Reviewerly/exHarmony huggingface dataset:
Stereo CenterNet-based 3D object detection for autonomous driving

Recently, three-dimensional (3D) detection based on stereo images has progressed remarkably; however, most advanced methods adopt anchor-based two-dimensional (2D) detection or depth estimation to address this problem. Nevertheless, high computational cost inhibits these methods from achieving real-time performance. In this study, we propose a 3D object detection method, Stereo CenterNet (SC), using geometric information in stereo imagery. SC predicts the four semantic key points of the 3D bounding box of the object in space and utilizes 2D left and right boxes, 3D dimension, orientation, and key points to restore the bounding box of the object in the 3D space. Subsequently, we adopt an improved photometric alignment module to further optimize the position of the 3D bounding box. Experiments conducted on the KITTI dataset indicate that the proposed SC exhibits the best speed-accuracy trade-off among advanced methods without using extra data.
Robust forecasting using predictive generalized synchronization in reservoir computing

Reservoir computers (RCs) are a class of recurrent neural networks (RNNs) that can be used for forecasting the future of observed time series data. As with all RNNs, selecting the hyperparameters in the network to yield excellent forecasting presents a challenge when training on new inputs. We analyze a method based on predictive generalized synchronization (PGS) that gives direction in designing and evaluating the architecture and hyperparameters of an RC. To determine the occurrences of PGS, we rely on the auxiliary method to provide a computationally efficient pre-training test that guides hyperparameter selection. We provide a metric for evaluating the RC using the reproduction of the input system's Lyapunov exponents that demonstrates robustness in prediction.
Image denoising for magnetic resonance imaging medical images using improved generalized cross‐validation based on the diffusivity function

Abstract Various image denoising algorithms have been developed for medical imaging. But some disadvantages have been found, including the block effect, which increases smoothing, and the loss of image detail. Using the statistical properties of observed noisy images, we propose a new diffusivity function‐based partial differential equation method for image denoising. This model incorporates a Quaternion Wavelet Transform for the generation of the various noisy image coefficients, an improved generalized cross‐validation function for generating the optimal threshold value via the soft threshold function, and a new diffusivity function for controlling the diffusion process. The fourth‐order PDE diffusivity function, which is presented in this article, is a novel diffusion coefficient that is more effective at removing noise and preserving edges than previous approaches. Finally, the performance of the proposed method is evaluated using the peak signal‐to‐noise ratio, mean square error, structural similarity index, and comparisons to other traditional methods.
Earthquake Reconnaissance Data Sources, a Literature Review

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting data on building damage and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 39 articles that indicate the sources used by different authors to collect data related to damage and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria depending on what questions are to be answered by these data. We conclude that modern reconnaissance missions cannot rely on a single data source. Different data sources should complement each other, validate collected data or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important data source.
Cyber Physical Systems Security for Maritime Assets

The integration of IT, OT, and human factor elements in maritime assets is critical for their efficient and safe operation and performance. This integration defines cyber physical systems and involves a number of IT and OT components, systems, and functions that involve multiple and diverse communication paths that are technologically and operationally evolving along with credible cyber security threats. These cyber security threats and risks as well as a number of known security breach scenarios are described in this paper to highlight the evolution of cyber physical systems in the maritime domain and their emerging cyber vulnerabilities. Current industry and governmental standards and directives related to cyber security in the maritime domain attempt to enforce the regulatory compliance and reinforce asset cyber security integrity for optimum and safe performance with limited focus, however, in the existing OT infrastructure and systems. The use of outside-of-the-maritime industry security risk assessment tools and processes, such the API STD 780 Security Risk Assessment (SRA) and the Bow Tie Analysis methodologies, can assist the asset owner to assess its IT and OT infrastructure for cyber and physical security vulnerabilities and allocate proper mitigation measures assuming their similarities to ICS infrastructure. The application of cyber security controls deriving from the adaptation of the NIST CSF and the MITRE ATT&amp;CK Threat Model can further increase the cyber security integrity of maritime assets, assuming they are periodically evaluated for their effectiveness and applicability. Finally, the improvement in communication among stakeholders, the increase in operational and technical cyber and physical security resiliency, and the increase in operational cyber security awareness would be further increased for maritime assets by the convergence of the distinct physical and cyber security functions as well as onshore- and offshore-based cyber infrastructure of maritime companies and asset owners.
Dynamic Analysis of College Physical Education Teaching Quality Evaluation Based on Network under the Big Data

Improving the quality of college physical education is of great significance to facilitating the integrated development of students' psyches and physical. Establishing a more systematic, effective, and social training needs of education quality evaluation hierarchy is also the centerpiece of the college physical culture education administration. Massive information technology provides new conception and methods to this, and supply advantage sustains for furtherance the education ecology development. Based on the network education system, this paper uses big data to quantify the evaluation indexes of physical education teaching, so as to actualize the timely dynamic evaluation of the process that is physical teaching and learning. This essay constructs the evaluation index system of college physical education teaching quality by combining mensurable and qualitative methods. On the basis of previous studies, an evaluation model of college physical education teaching quality based on artificial intelligence mass data calculation is designed. The experiment authenticates that the model evaluation risk coefficient is 1.93 lower than the optimized model. The experiment also proves that the model is conducive to elevating the education quality.
SAFRON: Stitching Across the Frontier Network for Generating Colorectal Cancer Histology Images

Automated synthesis of histology images has several potential applications including the development of data-efficient deep learning algorithms. In the field of computational pathology, where histology images are large in size and visual context is crucial, synthesis of large high-resolution images via generative modeling is an important but challenging task due to memory and computational constraints. To address this challenge, we propose a novel framework called SAFRON (Stitching Across the FROntier Network) to construct realistic, large high-resolution tissue images conditioned on input tissue component masks. The main novelty in the framework is integration of stitching in its loss function which enables generation of images of arbitrarily large sizes after training on relatively small image patches while preserving morphological features with minimal boundary artifacts. We have used the proposed framework for generating, to the best of our knowledge, the largest-sized synthetic histology images to date (up to 11K×8K pixels). Compared to existing approaches, our framework is efficient in terms of the memory required for training and computations needed for synthesizing large high-resolution images. The quality of generated images was assessed quantitatively using Frechet Inception Distance as well as by 7 trained pathologists, who assigned a realism score to a set of images generated by SAFRON. The average realism score across all pathologists for synthetic images was as high as that of real images. We also show that training with additional synthetic data generated by SAFRON can significantly boost prediction performance of gland segmentation and cancer detection algorithms in colorectal cancer histology images.
Rapid detection of mussels contaminated by heavy metals using near-infrared reflectance spectroscopy and a constrained difference extreme learning machine

The consumption of mussels contaminated with heavy metals can cause toxicity in humans. To realize quick, accurate, and non-destructive detection of heavy metals in mussels, a new method based on near-infrared reflection spectroscopy was developed in this study. Spectral data from 900 nm to 1700 nm of non-contaminated mussels and mussels contaminated with Cd, Zn, Pb, and Cu were collected using a near-infrared spectrometer. After pre-processing spectral data with multiplicative scatter correction, wavelength selection algorithms based on consistency measures of neighborhood rough sets were used to extract wavelengths for distinguishing non-contaminated and contaminated mussels. A constrained difference extreme learning machine was established as a classification model to detect contaminated mussels. In the proposed model, the weight and bias of the hidden layers are calculated by the difference vectors of samples between classes instead of being randomly selected. The results indicate that the proposed model performs significantly well in differentiating between non-contaminated and contaminated mussels. The average classification accuracy of 50 randomly generated test datasets reaches 97.53%, 95.67%, 99.00%, and 98.80% for the detection of Zn, Pb, Cd, and Cu contamination, respectively. This study demonstrates that near-infrared spectroscopy coupled with a constrained difference extreme learning can be used to rapidly and accurately detect mussels contaminated with heavy metals. This is of great significance for the evaluation of the quality and safety of mussels.
Multi-view fuzzy clustering of deep random walk and sparse low-rank embedding

Multi-view clustering aims to improve the learning performance by exploiting discriminative information from heterogeneous data sources. It has been capturing growing research attention due to its wide utilization in the areas of data mining and computer vision. However, the full use of complementarity and consistency from multi-view data is still a challenging task. Here, we propose a novel multi-view fuzzy clustering, which adopts the joint learning of deep random walk and sparse low-rank embedding. First, a deep random walk is employed to acquire a robust similarity matrix of data points and convert fuzzy membership matrix learning to adaptive graph learning. Second, the adaptive graph is restricted with sparse low-rank constraints, which ensures its strong discriminative ability and effective cluster assignments. Third, by exploiting the sparse low-rank property, the multi-view fuzzy clustering problem is formulated as optimizing a regularized graph adjacency matrix with distance metric learning and spectral norm minimization simultaneously. The distance metric learning is embedded to scatter all data points so that any two samples are projected onto a low-dimensional subspace with a large margin. Finally, an efficient algorithm is developed for the formulated problem and its convergence is also guaranteed. In order to demonstrate the superiority of the proposed method, extensive experiments are conducted by comparing state-of-the-arts on real-world databases.
Battling to keep education going: Chilean and Portuguese teacher experiences in COVID-19 Times

The article presents results of studies in Chile and Portugal during COVID-19 lockdowns and remote teaching conditions. In each of both countries, over two thousand teachers of all school levels and types were surveyed during a two-month period on their professional experiences in the first year of remote teaching. The article discusses teacher accounts of how their work changed, their difficulties, impact on well-being, as well as new professional learning brought about by the challenges involved. Conceptually, the study relied on notions of occupational professionalism, self-efficacy perceptionsand collegial support in challenging circumstances. Results in both countries highlighted an impact on teacher occupational professionalism and self-efficacy perceptions, mainly brought about by intermittent levels of engagement of students during remote teaching sessions, and difficulties in carrying out proper assessments of student learning. Student well-being was of greater concern to teachers than their own problems, despite feeling the stressful effect of much longer daily working hours, more so among Chilean than Portuguese teachers.The study also brought out teachers’ imaginative efforts and the use of unaccustomed pedagogic strategies to reach out to students and insure some level of learning, especially among those without internet connectivity.
CBAM-Unet++:easier to find the target with the attention module "CBAM"

There are already many methods based on U-net, however, due to the paricularity of medical images, we need to pay more attention to the target area to perform more detailed segmentation. In this paper, we present a CBAM-Unet++ module, which a more targeted architecture for medical image segmentation. It combines Unet++ and Convolutional block attention module to make it easier for architecture to ignore irrelevant background information, thereby paying more attention to the parts that we want to have.
GraphXSS: An efficient XSS payload detection approach based on graph convolutional network

With the rapid development of the Internet age today, Web applications have become very common in modern society. Web applications are often applied to a social network, media, management, etc., and usually contain a large amount of personal privacy information, which makes Web applications a common target for hackers. The most common method for stealing private information from web applications is cross-site scripting attacks. Attackers frequently use cross-site scripting vulnerabilities to steal victims' identity information or hijack login tokens. Therefore, we proposed a cross-site scripting payload detection model based on graph convolutional networks, which could identify the cross-site scripting payload in the content submitted by the user (We termed our implementation of this approach, GraphXSS). We preprocessed the sample, and constructed the processed data into a graph structure, and finally used the graph convolutional network and the residual network to train the cross-site scripting detection model. In experiments, the model based on graph convolutional network (GCN) could achieve AUC value of 0.997 under small sample conditions. Compared with the detection model after adding the residual network structure, the model could converge and stabilize under the multi-layer, and could make the accuracy rate reached 0.996.
Obstacle avoidance strategy for an autonomous surface vessel based on modified deep deterministic policy gradient

In the present paper, a decision-making agent based on reinforcement learning is designed for establishing an obstacle avoidance strategy of an autonomous surface vessel (ASV). To solve the sparse feedback in obstacle avoidance issue, the Modified Deep Deterministic Policy Gradient (MDDPG) method is proposed in the present study, using an actor-network to generate actions from the state of the ASV and a critic-network to evaluate the behavior of the actor-network. The gradient descend method is applied to improve the networks. Memory pool modification, success pool modification and target network modification are developed to smooth the training process of the ASV. A comparative analysis based on numerical simulations are carried out using Deep Q Network (DQN) method, Modified Deep Q Network (MDQN) method and MDDPG method. The results demonstrate that the MDDPG method smooths and accelerates the learning process. Compared with the other two methods, the effectiveness and viability of the proposed method is significantly increased. Random obstacles tests are also conducted to confirm the applicability and generalization of the method.
Investigating the impact of the Amazon Alexa on the development of L2 listening and speaking skills

With advances in technology, intelligent personal assistants (IPAs) have become available to assist users with a variety of tasks using voice commands. Because IPAs may induce dialogic interactions, researchers speculated that they may benefit second language learning, especially regarding pronunciation, listening and speaking skills. So far, very few experimental studies have examined the effects of IPAs. The present study examined the effects of one popular IPA, the Amazon Echo Show, on L2 learners’ listening and speaking skills as well as the learners’ perceptions of the IPA. Two classes were recruited and randomly assigned as a control and an experimental group. While the control group continued with their regular lessons, the experimental group received seven Alexa sessions (each of around 50 minutes). Both groups took mock TOEIC listening and speaking tests before and after the experiment and the experimental group completed a survey exploring their perceptions of working with the IPA. A significant difference was found regarding speaking, suggesting that learners can improve their speaking skills through interacting with an IPA. The learners reported that IPAs offered more oral interactions and also reduced their speaking anxieties. These positive findings suggest that researchers should further explore the potential of IPAs.
CCGIR: Information retrieval-based code comment generation method for smart contracts

A smart contract is a computer program, which is intended to automatically execute, control or document legally relevant events and actions according to the terms of a contract. About 10% of the security vulnerabilities in smart contracts are caused by misuse of codes without comments. Therefore, there is a need to design effective automatic code comment generation methods for smart contracts. In this study, we propose an information retrieval-based code comment generation method CCGIR for smart contracts. Since code clones are common in smart contract development, CCGIR finds the most similar code in the code repository and reuses its comment through an information retrieval approach from three aspects: semantic similarity, lexical similarity, and syntactic similarity of smart contract codes. We select a corpus, which contains 57,676 unique pairs of <method, comment> from 40,932 real-world smart contracts, as our experimental subject. Then we conduct empirical studies to evaluate the effectiveness of our proposed method. Experimental results show that CCGIR can outperform nine state-of-the-art baselines in terms of three performance measures. Moreover, we perform a human study to further verify that CCGIR can generate higher quality comments. Finally, we find CCGIR can achieve promising performance on the other two code comment generation tasks (i.e., code comment generation for Java and code comment generation for Python). Due to the simplicity and effectiveness of our proposed method, we recommend researchers can use our proposed method as the baseline when evaluating their proposed novel code comment generation methods.
Generalization error of random feature and kernel methods: Hypercontractivity and kernel matrix concentration

Consider the classical supervised learning problem: we are given data (yi,xi), i≤n, with yi a response and xi∈X a covariates vector, and try to learn a model fˆ:X→R to predict future responses. Random feature methods map the covariates vector xi to a point ϕ(xi) in a higher dimensional space RN, via a random featurization map ϕ. We study the use of random feature methods in conjunction with ridge regression in the feature space RN. This can be viewed as a finite-dimensional approximation of kernel ridge regression (KRR), or as a stylized model for neural networks in the so called lazy training regime. We define a class of problems satisfying certain spectral conditions on the underlying kernels, and a hypercontractivity assumption on the associated eigenfunctions. These conditions are verified by classical high-dimensional examples. Under these conditions, we prove a sharp characterization of the error of random feature ridge regression. In particular, we address two fundamental questions: (1) What is the generalization error of KRR? (2) How big N should be for the random feature approximation to achieve the same error as KRR? In this setting, we prove that KRR is well approximated by a projection onto the top ℓ eigenfunctions of the kernel, where ℓ depends on the sample size n. We show that the test error of random feature ridge regression is dominated by its approximation error and is larger than the error of KRR as long as N≤n1−δ for some δ>0. We characterize this gap. For N≥n1+δ, random features achieve the same error as the corresponding KRR, and further increasing N does not lead to a significant change in test error.
Egocentric Vision-based Action Recognition: A survey

The egocentric action recognition EAR field has recently increased its popularity due to the affordable and lightweight wearable cameras available nowadays such as GoPro and similars. Therefore, the amount of egocentric data generated has increased, triggering the interest in the understanding of egocentric videos. More specifically, the recognition of actions in egocentric videos has gained popularity due to the challenge that it poses: the wild movement of the camera and the lack of context make it hard to recognise actions with a performance similar to that of third-person vision solutions. This has ignited the research interest on the field and, nowadays, many public datasets and competitions can be found in both the machine learning and the computer vision communities. In this survey, we aim to analyse the literature on egocentric vision methods and algorithms. For that, we propose a taxonomy to divide the literature into various categories with subcategories, contributing a more fine-grained classification of the available methods. We also provide a review of the zero-shot approaches used by the EAR community, a methodology that could help to transfer EAR algorithms to real-world applications. Finally, we summarise the datasets used by researchers in the literature.