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Dual-encoder model for typhoon path prediction with multiscale spatiotemporal data fusion

Earth Science Informatics 2025年第2期18卷 1-17页

作者： Ren, Shuxia Zhong, Ruikun Guo, Zewei Zhang, Zining School of Software Engineering Tiangong University Tianjin 300387 China School of Computer Science and Technology Tiangong University Tianjin 300387 China

Extreme weather caused by typhoons poses a severe threat to human life safety and socio-economic development, making accurate prediction of typhoon paths crucial. However, existing prediction models struggle to effectively handle and integrate heterogeneous data, overlooking deep correlations between the data, which in turn affects the accuracy of the models. This paper proposes a Dual-Encoder Spatiotemporal Fusion Model for Typhoon Path Prediction (DESF-Typhoon), designed to effectively integrate data from different time scales and extract the underlying relationships between the data, thereby improving prediction accuracy. Specifically, we designed a dual-encoder module that effectively captures complex nonlinear data structures, accounting for the intricate influences of factors such as geography and environment on path, while integrating spatial features of typhoon at different time scales. Subsequently, we introduced a feature interaction module to explore the relationships between different features, which can adaptively learn the interaction weights between them, resulting in a richer feature representation. We evaluated the model using the dataset from the China Meteorological Administration (CMA) and compared its performance with traditional prediction methods and deep learning-based approaches. The results demonstrate that the model significantly improves accuracy and robustness, making innovative contributions in data fusion and spatiotemporal feature modeling. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Dual encoder Feature interaction Spatiotemporal data Tropical cyclone prediction

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ViF-SD2E: a robust weakly-supervised framework for neural decoding

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Neural Computing and Applications 2025年第9期37卷 6645-6661页

作者： Feng, Jingyi Luo, Yong Song, Shuang Hu, Han National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Neural decoding plays a vital role in the interaction between the brain and the outside world. Our task in this paper is to decode the movement track of a finger directly based on the neural data. Existing neural decoding solutions primarily perform some preprocessing operations on neural data before feeding them into existing models (such as LSTM) for decoding. However, these solutions either are prone to overfitting or cannot well exploit the spatial and temporal information. In our previous observations, there is a symmetrical phenomenon between the unsupervised decoded trajectory and the ground truth trajectory within the activity space. This precisely motivates us to propose (or derive) a robust weakly-supervised framework (or model structure), called ViF-SD2E, for neural decoding. In particular, it consists of a space-division (SD) module and an exploration–exploitation (2E) strategy, to effectively exploit both the spatial information of the outside world and the temporal information of neural activity, where the SD2E output is analogized with the weak 0/1 vision feedback (ViF) label for training. Extensive experiments demonstrate the effectiveness of our method, which can sometimes be comparable to supervised counterparts. Therefore, we redirect our attention to the information (hidden in data) ViF-SD2E conveys to us. In other words, we believe that the advantage of ViF-SD2E lies in the fact that its processing steps are objectively determined by the inherent attributes (i.e., symmetry) of the neural data, or rather, the model structure is fixed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Space division multiple access

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Network Traffic Anomaly Detection Based on DGBi-SA Model

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engineering Letters 2025年第3期33卷 612-619页

作者： Wang, Xin Dai, Hong Huang, Zheng Han, Yue University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China

Network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structuring and dynamics, the paper proposes a network traffic anomaly detection approach based on DGBi-SA model, which integrates Dynamic Graph Convolutional Networks (DyGCN), Bi-directional Long Short-Term Memory (Bi-LSTM), and the Self-Attention mechanism. To solve the problem of insufficient structuring, the model employs DyGCN to capture network traffic's dynamic graph structural characteristics proficiently. Simultaneously, combining Bi-LSTM and the Self-Attention mechanism enhances the model's ability to capture temporal and spatial dependencies. The main objective of the research in the paper is to improve the accuracy and detection efficiency of network traffic prediction, thereby achieving effective detection and timely response to abnormal behaviors in complex network environments. Through a sequence of experiments conducted on the CICIDS-2017 dataset, the paper verifies that the DGBi-SA model can effectively solve the above problems. The experimental results indicate that the model outperforms existing methods in key performance indicators such as AUC, precision, recall, and F1 score, thereby proving that the DGBi-SA model has important application value and scientific significance in network traffic anomaly detection. © 2025, International Association of Engineers. All rights reserved.

关键词： Long short-term memory

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Redactable Blockchain From Decentralized Chameleon Hash Functions, Revisited

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IEEE Transactions on computers 2025年第6期74卷 1911-1920页

作者： Li, Cong Shen, Qingni Wu, Zhonghai School of Computer Science Peking University Beijing100871 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Key Laboratory of High Confidence Software Technologies Ministry of Education Peking University Beijing100871 China PKUOCTA Laboratory for Blockchain and Privacy Computing Peking University Beijing100871 China School of Software and Microelectronics Peking University Beijing102600 China

Recently, redactable blockchains have attracted attention owing to enabling the contents of blocks to be re-written. The existing redactable blockchain solutions can be classified as two categories, the centralized one and decentralized one. In centralized solutions, a single blockchain node possessing the trapdoor conducts redaction operations. However, they suffer from the issue of single point of failure. In decentralized solutions, redaction operations are performed by numerous blockchain nodes cooperatively. But there also exists the issue of inefficiency or requiring a trusted party in these solutions. Subsequently, Jia et al. proposed a redactable blockchain solution from a decentralized chameleon hash function (DCH) they designed, which supports the threshold redaction, traceability and consistency check. Nevertheless, after carefully analyzing their solution, we find that it fails to achieve the security they claimed by presenting a concrete attack. To resolve this security issue, we propose a novel chameleon hash function scheme that achieves strong collision-resistant security while maintaining simple and efficient as the building block. Based on it, we then present an improved DCH scheme with sufficient security, so that the redactable blockchain from it can resist the presented attack. Theoretical and experimental analyses demonstrate that improved DCH achieves efficiency comparable to DCH. © 1968-2012 IEEE.

关键词： Decentralized control

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KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration

KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driv...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Chengyuan Zhou, Suyang Kong, Jieping Qi, Lei Xue, Hui College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350368741

Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large Language Model (GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question Answering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods. © 2025 IEEE.

关键词： Prompt Learning Vision-Language Models Zero-shot Anomaly Detection

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Cardinality estimation for property graph queries with gated learning approach on the graph database

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Multimedia Tools and Applications 2025年第11期84卷 9159-9183页

作者： He, Zhenzhen Yu, Jiong Du, Xusheng Guo, Binglei Li, Ziyang Li, Zhe School of Information Science and Engineering Xinjiang University Urumqi China School of Computer Engineering Hubei University of Arts and Science Xiangyang China School of Software Xinjiang University Urumqi China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the execution of query tasks. Accurate estimate query cardinality is crucial for property graph data model, which usually involves entities, multiple joins, and various types of properties (key-value pairs). While learning-based approaches have been used in query optimization, estimating the cardinality of property graph queries is still particularly challenging. In this paper, we first formally represent each property graph query and then divide the estimation process into three phases: query execution, training data generation, and model training. Secondly, we construct a data pool to deal the updating query workloads in the training data generation phase. Thirdly, we utilize the gate mechanism to develop a cardinality estimation framework based on deep learning neural networks for property graph queries in the Neo4j database, and we propose a hybrid loss function to optimize the training process. Finally, we adopt a method of pre-aggregating the underlying data to speed up query execution in the first phase. The result of the experiment on two real-world datasets shows that our optimized methods can effectively improve the estimation accuracy of the deep learning model, and our query estimator outperforms other deep learning models compared in terms of estimation accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Query processing

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SLwF: A Split Learning Without Forgetting Framework for Internet of Things

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IEEE Internet of Things Journal 2025年第9期12卷 12008-12020页

作者： Feng, Xingyu Jia, Renqi Luo, Chengwen Leung, Victor C. M. Xu, Weitao School of Information Engineering Beijing100190 China City University of Hong Kong Shenzhen Research Institute Hong Kong Hong Kong City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Split learning (SL) is widely regarded as a promising distributed machine learning framework with superior privacy-preserving properties, lower communication and computation costs. However, in real Internet of Things (IoT) scenarios, existing SL may not perform well because the local data of IoT devices often do not follow the same distribution. This leads to the model continuously adapting to the current data distribution in each training epoch, resulting in a catastrophic forgetting phenomenon. Existing methods typically attempt to add raw or generated data from previous devices in the current training epoch to review knowledge, but direct access to the local data of other devices carries serious privacy risks. Data augmentation techniques based on generative networks often have poor robustness and increase the computation cost on the device side. To address these challenges, we propose a new SL framework called SL without Forgetting (SLwF). To mitigate catastrophic forgetting without accessing any previous data, we propose a contrastive learning-based training method that leverages current training data to review previous knowledge, and learn new knowledge better. Furthermore, we adopt an exponential moving average (EMA)-based model update strategy to preserve lost knowledge, further alleviating the forgetting problem. We implement the SLwF framework in real IoT scenarios and extensively evaluated its performance using four publicly available datasets. Compared to other related research (e.g., IoTSL), SLwF performs better in terms of final accuracy and robustness while avoiding excessive device energy consumption. © 2014 IEEE.

关键词： Contrastive Learning

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BugWatch: A Comprehensive Approach to Classification and Redundancy Detection in software Testing 1

BugWatch: A Comprehensive Approach to Classification and Red...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Shinde, Sandeep Chrungoo, Mahi Mane, Aditya N. Maske, Pavan R. Kulkarni, Sakshi Chat, Sachin Vishwakarma Institute of Technology Dept of Computer Engineering Pune India Dassault Medidata Software Testing California United States

ISBN: (纸本)9798350355611

Countless software programs are created daily, leading to a parallel increase in software bugs. Each institution typically maintains a dedicated quality assurance team tasked with ensuring the integrity of their developed products. However, the sheer volume of bug reports received on a daily basis presents a formidable challenge for these teams to manually address each issue. The primary aim of this research is to streamline the process of bug classification and mitigate redundancy within reported bug data. To achieve this goal, the authors explored the utilization of dif erent classification algorithms, specifically SGD, based on the issue summary of each bug. The approach involves categorizing bugs into two main groups: major and minor. Additionally, a redundancy removal process was implemented to preprocess the data, flagging sentences that exhibit significant similarity above a predetermined threshold as duplicates. The results of the study demonstrate promising outcomes, with classification accuracies of 75.93% (Feature Classification) and 90.02% (Redundancy detection) achieved using SGD classifier, which gave us the best accuracy. © 2025 IEEE.

关键词： Bug Report Classification Duplicate detection Machine Learning Natural Language Processing SGD classifier software Testing

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Optimizing Deep Learning Models to Address the Long Tail Problem and its Application in Semantic Segmentation 24

Optimizing Deep Learning Models to Address the Long Tail Pro...

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4th International Conference on Signal Processing and Communication Technology, SPCT 2024

作者： Sun, Jiarui Zhao, Yong School of Software and Microelectronics Peking University Beijing China School of Electronic and Computer Engineering Peking University Beijing China

ISBN: (纸本)9798400710636

In the semantic segmentation task, the long-tail problem causes the recognition performance of minority categories to be significantly lower than that of majority categories, affecting the overall effect of the model. This paper aims to optimize the FPN (Feature Pyramid Networks) model to better deal with the long-tail problem and improve the accuracy of semantic segmentation. First, by analyzing the category distribution of the dataset and identifying the severity of the long-tail phenomenon, a semantic segmentation dataset containing multiple categories is constructed. Secondly, for minority categories, data enhancement technology is used and resampling is performed to ensure that the model could be exposed to more minority category samples during training. A focus loss function is then integrated to prioritize hard-to-segment regions and underrepresented categories. Experimental results on standard benchmarks show that the proposed FPN model achieves a Dice coefficient of 88.1% and an F1-Score of 86.4%, significantly outperforming other models such as U-Net (Dice: 85.6%, F1-Score: 83.9%), DeepLab v3 (Dice: 87.4%, F1-Score: 85.6%), and DeepLab v1 (Dice: 84.3%, F1-Score: 82.8%). Notably, the minority class F1-Score for FPN reaches 72.1%, demonstrating its superior ability to tackle the long-tail problem. The results indicate that targeted methods, including dataset augmentation and loss function adjustment, can significantly enhance deep learning model performance, offering new insights for improving semantic segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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Unpaired 3D Shape-to-Shape Translation via Gradient-Guided Triplane Diffusion

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 3376-3386页

作者： Zhang, Wenxiao Rahmani, Hossein Liu, Jun Hohai University College of Computer Science and Software Engineering Nanjing211100 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom

Unpaired shape-to-shape translation refers to the task of transforming the geometry and semantics of an input shape into a new shape domain without paired training data. Previous methods utilize GAN-based architectures to perform shape translation, employing adversarial training to transform the source shape encoding into the target domain in the low-dimensional latent feature space. However, these methods encounter difficulties in generating diverse and high-quality results, as they often suffer from issues such as "mode collapse". This leads to limited generation diversity and makes it challenging to find an accurate latent code that adequately represents the input shape. In this paper, we achieve unpaired shape-to-shape translation via a triplane diffusion model, in which we factorize 3D objects into triplane representations and conduct a diffusion process on these representations to accomplish shape domain transformation. We observe that by adding an appropriate amount of noise to an input object during the forward diffusion process, domain-specific shape structures are smoothed out while the overall structure is still preserved. Subsequently, we progressively remove the noise via an unconditional diffusion model trained on the target shape domain in the reverse diffusion process. This allows us to obtain a denoised output that retains the structural similarities of the source input while aligning with the distribution of the target shape domain. During this process, we propose two gradient-based guidance mechanisms to guide the translation process to guarantee more faithful results during the denoising process. We conduct extensive experiments on different shape domains, and the experimental results demonstrate that our method achieves superior shape fidelity with high quality compared to current state-of-the-art baselines. © 1995-2012 IEEE.

关键词： Signal encoding

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