Deep web data extraction based on visual information processing

Deep Web is a widely unexplored data source, is becoming an important research topic. Retrieving structured data from deep web pages is the challenging problem due to their complex structure. In this paper, Information extracts on the Deep Web pages based on the Deep Web Data Extraction technique (DWDR-IR). Search engines usually return a large number of pages in response to the user queries. To help the users to navigate in the result list, ranking methods are activated on the search results. In this paper, a page ranking mechanism called Coherence Ratio based Page (CRP) ranking algorithm is used. To retrieve the information accurately, an approach called WordNet is used. WordNet checks the similarity of data records and find the correct data region with higher precision using the semantic properties of data records. This concept is very important to display the valuable results occur on the top of the result list on the basis of browsing behavior of the user, it reduces the search space and provides high accuracy. This approach handles the visual features on the deep web data extraction, including data item extraction, data record extraction and visual wrapper generation. The proposed work removes all noise such as header, footer, irrelevant advertisement and irrelevant content using NoiSe Filter (NSFilter) algorithm. The proposed method retrieves perfect extraction of relevant results from the deep web pages. DWDE-IR results higher precision, recall and filter accuracy than the existing method ViDE.

This study explored whether benzodiazepines selectively affect aspects of attention and/or visual information processing, as they do memory. A cued visual-search paradigm was employed, using normal volunteers and a single dose of triazolam. This paradigm provided for a detailed examination of two aspects of visual attention and information processing: 1) controlled versus automatic attention allocation (via central and peripheral cues), and 2) the extent to which processing an item in a non-cued location affects performance (via cue-validity). Triazolam, compared to placebo, significantly increased response time, and Drug Condition interacted with Cue-Validity but not Cue-Type. Based on these data, we argue that triazolam does not affect attention allocation but does affect attentional disengagement and/or attention switching mechanisms.

In this study, we investigated how shapes are classified based on local and global features by four representative convolutional neural networks (CNNs), i.e., AlexNet, VGG, ResNet and Inception. While the local features are based on simple components, such as orientation of line segment, the global features are based on the whole object, such as whether an object has a hole. For example, solid triangles and solid squares are differentiated by local features, solid circles and rings are differentiated by a global feature. Two sets of experiments were performed in this research. In the first experiment, we examined how the four CNNs pre-trained on ImageNet (with transfer learning) learned to differentiate the regular shapes (equilateral-triangles, squares, circles and rings). Our results showed that the pre-trained CNNs exhibited faster learning rates in the tasks discriminating the local features than in the tasks discriminating the global feature. However, the transfer learning of discriminating the global feature in regular shapes were better generalized to irregular shapes than the transfer learning of discriminating local features. In the second experiment, the CNNs were trained from scratch (with random weights initialization) to discriminate local and global features in regular and irregular shapes. Different from the transfer learning, the CNNs exhibited faster learning rates in discriminating the global feature than the local features. Similar to transfer learning, the CNNs exhibited excellent learning generalization to discriminating the global feature of irregular shapes, but poor learning generalization to discriminating the local features in the irregular shapes. The overarching goal of this research is to create a paradigm and benchmark to directly compare how the CNNs and primate visual systems process geometrical invariants. In contrast to the ImageNet approach which employs natural images to train CNNs, we employed the “ShapeNet” approach which features geometrical shapes with well-defined properties. The ShapeNet approach will not only help elucidate the strengths and limitations of CNN computation, but also provide insights into visual information processing in the primates.

The article shows the importance of solving various production problems using processing, analysis and display of visual information. The existing systems for processing visual information are considered and the insufficiently developed use of automated image processing systems in production systems that solve rather complex and “intelligent” problems of image analysis is noted. The prospects of using digital modeling of object images are shown. The article provides a literary review of works on the development of methods and models for effective processing of visual control information, which showed that there are many unresolved issues. This work is aimed at solving some of them. The relevance of the topic and the objectives of the research are determined. Based on the results of the studies performed, a model of the visual information processing system was developed. From the object under study, a video stream arrives at the input of the model of the visual information processing system, which is first selected as a sequence of frames (frames) of a fixed dimension. Then there is a preliminary processing of the information received from the object, its segmentation, extraction of features and decision making. Here, the traditional number of homogeneous segments can be applied, into which the object is divided using the principle of maximum posterior probability. The result obtained is then used in the adaptation block. The proposed model of the visual information processing system was implemented in the Qt Creator environment in C ++.

Deep Web has been an important resource on the Web due to its rich and high quality information, leading to emerging a new application area in data mining and information retrieval and integrates. In Web scale deep Web data integration tasks, where there may be hundreds or thousands of data sources providing data of relevance to a particular domain, It must be inefficient to integrate all available deep Web sources. This paper proposes a data source selection approach based on the quality of deep Web source. It is used for automatic finding the highest quality set of deep Web sources related to a particular domain, which is a premise for effective deep Web data integration. The quality of data sources are assessed by evaluating quality dimensions represent the characteristics of deep Web source. Experiments running on real deep Web sources collected from the Internet show that our provides an effective and scalable solution for selecting data sources for deep Web data integration.

학습자의 정보처리유형에 따른 e-Book 인터페이스의 설계 및 개발은 학습자 중심의 적성처지교수를 위해 중요한 이슈가 된다. 이에 본 연구에서는 하이퍼텍스트 정보구조가 제공된 e-Book 학습환경에서 학습자의 정보처리유형이 학습내용 이해 및 사용 편의성에 있어 어떠한 차이를 보이는지를 검증해 봄으로써 보다 처방적인 e-Book 인터페이스의 설계전략에 대한 시사점을 제공하고자 하였다. 본 연구를 위해 대학생 68명이 참여하였으며, 하이퍼텍스트 정보구조가 제공된 e-Book 콘텐츠를 통해 학습한 후 정보처리유형(시각 정보처리, 언어 정보처리)에 따른 학업성취도 (텍스트 구조 파악 및 학습내용이해)와 사용 편의성에 대한 효과성을 검증하였다. 연구결과, 학업성취도에 있어서는 정보처리 유형에 따라 텍스트 구조 파악에는 유의미한 차이가 나타났으나, 학습내용 이해에는 유의미한 차이가 나타나지 않았다. 사용 편의성에 있어서는 학습자의 정보처리유형에 따라 유의미한 차이가 나타났으며, 구체적으로는 텍스트 구조 파악의 용이성, 텍스트 분량 파악의 용이성, 네비게이션의 용이성, 그리고 내용기억의 편의성 등에 있어 유의미한 차이가 나타났다. 또한 학업성취도에 영향을 미친 사용 편의성의 주요요인은 학습위치 파악의 용이성이었으며, 정보처리유형의 집단간 분류의 정확도는 79.4%로 비교적 높은 것으로 나타났다. 이러한 결과를 바탕으로 e-Book 콘텐츠 설계 및 개발에 대한 전략적 시사점에 논의 되었다. The purpose of this study is to examine difference of information processing style on lesson comprehension scores and usability ratings in e-Learning containing visual information structure. To address this goal, 68 university students were participated in this research. They were asked information processing style test, lesson comprehension test, and usability ratings after completed e-Learning lesson. According to the result, there was not significant difference between visual and verbal information process style on lesson comprehension as learn outcomes. However, students who are visual information processing style were significantly higher ratings than students who are verbal information processing style on 4 of 8 usability scales; awareness of lesson structure, awareness of lesson length, ease of navigation, and ease of lesson learning. These result indicate that there will be needed the design of aptitude treatment interaction for e-Book according to information processing style.

Acute effects of lorazepam on visual information processing, perceptual priming and explicit memory are well established. However, visual processing and perceptual priming have rarely been explored in long-term lorazepam users. By exploring these functions it was possible to test the hypothesis that difficulty in processing visual information may lead to deficiencies in perceptual priming. Using a simple blind procedure, we tested explicit memory, perceptual priming and visual perception in 15 long-term lorazepam users and 15 control subjects individually matched according to sex, age and education level. Explicit memory, perceptual priming, and the identification of fragmented pictures were found to be preserved in long-term lorazepam users, contrary to what is usually observed after an acute drug intake. The processing of visual contour, on the other hand, was still significantly impaired. These results suggest that the effects observed on low-level visual perception are independent of the acute deleterious effects of lorazepam on perceptual priming. A comparison of perceptual priming in subjects with low- vs. high-level identification of new fragmented pictures further suggests that the ability to identify fragmented pictures has no influence on priming. Despite the fact that they were treated with relatively low doses and far from peak plasma concentration, it is noteworthy that in long-term users memory was preserved.

This chapter presents first some background knowledge on how the human brain processes audio- and visual information. Then methods are presented for audio-, visual- and for the integrated audio and visual information processing using evolving spiking neural networks that include convolutional evolving spiking neural networks (CeSNN). Case studies are presented for person identification.

User attributes in processing 3D VR-enabled showroom: Gender, visual cognitive styles, and the sense of presence

Deep web is termed as data present on web but inaccessible to search engine. This data must be accessed through HTML forms filling manually and get access to the deep web i.e. forms. To access deep web data automatically we need to understand search interfaces. The deep web grows at very fast pace and it is always different to manage such data and to give it for deep web crawling. Smart crawler for hidden web interfaces consist of mainly two stages first is site locating another is insite exploring. Site locating starts from seed sites and obtains relevant websites through reverse searching and obtains relevant sites through feature space of URL, anchor and text around URL. Second stage takes input from site locating and goes to find relevant link from those sites. The adaptive link learner is used to find out relevant links with help of link priority and link rank. Proposed system is contributing the searching query as per-query and post-query to obtain wider coverage is obtained through DFS and SCFS classifier.

. Purpose: to study gender peculiarities of visual perception and information processing of female wrestlers. Material and methods. The research involved 23 female and 22 male wrestlers, members of the national teams of Ukraine. We studied the perception and decision-making in testing conditions using the hardware and software complex «Multispychometer-05». The perception of visual information was studied by the perceptual speed test. The athlete was asked to find the missing element of geometric shapes among the proposed on the display screen. The task was to quickly and accurately recognize the missing element. The following indicators were determined: speed, performance, accuracy and efficiency of visual perception. Decision making was studied by a choice test among three stimuli of different colors. The stimuli appeared randomly, the task of the athlete was to respond quickly and accurately to the corresponding stimulus with the right or left hand. The speed of sensory response was studied by the test of determining the latent time of a simple visual-motor reaction. The sportsman was offered to react to a random stimulus as quickly as possible. The latency and stability of the reaction were determined. Results: the study of gender peculiarities of visual perception and information processing in wrestlers was conducted. For sports wrestling perception and processing of information is an important property that provides adequate decision-making in the fight with the active action of the opponent. The best indicator in the study of decision-making time, in terms of choosing stimuli among different external stimuli, was found in men. They make decisions more quickly in conditions of time limit than women. At the same time, women have a faster level of perception of visual information with simultaneous psycho-emotional stress. Men have a faster nature of information processing and decision-making on external visual stimuli. It is advisable to use the results of the study for the construction of training load of women in wrestling. Conclusions. The results of the study showed that the faster nature of visual perception in women who specialize in wrestling than in men wrestlers. The presence of faster decision-making in men wrestlers compared to women in the choice of an external stimulus was revealed. The slowing down of the time of visual-motor reaction in women against the background of the growth of psycho-emotional tension is established. Keywords: freestyle wrestling, elite sportsmen, visual-motor reaction, gender peculiarities.

Chapter 4 - The Contribution of Human Superior Intraparietal Sulcus to Visual Short-Term Memory and Perception

Meetings are a common activity in professional contexts, and it remains challenging to endow vocal assistants with advanced functionalities to facilitate meeting management. In this context, a task like active speaker detection can provide useful insights to model interaction between meeting participants. Detection of the active speaker can be performed using only video based on the movements of the participants of a meeting. Depending on the assistant design and each participant position regarding the device, active speaker detection can benefit from information coming from visual and audio modalities. Motivated by our application context related to advanced meeting assistant, we want to combine audio and visual information to achieve the best possible performance. In this paper, we propose two different types of fusion (naive fusion and attention-based fusion) for the detection of the active speaker, combining two visual modalities and an audio modality through neural networks. In addition, the audio modality is mainly processed using neural networks. For comparison purpose, classical unsupervised approaches for audio feature extraction are also used. We expect visual data centered on the face of each participant to be very appropriate for detecting voice activity, based on the detection of lip and facial gestures. Thus, our baseline system uses visual data (video) and we chose a 3D Convolutional Neural Network (CNN) architecture, which is effective for simultaneously encoding appearance and movement. To improve this system, we supplemented the visual information by processing the audio stream with a CNN or an unsupervised speaker diarization system. We have further improved this system by adding visual modality information using motion through optical flow. We evaluated our proposal with a public and state-of-the-art benchmark: the AMI corpus. We analysed the contribution of each system to the merger carried out in order to determine if a given participant is currently speaking. We also discussed the results we obtained. Besides, we have shown that, for our application context, adding motion information greatly improves performance. Finally, we have shown that attention-based fusion improves performance while reducing the standard deviation.

The rapid advancement of industrialization has sparked the emergence of diverse art and design theories. As a trailblazer in the realm of industrial art and design theory, visual communication has transcended the boundaries of merely arranging and combining individual elements. Embracing the potential of artificial intelligence technology, the extraction of multidimensional abstract data and the acceleration of the art design process have gained considerable momentum. This study delves into the abstract emotional facets within the methodology of visual communication art design. Initially, convolutional neural networks (CNN) are employed to extract expressive features from the poster's visual information. Subsequently, these features are utilized to cluster emotional elements using a variational autoencoder (VAE). Through this clustering process, the poster images are categorized into positive, negative, and neutral classes. Experimental results demonstrate a silhouette coefficient surpassing 0.7, while the system framework exhibits clustering accuracy and efficiency exceeding 80% in single sentiment class testing. These outcomes underscore the efficacy of the proposed CNN-VAE-based clustering framework in analyzing the dynamic content of design elements. This framework presents a novel approach for future art design within the realm of visual communication.

Vision Based Deep Web data Extraction on Nested Query Result Records