Reviewerly/exHarmony huggingface dataset:
A graph model-based multiscale feature fitting method for unsupervised anomaly detection

Anomaly detection and localization without prior knowledge is a challenging problem in industrial manufacturing due to the complexity and variety of anomaly types. Most of the existing methods have achieved considerable anomaly detection performance based on the distance between normal features and abnormal features. However, when the defect area is hard to distinguish from the background or the defect area is small, the distance between normal and abnormal features will be too close to detect anomaly areas. In addition, existing methods do not consider the influences of features in different layers with different anomaly sizes. In this paper, a graph model-based multiscale feature fitting method is proposed for unsupervised anomaly detection. Specifically, we build a graph model based on the K nearest neighbors of an anchor image. The feature fitting and anomaly scores of the anchor images in the graph vertices are calculated next. Finally, a weighted multiscale anomaly map matching method is proposed to detect and locate the anomaly regions of test images. Compared with the state-of-the-art methods, our proposed method achieves competitive improvement in anomaly detection and localization on the MVTec AD dataset, the two KolektorSDD datasets, and the mSTC dataset.
Predictors of NFT Prices

This article aims to broaden the understanding of the non-fungible tokens (NFTs) pricing determinants by investigating features, both market- and network-related aspects. NFTs are uniquely identifiable digital assets stored on the blockchain. Ownership is assigned through smart contracts and can be transferred or resold by the owner. The authors analyzed a comprehensive dataset from Signex.io with over 19,183 datapoints on NFT prices and NFT social communities using automated machine learning (AML), a suitable technique to investigate the most impactful factors due to a lack of knowledge on the exact determinants. Findings show that network factors are the most important pricing determinants: Twitter members followed by Discord members. Online communities drive the price of NFTs, but not in a linear fashion. Given the newness of the phenomenon and no agreed upon pricing models, this article contributes by using AML to discover the most relevant determinants of non-fungible tokens (NFT) prices.
Small Visual Object Detection in Smart Waste Classification Using Transformers with Deep Learning

Smart object waste classification is relatively essential for protecting the environment and saving resources. This is considered a vital pathway towards sustainability. In waste classification, we see that it is challenging to detect waste of small visual objects with low resolutions that directly affect the overall performance of waste classification. While current visual object detection algorithms focus on the exploration of larger objects, the development of small object detection is being expanded relatively slowly due to the inability to acquire more visual information. In this paper, we propose a novel method combining contextual information and multiscale learning to improve small object detection performance in waste classification by enabling small object detection to obtain more feature information at high resolution. Furthermore, based on the advantages of parallel computing in Transformers, we utilize the DETR model to explore our method. The experimental results show that our method achieves high accuracy in the detection of a small object in waste.
Physically Secure and Conditional-Privacy Authenticated Key Agreement for VANETs

To avoid physically extracting secrets from the storage of devices, Physical Unclonable Function (PUF) is used in various authentication schemes. In these schemes, after the devices are registered on the Trusted Authority (TA), the subsequent authentication still must need the help of the TA. And these devices usually don't move. Due to the high mobility of vehicles, frequent involvement with the TA becomes expensive and impractical in motion. As a result, these schemes are not well suited to VANETs where devices move at high speeds. Motivated from this, we propose a physically secure and conditional-privacy authenticated key agreement scheme for VANETs. In the proposed scheme, PUF is utilized to prevent the physical extraction of secrets from vehicles and RSUs. Unlike others using PUF, the proposed scheme does not require the TA's participation during the authentication between the vehicles and RSUs. Meanwhile, the proposed scheme provides conditional-privacy with pseudonym mechanism, which enables the anonymity of legitimate vehicles and the tracking of malicious vehicles. Then, formal and informal security analysis show that the proposed scheme meets the expected goals and is secure against several known attacks in VANETs. Finally, compared with relevant studies, the proposed scheme has certain advantages in computation burden and communication burden.
Continual learning for neural regression networks to cope with concept drift in industrial processes using convex optimisation

Process models in industrial applications, e.g. predictive maintenance or automation, are subject to both divergence from the underlying system due to their time-variant nature and to high complexity resulting from a wide operational range being covered. Hence, regression models require high accuracy for the present system state and at the same time need to be valid across the whole system operating space. While accuracy for the current system state can be gained by updating the model on the current data, the overall validity must often be retrieved from historical or design data. We propose a method to find an appropriate compromise for these two demands. A pre-trained artificial neural network (ANN) is continually updated on the current sensor data stream using convex optimisation. Thus, a unique and optimal solution is generated in each update step, while robust regression accuracy on the domain that is not covered by the arriving data subset is maintained. This is achieved by introducing a data management system to provide some historical data, constraining the optimisation problem and manipulating the architecture of the ANN. Models updated with this method show reasonable stability but display plastic behaviour at the current operating point.
Discrete linear canonical transform on graphs

With the wide application of spectral and algebraic theory in discrete signal processing techniques in the field of graph signal processing, an increasing number of signal processing methods have been proposed, such as the graph Fourier transform, graph wavelet transform and windowed graph Fourier transform. In this paper, we propose and design the definition of the discrete linear canonical transform on graphs (GLCT), which is an extension of the discrete linear canonical transform (DLCT), just as the graph Fourier transform (GFT) is an extension of the discrete Fourier transform (DFT). First, based on the centrality and scalability of the DLCT eigendecomposition approach, the definition of the GLCT is proposed by combining graph chirp-Fourier transform, graph scale transform and graph fractional Fourier transform. Second, we derive and discuss the properties and special cases of GLCT. Finally, some GLCT examples of the graph signals and comparisons with the DLCT are given to illustrate the improvement of the transformation.
DRGAN: A dual resolution guided low-resolution image inpainting

Although image inpainting is a challenging task in computer vision, most existing image inpainting methods have achieved remarkable progress. However, occlusion and low resolution often appear on one image simultaneously in the real world, and only a few methods can address low-resolution image inpainting. To tackle this problem, we propose a novel method called the Dual Resolution Generative Adversarial Network (DRGAN), which formulates low-resolution (LR) image inpainting as a constrained image generation problem. The proposed DRGAN first trains a generative network to obtain high-quality images from noise vectors. Then a dual-resolution loss is designed to effectively integrate both high- and low-resolution information to optimize the input vectors initialized by random noise to generate high-quality images. Compared with the model that learns the mapping function from low- to high-quality images, the proposed model maps input noise vectors to images and can thus adapt more effectively to various types of degradation and large occlusions. Extensive experiments on synthetic LR data and real-world images demonstrate the effectiveness of the proposed DRGAN.
Mitigating robust overfitting via self-residual-calibration regularization

Overfitting in adversarial training has attracted the interest of researchers in the community of artificial intelligence and machine learning in recent years. To address this issue, in this paper we begin by evaluating the defense performances of several calibration methods on various robust models. Our analysis and experiments reveal two intriguing properties: 1) a well-calibrated robust model is decreasing the confidence of robust model; 2) there is a trade-off between the confidences of natural and adversarial images. These new properties offer a straightforward insight into designing a simple but effective regularization, called Self-Residual-Calibration (SRC). The proposed SRC calculates the absolute residual between adversarial and natural logit features corresponding to the ground-truth labels. Furthermore, we utilize the pinball loss to minimize the quantile residual between them, resulting in more robust regularization. Extensive experiments indicate that our SRC can effectively mitigate the overfitting problem while improving the robustness of state-of-the-art models. Importantly, SRC is complementary to various regularization methods. When combined with them, we are capable of achieving the top-rank performance on the AutoAttack benchmark leaderboard.
FedDKD: Federated learning with decentralized knowledge distillation

The heterogeneity of the data distribution generally influences federated learning performance in neural networks. For a well-performing global model, taking a weighted average of the local models, as in most existing federated learning algorithms, may not guarantee consistency with local models in the space of neural network maps. In this paper, we highlight the significance of the space of neural network maps to relieve the performance decay produced by data heterogeneity and propose a novel federated learning framework equipped with the decentralized knowledge distillation process (FedDKD). In FedDKD, we introduce a decentralized knowledge distillation (DKD) module to distill the knowledge of local models to teach the global model approaching the neural network map average by optimizing the divergence defined in the loss function, other than only averaging parameters as in the literature. Numerical experiments on various heterogeneous datasets reveal that FedDKD outperforms the state-of-the-art methods, especially on some extremely heterogeneous datasets.
What makes Ethereum blockchain transactions be processed fast or slow? An empirical study

The Ethereum platform allows developers to implement and deploy applications called ÐApps onto the blockchain for public use through the use of smart contracts. To execute code within a smart contract, a paid transaction must be issued towards one of the functions that are exposed in the interface of a contract. However, such a transaction is only processed once one of the miners in the peer-to-peer network selects it, adds it to a block, and appends that block to the blockchain This creates a delay between transaction submission and code execution. It is crucial for ÐApp developers to be able to precisely estimate when transactions will be processed, since this allows them to define and provide a certain Quality of Service (QoS) level (e.g., 95% of the transactions processed within 1 minute). However, the impact that different factors have on these times have not yet been studied. Processing time estimation services are used by ÐApp developers to achieve predefined QoS. Yet, these services offer minimal insights into what factors impact processing times. Considering the vast amount of data that surrounds the Ethereum blockchain, changes in processing times are hard for ÐApp developers to predict, making it difficult to maintain said QoS. In our study, we build random forest models to understand the factors that are associated with transaction processing times. We engineer several features that capture blockchain internal factors, as well as gas pricing behaviors of transaction issuers. By interpreting our models, we conclude that features surrounding gas pricing behaviors are very strongly associated with transaction processing times. Based on our empirical results, we provide ÐApp developers with concrete insights that can help them provide and maintain high levels of QoS.
Digital Competence of Training Teachers: Results of a Teaching Innovation Project

Today’s society is characterised by the impact of technology in all areas of life, which is why promoting the development of digital competence in future preschool and primary school teachers is key to achieving a digitally competent population and promoting social progress from schools. This paper describes the implementation of a funded teaching innovation project developed with students of the Degrees in Preschool Education and Primary School Education at the University of Valencia, Spain. The main objective was the development of digital competence from a cross-cutting perspective. The analyses confirm the improvement of the participants in all areas of digital competence and the influence of gender on the results obtained. This highlights the impact of the intervention for the improvement of technological skills for future teachers.
Natural Language Processing–Driven Similar Project Determination Using Project Scope Statements

Learning from previous projects in light of benchmarking criteria is a desirable and popular approach to reliable project development and planning in the preconstruction phase. Previous similar projects serve as a practical and proven source of knowledge that can be applicable to future projects. In the early preconstruction phase, the common practice of similar project determination involves leveraging simple and limited project characteristics, resulting in determination accuracy degradation. In order to deliver a project context-based similarity evaluation, this study develops and proposes a natural language processing (NLP)–driven method that can recommend similar previous projects by systematically measuring the similarity between project scope statements. NLP techniques enable systematic measurement of project scope similarity, which addresses the reliance on (1) individual experience and expertise for comprehending contents; and (2) time and efforts for reviewing all the unstructured descriptive narratives of project scopes. The proposed method extracts key work activities from project scope statements, evaluates the level of homogeneity (LOH) between extracted activities, and quantifies the project similarity based on the homogeneity evaluation results. The proposed method utilizes bidirectional encoder representations from transformers (BERT) models that can embed unstructured texts into computer-readable numeric formats by considering the context of texts. The output of the proposed model is a graphical map depicting similarities that can help project engineers quickly and intuitively recognize the similarity evaluation results. The validity test shows that the proposed method demonstrates better performance in determining the most highly similar past projects with an ongoing project. The proposed method is appropriate for enhancing an effective information acquisition process from previous projects, resulting in an improved and more efficient project planning process during the preconstruction phase.
Microblog Text Emotion Classification Algorithm Based on TCN-BiGRU and Dual Attention

Microblog is an important platform for mining public opinion, and it is of great value to conduct emotional analysis of microblog texts during the current epidemic. Aiming at the problem that most current emotional classification methods cannot effectively extract deep text features, and that traditional word vectors cannot dynamically obtain the semantics of words according to their context, which leads to classification bias, this research put forward a microblog text emotion classification algorithm based on TCN-BiGRU and dual attention (TCN-BiGRU-DATT). First, the vector representation of the text was obtained using ALBERT. Second, the TCN and BiGRU networks were used to extract the emotional information contained in the text through dual pathway feature extraction, to efficiently obtain the deep semantic features of the text. Then, the dual attention mechanism was introduced to allocate the global weight of the key information in the semantic features, and the emotional features were spliced and fused. Finally, the Softmax classifier was applied for emotion classification. The findings of a comparative experiment on a set of microblog text comments collected throughout the pandemic revealed that the accuracy, recall, and F1 value of the emotion classification method proposed in this paper reached 92.33%, 91.78%, and 91.52%, respectively, which was a significant improvement compared with other models.
MLOps

DevOps practices have increasingly been applied to software development as well as the machine learning lifecycle, in a process known as MLOps. Currently, many professionals have written about this topic, but still few results can be found in the academic and scientific literature on MLOps and how to to implement it effectively. Considering aspects of responsible AI, this number is even lower, opening up a field of research with many possibilities. This article presents five steps to guide the understanding and adoption of MLOps in the context of responsible AI. The study aims to serve as a reference guide for all those who wish to learn more about the topic and intend to implement MLOps practices to develop their systems, following responsible AI principles.
The origin and primary areas of application of natural language processing

Natural language processing with computers, i.e. NLP, is increasingly gaining ground in today's IT. The purpose of this study is to analyze the articles published in the NLP topic and to summarize it from several aspects, especially with regards to the development and practical application of the technology. The idea of NLP as a technology was not formulated in the 21st century, but already in the 1950s, relevant research was carried out and theories were created regarding how a machine can become capable of interpreting human speech. Technology has come a long way from simple algorithms to Deep Learning, but it has not yet solved the problem of machine processing of natural language. The research has confirmed the idea that mathematical modelling and models based on statistical probability dominated the field in the early days of the discipline and before the advent of computers. However, despite technological progress, no solution has yet been found to the comprehensive and all-encompassing problem of natural language processing with computers. To determine the future research area of NLP, we need to take into account the text files on which the technology is based. From this perspective, we can focus on the insurance and banking sectors, where a series of administrative processes take place where centralized paper processing is the incoming data set of the process and a database going back several years may be available.
Enhanced temporal resolution of vision in action video game players

Video game play has been suggested to improve visual and attention processing. Nevertheless, while action video game play is highly dynamic, there is scarce research on how information is temporally discriminated at the millisecond level. This cross-sectional study investigates whether temporal discrimination at the millisecond level in vision varies across action video game players (VGPs; N = 23) and non-video game players (NVGPs; N = 23). Participants discriminated synchronous from asynchronous onsets of two visual targets in virtual reality, while their EEG and oculomotor movements were recorded. Results show an increased sensitivity to short asynchronies (11, 33 and 66 ms) in VGPs compared with NVGPs, which was especially marked at the start of the task, suggesting better temporal discrimination abilities. Pre-targets oculomotor freezing - the inhibition of small fixational saccades - was associated with correct temporal discrimination, probably revealing attentional preparation. However, this parameter did not differ between groups. EEG and reconstruction analyses suggest that the enhancement of temporal discrimination in VGPs during temporal discrimination is related to parieto-occipital processing, and a reduction of alpha-band (8-14 Hz) power and inter-trial phase coherence. Overall, the study reveals an enhanced ability in action video game players to discriminate in time visual events in close temporal proximity combined with reduced alpha-band oscillatory activities. Consequently, playing action video games is associated with an improved temporal resolution of vision.
Diversity-Aware Marine Predators Algorithm for Task Scheduling in Cloud Computing

With the increase in cloud users and internet of things (IoT) applications, advanced task scheduling (TS) methods are required to reasonably schedule tasks in cloud computing. This study proposes a diversity-aware marine predators algorithm (DAMPA) for solving TS in cloud computing. In DAMPA, to enhance the premature convergence avoidance ability, the predator crowding degree ranking and comprehensive learning strategies were adopted in the second stage to maintain the population diversity and thereby inhibit premature convergence. Additionally, a stage-independent control of the stepsize-scaling strategy that uses different control parameters in three stages was designed to balance the exploration and exploitation abilities. Two case experiments were conducted to evaluate the proposed algorithm. Compared with the latest algorithm, in the first case, DAMPA reduced the makespan and energy consumption by 21.06% and 23.47% at most, respectively. In the second case, the makespan and energy consumption are reduced by 34.35% and 38.60% on average, respectively. Meanwhile, the algorithm achieved greater throughput in both cases.
R-VPCG: RGB image feature fusion-based virtual point cloud generation for 3D car detection

Although 3D object detection methods based on feature fusion have made great progress, the methods still have the problem of low precision due to sparse point clouds. In this paper, we propose a new feature fusion-based method, which can generate virtual point cloud and improve the precision of car detection. Considering that RGB images have rich semantic information, this method firstly segments the cars from the image, and then projected the raw point clouds onto the segmented car image to segment point clouds of the cars. Furthermore, the segmented point clouds are input to the virtual point cloud generation module. The module regresses the direction of car, then combines the foreground points to generate virtual point clouds and superimposed with the raw point cloud. Eventually, the processed point cloud is converted to voxel representation, which is then fed into 3D sparse convolutional network to extract features, and finally a region proposal network is used to detect cars in a bird's-eye view. Experimental results on KITTI dataset show that our method is effective, and the precision have significant advantages compared to other similar feature fusion-based methods.