DSpace community: 資訊工程學系暨研究所

Toward deployment-oriented long legal document classification: a segmentation-based framework for distributed legal evidence integration

Thu, 14 May 2026 04:05:13 GMT

title: Toward deployment-oriented long legal document classification: a segmentation-based framework for distributed legal evidence integration

MGGA: Make GeM Great Again via Regularization Branch to Mitigate Channel Vanishing in Visual Place Recognition

Thu, 16 Apr 2026 04:05:57 GMT

title: MGGA: Make GeM Great Again via Regularization Branch to Mitigate Channel Vanishing in Visual Place Recognition abstract: Deep-learning-based methods have achieved significant success in the Visual Place Recognition (VPR) task, which is important for autonomous driving and robotics systems. Recent advancements primarily focus on the sophisticated feature aggregation module. This paper argues for a shift in emphasis toward the backbone features. Through an in-depth analysis of GeM, one of the simplest pooling aggregator based VPR method, we identify a prevalent issue, termed ’Channel vanishing’. The issue manifests as a substantial proportion of channels in both the final GeM descriptor and the backbone output local features turning zero-valued and inactive during training, thereby drastically diminishing the representational capacity of the model and undermining its VPR performance. In order to solve this problem, we propose a regularization branch with a fully connected layer for the GeM pipeline. This branch successfully mitigates Channel vanishing and further enriches the diversity and representation of the backbone output features. During inference, our streamlined model, using only the GeM aggregator, achieves state-of-the-art performance among backbones that are not transformerbased. Notably, when utilizing the DINOv2-B backbone, our method derives 99.1% recall@1 and 100% recall@5 VPR scores on the Tokyo24/7 dataset. This result suggests that strengthening backbone features can substantially narrow the gap between simple GeM pooling and more complex aggregators; assessing how broadly this observation transfers to other aggregators is an interesting direction.

A Local Hierarchical LLM Framework for Privacy-Preserving Memory Forensics of Cryptocurrency Wallets

Thu, 16 Apr 2026 04:05:20 GMT

title: A Local Hierarchical LLM Framework for Privacy-Preserving Memory Forensics of Cryptocurrency Wallets abstract: Cryptocurrency-related crime continues to expand worldwide. Chainalysis reports that the global value of illicit cryptocurrency transactions has exceeded USD 50 billion, underscoring an urgent need for more advanced digital forensics. Cryptocurrency investigations are particularly difficult when critical evidence resides in volatile memory. Traditional workflows are time-consuming and heavily manual. Cloud-based large language models (LLMs) also pose unacceptable privacy risks in confidential law-enforcement investigations. This paper presents a multi-layer reasoning framework that integrates LangChain with a locally deployed LLM (LLaMA 3.1-8B). The framework acquires volatile evidence via memory dumping, extracts forensic artifacts via keyword and regular-expression search, and performs three-stage reasoning with a Single-Layer Baseline Architecture, a Dual-Layer Supervisor Architecture, and a Tri-Layer RAG-Decider Architecture. We evaluate the framework on 100 purpose-built crypto-wallet forensic questions. The Tri-Layer architecture achieves an average human-evaluation total score of 11.29, which is an 8.9% improvement over the Single-Layer baseline. It also reaches a BERT F1 score of 0.84 in automated metrics, improving by 15.1%. Notably, the local Tri-Layer system performs very close to the commercial cloud model ChatGPT-4o (only a 0.3% gap overall) and surpasses it on the reasoning dimension. These results demonstrate that local LLM deployment can effectively support memory forensics under strict confidentiality and limited compute resources. The proposed approach offers a practical, low-cost, and privacy-preserving tool for digital investigations. It also shows that optimized lightweight local models can approach the analytical quality of cloud-scale models.

Replacing Batch Normalization with Memory-Based Affine Transformation for Test-Time Adaptation

Thu, 16 Apr 2026 04:05:17 GMT

title: Replacing Batch Normalization with Memory-Based Affine Transformation for Test-Time Adaptation abstract: Batch normalization (BN) has become a foundational component in modern deep neural networks. However, one of its disadvantages is its reliance on batch statistics that may be unreliable or unavailable during inference, particularly under test-time domain shifts. While batch-statistics-free affine transformation methods alleviate this by learning per-sample scale and shift parameters, most treat samples independently, overlooking temporal or sequential correlations in streaming or episodic test-time settings. We propose LSTM-Affine, a memory-based normalization module that replaces BN with a recurrent parameter generator. By leveraging an LSTM, the module produces channel-wise affine parameters conditioned on both the current input and its historical context, enabling gradual adaptation to evolving feature distributions. Unlike conventional batch-statistics-free designs, LSTM-Affine captures dependencies across consecutive samples, improving stability and convergence in scenarios with gradual distribution shifts. Extensive experiments on few-shot learning and source-free domain adaptation benchmarks demonstrate that LSTM-Affine consistently outperforms BN and prior batch-statistics-free baselines, particularly when adaptation data are scarce or non-stationary. Keywords: batch normalization; affine transformation; LSTM; test-time adaptation; memory-based learning; domain adaptation; few-shot learning; normalization-free networks; deep neural networks; feature distribution shift

Physics-Aware Bottleneck-First Target Coverage Scheduling for Solar-Powered Wireless Rechargeable Sensor Networks

Tue, 14 Apr 2026 04:06:41 GMT

title: Physics-Aware Bottleneck-First Target Coverage Scheduling for Solar-Powered Wireless Rechargeable Sensor Networks abstract: Long-term target coverage in solar-powered wireless rechargeable sensor networks (WRSNs) is fundamentally challenged by sensing uncertainty, weather-driven energy variability, and the strong coupling between adjustable sensing ranges and energy consumption. Existing approaches often rely on simplified sensing or harvesting models, which may lead to unstable schedules and degraded coverage at vulnerable targets. This paper proposes Physics-aware Bottleneck-first Target Coverage Scheduling (PBTCS), a unified framework for sustainable target coverage in WRSNs under energy-neutral operation constraints. PBTCS integrates a physics-prior, interpretable day-ahead photovoltaic (PV) forecasting model to derive feasible and auditable energy budgets, and employs a budget-driven time partitioning mechanism to stabilize day-night operations. Based on the probabilistic sensing model, a bottleneck-first scheduling principle is introduced to explicitly prioritize the weakest space-time points, rather than optimizing average coverage metrics. To efficiently realize this objective under adjustable sensing radii, a closed-form marginal-gain decomposition and a budgeted dynamic programming scheme are developed for per-sensor schedule construction. Extensive simulations using real PV and meteorological data demonstrate that PBTCS consistently outperforms state-of-the-art methods in surveillance quality, coverage fairness, and long-term network sustainability across different seasons and network scales.

一種用以容置多離子感測分析裝置的容器

Fri, 27 Mar 2026 06:19:19 GMT

title: 一種用以容置多離子感測分析裝置的容器 abstract: 本發明提供一種用以容置多離子感測分析裝置的容器，包括：一上蓋，位於該容器之頂端；以及一容器本體，位於該上蓋之底端，包含一底部凹槽，具有一自該容器本體之一側邊向中心部位傾斜之傾斜面，及一位於該底部凹槽傾斜面之下方的排液開口，其中該上蓋設置有：一樣本注入口，位於鄰近該容器本體之側邊，用以注入一待測之樣本，至少一離子校正液注入口，設置於該樣本注入口之鄰近處，用以注入所需之離子校正液，及一多離子感測電極陣列試片，設置於該容器本體中，用以獲得該樣本之離子感測訊號。
description: 專利證號：I901009

Deep Learning-Based Identification of Rab Proteins: A Convolutional Neural Network Approach with Evolutionary Information Integration

Wed, 25 Mar 2026 04:05:49 GMT

title: Deep Learning-Based Identification of Rab Proteins: A Convolutional Neural Network Approach with Evolutionary Information Integration abstract: Rab proteins play a crucial role in membrane trafficking and are implicated in various human diseases. Accurate identification of Rab proteins within membrane proteins is of utmost importance for comprehending these diseases and establishing effective drug targets. In this study, we applied a two-dimensional convolutional neural network (CNN) integrated with evolutionary information to discern and identify Rab proteins present within general proteins. Our CNN model exhibited notable performance, achieving a sensitivity of 93.3%, specificity of 98%, accuracy of 96.9%, and a Matthews correlation coefficient (MCC) of 0.91 when tested on an independent dataset. In comparison to previously published methodologies, our approach displayed a substantial 25% improvement in the identification of Rab GTPases. These findings underscore the potential of deep learning techniques for accurately discerning Rab proteins and lay the groundwork for future investigations employing deep learning in the field of bioinformatics.

Enhancing Segmentation Performance for Cellular and Subcellular Structures in Micrographs: Leveraging ROI, Neighbor Extraction, and Size Constraints

Wed, 25 Mar 2026 04:05:46 GMT

title: Enhancing Segmentation Performance for Cellular and Subcellular Structures in Micrographs: Leveraging ROI, Neighbor Extraction, and Size Constraints

Optimal YOLO-based Model for Fabric Anomaly Detection

Wed, 25 Mar 2026 04:05:40 GMT

title: Optimal YOLO-based Model for Fabric Anomaly Detection abstract: Fabric anomaly detection is a crucial application in the industry. This study identifies the optimal YOLO (You Only Look Once) algorithm from a selection of YOLO versions for detecting fabric anomalies, including defect identification and region localization. Recent YOLO models, including YOLOv5, YOLOv7, YOLOv8, and YOLOv9, are evaluated with batch sizes of 4, 8, and 16. Additionally, computation times for detection are compared. The dataset is generated from numerous images extracted from a fabric video, with test images categorized as normal, line defect, or hole defect. Experimental results show that YOLOv9 batch 4 achieves the highest F1-score for defect detection, while YOLOv8 batch 16 offers a balance of optimal mAP and reduced training time. Larger batch sizes consistently enhance training efficiency across all models. Further experiments can extend this approach to other fabric datasets to detect various types of defects.

Funding Agencies Identification Using Pre-trained Large Language Models

Wed, 25 Mar 2026 04:05:35 GMT

title: Funding Agencies Identification Using Pre-trained Large Language Models

利用新聞與社群貼文標題的情緒分析用以預測個股股價走勢 – 以台積電為例

Wed, 25 Mar 2026 04:05:31 GMT

title: 利用新聞與社群貼文標題的情緒分析用以預測個股股價走勢 – 以台積電為例 abstract: 本研究探討中文大型語言模型（LLMs）於金融情緒分析在台灣股市應用之可行性，特別聚焦於台積電股價預測。透過蒐集2023年8月至2025年3月間之PTT股市版與鉅亨網關於台積電的標題資料，建立三種以BERT為基礎之情緒分析模型（EC_BERT、Dict_BERT、PN_BERT）。這三種模型分別採用Twitter財經標題、NTUSD中文情緒詞典與本研究自建資料進行訓練。實驗結果顯示，EC_BERT在新聞標題分析上表現最佳，準確率最高達57.14%；而Dict_BERT則於分析PTT貼文情緒用以預測股價時具備穩定預測力。相較之下，PN_BERT因訓練資料量與泛化能力(Generalization)限制，準確率略遜。研究亦發現社群媒體之情緒信號較易與股價變動產生關聯，惟新聞報導提供的穩定性與權威性亦具補充價值。整體而言，本研究驗證了LLMs於中文股市情緒分析之潛力，並強調模型微調訓練資料與應用場景是否相符對預測效能之重要性。

An Improved Data Smooth Method with its Application on Aerial Calligraphy

Mon, 23 Mar 2026 04:05:11 GMT

title: An Improved Data Smooth Method with its Application on Aerial Calligraphy

AI商業應用與實務

Fri, 20 Mar 2026 04:08:00 GMT

title: AI商業應用與實務

A Cross-Attention Enhanced Approach for Hybrid Text Summarization

Fri, 20 Mar 2026 04:05:33 GMT

title: A Cross-Attention Enhanced Approach for Hybrid Text Summarization

A Novel Time-Awareness Recommendation System

Fri, 20 Mar 2026 04:05:28 GMT

title: A Novel Time-Awareness Recommendation System

An Efficient Cluster-Based Continual Learning with Gradient Episodic Cache Memory

Fri, 20 Mar 2026 04:05:15 GMT

title: An Efficient Cluster-Based Continual Learning with Gradient Episodic Cache Memory

A Reinforcement Learning-Based Mobile Charging Agent for Wireless Rechargeable Sensor Networks

Thu, 19 Mar 2026 04:06:04 GMT

title: A Reinforcement Learning-Based Mobile Charging Agent for Wireless Rechargeable Sensor Networks abstract: Wireless Rechargeable Sensor Networks (WRSNs) are crucial for many applications, including environmental monitoring, healthcare, and smart cities. However, optimizing the charging schedule of a mobile charger to maximize network coverage while minimizing charging latency and energy consumption remains a challenge. In this paper, we design a reinforcement learning-based mobile charging agent (RLMCA), which integrates dynamic window search (DWS), Q-learning, and SARSA to improve charging efficiency. The proposed RLMCA combines dynamic threshold-based charging requests and an improved reward function that accounts for sensor coverage contribution. Extensive simulations demonstrate that RLMCA outperforms conventional methods in terms of charging latency, energy usage efficiency, and network coverage.

Enhancing Retrieval-Augmented Generation with Knowledge Graph-Based Soft-Labeling and Triplet Similarity SBERT

Thu, 19 Mar 2026 04:06:00 GMT

title: Enhancing Retrieval-Augmented Generation with Knowledge Graph-Based Soft-Labeling and Triplet Similarity SBERT abstract: In recent years, generative AI has made significant progress in natural language generation, including applications in customer service. Retrieval-Augmented Generation (RAG) technology has been widely used to enhance the accuracy and relevance of AI-generated responses by integrating external knowledge retrieval. This paper proposes an improved RAG system that employs knowledge graphs and a triplet similarity SBERT framework to refine text retrieval performance. The proposed model introduces soft-label generation for training, optimizing textual representation learning and retrieval quality. Experimental results demonstrate that our method outperforms existing retrieval models in accuracy and efficiency.

Question Answering System Based on Graph Neural Networks and Contrastive Learning Combined with Large Language Models

Thu, 19 Mar 2026 04:05:55 GMT

title: Question Answering System Based on Graph Neural Networks and Contrastive Learning Combined with Large Language Models abstract: In the era of information explosion, question-answering (QA) systems are crucial for efficient information retrieval. However, existing QA models face challenges in knowledge updating, semantic understanding, and computational efficiency. This study proposes a QA system integrating Graph Neural Networks (GNNs), Contrastive Learning, and Large Language Models (LLMs) within a Retrieval-Augmented Generation (RAG) framework. Our method enhances vector representation learning through GNNs and contrastive loss while leveraging RAG for efficient knowledge retrieval. Experimental results demonstrate significant improvements in accuracy and computational efficiency compared to baseline models.

Visual secret sharing for interlaced quick response codes

Tue, 17 Mar 2026 04:07:01 GMT

title: Visual secret sharing for interlaced quick response codes abstract: The quick response codes (QR codes) are commonly seen in our daily lives. They are mainly composed of black or white square blocks, containing the URL or other form of information. We may compose several QR codes together by using interlacing for layered QR codes. With the concept of visual secret sharing (VSS), we may separate the interlaced QR code into several shares, and once received shares is more than the threshold, decoding for layered QR codes can be performed. Even the separated QR codes are not 100% recoverable, they can still be scanned and the information therein can be extracted. Our experiments have presented the applicability of the composition of two QR codes with the concept of visual secret sharing in the proposed method.

An Efficient Cluster-Based Continual Learning with Gradient Episodic Cache Memory

Tue, 17 Mar 2026 04:06:54 GMT

title: An Efficient Cluster-Based Continual Learning with Gradient Episodic Cache Memory

A Novel Time-Awareness Recommendation System

Tue, 17 Mar 2026 04:06:47 GMT

title: A Novel Time-Awareness Recommendation System

應用生成對抗網路於動態社群網路預測

Tue, 17 Mar 2026 04:06:44 GMT

title: 應用生成對抗網路於動態社群網路預測

An Augmented User Context-Awareness Recommendation System

Tue, 17 Mar 2026 04:06:39 GMT

title: An Augmented User Context-Awareness Recommendation System

A Novel RNN-Based Ensemble Model for Link Prediction on Dynamic Social Network

Tue, 17 Mar 2026 04:06:35 GMT

title: A Novel RNN-Based Ensemble Model for Link Prediction on Dynamic Social Network

Error control for content-adaptive block compressive sensing with polar codes and unequal protection concepts

Tue, 17 Mar 2026 04:06:29 GMT

title: Error control for content-adaptive block compressive sensing with polar codes and unequal protection concepts abstract: Block compressive sensing (BCS) is one of the new compression techniques in recent years. In addition to looking for compression performances, protection of BCS plays another key role for robust data transmission. In our scenario, original image can be classified into smooth and active blocks, and then different measurement rates in BCS can be applied. BCS compressed data would be presented with the binary format to form bitstreams for transmitting over binary channels. Considering the concept of unequal error protection, different level of protection can be accomplished with the polar codes. Compressed bistreams protected with polar codes can be transmitted over the binary symmetric channel (BSC), and errors may be induced during transmission. After reception, polar decoding and BCS reconstruction can be utilized subsequently to obtain reconstructed image. Simulations have shown the advantages of using the inherent characteristics of original image and the concept of unequal error protection over conventional settings.

Reversible data hiding based on blockwise prediction with quadtree concepts

Tue, 17 Mar 2026 04:06:24 GMT

title: Reversible data hiding based on blockwise prediction with quadtree concepts abstract: Reversible data hiding is famous for the reversibility in information security researches. For assessing the effectiveness of reversible data hiding schemes, the marked image quality and the payload should be compared, and the reversibility should be guaranteed. It would be advantageous to take the inherent characteristics of original image into consideration to enhance the image quality and embedding payload. By generating predicted image and taking the differences between predicted and original image, a much enhanced amount of payload can be achieved. We employ quadtree decomposition for presenting image characteristics, choose different sets of prediction parameters, and perform embedding with reversible data hiding. Experimental results have provided the flexibility for performance enhancements.

Deep Spatiotemporal Forecasting and Reinforcement Optimization for Ambulance Allocation

Wed, 11 Mar 2026 04:06:10 GMT

title: Deep Spatiotemporal Forecasting and Reinforcement Optimization for Ambulance Allocation

An Effective Learning Model for Multi-label Melon Classification based on Ensemble Learning

Wed, 11 Mar 2026 04:06:06 GMT

title: An Effective Learning Model for Multi-label Melon Classification based on Ensemble Learning abstract: Melons are a popular fruit with various textures and types. Categorizing them before sale enables consumers make informed choices and enhances product appeal. While humans can classify melons by sight, this process is highly inefficient for large quantities. Automated deep-learning agricultural systems offer solutions to reduce costs and increase productivity. Therefore, this study addresses the problem of reduced recognition accuracy caused by multiple textures in a single instance. Specifically, it proposes a multi-label classification method. Four models were trained on our dataset: a custom CNN, VGG16, InceptionV3, and a decision tree. Using voting aggregation techniques, we combined their strengths to produce multi-label outputs. Our method achieved impressive results, with an accuracy of 94%, a precision of 94%, and an F1 score of 93%. Additionally, this work introduced a specialized CNN model for melon rind recognition, further improving accuracy by integrating existing techniques with voting ensemble learning. This advances in automated agriculture and inspires future research.

MeTa Learning-Based Optimization of Unsupervised Domain Adaptation Deep Networks

Wed, 11 Mar 2026 04:06:01 GMT

title: MeTa Learning-Based Optimization of Unsupervised Domain Adaptation Deep Networks

Heterogeneous Multi-AAV Cooperation With Data Gathering, Offloading, and Wireless Charging in Wireless Sensor Networks

Wed, 11 Mar 2026 04:05:55 GMT

title: Heterogeneous Multi-AAV Cooperation With Data Gathering, Offloading, and Wireless Charging in Wireless Sensor Networks

Content-Preserving Image Style Transfer via Reversible Networks with Meta ActNorm

Tue, 10 Mar 2026 04:09:21 GMT

title: Content-Preserving Image Style Transfer via Reversible Networks with Meta ActNorm abstract: Image style transfer aims to synthesize visually compelling images by blending the structural content of one image with the artistic style of another. While arbitrary style transfer methods such as AdaIN and WCT offer flexibility, they often suffer from content distortion and style leakage, particularly in complex or cross-domain scenarios. Recent approaches like ArtFlow address these issues through reversible architectures, effectively reducing distortion and leakage while providing consistent reconstruction. However, ArtFlow’s reliance on fixed normalization parameters limits adaptability across diverse content–style pairs, motivating further improvement. In this paper, we propose ISTMAF (Image Style Transfer based on Meta ArtFlow), a scalable and adaptive reversible framework that incorporates Meta ActNorm—a meta-network that dynamically generates input-specific normalization parameters. To further improve the integration of content and style, we introduce an algebraic–geometric parameter fusion strategy in the reverse process, along with a hierarchical aligned style loss to reduce artifacts and enhance visual coherence. Experiments on MS-COCO, WikiArt, and face datasets demonstrate that ISTMAF achieves superior content preservation and style consistency compared to recent state-of-the-art methods. Quantitative evaluations using SSIM and Gram difference further confirm its effectiveness. ISTMAF provides a flexible, high-fidelity solution for style transfer and shows strong generalization potential, paving the way for future extensions in multi-style fusion, video stylization, and 3D applications.

GNN-RM: A trajectory completion algorithm based on graph neural networks and regeneration modules

Tue, 10 Mar 2026 04:09:17 GMT

title: GNN-RM: A trajectory completion algorithm based on graph neural networks and regeneration modules abstract: Data about vehicle trajectories assumes a crucial role in applications such as intelligent connected vehicles. However, missing values resulting from sensors and other factors frequently affect real trajectory data. Currently, it is challenging to utilize trajectory completion methods to generate accurate real-time results at an affordable computing cost. This paper proposes GNN-RM, a trajectory completion algorithm based on graph neural networks and regeneration modules, encompassing feature extraction, subgraph construction, spatial interaction graph, and trajectory regeneration modules. The feature extraction algorithm extracts influential data as feature vectors based on certain conditions and organizes these feature vectors into different subgraphs according to categories. The spatial interaction graph constructed through graph neural networks extracts spatial interaction features between vehicles and the environment, while the regeneration modules constructed by multi-head attention mechanisms extract temporal features of vehicles, thereby completing the missing trajectories. The experimental results demonstrate that GNN-RM can achieve higher trajectory completion accuracy with fewer input parameters than multiple baseline models.

Cooperative Multi-User Task Allocation in Social-Based Crowdsensing Platform

Tue, 10 Mar 2026 04:09:11 GMT

title: Cooperative Multi-User Task Allocation in Social-Based Crowdsensing Platform abstract: Mobile crowdsensing (MCS) technologies usher in new distributed services that utilize user-provided resources to execute tasks in ubiquitous network environments. Conventional studies mainly emphasize user recruitment for simple task allocation with neither information exchange nor collaboration between users. Our study aims to investigate the new issue of collaborative multi-user (CMU) task allocation, while many users cooperate in complex tasks that require multi-fold resources from different users. To form a group of collaborative users, we adopt the social-tie notion to represent three group types, including basic, bridging, and linked user groups, in MCS contexts. By quantifying the strength of social connections between users, we formulate the measures of service capacity and cost with respect to any user group. Then, the fittingness of each group to a task can be calculated, and then a subset of user groups can be chosen to perform CMU tasks. We propose a group-based task allocation scheme, briefly named GTA, which can evaluate the cost-effectiveness of task processing by any particular group and thus allocate appropriate groups in accordance with different task requirements. Performance results by simulation manifest that the GTA scheme is promising in sustaining lower service time with only a minor influence on service cost, as compared with two typical schemes, MinCost and GoCC.

A 3D Spatial–Spectral–Temporal Deep Regression Model for Improving Mangrove Canopy Height Estimation Through Fusion of Optimized Red-Edge Sentinel-2 Bands and Sentinel-1 SAR Data

Tue, 10 Mar 2026 04:09:08 GMT

title: A 3D Spatial–Spectral–Temporal Deep Regression Model for Improving Mangrove Canopy Height Estimation Through Fusion of Optimized Red-Edge Sentinel-2 Bands and Sentinel-1 SAR Data

Elastic-Trust Hybrid Federated Learning

Tue, 10 Mar 2026 04:08:59 GMT

title: Elastic-Trust Hybrid Federated Learning

Bi-Phase LSTM: A LSTM-Based Autoencoder Architecture for Dynamic Social Network Prediction

Tue, 10 Mar 2026 04:08:54 GMT

title: Bi-Phase LSTM: A LSTM-Based Autoencoder Architecture for Dynamic Social Network Prediction abstract: In recent years, social networks have grown in popularity, with most people actively engaging on these platforms. These networks hold valuable insights into users' values and interests, allowing us to analyse relationships between connected individuals and even predict potential friendships. However, social networks are dynamic, and their structure evolves over time. To account for this, we employed a dual approach using a bi-phase LSTM autoencoder and a bi-phase LSTM predictor. These tools capture the changing characteristics of social networks and predict future graph structures. We rigorously tested our model on three datasets and compared its performance with other models. The bi-phase LSTM consistently delivered strong results across all datasets. Additionally, the model's hyperparameters were fine-tuned to improve predictive accuracy, demonstrating its reliability in forecasting the evolution of social network structures.

CARES: A Hybrid Caregivers Recommendation System Using Deep Learning and Knowledge Graphs

Tue, 10 Mar 2026 04:08:50 GMT

title: CARES: A Hybrid Caregivers Recommendation System Using Deep Learning and Knowledge Graphs abstract: Recommendation systems have prospered by leveraging user-item interactions and their features for personalized recommendations. Recent advancements in deep learning further enhance these recommendation systems with powerful backbones for learning from user-item data. However, solely depending on these interactions often leads to the cold-start problem, where items lacking historical data cannot be effectively recommended. Additionally, the issue of high similarity between user and item features frequently goes unresolved. This paper introduces a Hybrid Caregiver Recommendation mechanism, called CARES, designed to recommend suitable caregivers for postpartum women using deep learning and knowledge graphs. Initially, the proposed CARES utilizes Extreme Gradient Boosting (XGBoost) to identify important features, addressing the issue of feature similarity. Then it employs K-Means clustering to group postpartum women and caregivers based on similar features. Subsequently, it utilizes a Deep & Cross Network (DCN) to automatically learn feature interactions and constructs knowledge graphs to tackle the cold start problem. The proposed CARES also integrates exploration and exploitation strategies to balance the accuracy and diversity of recommendations. The proposed CARES compares with existing mechanisms on real datasets, and the simulation results demonstrate its effectiveness in terms of precision, recall, and F1-Score.

A Multiple Mobile Chargers Collaboratative Recharging Scheme for Enhanced Data Quailty and Sustainable Lifetime in WRSNs

Tue, 10 Mar 2026 04:08:44 GMT

title: A Multiple Mobile Chargers Collaboratative Recharging Scheme for Enhanced Data Quailty and Sustainable Lifetime in WRSNs abstract: With the rapid advancement of Internet of Things (IoT) technology, wireless sensor networks (WSNs) have become increasingly pivotal across various domains. However, the limited battery life of sensor nodes remains a critical bottleneck affecting their development and sustainability. In wireless rechargeable sensor networks (WRSNs), existing multiple mobile chargers (MCs) approach face challenges, including uneven node distribution, suboptimal charging station placement, and poor coordination, resulting in diminished recharging efficiency and degraded surveillance quality. Therefore, this study proposes a cooperative recharging mechanism for WRSNs based on multiple MCs. This mechanism first uses attractive-repulsive forces and Voronoi diagrams to achieve sensor relocation, resulting in a more balanced distribution of sensor nodes and improved spatial surveillance quality. Second, by optimizing the number and location of recharging stations (RSs) and dividing the surveillance area into multiple subregions, each served by a single MC, recharging efficiency is enhanced while satisfying the energy capacity constraints of MCs. Finally, the batch recharging strategy is adopted, and the sensing frequency of sensors are adjusted based on the recharging interval to improve temporal surveillance quality. Simulation results demonstrate that the proposed mechanism outperforms existing algorithms in terms of energy consumption, recharging efficiency, and surveillance quality, effectively extending the lifetime of WRSNs and improving surveillance quality, providing a new perspective for the sustainable development and application of WRSNs.

Teaching Authentic Sign Language Through Multiple Representation Learning

Mon, 09 Mar 2026 04:06:04 GMT

title: Teaching Authentic Sign Language Through Multiple Representation Learning abstract: Sign language recognition plays a crucial role in bridging the communication gap between hearing-impaired and hearing individuals. However, traditional teaching systems often rely on expert systems or rule-based approaches for recognition, which struggle to meet the needs of learners at different levels. This paper introduces a novel Sign Language Teaching and Scoring System (SLTS) based on multi-model collaboration, aimed at improving learning efficiency and accuracy for diverse learners. The proposed SLTS employs teaching strategies suitable for both beginners and advanced learners, offering a comprehensive solution for sign language education through multiple representation learning. Specifically, for beginners, it uses an improved Siamese Long Short-Term Memory (LSTM) module to facilitate passive learning. This approach analyzes individual gestures by comparing them to conventional sign language, allowing novices to focus on mimicking movements and establishing a solid foundation in sign language norms. For advanced learners, the proposed SLTS implements an active learning approach using an enhanced Convolutional LSTM (ConvLSTM) module to handle more complex sign language vocabulary. The system captures both spatial and temporal features of gestures, enhancing learners' fluency and expressiveness in real communication scenarios. The experimental results in real-world environments demonstrate that the proposed SLTS significantly outperforms existing methods in recognition accuracy, proving its effectiveness and advanced nature.