Reviewerly/exHarmony huggingface dataset:
UPRec: User-aware Pre-training for sequential Recommendation

Recent years witness the success of pre-trained models to alleviate the data sparsity problem in recommender systems. However, existing pre-trained models for recommendation mainly focus on leveraging universal sequence patterns from user behavior sequences and item information, whereas ignore heterogeneous user information to capture personalized interests, which has been shown to contribute to the personalized recommendation. In this paper, we propose a simple yet effective model, called User-aware Pre-training for Recommendation (UPRec), which could flexibly encode heterogeneous user information into the sequential modeling of user behaviors. Specifically, UPRec first encodes the sequential behavior to generate user embeddings, and then jointly optimizes the model with the sequential objective and user-aware objective constructed from the user attributes and structured social graphs. Comprehensive experimental results on two real-world large-scale recommendation datasets demonstrate that UPRec can effectively enrich the user representations with user attributes and social relations and thus provide more appropriate recommendations for users. The source code of UPRec will be released once accepted.
DSPose: Dual-Space-Driven Keypoint Topology Modeling for Human Pose Estimation

Human pose estimation is the basis of many downstream tasks, such as motor intervention, behavior understanding, and human-computer interaction. The existing human pose estimation methods rely too much on the similarity of keypoints at the image feature level, which is vulnerable to three problems: object occlusion, keypoints ghost, and neighbor pose interference. We propose a dual-space-driven topology model for the human pose estimation task. Firstly, the model extracts relatively accurate keypoints features through a Transformer-based feature extraction method. Then, the correlation of keypoints in the physical space is introduced to alleviate the error localization problem caused by excessive dependence on the feature-level representation of the model. Finally, through the graph convolutional neural network, the spatial correlation of keypoints and the feature correlation are effectively fused to obtain more accurate human pose estimation results. The experimental results on real datasets also further verify the effectiveness of our proposed model.
MetaverseBench: Instantiating and benchmarking metaverse challenges

The rapid evolution of the metaverse has led to the emergence of numerous metaverse technologies and productions. From a computer systems perspective, the metaverse system is a complex, large-scale system that integrates various state-of-the-art technologies, including AI, blockchain, big data, and AR/VR. It also includes multiple platforms, such as IoTs, edges, data centers, and diverse devices, including CPUs, GPUs, NPUs, and 3D glasses. Integrating these technologies and components to build a holistic system poses a significant challenge for system designers. The first step towards building the metaverse is to instantiate and evaluate the challenges and provide a comprehensive benchmark suite. However, to the best of our knowledge, no existing benchmark defines the metaverse challenges and evaluates state-of-the-art solutions from a holistic perspective. In this paper, we instantiate metaverse challenges from a system perspective and propose MetaverseBench, a holistic and comprehensive metaverse benchmark suite. Our preliminary experiments indicate that the existing system performance needs to catch up to the requirements of the metaverse by two orders of magnitude on average.
Perception of Facial Impressions Using Explicit Features of the Face (xFoFs)

We present a novel approach to perceiving facial impressions by defining the explicit features of the face (xFoFs) based on anthropometric studies. The xFoFs estimate 35 anthropometric features of human faces with normal expressions and frontalized poses. Using these xFoFs, we have developed a method to objectively measure facial impressions, compiling a dataset of approximately 4896 facial images to validate our method. The ranking of xFoFs among the face image dataset guides an objective and quantitative estimation of facial impressions. To further corroborate our study, we conducted two user studies: an examination of the first and strongest impression perception and a validation of the consistency of multiple important impression perceptions. Our work significantly contributes to the field of facial recognition and explainable artificial intelligence (XAI) by providing an effective solution for integrating xFoFs with existing facial recognition models.
The potential of extended reality in Rural Education’s future – perspectives from rural educators

Abstract Technology-enhanced education can potentially enhance teaching and learning outcomes for rural educators since they face limited educational resources and low job satisfaction. Recently, there has been a surge in extended reality (XR) as an immersive learning technology to improve teaching and learning in rural areas, but without focusing on rural educators’ perspectives. This study aims to bridge this gap by investigating rural educators’ interactions with XR educational applications and exhibiting their insights on using XR education to improve education quality in rural areas. After educators’ hands-on experiences in a pre-designed XR education workshop, qualitative data was collected from their discussions in focus groups. As a result, educators believed that XR could transform traditional educational practices and create opportunities for new patterns of rural education (e.g., public engagement with rurality and rural vocational education). Limitations include a lack of school infrastructure to apply XR and an absence of well-structured curriculum design to use XR in the classroom setting. We suggest that future studies explore the integration of effective XR practices into primary and elementary education in those areas with limited educational resources.
Enhancing Medical Image Segmentation: Ground Truth Optimization through Evaluating Uncertainty in Expert Annotations

The surge of supervised learning methods for segmentation lately has underscored the critical role of label quality in predicting performance. This issue is prevalent in the domain of medical imaging, where high annotation costs and inter-observer variability pose significant challenges. Acquiring labels commonly involves multiple experts providing their interpretations of the “true” segmentation labels, each influenced by their individual biases. The blind acceptance of these noisy labels as the ground truth restricts the potential effectiveness of segmentation algorithms. Here, we apply coupled convolutional neural network approaches to a small-sized real-world dataset of bovine cumulus oocyte complexes. This is the first time these methods have been applied to a real-world annotation medical dataset, since they were previously tested only on artificially generated labels of medical and non-medical datasets. This dataset is crucial for healthy embryo development. Its application revealed an important challenge: the inability to effectively learn distinct confusion matrices for each expert due to large areas of agreement. In response, we propose a novel method that focuses on areas of high uncertainty. This approach allows us to understand the individual characteristics better, extract their behavior, and use this insight to create a more sophisticated ground truth using maximum likelihood. These findings contribute to the ongoing discussion of leveraging machine learning algorithms for medical image segmentation, particularly in scenarios involving multiple human annotators.
Cryptic trans-lithospheric fault systems at the western margin of South America: implications for the formation and localization of gold-rich deposit superclusters

We present a review of frontier research advances in the investigation of cryptic structures that transect the South American Andes at oblique strike directions. The intersections between these cryptic structures and the superimposed Andean belt correlate with the spatial distribution of gold-rich mineral deposit clusters. The deposit clusters can be described as superclusters, as they comprise various gold deposit types that formed at multiple times throughout the Phanerozoic, impinging repeatedly on the structural intersections. However, the cryptic inherited fault structures are difficult to detect, because their deeper-seated roots are often overlain by younger supracrustal successions, and/or their exposed surface manifestations are structurally obscured by subsequent tectonic-magmatic activity. Thus, it also remains a challenge to constrain the nature and timing of formation, and the respective subsequent evolutionary path, of these proposed pre-Andean structures. Based on various case studies, we demonstrate that the localization of identified Phanerozoic gold deposit superclusters along the western South American margin is fundamentally controlled by structural inheritance often dating back to at least the Mesoproterozoic. Integration of multi-approach observations and datasets allows insights into a larger-scale tectonic history that showcases the successive inheritance of major structures originating from the Amazonian Craton, over the Paleozoic Gondwana margin, into the Cenozoic magmatic belts of the Andes, and even into recent fractures within the subducting oceanic Nazca plate, recording &amp;gt;1.2-billion-years of progressive structural inheritance and growth at one of the longest-lived tectonic margins in Earth history. In contrast to previous models of the spatial distribution of gold deposits, based on statistical approaches and spatial periodicity in self-organized systems focusing on single subduction and/or accretion episodes and belts, we propose that the structural inheritance and intersections are key to the localization of gold deposits in the Andes. In combination with bulk-geochemical data from magmatic rocks, we suggest that inherited structures maintained a trans-lithospheric connectivity to pre-fertilized gold enriched upper mantle reservoirs, which were tapped during multiple tectono-magmatic reactivation episodes.
Multi-Intent Natural Language Understanding Framework for Automotive Applications: A Heterogeneous Parallel Approach

Natural language understanding (NLU) is an important aspect of achieving human–machine interactions in the automotive application field, consisting of two core subtasks, multiple-intent detection, and slot filling (ID-SF). However, existing joint multiple ID-SF tasks in the Chinese automotive domain face two challenges: (1) There is a limited availability of Chinese multi-intent corpus data for research purposes in the automotive domain; (2) In the current models, the interaction between intent detection and slot filling is often unidirectional, which ultimately leads to inadequate accuracy in intent detection. A novel multi-intent parallel interactive framework based on heterogeneous graphs for the automotive applications field (Auto-HPIF) was proposed to overcome these issues. Its improvements mainly include three aspects: firstly, the incorporation of the Chinese bidirectional encoder representations from transformers (BERT) language model and Gaussian prior attention mechanism allow each word to acquire more comprehensive contextual information; secondly, the establishment of a heterogeneous graph parallel interactive network efficiently exploits intent and slot information, facilitating mutual guidance; lastly, the application of the cross-entropy loss function to the multi-intent classification task enhances the model’s robustness and adaptability. Additionally, a Chinese automotive multi-intent dataset (CADS) comprising 13,100 Chinese utterances, seven types of slots, and thirty types of intents were collected and annotated. The proposed framework model demonstrates significant improvements across various datasets. On the Chinese automotive multi-intent dataset (CADS), the model achieves an overall accuracy of 87.94%, marking a notable 2.07% enhancement over the previous best baseline. Additionally, the model performs commendably on two publicly available datasets. Specifically, it showcases a 3.0% increase in overall accuracy on the MixATIS dataset and a 0.7% improvement on the MixSNIPS dataset. These findings showcase the efficacy and generalizability of the proposed model in tackling the complexity of joint multiple ID-SF tasks within the Chinese automotive domain.
Simulation of Laser Profilometer Measurements in the Presence of Speckle Using Perlin Noise

In the manufacturing industry, inspection systems play a crucial role in ensuring product quality. High-resolution profilometric sensors have become increasingly popular for inspection due to their ability to provide detailed surface information. However, the development and testing of inspection systems can be costly and time-consuming. This paper presents the development of a simulation of an inspection system using a high-resolution profilometric sensor. A geometrical and noise model is proposed to simulate the readings of any actual profilometric sensor. The model replicates the sensor's movement on the CAD model of the inspected part. The model incorporates the physical properties of the sensor and combines noise sources from sensor uncertainty and speckle noise induced by the roughness of the material. Our contribution lies in noise modeling. This work proposes a combination of Perlin noise to simulate the speckle noise and Gaussian noise for the uncertainty-related noise. Perlin noise is generated based on the surface roughness parameters of the inspected part. The accuracy of the simulation system is evaluated by comparing the simulated scans with real scans. The results highlight the ability to simulate real scans of different parts, using commercial sensor specifications and the CAD model of the inspected part.
Hybrid optimization‐based deep neuro‐fuzzy network for designing <i>m</i>‐user multiple‐input multiple‐output interference channel

Summary Multiple‐input multiple‐outputs (MIMOs) have eminent quality in maximizing the throughput of wireless communication models. In MIMO, the antenna arrays can be utilized for fulfilling the needs of 5G by utilizing various spatial signatures of users. Even though 5G communication is imminent, there exist issues, like network interference that arise due to reused frequency spectrum resources. This delving presents an optimized deep model for suppressing interference occurring in the Rayleigh channel in the multiple‐user MIMO (MU‐MIMO) model. Here, an MU‐MIMO model is employed with correlated interference wherein there exist various users around the base station (BS) with several antennas at the transmitter and receiver. Here, a deep neuro‐fuzzy network (DNFN) is used to upgrade the performance of detectors underneath correlated interference. Here, the model comprises zero forcing‐maximum likelihood detection (ZF‐MLD) that assists to generate an initial estimate of broadcasted signals in a particular time slot. The DNFN is used to capture latent correlation among several symbols. Here, the DNFN training is performed using developed autoregressive Henry gas spider monkey optimization (RHGSMO), which is the combination of conditional autoregressive value at risk (CAViaR), Henry gas solubility optimization (HGSO), and spider monkey optimization (SMO). With the lowest symbol error rate (SER), bit error rate (BER), and signal to interference and noise ratio (SINR), the suggested RHGSMO‐based DNFN performed better than existing approaches.
Harnessing Institutional Agility for a More Effective and Efficient Government Organization

Every business relies on its employees, and their attitude toward their job and the results they achieve directly impact the organization's stability and performance. To ensure that the organizational effectiveness process runs smoothly, motivating employees to participate actively is crucial. Without their cooperation and assistance, considerable energy may be wasted. In a competitive global environment, employee productivity is linked to several issues that can hinder an organization's success. Therefore, this study aims to investigate the factors that influence employees' productivity, determine if there is a connection between productivity elements and employee work, and evaluate how incentives affect employees at work and their productivity. The study involved selecting a sample of 116 individuals from different Saudi government agencies, including administrative personnel and managers. The data was gathered using survey questions and analyzed using several statistical techniques. The study results indicate that out of the five tested factors, four of them significantly influence productivity. These factors are health issues, stress, workplace environment, and personality traits. On the other hand, sleep deprivation was found to have no impact on productivity. Accordingly, employees in government organizations acknowledge that addressing health issues, reducing stress levels, improving the work environment, and developing positive personality traits are all critical aspects of enhancing the organization's productivity and achieving its objectives.
Assessing the correlates of cyberattacks against high-visibility institutions

ABSTRACTWhile cyberattacks are a persistent problem, particularly web defacements, it is unclear what factors differentiate the likelihood of these attacks based on the type of institution that could be targeted. Military and government entities are high-visibility targets for some attackers, though it is unclear whether they are more likely to be defaced on the basis of their operational characteristics or the motives of the attacker. The same is true for educational institutions, which are frequently targeted by ideologically motivated hackers and others seeking to abuse their IT infrastructure. The current study attempted to address this gap in the literature through the application of routine activities theory to identify the factors significantly associated with web defacements against military, government, and educational web domains. Using a sample of 2.2 million web defacements, we examined the perceived value, inertia, visibility, and accessibility of the targets and the attack methods employed in each defacement. The implications of this analysis for our understanding of cyberattacks against high-visibility institutions are discussed in detail.KEYWORDS: Web defacementcyberattackshigh-visibility institutionscybercrimeroutine activity theory Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationNotes on contributorsJin R. LeeJin R. Lee is an Assistant Professor in the Department of Criminology, Law and Society at George Mason University. His research interests are in cybercrime, online interpersonal violence, cybersecurity, cyberpsychology, computer-mediated communications, and big data.Thomas J. HoltThomas J. Holt is a Professor in the School of Criminal Justice at Michigan State University. His research focuses on computer hacking, malware, and the role of the Internet in facilitating all manner of crime and deviance. He received his PhD from the University of Missouri-St. Louis in 2005.
Cybercrime victimization among Virginia businesses: frequency, vulnerabilities, and consequences of cybervictimization

ABSTRACTThe Commonwealth of Virginia, USA, is one of the most vulnerable states to cyberattacks and breaches. Analyzing data from 428 online surveys collected from Virginia businesses from multiple vendors and several unique resources, this study provides an in-depth view of the nature and extent of cybercrime victimization in Virginia, highlighting specific vulnerabilities, how the victimization occurred, the consequences of victimization, and if and to whom these breaches were reported. In addition, we describe the extent to which businesses perceive their vulnerabilities, the extent in which companies engage in behaviors that can potentially make them vulnerable, the policies and practices they have in place to reduce vulnerability, and their experiences with victimization. The results provide a quality baseline for understanding cybercrimes against businesses in Virginia.KEYWORDS: Cybercrimevictimizationcybersecurity Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingFunding provided by COVA CCI (Coastal Virginia Center for Cyber Innovation Commonwealth Cyber Initiative) Number: [C-Q122-ODU-05].
Enterprise Digital Financial Assets Modeling Based on Blockchain Technology

This article presents the concept of using digital financial assets based on blockchain technolo-gy and description of the main advantages of using assets at the enterprise level, including in-creased security, the absence of intermediaries in financial transactions, a higher level of auto-mation compared to the traditional approaches, better confidentiality and security. The article contains the determination of key aspects of the distributed blockchain networks usage analysis of the existing models of blockchain-technology application in the enterprises business pro-cesses. Blockchain business models have been divided into the following groups: blockchain token economy model, blockchain network toll collection model, P2P blockchain, blockchain as a service, development platforms, and blockchain-based software products. A dApp (decen-tralized application) application model at the enterprise level has been developed using the UML language notation.
Planning Integrated Unmanned Aerial Vehicle and Conventional Vehicle Delivery Operations under Restricted Airspace: A Mixed Nested Genetic Algorithm and Geographic Information System-Assisted Optimization Approach

Using Unmanned Aerial Vehicles (UAVs), commonly referred to as “drones”, as a supplementary mode for last-mile deliveries has been a research focus for some years now. Motivation lies in the reduced dependency on Conventional Vehicles (CVs) and fossil fuels and in serving remote areas and underprivileged populations. We are building a flexible, modular framework for integrated CV-UAV parcel delivery operations planning that is responsive to infrastructure and demand and offers an open and practical tool for future adaptations. The entire model and solution methodology are practical tools for decision making and strategic planning, with novelties such as the variable Launch Site types for Launch and Recovery Operations (LAROs), the tailored Assignment and Routing Optimization nested GA, the consideration of airspace restrictions of any shape and size, the inclusion of GIS tools in the process, the modularity of the platform, and most importantly, the inclusion of all the above in a single, comprehensive, and holistic approach. Because of the need for safe UAV deployment sites and the high presence of restricted airspace zones in urban environments, the intended field of application is assumed to be the delivery of small packages in rural and under-connected areas, the execution of inter-city deliveries, and the expansion of a city’s original service range. A single CV is equipped onboard with UAVs, while special locations, such as Remote Depots (RDs) with UAVs and Virtual Hubs (VHs) for UAV deployment facilitation, are introduced. The framework considers the presence of Restricted Zones (RZs) for UAV flights. Part of the methodology is implemented in a GIS environment, taking advantage of modern tools for spatial analysis and optimal path planning. We have designed a tailored nested GA method for solving the occurring mode assignment and vehicle routing optimization problems and have implemented our workflow on a devised case study with benchmark characteristics. Our model responds well to unfavorable network types and demand locations, while the presence of RZs notably affects the expected solution and should be considered in the decision-making process.