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Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods 预览
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作者 Jiajun Wang Tufan Kumbasar 《自动化学报:英文版》 CSCD 2019年第1期247-257,共11页
Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Althou... Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs. 展开更多
关键词 BIG bang-big crunch (BBBC) INTERVAL type-2 fuzzy neural networks (IT2FNNs) PARAMETER OPTIMIZATION particle SWARM OPTIMIZATION (PSO)
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HybridTune: Spatio-Temporal Performance Data Correlation for Performance Diagnosis of Big Data Systems
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作者 Rui Ren Jiechao Cheng +4 位作者 Xi-Wen He Lei Wang Jian-Feng Zhan Wan-Ling Gao Chun-Jie Luo 《计算机科学技术学报:英文版》 SCIE EI CSCD 2019年第6期1167-1184,共18页
With tremendous growing interests in Big Data, the performance improvement of Big Data systems becomes more and more important. Among many steps, the first one is to analyze and diagnose performance bottlenecks of the... With tremendous growing interests in Big Data, the performance improvement of Big Data systems becomes more and more important. Among many steps, the first one is to analyze and diagnose performance bottlenecks of the Big Data systems. Currently, there are two major solutions. One is the pure data-driven diagnosis approach, which may be very time-consuming;the other is the rule-based analysis method, which usually requires prior knowledge. For Big Data applications like Spark workloads, we observe that the tasks in the same stages normally execute the same or similar codes on each data partition. On basis of the stage similarity and distributed characteristics of Big Data systems, we analyze the behaviors of the Big Data applications in terms of both system and micro-architectural metrics of each stage. Furthermore, for different performance problems, we propose a hybrid approach that combines prior rules and machine learning algorithms to detect performance anomalies, such as straggler tasks, task assignment imbalance, data skew, abnormal nodes and outlier metrics. Following this methodology, we design and implement a lightweight, extensible tool, named HybridTune, and measure the overhead and anomaly detection effectiveness of HybridTune using the BigDataBench benchmarks. Our experiments show that the overhead of HybridTune is only 5%, and the accuracy of outlier detection algorithm reaches up to 93%. Finally, we report several use cases diagnosing Spark and Hadoop workloads using BigDataBench, which demonstrates the potential use of HybridTune. 展开更多
关键词 Big Data system spatio-temporal correlation rule-based diagnosis machine learning
‘Wonderful Night of Museum’Hopefully Becoming Normal in Shanghai 预览
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作者 Li Ting 《中外文化交流:英文版》 2019年第8期38-39,共2页
It is promising that museums in Shanghai are to open to the public at night on a regular basis.But it should be based on the actual understanding of audience demands and steady support by investigation.The big data fr... It is promising that museums in Shanghai are to open to the public at night on a regular basis.But it should be based on the actual understanding of audience demands and steady support by investigation.The big data from the pilot project is to provide a basis for further promotion. 展开更多
关键词 MUSEUMS PILOT project BIG data
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Joe's Favorite Butt on
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作者 Rhea Ashmore 《小学生时代:英文版》 2019年第3期10-17,共8页
Hi Big Mouth English readers! Button is an interesting word. It can be a thing: The button on the doorbell is broken. It can be an action: Butt on your coat! This story is about buttons that are things. Read to find o... Hi Big Mouth English readers! Button is an interesting word. It can be a thing: The button on the doorbell is broken. It can be an action: Butt on your coat! This story is about buttons that are things. Read to find out about Joe' s favorite button. 展开更多
关键词 Big MOUTH English INTERESTING word FAVORITE BUTTON
Small Town,Big Dream 预览
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作者 ZHANG XIAO 《今日中国:英文版》 2019年第2期47-49,共3页
THE small town of Zhili in Huzhou,east China’sZhejiang Province, has a population of450,000 living within an area of 25 square kilometers. Lying on the south of Taihu Lake is the Shoulder Pole Street, which once was ... THE small town of Zhili in Huzhou,east China’sZhejiang Province, has a population of450,000 living within an area of 25 square kilometers. Lying on the south of Taihu Lake is the Shoulder Pole Street, which once was a street with an area of 0.58 square kilometers. Today that street has developed into the larg-est children’s clothing industry cluster in China, and transformed Zhili from a once utterly backward town into a promising center of industry. 展开更多
关键词 SMALL TOWN BIG DREAM
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Identify crystal structures by a new paradigm based on graph theory for building materials big data
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作者 Mouyi Weng Zhi Wang +5 位作者 Guoyu Qian Yaokun Ye Zhefeng Chen Xin Chen Shisheng Zheng Feng Pan 《中国科学:化学英文版》 SCIE EI CAS CSCD 2019年第8期982-986,共5页
Material identification technique is crucial to the development of structure chemistry and materials genome project. Current methods are promising candidates to identify structures effectively, but have limited abilit... Material identification technique is crucial to the development of structure chemistry and materials genome project. Current methods are promising candidates to identify structures effectively, but have limited ability to deal with all structures accurately and automatically in the big materials database because different material resources and various measurement errors lead to variation of bond length and bond angle. To address this issue, we propose a new paradigm based on graph theory(GTscheme) to improve the efficiency and accuracy of material identification, which focuses on processing the "topological relationship" rather than the value of bond length and bond angle among different structures. By using this method, automatic deduplication for big materials database is achieved for the first time, which identifies 626,772 unique structures from 865,458 original structures.Moreover, the graph theory scheme has been modified to solve some advanced problems such as identifying highly distorted structures, distinguishing structures with strong similarity and classifying complex crystal structures in materials big data. 展开更多
关键词 structures identification GRAPH theory big data TOPOLOGICAL relationship materials DATABASE
Big Data Analytics for Healthcare Industry:Impact,Applications,and Tools
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作者 Sunil Kumar Maninder Singh 《大数据挖掘与分析(英文)》 2019年第1期48-57,共10页
In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by various institutions around the world and, collectively, this heterogeneous data is referred to as big data. T... In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by various institutions around the world and, collectively, this heterogeneous data is referred to as big data. The health industry sector has been confronted by the need to manage the big data being produced by various sources,which are well known for producing high volumes of heterogeneous data. Various big-data analytics tools and techniques have been developed for handling these massive amounts of data, in the healthcare sector. In this paper, we discuss the impact of big data in healthcare, and various tools available in the Hadoop ecosystem for handling it. We also explore the conceptual architecture of big data analytics for healthcare which involves the data gathering history of different branches, the genome database, electronic health records, text/imagery, and clinical decisions support system. 展开更多
关键词 BIG data healthcare HADOOP MAPREDUCE
Quality control of marine big data——a case study of real-time observation station data in Qingdao 预览
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作者 QIAN Chengcheng LIU Aichao +4 位作者 HUANG Rui LIU Qingrong XU Wenkun ZHONG Shan YU Le 《海洋湖沼学报(英文)》 SCIE CAS CSCD 2019年第6期1983-1993,共11页
Offshore waters provide resources for human beings,while on the other hand,threaten them because of marine disasters.Ocean stations are part of offshore observation networks,and the quality of their data is of great s... Offshore waters provide resources for human beings,while on the other hand,threaten them because of marine disasters.Ocean stations are part of offshore observation networks,and the quality of their data is of great significance for exploiting and protecting the ocean.We used hourly mean wave height,temperature,and pressure real-time observation data taken in the Xiaomaidao station(in Qingdao,China)from June 1,2017,to May 31,2018,to explore the data quality using eight quality control methods,and to discriminate the most effective method for Xiaomaidao station.After using the eight quality control methods,the percentages of the mean wave height,temperature,and pressure data that passed the tests were 89.6%,88.3%,and 98.6%,respectively.With the marine disaster(wave alarm report)data,the values failed in the test mainly due to the influence of aging observation equipment and missing data transmissions.The mean wave height is often affected by dynamic marine disasters,so the continuity test method is not effective.The correlation test with other related parameters would be more useful for the mean wave height. 展开更多
关键词 quality control REAL-TIME STATION DATA MARINE BIG DATA Xiaomaidao STATION MARINE DISASTER
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A Novel Clustering Technique for Efficient Clustering of Big Data in Hadoop Ecosystem
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作者 Sunil Kumar Maninder Singh 《大数据挖掘与分析(英文)》 2019年第4期240-247,共8页
Big data analytics and data mining are techniques used to analyze data and to extract hidden information.Traditional approaches to analysis and extraction do not work well for big data because this data is complex and... Big data analytics and data mining are techniques used to analyze data and to extract hidden information.Traditional approaches to analysis and extraction do not work well for big data because this data is complex and of very high volume. A major data mining technique known as data clustering groups the data into clusters and makes it easy to extract information from these clusters. However, existing clustering algorithms, such as k-means and hierarchical, are not efficient as the quality of the clusters they produce is compromised. Therefore, there is a need to design an efficient and highly scalable clustering algorithm. In this paper, we put forward a new clustering algorithm called hybrid clustering in order to overcome the disadvantages of existing clustering algorithms. We compare the new hybrid algorithm with existing algorithms on the bases of precision, recall, F-measure, execution time, and accuracy of results. From the experimental results, it is clear that the proposed hybrid clustering algorithm is more accurate, and has better precision, recall, and F-measure values. 展开更多
关键词 CLUSTERING HADOOP BIG data K-MEANS HIERARCHICAL
“大”器传神--三星堆文化器物造型的审美特征 预览
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作者 李社教 《湖北理工学院学报:人文社会科学版》 2019年第4期24-29,共6页
三星堆文化器物造型总体上呈现出一个显著的特征--"大"。"大"既现之于古蜀人器物的形制或造器的方法,也现之于通过器物表现的对象。三星堆文化器物造型的表达意图、表达内容、表达效果都指向神,同时也是借神扬人,... 三星堆文化器物造型总体上呈现出一个显著的特征--"大"。"大"既现之于古蜀人器物的形制或造器的方法,也现之于通过器物表现的对象。三星堆文化器物造型的表达意图、表达内容、表达效果都指向神,同时也是借神扬人,由此折射出了古蜀人的"崇高"的审美观念。 展开更多
关键词 三星堆文化 器物造型 审美特征
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Thoughts on the development of "data News" in the era of big data 预览
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作者 Shen Zhengbo Yuan Shaozhi 《探索-媒体与传播研究》 2019年第1期28-30,共3页
With"Robot Writing""Drone"And"Google Glasses"It is widely used in news reports. The trend of news report defocusing and defocusing is becoming more and more obvious,"Data news"H... With"Robot Writing""Drone"And"Google Glasses"It is widely used in news reports. The trend of news report defocusing and defocusing is becoming more and more obvious,"Data news"However, due to the current network environment, the professionalism of Data news has been questioned. Starting from the phenomenon, this paper discusses the problems existing in the development of data news. 展开更多
关键词 BIG DATA DATA NEWS NORMALIZATION OBJECTIVITY
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A Classifier Using Online Bagging Ensemble Method for Big Data Stream Learning
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作者 Yanxia Lv Sancheng Peng +4 位作者 Ying Yuan Cong Wang Pengfei Yin Jiemin Liu Cuirong Wang 《清华大学学报自然科学版(英文版)》 EI CAS CSCD 2019年第4期379-388,共10页
By combining multiple weak learners with concept drift in the classification of big data stream learning, the ensemble learning can achieve better generalization performance than the single learning approach. In this ... By combining multiple weak learners with concept drift in the classification of big data stream learning, the ensemble learning can achieve better generalization performance than the single learning approach. In this paper,we present an efficient classifier using the online bagging ensemble method for big data stream learning. In this classifier, we introduce an efficient online resampling mechanism on the training instances, and use a robust coding method based on error-correcting output codes. This is done in order to reduce the effects of correlations between the classifiers and increase the diversity of the ensemble. A dynamic updating model based on classification performance is adopted to reduce the unnecessary updating operations and improve the efficiency of learning.We implement a parallel version of EoBag, which runs faster than the serial version, and results indicate that the classification performance is almost the same as the serial one. Finally, we compare the performance of classification and the usage of resources with other state-of-the-art algorithms using the artificial and the actual data sets, respectively. Results show that the proposed algorithm can obtain better accuracy and more feasible usage of resources for the classification of big data stream. 展开更多
关键词 big data STREAM classification ONLINE BAGGING ensemble LEARNING concept DRIFT
Research on the big data feature mining technology based on the cloud computing 预览
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作者 WANG Yun 《国际英语教育研究:英文版》 2019年第3期52-54,共3页
The cloud computing platform has the functions of efficiently allocating the dynamic resources, generating the dynamic computing and storage according to the user requests, and providing the good platform for the big ... The cloud computing platform has the functions of efficiently allocating the dynamic resources, generating the dynamic computing and storage according to the user requests, and providing the good platform for the big data feature analysis and mining. The big data feature mining in the cloud computing environment is an effective method for the elficient application of the massive data in the information age. In the process of the big data mining, the method o f the big data feature mining based on the gradient sampling has the poor logicality. It only mines the big data features from a single-level perspective, which reduces the precision of the big data feature mining. 展开更多
关键词 CLOUD COMPUTING BIG data features MINING technology model method
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Subtitle Translation of The Big Bang Theory Based on the Functional Equivalence Theory 预览
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作者 WANG Jie ZOU Jianling 《美中外语:英文版》 2019年第5期219-225,共7页
Since The Big Bang Theory was introduced to our country, it has been warmly welcomed. It is its unique contents, characters of distinctive personalities, and humorous dialogues that enable it to be one of the most pop... Since The Big Bang Theory was introduced to our country, it has been warmly welcomed. It is its unique contents, characters of distinctive personalities, and humorous dialogues that enable it to be one of the most popular sitcoms now. The success of The Big Bang Theory is not only because of its own artistic charm but also because of its skillful and accurate subtitle translation. Chinese audience can learn about social values of foreign countries and also improve their oral English through subtitle translation. Therefore, subtitle translation is especially important. The thesis takes the subtitle translation of The Big Bang Theory as the example to analyze how subtitle translation helps audience to understand and enjoy the humorous dialogues from the perspective of Functional Equivalence Theory. Based on exemplification, the thesis discusses the principles and procedures in the subtitle translation from the aspects of linguistic, stylistic, and cultural equivalence to help people to better understand the subtitle translation and promote Chinese-English cultural communication. 展开更多
关键词 The BIG Bang THEORY SUBTITLE TRANSLATION Functional EQUIVALENCE THEORY SITCOM
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Research on College Students' Online Learning Process Control under the Background of Big Data 预览
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作者 Chen Xiaoxiao 《管理科学与研究:中英文版》 2019年第1期49-51,共3页
With the advent of the big data era, personalized self-adaptive learning is implemented for college students, which promotes the balanced development of all aspects of their abilities and leads to changes in their lea... With the advent of the big data era, personalized self-adaptive learning is implemented for college students, which promotes the balanced development of all aspects of their abilities and leads to changes in their learning styles. Quality assurance and personalized education of online education have become an important direction for the further development of distance education. At present, in the online education platform, learners' participation in learning activities is not high, and educational managers and teachers do not have in-depth understanding of learners' learning status. Based on this, the article explores the connotation and components of online learning based on the background of big data, and studies the online learning process control of college students from the aspects of information retrieval power, network self-control and practice transformation. The purpose is to explore the application of big data in learning process control, establish a learning process control strategy model based on big data, and verify the effectiveness of the strategy model and control strategy by designing a variety of different control experiments to provide ideas for controlling experiments. 展开更多
关键词 BIG Data COLLEGE STUDENTS ONLINE Learning
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PECS: Towards Personalized Edge Caching for Future Service-Centric Networks 预览
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作者 Ming Yan Wenwen Li +3 位作者 Chien Aun Chan Sen Bian Chih-Lin I André F. Gygax 《中国通信:英文版》 SCIE CSCD 2019年第8期93-106,共14页
Mobile operators face the challenge of how to best design a service-centric network that can effectively process the rapidly increasing number of bandwidth-intensive user requests while providing a higher quality of e... Mobile operators face the challenge of how to best design a service-centric network that can effectively process the rapidly increasing number of bandwidth-intensive user requests while providing a higher quality of experience(QoE). Existing content distribution networks(CDN) and mobile content distribution networks(mCDN) have both latency and throughput limitations due to being multiple network hops away from end-users. Here, we first propose a new Personalized Edge Caching System(PECS) architecture that employs big data analytics and mobile edge caching to provide personalized service access at the edge of the mobile network. Based on the proposed system architecture, the edge caching strategy based on user behavior and trajectory is analyzed. Employing our proposed PECS strategies, we use data mining algorithms to analyze the personalized trajectory and service usage patterns. Our findings provide guidance on how key technologies of PECS can be employed for current and future networks. Finally, we highlight the challenges associated with realizing such a system in 5G and beyond. 展开更多
关键词 BIG DATA DATA mining EDGE CACHING content network PERSONALIZED EDGE CACHING
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Application status and development of big data in medical education in China 预览
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作者 Ke-Jia Liu Yu-Di Cao +1 位作者 Yue Hu Li-Jiao Wei 《TMR医学数据挖掘》 2019年第3期118-125,共8页
With the development of information technology, big data has been widely used in medical education in China. Through the analysis of the definition, characteristics and development process of big data, summarized the ... With the development of information technology, big data has been widely used in medical education in China. Through the analysis of the definition, characteristics and development process of big data, summarized the transformation of domestic big data medical education mode compared with the traditional medical education mode in the teaching mode reform, research method innovation, teaching courses optimization, teaching key extension and the teaching quality monitoring. Based on this, this paper expounds the impact and challenge of big data on medical education, and makes an outlook on its development prospect, indicating that the future development of big data will be open, popular and trending. At the same time, some suggestions on further optimization direction of China's big data medical education are put forward. 展开更多
关键词 BIG data MEDICAL EDUCATION EDUCATION REFORM TRAINING MODE
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The Cultural Interpretations of "Golden Sound and Jade Vibration" 预览
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作者 Hu Jiansheng 《文学与艺术研究:英文版》 2019年第6期563-581,共19页
Limited by the writing habits of the small tradition, conventional scholars tend to interpret "golden sound and jade vibration" as a musical tempo representing virtues of saints. In fact, we can prove how &q... Limited by the writing habits of the small tradition, conventional scholars tend to interpret "golden sound and jade vibration" as a musical tempo representing virtues of saints. In fact, we can prove how "golden sound and jade vibration" are closely linked to the sacred material beliefs (e.g.,"holy gold","holy jade") and the prehistoric religious ritual activities, by using various oral cultures and physical images, deeply exploring the profound cultural roots of "golden sound" and "jade vibration" in the local knowledge tradition and tracing its genetic system back to the prehistoric Jade Age and Bronze Age. The concept of "golden sound and jade vibration" is the sacred aural symbol of the "sage" entering the imaginary and illusionary realm. It also highlights the genetic system of the sage having "great accomplishments" and the Chinese ritual music system. 展开更多
关键词 GOLDEN SOUND JADE VIBRATION big TRADITION SAGE great accomplishments
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Big Data and Knowledge Management:A Possible Course to Combine Them Together 预览
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作者 Sam Hijazi 《美中教育评论:B》 2019年第3期90-97,共8页
Big data (BD) is the buzz phrase these days.Everyone is talking about its potential,its volume,its variety,and its velocity.Knowledge management (KM) has been around since the mid-1990s.The goals of KM have been to co... Big data (BD) is the buzz phrase these days.Everyone is talking about its potential,its volume,its variety,and its velocity.Knowledge management (KM) has been around since the mid-1990s.The goals of KM have been to collect,store,categorize,mine,and process data into knowledge.The methods of knowledge acquisition varied from organizational culture to the next.Typical processes converted data into information through traditional databases and then applied business intelligence and data mining methodologies to extract knowledge.With the recent arrival of BD as a disruptive technology and the center of BD,this paper attempts to combine KM and BD fields together.Both areas could help each other tremendously.KM historically,when applied correctly,has helped managers to make decisions faster and better,prevented reinventing the wheel,preserved some talented processes through keeping track of best practices,and prompted innovation due to knowledge sharing and dissemination.BD deals with massive amount of data and does not require a traditional database to be effective.BD has its tools and requirement that can be enhanced through KM.The final aim of this paper is to recreate a model where both BD and KM coexist.The author hopes with a better understanding of both fields to develop a new course where the focus is a productive intersection of KM and BD.To keep up with changing times,this paper will bring the needed awareness of these fields for information systems and business students. 展开更多
关键词 big data KNOWLEDGE management model KNOWLEDGE value class design introductory learning business
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Research on Innovation of Ideological and Political Education Method in Colleges and Universities in Big Data Era 预览
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作者 Wang Hailong 《计算机科学与技术汇刊:中英文版》 2019年第1期49-52,共4页
The era of big data has brought about tremendous changes in people's daily life, work and thinking patterns. The ideological and political education of colleges and universities has also faced opportunities and ch... The era of big data has brought about tremendous changes in people's daily life, work and thinking patterns. The ideological and political education of colleges and universities has also faced opportunities and challenges. To this end, college ideological and political education workers must strengthen data awareness, innovate data dissemination methods, and transform research paradigms to meet the requirements of the development of the times. This requires universities to continue to innovate, advance with the times, and strive to use innovative methods to promote ideological and political work. Guided by Marxist theory and combined with relevant knowledge of pedagogy and educational psychology, this paper comprehensively and profoundly explores the impact of big data on people's life style, thinking mode and education. On this basis, the current situation and new ways of Ideological and political education in Colleges and universities in the era of big data are discussed in depth, with a view to providing effective reference for the industry. 展开更多
关键词 BIG Data Age Ideological and POLITICAL EDUCATION in COLLEGES and UNIVERSITIES INNOVATION
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