Annotating Exam Questions Through Automatic Learning Concept Classification

The process of education must apply with “Learning Process Skill”, not “Learning Concept”. Process approach marked with student centered curricula, not teacher centered. Role of teacher is as facilitator, mediator, dynamizing, organizing, and catalyst to apply “dialog” as spirit of education process. Critical education model is an education that independent from internal-institutional fetter, social hegemony, or structured to maintain political and economical stability. These happen in the length of our national history, then produce tame-weak human accorded to system condition. Whereas, education is human right, even people right to enhance its maturity, self-identity, and independence to serve his function to his God. .

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On the Automatic Learning of Sentiment Lexicons

10.3115/v1/n15-1159 ◽

2015 ◽

Cited By ~ 9

Author(s):

Aliaksei Severyn ◽

Alessandro Moschitti

Keyword(s):

Automatic Learning

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Competitive-integrated benchmarking as an instrument increase of competitiveness of the enterprise

Voprosy regionalnoj ekonomiki ◽

10.21499/2078-4023-2018-35-2-32-39 ◽

2018 ◽

Vol 35 (2) ◽

pp. 32-39

Author(s):

M. D. Dzhamaldinova ◽

N. O. Kurdukova

Keyword(s):

Development Strategy ◽

Assessment Methods ◽

The Development Strategy ◽

Concept Classification

Results of a research of a concept of competitiveness of the organization are presented in article, competitiveness assessment methods are studied and analysed. Special attention is paid to a benchmarking research: to studying of essence, concept, classification and stages of realization, as one of effective modern tools of an assessment of competitiveness and development of the development strategy of the organization.

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ATJ-Net: Auto-Table-Join Network for Automatic Learning on Relational Databases

Proceedings of the Web Conference 2021 ◽

10.1145/3442381.3449980 ◽

2021 ◽

Author(s):

Jinze Bai ◽

Jialin Wang ◽

Zhao Li ◽

Donghui Ding ◽

Ji Zhang ◽

...

Keyword(s):

Relational Databases ◽

Automatic Learning

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Machine learning techniques based on security management in smart cities using robots

Work ◽

10.3233/wor-203423 ◽

2021 ◽

pp. 1-12

Author(s):

Zhang Mengqi ◽

Wang Xi ◽

V.E. Sathishkumar ◽

V. Sivakumar

Keyword(s):

Machine Learning ◽

Information And Communication Technologies ◽

Smart City ◽

Smart Cities ◽

Security Management ◽

Machine Learning Techniques ◽

Quality Of Services ◽

Learning Techniques ◽

Learning Concept

BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities’ security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services. OBJECTIVES: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network. RESULTS: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis. CONCLUSION: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains.

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MLatom 2: An Integrative Platform for Atomistic Machine Learning

Topics in Current Chemistry ◽

10.1007/s41061-021-00339-5 ◽

2021 ◽

Vol 379 (4) ◽

Author(s):

Pavlo O. Dral ◽

Fuchun Ge ◽

Bao-Xin Xue ◽

Yi-Fan Hou ◽

Max Pinheiro ◽

...

Keyword(s):

Machine Learning ◽

Software Package ◽

State Of The Art ◽

Kernel Method ◽

Automatic Learning ◽

Hyperparameter Optimization ◽

Input And Output ◽

Spectrum Simulation ◽

Farthest Point ◽

Theoretical Foundations

AbstractAtomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.

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