Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition Using Sensor Fusion and Deep Learning for Ergonomics in Logistics Applications

The rapid growth within the e-commerce industry and the necessity of hiring more inexperienced labor in logistics made the warehouse safety more critical than ever. Monitoring the workers' activity during warehouse operations is a fundamental task to create an ergonomic workplace and to reduce operational costs. Therefore, an advanced Human Activity Recognition (HAR) solution is needed to monitor such industrial environments. In this paper, the development of an accurate and reliable HAR classifier using sensor fusion methods is investigated. Three Inertial Measurement Units (IMUs) are used to capture body movement while performing typical warehouse tasks. Deep Learning (DL) algorithms are employed to model the raw time series data and two sensor fusion techniques are applied to combine the data from the different sensors. Overall, the optimal HAR classifier was found to be a stacked model, being more practical and less computationally expensive for industrial applications.
The JavaScript Package Selection Task: A Comparative Experiment Using ChatGPT

When developing Java Script (JS) applications, the assessment and selection of JS packages have become challenging for developers due to the growing number of technology options available. Given a technology need, a common developers' strat-egy is to query Web repositories via search engines (e.g., NPM, Google) and shortlist candidate JS packages. However, these engines might return a long list of results. Furthermore, these results should be ranked according to the developer's criteria. To address these problems, we developed a recommender system called AIDT that assists developers in the package selection task. AIDT relies on meta-search and machine learning techniques to infer the relevant packages for a query. An initial evaluation of AIDT showed good search effectiveness. Recently, the emergence of ChatGPT has opened new opportunities for this kind of assistants, as reported by some experiments. Anyway, human developers should judge whether the recommendations (e.g., JS packages) of these tools are fit to purpose. In this paper, we report on a user study in which we used both AIDT and ChatGPT on a sample of JS-related queries, compared their results, and also validated them against developers' criteria and expectations for the task. Our initial findings show that ChatGPT is not yet on par with AIDT or even human efforts for the task at hand, but the model is flexible to be improved and furthermore, it can provide good arguments for its package choices.
Application of error level analysis in image spam classification using deep learning model

Image spam is a type of spam that contains text information inserted in an image file. Traditional classification systems based on feature engineering require manual extraction of certain quantitative and qualitative image features for classification. However, these systems are often not robust to adversarial attacks. In contrast, classification pipelines that use convolutional neural network (CNN) models automatically extract features from images. This approach has been shown to achieve high accuracies even on challenge datasets that are designed to defeat the purpose of classification. We propose a method for improving the performance of CNN models for image spam classification. Our method uses the concept of error level analysis (ELA) as a pre-processing step. ELA is a technique for detecting image tampering by analyzing the error levels of the image pixels. We show that ELA can be used to improve the accuracy of CNN models for image spam classification, even on challenge datasets. Our results demonstrate that the application of ELA as a pre-processing technique in our proposed model can significantly improve the results of the classification tasks on image spam datasets.
Performance Enhancement in a Two-Camera Visual Servoing System Using Artificial Neural Networks

Visual sensory information from cameras for robot guidance and feedback control increases the scope of industrial automation. Heterogeneous workspaces and parallel operations may require the data from more than one sensor. This paper illustrates a regulatory scheme in visual servo control, utilizing the cooperation of two cameras for robot manipulation with a switching approach in configuration and oversight. A supervisory camera placed at a convenient location monitors the workspace such that the end effector camera can extend its field of view in Image Based Visual Servoing (IBVS). The robot is steered by a hybrid control law prior to switching to IBVS in its stable and converging region. Selection of gain factors complementing each other ensures smooth changeover in control laws with smooth velocity profile using a qualitative approach. Alternately, trained Artificial Neural Networks (ANN) improve the performance through prediction servoing over regions of uncertainty, multiple switches and multivariable loop interactions.
DEEP LEARNING-BASED STEREO MATCHING FOR HIGH-RESOLUTION SATELLITE IMAGES: A COMPARATIVE EVALUATION

Abstract. Dense matching plays an important role in 3D modeling from satellite images. Its purpose is to establish pixel-by-pixel correspondences between two stereo images. The most well-known algorithm is the semi-global matching (SGM), which can generate high-quality 3D models with high computational efficiency. Due to the complex coverage and imaging condition, SGM cannot cope with these situation well. In recent years, deep learning-based stereo matching has attracted wide attention and shown overwhelming benefits over traditional algorithms in terms of precision and completeness. However, existing models are usually evaluated by using close-ranging datasets. Thus, this study investigates the recent deep learning models and evaluate their performance on both close-ranging and satellite image datasets. The results demonstrate that deep learning network can better adapt to the satellite dataset than the typical SGM. Meanwhile, the generalization ability of deep learning-based models is still low for the real application at recent time.
Installing VMware vSphere

One of the more popular uses for Workstation Pro, when it comes to development and learning, is to install VMware ESXi and vCenter Server running as virtual machine hosted by Workstation Pro. We touched on this back in Chapter 1 when we discussed nested environments.
268: DATA MISSINGNESS REPORTING AND THE USE OF METHODS TO ADDRESS IT IN CRITICAL CARE COHORT STUDIES

Devlin, John; Ting Wu, Ting; Patel, Emi; Vernooijl, Lisette; Smith, Louisa Author Information
Virtual learner experience (VLX)

Science educators need tools to assess to what extent learners’ knowledge can be transferred to novel real-world situations. Virtual reality learning environments (VRLEs) offer the possibility of creating authentic tools where situated learning and assessment can take place, but there is a lack of evidence-based guidelines to inform the design and development of the VRLEs focussing on the user, that is the learner experience, especially for secondary schools. Drawing on theoretical premises and guidelines from user experience, usability, and technologically enabled assessment literature, we designed, developed, evaluated, and refined a VRLE prototype for the authentic assessment of knowledge transfer in the secondary school science classroom as guided by the design science research approach. Lessons learnt from the implementation and iterative evaluation of the prototype are presented as a set of literature-based, empirically validated guidelines to support and guide educational designers and developers to create VRLEs focused on supporting the learner experience. The contribution of this study is a VRLE design model with the learner at its core, the definition of VLX to include learner-specific aspects of immersive environments, and guidelines for the development of an effective and efficient virtual reality environment for the assessment of knowledge transfer in science education.
Analisis Distribusi Dosis Berkas Foton pada Kanker Nasofaring dengan Teknik Intensity Modulated Radiation Therapy (IMRT) menggunakan Pesawat Linear Accelerator di Sub Instalasi Radioterapi RSUP Prof. Dr. I.G.N.G. Ngoerah

Analysis for the distribution of photon beam doses in nasopharyngeal cancer has been carried out with the Intensity Modulated Radiation Therapy (IMRT) technique at the Sub-Installation of RSUP Prof. Dr. I.G.N.G. Ngoerah. Calculations were carried out by analyzing the values of the Homogeneity Index (HI) and Conformity Index (CI) as well as radiation dose in Organs a t Risk (OAR). Data from 20 nasopharyngeal cancer patients were used, and a 6 MV energy photon beam with a total dose of 7000 cGy was used, using seven beams of irradiation direction. The data used are radiation doses of 2%, 50%, and 98%, target volume and 95%, and doses at OAR. These data are obtained from the Dose Volume Histogram (DVH) curve in the Treatment Planning System (TPS) process. The results obtained were the average dose distribution on cancer targets in the form of HI and CI values for 20 patients, namely the average HI value of 0.1231±0.0244, while the average CI value was 0.9856±0.022 which, when compared to ICRU report 83 of 2010 HI and CI values were still within the allowable standard limits. The average dose distribution in OAR for 20 patients was the mean spinal cord (4683.9±216.47) cGy, the average right eye (3909.7±605.45) cGy, the average left eye (3683.885±659.89) cGy, the average right optic nerve (4149.01±1099.68) cGy, the average left optic nerve (4174.355±1219.96) cGy, the average right lens (746.29±149.85) cGy, and the average left lens (753.03±144.32) cGy, all average values obtained by OAR are still within the allowable limits. Keywords: nasopharyngeal cancer, 6 MV, IMRT, homogeneity index, conformity index, organ at risk
Quantum algorithms for charged particle track reconstruction in the LUXE experiment

LUXE (Laser Und XFEL Experiment) is a new experiment in planning in Hamburg, which will study Quantum Electrodynamics at the strong-field frontier. LUXE intends to measure the positron production rate in this unprecedented regime by using, among others, a silicon tracking detector. The large number of expected positrons traversing the sensitive detector layers results in an extremely challenging combinatorial problem, which can become computationally expensive for classical computers. We investigate the potential future use of gate-based quantum computers for pattern recognition in track reconstruction, based on a quadratic unconstrained binary optimisation and a quantum graph neural network. This report introduces these approaches and compares their performance with a classical track reconstruction algorithm.
Fast and high precision phase recovery technology of single-shot ineterferogram based on depth convolution neural network

Abstract Phase retrieval from single interferogram is of great interest for the possibility of dynamic phase measurement. However, it is a very complicated process in reality since the unknowns and knows are highly unequal. In this paper, we propose a fast phase recovery method from single interferogram based on deep convolution neural network. The network is trained based on supervised learning to achieve the purpose of quickly obtaining unwrapped phase results from a single interferogram. To improve the detection accuracy, a modified set establishment model has been propose to improve the practicability of the fringe data. The simulation and experimental results show that the root mean square value of residual phase extraction error by this method is closed to 0.01 λ ( λ = 632.8 nm), and the constructed depth convolution neural network model has significant flexibility and effective generalization ability for phase recovery of single frame interference fringe.
E-Yolov7: A Life Jacket Detection Algorithm Based on Yolov7

As the core equipment of water safety, life jackets play an indispensable role in the safety field and rescue applications. Although existing deep learning techniques and object detection methods have achieved remarkable success in various fields, there are still many challenges in applying them to life jacket detection. Complex environmental changes, occlusion problems caused by water surface fluctuations, etc. will affect the detection effect. In addition, there are relatively few datasets suitable for life jacket detection, which makes many existing detection models lack stability and generalization ability in practical application scenarios. In order to address these challenges, this paper aims to propose an efficient and accurate detection method for life jacket object detection. Specifically, we first review the relevant work of sea rescue, then collect and label a life jacket-related dataset, and then introduce EVC block based on the Yolov7 network to aggregate the local corner area information of the image, and propose an improved life jacket target detection method E-Yolov7. In the experimental and evaluation section, the proposed method was tested, comparing it with Yolov5s, Yolov7-Tiny, Yolov7x, and Yolov7. Experiments show that the improved model E-Yolov7 has better performance in the life jacket detection task, and compared with Yolov7, the precision is increased by 3.7%, the mAP is increased by 2.5% and the F1 score is increased by 2%.
Current Challenge and Limitations in Quantum Computation

This paper reviews various engineering hurdles facing the field of quantum computing. Specifically, problems related to decoherence, state preparation, error correction, and implement ability of gates are considered. Computer computation is very advanced computers used in recent days for various applications, including cryptography, optimization problems, financial modelling, route and traffic optimization, manufacturing drug, chemical research, batteries, simulation of quantum systems, and even machine learning. In a quantum computer, two qubits can also represent the exact same four states (00,01,10 or 11). It’s an exciting field with lots of potential. But it faces lots of challenges and problems like maintaining qubits, interaction with the environment can cause decoherence. However, the adventure to harness the enormous power of quantum computer systems is packed with challenges and boundaries. In this discussion, we can embark on an exploration of the current boundaries that researchers and engineers face within the world of quantum computing, losing mild on the complexities and constraints that ought to be overcome to fully free up its ability. Reacher’s are working on error correction techniques to address this issue and make quantum computations more reliable. It’s fascinating area of ongoing research. In recent development in quantum computing on 4 October 2022 The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Physics 2022 to Alain Aspect (Institute d’Optique Graduate School – Université Paris-Saclay and École Polytechnique, Palaiseau, France ), John F. Clauser ( J.F. Clauser &amp; Assoc., Walnut Creek, CA, USA ) Anton Zeilinger University of Vienna, Austria“for experiments with entangled photons, establishing the violation of Bell inequalities and pioneering quantum
Underwater Animal Identification and Classification Using a Hybrid Classical-Quantum algorithm

Underwater Animal Identification and Classification is gaining significance in recent times as a result of the increased need for biodiversity monitoring and ecological surveillance. Although classical deep learning techniques have been widely used for these tasks, the effectiveness of identification and classification remains a bottleneck for several researchers due to the live capture of animals in complex environments, a limited sea-animal image dataset, and the complex topography of the seafloor, particularly in shallow waters, sediments, reefs, submarine ridges, and ship radiation. In this paper, three hybrid Classical-Quantum neural networks ResNet50-QCNN, ResNet18-QCNN and InceptionV3-QCNN have been proposed for underwater quantum-classical Animal Identification and Classification. By using quantum devices to reduce dimension and denoise datasets, it dramatically reduces the complexity of data processing on classical computers. The outcomes of the numerical simulation show that the quantum algorithm may effectively reduce dimensionality and increase classification accuracy. Even when quantum data is read out classically, the hybrid approach provides polynomial acceleration in dimension reduction beyond classical procedures. The three hybrid models, viz., ResNet50-QCNN, ResNet18-QCNN, and InceptionV3-QCNN, displayed classification test accuracy of 88%, 80.29%, and 70%, respectively, revealing that ResNet50-QCNN performed best in identifying and classifying underwater animals.
COARSE-TO-FINE SEISMIC ASSESSMENT BASED ON COMPUTER VISION

Recently, remote sensing satellites, unmanned aerial vehicles (UAVs), and smartphones have been extensively utilized in non-contact post-earthquake inspection at different scales with cutting-edge computer vision and machine learning techniques. This study establishes a computer-vision-based coarse-to-fine seismic assessment framework to localize dense buildings in urban areas, classify collapsed and noncollapsed states, recognize multi-type surface damage on structural components, and evaluate seismic performances. First, a Transformer-CNN deep learning architecture is designed for semantic segmentation of dense buildings and binary classification of collapsed states using large-scale remote sensing satellite images. It consists of a Swin Transformer encoder, multi-stage feature fusion module, and UPerNet decoder to extract global correlations and local features of dense buildings synchronously. Then, a multi-task learning strategy is proposed to simultaneously recognize multi-type structural components (column, beam, wall), seismic damage (concrete crack, spalling, and rebar exposure), and multi-level damage states (minor, moderate, major) using medium-scale UAV images. It contains a CNN-based encoder-decoder backbone with skip-connection modules and multi-head segmentation subnetworks for different tasks. The geometric consistency loss of split line, area, and curvature is further designed to refine the semantic segmentation of local details, increase boundary smoothness, and suppress inner voids. Finally, a machine learning neural network is established to quantify the seismic damage index of structural components using damage-related parameters (lengths, areas, and numbers of concrete crack, spalling, and rebar exposure) and design-related parameters (axial compression ratio, shear span ratio, and volumetric stirrup ratio) as inputs. A seismic damage indicator with an explicit bound of [0,1] can be obtained to reflect the nonlinear accumulation of seismic damage. The effectiveness and applicability under real-world post-earthquake scenarios have been validated by the 2017 Mexico City Earthquake M7.1, 2008 Beichuan Earthquake M8.0, 2010 Yushu Earthquake M6.9, 2015 Nepal Earthquake M7.8, 2016 Ecuador Earthquake M7.8, and 2016 Meinong Earthquake M6.7.
OPEN SCIENCE DATA CATALOGUE

Abstract. Open Science is a catalyst for innovation. Across the Earth Observation value chain, from R&amp;amp;D to prototyping new products and development of commercial applications, openness can play an important role by promoting long-term sustainable, community-contributed science and technology. The FAIR principles provide essential support to implementing Open Science, by offering guidelines for how researchers can adapt their EO and Earth Science practice to enable that their work (taking place increasingly in the cloud) and results are discovered, accessed, used, and reproduced by others. The Open Science Data Catalogue (OSC) (https://opensciencedata.esa.int) is an ESA Open Science activity aiming to enhance the discoverability and use of the various scientific and value-added results (i.e. data, code, documentation) achieved in Earth System Science research activities funded by ESA EO. The OSC provides open access for the scientific community to geoscience products (based on EO data from ESA and non-ESA missions and other geospatial information and models) across the whole spectrum of Earth Science domains. The OSC adheres to FAIR principles and promotes reproducibility of scientific studies. The OSC makes use of various Open-Source geospatial technologies such as pycsw, PySTAC, and OpenLayers and tries to contribute back to these projects in terms of software and standardisation. This paper reviews the EO OSC architecture, technology stack, and illustrates how this tool can be used to discover and publish Earth System Science products from ESA activities. It also looks at future evolutions of the product and how it contributes to ESA’s EO Open Science and Innovation goals.
Investigating Developers’ Perception on Success Factors for Research Software Development

Modern research depends on software libraries, tools, and applications, specially in Artificial Intelligence (AI) to support science, engineering, and business. Research software is often developed in academia by academic researchers in Information and Communication Technology (ICT). However, this software rarely achieves effective success: it is often developed and maintained in an inefficient and unsustainable way, resulting in failure, non-adoption, and abandonment. The goal of this work is to better understand how academic research software developers evaluate success factors in different categories: technical, organizational, and people. A survey with thirty Brazilian academic research software developers was conducted to gather information about the level of importance of the factors. An analysis of the collected data was conducted to identify the importance of these success factors. The results show the ranking of success factors into categories among the research developers. Our study indicates that academic developers can conduct more software development by being aware of the success factors that can provide software sustainability and successfully. The comprehension of the level of importance of each factor will help to improve and guide the support for research software developers on development.
A fusion algorithm of central classification index and local classification index based on B+ tree

This paper introduces a content-based, data-centered data search system for large-scale databases. Firstly, this paper designs the system from data source tracking module, data mining module and system warning module respectively. Then the software design flow chart and information retrieval algorithm are analyzed, and the software part of multimedia information retrieval system is designed. A fusion algorithm of central classification index and local classification index based on B+ tree is proposed to further improve the efficiency of classification. Experiments show that this method has high retrieval speed and accuracy.