Reviewerly/exHarmony huggingface dataset:
Financial Anti-Fraud Based on Dual-Channel Graph Attention Network

Authors:
raw_author_name: Suan Lee
raw_affiliation_string: School of Computer Science, Semyung University, Jecheon-si 27136, Republic of Korea

Abstract: This article addresses the pervasive issue of fraud in financial transactions by introducing the Graph Attention Network (GAN) into graph neural networks. The article integrates Node Attention Networks and Semantic Attention Networks to construct a Dual-Head Attention Network module, enabling a comprehensive analysis of complex relationships in user transaction data. This approach adeptly handles non-linear features and intricate data interaction relationships. The article incorporates a Gradient-Boosting Decision Tree (GBDT) to enhance fraud identification to create the GBDT–Dual-channel Graph Attention Network (GBDT-DGAN). In a bid to ensure user privacy, this article introduces blockchain technology, culminating in the development of a financial anti-fraud model that fuses blockchain with the GBDT-DGAN algorithm. Experimental verification demonstrates the model’s accuracy, reaching 93.82%, a notable improvement of at least 5.76% compared to baseline algorithms such as Convolutional Neural Networks. The recall and F1 values stand at 89.5% and 81.66%, respectively. Additionally, the model exhibits superior network data transmission security, maintaining a packet loss rate below 7%. Consequently, the proposed model significantly outperforms traditional approaches in financial fraud detection accuracy and ensures excellent network data transmission security, offering an efficient and secure solution for fraud detection in the financial domain.
Lip-Reading Advancements: A 3D Convolutional Neural Network/Long Short-Term Memory Fusion for Precise Word Recognition

Authors:
raw_author_name: Aristidis G. Vrahatis
raw_affiliation_string: Department of Informatics, Ionian University, 49100 Corfu, Greece

Abstract: Lip reading, the art of deciphering spoken words from the visual cues of lip movements, has garnered significant interest for its potential applications in diverse fields, including assistive technologies, human–computer interaction, and security systems. With the rapid advancements in technology and the increasing emphasis on non-verbal communication methods, the significance of lip reading has expanded beyond its traditional boundaries. These technological advancements have led to the generation of large-scale and complex datasets, necessitating the use of cutting-edge deep learning tools that are adept at handling such intricacies. In this study, we propose an innovative approach combining 3D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to tackle the challenging task of word recognition from lip movements. Our research leverages a meticulously curated dataset, named MobLip, encompassing various speech patterns, speakers, and environmental conditions. The synergy between the spatial information extracted by 3D CNNs and the temporal dynamics captured by LSTMs yields impressive results, achieving an accuracy rate of up to 87.5%, showcasing robustness to lighting variations and speaker diversity. Comparative experiments demonstrate our model’s superiority over existing lip-reading approaches, underlining its potential for real-world deployment. Furthermore, we discuss ethical considerations and propose avenues for future research, such as multimodal integration with audio data and expanded language support. In conclusion, our 3D CNN-LSTM architecture presents a promising solution to the complex problem of word recognition from lip movements, contributing to the advancement of communication technology and opening doors to innovative applications in an increasingly visual world.
Needs Analysis and Requirements Delivery: Guiding the Development Approach with an Intuitive Framework

Authors:
raw_author_name: Vijay Kanabar
raw_affiliation_string: Boston University

Abstract: Abstract The needs assessment and requirements engineering domains involve tasks that help us fully understand a project's motivation. The functions of this domain include exploring the problem or opportunity and recommending a possible solution and development approach to the problem. From a project perspective, this is a critical stage as we can understand the value a viable solution brings to the customer and identify an appropriate development approach. A business investing resources to develop a solution must be pragmatic about the product development approach. Our research paper presents a framework to aid decision-making within this context. It involves considering attributes such as delivery cadence, risk, degree of innovation, and requirements certainty. Knowledge of the impact of these attributes on project outcomes will help educate engineers to become successful entrepreneurs and product managers.
Exploring Virtual Reality as a Design Observation Training Tool for Engineering Students

Authors:
raw_author_name: Shanna Daly
raw_affiliation_string: University of Michigan
raw_author_name: Kathleen Sienko
raw_affiliation_string: University of Michigan

Abstract: Abstract Observation is an essential tool in human-centered engineering design. Observations allow engineering designers to collect rich data on design problem environments and stakeholder behaviors in ways that are not always possible with other methods, as well as allow for the identification of discrepancies in other data (e.g., verbal reports from stakeholders) (Blomberg & Burrel, 2009). Developing design observation skills presents challenges, however, as observation often involves uncontrolled environments and the management of significant levels of uncertainty, which engineering students have been shown to struggle with (Jordan, 2015). Practical observation experience may also require extra effort for students to reach observation locations outside the classroom. In addition, students may be confronted with high-pressure or sensitive observation environments like medical facilities in co-curricular and extracurricular design projects and internships, which are likely to be especially difficult to prepare for in a classroom. Virtual reality (VR) has been promoted as a promising design training tool for its ability to provide controlled, yet immersive access to scenarios that are otherwise difficult to replicate in a classroom (Özgen et al., 2021). There is mixed empirical evidence on the benefits and drawbacks of VR-based training simulations in higher education (Merchant et al., 2013), however, and no studies have characterized the use of VR for engineering design observation training. To explore the effectiveness of VR for supporting engineering design observation training, this study explored students' perceptions of a prototype VR-based design observation training tool. Four undergraduate engineering students were each interviewed twice: once after the VR training experience and again after in-person design observation practice within a clinical setting during a co-curricular design program. Approximately six hours of data were collected and analyzed inductively to characterize student perceptions of advantages, disadvantages, and effectiveness of VR as a training tool compared to conventional classroom training (i.e., lecture and in-classroom practice). We found that student participants reported VR-based design observation training to be a more engaging format than other classroom-based training formats, such as lectures or two-dimensional videos. Participants also reported that VR-based training did not fully prepare them for responding to the social queues and interactions present in in-person design observation experiences, however. Preliminary recommendations for VR-based observation training experiences and related future research are also discussed.
What makes a solar engineer?

Authors:
raw_author_name: Joseph Ranalli
raw_affiliation_string: Pennsylvania State University Hazleton

Abstract: The international response to climate change has driven growing demand for engineers trained to design solar energy systems and integrate them into electricity infrastructure.There is a need for some guidance as to what training is necessary for future engineers to meet this demand.This study gathers data from literature, industry perspectives and current educational practice to help establish suitable learning objectives for training undergraduate engineers to be prepared for solar project development.In general, results suggest that students be trained to understand the complete workflow of concepts related to design of photovoltaic systems including solar positioning, the solar resource and irradiance data sources, design of a photovoltaic system from both a solar resource and an electrical perspective and performing calculations to model or support validation of photovoltaic systems.Professionals also highlighted the importance of discussing various types of typical financing structures for solar energy systems, though these were less common in existing courses.The paper summarizes these outcomes with a proposed set of learning objectives that encompasses these highlight areas and that could form the backbone of an undergraduate course on solar energy engineering.
Exploring Magic Interactions for Collaboration in Virtual Reality Learning Factory

Authors:
raw_author_name: Tyler Hartleb
raw_affiliation_string: University of Calgary
raw_author_name: Haedong Kim
raw_affiliation_string: The Pennsylvania State University
raw_author_name: Richard Zhao
raw_affiliation_string: University of Calgary
raw_author_name: Faisal Aqlan
raw_affiliation_string: University of Louisville
raw_author_name: Hui Yang

Abstract: Abstract A hands-on curriculum that blends theory and practical skills is essential to teach manufacturing. An integral part of such a curriculum is a learning factory, which allows engineering students to experience the entire manufacturing cycle of a product in a realistic factory environment. In addition to learning the required technical skills, students can practice their collaborative skills and communication via teamwork in a learning factory. With virtual reality (VR), environments can be made using game engines that simulate their real-world equivalents, providing realistic experiences. Compared to traditional remote learning, VR-based learning together with online remote learning is experiential, allows for natural interaction, and is only limited by the capabilities of the hardware running the virtual environments. The cost of VR devices has dramatically reduced with standalone VR devices such as Meta Quest 2, making these devices a compelling option for specialized educational simulations. A VR Learning Factory should support synchronous collaboration of multiple learners in the same environment. This is a critical advantage of using VR, since collaboration is an essential skill for engineers. To maximize this benefit, it is imperative to develop an appropriate VR interaction mode, because it can greatly influence the effectiveness of collaborations. In this research, we explore multi-user interaction within the context of the VR Learning Factory and compare two modes of virtual user interaction that we call natural and magic. Magic interactions include three additional tools: object container, holographic representation, and multi-object selection. We conduct an analysis of the two modes of VR interaction in a craft production task and show increased performance of using magic interactions.
Brain-driven facial image reconstruction via StyleGAN inversion with improved identity consistency

Authors:
raw_author_name: Ziqi Ren
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xi’an 710071, China
raw_author_name: Jie Li
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xi’an 710071, China
raw_author_name: Xuetong Xue
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xi’an 710071, China
raw_author_name: Xin Li
raw_affiliation_string: Group 42 (G42), Abu Dhabi, United Arab Emirates
raw_author_name: Fan Yang
raw_affiliation_string: Group 42 (G42), Abu Dhabi, United Arab Emirates
raw_author_name: Zhicheng Jiao
raw_affiliation_string: Department of Diagnostic Imaging, Rhode Island Hospital, RI, USA; The Warren Alpert Medical School, Brown University, RI, USA
raw_author_name: Xinbo Gao
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xi’an 710071, China

Abstract: The reconstruction of visual stimuli from fMRI data represents a major technological and scientific challenge at the forefront of contemporary neuroscience research. Deep learning techniques have played a critical role in advancing decoding models for visual stimulus reconstruction from fMRI data. Particularly, the use of advanced GANs has resulted in significant improvements in the quality of image generation, providing a powerful tool for addressing the challenges of this complex task. However, none of these studies have taken into account the inherent characteristics of the stimulus contents themselves; This, in turn, leads to unsatisfactory outcomes, as demonstrated by the inconsistent identity between reconstructed faces and ground truth in the decoding of facial images. In order to tackle this challenge, we introduce a new framework aimed at enhancing the accuracy of reconstructing facial images from fMRI data. Our key innovation involves extracting and disentangling multi-level visual information from brain signals in the latent space and optimizing high-level features for facial identity control using identity loss. Specifically, our framework uses StyleGAN inversion to extract hierarchical latent codes from images, which are then bridged to fMRI data through transformation blocks. Additionally, we introduce a multi-stage refinement method to enhance the accuracy of reconstructed faces, which involves progressively updating fMRI latent codes with custom loss functions designed for both feature- and image-wise optimization. Our experimental results demonstrate that our proposed framework effectively achieves two critical objectives: (1) accurate facial image reconstruction from fMRI data and (2) preservation of identity characteristics with a high level of consistency.
Building bonds: an examination of relational bonding in continuous content contribution behaviors on metaverse-based non-fungible token platforms

Authors:
raw_author_name: Crystal T. Lee
raw_affiliation_string: Business School, Shantou University, Shantou, China

Abstract: Purpose The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their digital works. Despite this, no studies have examined the drivers of continuous content contribution behavior (CCCB) toward NFTs. Hence, this study draws on the theory of relational bonds to examine how various relational bonds affect feelings of psychological ownership, which, in turn, affects CCCB on metaverse platforms. Design/methodology/approach Using structural equation modeling and importance-performance matrix analysis, an online survey of 434 content creators from prominent NFT platforms empirically validated the research hypotheses. Findings Financial, structural, and social bonds positively affect psychological ownership, which in turn encourages CCCBs. The results of the importance-performance matrix analysis reveal that male content creators prioritized virtual reputation and social enhancement, whereas female content creators prioritized personalization and monetary gains. Originality/value We examine Web 3.0 and the NFT creators’ network that characterizes the governance practices of the metaverse. Consequently, the findings facilitate a better understanding of creator economy and meta-verse commerce.
Quantum steering and coherence evolution of two atoms under noisy environments

Authors:
raw_author_name: K. Berrada
raw_affiliation_string: Department of Physics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
raw_author_name: A. Sabik
raw_affiliation_string: Department of Physics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
raw_author_name: H. Eleuch
raw_affiliation_string: College of Arts and Sciences, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, P.O. Box 27272, United Arab Emirates; Institute for Quantum Science and Engineering, Texas A&M University, College Station, TX 77843, USA

Abstract: By considering two kinds of non-Markovian (NM) environments, the dynamical behavior of quantum steering, Bell nonlocality and coherence of a system of two atoms initially immersed in a vacuum has been studied. In this context, the cases of a cavity little off-resonance with the atomic transition frequency and non-perfect photonic band-gap (NPPBG) has been considered. The control and preservation of the quantumness measures is discussed in terms of the physical parameters. Furthermore, the situations for which the atoms state is steerable with the presence of Bell nonlocality and coherence has been illustrated. Finally, the monotonic dependence among the quantumness measures is displayed with respect to the model parameters.
Y-GAN: Learning dual data representations for anomaly detection in images

Authors:
raw_author_name: Vitomir Štruc
raw_affiliation_string: Faculty of electrical engineering, University of Ljubljana, Trzaska cesta 25, 1000 Ljubljana, Slovenia

Abstract: We propose a novel reconstruction-based model for anomaly detection in image data, called’Y-GAN’. The model consists of a Y-shaped auto-encoder and represents images in two separate latent spaces. The first captures meaningful image semantics, which are key for representing (normal) training data, whereas the second encodes low-level residual image characteristics. To ensure the dual representations encode mutually exclusive information, a disentanglement procedure is designed around a latent (proxy) classifier. Additionally, a novel representation-consistency mechanism is proposed to prevent information leakage between the latent spaces. The model is trained in a one-class learning setting using only normal training data. Due to the separation of semantically-relevant and residual information, Y-GAN is able to derive informative data representations that allow for efficacious anomaly detection across a diverse set of anomaly detection tasks. The model is evaluated in comprehensive experiments with several recent anomaly detection models using four popular image datasets, i.e., MNIST, FMNIST, CIFAR10, and PlantVillage. Experimental results show that Y-GAN outperforms all tested models by a considerable margin and yields state-of-the-art results. The source code for the model is made publicly available at https://github.com/MIvanovska/Y-GAN.
Self-Distillation and Pinyin Character Prediction for Chinese Spelling Correction Based on Multimodality

Authors:
raw_author_name: Li He
raw_affiliation_string: CNONIX National Standard Application and Promotion Lab, Beijing 100144, China; School of Information Science and Technology, North China University of Technology, Beijing 100144, China
raw_author_name: Feng Liu
raw_affiliation_string: CNONIX National Standard Application and Promotion Lab, Beijing 100144, China; School of Information Science and Technology, North China University of Technology, Beijing 100144, China
raw_author_name: Jie Liu
raw_affiliation_string: CNONIX National Standard Application and Promotion Lab, Beijing 100144, China; School of Information Science and Technology, North China University of Technology, Beijing 100144, China
raw_author_name: Jianyong Duan
raw_affiliation_string: CNONIX National Standard Application and Promotion Lab, Beijing 100144, China; School of Information Science and Technology, North China University of Technology, Beijing 100144, China
raw_author_name: Hao Wang
raw_affiliation_string: CNONIX National Standard Application and Promotion Lab, Beijing 100144, China; School of Information Science and Technology, North China University of Technology, Beijing 100144, China

Abstract: Chinese spelling correction (CSC) constitutes a pivotal and enduring goal in natural language processing, serving as a foundational element for various language-related tasks by detecting and rectifying spelling errors in textual content. Numerous methods for Chinese spelling correction leverage multimodal information, including character, character sound, and character shape, to establish connections between incorrect and correct characters. Research indicates that a majority of spelling errors stem from pinyin similarity, with character similarity accounting for half of the errors. Consequently, effectively modeling character pinyin and character relationships emerges as a key challenge in the CSC task. In this study, we propose enhancing the CSC task by introducing the pinyin character prediction task. We employ an adaptive weighting method in the pinyin character prediction task to address predictions in a more granular manner, achieving a balance between the two prediction tasks. The proposed model, SPMSpell, utilizes ChineseBERT as an encoder to capture multimodal feature information simultaneously. It incorporates three parallel decoders for character prediction, pinyin prediction, and self-distillation modules. To mitigate potential overfitting concerning pinyin, a self-distillation method is introduced to prioritize character information in predictions. Extensive experiments conducted on three SIGHAN benchmark tests showcase that the model introduced in this paper attains a superior level of performance. This substantiates the correctness and superiority of the adaptive weighted pinyin character prediction task and underscores the effectiveness of the self-distillation module.