MKEAH:Multimodal knowledge extraction and accumulation based on hyperplane embedding for knowledge-based visual question answering

Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.

Integrating GPT into the Journalism and Communication Industries:The Technological Logic,Ecosystem Reconstruction,and Ethical Chal enges

Generative AI,represented by GPT(Generative Pre-trained Transformer),is now leading the technological revolution and is reconstructing the journalism and communication industries'ecosystems because of its powerful generative capacity and diverse range of outputs.While GPT is busy revolutionizing and innovating the production of news content,working patterns,and operation modes,it has also given rise to ethical concerns in regard to news authenticity,data security,humanistic values,and other related aspects.Therefore,it is imperative to initiate strategies and approaches,such as establishing a mechanism for verifying information authenticity,enhancing data security and privacy regulations,and instituting an ethical supervision and governance framework for AI,in order to facilitate the systematic advancement of AI-based news production while reinstating public trust.

A Case Study of Periodical Terminologies’Mis-translations in History of Chinese Journalism by Ko Kung-chen under Analysis-Synthesis Translation Mode

From the perspective of analysis-synthesis translation mode,this paper discusses the translator’s responsibility and the translator’s role in the translation of historiography,based on the case of Ko Kung-chen’s History of Chinese Journalism.Missionary J.S.Roberts believed that translation is the logical combination of two processes of analysis and synthesis.The analysis process is characterized by understanding,emphasizing accurate understanding and professional analysis of the source text,while the synthesis process focuses on the transformation of language and culture.The two processes are dependent,forming the whole process of translation altogether.As the indisputable subject of translation,the translator should assume his own subjective responsibility in the two processes.The translator should be responsible for the fact and professional academics in the analysis process,and language and culture and translation itself in the synthesis process,playing the role of a reviewer,disseminator and translator to ensure that the translation text would be correct,accurate and professional so as to realize the value of himself and translation.

Knowledge-Based Classification in Automated Soil Mapping

A machine-learning approach was developed for automated building of knowledgebases for soil resources mapping by using a classification tree to generate knowledge from trainingdata. With this method, building a knowledge base for automated soil mapping was easier than usingthe conventional knowledge acquisition approach. The knowledge base built by classification tree wasused by the knowledge classifier to perform the soil type classification of Longyou County,Zhejiang Province, China using Landsat TM bi-temporal images and CIS data. To evaluate theperformance of the resultant knowledge bases, the classification results were compared to existingsoil map based on a field survey. The accuracy assessment and analysis of the resultant soil mapssuggested that the knowledge bases built by the machine-learning method was of good quality formapping distribution model of soil classes over the study area.

Knowledge-based bridge detection from SAR images

Automatic bridge detection is an important application of SAR images. Differed from the classical CFAR method, a new knowledge-based bridge detection approach is proposed. The method not only uses the backscattering intensity difference between targets and background but also applies the contextual information and spatial relationship between objects. According to bridges＇ special characteristics and scattering properties in SAR images, the new knowledge-based method includes three processes： river segmentation, potential bridge areas detection and bridge discrimination. The application to AIRSAR data shows that the new method is not sensitive to rivers＇ shape. Moreover, this method can detect bridges successfully when river segmentation is not very exact and is more robust than the radius projection method.

New Knowledge-based Genetic Algorithm for Excavator Boom Structural Optimization

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Structural Topology Design of Container Ship Based on Knowledge-Based Engineering and Level Set Method

Knowledge-Based Engineering （KBE） is introduced into the ship structural design in this paper. From the implementation of KBE, the design solutions for both Rules Design Method （RDM） and Interpolation Design Method （IDM） are generated. The corresponding Finite Element （FE） models are generated. Topological design of the longitudinal structures is studied where the Gaussian Process （GP） is employed to build the surrogate model for FE analysis. Multi-objective optimization methods inspired by Pareto Front are used to reduce the design tank weight and outer surface area simultaneously. Additionally, an enhanced Level Set Method （LSM） which employs implicit algorithm is applied to the topological design of typical bracket plate which is used extensively in ship structures. Two different sets of boundary conditions are considered. The proposed methods show satisfactory efficiency and accuracy.

Translation Aestheticsand Communication Theory of Journalism English

This paper describes the present translation theories and standards especially in journalism English translation and imply these theories in my issue. Moreover, I will explain how to apply the translation aesthetics and communication theory of translation in the course of journalism English translation activities. So that translation will achieve elegance in English news text and its Chinese version as well.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

Knowledge-based potentials in bioinformatics: From a physicist's viewpoint

Biological raw data are growing exponentially, providing a large amount of information on what life is. It is believed that potential functions and the rules governing protein behaviors can be revealed from analysis on known native structures of proteins. Many knowledge-based potentials for proteins have been proposed. Contrary to most existing review articles which mainly describe technical details and applications of various potential models, the main foci for the discussion here are ideas and concepts involving the construction of potentials, including the relation between free energy and energy, the additivity of potentials of mean force and some key issues in potential construction. Sequence analysis is briefly viewed from an energetic viewpoint.

A Knowledge-based System for the Analysis of the Ability of Paying back Loans

This paper describes the development of a knowledgebased system (KBS) for determining whether or not, and under what conditions, a bank Ioan officer should grant a business loan to a company. The prototype system developed focuses on what is bank loans risks management, how to prevent risk by the analysis of the ability of paying back loans. The paper makes the structural analysis involved in the system's decision situation, the structured situation diagram or model, dependency diagram and the document needed by the KBS prototype system thus are developed. Through testing the samples from loan business, the quality for the analysis of the ability of paying back loans can be effectively evaluated by the KBS prototype system.

Integration of Decision-Support and Knowledge-Based Techniques in a Problem-Solving Strategy for Selection Problems

The limitations of traditional approaches to selection problems are examined. A problemsolving strategy is presented in which decision-support and knowledge-based techniques play complementary roles. An approach to the representation of knowledge to support the problem-solving strategy is presented which avoids commitment to a specific programming language or implementation environment. The problem of choosing a home is used to illustrate the representation of knowledge in a specific problem domain. Techniques for implementation of the problem-solving strategy are described. Knowledge elicitation techniques and their implementation in a development shell for application of the problem-solving strategy to any selection problem are also described.

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

教育心理学视域下传播学慕课课程教学目标分析——以Journalism for Social Change为例

本文以美国加州伯克利分校的传播学慕课课程Journalism for Social Change为研究对象,采用观察法的研究方法,从教育心理学的视角,以教学目标设置为切入点,从教学内容、教学形式、互动与反馈等角度进行分析,并进一步整理和分析其教学设计方法中可资借鉴的内容,为传播学慕课课程教学设计提供相关建议。

Urban Journalism： Street by Street

The website philadelphianeighborhoods.com provides news coverage about more than 30 underreported and underserved communities in Philadelphia. Affiliated with Temple University, the website and bimonthly printed editions focus on hyperlocal journalism, or journalism at the street-by-street level. The website has won numerous state, regional, and national awards for its coverage. The goals focus on many aspects of journalism, but this paper will address the successes and failures of this hyperlocal approach and the potential applicability for other organizations.

MY PERSONAL STUDY ON KNOWLEDGE-BASED ECONOMY

The Financial Crisis in Asia is having a negative impacion the economic development of China, but it also enlightens us. It makes us consider and take measures to avoid such a crisis. I have put forward six measures, one of which is to promote the transformation of S&T knowledge into productive forces.

Research on Knowledge-based Connecting Elements Modeling

Steps of manipulation is required to complete the m od eling of the connection elements such as bolt, pin and the like in commerce CAD system. It leads to low efficiency, difficulty to assure the relative position, impossibility to express rules and knowledge. Based on the inner character analy sis of interpart, detail modification and assembly relation of mechanical connec ting element, the idea, which extends the feature modeling of part to the interp art feature modeling for assembly purpose, is presented, and virtual part based connecting element modeling is proposed. Virtual part is a complement set of lo cal modification of part to be connected. In assembly modeling, base part is mod ified by Boolean operation between base part and virtual part. The modeling and assembly is finished just in one operation, at the same time the rules and knowl edge of the connection elements are encapsulated through virtual part. According to this mechanism, a knowledge-based connecting elements rapid design module i s developed on commerce software package UG with satisfying results.

Optimizing the atom types of proteins through iterative knowledge-based potentials

Knowledge-based scoring functions have been widely used for protein structure prediction, protein-small molecule, and protein-nucleic acid interactions, in which one critical step is to find an appropriate representation of protein structures. A key issue is to determine the minimal protein representations, which is important not only for developing of scoring func- tions but also for understanding the physics of protein folding. Despite significant progresses in simplifying residues into alphabets, few studies have been done to address the optimal number of atom types for proteins. Here, we have investigated the atom typing issue by classifying the 167 heavy atoms of proteins through 11 schemes with 1 to 20 atom types based on their physicochemical and functional environments. For each atom typing scheme, a statistical mechanics-based iterative method was used to extract atomic distance-dependent potentials from protein structures. The atomic distance-dependent pair potentials for different schemes were illustrated by several typical atom pairs with different physicochemical proper- ties. The derived potentials were also evaluated on a high-resolution test set of 148 diverse proteins for native structure recognition. It was found that there was a crossover around the scheme of four atom types in terms of the success rate as a function of the number of atom types, which means that four atom types may be used when investigating the basic folding mechanism of proteins. However, it was revealed by a close examination of typical potentials that 14 atom types were needed to describe the protein interactions at atomic level. The present study will be beneficial for the development of protein related scoring functions and the understanding of folding mechanisms.

Journal Club教学法用于临床药学专业硕士研究生理论教学实践

目的探讨Journal Club教学法在临床药学专业硕士研究生理论教学中的应用效果。方法选取重庆医科大学2022年的2020级(4名)、2021级(6名)临床药学专业硕士研究生为研究对象。课前,2个年级的学生分别以小组为单位,围绕与“临床药物治疗学前沿”授课内容相关的2个临床用药问题,通过设定关键词、检索文献、分析评价、证据分级,制订最优用药方案;课中以幻灯片形式分享经典文献,并对最优用药方案进行循证药学证据分析。采用匿名问卷调查法统计对该教学法8个条目完全赞同、基本同意及否定的人数,并计算占比;授课教师对学生撰写的综述进行评分(90~100分、80~89分、70~79分、60~69分分别为优、良、中、及格);统计学生通过本轮教学的论文发表情况。结果2020级、2021级分别发放调查问卷4份、6份,均全部回收,有效回收率均为100.00%;对所有调查项目均完全赞同或基本同意的分别有29人次、47人次。期末考试成绩为优、良、中的,2020级分别有2名(50.00%)、1名(25.00%)、1名(25.00%),优良率为75.00%;2021级分别有2名(33.33%)、3名(50.00%)、1名(16.67%),优良率为83.33%。2021级学生有1篇综述发表在科学引文索引(SCI)期刊上。结论Journal Club教学法有助于提高学生的文献查阅能力和英文阅读能力,可调动自主学习的积极性,增强独立思考能力和科研创新意识。

A knowledge-based decision support system for engineering design

A knowledge-based decision supporting system, used for engineering design is introduced by describing the architecture, function, workflow of the system and its way of implementation. Based upon information composed of knowledge, models, data, cases, methods, etc, the system is designed to use such methods as knowledge-based reasoning, case-based reasoning, and multi-criteria evaluation techniques to provide effective tools to support the decision-making process.