OpenRaiser/Intern-Atlas huggingface dataset:
Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques
Venue: arXiv cs.AR 2025
Year: 2025

The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, over the last 15 years, the semiconductor industry has established power efficiency as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, which has resulted in novel approximation techniques for all the layers of the traditional computing stack. More specifically, during the last decade, a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories) have been proposed in the literature. The current article is Part I of a comprehensive survey on Approximate Computing. It reviews its motivation, terminology and principles, as well it classifies the state-of-the-art software & hardware approximation techniques, presents their technical details, and reports a comparative quantitative analysis.
Finding Optimal Diverse Feature Sets with Alternative Feature Selection
Venue: arXiv cs.LG 2025
Year: 2025

Feature selection is popular for obtaining small, interpretable, yet highly accurate prediction models. Conventional feature-selection methods typically yield one feature set only, which might not suffice in some scenarios. For example, users might be interested in finding alternative feature sets with similar prediction quality, offering different explanations of the data. In this article, we introduce alternative feature selection and formalize it as an optimization problem. In particular, we define alternatives via constraints and enable users to control the number and dissimilarity of alternatives. We consider sequential as well as simultaneous search for alternatives. Next, we discuss how to integrate conventional feature-selection methods as objectives. In particular, we describe solver-based search methods to tackle the optimization problem. Further, we analyze the complexity of this optimization problem and prove NP-hardness. Additionally, we show that a constant-factor approximation exists under certain conditions and propose corresponding heuristic search methods. Finally, we evaluate alternative feature selection in comprehensive experiments with 30 binary-classification datasets. We observe that alternative feature sets may indeed have high prediction quality, and we analyze factors influencing this outcome.
High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers
Venue: arXiv cs.LG 2025
Year: 2025

Adversarial attacks pose a major challenge to distributed learning systems, prompting the development of numerous robust learning methods. However, most existing approaches suffer from the curse of dimensionality, i.e. the error increases with the number of model parameters. In this paper, we make a progress towards high dimensional problems, under arbitrary number of Byzantine attackers. The cornerstone of our design is a direct high dimensional semi-verified mean estimation method. The idea is to identify a subspace with large variance. The components of the mean value perpendicular to this subspace are estimated using corrupted gradient vectors uploaded from worker machines, while the components within this subspace are estimated using auxiliary dataset. As a result, a combination of large corrupted dataset and small clean dataset yields significantly better performance than using them separately. We then apply this method as the aggregator for distributed learning problems. The theoretical analysis shows that compared with existing solutions, our method gets rid of $\sqrt{d}$ dependence on the dimensionality, and achieves minimax optimal statistical rates. Numerical results validate our theory as well as the effectiveness of the proposed method.
Evaluating Large Language Models for Radiology Natural Language Processing
Venue: arXiv cs.CL 2025
Year: 2025

The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they have made a significant impact in the medical field. Large language models are now more abundant than ever, and many of these models exhibit bilingual capabilities, proficient in both English and Chinese. However, a comprehensive evaluation of these models remains to be conducted. This lack of assessment is especially apparent within the context of radiology NLP. This study seeks to bridge this gap by critically evaluating thirty two LLMs in interpreting radiology reports, a crucial component of radiology NLP. Specifically, the ability to derive impressions from radiologic findings is assessed. The outcomes of this evaluation provide key insights into the performance, strengths, and weaknesses of these LLMs, informing their practical applications within the medical domain.
Random (Un)rounding : Vulnerabilities in Discrete Attribute Disclosure in the 2021 Canadian Census
Venue: arXiv cs.CR 2025
Year: 2025

The 2021 Canadian census is notable for using a unique form of privacy, random rounding, which independently and probabilistically rounds discrete numerical attribute values. In this work, we explore how hierarchical summative correlation between discrete variables allows for both probabilistic and exact solutions to attribute values in the 2021 Canadian Census disclosure. We demonstrate that, in some cases, it is possible to "unround" and extract the original private values before rounding, both in the presence and absence of provided population invariants. Using these methods, we expose the exact value of 624 previously private attributes in the 2021 Canadian census disclosure. We also infer the potential values of more than 1000 private attributes with a high probability of correctness. Finally, we propose how a simple solution based on unbounded discrete noise can effectively negate exact unrounding while maintaining high utility in the final product.
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
Venue: arXiv cs.CV 2025
Year: 2025

We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation. MAMo can augment and improve any single-image depth estimation networks into video depth estimation models, enabling them to take advantage of the temporal information to predict more accurate depth. In MAMo, we augment model with memory which aids the depth prediction as the model streams through the video. Specifically, the memory stores learned visual and displacement tokens of the previous time instances. This allows the depth network to cross-reference relevant features from the past when predicting depth on the current frame. We introduce a novel scheme to continuously update the memory, optimizing it to keep tokens that correspond with both the past and the present visual information. We adopt attention-based approach to process memory features where we first learn the spatio-temporal relation among the resultant visual and displacement memory tokens using self-attention module. Further, the output features of self-attention are aggregated with the current visual features through cross-attention. The cross-attended features are finally given to a decoder to predict depth on the current frame. Through extensive experiments on several benchmarks, including KITTI, NYU-Depth V2, and DDAD, we show that MAMo consistently improves monocular depth estimation networks and sets new state-of-the-art (SOTA) accuracy. Notably, our MAMo video depth estimation provides higher accuracy with lower latency, when omparing to SOTA cost-volume-based video depth models.
Fact-Checking of AI-Generated Reports
Venue: arXiv cs.AI 2025
Year: 2025

With advances in generative artificial intelligence (AI), it is now possible to produce realistic-looking automated reports for preliminary reads of radiology images. This can expedite clinical workflows, improve accuracy and reduce overall costs. However, it is also well-known that such models often hallucinate, leading to false findings in the generated reports. In this paper, we propose a new method of fact-checking of AI-generated reports using their associated images. Specifically, the developed examiner differentiates real and fake sentences in reports by learning the association between an image and sentences describing real or potentially fake findings. To train such an examiner, we first created a new dataset of fake reports by perturbing the findings in the original ground truth radiology reports associated with images. Text encodings of real and fake sentences drawn from these reports are then paired with image encodings to learn the mapping to real/fake labels. The utility of such an examiner is demonstrated for verifying automatically generated reports by detecting and removing fake sentences. Future generative AI approaches can use the resulting tool to validate their reports leading to a more responsible use of AI in expediting clinical workflows.
CLIP Brings Better Features to Visual Aesthetics Learners
Venue: arXiv cs.CV 2025
Year: 2025

Image Aesthetics Assessment (IAA) is a challenging task due to its subjective nature and expensive manual annotations. Recent large-scale vision-language models, such as Contrastive Language-Image Pre-training (CLIP), have shown their promising representation capability for various downstream tasks. However, the application of CLIP to resource-constrained and low-data IAA tasks remains limited. While few attempts to leverage CLIP in IAA have mainly focused on carefully designed prompts, we extend beyond this by allowing models from different domains and with different model sizes to acquire knowledge from CLIP. To achieve this, we propose a unified and flexible two-phase CLIP-based Semi-supervised Knowledge Distillation (CSKD) paradigm, aiming to learn a lightweight IAA model while leveraging CLIP's strong generalization capability. Specifically, CSKD employs a feature alignment strategy to facilitate the distillation of heterogeneous CLIP teacher and IAA student models, effectively transferring valuable features from pre-trained visual representations to two lightweight IAA models, respectively. To efficiently adapt to downstream IAA tasks in a low-data regime, the two strong visual aesthetics learners then conduct distillation with unlabeled examples for refining and transferring the task-specific knowledge collaboratively. Extensive experiments demonstrate that the proposed CSKD achieves state-of-the-art performance on multiple widely used IAA benchmarks. Furthermore, analysis of attention distance and entropy before and after feature alignment shows the effective transfer of CLIP's feature representation to IAA models, which not only provides valuable guidance for the model initialization of IAA but also enhances the aesthetic feature representation of IAA models. Code will be made publicly available.
Almost perfect nonlinear power functions with exponents expressed as fractions
Venue: arXiv cs.IT 2025
Year: 2025

Let $F$ be a finite field, let $f$ be a function from $F$ to $F$, and let $a$ be a nonzero element of $F$. The discrete derivative of $f$ in direction $a$ is $Δ_a f \colon F \to F$ with $(Δ_a f)(x)=f(x+a)-f(x)$. The differential spectrum of $f$ is the multiset of cardinalities of all the fibers of all the derivatives $Δ_a f$ as $a$ runs through $F^*$. An almost perfect nonlinear (APN) function is one for which the largest cardinality in its differential spectrum is $2$. Almost perfect nonlinear functions are of interest as cryptographic primitives. If $d$ is a positive integer, then the power function over $F$ with exponent $d$ is the function $f \colon F \to F$ with $f(x)=x^d$ for every $x \in F$. There is a small number of known infinite families of APN power functions. In this paper, we re-express the exponents for one such family in a more convenient form. This enables us not only to obtain the differential spectrum of each power function $f$ with an exponent in our family, but also to determine the elements that lie in an arbitrary fiber of the discrete derivative of $f$. This differential analysis, which is far more detailed than previous results, is achieved by composing the discrete derivative of $f$ with some permutations and a double covering of its domain to obtain a function whose fibers can more readily be analyzed.
EnrichEvent: Enriching Social Data with Contextual Information for Emerging Event Extraction
Venue: arXiv cs.CL 2025
Year: 2025

Social platforms have emerged as crucial platforms for distributing information and discussing social events, offering researchers an excellent opportunity to design and implement novel event detection frameworks. Identifying unspecified events and detecting events without prior knowledge enables governments, aid agencies, and experts to respond swiftly and effectively to unfolding situations, such as natural disasters, by assessing severity and optimizing aid delivery. Social data is characterized by misspellings, incompleteness, word sense ambiguation, and irregular language. While discussing an ongoing event, users share different opinions and perspectives based on their prior experience, background, and knowledge. Prior works primarily leverage tweets' lexical and structural patterns to capture users' opinions and views about events. In this study, we propose an end-to-end novel framework, EnrichEvent, to identify unspecified events from streaming social data. In addition to lexical and structural patterns, we leverage contextual knowledge of the tweets to enrich their representation and gain a better perspective on users' opinions about events. Compared to our baselines, the EnrichEvent framework achieves the highest values for Consolidation outcome with an average of 87% vs. 67% and the lowest for Discrimination outcome with an average of 10% vs. 16%. Moreover, the Trending Data Extraction module in the EnrichEvent framework improves efficiency by reducing Runtime by up to 50% by identifying and discarding irrelevant tweets within message blocks, making the framework highly scalable for processing streaming data. Our source code and dataset are available in our official replication package.
Extraction of Road Users' Behavior From Realistic Data According to Assumptions in Safety-Related Models for Automated Driving Systems
Venue: arXiv cs.RO 2025
Year: 2025

In this work, we utilized the methodology outlined in the IEEE Standard 2846-2022 for "Assumptions in Safety-Related Models for Automated Driving Systems" to extract information on the behavior of other road users in driving scenarios. This method includes defining high-level scenarios, determining kinematic characteristics, evaluating safety relevance, and making assumptions on reasonably predictable behaviors. The assumptions were expressed as kinematic bounds. The numerical values for these bounds were extracted using Python scripts to process realistic data from the UniD dataset. The resulting information enables Automated Driving Systems designers to specify the parameters and limits of a road user's state in a specific scenario. This information can be utilized to establish starting conditions for testing a vehicle that is equipped with an Automated Driving System in simulations or on actual roads.
An Efficient Recommendation System in E-commerce using Passer learning optimization based on Bi-LSTM
Venue: arXiv cs.MM 2025
Year: 2025

Online reviews play a crucial role in shaping consumer decisions, especially in the context of e-commerce. However, the quality and reliability of these reviews can vary significantly. Some reviews contain misleading or unhelpful information, such as advertisements, fake content, or irrelevant details. These issues pose significant challenges for recommendation systems, which rely on user-generated reviews to provide personalized suggestions. This article introduces a recommendation system based on Passer Learning Optimization-enhanced Bi-LSTM classifier applicable to e-commerce recommendation systems with improved accuracy and efficiency compared to state-of-the-art models. It achieves as low as 1.24% MSE on the baby dataset. This lifts it as high as 88.58%. Besides, there is also robust performance of the system on digital music and patio lawn garden datasets at F1 of 88.46% and 92.51%, correspondingly. These results, made possible by advanced graph embedding for effective knowledge extraction and fine-tuning of classifier parameters, establish the suitability of the proposed model in various e-commerce environments.
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
Venue: arXiv cs.LG 2025
Year: 2025

On tabular data, a significant body of literature has shown that current deep learning (DL) models perform at best similarly to Gradient Boosted Decision Trees (GBDTs), while significantly underperforming them on outlier data. However, these works often study idealized problem settings which may fail to capture complexities of real-world scenarios. We identify a natural tabular data setting where DL models can outperform GBDTs: tabular Learning-to-Rank (LTR) under label scarcity. Tabular LTR applications, including search and recommendation, often have an abundance of unlabeled data, and scarce labeled data. We show that DL rankers can utilize unsupervised pretraining to exploit this unlabeled data. In extensive experiments over both public and proprietary datasets, we show that pretrained DL rankers consistently outperform GBDT rankers on ranking metrics -- sometimes by as much as 38% -- both overall and on outliers.
FREIDA: A Framework for developing quantitative agent based models based on qualitative expert knowledge
Venue: arXiv cs.AI 2025
Year: 2025

Agent Based Models (ABMs) often deal with systems where there is a lack of quantitative data or where quantitative data alone may be insufficient to fully capture the complexities of real-world systems. Expert knowledge and qualitative insights, such as those obtained through interviews, ethnographic research, historical accounts, or participatory workshops, are critical in constructing realistic behavioral rules, interactions, and decision-making processes within these models. However, there is a lack of systematic approaches that are able to incorporate both qualitative and quantitative data across the entire modeling cycle. To address this, we propose FREIDA (FRamework for Expert-Informed Data-driven Agent-based models), a systematic mixed-methods framework to develop, train, and validate ABMs, particularly in data-sparse contexts. Our main technical innovation is to extract what we call Expected System Behaviors (ESBs) from qualitative data, which are testable statements that can be evaluated on model simulations. Divided into Calibration Statements (CS) for model calibration and Validation Statements (VS) for model validation, they provide a quantitative scoring mechanism on the same footing as quantitative data. In this way, qualitative insights can inform not only model specification but also its parameterization and assessment of fitness for purpose, which is a long standing challenge. We illustrate the application of FREIDA through a case study of criminal cocaine networks in the Netherlands.
Comparability of Automated Vehicle Crash Databases
Venue: arXiv cs.CY 2025
Year: 2025

Introduction: This paper reviewed current driving automation (DA) and baseline human-driven crash databases and evaluated their comparability. Method: Five sources of DA crash data and three sources of human-driven crash data were reviewed for consistency of inclusion criteria, scope of coverage, and potential sources of bias. Alternative methods to determine vehicle automation capability using vehicle identification number (VIN) from state-maintained crash records were also explored. Conclusions: Evaluated data sets used incompatible or nonstandard minimum crash severity thresholds, complicating crash rate comparisons. The most widely-used standard was "police-reportable crash," which itself has different reporting thresholds among jurisdictions. Although low- and no-damage crashes occur at greater frequencies and have more statistical power, they were not consistently reported for automated vehicles. Crash data collection can be improved through collection of driving automation exposure data, widespread collection of crash data form electronic data recorders, and standardization of crash definitions. Practical Applications: Researchers and DA developers may use this analysis to conduct more thorough and accurate evaluations of driving automation crash rates. Lawmakers and regulators may use these findings as evidence to enhance data collection efforts, both internally and via new rules regarding electronic data recorders.
A Pre-trained Data Deduplication Model based on Active Learning
Venue: arXiv cs.LG 2025
Year: 2025

In the era of big data, the issue of data quality has become increasingly prominent. One of the main challenges is the problem of duplicate data, which can arise from repeated entry or the merging of multiple data sources. These "dirty data" problems can significantly limit the effective application of big data. To address the issue of data deduplication, we propose a pre-trained deduplication model based on active learning, which is the first work that utilizes active learning to address the problem of deduplication at the semantic level. The model is built on a pre-trained Transformer and fine-tuned to solve the deduplication problem as a sequence to classification task, which firstly integrate the transformer with active learning into an end-to-end architecture to select the most valuable data for deduplication model training, and also firstly employ the R-Drop method to perform data augmentation on each round of labeled data, which can reduce the cost of manual labeling and improve the model's performance. Experimental results demonstrate that our proposed model outperforms previous state-of-the-art (SOTA) for deduplicated data identification, achieving up to a 28% improvement in Recall score on benchmark datasets.
GRDD: A Dataset for Greek Dialectal NLP
Venue: arXiv cs.CL 2025
Year: 2025

In this paper, we present a dataset for the computational study of a number of Modern Greek dialects. It consists of raw text data from four dialects of Modern Greek, Cretan, Pontic, Northern Greek and Cypriot Greek. The dataset is of considerable size, albeit imbalanced, and presents the first attempt to create large scale dialectal resources of this type for Modern Greek dialects. We then use the dataset to perform dialect idefntification. We experiment with traditional ML algorithms, as well as simple DL architectures. The results show very good performance on the task, potentially revealing that the dialects in question have distinct enough characteristics allowing even simple ML models to perform well on the task. Error analysis is performed for the top performing algorithms showing that in a number of cases the errors are due to insufficient dataset cleaning.