The literature often views the emergence of social tagging as a potential alternative method to controlled vocabulary for organizing and indexing large-scale information resources. In this paper, we present an in-depth examination of the relationship between social tagging and controlled vocabulary-based indexing and organization in two unique contexts: the information science domain and when comparing data gathered from both English and Chinese sources. Our results show that the information science domain has more overlap between social tags and controlled vocabulary-based subject terms. This is reflected in the higher percentage of overlapping terms between tags and subject terms, as well as in the strong similarity (measured by Jaccard’s coefficient) in frequently used keywords among tags and subject terms. However, social tags in the information science domain still possess limitations in terms of uncontrolled terms, where inconsistencies and noisy usages exist. Our results also show that language difference does have an impact on social tagging. The numbers of Chinese tags overall and per book are less than those of English tags. The most frequently used English tags are single-word terms, which are different from multi-word controlled vocabulary terms. In comparison, the character difference between the most frequently used Chinese tags and Chinese subject terms is just one character (3 vs 4). However, English and Chinese users do share many similar behaviours when they tag books in the information science domain. Many of the most frequently used tags are shared between the two languages and the patterns of overlap between topical tags and subject terms are also similar between the two languages. Overall, despite the application limitations for social tagging in cataloguing and indexing, we believe that tagging has the potential to become a complementary resource for expanding and enriching controlled vocabulary systems. With the help of future technology to regulate and promote features related to controlled vocabulary in social tags, a hybrid cataloguing and indexing system that integrates social tags with controlled vocabulary would greatly improve people’s organizational and access capabilities within information resources.

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Nowadays, thirteen ultra-high voltage direct current (UHVDC) transmission projects have commissioned in China, including Jinping-Sunan, Fulong-Fengxian, Yibin-Jinhua project, and so forth. Meanwhile, another two UHVDC transmission projects are under construction. The highest UHVDC voltage level is ±1100kV. The HVDC transmission is superior in high capacity and long transmission distance, and its control response put forward higher requirements for the transient characteristics of DC current transformer. At present, there is no test method and equipment for testing the transient characteristics of the DC current transformer. Therefore, mastering the transient response characteristics of operating DC current transformer remains to be a challenge. In this paper, a transient test system of DC current transformer is developed, which includes a step current output unit, a high precision acquisition unit and a calibrator algorithm. The system was used to test the rising time, maximum overshoot and transient delay of the DC current transformers installed on the pole line and valve outlet. This study can clarify the influence factors of the transient characteristics of DC transformer on the control and protection system of UHVDC, and can define the principle of selecting the transformer parameters to meet the requirements of the control and protection system of UHVDC. The results of this study have great significance to improve the performance of UHVDC control and protection system.

In order to shorten the image registration time and improve the imaging quality, this paper proposes a fuzzy medical computer vision image information recovery algorithm based on the fuzzy sparse representation algorithm. Firstly, by constructing a computer vision image acquisition model, the visual feature quantity of the fuzzy medical computer vision image is extracted, and the feature registration design of the fuzzy medical computer vision image is carried out by using the 3D visual reconstruction technology. Then, by establishing a multidimensional histogram structure model, the wavelet multidimensional scale feature detection method is used to achieve grayscale feature extraction of fuzzy medical computer vision images. Finally, the fuzzy sparse representation algorithm is used to automatically optimize the fuzzy medical computer vision images. The experimental results show that the proposed method has a short image information registration time, less than 10 ms, and has a high peak PSNR. When the number of pixels is 700, its peak PSNR can reach 83.5 dB, which is suitable for computer image restoration.

The most distinctive feature of conundrum is the deliberate misinterpretation in the course of its application. The present paper attempts a new cognitive approach to the understanding of conundrums. Based on psychological laws of mankind, the conundrum begins with the questioner's leaving a certain linguistic item on purpose, which will definitely cause deliberate misinterpretation. The process of its understanding is the result of the replier's taking advantage of the impartment and inheritance of the connotation and denotation to disguisedly replace another concept.Keywords: conundrums, the impartment and inheritance of connotation and denotation (IICD) approach, deliberate misinterpretation, disguised replacement of concept The Conundrum and Its FeaturesThe conundrum is an ambiguous question, a kind of word-play that people ask for fun, which is a combination of language features and mind-sets. All conundrums are unexceptionally composed of two parts: the first part is a question with a series of clues, which lead people to the stereotyped thought; the second part is the answer, to which people tumble at last. A Chinese scholar Wang (1996) defines conundrums as involving puns and other shrewd and witty questions. It is intentionally worded in a puzzling manner in order to be guessed, especially as a form of amusement. In order to have a clear understanding of the conundrum, let's look at the following examples: 1) What month do soldiers like least? Key: March.2) The farmers in this village used modern methods but harvested no apples this year? Why? Key: They planted peach trees.In example 1), the question seems to figure out soldiers' least favorite month of a year. It could be any month of a year so long as it holds water. However, this couldn't be the most appropriate answer to this conundrum as it cannot achieve any amusing effect. As a matter of fact, the key to this conundrum makes use of polysemy of the word "march": it seems to refer to the third month of a year, but actually to the action of marching. In this example, the questioner intentionally creates the context in which it seems to have favorable answers which are actually undesirable. In example 2), the question presupposes that the farmers planted apple trees this year. If the replier accepts the presupposition, then he is misled to a mental trap.The conundrum usually takes the form of a brief wh-question which is posed to be answered with an appropriate but amusing guess. The conundrum has its own features. First of all, it consists of a question and an answer. Dienhart (1998) labels the former "text 1 " and the latter "text 2 " respectively. The initial part is usually a shrewd and witty question with puns or not. Only people break through the stereotyped thought, can they arrive at the most appropriate solution. Secondly, the conundrum is not created to seek or provide information, but for fun-making and amusement. When people interpret a conundrum, they always work out the answer in a conventional way, which is maximum relevant to ...