Array最新文献 - Book学术

Combining computational linguistics with sentence embedding to create a zero-shot NLIDB 将计算语言学与句子嵌入相结合，创建零镜头 NLIDB

IF 2.3

Array Pub Date : 2024-10-24 DOI: 10.1016/j.array.2024.100368

引用次数: 0

Development of automatic CNC machine with versatile applications in art, design, and engineering 开发可广泛应用于艺术、设计和工程领域的自动数控机床

IF 2.3

Array Pub Date : 2024-10-21 DOI: 10.1016/j.array.2024.100369

引用次数: 0

Dual-model approach for one-shot lithium-ion battery state of health sequence prediction 一次性锂离子电池健康状态序列预测的双模型方法

IF 2.3

Array Pub Date : 2024-10-03 DOI: 10.1016/j.array.2024.100367

{"title":"Dual-model approach for one-shot lithium-ion battery state of health sequence prediction","authors":"","doi":"10.1016/j.array.2024.100367","DOIUrl":"10.1016/j.array.2024.100367","url":null,"abstract":"<div><div>Lithium-ion batteries play a crucial role in powering various applications, including Electric Vehicles (EVs), underscoring the importance of accurately estimating their State Of Health (SOH) throughout their operational lifespan. This paper introduces two novel models: a Transformer (TOPS-SoH) and a Long Short-Term Memory based (LSTM-OSoH) for One-shot Prediction of SOH. The LSTM-OSoHexcels in accuracy, achieving a Masked Mean Absolute Error (MMAE) of less than 0.01 for precise SOH estimation, while the TOPS-SoHdemonstrates simplicity and efficiency, with accuracy comparable to state-of-the-art models. The TOPS-SoHmodel also offers additional interpretability by providing insights into the attention scores between inputs and outputs, highlighting the cycles used for estimation. These models were trained using the MIT battery dataset, with auto-encoders employed to reduce the dimensionality of the input data. Additionally, the models’ effectiveness was validated against a Bidirectional LSTM (<em>BiLSTM</em>) baseline, demonstrating superior performance in terms of lower MMAE, MMSE, and MAPE values, making them highly suitable for integration into Battery Management Systems (BMS). These findings contribute to advancing SOH estimation up to the End Of Life (EOL), which is crucial for ensuring the reliability and longevity of lithium-ion batteries in diverse applications.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Maximizing influence via link prediction in evolving networks 通过演化网络中的链接预测实现影响力最大化

IF 2.3

Array Pub Date : 2024-09-28 DOI: 10.1016/j.array.2024.100366

{"title":"Maximizing influence via link prediction in evolving networks","authors":"","doi":"10.1016/j.array.2024.100366","DOIUrl":"10.1016/j.array.2024.100366","url":null,"abstract":"<div><div><em>Influence Maximization</em> (IM), targeting the optimal selection of <span><math><mi>k</mi></math></span> seed nodes to maximize potential information dissemination in prospectively social networks, garners pivotal interest in diverse realms like viral marketing and political discourse dissemination. Despite receiving substantial scholarly attention, prevailing research predominantly addresses the IM problem within the confines of existing networks, thereby neglecting the dynamic evolutionary character of social networks. An inevitable requisite arises to explore the IM problem in social networks of future contexts, which is imperative for certain application scenarios. In this light, we introduce a novel problem, Influence Maximization in Future Networks (IMFN), aimed at resolving the IM problem within an anticipated future network framework. We establish that the IMFN problem is NP-hard and advocate a prospective solution framework, employing judiciously selected link prediction methods to forecast the future network, and subsequently applying a greedy algorithm to select the <span><math><mi>k</mi></math></span> most influential nodes. Moreover, we present SCOL (Sketch-based Cost-effective lazy forward selection algorithm Optimized with Labeling technique), a well-designed algorithm to accelerate the query of our IMFN problem. Extensive experimental results, rooted in five real-world datasets, are provided, affirming the efficacy and efficiency of the proffered solution and algorithms.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing generalizability of Deep Reinforcement Learning algorithms for Automated Vulnerability Assessment and Penetration Testing 评估用于自动漏洞评估和渗透测试的深度强化学习算法的通用性

IF 2.3

Array Pub Date : 2024-09-27 DOI: 10.1016/j.array.2024.100365

{"title":"Assessing generalizability of Deep Reinforcement Learning algorithms for Automated Vulnerability Assessment and Penetration Testing","authors":"","doi":"10.1016/j.array.2024.100365","DOIUrl":"10.1016/j.array.2024.100365","url":null,"abstract":"<div><div>Modern cybersecurity best practices and standards require continuous Vulnerability Assessment (VA) and Penetration Test (PT). These activities are human- and time-expensive. The research community is trying to propose autonomous or semi-autonomous solutions based on Deep Reinforcement Learning (DRL) agents, but current proposals require further investigations. We observe that related literature reports performance tests of the proposed agents against a limited subset of the hosts used to train the models, thus raising questions on their applicability in realistic scenarios. The main contribution of this paper is to fill this gap by investigating the generalization capabilities of existing DRL agents to extend their VAPT operations to hosts that were not used in the training phase. To this purpose, we define a novel VAPT environment through which we devise multiple evaluation scenarios. While evidencing the limited capabilities of shallow RL approaches, we consider three state-of-the-art deep RL agents, namely Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Advantage Actor–Critic (A2C), and use them as bases for VAPT operations. The results show that the algorithm using A2C DRL agent outperforms the others because it is more adaptable to unknown hosts and converges faster. Our methodology can guide future researchers and practitioners in designing a new generation of semi-autonomous VAPT tools that are suitable for real-world contexts.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DART: A Solution for decentralized federated learning model robustness analysis DART：分散联合学习模型稳健性分析解决方案

IF 2.3

Array Pub Date : 2024-09-01 DOI: 10.1016/j.array.2024.100360

{"title":"DART: A Solution for decentralized federated learning model robustness analysis","authors":"","doi":"10.1016/j.array.2024.100360","DOIUrl":"10.1016/j.array.2024.100360","url":null,"abstract":"<div><p>Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (CFL) paradigm, utilize a central server for global aggregation, which exhibits limitations such as bottleneck and single point of failure. To address these issues, the Decentralized FL (DFL) paradigm has been proposed, which removes the client–server boundary and enables all participants to engage in model training and aggregation tasks. Nevertheless, as CFL, DFL remains vulnerable to adversarial attacks, notably poisoning attacks that undermine model performance. While existing research on model robustness has predominantly focused on CFL, there is a noteworthy gap in understanding the model robustness of the DFL paradigm. In this paper, a thorough review of poisoning attacks targeting the model robustness in DFL systems, as well as their corresponding countermeasures, are presented. Additionally, a solution called <em>DART</em> is proposed to evaluate the robustness of DFL models, which is implemented and integrated into a DFL platform. Through extensive experiments, this paper compares the behavior of CFL and DFL under diverse poisoning attacks, pinpointing key factors affecting attack spread and effectiveness within the DFL. It also evaluates the performance of different defense mechanisms and investigates whether defense mechanisms designed for CFL are compatible with DFL. The empirical results provide insights into research challenges and suggest ways to improve the robustness of DFL models for future research.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000262/pdfft?md5=435488fb30eb056a2cc218da941ac1cf&pid=1-s2.0-S2590005624000262-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Threat intelligence named entity recognition techniques based on few-shot learning 基于少量学习的威胁情报命名实体识别技术

IF 2.3

Array Pub Date : 2024-09-01 DOI: 10.1016/j.array.2024.100364

{"title":"Threat intelligence named entity recognition techniques based on few-shot learning","authors":"","doi":"10.1016/j.array.2024.100364","DOIUrl":"10.1016/j.array.2024.100364","url":null,"abstract":"<div><p>In today’s digital and internet era, threat intelligence analysis is of paramount importance to ensure network and information security. Named Entity Recognition (NER) is a fundamental task in natural language processing, aimed at identifying and extracting specific types of named entities from text, such as person names, locations, organization names, dates, times, currencies, and more. The quality of entities determines the effectiveness of upper-layer applications such as knowledge graphs. Recently, there has been a scarcity of training data in the threat intelligence field, and single models suffer from poor generalization ability. To address this, we propose a multi-view learning model, named the Few-shot Threat Intelligence Named Entity Recognition Model (FTM). We enhance the fusion method based on FTM, and further propose the FTM-GRU (Gate Recurrent Unit) model. The FTM model is based on the Tri-training algorithm to collaboratively train three few-shot NER models, leveraging the complementary nature of different model views to enable them to capture more threat intelligence domain knowledge at the coding level.FTM-GRU improves the fusion of multiple views. FTM-GRU uses the improved GRU model structure to control the memory and forgetting of view information, and introduces a relevance calculation unit to avoid redundancy of view information while highlighting important semantic features. We label and construct a few-shot Threat Intelligence Dataset (TID), and experiments on TID as well as the publicly available National Vulnerability Database (NVD) validate the effectiveness of our model for NER in the threat intelligence domain. Experimental results demonstrate that our proposed model achieves better recognition results in the task.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000304/pdfft?md5=d191f5b484b3734ea988ad3ecd18a1f3&pid=1-s2.0-S2590005624000304-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autonomous UAV navigation using deep learning-based computer vision frameworks: A systematic literature review 使用基于深度学习的计算机视觉框架进行无人机自主导航：系统性文献综述

IF 2.3

Array Pub Date : 2024-09-01 DOI: 10.1016/j.array.2024.100361

{"title":"Autonomous UAV navigation using deep learning-based computer vision frameworks: A systematic literature review","authors":"","doi":"10.1016/j.array.2024.100361","DOIUrl":"10.1016/j.array.2024.100361","url":null,"abstract":"<div><p>The increasing use of unmanned aerial vehicles (UAVs) in both military and civilian applications, such as infrastructure inspection, package delivery, and recreational activities, underscores the importance of enhancing their autonomous functionalities. Artificial intelligence (AI), particularly deep learning-based computer vision (DL-based CV), plays a crucial role in this enhancement. This paper aims to provide a systematic literature review (SLR) of Scopus-indexed research studies published from 2019 to 2024, focusing on DL-based CV approaches for autonomous UAV applications. By analyzing 173 studies, we categorize the research into four domains: sensing and inspection, landing, surveillance and tracking, and search and rescue. Our review reveals a significant increase in research utilizing computer vision for UAV applications, with over 39.5 % of studies employing the You Only Look Once (YOLO) framework. We discuss the key findings, including the dominant trends, challenges, and opportunities in the field, and highlight emerging technologies such as in-sensor computing. This review provides valuable insights into the current state and future directions of DL-based CV for autonomous UAVs, emphasizing its growing significance as legislative frameworks evolve to support these technologies.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000274/pdfft?md5=49538d0ae336567b2c721a5cb431f7e9&pid=1-s2.0-S2590005624000274-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling and supporting adaptive Complex Data-Intensive Web Systems via XML and the O-O paradigm: The OO-XAHM model 通过 XML 和 O-O 范式为自适应复杂数据密集型网络系统建模和提供支持：OO-XAHM 模型

IF 2.3

Array Pub Date : 2024-09-01 DOI: 10.1016/j.array.2024.100363

{"title":"Modeling and supporting adaptive Complex Data-Intensive Web Systems via XML and the O-O paradigm: The OO-XAHM model","authors":"","doi":"10.1016/j.array.2024.100363","DOIUrl":"10.1016/j.array.2024.100363","url":null,"abstract":"<div><p>The <em>data model</em> is a critical component of an <em>Adaptive Web System</em> (AWS). The major goals of such a data model are describing the <em>application domain</em> of the AWS and capturing data about the user in order to support the “adaptation effect”. There have been many proposals for data models, principally based on knowledge representation, machine learning, logic and reasoning, and, recently, ontologies. These models are focused on the implementation of the core layer of AWS, that is realizing the adaptation of contents and presentations of the system, but sometimes they are poor with respect to the application domain design. In this paper, we present an extension of the state-of-the-art <em>XML Adaptive Hypermedia Model</em> (XAHM), <em>Object-Oriented XAHM</em> (OO-XAHM) that supports the application domain modeling using an <em>object-oriented approach</em>. We also provide the formal definition of the model, its description via <em>Unified Modeling Language</em> (UML), and its implementation using <em>XML Schema</em>. Finally, we provide a complete case study that focuses the attention on the well-known Italian archaeological site <em>Pompeii</em>.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000298/pdfft?md5=6d2e89f7a240ad4c3e1b8653672e843f&pid=1-s2.0-S2590005624000298-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142238109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reimagining otitis media diagnosis: A fusion of nested U-Net segmentation with graph theory-inspired feature set 重塑中耳炎诊断：嵌套 U-Net 细分与图论启发特征集的融合

IF 2.3

Array Pub Date : 2024-09-01 DOI: 10.1016/j.array.2024.100362

{"title":"Reimagining otitis media diagnosis: A fusion of nested U-Net segmentation with graph theory-inspired feature set","authors":"","doi":"10.1016/j.array.2024.100362","DOIUrl":"10.1016/j.array.2024.100362","url":null,"abstract":"<div><p>Otitis media (OM) is a common infection or inflammation of the middle ear causing conductive hearing loss that primarily affects children and may delay speech, language, and cognitive development. OM can manifest itself in different forms, and can be diagnosed using (video) otoscopy (visualizing the tympanic membrane) or (video) pneumatic otoscopy and tympanometry. Accurate diagnosis of OM is challenging due to subtle differences in otoscopic features. This research aims to develop an automated computer-aided design (CAD) system to assist clinicians in diagnosing OM using otoscopy images. The ground truths, generated manually and validated by otolaryngologists, are utilized to train the proposed nested U-Net++ model. Ten clinically relevant gray level co-occurrence matrix (GLCM) and morphological features were extracted from the segmented Region of Interest (ROI) and validated for OM classification based on a statistical significance test. These features serve as input for a Graph Neural Network (GNN) model, the base model in our research. An optimized GNN model is proposed after ablation study of the base model. Three datasets, one private dataset, and two public ones have been used, where the private dataset is utilized for both training and testing, and the public datasets are used to test the robustness of the proposed GNN model only. The proposed GNN model obtained the highest accuracy in diagnosing OM: 99.38 %, 93.51 %, and 91.38 % for the private dataset, public dataset1, and public dataset2, respectively. The proposed methodology and results of this research might enhance clinicians' effectiveness in diagnosing OM.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000286/pdfft?md5=206b3948d729d466a159c76421c4e068&pid=1-s2.0-S2590005624000286-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0