Reviewerly/exHarmony huggingface dataset:
Research and Application of Cross-media Knowledge Discovery Service Based on Deep Learning Model

Abstract With the diversification and complexity of multimedia data on big data, it becomes increasingly important to realize accurate and effective mutual retrieval among cross-media knowledge service data. In this paper, we first improve the structure of cross-media knowledge deep relevance analysis and apply it to cross-media data to construct cross-media relevance learning evaluation metrics. Then deep learning is commonly used for training classification labels or mapping vectors to another vector space by supervision, and with the rapid growth of data size and hardware resources, the advantages of deep learning in handling large-scale complex data will become more and more obvious. According to the experimental scheme to extract the features of the original data of Wikipedia and NUS-WIDE and the comparative analysis of the results based on the CCA extension method, the performance of CMC-DCCA on the dataset is 0.319, 0.338, 0.363, and 0.372, respectively, and it outperforms the other four algorithms. This study constructs a correlation analysis model between different media data to mine the correlations between cross-media data, thus realizing cross-media knowledge discovery service research while spawning more intuitive and concrete multimedia information carriers so that users can obtain more comprehensive information.
Numerical implementation of level set image recovery based on partial differential equation model

Abstract Image recovery is a class of ill-posed inverse problems, so image recovery has been a challenging and fundamental topic in image processing. In this paper, firstly, to recover blurred images with noise, an adaptive fully variable partial differential image recovery model is proposed based on the fully variable partial differential equation model and Gaussian thermal diffusion model, where the adaptiveness is achieved logarithmically by regularizing the edge detection operator. Secondly, a finite difference method, which covers the gradient and scatters operators in the partial differential equation, is applied to the problem of fixing the global range boundary conditions of the image. Finally, the gradient of the image is restored, and then the gradient domain of the image is solved to obtain the complete recovered image. The results show that in the evaluation of the image restoration effect aspect by two indexes, PSNR and SSIM, the value of the adaptive fully variable partial differential image restoration model increases from 28 to 32, and the value of SSIM increases from 0.70 to 0.88, which has good image restoration performance. The adaptive fully variable partial differential image restoration model proposed in this paper has the characteristics of good edge protection and fast convergence, which is of guiding significance for the numerical implementation of image restoration techniques.
Keyword Extraction-based Library Intelligence Services: Challenges, Adaptations and Reinvention

Abstract This paper proposes a domain knowledge base construction platform for thematic intelligence services of library intelligence institutions. The platform proposes a keyword extraction algorithm applicable to the literature resources of library intelligence services based on a statistical model and N-Gram model combined with feature word information. The automatic summary algorithm based on statistics combines the information of sentence content and location to comprehensively evaluate the weights of candidate summary sentences and achieve automatic summary extraction. Based on the HDP topic model, a literature recommendation model oriented to the resource retrieval context is proposed, which can convert the literature feature similarity calculation to the topic similarity calculation with implied literature feature semantics from the perspective of semantic analysis and improve the accuracy of recommended literature. The technical feasibility of this system platform is verified through relevant data experiments. Regarding the accuracy of the literature recommendation, the platform of this paper and the traditional method are 86.36% and 75.16%, respectively, and the recall rate is 67.29% and 57.25%, respectively. The platform scored 3.85 on the comprehensive evaluation of library intelligence service, close to the excellent level. Therefore, the platform can realize various functions, from resource collection and organization to resource processing and utilization, with certain practical significance.
An informative teaching model of English-Chinese translation in universities based on the perceptual machine model

Abstract With the development of globalization, there is an increasing demand for English-Chinese translation talents in the country and society. In this paper, a text classifier based on multilayer perceptron is proposed, an MLP structure containing two layers of feedforward neural network is designed for the characteristics of English-Chinese translation corpus, the training samples are selected with text height mainly below 60 pixels, and the best sliding window of 13×13 is selected by comparison, supplemented by the smoothing method of mathematical morphology. The constructed MLP-based information-based teaching model is then applied to teaching English-Chinese translation in colleges and universities and is mainly evaluated in terms of student evaluation and teaching effectiveness. There were 28.48 percentage points more English-Chinese translation students who thought the class was more interesting than the original, while 20.56% and 20.50% more students evaluated the class as more helpful and less helpful, respectively. The average score of English-Chinese translation students increased by 15.88% compared to the original one through the comparison test between traditional and informational teaching modes. The MLP-based informatization teaching can mobilize students’ enthusiasm to participate in English-Chinese translation learning, strengthen the guidance of translation methods, and improve students’ translation levels.
Research on teaching reform of environmental design majors in colleges and universities based on the integration of rural revitalization into big data

Abstract The teaching reform of environmental art design majors is integrated into the rural revitalization strategy, which can best reflect the contemporary characteristics of education and teaching reform. This paper firstly explores and analyzes the significance of education reform of environmental art design majors in colleges and universities under the strategy of rural revitalization and proposes three teaching mode reform measures in combination with the current teaching situation. Secondly, studying the scheduling system based on genetic algorithm which is widely used at present, combines the characteristics of randomness and ergodicity of a chaotic system. Finally, the population diversity, crossover operation and variation operation are improved, thus forming a scheduling algorithm based on a chaotic genetic algorithm, which enables the most reasonable allocation of the existing teaching resources of university environmental design majors. The results show that the maximum running time of the genetic algorithm is 153 t / s , which is much larger than the maximum running time of the chaotic genetic algorithm 57 t / s . The minimum running time of the chaotic genetic algorithm is 19 t / s , which is 23.14% smaller than that of the genetic algorithm. The reform measures proposed in this paper for integrating rural revitalization into teaching provide the optimal and reasonable allocation of teaching resources for the major and employment opportunities for environmental art design students.
Research on the intelligent teaching model of integrated English reading and writing classrooms in colleges and universities based on big data technology

Abstract Because of the current relatively lagging writing ability and the current situation of English teaching that separates reading and writing, input and output, this paper proposes the reading and writing integration model to improve English teaching. This paper first introduces the definition of mutual information and interaction information, explains the information theory feature selection method of big data relationship mining technology, and uses the feature selection method to select the features related to the target variable, further derives the hidden structural information between the features, mines the correlation between the target variable and the features, and derives their causal relationship through correlation. Then the specific measures of reading and writing integration are elaborated, including developing students’ sense of language through reading and imitating writing. Finally, the effectiveness of this method is evaluated in terms of learning confidence, achievement and writing level. In terms of English achievement, the mean score after the experiment was 95.44, which was higher than the previous mean score of 82.78. In terms of writing ability, 76.5% of the students said that reading improved the quality of writing, and 65.4% said that the integrated teaching of reading and writing made their writing faster. The integrated teaching mode of reading and writing has improved students’ English proficiency and interest in learning.
Design of teaching quality evaluation system for higher vocational education based on data mining technology

Abstract It is advantageous for schools to realize exact education management, the cornerstone for developing abilities, by constructing higher vocational education teaching quality evaluation systems. The division of frequent item sets based on the modified Apriori algorithm under the association rule algorithm is used for the evaluation algorithm of this system to examine the evaluation data in the evaluation system, which is created in this work using data mining technology. Data validation was carried out from system performance and application analysis of association rules for the teaching quality evaluation system constructed in this paper. Regarding system performance, the highest response time of the system in this paper is 8.34s, which is about 56.94% and 40.68% shorter than that of the evaluation system based on AHP and gray correlation theory, respectively. This shows that using association rule algorithms can realize the operation of the evaluation system faster, improve evaluation efficiency, and use visual data to achieve precise education management in schools.
Analysis of the evolutionary path of early civilization agriculture based on the plain Bayesian model

Abstract First, this paper focuses on the problem of nonlinear association between attributes and categories, combines inverse document frequency information and deep attribute weighting, improves the distance correlation coefficient, and proposes an attribute-weighted polynomial plain Bayesian text classification algorithm based on the improved distance correlation coefficient. Then a specific scheme for stage classification of early Chinese civilization provides a theoretical premise and expository framework for systematically exploring the relationship between primitive agricultural types and early Chinese civilization and also provides conditions for forming the agricultural revolution of early Chinese civilization in the Central Plains region. Finally, according to the research objectives, the 7000-year-old Ningzhen area was selected as the research object, and the attribute-weighted polynomial plain Bayesian text classification algorithm was used to analyze the data of the research object. The results show that: in the Neolithic burials of the North Yingying culture period alone, there are 240 burial relics with a total of 1,580 pieces of all burial relics, including 554 pieces of stone production workers, but the fundamental reason is the active change of human for the survival crisis. This study is a preliminary attempt to conduct an interdisciplinary study on the history of agriculture and civilization and provides an academic path for the sublimation of agricultural history to the study of the history of agricultural civilization.
Multicomponent hyperspectral grade evaluation of ilmenite using spectral-spatial joint features

The grade information of nine components in ilmenite was evaluated from spectral and spatial dimensions based on hyperspectral imaging combined with chemometric methods.
SVMDnet: A Novel Framework for Elderly Activity Recognition based on Transfer Learning

Elderly Activity Recognition has become very crucial now-a-days because majority of elderly people are living alone and are vulnerable. Despite the fact that several researchers employ ML (machine learning) and DL (deep learning) techniques to recognize elderly actions, relatively lesser research specifically aimed on transfer learning based elderly activity recognition. Even transfer learning is not sufficient to handle the complexity levels in the HAR related problems because it is a more general approach. A novel transfer leaning based framework SVMDnet is proposed in which pre-trained deep neural network extracts essential action features and to classify actions, Support Vector Machine (SVM) is used as a classifier. The proposed model is evaluated on Stanford-40 Dataset and self-made dataset. The older volunteers over the age of 60 were recruited for the main dataset, which was compiled from their responses in a uniform environment with 10 kinds of activities. Results from SVMDnet on the two datasets shows that our model behaves well with human recognition and human-object interactions as well.
Design of a curved-target ultrashort throw projector based on predistortion and illumination precorrection

Ultrashort throw projector (UTP) technology has been a challenging research focus in the fields of large field of view and short-distance projection. In this paper, we propose a design method for a curved-target UTP based on image precorrection. A predistortion method (&lt;1pixel) using a radial basis function is presented to effectively correct irregular distortion. A grayscale precorrection method is used to address uneven illumination distribution. We provide a UTP design example and verify the correction methods using two different curved targets (aspherical and freeform), demonstrating effective distortion correction (&lt;1%) and illumination uniformity (&gt;90%). The structure similarity index measure for the projection images shows close or greater than 0.9 correlation.
Path planning for unmanned automaton based on improved artificial potential field method

The artificial potential field method (APF), a highly effective navigation technique, is currently utilized extensively in this area due to the rapid development of unmanned automatons. Traditional APF, however, have several shortcomings, including the issue of unreachable object points and the propensity to sink into local minima that prohibit the automaton from moving on. In this paper, a two-part improved APF model is created to address these issues. First, by including additional constraints, the repulsive field model at the stumbling block is enhanced to address the issue that the object point is impassable when too close a distance between the two stumbling blocks. Secondly, a new potential field is introduced to help the automaton walk out of the local minima. Analogue simulation show that the methods mentioned above can solve these problems better and make the route planning of unmanned automatons come true.
Radio frequency identification review and development prospect research

Based on the existing literature and data, this paper starts from the principle of radio frequency identification technology, analyses the situation of the application field of RFID and predicts its development prospect. The results show that RFID technology is a kind of technology that uses the transmission characteristics of RF signals through space alternating electromagnetic coupling to identify targets and thus realize rapid automatic identification. Since it was developed and used in warfare in the mid-twentieth century, RF identification technology has developed rapidly and developed a variety of applications. Today, with the development of electronic technology and communication technology, RF identification technology already has a wide and irreplaceable role in medicine, military and food safety. It is not difficult to imagine that with more joint applications of new technology, radio frequency-related algorithm improvement, radio frequency tag price reduction, and other conditions, the technology will have a more extensive and profound application in the future.
Review of unmanned aerial vehicle obstacle avoidance planning based on artificial potential field

With the development and widely use of unmanned aerial vehicles (UAVs) in recent years, the development of efficient path planning methods for automation has become crucial. Obstacle avoidance and path planning are the key components of UAV path planning. This article provides an overview of obstacle avoidance and path planning techniques for UAVs based on the artificial potential field method (APF method). This article begins with the explaining the principles of artificial potential field on this basis discusses its advantages and limitations. The article then summarizes the improvement strategies proposed by previous researchers to address issues like local minimum values and unreachable targets, such as introducing a new repulsive potential energy function, combining APF with other planning methods, and utilizing flow functions. Furthermore, it presents examples of the application and the performance of usage of these techniques in both static and dynamic environments. Based on this, the prospects and developing trend of UAV obstacle avoidance methods based on artificial potential field are foresee, such as combined with DRL and deep learning.
Advanced Sensing Techniques for Intelligent Human Activity Recognition Using Machine Learning

State-of-the-art network architectures ensure fast and dependable real-time communication with abundant data and minimal delays [...]
5G Enabled Moving Robot Captured Image Encryption with Principal Component Analysis Method

Estimating the captured image of moving robots is very difficult. These images are vital in analyzing earth's surface objects for many applications like studying environmental conditions, Land use and Land Cover changes, and change detection studies of worldwide change. Multispectral robot-captured images have a massive amount of low-resolution data, which is lost due to a lack of capture efficiency due to artificial and atmospheric reasons. The image transformation is required in a 5G network with effective transmission by reducing noise, inconsistent lighting, and low resolution, degrading image quality. In this paper, the authors proposed the machine learning dimensionality reduction technique i.e. Principle Component Analysis (PCA) and which is used for metastasizing the 5 G-enabled moving robot captured image to enrich the image's visual perception to analyze the exact information of global or local data. The encryption algorithm implanted for data reduction and transmission over the 5G network gives sophisticated results compared with other standard methods. This proposed algorithm gives better performance in developing data reduction, network convergence speed, reduces the training time for object classification, and improves accuracy for multispectral moving robot-captured images by the support of 5G network.
A Deep Learning Approach towards Cold Start Problem in Movie Recommendation System

Recommendation systems play an important role for e-commerce websites to make profits. It has a variety of applications in different domains. There are three types of categories in which recommendation systems are classified i.e. content based, collaborative and hybrid systems. These systems suffer when a redundant amount of information is not available to provide recommendations. This problem is known as the cold start problem. In this digital era, it is possible to collect meta information about a user and provide rich recommendations. Various approaches such as social media analysis, graph networks have been proposed to solve this problem. But they lack personalization and generate irrelevant recommendations affecting the system performance. The objective of this work is to resolve new user cold start problem in movie recommendation systems using a deep learning approach that utilizes demographic attributes to cluster similar users. This embedding is given to the deep neural network to generate the recommendations. From the analysis done, we verify the effectiveness of our approach..
Enhancing Face Recognition Performance with Multispectral Imaging and Machine Learning: Comparison from Sift and Sift-Freak Feature Extraction

The development of modern societies faces many security and identification challenges. To meet this expectation, computer vision offers biometric solutions. Much research in recent years has focused on face recognition. Traditional facial recognition that uses color images has had many shortcomings, such as variation in illumination, smoke, rain, disguise, face concealment, makeup, etc. Light-insensitive infrared (IR) imaging is presented as an alternative to facial recognition in the visible to overcome the shortcomings of uncontrolled environments. However, IR also has weaknesses, such as facial occlusion by glasses, variation in body temperature, perfusion, etc. This paper proposes a new facial recognition architecture that uses several classification algorithms, detectors, and feature descriptors in multispectral imaging. A combination of SIFT and FREAK, feature extraction tools, was associated with classification algorithms such as SVM, logistic regression, and Random forest to conduct this study. Several experiments were made to evaluate the performance of the proposed recognition system. The validation process of the proposed multispectral face recognition method involved several important steps. First, experiments were carried out on visible and infrared spectrum images to measure the recognition system's performance. These experiments made it possible to compare the recognition performances between these two types of images. Then, fuse visible and infrared images were used to assess multispectral facial recognition. The goal was to maximize each spectrum's advantages while minimizing their disadvantages. Metrics were evaluated to measure the accuracy of the multispectral face recognition method. The performances were compared with classical facial recognition methods, such as facial recognition based on the visible spectrum or infrared imagery alone. The results showed that the proposed multispectral facial recognition method performed better than traditional methods, reaching a facial recognition score ranging from 76% to 95% in the IRIS database.