Abstract With the rapid development of artificial intelligence technology, the design of evaluation methods for university English teaching effects based on intelligent algorithms has become a hot topic. To reduce evaluation errors in English teaching effects, a university English teaching effect evaluation method is proposed by combining intelligent algorithms. First, English teaching evaluation data is collected, and an intelligent selection of... With the rapid development of artificial intelligence technology, the design of evaluation methods for university English teaching effects based on intelligent algorithms has become a hot topic. To reduce evaluation errors in English teaching effects, a university English teaching effect evaluation method is proposed by combining intelligent algorithms. First, English teaching evaluation data is collected, and an intelligent selection of evaluation indicators within a calibrated scope is made. A versatile intelligent algorithm teaching effect evaluation model is established, using a stepwise target evaluation method for implementation. Through testing, it was found that the algorithm designed can effectively reduce the errors in teaching evaluation, even reducing them to below 1.5. This indicates that it can significantly enhance the overall evaluation accuracy. Read More Read Less

Abstract The investigation into lexical and conceptual representation in second language (L2) acquisition has garnered significant attention in linguistics and cognitive psychology as it sheds light on the intricate mechanisms underlying the development of language proficiency in non-native speakers. The issue of handling outlier data in response time (RT) measurements, a cornerstone in many psycholinguistic experiments, is meticulously examined. The paper... The investigation into lexical and conceptual representation in second language (L2) acquisition has garnered significant attention in linguistics and cognitive psychology as it sheds light on the intricate mechanisms underlying the development of language proficiency in non-native speakers. The issue of handling outlier data in response time (RT) measurements, a cornerstone in many psycholinguistic experiments, is meticulously examined. The paper argues that variance normalization as a preprocessing step offers a robust method for mitigating the influence of extreme values that can potentially distort the overall interpretation of the data. Variance normalization involves transforming the RT data such that each observation is expressed in terms of its deviation from the mean scaled by the standard deviation of the dataset, thereby ensuring that outliers do not disproportionately affect the statistical analyses. Read More Read Less

Abstract Contextual representation and reasoning are an open issue of the semantic web. Although long parties have practically limited local treatment of context representation for the semantic web, a rich and vast consensus has yet to be reached about the encoding, particularly for reasoning about contextual knowledge. This paper presents OWLC: a contextual two-dimensional web ontology language. Using the first dimension, we can reason about... Contextual representation and reasoning are an open issue of the semantic web. Although long parties have practically limited local treatment of context representation for the semantic web, a rich and vast consensus has yet to be reached about the encoding, particularly for reasoning about contextual knowledge. This paper presents OWLC: a contextual two-dimensional web ontology language. Using the first dimension, we can reason about context-dependent classes, properties, and axioms, while using the second dimension, we can reason about knowledge of contexts considered formal objects, as proposed by McCarthy [20]. We demonstrate how strong modeling and reasoning with OWLC can be through a real-world scenario arising from the domain of digital humanity. The Ferdinand de Saussure [15] use case is used for its naturally contextual and at the same time complex nature, making it possible to bring out many issues in context representation and reasoning. Read More Read Less