Reviewerly/exHarmony huggingface dataset:
Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Efficient and economical performance evaluation of pilots has become critical to the aviation industry. With the development of virtual reality (VR) and the combination of eye-tracking technology, solutions to meet these needs are becoming a reality. Previous studies have explored VR-based flight simulators, focusing mainly on technology validation and flight training. The current study developed a new VR flight simulator to evaluate pilots' flight performance based on eye movement and flight indicators in a 3D immersive scene. During the experiment, 46 participants were recruited: 23 professional pilots and 23 college students without flight experience. The experiment results showed significant differences in flight performance between participants with and without flight experience, the former being higher than the latter. In contrast, those with flight experience showed more structured and efficient eye-movement patterns. These results of the differentiation of flight performance demonstrate the validity of the current VR flight simulator as a flight performance assessment method. The different eye-movement patterns with flight experience provide the basis for future flight selection. However, this VR-based flight simulator has shortcomings like motion feedback compared to traditional flight simulators. This flight simulator platform is highly flexible except for the apparent low cost. It can meet the diverse needs of researchers (e.g., measuring situation awareness, VR sickness, and workload by adding relevant scales).
A Reliable and Secure Method for Sharing Genomic Data

Genomics has significantly impacted the field of medicine, with advances in DNA sequencing leading to personalized medicine and a deeper understanding of the genomic basis of various diseases. The ability to share genomic data is crucial for advancing this field and developing new approaches to understanding the genome. However, the sensitive nature of this data requires secure methods for protecting it during storage and transfer. In this paper, we present a new tool for the secure encryption and decryption of FASTA files without sharing a common secret and with a reduced number of shared keys between the pairs. Our proposal combines symmetric and asymmetric encryption techniques, including the AES (Advanced Encryption Standard) cypher and RSA (Rivest-Shamir-Adleman). The tool is fast, reliable, and secure, outperforming existing tools in terms of security and ease of use. This makes it a valuable solution for the secure sharing and use of sensitive genomic data, representing a significant advancement in the field of genomics.
The Media in Metaverse; Baudrillard’s Simulacra, Is Metaverse that Begins the Apocalypse?

The Metaverse is a multiuser environment that mixes physical reality with digital virtuality in a post-reality realm.It is built on the convergence of technologies (virtual reality, augmented reality, and mixed reality) that enable a variety of interactions with virtual environments, digital items, and people.As a result, the metaverse has transformed into a socially connected web of immersive experiences.Critical theories are practical and theoretical frameworks we may use to develop a qualitative criticism of the metaverse, in addition to a significant previous work that addresses these platforms' statistically and qualitatively measurable effects.While avoiding postmodernism's excesses and failures, the Metaverse platform setting provides a secure space for media and entertainment to achieve innovative societal transformation and spiritual development.The potential of metaverse to make improvements through better internal staff communication and relationship marketing and to replace the traditional ways of training, teaching and marketing in the fields of education, advertising and communication which argues against Baudrillard's simulacra which are based and grounded on the apocalypse's beginning.
Analysis of gender sentiment expression in network based on TF-LDA algorithm

With the popularity of networks, social media has become an important way to spread gender awareness, in which sentiment expressions can especially reflect the development of feminism and public opinion atmosphere. The team took two of the hottest gender-related news events in 2022 and used crawler technology to collect network data of Weibo comments. Then, we used the TF-LDA model combined with the TF-IDF algorithm and LDA topic model, to screen the keywords respectively, then built a new text based on the filtered results, and made sentiment analysis based on the BosonNLP sentiment lexicons.
Ada‐FFL: Adaptive computing fairness federated learning

Abstract As the scale of federated learning expands, solving the Non‐IID data problem of federated learning has become a key challenge of interest. Most existing solutions generally aim to solve the overall performance improvement of all clients; however, the overall performance improvement often sacrifices the performance of certain clients, such as clients with less data. Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning. In order to solve the above problem, the authors propose Ada‐FFL, an adaptive fairness federated aggregation learning algorithm, which can dynamically adjust the fairness coefficient according to the update of the local models, ensuring the convergence performance of the global model and the fairness between federated learning clients. By integrating coarse‐grained and fine‐grained equity solutions, the authors evaluate the deviation of local models by considering both global equity and individual equity, then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value, which can ensure that the update differences of local models are fully considered in each round of training. Finally, by combining a regularisation term to limit the local model update to be closer to the global model, the sensitivity of the model to input perturbations can be reduced, and the generalisation ability of the global model can be improved. Through numerous experiments on several federal data sets, the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.
How to Measure Knowledge Economy

The paper’s primary goal is to analyse the development of Knowledge Economy (KE) measurement methods ranging from those based on national income to indices identifying and combining the relevant indicators. The paper focuses on four current global and European KE level indices: Global Innovation Index (GII), Global Knowledge Index (GKI), European Innovation Scoreboard (EIS), and Digital Economy and Society Index (DESI), highlighting persistent significant differences in the perception of the very essence of KE, as there is no clear interdisciplinary definition of the initial concept of knowledge, leading to further problems with ambiguous and insufficiently specific definitions and measurement of KE. Tacit aspects of knowledge are rarely part of KE definitions or measurements, excluding a large part of the knowledge system from KE measurements. The results of the analysis show that the set of KE indicators used by the individual KE indices is heterogeneous, with the set of intersecting indicators having different weights in terms of importance. Frequent interventions in the indices by their authors were observed, such as changes in index methodology, the indicators used, main pillars (subindices), etc. Despite the high heterogeneity in the approach to measuring KE, we identified the pillars, which can be viewed as the core pillars of KE. These include, for example the level of ICT, R&amp;D, human resources, innovation, patents, and education.
Theoretical characterization of uncertainty in high-dimensional linear classification

Being able to reliably assess not only the \emph{accuracy} but also the \emph{uncertainty} of models' predictions is an important endeavour in modern machine learning. Even if the model generating the data and labels is known, computing the intrinsic uncertainty after learning the model from a limited number of samples amounts to sampling the corresponding posterior probability measure. Such sampling is computationally challenging in high-dimensional problems and theoretical results on heuristic uncertainty estimators in high-dimensions are thus scarce. In this manuscript, we characterise uncertainty for learning from limited number of samples of high-dimensional Gaussian input data and labels generated by the probit model. In this setting, the Bayesian uncertainty (i.e. the posterior marginals) can be asymptotically obtained by the approximate message passing algorithm, bypassing the canonical but costly Monte Carlo sampling of the posterior. We then provide a closed-form formula for the joint statistics between the logistic classifier, the uncertainty of the statistically optimal Bayesian classifier and the ground-truth probit uncertainty. The formula allows us to investigate calibration of the logistic classifier learning from limited amount of samples. We discuss how over-confidence can be mitigated by appropriately regularising.
Application of Boreholes Redox Mapping Technique for Sandstone-Type Uranium Exploration in Arlit, Niger

Exploration is a key fundamental step in the process of any uranium mining activity.Moreover, a new deposit has been discovered in the context of mine development.The objective of the present research studies is to apply the borehole redox mapping technique as a guide for uranium exploration in the sedimentary environment.The Tarat formation in the study area is the Tarat formation in Arlit area is divided into four units (U4, U3, U2, and U1) such as Tarat Unit4 (U4) consists of reduced gray fine consolidated sandstone alternating clay-silt; Tarat Unit 3 (U3) is composed of coarse to medium sandstone with increasing presence of micro-conglomerate towards the bottom; Tarat Unit 2 (U2) characterized by reduced fine gray sandstone with kaolinitic cement and Tarat Unit 1 (U1) consists of coarse to micro-conglomerate gray sandstone.The results of oxyhydroxides distribution maps show high radiometric accumulation in low oxyhydroxides zones of the Tarat Unit (U4) and Tarat Unit (U3), while the Tarat Unit (U2) and Tarat Unit (U1) show less radiometric accumulation in high oxyhydroxides zones.Therefore, the generalized oxyhydroxide distribution maps of the whole Tarat Formation show that moderate to fewer oxidation zones are suitable for uranium mineralization.In contrast, zones with intense oxidation are poorly mineralized.
Sequential Image Recovery Using Joint Hierarchical Bayesian Learning

Abstract Recovering temporal image sequences (videos) based on indirect, noisy, or incomplete data is an essential yet challenging task. We specifically consider the case where each data set is missing vital information, which prevents the accurate recovery of the individual images. Although some recent (variational) methods have demonstrated high-resolution image recovery based on jointly recovering sequential images, there remain robustness issues due to parameter tuning and restrictions on the type of sequential images. Here, we present a method based on hierarchical Bayesian learning for the joint recovery of sequential images that incorporates prior intra- and inter-image information. Our method restores the missing information in each image by “borrowing” it from the other images. More precisely, we couple sequential images by penalizing their pixel-wise difference. The corresponding penalty terms (one for each pixel and pair of subsequent images) are treated as weakly-informative random variables that favor small pixel-wise differences but allow occasional outliers. As a result, all of the individual reconstructions yield improved accuracy. Our method can be used for various data acquisitions and allows for uncertainty quantification. Some preliminary results indicate its potential use for sequential deblurring and magnetic resonance imaging.
A Systematic Review on Deep Convolutional Neural Network-based Breast Cancer Classification on Histopathological Images

Breast cancer (BC) is an increasingly prevalent malignant disease in females globally.Lately, early diagnoses and the best adjuvant therapy have considerably enhanced patient outcomes.However, new challenges have occurred as our growing understanding of tumors has exposed their complex nature, and diagnoses by histopathology have proved to be helpful in guiding BC treatment.We faced an absolute necessity for accurate histopathologic BC diagnoses to make better therapy decisions as patient demand for personalized BC therapy increases.Furthermore, current development in memory capacity and computational power resulted in the applications of medical image processing and Deep Learning (DL) techniques to analyze and process histopathological images (HIs) of BC.Therefore, this study performs a Systematic Review of Deep Convolutional Neural Network based BC Classification on HIs.This survey aims to review the conventional and recently developed DL techniques for BC diagnosis using HIs.Firstly, the role of machine learning (ML) and DL algorithms for HI classification for BC detection is elaborated briefly.Next, the recently developed DL-based HI classification models for BC are reviewed in detail.Moreover, a comparison stud of the reviewed models with result analysis is performed.Furthermore, an elaborate description of the challenging issues with possible future directions is identified at the end of the survey.
Optimization of the YOLOv7 Object Detection Algorithm for Estimating the Amount of Apple Harvest

The increasing population consumed in high production and food needs for survival. Apples are one of the crop harvest products in Indonesia whose needs are increasing, because they are not only needed for human vitamins but can be used as hand fruit or a form of gratitude to those who receive the fruit. In the process of harvesting apples in agricultural land, harvesting is often found which is not feasible in the hands of consumers because it takes too long for apples to not be harvested when the condition of the fruit is feasible in maturity. Therefore, the authors approach this problem by processing the image results obtained to form a detection model, whether the apples are said to be feasible to be harvested immediately and from the image results it can also be calculated the number of fruits captured by the image model , feature enhancements Estimates on objects from this image model are expected to provide more timely harvest predictions in order to provide longer aging of apples and good fruit quality after reaching consumers
Waterwave: A GPU Memory Flow Engine for Concurrent DNN Training

Training Deep Neural Networks (DNN) concurrently is becoming increasingly important for deep learning practitioners, e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hyperparameter optimization (HPO)</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neural architecture search (NAS)</i> . The GPU memory capacity is the impediment that prohibits multiple DNNs from being trained on the same GPU due to the large memory usage during training. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Waterwave</i> , a GPU memory flow engine for concurrent deep learning training. First, to address the memory explosion brought by the long time lag between memory allocation and deallocation time, we develop an allocator tailored for multi-streams. By making the allocator aware of the stream information, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prioritized allocation</i> is conducted based on the chunk's <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">synchronization</i> attributes, allowing us to provide useable memory after scheduling rather than waiting it to be really released after GPU computation. Second, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Waterwave</i> partitions the compute graph to a set of continuous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">node groups</i> and then performs finer-grained scheduling: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NodeGroup pipeline execution</i> , to guarantee a proper memory requests order. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Waterwave</i> can accomplish up to 96.8% of the maximum batch size of solo training. Additionally, in scenarios with high memory demand, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Waterwave</i> can outperform existing spatial sharing and temporal sharing by up to 12x and 1.49x, respectively.
Parallelization of Data Science Tasks, an Experimental Overview

The practice of data science and machine learning often involves training many kinds of models, for inferring some target variable, or extracting structured knowledge from data. Training procedures generally require lengthy and intensive computations, so a natural step for data scientists is to try to accelerate these procedures, typically through parallelization as supported by multiple CPU cores and GPU devices. In this paper, we focus on Python libraries commonly used by machine learning practitioners, and propose a case-based experimental approach to overview mainstream tools for software acceleration. For each use case, we highlight and quantify the optimizations from the baseline implementations to the optimized versions. Finally, we draw a taxonomy of the tools and techniques involved in our experiments, and identify common pitfalls, in view to provide actionable guidelines to data scientists and code optimization tools developers.
Self-supervised Multi-view Learning via Auto-encoding 3D Transformations

3D object representation learning is a fundamental challenge in computer vision to infer about the 3D world. Recent advances in deep learning have shown their efficiency in 3D object recognition, among which view-based methods have performed best so far. However, feature learning of multiple views in existing methods is mostly performed in a supervised fashion, which often requires a large amount of data labels with high costs. In contrast, self-supervised learning aims to learn multi-view feature representations without involving labeled data. To this end, we propose a novel self-supervised framework to learn Multi-View Transformation Equivariant Representations (MV-TER), exploring the equivariant transformations of a 3D object and its projected multiple views that we derive. Specifically, we perform a 3D transformation on a 3D object and obtain multiple views before and after the transformation via projection. Then, we train a representation encoding module to capture the intrinsic 3D object representation by decoding 3D transformation parameters from the fused feature representations of multiple views before and after the transformation. Experimental results demonstrate that the proposed MV-TER significantly outperforms the state-of-the-art view-based approaches in 3D object classification and retrieval tasks and show the generalization to real-world datasets. The code is available at https://github.com/gyshgx868/mvter .
Metaverse

The transition from a physical economy to a digital economy is happening across the globe, and since the worldwide pandemic, this process has accelerated substantially. People are spending more time online than offline, which means that work and daily life are becoming more and more dependent on the internet. Today's internet is frequently the primary means through which millions of people access information and services, engage in social interaction and commerce, and find entertainment. COVID-19 has also altered corporate practices, accelerated the growth of e-commerce, and altered workplace culture. As more people work remotely, businesses are beginning to prioritize virtual environments. The pandemic has so demonstrated that technology is essential to maintaining many jobs in operation. Since COVID-19 is becoming more and more widespread, the demand for virtual reality is rising, and the Metaverse sector is expanding. Blockchain technology, artificial intelligence, and the virtual environment known as the Metaverse are all merged here.
A New Energy-Based Latent-Variable Model for Unsupervised Feature Learning

본 논문은 비지도 특징학습을 위한 새로운 에너지기반 은닉변수 모델(EBLVM)을 제안한다. EBLVM의 결합 확률밀도함수는 심층 신경망에 의해 변환된 연속적인 가시변수와 은닉변수에 대한 새로운 에너지 함수를 정의한다. 경사도 기반 contrastive divergence 알고리즘을 사용하여 새로운 EBLVM의 파라미터를 훈련한다. EBLVM은 심층구조를 갖고 모든 은닉층을 연합하여 학습하기 때문에 각 층에서 특징학습을 위한 효과적인 특징들을 추출할 수 있다. Fashion MNIST와 CIFAR10 데이터를 사용한 비교사 특징학습 실험에서 제안된 기법은 기존의 stacked RBM, DBN, DBM 그리고 DEM보다 더 향상된 인식성능을 나타낸다. This paper proposes a new energy-based latent-variable model (EBLVM) for unsupervised feature learning. The joint probability density function of EBLVM defines a new energy function for the continuous visible and hidden variables in which the visible variable is transformed by the deep neural network. We train the parameters of a new EBLVM using a gradient-based contrastive divergence algorithm. Since EBLVM has a deep structure and learns by combining all hidden layers, effective features for feature learning can be extracted from each layer. In comparative feature learning experiments using Fashion MNIST and CIFAR10 data, the proposed method shows better recognition performance than the existing stacked RBM, DBN, DBM, and DEM.
Definition of Forensic Methodologies for Autonomous Vehicles

Digitisation is present more and more widely in the vehicle industry, the use of modern and increasingly self-driving vehicles is appearing more and more widely. Their use becomes more appropriate and necessary in industry, transport, military and logistics applications. The spread of these highly automated vehicles will have a direct impact on road accidents, violations and crimes. In the event of accidents, the determination of responsibility and the identification of the perpetrators of crimes will continue to be necessary in the future, for which tools and procedures are currently not available or not fully applicable. By connecting vehicles and infrastructure elements, the range of data related to events has been greatly enhanced. This information must be extracted and analysed during the investigations. The purpose of the investigations is to determine what, when, where and how it happened, who were involved, and to determine responsibility. The basis of the expert investigation of self-driving vehicles and cooperative transport systems is the classical forensic procedure and digital forensics. On the other hand, the field requires specific knowledge, preparation, specific tools and approach. In this study, the existing, already used digital forensics examination methods are described,the applicability of some of them, some steps or tools that require further examination in the case of modern and increasingly autonomous vehicles.