OpenRaiser/Intern-Atlas huggingface dataset:
CharGen: High Accurate Character-Level Visual Text Generation Model with MultiModal Encoder
Venue: arXiv cs.CV 2024
Year: 2024

Recently, significant advancements have been made in diffusion-based visual text generation models. Although the effectiveness of these methods in visual text rendering is rapidly improving, they still encounter challenges such as inaccurate characters and strokes when rendering complex visual text. In this paper, we propose CharGen, a highly accurate character-level visual text generation and editing model. Specifically, CharGen employs a character-level multimodal encoder that not only extracts character-level text embeddings but also encodes glyph images character by character. This enables it to capture fine-grained cross-modality features more effectively. Additionally, we introduce a new perceptual loss in CharGen to enhance character shape supervision and address the issue of inaccurate strokes in generated text. It is worth mentioning that CharGen can be integrated into existing diffusion models to generate visual text with high accuracy. CharGen significantly improves text rendering accuracy, outperforming recent methods in public benchmarks such as AnyText-benchmark and MARIO-Eval, with improvements of more than 8% and 6%, respectively. Notably, CharGen achieved a 5.5% increase in accuracy on Chinese test sets.
Selective Kalman Filter: When and How to Fuse Multi-Sensor Information to Overcome Degeneracy in SLAM
Venue: arXiv cs.RO 2024
Year: 2024

Research trends in SLAM systems are now focusing more on multi-sensor fusion to handle challenging and degenerative environments. However, most existing multi-sensor fusion SLAM methods mainly use all of the data from a range of sensors, a strategy we refer to as the all-in method. This method, while merging the benefits of different sensors, also brings in their weaknesses, lowering the robustness and accuracy and leading to high computational demands. To address this, we propose a new fusion approach -- Selective Kalman Filter -- to carefully choose and fuse information from multiple sensors (using LiDAR and visual observations as examples in this paper). For deciding when to fuse data, we implement degeneracy detection in LiDAR SLAM, incorporating visual measurements only when LiDAR SLAM exhibits degeneracy. Regarding degeneracy detection, we propose an elegant yet straightforward approach to determine the degeneracy of LiDAR SLAM and to identify the specific degenerative direction. This method fully considers the coupled relationship between rotational and translational constraints. In terms of how to fuse data, we use visual measurements only to update the specific degenerative states. As a result, our proposed method improves upon the all-in method by greatly enhancing real-time performance due to less processing visual data, and it introduces fewer errors from visual measurements. Experiments demonstrate that our method for degeneracy detection and fusion, in addressing degeneracy issues, exhibits higher precision and robustness compared to other state-of-the-art methods, and offers enhanced real-time performance relative to the all-in method. The code is openly available.
LegalAgentBench: Evaluating LLM Agents in Legal Domain
Venue: arXiv cs.CL 2024
Year: 2024

With the increasing intelligence and autonomy of LLM agents, their potential applications in the legal domain are becoming increasingly apparent. However, existing general-domain benchmarks cannot fully capture the complexity and subtle nuances of real-world judicial cognition and decision-making. Therefore, we propose LegalAgentBench, a comprehensive benchmark specifically designed to evaluate LLM Agents in the Chinese legal domain. LegalAgentBench includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge. We designed a scalable task construction framework and carefully annotated 300 tasks. These tasks span various types, including multi-hop reasoning and writing, and range across different difficulty levels, effectively reflecting the complexity of real-world legal scenarios. Moreover, beyond evaluating final success, LegalAgentBench incorporates keyword analysis during intermediate processes to calculate progress rates, enabling more fine-grained evaluation. We evaluated eight popular LLMs, highlighting the strengths, limitations, and potential areas for improvement of existing models and methods. LegalAgentBench sets a new benchmark for the practical application of LLMs in the legal domain, with its code and data available at \url{https://github.com/CSHaitao/LegalAgentBench}.
ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models
Venue: arXiv cs.SE 2024
Year: 2024

CodeLLMs have demonstrated remarkable advancements in software engineering tasks. However, while these models can generate functionally correct code, they often produce code that is inefficient in terms of runtime. This inefficiency is particularly problematic in resource-constrained environments, impacting software performance and sustainability. Existing approaches for optimizing code efficiency for CodeLLMs like SOAP and PIE exhibit certain limitations. SOAP requires a compatible execution environment and predefined test cases for iterative code modification, while PIE focuses on instruction tuning, improving efficiency but compromising correctness. These shortcomings highlight the need for a fine-tuning framework that optimizes both efficiency and correctness without relying on predefined test cases or specific execution environments. To bridge this gap, we introduce ACECode, a reinforcement learning-based fine-tuning framework that aligns CodeLLMs with dual objectives of efficiency and correctness. ACECode combines three key steps: (1) generating code with an actor CodeLLM, (2) calculating a training-free reward signal derived from code execution feedback for each generated code, and (3) optimizing the CodeLLM via Proximal Policy Optimization (PPO) algorithm. This reward signal enables joint assessment of efficiency and correctness without manual labeling. We evaluate ACECode by fine-tuning four SOTA (state-of-the-art) CodeLLMs and comparing their code with three baselines: original, instruction-tuned, and PIE-tuned CodeLLMs. Extensive experiment results suggest that \tool{} significantly improves the efficiency and correctness of generated code against all baselines for all CodeLLMs. Specifically, CodeLLMs fine-tuned with ACECode improve pass@1 by 1.84% to 14.51% and reduce runtime in 65% to 72% of cases compared to original CodeLLMs.
ChromaGazer: Unobtrusive Visual Modulation using Imperceptible Color Vibration for Visual Guidance
Venue: arXiv cs.HC 2024
Year: 2024

Visual guidance (VG) is critical for directing user attention in virtual and augmented reality applications. However, conventional methods using explicit visual annotations can obstruct visibility and increase cognitive load. To address this, we propose an unobtrusive VG technique based on color vibration, a phenomenon in which rapidly alternating colors at frequencies above 25 Hz are perceived as a single intermediate color. We hypothesize that an intermediate perceptual state exists between complete color fusion and perceptual flicker, where colors appear subtly different from a uniform color without conscious perception of flicker. To investigate this, we conducted two experiments. First, we determined the thresholds between complete fusion, the intermediate state, and perceptual flicker by varying the amplitude of color vibration pairs in a user study. Second, we applied these threshold parameters to modulate regions in natural images and evaluated their effectiveness in guiding users' gaze using eye-tracking data. Our results show that color vibration can subtly guide gaze while minimizing cognitive load, providing a novel approach for unobtrusive VG in VR and AR applications.
Enabling Time-series Foundation Model for Building Energy Forecasting via Contrastive Curriculum Learning
Venue: arXiv cs.LG 2024
Year: 2024

Advances in time-series forecasting are driving a shift from conventional machine learning models to foundation models (FMs) that are trained with generalized knowledge. However, existing FMs still perform poorly in the energy fields, such as building energy forecasting (BEF). This paper studies the adaptation of FM to BEF tasks. We demonstrate the shortcomings of fine-tuning FM straightforwardly from both the perspectives of FM and the data. To overcome these limitations, we propose a new \textit{contrastive curriculum learning}-based training method. Our method optimizes the ordering of training data in the context of TSFM adaptation. Experiments show that our method can improve the zero/few-shot performance by 14.6\% compared to the existing FMs. Our code and new TSFM will be available at <Anonymous Github Repo>.
Multi-Modal Grounded Planning and Efficient Replanning For Learning Embodied Agents with A Few Examples
Venue: arXiv cs.RO 2024
Year: 2024

Learning a perception and reasoning module for robotic assistants to plan steps to perform complex tasks based on natural language instructions often requires large free-form language annotations, especially for short high-level instructions. To reduce the cost of annotation, large language models (LLMs) are used as a planner with few data. However, when elaborating the steps, even the state-of-the-art planner that uses LLMs mostly relies on linguistic common sense, often neglecting the status of the environment at command reception, resulting in inappropriate plans. To generate plans grounded in the environment, we propose FLARE (Few-shot Language with environmental Adaptive Replanning Embodied agent), which improves task planning using both language command and environmental perception. As language instructions often contain ambiguities or incorrect expressions, we additionally propose to correct the mistakes using visual cues from the agent. The proposed scheme allows us to use a few language pairs thanks to the visual cues and outperforms state-of-the-art approaches. Our code is available at https://github.com/snumprlab/flare.
Popularity Estimation and New Bundle Generation using Content and Context based Embeddings
Venue: arXiv cs.IR 2024
Year: 2024

Recommender systems create enormous value for businesses and their consumers. They increase revenue for businesses while improving the consumer experience by recommending relevant products amidst huge product base. Product bundling is an exciting development in the field of product recommendations. It aims at generating new bundles and recommending exciting and relevant bundles to their consumers. Unlike traditional recommender systems that recommend single items to consumers, product bundling aims at targeting a bundle, or a set of items, to the consumers. While bundle recommendation has attracted significant research interest recently, extant literature on bundle generation is scarce. Moreover, metrics to identify if a bundle is popular or not is not well studied. In this work, we aim to fulfill this gap by introducing new bundle popularity metrics based on sales, consumer experience and item diversity in a bundle. We use these metrics in the methodology proposed in this paper to generate new bundles for mobile games using content aware and context aware embeddings. We use opensource Steam Games dataset for our analysis. Our experiments indicate that we can generate new bundles that can outperform the existing bundles on the popularity metrics by 32% - 44%. Our experiments are computationally efficient and the proposed methodology is generic that can be extended to other bundling problems e.g. product bundling, music bundling.
Complete Implementation of WXF Chinese Chess Rules
Venue: arXiv cs.AI 2024
Year: 2024

Unlike repetitions in Western Chess where all repetitions are draws, repetitions in Chinese Chess could result in a win, draw, or loss depending on the kind of repetition being made by both players. One of the biggest hurdles facing Chinese Chess application development is a proper system for judging games correctly. This paper introduces a complete algorithm for ruling the WXF rules correctly in all 110 example cases found in the WXF manual. We introduce several novel optimizations for speeding up the repetition handling without compromising the program correctness. This algorithm is usable in engines, and we saw a total increase in playing strength by +10 point rating increase, or an increased 5% winrate when integrating this approach into our prototype engine.
Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp)
Venue: arXiv cs.IR 2024
Year: 2024

Text embedding models play a crucial role in natural language processing, particularly in information retrieval, and their importance is further highlighted with the recent utilization of RAG (Retrieval- Augmented Generation). This study presents an efficient fine-tuning methodology encompassing data selection, loss function, and model architecture to enhance the information retrieval performance of pre-trained text embedding models. In particular, this study proposes a novel Contrastive Learning Penalty function that overcomes the limitations of existing Contrastive Learning. The proposed methodology achieves significant performance improvements over existing methods in document retrieval tasks. This study is expected to contribute to improving the performance of information retrieval systems through fine-tuning of text embedding models. The code for this study can be found at https://github.com/CreaLabs/Enhanced-BGE-M3-with-CLP-and-MoE, and the best-performing model can be found at https://huggingface.co/CreaLabs.
FRTP: Federating Route Search Records to Enhance Long-term Traffic Prediction
Venue: arXiv cs.AI 2024
Year: 2024

Accurate traffic prediction, especially predicting traffic conditions several days in advance is essential for intelligent transportation systems (ITS). Such predictions enable mid- and long-term traffic optimization, which is crucial for efficient transportation planning. However, the inclusion of diverse external features, alongside the complexities of spatial relationships and temporal uncertainties, significantly increases the complexity of forecasting models. Additionally, traditional approaches have handled data preprocessing separately from the learning model, leading to inefficiencies caused by repeated trials of preprocessing and training. In this study, we propose a federated architecture capable of learning directly from raw data with varying features and time granularities or lengths. The model adopts a unified design that accommodates different feature types, time scales, and temporal periods. Our experiments focus on federating route search records and begin by processing raw data within the model framework. Unlike traditional models, this approach integrates the data federation phase into the learning process, enabling compatibility with various time frequencies and input/output configurations. The accuracy of the proposed model is demonstrated through evaluations using diverse learning patterns and parameter settings. The results show that online search log data is useful for forecasting long-term traffic, highlighting the model's adaptability and efficiency.
PointVoxelFormer -- Reviving point cloud networks for 3D medical imaging
Venue: arXiv cs.CV 2024
Year: 2024

Point clouds are a very efficient way to represent volumetric data in medical imaging. First, they do not occupy resources for empty spaces and therefore can avoid trade-offs between resolution and field-of-view for voxel-based 3D convolutional networks (CNNs) - leading to smaller and robust models. Second, they provide a modality agnostic representation of anatomical surfaces and shapes to avoid domain gaps for generic geometric models. Third, they remove identifiable patient-specific information and may increase privacy preservation when publicly sharing data. Despite their benefits, point clouds are still underexplored in medical imaging compared to volumetric 3D CNNs and vision transformers. To date both datasets and stringent studies on comparative strengths and weaknesses of methodological choices are missing. Interactions and information exchange of spatially close points - e.g. through k-nearest neighbour graphs in edge convolutions or point transformations - within points clouds are crucial for learning geometrically meaningful features but may incur computational bottlenecks. This work presents a hybrid approach that combines point-wise operations with intermediate differentiable rasterisation and dense localised CNNs. For deformable point cloud registration, we devise an early fusion scheme for coordinate features that joins both clouds within a common reference frame and is coupled with an inverse consistent, two-step alignment architecture. Our extensive experiments on three different datasets for segmentation and registration demonstrate that our method, PointVoxelFormer, enables very compact models that excel with threefold speed-ups, fivefold memory reduction and over 30% registration error reduction against edge convolutions and other state-of-the-art models in geometric deep learning.
Towards Intrinsic Self-Correction Enhancement in Monte Carlo Tree Search Boosted Reasoning via Iterative Preference Learning
Venue: arXiv cs.LG 2024
Year: 2024

With current state-of-the-art approaches aimed at enhancing the reasoning capabilities of Large Language Models(LLMs) through iterative preference learning inspired by AlphaZero, we propose to further enhance the step-wise reasoning capabilities through intrinsic self-correction to some extent. Our work leverages step-wise preference learning to enhance self-verification via reinforcement learning. We initially conduct our work through a two-stage training procedure. At the first stage, the self-correction reasoning ability of an LLM is enhanced through its own predictions, relying entirely on self-generated data within the intrinsic self-correction to some extent. At the second stage, the baseline step-wise preference learning is leveraged via the application of the enhanced self-correct policy achieved at the first stage. In the evaluation of arithmetic reasoning tasks, our approach outperforms OpenMath2-Llama3.1-8B, dart-math-mistral-7b-uniform on MATH with increases in accuracy to 71.34%(+4.18%) and 48.06%(+4.94%) and LLama-3.1-8B-Instruct, Mistral-7B-Instruct-v0.1 on GSM8K with increases in accuracy to 86.76%(+2.00%) and 38.06%(+2.28%).
Applying LLM and Topic Modelling in Psychotherapeutic Contexts
Venue: arXiv cs.LG 2024
Year: 2024

This study explores the use of Large language models to analyze therapist remarks in a psychotherapeutic setting. The paper focuses on the application of BERTopic, a machine learning-based topic modeling tool, to the dialogue of two different groups of therapists (classical and modern), which makes it possible to identify and describe a set of topics that consistently emerge across these groups. The paper describes in detail the chosen algorithm for BERTopic, which included creating a vector space from a corpus of therapist remarks, reducing its dimensionality, clustering the space, and creating and optimizing topic representation. Along with the automatic topical modeling by the BERTopic, the research involved an expert assessment of the findings and manual topic structure optimization. The topic modeling results highlighted the most common and stable topics in therapists speech, offering insights into how language patterns in therapy develop and remain stable across different therapeutic styles. This work contributes to the growing field of machine learning in psychotherapy by demonstrating the potential of automated methods to improve both the practice and training of therapists. The study highlights the value of topic modeling as a tool for gaining a deeper understanding of therapeutic dialogue and offers new opportunities for improving therapeutic effectiveness and clinical supervision.
A Temporal Convolutional Network-based Approach for Network Intrusion Detection
Venue: arXiv cs.CR 2024
Year: 2024

Network intrusion detection is critical for securing modern networks, yet the complexity of network traffic poses significant challenges to traditional methods. This study proposes a Temporal Convolutional Network(TCN) model featuring a residual block architecture with dilated convolutions to capture dependencies in network traffic data while ensuring training stability. The TCN's ability to process sequences in parallel enables faster, more accurate sequence modeling than Recurrent Neural Networks. Evaluated on the Edge-IIoTset dataset, which includes 15 classes with normal traffic and 14 cyberattack types, the proposed model achieved an accuracy of 96.72% and a loss of 0.0688, outperforming 1D CNN, CNN-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-GRU-LSTM models. A class-wise classification report, encompassing metrics such as recall, precision, accuracy, and F1-score, demonstrated the TCN model's superior performance across varied attack categories, including Malware, Injection, and DDoS. These results underscore the model's potential in addressing the complexities of network intrusion detection effectively.
Is ChatGPT Massively Used by Students Nowadays? A Survey on the Use of Large Language Models such as ChatGPT in Educational Settings
Venue: arXiv cs.CY 2024
Year: 2024

The rapid adoption of Generative AI (GenAI) based on Large Language Models (LLMs) such as ChatGPT has recently and profoundly impacted education, offering transformative opportunities while raising significant concerns. In this study we present the results of a survey that investigates how 395 students aged 13 to 25 years old in France and Italy integrate LLMs into their educational routines. Key findings include the widespread use of these tools across all age groups and disciplines, with older students and male students demonstrating higher usage frequencies, particularly in scientific contexts. The results also show gender disparities, raising concerns about an emerging AI literacy and technological gender gap. Additionally, while most students utilise LLMs constructively, the lack of systematic proofreading and critical evaluation among younger users suggests potential risks to cognitive skills development, including critical thinking and foundational knowledge. The survey results underscore the need for educational institutions to adapt their curricula to integrate AI tools effectively, promoting ethical use, critical thinking, and awareness of AI limitations and environmental costs. This paper provides actionable recommendations for fostering equitable and effective cohabitation of LLMs and education while addressing emerging challenges.
Dataset for Real-World Human Action Detection Using FMCW mmWave Radar
Venue: arXiv cs.CV 2024
Year: 2024

Human action detection using privacy-preserving mmWave radar sensors is studied for its applications in healthcare and home automation. Unlike existing research, limited to simulations in controlled environments, we present a real-world mmWave radar dataset with baseline results for human action detection.