Reviewerly/exHarmony huggingface dataset:
A Microservice-based SaaS Deployment in a Data Center Considering Computational Server and Network Energy Consumption

In a data center, deploying a microservice-based SaaS will result in a spike in energy usage, since the computation servers where the microservices are deployed will consume more energy as a result of an increase in computation workload. The data center's communications network will also consume more energy due to the increased communication among the microservices of the SaaS. Due to the fact that microservice-based SaaS deployment is handled by the developer of the microservice-based SaaS and can only be deployed on virtual machines rented by the developer, this issue cannot be taken into account in traditional microservice-based SaaS deployment methods. In this paper, a new microservice-based SaaS deployment method is proposed. The new approach relies on the data center determining where microservices should be deployed. The new microservice-based SaaS deployment method considers the energy increase in the computation servers and in the communication network. The microservice-based SaaS deployment problem is a combinatorial optimization problem. Thus, a genetic algorithm with repairing mechanism is proposed to solve the problem. Compared to traditional deployment approach, the proposed method is capable of reducing the energy consumption associated with microservice-based SaaS deployment by 37.55%.
Improving the Convergence of the PSO Algorithm with a Stagnation Variable and Fuzzy Logic

Particle swarm optimization (PSO) is essential to evolutionary computation algorithms (ECA). The PSO has some drawbacks as premature convergence and stagnation at local minima. Inertia weight is a parameter that controls the global and local exploration and exploitation capability in the PSO by determining the influence of the previous velocity on its current motion. This article proposes using a stagnation counter that verifies the times the PSO is stuck in the same fitness value. In the proposed fuzzy controlled PSO with stagnation coefficient (FCPSO), a fuzzy controller is designed to tune the inertia weight based on the population's diversity and the search's stagnation. This modification allows the PSO to escape from suboptimal values enhancing its search capabilities. The FCPSO is tested over 28 benchmark functions in 50 dimensions. Besides, it has been compared with nine optimization algorithms from the state-of-the-art. The experiments and comparisons suggest that the FCPSO is an interesting tool for solving complex optimization problems.
Digital Image Correlation Technique for Laboratory Structural Tests and Applications: A Systematic Literature Review

Digital Image Correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the DIC methodology has validated its effectiveness as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.
Plato: An Open-Source Research Framework for Production Federated Learning

As existing works on federated learning (FL) have not typically shared their implementations as open-source, and existing open-source FL frameworks fell short of evaluating FL mechanisms appropriately, in the past two years, we have designed and implemented Plato, a new open-source research framework for scalable federated learning research from scratch. Development on Plato started in November 2020, and so far involved more than 50 person-month of research and development time. Plato is designed and built with several key objectives in mind: it is scalable to a large number of clients; extensible to accommodate a wide variety of datasets, models, and FL algorithms; and agnostic to deep learning frameworks such as TensorFlow and PyTorch. In Plato, clients communicate with servers over industry-standard WebSockets, while servers may either run in the same GPU-enabled physical machine as its clients — suitable for an emulation research testbed — or deployed in a cloud datacenter. We provided a large variety of popular datasets and models, as well as algorithms proposed in the literature as examples.
Application of Glasses-Free Augmented Reality Localization in Neurosurgery

The accurate localization of intracranial lesions is critical in neurosurgery. Most surgeons locate the vast majority of neurosurgical sites through skull surface markers, combined with neuroimaging examination and marking lines. This project’s primary purpose was to develop an augmented reality (AR) technology or tool that can be used for surgical positioning using the naked eye. Brain models were predesigned with intracranial lesions using computerized tomography scan, and Digital Imaging and Communications in Medicine data were segmented and modeled by 3D slicer software. The processed data were imported into a smartphone 3D viewing software application (Pusee 3D) and were used by a Remebot surgical robot. The localization of intracranial lesions was performed, and the AR localization error was calculated compared with standard robot localization. After mastering the AR localization registration method, surgeons achieved an average localization error of 1.39 ± 0.82 mm. The error of AR positioning technology in surgical simulation tests based on brain modelling was millimeter level, which has verified the feasibility of clinical application. More efficient registration remains a need that should be addressed.
Design of a College English Smart Teaching Platform Based on Big Multimedia Data Technology

College English teaching should give full play to the important role of network resources, implement the student-centered network autonomous learning model, and give full play to learners' autonomous learning potential. At present, although some universities have tried this new model, its application in college English lacks practical research. Only by establishing an intelligent English teaching platform based on big multimedia data technology can big multimedia data play an important role in college English teaching. Based on the analysis of the current situation of English classroom teaching in colleges and universities in China, this article puts forward the corresponding optimization plan according to the existing better teaching experience and the “suiting measures to local conditions” model. Large multimedia data technology provides a convenient learning platform and learning space for English learners, and a very convenient learning environment and learning platform for users with English learning needs.
Entrepreneurial Firms and Bank Financing:Do Business Angels Play a Role?

This paper aims to investigate the contribution provided by business angels’ (BAs’) post-investment intervention to the interaction between entrepreneurial firms and bank-lending decisions within the entrepreneurial-finance ecosystem. The analysis relies on a dataset made up of 612 Italian firms, 114 of which are BA-backed over the 2010–2018 time period, whereas the remaining 498 constitute the propensity score-matched control group. We find strong evidence that BAs’ support makes it easier for entrepreneurial firms to raise follow-on bank financing. Additionally, we document positive effects of both BAs’ human capital and their investment practices on the funded ventures’ capital-structure choices. These effects can be attributed to BAs’ value-added contributions and their role in mitigating information asymmetries perceived by debt providers. The results are robust to several tests. The study is the first to provide quantitative empirical evidence on the role played by informal investors in nurturing bank-firm lending relationships within the start-up ecosystem.
Multi-person Activity Recognition and Tracking Based on Skeletal Keypoint Detection

Currently, most action recognition networks have deep overall structures, large model parameters, and high requirements for computer hardware equipment. As a result, it is easy to over-fit in the recognition process for too deep network layers. Furthermore, it is also difficult to extract features because of the video's interference information, such as illumination and occlusion. To solve the above problems, we propose a multi-person action recognition and tracking algorithm based on skeletal keypoint detection. Firstly, the n network combining the improved dense convolutional network and part affinity field (PAF) are used to extract the skeletal information points of the human body. Then, we present an improved Deepsort network for multi-person target tracking, which contains a Hungarian matching algorithm based on the generalized intersection over union (GIoU) and a pedestrian re-identification network combining GhostNet and Feature Pyramid Network (FPN). Finally, we construct a deep neural network model to classify the extracted human skeletal information and realize action recognition. Experiment results show that the multi-person action recognition and tracking algorithm achieves an action recognition accuracy of 98%. In addition, The multi-target tracking accuracy of the proposed algorithm is improved by 4.2% on the MOT16 dataset. Compared to other common algorithms, the proposed algorithm can achieve high accuracy in detecting keypoints of the human body and improve the accuracy of multi-person action recognition with fewer parameters and complexity of operations.
Event-Triggered Adaptive Neural Control for MIMO Nonlinear Systems With Rate-Dependent Hysteresis and Full-State Constraints via Command Filter

This article presents an event-triggered adaptive NN command-filtered control for a class of multi-input and multi-output (MIMO) nonlinear systems with unknown rate-dependent hysteresis in the actuator and the constraints on full states. The ETM is used to reduce the communication frequency between controller and actuator. The command filter technique is first employed to solve the dilemma between the nondifferentiable control signal at triggering instants and rate-dependent hysteresis input premise while avoiding the "explosion of complexity" problem. During the backstepping design, the barrier Lyapunov functions are utilized to guarantee that system states will stay in certain regions and the unknown nonlinear items are approximated by adaptive neural networks. The compensating signals are constructed to eliminate filtering errors. The estimates of unknown hysteresis parameters are updated by adaptive laws. The stability analysis is given and the effectiveness of the proposed method is verified by simulation.
FDEPCA: A Novel Adaptive Nonlinear Feature Extraction Method via Fruit Fly Olfactory Neural Network for IoMT Anomaly Detection

With the rapid development of 5G communication technology, the data in the Internet of Medical Things (IoMT) application systems exhibits complex characteristics such as large volume, high dimensionality, nonlinearity, and diversity, which significantly affect the efficiency and detection performance of anomaly detection tasks. How to efficiently extract nonlinear features from high-dimensional data in the context of the IoMT while minimizing information distortion in data objects are challenging problems in recent academic research. A novel adaptive nonlinear feature extraction method via fruit fly olfactory neural network (Fly dimension expansion projection and remain main components by PCA, FDEPCA) is proposed, where 1) the data are mean-centered; 2) a binary sparse random projection matrix is used for dimension expansion projection; and 3) PCA is used to extract principal component information. The proposed method overcomes the problems of present nonlinear feature extraction in the face of high-dimensional outliers where the intrinsic geometric structure of the data is severely distorted and computationally expensive. The dataset after nonlinear feature extraction by the FDEPCA algorithm is applied to specific anomaly detection models, using ROC curves and AUC as evaluation metrics for classification performance. Extensive comparison experiments are conducted on eight publicly available datasets, and experimental results show that compared with the popular nonlinear feature extraction algorithms, the FDEPCA algorithm has better classification performance and projection time advantage. When applied to proximity-based, probability-based, and ensemble-based different anomaly detection models respectively, the FDEPCA algorithm exhibits strong applicability in different types of anomaly detection classifiers.
Quality Evaluation of Single-Crystal Wafer X-Ray Detection Using Random Vector Functional-Link Network

An X-ray orientation instrument is a primary non-destructive means to detect the quality of single-crystal wafers. However, some problems exist, such as low accuracy and low efficiency, which hinder its industrial application. This paper proposes an original approach to wafer quality evaluation. The limited multi-point strategy, instead of the single-point strategy, is used first to evaluate quality. Then, using interval analysis theory for feature expansion, the point-valued data is transformed into the interval data to reduce the number of detection points and cope with small sample situation. Finally, combining interval analysis with an improved cross-layer point-valued random vector functional-link (CP-RVFL) network, an cross-layer interval RVFL (CI-RVFL) network was designed to evaluate the quality of single-crystal wafers using interval data. The proposed approach effectively solves various difficulties in evaluating wafer quality and can be seen as a relatively general quality evaluation method. Several typical industrial data experiments have demonstrated the feasibility of CI-RVFL, especially when the number of training samples is small. Its performance is significantly better than that of general point-valued RVFL (P-RVFL) and CP-RVFL.
Dynamic Graph-Based Anomaly Detection in the Electrical Grid

Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs? A key part of achieving this goal is to use the network of power grid sensors to quickly detect, in real-time, when any unusual events, whether natural faults or malicious, occur on the power grid. Existing bad-data detectors in the industry lack the sophistication to robustly detect broad types of anomalies, especially those due to emerging cyber-attacks, since they operate on a single measurement snapshot of the grid at a time. New ML methods are more widely applicable, but generally do not consider the impact of topology change on sensor measurements and thus cannot accommodate regular topology adjustments in historical data. Hence, we propose DYNWATCH, a domain knowledge based and topology-aware algorithm for anomaly detection using sensors placed on a dynamic grid. Our approach is accurate, outperforming existing approaches by 20% or more (F-measure) in experiments; and fast, averaging less than 1.7ms per time tick per sensor on a 60K+ branch case using a laptop computer, and scaling linearly with the size of the graph.
Modular Multi-Tree Genetic Programming for Evolutionary Feature Construction for Regression

Evolutionary feature construction is a key technique in evolutionary machine learning, with the aim of constructing high-level features that enhance performance of a learning algorithm. In real-world applications, engineers typically construct complex features based on a combination of basic features, re-using those features as modules. However, modularity in evolutionary feature construction is still an open research topic. This paper tries to fill that gap by proposing a modular and hierarchical multitree genetic programming (GP) algorithm that allows trees to use the output values of other trees, thereby representing expressive features in a compact form. Based on this new representation, we propose a macro parent-repair strategy to reduce redundant and irrelevant features, a macro crossover operator to preserve interactive features, and an adaptive control strategy for crossover and mutation rates to dynamically balance the trade-off between exploration and exploitation. A comparison with seven bloat control methods on 98 regression datasets shows that the proposed modular representation achieves significantly better results in terms of test performance and smaller model size. Experimental results on the state-of-the-art symbolic regression benchmark demonstrate that the proposed symbolic regression method outperforms 22 existing symbolic regression and machine learning algorithms, providing empirical evidence for the superiority of the modularized evolutionary feature construction method.
Nonlinear decoupling of 3d force sensors based on WSO-ELM

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dbt in Production

As you’ve learned throughout this book, dbt is a fantastic tool for building, maintaining, and scaling your data transformation processes. We’ve covered many aspects of dbt ranging from utilizing seed files, building incremental models, and using Jinja and macros to fill in the gaps where SQL gets repetitive. So far, we have only run these transformations in a development environment, but once you are ready for data consumers to utilize the data models that you have built, you will need to deploy dbt to a stable production environment.
Jinja, Macros, and Packages

Throughout this book so far, we have covered topics such as models and snapshots which primarily make use of mostly straightforward SQL transformations. However, have you ever found yourself writing repetitive SQL code that you wish you could easily use throughout your transformations without needing to rewrite the same code every time? This is where Jinja in conjunction with dbt comes in and adds extensible programmatic capability to your SQL code.
Introduction to dbt

In 2006, Clive Humby, a British mathematician and entrepreneur in the field of data science, coined the phrase “Data is the new oil” because of its incredible high value. While both are valuable in their raw state, they must first be processed in order to create something of value. Crude oil is sent to refineries where it is separated into petroleum products like fuel, oils, asphalts, etc. which turn it into staples of everyday life. Data is refined by people, tools, and services into reports, data warehouses, machine learning models, etc. to extract its value. Take both data and oil and leave it in its raw form and what you have is a wasted opportunity.
42063 Image Quality Assessment using Convolutional Neural Network in Clinical Skin Images

Introduction: Convolutional neural networks (CNNs) can detect image characteristics such as blurriness, contrast, and exposure in clinical images and can be utilized for real time image quality feedback to streamline clinical workflows via an Image Quality Analysis (IQA) model.
43145 Dermatology and the metaverse: a simulation of the future

Background: The metaverse has rapidly become a part of life for the digital native population amongst other emerging technologies. We explore how the metaverse, a computer-generated reality and next- generation infrastructure of the internet, could impact dermatology in the future. After conducting a literature review, we created a simulated virtual experience of dermatology in the metaverse to showcase potential future opportunities. Our research demonstrates that Artificial intelligence (AI) is becoming central to dermatological diagnostics through convolutional neural networks (CNNs) (1).
On Fully-Secure Honest Majority MPC Without $$n^2$$ Round Overhead

Fully secure multiparty computation (or guaranteed output delivery) among n parties can be achieved with perfect security if the number of corruptions t is less than n/3, or with statistical security with the help of a broadcast channel if $$t<n/2$$ . In the case of $$t<n/3$$ , it is known that it is possible to achieve linear communication complexity, but at a cost of having a round count of $$\varOmega (\textsf{depth}(C) + n)$$ in the worst case. The number of rounds can be reduced to $$O(\textsf{depth}(C))$$ by either increasing communication, or assuming some correlated randomness (a setting also known as the preprocesing model). For $$t<n/2$$ it is also known that linear communication complexity is achievable, but at the cost of $$\varOmega (\textsf{depth}(C) + n^2)$$ rounds, due to the use of a technique called dispute control. However, in contrast to the $$t<n/3$$ setting, it is not known how to reduce this round count for $$t<n/2$$ to $$O(\textsf{depth}(C))$$ , neither allowing for larger communication, or by using correlated randomness. In this work we make progress in this direction by taking the second route above: we present a fully secure protocol for $$t<n/2$$ in the preprocessing model, that achieves linear communication complexity, and whose round complexity is only $$O(\textsf{depth}(C))$$ , without the additive $$n^2$$ term that appears from the use of dispute control. While on the $$t<n/3$$ such result requires circuits of width $$\varOmega (n)$$ , in our case circuits must be of width $$\varOmega (n^2)$$ , leaving it as an interesting future problem to reduce this gap. Our $$O(\textsf{depth}(C))$$ round count is achieved by avoiding the use of dispute control entirely, relying on a different tool for guaranteeing output. In the $$t<n/3$$ setting when correlated randomness is available, this is done by using error correction to reconstruct secret-shared values, but in the $$t<n/2$$ case the equivalent is robust secret-sharing, which guarantees the reconstruction of a secret in spite of errors. However, we note that a direct use of such tool would lead to quadratic communication, stemming from the fact that each party needs to authenticate their share towards each other party. At the crux of our techniques lies a novel method for reconstructing a batch of robustly secret-shared values while involving only a linear amount of communication per secret, which may also be of independent interest.