Reviewerly/exHarmony huggingface dataset:
Inverse Image Frequency for Long-Tailed Image Recognition

The long-tailed distribution is a common phenomenon in the real world. Extracted large scale image datasets inevitably demonstrate the long-tailed property and models trained with imbalanced data can obtain high performance for the over-represented categories, but struggle for the under-represented categories, leading to biased predictions and performance degradation. To address this challenge, we propose a novel de-biasing method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inverse Image Frequency (IIF)</i> . IIF is a multiplicative margin adjustment transformation of the logits in the classification layer of a convolutional neural network. Our method achieves stronger performance than similar works and it is especially useful for downstream tasks such as long-tailed instance segmentation as it produces fewer false positive detections. Our extensive experiments show that IIF surpasses the state of the art on many long-tailed benchmarks such as ImageNet-LT, CIFAR-LT, Places-LT and LVIS, reaching 55.8% top-1 accuracy with ResNet50 on ImageNet-LT and 26.3% segmentation AP with MaskRCNN ResNet50 on LVIS. Code available at https://github.com/kostas1515/iif.
Consumer Behavior in the Metaverse

In a world where all living things have consumer identities, and this varies according to the periods, it is normal for people to have differentiations in their consumer behavior in accordance with the “Metaverse” environment. Consumer behaviors that are unique to the “Metaverse” are expected to vary from sectors, product types, and psychological situations to sociological phenomena. Consumer behavior in the “Metaverse”, which first started in gaming, event management, marketing communications, corporate marketing, architectural products, real estate, and money markets, is becoming increasingly widespread. Cultural heritage marketing, service, and commodity consumption in the health sector, cosmetics, and personal care, jewelry sectors are among the most up-to-date developments. Immersive consumption, content creation, decentralization, and development of behaviors with personalized dimensions are among the prominent aspects. The end of the sustainability debate and the unlimited power of the consumer to determine their behavior by existing in a different universe will be one of the main issues to be emphasized. It is necessary to emphasize the importance of ease, flexibility, and interactive activity as compelling and varied as the positives. Metaverse, along with its features that emphasize the immersive consumption, and uniqueness of each consumer, can develop creativity in consumer behavior, on the other hand, it may harbor the risk of alienation from self-identities. In the “Metaverse” which already emphasizes enjoyment, pleasure, speed, and agility, a preparation for some forward-looking hazards will enhance validity, value, meaning, and the benefits of the context.
Monitoring the source noise of a plug-and-play continuous variable quantum key distribution system

The source noise in plug-and-play continuous variable quantum key distribution (CV-QKD) system plays a crucial role in determining the secret key rate and transmission distance. In general, the source noise is considered untrusted and fully controlled by Eve, which is because the laser travels through the unsecure channel before being modulated. However, this may overestimate the key information stolen by Eve leading to an underestimation of the key rate share between the legal communication parties. Here, we use a beam-splitter with signal attenuation to model the source noise combined with source monitoring scheme to characterize the source imperfection in the plug-and-play CV-QKD system. We show that the performance of the plug-and-play CV-QKD can be significantly improved under the above scheme compared to the untrusted source model. Our numerical simulation results also show that the plug-and-play CV-QKD with source monitoring has a key generation rate close to that of a trusted source under the same simulation parameters.
Camera calibration method based on improved DLT algorithm

Camera calibration is one of the key tasks in the field of computer vision, finding extensive applications in various domains, including photogrammetry, 3D reconstruction, augmented reality, and autonomous driving. The Direct Linear Transform (DLT) algorithm, a classical approach for camera calibration, estimates camera parameters by solving a system of linear equations. However, traditional DLT methods may face accuracy and stability issues when dealing with noise, distortion, and nonlinear effects. To address these limitations, this paper introduces a camera calibration method based on the Improved DLT algorithm. This method incorporates distortion models into the traditional DLT algorithm and utilizes Levenberg-Marquardt (LM) optimization techniques to enhance calibration accuracy and stability. The key steps involve data preparation, DLT estimation, and nonlinear optimization. Experimental results demonstrate that the Improved DLT algorithm outperforms traditional DLT methods, particularly in cameras with significant distortion and wide field of view. It exhibits smaller reprojection errors and achieves a more uniform error distribution, especially at image edges. This research contributes by providing a more accurate and robust camera calibration method, offering valuable tools for computer vision applications, and advancing the development and application of computer vision technology.
Small object detection for mobile behavior recognition based on Wasserstein distance and partial convolution

While mobile phones offer convenience in our daily lives, they also introduce associated security risks. For instance, in high-security settings like confidential facilities, casual mobile phone usage and calls can inadvertently lead to the leakage of sensitive information. In response to such security concerns, this paper proposes an algorithm for recognizing mobile phone behaviors in high-resolution images with a wide field of view.To improve inference speed, we introduce the C3_Faster module. To address the challenge of detecting small-sized targets in images, we propose a boundary loss function. This reduces the scale sensitivity of IoU loss and mitigates model underperformance in detecting small objects. Experimental results demonstrate that, our improved algorithm achieved a 7.6% increase in mAP and a 38% improvement in inference speed. These findings highlight the effectiveness of our enhanced algorithm, making it well-suited for the task of mobile behavior recognition in secure environments.
[Study on the influence of Barbecure combined with Epley on residual dizziness of horizontal canal benign paroxysmal positional vertigo by SRM-vertigo diagnosis system].

Objective:To investigate the influence of Barbecure combined with Epley on residual dizziness of horizontal canal benign paroxysmal positional vertigo（HC-BPPV） by SRM-vertigo diagnosis system. Methods:A total of 406 patients diagnosed with HC-BPPV from Nov 2021 to Nov 2022 were enrolled by rapid axial roll test and Dix-Hallpike in the department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Xi'an Jiaotong University. The patients were divided into two groups by hospital card numbers, in which the numbers that were odd were considered as group A, and the numbers that were even were considered as group B. The group A underwent two circles of Barbecure repositioning procedure by SRM-vertigo diagnosis system, while the group B underwent two circles Barbecure combined with Epley repositioning procedure by SRM-vertigo diagnosis system. The treatment was stopped on the next day when two groups of patients were cured, and those who were not cured will continue treatment with the same method. Results:The cure rate of group A was 83.41%, and the cure rate of group B was 80.51%, the difference between the two groups was not-statistically significant difference（P>0.05）. The rate of residual dizziness of group A was 23.30%, the rate of residual dizziness of group B was 11.46%, the difference between the two groups was statistically significant（P<0.05）. Conclusion:The Barbecure combined with Epley otoliths repositioning maneuver by SRM-vertigo diagnosis system can significantly reduce the rate of residual dizziness after the treatment of HC-BPPV, and improve the quality of life of patients.目的：探讨SRM眩晕诊疗系统中Barbecure联合Epley对水平半规管BPPV（horizontal canal benign paroxysmal positional vertigo，HC-BPPV）残余症状影响的研究。 方法：选取2021年11月—2022年11月在西安交通大学第一附属医院耳鼻咽喉头颈外科以快速轴位滚转试验及Dix-Hallpike试验确诊的HC-BPPV 406例患者，按就诊卡单双号分为2组，A组（奇数）：采取Barbecure复位2圈；B组（偶数）：采取Barbecure联合Epley复位2圈。2组患者次日重新体位诱发痊愈即停止治疗，未痊愈者同法继续治疗。观察痊愈率及30 d内残余症状发生率。 结果：A组痊愈率83.41%，B组痊愈率80.51%，2组痊愈率差异无统计学意义（P>0.05）。A组残余症状发生率23.30%，B组残余症状发生率11.46%。2组残余症状发生率差异有统计学意义（P<0.05）。 结论：SRM眩晕诊疗系统中Barbecure联合Epley复位法可显著降低HC-BPPV痊愈后残余症状的发生率，明显改善患者生活质量。.
[Distribution, Sources, and Ecological Risk Evaluation of Heavy Metals in Surface Sediments of Taihu Lake].

For the surface sediment samples of Taihu Lake in 2010, the eight physicochemical indices of pH, temperature, Eh, water content, porosity, grain size, total phosphorus, and Loss-on-ignition were measured and analyzed, along with the contents of nine heavy metals:Cu, Zn, Ni, Cr, Pb, Ba, Mn, Co, and V. The order of magnitudes of heavy metal content of surface sediments in Taihu Lake was:Mn>Ba>Zn>Cr>V>Ni>Pb>Cu>Co. This suggested that the contents of the nine heavy metals were beyond the background value, which had a close connection to the geology of the Taihu Lake Basin and were influenced by human activity to varying degrees. The clustering analysis and the spatial distribution of the heavy metals revealed that the concentrations of heavy metals in the North and South Taihu Lake sections decreased from the lake shore to the lake center, the concentrations of heavy metals in the West Taihu Lake section increased from the lake shore to the lake center, and the distribution of heavy metals in the center of the lake remained relatively uniform. According to the correlation study, the metal elements were positively correlated with one another to varying degrees, indicating that they originate from the same source of pollution. According to the PCA and PMF analyses, there were some different sources of heavy metals in Taihu Lake, in which the transportation and industrial complex source were the most important sources, the diagenesis was the second major source, and agriculture was the third major source. Furthermore, the heavy metal pollution was evaluated using the geoaccumulation and the potential ecological risk indices. This offers a solid theoretical backing for the future management of heavy metal pollution in Taihu Lake.
The Study of Velocity Field in Front Opening Unified Pod by CAE

The front opening unified pod (FOUP) is a packing box for contamination control for semiconductor wafer transport. As the wafer fabrication process developes towards nanoor atom level, the semiconductor wafer storage device should advance from the particle prevention function into the airborne molecular contamination (AMC) removal function. Therefore, it is necessary to design/redesign a function for removing AMC or moisture inside the FOUP. This study used the design of leading diffuser tubes in the FOUP and pores in the surfaces of diffuser tubes to generate gas diffusion. This is to achieve a uniform distribution of the wafer surface velocity field and a uniform dehumidification function of the wafer surface. Based on the analysis results, when circular diffuser tubes are introduced in the FOUP and the intake air flow was set at 0.2-0.3 m 3 /hr, the interlayer wafer surface in the FOUP could achieve uniform distribution of velocity field. As a result, the humidity difference among various zones of wafer surface could be reduced, and the yield and quality of the wafer cutting process could be controlled.
A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave “picking” directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution.
Comprehensive robustness evaluation of an automatic writer identification system using convolutional neural networks

&lt;p&gt;This research paper presents a convolutional neural network (CNN) model for identifying handwritten Urdu characters. A dataset of 38 fundamental Urdu characters from 100 different writers in the Kashmir valley was manually collected. The developed system was trained on a training dataset of 30,400 samples and verified on a test dataset of 7600 samples, and it outperformed previously proposed AI based writer identification systems in Urdu language with an identification rate of 91.44 percent for 38 classes. This study highlights the effectiveness of deep learning techniques in solving the challenging task of the Urdu writer identification. The findings demonstrate the potential of the developed CNN model for real-world applications in handwritten character recognition and verification systems. Future work involves expanding the dataset to include numerals and isolated characters for improved system performance.&lt;/p&gt;
Application Of VR In the Field of Education

VR in virtual reality and augmented reality technology is a new technology applied in the field of education in recent years. This paper combs and analyses the educational application research from three aspects: educational games, art education and political education. First, starting from education, the author expounds that virtual reality education is based on the teaching concept of "interesting" games and takes education and teaching objectives as the design concept. Then, it introduces the performance of virtual reality technology in the field of art. This part explains the important role of virtual reality technology in the field of art education. It mainly expounds the practical exploration of VR in teaching, the value of VR in art design teaching, the content of VR in the new path of art design curriculum reform, and the innovation of VR teaching. Finally, the application of virtual reality technology in ideological and political education is proposed. At the end of this paper, the application of VR in these three aspects is summarized and prospected.
Obstacles to Using E-learning and Distance Learning in Jordanian Universities in the light of the Coronavirus Pandemic from the Students Point of View

This study aims to identify barriers to the use of e-learning and distance learning in Jordanian universities from the perspective of students in Jordan during the outbreak of the COVID-19 pandemic. The study used descriptive methodology to develop a questionnaire with 50 items, all of which were arranged in a behavioral order to match the structure of the interview items. Validity and reliability of the instrument were evaluated, and the instrument was applied to a sample of students (716), who were randomly selected. The results of the study showed that there are barriers to using e-learning and distance learning in Jordanian universities according to students opinions about the coronavirus pandemic. The results also show that there are significant differences in student responses regarding barriers to online learning. It also covered the barriers and challenges that prevent this change, such as poor internet connection and lack of interest, showing the need for new courses to improve students experience in online learning.
Binocular vision vibration measurement based on pixel coordinate matching of inner corner points in a chequerboard

A binocular vision measurement system provides a simple method for obtaining three-dimensional vibration data from moving objects, which is suitable for vibration monitoring of large structures such as bridges. Aiming to address the problem that the feature selection process for binocular visual inspection affects the measurement accuracy, chequerboard feature points are selected in this paper for carrying out a visual displacement measurement method. Firstly, pixel coordinate matching of the inner corner points in the chequerboard is completed and then a binocular vision measurement system is established. The measurement results are compared with using circular feature points. Secondly, the binocular vision measurement model is applied to the vibration measurement of a cantilever beam. Using comparisons with a three-axis acceleration sensor, the effectiveness and accuracy of this method are evaluated. Finally, the method is applied to measure the vibration of the cantilever beam under different load conditions and its vibration characteristics are analysed. The results show that the accuracy of the binocular vision measurement method based on pixel coordinate matching of the inner corner points in the chequerboard is higher than that using circular feature points. From comparisons with the acceleration sensor, the measurement error of this method is found to be small. In addition, the method can effectively analyse the vibration performance of a cantilever beam under different load conditions. Therefore, this measurement method is effective and provides a theoretical basis for the identification of vibration characteristics in large engineering structures.
Augmenting Home Objects to Promote Physical Activity at Home

Exercise video games (exergames) can facilitate home exercising, which can help overcome some barriers to exercising. Augmented Reality (AR) headsets offer the potential to make in-home exergaming more safe, immersive, inclusive, and accessible, but there is limited research on AR for home-based exercising, let alone exergaming. We conducted semi-structured interviews as part of a participatory design approach to investigate how homes can be augmented to facilitate exercising. Thematic analysis revealed themes regarding suitable home exercises, and physical objects at home that can be augmented to facilitate different exercises. Results from our initial exploration can be used to guide the design of future AR systems for exercising and exergaming.
Comparative Analysis of the Readability of English Research Articles Written by Chinese and English Native Writers

This research tries to explore the linguistic factors affecting the citation rate of Chinese writer’s research articles. We selected 50 SCI journal article introductions written by English Native and Chinese writers respectively. We measured their readability using Coh-Metrix. We then analyzed the data based on Graesser &amp; McNamara’s theory of multi-level discourse comprehension. Our results show that the introductions written by the two types of writers differ significantly at the following levels: (1) at the discourse level, the referential cohesion score of the Chinese writer’s introductions was higher than that of the native speaker’s, indicating a higher readability; (2) at the situation model level, the deep cohesion and time sequential scores of the native writer’s introductions were higher than those of the Chinese writer’s, suggesting higher readability. Our findings also imply that although some readability indices of Chinese writers’ introductions are higher than those of the native writer’s, it does not necessarily indicate high readability literally, nor good writing quality, which could also be an explanation for the low citation rate. Moreover, the results show that the readability parameters of Chinese and native writer’s introductions differ at various levels, so it is hard to conclude that the readability alone impacts the citation rate.
Feature Space Augmentation for Negation Handling on Sentiment Analysis

One crucial issue affecting the performance of sentiment analysis tasks is negation. Handling negation involves determining the negation scope and negation cue. Feature space augmentation is one approach used to address negation. Feature space augmentation has been carried out by some previous researchers using a negation flag with the rule that the negation scope includes all words from the explicit negation cue to the punctuation mark. This study aimed to analyze the classifier's performance when negation handling was applied by adding a new rule for the negation scope. The new rule for determining the negation scope no longer took all words from the negation cue to the punctuation mark, but only considered or ignored words with certain POS tags. The results of this study showed that using the new rule for negation scope contributed to improving the performance of the classifier in sentiment analysis tasks. The proposed approach for negation handling was better than the previous approach in terms of accuracy, precision, recall, and f1-score.
Comparative Study of Herbal Leaves Classification using Hybrid of GLCM-SVM and GLCM-CNN

Indonesia is a tropical country with a diverse range of plants that ancient people used for traditional medicines. However, the similarity in shape of the leaves became an obstacle to distinguishing them. Therefore, technological advancements are expected to help identify the herbal leaves to use them right on target according to their efficacy. In this research, image classification of katuk (Sauropus Androgynus) and kelor (Moringa Oleifera) leaves is applied using 3 different algorithms i.e hybrid of Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Support Vector Machine (SVM) implementing 4 kernels namely linear, RBF, polynomial, and sigmoid; hybrid of GLCM and Convolutional Neural Network (CNN); and pure CNN. A dataset of 480 images has been collected with 2 different scenarios, including bright and dark intensities. Based on the result, a hybrid of GLCM and SVM showed the highest accuracy of 96% in the dark intensity test using a linear kernel, while sigmoid obtained the lowest accuracy of 35%. On the other hand, it has been discovered that CNN obtained the highest performance in the bright intensity test with an accuracy of 98%. While in the dark intensity test, a hybrid of GLCM and CNN is superior, obtaining 96% accuracy. In conclusion, CNN is more powerful for image classification with bright intensity. For dark intensity images, both the hybrid of GLCM+SVM (linear) and the hybrid of GLCM+CNN are fairly recommended.