-
Automated Code Editing with Search-Generate-Modify IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-27 Changshu Liu, Pelin Cetin, Yogesh Patodia, Baishakhi Ray, Saikat Chakraborty, Yangruibo Ding
-
Understanding and Detecting Real-World Safety Issues in Rust IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-25 Boqin Qin, Yilun Chen, Haopeng Liu, Hua Zhang, Qiaoyan Wen, Linhai Song, Yiying Zhang
-
MASTER: Multi-Source Transfer Weighted Ensemble Learning for Multiple Sources Cross-Project Defect Prediction IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-25 Haonan Tong, Dalin Zhang, Jiqiang Liu, Weiwei Xing, Lingyun Lu, Wei Lu, Yumei Wu
-
GIPC: Fast and Stable Gauss-Newton Optimization of IPC Barrier Energy ACM Trans. Graph. (IF 6.2) Pub Date : 2024-03-23 Kemeng Huang, Floyd M. Chitalu, Huancheng Lin, Taku Komura
Barrier functions are crucial for maintaining an intersection- and inversion-free simulation trajectory but existing methods, which directly use distance can restrict implementation design and performance. We present an approach to rewriting the barrier function for arriving at an efficient and robust approximation of its Hessian. The key idea is to formulate a simplicial geometric measure of contact
-
Self-supervised High Dynamic Range Imaging: What Can Be Learned from a Single 8-bit Video? ACM Trans. Graph. (IF 6.2) Pub Date : 2024-03-23 Francesco Banterle, Demetris Marnerides, Thomas Bashford-rogers, Kurt Debattista
Recently, Deep Learning-based methods for inverse tone mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular. These methods manage to fill over-exposed areas convincingly both in terms of details and dynamic range. To be effective, deep learning-based methods need to learn from large datasets and transfer this knowledge to the network weights
-
Evaluating Search-Based Software Microbenchmark Prioritization IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-22 Christoph Laaber, Tao Yue, Shaukat Ali
-
Shaken, Not Stirred. How Developers Like Their Amplified Tests IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-22 Carolin Brandt, Ali Khatami, Mairieli Wessel, Andy Zaidman
-
Toward a Theory of Causation for Interpreting Neural Code Models IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-21 David N. Palacio, Alejandro Velasco, Nathan Cooper, Alvaro Rodriguez, Kevin Moran, Denys Poshyvanyk
-
Microservice Extraction Based on a Comprehensive Evaluation of Logical Independence and Performance IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-21 Zhijun Ding, Yuehao Xu, Binbin Feng, Changjun Jiang
-
Toward Cost-effective Adaptive Random Testing: An Approximate Nearest Neighbor Approach IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-21 Rubing Huang, Chenhui Cui, Junlong Lian, Dave Towey, Weifeng Sun, Haibo Chen
-
hmCodeTrans: Human-Machine Interactive Code Translation IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-20 Jiaqi Liu, Fengming Zhang, Xin Zhang, Zhiwen Yu, Liang Wang, Yao Zhang, Bin Guo
-
Distinguished Reviewers 2023 IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-18 Sebastian Uchitel
Lists the reviewers who contributed to this publication in 2023.
-
Methods and Benchmark for Detecting Cryptographic API Misuses in Python IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-18 Miles Frantz, Ya Xiao, Tanmoy Sarkar Pias, Na Meng, Danfeng Daphne Yao
-
Mutation Testing in Practice: Insights from Open-Source Software Developers IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-18 Ana B. Sánchez, José A. Parejo, Sergio Segura, Amador Durán, Mike Papadakis
-
Asking and Answering Questions During Memory Profiling IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-13 Alison Fernandez Blanco, Araceli Queriolo Córdova, Alexandre Bergel, Juan Pablo Sandoval Alcocer
-
Active Code Learning: Benchmarking Sample-Efficient Training of Code Models IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-13 Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon
-
DMHomo: Learning Homography with Diffusion Models ACM Trans. Graph. (IF 6.2) Pub Date : 2024-03-11 Haipeng Li, Hai Jiang, Ao Luo, Ping Tan, Haoqiang Fan, Bing Zeng, Shuaicheng Liu
Supervised homography estimation methods face a challenge due to the lack of adequate labeled training data. To address this issue, we propose DMHomo, a diffusion model-based framework for supervised homography learning. This framework generates image pairs with accurate labels, realistic image content, and realistic interval motion, ensuring they satisfy adequate pairs. We utilize unlabeled image
-
Evaluation framework for autonomous systems: the case of Programmable Electronic Medical Systems IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-11 Andrea Bombarda, Silvia Bonfanti, Martina De Sanctis, Angelo Gargantini, Patrizio Pelliccione, Elvinia Riccobene, Patrizia Scandurra
-
Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-07 Yuanyuan Yuan, Qi Pang, Shuai Wang
-
Exploring the Role of Team Security Climate in the Implementation of Security by Design: A Case Study in the Defense Sector IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-06 Micha Prudjinski, Irit Hadar, Gil Luria
-
An Empirical Study of JVMs’ Behaviors on Erroneous JNI Interoperations IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-05 Sungjae Hwang, Sungho Lee, Sukyoung Ryu
-
The model-as-a-resource paradigm for geoscience digital ecosystems Environ. Model. Softw. (IF 4.9) Pub Date : 2024-03-04 Paolo Mazzetti, Stefano Nativi
A long-term goal of environmental science and Earth observation is to enable the creation of a “Model Web” of semantically interconnected data and models. Geospatial models are usually exposed on the Web as services accessible through heterogeneous interfaces. However, such services, which represent instances of the paradigm called Model-as-a-Service (MaaS), cannot be easily exploited beyond their
-
A prototype field-to-publication data system for a multi-variable permafrost observation network Environ. Model. Softw. (IF 4.9) Pub Date : 2024-03-04 Nicholas Brown, Stephan Gruber, Peter Pulsifer, Amos Hayes
Analysis and prediction of permafrost change are hampered by lack of observational data. We implement a permafrost data management system to support multi-variable permafrost observation networks. It addresses five key challenges we identified for permafrost data management and publication: (1) existing data management strategies do not scale well, (2) data users have different skills and needs, (3)
-
HydroCompute: An open-source web-based computational library for hydrology and environmental sciences Environ. Model. Softw. (IF 4.9) Pub Date : 2024-03-03 Carlos Erazo Ramirez, Yusuf Sermet, Ibrahim Demir
We present HydroCompute, a high-performance client-side computational library specifically designed for web-based hydrological and environmental science applications. Leveraging state-of-the-art technologies in web-based scientific computing, the library facilitates both sequential and parallel simulations, optimizing computational efficiency. Employing multithreading via web workers, HydroCompute
-
Towards reusable building blocks for agent-based modelling and theory development Environ. Model. Softw. (IF 4.9) Pub Date : 2024-03-03 Uta Berger, Andrew Bell, C. Michael Barton, Emile Chappin, Gunnar Dreßler, Tatiana Filatova, Thibault Fronville, Allen Lee, Emiel van Loon, Iris Lorscheid, Matthias Meyer, Birgit Müller, Cyril Piou, Viktoriia Radchuk, Nicholas Roxburgh, Lennart Schüler, Christian Troost, Nanda Wijermans, Tim G. Williams, Marie-Christin Wimmler, Volker Grimm
Despite the increasing use of standards for documenting and testing agent-based models (ABMs) and sharing of open access code, most ABMs are still developed from scratch. This is not only inefficient, but also leads to and often inconsistent implementations of the same theories in computational code and delays progress in the exploration of the functioning of complex social-ecological systems (SES)
-
An integrative modelling framework for predicting the compound flood hazards induced by tropical cyclones in an estuarine area Environ. Model. Softw. (IF 4.9) Pub Date : 2024-03-03 Haoxuan Du, Kai Fei, Jiahao Wu, Liang Gao
In this study, a novel numerical model is first developed, which can simulate compound floods under a framework comprehensively considering the combined effects of tide, river flow, rainfall, and wind. The modelling framework is applied to reproduce an extreme compound flood event in the Pearl River Delta caused by Typhoon Hato (2017). The model is estimated to perform reasonably well, which can yield
-
Modelling forests as social-ecological systems: A systematic comparison of agent-based approaches Environ. Model. Softw. (IF 4.9) Pub Date : 2024-03-02 Hanna Ekström, Nils Droste, Mark Brady
The multifunctionality of forest systems calls for appropriately complex modelling approaches to capture social and ecosystem dynamics. Using a social-ecological systems framework, we review the functionality of 31 existing agent-based models applied to managed forests. Several applications include advanced cognitive and emotional decision-making, crucial for understanding complex sustainability challenges
-
HQ3DAvatar: High Quality Implicit 3D Head Avatar ACM Trans. Graph. (IF 6.2) Pub Date : 2024-02-29 Kartik Teotia, Mallikarjun B R, Xingang Pan, Hyeongwoo Kim, Pablo Garrido, Mohamed Elgharib, Christian Theobalt
Multi-view volumetric rendering techniques have recently shown great potential in modeling and synthesizing high-quality head avatars. A common approach to capture full head dynamic performances is to track the underlying geometry using a mesh-based template or 3D cube-based graphics primitives. While these model-based approaches achieve promising results, they often fail to learn complex geometric
-
A Dual-Particle Approach for Incompressible SPH Fluids ACM Trans. Graph. (IF 6.2) Pub Date : 2024-02-29 Shusen Liu, Xiaowei He, Yuzhong Guo, Yue Chang, Wencheng Wang
Tensile instability is one of the major obstacles to particle methods in fluid simulation, which would cause particles to clump in pairs under tension and prevent fluid simulation to generate small-scale thin features. To address this issue, previous particle methods either use a background pressure or a finite difference scheme to alleviate the particle clustering artifacts, yet still fail to produce
-
Joint Stroke Tracing and Correspondence for 2D Animation ACM Trans. Graph. (IF 6.2) Pub Date : 2024-02-29 Haoran Mo, Chengying Gao, Ruomei Wang
To alleviate human labor in redrawing keyframes with ordered vector strokes for automatic inbetweening, we for the first time propose a joint stroke tracing and correspondence approach. Given consecutive raster keyframes along with a single vector image of the starting frame as a guidance, the approach generates vector drawings for the remaining keyframes while ensuring one-to-one stroke correspondence
-
Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians ACM Trans. Graph. (IF 6.2) Pub Date : 2024-02-28 Jiawei Huang, Akito Iizuka, Hajime Tanaka, Taku Komura, Yoshifumi Kitamura
Importance sampling techniques significantly reduce variance in physically-based rendering. In this paper we propose a novel online framework to learn the spatial-varying distribution of the full product of the rendering equation, with a single small neural network using stochastic ray samples. The learned distributions can be used to efficiently sample the full product of incident light. To accomplish
-
Spatially targeted afforestation to minimize sediment loss from a catchment: An efficient hill climbing method considering spatial interaction Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-29 Grethell Castillo-Reyes, René Estrella, Dirk Roose, Floris Abrams, Gerdys Jiménez-Moya, Jos Van Orshoven
Based on soil erosion and sediment transport processes, CAMF (Cellular Automata-based heuristic for Minimizing Flow) selects sites for afforestation to minimize sediment influx at a catchment’s outlet. CAMF uses a raster representation of the catchment and a steepest ascent hill-climbing optimization heuristic, safeguarding spatial interaction. Its execution time can be prohibitively long for large
-
Development of a Spatially Distributed Snow and Glacier Melt Runoff Model (SDSGRM) for data scarce high-altitude river basins Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-29 V. Nunchhani, Ngahorza Chiphang, Arnab Bandyopadhyay, Aditi Bhadra
Spatially Distributed Snow and Glacier-melt Runoff Model (SDSGRM) was developed to evaluate the glacier-melt (GM), snowmelt (SM) and rainfall induced runoff contribution to the total stream runoff. It includes temperature index, radiation-temperature index, advection driven index and energy balance methods and generates gridded outputs. The model was successfully calibrated (NSE and R more than 0.7
-
NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks ACM Trans. Graph. (IF 6.2) Pub Date : 2024-02-28 Doyub Kim, Minjae Lee, Ken Museth
We introduce NeuralVDB, which improves on an existing industry standard for efficient storage of sparse volumetric data, denoted VDB [Museth 2013], by leveraging recent advancements in machine learning. Our novel hybrid data structure can reduce the memory footprints of VDB volumes by orders of magnitude, while maintaining its flexibility and only incurring small (user-controlled) compression errors
-
Multiple clusterings: Recent advances and perspectives Comput. Sci. Rev. (IF 12.9) Pub Date : 2024-02-26 Guoxian Yu, Liangrui Ren, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang
Clustering is a fundamental data exploration technique to discover hidden grouping structure of data. With the proliferation of big data, and the increase of volume and variety, the complexity of data multiplicity is increasing as well. Traditional clustering methods can provide only a single clustering result, which restricts data exploration to one single possible partition. In contrast, multiple
-
Automatic Debugging of Design Faults in MapReduce Applications IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-26 Jesús Morán, Antonia Bertolino, Claudio de la Riva, Javier Tuya
-
Pyraingen: A python package for constrained continuous rainfall generation Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-26 Caleb Dykman, Ashish Sharma, Conrad Wasko, Rory Nathan
Continuous rainfall is often required for flood estimation and water resources assessment. However, stochastically generated continuous rainfall records are typically inconsistent with intensity-frequency-durations (IFDs) used in design, and do not simulate the rainfall behaviour we can expect in a future warmer climate. Here, we present the python package to generate ensembles of continuous subdaily
-
Factoring Expertise, Workload, and Turnover into Code Review Recommendation IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-23 Fahimeh Hajari, Samaneh Malmir, Ehsan Mirsaeedi, Peter C. Rigby
-
A Testing Program and Pragma Combination Selection Based Framework for High-Level Synthesis Tool Pragma-Related Bug Detection IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-22 He Jiang, Zun Wang, Zhide Zhou, Xiaochen Li, Shikai Guo, Weifeng Sun, Tao Zhang
-
Data-driven early warning indicator for the overall stability of rock slopes: An example in hydropower engineering Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-22 Jietao Sun, Haifeng Li, Yi Liu
In hydropower engineering, monitoring the instability of rock slopes is a crucial undertaking. Current early warning models of rock slopes lack consideration of the overall stability and cannot reflect the stability differences of different structure forms. Addressing these issues, we have successfully developed an integrative early warning indicator for the overall stability of rock slopes, utilizing
-
-
Importance Sampling BRDF Derivatives ACM Trans. Graph. (IF 6.2) Pub Date : 2024-02-21 Yash Belhe, Bing Xu, Sai Praveen Bangaru, Ravi Ramamoorthi, Tzu-Mao Li
We propose a set of techniques to efficiently importance sample the derivatives of a wide range of BRDF models. In differentiable rendering, BRDFs are replaced by their differential BRDF counterparts which are real-valued and can have negative values. This leads to a new source of variance arising from their change in sign. Real-valued functions cannot be perfectly importance sampled by a positive-valued
-
Nature-inspired optimal tuning of input membership functions of fuzzy inference system for groundwater level prediction Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-21 Vipul Bhadani, Abhilash Singh, Vaibhav Kumar, Kumar Gaurav
We present a novel regression algorithm that combines a Fuzzy Inference System (FIS) with a nature-inspired algorithm to predict variations in GroundWater Levels (GWLs). Initially, we considered several input features, including precipitation, temperature, evaporation, relative humidity, soil type, and GWL Lag. A feature importance analysis using regression tree ensemble learning reveals GWL lag as
-
On the Understandability of MLOps System Architectures IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-20 Stephen John Warnett, Uwe Zdun
-
Software Testing with Large Language Models: Survey, Landscape, and Vision IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-20 Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, Qing Wang
-
Optimizing sediment transport models by using the Monte Carlo simulation and deep neural network (DNN): A case study of the Riba-Roja reservoir Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-20 Danial Dehghan-Souraki, David López-Gómez, Ernest Bladé-Castellet, Antonia Larese, Marcos Sanz-Ramos
This study emphasizes the importance of accurate calibration in sediment transport models and highlights the transformative role of artificial intelligence (AI), specifically machine learning, in improving accuracy and computational efficiency. Extensive experiments were carried out in the Riba-Roja reservoir, which is located in the northeastern Iberian Peninsula. The accumulated sediment volume (ASV)
-
Mask–Mediator–Wrapper architecture as a Data Mesh driver IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-19 Juraj Dončević, Krešimir Fertalj, Mario Brcic, Mihael Kovač
-
Agent-based models of groundwater systems: A review of an emerging approach to simulate the interactions between groundwater and society Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-17 Marcos Canales, Juan Castilla-Rho, Rodrigo Rojas, Sebastian Vicuña, James Ball
-
A three-dimensional numerical model for variably saturated groundwater flow using meshless weak-strong form method Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-17 Jiayu Fang, Mohammad Z. Al-Hamdan, Andrew M. O'Reilly, Yavuz Ozeren, James R. Rigby
Meshless numerical models have attracted much attention due to the circumvention of troublesome mesh generation. Current meshless numerical groundwater (GW) models either focus on only pumping in fully saturated zone or merely simulate variably saturated GW flow without pumping. However, these two components are both essential for a GW model to represent practical real-world conditions. This gap is
-
Guess the State: Exploiting Determinism to Improve GUI Exploration Efficiency IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-16 Diego Clerissi, Giovanni Denaro, Marco Mobilio, Leonardo Mariani
-
Coverage Goal Selector for Combining Multiple Criteria in Search-Based Unit Test Generation IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-16 Zhichao Zhou, Yuming Zhou, Chunrong Fang, Zhenyu Chen, Xiapu Luo, Jingzhu He, Yutian Tang
-
Challenges and opportunities when bringing machines onto the team: Human-AI teaming and flood evacuation decisions Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-16 Vidya Samadi, Keri K. Stephens, Amanda Hughes, Pamela Murray-Tuite
-
DeadWood: Including Disturbance and Decay in the Depiction of Digital Nature ACM Trans. Graph. (IF 6.2) Pub Date : 2024-02-14 Adrien Peytavie, James Gain, Eric Guérin, Oscar Argudo, Eric Galin
The creation of truly believable simulated natural environments remains an unsolved problem in Computer Graphics. This is, in part, due to a lack of visual variety. In nature, apart from variation due to abiotic and biotic growth factors, a significant role is played by disturbance events, such as fires, windstorms, disease, and death and decay processes, which give rise to both standing dead trees
-
Spectral Total-variation Processing of Shapes—Theory and Applications ACM Trans. Graph. (IF 6.2) Pub Date : 2024-02-14 Jonathan Brokman, Martin Burger, Guy Gilboa
We present a comprehensive analysis of total variation (TV) on non-Euclidean domains and its eigenfunctions. We specifically address parameterized surfaces, a natural representation of the shapes used in 3D graphics. Our work sheds new light on the celebrated Beltrami and Anisotropic TV flows and explains experimental findings from recent years on shape spectral TV [Fumero et al. 2020] and adaptive
-
Deep learning for intelligent demand response and smart grids: A comprehensive survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2024-02-14 Prabadevi Boopathy, Madhusanka Liyanage, Natarajan Deepa, Mounik Velavali, Shivani Reddy, Praveen Kumar Reddy Maddikunta, Neelu Khare, Thippa Reddy Gadekallu, Won-Joo Hwang, Quoc-Viet Pham
Electricity is one of the mandatory commodities for mankind today. To address challenges and issues in the transmission of electricity through the traditional grid, the concepts of smart grids and demand response have been developed. In such systems, a large amount of data is generated daily from various sources such as power generation (e.g., wind turbines), transmission and distribution (microgrids
-
An annotated timeline of sensitivity analysis Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-14 Stefano Tarantola, Federico Ferretti, Samuele Lo Piano, Mariia Kozlova, Alessio Lachi, Rossana Rosati, Arnald Puy, Pamphile Roy, Giulia Vannucci, Marta Kuc-Czarnecka, Andrea Saltelli
The last half a century has seen spectacular progresses in computing and modelling in a variety of fields, applications, and methodologies. Over the same period, a cross-disciplinary field known as sensitivity analysis has been making its first steps, evolving from the design of experiments for laboratory or field studies, also called ‘in-vivo’, to the so-called experiments ‘in-silico’. Some disciplines
-
Enhanced watershed model evaluation incorporating hydrologic signatures and consistency within efficient surrogate multi-objective optimization Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-14 Wei Xia, Taimoor Akhtar, Wei Lu, Christine A. Shoemaker
This paper presents a new framework for calibrating computationally expensive watershed models with multi-objective optimization methods and hydrological consistency analysis. The analysis evaluates different algorithms' efficiencies for finding watershed model calibration solutions within a limited budget. Two surrogate multi-objective algorithms GOMORS and ParEGO are compared to five evolutionary
-
Sustainable computing across datacenters: A review of enabling models and techniques Comput. Sci. Rev. (IF 12.9) Pub Date : 2024-02-13 Muhammad Zakarya, Ayaz Ali Khan, Mohammed Reza Chalak Qazani, Hashim Ali, Mahmood Al-Bahri, Atta Ur Rehman Khan, Ahmad Ali, Rahim Khan
The growth rate in big data and internet of things (IoT) is far exceeding the computer performance rate at which modern processors can compute on the massive amount of data. The cluster and cloud technologies enriched by machine learning applications had significantly helped in performance growths subject to the underlying network performance. Computer systems have been studied for improvement in performance
-
Improving probabilistic streamflow predictions through a nonparametric residual error model Environ. Model. Softw. (IF 4.9) Pub Date : 2024-02-13 Jiyu Liang, Shuguang Liu, Zhengzheng Zhou, Guihui Zhong, Yiwei Zhen
Reliable probabilistic hydrological prediction requires appropriate handling of residual errors, which can pose considerable complexity. This paper proposes a nonparametric residual error (NRE) model that effectively captures the statistical characteristics of raw residuals. The NRE model employs a local linear estimator with a robust bandwidth selector to estimate the regression and conditional volatility
-
Measuring and Characterizing (mis)compliance of the Android permission system IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-12 Anna Barzolevskaia, Enrico Branca, Natalia Stakhanova