Reviewerly/exHarmony huggingface dataset:
An adversarial cross-modal retrieval method based on collaborative attention networks

Serious feature heterogeneity and semantic gaps exist between multi-source heterogeneous data, and existing cross-modal retrieval methods cannot effectively extract common semantic and complementary information between multi-source heterogeneous data. In this regard, an adversarial cross-modal retrieval method that fuses collaborative attention networks is proposed. The method addresses two major challenges in the cross-modal retrieval process, firstly, an information extraction algorithm based on the cooperative attention mechanism is designed, and secondly, the cooperative attention network is combined with the adversarial subspace learning algorithm to enhance the complementary capability of information in the feature subspace. The experimental results show that the proposed method has better retrieval results than similar cross-modal retrieval methods in terms of MAP metrics.
A hybrid digital watermarking scheme based on silicon photonic microcavity chaos (SPM-Chaos) and DWT

We proposed a hybrid digital watermarking scheme in wavelet domain with double encryption of silicon photonic microcavity chaotic encryption and Arnold transform. In particular, the SPM-Chaos is four-dimensional dynamic chaos generated by silicon-based photonic crystal optomechanical microcavity, which brings higher randomness and larger keyspace. In this article, the hybrid encryption of SPM-Chaos and Arnold transform can greatly enhance security. Meanwhile, using DWT due to wavelet’s time-frequency localization and multi-resolution characteristics which are applicable for image processing. The experiment results show that proposed scheme has good visual invisibility, and robustness against various attacks such as noise, filter, rotation and compression.
Modified tuna swarm optimization algorithm using levy flight

In this article, aiming at the defects of Tuna Swarm Optimization algorithm (TSO), which is easy to fall into local optimum and slow convergence speed, an improved tuna swarm optimization algorithm based on Levy flight (LTSO) is proposed. This algorithm updates the position of the optimal individual and its followers in the tuna swarm by using the characteristics of the long and short jump of Levy flight. This method can effectively enhance the global exploration effect of the algorithm and avoid the algorithm being attracted by local extreme values. In this paper, 12 benchmark functions are used to design two groups of experiments to test the performance of LTSO on high-dimensional and low-dimensional problems respectively, and then compared with TSO and other popular algorithms. It can be seen from the test results that the speed of finding the optimal value, the accuracy of the answer and the robustness of the algorithm are better than those of TSO and other algorithms. In addition, the test results of these algorithms in these benchmark functions are analyzed using Wilcoxon statistical method and Friedman method. The statistical analysis results indicate that LTSO has significant advantages over other algorithms.
Research on construction of learner portrait based on learning analytics technology

With the development of big data, artificial intelligence, and wearable technology, the data generated by learners in the learning process can be fully recorded and stored. Using these data to study the characteristics of different types of learners and provide them with precise and personalized learning guidance is an urgent problem to be solved. This paper proposes a learner portrait construction method based on learning analysis technology. Firstly, the case frame relationship with "learning" as the central verb is established by using case grammar. Secondly, constructing a "learning-learner" feature layer with 6 sub-layers through qualitative analysis, and organizing the primary data of learners into 6 kinds of integrated data with specific meanings. Then, two methods of classifying learner types, cluster analysis and standard establishment, are proposed. Finally, giving an example of learner portrait construction.
No-reference image quality assessment based on local and global using attention features

No-reference image quality assessment is the prediction of image quality without the original image and has a wide range of applications in fields such as image recognition. The wide content of images and the diversity of distortions acquired by mobile devices, make it a challenge to evaluate the quality of such truly distorted images captured by mobile devices. Therefore, in this paper, we propose to use global and local features of images to deal with the diversity of image distortions, using an attention module to extract global features to improve the characterization ability of global features. The global semantic features are used as the input of an adaptive weight and bias generation network, which can generate the weights and biases of the final quality prediction network based on the image content. In this paper, experiments are conducted on three real distortion databases, KonIQ-10k, BID, and LIVEC, and the results show that the proposed method outperforms existing no-reference image quality assessment methods. Also, experiments are conducted on two synthetic distortion databases LIVE and TID2013, and the results show that the method also achieves competitive results in evaluating synthetic distorted images.
Prediction of heart disease using graph neural networks

Cardiovascular Diseases (CVDs) have become increasingly crucial in recent years and have been regarded as the leading cause of death worldwide. Although it is necessary to detect and treat CVDs in their early stages, only 67% of heart diseases could be predicted by medical professionals. Motivated by recent advances in Graph Neural Networks (GNNs) that have ramified in a variety of industries, in this paper, we utilize three novel GNN models, including Graph Convolutional Network (GCN), GraphSAGE, and Graph Attention Network (GAT), to conduct the heart disease prediction task, comprised of two stages: table-to-graph transformation and Graph Neural Networks prediction. Experimental results show significant improvements with the utilization of GNNs compared with three novel Machine Learning models: Logistic Regression (LR), Naïve Bayes (NB), and Multi-Layer Perceptron (MLP), and GAT performs optimally among the three GNN models. To the best of our knowledge, this is the first work that predicts heart disease using GNNs.
Creating a Sentiment Analyzer for Text Messages

The work is devoted to the use of Logistic Regression and Neural Network methods to develop a methodology for creating and implementing an analyzer for determining the sentiment of a text in Armenian. Based on a selection of data from social networks, this analyzer determines the tone of the message entered by the user. Note that such work with Armenian texts is carried out for the first time. Sentiment analysis of the text helps to form an opinion about the content of the message without reading the entire text, freeing the user from unnecessary work and time. Sentiment analyzer based on various machine-learning methods for text classification has been created. The paper presents the results of a comparative analysis of the models underlying this analyzer. The created analyzer is used in processing the results of a social survey of students both to determine the students’ opinion about the relevance of teaching a given subject and to assess the quality of teaching a subject by a teacher. It is also supposed to use the analyzer to determine the psychological state of the student before the exam in order to adapt the exam to student.
A Data Driven Solution to the Dark MatterProblem

A data driven solution to the dark matter problem is presented. This short and self-contained overview is intended for a wide audience, with full technical details available in the cited references. We present redundant, independent and consistent measurements of the dark matter particle comoving root-mean-square velocity vhrms(1), or equivalently, of the dark matter temperature-to-mass ratio. These measurements agree with the “no freeze-in and no freeze-out” scenario of spin zero dark matter that decouples early on from the Standard Model sector, e.g., spin zero dark matter coupled to the Higgs boson or to the top quark. 1
On the Security of Privacy-Preserving Federated Learning via Functional Encryption

&lt;p&gt;Federated learning (FL) is a promising collaborative machine learning (ML) framework allowing models to be trained on sensitive real-world data while preserving its privacy. Unfortunately, recent attacks on FL demonstrate that local gradients provided by participating users may leak information about their local training data. To address this privacy issue, the notion of secure aggregation was proposed and has been achieved by different approaches including secure multiparty computation (MPC), homomorphic encryption (HE), functional encryption (FE), and double-masking.&lt;/p&gt; &lt;p&gt;In 2023, Chang et al. proposed a novel privacy-preserving federated learning (PPFL) based on FE and claimed that their PPFL resolves the aforementioned privacy issue. However, in this paper, we demonstrate that their PPFL is vulnerable to two attacks we design, and therefore their PPFL is insecure due to the above privacy issue. We hope that by identifying the security problem, similar errors can be avoided in future designs of PPFL.&lt;/p&gt;
Search-based motion planning algorithms for quadrotors

Motion Planning is an essential module for quadrotors, especially for automated navigation tasks. This paper introduces a search-based motion planning method for the quadrotor. The method can generate minimum time and smooth trajectory for quadrotors in a cluttered environment. The method explores the space with short-duration motion primitives based on obstacle information and dynamic constraints and generates an induced finite lattice discretization map. Then, the searchbased algorithm traverses through the map and returns a preferred trajectory. With the help of the heuristic function, the trajectory planning only takes a short time and is available for fast replanning during the flight. The performance of the proposed approach is evaluated using simulation and comparison with other state-of-art trajectory generation approaches.
An In-Depth Study of Cryptocurrency Exchanges

This research paper provides an in-depth analysis of cryptocurrency exchanges by examining their types, regulatory environment, challenges, and user behavior. We conducted a comparative study of ten popular cryptocurrency exchanges and collected data on user behavior and preferences through surveys, interviews, and website analysis. Our findings reveal that crypto exchanges face numerous challenges such as security, liquidity, and regulatory compliance. We also found that users prefer exchanges that offer a wide range of cryptocurrencies, high liquidity, low fees, and strong security measures. This research contributes to the understanding of the cryptocurrency industry and provides insights for policymakers, investors, and users. Keywords : Cryptocurrency Exchanges, Bitcoin, Trading, Portability and Vulnerability
A HIGHER-LEVEL SECURITY SCHEME FOR KEY ACCESS MANAGEMENT ON CLOUD COMPUTING

In this work, we make a crucial access operation scheme that translates any access policy with a hierarchical structure to the digital media. According to the proposed plan, any google cloud system can be used as a corporate cloud. We view the data proprietor as a reality comprising numerous organizational rudiments. Each stoner of this reality has access to a secure medium from both inside and outside the company's network to pierce the Google Cloud. Our crucial access control strategy, which is grounded on the polynomial interpolation system and Shamir's secret sharing algorithm, is particularly well suited for hierarchical organizational systems. It provides a hierarchical, secure, and customizable crucial access system for businesses using charge- critical data. also, it lessens worries about releasing charge-critical data to the general public. KEYWORDS: Interpolation, Organization, Access policy, Secret sharing, Hierarchical, Secure.
Multiple Game and Computer-Aided Evaluation System Design of Computer Software Legal Protection Policy

This article has deeply studied the two kinds of computer software legal protection policies ISO17799 and ISO13355, and analyzed the conceptual model and relational model of the multiple game. On this basis, the concept of computer-aided evaluation based on computer law is proposed, and a computer-aided evaluation system is designed, which effectively realizes asset evaluation based on multiple games and threat and vulnerability evaluation based on probability statistics. And on the basis of referring to the risk assessment process and based on the actual needs of the system, a highly operable assessment process that is convenient for computer implementation is proposed.
Graphical Password Authentication for Web-Based System

Graphical password authentication is a method that uses passwords in the form of images or symbols. There can be various implementations based on its usage. Digitization and automation across all industries have resulted in improvements in the efficiencies and effectiveness of systems and processes, and the tech sector is not immune to attacks through hackers. Graphical Password authentication offers a systematic and secure alternative to the old text-based password designed for the authentication and security of the user. KEYWORDS Hashing, Encryption, Authentication, Graphical Passwords, Image Password Authentication
Design of game software for children with autism spectrum disorder based on augmented reality

Traditional rehabilitation therapy for children with autism (ASD) relies on manpower, is expensive, and lacks teaching aids. Based on augmented reality technology (AR), this research designs a game training software for children with ASD in terms of social cognition and motor perception, and combines the data monitoring module to record the children's usage in real time. The AHP-fuzzy comprehensive evaluation method is used to evaluate the software to verify the effectiveness of the software.
Handwritten Chinese character text image correction method based on block similarity

Text image aberration correction, as a pre-processing step in Chinese character image processing techniques, has an important impact on its final results. The existing methods are difficult to obtain satisfactory correction results for text images of handwritten Chinese characters due to the non-uniform types of aberrations. To address this problem, a new correction method is proposed in this paper. The method uses Pearson correlation coefficients to classify text lines based on text line trends and combines document distortion models to unify text lines with different distortion types; meanwhile, a data expansion method based on historical text information is proposed to transform the irregular text line correction problem into a model-based text block optimization problem to deal with the situation where the number of text images is small, resulting in insufficient information for distortion models. situation. The comparison experiments with existing methods on synthetic and real datasets show that the method in this paper has better visual correction effect and higher values of PSNR, SSIM and MS-SSIM evaluation indexes.
Improved EAST scene text detection based on ResNet-50

With the development of image technology and artificial intelligence, the detection and recognition of image text is increasing. Aiming at the different intensity of text size and complex background in natural scenes, this article uses an improved text detection method based on ResNet-50. ResNet is used to implement identity mapping to solve the problem of redundant layers in deep networks, avoiding gradient descent or gradient explosion, thereby improving the overall performance of the algorithm. Compared with the EAST algorithm, this method improves the F value by 1.79 % on the ICDAR2015 dataset.
Pengayaan Materi Matematika Bagi Siswa Kelas 3 di SD Negeri 040457 Berastagi

The purpose of this service activity is to provide enrichment of math material for grade 3 students who support in solving elementary math problems, and provide training in working on elementary math problems. This service activity lasts for a month and is carried out after school hours are finished. Free tutoring activities are carried out with the aim of teaching students to be able to overcome the problem of decreased interest in learning and low ability of students in mathematics, due to the length of the online learning process due to the Covid-19 pandemic. The community service team carried out PKM activities at SD Negeri 040457 Berastagi from February 2 2022 to February 26 2022, every Tuesday to Friday for one and a half hours. This service activity is carried out with an emphasis on strategies for working on short questions and description questions. Evaluation of the implementation of activities is carried out by holding a free test / giving questions to work on before the activity and the post test is retested after the activity. The learning process is carried out with guidance in small classes and using learning media such as lists of numbers, addition tables, multiplication tables, etc. to attract students' interest. The results of the activity showed an increase in students' interest in learning and math skills up to 75%. This proves that students can be more enthusiastic about learning if mentoring and direct interaction are provided.
YOLOX-Lite: an efficient model based on YOLOX for object detection

As a high precision target detection model, YOLOX still has the disadvantage of slow detection speed and is difficult to apply to the work scene with limited computing resources. Thus, an efficient target detection network, called YOLOX-Lite, which balances detection speed and detection accuracy, is proposed in this paper. Firstly, the mixed efficient channel attention module is designed to realize the adaptive refinement of spatial features and channel features in the network. Then the feature extraction ability of YOLOX-Lite network can be improved. Secondly, the optimized MobileNetv3 is used as the backbone network to replace Darknet53, so as to significantly reduce the computational complexity of the backbone network in feature extraction. Finally, efficient down_ sampler with focus is designed, which can efficiently integrate the low dimensional details in the backbone network with the high-dimensional semantic information in the neck layer. At the same time, when constructing the neck layer, the depth separable convolution is combined with PANet. It can reduce a lot of computing overhead caused by excessive multiplexing of standard convolutions. The experimental results on PASCAL VOC and TT100K datasets show that the mAP values of YOLOX Lite are 84.3% and 88.1% respectively, and the FPS value reaches 56.7. Thus, while ensuring the network detection accuracy, the detection speed of YOLOX Lite is increased by about 17% compared with the original YOLOX.
Non-reference quality assessment method based on occlusion region information estimation and natural scene statistics

Most of the laser interfered image quality assessment algorithms need to know the reference images or partial information of reference images. However, in practical application, the reference image or its related information is difficult to obtain, which makes the application scenario of laser interference image quality evaluation algorithm is greatly limited. To solve this problem, this paper starts with the prediction processing of the obscured information and improves the Markov Random Field estimation algorithm (MRF) to realize the real-time estimation of the obscured area information. Then, proposes a non-reference image quality assessment method based on occlusion area information estimation and natural scene statistics (IENSS), which analyzes the statistical characteristics of laser interfered images in natural scenes. The model is trained by machine learning. Finally, simulation experiments are carried out to verify the effectiveness of the proposed method.
Optimization of phase correction method based on multi frequency heterodyne principle

A phase correction method based on multi frequency heterodyne principle is proposed for the phase jump error caused by multi frequency heterodyne phase solution. The traditional phase shifting method and multi frequency heterodyne are used to calculate the wrapping phase and the unwrapping phase. Aiming at the jump error in the unwrapping phase, the unwrapping phase is used to locate the error position. The wrapping phase is used as a reference to correct the position of the positioning error to obtain a new unwrapping phase. The experimental results show that under the optimization method, this method can effectively judge the jump, and obtain the unwrapping phase without jump error after eliminating the jump, which greatly improves the unwrapping accuracy and accuracy of the fringe pattern after phase unwrapping.
Pelatihan Menulis dengan Pendekatan Talk For Writing Bagi Guru Bahasa Inggris

The quality of students’ writing English is the reason for conducting Talk for Writing (TfW) workshop. This workshop aims to provide insight into how to teach writing in an interesting and fun way. English teachers in Merauke, South Papua, are the main targets for this activity, considering that teachers in this location rarely get refreshments for English teaching strategy. The method to implement this activity is carried out offline through several stages and namely, the lecture method used to introduce the concept of TfW. The second stage is a demonstration method showing a work process or stage of making a magpie that will be used in the TfW process. The third stage is the practice method. At this stage, the participants practiced creating a magpie and writing using the TfW approach. The final stage was an evaluation where the teachers received feedback based on the magpie and the writing they had made. During the workshop, the enthusiasm of the participants was very high. In addition, the knowledge and understanding of the use of TfW techniques have increased.
Workshop Menulis Dongeng Berbasis Augmented Reality untuk Meningkatkan Keterampilan Literasi Digital Guru SD

The location of SD N Tukharjo, which is about 30 km from the Kulon Progo district center, makes educational facilities such as the regional library difficult to reach for students and teachers. The mobile library car run by the Kulonprogo regional library has also not reached SD N Tukharjo. The impact is that students and teachers only use the school library as literacy material. Currently Face-to-Face Learning (PTM) also limits the circulation of borrowed books in the school library. Through this activity, teachers will be given a deeper understanding of the nature of the literacy-numeration school program. Teachers are also encouraged to produce works in the form of fairy tale books that can add to reading material in reading corners or libraries, as well as increase skills regarding digital literacy by making literacy books based on augmented reality (AR). This service activity is carried out through four stages, namely planning, implementation, mentoring, and evaluation and reflection. From the results of the evaluation and reflection activities it can be concluded: 1) there is an increase in knowledge and digital literacy skills for teachers at SD Negeri Tukharjo, and 2) teachers at SD Negeri Tukharjo have succeeded in composing augmented reality fairy tales and have been ISBN-approved.