ppxscal/embedding_network huggingface dataset:
SIP-Based User-Oriented Seamless Handoff Architecture and Control Schemes
Authors: Hsu-Yang Kung, Chi-Yu Hsu
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

This paper proposes a user-oriented handoff system architecture to solve the detection and collection of mobile user locations using Wireless Sensor Network (WSNs) and Location-aware technologies. The proposed User-Oriented Seamless Handoff (USH) system adopts two seamless handoff control schemes: Proactive Buffering (PB) as well as Double Queue (DQ), to minimize handoff delay and solve the packet out-oforder problem when the network traffic situation is not stable. SUH system effectively achieves seamless userlevel handoff and guarantees quality of communication during handoff.
Localized construction of low weighted structure and its applications in wireless ad hoc networks
Authors: Xiang-Yang Li
Venue: Wireless Networks (2005)

We consider a wireless network composed of a set of n wireless nodes distributed in a two dimensional plane. The signal sent by every node can be received by all nodes within its transmission range, which is uniform and normalized to one unit. We present the first distributed method to construct a bounded degree planar connected structure LRNG, whose total link length is within a constant factor of the minimum spanning tree1 using total O(n) messages under the broadcast communication model. Moreover, in our method, every node only uses its two-hop information to construct such structure, i.e., it is localized method. We show that some two-hop information is necessary to construct any low-weighted structure. We also study the application of this structure in efficient broadcasting in wireless ad hoc networks. We prove that, for broadcasting, the relative neighborhood graph (RNG), which is the previously best-known sparse structure that can be constructed locally, could use energy O(n) times the total energy used by our structure LRNG. Our simulations show that the broadcasting based on LRNG consumes energy about 36% more than that by MST, and broadcasting based on RNG consumes energy about 64% more than that by MST. We also show that no localized method can construct a structure for broadcasting with total power consumption asymptotically better than LRNG.
Inter-Model Consistency in UML Based on CPN Formalism
Authors: Yoshiyuki Shinkawa
Venue: Proceedings of the XIII Asia Pacific Software Engineering Conference (2006)

The Unified Modeling Language (UML) is one of the most widely used industry-standard modeling tools in object orientation. It provides us with variety of diagrams with rigorous syntax and semantics to build precise analysis models, design models, and implementation models for software development. Whereas this variety helps us to model the problem domain from various viewpoints, it makes it difficult to keep consistency between the models presented by different diagrams. This paper proposes a complementary modeling process to UML modeling, which makes it easy to keep consistency between heterogeneous UML models. The process is based on the use case driven approach and Colored Petri Net (CPN) formalism. The consistency is defined along the use case driven modeling process, using CPN formalism. The paper focuses on four major UML diagrams, among which the consistency is kept and assured. At the end of the process, we obtain four kinds of UML compatible CPN models, which can easily converted to the corresponding UML diagrams for further software development.
Hearing Two Things at Once: Neurophysiological Indices of Speech Segregation and Identification
Authors: Claude Alain, Karen Reinke, Yu He, Chenghua Wang, Nancy Lobaugh
Venue: Journal of Cognitive Neuroscience (2005)

The discrimination of concurrent sounds is paramount to speech perception. During social gatherings, listeners must extract information from a composite acoustic wave, which sums multiple individual voices that are simultaneously active. The observers' ability to identify two simultaneously presented vowels improves with increasing separation between the fundamental frequencies (f0) of the two vowels. Event-related potentials to stimuli presented during attend and ignore conditions revealed activity between 130 and 170 msec after sound onset that reflected the f0 differences between the two vowels. Another, more posterior and right-lateralized, negative wave maximal at 250 msec, and a central-parietal slow negativity were observed only during vowel identification and may index stimulus categorization. This sequence of neural events supports a multistage model of auditory scene analysis in which the spectral pattern of each vowel constituent is automatically extracted and then matched against representations of those vowels in working memory.
A new topology-based watermarking method for layered 3D triangular mesh models
Authors: Mahfuzur Rahman, Kazufumi Kaneda, Koichi Harada
Venue: Machine Graphics Vision International Journal (2005)

A new topology-based watermarking method is proposed to embed information in objects with layered 3D triangular meshes such as those reconstructed from CT or MRI data. The main idea of the method is to compare the heights of the vertices of a triangle lying in the same layer. A watermark message is converted into a binary bit sequence, and then embedded into the modelin such a way that the first vertex of a triangle in the upper level carries information 1, and the first vertex of a triangle in the lower level carries information 0. For experimental purposes, a watermark message is embedded in a mouse embryo model. It is robust against translation, rotation, re-sectioning, local deformation and scaling. It left some artifacts after re-arrangement of local or global numbering. It is useful for shape sensitive 3D geometric models.
Support Vector Machines Optimization - An Income Prediction Study
Authors: Alina Lazar, Robert Zaremba
Venue: Proceedings of the International Multi-Conference on Computing in the Global Information Technology (2006)

Relevant features selection through principal component analysis is employed to increase the efficiency of support vector machine (SVM) methods. In particular, a detailed study is presented on the effects of this statistical narrowing, when used to generate income prediction data based on the Current Population Survey provided by the U.S. Census Bureau. A systematic analysis of the grid parameter search, training time, accuracy, and number of support vectors shows increases not only in the efficiency of the SVM methods, but also in the classification accuracy. Proper identification of the relevant features for specific problems allows accuracy values as high as 93% against a test population, to be obtained, while reducing the total computational. Tailoring computational methods around specific real data sets is critical in designing powerful algorithms.
Joint resolution enhancement and artifact reduction for MPEG-2 encoded digital video
Authors: Yibin Yang, L. Boroczky
Venue: Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1 (2003)

A joint approach of resolution enhancement and artifact reduction for MPEG-2 encoded video is proposed in this paper. This new method uses a unified metric for digital video processing (UMDVP). The UMDVP is defined based on the coding information of MPEG-2 encoded video. It also takes the local scene content into account. Experimental results have demonstrated that the joint approach using the UMDVP outperforms the system of resolution enhancement and artifact reduction without the help of UMDVP. The proposed method can improve the performance of various emerging video systems using MPEG-2 compression.
Neural network flux optimization using a model of losses in induction motor drives
Authors: Bogdan Pryymak, Juan M. Moreno-Eguilaz, Juan Peracaula
Venue: Mathematics and Computers in Simulation (2006)

This paper focuses on loss minimization in induction motor (IM) drives. In many applications Induction Motor drives work below the nominal torque most of the time. In these circumstances the IM efficiency can be improved lowering the flux. For a given torque, this decreases iron looses and increases copper losses. With appropriate algorithms an optimum point for the flux can be achieved in order to minimize IM total power losses. Using an IM model, a neural network (NN) based approach is used to improve efficiency in a vector control of the induction motor drive. A complex loss model of the motor, including magnetic and thermal deviations of its parameters, is used to estimate losses. Based on this model, the neural network is trained to estimate the optimum rotor flux. Inputs to the NN are torque, speed and rotor resistance of the IM and the output is the rotor flux. Analysis, modeling and simulation results are presented to demonstrate the validity of the proposed method.
Design choices for learning-by-doing software: when to choose Advise
Authors: Michael Korcuska, Alex Kass, Menachem Y. Jona
Venue: Proceedings of the 1996 international conference on Learning sciences (1996)

While there is growing awareness of the value of developing computer-based learning-by-doing environments, there is still a need to analyze the strengths and weaknesses of particular architectures. This paper examines some of the common design issues faced when building learning-by-doing environments and analyzes the relationship between those design issues and the key features of learning architectures, especially an architecture we are currently developing called Advise. We also discuss the circumstances under which Advise is appropriate and when other architectures would be a better choice.
Modeling and simulation of universal digital AC bridge using ANN technique
Authors: S. Edward Rajan, P. S. Kannan, K. Muneeswaran, T. Revathi
Venue: International Journal of Modelling and Simulation (2006)

Bridges are the measuring devices used for accurate measurement of impedance. As such, there is no universal bridge circuit that measures impedance of all types of passive components. Moreover, the bridges available cannot be used for high-frequency impedance measurements. This paper proposes an automatic digital AC bridge model, which is considered to be universal and suitable for high-frequency impedance measurement up to 1 MHz. In this adaptive digital AC Bridge, balancing is achieved by means of Artificial Neural Network based on stochastic gradient search algorithm. Among the various gradient search techniques, the Widrow-Hoff's Least Mean Square (LMS) technique has been chosen as it involves less computational burden. The digital AC bridge operations have been verified by simulation. The simulated system was tested for measuring impedances of pure and impure inductors, capacitors, resistors, and a series combination of an inductance and a capacitance. The effects of various parameters like supply voltage, reference resistance, learning rate, and sampling rate were considered to investigate the performance of universal high-precision AC Bridge, and the results are reported. This model is advantageous over the traditional heuristic methods in terms of higher accuracy, faster convergence, and greater reproducibility and reliability.
Performance measures of a call admission control in mobile networks using SWN
Authors: Lynda Mokdad, Mbaye Sene
Venue: Proceedings of the 1st international conference on Performance evaluation methodolgies and tools (2006)

Call Admission Control (CAC) is an important mechanism in mobile networks. Many works have been done on GSM/GPRS and on UMTS system with mixed voice and data. However, CAC models and wireless communication proposed in the literature are represented with a uni-dimensional Markov chain, in general based on voice calls in order to reduce the state-space of the chain. Another drawback of those studies is lack of the synchronization between mobiles and servers. In this paper, we aim to evaluate the performance of a CAC scheme which takes into account voice connections, synchronous and asynchronous data connections. This scheme can be represented by a multi-dimensional Markov chain with a very large state-space. Thus, we propose to use Stochastic Well formed Petri Nets (SWN) to model the system interaction which consists of several mobiles, gateways, cells and servers. Performance measures are carried-out using symbolic simulation.
Semantic QoS Metric Matching
Authors: Kyriakos Kritikos, Dimitris Plexousakis
Venue: Proceedings of the European Conference on Web Services (2006)

As the Web Service paradigm gains popularity for its promise to transform the way business is conducted, the number of deployed Web Services grows with a fast rate. While sophisticated semantic discovery mechanisms have been invented to overcome the UDDI's syntactic discovery solution in order to provide more recallable and precise results, the amount of functionally equivalent Web Services returned is still large. The solution to this problem is the description of the QoS non-functional aspect of Web Services. QoS encompasses the performance of Web Services and can be used as a discriminator factor for refining Web Service advertisement result lists. However, most scientific efforts presented so far are purely syntactic and are not capturing all aspects of QoS-based Web Service description leading to imprecise syntactic discovery mechanisms. This paper presents a novel, rich and extensible ontology-based approach for describing QoS of Web Services that complements OWL-S. It is shown that, by using this approach and by introducing the concept of semantic QoS metric matching, QoS-based syntactic matchmaking and selection algorithms are transformed to semantic ones leading to better results.
Layering Model and Fault Diagnosis Algorithm for Internet Services
Authors: Xiaohui Huang, Shihong Zou, Wendong Wang, Shiduan Cheng
Venue: Proceedings of the International Multi-Conference on Computing in the Global Information Technology (2006)

Challenges of Internet service fault management are analyzed in this paper, and a layering model is recommended. Bipartite graph is chosen to be the fault propagation model (FPM) for each layer. A window-based fault diagnosis algorithm MAlg (Multi-window Algorithm) is proposed for the bipartite FPM. MAlg takes into account the correlation of adjacent time windows. As a result it can reduce the impact of improper time window setting. Simulation results prove the validity and efficiency of MAlg.
Delayed Response to Animate Implied Motion in Human Motion Processing Areas
Authors: Jeannette A. M. Lorteije, J. Leon Kenemans, Tjeerd Jellema, Rob H. J. Van Der Lubbe, Frederiek De Heer, Richard J. A. Van Wezel
Venue: Journal of Cognitive Neuroscience (2006)

Viewing static photographs of objects in motion evokes higher fMRI activation in the human medial temporal complex (MT+) than looking at similar photographs without this implied motion. As MT+ is traditionally thought to be involved in motion perception (and not in form perception), this finding suggests feedback from object-recognition areas onto MT+. To investigate this hypothesis, we recorded extracranial potentials evoked by the sight of photographs of biological agents with and without implied motion. The difference in potential between responses to pictures with and without implied motion was maximal between 260 and 400 msec after stimulus onset. Source analysis of this difference revealed one bilateral, symmetrical dipole pair in the occipital lobe. This area also showed a response to real motion, but approximately 100 msec earlier than the implied motion response. The longer latency of the implied motion response in comparison to the real motion response is consistent with a feedback projection onto MT+ following object recognition in higher-level temporal areas.
Total performance by local agent selection strategies in multi-agent systems
Authors: Toshiharu Sugawara, Satoshi Kurihara, Toshio Hirotsu, Kensuke Fukuda, Shinya Sato, Osamu Akashi
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

In order to achieve efficient progress in activities such as e-commerce and e-transactions in an open environment like the Internet, an agent must choose appropriate partner agents for collaboration. However, agents have no global information about the whole multi-agent system (MAS) and the state of the Internet; therefore, they must select the appropriate partners based on local knowledge and local observations. In this paper, using a multi-agent simulation, we discuss how total MAS performances are affected by local decisions when agents select partners to collaborate with. We also investigate how MAS performances change and how network structures between agents shift according to the progress of agents' local learning and observations. We then discuss the relationship between task load and agent network structure. This relates to establishing the optimum time when agents should learn about appropriate partners in an actual environment.
Quantization of Global Gene Expression Data
Authors: Tae-Hoon Chung, Marcel Brun, Seungchan Kim
Venue: Proceedings of the 5th International Conference on Machine Learning and Applications (2006)

Many researchers are investigating the possibility of utilizing global gene expression profile data as a platform to infer gene regulatory networks. However, heavy computational burden and measurement noises render these efforts difficult and approaches based on quantized levels are vigorously investigated as an alternative. Methods based on quantized values require a procedure to convert continuous expression values into discrete ones. Although there have been algorithms to quantize values into multiple discrete states, these algorithms assumed strict state mixtures (SSM) so that all expression profiles were divided into pre-specified number of states. We propose two novel quantization algorithms (QAs), model-based quantization algorithm and model-free quantization algorithm, that generalize SSM algorithms in two major aspects. First, our QAs assume the maximum number of expression states (Es) be arbitrary. Second, expression profiles can exhibit any combinations of Es possible states. In this paper, we compare the performances between SSM algorithms and QAs using simulation studies as well as applications to actual data and show that quantizing gene expression data using adaptive algorithms is an effective way to reduce data complexity without sacrificing much of essential information.
Brain Activation during Face Perception: Evidence of a Developmental Change
Authors: E. H. Aylward, J. E. Park, K. M. Field, A. C. Parsons, T. L. Richards, S. C. Cramer, A. N. Meltzoff
Venue: Journal of Cognitive Neuroscience (2005)

Behavioral studies suggest that children under age 10 process faces using a piecemeal strategy based on individual distinctive facial features, whereas older children use a configural strategy based on the spatial relations among the face's features. The purpose of this study was to determine whether activation of the fusiform gyrus, which is involved in face processing in adults, is greater during face processing in older children (12&ndash;14 years) than in younger children (8&ndash;10 years). Functional MRI scans were obtained while children viewed faces and houses. A developmental change was observed: Older children, but not younger children, showed significantly more activation in bilateral fusiform gyri for faces than for houses. Activation in the fusiform gyrus correlated significantly with age and with a behavioral measure of configural face processing. Regions believed to be involved in processing basic facial features were activated in both younger and older children. Some evidence was also observed for greater activation for houses versus faces for the older children than for the younger children, suggesting that processing of these two stimulus types becomes more differentiated as children age. The current results provide biological insight into changes in visual processing of faces that occur with normal development.
A Distributed Fuzzy Qualitative Evaluation System
Authors: K. K. F. Yuen, H. C. W. Lau
Venue: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology (2006)

In an enterprise, decision makers always make decisions under the environment of uncertainty, vagueness, fuzziness and incomplete information. Evaluation activities such as rating and surveying play an important role for the enterprise decision making. In order to enhance the evaluation result accuracy and evaluation process efficiency, this paper presents a Distributed Fuzzy Qualitative Evaluation System (DFQES) to be applied in complex distributed evaluation scenarios. DFQES applies our developed fuzzy algorithms which are translated into objects distributed to remote agents to solve the similar problems using the agent technology on the basis of Web Services-based Service Oriented Architecture (WS-SOA). In addition, we also present the approach modeling the fuzzy system as an intelligent agent for further development. DFQES can be a generic framework for designing and implementing the enterprise qualitative evaluation applications such as customer satisfaction evaluations, employee satisfaction surveys, vendor surveys, and risk assessments.
Template Based Chinese News Event Summarization
Authors: Ying Han, Fang Li, KeBin Liu, Lei Liu
Venue: Proceedings of the Second International Conference on Semantics, Knowledge, and Grid (2006)

Summarization can facilitate users to acquire large amount of information. This paper introduces a Chinese news events summarization system based on a predefined template. The system automatically collects query-related news online, applies Language model to detect event related information, calculates word frequencies based on semantic meaning and uses information fusion techniques to merge the results. After experiments on 50 events, the system can achieve the average of 82.1% precision based on human judgment.
Development of a Semantic Awareness Framework for Textual Content Management in e-Learning
Authors: Ming-Yen Chen, Ming-Fen Yang, Yuh-Ming Chen, Hui-Chuan Chu
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

This study proposes a semantic-awareness content management model, based on which a semantic-based environment that features a high interoperability of e-Learning content can be constructed, to realize the goals and values of e-Learning by effectively bridging semantic gaps in existing LCMSs.
Off-road Path Following using Region Classification and Geometric Projection Constraints
Authors: Yaniv Alon, Andras Ferencz, Amnon Shashua
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

We describe a realtime system for finding and tracking unstructured paths in off-road conditions. The system was designed as part of the recent Darpa Grand Challenge and was tested over hundreds of miles of off-road driving. The unique feature of our approach is to combine geometric projection used for recovering Pitch and Yaw with Learning approaches for identifying familiar "drivable" regions in the scene. The region-based component segments the image to "path" and "non-path" regions based on texture analysis borne out of a learning-by-examples principle. The boundary-based component looks for the path bounding lines assuming a geometric model of a planar pathway bounded by parallel edges taken by a perspective camera. The combined effect of both sub-systems forms a robust system capable of finding the path even in situations where the vehicle is positioned out of the path - a situation which is not common for human drivers but is relevant for autonomous driving where the vehicle may find itself occasionally veering out of the path.