Reviewerly/exHarmony huggingface dataset:
Research on Information Literacy Education Model in the Big Data Environment - A Case Study of six Hainan high school Libraries

Abstract Under the overwhealing trend of big data, information literacy is gradually embedded in various applications with broader contents. Due to the new concepts and new technologies, the contents and methods of information literacy education have also changed. In order to further study the mode of information literacy education under the environment of big data, this paper takes six Hainan high school libraries as an example to investigate the students’ understanding level of information literacy education - their perception of the role of information literacy education and their satisfaction with information literacy education. In term of students’ knowledge, 28.25% of students have never heard literacy education, 37.28% have heard but are not sure of the details, and 34.47% know about information literacy education. In terms of students’ perceptions of the role of education, 48.27% thought that information literacy education was important, 48.15% thought that information literacy education was neutral, and 3.58% thought that information literacy education in libraries was not useful at all. In terms of satisfaction, students who are very satisfied, satisfied, and not satisfied with information literacy education is 33.33%, 52.12%, and 14.55% respectively. Our result showed that information literacy education in college libraries still lacks publicity, and developed mulitiple teaching model to attract student attention is necessary. Therefore, we purposed that information literacy education in college libraries should develop diversified publicity based on various media, particular follow the new characteristics of college students’ information acquisition behavior, and expand the scope of information literacy education.
Research on the innovation path of teaching interior design majors in universities based on big data analysis

Abstract Presently, the teaching of interior design in most higher education institutions still uses more traditional and conservative teaching methods, which to some extent, hinders the reform of interior design teaching in the era of big data. Interior design originally required students to grasp the operation fully and use CAD, 3D, and other design software, which makes the operation of complex commands, and model construction diversity, difficult for students to learn. This situation still uses the traditional lecture-based indoctrination teaching can play a very small role. This paper first focuses on the field of interior design education, based on the background of the times, provides a deep overview of the current situation of the relevant research involved in interior design, and explains the purpose, ideas, content, and methods of this paper. Secondly, we use data mining technology to build a smart classroom to visually present interior design. Finally, a comparison experiment between the traditional teaching mode of interior design and the smart teaching mode of big data is carried out, and three groups of experimental subjects are investigated after nearly one month of teaching experience. The results of this study show that the big data wisdom teaching model proposed in this paper compared with the traditional teaching model, through the one-month teaching experience post-test results, the average increase of 7 points in school A, 11 points in school K, and 14 points in school Z. The wisdom teaching system based on big data built in this paper enhanced the relevance and initiative of interior design students’ learning, improved students’ interest in learning and classroom efficiency and promoted students’ comprehensive ability while playing a positive and active role.
Research on the evaluation of the employment effect of Jiangsu artificial intelligence based on neural network on colleges and universities

Abstract Under the wave of digitization of big data information, the development of artificial intelligence technology continues to deepen the information technology revolution, which has a profound impact on human society and will also revolutionize the employment of different industries and groups. In this paper, we study the evaluation of employment effects of universities based on the neural network Jiangsu artificial intelligence. Since a neural network model needs to be built for training and there is a certain requirement for sample size, Jiangsu AI is selected for the study on the evaluation of employment effects in colleges and universities. The results show that the Dense Net model (C2) has better overall performance than the Vgg Net model, with weight and efficiency values of 89 and 88%, respectively, under the index of the educational level of college employment. Analyzing the impact of AI on college employment and unemployment in Jiangsu for the eight years from 2015 to 2022, it is concluded that with the development of time, the employment rate of college graduates reaches 87%, and the unemployment rate is 13% in 2022. This study has a guiding value for the employment of college graduates, and colleges and universities should focus on the future talent demand, guide students to cope with the possible impact of AI technology actively, cultivate students to acquire core competencies and literacy that AI does not easily replace, and promote high-quality employment of college students.
A flexible dataflow CNN accelerator on FPGA

With the increasing diversity of neural networks, accelerators supporting multiple dataflows are more efficient than those only supporting a specific dataflow. However, due to different storage structures and transmission patterns in different dataflows, deploying an accelerator with multiple heterogeneous cores on FPGA that lacks resources is challenging. In this paper, we present an instruction-based CNN accelerator supporting flexible dataflow. Towards reducing the resource occupation of FPGA, we propose a novel architecture with a delicate data path reusing the storage module and the computing module. In addition, a special instruction mechanism is designed to control the data transmission. As a case study, we implement a prototype and test it on ResNet18 and MobileNetv1. Results show, compared to the multi-core accelerator that supports the flexible dataflow, we achieve 1.3-2.7x speedup.
Library Collection Document Lending System Based on The Statistical Equation of Probability Theory

This paper establishes a library-collected document lending system based on data mining technology. This paper uses the statistical method of probability theory of association rules between multi-dimensional attributes to mine the regularity of document usage. Through this method, corresponding decisions can be made to optimize the layout of the library's collections. This paper uses data mining methods such as cluster analysis and association analysis to establish a data mining processing model. This method records the retrieval behavior of teachers and students. In this way, the interest points of teachers and students' demand books can be summarized. Finally, according to the above scheme, a simple experimental system is implemented, and the experimental results are verified and tested. The research finds that this paper's bibliography recommended by the book recommendation system using data mining technology is consistent with the borrowed bibliography. Compared with the traditional method, the judgment accuracy of the algorithm is greatly improved.
Towards Robust Knowledge Tracing Models via k-Sparse Attention

Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interaction sequences. With the advanced capability of capturing contextual long-term dependency, attention mechanism becomes one of the essential components in many deep learning based KT (DLKT) models. In spite of the impressive performance achieved by these attentional DLKT models, many of them are often vulnerable to run the risk of overfitting, especially on small-scale educational datasets. Therefore, in this paper, we propose sparseKT, a simple yet effective framework to improve the robustness and generalization of the attention based DLKT approaches. Specifically, we incorporate a k-selection module to only pick items with the highest attention scores. We propose two sparsification heuristics: (1) soft-thresholding sparse attention and (2) top-K sparse attention. We show that our sparseKT is able to help attentional KT models get rid of irrelevant student interactions and improve the predictive performance when compared to 11 state-of-the-art KT models on three publicly available real-world educational datasets. To encourage reproducible research, we make our data and code publicly available at https://github.com/pykt-team/pykt-toolkit1..
Using Entropy for Group Sampling in Pairwise Ranking from implicit feedback

In recent years, pairwise methods, such as Bayesian Personalized Ranking (BPR), have gained significant attention in the field of collaborative filtering for recommendation systems. Group BPR is an extension of BPR that incorporates user groups to relax the strict assumption of independence between two users. However, the reliability of its user groups may be compromised as they only focus on a few behavioral similarities. To address this problem, this paper proposes a new entropy-weighted similarity measure for implicit feedback to quantify the relation between two users and sample like-minded user groups. We first introduce the group preference into several pairwise ranking algorithms and then utilize the entropy-weighted similarity to sample groups to further improve these algorithms. Unlike other approaches that rely solely on common item ratings, our method incorporates global information into the similarity measure, resulting in a more reliable approach to group sampling. We conducted experiments on two real-world datasets and evaluated our method using different metrics. The results show that our method can construct better user groups from sparse data and produce more accurate recommendations. Our approach can be applied to a wide range of recommendation systems, and this can significantly improve the performance of pairwise ranking algorithms, making it an effective tool for pairwise ranking.
Multi-order Matched Neighborhood Consistent Graph Alignment in a Union Vector Space

In this paper, we study the unsupervised plain graph alignment problem, which aims to find node correspondences across two graphs without any side information. The majority of previous works addressed UPGA based on structural information, which will inevitably lead to subgraph isomorphism issues. That is, unaligned nodes could take similar local structural information. To mitigate this issue, we present the Multi-order Matched Neighborhood Consistent (MMNC) which tries to match nodes by aligning the learned node embeddings with only a small number of pseudo alignment seeds. In particular, we extend matched neighborhood consistency (MNC) to vector space and further develop embedding-based MNC (EMNC). By minimizing the EMNC-based loss function, we can utilize the limited pseudo alignment seeds to approximate the orthogonal transformation matrix between two groups of node embeddings with high efficiency and accuracy. Through extensive experiments on public benchmarks, we show that the proposed methods achieve a good balance between alignment accuracy and speed over multiple datasets compared with existing methods.
Development and Evaluation of a Gesture Recognition-Based Artificial Intelligence Science Popularization System

Currently, the Chinese government is actively promoting the integration of artificial intelligence (AI) technology into secondary school classrooms.However, the lack of teaching resources, heavy academic pressure, and unexpected public health events hinder the normal development of popular science teaching.In this study, based on the AI curriculum requirements in Beijing's Haidian District, a remote-control system for experiential teaching was developed using Mediapipe's keypoint recognition technology.This system enables remote control of smart homes and manipulation of robotic arm movements, allowing students to experience AI technology in a multi-sensory manner and overcome spatial limitations.To evaluate the effectiveness of this system in fostering students' AI literacy, 40 students from a high school in Hunan Province were selected as the research subjects.The t-value of the test scores between the experimental group and the control group was found to be 4.173.The results indicate that the adoption of the popular science system as a learning aid significantly improves students' mastery of AI knowledge.
A Novel Strategy for Improvements in Binary Swarm Optimization Algorithms

Binary swarm optimization algorithms use transfer functions to map a continuous search space to a discrete search space; therefore, the transfer function has an important impact on the performance of the algorithm. In this paper, a Hybrid Binary Particle Swarm Optimization (HBPSO) is proposed, which is implemented using a hybrid S-shaped transfer function made from two modified S-shaped transfer functions. The HBPSO is tested on 13 benchmark test functions and evaluated in terms of both local optimum avoidance and convergence speed. A comparative study with four classical S-shaped transfer functions is also implemented to verify the hybrid transfer function's performance. The results show that HBPSO using the modified hybrid S-shaped transfer function significantly improves the original Binary Particle Swarm Optimization performance.
Multi-group Adaptive Fish Migration Optimization Algorithm

Meta-heuristic algorithm is an powerful tool to solve optimization problems in many fields. How to improve the search ability of meta-heuristic algorithm is a challenge, and many novel algorithm is proposed in recent years. The Adaptive Fish Migration Optimization (AFMO) is an excellent meta-heuristic algorithm in solving complex optimization problems. For further enhance the performance of AFMO, this paper proposes an Muli-group Adaptive Fish Migration Optimization algorithm (MgAFMO), which improves the search ability of the evolutionary algorithm by using muliti-group strategy. The new algorithm is verified by the CEC2013 benchmark function. In addition, the new algorithm is also compared with heuristic algorithms such as PSO, HB, and WOA. The results show that the MgAFMO has strong optimization ability.
Exploration on Breast Cancer Prediction and Recurrence Diagnosis Using Bio-Inspired Algorithms

The most harmful disease among women that raises the demise rate among them is breast cancer. Breast cancer can be prevented, and its death rate can be reduced by early diagnosis. Unfortunately, cancer cell growth will start within few years again after the completion of treatment because cancer cells may still be present in the body. This is a significant issue for women because they are afraid of having to undergo the same treatment twice. Therefore, early cancer detection and recurrence diagnosis represent a significant medical challenge. To address these types of medical challenges, Machine learning algorithms make it quick and easy. In this paper, a thorough and organized review for breast cancer prediction and recurrence is discussed. The paper describes the background and current state of art on bio-inspired algorithms for feature selection and machine learning algorithms for classification. Furthermore, this paper helps to understand which machine learning classification algorithm performs best and under what environmental conditions the results can be compared. This literature represents a research front that is critically important in health sector.
Image Enhancement Using Histogram Shifting Technique for Reversible Data Hiding

Digital image processing is the process of enhancing the clarity and quality of a picture.Also, owing to the requirement for security while emailing the recipient a picture.Reversible data hiding (RDH) is a key component of many current systems used to encrypt and decrypt pictures, allowing for easy improvement of the original image's quality without pixel loss once it has been embedded.Previous methods for embedding data into encrypted images by reversibly releasing space from them may have had drawbacks for data extraction and image restoration.Hence, by reducing data loss, this technique enhances picture security while also improving image quality at the receiver end.The rapid spread of information and the development of digital technologies, which have made it possible for information to be concealed in multimedia data, have made it easier to access digital data and made it possible for it to be stored, transferred, and processed in a reliable, quick, and efficient manner.This has had the unintended consequence of making it simple and covert to produce and distribute digital media illegally.
Computer Information Teaching Reform Based on Internet of Things

With the rapid development of modern communication technology, the major of computer application has attracted much attention, and now it has become a popular major in secondary vocational schools.Under the background of the new era, the computer application specialty is facing a broader development space, and the demand of society for computer application professionals is getting higher and higher.Secondary vocational schools must strengthen the investigation of the industry and the market, compare the social needs with the current situation of talent cultivation, education and teaching methods, constantly optimize computer teaching, improve teaching objectives and teaching plans, and improve the quality of talent cultivation.This paper analyzes the influence of Internet of Things technology on computer application specialty, analyzes the reform strategy of computer application specialty's practice curriculum from the aspects of practice curriculum system reform, practice teaching environment reform and assessment method reform, and puts forward the specific process of integrated teaching.
Active contour model-guided 3D NMF kidney segmentation from CT scans.

Nephrolithiasis, or kidney stone disease, is surprisingly common in developed countries.Small kidney stones often pass away on their own.Often, no therapy is necessary for these people.Some people with nephrolithiasis, however, develop large stones that, if left untreated, may cause significant morbidity in the form of acute signs and symptoms and persistent headaches.However, effective treatment and prevention have the potential to eradicate the disease entirely.To achieve this victory, we developed the wavelet technique, which sidesteps the need for either log or exponential processing by seeing the highly complex speckle as additive signal-structured noise with zero means.The proposed method during wavelet transformation can combine data from different frequency bands, accurately evaluate the local regularity of image functions, and ornament the image using watershed rules in a highly satisfying way, all while being classified by a neural network.
Histopathological image classification for breast cancer using deep transfer learning techniques

Among female cancers, breast cancer has the highest incidence rate.The survival rate may be improved by early diagnosis of breast cancer.In order to reduce the burden on pathologists and enhance the quality of interpretation, automatic image analysis approaches are required.The first step in cell identification is finding their nuclei.Histopathological imaging may also reveal single-cell division and the metastasis of cancer from one organ to another.Detecting breast cancer from histopathology pictures is reviewed here..
Multi-Class Support Vector Machines for Text Classification Using a Class-Incremental Learni

The challenging topic of multiclass picture classification has been extensively studied in the past.Methods based on decomposition are often used to deal with it.These techniques often involve breaking down a complex issue into simpler subproblems that may then be tackled using any number of tried-and-true learning algorithms that would not otherwise be applicable.This study examines the effectiveness of decomposition-based approaches and offers some suggestions for enhancing the meta-learning stage.This study presents four strategies for improving multiclass classification ensembles.The first one shows how decomposition-based approaches for multiclass issues may benefit by using a combination of experts scheme to minimise the number of operations required during training.Bayes' theorem underpins the second method for merging learner-based outcomes.Training complexity is reduced in relation to the number of classifiers when the Bayes rule is combined with arbitrary decompositions.Decomposing the original problem into smaller ones and assembling the output of the base learners together with that of a multiclass classifier are two additional strategies recommended for improving the final classification accuracy.Finally, four datasets with varied degrees of categorization complexity are used to test the effectiveness of the suggested unique meta-learning strategies.The suggested approaches consistently show a significant increase in accuracy over classic Text categorization methods.
Applications in medical imaging using distributed vector processing of the new local MultiScale Fourier transform

Recent work in geophysics has resulted in the Stockwell transform, which combines elements of the Fourier, Gabor, and wavelet transforms to show temporal or spatial variation in frequency.This helpful data is produced by doing a Fourier analysis on a short sample of a signal.The Fourier spectrum may be localised by filtering the signal using Gaussian scaling windows that vary in width depending on frequency.This time-frequency analysis at many scales reveals both the types of frequencies present and, more crucially, their occurrence times.More so, the Fourier domain may be deduced from the Stockwell domain, and vice versa.The ST's capabilities in these areas suggest it might be a useful tool for analysing and processing medical imaging data.The ST has found use in noise suppression and tissue texture analysis.Here, we discuss the methodology and use of the ST in diagnostic imaging.Using fMRI data, we describe and illustrate its usefulness and efficacy in contrast to other linear time-frequency transformations such the Gabor and wavelet transforms.
Classification of Voice Signals for Direct Speech-To-Image Translation with Enhance Feature Selection

Voice recognition and classification involves the analysis of various speech signals with the goal of enhancing the accuracy of either human recognition or machine decoding.Utilizing characteristics and feature matching, speech recognition algorithms aim to enhance the performance of communication systems.The matching name of the item will thus be detected in this procedure based on the speech characteristics added to the voice signal used as the input question.Also, a number of web-based picture annotation tools have been developed to enhance the quality of image retrieval in response to the rise of social web apps and the semantic web.The primary goals of this procedure are to categorize the voice signal, get the picture from the dataset, and enhance feature classification.