Reviewerly/exHarmony huggingface dataset:
Modeling Method of 3D Environment Design Based on Genetic Algorithm

At present, mobile robots have been applied in various fields, and technologies such as autonomous research and environmental modeling have also received attention. Although this technology has been researched according to certain rules, it still faces certain problems when it comes to solving more complex 3D environment applications. Therefore, this paper studies the 3D environment design modeling method of genetic algorithm, understands the relevant knowledge of 3D environment design modeling on the basis of literature data, and then constructs the 3D environment design modeling method based on genetic algorithm. The algorithm is tested, and the test results show that the algorithm in this paper can eliminate noise very well. In the case of 5% noise, the final fuzzy system has 10 division points (5X5 rules).
A Fast Method of Legal Decision Recommendation System

Current engines search based on keywords, but in the process of legal case search recommendation, only some keywords are not enough. This work mainly completes the classification task of legal texts in three aspects: crime prediction, articles of law recommendation, and prison term prediction. We use TextCNN as a baseline. First of all, we make different attempts on the receptive field of the convolutional layer to verify its impact on the task of this project. Secondly, in order to better reflect the impact of context on the task of this project, we combined TextCNN and bidirectional GRU or Attention. In order to verify the effect of the above method, we use the CAIL2018 dataset for verification. The dataset contains more than 2.6 million cases. The experimental results show that the change of the receptive field and the introduction of attention have a specific impact on this project, and the combination of TextCNN and Attention has also achieved good results for the comprehensive performance of the three tasks of this work.
Design and Implementation of PSO Based on Ternary Optical Computer

As particle swarm optimization is widely used in communication, electronic system design and other fields, it cannot meet the high real-time requirements. To this end, this paper intends to use the giant bitness and parallelism of the ternary optical computer, and adjust the effect of fixed parameters on particles to make it dynamic adaptive, so as to speed up the convergence speed of the particle and the ability to find the global optimal solution. The simulation results show that, compared with other literature, the convergence speed of the particle swarm optimization algorithm based on ternary optical computers is significantly improved.
Decentralized Counterfactual Value with Threat Detection in Multi-Agent Mixed Cooperative and Competitive Environment

This paper introduces a fully decentralized method to address the problem of general mixed cooperation and competition in multi-agent setting. There are two main types of traditional Multi-Agent Reinforcement Learning (MARL) methods. The first kind is communication-based method which is designed to achieve coordination to deal with the non-stationary problem. However, it causes great communication overhead and can only be applied in fully cooperative environment. Another kind of methods follows Centralized Training and Decentralized Execution (CTDE) framework, which needs a centralized critic in the training process resulting in the huge computational complexity as the number of agents grows. In addition, neither approach is fully decentralized, because they either need to communicate the implicit information, or need to know the rewards of other agents. Different to the above methods, this paper introduces a Decentralized Counterfactual Value (DCV) to model other agents' behaviors, combined with the Threat Detection mechanism to learn the latent competitive or cooperative relationship with others. Finally, we prove that our method DCVTD does not affect the convergence of the basic algorithm and validate that DCVTD outperforms other fully decentralized methods, CTDE methods and other agent modeling or social influence methods in the experiments.
Research on Composite Index Construction Method Based on Master-Slave Blockchain Structure

Blockchain is a new information processing technology that uses efficient cryptographic principles for the trustworthy storage of big data. With the exponential growth of the scale of data on the chain, the problems of low query efficiency and long traceability time of the existing blockchain system become more and more serious. To solve the above problems, this paper proposes a composite index construction method based on a master-slave blockchain structure. Firstly, a weight matrix is introduced to slice the whole master-slave blockchain based on the master chain structure; secondly, a master index construction method based on jump consistency hash is proposed for the master blockchain within each slice; finally, based on an improved Bloom filter, a slave composite index is constructed for each master block corresponding to the slave blockchain. Experimental results show that, compared with the existing methods, the proposed method can reduce the index construction time by 4.63% on average, improve the query efficiency by 6.71%, and reduce the memory overhead by 24.4%.
Cross-platform Product Matching Based on Knowledge Graph

Product matching aims to identify similar or identical products sold on different platforms, which is crucial for retailers to adjust investment strategies. By building knowledge graphs (KGs), the product matching problem can be converted to the Entity Alignment (EA) task, which aims to discover the equivalent entities from diverse KGs. This paper introduces a two-stage pipeline consisted of rough filter and fine filter. Based on product names and categories, we roughly match products in rough filtering. For fine filtering, a new framework for Entity Alignment, Relation-aware, and Attribute-aware Graph Attention Networks for Entity Alignment (RAEA), is employed. Experiments on eBay-Amazon dataset indicated that the two-stage pipeline performs well on the problem of cross-platform product matching.
Towards a Unified Storage Scheme for Dual Data Models of Knowledge Graphs

As an important cornerstone of artificial intelligence, the knowledge graph is one of the indispensable foundations of the new generation of artificial intelligence from perception to cognition. RDF graphs and property graphs are the two main data models of KGs, and various data management methods have been developed for the two models. However, differences in data models will lead to differences in how the data is stored and manipulated, which will further hinder the widespread application of knowledge graphs. In this paper, we propose a novel unified storage scheme, UniS, which considers the characteristics of the two data model comprehensively. Unlike the existing approaches, the detailed conversion process for different storage forms of data that we devised will make it easier to manage multiple KGs in one database. Meanwhile, the experimental results show that UniS improves the storage and query efficiency by up to an order of magnitude than the state-of-the-art storage engines.
Vehicle Trajectory Obfuscation and Detection

A vehicle in road networks shares location data with other vehicles and location-based services (LBS) through Internet-of-vehicles (IoV). By analyzing the location data from vehicles, LBS providers can offer vehicles better services. However, fake trajectories created by adversaries and malicious drivers diminish the location data utility in IoV and breach the quality of LBS. Illegal trajectory detection is vital to ensure location data utility in IoV. Existing location privacy-preserving schemes like obfuscation schemes add noise to actual location data increasing difficulties in detecting illegal trajectories. In this paper, we detect illegal trajectory in the case that all drivers in road networks protect location privacy by using obfuscation. We propose a new personalized obfuscation mechanism to dynamically and adaptively protect the location privacy of drivers in road networks. Considering uneven protection, we propose a trajectory detection scheme to classify trajectories in IoV. We evaluate our detection method with the data of real-world road networks, which is an important scenario of smart cities. The experiment results show that the proposed classifier outperforms existing studies in detecting malicious obfuscated trajectories with at least 94% of the Area Under the Curve (AUC) score.
Multi-source Heterogeneous Blockchain Data Quality Assessment Model

Blockchain-based applications are becoming more and more widespread in business operations. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method realizes the consistency assessment by calculating the similarity of block information.After that, a trustworthiness characterisation method is proposed based on information sources and information comments, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain.
Investing information and communication technology to develop e-learning in Iraq: A survey

Despite the rapid development and spread of Information and Communication Technology (ICT) during the past four decades, which has become a prominent feature of the era, Iraq is still at the bottom of the list of developing countries, especially in the field of technology and communication by all standards. In fact, Iraq lies outside the scope of education quality alongside other six Arab countries. This, however, is attributed to the constant mis-management and lack of knowledge about issues relevant to the modes of technology development. What makes things worse is the outbreak and rapid spread of Covid-19. This, so to speak, has imposed a number of restrictions on education to the extent that the government has resorted to E-Learning.The researchers, however, briefly address the concept of (ICT) as well as the global technological diffusion indicator for the Arab group in which Iraq's position is analysed according to the main and sub-indicators. Moreover, the E-Learning, its advantages and disadvantages, is being brought into focus. To amend all the obstacles, solutions have been proposed in accordance with the general technological indicator. Besides, the research-paper concludes with recommendations and suggestions which will definitely contribute to the attainment of the goal aspired for.
Transfer Learning for Convolutional Neural Networks in Tiny Deep Learning Environments

Tiny Machine Learning (TinyML) and Transfer Learning (TL) are two widespread methods of successfully deploying ML models to resource-starving devices. Tiny ML provides compact models, that can run on resource-constrained environments, while TL contributes to the performance of the model by using pre-existing knowledge. So, in this work we propose a simple but efficient TL method, applied to three types of Convolutional Neural Networks (CNN), by retraining more than the last fully connected layer of a CNN in the target device, and specifically one or more of the last convolutional layers. Our results shown that our proposed method (FxC1) achieves about increase in accuracy and increase in convergence speed, while it incurs a bit larger energy consumption overhead, compared to two baseline techniques, namely one that retrains the last fully connected layer, and another that retrains the whole network.
Supplementary Figure S2 from Improving the Quality of Biomarker Discovery Research: The Right Samples and Enough of Them

&lt;p&gt;Supplementary Figure S2. ROC curves for two biomarkers, A (red) and B (blue).&lt;/p&gt;
Data from Nonlinear Optics with Near-Infrared Excitation Enable Real-Time Quantitative Diagnosis of Human Cervical Cancers

&lt;div&gt;Abstract&lt;p&gt;Histopathologic analysis through biopsy has been one of the most useful methods for the assessment of malignant neoplasms. However, some aspects of the analysis such as invasiveness, evaluation range, and turnaround time from biopsy to report could be improved. Here, we report a novel method for visualizing human cervical tissue three-dimensionally, without biopsy, fixation, or staining, and with sufficient quality for histologic diagnosis. Near-infrared excitation and nonlinear optics were employed to visualize unstained human epithelial tissues of the cervix uteri by constructing images with third-harmonic generation (THG) and second-harmonic generation (SHG). THG images enabled evaluation of nuclear morphology in a quantitative manner with six parameters after image analysis using deep learning. It was also possible to quantitatively assess intraepithelial fibrotic changes based on SHG images and another deep learning analysis. Using each analytical procedure alone, normal and cancerous tissue were classified quantitatively with an AUC ≥0.92. Moreover, a combinatory analysis of THG and SHG images with a machine learning algorithm allowed accurate classification of three-dimensional image files of normal tissue, intraepithelial neoplasia, and invasive carcinoma with a weighted kappa coefficient of 0.86. Our method enables real-time noninvasive diagnosis of cervical lesions, thus constituting a potential tool to dramatically change early detection.&lt;/p&gt;Significance:&lt;p&gt;This study proposes a novel method for diagnosing cancer using nonlinear optics, which enables visualization of histologic features of living tissues without the need for any biopsy or staining dye.&lt;/p&gt;&lt;/div&gt;
Data from Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer

&lt;div&gt;Abstract&lt;p&gt;The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). To facilitate the study of cellular spatial organization and interactions, we developed Histology-based Digital-Staining, a deep learning-based computation model, to segment the nuclei of tumor, stroma, lymphocyte, macrophage, karyorrhexis, and red blood cells from standard hematoxylin and eosin–stained pathology images in lung adenocarcinoma. Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas lung adenocarcinoma dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (&lt;i&gt;P&lt;/i&gt; = 0.001), with a HR of 2.23 (1.37–3.65) after adjusting for clinical variables. Furthermore, the image-derived TME features significantly correlated with the gene expression of biological pathways. For example, transcriptional activation of both the T-cell receptor and programmed cell death protein 1 pathways positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway positively correlated with the density of stromal cells. In summary, we demonstrate that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways.&lt;/p&gt;Significance:&lt;p&gt;These findings present a deep learning-based analysis tool to study the TME in pathology images and demonstrate that the cell spatial organization is predictive of patient survival and is associated with gene expression.&lt;/p&gt;&lt;p&gt;&lt;i&gt;See related commentary by Rodriguez-Antolin, p. 1912&lt;/i&gt;&lt;/p&gt;&lt;/div&gt;
Anime Based on Content-Based Filtering

Anime comes from the word "Animation", nowadays it is very popular among people, mostly kids and teenagers. They are being streamed on large platforms such as Netflix and Amazon Prime. Millions of people watch anime nowadays regularly. It has a huge fan base. Due to this increment in the popularity of anime, there is a requirement for a recommendation system that can suggest user watch the best-suited anime for the user. The recommendation system is used to predict the preference of the user based on user interest. In this paper, we have proposed an anime recommendation system. It is based on a content-based filtering approach that makes use of the information provided by users, analyzes them and then recommends the different anime that is best suited to the user. This method helps users to find anime that they are interested in and can watch without concern about the content of the show. They don't have to ask other people for suggestions about the anime and they can have a lot of options at the same place. The recommended anime list is sorted according to the ratings given to these animations by the user that is stored in the dataset and it uses various algorithms such as content-based filtering and sectorization for this purpose. It doesn't allow users to waste time because of its efficient and effective technique. This system has been developed in Jupyter Notebook with the help of python and machine learning algorithms as the backend and for the front end python idle is used with various libraries.
Reasoning-Based DCNs

This chapter first provides a brief overview of reasoning-based Deep Cognitive Networks (DCNs). Then, representative models from two aspects in terms of analogical reasoning and deductive reasoning are introduced and analyzed, as well as their relation to important theories, computational models and experimental evidences in cognitive psychology. At last, this chapter is briefly summarized.
Conclusions and Future Trends

This chapter first provides conclusions of the whole book, by summarizing major properties of different Deep Cognitive Networks (DCNs). Afterwards, we discuss some open problems for future research directions.
Lightweight Multi-level Information Fusion Network for Facial Expression Recognition

The increasing capability of networks for facial expression recognition with disturbing factors is often accompanied by a large computational burden, which imposes limitations on practical applications. In this paper, we propose a lightweight multi-level information fusion network with distillation loss, which can be more lightweight compared with other methods under the premise of not losing accuracy. The multi-level information fusion block uses fewer parameters to focus on information from multiple levels with greater detail awareness, and the channel attention used in this block allows the network to concentrate more on sensitive information when processing facial images with disturbing factors. In addition, the distillation loss makes the network less susceptible to the errors of the teacher network. The proposed method has the fewest parameters of 0.98 million and GFLOPs of 0.142 compared with the state-of-the-art methods while achieving 88.95 $$\%$$ , 64.77 $$\%$$ , 60.63 $$\%$$ , and 62.28 $$\%$$ on the datasets RAF-DB, AffectNet-7, AffectNet-8, and SFEW, respectively. Abundantly experimental results show the effectiveness of the method. The code is available at https://github.com/Zzy9797/MLIFNet .
Equipment Health Assessment Based on Node Embedding

Equipment health assessment is a fundamental task in predictive equipment maintenance practice, which aims to predict the health of equipment based on information about the equipment and its operation, thus avoiding unexpected equipment failures. In the current context, equipment health assessment based on sequential deep learning methods is becoming more and more popular, however, such methods ignore the inter-device correlations, leading to their lack of readiness for health assessment of a large number of devices. To address this problem, this paper proposes a node-embedding-based device health assessment method, which creatively introduces a graph model for device health assessment and effectively improves the performance of health assessment. Firstly, this paper proposes a way to define equipment association graphs. Secondly, we introduce the node embedding technique to extract graph information. Finally, an equipment health assessment method based on the equipment association graph is proposed. Experiments show that the proposed method outperforms the existing prevailing methods.
Research on the Strategy of Improving the Educational Service Function of Big Data Financial Management Major to Promote the Harmonious Development of Regional Economy

In order to further promote the harmonious development of regional economy, meet the demand of the new era of talents training, the local government should attach importance to the deepening transformation of vocational education concept, clear promote big data financial management professional education for service the importance of regional economic development, develop scientific financial management personnel training system, and guarantee financial management personnel in the new period to meet the demand of time development.
Construction Practice of Cloud Billing Message Based on Stream Native

It is necessary to accelerate digital development to make digital economy, society and government shine brightly in the future. Digital technology and the real economy will be deeply integrated, and a large number of new industries and new models will emerge. Cloud computing is an important industry to create further advantages in the digital economy. As the key to the development of enterprise business, cloud computing is the decisive factor. The ability to support billing for the entire cloud is a vital link, and it is the core capability of the whole system. It requires high accuracy and performance. Based on cloud billing, this paper analyzes the challenges cloud computing service support faces. It studies the architecture upgrade, builds the cloud service billing support capability based on stream native technology, meets the flexible billing needs of cloud services, and realizes the rapid and efficient operation support of cloud services. This system has been put into production and played a significant role strongly supporting the high-quality development needs of cloud business billing.