ppxscal/embedding_network huggingface dataset:
Computationally efficient techniques for data-driven haptic rendering
Authors: Raphael Hover, Massimiliano Di Luca, Gabor Szekely, Matthias Harders
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

Data-driven haptic rendering requires processing of raw recorded signals, which leads to high computational effort for large datasets. To achieve real-time performance, one possibility is to reduce the parameter space of the employed interpolation technique, which generally decreases the accuracy in the rendering. In this paper, we propose a method for guiding this parameter reduction to maintain high accuracy with respect to the just noticeable difference for forces. To this end, we performed a user study to estimate this perception threshold. The threshold is used to assess the final error in the rendered forces as well as for the parameter reduction process. Comparison with measured data from real object interactions confirms the accuracy of our method and highlights the reduced computational effort.
Forward transmission over a multipath faded channel in a DS CDMA system
Authors: Abdel-Rahman Al-Qawasmi, Omar Al-Ayasrah, Ayman Al-Lawama
Venue: WSEAS Transactions on Circuits and Systems (2009)

The pseudorandom spreading sequence in the forward transmission in the IS -95 system is assigned only to individual base stations. To separate users, the system assigns orthogonal (Hadamard) sequences to each of them. In this paper, a different scheme for the forward transmission is considered. In this scheme pseudorandom sequences are used, instead of employing orthogonal spreading sequence for individual users. The effect of fade amplitudes, phase shifts, time -mismatching and AWGN on the detection process of Gold Sequence Spread Spectrum Signal (GSSSS) is considered.
Analysis of a DAR IMPATT diode for high frequency part of millimetric region
Authors: Alexander Zemliak, Santiago Cabrera
Venue: Proceedings of the 8th WSEAS international conference on Microelectronics, nanoelectronics, optoelectronics (2009)

The analysis and optimization of the n+pvnp+ avalanche diode structure that includes two avalanche regions have been realized on basis of the nonlinear model and special optimization procedure. The admittance characteristics of the DAR diode were analyzed in very wide frequency band from 30 up to 360 GHz and had been optimized for the third frequency band near the 300 GHz.
Use of Cluster OpenMP with the Gaussian Quantum Chemistry Code: A Preliminary Performance Analysis
Authors: Rui Yang, Jie Cai, Alistair P. Rendell, V. Ganesh
Venue: Proceedings of the 5th International Workshop on OpenMP: Evolving OpenMP in an Age of Extreme Parallelism (2009)

The Intel Cluster OpenMP (CLOMP) compiler and associated runtime environment offer the potential to run OpenMP applications over a few nodes of a cluster. This paper reports on our efforts to use CLOMP with the Gaussian quantum chemistry code. Sample results on a four node quad core Intel cluster show reasonable speedups. In some cases it is found preferable to use multiple nodes compared to using multiple cores within a single node. The performances of the different benchmarks are analyzed in terms of page faults and by using a critical path analysis technique.
Flexible decision tree for data stream classification in the presence of concept change, noise and missing values
Authors: Sattar Hashemi, Ying Yang
Venue: Data Mining and Knowledge Discovery (2009)

In recent years, classification learning for data streams has become an important and active research topic. A major challenge posed by data streams is that their underlying concepts can change over time, which requires current classifiers to be revised accordingly and timely. To detect concept change, a common methodology is to observe the online classification accuracy. If accuracy drops below some threshold value, a concept change is deemed to have taken place. An implicit assumption behind this methodology is that any drop in classification accuracy can be interpreted as a symptom of concept change. Unfortunately however, this assumption is often violated in the real world where data streams carry noise that can also introduce a significant reduction in classification accuracy. To compound this problem, traditional noise cleansing methods are incompetent for data streams. Those methods normally need to scan data multiple times whereas learning for data streams can only afford one-pass scan because of data's high speed and huge volume. Another open problem in data stream classification is how to deal with missing values. When new instances containing missing values arrive, how a learning model classifies them and how the learning model updates itself according to them is an issue whose solution is far from being explored. To solve these problems, this paper proposes a novel classification algorithm, flexible decision tree (FlexDT), which extends fuzzy logic to data stream classification. The advantages are three-fold. First, FlexDT offers a flexible structure to effectively and efficiently handle concept change. Second, FlexDT is robust to noise. Hence it can prevent noise from interfering with classification accuracy, and accuracy drop can be safely attributed to concept change. Third, it deals with missing values in an elegant way. Extensive evaluations are conducted to compare FlexDT with representative existing data stream classification algorithms using a large suite of data streams and various statistical tests. Experimental results suggest that FlexDT offers a significant benefit to data stream classification in real-world scenarios where concept change, noise and missing values coexist.
Incorporating assessment in a pattern-based design process for CSCL scripts
Authors: Eloy D. Villasclaras-Fernández, Davinia Hernández-Leo, Juan I. Asensio-Pérez, Yannis Dimitriadis
Venue: Computers in Human Behavior (2009)

Scripting has already been shown to be a way of increasing the chances of effective collaborative learning, and especially when computer support is available. Formal specifications, such as IMS-LD, and software authoring tools have provided instructional designers and educational practitioners with mechanisms for the creation and automation of flexible collaborative learning scripts ready to run in real scenarios. Within the context of Computer Supported Collaborative Learning (CSCL) script design, the application of design patterns has been proposed as the basis for a design process that promotes the reuse of good practices, implemented in an IMS-LD authoring tool, Collage. However, it is well known that assessment is a key aspect in real learning scenarios, and therefore it should be included in CSCL scripts. In this sense, this paper reports data and findings derived from two case studies performed to explore how assessment design can be included in the aforementioned pattern-based process. Results provide preliminary evidence that additional support is necessary for assessment design within this process for non-expert users. In order to tackle this limitation, the use of assessment patterns is analyzed. Evidence gathered in the case studies supports the need, adequacy and feasibility of a more systematic assessment-aware design process for CSCL scripts.
Generalizations of E-convex and B-vex functions
Authors: Yu-Ru Syau, Lixing Jia, E. Stanley Lee
Venue: Computers Mathematics with Applications (2009)

A class of functions called E-B-vex functions is defined as a generalization of E-convex and B-vex functions. Similarly, a class of E-B-preinvex functions, which are generalizations of E-convex and B-preinvex functions, is introduced. In addition, the concept of B-linear functions is also generalized to E-B-linear functions. Some properties of these proposed classes are studied. Furthermore, the equivalence between the class of E-B-vex functions and that of E-quasiconvex functions is proved.
Lossless Quantum Data Compression and Secure Direct Communication- New concepts and methods for quantum information theory
Authors: Kim Joris Bostrm
Venue:  (2007)

Quantum mechanics has radically changed our classical conception of information and communication. The elementary unit of information, the "bit", is replaced by the "qubit" which is a superposition of zero and one. Several qubits can be entangled among each other so that each qubit alone is neither zero nor one, nor any superposition of it. If qubits are used to encode messages then these "quantum messages" do not behave like ordinary ones. They cannot be compressed without loss of information, and the process of reading disturbs their content. Such apparent weaknesses, however, give rise to a revolutionary concept of secure communication... This book reviews the basic concepts of quantum information, develops a theoretical framework for variable-length quantum messages, derives some theorems, and introduces a novel secure communication scheme, the so-called "Ping-Pong protocol", which has attracted some interest in the scientific community and has recently been experimentally realized. The reader should be roughly familiar with probability theory, linear algebra and basic quantum mechanics. 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
Farming Human Pathogens: Ecological Resilience and Evolutionary Process, 1st edition
Authors: Rodrick Wallace, Deborah Wallace, Robert G Wallace
Venue:  (2009)

Farming Human Pathogens: Ecological Resilience and Evolutionary Process introduces a cutting-edge mathematical formalism based on the asymptotic limit theorems of information theory to describe how punctuated shifts in mesoscale ecosystems can entrain patterns of gene expression and organismal evolution. The authors apply the new formalism toward characterizing a number of infectious diseases that have evolved in response to the world as humans have made it. Many of the human pathogens that are emerging out from underneath epidemiological control are 'farmed' in the metaphorical sense, as the evolution of drug-resistant HIV makes clear, but also quite literally, as demonstrated by avian influenza's emergence from poultry farms in southern China. The most successful pathogens appear able to integrate selection pressures humans have imposed upon them from a variety of socioecological scales. The book also presents a related treatment of Eigen's Paradox and the RNA 'error catastrophe' that bedevils models of the origins of viruses and of biological life itself.
A Heuristic-Based Approach for a Betting Strategy in Texas Hold'em Poker
Authors: Teemu Saukonoja, Tomi A. Pasanen
Venue: Proceeding of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008 (2008)

Artificial intelligence research in Texas Hold'em poker has recently mainly focused on heads-up fixed-limit games. Game theoretic methods used in the poker agents capable of playing at the very best level are not easily generalized to other forms of Texas Hold'em poker. In this paper we present a general heuristic-based approach to build a poker agent, where the betting strategy is defined by estimating the expected values of the actions. The approach is well suited for a larger number of players and it is also easily modified to suite no limit games.
An interview with John H. Holland
Authors: Lashon B. Booker
Venue: ACM SIGEVOlution (2008)

John Holland began his academic career as a graduate student at the University of Michigan studying with Arthur Burks, a designer of early programmable computers and colleague of John von Neumann. Under Burks' supervision, in 1959 Holland received what may have been the first Ph.D. in the world in the emerging field of computer science. He was subsequently hired as one of the first professors in the new department of Computer and Communication Science at the University of Michigan. As the inventor of genetic algorithms, John established himself as one of the early pioneers in understanding the foundations of adaptation, learning, and modeling in both natural and artificial systems. His extensions of the genetic algorithm, first to a cognitive architecture called classifier systems and later to an ecological architecture known as Echo, have increased the reach of his ideas well beyond computer science. John Holland's imaginative, interdisciplinary approach to thinking about adaptation, emergence and complexity has made him one of the world's acknowledged leaders in the field of complex adaptive systems. In addition to his many intellectual achievements, John Holland has also been a valued mentor and friend for those of us who have had the privilege to be his students. Holland is currently a Professor of Psychology and Professor of Electrical Engineering & Computer Science at the University of Michigan. He is also a member of the Board of Trustees and an External Professor at the Santa Fe Institute. Holland was made a MacArthur Fellow in 1992 and is a Fellow of the World Economic Forum.
Leakage-aware energy synchronization for wireless sensor networks
Authors: Ting Zhu, Ziguo Zhong, Yu Gu, Tian He, Zhi-Li Zhang
Venue: Proceedings of the 7th international conference on Mobile systems, applications, and services (2009)

To ensure sustainable operations of wireless sensor systems, environmental energy harvesting has been regarded as the right solution for long-term applications. In energy-dynamic environments, energy conservation is no longer considered necessarily beneficial, because energy storage units (e.g., batteries or capacitors) are limited in capacity and leakage-prone. In contrast to legacy energy conservation approaches, we aim at energy synchronization for wireless sensor devices. The starting point of this work is TwinStar, which uses ultra-capacitor as the only energy storage unit. To efficiently use the harvested energy, we design and implement leakage-aware feedback control techniques to match local and network-wide activity of sensor nodes with the dynamic energy supply from environments. We conduct system evaluation under three typical real-world settings - indoor, outdoor, and mobile backpack under a wide range of system settings. Results indicate our leakage-aware control can effectively utilize energy that could otherwise leak away. Nodes running leakage-aware control can enjoy 70% more energy than the ones running non-leakage-aware control and application performance (e.g., event detection) can be improved significantly.
AI-SIMCOG: a simulator for spiking neurons and multiple animats&#x2019; behaviours
Authors: Andr&#x00e9; Cyr, Mounir Boukadoum, Pierre Poirier
Venue: Neural Computing and Applications (2009)

Designing a biologically inspired neural architecture as a controller for a complete animat or physical robot environment, to test the hypotheses on intelligence or cognition is non-trivial, particularly, if the controller is a network of spiking neurons. As a result, simulators that integrate spike coding and artificial or real-world platforms are scarce. In this paper, we present artificial intelligence simulator of cognition, a software simulator designed to explore the computational power of pulsed coding at the level of small cognitive systems. Our focus is on convivial graphical user interface, real-time operation and multilevel Hebbian synaptic adaptation, accomplished through a set of non-linear dynamic weights and on-line, life-long modulation. Inclusions of transducer and hormone components, intrinsic oscillator and several learning functions in a discrete spiking neural algorithm are distinctive features of the software. Additional features are the easy link between the production of specific neural architectures and an artificial 2D-world simulator, where one or more animats implement an input&#x2013;output transfer function in real-time, as do robots in the real world. As a result, the simulator code is exportable to a robot&#x2019;s microprocessor. This realistic neural model is thus amenable to investigate several time related cognitive problems.
Probabilistic dyadic data analysis with local and global consistency
Authors: Deng Cai, Xuanhui Wang, Xiaofei He
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Dyadic data arises in many real world applications such as social network analysis and information retrieval. In order to discover the underlying or hidden structure in the dyadic data, many topic modeling techniques were proposed. The typical algorithms include Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). The probability density functions obtained by both of these two algorithms are supported on the Euclidean space. However, many previous studies have shown naturally occurring data may reside on or close to an underlying submanifold. We introduce a probabilistic framework for modeling both the topical and geometrical structure of the dyadic data that explicitly takes into account the local manifold structure. Specifically, the local manifold structure is modeled by a graph. The graph Laplacian, analogous to the Laplace-Beltrami operator on manifolds, is applied to smooth the probability density functions. As a result, the obtained probabilistic distributions are concentrated around the data manifold. Experimental results on real data sets demonstrate the effectiveness of the proposed approach.
Cognitive Context and Arguments from Ontologies for Learning
Authors: Christiana Panayiotou, Brandon Bennett
Venue: Proceeding of the 2008 conference on Formal Ontology in Information Systems: Proceedings of the Fifth International Conference (FOIS 2008) (2008)

The deployment of learning resources on the web by different experts has resulted in the accessibility of multiple viewpoints about the same topics. In this work we assume that learning resources are underpinned by ontologies. Different formalizations of domains may result from different contexts, different use of terminology, incomplete knowledge or conflicting knowledge. We define the notion of cognitive learning context which describes the cognitive context of an agent who refers to multiple and possibly inconsistent ontologies to determine the truth of a proposition. In particular we describe the cognitive states of ambiguity and inconsistency resulting from incomplete and conflicting ontologies respectively. Conflicts between ontologies can be identified through the derivation of conflicting arguments about a particular point of view. Arguments can be used to detect inconsistencies between ontologies. They can also be used in a dialogue between a human learner and a software tutor in order to enable the learner to justify her views and detect inconsistencies between her beliefs and the tutor's own. Two types of arguments are discussed, namely: arguments inferred directly from taxonomic relations between concepts, and arguments about the necessary and jointly sufficient features that define concepts.
Computer daylight simulations in buildings
Authors: Hasim Altan, Ian Ward, Frantisek Vajkay, Jitka Mohelnikova
Venue: Proceedings of the 7th WSEAS international conference on System science and simulation in engineering (2008)

Newly designed buildings should be optimized with respect to sufficient indoor illuminance and solar protection against glare effect and indoor overheating. The optimized building design must be based on solutions of many design alternatives and case studies, as well as necessary assessments should be carried out in order to find optimum solutions which can be completed via the help of computer simulation programmes. Some of the simulation programmes could be helpful for modelling of indoor daylighting in rooms and spaces with various types of daylight systems and their positioning in buildings. The paper focuses on a series of computer daylight simulations completed for the selected room with four different types of window openings specified for optimizing the building design for daylighting.
Robust fitting of implicitly defined surfaces using&#x00a0;Gauss&#x2013;Newton-type techniques
Authors: Martin Aigner, Bert J&#x00fc;ttler
Venue: The Visual Computer: International Journal of Computer Graphics (2009)

We describe Gauss&#x2013;Newton-type methods for fitting implicitly defined curves and surfaces to given unorganized data points. The methods are suitable not only for least-squares approximation, but they can also deal with general error functions, such as approximations to the &#x2113; 1 or &#x2113; &#x221e; norm of the vector of residuals. Two different definitions of the residuals will be discussed, which lead to two different classes of methods: direct methods and data-based ones. In addition we discuss the continuous versions of the methods, which furnish geometric interpretations as evolution processes. It is shown that the data-based methods&#x2014;which are less costly, as they work without the computation of the closest points&#x2014;can efficiently deal with error functions that are adapted to noisy and uncertain data. In addition, we observe that the interpretation as evolution process allows to deal with the issues of regularization and with additional constraints.
Transition dependency: a gene-gene interactionmeasure for times seriesmicroarray data
Authors: Xin Gao, Daniel Q. Pu, Peter X.-K. Song
Venue: EURASIP Journal on Bioinformatics and Systems Biology (2009)

Gene-Gene dependency plays a very important role in system biology as it pertains to the crucial understanding of different biological mechanisms. Time-course microarray data provides a new platform useful to reveal the dynamic mechanism of gene-gene dependencies. Existing interaction measures are mostly based on association measures, such as Pearson or Spearman correlations. However, it is well known that such interaction measures can only capture linear or monotonic dependency relationships but not for nonlinear combinatorial dependency relationships. With the invocation of hidden Markov models, we propose a new measure of pairwise dependency based on transition probabilities. The new dynamic interaction measure checks whether or not the joint transition kernel of the bivariate state variables is the product of two marginal transition kernels. This new measure enables us not only to evaluate the strength, but also to infer the details of gene dependencies. It reveals nonlinear combinatorial dependency structure in two aspects: between two genes and across adjacent time points. We conduct a bootstrap-based χ2 test for presence/absence of the dependency between every pair of genes. Simulation studies and real biological data analysis demonstrate the application of the proposed method. The software package is available under request.