Semantic Discriminant mapping for classification and browsing of remote sensing textures and objects

We present a new approach based on Discriminant Analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1. each dimension corresponds to a semantic likelihood, 2. an efficient and simple multiclass classifier is proposed and 3. it is low dimensional. This mapping is learnt from a given set of labeled images with a class groundtruth. In the new space a classifier is naturally derived which performs as well as a linear SVM. We will show that projecting images in this new space provides a database browsing tool which is meaningful to the user. Results are presented on a remote sensing database with eight classes, made available online. The output semantic space is a low dimensional feature space which opens perspectives for other recognition tasks. © 2005 IEEE.

Initializing Vision Based Trackers Using Semantic Texton Forests

Vision based tracking can provide the spatial location of project related entities such as equipment, workers, and materials in a large-scale congested construction site. It tracks entities in a video stream by inferring their motion. To initiate the process, it is required to determine the pixel areas of the entities to be tracked in the following consecutive video frames. For the purpose of fully automating the process, this paper presents an automated way of initializing trackers using Semantic Texton Forests (STFs) method. STFs method performs simultaneously the segmentation of the image and the classification of the segments based on the low-level semantic information and the context information. In this paper, STFs method is tested in the case of wheel loaders recognition. In the experiments, wheel loaders are further divided into several parts such as wheels and body parts to help learn the context information. The results show 79% accuracy of recognizing the pixel areas of the wheel loader. These results signify that STFs method has the potential to automate the initialization process of vision based tracking.

Semantic transform: Weakly supervised semantic inference for relating visual attributes

Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets. © 2013 IEEE.

semantic oriented ontology cohesion metrics for ontology-based systems

Ontologies play a core role to provide shared knowledge models to semantic-driven applications targeted by Semantic Web. Ontology metrics become an important area because they can help ontology engineers to assess ontology and better control project management and development of ontology based systems, and therefore reduce the risk of project failures. In this paper, we propose a set of ontology cohesion metrics which focuses on measuring (possibly inconsistent) ontologies in the context of dynamic and changing Web. They are: Number of Ontology Partitions (NOP), Number of Minimally Inconsistent Subsets (NMIS) and Average Value of Axiom Inconsistencies (AVAI). These ontology metrics are used to measure ontological semantics rather than ontological structure. They are theoretically validated for ensuring their theoretical soundness, and further empirically validated by a standard test set of debugging ontologies. The related algorithms to compute these ontology metrics also are discussed. These metrics proposed in this paper can be used as a very useful complementarity of existing ontology cohesion metrics.

应用解析水平理论考察时间距离对决策的影响

Construal Level Theory is a kind of social cognitive theory, with the main conclusion of that the more time distance is, the more people use high level construal and the more high level construal affect in judging, decision making and such of these cognitive processes, and the low level construal has the mirror process as well. In these ten years, construal level theory is developing very fast and some of the researches of consumer behavior has concerned this developed trend very much. In 2007’s Journal of Consumer psychology, half of the papers focused on construal topics and ask for more researches about these topics. So our research mainly focused on such topics, the application of construal level theory in consumer behavior research. The first part is contained 3 experiments which mean to discuss the questions from reviewing the prior literatures. The research methods, manipulated definition, construal level factors VS construal level events are the goal of first part of this study. The results showed that construal level’s effect might be a consecutive variable and has a consecutive trend when time goes by. Time distance might not be a semantic cognitive effect to construal level but a internal ones for when there’s no verbal information of time distance the results would have the same one as well. The second part of the study is focus on a simplified model of decision-making, which contained two times decision-making, to detect whether and how would the prior decision making affect the next decision making. The study conducted the time distance in first decision making with the situation for the decision and conducted the second time distance in the time distance between the two decision situations. Although we couldn’t get the detail of the weight changes in construal level factors limited by our research, we can have a logical hypothesis based on “one-down- the other-up”. The results showed that the level of construal factors can affect the memory and the more the factors affected, the bigger the error would be. The first time distance and second time distance could effect the second decision in a interaction-way, and support the hypothesis. The whole research is mean to explore the application of construal level theory in consumer behavior research. The conclusion suggests that the periods of products and the consuming may need different marketing strategies in a long-term perspective and time distance should be control are detected in marketing analysis even there’s no time information on the paper.

聋童与正常儿童中文阅读信息加工过程的比较研究

Based upon the experiments showing three traits of Chinese characters as stimulus, the comparison has been made between the hearing-impaired and normal children in RT functions for the judgment involved in the verbal recording. The results show that the function of semantic code is more strongly correlated with recognition of Chinese character than the phonetic code and graphic code for both the hearing-impaired and normal children. For hearing-impaired children, a direct access that a semantic code is produced directly from the graphic code is observed. However, for normal children the strategly of grapheme-phoneme conversion rules to interpret the meaning of words is used. It's shown that no significant differences of mental processing has been found between the hearing-impaired and normal children. Perhaps both the direct access and the grapheme-phoneme conversion form a comprehensive information procession activating each other. In reading Chinese character, the hearing-impaired and normal children is significantly effected by stroke under and character frequency. It is found that the normal children have more depth processing in reading Chinese sentences than the hearing-impaired children.

记忆年老化与焦虑

It is well established that memory functioning deteriorates with advancing age. However, research indicates that the magnitude of age-related memory deficits varies across different types of memory, and broad individual differences can be observed in the rate and timing of memory aging. The general aim of this study was to investigate the selectivity and variability of memory functioning in relation to anxiety. Firstly, memory effectiveness was assessed in episodic memory tasks with reality monitoring and external source monitoring paradigms, semantic memory tasks referred to general knowledge and word fluency, and perceptual priming task reflected in word completion. According to the scores on trait version of STAI, the high-trait and low-trait anxious subjects were screened respectively from young and old participants matched for educational level. Secondly, based on the results of the first part, concurrent primary and secondary tasks with probe technique assessing spare processing capacity were used to explore the relation between memory efficiency and anxiety. The first main findings were that: (a) there were no age-related differences in semantic memory assessed by general knowledge and PRS, whereas age effects were observed in episodic memory and semantic memory assessed by word fluency with stringent time restraints. (b) Furthermore, comparison of age-related deficits in source and item was not related to the presentation ways and encoding effort for source, but was affected by types of source. Specifically, memory was more sensitive to aging than item memory in external source monitoring processes involved in discriminating two external sources (i.e., female vs. male voices), but not in reality monitoring processes in discriminating between internal and external sources (i.e., acting vs. listening). The second main findings were that: (a) Anxiety had no effects on the effectiveness and efficiency of semantic memory in recall of general knowledge and PRS, but impaired those of semantic memory in word fluency. (b) The effects of anxiety on episodic memory were different between the old and the young. Both the effectiveness and the efficiency of episodic memory of the old were affected adversely by anxiety. More importantly, source recall in external source monitoring processes was observed to be more vulnerable to anxiety than item memory. The effectiveness of episodic memory of the young was relatively unrelated to anxiety, while anxiety might have adverse effect on their memory efficiency. These results indicated that: First, the selectivity of age-related memory deficits existed not only between memory systems, but also within episodic memory system. The tendency to forget the source even when the fact was retained in external source monitoring was suggested to be a specific feature of cognitive aging. Second, anxiety had adverse impact on the individual differences in memory aging, and mediated partial age-related differences in episodic memory performance.

SIR: A Computer Program for Semantic Information Retrieval

SIR is a computer system, programmed in the LISP language, which accepts information and answers questions expressed in a restricted form of English. This system demonstrates what can reasonably be called an ability to "understand" semantic information. SIR's semantic and deductive ability is based on the construction of an internal model, which uses word associations and property lists, for the relational information normally conveyed in conversational statements. A format-matching procedure extracts semantic content from English sentences. If an input sentence is declarative, the system adds appropriate information to the model. If an input sentence is a question, the system searches the model until it either finds the answer or determines why it cannot find the answer. In all cases SIR reports its conclusions. The system has some capacity to recognize exceptions to general rules, resolve certain semantic ambiguities, and modify its model structure in order to save computer memory space. Judging from its conversational ability, SIR, is a first step toward intelligent man-machine communication. The author proposes a next step by describing how to construct a more general system which is less complex and yet more powerful than SIR. This proposed system contains a generalized version of the SIR model, a formal logical system called SIR1, and a computer program for testing the truth of SIR1 statements with respect to the generalized model by using partial proof procedures in the predicate calculus. The thesis also describes the formal properties of SIR1 and how they relate to the logical structure of SIR.

Generating Semantic Descriptions From Drawings of Scenes With Shadows

The research reported here concerns the principles used to automatically generate three-dimensional representations from line drawings of scenes. The computer programs involved look at scenes which consist of polyhedra and which may contain shadows and various kinds of coincidentally aligned scene features. Each generated description includes information about edge shape (convex, concave, occluding, shadow, etc.), about the type of illumination for each region (illuminated, projected shadow, or oriented away from the light source), and about the spacial orientation of regions. The methods used are based on the labeling schemes of Huffman and Clowes; this research provides a considerable extension to their work and also gives theoretical explanations to the heuristic scene analysis work of Guzman, Winston, and others.

Comparing Student Software Designs Using Semantic Categorization

Eckerdal, A. McCartney, R. Mostr?m, J.E. Ratcliffe, M. Zander, C. Comparing Student Software Designs Using Semantic Categorization. Proceedings of the Fifth Finnish/Baltic Sea Conference on Computer Science Education, 2005