Reviewerly/exHarmony huggingface dataset:
U-Net based neural network for fringe pattern denoising

Fringe patterns from different optical measurement systems are widely used in scientific and engineering applications. However, fringe patterns are often corrupted by speckle noise, which is necessary to be removed to accurately recover the information encoded in the phase of the fringe pattern. In this paper we propose a lightweight residual dense neural network based on the U-net neural network model (LRDUNet) for fringe pattern denoising. The encoding and decoding layers of the LRDUNet consist of grouped densely connected convolutional layers for the sake of reusing the feature maps and reducing the number of trainable parameters. Additionally, local residual learning is used to avoid the vanishing gradient problem and speed up the learning process. We compare the proposed method versus state-of-the-art methods and present a study of parameters where we demonstrate that computationally simpler versions of the proposed model are still quite competitive. Experiments on simulated and real fringe patterns show that the proposed method outperforms state-of-the-art methods by restoring the main features of the fringe patterns, achieving an average of 41 dB of PSNR on simulated images.
CAROM - Vehicle Localization and Traffic Scene Reconstruction from Monocular Cameras on Road Infrastructures

Traffic monitoring cameras are powerful tools for traffic management and essential components of intelligent road infrastructure systems. In this paper, we present a vehicle localization and traffic scene reconstruction framework using these cameras, dubbed as CAROM, i.e., "CARs On the Map". CAROM processes traffic monitoring videos and converts them to anonymous data structures of vehicle type, 3D shape, position, and velocity for traffic scene reconstruction and replay. Through collaborating with a local department of transportation in the United States, we constructed a benchmarking dataset containing GPS data, roadside camera videos, and drone videos to validate the vehicle tracking results. On average, the localization error is approximately 0.8 m and 1.7 m within the range of 50 m and 120 m from the cameras, respectively.
The Effects of Virtual Reality-Based Education on Empathy and Understanding of the Physical Environment for Dementia Care Workers in Australia: A Controlled Study

There is support for the effectiveness of virtual reality (VR) technology in dementia education. However, it is not yet known if VR is a useful tool for improving empathy and understanding of dementia care environments among dementia care workers.This study compared learning outcomes of VR versus non-VR (control) workshops for dementia care workers of different ages and English-speaking backgrounds.Dementia care workers enrolled in workshops on dementia care principles. Once participants were enrolled, workshops were assigned at random to deliver non-VR or VR-based education. Participants (N = 114, 91.8%female, mean age = 46.4; SD = 13.2; n = 60VR condition, 54control condition) completed self-report measures of empathy towards people living with dementia, understanding of dementia care environments, dementia knowledge, and attitudes towards dementia at pre- and post-workshop.Significant pre-post main effects were observed for empathy, understanding of dementia care environments, and attitudes. Interaction effects were not found; improvements in outcomes were similar between conditions. However, interaction effects were observed for subgroups. Empathy improved significantly more in the VR condition for older participants. Understanding of dementia care environments improved more in the VR condition for younger and non-English-speaking background participants.Using VR may not augment teaching outcomes for all learners. VR may differentially assist leaners of different ages and English-speaking backgrounds. More research is needed to understand for which variables and for whom VR is a useful teaching tool.
MRPB 1.0: A Unified Benchmark for the Evaluation of Mobile Robot Local Planning Approaches

Local planning is one of the key technologies for mobile robots to achieve full autonomy and has been widely investigated. To evaluate mobile robot local planning approaches in a unified and comprehensive way, a mobile robot local planning benchmark called MRPB 1.0 is newly proposed in this paper. The benchmark facilitates both motion planning researchers who want to compare the performance of a new local planner relative to many other state-of-the-art approaches as well as end users in the mobile robotics industry who want to select a local planner that performs best on some problems of interest. We elaborately design various simulation scenarios to challenge the applicability of local planners, including large-scale, partially unknown, and dynamic complex environments. Furthermore, three types of principled evaluation metrics are carefully designed to quantitatively evaluate the performance of local planners, wherein the safety, efficiency, and smoothness of motions are comprehensively considered. We present the application of the proposed benchmark in two popular open-source local planners to show the practicality of the benchmark. In addition, some insights and guidelines about the design and selection of local planners are also provided. The benchmark website [1] contains all data of the designed simulation scenarios, detailed descriptions of these scenarios, and example code.
AKECP: Adaptive Knowledge Extraction from Feature Maps for Fast and Efficient Channel Pruning

Pruning can remove redundant parameters and structures of Deep Neural Networks (DNNs) to reduce inference time and memory overhead. As an important component of neural networks, the feature map (FM) has stated to be adopted for network pruning. However, the majority of FM-based pruning methods do not fully investigate effective knowledge in the FM for pruning. In addition, it is challenging to design a robust pruning criterion with a small number of images and achieve parallel pruning due to the variability of FMs. In this paper, we propose Adaptive Knowledge Extraction for Channel Pruning (AKECP), which can compress the network fast and efficiently. In AKECP, we first investigate the characteristics of FMs and extract effective knowledge with an adaptive scheme. Secondly, we formulate the effective knowledge of FMs to measure the importance of corresponding network channels. Thirdly, thanks to the effective knowledge extraction, AKECP can efficiently and simultaneously prune all the layers with extremely few or even one image. Experimental results show that our method can compress various networks on different datasets without introducing additional constraints, and it has advanced the state-of-the-arts. Notably, for ResNet-110 on CIFAR-10, AKECP achieves 59.9% of parameters and 59.8% of FLOPs reduction with negligible accuracy loss. For ResNet-50 on ImageNet, AKECP saves 40.5% of memory footprint and reduces 44.1% of FLOPs with only 0.32% of Top-1 accuracy drop.
Discriminating Tectonic Setting of Igneous Rocks Using Biotite Major Element chemistry−A Machine Learning Approach

Abstract The composition of igneous biotite is a potential indicator of the geologic environment of its host rock. Here, we apply two machine learning models−eXtreemly Greedy tree Boosting (XGBoost) and light gradient boosting machine (LightGBM) to classify biotite in igneous rocks from five tectonic settings−oceanic intraplate, continental intraplate, continental arc, island arc, and rift, using their major element chemistry compiled from a global dataset. The two models are successfully able to discriminate among biotite from the five tectonic settings. The classifiers quantitatively search for unique geochemical signatures in the training dataset, mapping each biotite analysis to its corresponding tectonic setting. Both models yielded good classification accuracy (∼90% on average) on an unseen dataset, suggesting that biotite major element chemistry can successfully discriminate the tectonic setting of many igneous rocks. The Shapley Additive exPlanations algorithm, which measures the impact of every element, indicates that Na, Mn, Ba, Mg, Al, Cr, and O 2− constitute important geochemical discriminators. According to both models, high Na, low Na, low Ba, high Mn, and low Mn of biotite have the strongest influence on the prediction of continental arc, continental intraplate, island arc, oceanic intraplate, and rift settings, respectively. The classifier models have been applied to investigate the Neoproterozoic geodynamics of the Aravalli‐Delhi Belt, northwestern India. The models show that igneous activity related to the Erinpura granites (∼835 Ma) and the Malani Igneous Suite (∼750 Ma) can be ascribed to continental intraplate setting related to extensional tectonics prior to the break‐up of the Rodinia supercontinent.
Probabilistic learning on manifolds (PLoM) with partition

Abstract The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved difficult supervised problems for the “small data” limit where the number N of points in the training set is small. Many extensions have since been proposed, making it possible to deal with increasingly complex cases. However, the performance limit has been observed and explained for applications for which N is very small and for which the dimension of the diffusion‐map basis is close to N . For these cases, we propose a novel extension based on the introduction of a partition in independent random vectors. We take advantage of this development to present improvements of the PLoM such as a simplified algorithm for constructing the diffusion‐map basis and a new mathematical result for quantifying the concentration of the probability measure in terms of a probability upper bound. The analysis of the efficiency of this extension is presented through two applications.
Classical-To-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks

This work investigates an extension of transfer learning applied in machine learning algorithms to the emerging hybrid end-to-end quantum neural network (QNN) for spoken command recognition (SCR). Our QNN-based SCR system is composed of classical and quantum components: (1) the classical part mainly relies on a 1D convolutional neural network (CNN) to extract speech features; (2) the quantum part is built upon the variational quantum circuit with a few learnable parameters. Since it is inefficient to train the hybrid end-to-end QNN from scratch on a noisy intermediate-scale quantum (NISQ) device, we put forth a hybrid transfer learning algorithm that allows a pre-trained classical network to be transferred to the classical part of the hybrid QNN model. The pre-trained classical network is further modified and augmented through jointly fine-tuning with a variational quantum circuit (VQC). The hybrid transfer learning methodology is particularly attractive for the task of QNN-based SCR because low-dimensional classical features are expected to be encoded into quantum states. We assess the hybrid transfer learning algorithm applied to the hybrid classical-quantum QNN for SCR on the Google speech command dataset, and our classical simulation results suggest that the hybrid transfer learning can boost our baseline performance on the SCR task.
SAR Ship Detection Based on an Improved Faster R-CNN Using Deformable Convolution

With the rise of Deep Learning (DL), numerous DL-based SAR ship detectors, represented by Faster R-CNN, is constantly breaking the record of detection accuracy. However, these detectors still face huge challenges in modeling the geometric transformation of shape-changeable ships, due to their used conventional convolution kernels whose structure is fixed. Therefore, to address this problem, we propose an improved Faster R-CNN by using deformable convolution kernels for SAR ship detection. We substitute some conventional shape-changeless convolution kernels in Faster R-CNN with deformable convolution ones that can adaptively learn additional 2-D offsets of the raw convolution kernels, to better model the geometric transformation of shape-changeable ships. Finally, the experimental results on the open SAR Ship Detection Dataset (SSDD) reveal that our improved Faster R-CNN achieves a 2.02% mean Average Precision (mAP) improvement than the raw Faster R-CNN.
Disciplining the Akratic user: Constructing digital (un) wellness

Contemporary discourse around digital well-being tends to focus on self-control when it comes to “addicting” social media apps and digital devices. By acting on behalf of users, designers and engineers promote various self-regulating products and services in order for users to “fix” the distractible brains of typical users. This paper explores the role of the history of psychology on user-experience thinking and engineering and provides a critical genealogy of digital well-being discourse and persuasive technology. In particular, I expand on the role of dual-process models in human–computer interaction and health behavior change from the 1980s to today. By exploring the social construction of the distracted, impulsive, “primitive” animal brain (system 1), I find that it is this part of the mind that the engineer wishes to “treat,” via app design. In order to tame this “primitive” brain, researchers and engineers have turned to behavioral science, hoping to better structure user options and encourage users to manage their own time and normalize screen habits. I argue that normality discourses like this are founded upon ideas of time management and delay of gratification, whereas abnormality is tied to ideas of immediate gratification and time wasting. This dichotomy is not simply to enforce social norms around time wasting, but reinforces social and econoimc inequities. Therefore, unlike some other approaches to digital well-being, I urge future scholarship on the subject to examine the taken-for-granted social-cultural context, which will lead not only to a more politically nuanced understanding of the subject but may also lead to further discussions over how digital well-being could be conceived otherwise.
Direction Relation Transformer for Image Captioning

Image captioning is a challenging task that combines computer vision and natural language processing for generating a textual description of the content within an image. Recently, Transformer-based encoder-decoder architectures have shown great success in image captioning, where multi-head attention mechanism is utilized to capture the contextual interactions between object regions. However, such methods regard region features as a bag of tokens without considering the directional relationships between them, making it hard to understand the relative position between objects in the image and generate correct captions effectively. In this paper, we propose a novel Direction Relation Transformer to improve the orientation perception between visual features by incorporating the relative direction embedding into multi-head attention, termed DRT. We first generate the relative direction matrix according to the positional information of the object regions, and then explore three forms of direction-aware multi-head attention to integrate the direction embedding into Transformer architecture. We conduct experiments on challenging Microsoft COCO image captioning benchmark. The quantitative and qualitative results demonstrate that, by integrating the relative directional relation, our proposed approach achieves significant improvements over all evaluation metrics compared with baseline model, e.g., DRT improves task-specific metric CIDEr score from 129.7% to 133.2% on the offline ''Karpathy'' test split.
Enhancing Knowledge Tracing via Adversarial Training

We study the problem of knowledge tracing (KT) where the goal is to trace the students' knowledge mastery over time so as to make predictions on their future performance. Owing to the good representation capacity of deep neural networks (DNNs), recent advances on KT have increasingly concentrated on exploring DNNs to improve the performance of KT. However, we empirically reveal that the DNNs based KT models may run the risk of overfitting, especially on small datasets, leading to limited generalization. In this paper, by leveraging the current advances in adversarial training (AT), we propose an efficient AT based KT method (ATKT) to enhance KT model's generalization and thus push the limit of KT. Specifically, we first construct adversarial perturbations and add them on the original interaction embeddings as adversarial examples. The original and adversarial examples are further used to jointly train the KT model, forcing it is not only to be robust to the adversarial examples, but also to enhance the generalization over the original ones. To better implement AT, we then present an efficient attentive-LSTM model as KT backbone, where the key is a proposed knowledge hidden state attention module that adaptively aggregates information from previous knowledge hidden states while simultaneously highlighting the importance of current knowledge hidden state to make a more accurate prediction. Extensive experiments on four public benchmark datasets demonstrate that our ATKT achieves new state-of-the-art performance. Code is available at: https://github.com/xiaopengguo/ATKT.
Breaking bad? Semantic versioning and impact of breaking changes in Maven Central

Just like any software, libraries evolve to incorporate new features, bug fixes, security patches, and refactorings. However, when a library evolves, it may break the contract previously established with its clients by introducing Breaking Changes (BCs) in its API. These changes might trigger compile-time, link-time, or run-time errors in client code. As a result, clients may hesitate to upgrade their dependencies, raising security concerns and making future upgrades even more difficult. Understanding how libraries evolve helps client developers to know which changes to expect and where to expect them, and library developers to understand how they might impact their clients. In the most extensive study to date, Raemaekers et al. investigate to what extent developers of Java libraries hosted on the Maven Central Repository (MCR) follow semantic versioning conventions to signal the introduction of BCs and how these changes impact client projects. Their results suggest that BCs are widespread without regard for semantic versioning, with a significant impact on clients. In this paper, we conduct an external and differentiated replication study of their work. We identify and address some limitations of the original protocol and expand the analysis to a new corpus spanning seven more years of the MCR. We also present a novel static analysis tool for Java bytecode, Maracas, which provides us with: (i) the set of all BCs between two versions of a library, and; (ii) the set of locations in client code impacted by individual BCs. Our key findings, derived from the analysis of 119,879 library upgrades and 293,817 clients, contrast with the original study and show that 83.4% of these upgrades do comply with semantic versioning. Furthermore, we observe that the tendency to comply with semantic versioning has significantly increased over time. Finally, we find that most BCs affect code that is not used by any client, and that only 7.9% of all clients are affected by BCs. These findings should help (i) library developers to understand and anticipate the impact of their changes; (ii) library users to estimate library upgrading effort and to pick libraries that are less likely to break, and; (iii) researchers to better understand the dynamics of library-client co-evolution in Java.
Saliency-Guided Transformer Network combined with Local Embedding for No-Reference Image Quality Assessment

No-Reference Image Quality Assessment (NR-IQA) methods based on Vision Transformer have recently drawn much attention for their superior performance. Unfortunately, being a crude combination of NR-IQA and Transformer, they can hardly take the advantage of their strengths. In this paper, we propose a novel Saliency-Guided Transformer Network combined with Local Embedding (TranSLA) for No-Reference Image Quality Assessment. Our TranSLA integrates different-level information for a robust representation. Existed researches have shown that the human vision system concentrates more on the Region-of-interest (RoI) when assessing the image quality. Thus we combine saliency prediction with Transformer to guide the model highlight the RoI when aggregating the global information. Besides, we import local embedding for Transformer with gradient map. Since the gradient map focuses on extracting structured feature in detail, it can be used as a supplement to offer local information for Transformer. Then, the local and non-local information can be utilized. Moreover, to accelerate the aggregation of information from all tokens, we introduce a Boosting Interaction Module (BIM) to enhance feature aggregation. BIM forces patch tokens to interact better with class tokens at all levels. Experiments on two large-scale NR-IQA benchmarks demonstrate that our method significantly outperforms the state-of-the-art.
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization

The theory of evolutionary computation for discrete search spaces has made significant progress in the last ten years. This survey summarizes some of the most important recent results in this research area. It discusses fine-grained models of runtime analysis of evolutionary algorithms, highlights recent theoretical insights on parameter tuning and parameter control, and summarizes the latest advances for stochastic and dynamic problems. We regard how evolutionary algorithms optimize submodular functions and we give an overview over the large body of recent results on estimation of distribution algorithms. Finally, we present the state of the art of drift analysis, one of the most powerful analysis technique developed in this field.
Ontology Driven Testing Strategies for IoT Applications

Internet-of-Things (IoT) has attained a major share in embedded software development. The new era of specialized intelligent systems requires adaptation of customized software engineering approaches. Currently, software engineering has merged the development phases with the technologies provided by industrial automation. The improvements are still required in testing phase for the software developed to IoT solutions. This research aims to assist in developing the testing strategies for IoT applications, therein ontology has been adopted as a knowledge representation technique to different software engineering processes. The proposed ontological model renders 101 methodology by using Protégé. After completion, the ontology was evaluated in three-dimensional view by the domain experts of software testing, IoT and ontology engineering. Satisfied results of the research are showed in interest of the specialists regarding proposed ontology development and suggestions for improvements. The Proposed reasoning-based ontological model for development of testing strategies in IoT application contributes to increase the general understanding of tests in addition to assisting for the development of testing strategies for different IoT devices.