Reviewerly/exHarmony huggingface dataset:
- Evaluation of an Open Access Version of the Trier Social Stress Test in Virtual Reality

Authors:
raw_author_name: Daniel Zielasko
raw_affiliation_string: Department of Human-Computer-Interaction, University of Trier
raw_author_name: Benjamin Weyers
raw_affiliation_string: Department of Human-Computer-Interaction, University of Trier
raw_author_name: Gregor Domes
raw_affiliation_string: Department of Biological and Clinical Psychology, University of Trier

Abstract: The Trier Social Stress Test (TSST) is one of the most frequently used psychosocial stressors under laboratory conditions. However, the implementation causes significant costs and is sensitive towards protocol variations, for instance the conduct of the judges. Therefore, in recent years, there has been growing interest in adapting the TSST in virtual reality (VR) to increase the standardization and economy of the procedure. This study aimed to validate a freely accessible version of the TSST-VR. Heart rate, salivary cortisol, and subjective stress ratings were measured in fifty male participants randomly assigned to the TSST-VR or a control procedure in VR. The analysis revealed a significant rise in subjective stress as well as in salivary cortisol in the stress condition compared to the control group indicating a significant induction of stress. Based on these findings, we conclude that the Open TSST-VR provides a suitable tool for eliciting psychosocial stress. In addition, this open access version offers various options to customize the VR environment to individual needs and enables collaborations between research groups.
UCGAN: a contrastive learning-based unaligned image translation model with importance reweighting

Authors:
raw_author_name: Shibin Wang
raw_affiliation_string: Henan Normal University, School of Computer and Information Engineering, Xinxiang, China (China); Key Laboratory of Artifcial Intelligence and Personalized Learning in Education of Henan Province, Xinxiang, China (China)
raw_author_name: Shiying Zhang
raw_affiliation_string: Henan Normal University, School of Computer and Information Engineering, Xinxiang, China (China)
raw_author_name: Dong Liu
raw_affiliation_string: Henan Normal University, School of Computer and Information Engineering, Xinxiang, China (China); Key Laboratory of Artifcial Intelligence and Personalized Learning in Education of Henan Province, Xinxiang, China (China)
raw_author_name: Jinghui Zhong
raw_affiliation_string: South China University of Technology, School of Computer Science and Engineering, Guangzhou, China (China)

Abstract: Image-to-image translation with unaligned domains is a seminal work that is capable of automatically selecting suitable image of unaligned dataset for translation. Unaligned datasets often contain irrelevant images that disrupt translation. However, the accuracy of selecting appropriate images is an issue that needs to be urgently improved. To improve the grasp of image features and enhance the performance of unaligned image-to-image translation without changing much semantic information, we propose a novel framework unaligned image-to-image translation model based on contrastive learning and generative adversarial networks (UCGAN), which can focus on both local and global features of the image. Inspired by contrastive learning, here we propose the patch-by-patch feature contrastive (PPFC). PPFC reuses the encoder of the generator to extract multi-layer features of different image patches. We then normalize the various features using L1-norm instead of L2-norm. To further improve translation performance, we also set up paired feature comparisons. We compare our proposed method UCGAN with state-of-the-art algorithms and the experimental results have demonstrated the superiority and practicality of our method.
Quantifying the Impact of Motions on Human Aiming Performance: Evidence from Eye Tracking and Bio-Signals

Authors:
raw_author_name: Yuzhang Li
raw_affiliation_string: Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
raw_author_name: Xinming Li
raw_affiliation_string: Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
raw_author_name: Bin Zheng
raw_affiliation_string: Department of Surgery, University of Alberta, Edmonton, AB T6G 2B7, Canada

Abstract: Working on a moving platform can significantly impede human performance. Previous studies on moving vehicles have often focused on the overall impact on general task performance, whereas our study's emphasis is on precise hand movements, exploring the interaction between body motion and the escalation of task difficulty. We recruited 28 participants to engage in reciprocal aiming tasks, following Paul Fitts's setting, under both in-motion and stationary conditions. The task index of difficulty (ID) was manipulated by varying the width of the targets and the distance between the targets. We measured participants' movement time (MT), performance errors, and monitored their eye movements using an eye-tracking device, heart rate (HR), and respiration rate (RR) during the tasks. The measured parameters were compared across two experimental conditions and three ID levels. Compared to the stationary conditions, the in-motion conditions degraded human aiming performance, resulting in significantly prolonged MT, increased errors, and longer durations of eye fixations and saccades. Furthermore, HR and RR increased under the in-motion conditions. Linear relationships between MT and ID exhibited steeper slopes under the in-motion conditions compared to the stationary conditions. This study builds a foundation for us to explore the control mechanisms of individuals working in dynamic and demanding environments, such as pilots in airplanes and paramedics in ambulances.
Use of VR in Higher Education: A Systematic Review (2014-2023)

Authors:
raw_author_name: Melor Md Yunus
raw_author_name: Karmila Rafiqah M. Rafiq

Abstract: With the ongoing advancement of intelligent information technology, virtual reality (VR) higher education, during this new era of reform, the incorporation of VR technology has led to an evolution of teaching methods. VR technology has been integrated into education and teaching, for example through virtual laboratories, digital environments and simulation-based training, thus providing higher education institutions with diverse teaching approaches and developmental opportunities.This study systematically categorises the utilisation of VR technology in tertiary education through the analysis of two databases, Web of Science (WoS) and Scopus.The systematic review process was conducted by referring to PRISMA (Preferred Reporting Items forSystematic Review and Meta-Analyses) as a guide, and 29 articles were extracted from the 478 articles for the period 2014 to 2023, taking into account exclusion and inclusion criteria.Firstly, the review revealed significant benefits of using VR applications in higher education, which were categorised into three categories. Secondly, the review examined challenges related to the use of VR in higher education content and categorised them into five categories.Overall, this review consolidates research discoveries and pragmatic insights to offer a refined comprehension of the present condition of VR in advanced education. Moreover, it provides recommendations for future development, which holds significant value for the effective use of VR technology in higher education.
PONG: A computational model of visual word recognition through bihemispheric activation.

Authors:
raw_author_name: Joshua Snell
raw_affiliation_string: Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam.

Abstract: Orthographic processing is an open problem. Decades of visual word recognition research have fueled the development of various theoretical frameworks. Although these frameworks have had good explanatory power, various recent results cannot be satisfactorily captured in any model. In order to account for old and new phenomena alike, here I present a new theory of how the brain computes letter positions. According to PONG (which describes the Positional Ordering of N-Grams), each hemisphere of the brain comprises a set of mono- and multigram detectors. The crux is that the detectors for a given N-gram are activated to different extents in their respective hemispheres, depending on where in the visual field the N-gram is located. This differential activity allows the brain to estimate the leftness or rightness of that N-gram, whereby word activation is a function of the N-gram's identity plus its laterality relative to that of other activated N-grams. Simulations with PONG suggest that the framework effectively accounts for classic phenomena, as well as newer phenomena and cross-linguistic differences that cannot be explained by other models. I also reflect on the neurophysiological plausibility of the model and avenues for future inquiry. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
The application of NLP in information retrieval

Authors:
raw_author_name: Xurui Wang
raw_affiliation_string: Dalian University of Technology

Abstract: The field of Natural Language Processing (NLP) has experienced impressive advancements and has found diverse applications. This paper presents a comprehensive review of the development of NLP in the field of information retrieval. It explores different stages of NLP techniques and methods, including keyword matching, rule-based approaches, statistical methods, and the utilization of machine learning and deep learning technologies. Furthermore, the paper provides detailed insights into the specific applications of NLP in domains such as academic information retrieval, medical information retrieval, travel information retrieval, and e-commerce information retrieval. It analyzes the current state of NLP applications in these domains, highlights their advantages, and discusses their associated limitations. Finally, the paper emphasizes the continuous advancement of the NLP field, with a particular focus on semantic understanding, personalized retrieval, and multimodal information retrieval, to better adapt to diverse data types and user requirements. The paper concludes by summarizing the main points discussed and providing future directions.
Demixing microwave signals using system-on-chip photonic processor

Authors:
raw_author_name: Chu Wu
raw_affiliation_string: Department of Electronic Engineering, Tsinghua University, 100084, Beijing, China
raw_author_name: Xing Lin
raw_affiliation_string: Beijing National Research Center for Information Science and Technology, Tsinghua University, 100084, Beijing, China

Abstract: Abstract The integrated photonic processor, co-packaged with electronic peripherals, is proposed for blind source separation of microwave signals, which separates signal-of-interest from dynamic interference with real-time adaptability.
Three-dimensional Image Authentication from Multi-view Images

Authors:
raw_author_name: Zhen Leng
raw_author_name: Bo Liu

Abstract: Three-dimensional (3D) optical authentication is important for modern information security. Existing 3D optical authentication methods rely on integral imaging devices, necessitating meticulous calibration and incurring high transmission overhead. To streamline the acquisition of 3D information, this paper introduces a novel 3D optical authentication approach, to the best of our knowledge, based on the construction of 3D data from multi-view images. The proposed method simplifies 3D projection by generating fixed-viewpoint elemental images, eliminating the need for additional viewpoint information during transmission and authentication. Compressed sensing is used for compression during transmission, and a deep learning network is designed for 3D reconstruction, enhancing the recovery. Experimental outcomes confirm the efficiency of our proposed approach for 3D authentication across diverse datasets.
Application of the Savitzky-Golay filter in multi-spectral signal processing

Authors:
raw_author_name: Syahrial Syahrial
raw_affiliation_string: Department of Electrical Engineering and Computer, Engineering Faculty, Universitas Syiah Kuala
raw_author_name: Melinda Melinda
raw_affiliation_string: Department of Electrical Engineering and Computer, Engineering Faculty, Universitas Syiah Kuala
raw_author_name: Junidar Junidar
raw_affiliation_string: Department of informatics, Mathematics and Natural Sciences Faculty, Universitas Syiah Kuala
raw_author_name: Safrizal Razali
raw_affiliation_string: Department of Electrical Engineering and Computer, Engineering Faculty, Universitas Syiah Kuala
raw_author_name: Zulhelmi Zulhelmi
raw_affiliation_string: Department of Electrical Engineering and Computer, Engineering Faculty, Universitas Syiah Kuala

Abstract: Multi-spectral signals are the result of the interaction between electromagnetic energy and the test material, which is then displayed by the signal fluctuation pattern of the test material. Signal fluctuations are inaccuracies in the peak amplitude of a signal caused by noise in the data. This fluctuation pattern reflects the properties of the test material, especially in this case H2O. To overcome this problem, it is necessary to use the right filter to smooth the signal and reduce the noise in the data so that the fluctuation pattern obtained is clearer and more accurate. This research involves the segmentation of HF fluctuation patterns, followed by the application of a Savitzky-Golay filter for signal smoothing. Signal quality is assessed objectively by calculating the Signal to Noise Ratio (SNR) and Mean Square Error (MSE). The research results show that the Savitzky-Golay filter succeeded in reducing noise and producing clearer fluctuation patterns. The SNR value varies, with the largest value reaching 16.6146 dB, and the smallest value being 3.0171 dB. This research contributes to a new method, namely the Savitzky-Golay adaptive filter, to identify multi-spectral signal fluctuation patterns more effectively, thereby enabling more accurate identification of fluctuation patterns. Apart from that, this research also provides insight into the characteristics of H2O which can be identified through fluctuation patterns, especially in certain segments with high amplitude. This method has potential for applications in various fields, especially in precise multi-spectral signal analysis.
Ensemble and Pre-Training Approach for Echo State Network and Extreme Learning Machine Models

Authors:
raw_author_name: Lingyu Tang
raw_affiliation_string: School of Science, Civil Aviation Flight University of China, Guanghan 618307, China
raw_author_name: Mengyao Wang
raw_affiliation_string: Business School, Sichuan Normal University, Chengdu 610101, China
raw_author_name: Chunyu Zhao
raw_affiliation_string: Business School, Sichuan Normal University, Chengdu 610101, China

Abstract: The echo state network (ESN) is a recurrent neural network that has yielded state-of-the-art results in many areas owing to its rapid learning ability and the fact that the weights of input neurons and hidden neurons are fixed throughout the learning process. However, the setting procedure for initializing the ESN's recurrent structure may lead to difficulties in designing a sound reservoir that matches a specific task. This paper proposes an improved pre-training method to adjust the model's parameters and topology to obtain an adaptive reservoir for a given application. Two strategies, namely global random selection and ensemble training, are introduced to pre-train the randomly initialized ESN model. Specifically, particle swarm optimization is applied to optimize chosen fixed and global weight values within the network, and the reliability and stability of the pre-trained model are enhanced by employing the ensemble training strategy. In addition, we test the feasibility of the model for time series prediction on six benchmarks and two real-life datasets. The experimental results show a clear enhancement in the ESN learning results. Furthermore, the proposed global random selection and ensemble training strategies are also applied to pre-train the extreme learning machine (ELM), which has a similar training process to the ESN model. Numerical experiments are subsequently carried out on the above-mentioned eight datasets. The experimental findings consistently show that the performance of the proposed pre-trained ELM model is also improved significantly. The suggested two strategies can thus enhance the ESN and ELM models' prediction accuracy and adaptability.
Using Metaverse-based Learning as a Strategy to Improve Higher Education: A Systematic Review (2019-2023)

Authors:
raw_author_name: Melor Md Yunus
raw_author_name: Karmila Rafiqah M. Rafiq

Abstract: With the leap of modern technology, metaverse-based learning is booming. It breaks through the limitations of space and time and promotes the sharing of high-quality educational resources. Among them, user attitudes of using metaverse-based learning technology in higher education is one of the most important contents. Because students of every major are involved in improving their professional ability, and not everyone can experience a professional environment, metaverse-based learning plays an important role in higher education and professional training. Improving teaching and learning quality of higher education institutions in the form of metaverse-based learning is one of the crucial factors to ensure its sustainable development. However, the studies on metaverse-based learning are insufficient though it has great significance in the higher education area. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, this study takes past articles as the research objects, and one main database named Scopus with one supporting database named Web of Science (WoS) was used. A total of 12 articles were selected and 3 themes emerged from it, namely learning acceptance construction, learning environment construction, and learning assessment construction. Last, some points were implemented in the study which can be the keystone of future research.
Identification of Multi-Mineral-Species Geochemical Anomalies using BME and the Spectrum Separable Module-constrained Variational Autoencoder

Authors:
raw_author_name: Bo Zhao
raw_affiliation_string: Research Center of Big Data Technology, Nanhu Laboratory, Jiaxing 314000, China
raw_author_name: Dehui Zhang
raw_affiliation_string: School of Earth Sciences and Resources, China University of Geosciences, Beijing 100086, China; Shaanxi Geo-Mineral Comprehensive Geological Brigade Co., Ltd, Weinan 714000, China
raw_author_name: Panpan Tang
raw_affiliation_string: Research Center of Big Data Technology, Nanhu Laboratory, Jiaxing 314000, China
raw_author_name: Lin An
raw_affiliation_string: Shaanxi Geo-Mineral Comprehensive Geological Brigade Co., Ltd, Weinan 714000, China

Abstract: This study presented a dual-drive multi-mineral-species anomaly detection system which involves the combined use of BME (Bayesian Maximum Entropy), SSM (Spectral Separable Module), High-Order Factor Analysis, a geologically constrained loss function, as well as a mixed Gaussian distribution-based thresholding algorithm, attaching to a deep CAE (Convolutional Auto-Encoder). We have achieved several firsts rarely considered in the existing literature, e.g., previous works focus mainly on separating single-mineral-species but multi-elemental anomalies, while we attempted to recognize the multi-mineral-species anomalies; previous works pay more attention to data, while we answered how to discover the ore-related correlations hidden within the input data; previous works fail to integrate soft data in a quantitative fashion, while we achieved that by capitalizing on BME and the stratigraphic combination entropy. A series of comparative experiments have demonstrated its advantages over other state-of-the-art approaches. Finally, we obtained a mineral-occurrence identification rate ( δ ) ranging from 36.87% to 61.46% versus the anomaly area ranging from 33.75% to 55.38% for each metalliferous anomaly division.
Exploring the Impact and Factors to Consider in Higher Education Practice: A Study in Reference to Generative Artificial Intelligence

Authors:
raw_author_name: Dr. Ajay Kumar Varshney, Dr. Ankit Garg, Dr. Sarjue Pandita

Abstract: This study examines the impact of generative artificial intelligence (GAI) on higher education, focusing on the experiences of international students. It highlights the challenges of recognizing AI content and the potential for unfair charges against students. The essay also addresses biases in AI models and the need for justice and equity in AI judgments. It advocates for a well-rounded strategy that addresses both potential and problems, emphasizing AI literacy and ethical considerations. The essay incorporates the AI competency framework to ensure fair use of AI in the classroom.
Reflection of Federal Data Protection Standards on Cloud Governance

Authors:
raw_author_name: Ebru Celikel Cankaya

Abstract: As demand for more storage and processing power increases rapidly, cloud services in general are becoming more ubiquitous and popular. This, in turn, is increasing the need for developing highly sophisticated mechanisms and governance to reduce data breach risks in cloud-based infrastructures. Our research focuses on cloud governance by harmoniously combining multiple data security measures with legislative authority. We present legal aspects aimed at the prevention of data breaches, as well as the technical requirements regarding the implementation of data protection mechanisms. Specifically, we discuss primary authority and technical frameworks addressing least privilege in correlation with its application in Amazon Web Services (AWS), one of the major Cloud Service Providers (CSPs) on the market at present.
Research on Speech Emotion Recognition Analysis Based on Deep Learning

Authors:
raw_author_name: Chunhao Li

Abstract: Speech is a common way of communication in human daily life, which is very natural and easy to understand. In the process of communication, one will also express their emotions to the other party. Through emotional expression, the distance between people is smaller and communication is more convenient. Speech emotion recognition is also the key to human-computer emotional interaction, and effective recognition of speech emotions can improve speech intelligibility. The number of speech datasets is usually large, with several G or even larger. This article proposes a method of preprocessing the speech dataset to extract feature data folders, and then using the feature data folder directory as input to train the model. This method helps to improve the efficiency, repeatability, and manageability of data processing.