A Review on Human-Computer Interaction and Intelligent Robots

Ren, Fuji; Bao, Yanwei

doi:10.1142/s0219622019300052

Cited by 89 publications

(35 citation statements)

References 217 publications

(186 reference statements)

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“…In addition to the above roles of text similarity detection, its direct application has considerable practical value [2]. For example, it has a significant role in protecting the intellectual property rights of electronic texts and combating illegal copying and plagiarism of academic results.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data

Liu

Tsai

2021

Wireless Communications and Mobile Computing

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In this paper, we conduct in-depth research and analysis on the intelligent recognition and teaching of English fuzzy text through parallel projection and region expansion. Multisense Soft Cluster Vector (MSCVec), a multisense word vector model based on nonnegative matrix decomposition and sparse soft clustering, is constructed. The MSCVec model is a monolingual word vector model, which uses nonnegative matrix decomposition of positive point mutual information between words and contexts to extract low-rank expressions of mixed semantics of multisense words and then uses sparse. It uses the nonnegative matrix decomposition of the positive pointwise mutual information between words and contexts to extract the low-rank expressions of the mixed semantics of the polysemous words and then uses the sparse soft clustering algorithm to partition the multiple word senses of the polysemous words and also obtains the global sense of the polysemous word affiliation distribution; the specific polysemous word cluster classes are determined based on the negative mean log-likelihood of the global affiliation between the contextual semantics and the polysemous words, and finally, the polysemous word vectors are learned using the Fast text model under the extended dictionary word set. The advantage of the MSCVec model is that it is an unsupervised learning process without any knowledge base, and the substring representation in the model ensures the generation of unregistered word vectors; in addition, the global affiliation of the MSCVec model can also expect polysemantic word vectors to single word vectors. Compared with the traditional static word vectors, MSCVec shows excellent results in both word similarity and downstream text classification task experiments. The two sets of features are then fused and extended into new semantic features, and similarity classification experiments and stack generalization experiments are designed for comparison. In the cross-lingual sentence-level similarity detection task, SCLVec cross-lingual word vector lexical-level features outperform MSCVec multisense word vector features as the input embedding layer; deep semantic sentence-level features trained by twin recurrent neural networks outperform the semantic features of twin convolutional neural networks; extensions of traditional statistical features can effectively improve cross-lingual similarity detection performance, especially cross-lingual topic model (BL-LDA); the stack generalization integration approach maximizes the error rate of the underlying classifier and improves the detection accuracy.

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Section: Introductionmentioning

confidence: 99%

Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data

Liu

Tsai

2021

Wireless Communications and Mobile Computing

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“…Distributed SCADA (Supervisory Control And Data Acquisition, system control, and data acquisition) is usually used for integrated monitoring of large industrial processes and power systems. However, general SCADA system construction and operation and maintenance costs are high, so special operation and maintenance personnel are required and almost all of them rely on the graphical user interface (Graph User Interface, GUI) for human-computer interaction [1]. By observing the chart data, clicking on the button control to set up and deploy the SCADA system, operation and maintenance engineers with special knowledge are needed to complete the limited operation under the fixed program through the Windows interface.…”

Section: Introductionmentioning

confidence: 99%

SCADA-NLI: A Natural Language Query and Control Interface for Distributed Systems

Shen

et al. 2021

IEEE Access

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Human-Computer natural language interaction is helpful to reduce the operation and maintenance cost of the SCADA system, and it is necessary to solve the complex natural language interface problem that supports data query and real-time control. According to the complexity of natural language instructions, a hierarchical classification of semantic parsing algorithm is adopted. Firstly, the KWECS method is used to classify the intent of natural language instruction, then the TF-IDF keyword extraction algorithm combined with the cosine similarity is used to structure the key-value of the classified natural language instructions which was used into SCADA control intermediate language and then formally converted into actual control or query instruction. If the analysis fails, the complex control and query instruction analysis are carried out according to the classification results, structuring instruction parsing based on dependency parsing and SQL natural language parsing based on deep learning are adopted respectively to implement real-time control interface and database query interface. Our experimental results show that the proposed hierarchical classification of natural language comprehensive query and control interface can better solve the problem of human-computer natural language interaction in the SCADA system, and the accuracy of intent recognition reaches 96.5%. In more detail, the accuracy, precision, recall, and F-score of instruction parsing reach 88.47%, 90.21%, 89.48%, and 89.72% respectively. Especially, it provides more convenient interactive means for industrial and agricultural information management and control.INDEX TERMS Natural language interface, intent classification, semantic parsing, scada systems, humancomputer interaction.

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“…This is thought to be because the relationship between humans and robots lacks development over time, and humans are entering the burnout phase, where they are not interested in agents [5]. There are also studies that points out other causes such as low intelligence, little variety of reactions in the robot and the lack of established methods to keep people interested in the robot [6,7], and designs robots or agents to keep users interested in them and build relationships with them [8][9][10]. As a solution to this problem, we intend to create a friendly and continuous relationship between a person and an agent by letting the agent generate personalized behaviors for the user.…”

Section: Introductionmentioning

confidence: 99%

Construction and Evaluation of QOL Specialized Dictionary SqolDic Using Vocabulary Meaning and QOL Scale

2021

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Agents that build interactive relationships with people can provide appropriate support and generate behaviors by accurately grasping the state of the person. This study focuses on the quality of life (QOL), which can be assessed multidimensionally, and aims to estimate QOL scores in the process of human interaction. Although vision-based estimation has been the main method for QOL estimation, we proposed a new text-based estimation method. We created a QOL-specific dictionary called SqolDic, which is based on large-scale Japanese textual data. To evaluate the effectiveness of SqolDic, we implemented a system that outputs the time-series variation of a user’s conversation content and the QOL scores based on it. In an experiment for estimating the content of user conversations based on a QOL scale by inputting data from actual human conversations, we achieved a maximum estimation accuracy of 91.2%. Additionally, in an experiment to estimate QOL score variability, we successfully estimated the mental health state and one of the QOL scales with a smaller distribution of error than that in previous studies. The experimental results demonstrated the effectiveness of our system in estimating conversation content and QOL scores as well as the effectiveness of our newly proposed QOL dictionary.

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A Review on Human-Computer Interaction and Intelligent Robots

Cited by 89 publications

References 217 publications

Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data

Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data

SCADA-NLI: A Natural Language Query and Control Interface for Distributed Systems

Construction and Evaluation of QOL Specialized Dictionary SqolDic Using Vocabulary Meaning and QOL Scale

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