Reviewerly/exHarmony huggingface dataset:
Metaverse as Tech for Good: Current Progress and Emerging Opportunities

Metaverse is an upcoming transformative technology that will impact our future society with immersive experiences. The recent surge in the adoption of new technologies and innovations in connectivity, interaction technology, and artificial realities can fundamentally change the digital world. The Metaverse concept is the most recent trend to encapsulate and define the potential new digital landscape. However, with the introduction of 5G with high speed and low latency advancements in the hardware and software with the graphics power to display millions of polygons in 3D and blockchain technology, this concept is no longer fiction. This transition from today’s Internet to a spatially embodied Internet is, at its core, a transition from 2D to 3D interactions taking place in multiple virtual universes. In recent years, augmented virtual reality has created possibilities in the private and professional spheres. The new Virtual Reality (VR) headsets and Augmented Reality (AR) glasses can provide immersion in the physical sense. Technology must offer realistic experiences for users to turn this concept into reality. This paper focuses on the potential use cases and benefits of the Metaverse as a tech for good. The research paper outlines the potential areas where a positive impact could occur, highlights recent progress, and discusses the issues around trust, ethics, and cognitive load.
A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data

Federal administrative data, such as tax data, are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few individuals. An alternative to sharing microlevel data is to allow individuals to query statistics without directly accessing the confidential data. This article studies the feasibility of using differentially private (DP) methods to make certain queries while preserving privacy. We also include new methodological adaptations to existing DP regression methods for using new data types and returning standard error estimates. We define feasibility as the impact of DP methods on analyses for making public policy decisions and the queries accuracy according to several utility metrics. We evaluate the methods using Internal Revenue Service data and public-use Current Population Survey data and identify how specific data features might challenge some of these methods. Our findings show that DP methods are feasible for simple, univariate statistics but struggle to produce accurate regression estimates and confidence intervals. To the best of our knowledge, this is the first comprehensive statistical study of DP regression methodology on real, complex datasets, and the findings have significant implications for the direction of a growing research field and public policy. Supplementary materials for this article are available online.
Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel

Images acquired in low-light conditions often have poor visibility. These images considerably degrade the performance of algorithms when used in computer vision and multi-media systems. Several methods for low-light image enhancement have been proposed to address these issues; furthermore, various techniques have been used to restore close-to-normal light conditions or improve visibility. However, there are problems with the enhanced image, such as saturation of local light sources, color distortion, and amplified noise. In this study, we propose a low-light image enhancement technique using illumination component estimation and a local steering kernel to address this problem. The proposed method estimates the illumination components in low-light images and obtains the images with illumination enhancement based on Retinex theory. The resulting image is then color-corrected and denoised using a local steering kernel. To evaluate the performance of the proposed method, low-light images taken under various conditions are simulated using the proposed method, and it demonstrates visual and quantitative superiority to the existing methods.
Selection of pedagogical conditions for training STEM teachers to use augmented reality technologies in their work

Abstract The focus of the study is on choosing the pedagogical conditions that will best prepare STEM teachers to utilize augmented reality tools in their classrooms. 20 conditions were initially proposed during a brainstorming session and were then sorted into five groups: (1) conditions relating to the material support of the educational process, (2) methods and forms of training, (3) best practices for utilizing augmented reality in education, (4) unique circumstances made in order to achieve the goal of training STEM teachers; and (5) conditions relating to the psychological and pedagogical support of students. A survey was conducted with 94 participants to determine the importance of these conditions. The processing of the survey data allowed for the selection of the following conditions: (a) accessibility of immersive digital educational resources for STEM teachers and mobile hardware for augmented reality (laptops, tablets, smartphones, augmented reality glasses, etc.), (b) inclusion of augmented reality-related topics in STEM teachers’ curricula, (c) use of research methodologies and interactive technologies in the STEM classroom, (d) having hands-on experience with the use of augmented reality technologies in STEM instruction.
Federated contrastive prototype learning: An efficient collaborative fault diagnosis method with data privacy

Data-driven fault diagnosis approaches have attracted considerable attention in the past few years, and promising diagnostic performance has been achieved with sufficient monitoring data. However, in real industrial scenarios, individual users often struggle to collect enough labeled data. Meanwhile, direct data aggregation from multiple users is not always feasible due to data privacy concerns and conflicts of interest. To solve this issue, a novel federated contrastive prototype learning scheme is proposed for collaborative fault diagnosis of rotating machinery. The collaborative modeling between the central server and multiple clients is implemented to establish a global fault diagnostic model with data privacy. A contrastive prototype learning module is designed to align the prototypes of the same classes across different clients while separating them away from other class prototypes, thus effectively eliminating distribution discrepancies across clients and learning domain-invariant discriminative features. To remove the bias of the global model during federated communication, an unbiased prototype learning module is constructed, which aligns the class prototypes of different clients to the global prototype center and enhances the generalization ability of the proposed approach under unseen conditions. Experimental results on two self-built testbeds and a laboratory dataset demonstrate that the proposed approach is a potential solution for real-world fault diagnosis applications.
Txt2Img-MHN: Remote Sensing Image Generation From Text Using Modern Hopfield Networks

The synthesis of high-resolution remote sensing images based on text descriptions has great potential in many practical application scenarios. Although deep neural networks have achieved great success in many important remote sensing tasks, generating realistic remote sensing images from text descriptions is still very difficult. To address this challenge, we propose a novel text-to-image modern Hopfield network (Txt2Img-MHN). The main idea of Txt2Img-MHN is to conduct hierarchical prototype learning on both text and image embeddings with modern Hopfield layers. Instead of directly learning concrete but highly diverse text-image joint feature representations for different semantics, Txt2Img-MHN aims to learn the most representative prototypes from text-image embeddings, achieving a coarse-to-fine learning strategy. These learned prototypes can then be utilized to represent more complex semantics in the text-to-image generation task. To better evaluate the realism and semantic consistency of the generated images, we further conduct zero-shot classification on real remote sensing data using the classification model trained on synthesized images. Despite its simplicity, we find that the overall accuracy in the zero-shot classification may serve as a good metric to evaluate the ability to generate an image from text. Extensive experiments on the benchmark remote sensing text-image dataset demonstrate that the proposed Txt2Img-MHN can generate more realistic remote sensing images than existing methods. Code and pre-trained models are available online (https://github.com/YonghaoXu/Txt2Img-MHN).
Victimological Characteristics of Fraud in the Banking Sphere (Based on the Materials from Siberian Federal District)

The article presents an analysis of victimological characteristics of bank fraud committed in Siberian Federal District in 2015-2021. The research results reveal a high level of victimization from bank fraud in Siberian Federal District, and show that the structure of victimization is dominated by fraud with the use of electronic means of payment. The authors draw attention to a specific feature of the victims of this type of fraud, who could be both physical and juridical persons. It was determined that in the time period under consideration, juridical persons were mainly victims of fraud in the sphere of credit financing (Art. 159.1 of the CC of the RF), while physical persons more often became victims of fraud with the use of electronic means of payment (Art. 159.3 of the CC of the RF), besides, the share of physical persons grew 17 times and, by the end of the period, constituted 84 % of the total number of victims. The analysis of the personality of a bank frauds victim showed that the relevant factors for victimization from fraud are age and marital status, while the level of education is not relevant. Several types of victimity were singled out among victimogenic factors, which were often manifested in the careless actions of victims. For juridical persons, personal victimity types are aggravated by role victimity, while for physical persons their victim behavior, which facilitated fraud, is quite often unconnected with their victimity. The authors also analyzed other victimogenic factors determining victimization from bank fraud. It was established that objective factors are mainly typical for the victimization of juridical persons, and their role is minimal for the victimization of physical persons. They point out the existing problems of victimological prevention and the general directions of its development.
Learning the Deep and the Shallow: Deep-Learning-Based Depth Phase Picking and Earthquake Depth Estimation

Abstract Automated teleseismic earthquake monitoring is an essential part of global seismicity analysis. Although constraining epicenters in an automated fashion is an established technique, constraining event depths is substantially more difficult. One solution to this challenge is teleseismic depth phases, but these can currently not be identified precisely by automatic detection methods. Here, we propose two deep-learning models, DepthPhaseTEAM and DepthPhaseNet, to detect and pick depth phases. For training the models, we create a dataset based on the ISC-EHB bulletin—a high-quality catalog with detailed phase annotations. We show how backprojecting the predicted phase arrival probability curves onto the depth axis yields accurate estimates of earthquake depth. Furthermore, we show how a multistation model, DepthPhaseTEAM, leads to better and more consistent predictions than the single-station model, DepthPhaseNet. To allow direct application of our models, we integrate them within the SeisBench library.
Exploring the physical–digital interface in blockchain applications: Insights from the luxury watch industry

Counterfeit products are an increasing issue across the luxury sector. Blockchain applications exhibit the potential to mitigate counterfeit risks and facilitate the growing secondary markets by enabling product authentication and digital proof of ownership. In this study, we adopt a qualitative multiple-case study design and conduct interviews with informants from four blockchain projects in the Swiss luxury watch industry. We identify the industry-specific drivers and barriers to blockchain adoption and specifically explore the challenges at the physical–digital interface. Beyond an in-depth description of the problems, our findings suggest two main data management hurdles related to the validity of input data and data synchronicity. The main challenge, however, is to establish an unequivocal link between the physical watch to its digital record on the blockchain. We present several invasive and non-invasive linking approaches and discuss their potential advantages and disadvantages. Further, we outline different areas for future research. Overall, our study addresses a timely issue of blockchain applications, contributing to the operations and supply chain management literature and supporting blockchain use case design in practice.
Variational quantum metrology for multiparameter estimation under dephasing noise

We present a hybrid quantum-classical variational scheme to enhance precision in quantum metrology. In the scheme, both the initial state and the measurement basis in the quantum part are parameterized and optimized via the classical part. It enables the maximization of information gained about the measured quantity. We discuss specific applications to 3D magnetic field sensing under several dephasing noise models. Indeed, we demonstrate its ability to simultaneously estimate all parameters and surpass the standard quantum limit, making it a powerful tool for metrological applications.
<i>faas‐sim</i>: A trace‐driven simulation framework for serverless edge computing platforms

Abstract This paper presents faas‐sim , a simulation framework tailored to serverless edge computing platforms. In serverless computing, platform operators are tasked with efficiently managing distributed computing infrastructure completely abstracted from application developers. To that end, platform operators and researchers need tools to design, build, and evaluate resource management techniques that efficiently use of infrastructure while optimizing application performance. This challenge is exacerbated in edge computing scenarios, where, compared to cloud computing, there is a lack of reference architectures, design tools, or standardized benchmarks. faas‐sim bridges this gap by providing (a) a generalized model of serverless systems that builds on the function‐as‐a‐service abstraction, (b) a simulator that uses trace data from real‐world edge computing testbeds and representative workloads, and (c) a network topology generator to model and simulate distributed and heterogeneous edge‐cloud systems. We present the conceptual design, implementation, and a thorough evaluation of faas‐sim . By running experiments on both real‐world test beds and replicating them using faas‐sim , we show that the simulator provides accurate results and reasonable simulation performance. We have profiled a wide range of edge computing infrastructure and workloads, focusing on typical edge computing scenarios such as edge AI inference or data processing. Moreover, we present several instances where we have successfully used faas‐sim to either design, optimize, or evaluate serverless edge computing systems.
DGSN: Learning how to segment pedestrians from other datasets for occluded person re-identification

In this paper, we present three major challenges in occluded person Re-Identification (ReID): different occlusions, background interference, and dataset bias. To address the first and second challenges, our approach incorporates pedestrian segmentation to distinguish between pedestrian and non-pedestrian regions. Additionally, to tackle the third challenge, we introduce an effective image enhancement method called Enhanced Random Occluding (ERO). ERO leverages other datasets with segmentation annotations to compensate for the lack of detailed annotations in ReID datasets. We compare the effectiveness of ERO with existing methods. To utilize the prior knowledge obtained by ERO, we introduce the Priori Segmentation Module (PSM) and the Domain Generalization Module (DGM). The PSM module enables learning out-of-domain prior knowledge without relying on external networks, while the DGM module transfers this knowledge to the current domain. Finally, we utilize the obtained segmentation results as attention maps for feature aggregation. ERO, PSM, and DGM together constitute the Domain Generalization Segmentation Network (DGSN). Our experimental results on occluded and holistic person ReID benchmarks demonstrate the superiority of the proposed DGSN. On the Occluded-Duke dataset, we achieved a mAP of 69.9% (+ 2.0%) and a rank-1 accuracy of 60.7% (+ 0.3%), surpassing state-of-the-art methods significantly.
Statistical Modeling in Healthcare

Recent math developments, especially statistical modeling, profoundly impact healthcare. Firstly, statistical modeling equips healthcare professionals with advanced tools for analyzing complex healthcare datasets, improving diagnostic accuracy and treatment planning by revealing hidden insights. Secondly, it supports evidence-based decision-making by quantifying treatment effectiveness, assessing risks, and evaluating interventions. By relying on empirical evidence rather than intuition, healthcare providers can make informed decisions that optimize patient outcomes. Additionally, it empowers early identification of high-risk patients, personalizes interventions, optimizes resource allocation, and enhances healthcare efficiency. Lastly, statistical modeling aids in quality improvement by identifying areas for enhancement and monitoring performance indicators. Overall, statistical modeling transforms healthcare, enhancing patient care, resource allocation, and decision-making, paving the way for a data-driven future in medicine.
Nitty-Gritty of Deep Reinforcement Learning for the Healthcare Sector

Deep reinforcement learning (DRL) is one of the emerging areas of machine learning which focuses on maximized rewards. DRL is a type of machine learning that combines reinforcement learning and deep learning. It uses a series of algorithms to enable an agent to learn how to make decisions in a complex environment. DRL is a subset of artificial intelligence that focuses on making decisions based on the environment and the rewards associated with each action.The goal of DRL is to maximize the long-term reward of an agent. In order to do this, the agent must use a combination of deep learning, reinforcement learning and other AI techniques to learn which actions will lead to the highest reward. DRL is used to solve a variety of problems, from playing video games to controlling robots. It is also used in autonomous driving and robotics, as well as for financial trading. DRL is a powerful tool for solving complex problems and has been used in a variety of research projects. DRL has the potential to revolutionize the way we interact with machines and the environment.
Does DeFi remove the need for trust? Evidence from a natural experiment in stablecoin lending

Decentralized Finance (DeFi) is built on a fundamentally different paradigm. Rather than trusting individuals and institutions, DeFi participants, in principle, only need to trust computer code enforced by a decentralized network of computers. We examine a natural experiment that exogenously stress-tests this alternative paradigm by revealing the identities of individuals (including a convicted criminal) associated with the Abracadabra lending protocol. The revelation seriously damaged the protocol, indicating that, in practice, DeFi does not (yet) entirely remove the need for trust in individuals. Our findings suggest that smart contracts are incomplete and are subject to protocol-run risk. Thus, individuals' character and trust remain relevant in this alternative financial system.
Semantic segmentation of autonomous driving scenes based on multi-scale adaptive attention mechanism

Semantic segmentation is a crucial visual representation learning task for autonomous driving systems, as it enables the perception of surrounding objects and road conditions to ensure safe and efficient navigation.In this paper, we present a novel semantic segmentation approach for autonomous driving scenes using a Multi-Scale Adaptive Mechanism (MSAAM). The proposed method addresses the challenges associated with complex driving environments, including large-scale variations, occlusions, and diverse object appearances. Our MSAAM integrates multiple scale features and adaptively selects the most relevant features for precise segmentation. We introduce a novel attention module that incorporates spatial, channel-wise and scale-wise attention mechanisms to effectively enhance the discriminative power of features.The experimental results of the model on key objectives in the Cityscapes dataset are: ClassAvg:81.13, mIoU:71.46. The experimental results on comprehensive evaluation metrics are: AUROC:98.79, AP:68.46, FPR95:5.72. The experimental results in terms of computational cost are: GFLOPs:2117.01, Infer. Time (ms):61.06. All experimental results data are superior to the comparative method model.The proposed method achieves superior performance compared to state-of-the-art techniques on several benchmark datasets demonstrating its efficacy in addressing the challenges of autonomous driving scene understanding.
Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

Optimizing large-scale numerical problems is a significant challenge with numerous real-world applications. The optimization process is complex due to the multi-dimensional search spaces and possesses several locally optimal regions. In response to this issue, various metaheuristic algorithms and variations have been developed, including evolutionary and swarm intelligence algorithms and hybrids of different artificial intelligence techniques. Previous studies have shown that swarm intelligence algorithms like PSO perform poorly in high-dimensional spaces, even with algorithms focused on reducing the search space. However, we propose a modified version of the PSO algorithm called Dynamical Sphere Regrouping PSO (DSRegPSO) to avoid stagnation in local optimal regions. DSRegPSO is based on the PSO algorithm and modifies inertial behavior with a regrouping dynamical sphere mechanism and a momentum conservation physics effect. These behaviors maintain the swarm’s diversity and regulate the exploration and exploitation of the search space while avoiding stagnation in optimal local regions. The DSRegPSO mechanisms mimic the behavior of birds, moving particles similar to birds when they look for a new food source. Additionally, the momentum conservation effect mimics how birds react to collisions with the boundaries in their search space or when they are looking for food. We evaluated DSRegPSO by testing 15 optimizing functions with up to 1000 dimensions of the CEC’13 benchmark, a standard for evaluating Large-Scale Global Optimization used in Congress on Evolutionary Computation, and several journals. Our proposal improves the behavior of all variants of PSO registered in the toolkit of comparison for CEC’13 and obtains the best result in the non-separable functions against all the algorithms.