ppxscal/embedding_network huggingface dataset:
Navigation and interaction in physical spaces using RFID enabled spatial sensing
Authors: Muhammad Atif Mehmood, Lars Kulik, Egemen Tanin
Venue: Proceedings of the 5th international conference on Embedded networked sensor systems (2007)

In this demonstration we show how RFID technology can be used for sensing spatial information in indoor environments. In particular, we demonstrate how this sensed information can be used for navigation and interaction within an environment.
Conditional independence and chain event graphs
Authors: Jim Q. Smith, Paul E. Anderson
Venue: Artificial Intelligence (2008)

Graphs provide an excellent framework for interrogating symmetric models of measurement random variables and discovering their implied conditional independence structure. However, it is not unusual for a model to be specified from a description of how a process unfolds (i.e. via its event tree), rather than through relationships between a given set of measurements. Here we introduce a new mixed graphical structure called the chain event graph that is a function of this event tree and a set of elicited equivalence relationships. This graph is more expressive and flexible than either the Bayesian network-equivalent in the symmetric case-or the probability decision graph. Various separation theorems are proved for the chain event graph. These enable implied conditional independencies to be read from the graph's topology. We also show how the topology can be exploited to tease out the interesting conditional independence structure of functions of random variables associated with the underlying event tree.
Wavelet Energy Entropy as a New Feature Extractor for Face Recognition
Authors: Cunjian Chen, Jiashu Zhang
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

In face recognition, it is important how to select the invariant facial features especially faces with various pose and expression changes. This paper presents wavelet energy entropy as new facial features to recognize faces under various pose and expression changes. Preliminary experiment results on ORL, YALE face databases with different pose and expression changes indicate that the proposed wavelet energy entropy feature face recognition method is fast and effective compared to other tradition algorithms.
BSS transition optimizations and analysis for VoIP over WLAN
Authors: Emily Qi, Sangeetha Bangolae, Kapil Sood, Jesse Walker
Venue: Wireless Personal Communications: An International Journal (2007)

Voice over IP (VoIP) is emerging as a critical application for IEEE 802.11 Wireless Local Area Networks (WLANs). However, the limited range of the IEEE 802.11 radio forces frequent transitions from one access point to another. Moreover, the introduction of IEEE 802.11i security and IEEE 802.11e Quality of Service (QoS) has increased the average transition time from ms to seconds, leaving mobile users with the unenviable dilemma of choosing good security and QoS while sacrificing real-time performance, or gaining acceptable real-time performance at the expense of security and QoS. Thus, optimizations to device transitioning that will provide an acceptable balance of latency, security, and QoS are needed to enable VoIP. This paper analyzes WLANs MAC layer transition procedure and optimizations being considered by IEEE 802.11 Working Group to improve transition times while retaining good security and QoS. The transition time improvements proposed in this paper are achieved through discovery phase optimizations and transition phase optimizations. The selective scanning and smart AP selection algorithms are designed to optimize the discovery phase to enable the mobile device to better exploit its ambient radio resource environment. The transition phase optimization seeks to accelerate device transition without compromising security. The paper then identifies security flaws in the current design and proposes simple corrections. Finally, experiment results for transition optimization are explained that demonstrate a significant increase in transition efficiency.
An ANFIS-based Transformer Insulation Fault Diagnosis Method Using Emotional Learning
Authors: Hongsheng Su
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

To tackle the flaws in transformer fault diagnosis such as long computing time, weak generalized ability and fuzzy knowledge acquisition difficulty, a self-adaptive neuro-fuzzy inference system (ANFIS) is proposed based on emotional learning in this paper. The method can automatically adapt itself to the change of input information characteristics, and compensate for the flaws of the imperfectness of the 3-ratio-code. In addition, Due to applying emotional learning, the structure complexity and learning time of the networks are dramatically reduced, and the forecast accuracy is also improved. Finally, a practical example in transformer fault diagnosis indicates the availability of the method.
Ensembles of Region Based Classifiers
Authors: Sungha Choi, Byungwoo Lee, Jihoon Yang
Venue: Proceedings of the 7th IEEE International Conference on Computer and Information Technology (2007)

In machine learning, ensemble classifiers have been introduced for more accurate pattern classification than single classifiers. We propose a new ensemble learning method that employs a set of region based classifiers. Since the distribution of data can be different in different regions in the feature space, we split the data and generate classifiers based on each region and apply a weighted voting among the classifiers. We used 11 data sets from the UCI Machine Learning Repository to compare the performance of our new ensemble method with that of individual classifiers as well as other ensemble methods such as bagging and boosting. As a result, we found that our method improve performance, particularly when the base learner is Naïve Bayes or SVM.
Cathedral-II: A Silicon Compiler for Digital Signal Processing
Authors: H. Man, J. Rabaey, P. Six, L. Claesen
Venue: IEEE Design Test (1986)

The article describes the status of work at IMEC on the Cathedral-II silicon compiler. The compiler was developed to synthesizesynchronous multiprocessor system chips for digital signal processing. It is a continuation of work on the Cathedral-I operationalsilicon compiler for bit-serial digital filters. Cathedral-II is based on a ?meet in the middle? design method that encouragesa total separation between system design and reusable silicon design. The CAD system includes a rule-based synthesis program,a procedural program, and a controller synthesis environment. Processors are synthesized in terms of modules called from automatedreusable module generators. Chip layout is done on a floor planner. An expert subsystem verifies correctness during silicondesign and generates functional and timing models for verification at the module and chip levels.
Dynamic Knowledge Inference and Learning of Fuzzy Petri Net Expert System Based on Self-Adaptation Learning Techniques
Authors: Zipeng Zhang, Shuqing Wang, Suyi Liu
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01 (2007)

It is rather limited for fuzzy production rules to describe the vague and modified knowledge of expert system, an automatic fuzzy reasoning and learning framework based on fuzzy Petri net are presented for design a dynamic expert knowledge system in this paper. Fuzzy Petri net may describe the relative degree of each proposition in the antecedent contributing to the consequent accurately. In order to reason and learn expediently, FPN without loop is transformed into hierarchy model and continuous functions to approximate transition firing and fuzzy reasoning. The self-adaptation learning techniques based on back-propagation are used to learn and train parameters of fuzzy production rules of FPN. Simulation experiment shows that the improved adaptive learning techniques can make rule parameters obtain optimal or at least nearly optimal convergence rapidly. Key words: Expert system, fuzzy Petri net, dynamic fuzzy reasoning, fuzzy production rules, neural network, self- adaptation learning
Sampling from the Multivariate Gaussian Distribution using Reconfigurable Hardware
Authors: David B. Thomas, Wayne Luk
Venue: Proceedings of the 15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (2007)

The multivariate Gaussian distribution models random processes as vectors of Gaussian samples with a fixed correlation matrix. Such distributions are useful for modelling real-world multivariate time-series such as equity returns, where the returns for businesses in the same sector are likely to be correlated. Generating random samples from such a distribution presents a computational challenge due to the dense matrix-vector multiplication needed to introduce the required correlations. This paper proposes a hardware architecture for generating random vectors, utilising the embedded block RAMs and multipliers found in contemporary FPGAs. The approach generates a new n dimensional random vector every n clock cycles, and has a raw generation rate over 200 times that of a single Opteron 2.2GHz using an optimised BLAS package for linear algebra computation. The generation architecture is an ideal source for both software simulations connected via high bandwidth connection, and for completely FPGA based simulations. Practical performance is explored in a case study in Delta-Gamma Value-at-Risk, where a standalone Virtex-4 xc4vsx55 solution at 400 MHz is 33 times faster than a quad Opteron 2.2GHz SMP. The FPGA solution also scales well for larger problem sizes, allowing larger portfolios to be simulation.
Research on Translation-Invariant Wavelet Transform for Classification in Mammograms
Authors: Lei Zhang, Xieping Gao
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

Classification of benign and malignant microcalcifications in mammograms through Computer-aided Diagnosis (CADx) is vital for the early diagnosis of the breast cancer. To this end, wavelet-based textural feature has been proved to be an effective feature extraction method. However, a majority of these methods is restricted to decimated wavelet transform, which lacks the property of translation invariance that is useful in signal processing. In this paper, we apply the translation-invariant (TI) wavelet transform to microcalcifications classification. A set of features, combining the TI wavelet based features and co-occurrence features, is employed to get better classification results than the conventional methods. The area under ROC curve ranged from 0.87 to 0.91 when using the proposed method. Experimental results show that the TI -wavelet method outperforms the one based on multiwavelet, which achieved the best results in 2004 on the same database as ours.
MBMS Handover control: A new approach for efficient handover in MBMS enabled 3G cellular networks
Authors: Andreas Pitsillides, Christophoros Christophorou
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2007)

With the introduction of Multimedia Broadcast Multicast Service (MBMS) system in 3rd Generation (3G) mobile cellular networks, the Radio Network Controller (RNC) for radio resource efficiency can use either Point-to-Point (one Dedicated Channel per User Equipment (UE) in the Cell) or Point-to-Multipoint (one Common Channel shared by all the UEs in the Cell) transmission mode, to distribute the same MBMS content in a Cell. Thus, the mobile users that are on the move and receive an MBMS service may have to deal with dynamic changes of network resources when crossing the Cell's edge, introducing new types of handovers (MBMS Handovers); from Point-to-Point to Point-to-Multipoint transmission mode Cell and vice versa. Executing an MBMS Handover using the existing handover control approach, as described in 3GPP TR 25.922 specifications, results in inefficiencies due to the approach used. In this paper, we highlight these inefficiencies and propose and evaluate a new handover control approach to address them. As illustrated in our performance evaluation, compared to the existing approach, the proposed approach improves the overall system capacity and link performance and avoids any QoS degradation (seamless handover) during an MBMS Handover.
Application semantics in query optimization for WSNs
Authors: Egemen Tanin, Songting Chen, Junichi Tatemura, Wang-Pin Hsiung
Venue: Proceedings of the 5th international conference on Embedded networked sensor systems (2007)

Efficient data acquisition in WSNs has attracted significant interest. For example, TinyDB [2] introduced query dissemination and data aggregation trees. Later, a probabilistic model of the physical world is used in [1]. Recently, [3] argues that probabilistic models of the physical world used in acquisition may miss outliers and introduces spatio-temporal suppression-based methods. We classify these established approaches as query-and-data centric approaches for optimizing the data acquisition process.
A Generalization of Eklundh's Algorithm for Transposing Large Matrices
Authors: H. K. Ramapriyan
Venue: IEEE Transactions on Computers (1975)

The purpose of this paper is to present an algorithm for transposing large rectangular matrices. This is basically a generalization of Eklundh's algorithm. Eklundh's method is designed to handle arrays in place of using powers-of-2 approach wherein partitions of the matrix have dimensions which are powers of 2. The algorithm presented here does not have this restriction. The choice of partitions is posed as an optimal control problem amenable to solution using Bellman's principle of optimality. The optimal partitioning of the matrix can be determined depending on the computer configuration (i.e., core size, whether moving-head or fixed-head disk drives are used, number of words per track, etc.) and the matrix dimensions. Simple modifications are noted for transposing complex/double precision and packed integer matrices. Experimental results are presented comparing the times needed for transposition using optimal partitioning and powers-of-2 partitioning. It is found in the cases tested that a speed gain of up to 5.55 is obtained by optimizing the partition sizes.
Multiple-Valued Logic: An Introduction and Overview
Authors: Z. G. Vranesic
Venue: IEEE Transactions on Computers (1977)

MULTIPLE-VALUED logic has been a subject of research work for many years [1], [2]. Until the late 1960's most of the reported work was of a theoretical nature, leaving little impact on the firmly established binary approaches in the design of digital systems. Then, as LSI technology proceeded to develop rapidly, it became apparent that multiple-valued logic offers not only a rich logic structure, but also a considerable potential for application in physical systems [2], [3].
Pentacene thin-film transistors with sol-gel derived amorphous Ba0.6Sr0.4TiO3 gate dielectric
Authors: Wei Wang, Guifang Dong, Liduo Wang, Yong Qiu
Venue: Microelectronic Engineering (2008)

An amorphous Ba"0"."6Sr"0"."4TiO"3 (BST) film with the thickness of 200nm was deposited on indium-tin-oxide (ITO)-coated glass substrate through sol-gel route and post-annealing at 500^oC. The dielectric constant of the BST film was determined to be 20.6 at 100kHz by measuring the Ag/BST/ITO parallel plate capacitor, and no dielectric tunability was observed with the bias voltage varying from -5 to 5V. The BST film shows a dense and uniform microstructure as well as a smooth surface with the root-mean-square (RMS) roughness of about 1.4nm. The leakage current density was found to be 3.5x10^-^8A/cm^2 at an applied voltage of -5V. The transmittance of the BST/ITO/glass structure is more than 70% in the visible region. Pentacene based transistor using the as-prepared BST film as gate insulator exhibits a low threshold voltage of -1.3V, the saturation field-effect mobility of 0.68cm^2/Vs, and the current on/off ratio of 3.6x10^5. The results indicate that the sol-gel derived BST film is a promising high-k gate dielectric for large-area transparent organic transistor arrays on glass substrate.
Reasoning within the Extended Fuzzy Description Logics with Restricted Terminological Boxes
Authors: Dazhou Kang, Baowen Xu, Yanhui Li
Venue: Proceedings of the International Conference on Semantic Computing (2007)

The extended fuzzy description logics extend the classical description logics by introducing cut concepts and cut roles to support expressive representation and reasoning for fuzzy knowledge. But the current extended fuzzy description logics lack reasoning algorithms with TBoxes. The paper proposes the sat-domain problem of the extended fuzzy description logic EFALCR+ with restricted TBoxes, and gives an optimized reasoning algorithm, which is proved to be sound, complete and with a worst complexity of EXPTime; then proves the complexity of the problem is EXPTimecomplete.
Supporting frequent updates in R-trees: a bottom-up approach
Authors: Mong Li Lee, Wynne Hsu, Christian S. Jensen, Bin Cui, Keng Lik Teo
Venue: Proceedings of the 29th international conference on Very large data bases - Volume 29 (2003)

Advances in hardware-related technologies promise to enable new data management applications that monitor continuous processes. In these applications, enormous amounts of state samples are obtained via sensors and are streamed to a database. Further, updates are very frequent and may exhibit locality. While the R-tree is the index of choice for multi-dimensional data with low dimensionality, and is thus relevant to these applications, R-tree updates are also relatively inefficient. We present a bottom-up update strategy for R-trees that generalizes existing update techniques and aims to improve update performance. It has different levels of reorganization--ranging from global to local--during updates, avoiding expensive top-down updates. A compact main-memory summary structure that allows direct access to the R-tree index nodes is used together with efficient bottom-up algorithms. Empirical studies indicate that the bottom-up strategy outperforms the traditional top-down technique, leads to indices with better query performance, achieves higher throughput, and is scalable.
A social network-based system for supporting interactive collaboration in knowledge sharing over peer-to-peer network
Authors: Stephen J. H. Yang, Irene Y. L. Chen
Venue: International Journal of Human-Computer Studies (2008)

Knowledge sharing enables people in virtual communities to access relevant knowledge (explicit or tacit) from broader scope of resources. The performance in such environments is fundamentally based on how effectively the explicit and tacit knowledge can be shared across people, and how efficiently the created knowledge can be organized and disseminated to enrich digital content. This study will address how to apply social network-based system to support interactive collaboration in knowledge sharing over peer-to-peer networks. Results of this study demonstrate that applying such social network-based collaboration support to knowledge sharing helps people find relevant content and knowledgeable collaborators who are willing to share their knowledge.
Special Feature Interactive Compuer Graphics: Poised for Takeoff?
Authors: W. Myers
Venue: Computer (1978)

After 15 years of development, much promise, but limited use, is interactive computer graphics finally due for a sharp growth spurt? If the mood projected at the Fourth Annual Conference on Computer Graphics and Interactive Techniques held in San Jose recently is any indication, the answer would seem to be yes. On the hardware side, costs are coming down and capability is going up, reflecting trends that have characterized the computer industry in recent years. On the software side, although programming for computer-aided design tends to be specialized and hence not broadly available, many standardized display packages are on the market.1 In applications, there is activity in more than 25 areas, ranging from stereotaxic surgery to landfills after strip mining. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Forecasting GDP Growth Using Genetic Programming
Authors: Meifang Li, Guoxin Liu, Yongxiang Zhao
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 04 (2007)

Monetary policy affects the economy with long and variable lags, and for this reason policy-makers require reliable forecasts of economic activity. Hence, Forecasts of real GDP growth have become more and more necessary. Recently, Haiming Guo (2006) proposed a new modified ARIMA model and used it to forecast the GDP growth of China from 1978 to 2004. Their experimental data show that the modified ARIMA model could provide more accurate forecasts than conventional ARIMA. However, all these models are linear. In this paper, we propose a new genetic programming method to forecast the GDP time series of China, United States and Japan from 1980 to 2006. Experimental results show that genetic programming yield statistically lower forecast errors for the year-over- year GDP data relative to modified linear ARIMA models. Moreover, we use the proposed method to forecast the future GDP growth of China, United States and Japan from 2007 to 2020, and we surprisingly find that the GDP of Japan fluctuates periodically, however the GDP of China and United States increases stably in the near future. According to the predicted data we can see that the GDP of China will exceed the GDP of Japan for the first time in 2020 or so.
Object ownership profiling: a technique for finding and fixing memory leaks
Authors: Derek Rayside, Lucy Mendel
Venue: Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering (2007)

We introduce object ownership profiling, a technique forfinding and fixing memory leaks in object-oriented programs. Object ownership profiling is the first memory profiling technique that reports both a hierarchy of allocated objects along with size and time information aggregated up that hierarchy. In addition, it reveals the cross-hierarchy interactions that areessential to pinpointing the source of the leak. We identify five memory management anti-patterns, including two that involve rich heap structure and object interactions, and are novel contributions of this work. We apply object ownership profiling to find and fix memory leaks in the Alloy IDE v3 that users had complained about for years, and that had eluded detection by code reviews and type-based commercial memory profilers.