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304 ENTERPRISE INFORMATION SYSTEMS VI to time evolution. Detection of the position and shape of the mouth, eyes, particularly eyelids, wrinkles and extraction of features related to them are the targets of techniques applied to still images of humans. It has, however, been shown (Bassili, 1979), that facial expressions can be more accurately recognized from image sequences, than from a single still image. His experiments used point-light conditions, i.e. subjects viewed image sequences in which only white dots on a darkened surface of the face were visible. Expressions were recognized at above chance levels when based on image sequences, whereas only happiness and sadness were recognized at above chance levels when based on still images. Techniques which attempt to identify facial gestures for emotional expression characterization face the problems of locating or extracting the facial regions or features, computing the spatio-temporal motion of the face through optical flow estimation, and introducing geometric or physical muscle models describing the facial structure or gestures. In general, facial expressions and emotions are described by a set of measurements and transformations that can be considered atomic with respect to the MPEG-4 standard; in this way, one can describe both the anatomy of a human face –basically through FDPs, as well as animation parameters, with groups of distinct tokens, eliminating the need for specifying the topology of the underlying geometry. These tokens can then be mapped to automatically detected measurements and indications of motion on a video sequence and, thus, help to approximate a real expression conveyed by the subject by means of a synthetic one. 5 GESTURES AND POSTURES The detection and interpretation of hand gestures has become an important part of human computer interaction (MMI) in recent years (Wu & Huang, 2001). Sometimes, a simple hand action, such as placing one’s hands over their ears, can pass on the message that he has had enough of what he is hearing; this is conveyed more expressively than with any other spoken phrase. To benefit from the use of gestures in MMI it is necessary to provide the means by which they can be interpreted by computers. The MMI interpretation of gestures requires that dynamic and/or static configurations of the human hand, arm, and even other parts of the human body, be measurable by the machine. First attempts to address this problem resulted in mechanical devices that directly measure hand and/or arm joint angles and spatial position. The so-called glove-based devices best represent this solutions’ group. Human hand motion is highly articulate, because the hand consists of many connected parts that lead to complex kinematics. At the same time, hand motion is also highly constrained, which makes it difficult to model. Usually, the hand can be modeled in several aspects such as shape (Kuch & Huang, 1995), kinematical structure (Lin, Wu & Huang, 200), dynamics (Quek, 1996), (Wilson & Bobick, 1998) and semantics. Gesture analysis research follows two different approaches that work in parallel. The first approach treats a hand gesture as a two- or three dimensional signal that is communicated via hand movement from the part of the user; as a result, the whole analysis process merely tries to locate and track that movement, so as to recreate it on an avatar or translate it to specific, predefined input interface, e.g. raising hands to draw attention or indicate presence in a virtual classroom. The low level results of the approach can be extended, taking into account that hand gestures are a powerful expressive means. The expected result is to understand gestural interaction as a higher-level feature and encapsulate it into an original modal, complementing speech and image analysis in an affective MMI system (Wexelblat, 1995). This transformation of a gesture from a time-varying signal into a symbolic level helps overcome problems such as the proliferation of available gesture representations or failure to notice common features in them. In general, one can classify hand movements with respect to their function as: �� Semiotic: these gestures are used to communicate meaningful information or indications �� Ergotic: manipulative gestures that are usually associated with a particular instrument or job and �� Epistemic: again related to specific objects, but also to the reception of tactile feedback. Semiotic hand gestures are considered to be connected, or even complementary, to speech in order to convey a concept or emotion. Especially two major subcategories, namely deictic gestures and beats, i.e. gestures that consist of two discrete phases, are usually semantically related to the spoken content and used to emphasize or clarify it. This relation is also taken into account in (Kendon, 1988) and provides a positioning of gestures along a continuous space.
6 FROM FEATURES TO SYMBOLS 6.1 Face In order to estimate the users' emotional state in a MMI context, we must first describe the six archetypal expressions (joy, sadness, anger, fear, disgust, surprise) in a symbolic manner, using easily and robustly estimated tokens. FAPs and BAPs or BBA representations make good candidates for describing quantitative facial and hand motion features. The use of these parameters serves several purposes such as compatibility of created synthetic sequences with the MPEG-4 standard and increase of the range of the described emotions – archetypal expressions occur rather infrequently and in most cases emotions are expressed through variation of a few discrete facial features related with particular FAPs. Based on elements from psychological studies (Ekman, 1993), (Parke, 1996), (Faigin, 1990), we have described the six archetypal expressions using MPEG-4 FAPs, which is illustrated in Table 1. In general, these expressions can be uniformly recognized across cultures and are therefore invaluable in trying to analyze the users' emotional state. Table 1: FAPs vocabulary for archetypal expression description Joy open_jaw(F3), lower_t_midlip(F4), raise_b_midlip(F5), stretch_l_cornerlip(F6), stretch_r_cornerlip(F7), raise_l_cornerlip(F12), raise_r_cornerlip(F13),close_t_l_eyelid(F19), close_t_r_eyelid(F20), close_b_l_eyelid(F21), close_b_r_eyelid(F22), raise_l_m_eyebrow (F33), raise_r_m_eyebrow(F34), lift_l_cheek (F41), lift_r_cheek(F42), stretch_l_cornerlip_o (F53), stretch_r_cornerlip_o(F54) Sadness close_t_l_eyelid(F19), close_t_r_eyelid(F20), close_b_l_eyelid(F21),close_b_r_eyelid(F22), raise_l_i_eyebrow(F31), raise_r_i_eyebrow (F32), raise_l_m_eyebrow(F33), raise_r_m_eyebrow(F34), raise_l_o_eyebrow (F35), raise_r_o_eyebrow(F36) Anger lower_t_midlip(F4), raise_b_midlip(F5), push_b_lip(F16), depress_chin(F18), close_t_l_eyelid(F19), close_t_r_eyelid(F20), close_b_l_eyelid(F21),close_b_r_eyelid(F22), raise_l_i_eyebrow(F31), raise_r_i_eyebrow (F32), raise_l_m_eyebrow(F33), raise_r_m_eyebrow(F34),raise_l_o_eyebrow (F35), raise_r_o_eyebrow(F36), squeeze_l_eyebrow(F37), squeeze_r_eyebrow (F38) EMOTION SYNTHESIS IN VIRTUAL ENVIRONMENTS Fear 305 open_jaw(F3), lower_t_midlip(F4), raise_b_midlip(F5), lower_t_lip_lm(F8), lower_t_lip_rm(F9), raise_b_lip_lm (F10), raise_b_lip_rm(F11), close_t_l_eyelid (F19), close_t_r_eyelid(F20), close_b_l_eyelid (F21), close_b_r_eyelid(F22), raise_l_i_eyebrow (F31), raise_r_i_eyebrow(F32), raise_l_m_eyebrow(F33), raise_r_m_eyebrow (F34), raise_l_o_eyebrow(F35), raise_r_o_eyebrow (F36), squeeze_l_eyebrow (F37), squeeze_r_eyebrow (F38) Disgust open_jaw (F3), lower_t_midlip (F4), raise_b_midlip (F5), lower_t_lip_lm (F8), lower_t_lip_rm (F9), raise_b_lip_lm (F10), raise_b_lip_rm (F11), close_t_l_eyelid (F19), close_t_r_eyelid (F20), close_b_l_eyelid (F21), close_b_r_eyelid(F22), raise_l_m_eyebrow (F33), raise_r_m_eyebrow(F34), lower_t_lip_lm_o (F55), lower_t_lip_rm_o (F56), raise_b_lip_lm_o (F57), raise_b_lip_rm_o (F58), raise_l_cornerlip_o (F59), raise_r_cornerlip_o (F60) Surprise open_jaw (F3), raise_b_midlip (F5), stretch_l_cornerlip (F6) , stretch_r_cornerlip (F7), raise_b_lip_lm(F10),raise_b_lip_rm(F11), close_t_l_eyelid (F19), close_t_r_eyelid (F20), close_b_l_eyelid (F21), close_b_r_eyelid (F22), raise_l_i_eyebrow(F31), raise_r_i_eyebrow (F32), raise_l_m_eyebrow (F33), raise_r_m_eyebrow (F34), raise_l_o_eyebrow (F35), raise_r_o_eyebrow (F36), squeeze_l_eyebrow (F37), squeeze_r_eyebrow (F38), stretch_l_cornerlip_o (F53), stretch_r_cornerlip_o (F54) Although FAPs provide all the necessary elements for MPEG-4 compatible animation, we cannot use them for the analysis of expressions from video scenes, due to the absence of a clear quantitative definition. In order to measure FAPs in real image sequences, we define a mapping between them and the movement of specific FDP feature points (FPs), which correspond to salient points on the human face. This quantitative description of FAPs provides the means of bridging the gap between expression analysis and synthesis. In the expression analysis case, the non-additive property of the FAPs can be addressed by a fuzzy rule system. Quantitative modeling of FAPs is implemented using the features labeled as fi (i=1..15) in Table 2 (Karpouzis, Tsapatsoulis & Kollias, 2000). The feature set employs feature points that lie in the facial area and, in the controlled environment of MMI applications, can be automatically detected and tracked. It consists of distances, noted as s(x,y), where x and y correspond to Feature Points (Tekalp & Ostermann, 2000), between these protuberant points, some of which are constant during expressions and are used as reference points; distances between these reference points are used for normali
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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The major limit A of the operation
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RELATIONAL SAMPLING FOR DATA QUALIT
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TOWARDS DESIGN RATIONALES OF SOFTWA
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((Brown et al., 1998), pp. 159-190,
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sive data filtering, presentation (
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• implementation changes in servi
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MEMORY MANAGEMENT FOR LARGE SCALE D
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Data Rate [bytes/sec] 1600000 14000
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E.g., DV Camcorder Camera Packets (
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Procedure CheckOptimal (n rs 1 ...n
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MEMORY MANAGEMENT FOR LARGE SCALE D
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PART 2 Artificial Intelligence and
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110 ENTERPRISE INFORMATION SYSTEMS
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DYNAMIC MULTI-AGENT BASED VARIETY F
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AN EXPERIENCE IN MANAGEMENT OF IMPR
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ANALYSIS AND RE-ENGINEERING OF WEB
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Module p0 Customer Itinerary Send I
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departments: the marketing dept. se
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• An acyclic module M is usable,
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BALANCING STAKEHOLDER’S PREFERENC
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The scenarios will provide an abstr
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BALANCING STAKEHOLDER'S PREFERENCES
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BALANCING STAKEHOLDER'S PREFERENCES
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A POLYMORPHIC CONTEXT FRAME TO SUPP
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A POLYMORPHIC CONTE XT FRAME TO SUP
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FEATURE MATCHING IN MODEL-BASED SOF
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combination of solution domains - a
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• features for all the concepts:
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Existence In both steps it is possi
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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elementary message (ae-message) can
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problems on a pragmatic level, whic
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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INTRUSION DETECTION SYSTEMS USING A
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(k� 1) (k) (k�1) (k) p � w
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Table 5: Performance Comparison of
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A USER-CENTERED METHODOLOGY TO GENE
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carries out the second phase of the
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1 1 1 1 2 PROCESS STORE ENTITY EDGE
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constraints rules. KOGGE (Ebert et
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PART 4 Software Agents and Internet
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