OpenRaiser/Intern-Atlas huggingface dataset:
Unreflected Use of Tabular Data Repositories Can Undermine Research Quality
Venue: arXiv cs.LG 2025
Year: 2025

Data repositories have accumulated a large number of tabular datasets from various domains. Machine Learning researchers are actively using these datasets to evaluate novel approaches. Consequently, data repositories have an important standing in tabular data research. They not only host datasets but also provide information on how to use them in supervised learning tasks. In this paper, we argue that, despite great achievements in usability, the unreflected usage of datasets from data repositories may have led to reduced research quality and scientific rigor. We present examples from prominent recent studies that illustrate the problematic use of datasets from OpenML, a large data repository for tabular data. Our illustrations help users of data repositories avoid falling into the traps of (1) using suboptimal model selection strategies, (2) overlooking strong baselines, and (3) inappropriate preprocessing. In response, we discuss possible solutions for how data repositories can prevent the inappropriate use of datasets and become the cornerstones for improved overall quality of empirical research studies.
AI-Driven Decision Support in Oncology: Evaluating Data Readiness for Skin Cancer Treatment
Venue: arXiv cs.AI 2025
Year: 2025

This research focuses on evaluating and enhancing data readiness for the development of an Artificial Intelligence (AI)-based Clinical Decision Support System (CDSS) in the context of skin cancer treatment. The study, conducted at the Skin Tumor Center of the University Hospital Münster, delves into the essential role of data quality, availability, and extractability in implementing effective AI applications in oncology. By employing a multifaceted methodology, including literature review, data readiness assessment, and expert workshops, the study addresses the challenges of integrating AI into clinical decision-making. The research identifies crucial data points for skin cancer treatment decisions, evaluates their presence and quality in various information systems, and highlights the difficulties in extracting information from unstructured data. The findings underline the significance of high-quality, accessible data for the success of AI-driven CDSS in medical settings, particularly in the complex field of oncology.
Blockchain Data Analytics: Review and Challenges
Venue: arXiv cs.CR 2025
Year: 2025

The integration of blockchain technology with data analytics is essential for extracting insights in the cryptocurrency space. Although academic literature on blockchain data analytics is limited, various industry solutions have emerged to address these needs. This paper provides a comprehensive literature review, drawing from both academic research and industry applications. We classify blockchain analytics tools into categories such as block explorers, on-chain data providers, research platforms, and crypto market data providers. Additionally, we discuss the challenges associated with blockchain data analytics, including data accessibility, scalability, accuracy, and interoperability. Our findings emphasize the importance of bridging academic research and industry innovations to advance blockchain data analytics.
Dynamic Feature Selection from Variable Feature Sets Using Features of Features
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning models usually assume that a set of feature values used to obtain an output is fixed in advance. However, in many real-world problems, a cost is associated with measuring these features. To address the issue of reducing measurement costs, various methods have been proposed to dynamically select which features to measure, but existing methods assume that the set of measurable features remains constant, which makes them unsuitable for cases where the set of measurable features varies from instance to instance. To overcome this limitation, we define a new problem setting for Dynamic Feature Selection (DFS) with variable feature sets and propose a deep learning method that utilizes prior information about each feature, referred to as ''features of features''. Experimental results on several datasets demonstrate that the proposed method effectively selects features based on the prior information, even when the set of measurable features changes from instance to instance.
Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs
Venue: arXiv cs.CR 2025
Year: 2025

The deployment of deep neural networks (DNNs) in privacy-sensitive environments is constrained by computational overheads in fully homomorphic encryption (FHE). This paper explores unstructured sparsity in FHE matrix multiplication schemes as a means of reducing this burden while maintaining model accuracy requirements. We demonstrate that sparsity can be exploited in arbitrary matrix multiplication, providing runtime benefits compared to a baseline naive algorithm at all sparsity levels. This is a notable departure from the plaintext domain, where there is a trade-off between sparsity and the overhead of the sparse multiplication algorithm. In addition, we propose three sparse multiplication schemes in FHE based on common plaintext sparse encodings. We demonstrate the performance gain is scheme-invariant; however, some sparse schemes vastly reduce the memory storage requirements of the encrypted matrix at high sparsity values. Our proposed sparse schemes yield an average performance gain of 2.5x at 50% unstructured sparsity, with our multi-threading scheme providing a 32.5x performance increase over the equivalent single-threaded sparse computation when utilizing 64 cores.
N2C2: Nearest Neighbor Enhanced Confidence Calibration for Cross-Lingual In-Context Learning
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements of in-context learning (ICL) show language models can significantly improve their performance when demonstrations are provided. However, little attention has been paid to model calibration and prediction confidence of ICL in cross-lingual scenarios. To bridge this gap, we conduct a thorough analysis of ICL for cross-lingual sentiment classification. Our findings suggest that ICL performs poorly in cross-lingual scenarios, exhibiting low accuracy and presenting high calibration errors. In response, we propose a novel approach, N2C2, which employs a -nearest neighbors augmented classifier for prediction confidence calibration. N2C2 narrows the prediction gap by leveraging a datastore of cached few-shot instances. Specifically, N2C2 integrates the predictions from the datastore and incorporates confidence-aware distribution, semantically consistent retrieval representation, and adaptive neighbor combination modules to effectively utilize the limited number of supporting instances. Evaluation on two multilingual sentiment classification datasets demonstrates that N2C2 outperforms traditional ICL. It surpasses fine tuning, prompt tuning and recent state-of-the-art methods in terms of accuracy and calibration errors.
LREF: A Novel LLM-based Relevance Framework for E-commerce
Venue: arXiv cs.IR 2025
Year: 2025

Query and product relevance prediction is a critical component for ensuring a smooth user experience in e-commerce search. Traditional studies mainly focus on BERT-based models to assess the semantic relevance between queries and products. However, the discriminative paradigm and limited knowledge capacity of these approaches restrict their ability to comprehend the relevance between queries and products fully. With the rapid advancement of Large Language Models (LLMs), recent research has begun to explore their application to industrial search systems, as LLMs provide extensive world knowledge and flexible optimization for reasoning processes. Nonetheless, directly leveraging LLMs for relevance prediction tasks introduces new challenges, including a high demand for data quality, the necessity for meticulous optimization of reasoning processes, and an optimistic bias that can result in over-recall. To overcome the above problems, this paper proposes a novel framework called the LLM-based RElevance Framework (LREF) aimed at enhancing e-commerce search relevance. The framework comprises three main stages: supervised fine-tuning (SFT) with Data Selection, Multiple Chain of Thought (Multi-CoT) tuning, and Direct Preference Optimization (DPO) for de-biasing. We evaluate the performance of the framework through a series of offline experiments on large-scale real-world datasets, as well as online A/B testing. The results indicate significant improvements in both offline and online metrics. Ultimately, the model was deployed in a well-known e-commerce application, yielding substantial commercial benefits.
Smart Feeding Station: Non-Invasive, Automated IoT Monitoring of Goodman's Mouse Lemurs in a Semi-Natural Rainforest Habitat
Venue: arXiv cs.SE 2025
Year: 2025

In recent years, zoological institutions have made significant strides to reimagine ex situ animal habitats, moving away from traditional single-species enclosures towards expansive multi-species environments, more closely resembling semi-natural ecosystems. This paradigm shift, driven by a commitment to animal welfare, encourages a broader range of natural behaviors through abiotic and biotic interactions. This laudable progression nonetheless introduces challenges for population monitoring, adapting daily animal care, and automating data collection for long-term research studies. This paper presents an IoT-enabled wireless smart feeding station tailored to Goodman's mouse lemurs (Microcebus lehilahytsara). System design integrates a precise Radio Frequency Identification (RFID) reader to identify the animals' implanted RFID chip simultaneously recording body weight and visit duration. Leveraging sophisticated electronic controls, the station can selectively activate a trapping mechanism for individuals with specific tags when needed. Collected data or events like a successful capture are forwarded over the Long Range Wide Area Network (LoRaWAN) to a web server and provided to the animal caretakers. To validate functionality and reliability under harsh conditions of a tropical climate, the feeding station was tested in the semi-natural Masoala rainforest biome at Zoo Zurich over two months. The station detected an animal's RFID chip when visiting the box with 98.68 % reliability, a LoRaWAN transmission reliability of 97.99 %, and a deviation in weighing accuracy below 0.41 g. Beyond its immediate application, this system addresses the challenges of automated population monitoring advancing minimally intrusive animal care and research on species behavior and ecology.
Prompt Inference Attack on Distributed Large Language Model Inference Frameworks
Venue: arXiv cs.CR 2025
Year: 2025

The inference process of modern large language models (LLMs) demands prohibitive computational resources, rendering them infeasible for deployment on consumer-grade devices. To address this limitation, recent studies propose distributed LLM inference frameworks, which employ split learning principles to enable collaborative LLM inference on resource-constrained hardware. However, distributing LLM layers across participants requires the transmission of intermediate outputs, which may introduce privacy risks to the original input prompts - a critical issue that has yet to be thoroughly explored in the literature. In this paper, we rigorously examine the privacy vulnerabilities of distributed LLM inference frameworks by designing and evaluating three prompt inference attacks aimed at reconstructing input prompts from intermediate LLM outputs. These attacks are developed under various query and data constraints to reflect diverse real-world LLM service scenarios. Specifically, the first attack assumes an unlimited query budget and access to an auxiliary dataset sharing the same distribution as the target prompts. The second attack also leverages unlimited queries but uses an auxiliary dataset with a distribution differing from the target prompts. The third attack operates under the most restrictive scenario, with limited query budgets and no auxiliary dataset available. We evaluate these attacks on a range of LLMs, including state-of-the-art models such as Llama-3.2 and Phi-3.5, as well as widely-used models like GPT-2 and BERT for comparative analysis. Our experiments show that the first two attacks achieve reconstruction accuracies exceeding 90%, while the third achieves accuracies typically above 50%, even under stringent constraints. These findings highlight privacy risks in distributed LLM inference frameworks, issuing a strong alert on their deployment in real-world applications.
MonoSLAM: Robust Monocular SLAM with Global Structure Optimization
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a robust monocular visual SLAM system that simultaneously utilizes point, line, and vanishing point features for accurate camera pose estimation and mapping. To address the critical challenge of achieving reliable localization in low-texture environments, where traditional point-based systems often fail due to insufficient visual features, we introduce a novel approach leveraging Global Primitives structural information to improve the system's robustness and accuracy performance. Our key innovation lies in constructing vanishing points from line features and proposing a weighted fusion strategy to build Global Primitives in the world coordinate system. This strategy associates multiple frames with non-overlapping regions and formulates a multi-frame reprojection error optimization, significantly improving tracking accuracy in texture-scarce scenarios. Evaluations on various datasets show that our system outperforms state-of-the-art methods in trajectory precision, particularly in challenging environments.
xVLM2Vec: Adapting LVLM-based embedding models to multilinguality using Self-Knowledge Distillation
Venue: arXiv cs.CL 2025
Year: 2025

In the current literature, most embedding models are based on the encoder-only transformer architecture to extract a dense and meaningful representation of the given input, which can be a text, an image, and more. With the recent advances in language modeling thanks to the introduction of Large Language Models, the possibility of extracting embeddings from these large and extensively trained models has been explored. However, current studies focus on textual embeddings in English, which is also the main language on which these models have been trained. Furthermore, there are very few models that consider multimodal and multilingual input. In light of this, we propose an adaptation methodology for Large Vision-Language Models trained on English language data to improve their performance in extracting multilingual and multimodal embeddings. Finally, we design and introduce a benchmark to evaluate the effectiveness of multilingual and multimodal embedding models.
FpgaHub: Fpga-centric Hyper-heterogeneous Computing Platform for Big Data Analytics
Venue: arXiv cs.DC 2025
Year: 2025

Modern data analytics requires a huge amount of computing power and processes a massive amount of data. At the same time, the underlying computing platform is becoming much more heterogeneous on both hardware and software. Even though specialized hardware, e.g., FPGA- or GPU- or TPU-based systems, often achieves better performance than a CPU-only system due to the slowing of Moore's law, such systems are limited in what they can do. For example, GPU-only approaches suffer from severe IO limitations. To truly exploit the potential of hardware heterogeneity, we present FpgaHub, an FPGA-centric hyper-heterogeneous computing platform for big data analytics. The key idea of FpgaHub is to use reconfigurable computing to implement a versatile hub complementing other processors (CPUs, GPUs, DPUs, programmable switches, computational storage, etc.). Using an FPGA as the basis, we can take advantage of its highly reconfigurable nature and rich IO interfaces such as PCIe, networking, and on-board memory, to place it at the center of the architecture and use it as a data and control plane for data movement, scheduling, pre-processing, etc. FpgaHub enables architectural flexibility to allow exploring the rich design space of heterogeneous computing platforms.
DAVE: Diagnostic benchmark for Audio Visual Evaluation
Venue: arXiv cs.CV 2025
Year: 2025

Audio-visual understanding is a rapidly evolving field that seeks to integrate and interpret information from both auditory and visual modalities. Despite recent advances in multi-modal learning, existing benchmarks often suffer from strong visual bias -- when answers can be inferred from visual data alone -- and provide only aggregate scores that conflate multiple sources of error. This makes it difficult to determine whether models struggle with visual understanding, audio interpretation, or audio-visual alignment. In this work, we introduce DAVE: Diagnostic Audio Visual Evaluation, a novel benchmark dataset designed to systematically evaluate audio-visual models across controlled settings. DAVE alleviates existing limitations by (i) ensuring both modalities are necessary to answer correctly and (ii) decoupling evaluation into atomic subcategories. Our detailed analysis of state-of-the-art models reveals specific failure modes and provides targeted insights for improvement. By offering this standardized diagnostic framework, we aim to facilitate more robust development of audio-visual models. Dataset: https://huggingface.co/datasets/gorjanradevski/dave Code: https://github.com/gorjanradevski/dave
Energy Optimized Piecewise Polynomial Approximation Utilizing Modern Machine Learning Optimizers
Venue: arXiv cs.LG 2025
Year: 2025

This work explores an extension of machine learning-optimized piecewise polynomial approximation by incorporating energy optimization as an additional objective. Traditional closed-form solutions enable continuity and approximation targets but lack flexibility in accommodating complex optimization goals. By leveraging modern gradient descent optimizers within TensorFlow, we introduce a framework that minimizes elastic strain energy in cam profiles, leading to smoother motion. Experimental results confirm the effectiveness of this approach, demonstrating its potential to Pareto-efficiently trade approximation quality against energy consumption.
CyberLLMInstruct: A Pseudo-malicious Dataset Revealing Safety-performance Trade-offs in Cyber Security LLM Fine-tuning
Venue: arXiv cs.CR 2025
Year: 2025

The integration of large language models (LLMs) into cyber security applications presents both opportunities and critical safety risks. We introduce CyberLLMInstruct, a dataset of 54,928 pseudo-malicious instruction-response pairs spanning cyber security tasks including malware analysis, phishing simulations, and zero-day vulnerabilities. Our comprehensive evaluation using seven open-source LLMs reveals a critical trade-off: while fine-tuning improves cyber security task performance (achieving up to 92.50% accuracy on CyberMetric), it severely compromises safety resilience across all tested models and attack vectors (e.g., Llama 3.1 8B's security score against prompt injection drops from 0.95 to 0.15). The dataset incorporates diverse sources including CTF challenges, academic papers, industry reports, and CVE databases to ensure comprehensive coverage of cyber security domains. Our findings highlight the unique challenges of securing LLMs in adversarial domains and establish the critical need for developing fine-tuning methodologies that balance performance gains with safety preservation in security-sensitive domains.
Post-interactive Multimodal Trajectory Prediction for Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

Modeling the interactions among agents for trajectory prediction of autonomous driving has been challenging due to the inherent uncertainty in agents' behavior. The interactions involved in the predicted trajectories of agents, also called post-interactions, have rarely been considered in trajectory prediction models. To this end, we propose a coarse-to-fine Transformer for multimodal trajectory prediction, i.e., Pioformer, which explicitly extracts the post-interaction features to enhance the prediction accuracy. Specifically, we first build a Coarse Trajectory Network to generate coarse trajectories based on the observed trajectories and lane segments, in which the low-order interaction features are extracted with the graph neural networks. Next, we build a hypergraph neural network-based Trajectory Proposal Network to generate trajectory proposals, where the high-order interaction features are learned by the hypergraphs. Finally, the trajectory proposals are sent to the Proposal Refinement Network for further refinement. The observed trajectories and trajectory proposals are concatenated together as the inputs of the Proposal Refinement Network, in which the post-interaction features are learned by combining the previous interaction features and trajectory consistency features. Moreover, we propose a three-stage training scheme to facilitate the learning process. Extensive experiments on the Argoverse 1 dataset demonstrate the superiority of our method. Compared with the baseline HiVT-64, our model has reduced the prediction errors by 4.4%, 8.4%, 14.4%, 5.7% regarding metrics minADE6, minFDE6, MR6, and brier-minFDE6, respectively.
Quantum Computing and Cybersecurity Education: A Novel Curriculum for Enhancing Graduate STEM Learning
Venue: arXiv cs.CR 2025
Year: 2025

Quantum computing is an emerging paradigm with the potential to transform numerous application areas by addressing problems considered intractable in the classical domain. However, its integration into cyberspace introduces significant security and privacy challenges. The exponential rise in cyber attacks, further complicated by quantum capabilities, poses serious risks to financial systems and national security. The scope of quantum threats extends beyond traditional software, operating system, and network vulnerabilities, necessitating a shift in cybersecurity education. Traditional cybersecurity education, often reliant on didactic methods, lacks hands on, student centered learning experiences necessary to prepare students for these evolving challenges. There is an urgent need for curricula that address both classical and quantum security threats through experiential learning. In this work, we present the design and evaluation of EE 597: Introduction to Hardware Security, a graduate level course integrating hands-on quantum security learning with classical security concepts through simulations and cloud-based quantum hardware. Unlike conventional courses focused on quantum threats to cryptographic systems, EE 597 explores security challenges specific to quantum computing itself. We employ a mixed-methods evaluation using pre and post surveys to assess student learning outcomes and engagement. Results indicate significant improvements in students' understanding of quantum and hardware security, with strong positive feedback on course structure and remote instruction (mean scores: 3.33 to 3.83 on a 4 point scale).