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Mitigating Noise in Quantum Software Testing Using Machine Learning IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-09-18 Asmar Muqeet, Tao Yue, Shaukat Ali, Paolo Arcaini
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Measuring the Fidelity of a Physical and a Digital Twin Using Trace Alignments IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-09-18 Paula Muñoz, Manuel Wimmer, Javier Troya, Antonio Vallecill
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The Effects of Computational Resources on Flaky Tests IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-09-18 Denini Silva, Martin Gruber, Satyajit Gokhale, Ellen Arteca, Alexi Turcotte, Marcelo d'Amorim, Wing Lam, Stefan Winter, Jonathan Bell
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D3: Differential Testing of Distributed Deep Learning with Model Generation IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-09-16 Jiannan Wang, Hung Viet Pham, Qi Li, Lin Tan, Yu Guo, Adnan Aziz, Erik Meijer
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ISO/IEC quality standards for AI engineering Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-09-14 Jesús Oviedo, Moisés Rodriguez, Andrea Trenta, Dino Cannas, Domenico Natale, Mario Piattini
Artificial Intelligence (AI) plays a crucial role in the digital transformation of organizations, with the influence of AI applications expanding daily. Given this context, the development of these AI systems to guarantee their effective operation and usage is becoming more essential. To this end, numerous international standards have been introduced in recent years. This paper offers a broad review
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Digital to quantum watermarking: A journey from past to present and into the future Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-09-14 Swapnaneel Dhar, Aditya Kumar Sahu
With the amplification of digitization, the surge in multimedia content, such as text, video, audio, and images, is incredible. Concomitantly, the incidence of multimedia tampering is also apparently increasing. Digital watermarking (DW) is the means of achieving privacy and authentication of the received content while preserving integrity and copyright. Literature has produced a plethora of state-of-the-art
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Mimicking Production Behavior with Generated Mocks IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-09-11 Deepika Tiwari, Martin Monperrus, Benoit Baudry
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Understanding Code Understandability Improvements in Code Reviews IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-09-10 Delano Oliveira, Reydne Santos, Benedito de Oliveira, Martin Monperrus, Fernando Castor, Fernanda Madeiral
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Comprehensive survey on resource allocation for edge-computing-enabled metaverse Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-09-09 Tanmay Baidya, Sangman Moh
With the rapid evaluation of virtual and augmented reality, massive Internet of Things networks and upcoming 6 G communication give rise to an emerging concept termed the “metaverse,” which promises to revolutionize how we interact with the digital world by offering immersive experiences between reality and virtuality. Edge computing, another novel paradigm, propels the metaverse functionality by enhancing
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HetFL: Heterogeneous Graph-based Software Fault Localization IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-09-05 Xin Chen, Tian Sun, Dongling Zhuang, Dongjin Yu, He Jiang, Zhide Zhou, Sicheng Li
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Does the Vulnerability Threaten Our Projects? Automated Vulnerable API Detection for Third-Party Libraries IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-09-05 Fangyuan Zhang, Lingling Fan, Sen Chen, Miaoying Cai, Sihan Xu, Lida Zhao
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Evaluating Diverse Large Language Models for Automatic and General Bug Reproduction IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-09-04 Sungmin Kang, Juyeon Yoon, Nargiz Askarbekkyzy, Shin Yoo
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stagg:: A data pre-processing R package for climate impacts analysis Environ. Model. Softw. (IF 4.8) Pub Date : 2024-09-02 Tyler Liddell, Anna S. Boser, Sara Orofino, Tracey Mangin, Tamma Carleton
The increasing availability of high-resolution climate data has greatly expanded the study of how the climate impacts humans and society. However, the processing of these multi-dimensional datasets poses significant challenges for researchers in this growing field, most of whom are social scientists. This paper introduces stagg, or “space-time aggregator”, a new R package that streamlines three critical
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Review of Jamal Mabrouki, Azrour Maroude, and Azeem Irshad (eds.), Artificial Intelligence Systems in Environmental Engineering.. Environ. Model. Softw. (IF 4.8) Pub Date : 2024-09-02 Fransiskus Serfian Jogo
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PyMTRD: A Python package for calculating the metrics of temporal rainfall distribution Environ. Model. Softw. (IF 4.8) Pub Date : 2024-09-01 Zhengxu Guo, Yang Wang, Caiqin Liu, Wanhong Yang, Junzhi Liu
Temporal rainfall distribution facilitates the understanding of rainfall patterns at various time scales, extreme events, and corresponding water resources implications. Researchers have developed various metrics of temporal rainfall distribution but there exist no easy-to-use software packages for calculating these metrics. To address this gap, we developed the package, which can be conveniently used
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Quantum secure authentication and key agreement protocols for IoT-enabled applications: A comprehensive survey and open challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-31 Ponnuru Raveendra Babu, Sathish A.P. Kumar, Alavalapati Goutham Reddy, Ashok Kumar Das
This study provides an in-depth survey of quantum secure authentication and key agreement protocols, investigating the dynamic landscape of cryptographic techniques within the realm of quantum computing. With the potential threat posed by quantum computing advancements to classical cryptographic protocols, the exploration of secure authentication and key agreement in the quantum era becomes imperative
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Analysis of the spatial heterogeneity of glacier melting in Tibet Autonomous Region and its influential factors using the K-means and XGBoost-SHAP algorithms Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-31 Tingting Xu, Aohua Tian, Jay Gao, Haoze Yan, Chang Liu
This study employed machine learning to comprehensively analyze glacier melting in Tibet Autonomous Region (TAR) and its vital influencing factors. Existing machine learning research often lacks detailed explanations, leading to generalized predictions without considering essential driving factors necessary for yielding an insightful understanding of glacier melting dynamics. To overcome these limitations
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Internet of everything meets the metaverse: Bridging physical and virtual worlds with blockchain Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-30 Wajid Rafique, Junaid Qadir
The Metaverse is an evolving technology that leverages the Internet infrastructure and the massively connected Internet of Everything (IoE) to create an immersive virtual world. In the Metaverse, humans engage in activities similar to those in the real world, such as socializing, working, attending events, exploring virtual landscapes, creating and trading digital assets, participating in virtual economies
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Role of human physiology and facial biomechanics towards building robust deepfake detectors: A comprehensive survey and analysis Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-30 Rajat Chakraborty, Ruchira Naskar
AI based multimedia content generation, already having achieved hyper-realism, deeply influences human perception and trust. Since emerging around late 2017, deepfake technology has rapidly gained popularity due to its diverse applications, raising significant concerns regarding its malicious and unethical use. Although many deepfake detectors have been developed by forensic researchers in recent years
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RLocator: Reinforcement Learning for Bug Localization IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-30 Partha Chakraborty, Mahmoud Alfadel, Meiyappan Nagappan
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Leveraging Large Language Model for Automatic Patch Correctness Assessment IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-30 Xin Zhou, Bowen Xu, Kisub Kim, DongGyun Han, Hung Huu Nguyen, Thanh Le-Cong, Junda He, Bach Le, David Lo
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XR-based interactive visualization platform for real-time exploring dynamic earth science data Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-29 Xuelei Zhang, Hu Yang, Chunhua Liu, Qingqing Tong, Aijun Xiu, Lingsheng Kong, Mo Dan, Chao Gao, Meng Gao, Huizheng Che, Xin Wang, Guangjian Wu
The transition from 2D planar displays to immersive holographic 3D environments has brought advancements in visualization technology. However, there remains a lack of effective interactive visualization tools for complex multi-dimensional structured or unstructured datasets in immersive space. To address this gap, we have developed MetIVA, a state-of-the-art multiscale interactive data visualization
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3Erefactor: Effective, Efficient and Executable Refactoring Recommendation for Software Architectural Consistency IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-28 Jingwen Liu, Wuxia Jin, Junhui Zhou, Qiong Feng, Ming Fan, Haijun Wang, Ting Liu
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Method-Level Test-to-Code Traceability Link Construction by Semantic Correlation Learning IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-27 Weifeng Sun, Zhenting Guo, Meng Yan, Zhongxin Liu, Yan Lei, Hongyu Zhang
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Hazy to hazy free: A comprehensive survey of multi-image, single-image, and CNN-based algorithms for dehazing Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-26 Jehoiada Jackson, Kwame Obour Agyekum, kwabena Sarpong, Chiagoziem Ukwuoma, Rutherford Patamia, Zhiguang Qin
The natural and artificial dispersal of climatic particles transforms images obtained in open-air conditions. Due to visibility diminishing aerosols, unfavorable climate situations such as mist, fog, and haze cause color change and reduce the contrast of the obtained image. Images seem deformed and inadequate in contrast saturation, affecting computer vision techniques considerably. Haze removal aims
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A survey on reconfigurable intelligent surfaces assisted multi-access edge computing networks: State of the art and future challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-26 Manzoor Ahmed, Salman Raza, Aized Amin Soofi, Feroz Khan, Wali Ullah Khan, Fang Xu, Symeon Chatzinotas, Octavia A. Dobre, Zhu Han
This survey provides a comprehensive analysis of the integration of Reconfigurable Intelligent Surfaces (RIS) with edge computing, underscoring RIS’s critical role in advancing wireless communication networks. The examination begins by demystifying edge computing, contrasting it with traditional cloud computing, and categorizing it into several types. It further delves into advanced edge computing
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A systematic literature review on chaotic maps-based image security techniques Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-26 Dilbag Singh, Sharanpreet Kaur, Mandeep Kaur, Surender Singh, Manjit Kaur, Heung-No Lee
Images play a substantial role in various applications such as medical imaging, satellite imaging, and military communications. These images often contain confidential and sensitive information, and are typically transmitted over public networks. As a result, they are vulnerable to various security threats. Therefore, efficient image security techniques are necessary to protect these images from unauthorized
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A survey of blockchain, artificial intelligence, and edge computing for Web 3.0 Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-24 Jianjun Zhu, Fan Li, Jinyuan Chen
Web 3.0, as the third generation of the World Wide Web, aims to solve contemporary problems of trust, centralization, and data ownership. Driven by the latest advances in cutting-edge technologies, Web 3.0 is moving towards a more open, decentralized, intelligent, and interconnected network. Currently, increasingly widespread data breaches have raised awareness of online privacy and security of personal
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Bayesian Optimization for Anything (BOA): An open-source framework for accessible, user-friendly Bayesian optimization Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-24 Madeline E. Scyphers, Justine E.C. Missik, Haley Kujawa, Joel A. Paulson, Gil Bohrer
We introduce Bayesian Optimization for Anything (BOA), a high-level Bayesian Optimization (BO) framework and model wrapping toolkit, which presents a novel approach to simplifying BO, with the goal of making it more accessible and user-friendly, particularly for those with limited expertise in the field. BOA addresses common barriers in implementing BO, focusing on ease of use, reducing the need for
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A comprehensive survey of text classification techniques and their research applications: Observational and experimental insights Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-23 Kamal Taha, Paul D. Yoo, Chan Yeun, Dirar Homouz, Aya Taha
The exponential growth of textual data presents substantial challenges in management and analysis, notably due to high storage and processing costs. Text classification, a vital aspect of text mining, provides robust solutions by enabling efficient categorization and organization of text data. These techniques allow individuals, researchers, and businesses to derive meaningful patterns and insights
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Follow-up Attention: An Empirical Study of Developer and Neural Model Code Exploration IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-23 Matteo Paltenghi, Rahul Pandita, Austin Z. Henley, Albert Ziegler
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EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-23 Chengjie Lu, Shaukat Ali, Tao Yue
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ForestAdvisor: A multi-modal forest decision-making system based on carbon emissions Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-23 Tong Ji, Yifeng Lin, Yuer Yang
Effectively balancing carbon emission reduction with economic viability through regional forest management is a significant challenge for global ecosystems. This paper introduces an innovative multi-modal forest decision-making system, integrating deep learning and natural language processing technologies, aimed at optimizing forest management strategies. Experimental validation of this system was
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Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-22 Deepak Adhikari, Wei Jiang, Jinyu Zhan, Danda B. Rawat, Asmita Bhattarai
This paper provides a comprehensive survey of anomaly detection for the Internet of Things (IoT). Anomaly detection poses numerous challenges in IoT, with broad applications, including intrusion detection, fraud monitoring, cybersecurity, industrial automation, etc. Intensive attention has been received by network security analytics and researchers, particularly on anomaly detection in the network
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Yuga: Automatically Detecting Lifetime Annotation Bugs in the Rust Language IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-22 Vikram Nitin, Anne Mulhern, Sanjay Arora, Baishakhi Ray
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iTCRL: Causal-intervention-based Trace Contrastive Representation Learning for Microservice Systems IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-20 Xiangbo Tian, Shi Ying, Tiangang Li, Mengting Yuan, Ruijin Wang, Yishi Zhao, Jianga Shang
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Runtime Verification and Field-based Testing for ROS-based Robotic Systems IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-19 Ricardo Caldas, Juan Antonio Piñera García, Matei Schiopu, Patrizio Pelliccione, Genaína Rodrigues, Thorsten Berger
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Increasing parameter identifiability through clustered time-varying sensitivity analysis Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-19 Lu Wang, Yue-Ping Xu, Jiliang Xu, Haiting Gu, Zhixu Bai, Peng Zhou, Hongjie Yu, Yuxue Guo
Hydrological models are becoming progressively complex, leading to unclear internal model behavior, increasing uncertainty, and the risk of equifinality. Accordingly, our study provided a research framework based on global sensitivity analysis, aiming at unraveling the process-level behavior of high-complexity models, teasing out the main information, and ultimately exploiting its usage for model parameterization
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How to assess conditions for the acceptance of climate change adaptation measures by applying implementation probability Bayesian Networks in participatory processes Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-14 Laura Müller, Max Czymai, Birgit Blättel-Mink, Petra Döll
Climate change adaptation measures are best identified participatorily, yet their implementation poses challenges. While Bayesian Network (BN) modeling has been widely used to assess how adaptation measures mitigate risks, we present how to develop, in a participatory process, an innovative BN type that quantifies the implementation probability of adaptation measures by considering conditions for actors’
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Learning to Generate Structured Code Summaries from Hybrid Code Context IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-13 Ziyi Zhou, Mingchen Li, Huiqun Yu, Guisheng Fan, Penghui Yang, Zijie Huang
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rCanary: Detecting Memory Leaks Across Semi-automated Memory Management Boundary in Rust IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-13 Mohan Cui, Hui Xu, Hongliang Tian, Yangfan Zhou
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Predicting the First Response Latency of Maintainers and Contributors in Pull Requests IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-13 SayedHassan Khatoonabadi, Ahmad Abdellatif, Diego Elias Costa, Emad Shihab
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Modernizing the US National Fire Danger Rating System (version 4): Simplified fuel models and improved live and dead fuel moisture calculations Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-13 W. Matt Jolly, Patrick H. Freeborn, Larry S. Bradshaw, Jon Wallace, Stuart Brittain
The US National Fire Danger Rating System (USNFDRS) supports wildfire management decisions nationwide, but it has not been updated since 1988. Here we implement new fuel moisture models, and we simplify the fuel models while maintaining the overall USNFDRS structure. Modeled and measured live fuel moisture content values were highly correlated ( with defaults and when species and location optimized)
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PyCHAMP: A crop-hydrological-agent modeling platform for groundwater management Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-13 Chung-Yi Lin, Maria Elena Orduna Alegria, Sameer Dhakal, Sam Zipper, Landon Marston
The Crop-Hydrological-Agent Modeling Platform (PyCHAMP) is a Python-based open-source package designed for modeling agro-hydrological systems. The modular design, incorporating aquifer, crop field, groundwater well, finance, and behavior components, enables users to simulate and analyze the interactions between human and natural systems, considering both environmental and socio-economic factors. This
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Chain-of-Thought in Neural Code Generation: From and For Lightweight Language Models IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-08-12 Guang Yang, Yu Zhou, Xiang Chen, Xiangyu Zhang, Terry Yue Zhuo, Taolue Chen
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Web application of an integrated simulation for aquatic environment assessment in coastal and estuarine areas Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-12 Yoshitaka Matsuzaki, Tetsunori Inoue, Masaya Kubota, Hiroki Matsumoto, Tomoyuki Sato, Hikari Sakamoto, Daisuke Naito
This paper introduces the web application-type Graphical User Interface that has been developed and also presents an application example. The introduced simulator conducts hydrodynamics and ecosystems in coastal and estuarine areas. It consists of (1) a hydrodynamic model that can simulate the current velocity, water temperature, salinity, and water level; (2) an ecosystem model that can simulate dissolved
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Modelling vegetation dynamics for future climates in Australian catchments: Comparison of a conceptual eco-hydrological modelling approach with a deep learning alternative Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-12 Hui Zou, Lucy Marshall, Ashish Sharma, Jie Jian, Clare Stephens, Philippa Higgins
Dynamically simulating leaf area index assists in modelling the feedbacks between eco-hydrologic and climatic processes. The particular challenge for Australia is the prevalence of arid and semi-arid ecosystems where water availability plays a crucial role in vegetation productivity. To understand whether existing LAI models can capture plant dynamics under changing climates, we tested two competing
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An explainable MHSA enabled deep architecture with dual-scale convolutions for methane source classification using remote sensing Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-12 Kamakhya Bansal, Ashish Kumar Tripathi
Methane is the second most abundant greenhouse gas after carbon dioxide. Anthropogenic sources are the dominant emitters of methane. The poor spatial resolution of satellite imagery, high interclass similarity, the multi-scalar nature of features, and the dominance of background limit the performance of the previous approaches. Further, the reliance on high-resolution imagery limits the cost-effective
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Cloud-based system for monitoring event-based hydrological processes based on dense sensor network and NB-IoT connectivity Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-11 Ernesto Sanz, Jorge Trincado, Jorge Martínez, Jorge Payno, Omer Morante, Andrés F. Almeida-Ñaulay, Antonio Berlanga, José M. Molina, Sergio Zubelzu, Miguel A. Patricio
Hydrologists claim high-quality experimental data are required to improve the understanding of hydrological processes. Though accurate devices for measuring hydrological processes are available, the on-site deployment and operation of effective monitoring networks face many relevant issues caused by the peculiar characteristics of hydrological systems. In this manuscript, we present a self-developed
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Extending our understanding on the retrievals of surface energy fluxes and surface soil moisture from the “triangle” technique Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-10 George P. Petropoulos
The present study demonstrates the capability of an inversion modelling scheme so-called the “triangle” to retrieve spatiotemporal estimates of surface energy fluxes and soil surface moisture (SSM) at high resolution using ASTER satellite imagery synergistically with SimSphere land biosphere model. In addition, as a further objective of this study is to examine the use of the technique for retrieving
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Development and evaluation of a general approach for predicting pathogen decay in surface waters using hierarchical Bayesian modeling Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-10 Kara Dean, Jade Mitchell
A general approach for predicting indicator and pathogen decay in surface waters was developed using Bayesian hierarchical modeling, a persistence database, and a two-parameter model form. The resulting hierarchical regression describes general persistence behaviors with target-level intercepts and population-level coefficients. Uncertainty factors calculated with the approach suggest fecal indicator
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An introduction to data-driven modelling of the water-energy-food-ecosystem nexus Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-10 Elise Jonsson, Andrijana Todorović, Malgorzata Blicharska, Andreina Francisco, Thomas Grabs, Janez Sušnik, Claudia Teutschbein
Attaining resource security in the ater, nergy, ood, and cosystem (WEFE) sectors, the WEFE nexus, is paramount. This necessitates the use of quantitative modelling, which presents many challenges, as this is a complex system acting at the intersection of the physical- and social sciences. However, as WEFE data is becoming more widely available, data-driven methods of modelling this system are becoming
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Pywr-DRB: An open-source Python model for water availability and drought risk assessment in the Delaware River Basin Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-10 Andrew L. Hamilton, Trevor J. Amestoy, Patrick M. Reed
The Delaware River Basin (DRB) in the Mid-Atlantic region of the United States is an institutionally complex water resources system that provides drinking water for 13.5 million people, plus water for energy, industry, recreation, and ecosystems. This paper introduces Pywr-DRB, an open-source Python model exploring the impacts of reservoir operations, transbasin diversions, and minimum flow targets
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A comprehensive review of vulnerabilities and AI-enabled defense against DDoS attacks for securing cloud services Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-08 Surendra Kumar, Mridula Dwivedi, Mohit Kumar, Sukhpal Singh Gill
The advent of cloud computing has made a global impact by providing on-demand services, elasticity, scalability, and flexibility, hence delivering cost-effective resources to end users in pay-as-you-go manner. However, securing cloud services against vulnerabilities, threats, and modern attacks remains a major concern. Application layer attacks are particularly problematic because they can cause significant
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Digital image watermarking using deep learning: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-07 Khalid M. Hosny, Amal Magdi, Osama ElKomy, Hanaa M. Hamza
Lately, a lot of attention has been paid to securing the ownership rights of digital images. The expanding usage of the Internet causes several problems, including data piracy and data tampering. Image watermarking is a typical method of protecting an image's copyright. Robust watermarking for digital images is a process of embedding watermarks on the cover image and extracting them correctly under
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A survey on the parameterized complexity of reconfiguration problems Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-06 Nicolas Bousquet, Amer E. Mouawad, Naomi Nishimura, Sebastian Siebertz
A graph vertex-subset problem defines which subsets of the vertices of an input graph are feasible solutions. We view a feasible solution as a set of tokens placed on the vertices of the graph. A reconfiguration variant of a vertex-subset problem asks, given two feasible solutions of size , whether it is possible to transform one into the other by a sequence of token slides (along edges of the graph)
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A systematic survey on fault-tolerant solutions for distributed data analytics: Taxonomy, comparison, and future directions Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-05 Sucharitha Isukapalli, Satish Narayana Srirama
Fault tolerance is becoming increasingly important for upcoming exascale systems, supporting distributed data processing, due to the expected decrease in the Mean Time Between Failures (MTBF). To ensure the availability, reliability, dependability, and performance of the system, addressing the fault tolerance challenge is crucial. It aims to keep the distributed system running at a reduced capacity
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Segmentation of underwater fish in complex aquaculture environments using enhanced Soft Attention Mechanism Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-05 Dashe Li, Yufang Yang, Siwei Zhao, Jinqiang Ding
Underwater fish segmentation technology serves as a crucial foundation for extracting aquatic biological information. However, due to intricate and fluctuating underwater environments, existing segmentation models fail to precisely focus on key image regions. Based on this, the paper developed an underwater fish segmentation model, Receptive Field Expansion Model(RFEM), by enhancing soft attention
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A modeller’s fingerprint on hydrodynamic decision support modelling Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-02 J.O.E. Remmers, A.J. Teuling, L.A. Melsen
Model results can have far-reaching societal implications, requiring fit-for-purpose models. However, model output is resulting from a particular path chosen with each modelling decision. We interviewed fourteen modellers in the Dutch water management sector in order to study how decision support hydrodynamic modellers make modelling decisions. An inductive-content analysis was performed. We identified
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Deep learning for hyperspectral image classification: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-01 Vinod Kumar, Ravi Shankar Singh, Medara Rambabu, Yaman Dua
Hyperspectral image (HSI) classification is a significant topic of discussion in real-world applications. The prevalence of these applications stems from the precise spectral information offered by each pixelś data in hyperspectral imaging (HS). Classical machine learning (ML) methods face challenges in precise object classification with HSI data complexity. The intrinsic non-linear relationship between