Systematic review: Factors influencing creativity in the design discipline and assessment criteria

Using psychological instrument to measure creativity is getting popular in design research. However, unlike quantifying general creativity using divergent thinking, the complexity and interdisciplinarity of the design discipline have made it difficult to explore research on design creativity. Therefore, to better quantify and measure design creativity, 31 relevant studies were retrieved by Google Scholar and the University of London Common Research in this article. This study summarizes the factors that influence design creativity in different design disciplines, the rules for setting the internal dimensions, and the valid instruments for measuring design creativity. The factors affecting design creativity can be divided into internal factors (aesthetic, spatial ability, and ambiguity tolerance) and external factors (environment and visual stimulation). Among these factors, different instruments and evaluation criteria considerably impact the result, while the measurement of design creativity is still not mature enough. A single scale evaluation or creative task evaluation cannot comprehensively evaluate the design creativity, which consists of aesthetic, functional, and technical aspects. In addition, the reference value of ordinary creativity remains to be further discussed in design. Under some professional design fields, the effect of widely recognized factors closely related to creativity, such as divergent thinking, imagination, and personality, is insignificant

Fault Diagnosis Algorithm Based on Power Outage Data in Power Grid

INTRODUCTION: With the rapid development of the power industry, the power system has become more and more complex and prone to failures, which seriously impacts power supply and safety. OBJECTIVES: Development of efficient and accurate fault diagnosis algorithms for power systems. METHODS:Proposes a fault diagnosis algorithm based on outage data to construct an outage fault prediction model using accurate data. First, the outage data are collected, pre-processed, feature extracted and reduced to obtain a more efficient data set. Then, an optimized fault diagnosis algorithm is designed based on logit, support vector machine (SVM) and decision tree (DT) to improve the accuracy and efficiency of fault diagnosis. RESULTS: The method is applied to the natural power system, and the results show that the optimization algorithm outperforms the traditional methods.   Specifically, the accuracy of the optimization algorithm can reach 100%, while the accuracy of the traditional logit algorithm and SVM algorithm is only 84% and 93%, which is a significant improvement in the model prediction performance. CONCLUSION: The author can significantly optimize the performance of its model and construct an outage data mining algorithm with a good predictive ability to achieve grid fault research and judgment, which has a specific application value in the practical field

Experimental observation of topological Fermi arcs in type-II Weyl semimetal MoTe2

Weyl semimetal is a new quantum state of matter [1-12] hosting the condensed matter physics counterpart of relativisticWeyl fermion [13] originally introduced in high energy physics. The Weyl semimetal realized in the TaAs class features multiple Fermi arcs arising from topological surface states [10, 11, 14-16] and exhibits novel quantum phenomena, e.g., chiral anomaly induced negative mag-netoresistance [17-19] and possibly emergent supersymmetry [20]. Recently it was proposed theoretically that a new type (type-II) of Weyl fermion [21], which does not have counterpart in high energy physics due to the breaking of Lorentz invariance, can emerge as topologically-protected touching between electron and hole pockets. Here, we report direct spectroscopic evidence of topological Fermi arcs in the predicted type-II Weyl semimetal MoTe2 [22-24]. The topological surface states are confirmed by directly observing the surface states using bulk-and surface-sensitive angle-resolved photoemission spectroscopy (ARPES), and the quasi-particle interference (QPI) pattern between the two putative Fermi arcs in scanning tunneling microscopy (STM). Our work establishes MoTe2 as the first experimental realization of type-II Weyl semimetal, and opens up new opportunities for probing novel phenomena such as exotic magneto-transport [21] in type-II Weyl semimetals.Comment: submitted on 01/29/2016. Nature Physics, in press. Spectroscopic evidence of the Fermi arcs from two complementary surface sensitive probes - ARPES and STS. A comparison of the calculated band structure for T_d and 1T' phase to identify the topological Fermi arcs in the T_d phase is also included in the supplementary informatio

Sentiment Classification through Combining Classifiers with Multiple Feature Sets

Sentiment classification aims at assigning a document to a predefined category according to the polarity of its subjective information (e.g. ‘thumbs up ’ or ‘thumbs down’). In this paper, we present a classifier combination approach to this task. First, different classifiers are generated through training the review data with different features: unigram and some POS features. Then, classifier selection method is used to select a part of the classifiers for the next-step combination. Finally, these selected classifiers are combined using several combining rules. The experimental results show that all the combination approaches with different combining rules outperform individual classifiers and the sum rule achieves the best performance with an improvement of 2.56 % over the best individual classifier.

Reinforced Transformer with Cross-Lingual Distillation for Cross-Lingual Aspect Sentiment Classification

Though great progress has been made in the Aspect-Based Sentiment Analysis(ABSA) task through research, most of the previous work focuses on English-based ABSA problems, and there are few efforts on other languages mainly due to the lack of training data. In this paper, we propose an approach for performing a Cross-Lingual Aspect Sentiment Classification (CLASC) task which leverages the rich resources in one language (source language) for aspect sentiment classification in a under-resourced language (target language). Specifically, we first build a bilingual lexicon for domain-specific training data to translate the aspect category annotated in the source-language corpus and then translate sentences from the source language to the target language via Machine Translation (MT) tools. However, most MT systems are general-purpose, it non-avoidably introduces translation ambiguities which would degrade the performance of CLASC. In this context, we propose a novel approach called Reinforced Transformer with Cross-Lingual Distillation (RTCLD) combined with target-sensitive adversarial learning to minimize the undesirable effects of translation ambiguities in sentence translation. We conduct experiments on different language combinations, treating English as the source language and Chinese, Russian, and Spanish as target languages. The experimental results show that our proposed approach outperforms the state-of-the-art methods on different target languages

Random Walks for Opinion Summarization on Conversations

Abstract. Opinion summarization on conversations aims to generate a sentimental summary for a dialogue and is shown to be much more challenging than traditional topic-based summarization and general opinion summarization, due to its specific characteristics. In this study, we propose a graph-based framework to opinion summarization on conversations. In particular, a random walk model is proposed to globally rank the utterances in a conversation. The main advantage of our approach is its ability of integrating various kinds of important information, such as utterance length, opinion, and dialogue structure, into a graph to better represent the utterances in a conversation and the relationship among them. Besides, a global ranking algorithm is proposed to optimize the graph. Empirical evaluation on the Switchboard corpus demonstrates the effectiveness of our approach. Keywords: Opinion Summarization on Conversations, Graph, Random Walk, Global Ranking. Introduction Opinion summarization aims to generate a sentimental summary on opinions in a text and has been drawing more and more attention recently in NLP due to its significant contribution to various real applications As pilots in opinion summarization on conversations, Wang and Li

Leveraging Interactive Knowledge and Unlabeled Data in Gender Classification with Co-training

Abstract. Conventional approaches to gender classification much rely on a large scale of labeled data, which is normally hard and expensive to obtain. In this paper, we propose a co-training approach to address this problem in gender classification. Specifically, we employ both non-interactive and interactive texts, i.e., the message and comment texts, as two different views in our cotraining approach to well incorporate unlabeled data. Experimental results on a large data set from micro-blog demonstrate the appropriateness of leveraging interactive knowledge in gender classification and the effectiveness of the proposed co-training approach in gender classification