OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG
Venue: arXiv cs.IR 2024
Year: 2024

Ranking models play a crucial role in enhancing overall accuracy of text retrieval systems. These multi-stage systems typically utilize either dense embedding models or sparse lexical indices to retrieve relevant passages based on a given query, followed by ranking models that refine the ordering of the candidate passages by its relevance to the query. This paper benchmarks various publicly available ranking models and examines their impact on ranking accuracy. We focus on text retrieval for question-answering tasks, a common use case for Retrieval-Augmented Generation systems. Our evaluation benchmarks include models some of which are commercially viable for industrial applications. We introduce a state-of-the-art ranking model, NV-RerankQA-Mistral-4B-v3, which achieves a significant accuracy increase of ~14% compared to pipelines with other rerankers. We also provide an ablation study comparing the fine-tuning of ranking models with different sizes, losses and self-attention mechanisms. Finally, we discuss challenges of text retrieval pipelines with ranking models in real-world industry applications, in particular the trade-offs among model size, ranking accuracy and system requirements like indexing and serving latency / throughput.
Situated Visualization in Motion for Swimming
Venue: arXiv cs.HC 2024
Year: 2024

Competitive sports coverage increasingly includes information on athlete or team statistics and records. Sports video coverage has traditionally embedded representations of this data in fixed locations on the screen, but more recently also attached representations to athletes or other targets in motion. These publicly used representations so far have been rather simple and systematic investigations of the research space of embedded visualizations in motion are still missing. Here we report on our preliminary research in the domain of professional and amateur swimming. We analyzed how visualizations are currently added to the coverage of Olympics swimming competitions and then plan to derive a design space for embedded data representations for swimming competitions. We are currently conducting a crowdsourced survey to explore which kind of swimming-related data general audiences are interested in, in order to identify opportunities for additional visualizations to be added to swimming competition coverage.
Visualization in Motion in Video Games for Different Types of Data
Venue: arXiv cs.HC 2024
Year: 2024

We contribute an analysis of situated visualizations in motion in video games for different types of data, with a focus on quantitative and categorical data representations. Video games convey a lot of data to players, to help them succeed in the game. These visualizations frequently move across the screen due to camera changes or because the game elements themselves move. Our ultimate goal is to understand how motion factors affect visualization readability in video games and subsequently the players' performance in the game. We started our work by surveying the characteristics of how motion currently influences which kind of data representations in video games. We conducted a systematic review of 160 visualizations in motion in video games and extracted patterns and considerations regarding was what, and how visualizations currently exhibit motion factors in video games.
Polarization Detection on Social Networks: dual contrastive objectives for Self-supervision
Venue: arXiv cs.SI 2024
Year: 2024

Echo chambers and online discourses have become prevalent social phenomena where communities engage in dramatic intra-group confirmations and inter-group hostility. Polarization detection is a rising research topic for detecting and identifying such polarized groups. Previous works on polarization detection primarily focus on hand-crafted features derived from dataset-specific characteristics and prior knowledge, which fail to generalize to other datasets. This paper proposes a unified self-supervised polarization detection framework, outperforming previous methods in unsupervised and semi-supervised polarization detection tasks on various publicly available datasets. Our framework utilizes a dual contrastive objective (DocTra): (1) interaction-level: to contrast between node interactions to extract critical features on interaction patterns, and (2) feature-level: to contrast extracted polarized and invariant features to encourage feature decoupling. Our experiments extensively evaluate our methods again 7 baselines on 7 public datasets, demonstrating significant performance improvements.
Unsupervised node clustering via contrastive hard sampling
Venue: arXiv cs.SI 2024
Year: 2024

This paper introduces a fine-grained contrastive learning scheme for unsupervised node clustering. Previous clustering methods only focus on a small feature set (class-dependent features), which demonstrates explicit clustering characteristics, ignoring the rest of the feature spaces (class-invariant features). This paper exploits class-invariant features via graph contrastive learning to discover additional high-quality features for unsupervised clustering. We formulate a novel node-level fine-grained augmentation framework for self-supervised learning, which iteratively identifies competitive contrastive samples from the whole feature spaces, in the form of positive and negative examples of node relations. While positive examples of node relations are usually expressed as edges in graph homophily, negative examples are implicit without a direct edge. We show, however, that simply sampling nodes beyond the local neighborhood results in less competitive negative pairs, that are less effective for contrastive learning. Inspired by counterfactual augmentation, we instead sample competitive negative node relations by creating virtual nodes that inherit (in a self-supervised fashion) class-invariant features, while altering class-dependent features, creating contrasting pairs that lie closer to the boundary and offering better contrast. Consequently, our experiments demonstrate significant improvements in supervised node clustering tasks on six baselines and six real-world social network datasets.
Large Language Models are Pattern Matchers: Editing Semi-Structured and Structured Documents with ChatGPT
Venue: arXiv cs.LG 2024
Year: 2024

Large Language Models (LLMs) offer numerous applications, the full extent of which is not yet understood. This paper investigates if LLMs can be applied for editing structured and semi-structured documents with minimal effort. Using a qualitative research approach, we conduct two case studies with ChatGPT and thoroughly analyze the results. Our experiments indicate that LLMs can effectively edit structured and semi-structured documents when provided with basic, straightforward prompts. ChatGPT demonstrates a strong ability to recognize and process the structure of annotated documents. This suggests that explicitly structuring tasks and data in prompts might enhance an LLM's ability to understand and solve tasks. Furthermore, the experiments also reveal impressive pattern matching skills in ChatGPT. This observation deserves further investigation, as it may contribute to understanding the processes leading to hallucinations in LLMs.
GatedUniPose: A Novel Approach for Pose Estimation Combining UniRepLKNet and Gated Convolution
Venue: arXiv cs.CV 2024
Year: 2024

Pose estimation is a crucial task in computer vision, with wide applications in autonomous driving, human motion capture, and virtual reality. However, existing methods still face challenges in achieving high accuracy, particularly in complex scenes. This paper proposes a novel pose estimation method, GatedUniPose, which combines UniRepLKNet and Gated Convolution and introduces the GLACE module for embedding. Additionally, we enhance the feature map concatenation method in the head layer by using DySample upsampling. Compared to existing methods, GatedUniPose excels in handling complex scenes and occlusion challenges. Experimental results on the COCO, MPII, and CrowdPose datasets demonstrate that GatedUniPose achieves significant performance improvements with a relatively small number of parameters, yielding better or comparable results to models with similar or larger parameter sizes.
From Uncertainty to Clarity: Uncertainty-Guided Class-Incremental Learning for Limited Biomedical Samples via Semantic Expansion
Venue: arXiv cs.CV 2024
Year: 2024

In real-world clinical settings, data distributions evolve over time, with a continuous influx of new, limited disease cases. Therefore, class incremental learning is of great significance, i.e., deep learning models are required to learn new class knowledge while maintaining accurate recognition of previous diseases. However, traditional deep neural networks often suffer from severe forgetting of prior knowledge when adapting to new data unless trained from scratch, which undesirably costs much time and computational burden. Additionally, the sample sizes for different diseases can be highly imbalanced, with newly emerging diseases typically having much fewer instances, consequently causing the classification bias. To tackle these challenges, we are the first to propose a class-incremental learning method under limited samples in the biomedical field. First, we propose a novel cumulative entropy prediction module to measure the uncertainty of the samples, of which the most uncertain samples are stored in a memory bank as exemplars for the model's later review. Furthermore, we theoretically demonstrate its effectiveness in measuring uncertainty. Second, we developed a fine-grained semantic expansion module through various augmentations, leading to more compact distributions within the feature space and creating sufficient room for generalization to new classes. Besides, a cosine classifier is utilized to mitigate classification bias caused by imbalanced datasets. Across four imbalanced data distributions over two datasets, our method achieves optimal performance, surpassing state-of-the-art methods by as much as 53.54% in accuracy.
Exploring Kolmogorov-Arnold networks for realistic image sharpness assessment
Venue: arXiv cs.CV 2024
Year: 2024

Score prediction is crucial in evaluating realistic image sharpness based on collected informative features. Recently, Kolmogorov-Arnold networks (KANs) have been developed and witnessed remarkable success in data fitting. This study introduces the Taylor series-based KAN (TaylorKAN). Then, different KANs are explored in four realistic image databases (BID2011, CID2013, CLIVE, and KonIQ-10k) to predict the scores by using 15 mid-level features and 2048 high-level features. Compared to support vector regression, results show that KANs are generally competitive or superior, and TaylorKAN is the best one when mid-level features are used. This is the first study to investigate KANs on image quality assessment that sheds some light on how to select and further improve KANs in related tasks.
Reimagining Linear Probing: Kolmogorov-Arnold Networks in Transfer Learning
Venue: arXiv cs.LG 2024
Year: 2024

This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. Linear probing, often applied to the final layer of pre-trained models, is limited by its inability to model complex relationships in data. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based representations to approximate intricate functions. In this study, we integrate KAN with a ResNet-50 model pre-trained on ImageNet and evaluate its performance on the CIFAR-10 dataset. We perform a systematic hyperparameter search, focusing on grid size and spline degree (k), to optimize KAN's flexibility and accuracy. Our results demonstrate that KAN consistently outperforms traditional linear probing, achieving significant improvements in accuracy and generalization across a range of configurations. These findings indicate that KAN offers a more powerful and adaptable alternative to conventional linear probing techniques in transfer learning.
Quaternion Nuclear Norm minus Frobenius Norm Minimization for color image reconstruction
Venue: arXiv cs.CV 2024
Year: 2024

Color image restoration methods typically represent images as vectors in Euclidean space or combinations of three monochrome channels. However, they often overlook the correlation between these channels, leading to color distortion and artifacts in the reconstructed image. To address this, we present Quaternion Nuclear Norm Minus Frobenius Norm Minimization (QNMF), a novel approach for color image reconstruction. QNMF utilizes quaternion algebra to capture the relationships among RGB channels comprehensively. By employing a regularization technique that involves nuclear norm minus Frobenius norm, QNMF approximates the underlying low-rank structure of quaternion-encoded color images. Theoretical proofs are provided to ensure the method's mathematical integrity. Demonstrating versatility and efficacy, the QNMF regularizer excels in various color low-level vision tasks, including denoising, deblurring, inpainting, and random impulse noise removal, achieving state-of-the-art results.
Controllable Synthetic Clinical Note Generation with Privacy Guarantees
Venue: arXiv cs.CL 2024
Year: 2024

In the field of machine learning, domain-specific annotated data is an invaluable resource for training effective models. However, in the medical domain, this data often includes Personal Health Information (PHI), raising significant privacy concerns. The stringent regulations surrounding PHI limit the availability and sharing of medical datasets, which poses a substantial challenge for researchers and practitioners aiming to develop advanced machine learning models. In this paper, we introduce a novel method to "clone" datasets containing PHI. Our approach ensures that the cloned datasets retain the essential characteristics and utility of the original data without compromising patient privacy. By leveraging differential-privacy techniques and a novel fine-tuning task, our method produces datasets that are free from identifiable information while preserving the statistical properties necessary for model training. We conduct utility testing to evaluate the performance of machine learning models trained on the cloned datasets. The results demonstrate that our cloned datasets not only uphold privacy standards but also enhance model performance compared to those trained on traditional anonymized datasets. This work offers a viable solution for the ethical and effective utilization of sensitive medical data in machine learning, facilitating progress in medical research and the development of robust predictive models.
Over-the-Air Federated Learning via Weighted Aggregation
Venue: arXiv cs.IT 2024
Year: 2024

This paper introduces a new federated learning scheme that leverages over-the-air computation. A novel feature of this scheme is the proposal to employ adaptive weights during aggregation, a facet treated as predefined in other over-the-air schemes. This can mitigate the impact of wireless channel conditions on learning performance, without needing channel state information at transmitter side (CSIT). We provide a mathematical methodology to derive the convergence bound for the proposed scheme in the context of computational heterogeneity and general loss functions, supplemented with design insights. Accordingly, we propose aggregation cost metrics and efficient algorithms to find optimized weights for the aggregation. Finally, through numerical experiments, we validate the effectiveness of the proposed scheme. Even with the challenges posed by channel conditions and device heterogeneity, the proposed scheme surpasses other over-the-air strategies by an accuracy improvement of 15% over the scheme using CSIT and 30% compared to the one without CSIT.
Enabling Cost-Effective UI Automation Testing with Retrieval-Based LLMs: A Case Study in WeChat
Venue: arXiv cs.SE 2024
Year: 2024

UI automation tests play a crucial role in ensuring the quality of mobile applications. Despite the growing popularity of machine learning techniques to generate these tests, they still face several challenges, such as the mismatch of UI elements. The recent advances in Large Language Models (LLMs) have addressed these issues by leveraging their semantic understanding capabilities. However, a significant gap remains in applying these models to industrial-level app testing, particularly in terms of cost optimization and knowledge limitation. To address this, we introduce CAT to create cost-effective UI automation tests for industry apps by combining machine learning and LLMs with best practices. Given the task description, CAT employs Retrieval Augmented Generation (RAG) to source examples of industrial app usage as the few-shot learning context, assisting LLMs in generating the specific sequence of actions. CAT then employs machine learning techniques, with LLMs serving as a complementary optimizer, to map the target element on the UI screen. Our evaluations on the WeChat testing dataset demonstrate the CAT's performance and cost-effectiveness, achieving 90% UI automation with $0.34 cost, outperforming the state-of-the-art. We have also integrated our approach into the real-world WeChat testing platform, demonstrating its usefulness in detecting 141 bugs and enhancing the developers' testing process.
Classifying Images with CoLaNET Spiking Neural Network -- the MNIST Example
Venue: arXiv cs.NE 2024
Year: 2024

In the present paper, it is shown how the columnar/layered CoLaNET spiking neural network (SNN) architecture can be used in supervised learning image classification tasks. Image pixel brightness is coded by the spike count during image presentation period. Image class label is indicated by activity of special SNN input nodes (one node per class). The CoLaNET classification accuracy is evaluated on the MNIST benchmark. It is demonstrated that CoLaNET is almost as accurate as the most advanced machine learning algorithms (not using convolutional approach).
Measuring the limit of perception of bond stiffness of interactive molecules in VR via a gamified psychophysics experiment
Venue: arXiv cs.HC 2024
Year: 2024

Molecular dynamics (MD) simulations provide crucial insight into molecular interactions and biomolecular function. With interactive MD simulations in VR (iMD-VR), chemists can now interact with these molecular simulations in real-time. Our sense of touch is essential for exploring the properties of physical objects, but recreating this sensory experience for virtual objects poses challenges. Furthermore, employing haptics in the context of molecular simulation is especially difficult since \textit{we do not know what molecules actually feel like}. In this paper, we build upon previous work that demonstrated how VR-users can distinguish properties of molecules without haptic feedback. We present the results of a gamified two-alternative forced choice (2AFC) psychophysics user study in which we quantify the threshold at which iMD-VR users can differentiate the stiffness of molecular bonds. Our preliminary analysis suggests that participants can sense differences between buckminsterfullerene molecules with different bond stiffness parameters and that this limit may fall within the chemically relevant range. Our results highlight how iMD-VR may facilitate a more embodied way of exploring complex and dynamic molecular systems, enabling chemists to sense the properties of molecules purely by interacting with them in VR.
MSMF: Multi-Scale Multi-Modal Fusion for Enhanced Stock Market Prediction
Venue: arXiv cs.CE 2024
Year: 2024

This paper presents MSMF (Multi-Scale Multi-Modal Fusion), a novel approach for enhanced stock market prediction. MSMF addresses key challenges in multi-modal stock analysis by integrating a modality completion encoder, multi-scale feature extraction, and an innovative fusion mechanism. Our model leverages blank learning and progressive fusion to balance complementarity and redundancy across modalities, while multi-scale alignment facilitates direct correlations between heterogeneous data types. We introduce Multi-Granularity Gates and a specialized architecture to optimize the integration of local and global information for different tasks. Additionally, a Task-targeted Prediction layer is employed to preserve both coarse and fine-grained features during fusion. Experimental results demonstrate that MSMF outperforms existing methods, achieving significant improvements in accuracy and reducing prediction errors across various stock market forecasting tasks. This research contributes valuable insights to the field of multi-modal financial analysis and offers a robust framework for enhanced market prediction.
Building a Cybersecurity Risk Metamodel for Improved Method and Tool Integration
Venue: arXiv cs.CR 2024
Year: 2024

Nowadays, companies are highly exposed to cyber security threats. In many industrial domains, protective measures are being deployed and actively supported by standards. However the global process remains largely dependent on document driven approach or partial modelling which impacts both the efficiency and effectiveness of the cybersecurity process from the risk analysis step. In this paper, we report on our experience in applying a model-driven approach on the initial risk analysis step in connection with a later security testing. Our work rely on a common metamodel which is used to map, synchronise and ensure information traceability across different tools. We validate our approach using different scenarios relying domain modelling, system modelling, risk assessment and security testing tools.
Tidal MerzA: Combining affective modelling and autonomous code generation through Reinforcement Learning
Venue: arXiv cs.HC 2024
Year: 2024

This paper presents Tidal-MerzA, a novel system designed for collaborative performances between humans and a machine agent in the context of live coding, specifically focusing on the generation of musical patterns. Tidal-MerzA fuses two foundational models: ALCAA (Affective Live Coding Autonomous Agent) and Tidal Fuzz, a computational framework. By integrating affective modelling with computational generation, this system leverages reinforcement learning techniques to dynamically adapt music composition parameters within the TidalCycles framework, ensuring both affective qualities to the patterns and syntactical correctness. The development of Tidal-MerzA introduces two distinct agents: one focusing on the generation of mini-notation strings for musical expression, and another on the alignment of music with targeted affective states through reinforcement learning. This approach enhances the adaptability and creative potential of live coding practices and allows exploration of human-machine creative interactions. Tidal-MerzA advances the field of computational music generation, presenting a novel methodology for incorporating artificial intelligence into artistic practices.
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Venue: arXiv cs.NI 2024
Year: 2024

Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we introduce WirelessAgent, a novel approach leveraging large language models (LLMs) to develop AI agents capable of managing complex tasks in wireless networks. It can effectively improve network performance through advanced reasoning, multimodal data processing, and autonomous decision making. Thereafter, we demonstrate the practical applicability and benefits of WirelessAgent for network slicing management. The experimental results show that WirelessAgent is capable of accurately understanding user intent, effectively allocating slice resources, and consistently maintaining optimal performance.