OpenRaiser/Intern-Atlas huggingface dataset:
Sentiment Analysis in Twitter Social Network Centered on Cryptocurrencies Using Machine Learning
Venue: arXiv cs.CL 2025
Year: 2025

Cryptocurrency is a digital currency that uses blockchain technology with secure encryption. Due to the decentralization of these currencies, traditional monetary systems and the capital market of each they, can influence a society. Therefore, due to the importance of the issue, the need to understand public opinion and analyze people's opinions in this regard increases. To understand the opinions and views of people about different topics, you can take help from social networks because they are a rich source of opinions. The Twitter social network is one of the main platforms where users discuss various topics, therefore, in the shortest time and with the lowest cost, the opinion of the community can be measured on this social network. Twitter Sentiment Analysis (TSA) is a field that analyzes the sentiment expressed in tweets. Considering that most of TSA's research efforts on cryptocurrencies are focused on English language, the purpose of this paper is to investigate the opinions of Iranian users on the Twitter social network about cryptocurrencies and provide the best model for classifying tweets based on sentiment. In the case of automatic analysis of tweets, managers and officials in the field of economy can gain knowledge from the general public's point of view about this issue and use the information obtained in order to properly manage this phenomenon. For this purpose, in this paper, in order to build emotion classification models, natural language processing techniques such as bag of words (BOW) and FastText for text vectorization and classical machine learning algorithms including KNN, SVM and Adaboost learning methods Deep including LSTM and BERT model were used for classification, and finally BERT linguistic model had the best accuracy with 83.50%.
Enhancing Generalization in Chain of Thought Reasoning for Smaller Models
Venue: arXiv cs.LG 2025
Year: 2025

Chain-of-Thought (CoT) reasoning in smaller language models is a challenging natural language process problem yet highly desirable in many real-life applications. Existing CoT knowledge distillation methods often suffer from overly conservative memorization in smaller LLMs, leading to low generalization confidence. As fully preserving the CoT ability of teacher model is impossible, we hypothesize that adversarial CoT fine-tuning is crucial for developing smaller LLM with robust CoT generalization. To this end, we propose \textit{PRompt-Assisted Domain-Adversarial fine-tuning} (PRADA), a principled fine-tuning framework that integrates diverse CoT domains. Specifically, PRADA pioneers two CoT improvements in smaller LLM: (1) Recovering the domain-invariant feature insight which typically lost during distillation with domain adversarial fine-tuning; (2) Enhancing the domain adaptability of CoT prompt engineering by employing domain-adversarial approaches. We theoretically demonstrate the effectiveness of our approach and empirically show that it significantly outperforms the state of the arts in a wide range of tasks. Moreover, our empirical findings reveal that the smaller LLM, when leveraging PRADA, aligns closely with domain knowledge, thereby improving the explainability of our approach.
Ruling the Unruly: Designing Effective, Low-Noise Network Intrusion Detection Rules for Security Operations Centers
Venue: arXiv cs.CR 2025
Year: 2025

Many Security Operations Centers (SOCs) today still heavily rely on signature-based Network Intrusion Detection Systems (NIDS) such as Suricata. The specificity of intrusion detection rules and the coverage provided by rulesets are common concerns within the professional community surrounding SOCs, which impact the effectiveness of automated alert post-processing approaches. We postulate a better understanding of factors influencing the quality of rules can help address current SOC issues. In this paper, we characterize the rules in use at a collaborating commercial (managed) SOC serving customers in sectors including education and IT management. During this process, we discover six relevant design principles, which we consolidate through interviews with experienced rule designers at the SOC.We then validate our design principles by quantitatively assessing their effect on rule specificity. We find that several of these design considerations significantly impact unnecessary workload caused by rules. For instance, rules that leverage proxies for detection, and rules that do not employ alert throttling or do not distinguish (un)successful malicious actions, cause significantly more workload for SOC analysts. Moreover, rules that match a generalized characteristic to detect malicious behavior, which is believed to increase coverage, also significantly increase workload, suggesting a tradeoff must be struck between rule specificity and coverage. We show that these design principles can be applied successfully at a SOC to reduce workload whilst maintaining coverage despite the prevalence of violations of the principles.
CrossModalityDiffusion: Multi-Modal Novel View Synthesis with Unified Intermediate Representation
Venue: arXiv cs.CV 2025
Year: 2025

Geospatial imaging leverages data from diverse sensing modalities-such as EO, SAR, and LiDAR, ranging from ground-level drones to satellite views. These heterogeneous inputs offer significant opportunities for scene understanding but present challenges in interpreting geometry accurately, particularly in the absence of precise ground truth data. To address this, we propose CrossModalityDiffusion, a modular framework designed to generate images across different modalities and viewpoints without prior knowledge of scene geometry. CrossModalityDiffusion employs modality-specific encoders that take multiple input images and produce geometry-aware feature volumes that encode scene structure relative to their input camera positions. The space where the feature volumes are placed acts as a common ground for unifying input modalities. These feature volumes are overlapped and rendered into feature images from novel perspectives using volumetric rendering techniques. The rendered feature images are used as conditioning inputs for a modality-specific diffusion model, enabling the synthesis of novel images for the desired output modality. In this paper, we show that jointly training different modules ensures consistent geometric understanding across all modalities within the framework. We validate CrossModalityDiffusion's capabilities on the synthetic ShapeNet cars dataset, demonstrating its effectiveness in generating accurate and consistent novel views across multiple imaging modalities and perspectives.
Evolving Deeper LLM Thinking
Venue: arXiv cs.AI 2025
Year: 2025

We explore an evolutionary search strategy for scaling inference time compute in Large Language Models. The proposed approach, Mind Evolution, uses a language model to generate, recombine and refine candidate responses. The proposed approach avoids the need to formalize the underlying inference problem whenever a solution evaluator is available. Controlling for inference cost, we find that Mind Evolution significantly outperforms other inference strategies such as Best-of-N and Sequential Revision in natural language planning tasks. In the TravelPlanner and Natural Plan benchmarks, Mind Evolution solves more than 98% of the problem instances using Gemini 1.5 Pro without the use of a formal solver.
Learning from Mistakes: Understanding Ad-hoc Logs through Analyzing Accidental Commits
Venue: arXiv cs.SE 2025
Year: 2025

Developers often insert temporary "print" or "log" instructions into their code to help them better understand runtime behavior, usually when the code is not behaving as they expected. Despite the fact that such monitoring instructions, or "ad-hoc logs," are so commonly used by developers, there is almost no existing literature that studies developers' practices in how they use them. This paucity of knowledge of the use of these ephemeral logs may be largely due to the fact that they typically only exist in the developers' local environments and are removed before they commit their code to their revision control system. In this work, we overcome this challenge by observing that developers occasionally mistakenly forget to remove such instructions before committing, and then they remove them shortly later. Additionally, we further study such developer logging practices by watching and analyzing live-streamed coding videos. Through these empirical approaches, we study where, how, and why developers use ad-hoc logs to better understand their code and its execution. We collect 27 GB of accidental commits that removed 548,880 ad-hoc logs in JavaScript from GitHub Archive repositories to provide the first large-scale dataset and empirical studies on ad-hoc logging practices. Our results reveal several illuminating findings, including a particular propensity for developers to use ad-hoc logs in asynchronous and callback functions. Our findings provide both empirical evidence and a valuable dataset for researchers and tool developers seeking to enhance ad-hoc logging practices, and potentially deepen our understanding of developers' practices towards understanding of software's runtime behaviors.
Decoding Patterns of Data Generation Teams for Clinical and Scientific Success: Insights from the Bridge2AI Talent Knowledge Graph
Venue: arXiv cs.DL 2025
Year: 2025

High-quality biomedical datasets are essential for medical research and disease treatment innovation. The NIH-funded Bridge2AI project strives to facilitate such innovations by uniting top-tier, diverse teams to curate datasets designed for AI-driven biomedical research. We examined 1,699 dataset papers from the Nucleic Acids Research (NAR) database issues and the Bridge2AI Talent Knowledge Graph. By treating each paper's authors as a team, we explored the relationship between team attributes (team power and fairness) and dataset paper quality, measured by scientific impact (Relative Citation Ratio percentile) and clinical translation power (APT, likelihood of citation by clinical trials and guidelines). Utilizing the SHAP explainable AI framework, we identified correlations between team attributes and the success of dataset papers in both citation impact and clinical translation. Key findings reveal that (1) PI (Principal Investigator) leadership and team academic prowess are strong predictors of dataset success; (2) team size and career age are positively correlated with scientific impact but show inverse patterns for clinical translation; and (3) higher female representation correlates with greater dataset success. Although our results are correlational, they offer valuable insights into forming high-performing data generation teams. Future research should incorporate causal frameworks to deepen understanding of these relationships.
Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Dialogue benchmarks are crucial in training and evaluating chatbots engaging in domain-specific conversations. Knowledge graphs (KGs) represent semantically rich and well-organized data spanning various domains, such as DBLP, DBpedia, and YAGO. Traditionally, dialogue benchmarks have been manually created from documents, neglecting the potential of KGs in automating this process. Some question-answering benchmarks are automatically generated using extensive preprocessing from KGs, but they do not support dialogue generation. This paper introduces Chatty-Gen, a novel multi-stage retrieval-augmented generation platform for automatically generating high-quality dialogue benchmarks tailored to a specific domain using a KG. Chatty-Gen decomposes the generation process into manageable stages and uses assertion rules for automatic validation between stages. Our approach enables control over intermediate results to prevent time-consuming restarts due to hallucinations. It also reduces reliance on costly and more powerful commercial LLMs. Chatty-Gen eliminates upfront processing of the entire KG using efficient query-based retrieval to find representative subgraphs based on the dialogue context. Our experiments with several real and large KGs demonstrate that Chatty-Gen significantly outperforms state-of-the-art systems and ensures consistent model and system performance across multiple LLMs of diverse capabilities, such as GPT-4o, Gemini 1.5, Llama 3, and Mistral.
Threat-Specific Risk Assessment for IP Multimedia Subsystem Networks Based on Hierarchical Models
Venue: arXiv cs.CR 2025
Year: 2025

Over the years, IP Multimedia Subsystems (IMS) networks have become increasingly critical as they form the backbone of modern telecommunications, enabling the integration of multimedia services such as voice, video, and messaging over IP-based infrastructures and next-generation networks. However, this integration has led to an increase in the attack surface of the IMS network, making it more prone to various forms of cyber threats and attacks, including Denial of Service (DoS) attacks, SIP-based attacks, unauthorized access, etc. As a result, it is important to find a way to manage and assess the security of IMS networks, but there is a lack of a systematic approach to managing the identification of vulnerabilities and threats. In this paper, we propose a model and a threat-specific risk security modeling and assessment approach to model and assess the threats of the IMS network. This model will provide a structured methodology for representing and analyzing threats and attack scenarios in layers within a hierarchical model. The proposed model aims to enhance the security posture of IMS networks by improving vulnerability management, risk evaluation, and defense evaluation against cyber threats. We perform a preliminary evaluation based on vulnerability collected from the National Vulnerability Database for devices in the IMS network. The results showed that we can model and assess the threats of IMS networks. IMS network defenders can use this model to understand their security postures taking into account the threat and risk posed by each vulnerability.
A Multi-Scale Feature Extraction and Fusion Deep Learning Method for Classification of Wheat Diseases
Venue: arXiv cs.CV 2025
Year: 2025

Wheat is an important source of dietary fiber and protein that is negatively impacted by a number of risks to its growth. The difficulty of identifying and classifying wheat diseases is discussed with an emphasis on wheat loose smut, leaf rust, and crown and root rot. Addressing conditions like crown and root rot, this study introduces an innovative approach that integrates multi-scale feature extraction with advanced image segmentation techniques to enhance classification accuracy. The proposed method uses neural network models Xception, Inception V3, and ResNet 50 to train on a large wheat disease classification dataset 2020 in conjunction with an ensemble of machine vision classifiers, including voting and stacking. The study shows that the suggested methodology has a superior accuracy of 99.75% in the classification of wheat diseases when compared to current state-of-the-art approaches. A deep learning ensemble model Xception showed the highest accuracy.
Client-Centric Federated Adaptive Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private. With an increasing scale of clients and models, FL encounters two key challenges, client drift due to a high degree of statistical/system heterogeneity, and lack of adaptivity. However, most existing FL research is based on unrealistic assumptions that virtually ignore system heterogeneity. In this paper, we propose Client-Centric Federated Adaptive Optimization, which is a class of novel federated adaptive optimization approaches. We enable several features in this framework such as arbitrary client participation, asynchronous server aggregation, and heterogeneous local computing, which are ubiquitous in real-world FL systems but are missed in most existing works. We provide a rigorous convergence analysis of our proposed framework for general nonconvex objectives, which is shown to converge with the best-known rate. Extensive experiments show that our approaches consistently outperform the baseline by a large margin across benchmarks.
Surface-SOS: Self-Supervised Object Segmentation via Neural Surface Representation
Venue: arXiv cs.CV 2025
Year: 2025

Self-supervised Object Segmentation (SOS) aims to segment objects without any annotations. Under conditions of multi-camera inputs, the structural, textural and geometrical consistency among each view can be leveraged to achieve fine-grained object segmentation. To make better use of the above information, we propose Surface representation based Self-supervised Object Segmentation (Surface-SOS), a new framework to segment objects for each view by 3D surface representation from multi-view images of a scene. To model high-quality geometry surfaces for complex scenes, we design a novel scene representation scheme, which decomposes the scene into two complementary neural representation modules respectively with a Signed Distance Function (SDF). Moreover, Surface-SOS is able to refine single-view segmentation with multi-view unlabeled images, by introducing coarse segmentation masks as additional input. To the best of our knowledge, Surface-SOS is the first self-supervised approach that leverages neural surface representation to break the dependence on large amounts of annotated data and strong constraints. These constraints typically involve observing target objects against a static background or relying on temporal supervision in videos. Extensive experiments on standard benchmarks including LLFF, CO3D, BlendedMVS, TUM and several real-world scenes show that Surface-SOS always yields finer object masks than its NeRF-based counterparts and surpasses supervised single-view baselines remarkably. Code is available at: https://github.com/zhengxyun/Surface-SOS.
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs
Venue: arXiv cs.CL 2025
Year: 2025

To mitigate the hallucination and knowledge deficiency in large language models (LLMs), Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) has shown promising potential by utilizing KGs as external resource to enhance LLMs reasoning. However, existing KG-RAG approaches struggle with a trade-off between flexibility and retrieval quality. Modular methods prioritize flexibility by avoiding the use of KG-fine-tuned models during retrieval, leading to fixed retrieval strategies and suboptimal retrieval quality. Conversely, coupled methods embed KG information within models to improve retrieval quality, but at the expense of flexibility. In this paper, we propose a novel flexible modular KG-RAG framework, termed FRAG, which synergizes the advantages of both approaches. FRAG estimates the hop range of reasoning paths based solely on the query and classify it as either simple or complex. To match the complexity of the query, tailored pipelines are applied to ensure efficient and accurate reasoning path retrieval, thus fostering the final reasoning process. By using the query text instead of the KG to infer the structural information of reasoning paths and employing adaptable retrieval strategies, FRAG improves retrieval quality while maintaining flexibility. Moreover, FRAG does not require extra LLMs fine-tuning or calls, significantly boosting efficiency and conserving resources. Extensive experiments show that FRAG achieves state-of-the-art performance with high efficiency and low resource consumption.
A Survey on Multi-Turn Interaction Capabilities of Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Multi-turn interaction in the dialogue system research refers to a system's ability to maintain context across multiple dialogue turns, enabling it to generate coherent and contextually relevant responses. Recent advancements in large language models (LLMs) have significantly expanded the scope of multi-turn interaction, moving beyond chatbots to enable more dynamic agentic interactions with users or environments. In this paper, we provide a focused review of the multi-turn capabilities of LLMs, which are critical for a wide range of downstream applications, including conversational search and recommendation, consultation services, and interactive tutoring. This survey explores four key aspects: (1) the core model capabilities that contribute to effective multi-turn interaction, (2) how multi-turn interaction is evaluated in current practice, (3) the general algorithms used to enhance multi-turn interaction, and (4) potential future directions for research in this field.
Multi-Modal Attention Networks for Enhanced Segmentation and Depth Estimation of Subsurface Defects in Pulse Thermography
Venue: arXiv cs.CV 2025
Year: 2025

AI-driven pulse thermography (PT) has become a crucial tool in non-destructive testing (NDT), enabling automatic detection of hidden anomalies in various industrial components. Current state-of-the-art techniques feed segmentation and depth estimation networks compressed PT sequences using either Principal Component Analysis (PCA) or Thermographic Signal Reconstruction (TSR). However, treating these two modalities independently constrains the performance of PT inspection models as these representations possess complementary semantic features. To address this limitation, this work proposes PT-Fusion, a multi-modal attention-based fusion network that fuses both PCA and TSR modalities for defect segmentation and depth estimation of subsurface defects in PT setups. PT-Fusion introduces novel feature fusion modules, Encoder Attention Fusion Gate (EAFG) and Attention Enhanced Decoding Block (AEDB), to fuse PCA and TSR features for enhanced segmentation and depth estimation of subsurface defects. In addition, a novel data augmentation technique is proposed based on random data sampling from thermographic sequences to alleviate the scarcity of PT datasets. The proposed method is benchmarked against state-of-the-art PT inspection models, including U-Net, attention U-Net, and 3D-CNN on the Université Laval IRT-PVC dataset. The results demonstrate that PT-Fusion outperforms the aforementioned models in defect segmentation and depth estimation accuracies with a margin of 10%.
Deep Learning for Early Alzheimer Disease Detection with MRI Scans
Venue: arXiv cs.CV 2025
Year: 2025

Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function. The study primarily focuses on the individuals above age 40, affecting their memory, behavior, and cognitive processes of the brain. Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients. This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis, specifically focusing on the Convolutional Neural Network, Bayesian Convolutional Neural Network, and the U-net model with the Open Access Series of Imaging Studies brain MRI dataset. Besides, to ensure robustness and reliability in the model evaluations, we address the challenge of imbalance in data. We then perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency. This comparative analysis would shed light on the future role of AI in revolutionizing AD diagnostics but also paved ways for future innovation in medical imaging and the management of neurodegenerative diseases.