• 품질경영학회지
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• 제20권2호
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• pp.109-117
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• 1992

An inspection task largely can be seen as a job divided up into a series of visual search and classification subtasks. In these subtasks, an Inspector must performs to compare the standard references proposed in visual environments and recalled in his memory with the visual stimuli to be inspected. It means that the judgement of similarity should be demanded on inspection tasks. Therefore, the inspector's ability for the judgement of similarity and the difference similarity between inspection materials are important factors to effect on performances in inspection tasks. In this paper, to analysis the effect of these factors on inspection time, an inspection task is designed and suggested by means of computer simulator. Especially, the skin conductance responses(SCR) of subjects are measured to evaluate the complexity of tasks due to the difference of similarity between materials. In the results of experiment, the more similar or different the difference of similarity between materials is, the shorter the inspection time is because of the reduction of task complexity. And, When the inspector's cognition for similarity between materials is consistanct, the inpsection time is improved. Concludingly, the consistency of reponses for similarity judgement becomes a measurement to present the performance levels. And the information of inspection time that due to the difference of similarity between materials must be considered in planning and scheduling inspection tasks.

• Journal of Multimedia Information System
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• 제3권3호
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• pp.53-61
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• 2016

This research proposes the index optimization as a classification task and application of the graph based KNN. We need the index optimization as an important task for maximizing the information retrieval performance. And we try to solve the problems in encoding words into numerical vectors, such as huge dimensionality and sparse distribution, by encoding them into graphs as the alternative representations to numerical vectors. In this research, the index optimization is viewed as a classification task, the similarity measure between graphs is defined, and the KNN is modified into the graph based version based on the similarity measure, and it is applied to the index optimization task. As the benefits from this research, by modifying the KNN so, we expect the improvement of classification performance, more graphical representations of words which is inherent in graphs, the ability to trace more easily results from classifying words. In this research, we will validate empirically the proposed version in optimizing index on the two text collections: NewsPage.com and 20NewsGroups.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1053-1056
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• 2004

In this article we have applied Schweizer & Sklars t-norm based similarity measures to classification task. We will compare results to fuzzy similarity measure based classification and show that sometimes better results can be found by using these measures than fuzzy similarity measure. We will also show that classification results are not so sensitive to p values with Schweizer & Sklars measures than when fuzzy similarity is used. This is quite important when one does not have luxury of tuning these kind of parameters but needs good classification results fast.

• 아동학회지
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• 제33권5호
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• pp.53-70
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• 2012

The purpose of this study was to investigate the effects of task conditions (physical similarity between the spatial product and the reference space, presentation place of the spatial product) on children's spatial representation. The participants consisted of 40 3-year-olds and 40 4-year-olds. The results of this study are as follows. Both 3-year-olds and 4-year-olds were capable of a greater degree of spatial representation when there was a high level of physical similarity between the spatial product and the reference space, and when the presentation place of the spatial product was in the reference space. 4-year-olds were capable of more accurate spatial representation than 3-year-olds. There was no significant difference in the children's spatial representation depending on the type of spatial product (scale model, map). The results revealed that the physical similarity between the spatial product and the reference space and the presentation place of the spatial product are essential in young children's spatial representation. Additionally, the results indicated that spatial representation of children develops gradually from when they are three to when they turn four.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.

• 대한음성학회:학술대회논문집
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• 대한음성학회 1997년도 7월 학술대회지
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• pp.142-143
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• 1997

The purpose of this paper is to assess the psychological status of the phoneme, syllable, and various postulated subsyllabic units in Korean by applying the Sound Similarity Judgment (SSJ) task, to compare the results with those in English, and to discuss the advantage and disadvantage of the SSJ task as a tool for linguistic research. In Experiment 1, 30 subjects listened to pairs of 56 eve words which were systematically varied from 'totally different' (e.g., pan-met) to 'identical' (e.g., pan-pan). Subjects were then asked to rate sound similarity of each pair on a 10-point scale. Not very surprisingly, there was a strong correlation between the number of phonemic segments matched and the similarity score provided by the subjects. This result was in accord with the previous results from English (e.g., Vitz & Winkler, 1973; Derwing & Nearey, 1986) and supported the assumption that the phoneme is the basic phonological unit in Korean and English. However, there were sharply contrasting results between the two languages. When the pairs shared two phonemes (e.g., pan-pat; pan-pen; pan-man), the pairs sharing the fIrst two phonemes were judged significantly more similar than the other two types of pairs. Quite to the contrary, in the comparable English experiments, the pairs sharing the last two phonemes were judged significantly more similar than the other two types of pairs. Experiment 2 was designed to conflrm the results of Experiment 1 by controlling the 'degree' of similarity between phonemes. For example, the pair pan-pam can be judged more similar than the pair pan-nan, although both pairs share the same number of phonemes. This could be interpreted either as confirming the result of Experiment 1 or as the fact that /n/ is more similar to /m/ than /p/ is to /n/ in terms of shared number of distinctive features. The results of Experiment 2 supported the former interpretation. Thus, the results of both experiments clearly showed that, although the 'number' of matched phonemes is the important predictor in judging sound similarity of monosyllabic pairs of both languages, the 'position' of the matched phonemes exerts a different influence in judging sound similarity in the two languages. This contrasting set of results may provide interesting implications for the internal structure of the syllable in the two languages.

• 한국건설관리학회논문집
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• 제21권6호
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• pp.125-134
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• 2020

시공 서류에서 접하는 자연어는 당국에서 권장하는 언어와 크게 다르다. 일관성이 부족한 이러한 관행은 자동화를 통한 통합 연구를 방해하고 장기적으로 업계의 생산성을 저하시킬 것이다. 이 연구는 여러 문자열 유사성(문자열 일치) 알고리즘을 비교하여 여러 다른 방법으로 작성된 동일한 작업 이름을 인식하는 각 알고리즘의 성능을 비교하는 것을 목표로 한다. 우리는 또한 앞서 언급 한 편차가 얼마나 널리 퍼져 있는지에 대한 토론을 시작하는 것을 목표로 한다. 마지막으로, 우리는 실제로 발견된 시공 작업 이름을 형식에 비해 덜 복잡한 해당 작업 이름과 연결하는 작은 데이터 세트를 구성했다. 이 데이터 세트를 사용하여 미래의 자연어 처리 접근방식을 검증 할 수 있을 것으로 기대한다.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• 감성과학
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• 제13권3호
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• pp.541-550
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• 2010

본 연구에서는 P300-기반 숨긴정보검사(P300 CIT)에서 검사자극들 간의 물리적 유사성이 P300 진폭과 검사의 효율성에 미치는 영향을 검증하였다. 사고를 당하여 자신의 이름을 기억하지 못한다고 허위로 주장하는 허위기억상실을 가정한 상황에서, 실험참여자의 이름을 숨긴정보(관련자극)로 사용하여 P300 CIT를 실시하였다. 이 검사에서 실험참여자의 과제는 목표자극과 나머지 자극을 변별하는 것이었다. 한 집단의 실험참여자들은 목표자극과 관련자극, 무관련자극들 간의 물리적 유사성이 낮은 조건(저난도 조건)에서 검사를 받았으며, 다른 한 집단의 실험참여자들은 검사자극들 간의 물리적 유사성이 높은 조건(고난도 조건)에서 검사를 받았다. 기저선-정점 P300 진폭을 측정치로 사용한 경우, 난이도 조건과 자극유형의 상호작용효과가 $\alpha$=.10 수준에서 유의하였다(p=.052). 저난도 조건에서는 관련자극과 무관련자극 간의 P300 진폭차이가 유의하였으며, 고난도 조건에서는 관련자극과 무관련자극 간의 P300 진폭차이가 유의하지 않았다. 정점-정점 P300 진폭을 측정치로 사용한 경우, 난이도 조건과 자극유형의 상호작용효과가 유의하지 않았으며, 저난도 조건과 고난도 조건 모두에서 관련자극과 무관련자극간의 P300 진폭차이가 유의하였다. 기저선-정점 P300 진폭을 이용한 개인별 판단결과, 저난도 조건과 고난도 조건 간의 정확판단율 차이가 유의하지 않았으며, 정점-정점 P300 진폭을 이용한 개인별 판단결과도 저난도 조건과 고난도 조건 간의 정확판단율 차이가 유의하지 않았다. 그러나, 난이도 조건 간의 정확판단율 차이가 기저선-정점 P300 진폭을 이용한 경우보다 정점-정점 P300 진폭을 이용한 경우에 더 작은 경향이 있었다. 이러한 결과는 검사자극들 간의 물리적 유사성이 높을 때에도 P300 CIT의 효율성이 크게 감소하지 않는다는 것을 의미한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3991-4010
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• 2021

The Question Similarity Measurement of Chinese Crop Diseases and Insect Pests (QSM-CCD&IP) aims to judge the user's tendency to ask questions regarding input problems. The measurement is the basis of the Agricultural Knowledge Question and Answering (Q & A) system, information retrieval, and other tasks. However, the corpus and measurement methods available in this field have some deficiencies. In addition, error propagation may occur when the word boundary features and local context information are ignored when the general method embeds sentences. Hence, these factors make the task challenging. To solve the above problems and tackle the Question Similarity Measurement task in this work, a corpus on Chinese crop diseases and insect pests(CCDIP), which contains 13 categories, was established. Then, taking the CCDIP as the research object, this study proposes a Chinese agricultural text similarity matching model, namely, the AgrCQS. This model is based on mixed information extraction. Specifically, the hybrid embedding layer can enrich character information and improve the recognition ability of the model on the word boundary. The multi-scale local information can be extracted by multi-core convolutional neural network based on multi-weight (MM-CNN). The self-attention mechanism can enhance the fusion ability of the model on global information. In this research, the performance of the AgrCQS on the CCDIP is verified, and three benchmark datasets, namely, AFQMC, LCQMC, and BQ, are used. The accuracy rates are 93.92%, 74.42%, 86.35%, and 83.05%, respectively, which are higher than that of baseline systems without using any external knowledge. Additionally, the proposed method module can be extracted separately and applied to other models, thus providing reference for related research.