OpenRaiser/Intern-Atlas huggingface dataset:
HyCoT: A Transformer-Based Autoencoder for Hyperspectral Image Compression
Venue: arXiv cs.CV 2024
Year: 2024

The development of learning-based hyperspectral image (HSI) compression models has recently attracted significant interest. Existing models predominantly utilize convolutional filters, which capture only local dependencies. Furthermore,they often incur high training costs and exhibit substantial computational complexity. To address these limitations, in this paper we propose Hyperspectral Compression Transformer (HyCoT) that is a transformer-based autoencoder for pixelwise HSI compression. Additionally, we apply a simple yet effective training set reduction approach to accelerate the training process. Experimental results on the HySpecNet-11k dataset demonstrate that HyCoT surpasses the state of the art across various compression ratios by over 1 dB of PSNR with significantly reduced computational requirements. Our code and pre-trained weights are publicly available at https://git.tu-berlin.de/rsim/hycot .
Comparative Analysis of Generative Models: Enhancing Image Synthesis with VAEs, GANs, and Stable Diffusion
Venue: arXiv cs.CV 2024
Year: 2024

This paper examines three major generative modelling frameworks: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Stable Diffusion models. VAEs are effective at learning latent representations but frequently yield blurry results. GANs can generate realistic images but face issues such as mode collapse. Stable Diffusion models, while producing high-quality images with strong semantic coherence, are demanding in terms of computational resources. Additionally, the paper explores how incorporating Grounding DINO and Grounded SAM with Stable Diffusion improves image accuracy by utilising sophisticated segmentation and inpainting techniques. The analysis guides on selecting suitable models for various applications and highlights areas for further research.
FourierKAN outperforms MLP on Text Classification Head Fine-tuning
Venue: arXiv cs.CL 2024
Year: 2024

In resource constraint settings, adaptation to downstream classification tasks involves fine-tuning the final layer of a classifier (i.e. classification head) while keeping rest of the model weights frozen. Multi-Layer Perceptron (MLP) heads fine-tuned with pre-trained transformer backbones have long been the de facto standard for text classification head fine-tuning. However, the fixed non-linearity of MLPs often struggles to fully capture the nuances of contextual embeddings produced by pre-trained models, while also being computationally expensive. In our work, we investigate the efficacy of KAN and its variant, Fourier KAN (FR-KAN), as alternative text classification heads. Our experiments reveal that FR-KAN significantly outperforms MLPs with an average improvement of 10% in accuracy and 11% in F1-score across seven pre-trained transformer models and four text classification tasks. Beyond performance gains, FR-KAN is more computationally efficient and trains faster with fewer parameters. These results underscore the potential of FR-KAN to serve as a lightweight classification head, with broader implications for advancing other Natural Language Processing (NLP) tasks.
RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba
Venue: arXiv cs.CV 2024
Year: 2024

Existing RGBT tracking methods often design various interaction models to perform cross-modal fusion of each layer, but can not execute the feature interactions among all layers, which plays a critical role in robust multimodal representation, due to large computational burden. To address this issue, this paper presents a novel All-layer multimodal Interaction Network, named AINet, which performs efficient and effective feature interactions of all modalities and layers in a progressive fusion Mamba, for robust RGBT tracking. Even though modality features in different layers are known to contain different cues, it is always challenging to build multimodal interactions in each layer due to struggling in balancing interaction capabilities and efficiency. Meanwhile, considering that the feature discrepancy between RGB and thermal modalities reflects their complementary information to some extent, we design a Difference-based Fusion Mamba (DFM) to achieve enhanced fusion of different modalities with linear complexity. When interacting with features from all layers, a huge number of token sequences (3840 tokens in this work) are involved and the computational burden is thus large. To handle this problem, we design an Order-dynamic Fusion Mamba (OFM) to execute efficient and effective feature interactions of all layers by dynamically adjusting the scan order of different layers in Mamba. Extensive experiments on four public RGBT tracking datasets show that AINet achieves leading performance against existing state-of-the-art methods.
DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Models
Venue: arXiv cs.CV 2024
Year: 2024

Vision-language models (VLMs), e.g., CLIP, have shown remarkable potential in zero-shot image classification. However, adapting these models to new domains remains challenging, especially in unsupervised settings where labeled data is unavailable. Recent research has proposed pseudo-labeling approaches to adapt CLIP in an unsupervised manner using unlabeled target data. Nonetheless, these methods struggle due to noisy pseudo-labels resulting from the misalignment between CLIP's visual and textual representations. This study introduces DPA, an unsupervised domain adaptation method for VLMs. DPA introduces the concept of dual prototypes, acting as distinct classifiers, along with the convex combination of their outputs, thereby leading to accurate pseudo-label construction. Next, it ranks pseudo-labels to facilitate robust self-training, particularly during early training. Finally, it addresses visual-textual misalignment by aligning textual prototypes with image prototypes to further improve the adaptation performance. Experiments on 13 downstream vision tasks demonstrate that DPA significantly outperforms zero-shot CLIP and the state-of-the-art unsupervised adaptation baselines.
SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Image segmentation plays an important role in vision understanding. Recently, the emerging vision foundation models continuously achieved superior performance on various tasks. Following such success, in this paper, we prove that the Segment Anything Model 2 (SAM2) can be a strong encoder for U-shaped segmentation models. We propose a simple but effective framework, termed SAM2-UNet, for versatile image segmentation. Specifically, SAM2-UNet adopts the Hiera backbone of SAM2 as the encoder, while the decoder uses the classic U-shaped design. Additionally, adapters are inserted into the encoder to allow parameter-efficient fine-tuning. Preliminary experiments on various downstream tasks, such as camouflaged object detection, salient object detection, marine animal segmentation, mirror detection, and polyp segmentation, demonstrate that our SAM2-UNet can simply beat existing specialized state-of-the-art methods without bells and whistles. Project page: \url{https://github.com/WZH0120/SAM2-UNet}.
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series
Venue: arXiv cs.LG 2024
Year: 2024

Automating anomaly detection is an open problem in many scientific fields, particularly in time-domain astronomy, where modern telescopes generate millions of alerts per night. Currently, most anomaly detection algorithms for astronomical time-series rely either on hand-crafted features or on features generated through unsupervised representation learning, coupled with standard anomaly detection algorithms. In this work, we introduce a novel approach that leverages the latent space of a neural network classifier for anomaly detection. We then propose a new method called Multi-Class Isolation Forests (MCIF), which trains separate isolation forests for each class to derive an anomaly score for an object based on its latent space representation. This approach significantly outperforms a standard isolation forest when distinct clusters exist in the latent space. Using a simulated dataset emulating the Zwicky Transient Facility (54 anomalies and 12,040 common), our anomaly detection pipeline discovered $46\pm3$ anomalies ($\sim 85\%$ recall) after following up the top 2,000 ($\sim 15\%$) ranked objects. Furthermore, our classifier-based approach outperforms or approaches the performance of other state-of-the-art anomaly detection pipelines. Our novel method demonstrates that existing and new classifiers can be effectively repurposed for real-time anomaly detection. The code used in this work, including a Python package, is publicly available, https://github.com/Rithwik-G/AstroMCAD.
Bayesian inference to improve quality of Retrieval Augmented Generation
Venue: arXiv cs.IR 2024
Year: 2024

Retrieval Augmented Generation or RAG is the most popular pattern for modern Large Language Model or LLM applications. RAG involves taking a user query and finding relevant paragraphs of context in a large corpus typically captured in a vector database. Once the first level of search happens over a vector database, the top n chunks of relevant text are included directly in the context and sent as prompt to the LLM. Problem with this approach is that quality of text chunks depends on effectiveness of search. There is no strong post processing after search to determine if the chunk does hold enough information to include in prompt. Also many times there may be chunks that have conflicting information on the same subject and the model has no prior experience which chunk to prioritize to make a decision. Often times, this leads to the model providing a statement that there are conflicting statements, and it cannot produce an answer. In this research we propose a Bayesian approach to verify the quality of text chunks from the search results. Bayes theorem tries to relate conditional probabilities of the hypothesis with evidence and prior probabilities. We propose that, finding likelihood of text chunks to give a quality answer and using prior probability of quality of text chunks can help us improve overall quality of the responses from RAG systems. We can use the LLM itself to get a likelihood of relevance of a context paragraph. For prior probability of the text chunk, we use the page number in the documents parsed. Assumption is that that paragraphs in earlier pages have a better probability of being findings and more relevant to generalizing an answer.
Privacy in Federated Learning
Venue: arXiv cs.CR 2024
Year: 2024

Federated Learning (FL) represents a significant advancement in distributed machine learning, enabling multiple participants to collaboratively train models without sharing raw data. This decentralized approach enhances privacy by keeping data on local devices. However, FL introduces new privacy challenges, as model updates shared during training can inadvertently leak sensitive information. This chapter delves into the core privacy concerns within FL, including the risks of data reconstruction, model inversion attacks, and membership inference. It explores various privacy-preserving techniques, such as Differential Privacy (DP) and Secure Multi-Party Computation (SMPC), which are designed to mitigate these risks. The chapter also examines the trade-offs between model accuracy and privacy, emphasizing the importance of balancing these factors in practical implementations. Furthermore, it discusses the role of regulatory frameworks, such as GDPR, in shaping the privacy standards for FL. By providing a comprehensive overview of the current state of privacy in FL, this chapter aims to equip researchers and practitioners with the knowledge necessary to navigate the complexities of secure federated learning environments. The discussion highlights both the potential and limitations of existing privacy-enhancing techniques, offering insights into future research directions and the development of more robust solutions.
What should I wear to a party in a Greek taverna? Evaluation for Conversational Agents in the Fashion Domain
Venue: arXiv cs.IR 2024
Year: 2024

Large language models (LLMs) are poised to revolutionize the domain of online fashion retail, enhancing customer experience and discovery of fashion online. LLM-powered conversational agents introduce a new way of discovery by directly interacting with customers, enabling them to express in their own ways, refine their needs, obtain fashion and shopping advice that is relevant to their taste and intent. For many tasks in e-commerce, such as finding a specific product, conversational agents need to convert their interactions with a customer to a specific call to different backend systems, e.g., a search system to showcase a relevant set of products. Therefore, evaluating the capabilities of LLMs to perform those tasks related to calling other services is vital. However, those evaluations are generally complex, due to the lack of relevant and high quality datasets, and do not align seamlessly with business needs, amongst others. To this end, we created a multilingual evaluation dataset of 4k conversations between customers and a fashion assistant in a large e-commerce fashion platform to measure the capabilities of LLMs to serve as an assistant between customers and a backend engine. We evaluate a range of models, showcasing how our dataset scales to business needs and facilitates iterative development of tools.
GeneticPrism: Multifaceted Visualization of Scientific Impact Evolutions
Venue: arXiv cs.DL 2024
Year: 2024

Understanding the evolution of scholarly impact is essential for many real-life decision-making processes in academia, such as research planning, frontier exploration, and award selection. Popular platforms like Google Scholar and Web of Science rely on numerical indicators that are too abstract to convey the context and content of scientific impact, while most existing visualization approaches on mapping science do not consider the presentation of individual scholars' impact evolution using curated self-citation data. This paper builds on our previous work and proposes an integrated pipeline to visualize a scholar's impact evolution from multiple topic facets. A novel 3D prism-shaped visual metaphor is introduced as the overview of a scholar's impact, whilst their scientific evolution on each topic is displayed in a more structured manner. Additional designs by topic chord diagram, streamgraph visualization, and inter-topic flow map, optimized by an elaborate layout algorithm, assist in perceiving the scholar's scientific evolution across topics. A new six-degree-impact glyph metaphor highlights key interdisciplinary works driving the evolution. The proposed visualization methods are evaluated through case studies analyzing the careers of prestigious Turing award laureates and a major visualization venue.
A Survey on Blockchain-based Supply Chain Finance with Progress and Future directions
Venue: arXiv cs.CR 2024
Year: 2024

Supply Chain Finance is very important for supply chain competition, which is an important tool to activate the capital flow in the supply chain. Supply Chain Finance-related research can support multiple applications and services, such as providing accounts receivable financing, enhancing risk management, and optimizing supply chain management. For more than a decade, the development of Blockchain has attracted widely attention in various fields, especially in finance. With the characteristics of data tamper-proof, forgery-proof, cryptography, consensus verification, and decentralization, Blockchain fits well with the realistic needs of Supply Chain Finance, which requires data integrity, authenticity, privacy, and information sharing. Therefore, it is time to summarize the applications of Blockchain technology in the field of Supply Chain Finance. What Blockchain technology brings to Supply Chain Finance is not only to alleviate the problems of information asymmetry, credit disassembly, and financing cost, but also to improve Supply Chain Finance operations through smart contracts to intelligent Supply Chain Finance and in combination with other technologies, such as artificial intelligence, cloud computing, and data mining, jointly. So there has been some work in Blockchain-based Supply Chain Finance research for different Supply Chain Finance oriented applications, but most of these work are at the management level to propose conceptual frameworks or simply use Blockchain without exploiting its deep applications. Moreover, there are few systematic reviews providing a comprehensive summary of current work in the area of Blockchain-based Supply Chain Finance. In this paper, we ...
Single Atom Convolutional Matching Pursuit: Theoretical Framework and Application to Lamb Waves based Structural Health Monitoring
Venue: arXiv cs.CE 2024
Year: 2024

Structural Health Monitoring (SHM) aims to monitor in real time the health state of engineering structures. For thin structures, Lamb Waves (LW) are very efficient for SHM purposes. A bonded piezoelectric transducer (PZT) emits LW in the structure in the form of a short tone burst. This initial wave packet (IWP) propagates in the structure and interacts with its boundaries and discontinuities and with eventual damages generating additional wave packets. The main issues with LW based SHM are that at least two LW modes are simultaneously excited and that those modes are dispersive. Matching Pursuit Method (MPM), which consists of approximating a signal as a sum of different delayed and scaled atoms taken from an a priori known learning dictionary, seems very appealing in such a context, however is limited to nondispersive signals and relies on a priori known dictionary. An improved version of MPM called the Single Atom Convolutional Matching Pursuit method (SACMPM), which addresses the dispersion phenomena by decomposing a measured signal as delayed and dispersed atoms and limits the learning dictionary to only one atom, is proposed here. Its performances are illustrated when dealing with numerical and experimental signals as well as its usage for damage detection. Although the signal approximation method proposed in this paper finds an original application in the context of SHM, this method remains completely general and can be easily applied to any signal processing problem.
DePrompt: Desensitization and Evaluation of Personal Identifiable Information in Large Language Model Prompts
Venue: arXiv cs.CR 2024
Year: 2024

Prompt serves as a crucial link in interacting with large language models (LLMs), widely impacting the accuracy and interpretability of model outputs. However, acquiring accurate and high-quality responses necessitates precise prompts, which inevitably pose significant risks of personal identifiable information (PII) leakage. Therefore, this paper proposes DePrompt, a desensitization protection and effectiveness evaluation framework for prompt, enabling users to safely and transparently utilize LLMs. Specifically, by leveraging large model fine-tuning techniques as the underlying privacy protection method, we integrate contextual attributes to define privacy types, achieving high-precision PII entity identification. Additionally, through the analysis of key features in prompt desensitization scenarios, we devise adversarial generative desensitization methods that retain important semantic content while disrupting the link between identifiers and privacy attributes. Furthermore, we present utility evaluation metrics for prompt to better gauge and balance privacy and usability. Our framework is adaptable to prompts and can be extended to text usability-dependent scenarios. Through comparison with benchmarks and other model methods, experimental evaluations demonstrate that our desensitized prompt exhibit superior privacy protection utility and model inference results.
A Factored MDP Approach To Moving Target Defense With Dynamic Threat Modeling and Cost Efficiency
Venue: arXiv cs.CR 2024
Year: 2024

Moving Target Defense (MTD) has emerged as a proactive and dynamic framework to counteract evolving cyber threats. Traditional MTD approaches often rely on assumptions about the attackers knowledge and behavior. However, real-world scenarios are inherently more complex, with adaptive attackers and limited prior knowledge of their payoffs and intentions. This paper introduces a novel approach to MTD using a Markov Decision Process (MDP) model that does not rely on predefined attacker payoffs. Our framework integrates the attackers real-time responses into the defenders MDP using a dynamic Bayesian Network. By employing a factored MDP model, we provide a comprehensive and realistic system representation. We also incorporate incremental updates to an attack response predictor as new data emerges. This ensures an adaptive and robust defense mechanism. Additionally, we consider the costs of switching configurations in MTD, integrating them into the reward structure to balance execution and defense costs. We first highlight the challenges of the problem through a theoretical negative result on regret. However, empirical evaluations demonstrate the frameworks effectiveness in scenarios marked by high uncertainty and dynamically changing attack landscapes.
Phishing Codebook: A Structured Framework for the Characterization of Phishing Emails
Venue: arXiv cs.CR 2024
Year: 2024

Phishing is one of the most prevalent and expensive types of cybercrime faced by organizations and individuals worldwide. Most prior research has focused on various technical features and traditional representations of text to characterize phishing emails. There is a significant knowledge gap about the qualitative traits embedded in them, which could be useful in a range of phishing mitigation tasks. In this paper, we dissect the structure of phishing emails to gain a better understanding of the factors that influence human decision-making when assessing suspicious emails and identify a novel set of descriptive features. For this, we employ an iterative qualitative coding approach to identify features that are descriptive of the emails. We developed the ``Phishing Codebook'', a structured framework to systematically extract key information from phishing emails, and we apply this codebook to a publicly available dataset of 503 phishing emails collected between 2015 and 2021. We present key observations and challenges related to phishing attacks delivered indirectly through legitimate services, the challenge of recurring and long-lasting scams, and the variations within campaigns used by attackers to bypass rule-based filters. Furthermore, we provide two use cases to show how the Phishing Codebook is useful in identifying similar phishing emails and in creating well-tailored responses to end-users. We share the Phishing Codebook and the annotated benchmark dataset to help researchers have a better understanding of phishing emails.