Yuancong Chai, Yuefei Zhu^*, Wei Lin, Ding Li

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1223-1243, 2024, DOI:10.32604/cmc.2024.049904

Abstract With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has become a core key technology in network supervision. In recent years, many different solutions have emerged in this field. Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-level features of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites, temporal features can exhibit significant variations due to changes in communication links and transmission quality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission. Faced with these challenges, identifying… More >

Xiangyan Tang^1,2, Chengchun Ruan^1,2,*, Xiulai Li^2,3, Binbin Li^1,2, Cebin Fu^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 983-1003, 2024, DOI:10.32604/cmc.2024.047541

Abstract Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in the field of small object detection on unmanned aerial vehicles (UAVs). This task is challenging due to variations in UAV flight altitude, differences in object scales, as well as factors like flight speed and motion blur. To enhance the detection efficacy of small targets in drone aerial imagery, we propose an enhanced You Only Look Once version 7 (YOLOv7) algorithm based on multi-scale spatial context. We build the MSC-YOLO model, which incorporates an additional prediction head, denoted as P2, to improve adaptability for small objects.… More >

Linlin Yuan^1,2, Tiantian Zhang^1,3, Yuling Chen^1,*, Yuxiang Yang¹, Huang Li¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1561-1579, 2024, DOI:10.32604/cmc.2023.046907

Abstract The development of technologies such as big data and blockchain has brought convenience to life, but at the same time, privacy and security issues are becoming more and more prominent. The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’ privacy by anonymizing big data. However, the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability. In addition, ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced. Based on this, we propose a… More >

Yunhe Wang¹, Yuxin Xia², Shuai Liu^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4489-4507, 2024, DOI:10.32604/cmc.2024.048813

Abstract In recent years, skeleton-based action recognition has made great achievements in Computer Vision. A graph convolutional network (GCN) is effective for action recognition, modelling the human skeleton as a spatio-temporal graph. Most GCNs define the graph topology by physical relations of the human joints. However, this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs, resulting in a low recognition rate for specific actions with implicit correlation between joint pairs. In addition, existing methods ignore the trend correlation between adjacent frames within an action and context clues, leading to… More >

Jacques Fize¹, Lucile Sautot², Martin Lentschat³, Laurence Dujourdy⁴, Ludovic Journaux⁵, Mohamed Hilal⁶

Revue Internationale de Géomatique, Vol.31, No.2, pp. 329-354, 2022, DOI:10.3166/RIG.31.329-354© 2022

Abstract Le Grand Débat National, lancé début 2019 par Emmanuel Macron, président de la République, pour répondre au mouvement social des « Gilets jaunes », a permis de collecter les contributions de citoyens sur la transition écologique via une plateforme en ligne. Dans cet article, nous exploitons le corpus constitué par ces contributions pour identifier des zones où les participants demandent le développement de pistes cyclables et d’équipements ferroviaires. Pour cela, nous avons créé un modèle de classification permettant d’identifier les contributions traitant de la thématique du transport et proposé une méthode d’extraction de contributions traduisant les propositions des contributeurs. A… More >

Thomas Leduc, Xenia Stavropulos-Laffaille, Ignacio Requena-Ruiz

Revue Internationale de Géomatique, Vol.31, No.2, pp. 241-263, 2022, DOI:10.3166/RIG.31.241-263©2022

Abstract The adaptation of public spaces to episodes of intense heat is now a major challenge for cities. With this in mind, this article presents a contribution aimed at delineating and handling the shadows on the ground or in a horizontal plane at a given height, whether it comes from buildings, street furniture or the tree cover. After a comparison with shadows obtained via two reference tools, we present two urban sites that mix shadows of different origins and, in addition, different indicators. The results of the simulations are compared with pyranometric surveys carried out on site. The aim of these… More >

Chili Li¹, Xinxin Zhao², Ziwen Pan³, Ting Yi⁴, Long Qian^5,6,*

International Journal of Mental Health Promotion, Vol.26, No.2, pp. 161-175, 2024, DOI:10.32604/ijmhp.2023.029896

Abstract While a plethora of studies has been conducted to explore demotivation and its impact on mental health in second language (L2) education, scanty research focuses on demotivation in L2 speaking learning. Particularly, little research explores the measures to quantify L2 speaking demotivation. The present two-phase study attempts to develop and validate an English Speaking Demotivation Scale (ESDS). To this end, an independent sample of 207 Chinese tertiary learners of English as a Foreign Language (EFL) participated in the development phase, and another group of 188 Chinese EFL learners was recruited for the validation of the scale. Exploratory Factor Analysis (EFA)… More >

Shahad Alzahrani¹, Hatim Alsuwat², Emad Alsuwat^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1635-1654, 2024, DOI:10.32604/cmes.2023.044718

Abstract Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables. However, the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams. One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks, wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance. In this research paper, we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms. Our framework utilizes latent variables to quantify… More >

Nadeem Salamat¹, Muhammad Kamran^1,2,*, Shahzaib Ashraf¹, Manal Elzain Mohammed Abdulla³, Rashad Ismail³, Mohammed M. Al-Shamiri³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1893-1933, 2024, DOI:10.32604/cmes.2023.044697

Abstract The main goal of informal computing is to overcome the limitations of hypersensitivity to defects and uncertainty while maintaining a balance between high accuracy, accessibility, and cost-effectiveness. This paper investigates the potential applications of intuitionistic fuzzy sets (IFS) with rough sets in the context of sparse data. When it comes to capture uncertain information emanating from both upper and lower approximations, these intuitionistic fuzzy rough numbers (IFRNs) are superior to intuitionistic fuzzy sets and pythagorean fuzzy sets, respectively. We use rough sets in conjunction with IFSs to develop several fairly aggregation operators and analyze their underlying properties. We present numerous… More > Graphic Abstract

Canghong Jin^1,*, Jiapeng Chen¹, Shuyu Wu¹, Hao Wu², Shuoping Wang¹, Jing Ying³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 873-891, 2024, DOI:10.32604/cmes.2023.043230

Abstract Time series data plays a crucial role in intelligent transportation systems. Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval. Existing approaches, including sequence periodic, regression, and deep learning models, have shown promising results in short-term series forecasting. However, forecasting scenarios specifically focused on holiday traffic flow present unique challenges, such as distinct traffic patterns during vacations and the increased demand for long-term forecastings. Consequently, the effectiveness of existing methods diminishes in such scenarios. Therefore, we propose a novel long-term forecasting model based on scene matching and embedding fusion representation to… More >