ppxscal/embedding_network huggingface dataset:
XML Data Integration Based on Content and Structure Similarity Using Keys
Authors: Waraporn Viyanon, Sanjay K. Madria, Sourav S. Bhowmick
Venue: Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems: (2008)

This paper proposes a technique for approximately matching XML data based on the content and structure by detecting the similarity of subtrees clustered semantically using leaf-node parents. The leaf-node parents are considered as a root of a subtree which is then recursively traversed bottom-up for matching. First, we take advantage of the "key" for matching subtrees which reduces the number of comparisons dramatically. Second, we measure the similarity degree based on data and structures of the two XML documents. The results show that our approach finds much more accurate matches with or without the presence of keys in XML subtrees. Other approaches experience problems with similarity matching thresholds as they either ignore semantic information available or have problems in handling complex XML data.
Integrating tags in a semantic content-based recommender
Authors: Marco de Gemmis, Pasquale Lops, Giovanni Semeraro, Pierpaolo Basile
Venue: Proceedings of the 2008 ACM conference on Recommender systems (2008)

Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object. The Web 2.0 (r)evolution and the advent of user generated content have changed the game for personalization, since the role of people has evolved from passive consumers of information to that of active contributors. One of the forms of user generated content that has drawn more attention from the research community is folksonomy, a taxonomy generated by users who collaboratively annotate and categorize resources of interests with freely chosen keywords called tags. In this paper, we investigate whether folksonomies might be a valuable source of information about user interests. The main contribution is a strategy that enables a content-based recommender to infer user interests by applying machine learning techniques both on the "official" item descriptions provided by a publisher, and on tags which users adopt to freely annotate relevant items. Static content and tags are preventively analyzed by advanced linguistic techniques in order to capture the semantics of the user interests often hidden behind keywords. The proposed approach has been evaluated in the context of cultural heritage personalization. Preliminary experiments involving 30 real users show an improvement in the predictive accuracy of the tag-augmented recommender compared to the pure content-based one.
A silicon test chip for the thermomechanical analysis of MEMS packagings
Authors: Soeren Majcherek, Thomas Leneke, Soeren Hirsch
Venue: Microsystem Technologies (2008)

This paper reports on a method for the investigation of mechanical stress on MEMS sensor and actuator structures due to packaging processes. A silicon test chip is developed and manufactured to validate the simulation results. Finite element analysis (FEA) is used to optimize the geometric parameters and to find a stress sensitive sensor geometry. A diaphragm structure is used as mechanical amplifier for bulk induced stresses during the packaging process. Piezo resistive solid state resistors are doped into the surface of the chip to measure the stress in the diaphragms and at the contact pads being most significant locations for analysis. A high precision ohmmeter was used to measure the resistance prior and past the packaging process. The captured data allows for computation of the resulting stress loads in magnitude. Therefore, a stress evaluation of different packaging technologies is conducted and the impact of the packaging process on reliability can be estimated immediately.
Integrating Case-Based Reasoning and Meta-Learning for a Self-Improving Intelligent Tutoring System
Authors: Leen-Kiat Soh, Todd Blank
Venue: International Journal of Artificial Intelligence in Education (2008)

A framework integrating case-based reasoning (CBR) and meta-learning is proposed in this paper as the underlying methodology enabling self-improving intelligent tutoring systems (ITSs). Pedagogical strategies are stored in cases, each dictating, given a specific situation, which tutoring action to make next. Reinforcement learning is used to improve various aspects of the CBR module - cases are learned and retrieval and adaptation are improved, thus modifying the pedagogical strategies based on empirical feedback on each tutoring session. To minimize canceling out effects due to the multiple strategies used for meta-learning - for example, the learning result of one strategy undoes or reduces the impact of the learning result of another strategy, a principled design that is both cautious and prioritized is put in place. An ITS application, called Intelligent Learning Material Delivery Agent (ILMDA), has been implemented, powered by this framework, on introductory computer science topics, and deployed at the Computer Science and Engineering Department of the University of Nebraska. Studies show the feasibility of such a framework and impact analyses are reported on pedagogical strategies and outcomes.
Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data
Authors: Huai Li, Ming Zhan
Venue: Bioinformatics (2008)

Motivation: Unraveling the transcriptional regulatory program mediated by transcription factors (TFs) is a fundamental objective of computational biology, yet still remains a challenge. Method: Here, we present a new methodology that integrates microarray and TF binding data for unraveling transcriptional regulatory networks. The algorithm is based on a two-stage constrained matrix decomposition model. The model takes into account the non-linear structure in gene expression data, particularly in the TF-target gene interactions and the combinatorial nature of gene regulation by TFs. The gene expression profile is modeled as a linear weighted combination of the activity profiles of a set of TFs. The TF activity profiles are deduced from the expression levels of TF target genes, instead directly from TFs themselves. The TF-target gene relationships are derived from ChIP-chip and other TF binding data. The proposed algorithm can not only identify transcriptional modules, but also reveal regulatory programs of which TFs control which target genes in which specific ways (either activating or inhibiting). Results: In comparison with other methods, our algorithm identifies biologically more meaningful transcriptional modules relating to specific TFs. We applied the new algorithm on yeast cell cycle and stress response data. While known transcriptional regulations were confirmed, novel TF-gene interactions were predicted and provide new insights into the regulatory mechanisms of the cell. Contact: zhanmi@mail.nih.gov Supplementary information: Supplementary data are available at Bioinformatics online.
Multi-optimization power management for chip multiprocessors
Authors: Ke Meng, Russ Joseph, Robert P. Dick, Li Shang
Venue: Proceedings of the 17th international conference on Parallel architectures and compilation techniques (2008)

The emergence of power as a first-class design constraint has fueled the proposal of a growing number of run-time power optimizations. Many of these optimizations trade-off power saving opportunity for a variable performance loss which depends on application characteristics and program phase. Furthermore, the potential benefits of these optimizations are sometimes non-additive, and it can be difficult to identify which combinations of these optimizations to apply. Trial-and-error approaches have been proposed to adaptively tune a processor. However, in a chip multiprocessor, the cost of individually configuring each core under a wide range of optimizations might be prohibitive under simple trial-and-error approaches. In this work, we introduce an adaptive, multi-optimization power saving strategy for multi-core power management. Specifically, we solve the problem of meeting a global chip wide power budget through run-time adaptation of highly configurable processor cores. Our approach applies analytic modeling to reduce exploration time and decrease the reliance on trial-and-error methods. We also introduce risk evaluation to balance the benefit of various power saving optimizations versus the potential performance loss. Overall, we find that our approach can significantly reduce processor power consumption compared to alternative optimization strategies.
Fast monitoring of traffic subpopulations
Authors: Anirudh Ramachandran, Srinivasan Seetharaman, Nick Feamster, Vijay Vazirani
Venue: Proceedings of the 8th ACM SIGCOMM conference on Internet measurement (2008)

Network accounting, forensics, security, and performance monitoring applications often need to examine detailed traces from subsets of flows ("subpopulations"), where the application desires flexibility in specifying the subpopulation (e.g., to detect a portscan, the application must observe many packets between a source and a destination with one packet to each port). However, the dynamism and volume of network traffic on many high-speed links necessitates traffic sampling, which adversely affects subpopulation monitoring: because many subpopulations of interest to operators are low-volume flows, conventional sampling schemes (e.g., uniform random sampling) miss much of the subpopulation's traffic. Today's routers and network devices provide scant support for monitoring specific traffic subpopulations. This paper presents the design, implementation, and evaluation of FlexSample, a traffic monitoring engine that dynamically extracts traffic from subpopulations that operators define using conditions on packet header fields. FlexSample uses a fast, flexible counter array to provide rough estimates of packets' membership in respective subpopulations. Based on these coarse estimates, FlexSample then makes per-packet sampling decisions to sample proportionately from each subpopulation (as specified by a network operator), subject to an overall sampling constraint. We apply FlexSample to extract subpopulations such as port scans and traffic to high-degree nodes and find that it is able to capture significantly more packets from these subpopulations than conventional approaches.
Evaluation of MODIS ocean colour products at a northeast United States coast site near the Martha's Vineyard Coastal Observatory
Authors: H. Feng, D. Vandemark, J. W. Campbell, B. N. Holben
Venue: International Journal of Remote Sensing (2008)

Moderate Resolution Imaging Spectroradiometer (MODIS) marine and atmospheric products were evaluated using match-ups of MODIS and in situ measurements collected by an above-water radiometric system, the SeaWiFS Photometer Revision for Incident Surface Measurements (SeaPRISM), deployed near the Martha's Vineyard Coastal Observatory from 2004 to 2005. The products evaluated include the normalized water-leaving radiance L wn in the visible and near-infrared bands, and the aerosol optical thickness at 870 nm τa(870), and the Ångstrom exponent α(531). With a restricted match-up criterion, the result shows that the MODIS-retrieved L wn at 488, 531 and 551 nm agree very well with SeaPRISM measurements, giving mean per cent differences δ(%) of 3-7%, absolute mean per cent differences |δ|(%) of ∼16%, and coefficient of determination R 2 of 0.84-0.88. However, the MODIS-retrieved L wn at 412 nm are underestimated significantly with δ(%), |δ|(%) and R 2 of -35%, 57% and 0.32, respectively, corresponding to a consistent overestimation and underestimation for the MODIS-retrieved τa(870) and α(531), respectively. Temporal patterns of match-ups revealing two distinct cases of the discrepancy of MODIS retrievals from in situ SeaPRISM measurements are discussed.
Software quality and assurance in waterfall model and XP: a comparative study
Authors: Sana'a Jawdat Khalaf, Mohamed Noor Al-Jedaiah
Venue: WSEAS Transactions on Computers (2008)

Dealing with an increasingly volatile organizational environment is a serious challenge for managers of any software development. Traditional formal software development methodologies can be characterized as reflecting linear, sequential processes, and the related management approaches, and be effective in development software with stable, known, consistent requirements. Yet most real-world development efforts are much more likely to be conducted in more volatile environments, as organizations adapt to changing technology, markets, and social conditions. Requirements for systems must be able to change right along with them, often at "Internet speed" [1]. Project management approaches based on the traditional linear development methodologies are mismatched with such dynamic systems. The support of software quality in a software development process may be regarded under two aspects: first, by providing techniques, which support the development of high quality software and second, by providing techniques, which assure the required quality attributes in existing artifacts. Both approaches have to be combined to achieve effective and successful software engineering [2]. Agile methods may produce software faster but we also need to know how they meet our quality requirements. In this paper we compare the waterfall model with agile processes to show how agile methods achieve software quality under time pressure and in an unstable requirements environment, i.e. we analyze agile software quality assurance. We present a detailed waterfall model showing its software quality support processes. We then show the quality practices that agile methods have integrated into their processes. This allows us to answer the question "Can agile methods ensure quality even though they develop software faster and can handle unstable requirements?"[3]
Predicting sub-Golgi localization of type II membrane proteins
Authors: A. D. J. van Dijk, D. Bosch, C. J. F. ter Braak, A. R. van der Krol, R. C. H. J. van Ham
Venue: Bioinformatics (2008)

Motivation: Recent research underlines the importance of.negrained knowledge on protein localization. In particular, subcompartmental localization in the Golgi apparatus is important, for example, for the order of reactions performed in glycosylation pathways or the sorting functions of SNAREs, but is currently poorly understood. Results: We assemble a dataset of type II transmembrane proteins with experimentally determined sub-Golgi localizations and use this information to develop a predictor based on the transmembrane domain of these proteins, making use of a dedicated proteinstructure based kernel in an SVM. Various applications demonstrate the power of our approach. In particular, comparison with a large set of glycan structures illustrates the applicability of our predictions on a ‘glycomic’ scale and demonstrates a signi.cant correlation between sub-Golgi localization and the ordering of different steps in glycan biosynthesis. Contact: roeland.vanham@wur.nl Supplementary information: Supplementary data are available at Bioinformatics online.
Grasping product pragmatics: a case with internet on tv
Authors: Aylin Koca, Mathias Funk, Evangelos Karapanos, Anne Rozinat, Nico van der Gaarden
Venue: Proceeding of the 1st international conference on Designing interactive user experiences for TV and video (2008)

With the emergence of highly interactive products in the domestic space, consumer electronics brands are facing an increasing challenge in predicting the way their products are being used and experienced. Unforeseen experiences relating to functional, emotional and social/contextual aspects of product use lead to a large and increasing share of field complaints that cannot be attributed to a violation of products' specifications. In a project called Soft Reliability, we are trying to develop a product evaluation ecology that enables the anticipation of product use by gathering behavioral and attitudinal data early in the product development process, through longitudinal field studies with working prototypes. This paper introduces a novel framework for behavioral and attitudinal data collection and analysis. The framework enables instrumentation that relies on the event-based experience sampling method, and as such deploys an analysis methodology that includes process mining techniques for the analysis of usage patterns, multivariate techniques for the analysis of longitudinal attitudinal data, and product quality analysis techniques for the analysis of combined information. We illustrate the value and applicability of this framework in practice, through the findings of an ongoing project concerning the conceptualization of an innovative Internet on TV product, which is being conducted in collaboration with Philips, a multi-national consumer electronics company.
Axiomatic considerations of a rule based mechanism for the determination of a building's egress capability
Authors: H. A. Donegan, G. Livesey, T. B. Mcmaster, G. J. Mcaleavy
Venue: Artificial Intelligence Review (2007)

When a building is designed it possesses a spatial layout of routes and passageways all of which contribute to the building's egress capability--a key consideration in the context of health and safety for occupants. In this paper we examine how such a spatial layout can be interpreted as a vital system of egress, which if processed exhaustively by a naïve agent will identify with a measure of egress complexity. We further examine how the established rule-based mechanism for the determination of egress complexity satisfies the generically accepted axioms of complexity and we illustrate some of the algorithms of egress complexity with examples.
A scaling algorithm for polynomial constraint satisfaction problems
Authors: Ferenc Domes, Arnold Neumaier
Venue: Journal of Global Optimization (2008)

Good scaling is an essential requirement for the good behavior of many numerical algorithms. In particular, for problems involving multivariate polynomials, a change of scale in one or more variable may have drastic effects on the robustness of subsequent calculations. This paper surveys scaling algorithms for systems of polynomials from the literature, and discusses some new ones, applicable to arbitrary polynomial constraint satisfaction problems.
Convergence analysis of convex incremental neural networks
Authors: Lei Chen, Hung Keng Pung
Venue: Annals of Mathematics and Artificial Intelligence (2008)

Recently, a convex incremental algorithm (CI-ELM) has been proposed in Huang and Chen (Neurocomputing 70:3056---3062, 2007), which randomly chooses hidden neurons and then analytically determines the output weights connecting with the hidden layer and the output layer. Though hidden neurons are generated randomly, the network constructed by CI-ELM is still based on the principle of universal approximation. The random approximation theory breaks through the limitation of most conventional theories, eliminating the need for tuning hidden neurons. However, due to the random characteristic, some of the neurons contribute little to decrease the residual error, which eventually increase the complexity and computation of neural networks. Thus, CI-ELM cannot precisely give out its convergence rate. Based on Lee's results (Lee et al., IEEE Trans Inf Theory 42(6):2118---2132, 1996), we first show the convergence rate of a maximum CI-ELM, and then systematically analyze the convergence rate of an enhanced CI-ELM. Different from CI-ELM, the hidden neurons of the two algorithms are chosen by following the maximum or optimality principle under the same structure as CI-ELM. Further, the proof process also demonstrates that our algorithms achieve smaller residual errors than CI-ELM. Since the proposed neural networks remove these "useless" neurons, they improve the efficiency of neural networks. The experimental results on benchmark regression problems will support our conclusions.
Inverse dynamics learned gait planner for a two-legged robot moving on uneven terrains using neural networks
Authors: Pandu Ranga Vundavilli, Dilip Kumar Pratihar
Venue: International Journal of Advanced Intelligence Paradigms (2008)

This paper proposes a method for design of inverse dynamics learned, neural network-based gait planner for a two-legged robot negotiating uneven terrains. The lower limbs' gaits are generated utilising inverse kinematics, and those of the trunk and swing foot are derived using a neural network aiming to maximise the dynamic balance margin. A genetic algorithm is used to provide training off-line to the gait planner. Its performance has been tested through computer simulations on different terrains, namely staircase, sloping surface and ditch. Simulation results show that the developed planner has successfully generated appropriate gaits to negotiate the uneven terrains.
A (computational) social science perspective on societal transitions
Authors: Flaminio Squazzoni
Venue: Computational Mathematical Organization Theory (2008)

This paper aims to illustrate how social sciences, sociology in particular, have theorized on societal transitions. The first section introduces some preliminary definitions. The assumption is that a societal transition is more than a social, economic or technological change. It is a large-scale and long-term macro process through which a given social system radically changes its structural basis, in terms of new socio-technical practices, governance rules, social and economic institutions, cultural frames, and patterns of social life. The second section provides an excursus on social science accounts on transitions. In particular, the attention has been given to Norbert Elias' famous study on the rise and fall of the court society in France, between 17th and 18th century and to Manuel Castells' recent analysis of the emergence of the network society. The third section discusses problems and challenges of standard approaches and suggests some building blocks of societal transition models, taken from complexity and social simulation literature. The concluding section suggests some way-forward measures towards a computational social science approach to societal transitions.
Application Domain Specific Embedded FPGAs for Flexible ISA-Extension of ASIPs
Authors: B. Neumann, T. Sydow, H. Blume, T. G. Noll
Venue: Journal of Signal Processing Systems (2008)

This paper presents a novel architecture combining an application specific instruction set processor (ASIP) core and an application domain specific embedded FPGAs (eFPGAs) used as flexible accelerator for the ASIP. The eFPGA is based on a parametrisable architecture template optimised for arithmetic oriented applications. It was designed as a physically optimised VLSI-macro using a flexible design methodology also sketched in this paper. Quantitative comparisons of the eFPGA with a commercial standard FPGA show significant improvements in energy, area and timing delays. Simulations of the new ASIP-eFPGA architecture have been conducted using a model based approach to evaluate its efficiency. The results show that power- and area-efficiencies similar to an FPGA can be achieved for the flexible ASIP-eFPGA while preserving the flexibility of a software programmable processor.