Reviewerly/exHarmony huggingface dataset:
How Software Developers Mitigate Their Errors When Developing Code

Code remains largely hand-made by humans and, as such, writing code is prone to error. Many previous studies have focused on the technical reasons for these errors and provided developers with increasingly sophisticated tools. Few studies have looked in detail at why code errors have been made from a human perspective. We use Human Error Theory to frame our exploratory study and use semi-structured interviews to uncover a preliminary understanding of the errors developers make while coding. We look particularly at the Skill-based (SB) errors reported by 27 professional software developers. We found that the complexity of the development environment is one of the most frequently reported reasons for errors. Maintaining concentration and focus on a particular task also underpins many developer errors. We found that developers struggle with effective mitigation strategies for their errors, reporting strategies largely based on improving their own willpower to concentrate better on coding tasks. We discuss how using Reason’s Swiss Cheese model may help reduce errors during software development. This model ensures that layers of tool, process and management mitigation are in place to prevent developer errors from causing system failures.
How Do Developers Refactor Code to Improve Code Reusability?

Refactoring is the de-facto practice to optimize software health. While there has been several studies proposing refactoring strategies to optimize software design through applying design patterns and removing design defects, little is known about how developers actually refactor their code to improve its reuse. Therefore, we extract, from 1,828 open source projects, a set of refactorings which were intended to improve the software reusability. We analyze the impact of reusability refactorings on state-of-the-art reusability metrics, and we compare the distribution of reusability refactoring types, with the distribution of the remaining mainstream refactorings. Overall, we found that the distribution of refactoring types, applied in the context of reusability, is different from the distribution of refactoring types in mainstream development. In the refactorings performed to improve reusability, source files are subject to more design level types of refactorings. Reusability refactorings significantly impact, high-level code elements, such as packages, classes, and methods, while typical refactorings, impact all code elements, including identifiers, and parameters.
A New Method for Searching Optimal Differential and Linear Trails in ARX Ciphers

In this paper, we propose an automatic tool to search for optimal differential and linear trails in ARX ciphers. It’s shown that a modulo addition can be divided into sequential small modulo additions with carry bit, which turns an ARX cipher into an S-box-like cipher. From this insight, we introduce the concepts of carry-bit-dependent difference distribution table (CDDT) and carry-bit-dependent linear approximation table (CLAT). Based on them, we give efficient methods to trace all possible output differences and linear masks of a big modulo addition, with returning their differential probabilities and linear correlations simultaneously. Then an adapted Matsui’s algorithm is introduced, which can find the optimal differential and linear trails in ARX ciphers. Besides, the superiority of our tool’s potency is also confirmed by experimental results for round-reduced versions of HIGHT and SPECK. More specifically, we find the optimal differential trails for up to 10 rounds of HIGHT, reported for the first time. We also find the optimal differential trails for 10, 12, 16, 8 and 8 rounds of SPECK32/48/64/96/128, and report the provably optimal differential trails for SPECK48 and SPECK64 for the first time. The optimal linear trails for up to 9 rounds of HIGHT are reported for the first time, and the optimal linear trails for 22, 13, 15, 9 and 9 rounds of SPECK32/48/64/96/128 are also found respectively. These results evaluate the security of HIGHT and SPECK against differential and linear cryptanalysis. Also, our tool is useful to estimate the security in the design of ARX ciphers.
Optical See-Through Head-Mounted Displays With Short Focal Distance: Conditions for Mitigating Parallax-Related Registration Error

Optical see-through (OST) augmented reality head-mounted displays are quickly emerging as a key asset in several application fields but their ability to profitably assist high precision activities in the peripersonal space is still sub-optimal due to the calibration procedure required to properly model the user's viewpoint through the see-through display. In this work, we demonstrate the beneficial impact, on the parallax-related AR misregistration, of the use of optical see-through displays whose optical engines collimate the computer-generated image at a depth close to the fixation point of the user in the peripersonal space. To estimate the projection parameters of the OST display for a generic viewpoint position, our strategy relies on a dedicated parameterization of the virtual rendering camera based on a calibration routine that exploits photogrammetry techniques. We model the registration error due to the viewpoint shift and we validate it on an OST display with short focal distance. The results of the tests demonstrate that with our strategy the parallax-related registration error is submillimetric provided that the scene under observation stays within a suitable view volume that falls in a ±10 cm depth range around the focal plane of the display. This finding will pave the way to the development of new multi-focal models of OST HMDs specifically conceived to aid high-precision manual tasks in the peripersonal space.
Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion

Detecting baggage threats is one of the most difficult tasks, even for expert officers. Many researchers have developed computer-aided screening systems to recognize these threats from the baggage X-ray scans. However, all of these frameworks are limited in identifying the contraband items under extreme occlusion. This paper presents a novel instance segmentation framework that utilizes trainable structure tensors to highlight the contours of the occluded and cluttered contraband items (by scanning multiple predominant orientations), while simultaneously suppressing the irrelevant baggage content. The proposed framework has been extensively tested on four publicly available X-ray datasets where it outperforms the state-of-the-art frameworks in terms of mean average precision scores. Furthermore, to the best of our knowledge, it is the only framework that has been validated on combined grayscale and colored scans obtained from four different types of X-ray scanners.
Landsat-8 OLI Multispectral Image Dehazing Based on Optimized Atmospheric Scattering Model

Optical satellite images are often affected by haze atmospheric conditions, which degrades the quality of remote sensing (RS) data and reduces the accuracy of interpretation and classification. Hence, haze removal becomes a necessary preprocessing step for most of the applications of RS image. In this article, we propose a novel haze removal method for Landsat-8 OLI multispectral image based on an optimized atmospheric scattering model. We focus on adaptively estimating the haze transmission map of each band by taking into account the effect of both wavelength and haze atmospheric conditions (haze particle size and haze particle concentration) thus improving dehazing performance. The experimental results on Landsat-8 OLI multispectral images show that the proposed dehazing model is able to remove haze successfully and significantly improve the image visibility as well as correct the spectral bias to some degree. Moreover, this method is simple and feasible, and has good practical value.
Meta-Generating Deep Attentive Metric for Few-Shot Classification

Learning to generate a task-aware base learner proves a promising direction to deal with few-shot learning (FSL) problem. Existing methods mainly focus on generating an embedding model utilized with a fixed metric (e.g., cosine distance) for nearest neighbour classification or directly generating a linear classifier. However, due to the limited discriminative capacity of such a simple metric or classifier, these methods fail to generalize to challenging cases appropriately. To mitigate this problem, we present a novel deep metric meta-generation method that turns to an orthogonal direction, i.e., learning to adaptively generate a specific metric for a new FSL task based on the task description (e.g., a few labelled samples). In this study, we structure the metric using a three-layers deep attentive network that is flexible enough to produce a discriminative metric for each task. Moreover, different from existing methods that utilize an uni-modal weight distribution conditioned on labelled samples for network generation, the proposed meta-learner establishes a multi-modal weight distribution conditioned on cross-class sample pairs using a tailored variational autoencoder, which can separately capture the specific inter-class discrepancy statistics for each class and jointly embed the statistics for all classes into metric generation. By doing this, the generated metric can be appropriately adapted to a new FSL task with pleasing generalization performance. To demonstrate this, we test the proposed method on three benchmark FSL datasets and gain competitive results with state-of-the-art competitors.
Comparative Analysis of Real-Time Fall Detection Using Fuzzy Logic Web Services and Machine Learning

Falls are the main cause of susceptibility to severe injuries in many humans, especially for older adults aged 65 and over. Typically, falls are being unnoticed and interpreted as a mere inevitable accident. Various wearable fall warning devices have been created recently for older people. However, most of these devices are dependent on local data processing. Various algorithms are used in wearable sensors to track a real-time fall effectively, which focuses on fall detection via fuzzy-as-a-service based on IEEE 1855–2016, Java Fuzzy Markup Language (FML) and service-oriented architecture. Moreover, several approaches are used to detect a fall using machine learning techniques via human movement positional data to avert any accidents. For fuzzy logic web services, analysis is performed using wearable accelerometer and gyroscope sensors, whereas in machine learning techniques, k-NN, decision tree, random forest and extreme gradient boost are used to differentiate between a fall and non-fall. This study aims to carry out a comparative analysis of real-time fall detection using fuzzy logic web services and machine learning techniques and aims to determine which one is better for real-time fall detection. Research findings exhibit that the proposed fuzzy-as-a-service could easily differentiate between fall and non-fall occurrences in a real-time environment with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively, while the random forest algorithm of machine learning achieved 99.19%, 98.53% and 99.63%, respectively.
Developing Future Wearable Interfaces for Human-Drone Teams through a Virtual Drone Search Game

Autonomous robotic vehicles (i.e., drones) are potentially transformative for search and rescue (SAR). This paper works toward wearable interfaces, through which humans team with multiple drones. We introduce the Virtual Drone Search Game as a first step in creating a mixed reality simulation for humans to practice drone teaming and SAR techniques. Our goals are to (1) evaluate input modalities for the drones, derived from an iterative narrowing of the design space, (2) improve our mixed reality system for designing input modalities and training operators, and (3) collect data on how participants socially experience the virtual drones with which they work. In our study, 17 participants played the game with two input modalities (Gesture condition, Tap condition) in counterbalanced order. Results indicated that participants performed best with the Gesture condition. Participants found the multiple controls challenging, and future studies might include more training of the devices and game. Participants felt like a team with the drones and found them moderately agentic. In our future work, we will extend this testing to a more externally valid mixed reality game.
Calibration for Camera–Projector Pairs Using Spheres

A newly developed calibration algorithm for camera-projector system using spheres is presented in this paper. Previous studies have exploited image conics of sphere to calibrate the camera, whereas this approach can be strengthened to apply in the projector and ultimately achieve the overall calibration for single or multiple pairs of camera-projector. Following the concept of taking the projector as an inverse camera, we retrieve the image conic of the sphere on the projector plane based on a pole-polar relationship we found. At least 3 image conics on the image plane of each device are required to calculate the intrinsic parameters of the device. The extrinsic parameters for all devices in the system are determined by the position of sphere centers in each coordinates frame of the device. Based on the isotropy of the calibration object (sphere), this work is mainly interested in accomplishing the entire calibration for multiple camera-projector systems in which sensors surround a central observation volume. Experiments are conducted on both synthetic and real datasets to evaluate its performance.
Optimization of Simultaneous Measurement for Variational Quantum Eigensolver Applications

Variational quantum eigensolver (VQE) is a promising algorithm suitable for near-term quantum computers. VQE aims to approximate solutions to exponentially-sized optimization problems by executing a polynomial number of quantum subproblems. However, the number of subproblems scales as N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> for typical problems of interest-a daunting growth rate that poses a serious limitation for emerging applications such as quantum computational chemistry. We mitigate this issue by exploiting the simultaneous measurability of subproblems corresponding to commuting terms. Our technique transpiles VQE instances into a format optimized for simultaneous measurement, ultimately yielding 8-30x lower cost. Our work also encompasses a synthesis tool for compiling simultaneous measurement circuits with minimal overhead. We demonstrate experimental validation of our techniques by estimating the ground state energy of deuteron with a quantum computer. We also investigate the underlying statistics of simultaneous measurement and devise an adaptive strategy for mitigating harmful covariance terms.
Human behaviour centered design

This paper introduces an integrated framework for sustainability and urban security socio-technical systems. The focus is to design and develop a hardware/software system based on human expected and real behaviour.
Image Segmentation with Partial Convexity Shape Prior Using Discrete Conformality Structures

Image segmentation aims to partition an image into meaningful regions and extract important objects therein. In real applications, the given images may contain multiple overlapping objects with noisy background, creating great challenges to the segmentation task. In these cases, prior information of the target object is essential for an accurate and meaningful segmentation result. In this paper, we present a new convexity shape prior segmentation framework to guarantee the segmented region to be fully or partially convex according to the user's preference. The basic idea is to incorporate a registration-based segmentation model with a specially designed convexity constraint. The convexity constraint is based on the discrete conformality structures of the image mesh. To solve the segmentation model, we propose an iterative scheme, which smoothly deforms a template object to trace the boundary of the target object. A projection is carried out to enforce the convexity constraint. The target object is then captured by a (fully or partially) convex region. Convexity is the only prior information needed for a (fully) convex shape, whereas the location of partial convexity is needed for a partially convex shape. Experiments have been carried out on both synthetic and real images and the results demonstrate the effectiveness of our proposed framework.
Random convergence analysis of particle swarm optimization algorithm with time‐varying attractor

The PSO convergence analysis is mainly based on the constant attractor, however, the attractor of PSO algorithm in the evolutionary process is time-varying point. So, the objective of this study is to mainly discuss the random convergence analysis of the standard and improved particle swarm optimization with the time-varying attractor. Its mathematical PSO model with the time-varying attractor is provided to calculate the convergence condition and the corresponding convergence speed. Specifically speaking, spectral radii of the random transfer matrix and the product of two adjacency random transfer matrices are calculated to determine the convergence or the divergence, together with the corresponding convergence speed. Additionally, it also calculates the mean and variance of the first and second order particle swarm optimization system with time-varying attractor. Numerical results highlight that the spectral analysis on some benchmark optimization functions is described to show the effectiveness of the obtained results, while the corresponding analysis is closely related to the objective fitness, the convergence speed, the time-varying attractor, the spectral radius of M(t) and M(t+1)M(t), and swarm convergence behavior in the evolutionary process.
Multilayer Detection of Network Steganography

This paper presents a new method for steganography detection in network protocols. The method is based on a multilayer approach for the selective analysis of derived and aggregated metrics utilizing machine learning algorithms. The main objective is to provide steganalysis capability for networks with large numbers of devices and connections. We discuss considerations for performance analysis and present results. We also describe a means of applying our method for multilayer detection of a popular RSTEG (Retransmission Steganography) technique.
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution

Super-resolution (SR) is by definition ill-posed. There are infinitely many plausible high-resolution variants for a given low-resolution natural image. Most of the current literature aims at a single deterministic solution of either high reconstruction fidelity or photo-realistic perceptual quality. In this work, we propose an explorative facial super-resolution framework, DeepSEE, for Deep disentangled Semantic Explorative Extreme super-resolution. To the best of our knowledge, DeepSEE is the first method to leverage semantic maps for explorative super-resolution. In particular, it provides control of the semantic regions, their disentangled appearance and it allows a broad range of image manipulations. We validate DeepSEE on faces, for up to \(32\times \) magnification and exploration of the space of super-resolution. Our code and models are available at: https://mcbuehler.github.io/DeepSEE/.
Rapid Ship Detection in SAR Images Based on YOLOv3

With the increasing resolution and data volume of synthetic aperture radar images, ship detection in synthetic aperture radar images has become one of the hot spots of academic research. In recent years, object detection methods based on deep convolutional neural networks have gradually become the mainstream methods in the field of object detection based on natural images. To address the problems of low accuracy rate and detection speed of ship detection methods in synthetic aperture radar images, an end-to-end ship detection method based on YOLOv3 is proposed. Unlike the previous predicted position offset, we directly predict the position coordinates of the detection frame and set the parameters of the anchor frame through dimension clusters. The multi-scale output combines high-level semantic information from high-level feature maps and detailed information from low-level feature maps. The simulation results show that our proposed method is more accurate and faster than other methods.
Empowering teachers to create augmented reality experiences: the effects on the educational experience

Well-designed educational Augmented Reality (AR) experiences support collaboration, transferability to the real world, situated, and experiential learning. Nevertheless, envisioning engaging yet effective learning and teaching experiences is a challenge. Most teachers are not technologically ready to create AR experiences and rely upon closed products often regarded as fun activities but whose learning affordances might remain unnoticed and whose integration within the course activities is often shoehorned. End-User Development tools empowering not technical users like teachers to create their own AR experience could contribute to spread the use of this technology in educational settings. In this paper, we analyze whether a EUD AR tool based on mobile technology can have positive effects on the educational experience from a teaching and learning perspective. We performed qualitative and quantitative research in a high school analyzing the tool in a focus group with teachers and assessing its effects on the educational experience by using it in two real classes. Results show that even if using the tool implies an additional workload for teachers, results can be rewarding for both, teachers and students that are skilled enough to use AR as far as it is based on easy, familiar, and affordable devices like mobile devices.
A pre-processing method for digital image correlation on rotating structures

Inter-frame structural motions are required to be small for classic Digital Image Correlation (DIC) algorithms, which severely limits the application of the technique in large rotation cases. To solve the problem, multiple methods to obtain initial guesses of deformation parameters have been introduced over recent years. However, the success rate is still limited by rotation angles and the efficiency is low due to extra calculations with these methods. To further develop the application of DIC in large rotation cases, Phase Mapping Method (PMM) is introduced in this paper. The principle of the proposed method is to rearrange the obtained images to be sorted by the structural rotating phases, which are previously mapped into the range of 0 to 2π. As a result, for an image set of n frames, the average inter-frame rotating angle is reduced to 2πn-1, and the original image set is transferred into an equivalent semi-static one by PMM. Additionally, a fast phase-detection approach is introduced to automate PMM in cases that the structural rotating speeds are unknown. By performing multiple tests, it is proved that PMM, as a pre-processing method, can significantly improve the success rate and the efficiency of DIC, whether used alone or concurrently with other algorithms. The first natural frequencies and mode shapes of a rotating beam under different rotating speeds are extracted through vibration test, from which the hardening effect is observed.
Optimal policy trees

We propose an approach for learning optimal tree-based prescription policies directly from data, combining methods for counterfactual estimation from the causal inference literature with recent advances in training globally-optimal decision trees. The resulting method, Optimal Policy Trees, yields interpretable prescription policies, is highly scalable, and handles both discrete and continuous treatments. We conduct extensive experiments on both synthetic and real-world datasets and demonstrate that these trees offer best-in-class performance across a wide variety of problems.