodes in hypergraphs, to breed high-quality offspring. In the experiments, several simulations on both synthetic and real hypergraphs have been conducted, and the results demonstrate that the proposed method outperforms the compared methods.
Advancing Explainable AI with Causal Analysis in Large-Scale Fuzzy Cognitive Maps
Venue: arXiv cs.AI 2024
Year: 2024

In the quest for accurate and interpretable AI models, eXplainable AI (XAI) has become crucial. Fuzzy Cognitive Maps (FCMs) stand out as an advanced XAI method because of their ability to synergistically combine and exploit both expert knowledge and data-driven insights, providing transparency and intrinsic interpretability. This letter introduces and investigates the "Total Causal Effect Calculation for FCMs" (TCEC-FCM) algorithm, an innovative approach that, for the first time, enables the efficient calculation of total causal effects among concepts in large-scale FCMs by leveraging binary search and graph traversal techniques, thereby overcoming the challenge of exhaustive causal path exploration that hinder existing methods. We evaluate the proposed method across various synthetic FCMs that demonstrate TCEC-FCM's superior performance over exhaustive methods, marking a significant advancement in causal effect analysis within FCMs, thus broadening their usability for modern complex XAI applications.
A first look into Utiq: Next-generation cookies at the ISP level
Venue: arXiv cs.CR 2024
Year: 2024

Online privacy has become increasingly important in recent years. While third-party cookies have been widely used for years, they have also been criticized for their potential impact on user privacy. They can be used by advertisers to track users across multiple sites, allowing them to build detailed profiles of their behavior and interests. However, nowadays, many browsers allow users to block third-party cookies, which limits their usefulness for advertisers. In this paper, we take a first look at Utiq, a new way of user tracking performed directly by the ISP, to substitute the third-party cookies used until now. We study the main properties of this new identification methodology and their adoption on the 10K most popular websites. Our results show that, although still marginal due to the restrictions imposed by the system, between 0.7% and 1.2% of websites already include Utiq as one of their user identification methods.
Bridging the gap in online hate speech detection: a comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter
Venue: arXiv cs.CL 2024
Year: 2024

Our study addresses a significant gap in online hate speech detection research by focusing on homophobia, an area often neglected in sentiment analysis research. Utilising advanced sentiment analysis models, particularly BERT, and traditional machine learning methods, we developed a nuanced approach to identify homophobic content on X/Twitter. This research is pivotal due to the persistent underrepresentation of homophobia in detection models. Our findings reveal that while BERT outperforms traditional methods, the choice of validation technique can impact model performance. This underscores the importance of contextual understanding in detecting nuanced hate speech. By releasing the largest open-source labelled English dataset for homophobia detection known to us, an analysis of various models' performance and our strongest BERT-based model, we aim to enhance online safety and inclusivity. Future work will extend to broader LGBTQIA+ hate speech detection, addressing the challenges of sourcing diverse datasets. Through this endeavour, we contribute to the larger effort against online hate, advocating for a more inclusive digital landscape. Our study not only offers insights into the effective detection of homophobic content by improving on previous research results, but it also lays groundwork for future advancements in hate speech analysis.
Preconceptual Modeling in Software Engineering: Metaphysics of Diagrammatic Representations
Venue: arXiv cs.SE 2024
Year: 2024

According to many researchers, conceptual model (CM) development is a hard task, and system requirements are difficult to collect, causing many miscommunication problems. CMs require more than modeling ability alone - they first require an understanding of the targeted domain that the model attempts to represent. Accordingly, a preconceptual modeling (pre-CM) stage is intended to address ontological issues before typical CM development is initiated. It involves defining a portion of reality when entities and processes are differentiated and integrated as unified wholes. This pre-CM phase forms the focus of research in this paper. The purpose is not show how to model; rather, it is to demonstrate how to establish a metaphysical basis of the involved portion of reality. To demonstrate such a venture, we employ the so-called thinging machine (TM) modeling that has been proposed as a high-level CM. A TM model integrates staticity and dynamism grounded in a fundamental construct called a thimac (things/machine). It involves two modes of reality, existence (events) and subsistence (regions - roughly, specifications of things and processes). Currently, the dominant approach in CM has evolved to limit its scope of application to develop ontological categorization (types of things). In the TM approach, pre-CM metaphysics is viewed as a part and parcel of CM itself. The general research problem is how to map TM constructs to what is out there in the targeted domain. Discussions involve the nature of thimacs (things and processes) and subsistence and existence as they are superimposed over each other in reality. Specifically, we make two claims, (a) the perceptibility of regions as a phenomenon and (b) the distinctiveness of existence as a construct for events. The results contribute to further the understanding of TM modeling in addition to introducing some metaphysical insights.
Measurements of Building Attenuation in 450 MHz LTE Networks
Venue: arXiv cs.NI 2024
Year: 2024

This work reports on a measurement study to estimate the attenuation of 450 MHz LTE networks. The LTE band 72 is currently deployed in Germany, in particular for smart grid applications. Due to this use-case, we assume that a significant amount of future devices will be deployed stationary and indoor which motivated our campaign. We designed a custom measurement device which uses commercial off-the-shelf hardware to assess the downlink RSRP of a public mobile network. In addition, a software has been developed to provide non-experts the possibility to conduct these measurements in the future. This software provides the possibility to determine the indoor position based on ground plans. We conducted measurements at three different buildings. Our results reveal, that the building attenuation of 450 MHz LTE networks is highly heterogeneous and mainly depends on the type of the building, the indoor position and in particular the height of the floor where the device is located.
Branch-and-price with novel cuts, and a new Stackelberg Security Game
Venue: arXiv cs.GT 2024
Year: 2024

Anticipating the strategies of potential attackers is crucial for protecting critical infrastructure. We can represent the challenge of the defenders of such infrastructure as a Stackelberg security game. The defender must decide how to allocate limited resources to protect specific targets, aiming to maximize their expected utility (such as minimizing the extent of damage) and considering that attackers will respond in a way that is most advantageous to them. We present novel valid inequalities to find a Strong Stackelberg Equilibrium in both Stackelberg games and Stackelberg security games. We also consider a Stackelberg security game that aims to protect targets with a defined budget. We use branch-and-price in this game to show that our approach outperforms the standard formulation in the literature, and we conduct an extensive computational study to analyze the impact of various branch-and-price parameters on the performance of our method in different game settings.
DemOpts: Fairness corrections in COVID-19 case prediction models
Venue: arXiv cs.LG 2024
Year: 2024

COVID-19 forecasting models have been used to inform decision making around resource allocation and intervention decisions e.g., hospital beds or stay-at-home orders. State of the art deep learning models often use multimodal data such as mobility or socio-demographic data to enhance COVID-19 case prediction models. Nevertheless, related work has revealed under-reporting bias in COVID-19 cases as well as sampling bias in mobility data for certain minority racial and ethnic groups, which could in turn affect the fairness of the COVID-19 predictions along race labels. In this paper, we show that state of the art deep learning models output mean prediction errors that are significantly different across racial and ethnic groups; and which could, in turn, support unfair policy decisions. We also propose a novel de-biasing method, DemOpts, to increase the fairness of deep learning based forecasting models trained on potentially biased datasets. Our results show that DemOpts can achieve better error parity that other state of the art de-biasing approaches, thus effectively reducing the differences in the mean error distributions across more racial and ethnic groups.
MGSER-SAM: Memory-Guided Soft Experience Replay with Sharpness-Aware Optimization for Enhanced Continual Learning
Venue: arXiv cs.LG 2024
Year: 2024

Deep neural networks suffer from the catastrophic forgetting problem in the field of continual learning (CL). To address this challenge, we propose MGSER-SAM, a novel memory replay-based algorithm specifically engineered to enhance the generalization capabilities of CL models. We first intergrate the SAM optimizer, a component designed for optimizing flatness, which seamlessly fits into well-known Experience Replay frameworks such as ER and DER++. Then, MGSER-SAM distinctively addresses the complex challenge of reconciling conflicts in weight perturbation directions between ongoing tasks and previously stored memories, which is underexplored in the SAM optimizer. This is effectively accomplished by the strategic integration of soft logits and the alignment of memory gradient directions, where the regularization terms facilitate the concurrent minimization of various training loss terms integral to the CL process. Through rigorous experimental analysis conducted across multiple benchmarks, MGSER-SAM has demonstrated a consistent ability to outperform existing baselines in all three CL scenarios. Comparing to the representative memory replay-based baselines ER and DER++, MGSER-SAM not only improves the testing accuracy by $24.4\%$ and $17.6\%$ respectively, but also achieves the lowest forgetting on each benchmark.