Reviewerly/exHarmony huggingface dataset:
A Decentralized Auction Model for Sustainable Housing Rental Market

The Internet of Things has driven the transformation of the traditional offline rental model to online rental platforms such as Zillow, Zigbang, and Lianjia. These platforms provide abundant housing options, detailed information, convenient communication, and greater price transparency. However, existing online rental platforms are still centralized and rely on third-party intermediaries for settlement, which raises concerns about the integrity of real estate data, transaction security, and price transparency. To improve the real estate leasing business, we propose leveraging the decentralized nature of blockchain technology. By eliminating intermediaries, we can ensure customer privacy and reduce transaction costs. This strategy revolves around smart contracts as a core component, automatically matching landlords and tenants based on their offers, achieving transparency, and maintaining anonymity for both parties. We introduce a novel auction system that combines the features of interactive auctions and sealed-bid auctions, enabling landlords and tenants to engage in range bidding. In this study, we developed a detailed algorithm to define how smart contracts function during the auction process. Through a cost analysis, we demonstrate the economic feasibility and provide a secure, transparent, and reliable approach for online auctions.
The Relationship Between the Intensity of Gaming and Aggressive Behavior in Adolescents Playing Games MMOFPS (Massively Multiplayer Online First- Person Shooter)

This study aimed to analyze the association between the frequency of playing games and aggressive behavior. This study consisted of 350 subjects between 12 and- 24 years old who were selected through purposive sampling. The data were tested using validity and reliability tests to measure the level of validity of the study instrument with a significance transfer of 0.5%, while also using the Pearson correlation test and the normality test. The results of the analysis showed that there was a positive correlation value with a significance value of (sig) of 0.786 &lt;0.05. This shows that there is a positive relationship between the frequency of playing games MMOPS and aggression behavior.&#x0D; Keywords: intensity of Gaming, aggressive behavior, MMOFPS
Multimodal fusion sensitive information classification based on mixed attention and CLIP model1

Social network attackers leverage images and text to disseminate sensitive information associated with pornography, politics, and terrorism,causing adverse effects on society.The current sensitive information classification model does not focus on feature fusion between images and text, greatly reducing recognition accuracy.To address this problem, we propose an attentive cross-modal fusion model (ACMF), which utilizes mixed attention mechanism and the Contrastive Language-Image Pre-training model.Specifically, we employ a deep neural network with a mixed attention mechanism as a visual feature extractor. This allows us to progressively extract features at different levels. We combine these visual features with those obtained from a text feature extractor and incorporate image-text frequency domain information at various levels to enable fine-grained modeling. Additionally, we introduce a cyclic attention mechanism and integrate the Contrastive Language-Image Pre-training model to establish stronger connections between modalities, thereby enhancing classification performance.Experimental evaluations conducted on sensitive information datasets collected demonstrate the superiority of our method over other baseline models. The model achieves an accuracy rate of 91.4% and an F1-score of 0.9145. These results validate the effectiveness of the mixed attention mechanism in enhancing the utilization of important features. Furthermore, the effective fusion of text and image features significantly improves the classification ability of the deep neural network.
Volume changes measurement of elastomers using 3D DIC

The presented research aims at measuring the volume changes of elastomers using digital image correlation (DIC) in the 3D configuration (stereo DIC). The deformation measurement method in stereo configuration was applied during the mechanical testing of two types of test specimens (dumbbells, cylinders) in uniaxial tension. In this configuration, two cameras were used. The test specimens were measured up to a specified strain value, and the measured data were used to obtain the strain dependence of Poisson’s ratio and the bulk modulus, which are crucial for hyperelastic models. The measurement results reveal that the stereo DIC method provides relatively less scattered data in the low-strain regions for dumbbell-shaped test specimens and is suitable for measuring various test specimen shapes.
Analysis of peel and shear strains in cracked lap shear specimens subjected to fatigue loading using digital image correlation

Adhesive bonding presents many advantages, such as efficient manufacturing and improved structural performance [1]. However, in structures subjected to fatigue, cracks might initiate and propagate in joints, leading to in-service failure [2]. Most adhesively bonded joints are subjected to combination of peel and shear loads, so mixed I+II mode loading conditions are present [3]. In this work, Cracked Lap Shear specimens, which feature mixed I+II mode loading conditions, were tested under fatigue loading. During tests, crack growth was monitored using Visual Testing and Digital Image Correlation. With Digital Image Correlation, opening and sliding displacements in the bondline were extracted from the substrates’ displacement fields and compared against a Finite Element Model, revealing a highly strained process zone ahead of the crack tip. Results highlight the usefulness of DIC in capturing the deformation behaviour of adhesive joints under mixed mode loading conditions.
Pengembangan Media Permainan Fruit word puzzle pada Pembelajaran Bahasa Indonesia pada Siswa Tunarungu di SDN Mojorejo 1 Kota Batu

This research aims to find out whether the fruit word puzzle game media can be used as a learning medium in Indonesian language learning, to find out the reactions of deaf students to the use of fruit word puzzle game media, and to develop the fruit word puzzle game media for deaf students. The method used in this research is development or Research and Development (R&amp;D), using the ADDIE development model (Analysis, Design, Develop, Implementation and Evaluation). The subject of this research uses a single subject. Research data collection methods include interviews, observation, observation sheets on 3 aspects (cognitive, psychomotor and affective aspects), and documentation. The results of the research show that the fruit word puzzle game media can be used as a learning medium in Indonesian language learning, deaf students give very strong reactions to the fruit word puzzle game media, and the fruit word puzzle game media is very valid, practical and effective for use with students deaf.
Reconstruction of narrowband solar radiation for enhanced spectral selectivity in building-integrated solar energy simulations

Solar radiation is a critical factor in advanced envelope design and solar energy's building integration, necessitating a shift from broadband solar radiation analyses towards more precise narrowband or spectrum-focused approaches. Understanding the performance potential of spectral-selective materials or structures requires accurate solar spectral information at specified locations, a feature often overlooked by conventional modeling tools. This work presents an innovative solar decomposing model capable of differentiating key solar irradiation components—visible and infrared—from broadband solar irradiance, without the need for expensive spectrum measurements. Our approach employs the extreme boosting regression tree method and leverages existing or easily derivable data from typical weather files. An exploratory analysis of the importance and interaction of different features in predicting solar irradiation components is also conducted. The results show that the proposed algorithm has an R2 of 0.981 and 0.990, RMSE of 18.280 and 18.390, and MAE of 7.989 and 8.011, for predicting VIS and NIR amount in DNI, respectively (for the strongest model using all the predictors within the dataset). This research offers an added layer of practicality by including case studies demonstrating how the solar decomposition models serve in real-world applications, especially in the integration of wavelength-selective devices like window systems and transparent solar cells into advanced envelope designs. Such real-world testing has verified the presence of a disparity between the power output calculation of NIR-selective transparent photovoltaics upon the broadband solar radiation data and the suggested narrowband solar radiation data, potentially resulting in a maximum deviation of 15.7 %. The decomposing models developed empower researchers and designers to generate new weather files comprising narrowband solar irradiance data, thereby enhancing their capacity to examine the influence of spectral-selective materials on a building's solar performance using existing solar simulation programs. The proposed method will be further improved by including more data from different climate zones and weather characteristics in the training model and validating through field measurements.
Real-time risk assessment of road vehicles based on inverse perspective mapping

Pan/Tilt/Zoom (PTZ) cameras play an important role in traffic scenes due to their wide monitoring fields and high flexibility. However, since the focal length and angle of PTZ cameras change irregularly with the monitoring needs, it is difficult to obtain accurate physical information about the real world from the image information of PTZ cameras. Aiming to address the need for real-time risk assessment of road vehicles in traffic monitoring scenarios, a vehicle position and velocity measurement scheme based on camera inverse perspective transformation is proposed, along with a method for real-time risk assessment based on the position and velocity. Specifically, Firstly, the vehicle target in the video is detected and tracked by deep learning YOLO detection algorithm and optical flow tracking algorithm. According to the obtained trajectory set, the vanishing points in the road direction are calculated by Cascade Hough Transform and the road marking lines are detected. Then, according to the vanishing point and marking line, the camera calibration task is accomplished via exploratory focal length. After camera calibration, the camera-to-road inverse perspective transformation is applied to project the image plane onto the road surface and obtain, the actual position information of vehicles. Finally, the vehicle speed measurement and real-time road risk assessment are achieved by calculating the average of instantaneous velocities across multiple frames. Simulation experiment results in a traffic monitoring scenario demonstrate that this perspective-based method for real-time road vehicle risk assessment achieves good stability and practicality, which meets the requirements for vehicle speed measurement and real-time road risk assessment.
Improved Meta-Heuristic Model for Text Document Clustering by Adaptive Weighted Similarity

This paper intends to develop a novel framework for text document clustering with the aid of a new improved meta-heuristic algorithm. Initially, the features are selected from the text document by subjecting each word under Term Frequency-Inverse Document Frequency (TF-IDF) computation. Subsequently, centroid selection plays a vital role in cluster formation, which is done using a new Improved Lion Algorithm (LA) termed as Cross over probability-based LA model (CP-LA). As a novelty, this paper introduced a new inter and intracluster similarity model. Moreover, this centroid selection is made in such a way that the proposed adaptive weighted similarity should be minimal. Based on the characteristics of the document, the weights are automatically adapted with the similarity measure. The proposed adaptive weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents. Finally, the superiority of the proposed over other models is proved under different performance measures.
Pose-guided self and external attention feature matching and aggregation network for person re-identification

Traditional methods that use pose information for person re-identification (ReID) often focus only on relationships between different parts of the same sample and ignore the correlation between poses of different samples, which hinders further improvement in recognition accuracy. In view of this, this paper proposes a pose-guided self and external attention feature matching and aggregation net which contains a visual context self and external attention module and a pose-guided feature matching and aggregation module. It can divide global features into local features without strict spatial feature alignment, and enables the model to focus on pose correlation between different samples. Thus, it not only learns pose-related identity identification features, but also avoids interference from the samples with large pose changes. The visual context self and external attention module realizes feature embedding through a transformer-based image classification model ViT and a human pose estimation model. Also, it extracts encoder features and local features containing rich pose information by adding an external attention. The pose-guided feature matching and aggregation module obtains a learnable part semantic view through a transformer decoder and pose heat maps. Then, it matches and aggregates with local features, adaptively learns pose-related features, and enhances the robustness of the model to background and occlusion. In this paper, experiments are conducted based on three data sets, including Market-1501, DukeMTMC-reID and MSMT17. The Rank-1 values are 95.3%, 90.1%, and 82.5%, respectively, and the mAP values are 88.3%, 81.1%, and 64.1%, respectively. In conclusion, our method introduces an external attention into the ReID task, and the ReID model can obtain more accurate results by extracting pose-related identity identification features and avoiding interference from samples with large pose changes.
PV resource evaluation based on Xception and VGG19 two-layer network algorithm

With the increasing global demand for new energy sources, Photovoltaic (PV) is increasingly emphasized as a renewable energy source globally. Consequently, the assessment of PV resources has become crucial. Existing single frameworks and algorithms for PV resource assessment lead to low assessment accuracy. To alleviate the deficiency, this study proposes a two-layer network algorithm based on Xception and VGG19 for the evaluation of PV resources. The proposed method combines Xception convolutional neural network and VGG19 convolutional neural network. In addition, this study constructs a two-layer network framework based on the two-layer network algorithm. The feasibility and reliability of the proposed method are verified by simulating the proposed method under the case. Compared with existing algorithms, the proposed method and framework can improve the accuracy of PV resource assessment.
Assessing cyber security coverage in non-computing disciplines

Cyber security is now the concern and responsibility of everyone who uses technology, regardless of whether they are IT or security practitioners. And this needs to be reflected in education. There are many disciplines, not directly related to cyber security, where knowledge of the threats and the basic skills to combat them are becoming necessary if graduates are to fully contribute to a society safe from cyber risks.
Efficient Privacy-preserving Multi-Dimensional Range Query with Hierarchical Hilbert Code-based Tree

With the development of cloud computing, more enterprises choose the cloud to manage data. However, the issue of privacy has been a stumbling block to cloud computing. Encrypting data, as a powerful way to figure out the problem, masks the semantic information, limiting query performance. This paper presents a multi-dimensional index structure (Hierarchical Hilbert Counting Bloom Filter Tree, HHCBtree) based on hierarchical Hilbert coding system, which supports efficient privacy-preserving multi-dimensional range query. On the one hand, Hierarchical Hilbert coding is used to divide the multi-dimensional space into fine-grained partitions to reduce the redundant data contained in the query results, and then the multi-dimensional data in the partitioned space is converted into hierarchical coding to avoid the disclosure of data order characteristics. On the other hand, the index structure is built on the encoded data, and the traditional tree type index node structure is replaced by the Counting Bronn Filter (Counting Bloom Filter, CBF) to implement secure query over encrypted data. Moreover, an index optimization is proposed to upgrade the efficiency of index traversal. Theoretical analysis shows that the scheme meets the IND-CKA security model, and the experimental results show that the proposed scheme has significantly improved the query efficiency compared with the existing scheme.
Virtual Reality Tourism, can it replace the real tourism experience?

The Covid-19 pandemic brought a severe impact on tourism industry, as traveling for leisure was very restricted. Yet, the desire to travel and explore tourist destination created a necessity for various reasons. The aim of this study is to explore virtual reality usage to replace such a travel limitation. Previous studies mention that virtual reality application has been used to enhance tourism promotion. Therefore, this study seeks evidences to go beyond enhancing the experience. The data were collected by questionnaires that were distributed to the Millennials, and the returned questionnaires were from 280 respondents, with 230 valid respondents. The results indicate that presence and authenticity have significant influences on virtual reality usage, however, they have no significant influence on tourism experience. Emotion has no significant influence on virtual reality usage, but it has a significant influence on tourism experience. Virtual reality usage has a significant influence on tourism experience. Virtual reality also acts as a mediator between presence and authenticity on tourism experience. At the moment, virtual reality is affecting the tourism experience, although it is relatively weak.
Design of Joint Control System for Target Detection UAV and Ground Vehicle Based on YOLO Algorithm

Nowadays, there is an increasing need for faster collection and processing of environmental information in technology, battlefield, and urban construction. In complex environments where human activity is difficult, drones can be dispatched to search for targets. Whether in military or civilian applications, the research on detection and positioning algorithms for search targets is crucial in the process of drone search. This also requires us to continuously improve the drone's ability to detect and locate targets. The breakthrough developments in computer vision and deep learning have greatly promoted the application of visual image technology in intelligent target recognition, localization, tracking, and target trajectory prediction. Due to its flexible flight characteristics, unmanned aerial vehicles (UAVs) are capable of flying in various complex environments. Therefore, the combination of drones and machine vision is increasingly sought after by people. Therefore, the research on unmanned aerial vehicle target detection and joint control technology based on YOLO algorithm has practical requirements and significance. Based on the characteristics of mainstream object detection algorithms in image technology and their performance comparison, a suitable object detection algorithm was selected for the scenario in this article. Based on the analysis of the trained results and the retested detection results, the YOLO detection algorithm that is most suitable for this article was selected. Then, the prior boxes of the algorithm were re clustered, and the clustered algorithm was re trained and tested to improve detection accuracy. The method used to improve detection accuracy is to analyze the feature layer information of each layer again, improve the structure of the clustered detection algorithm, and retrain and test the algorithm. Finally, drones provide more application scenarios for the YOLO algorithm, which can help drones complete more novel tasks. Through this approach, drone technology and YOLO algorithms can further promote people's daily lives while contributing to the productivity of their respective industries. The experiment shows that the design of the target detection unmanned aerial vehicle and ground vehicle joint control system based on YOLO algorithm in this paper is feasible, achieving the expected results, and has the advantages of stability and simple operation.
Real-Time Hand Gesture Recognition for Individuals with Aphasia using Artificial Neural Network

The researchers created a real-time hand gesture recognition for individuals with aphasia using Support Vector Machine and Artificial Neural Network to make the communication between the patient and nurse/doctor/family easier. Basically, this study gives emphasis to the accuracy of the system. In order to achieve a better accuracy, Support Vector Machine and Artificial Neural Network were used. Based on the result of the degree of accuracy in terms of distance between patients and nurse/doctors stated that, the closer the object that is recognized the higher is the accuracy. Based on the result of the degree of accuracy in terms of light, the lower the light the higher the accuracy of the object. Though, the system enables to automatically generate hand gesture, some of hand features like skin tone, hand marks and accessories, and hand disability are still can be considered for future works. Also, female hands can be a good variety for the study. For the technique used – Artificial Neural Network (ANN), time constraint is still a problem, with that it is hoped to be improved by combining some algorithms and techniques that can improve the time constraint of the hand gesture generation.
Adaptive VQVAE: a learning-based image compression framework with vector quantization

With the promising effectiveness of learned image compression, a substantial amount of research has been devoted to this field, following the publication of Ballé's paper. However, there are still some issues to be concerned before it becomes truly practical tools. The JPEG AI standard proposed by the JPEG team advocates that all machine vision tasks should be uniformly expressed under the same specifications of learned image format. While the JPEG AI standard is still under development, vector quantization (VQ) which is an important signal compression technique presents advantages in this context. This paper focuses on the research of vector quantization for image compression, and proposes an improved method called Adaptive VQVAE (Vector-Quantized Variational Autoencoder) to compactly represent the latent space of convolutional neural network. By transmitting an additional 1% of auxiliary information, the original performance of VQ is enhanced by approximately 1 dB PSNR for encoding the information from the latent space in an autoencoder architecture. In addition to the rate-distortion performance, the performance of vector quantized feature maps is also evaluated in terms of accuracy of computer vision tasks with the compressed format. The implementation results of Adaptive VQVAE have been uploaded to GitHub and are available for research purposes and comparison.