ppxscal/embedding_network huggingface dataset:
A universal strong law of large numbers for conditional expectations via nearest neighbors
Authors: Harro Walk
Venue: Journal of Multivariate Analysis (2008)

For k"n-nearest neighbor estimates of a regression Y on X (d-dimensional random vector X, integrable real random variable Y) based on observed independent copies of (X,Y), strong universal pointwise consistency is shown, i.e., strong consistency P"X-almost everywhere for general distribution of (X,Y). With tie-breaking by indices, this means validity of a universal strong law of large numbers for conditional expectations E(Y|X=x).
Knowledge formalization in experience feedback processes: An ontology-based approach
Authors: B. Kamsu Foguem, T. Coudert, C. Béler, L. Geneste
Venue: Computers in Industry (2008)

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving the missions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable.
INDEPENDENT COMPONENT ANALYSIS OF EEG RECORDED DURING TWO-PERSON GAME PLAYING
Authors: A. Flexer, S. Makeig
Venue: Applied Artificial Intelligence (2007)

We report a study on two-person game playing involving simultaneous EEG recording from both subjects. Independent component analysis is used to identify activities of individual cortical EEG sources. Activity of a midline fronto-central component is identified in four of five subjects. This component accounts for the P300 waveform whose amplitude varies, depending on the success in the gaming situation.
Comments on "Dynamic routing of restorable bandwidth-guaranteed tunnels using aggregated network resource usage information"
Authors: William Lau, Gustav Filip Rosenbaum, Sanjay Jha
Venue: IEEE/ACM Transactions on Networking (TON) (2008)

This paper identifies and corrects two flaws in the paper "Dynamic routing of restorable bandwidth-guaranteed tunnels using aggregate network resource usage information", Kodialam and Lakshman, IEEE/ACM Trans. Networking, 2003.
Enumerating the orientable 2-cell imbeddings of complete bipartite graphs
Authors: Bruce P. Mull
Venue: Journal of Graph Theory (1999)

A formula is developed for the number of congruence classes of 2-cell imbeddings of complete bipartite graphs in closed orientable surfaces. &copy; 1999 John Wiley & Sons, Inc. J Graph Theory 30: 77&ndash;90, 1999
Deciding polynomial-exponential problems
Authors: Melanie Achatz, Scott McCallum, Volker Weispfenning
Venue: Proceedings of the twenty-first international symposium on Symbolic and algebraic computation (2008)

This paper presents a decision procedure for a certain class of sentences of first order logic involving integral polynomials and the exponential function in which the variables range over the real numbers. The inputs to the decision procedure are prenex sentences in which only the outermost quantified variable can occur in the exponential function. The decision procedure has been implemented in the computer logic system REDLOG. Closely related work is reported in [2, 7, 16, 20, 24].
Decomposable algorithms for nearest neighbor computing
Authors: Ahmed M. Khedr, Ahmed Salim
Venue: Journal of Parallel and Distributed Computing (2008)

Privacy and security concerns can prevent sharing of data, derailing many data projects. Distributed knowledge computing, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. In the present study, two novel algorithms have been developed to find the nearest neighbor from known points, and a pair of nearest neighbors in a given set data points in d-dimensional space in distributed databases. These algorithms preserve the privacy and security of the data at individual sites by requiring transmission of only minimal information to other sites.
To combine steady-state genetic algorithm and ensemble learning for data clustering
Authors: Yi Hong, Sam Kwong
Venue: Pattern Recognition Letters (2008)

This paper proposes a data clustering algorithm that combines the steady-state genetic algorithm and the ensemble learning method, termed as genetic-guided clustering algorithm with ensemble learning operator (GCEL). GCEL adopts the steady-state genetic algorithm to perform the search task, but replaces its traditional recombination operator with an ensemble learning operator. Therefore, GCEL can avoid the problems of clustering invalidity and context insensitivity of the traditional recombination operator of genetic algorithms. In addition, GCEL generates its initial population of candidate clustering solutions by using the random subspaces method. Therefore, less fitness evaluations are required to converge. The proposed GCEL is tested on one synthetic and several real data sets. Experimental results demonstrate that GCEL is able to achieve a comparative or better clustering solution with less fitness evaluations when compared with several other existing genetic-guided clustering algorithms.
Efficient implementation of the Lanczos method for magnetic systems
Authors: Jürgen Schnack, Peter Hage, Heinz-Jürgen Schmidt
Venue: Journal of Computational Physics (2008)

Numerically exact investigations of interacting spin systems provide a major tool for an understanding of their magnetic properties. For medium size systems the approximate Lanczos diagonalization is the most common method. In this article we suggest two improvements: efficient basis coding in subspaces and simple restructuring for openMP parallelization.
Image interpolation using consistent neighborhood correlations
Authors: Ronen Sher, Moshe Porat
Venue: Proceedings of the 5th WSEAS/IASME International Conference on Systems Theory and Scientific Computation (2005)

We present a new approach to image interpolation using consistent relationships between adjacent pixels in an image. In the first stage, the localized relationships are learned from the input image. In the second stage, the relationships and the concluded governing rules are used to build an interpolated image. Our method is compared with other interpolation methods such as bilinear, bi-cubic and spline for enlargement of images by multiplications of 2. The results indicate significant reduction in the blockiness and smoothing effects compared to existing methods. We present additional results for related applications of audio signals and for color imaging.
Seeing is retrieving: building information context from what the user sees
Authors: Karl Gyllstrom, Craig Soules
Venue: Proceedings of the 13th international conference on Intelligent user interfaces (2008)

As the user's document and application workspace grows more diverse, supporting personal information management becomes increasingly important. This trend toward diversity renders it difficult to implement systems which are tailored to specific applications, file types, or other information sources. We developed SeeTrieve, a personal document retrieval and classification system which abstracts applications by considering only the text they present to the user through the user interface. Associating the visible text which surrounds a document in time, SeeTrieve is able to identify important information about the task within which a document is used. This context enables novel, useful ways for users to retrieve their personal documents. When compared to content based systems, this context based retrieval achieved substantial improvements in document recall.
Anchored mobilities: mobile technology and transnational migration
Authors: Amanda Williams, Ken Anderson, Paul Dourish
Venue: Proceedings of the 7th ACM conference on Designing interactive systems (2008)

Mobile technologies are deployed into diverse social, cultural, political and geographic settings, and incorporated into diverse forms of personal and collective mobility. We present an ethnography of transnational Thai retirees and their uses of mobile technology, highlighting forms of mobility that are spatially, temporally, and infrastructurally anchored, and concepts of the house as a kinship network that may be globally distributed. We conclude in pointing out several ways in which our observations and analysis can influence design.
An efficient scheduling and admission control algorithm for IEEE 802.11e WLAN
Authors: Geun-Hyuk Yang, Jin-Chul Choi, Se-Jin Oh, Chae-Woo Lee
Venue: Proceedings of the 2nd international conference on Ubiquitous information management and communication (2008)

IEEE 802.11e is being proposed to improve QoS (Quality of Service) by IEEE 802.11 working group. HCCA (HCF Controlled Channel Access), a centralized polling based mechanism of IEEE 802.11e, needs a scheduling algorithm that decides on how the available radio resources are allocated to the polled stations. In IEEE 802.11e a standard Reference Scheduler is presented. The Reference Scheduler polls all stations in a polling list by the same interval which causes ineffectively frequent polling. It increases the overhead and also decreases the TXOP (Transmission Opportunity) utilization. In this paper, we propose a scheduling and admission control algorithm that polls stations depending on the MSI (Maximum Service Interval) of stations in order to solve these shortcomings. In our proposed algorithm a station is polled by an interval close to its MSI, thus polling overhead decreases and TXOP utilization increases in comparison to those of the Reference Scheduler. Simulation results show that our algorithm outperforms the Reference Scheduler. Our algorithm maintains higher aggregated throughput and serves more stations than the Reference Scheduler.
Using VR for Human Development in Africa
Authors: Dave Lockwood, Erik Kruger
Venue: IEEE Computer Graphics and Applications (2008)

Virtual Reality (VR) is often seen as a "First World" technology; however, over the past eight years, the Naledi3d Factory in South Africa has been quietly pioneering the use of VR as an educational training tool in under-developed communities across Africa.
Probabilistic Model Checking of Contention Resolution in the IEEE 802.15.4 Low-Rate Wireless Personal Area Network Protocol
Authors: Matthias Fruth
Venue: Proceedings of the Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (2006)

The international standard IEEE 802.15.4 defines low-rate wireless personal area networks, a central communication infrastructure of pervasive computing. In order to avoid conflicts caused by multiple devices transmitting at the same time, it uses a contention resolution algorithm based on randomised exponential backoff that is similar to the ones used in IEEE 802.3 for Ethernet and IEEE 802.11 for Wireless LAN. We model the protocol using probabilistic timed automata, a formalism in which both nondeterministic and probabilistic choice can be represented. The probabilistic timed automaton is transformed into a finite-state Markov decision process via a property-preserving integral-time semantics. Using the probabilistic model checker PRISM, we verify correctness properties, compare different operation modes of the protocol, and analyse performance and accuracy of different model abstractions.
On multi-view active learning and the combination with semi-supervised learning
Authors: Wei Wang, Zhi-Hua Zhou
Venue: Proceedings of the 25th international conference on Machine learning (2008)

Multi-view learning has become a hot topic during the past few years. In this paper, we first characterize the sample complexity of multi-view active learning. Under the &alpha;-expansion assumption, we get an exponential improvement in the sample complexity from usual &Otilde;(1/&epsilon;) to &Otilde;(log 1/&epsilon;), requiring neither strong assumption on data distribution such as the data is distributed uniformly over the unit sphere in Rd nor strong assumption on hypothesis class such as linear separators through the origin. We also give an upper bound of the error rate when the &alpha;-expansion assumption does not hold. Then, we analyze the combination of multi-view active learning and semi-supervised learning and get a further improvement in the sample complexity. Finally, we study the empirical behavior of the two paradigms, which verifies that the combination of multi-view active learning and semi-supervised learning is efficient.
A pairwise key establishment scheme for heterogeneous sensor networks
Authors: Zhihong Liu, Jianfeng Ma, Qiping Huang, SangJae Moon
Venue: Proceeding of the 1st ACM international workshop on Heterogeneous sensor and actor networks (2008)

Motivated by a key agreement scheme proposed by Leighton and Micali (LM-KDS), we present a key establishment mechanism for heterogeneous sensor networks which combines random key pre-distribution schemes and LM-KDS. Using LM-KDS as its building block, proposed scheme divides nodes into groups and distributes a small number of micro key servers among nodes. This scheme consists of two phases. In the first one, some secret information is sent by the TA to every node off-line, then the TA assigns public keying material to every micro key server. In the second phase, if any pair of nodes cannot establish session key using their preloaded keying material, they may get public keying material from a micro key server through an open channel. Besides, all the public keying material preloaded in micro key servers does not need to be secret. This scheme preserves properties of LM-KDS yet takes advantage of flexibility of probabilistic approaches to yield improved resilience against node capture attacks by trading off increased communication overhead and storage complexity.
Detection and substitution of clouds/hazes and their cast shadows on IKONOS images
Authors: D. Lu
Venue: International Journal of Remote Sensing (2007)

Frequently cloudy conditions in moist tropical regions often cause difficulty in acquiring good-quality remote-sensing images. This paper explores approaches to detect clouds/hazes and their cast shadows on IKONOS images. Two approaches based on maximum and minimum filters and the second-moment texture measures were tested. The detected clouds/hazes and their cast shadows in one image were then replaced with the pixel values from the same location of another image after image normalization. This research indicates that both the approaches developed in this paper have successfully detected the clouds/hazes and their cast shadows on IKONOS images. In particular, the second-moment texture approach is recommended.
Vehicle spacing control using robust fuzzy control with pole placement in LMI region
Authors: P. -F. Toulotte, S. Delprat, T. -M. Guerra, J. Boonaert
Venue: Engineering Applications of Artificial Intelligence (2008)

This work presents a robust pole placement in an LMI region for Takagi-Sugeno (TS) (Takagi T., Sugeno M., 1985. Fuzzy identification of systems and its application to modelling and control. IEEE Transactions on SMC 15 (1), 116-132.) fuzzy models. The objective is to find a set of linear matrix inequalities in order to ensure that the linear model poles of the nonlinear TS model remain in a specified region of the complex plane, even in presence of model uncertainties. As an illustration, the obtained conditions are applied to the spacing policy control of an automated electric vehicle. Simulation and real-time results are presented.
A Methodology for Characterization of Large Macro Cells and IP Blocks Considering Process Variations
Authors: Amit Goel, Sarma Vrudhula, Feroze Taraporevala, Praveen Ghanta
Venue: Proceedings of the 9th international symposium on Quality Electronic Design (2008)

Integrated circuits today rely on extensive re-use of precharacterized IP bocks and macro cells to meet the demand for high performance system on chip (SoC). In this paper we propose a methodology for characterization of IP blocks and macro cells for statistical timing analysis considering process variations and spatial correlations. We develop efficient models for capturing both inter-die and intra-die variations in devices and interconnects. Increasing variability of the process parameters in sub-nanometer designs requires instance-specific characterization of these design blocks. We propose a technique for instancespecific calibration of pre-characterized timing model. The proposed approach was evaluated on large industrial designs of 1.2M and 3.5M gates in 65nm technology and validated against SPICE for accuracy.
A framework for software project estimation based on cosmic, dsm and rework characterization
Authors: Sharareh Afsharian, Marco Giacomobono, Paola Inverardi
Venue: Proceedings of the 1st international workshop on Business impact of process improvements (2008)

Effective software project estimation is one of the most challenging activities in software development. In today's highly competitive world, accurate software estimation can make the difference between successful projects and dismal failures. Proper project planning and control is not possible without a sound and reliable estimate. In this paper we propose a framework, developed by Ericsson R&D Italy, for project time and cost estimation for software development projects in the telecommunications domain. The customization of Design Structure Matrix (DSM), the application of COSMIC and the study of defect complexity curves are the components of this new estimation framework. The joint application of these three components allows all stakeholders interested in the estimation result to have a common view based on objective data and to understand how a change to functional and quality requirements can impact the result.
Forecasting and explanation of algal dynamics in two shallow lakes by recurrent artificial neural network and hybrid evolutionary algorithm
Authors: A. Talib, F. Recknagel, H. Cao, D. T. van der Molen
Venue: Mathematics and Computers in Simulation (2008)

Long-term time-series of the two eutrophic Dutch lakes Veluwemeer and Wolderwijd were subject to predictive modelling by recurrent supervised ANN (RANN) and hybrid evolutionary algorithms (HEA). A combination of bottom-up and top-down eutrophication control measures has been implemented in both lakes since 1979. Dividing the time-series data into training and validation datasets based on three distinctive management periods has facilitated a comparative analysis of the two lakes regarding the long-term dynamics in response to eutrophication control. Results of the study have demonstrated that RANN and HEA can be applied for (1) 5-day-ahead prediction of Oscillatoria spp. and (2) 5-day-ahead prediction of Scenedesmus spp. Firstly RANN achieved reasonably accurate results for 5-day-ahead forecasting of abundances of blue-green algae Oscillatoria and green algae Scenedesmus in both lakes. Secondly HEA achieved similar good forecasting results and also provided model representations for both algae species in the form of rule sets. The limitation with single lake models is that the rule sets discovered are lake specific. Merging of the two lake datasets using merged lake models for both training and testing have produced simpler and generic rule sets that explain the dynamics of Oscillatoria and Scenedesmus for both lakes. The results from this study have shown that both external nutrient control combined with food web manipulation have turned the lakes from hypereutrophic conditions and from Oscillatoria to Scenedesmus dominance. These complex dynamics that are associated with the shift from periods of turbid with Oscillatoria dominance to clear-water conditions with increased Scenedesmus in both lakes are predictable and can be explained by the key-driving variables in the generic rule sets discovered.
Note: On the Sn-module structure of the noncommutative harmonics
Authors: Emmanuel Briand, Mercedes Rosas, Mike Zabrocki
Venue: Journal of Combinatorial Theory Series A (2008)

Using a noncommutative analog of Chevalley's decomposition of polynomials into symmetric polynomials times coinvariants due to Bergeron, Reutenauer, Rosas, and Zabrocki we compute the graded Frobenius characteristic for their two sets of noncommutative harmonics with respect to the left action of the symmetric group (acting on variables). We use these results to derive the Frobenius series for the enveloping algebra of the derived free Lie algebra in n variables.
Assessing the effect of attribute uncertainty on the robustness of choropleth map classification
Authors: Ningchuan Xiao, Catherine A. Calder, Marc P. Armstrong
Venue: International Journal of Geographical Information Science (2007)

Choropleth maps are often used to visualize the spatial distribution of information collected for enumeration units. Such maps, however, are normally produced without considering the effect of uncertainty associated with data, which can contribute to incorrect interpretation. The purpose of this paper is to develop a method that can be used to evaluate the classification robustness of choropleth maps when the attribute uncertainty associated with the data is known or can be estimated. We first develop a measure to indicate the robustness of classification schemes. We then design a set of experiments to examine the robustness of different choropleth map classifications under various levels and types of uncertainty. Our experiments suggest that the robustness of a choropleth classification scheme is a function of uncertainty and the number of classes used. Increases in data uncertainty will decrease map robustness. However, it is possible to increase map robustness by choosing a smaller number of classes. We also discuss a visualization approach that can be used to display the classification robustness of each enumeration unit within a choropleth map.