AN INVESTIGATION OF COMMON CODING ERRORS IN OPEN SOURCE GRAPHICAL USER INTERFACE CODE

Introduction: This thesis investigates the occurrence of coding errors in the Open Source Software (OSS) Graphical User Interface (GUI) code. Characteristics of coding errors in the OSS GUI code are explored and analyzed so that guidelines are proposed to lower the influence of coding errors in OSS GUI software. Background: This thesis recognizes the increased prominence of, as well as the increased total volume of OSS GUI code within modern software applications. This thesis seeks to identify whether specific types of errors manifest themselves more frequently in GUI code. The rationale behind this investigation is that: if specific errors are known to occur in specific locations then they can be more easily identified; if specific errors are due to specific causes then they can be more easily recognized in earlier stages of software development. Methods: Common coding errors were selected and examined in example OSS code using an automatic code inspection. An analysis of results from this inspection identified the frequency and location (i.e. in GUI or non-GUI code) of the common coding error. An initial sample of OSS GUI projects was selected to be examined and a wider range of OSS GUI projects was randomly chosen to evaluate the results that were obtained from the initial sample. Results: It was found that there are some differences in the type of errors within differing portions of source code. Certain types of coding errors were more frequently identified in GUI code than non-GUI code and corresponding typical GUI coding error-prone behaviors were summarized. Discussion: Awareness of these differences helps predict errors during the earlier stages of software development. Comprehension of these GUI coding error-prone behaviours contributes to prevent typical GUI coding errors as much as possible during the whole lifecycle of OSS GUI projects

Risk Minimization, Regret Minimization and Progressive Hedging Algorithms

This paper begins with a study on the dual representations of risk and regret measures and their impact on modeling multistage decision making under uncertainty. A relationship between risk envelopes and regret envelopes is established by using the Lagrangian duality theory. Such a relationship opens a door to a decomposition scheme, called progressive hedging, for solving multistage risk minimization and regret minimization problems. In particular, the classical progressive hedging algorithm is modified in order to handle a new class of linkage constraints that arises from reformulations and other applications of risk and regret minimization problems. Numerical results are provided to show the efficiency of the progressive hedging algorithms.Comment: 21 pages, 2 figure

Effects of motion-induced aerodynamic force on the performance of active buffeting control

During buffeting control of an aircraft, there consequently is a motion-induced aerodynamic force. However, it is not yet clear whether this additional force must be considered in design of control law. In this paper, to hopefully answer this interesting question, effects of the motion-induced aerodynamic force on the active buffeting control during control law design are studied. The macro fiber composite (MFC) actuator is modeled by employing the load simulation method, and the motion-induced unsteady aerodynamic forces are computed by the doublet-lattice method. Two different controllers, i.e. one with the motion-induced aerodynamic force and another without it, are simultaneously designed based on the linear quadratic Gaussian (LQG) control method. And, two corresponding models are respectively developed. Then, the control effects of the two models are compared and the physical mechanisms are discussed. From our simulation results it is found that the motion-induced aerodynamic forces do influence the buffeting responses depending on airflow velocity. The differences of the control effects of the two models are smaller at lower airflow velocity below the flutter velocity, however with the increase of the airflow velocity the control effect of the model considering the motion-induced aerodynamic force is much better. The larger the velocity is, the more significant the differences are. Finally, the energy dissipation of the motion-induced aerodynamic force is examined and found to be a main factor influencing the differences of the two models

A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm

Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images. This paper aims to establish a Euler's Elastica based approach that properly deals with random noises to improve the segmentation performance for noisy images. We solve the corresponding optimization problem via using the progressive hedging algorithm (PHA) with a step length suggested by the alternating direction method of multipliers (ADMM). Technically, all the simplified convex versions of the subproblems derived from the major framework of PHA can be obtained by using the curvature weighted approach and the convex relaxation method. Then an alternating optimization strategy is applied with the merits of using some powerful accelerating techniques including the fast Fourier transform (FFT) and generalized soft threshold formulas. Extensive experiments have been conducted on both synthetic and real images, which validated some significant gains of the proposed segmentation models and demonstrated the advantages of the developed algorithm

Universal scheme to generate metal-insulator transition in disordered systems

We propose a scheme to generate metal-insulator transition in random binary layer (RBL) model, which is constructed by randomly assigning two types of layers. Based on a tight-binding Hamiltonian, the localization length is calculated for a variety of RBLs with different cross section geometries by using the transfer-matrix method. Both analytical and numerical results show that a band of extended states could appear in the RBLs and the systems behave as metals by properly tuning the model parameters, due to the existence of a completely ordered subband, leading to a metal-insulator transition in parameter space. Furthermore, the extended states are irrespective of the diagonal and off-diagonal disorder strengths. Our results can be generalized to two- and three-dimensional disordered systems with arbitrary layer structures, and may be realized in Bose-Einstein condensates.Comment: 5 ages, 4 figure

Effects of motion-induced aerodynamic force on the performance of active buffeting control

During buffeting control of an aircraft, there consequently is a motion-induced aerodynamic force. However, it is not yet clear whether this additional force must be considered in design of control law. In this paper, to hopefully answer this interesting question, effects of the motion-induced aerodynamic force on the active buffeting control during control law design are studied. The macro fiber composite (MFC) actuator is modeled by employing the load simulation method, and the motion-induced unsteady aerodynamic forces are computed by the doublet-lattice method. Two different controllers, i.e. one with the motion-induced aerodynamic force and another without it, are simultaneously designed based on the linear quadratic Gaussian (LQG) control method. And, two corresponding models are respectively developed. Then, the control effects of the two models are compared and the physical mechanisms are discussed. From our simulation results it is found that the motion-induced aerodynamic forces do influence the buffeting responses depending on airflow velocity. The differences of the control effects of the two models are smaller at lower airflow velocity below the flutter velocity, however with the increase of the airflow velocity the control effect of the model considering the motion-induced aerodynamic force is much better. The larger the velocity is, the more significant the differences are. Finally, the energy dissipation of the motion-induced aerodynamic force is examined and found to be a main factor influencing the differences of the two models

Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memory

Semantic segmentation is a key technique involved in automatic interpretation of high-resolution remote sensing (HRS) imagery and has drawn much attention in the remote sensing community. Deep convolutional neural networks (DCNNs) have been successfully applied to the HRS imagery semantic segmentation task due to their hierarchical representation ability. However, the heavy dependency on a large number of training data with dense annotation and the sensitiveness to the variation of data distribution severely restrict the potential application of DCNNs for the semantic segmentation of HRS imagery. This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet constructs an output space adversarial learning scheme to bridge the domain distribution discrepancy between source domain and target domain and to narrow the influence of domain shift. Specifically, we embed an invariant feature memory module to store invariant domain-level context information because the features obtained from adversarial learning only tend to represent the variant feature of current limited inputs. This module is integrated by a category attention-driven invariant domain-level context aggregation module to current pseudo invariant feature for further augmenting the pixel representations. An entropy-based pseudo label filtering strategy is used to update the memory module with high-confident pseudo invariant feature of current target images. Extensive experiments under three cross-domain tasks indicate that our proposed MemoryAdaptNet is remarkably superior to the state-of-the-art methods.Comment: 17 pages, 12 figures and 8 table

Trench formation and corner rounding in vertical GaN power devices

Trench formation and corner rounding are the key processes to demonstrate high-voltage trenchbased vertical GaN devices. In this work, we developed a damage-free corner rounding technology combining Tetramethylammonium hydroxide wet etching and piranha clean. By optimizing the inductively coupled plasma dry etching conditions and applying the rounding technology, two main trench shapes were demonstrated: flat-bottom rounded trench and tapered-bottom rounded trench. TCAD simulations were then performed to investigate the impact of trench shapes and round corners on device blocking capability. GaN trench metal-insulator-semiconductor barrier Schottky rectifiers with different trench shapes were fabricated and characterized. A breakdown voltage over 500V was obtained in the device with flat-bottom rounded trenches, compared to 350V in the device with tapered-bottom rounded trenches and 150V in the device with nonrounded trenches. Both experimental and simulation results support the use of rounded flat-bottom trenches to fabricate high-voltage GaN trench-based power devices

Precise Facial Landmark Detection by Reference Heatmap Transformer

Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results. However, when the face image is suffering from large poses, heavy occlusions and complicated illuminations, they cannot learn discriminative feature representations and effective facial shape constraints, nor can they accurately predict the value of each element in the landmark heatmap, limiting their detection accuracy. To address this problem, we propose a novel Reference Heatmap Transformer (RHT) by introducing reference heatmap information for more precise facial landmark detection. The proposed RHT consists of a Soft Transformation Module (STM) and a Hard Transformation Module (HTM), which can cooperate with each other to encourage the accurate transformation of the reference heatmap information and facial shape constraints. Then, a Multi-Scale Feature Fusion Module (MSFFM) is proposed to fuse the transformed heatmap features and the semantic features learned from the original face images to enhance feature representations for producing more accurate target heatmaps. To the best of our knowledge, this is the first study to explore how to enhance facial landmark detection by transforming the reference heatmap information. The experimental results from challenging benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art methods in the literature.Comment: Accepted by IEEE Transactions on Image Processing, March 202