ppxscal/embedding_network huggingface dataset:
I-Ex: Intelligent Extreme Expedition Support
Authors: Stephen Potter, Austin Tate, Rob Milne
Venue: Proceeding of the 2006 conference on Rob Milne: A Tribute to a Pioneering AI Scientist, Entrepreneur and Mountaineer (2006)

The aim of the I-X research programme is to provide a general framework for performing mixed-initiative synthesis tasks, along with a set of tools that supports its use. This framework arises from and builds upon seminal work at the University of Edinburgh in the field of Artificial Intelligence planning. In this paper we describe the framework and tools, before describing the application of I-X to the task of planning and coordinating expeditions to remote locations --such as an attempt on Everest. We call this application I-Ex.
A novel framework for optimised privacy preserving data mining using the innovative desultory technique
Authors: J. Indumathi, G. V. Uma
Venue: International Journal of Computer Applications in Technology (2009)

Computing is an ogle spectator witnessing afloat similar to a tornado. The chic data analysis and mining techniques compromise privacy and their exploitation have reached the pinnacle, demanding our attention. Amongst the available diverse techniques available for privacy preservation, the existing techniques involve only individual preservations. This work proposes novel three-tier architecture. The subsequent coalescing of three best secure techniques endows us with a three-fold privacy preservation, namely access control limitation technique, randomisation and Privacy Preserving Clustering (PPC). Thus, our framework gives an efficient control system comprising authentication, authorisation and access for each database application; efficiency and economical benefits of randomisation and advantages of PPC. We have further proposed a method for computing the object-based dissimilarity for secure computation for all different attribute types. PPC uses a combination of CLARANS with Simulating Annealing in order to achieve increased privacy, efficient scaling and improvised performance.
Retardation of electromagnetic waves in multi-walled carbon nanotubes: the role of quantum recoil
Authors: Konstantin Batrakov, Polina Kuzhir, Sergey Maksimenko
Venue: Proceedings of the 1st WSEAS international conference on Nanotechnology (2009)

The dispersion of surface plasmons in multi-walled carbon nanotubes is theoretically studied. The possibility of strong retardation of electromagnetic wave is demonstrated exceeding the retardation in single-walled CNTs. The quantum field consideration using the creation and annihilation operators for electron is used for the derivation of the surface plasmon dispersion equation. The analysis performed demonstrate a high potentiality of multi-walled CNTs for the nano-scale realization of the traveling wave tube.
Adobe Camera Raw for Digital Photographers Only, 2nd edition
Authors: Rob Sheppard
Venue:  (2008)

Expert photographer Rob Sheppard explains the details of Camera Raw, the steps for using it, the workflow process, and certain best practices that demonstrates how Camera Raw can empower the digital photographer. Encouraging you to use it as you see fit, he explores the enhancements in the newest generation and helps you deal with RAW's limitations, manage white balance and exposure, reduce noise (especially in night shots,) and learn to use camera settings that make the most of RAW capabilities. 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
Stream warehousing with DataDepot
Authors: Lukasz Golab, Theodore Johnson, J. Spencer Seidel, Vladislav Shkapenyuk
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

We describe DataDepot, a tool for generating warehouses from streaming data feeds, such as network-traffic traces, router alerts, financial tickers, transaction logs, and so on. DataDepot is a streaming data warehouse designed to automate the ingestion of streaming data from a wide variety of sources and to maintain complex materialized views over these sources. As a streaming warehouse, DataDepot is similar to Data Stream Management Systems (DSMSs) with its emphasis on temporal data, best-effort consistency, and real-time response. However, as a data warehouse, DataDepot is designed to store tens to hundreds of terabytes of historical data, allow time windows measured in years or decades, and allow both real-time queries on recent data and deep analyses on historical data. In this paper we discuss the DataDepot architecture, with an emphasis on several of its novel and critical features. DataDepot is currently being used for five very large warehousing projects within AT&T; one of these warehouses ingests 500 Mbytes per minute (and is growing). We use these installations to illustrate streaming warehouse use and behavior, and design choices made in developing DataDepot. We conclude with a discussion of DataDepot applications and the efficacy of some optimizations.
Learner attending auto-monitor in distance learning using image recognition and Bayesian Networks
Authors: Kuo-An Hwang, Chia-Hao Yang
Venue: Expert Systems with Applications: An International Journal (2009)

Distance learning is one of the common education methods. Its advantage lies in that the student can learn at anytime or anyplace. However, such a learning mode relies highly on the dependence of the student. Under different environments and conditions, not all the students can be attentive. In this research, an auto-detection system has been designed, using image processing and recognition technique, for defining the facial expressions and behavior easily found when a learner is inattentive or in bad mentality under distance learning environment. From the learner's facial expressions and behavior features, the attentiveness of the student during distance learning can be determined by Bayesian Networks Model. After implementing the system of this research, and performing practical test, it is found that the accuracy of identifying the features of bad mentality and inattentive behavior is high. From Bayesian Networks assessment and inference, the learning attentiveness of the student can be determined precisely to have the teacher control the learning condition of the student explicitly.
Lectures in Supercomputational Neuroscience: Dynamics in Complex Brain Networks, 1st edition
Authors: Peter beim Graben, Changsong Zhou, Marco Thiel, Jurgen Kurths
Venue: Understanding Complex Systems (2007)

Computational Neurosciences is a burgeoning field of research where only the combined effort of neuroscientists, biologists, psychologists, physicists, mathematicians, computer scientists, engineers and other specialists, e.g. from linguistics and medicine, seem to be able to expand the limits of our knowledge. The present volume is an introduction, largely from the physicists' perspective, to the subject matter with in-depth contributions by system neuroscientists. A conceptual model for complex networks of neurons is introduced that incorporates many important features of the real brain, such as various types of neurons, various brain areas, inhibitory and excitatory coupling and the plasticity of the network. The computational implementation on supercomputers, which is introduced and discussed in detail in this book, will enable the readers to modify and adapt the algortihm for their own research. Worked-out examples of applications are presented for networks of Morris-Lecar neurons to model the cortical connections of a cat's brain, supported with data from experimental studies. This book is particularly suited for graduate students and nonspecialists from related fields with a general science background, looking for a substantial but hands-on introduction to the subject matter.
Access network selection for coexisted WWAN, WMAN and WLAN using combined fuzzy logic and AHP
Authors: M. M. Alkhawlani, A. Ayesh
Venue: International Journal of Innovative Computing and Applications (2008)

Access network selection (ANS) is the radio resource mechanism that is responsible for selecting the most optimal and promising access network (AN) for a new service request in the next generation wireless network (NGWN). Selecting the most optimal and promising AN is an important consideration for overall networks stability, resource utilisation, user satisfaction, and quality of service (QoS) provisioning. Choosing a non optimal network can result in problems such as the use of expensive access types or poor service experience. This paper presents and designs a multi criteria ANS algorithm that considers NGWN environment with a co-existed WWAN, WMAN, and WLAN. A combination of parallel fuzzy logic and analytic hierarchy process (AHP) has been used to achieve scalable, flexible, simple, general, and adaptable solution. The simulation results show that the proposed algorithm has better and more robust performance over the random based, the terminal speed based, and the service type based selection algorithms that are used as reference algorithms.
Compositions inside a rectangle and unimodality
Authors: Bruce E. Sagan
Venue: Journal of Algebraic Combinatorics: An International Journal (2009)

Let c k,l (n) be the number of compositions (ordered partitions) of the integer n whose Ferrers diagram fits inside a k×l rectangle. The purpose of this note is to give a simple, algebraic proof of a conjecture of Vatter that the sequence c k,l (0),c k,l (1),¿,c k,l (kl) is unimodal. The problem of giving a combinatorial proof of this fact is discussed, but is still open.
Complexity Properties of Restricted Abstract Argument Systems
Authors: Paul E. Dunne
Venue: Proceeding of the 2006 conference on Computational Models of Argument: Proceedings of COMMA 2006 (2006)

One difficulty that arises in abstract argument systems is that many natural questions regarding argument acceptability are, in general, computationally intractable having been classified as complete for classes such as NP, CO-NP, and Πp2. In consequence, a number of researchers have considered methods for specialising the structure of such systems so as to identify classes for which efficient decision processes exist. In this paper the effect of a number of graph-theoretic restrictions is considered. For the class of bipartite graphs, it is shown that determining the acceptability status of a specific argument can be accomplished in polynomial time under both credulous and sceptical semantics. In contrast to these positive results, however, deciding whether an arbitrary set of arguments is “collectively acceptable” remains NP--complete in bipartite systems. In addition, a construction is presented by means of which questions posed of arguments in any given finite argument system may be expressed as questions within a related system in which every argument attacks and is attacked by at most two arguments. It follows that bounding the number of attacks on individual arguments is unlikely to produce a computationally more tractable environment.
Co-design of sensor networking technology and privacy policy
Authors: Stephen B. Wicker
Venue:  (2008)

Sensor networks are infrastructure-less, possibly multi-hop wireless networks where every mote (networked device) can be either a host or a router, forwarding packets to other motes in the network. These networks usually consist of numerous sensor-equipped motes which are used for gathering data related to physical phenomena. Sensor networks are becoming widely integrated into the critical, physical as well as personal infrastructures. In this dissertation, we address the problem of enhancing privacy protections for individuals exposed to these networks. In this dissertation, we define the human value of privacy and describe its importance in the sensor networking domain. Next, we describe sensor network policies and protocols to preserve network stakeholder privacy. The goal of privacy is the broken into two preliminary objectives: the design of a robust sensor network trust protocol and the design and analysis of network confidentiality-preserving policies and protocols. We first present what problems could arise if these two objectives are not met and then discuss solutions. We discuss privacy problems associated with mote-class, active, insider attackers; the lack of content confidentiality; and the lack of transactional confidentiality in sensor networks. We then examine the fundamental design issues involved with creating robust sensor network trust protocols, describe and analyze a new protocol that we created, and analyze equilibrium-induced best strategies in a game theoretic model of such types of protocols. We show how trust protocols mitigate the problems created by insider attackers. Next, we examine the design of solutions to enhance content confidentiality in the context of high-fidelity camera sensor networks. We also discuss transactional confidentiality and the complexity of designing solutions to protect the transactions in a sensor network. We conclude using a case study involving our deployment of a sensor network testbed in a public space. We elaborate on our testbed architecture design and our privacy considerations.
Advances in Information Processing and Protection, 1st edition
Authors: Jerzy Pejas, Jerzy Pejas, Khalid Saeed
Venue:  (2007)

This book contains a selection of the best papers given at the Advanced Computer Systems Conference in October 2006 in Poland. The book is partitioned into four topical areas: The Artificial Intelligence section includes papers on Humatronics, Fuzzy Logic, Robotics, Cell, Genetic and Probabilistic Algorithms, Incomplete Data and Similarity Relations, and Fast Neural Networks. The Computer Security and Safety section covers a wide range of issues, from Error Detection in Block Ciphers to Visual Cryptography Methods. The Image Analysis, Graphics and Biometrics section deals with the extraction of meaningful information from images as well as methods for uniquely recognizing humans based upon intrinsic physical or behavioral traits. The Computer Simulation and Data Analysis section concerns computer programs that attempt to simulate an abstract model of a particular system and transform data with the aim of extracting useful information and facilitating conclusions.
Learning with Computer Modeling Software in a High School Chemistry Class: A Study of Conceptual Growth and Transfer
Authors: HyungShin Kim
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

This study was designed to investigate the effects of computer modeling software on how twenty Chicago high school students learned the physics concept of pressure. Hypermedia scaffolding was specially designed into the modeling system in order to help students explore models in the system. The results show that exploring computer models had a positive effect on students' conceptual understanding of scientific phenomenon of pressure.
Automatic constraint based test generation for behavioral HDL models
Authors: Siva Kumar Sastry Hari, Vishnu Vardhan Reddy Konda, V. Kamakoti, Vivekananda M. Vedula, Kailasnath S. Maneperambil
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2008)

With the emergence of complex high-performance microprocessors, functional test generation has become a crucial design step. Constraint-based test generation is a well-studied directed behavioral level functional test generation paradigm. The paradigm involves conversion of a given circuit model into a set of constraints and employing constraint solvers to generate tests for it. However, automatic extraction of constraints from a given behavioral hardware design language (HDL) model remained a challenging open problem. This paper proposes an approach for automatic extraction of word-level model constraints from the behavioral verilog HDL description. The scenarios to be tested are also expressed as constraints. The model and the scenario constraints are solved together using an integer solver to arrive at the necessary functional test. The effectiveness of the approach is demonstrated by automatically generating the constraint models for: 1) an exclusive-shared-invalid multiprocessor cache coherency model and 2) the 16-bit DLX-architecture, from their respective Verilog-based behavioral models. Experimental results that generate test vectors for high level scenarios like pipeline hazards, cache miss, etc., spanning over multiple time-frames are presented.
Broadleaf species recognition with in situ hyperspectral data
Authors: Ruiliang Pu
Venue: International Journal of Remote Sensing (2009)

Timely and accurate identification of tree species by spectral methods is crucial for forest and urban ecological management. In this study, a total of 394 reflectance spectra (between 350 and 2500 nm) from foliage branches or canopy of 11 important urban forest broadleaf species were measured in the City of Tampa, Florida, USA with a spectrometer. The 11 species include American elm (Ulmus americana), bluejack oak (Quercus incana), crape myrtle (Lagerstroemia indica), laurel oak (Q. laurifolia), live oak (Q. virginiana), southern magnolia (Magnolia grandiflora), persimmon (Diospyros virginiana), red maple (Acer rubrum), sand live oak (Q. geminata), American sycamore (Platanus occidentalis), and turkey oak (Q. laevis). A total of 46 spectral variables, including normalized spectra, derivative spectra, spectral vegetation indices, spectral position variables, and spectral absorption features were extracted and analysed from the in situ hyperspectral measurements. Two classification algorithms were used to identify the 11 broadleaf species: a nonlinear artificial neural network (ANN) and a linear discriminant analysis (LDA). An analysis of variance (ANOVA) indicates that the 30 selected spectral variables are effective to differentiate the 11 species. The 30 selected spectral variables account for water absorption features at 970, 1200, and 1750 nm and reflect characteristics of pigments and other biochemicals in tree leaves, especially variability of chlorophyll content in leaves. The experimental results indicate that both classification algorithms (ANN and LDA) have produced acceptable accuracies (overall accuracy from 86.3% to 87.8%, kappa from 0.83 to 0.87) and have a similar performance for classifying the 11 broadleaf species with input of the 30 selected spectral variables. The preliminary results of identifying the 11 species with the in situ hyperspectral data imply that with current remote sensing techniques, including high spatial and spectral resolution data, it is still difficult but possible to identify similar species to such 11 broadleaf species with an acceptable accuracy.
Extracting Clinically Relevant Circular Mapping and Coronary Sinus Catheter Potentials from Atrial Simulations
Authors: Frank M. Weber, Christopher Schilling, Dorothee Straub, Sandeep Gurm, Gunnar Seemann, Cristian Lorenz, Olaf Dössel
Venue: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart (2009)

Patient-specific model adaptation and validation requires a comparison of simulations with measured patient data. For patients suffering from atrial fibrillation, such data is mainly available as intracardiac catheter signals. In this work, we demonstrate the simulation of clinically relevant catheter data as measured using circular mapping catheters (such as Lasso® or Orbiter®) and coronary sinus catheters using atrial simulations on a realistic geometry. Four circular catheters are modeled using a projection technique for two distinct types of application. We show that in sinus rhythm, the choice of a distinct electrophysiological model does not impair the signal quality. Finally, we compare simulated potentials to a real clinical measurement. In the future, with patient-specific models available, such comparisons can constitute an important interface for personalizing cardiac models.
The considerations of the web page design
Authors: Jenn Tang
Venue: WSEAS Transactions on Information Science and Applications (2009)

The web page interface is often one of the key factors that determine the browsers to stay or to leave. Limited visual space should be utilized to design the optimal layout in order to offer the important message of web page, increase the surfing, and, furthermore, fully stimulate the click-through rate. Based on content analysis method, this study takes 326 female shopping sites as examples to categorize 45 main entities from most websites and further obtains the web page layout of e-commerce in which there are 7 principles of uniformity for general entities and 5 for specific entities. According to these findings, another usability tests been conducted that are user friendly, usefulness, ease of use regarding other random searched shopping sites for verification. We did discover that the entities possess the characteristics of position-oriented. Our results offer guidelines for planning e-commerce web pages.
Structure learning of Bayesian networks using constraints
Authors: Cassio P. de Campos, Zhi Zeng, Qiang Ji
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful properties that strongly reduce the time and memory costs of many known methods such as hill-climbing, dynamic programming and sampling variable orderings. Secondly, a branch and bound algorithm is presented that integrates parameter and structural constraints with data in a way to guarantee global optimality with respect to the score function. It is an any-time procedure because, if stopped, it provides the best current solution and an estimation about how far it is from the global solution. We show empirically the advantages of the properties and the constraints, and the applicability of the algorithm to large data sets (up to one hundred variables) that cannot be handled by other current methods (limited to around 30 variables).
70-290: MCSE Guide to Managing a Microsoft Windows Server 2003 Environment, Enhanced, 1st edition
Authors: Dan DiNicolo, Brian T. McCann
Venue:  (2007)

This full-color hands-on guide book has been enhanced to make learning even easier by offering more detailed lab setup instructions, expanded chapter summaries, and new practice exams that will reinforce the skills learned.