Reviewerly/exHarmony huggingface dataset:
Attention Analysis and Information Dissemination Effect of Young People Based on Multisource Sensors under the Background of Wireless Communication and Artificial Intelligence

In recent years, the development of wireless communication and artificial intelligence technology has brought tremendous convenience to human life. Especially in the fields of education and medical treatment, the functions of wireless communication are the most representative. In order to enhance the attention of young people and the effect of information dissemination, scholars have conducted many analyses on its influencing factors. Studies have found that strong noise environments have obvious negative effects on attention and information dissemination effects. Therefore, this paper proposes a multisource sensor-based method for young people’s attention analysis and information dissemination effect enhancement in a strong noise environment. Multisource sensors are selected to perform young people’s attention analysis on speech in a strong noise environment, combined with the information dissemination effect to strengthen the model obtained by combining attention analysis and information dissemination effect strengthened speech. Through the multisource sensor system, it is demonstrated that the method in this paper is superior to the traditional method in a strong noise environment. And through the comparative analysis of multisource sensors, the corresponding improvement plan for the strong noise environment was formulated, and the numerical simulation experiment of young people's attention analysis and information dissemination effect was carried out to verify the optimization results. This research, based on wireless communication and artificial intelligence decision-making technology, proposes to evaluate and optimize the attention analysis and information dissemination effect of young people in a strong noisy environment based on multisource sensors and gives useful ideas to ensure the system design method in a strong noisy environment and the environmental quality after implementation. It also provides support and help for people to more specifically prevent the adverse effects on people’s attention analysis and information dissemination effects in a strong noise environment. This paper designs a model based on multisource sensors to study the effect of young people's attention analysis and information dissemination in a strong noise environment, which is of great significance to the development of people’s high-quality life.
Educational Opportunities

Journal AWWAVolume 114, Issue 2 p. 80-80 Departments Educational Opportunities First published: 04 March 2022 https://doi.org/10.1002/awwa.1874Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume114, Issue2March 2022Pages 80-80 RelatedInformation
Blind image quality assessment based on Multi-resolution Local Structures

Our approach to no-reference image quality assessment (IQA) is grounded on the assumption that natural scenes have certain statistical properties which are altered in the presence of distortion. Therefore, by measuring this statistic one can recognize the distortion affecting the image and provide a no-reference (NR) IQA. In this paper, we extract local entropy and standard deviation from non-overlapping 8 × 8 blocks in the image and its gradient version from 2 scales. Extracting features from gradient images can help to better distinguishing blur and noise effects on images. Having extracted local measures, we aggregate them by finding the parameters of a Gaussian mixture model fitted on these local measures. Finally, our quality metric is achieved via a Neural Network machine learning tool, and its performance is evaluated on a standard LIVE database. The experimental results point out that our proposed features not only can identify the type of distortion but also are consistent with the DMOS in subjective assessment in terms of the correlation coefficient.
A Spatial-Frequency Feature Ensemble for Detecting Cervical Dysplasia from Pap Smear Images

Among women, cervical cancer is the commonest and the most treatable and preventable type of cancer. In most cases, cervical cancer begins as precancerous changes which gradually develop into cancer. Pap smear is widely used for cervical cancer diagnosis. Cell analysis is a time-consuming and cumbersome job; thus, an automatic detecting framework is proposed. Wavelet transforms offer the associated coefficients as the input image data representation, used as feature vectors. Artificial Neural Networks (ANNs) have outstanding attributes such as enhanced input-to-output mapping, non-linearity, fault tolerance, adaptively, and self-learning. Classification of cervical cancers employs neural network systems that have a huge role in most applications related to image processing. For application in diverse fields such as bioinformatics and pattern recognition, most researchers choose ensemble classifiers. A spatial-frequency feature ensemble has been proposed in this work to identify cervical dysplasia from images of Pap smears.
A study on the application of mission-based cybersecurity testing and evalulation of weapon systems

In this paper, we investigate the ongoing research on ways to improve cybersecurity throughout the life cycle of weapon systems applied in advanced countries such as the United States(U.S.), and present effective security evaluation measures by analyzing restrictions on acquiring weapon systems in Republic of Korea. We consistently performed mission-based risk assessment in cybersecurity tests and evaluation plans at the entire stage to support decision-making by providing key information to major decision-making organizations in a timely manner. We propose a plan to carry out simulated penetration by establishing rules of engagement so that protection measures can be verified for vulnerabilities identified in terms of cybersecurity. In addition, we identified the areas where artificial intelligence can be applied to the proposed cybersecurity test and evaluation system, and suggested future development plans. Through this, we supplemented our ability to support major decisions by integrating mission-based risk assessment factors into the cybersecurity test and evaluation system research conducted so far to identify risks in a timely manner between acquisition projects.
Research on Splicing of Horizontal and Longitudinal Shredded Paper Based on Hungarian Algorithm

In order to solve the problems of traditional heuristic algorithm for solving the splicing of horizontal and longitudinal shredded paper, such as high complexity and low success rate when the order of magnitude is large. A method is proposed to transform the horizontal and longitudinal shredded paper splicing problem into an assignment problem. Then the Hungarian algorithm is used to solve the problem: first, the fragments are numbered and the position information on both sides of the fragments is extracted. For the shredded paper with both crosscutting and longitudinal cutting. The K-Means clustering algorithm is used to splice each row successfully. Then, one side of any fragment is regarded as the person completing the task, and the other side is regarded as the task, and the gray difference of the corresponding position on both sides is regarded as the cost for the person to complete the task (if the blank row is cut, the line spacing difference is used as the cost). Thus the cost matrix is constructed. Finally, through the minimum cost sum obtained by the row-column transformation of the Hungarian algorithm, the optimal solution of the problem is obtained and the fragment splicing sequence is obtained. This method transforms the nature of the problem ideologically and adopts a solution different from the traditional heuristic algorithm. Compared with the simulated annealing algorithm, the complexity of the Hungarian algorithm is obviously lower than that of the simulated annealing algorithm. The amount of calculation is greatly reduced.
Research on Security Protection Warning Model Based on Multiple Data Monitoring

Due to the expansion of data types and collection channels of power grid energy consumption systems, it is difficult to effectively monitor new security attacks. In order to improve the power grid enterprises' perception of the energy consumption status of users, a monitoring and early warning method for grid users' energy consumption based on multiple data perception is proposed. First, by collecting energy consumption monitoring data, multiple data perception characteristics are obtained, and the intrusion behavior sample data processing is performed in combination with the working condition characteristics of the energy consumption equipment. Subsequently, the fuzzy monitoring method is used to control the abnormal data according to the set authority threshold, and the monitoring and early warning level is set accordingly. Finally, the combination of multi-attention and GRU (Gate Recurrent Unit) is used to construct the security protection warning model, so as to realize state safety monitoring and early warning. The simulation result showed that this method further improved the system's monitoring and early warning accuracy by improving the multiple perception capabilities of the collected data.
Few Shot Model based on Weight Imprinting with Multiple Projection Head

Few-shot learning models based on imprinted weights have achieved excellent results on several benchmarks. In these methods, the network model directly sets the weights of the final layers for novel classes from the latent representations of the training classes. As a result, the learned representations lead to good performance accuracy in training classes. However, the performance accuracy may be poor on unseen classes. This paper provides an alternative training technique for imprinted weight models. We find that adding projection heads can yield substantial improvements over the baseline model. Our experiments show that (1) introducing nonlinear projection heads in-between the feature extractor and the classifier substantially improves generalization, (2) imprinting from the task-specific layer does not provide better generalization for novel classes. Instead, we propose imprinting from the task-agnostic layer, and (3) our design choice benefits from a large latent dimension. We validate our findings by achieving 5.6 and 4.1% improvement on the MNIST dataset trained with the Omniglot dataset
Application of Harmony Search Algorithm to Optimize SPARQL Protocol and Resource Description Framework Query Language Queries in Healthcare Data

The rapid developing international of internet, Semantic Web has become a platform for intelligent agents mainly in the healthcare sector. Inside the beyond few years there is a widening in the Semantic web data field in the healthcare industry. With a growth in the quantity of Semantic web data field in health industry, there exist some challenges to be resolved. One such challenge is to provide an efficient querying mechanism that can handle large number of Semantic web data. Consider many query languages; especially SPARQL (SPARQL Protocol and RDF Query Language) is the most popular query language. Each of these query languages has their own design strategy and it was identified in research that it is difficult to handle and query large quantity of RDF data efficiently using these languages. In the proposed process, Harmony search identify met heuristic algorithm to optimize the SPARQL queries in the healthcare data in the applicable manner. The application of Harmony search algorithm is evaluated with large Resource Description Framework (RDF) datasets and SPARQL queries. To assess performance, the algorithm’s implementation is compared to existing nature-inspired algorithms. The performance analysis shows that the proposed application performs well for large RDF datasets.
Smart Voting System using Face Detection and Recognition Algorithms

The electoral system is the backbone of democracy and organization. The electoral system has experienced many efficient changes within the past few decades. India being a majority rule government, the world's biggest, still directs its races utilizing either Secret Ballet Voting (SBV) or Electronic Voting Machines (EVM), the two of which include significant expenses, physical work and are wasteful. In the existing system, it verified only identification proof, which made more chances for fake voting. To avoid the above issues, we developed a web-based smart voting system along with novel face detection and recognition approach. The entire online framework empowers individuals to protect their votes from any place on the planet. Utilizing the ID of appearances lessens the shot at copying a vote and the individuals who are enlisted advance to the political race and are perceived by the framework will be permitted to cast a ballot. Accordingly, the methodology makes the framework the most ideal approach to make the choice.
Edge Detection of Microstructure Images of Magnetic Multilayer Materials via a Richer Convolutional Features Network

Magnetic multilayer materials are extensively used in micro-devices and nanoelectronics areas. It is significant to implement edge detection and extraction for the microstructure images of the multilayer materials. This research deals with the edge detection and extraction of microstructures images of the magnetic multilayer material based on a richer convolutional features (RCF) network. First, an RCF network model on a 20-fold expanded Berkeley Segmentation Data Set and benchmark 500 (BSDS500) dataset is retrained. Then, such model is applied to the edge detection test on the given microstructure images of the magnetic multilayer material, and the edge probability maps containing coarse and obvious boundaries between the layers of magnetic materials are obtained. Third, the non-maximum suppression (NMS) algorithm is introduced to further refine the thick edges of the microstructure images. The results demonstrate that the RCF-based edge detection method is capable of detecting light and unclear boundaries of the magnetic multilayer material from their images, and outperforms the existing other edge detection algorithms includes Canny operator and HED network. In addition, under the expanded RCF model combining with the NMS algorithm, the edge probability map of the microstructure images of the magnetic multilayer material are almost the same as the ground truth.
Multimedia Content Mining Based on Web Categorization (MCMWC) Using AlexNet and Ensemble Net

Nowadays, a large amount of web content available over the web tends to grow exponentially. From this big data environment, extracting meaningful information according to user adaptability and making better decisions is a big challenge. For this Web Page, categorization approaches help to classify web pages into a number of predefined categories. Normal web categorization approaches work for either text or images. But to extract information from multimedia sort of webpages is again a big challenge. Hence in this paper, a method of categorization has been proposed which is applicable over the webpages consisting of multimedia type of data (text and images both). Experimental results based on real-world disaster dataset show that the proposed MCMWC model provides better performance (3% more accuracy in comparison to Text Modality and 2% more accuracy in comparison Image modality) in comparison to models trained using a single modality (either text or image).
Multiword Expression Extraction Using Supervised ML for Dogri Language

The multiword expression identification in NLP remains an interesting topic of research and number of research techniques has been proposed for MWE for different Indian languages. In this paper, a machine learning-based approach is applied that can be used to extract the multiword expressions from corpora. The MWE relies on simple heuristics that take the co-occurrence of MWEs into consideration. Novel technique for multiword expression extraction of bigrams using supervised machine learning is proposed for Dogri language. The proposed technique is applied on a variety of test datasets and a decent measure of precision, recall, and F-score is obtained.
Semi-Supervised Deep Clustering with Soft Membership Affinity

As an effective deep clustering method, improved deep embedding clustering can process large-scale high-dimensional data. However, the method only focuses on the global data and does not consider the local graph structure between data points. In this paper, a semi-supervised deep clustering algorithm with soft membership affinity is proposed to cluster high-dimensional datasets. The proposed algorithm is composed of three parts: the reconstruction loss is adopted to recover data and extract important features on latent space, the KL divergence between the soft assignment and the target distribution is utilized to make samples in each cluster distribute more densely, and the novel soft membership affinity, which is regarded as the semi-supervised information, is introduced to the IDEC model to constrain the relationship between data points and their neighbors, so as to further enhance the clustering performance. Experiments on datasets show that the algorithm is effective compared with other deep clustering algorithms.
Improved Image Retrieval Technology Based on Singular Value Decomposition

An image retrieval technology combining singular value decomposition, bicubic interpolation and deep learning is proposed. Since the accuracy of image retrieval is affected by the quality of the input image, images with distinct features can accurately match the target image. For image retrieval and feature training that require input images of different scales, traditional image scaling methods will degrade image features. This paper uses singular value decomposition (SVD) and bicubic interpolation algorithm to improve the quality of the zoomed image, so that the deep neural network (DNN) can extract more accurate features. The experimental results show that the proposed algorithm can improve the accuracy of various image retrieval of power grid companies
Video Instance Segmentation of Rock Particle Based on MaskTrack R-CNN

Video instance segmentation (VIS) of rock particles in motion is the basis for revealing the laws of motion and quantitative analysis. It has important scientific and engineering value. Use an end-to-end network called MaskTrack R-CNN to complete the VIS task for rock particles. The network introduces a new tracking branch on Mask R-CNN. It integrates particle detection, segmentation, and tracking tasks into the framework. The tracking branch primarily uses appearance similarity cues to linearly combine cues such as semantic consistency and spatial correlation to improve tracking accuracy. To facilitate the study of rock particle visibility, we have created a set of experimental equipment for collecting rock particle datasets. We conducted training and testing experiments to verify the effectiveness of the algorithm and compared it to some baselines of our own dataset. Experimental results show that MaskTrack R-CNN uses ResNet-50 to get 33.1% AP. It better than other two-stage models. This work provides an intelligent solution for meso-analyzing particles.
Cross-scale feature extraction module for efficient RGBD images semantic segmentation

Image semantic segmentation plays an important role in assisted driving systems and motor vehicle auto driving system. Due to the complexity of outdoor scenes and driving scenarios, algorithms that only use texture images have low robustness. In order to improve the performance of semantic segmentation, depth images can be used to assist texture images. In addition, the assisted driving system requires that the algorithm need to achieve real-time performance, but the existing algorithm is limited by the complexity of semantic segmentation, resulting in low operating efficiency. To address the above problems, a cross-scale feature extraction module for efficient RGBD image semantic segmentation is proposed. The cross-scale feature extraction module has the characteristics of small parameter amount, large receptive field, and the ability to merge multi-scale features, which can efficiently extract context features. The proposed model achieves a segmentation accuracy of 69.4% mIoU on the RGBD original resolution image of the outdoor scene dataset Cityscapes, and runs at a speed of up to 120 frames per second. Compared with related algorithms, the model proposed in this paper has obvious advantages in running speed, and has achieved a good balance between performance and efficiency.
Multi-view Spectral Clustering With Adaptive Local Neighbors

Some existing multi-view spectral clustering methods calculate a fixed similarity matrix. However, the fixed similarity is hard to reflect comprehensive and correct relationship between data points and may not be optimal for learning sample embedding, leading to the suboptimal result. In this paper, we propose a novel multi-view spectral clustering (MSCAN) algorithm to address these issues. Specifically, the proposed algorithm generates the dynamic similarity matrix with adaptive neighbors for each view instead of using the fixed similarity matrix. It enhances the representative capacity of the similarity matrix and accuracy of the sample embedding. Meanwhile, the knowlege trnasfer is imposed to the sample embedding for all view, such that the sample embedding learns shared information betwwen the feature embeddings to keep consistency. Additionally, the feature embedding of each view learns from the shared sample embedding to obtian complementary information. An alternating iterative optimization algorithm is presented to optimize the objective function. Experimental results on several benchmark databases show the superior performance compared to the previous state-of-the-arts.
Long Context Question Answering With BERT

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An Approach to Improving Anomaly Detection Using Multiple Detectors

Anomaly detection performs well in situations where signature (and other rule-based) methods fail; there is no need to identify every threat as long as it is different from the norm. The tradeoff is that anomaly detection often results in a large number of false positives. While previous work has capitalized on the data imbalance problem to train models with only one set of data (one-class classification), few have utilized the limiting set for anything other than testing purposes. This paper seeks to utilize two anomaly detectors: one that is trained on the positive set and one that is trained on the negative set. By utilizing multiple detectors, we can encode more information about each class and ensure that a data point is not only different from one class, but also similar to the other. We present a new approach to anomaly detection and show its effectiveness at reducing false positives with limited effect on detection rates.