Reviewerly/exHarmony huggingface dataset:
A System of Data Assets Registration and Catalog Management

As the amount of data storage continues to be enriched and the areas covered by data continue to be extended, the importance of data at the policy, economic, social and technological levels is becoming apparent. Building a unified data asset registration system is the key to fostering a data circulation market. In this paper, we proposed a data asset registration and catalog management system for governments, enterprises and individuals. The system has the functions of data asset catalog, metadata knowledge map, data asset confirmation, data asset quality evaluation, etc., and finally forms a unified data asset catalog across the network to realize data interconnection and interoperability across organizations, systems, regions and fields. The system has the following advantages: 1) It helps users to comprehensively grasp the status of data assets and fully integrate multi-source heterogeneous data. It forms a unified data view among different enterprises and organizations and effectively solves the problem of data silos. 2) Establishing a unified data registration standard can effectively avoid the problem of data confusion and conflicts. 3) We propose a metadata knowledge graph construction method to automatically discover the association between different data assets, so as to quickly find all the data required in different scenarios.
Few-Shot Relation Classification Method Based on Semantic Feature Fusion and Relation Network

Current few-shot relation classification models based on prototype networks ignore the dependencies between query instances and support set instance representation vectors, and ignore local features of text leading to bias in the calculation of prototype centers, which affects the classification performance of the model. To solve the problem, a few-shot relation classification method based on semantic feature fusion and relation networks is proposed to enrich the feature vector representation by learning the dependencies between instances in the character layer and semantic layer, combining the relation networks to obtain the relation score, adjusting the calculation of the prototype center to enhance the support instances that are more closely related to the query instances as well as the categories. Tested on the FewRell.0 dataset, the accuracy of the method is higher than that of other few-shot learning models, which proves that the method can effectively enhance the prototype network and significantly increase the accuracy of few-shot relation classification task.
Research on Intelligent System of Automatic Cleaning Robot Based on Data Analysis Algorithm

The article explores the design and implementation of an intelligent system for automatic cleaning robots based on data analysis algorithms. This system relies on data analysis technology to obtain information about cleaning tasks and optimizes the cleaning route through algorithms. Key technologies include image processing, path planning, and motion control. The system design combines machine learning algorithms to improve system reliability and stability. Sensing technology provides the robot with more comprehensive and accurate environmental information and transmits data in real-time to the cloud, further optimizing system performance. In terms of implementation, the system achieves autonomous perception of the environment and obstacle avoidance through functions such as automatic obstacle recognition and avoidance, intelligent path planning, and autonomous cleaning. In terms of control accuracy, the sensitivity and control accuracy of the robot are improved by analyzing its motion trajectory. The research shows that the intelligent system for automatic cleaning robots based on data analysis algorithms has higher accuracy and efficiency and can achieve autonomous perception of the environment and avoid obstacles. This system has broad application prospects in the consumer market and smart home field, providing more efficient and convenient services for the cleaning industry and promoting the development of intelligent manufacturing and smart homes.
Mixed text detection and classification method based on attention mechanism and YOLOv7

To improve the accuracy of mixed text classification, CBAM attention mechanism is introduced in the backbone network of YOLOv7 to highlight the key features of dominant category determination. At the same time, aiming at the information loss of PANet route aggregation network, the ASFF adaptive feature fusion mechanism is embedded after it, and the feature map is adaptively weighted by pixels to better preserve the details of the feature map. Experiments show that the Macro-F1 value is improved by 1.9% compared with PSENet + SVM method which detects and classifies first.
Research on deep learning recognition model of human behavior for financial institution monitoring based on RFID

With the continuous development of the national economy, people's demand for the security of financial institutionsiscontinually increasing. Compared with human monitoring, with the help of human behavior recognition technology, collecting data from sensors to identify the behavior of criminals has the advantages of fast data processing and lowcost. This paper takes the human behavior recognition project based on RFID and deep learning as the research object, andstudies how to effectively extract behavior features and judge human actions according to the value of features. Byoptimizing the convolutional neural network, the project has both good feature construction ability and memory abilitytoextract action marks. Hopefully, all-weather, fast and accurate response to illegal acts in self-service banks andother places can be provided, and the informatization of financial institutions can be promoted.
Implementation of relational graph for multiple scenarios

Common relational graph calculation and display can adopt different calculation methods for various application scenarios to achieve the best display effect. This paper provides a multi scenario display method for the display of daily relationship graph. It can flexibly display the relationship between specified entities according to the user's needs. This technology fundamentally solves the problem of single computing and presentation mode in existing Internet relationship mapping products.
Query reduction based on multiple types of information

To solve the problem of failing to effectively match bug report in query reduction, we propose a query reduction method based on multiple types of information, MIQR. The method divides the information in the description of bug report into three different types: stack trace, program element, and natural language. Different term weighting algorithms are used to calculate term weights for different information, and keywords are extracted based on term weights. A new query is constructed based on the summary and keywords. We conducted experiments on 5,139 bug reports. The results show that our method is superior to the state-of-the-art method.
Cognitive sensors for rehabilitation and therapeutic treatment

Cognitive sensors (CS) for healthcare are devices that utilize various sensor technologies to monitor a person&apos;s health condition and provide them with appropriate rehabilitation services. These sensors can be worn on the body, embedded in clothing, or integrated into other devices such as smartphones, smartwatches, and tablets. These sensors can monitor various indices of health and diseased conditions. Data related to human health are increasingly integrated with cognitive sensors, which facilitates decision-making and better disease treatment. Further, real-time communication and patient monitoring devices have become a central concept of smart healthcare services. The CS networks consist of a large number of sensors that can collect localized information automatically and intelligently. In addition to information fusion, intelligent sensors, sensing grids, communication protocols, network routing, and complex event processing architectures, CS intersects with other trends in computing. It has been shown that such CS can significantly improve the quality of life and support for the elderly and physically handicapped by designing interactive, adaptable, and intelligent multisensory surveillance systems, which can be equipped with panic buttons, current, bed, and water flow sensors. The use of CS technology, such as sensors, can reduce directional errors and assist in the neural health of patients. Many cognitive sensor systems have been developed for rehabilitation purposes to assist various groups of individuals in overcoming physical or mental health conditions. However, only a few attempts have been made to assist individuals with traumatic brain damage, schizophrenia, or dementia.
Global Earthquake Analysis Seeks Elusive Foreshock Signal

Spotting foreshocks for what they are could help seismologists forecast large earthquakes.
An ensemble machine learning-based intelligent system for human activity recognition using sensory data

Human activity recognition (HAR) from sensor data has become an attractive research topic due to its application in areas such as healthcare, human-computer interaction and smart environments. Although several research works have been done on HAR, the current studies are not enough to produce optimised performance balancing efficiency and accuracy. To solve this issue, this chapter proposes a new hybrid scheme based on permutation entropy integrated with quadratic sample entropy and ensemble classifier. First, the data are segmented into intervals, then, a combination of entropy features is extracted and evaluated. The extracted features are fed to an ensemble machine learning algorithm to classify the features vector into different activities. The proposed model is tested on a publicly available sensor dataset. The results demonstrate that the proposed scheme is efficient to improve HAR while obtaining an accuracy between 95% and 96% and outperforms considerably the existing methods.
The ‘dishonesty’ of culture and the feminist ‘honesty’ in Elena Ferrante’s &lt;i&gt;The Lying Life of Adults&lt;/i&gt;

The article discusses the latest novel by one of the most celebrated contemporary writers (who may have been intentionally mystifying their audiences): Elena Ferrante. In her analysis of The Lying Life of Adults [ La vita bugiarda degli adulti ], the critic offers a consecutive examination of Ferrante’s take on such problems as coming of age, national identity, socio-cultural stratification and, finally, the problem of falsehood, treatment of which defines the novel’s narrative. In Ferrante’s interpretation, the story of the protagonist Giovanna logically follows from the dishonesty of culture and religion. However, the critic argues that this ideological charge kills the novel, turning an overall masterly psychological tale of a teenage girl’s coming of age into a platform for a feminist jab at culture. The only redeeming quality that balances off the plot’s ideological dictate, something immediately associated with the literature of social realism in the mind of a Russian reader, is the image of the narrator, whose consciousness is depicted by Ferrante to be more nuanced and psychologically convincing than the heroine’s radicalism.
Estimation of Parameters and Error Prediction for Instrument Transformer Using Machine Learning

Transformers are employed widely for varying purposes.In the making of different kinds of transformers, various different parameters are present which need to be precisely calculated before the actual manufacturing of the product.From input parameters to materials required for the manufacturing process of the product, every value needs to be calculated accurately to avoid errors and wastage of raw materials.Even today these values are being calculated manually, which leads to errors.So, the automation of the manual process of computation is necessary to get the desired requirements of the final product.Based on the purpose and the inputs provided by user the application does the automated computation and displays the results and 3D model of the transformer that provides a better view, also the phase and ratio errors are predicted before testing of transformer using Machine Learning model and the design sheet is printed.
A new animation landscape generator based on RGB and HSV model

For now, cartoonization becomes more and more popular in image processing field. For example, cartoonization can be applied to image processing softwares as a filter. By now, there are some cartoonization Generative Adversarial Networks (GAN) has been proposed. However, most of these models just use RGB image as input. Different from these approaches, our cartoon landscape GAN use HSV and RGB image as input to learn cartoon images’ saturation feature and color feature. To learn cartoon images’ texture feature, we used Gramian matrix as images’ texture feature and use the texture discriminator makes generator to learn cartoon images’ texture feature. By this way, we get a great cartoon landscape generator. Our generated images have high contrast like cartoon images.
A practical three-stage hybrid feature selection method using discrete state transition algorithm

The development of sensor technology has driven the growth of data scale and data dimensions. While the dimension of the dataset is rapidly increasing, efficiently processing high-dimensional and small-scale datasets is still a challenge for traditional machine learning methods. Currently, feature selection is an effective method used in this case. In this study, we propose a practical hybrid feature selection method called mRMR-FBSTA. The algorithm can be divided into three stages: the first stage is the filter part that provides a preliminary feature subset and necessary feature weights to avoid high computational costs caused by using only the wrapper part, the second stage called fast iteration stage which used to quickly find the acceptable solution and the third stage called global iteration stage that aims to find the global optimal solution based on the acceptable solution found by the fast iteration stage. The experimental results show that the proposed method achieves excellent performance on high-dimensional and small-scale datasets.
Next POI recommendation method based on heterogeneous graph neural network and GRU

With the development of mobile online social networks, more and more attention has been paid to the task of recommending points of interest. Existing studies have proposed using user behavior sequence information and point-ofinterest information to make recommendations, but they have not fully explored the complex interaction relationships of users. Users' check-in behavior is uncertain, which increases the complexity of recommendation work. In this paper, a point of interest recommendation algorithm model based on heterogeneous graph neural network and GRUs is designed. First, this paper constructs a heterogeneous graph that includes user social relations, user check-ins, co-occurrence relations of POIs, and proximity relations between locations. The RGCN model is then applied to the constructed heterogeneous graph to obtain user and POI embeddings. We take user and POI embeddings as input to GRU. We predict where the user is likely to arrive next based on the existing check-in location. Then, based on the location score, we recommend k locations that the user might be interested in. This paper conducts experimental comparison and analysis on a real dataset Gowalla. Experiments show that this method has a better effect than the baseline model.
Knowledge-enhanced recommendation algorithms for multi-task learning with interactive attention

Compared with the traditional knowledge graph-enhanced recommendation method, this paper introduces a multi-task learning module to alternately train knowledge graphs and recommendations to alleviate the data sparsity and cold start problems in traditional recommendation methods. Specifically, in the multi-task learning module, the item features and contextual content features are taken, and the features after feature interaction are obtained using the interactive attention network, as a way to learn finer-grained features, and then the gating mechanism processes the item features and entity features that fuse the contextual content, which can filter the unimportant features and obtain the important potential features, and can capture the implicit higher-order feature interaction more effectively. Optimized for multi-task learning tasks. The validity of our model was verified on three publicly available datasets.
A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images

The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model’s guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model’s superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities.
The factors obstructing the blockchain adoption in supply chain finance: a hybrid fuzzy DELPHI-AHP-DEMATEL approach

Purpose Supply chain finance (SCF) is an advanced financing solution for medium and small organizations. However, due to its nascent stage, SCF is still struggling with its application in the supply chain. The blockchain application in SCF can bring lots of opportunities and smart techniques to deal with financing to organizations. However, some factors restrict the proper adoption of blockchain in SCF. Therefore, this study aims to identify and analyze the factors obstructing blockchain adoption in SCF. Design/methodology/approach The present study applied a hybrid fuzzy DELPHI-AHP-DEMATEL to validate and analyze the factors. The fuzzy Delphi techniques were adopted to validate the identified factors. Thereafter, the analytic hierarchy process (AHP) has been used to prioritize the validated factors. Further, the decision-making trial and evaluation laboratory (DEMATEL) method has opted to classify the factors into cause-and-effect groups. Findings The result of the study revealed that lack of technological competence and confidentiality leaking are the most prioritized factors and act as the prime factors for the obstacles in the blockchain application in the SCF. The application of the DEMATEL method revealed that the highest causing factor is authenticity, and the highest effecting factor is high cost. Practical implications The current study enhances the knowledge and information of managers and policymakers about the challenges associated with the blockchain application in SCF as well as provides better insight to deal with various factors obstructing blockchain adoption in SCF. Originality/value The current study is the first of its kind, where factors obstructing blockchain adoption in SCF are identified and validated with the help of experts and practitioners by incorporating the fuzzy Delphi method. Thereafter, the validated factors are prioritized by using fuzzy AHP and fuzzy DEMATEL techniques helps to identify the cause-and-effect group, which opens the door for future research work.
The Effectiveness of a Suggested E-learning Program Using Keeler’s Strategy to Build a Basic Model for a Girl’s Dress in an Innovative Way

The process of preparing patterns is considered one of the most accurate and important jobs on which the ready-made garment industry relies, as the success of the design and production depends entirely on it.Children's patterns are among the most important types of patterns, whose control and method of construction affects the comfort of the child, which calls on researchers to continuously develop ways to build these patterns.It is characterized by exactness and ease of construction and learning, and after many studies have proven the effectiveness of electronic educational and training programs with appropriate education strategies in learning to prepare patterns.The pattern, and the use of Keeler's strategy to master his learning, and the research followed the descriptive approach, and the applied approach.The child achieves excellent control over the body for all sizes, according to the opinions of the arbitrators, where The value of the quality coefficient for adjusting sizes ranged between (94.13) to (95.07), which represents a high degree of acceptance (excellent), and the value of the quality coefficient for adjusting (front, side, back, sleeve) for different sizes ranged between (93.94) to (94.74), which represents A high degree of acceptance (excellent), and the evaluation of the proposed program achieved an excellent evaluation according to the opinions, achieving the arithmetic mean (4.93), and the relative weight (98.67).Finally, the training program achieved a significant impact on the level of knowledge and skill achievement of the trainees.
Robust zero-watermarking scheme using DT CWT and improved differential entropy for color medical images

The advancement of internet has greatly facilitated development of healthcare technology, particularly in the field of remote diagnosis. However, during the transmission of medical images and electronic medical records through internet, there are risks such as theft, tampering, and compression, which may result in leakage or damage of patients’ information. A robust zero-watermarking scheme using DTCWT and improved differential entropy is proposed to protect the copyright and the security of medical images. Firstly, the low-frequency information of medical image is extracted by DTCWT to weaken the impact of non-geometric attacks. Then, a sub-block selection method based on variance is used to select locally stable sub-blocks, and the traditional differential entropy method is improved by introducing singular values and its mean, which improves its robustness against geometric attacks. In order to ensure the security of zero-watermarking, Arnold scrambling and Logistic mapping encryption mechanisms are applied to watermark images. Experimental results show that the proposed scheme provides a high level of robustness with average normalized correlation values of 0.9928. Compared with other advanced schemes, the robustness of the proposed scheme can be improved by 9 percentage points under same attacks, which verifies the superiority of the proposed scheme.