Reviewerly/exHarmony huggingface dataset:
Research on the development of urban and rural basic education based on analytic hierarchy method

In order to analyze the factors affecting basic education, such as the number of schools, the condition of basic equipment, the number of teachers, and the proportion of school area in elementary, middle, and high schools, we derived the time difference between urban and rural basic education for this period by finding various types of data for rural and urban areas from 2010 to 2022 and then comparing and analyzing the various types of data for rural and urban areas. The analytical hierarchy was then used to determine the importance of these indicators. This shows that the gap between urban and rural basic education has widened over time in these areas discussed. From 2010 to 2022, the changes in the penetration of information technology, the number of multimedia devices, the promotion rate, and the students' and physical education; satisfaction with the use of multimedia devices and the problems that teachers encounter when using multimedia were analyzed for both urban and rural primary and secondary schools. The TOPSIS integrated analysis was used to determine the most important influencing factors and their impact on the level of basic education. Finally, it was concluded that multimedia technology has both favorable and unfavorable effects on the level of basic education, but the favorable effects far outweigh the unfavorable effects, and overall it still greatly promotes the development of the level of basic education and helps to promote the balanced development of education. Before the implementation of the double reduction policy, we looked for the extracurricular tuition of rural and urban primary, middle and high school students, the promotion rate of rural and urban primary and secondary schools, and the proportion of extracurricular activities. Then we looked for corresponding data after the implementation of the double reduction policy, analyzed the correlation of these data, determined the impact of these data on the level of basic education, and used the analytical hierarchy to rank the importance of these factors. This policy has reduced the pressure of involutional competition for urban students and enriched their after-school life. For rural children, transfer rates have increased.
An Iterative Learning Scheme with Binary Classifier for Improved Event Detection in Surveillance Video

This paper presents an iterative training framework with a binary classifier to improve the learning capability of a deep learning model for detecting abnormal behaviors in surveillance video. When a deep learning model trained on data from one surveillance video is deployed to monitor another video stream, its abnormal behavior detection performance often decreases significantly. To ensure the desired performance in new environments, the deep learning model needs to be retrained with additional training data from the new video stream. Iterative training requires manual annotation of the additional training data during the fine-tuning process, which is a tedious and error-prone task. To address this issue, this paper proposes a binary classifier to automatically label false positive data without human intervention. The binary classifier is trained on bounding boxes extracted from the detection model to identify which boxes are true positives or false positives. The proposed learning framework incrementally enhances the performance of the deep learning model for detecting abnormal behaviors in a surveillance video stream through repeated iterative learning cycles. Experimental results demonstrate that the accuracy of the detection model increases from 0.35 (mAP = 0.74) to 0.91 (mAP = 0.99) in just a few iterations.
FRIMFL: A Fair and Reliable Incentive Mechanism in Federated Learning

Federated learning (FL) enables data owners to collaboratively train a machine learning model without revealing their private data and sharing the global models. Reliable and continuous client participation is essential in FL for building a high-quality global model via the aggregation of local updates from clients over many rounds. Incentive mechanisms are needed to encourage client participation, but malicious clients might provide ineffectual updates to receive rewards. Therefore, a fair and reliable incentive mechanism is needed in FL to promote the continuous participation of clients while selecting clients with high-quality data that will benefit the whole system. In this paper, we propose an FL incentive scheme based on the reverse auction and trust reputation to select reliable clients and fairly reward clients that have a limited budget. Reverse auctions provide candidate clients to bid for the task while reputations reflect their trustworthiness and reliability. Our simulation results show that the proposed scheme can accurately select users with positive contributions to the system based on reputation and data quality. Therefore, compared to the existing schemes, the proposed scheme achieves higher economic benefit encouraging higher participation, satisfies reward fairness and accuracy to promote stable FL development.
Image enhancement method based on multi algorithm fusion

This research suggests an image enhancement method based on multi algorithm fusion. The illumination component and reflection component are obtained by morphological closure. On the one hand, multiple inputs of the estimated illumination and the corresponding weights are fused by multi-scale fusion technology. On the other hand, space conversion technology is used. Two adjusted images are obtained based on multi-scale Gaussian filter and adaptive brightness enhancement algorithm. Then details are extracted from these two images through PCA fusion strategy. The reflection component is improved significantly after above operations. Finally, combine the illumination and reflection components. Compared with the classical algorithm, our method can simultaneously achieve brightness improvement, naturalness maintenance and contrast enhancement.
OO-MA-KP-ABE-CRF: Online/Offline Multi-Authority Key-Policy Attribute-Based Encryption with Cryptographic Reverse Firewall for Physical Ability Data

In many universities, students’ physical ability data are collected and stored in the cloud through various sensing devices to save computational and storage costs. Therefore, how to effectively access data while ensuring data security has become an urgent issue. Key-policy attribute-based encryption (KP-ABE) not only enables secure one-to-many communication and fine-grained access control but also adapts to data sharing in static scenarios, making it more suitable for the cloud sharing of physical ability data. In this paper, we construct an online/offline multi-authority key-policy attribute-based encryption with a cryptographic reverse firewall for physical ability data. This scheme uses multi-authority to avoid the single point of failure crisis of a single authority, and is combined with a cryptographic reverse firewall to resist backdoor attacks. In addition, the scheme uses outsourcing decryption to save users’ computing costs, and utilizes offline/online technology to move a large amount of computing offline, reducing the online burden. Finally, the experiment shows the feasibility of the scheme.
Rotated Object Detection with Circular Gaussian Distribution

Rotated object detection is a challenging task due to the difficulties of locating the rotated objects and separating them effectively from the background. For rotated object prediction, researchers have explored numerous regression-based and classification-based approaches to predict a rotation angle. However, both paradigms are constrained by some flaws that make it difficult to accurately predict angles, such as multi-solution and boundary issues, which limits the performance upper bound of detectors. To address these issues, we propose a circular Gaussian distribution (CGD)-based method for angular prediction. We convert the labeled angle into a discrete circular Gaussian distribution spanning a single minimal positive period, and let the model predict the distribution parameters instead of directly regressing or classifying the angle. To improve the overall efficiency of the detection model, we also design a rotated object detector based on CenterNet. Experimental results on various public datasets demonstrated the effectiveness and superior performances of our method. In particular, our approach achieves better results than state-of-the-art competitors, with improvements of 1.92% and 1.04% in terms of AP points on the HRSC2016 and DOTA datasets, respectively.
Analysis of Distinguishable Security between the One-Time Password Extraction Function Family and Random Function Family

A one-time password is a security system that uses a password that is only used once for authentication, and it is commonly used in multi-factor authentication systems. The process of generating an OTP is very similar to generating pseudorandom sequences in cryptography. However, since only a part of the bit string is used in OTP, an algorithm is needed to extract that part. In addition, the OTP process also includes converting the value of the bit string value into decimal form for human perception. This paper focuses on analyzing the extraction function, which is the step before the hexadecimal is reprocessed into the decimal form. We analyze a function family, which includes functions used in the process of extracting a bit string in terms of distinguishable security. As a result, we conclude that the OTP extraction function family is vulnerable in terms of distinguishable security compared to the random function family.
Event-Triggered Robust Fusion Estimation for Multi-Sensor Time-Delay Systems with Packet Drops

This paper investigates the robust fusion estimation problem for multi-sensor systems with communication constraints, parameter uncertainty, d-step state delays, and deterministic control inputs. The multi-sensor system consists of a fusion center and some sensor nodes with computational capabilities, between which there are random packet drops. The state augmentation method is utilized to transform a time-delay system into a non-time-delay one. The robust state estimation algorithm is derived based on the sensitivity penalty for each sensor node to reduce the impact of modelling errors, and modelling errors here are not limited to a unique form, which implies that the fusion estimator applies to a wide range of situations. An event-triggered transmission strategy has been adopted to effectively alleviate the communication burden from the sensor node to the fusion center. Moreover, the fusion estimator handles packet drops arising from unreliable channels, and the corresponding pseudo-cross-covariance matrix is provided. Some conditions are given to ensure that the estimation error of the robust fusion estimator is uniformly bounded. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.
Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion

This study focuses on improving the objective alignment of image quality assessment (IQA) algorithms with human visual perception. Existing methodologies, predominantly those based on the Laplacian of Gaussian (LoG) filter, often neglect the impact of color channels on human visual perception. Consequently, we propose a full-reference IQA method that integrates multi-channel visual information in color images. The methodology begins with converting red, green, blue (RGB) images into the luminance (L), red–green opponent color channel (M), blue–yellow opponent color channel (N) or LMN color space. Subsequently, the LoG filter is separately applied to the L, M, and N channels. The convoluted components are then fused to generate a contrast similarity map using the root-mean-square method, while the chromaticity similarity map is derived from the color channels. Finally, multi-channel LoG filtering, contrast, and chromaticity image features are connected. The standard deviation method is then used for sum pooling to create a full-reference IQA computational method. To validate the proposed method, distorted images from four widely used image databases were tested. The evaluation, based on four criteria, focused on the method’s prediction accuracy, computational complexity, and generalizability. The Pearson linear correlation coefficient (PLCC) values, recorded from the databases, ranged from 0.8822 (TID2013) to 0.9754 (LIVE). Similarly, the Spearman rank-order correlation coefficient (SROCC) values spanned from 0.8606 (TID2013) to 0.9798 (LIVE). In comparison to existing methods, the proposed IQA method exhibited superior visual correlation prediction accuracy, indicating its promising potential in the field of image quality assessment.
A Survey on Bug Deduplication and Triage Methods from Multiple Points of View

To address the issue of insufficient testing caused by the continuous reduction of software development cycles, many organizations maintain bug repositories and bug tracking systems to ensure real-time updates of bugs. However, each day, a large number of bugs is discovered and sent to the repository, which imposes a heavy workload on bug fixers. Therefore, effective bug deduplication and triage are of great significance in software development. This paper provides a comprehensive investigation and survey of the recent developments in bug deduplication and triage. The study begins by outlining the roadmap of the existing literature, including the research trends, mathematical models, methods, and commonly used datasets in recent years. Subsequently, the paper summarizes the general process of the methods from two perspectives—runtime information-based and bug report-based perspectives—and provides a detailed overview of the methodologies employed in relevant works. Finally, this paper presents a detailed comparison of the experimental results of various works in terms of usage methods, datasets, accuracy, recall rate, and F1 score. Drawing on key findings, such as the need to improve the accuracy of runtime information collection and refine the description information in bug reports, we propose several potential future research directions in the field, such as stack trace enrichment and the combination of new NLP models.
An Improved YOLOv5s-Based Helmet Recognition Method for Electric Bikes

This paper proposes an improved model based on YOLOv5s, specifically designed to overcome the challenges faced by current target detection algorithms in the field of electric bike helmet detection. In order to enhance the model’s ability to detect small targets and densely populated scenes, a specialized layer dedicated to small target detection and a novel loss function called Normalized Wasserstein Distance (NWD) are introduced. In order to solve the problem of increasing model parameters and complexity due to the inclusion of a small target detection layer, a Cross-Stage Partial Channel Mixing (CSPCM) on top of Convmix is designed. The collaborative fusion of CSPCM and the Deep Feature Consistency (DFC) attention mechanism makes it more suitable for hardware devices. In addition, the conventional Nearest Upsample technology is replaced with the advanced CARAFE Upsample module, further improving the accuracy of the model. Through rigorous experiments on carefully constructed datasets, the results show significant improvements in various evaluation indicators such as precision, recall, mAP.5, and mAP.95. Compared with the unmodified YOLOv5s algorithm, the proposed enhanced model achieves significant improvements of 1.1%, 8.4%, 5.2%, and 8.6% on these indicators, respectively, and these enhancements are accompanied by a reduction of 778,924 parameters. The experimental results on our constructed dataset demonstrate the superiority of the improved model and elucidate its potential applications. Furthermore, promising improvements for future research are suggested. This study introduces an efficient approach for improving the detection of electric bike helmets and verifies the effectiveness and practicality of the model through experiments. Importantly, the proposed scheme has implications for other target detection algorithms, especially in the field of small target detection.
Utilizing Stereography to Compare Cultural Heritage in the Past and Now: An Interactive AR Application

Due to the astonishing speed in technological progress for new heterogeneous experiences, the Mixed Reality continuum represents nowadays an invaluable tool for cultural heritage applications, and the possibility of seemingly natural interaction with virtual elements is paving the way for more immerse, ve and deeply enjoyable experiences, both in dedicated spaces, such as museums and exhibitions, and in public spaces via the latest mobile AR technologies. This paper illustrates the complete workflow followed for the design and implementation of an Augmented Reality application on a holographic table, along with critical latency-quality tradeoffs encountered when combining both high-fidelity surveys and latency-sensibility devices and experiences. A workaround is provided in order to mitigate the impact of low-importance models on the overall latency utilizing a software toolchain employing Google Maps/Earth web applications; each software comprises the toolchain Free and Open-Source Software (FOSS) for a more adaptive workaround with a wider range of suitable applications. The application is then tested with the students of LaborA, which is the modeling laboratory of “Politecnico di Milano”.
Efficient Latent Space Compression for Lightning-Fast Fine-Tuning and Inference of Transformer-Based Models

This paper presents a technique to reduce the number of parameters in a transformer-based encoder–decoder architecture by incorporating autoencoders. To discover the optimal compression, we trained different autoencoders on the embedding space (encoder’s output) of several pre-trained models. The experiments reveal that reducing the embedding size has the potential to dramatically decrease the GPU memory usage while speeding up the inference process. The proposed architecture was included in the BART model and tested for summarization, translation, and classification tasks. The summarization results show that a 60% decoder size reduction (from 96 M to 40 M parameters) will make the inference twice as fast and use less than half of GPU memory during fine-tuning process with only a 4.5% drop in R-1 score. The same trend is visible for translation and partially for classification tasks. Our approach reduces the GPU memory usage and processing time of large-scale sequence-to-sequence models for fine-tuning and inference. The implementation and checkpoints are available on GitHub.
Bread Sales Information System at Satria Bread Shop

In the current era of globalization, technological developments are progressing very rapidly so that with the advancement of this technology it can facilitate humans in various activities. In this research, a Web-Based Bread Sales Information System will be built at the Satria Roti Shop, Sidoarjo, which currently still uses the manual way of transacting with customers, so that customer service is still not optimal and efficient.The method used in this study is the waterfall method and software as follows: Visual code as an editor, React programming language, Javascript framework, Apache web server and MySQL as the database.This research will produce a Web-Based Bread Sales Information System at the Satria Roti Shop, Sidoarjo, which has an appropriate interface and can facilitate the process of selling bread transactions efficiently and practically.
Algorithm for automation of the method of testing statistical hypotheses according to the Pearson criterion by the means of MATLAB

The purpose of the work is to develop and improve the methods of computer modeling and digitalization of procedures for testing statistical hypotheses according to consent criteria for use in high-tech software and measuring complexes of automated control systems. In most cases that are important for practice, the analysis of the statistical properties of distributions requires multiple checks performed using numerical methods, which require the tools of powerful software and computing environments. Examples of such cases are the procedures for finding information subsets of features in regression and statistical analysis, as well as identifying parameter deviations in automated control systems. It is noted that the tasks of processing experimental data do not always fit into the framework of the theory of normal processes and have exclusively such hypotheses. To test such hypotheses, modern mathematical statistics has developed criteria based on specially developed probabilistic rules that distinguish between zero and alternative hypotheses. Moreover, if for the testable (zero) hypothesis the distribution of statistics is known exactly or approximately, then for the alternative it has a greater degree of uncertainty and it (the alternative) itself is not a complete negation of the null hypothesis. As the zero and alternative hypotheses move away from each other, the statistics of the consent criterion begins to take a larger absolute value than with the null hypothesis, which allows us to build a critical area on the basis of estimating the set of this statistics values. However, the use of analytical methods for statistical analysis to determine the discrepancy between empirical and hypothetical statistics in practice is associated with the use, as a rule, of complex computational calculation algorithms and the implementation of high requirements for the assessment accuracy, which they cannot provide. To solve this problem, it is proposed to use numerical methods of computer modeling and integration of probability density of distribution according to the Simpson algorithm using the tools of built-in functions of the Statistics Toolbox of the MATLAB environment. The proposed method for automation of statistical analysis of zero and alternative hypotheses is implemented on the example of applying the Pearson criterion to verify compliance with the normal distribution of two random data samples, one of which meets the normal distribution law. The obtained graphic and numerical results of computer modeling confirm the correspondence of one of the testable distributions to a given normal distribution according to the null hypothesis.
A face recognition algorithm based on the combine of image feature compensation and improved PSO

Abstract Face recognition systems have been widely applied in various scenarios in people's daily lives. The recognition rate and speed of face recognition systems have always been the two key technical factors that researchers focus on. Many excellent recognition algorithms achieve high recognition rates or good recognition speeds. However, more research is needed to develop algorithms that can effectively balance these two indicators. In this study, we introduce an improved particle swarm optimization algorithm into a face recognition algorithm based on image feature compensation techniques. This allows the system to achieve high recognition rates while simultaneously enhancing the recognition efficiency, aiming to strike a balance between the two aspects. This approach provides a new perspective for the application of image feature compensation techniques in face recognition systems. It helps achieve a broader range of applications for face recognition technology by reducing the recognition speed as much as possible while maintaining a satisfactory recognition rate. Ultimately, this leads to an improved user experience.