Reviewerly/exHarmony huggingface dataset:
Incremental Reasoning for Dynamic ABox Materialization Based on Subgraph

The ontology knowledge base mainly includes: TBox and ABox, the former models schema-level knowledge in the domain, and the latter is a statement of a set of instance assertions or facts. ABox materialization is the process of discovering the implicit knowledge in ABox by reasoning based on the existing knowledge, which has important value in the application of knowledge base. In practice, TBox has stability and durability, and is suitable for one-time construction and repeated use, while ABox is usually in a state of continuous growth and change. Even with efficient reasoning techniques, recomputing all possible results whenever the ABox changes is a time-and-resource-intensive operation, which can cause a significant delay, and is unacceptable for some application scenarios. In response to this problem, this paper proposes a subgraph-based incremental reasoning method for dynamic ABox materialization, which first divides the ABox into instance-centric subgraphs, and uses the collection of extended subgraphs of each instance that reasoning under the complete TBox as the materialized result. When the assertion is changed, the impact on the materialization result can be regarded as a limited step of information propagation between subgraphs, and only a small number of relevant subgraphs need to be re-derived, thus realizing the incremental reasoning of ABox materialization. The experimental results on two open-source ontology knowledge bases show that the method in this paper has high reasoning quality, and can effectively reduce the response time for obtaining materialized results when ABox changes dynamically.
A Survey of Defense Methods Against Adversarial Examples

With the development of artificial intelligence technology, to our surprise, deep neural networks have made a series of breakthroughs and achieved optimal results in lots of machine learning tasks, while the image field is the most prominent. Although deep learning technology has brought great convenience to our lives, the security problems of artificial intelligence have gradually become prominent. But deep neural networks are very fragile, and usually a small perturbation can have a serious impact on the neural network, especially adversarial examples. As a small imperceptible perturbation, it can cause malicious effects on the results of image classification. What can we do to protect clean examples from perturbations has become the most important and practical research direction in the current image field. A large number of scholars have begun to invest in adversarial examples research, especially in defense, the achievements are more abundant, and related research results have also shown a blowout state. Based on the research status of recent years, this paper first explains the concept of adversarial examples, organizes the current attack methods, and then summarizes different defense methods according to the criteria of detection defense and robust defense. Improvements are discussed to help further development of adversarial defense of images.
Integrating R-Drop and Pre-trained Language Model for Short Text Classification

At present, the rapid development of computer technology has led to the emergence of massive short text data represented by e-commerce user comments. Due to the sparse feature and fast update speed of short text data, effective classification methods of short text have been widely concerned and applied. In this paper, a short text classification method integrating R-Drop and pre-trained language model IRDP-STC is proposed. The original data and generated data are input into the pre-trained language model Bert with Dropout, and KL divergence loss is added to approximate the two outputs. IRDP-STC makes full use of short text information, takes into account the robustness of the model on the basis of ensuring the high classification performance of the model, and is more suitable for low training sample scenarios. The experimental results on THUCNews dataset are better than the baseline model, which verifies the effectiveness and universality of the proposed method.
Cloud Computing and its Application in The Future Fire Control System of Ground Combat Platform

This paper first discusses the concept, architecture and technical advantages of cloud computing. Secondly, focusing on the operational requirements of future information warfare, the development trend of existing fire control platforms is analyzed and the concept of cloud fire control is proposed. Taking the ground combat platform as the object, the basic principle, system structure and composition of the cloud fire control of the ground combat platform are analyzed and designed in depth. Finally, the key technologies of distributed dynamic networking, mobile cooperative awareness, and multi-platform collaborative attack and decision-making that need to be solved in cloud fire control system are pointed out, which can provide reference for the development of cloud fire control system for future ground combat platforms.
Predicting Social Media User Occupation with Content-aware Hierarchical Neural Networks

Inferring users' occupational categories on the basis of user-generated content becomes an important issue in user profiling and applications such as personalised recommendation systems with the rapid explosion usage of online social me-dia. Although previous work has demonstrated that language features extracted from social media content can effectively predict users' occupations, work on overcoming the challenge of time-consuming, expensive expert knowledge and low prediction performance is fairly limited and mostly based on English social platforms. In this paper, we first investigate the relationship between users' language usage in their Chinese blogs and users' occupations, employing tools to extract quantitative features related to users' psychological states and social relationships. Additionally, We propose a novel content-aware hierarchical model called T-LSTM for the user occupation prediction, which is mainly divided into a word-level Transformer encoder layer overcoming the problem of neglecting mining the importance of words in users' texts and a blog-level bidirectional LSTM layer exploiting temporal information of blogs to obtain users' representations. Our experimental results on our collected real-world Chinese social media dataset shows that the proposed model greatly outperforms the baseline methods for occupation prediction and verifies the effectiveness of components as well as the robustness of the model.
Kernel-Based Causal Discovery in Large Time Series Datasets

Discovering causal relationships from a large-scale observational time series dataset is an emerging topic in the current field of research, such as neuroscience and system monitoring. Traditional causal inference approaches in such fields are challenging since the curse of dimensionality and the large sample size. Here, we propose a kernel-based causal discovery (KCD) method by capitalizing on a characterization of conditional independence in reproducing kernel Hilbert spaces. Specifically, KCD uses an iterative and heuristic procedure to add variables into the causal parents set and then adopts the kernel-based conditional independence test to determine the true causal parents. We validated this approach on a large-scale synthetic dataset that simulates the typical properties of real-world data. The results of the experiments have shown that the proposed method is superior to currently existing technologies in terms of detection capabilities, which provides a completely new way to explore how to discover and quantify causal networks from time series in the current research field.a
An Improved Flocking Model for UAVs in Constrained Environments

The research of UAV flocking is getting more and more popular these days. Some flocking model is established on the commonly used environment and complex environment with narrow paths and broad barriers will reduce the performance of the model. In this paper, we design a complex environment which includes narrow paths and broad barriers. We propose a mission that UAVs move at the start point and move into the expected area after crossing barriers and walls. We propose a evaluation function which contains the mission execution time and collisions. Then we conducted a simulation experiment, using a single-objective genetic algorithm to optimize the parameters of the model. We proof that the performance of the optimized model is superior to the performance of the original model and we get better mission execution time and the number of collisions.
A Bayesion Rule based Aggregation Method In Federated Learning

Federal Learning(FL) has been proposed to solve the problem of the data isolation. The traditional Federated Averaging Alogrithm(Fed-Avg) [1] requires IID dataset and treats the client indiscriminately. However, due to the data heterogeneity, the client models has different equality, so it is unreasonable to aggregate the client models equally. To address the issues, we raise a new aggregation method based on Bayesian rules. This method sets the accuracy of each client models on a global validation dataset as the prior probality, then get the likelihood probility with the 2-norm of the weights between each client and server. Furthermore, we also do research on the Non-IID splits [2] and use regularization to improve the generalization ability. Experienments on the CIFAR-10 [3] and private image collections from Internet show that the Bayesion Method based model converges faster and enhances the global model compared with Federated Averaging Alogrithm in both situations, and the accuracy has 1%-2% increase.
Improving Document Ranking with Relevance-based Entity Embeddings

Document ranking aims to rank and return a group of documents in accordance to their relevance to queries. Traditional document ranking solutions utilize sparse vectors to represent query and documents, and then rank documents in accordance to the similarity between sparse vectors. Over recent years, with the advance of big data technology, such as the emergence of knowledge graph (KG), entities are considered as more essential pivots to connect queries and documents, which are used to improve the document ranking process. However, state-of-the-art entity embedding methods usually place entities with close proximity or similar contexts into the same area in the entity embedding space, which does not meet the goal of document ranking, in which entity relevance plays a more important role. As thus, we propose to enhance document ranking with relevance-based entity embedding. In particular, we first introduce the neural network for training such embeddings, and set the objective that given information needs, i.e., query entities, the frequently occurring entities in the top retrieved documents should be predicted. Then, we trained the model based on Wikipedia articles, and used it to improve the baseline document ranking framework. Empirical experiments validate the superiority of the proposed method.
Research on Dual-Dimensional Entity Association-Based Question and Answering Technology for Smart Medicine

With the development of the Internet of Things, intelligent medical devices and intelligent consultation platforms have been rapidly popularized, providing great convenience for medical treatment to patients and consultation to doctors. In the face of large-scale medical electronic information data, how to automatically and accurately learn professional knowledge and realize application is very important. The existing intelligent medical question answering models typically use query expansion to improve the accuracy of model matching answers but ignore the corresponding entity association between questions and answers, and the method of randomly generating negative samples cannot train the model to capture more semantic information. To solve these problems, a question answering method based on dual-dimensional entity association for intelligent medicine is proposed. This method learns semantics from the dual-dimension of question and answer respectively. In the question dimension, query extension words with strong relevance to query intention are obtained through entity association in the medical knowledge graph. In the answer dimension, answer sentences are segmented and sampled by employing a variety of similarity distances to generate negative samples in different ranges, provide different levels of correlation information between entities for model training, and then integrate the trained model to improve the accuracy and robustness of the question answering model. The experimental results show that the question answering model proposed in this paper has a good improvement in accuracy.
Joint Event Relation Identification Based on Multiscale Convolutional Neural Network and Sharing Strategy

At present, most of the event relation identification work mainly focuses on the sequential temporal and explicit causal relation between events. These methods usually ignore the role of synchronous temporal and implicit causal relation in sentences, which makes the semantic understanding of the model deviate from the text. In this paper, we propose a joint event relation identification model. The model uses bidirectional GRU and multiscale convolutional neural network to obtain the context semantic features and multiscale local semantic features of text, respectively. Then, these two kinds of features are fused to fully obtain the semantics of the text itself. In addition, we build encoders and decoders of event temporal and causal relation, respectively, to obtain the event temporal and causal semantic features from text. In this process, considering the correlation between event timing and causality, we use three different parameter sharing strategies to realize the interaction between event temporal and causal semantic features. The experimental results on the legal field dataset we constructed show that our model has made significant improvements compared with the baseline model. Through experimental analysis, our method can effectively improve the identification performance of synchronous temporal and implicit causality relation.
Decision-making Control in Military Combat System of Systems: A Prospect Theory Approach

In the process of military operation, the choice of action plan is a typical decision-making control problem. Decision makers' judgment and subjective preference for combat situation have a great impact on the selection results. The action plan is represented in this study as an operation loop model based on OODA theory. Therefore, this paper mainly studies the influence of decision-maker preference on operation loop selection control from the perspective of prospect theory. Firstly, the network modeling of kill-web is carried out based on operation loop, in which the preference attributes of decision maker and three kinds of command & control relation are described. Secondly, considering decision preference, the judgment method of damage degree and hit probability of operation loop is analyzed, and an operation loop decision method is proposed. Finally, we verify the feasibility and effectiveness of this method through a case study, and analyze the sensitivity of decision preference attributes. The results show that reference point and risk attitude of decision maker are the key factors that affect the decision of operation loop. Prospect theory is an effective method to analyze the behavior of decision-makers in military combat control.
Geometrically Constrained Phase Unwrapping Method Based On Variable-frequency Fringe Pattern And Neighborhood Feature Matching

Structured light measurement technology has the advantages of non-contact, full field, high robustness, high speed, and high precision, and has been widely used in industrial inspection, machine vision, cultural relics digitization, medicine, and other fields. Multi-view structured light method introduces an auxiliary camera on the basis of single-view structured light. According to the principle of geometric constraints phase unwrapping, the wrapped phase can be unwrapped without projecting additional fringe patterns. Multi-view structured light has become the focus of structured light measurement technology research in recent years because of its advantages of small number of projected fringe patterns, fast phase unwrapping, and high efficiency of point cloud reconstruction. However, the current multi-view structured light geometric constraints phase unwrapping algorithm still has the problems of high matching difficulty and low phase unwrapping accuracy under the high-frequency fringes. Addressing these problems, this paper proposes a geometric constraints phase unwrapping method based on variable-frequency fringe patterns and neighborhood features matching. The method calculates neighborhood features of the pixel points in left camera wrapped phase image and the candidate points in right camera wrapped phase image. Then, the best matching point among the candidate points is obtained by using the difference of neighborhood features of the pixel points with the same phase in the different frequency fringe. Therefore, the pixel points matching between the left camera view and right camera view can be implemented, and the geometric constraints phase unwrapping can be realized. The proposed method does not need to project additional fringe patterns. It can thus reduce the time cost of projecting patterns, capturing images, and unwrapping phases. In addition, the proposed method can increase the difference of neighborhood features of the candidate points with the same phase in the different frequency fringe, and thus improve the reliability of pixel points matching and the accuracy of geometric constraint unwrapping. Experimental results show that this method can effectively reconstruct the 3D point cloud of objects with spatial discontinuities and complex geometric shapes by only projecting four variable-frequency sinusoidal fringe patterns.
MIAR: Interest-Activated News Recommendation by Fusing Multichannel Information

The different news clicked by users reflects the diverse interests of users. Most of the existing news recommendation methods do not consider the interaction with candidate news in the process of modeling user interest representation. This method makes it challenging to precisely match candidate news to specific user interests. We propose a user interest activation recommendation method that fuses multichannel information—MIAR. It utilizes the word embedding of the user’s historical clicked news and the news title embedding generated by aggregation and interacts with the candidate news, respectively, to better match the candidate news with the user’s interests. Our proposed method contains two frameworks (interactive framework and distributed framework). In the interactive framework, we propose a user multichannel interest modeling framework MIF from the word embedding level of news headlines to capture more semantic cues related to user interests. In the distributed framework, we design a candidate-aware interest activation module TAR from the news embedding representation level obtained by attention aggregation. It uses different candidate news vectors to adjust the user representations learned from the user’s historical reading records. This allows the model to build candidate-guided user representations to accurately match candidate news to parts of user interests that are relevant to the candidate news. Finally, we effectively assign the weights of the two frame scores so that the models can fuse better. Extensive experiments on the MIND news recommendation dataset demonstrate the effectiveness of our method.
Retraction: Research on Robot Path Planning Based on Improved Ant Colony Algorithm under Computer Background (J. Phys.: Conf. Ser. 1992 032050)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 9 September 2022
Development of Artificial Intelligence‐based chatbot for smart aquafarm practices

Abstract This study includes the development of a farm advisory framework known as the “AquaProFAN framework,” which is concerned with the study of various requirements by the aquafarmers and stakeholder community in fisheries. AquaProFAN (Aqua Professional Farm Advisory Network) advisory committee provided careful consideration to the aquafarmers' inquiries and proper clarification with many innovative ideas, and the feasibility study on chatbot implementation was also performed. The end product, called AquaGent, is an AI‐based chatbot designed for shrimp aquafarmers and support services. It was developed and deployed in social media platforms like Facebook Messenger and Telegram for research and dissemination purposes. The effectiveness of the chatbot was analysed and the findings indicated that: (i) informational requests are more satisfactory than emotional requests, (ii) the performance of chatbot is comparatively better than other software applications concerning informational requests, (iii) participants perceive ‘AquaGent’ chatbot to be more user friendly and time saving. It can be concluded that the implementation of this state‐of‐art technology in aquaculture sector will improve stakeholders' understanding for future efficient and profitable production.
College Students’ Perspectives and Attitudes Toward the Use of Gather in Online Classes

Metaverse has been recognized as an educational platform in the field of education. This study aims to examine perspectives and attitudes of college students to the metaverse platform, Gather, in online English classes. A total of 36 college students participated in the study and completed a survey including demographic information, class satisfaction, and perceptions of Gather. The results showed that participants showed increasingly positive attitudes to Gather during four classes. In addition, students’ satisfaction gradually increased. The advantages of using Gather include interaction, virtual world, and group activities, and the use of avatars. The disadvantages of using Gather include being unfamiliar with the metarverse platform and technical functions. The results imply that metarverse can reduce learners’ anxiety and stress in language learning by providing a virtual world as it allows language learners to interact using online resources and avatars connected with the metarverse platform. Metaverse could play an important role in English education in the future as it can cope with the rapid changes caused by the 4th industrial revolution and non-face-to-face educational environments, although thorough class design is required for it to be successful.