计算机科学最新文献

{"title":"Bidirectional cross-frequency guided wavelet diffusion for detail-preserving low-light image enhancement.","authors":"Pingping Liu, Jiahao Liu, Shihui Pei, Tongshun Zhang, Qingyi Xue, Kai Zhang, Qiuzhan Zhou","doi":"10.1016/j.neunet.2026.109650","DOIUrl":"10.1016/j.neunet.2026.109650","url":null,"abstract":"<p><p>Low-light image enhancement is challenging due to noise and poor visibility. While diffusion models have shown remarkable generative capabilities, applying them directly to this task in the pixel domain remains problematic. They tend to struggle with the trade-off between global illumination correction and local detail preservation, often resulting in unnatural textures or amplified noise due to the lack of frequency-specific constraints. To address these limitations, we propose a novel Bidirectional Cross-Frequency Guided Wavelet Diffusion model (BCF-Diff), which operates in the frequency domain for efficient and detail-preserving enhancement. Our approach uses K-level Discrete Wavelet Transform to decompose images. We introduce a two-pronged strategy to tackle the core challenges. First, we focus the diffusion model exclusively on restoring the compact low-frequency component for efficiency and stability with a detail-weighted loss mechanism. This mechanism computes weights from the high-frequency components to encourage the model to prioritize the reconstruction of detail-rich regions, ensuring a high-quality structural base. Then, we propose an innovative Cross-Frequency Gated Refinement (CFGR), which leverages the newly restored low-frequency component as a guidance to direct the enhancement of high-frequency details, and subsequently employs a Spatial Gated Refinement Module to adaptively suppress noise and sharpen textures. Our bidirectional cross-frequency guidance ensures that textures and edges are generated coherently with the overall scene structure. Experiments show that BCF-Diff achieves competitive overall performance and particularly strong structural similarity on the evaluated paired datasets, while providing visually detailed enhancement results under low-light conditions.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"205 Pt C","pages":"109650"},"PeriodicalIF":7.2,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair.","authors":"Meghdad Dehghan, Jie Jw Wu, Fatemeh H Fard, Ali Ouni","doi":"10.1007/s10664-026-10943-y","DOIUrl":"10.1007/s10664-026-10943-y","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Large Language Models (LLMs) have shown high capabilities in several software development-related tasks such as program repair, documentation, code refactoring, debugging, and testing. However, training these models requires massive amount of data and significant computational resources. Adapters are specialized, small modules designed for parameter efficient fine-tuning of LLMs for specific tasks, domains, or applications without requiring extensive retraining of the entire model. These adapters offer a more efficient way to customize LLMs for particular needs, leveraging the pre-existing capabilities of the large model. Model (and adapter) merging have emerged as a technique to develop one model capable of multiple tasks, with minimal or no training required. Although model and adapter merging has shown promising performance in domains such as natural language processing and computer vision, its applicability to software engineering tasks remains underexplored. In this paper, we investigate the effectiveness of merged adapters within the context of software engineering, with a particular focus on the Automated Program Repair (APR) task, through our approach, &lt;b&gt;MergeRepair&lt;/b&gt;. In particular, we merge multiple task-specific adapters using three different merging methods, including weight-averaging, TIES-Merging, and DARE-TIES, and evaluate the performance of the merged adapter on the APR task. We introduce a &lt;b&gt;continual merging&lt;/b&gt; approach, a novel method in which we sequentially merge the task-specific adapters where the order and weight of the merged adapters play a significant role. We further compare the performance of our approach with a baseline method consisting of equal-weight merging applied on parameters of different adapters, where all adapters are of equal importance. Through continual merging, we explore the capability of merged adapters and the effect of task order, as it occurs in real-world software projects. To evaluate our approach, we consider two LLMs, StarCoder2 and Granite models on an APR benchmark, HumanEvalFix, with various combinations of the merged adapters trained on a subset of the CommitPackFT dataset that contains four tasks and APR in Python. The results show that merging task-specific adapters can enhance the performance of models on the APR task, &lt;i&gt;without additional training&lt;/i&gt;. In particular, our approach achieves up to 2.38% performance improvement over APR task in terms of pass@1 score when merging APR with Improvement and Misc task-adapters on top of the StarCoder2 model. Performance rises to 4.01% for pass@10 score with the same model and merged adapters. This improvement could be significantly influenced by the base model and the specific task adapters merged, rather than only the number of adapters. The proposed continual merging approach can be effective if the order of merging is efficiently determined. Results show that performance improves when the most effective task-adapter is added last","PeriodicalId":11525,"journal":{"name":"Empirical Software Engineering","volume":"32 1","pages":"37"},"PeriodicalIF":3.4,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scalable memory synthesis for exemplar-free lifelong person re-identification.","authors":"Hancai Liu, Jican Tan, Haifeng Yang, Jinjia Peng, Huibing Wang","doi":"10.1016/j.neunet.2026.109649","DOIUrl":"10.1016/j.neunet.2026.109649","url":null,"abstract":"<p><p>Lifelong person re-identification (LReID) aims to learn from streaming data sources step by step, which suffers from catastrophic forgetting. In the exemplar-free LReID setting, some existing works generate pseudo-images to approximate past-domain data distributions in order to alleviate forgetting. However, such pixel-level replay strategies mainly align low-level appearance statistics such as color and style, and often neglect explicit constraints on the geometric structure of old identities in the embedding space, which tends to lead to continuous performance degradation and aggravated forgetting on past domains. To address this issue, this paper proposes Scalable Memory Synthesis (SMSyn), which constructs a controllable synthetic memory to maintain instance-level separability during continual updates. The Prototype Variational Synthesizer (PVS) generates pseudo-samples around historical instances to enrich and reconstruct historical knowledge. Then, the Feature Space Preservation (FSP) module constrains samples to concentrate around their corresponding class centers while maintaining inter-class relationships and local neighborhood structures, thereby counteracting geometric inflation and suppressing the accumulation of identity-geometry degradation. Experiments demonstrate that SMSyn outperforms state-of-the-art LReID methods.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"205 Pt C","pages":"109649"},"PeriodicalIF":7.2,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149031808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MNet: A Mamba-based teacher-student-decoder framework for multimodal remote sensing segmentation.","authors":"Mengmeng Liu, Ronghua Shang, Jingyu Zhong, Kang Liu, Yangyang Li, Jie Feng","doi":"10.1016/j.neunet.2026.109645","DOIUrl":"10.1016/j.neunet.2026.109645","url":null,"abstract":"<p><p>Remote sensing image segmentation serves as a fundamental step in connecting raw data with high-level vision understanding. However, single-modality inputs, such as optical or SAR imagery, often provide incomplete semantics, thereby limiting segmentation accuracy. While recent state space models (e.g., Mamba) have shown strong performance in unimodal segmentation by modeling long-range dependencies efficiently, their potential in multimodal settings remains largely unexplored. To address this gap, we propose MNet, a Mamba-based Teacher-Student-Decoder framework for multimodal remote sensing segmentation. First, we develop a self-adaptive weighting fusion strategy that employs attention-guided, learnable weighting to dynamically integrate optical and SAR features, enhancing cross-modal complementarity, suppressing redundancy, and producing a unified multimodal representation. Second, we introduce an omnidirectional additive state space block, which leverages eight-directional selective scanning and residual aggregation to capture anisotropic spatial features from an overhead perspective. Finally, a teacher network provides global semantic supervision, guiding the student network toward consistent local-global feature alignment, while a decoder refines structural details for precise segmentation. Extensive experiments on the WHU-OPT-SAR and DDHR-Pohang datasets demonstrate that MNet achieves state-of-the-art performance on both multimodal and unimodal tasks, highlighting the potential of Mamba-based architectures for multimodal remote sensing analysis.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"205 Pt C","pages":"109645"},"PeriodicalIF":7.2,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Lingual Real-Time Speech Interaction for Embodied Intelligence: Architecture, Challenges, and Prospects.","authors":"Shuai Wang, Guanzhe Jiao, Yicheng Li","doi":"10.1177/2167647X261487329","DOIUrl":"10.1177/2167647X261487329","url":null,"abstract":"<p><p>Embodied intelligence is rapidly evolving toward natural human-machine interaction, where real-time cross-lingual speech processing is essential for robots to understand and execute commands across languages. This study reviews research progress from January 2010 to November 1, 2025, using CiteSpace to analyze knowledge structure, hotspots, and trends. The year 2010 was selected as the start date because it marks the early emergence of foundational theoretical discussions on embodied intelligence internationally, providing a comprehensive baseline to observe the entire evolutionary trajectory of the field, even though substantive domestic research emerged later around 2015. Results show a sharp rise in research interest after 2023, driven by multilingual interaction and robotics applications. Current hotspots include multimodal representation learning, low-latency speech-to-text models, cross-lingual semantic alignment, and embodied control. The field has progressed through three stages: conceptual foundations, real-time neural integration, and large-model-driven general embodiment. We summarize the technical framework spanning speech recognition, semantic grounding, and action execution, and identify challenges such as latency reduction, noise robustness, and cross-lingual knowledge transfer. Future work should focus on unified speech-action representation, scalable low-latency architectures, adaptive learning, and ethical governance. This study outlines development trends and prospects for cross-lingual real-time speech interaction in embodied intelligence.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"380-394"},"PeriodicalIF":2.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149128665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"D3I-COMCF: Dissimilarity-driven dual-interaction via co-occurrence motifs and complementary fragments for drug-drug interaction prediction.","authors":"Wentao Xie, Min Jin, Mingjian Yang, Yulu Zhou, Xinhua Liu, Meng Li, Jagath C Rajapakse","doi":"10.1016/j.neunet.2026.109328","DOIUrl":"10.1016/j.neunet.2026.109328","url":null,"abstract":"<p><p>Drug-drug interaction (DDI) prediction seeks to identify pharmacodynamic and pharmacokinetic interactions arising from the co-administration of multiple drugs with differing physicochemical properties. However, most existing methods predominantly focus on global molecular or atomic topologies, overlooking the critical role of dynamically derived functional substructures (motifs) in modulating drug activity and the occurrence and strength of DDI. To this end, we propose a dissimilarity-driven dual-interaction via co-occurrence motifs and complementary fragments (D3I-COMCF) framework that deeply integrates molecular-, motif-, and atomic-level information for accurate DDI prediction. Specifically, we construct, for the first time, a co-occurrence motifs molecular interaction graph (CMMIG) that unifies the global molecular structure, local physicochemical properties of motifs, and molecule-motif interaction topology. By weighting rare, dissimilar but information-rich motifs, D3I-COMCF was guided to focus on more discriminative functional units. Moreover, based on the maximum common substructure mapping and its dissimilar one-hop neighbor extension, we dynamically incorporated complementary fragments attached to the shared backbone into a fragment interaction graph, revealing electronic complementarity and spatial cooperativity with computational efficiency. Finally, hierarchical multi-block integration of the three levels enables cross-scale joint representation of drug pairs. Extensive experiments on multiple public datasets verified the effectiveness of D3I-COMCF, which achieved strong and stable performance under standard benchmark protocols and showed promising cold-start generalization and interpretability. Additional hard-negative analysis further indicates that D3I-COMCF remains sensitive to known positive DDIs, while controlling false positives among structurally similar candidate pairs not annotated as known positives remains challenging.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"204 ","pages":"109328"},"PeriodicalIF":7.2,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148406673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Product Carbon Footprint Estimation via Low-Latency Neural Language Processing in Multilingual, Real-Time Supply Chains.","authors":"Shixiong Guo, Xinzhi Wang","doi":"10.1177/2167647X261481461","DOIUrl":"10.1177/2167647X261481461","url":null,"abstract":"<p><p>Real-time product carbon footprint (PCF) reporting is increasingly required in global supply chains, yet conventional life cycle assessment (LCA) remains difficult to scale due to fragmented multitier data, heterogeneous formats, and substantial manual effort. This article proposes Auto-LCA, a system-level architecture for continuous, product-level carbon estimation that integrates (i) automated supply-chain data acquisition, (ii) low-latency multilingual natural language processing for extracting life-cycle-relevant attributes from unstructured documents, (iii) a structured product carbon data model, and (iv) machine-learning-based completion of missing life cycle inventory parameters with provenance and confidence tags. A central mechanism is the \"carbon header,\" which packages accumulated embodied and operational emissions as transferable, version-controlled metadata associated with product identifiers and propagated incrementally across supply-chain tiers. Auto-LCA is designed as an open Application Programming Interface (API) layer to interface with enterprise systems (e.g., ERP/PLM/MES) and supports update-driven recomputation that recalculates only affected nodes in the supply-chain graph, improving scalability for large product portfolios and deep supplier networks. Case studies in the steel, cement, and automotive sectors demonstrate that Auto-LCA yields estimates close to representative conventional LCA benchmarks (typically within ±4%), while enabling broader coverage and more frequent updates under realistic data availability constraints. The framework also aligns with emerging digital product passport and sustainability reporting needs by enabling traceable, machine-readable carbon metadata exchange across organizations.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"446-461"},"PeriodicalIF":2.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148914190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A boundary-regularization-enhanced video anomaly detection network based on context-adaptive spatio-temporal conditional diffusion.","authors":"Huilin Liu, Guanghan Sun, Xiaolong Hu, Tian Liu, Wanqi Ma","doi":"10.1016/j.neunet.2026.109330","DOIUrl":"10.1016/j.neunet.2026.109330","url":null,"abstract":"<p><p>Unsupervised video anomaly detection aims to learn normal spatio-temporal patterns from unlabeled surveillance data and identify deviations during inference. Existing methods often struggle to capture long-term temporal dependencies and adapt to spatial variations caused by target deformation and viewpoint changes. Although diffusion-based models provide stable distribution modeling, their limited temporal conditioning and fixed spatial representations restrict anomaly discrimination in complex scenes. To address these issues, we propose a boundary regularization enhanced video anomaly detection network based on context-adaptive spatio-temporal conditional diffusion. Rather than relying on rigid feature extraction, our method explicitly models long-range motion evolution and dynamically adapts to spatial variations caused by target deformation and viewpoint shifts. Furthermore, to prevent overfitting to normal behaviors, we introduce a hybrid pseudo-anomaly generation strategy that acts as a boundary regularization constraint, explicitly sharpening the model's discriminative capability against abnormal patterns. Experiments on benchmark datasets including UCSD Ped2, CUHK Avenue, and ShanghaiTech demonstrate that our method significantly improves detection accuracy. By effectively integrating context-adaptive conditioning and boundary regularization, this work establishes a highly discriminative and generalizable paradigm for video anomaly detection, demonstrating strong robustness in complex, real-world surveillance scenarios.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"204 ","pages":"109330"},"PeriodicalIF":7.2,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148399058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Long Short-Term Memory Framework Using Hybrid Meta-Heuristic Optimization Techniques for Traffic Flow Forecasting.","authors":"Rajalakshmi Gurusamy","doi":"10.1177/2167647X261481477","DOIUrl":"10.1177/2167647X261481477","url":null,"abstract":"<p><p>Short-term traffic flow forecasting is essential for travel safety, congestion avoidance, and effective traffic management as an integral part of intelligent transport systems. Long short-term memory (LSTM) has become a promising technique for forecasting traffic flow. Unfortunately, the LSTM model does not achieve adequate forecast accuracy due to noisy traffic data and poor selection of hyperparameter optimization values. To address these limitations, this study proposes a composite SFFSDF-LSTM model that integrates sinusoidal firefly feature selection (SFFS) and dragonfly (DF) algorithms with an LSTM network. The SFFS algorithm performs adaptive feature selection, while the DF algorithm optimizes hyperparameters to improve convergence and prediction accuracy. The proposed model efficiently identifies the optimal set of LSTM features, weights, biases, and hyperparameters for domain traffic flow prediction while minimizing training errors. From the perspective of error analysis and predictive analytics, the predictive accuracy of the combined model is evaluated and found to be superior to traditional nonparametric and baseline deep learning models.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"395-409"},"PeriodicalIF":2.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Gas Network-Based Distinction Between Brain Cysts and Tumors from Magnetic Resonance Images Using a Hybrid Deep Learning Algorithm.","authors":"Mete Yağanoğlu, Öznur Özaltın, Orhan Coşkun","doi":"10.1177/2167647X261490062","DOIUrl":"10.1177/2167647X261490062","url":null,"abstract":"<p><p>The early and accurate diagnosis of brain tumors is critically important, as timely intervention significantly reduces mortality and improves patient outcomes. While magnetic resonance imaging (MRI) is the preferred diagnostic tool, differentiating malignant brain tumors from benign cysts remains a clinical challenge due to overlapping visual features. This study introduces a technically novel hybrid deep learning framework that integrates denoising, segmentation, and classification stages in a unified pipeline. First, a neural gas network is employed for precise segmentation of MRI images, enhancing boundary delineation between cystic and tumorous regions. To mitigate class imbalance and improve generalization, the Synthetic Minority Oversampling Technique is applied. A unique architectural contribution of this study lies in the integration of three pretrained convolutional neural networks (CNNs)-EfficientNetB0, MobileNetV2, and ResNet50-into a fused hybrid model that combines their complementary feature extraction capabilities. This model is fine-tuned using a grid search-based hyperparameter optimization strategy, early stopping, and cross-validation. Comparative experiments on four distinct datasets (including balanced and segmented versions) demonstrate that the proposed hybrid model consistently outperforms conventional models such as VGG16, InceptionV3, and individual CNN baselines. Achieving a peak accuracy of 97% on the balanced, augmented, and segmented dataset, this work highlights a novel and robust Artificial Intelligence (AI)-based strategy for distinguishing brain cysts from tumors in MRI, with superior performance in complex diagnostic scenarios.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"410-445"},"PeriodicalIF":2.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149065779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}