ppxscal/embedding_network huggingface dataset:
Automatic Extraction of Pedagogic Metadata from Learning Content
Authors: Devshri Roy, Sudeshna Sarkar, Sujoy Ghose
Venue: International Journal of Artificial Intelligence in Education (2008)

Annotating learning material with metadata allows easy reusability by different learning/tutoring systems. Several metadata standards have been developed to represent learning objects and courses. A learning system needs to use pedagogic attributes including document type, topic, coverage of concepts, and for each concept the significance and the role. Moreover, in order to have a flexible and reusable repository of e-learning materials, it is necessary that the annotation of the documents with such metadata be done in an automatic fashion as far as possible. This paper describes the attributes that represent some important pedagogic characteristics of learning materials. To reduce the overhead of manual annotation we have explored the feasibility of automatic annotation of learning materials with metadata. This facilitates the creation of an elearning open repository for storing these annotated learning materials, which can be used by learning systems. The automatic annotation is based on a domain knowledge base and a number of algorithms like standard classification algorithms, parsing and analysis of documents have been used for this purpose. The results show a fair degree of accuracy, which may be improved in future using more sophisticated algorithms.
Non-symmetric Preferences in the IPA Market with Reinforcement Learning
Authors: Eduardo Rodrigues Gomes, Ryszard Kowalczyk
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02 (2008)

Machine Learning has been proposed to support and optimize market-based resource allocation. In particular, Reinforcement Learning (RL) has been used to improve the allocation in terms of the utility received by resource requesting agents in the Iterative Price Adjustment (IPA) mechanism. In such an approach, utility functions describe the agents' preferences for resource attributes and are the basis for RL to learn demand functions that are optimized for the market. It has been shown that the reward functions based on the individual utility of the agents and the social welfare of the allocation can deliver similar social results when the market consists only of learning agents with symmetric preferences. In this paper we investigate the IPA market-based resource allocation with RL for the case of agents with non-symmetric preferences. We show through experimental investigation that the results observed above are also held in this case. In particular, we show that the individual-based reward function is able to approximate the solution to the fairest Pareto-Optimal allocation in situations where the social-based reward function fails.
Educational Uses of the e-Book: An Experience in a Virtual University Context
Authors: Eva Patrícia Gil-Rodríguez, Jordi Planella-Ribera
Venue: Proceedings of the 4th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society on HCI and Usability for Education and Work (2008)

Despite the fact that the Digital Ink Book (DIB) has been developing over the past 10 years in different contexts, it has not made its presence felt within university education. This paper focuses on experimental educational use of the e-book in the context of a virtual university. Based on the available technologies, this research for technological innovation in education aims to carry out a pilot test with the students on the Digital Literacy and Exclusion course from a user centred design (UCD) perspective. Each student received a Digital Ink Book (DIB) for use in both the reading and working notes necessary for learning the different topics that make up the course. The data gathering was divided into a field diary kept by students participating in the DIB pilot test, a final report from each student about their own process of acquiring digital literacy in relation to the DIB, a virtual forum for the course, where the students commented on their difficulties and discoveries in using the DIB, and individual interviews with the students participating in the project once the experiment was finished. Through a content analysis of collected data we mapped the processes used by the students as strategies for the study of the university material using the DIB from a UCD perspective.
Identification of QRS complexes in 12-lead electrocardiogram
Authors: S. S. Mehta, N. S. Lingayat
Venue: Expert Systems with Applications: An International Journal (2009)

A method for the detection of QRS complexes in 12-lead electrocardiogram (ECG) using support vector machine (SVM) is presented in this paper. Digital filtering techniques are used to remove base line wander and power line interference. SVM is used for the identification of QRS complexes in the processed signal. The performance of the algorithm is evaluated against the standard CSE ECG database. The results indicated that the algorithm achieved 99.75% of the identification rate. The percentage of false positive and false negative is 1.61% and 0.26%, respectively. The performance of the proposed algorithm is found to be better than published results of the other QRS detectors tested on the same database. The proposed method functions reliably even under the conditions of poor signal quality in the ECG data.
Provenance and the Price of Identity
Authors: Adriane Chapman, H. V. Jagadish
Venue: Provenance and Annotation of Data and Processes: Second International Provenance and Annotation Workshop, IPAW 2008, Salt Lake City, UT, USA, June 17-18, 2008. Revised Selected Papers (2008)

As developers acknowledge that provenance is essential, more and more datasets are attempting to keep provenance records describing how they were created. Some of these datasets are constructed using workflows, others cobble together processes and applications to manipulate the data. While the provenance needs are the same, the inputs and set of processes used must be kept, the identity needs are very different. We outline several identification strategies that can be used for data manipulation outside of workflows. We evaluate these strategies in terms of time to create and store identity, and the space needed to keep this information. Additionally, we discuss the strengths and weaknesses of each strategy.
Proceedings of the 4th ACM workshop on Wireless multimedia networking and performance modeling
Venue: International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems (2008)

Welcome to the Fourth ACM Workshop on Wireless Multimedia Networking and Performance Modeling -WMUNeP. The demand for wireless multimedia communications thrives in today's consumer and corporate market. The need to evolve multimedia applications and services, and their associated protocols for emerging networks is at a critical point given the proliferation and integration of wireless systems to intelligent and broadband networks, mobility of people, data/voice convergence and the integration of computing and communication in mobile devices. The workshop will provide a forum for researchers and practitioners to share and exchange their experience, discuss challenges, and report the state of-the-art and in progress research related to different aspects of wireless multimedia networking and performance modeling for WLANs, WPANs, WMANs, WWANs, MANETs, VANETs and sensor networks such as wireless video and wireless streaming, systematic design methodologies, algorithms, synchronization, analysis and performance modeling. The workshop is held in conjunction with the 11th ACM/IEEE International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems -- MSWiM. The venue is Vancouver, Canada, one of the liveliest cities in the world with spectacular natural scenery.
On adaptive replacement based on LRU with working area restriction algorithm
Authors: Edson T. Midorikawa, Ricardo L. Piantola, Hugo H. Cassettari
Venue: ACM SIGOPS Operating Systems Review (2008)

Adaptive algorithms are capable of modifying their own behavior through time, depending on the execution characteristics. Recently, we have proposed LRU-WAR, an adaptive replacement algorithm whose objective is to minimize failures detected in LRU policy, preserving its simplicity and low overhead. In this paper, we present our contribution to the study of adaptive replacement algorithms describing their behavior under a number of workloads. Simulations include an analysis of the performance sensibility with the variation of the control parameters and its application in a multiprogrammed environment. In order to address LRU-WAR weakness as a global policy, we also introduce LRU-WARlock. The simulation results show that substantial performance improvements can be obtained.
Mining changing customer segments in dynamic markets
Authors: Mirko Böttcher, Martin Spott, Detlef Nauck, Rudolf Kruse
Venue: Expert Systems with Applications: An International Journal (2009)

Identifying customer segments and tracking their change over time is an important application for enterprises who need to understand what their customers expect from them - now and in the future. This in particular is important for businesses which operate in dynamic markets with customers who, driven by new innovations and competing products, have highly changing demands and attitudes. Customer segmentation is typically done by applying some form of cluster analysis to obtain a set of segments to which future customers are assigned to. In this paper, we present a system for customer segmentation which accounts for the dynamics of today's markets. It employs an approach based on the discovery of frequent itemsets and the analysis of their change over time which, finally, results in a change-based notion of segment interestingness. Our approach allows us to detect arbitrary segments and analyse their temporal development. Thereby, our approach is assumption-free and pro-active and can be run continuously. Newly discovered segments or relevant changes will be reported automatically based on the application of several interestingness measures.
Preprogramming knowledge: a major source of misconceptions in novice programmers
Authors: Jeffrey Bonar, Elliot Soloway
Venue: Human-computer interaction (1985)

We present a process model to explain bugs produced by novices early in a programming course. The model was motivated by interviews with novice programmers solving simple programming problems. Our key idea is that many programming bugs can be explained by novices inappropriately using their knowledge of step-by-step procedural specifications in natural language. We view programming bugs as patches generated in response to an impasse reached by the novice while developing a program. We call such patching strategies bug generators. Several of our bug generators describe how natural language preprogramming knowledge is used by novices to create patches. Other kinds of bug generators are also discussed. We describe a representation both for novice natural language preprogramming knowledge and novice fragmentary programming knowledge. Using these representations and the bug generators, we evaluate the model by analyzing four interviews with novice programmers.
Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion
Authors: Sumantra Mandal, P. V. Sivaprasad, S. Venugopal, K. P. N. Murthy
Venue: Applied Soft Computing (2009)

The deformation behavior of type 304L stainless steel during hot torsion is investigated using artificial neural network (ANN). Torsion tests in the temperature range of 600-1200^oC and in the (maximum surface) strain rate range of 0.1-100s^-^1 were carried out. These experiments provided the required data for training the neural network and for subsequent testing. The input parameters of the model are strain, log strain rate and temperature while torsional flow stress is the output. A three layer feed-forward network was trained with standard back propagation (BP) and Resilient propagation (Rprop) algorithm. The paper makes a robust comparison of the performances of the above two algorithms. The network trained with Rprop algorithm is found to perform better and also needs less number of iterations for convergence. The developed ANN model employing this algorithm could efficiently track the work hardening, dynamic softening and flow localization regions of the deforming material. Sensitivity analysis showed that temperature and strain rate are the most significant parameters while strain affects the flow stress only moderately. The ANN model, described in this paper, is an efficient quantitative tool to evaluate and predict the deformation behavior of type 304L stainless steel during hot torsion.
Group analysis for natural convection from a vertical plate
Authors: A. S. Rashed, M. M. Kassem
Venue: Journal of Computational and Applied Mathematics (2008)

The steady laminar natural convection of a fluid having chemical reaction of order n past a semi-infinite vertical plate is considered. The solution of the problem by means of one-parameter group method reduces the number of independent variables by one leading to a system of nonlinear ordinary differential equations. Two different similarity transformations are found. In each case the set of differential equations are solved numerically using Runge-Kutta and the shooting method. For each transformation different Schmidt numbers and chemical reaction orders are tested.
Improving robustness of MLLR adaptation with speaker-clustered regression class trees
Authors: Arindam Mandal, Mari Ostendorf, Andreas Stolcke
Venue: Computer Speech and Language (2009)

We introduce a strategy for modeling speaker variability in speaker adaptation based on maximum likelihood linear regression (MLLR). The approach uses a speaker-clustering procedure that models speaker variability by partitioning a large corpus of speakers in the eigenspace of their MLLR transformations and learning cluster-specific regression class tree structures. We present experiments showing that choosing the appropriate regression class tree structure for speakers leads to a significant reduction in overall word error rates in automatic speech recognition systems. To realize these gains in unsupervised adaptation, we describe an algorithm that produces a linear combination of MLLR transformations from cluster-specific trees using weights estimated by maximizing the likelihood of a speaker's adaptation data. This algorithm produces small improvements in overall recognition performance across a range of tasks for both English and Mandarin. More significantly, distributional analysis shows that it reduces the number of speakers with performance loss due to adaptation across a range of adaptation data sizes and word error rates.
Intrinsic disorder prediction from the analysis of multiple protein fold recognition models
Authors: Liam J. McGuffin
Venue: Bioinformatics (2008)

Motivation: Intrinsic protein disorder is functionally implicated in numerous biological roles and is, therefore, ubiquitous in proteins from all three kingdoms of life. Determining the disordered regions in proteins presents a challenge for experimental methods and so recently there has been much focus on the development of improved predictive methods. In this article, a novel technique for disorder prediction, called DISOclust, is described, which is based on the analysis of multiple protein fold recognition models. The DISOclust method is rigorously benchmarked against the top.ve methods from the CASP7 experiment. In addition, the optimal consensus of the tested methods is determined and the added value from each method is quantified. Results: The DISOclust method is shown to add the most value to a simple consensus of methods, even in the absence of target sequence homology to known structures. A simple consensus of methods that includes DISOclust can significantly outperform all of the previous individual methods tested. Availability: http://www.reading.ac.uk/bioinf/DISOclust/ Contact: l.j.mcguf.n@reading.ac.uk Supplementary information: Supplementary data are available at http://www.reading.ac.uk/bioinf/DISOclust/suppl.pdf
Managing Proof Documents for Asynchronous Processing
Authors: Holger Gast
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

Asynchronous proof processing is a recent approach at improving the usability and performance of interactive theorem provers. It builds on a simple metaphor: the user edits a proof document while the prover checks its consistency in the background without explicit requests from the user. This paper presents a software architecture for asynchronous proof processing. Its foundation is a novel state model for commands that synchronizes the possibly parallel accesses of the user interface and prover. The state model is complemented by a communication protocol that places minimal requirements on the prover. The model also allows asynchronous processing to be emulated by existing linear-processing proof engines, such that the migration to the new communication protocol is simplified. A prototype implementation that works with the current development version of Isabelle is presented.
Towards an evaluation framework for knowledge management systems
Authors: Dorit Nevo, Brent Furneaux, Yair Wand
Venue: Information Technology and Management (2008)

In this paper we develop an evaluation framework for Knowledge Management Systems (KMS). The framework builds on the theoretical foundations underlying organizational Knowledge Management (KM) to identify key KM activities and the KMS capabilities required to support each activity. These capabilities are then used to form a benchmark for evaluating KMS. Organizations selecting KMS can use the framework to identify gaps and overlaps in the extent to which the capabilities provided and utilized by their current KMS portfolio meet the KM needs of the organization. Other applications of the framework are also discussed.
An Applicable Data Quality Model for Web Portal Data Consumers
Authors: Coral Calero, Angélica Caro, Mario Piattini
Venue: World Wide Web (2008)

Web portals have emerged as an important means by which to access data on the worldwide. The people that use these applications need to ensure that the data recovered is suitable for the task at hand. That is, they need to know the level of quality of the data obtained. This paper introduces the PoDQA (Portal Data Quality Assessment) tool which implements PDQM, a Portal Data Quality Model, which is centered upon the data consumer perspective. Thus, the measurement of data quality is carried out by using the point of view of data consumers. Our work aims to fill the lack of specific proposals for the DQ evaluation in Web portals and tools that put these proposals into practice. The paper illustrate how PoDQA tool works and how it can be used by data consumers in order to, for example, discover the data quality of a specific web portal. PoDQA also suggests several corrective maintenance activities for users who are interested in the improvement of the data quality of their Web portals.
Data-driven extraction of relative reasoning rules to limit combinatorial explosion in biodegradation pathway prediction
Authors: Kathrin Fenner, Junfeng Gao, Stefan Kramer, Lynda Ellis, Larry Wackett
Venue: Bioinformatics (2008)

Motivation: The University of Minnesota Pathway Prediction System (UM-PPS) is a rule-based expert system to predict plausible biodegradation pathways for organic compounds. However, iterative application of these rules to generate biodegradation pathways leads to combinatorial explosion. We use data from known biotransformation pathways to rationally determine biotransformation priorities (relative reasoning rules) to limit this explosion. Results: A total of 112 relative reasoning rules were identified and implemented. In one prediction step, i.e. as per one generation predicted, the use of relative reasoning decreases the predicted biotransformations by over 25% for 50 compounds used to generate the rules and by about 15% for an external validation set of 47 xenobiotics, including pesticides, biocides and pharmaceuticals. The percentage of correctly predicted, experimentally known products remains at 75% when relative reasoning is used. The set of relative reasoning rules identified, therefore, effectively reduces the number of predicted transformation products without compromising the quality of the predictions. Availability: The UM-PPS server is freely available on the web to all users at the time of submission of this manuscript and will be available following publication at http://umbbd.msi.umn.edu/predict/. Contact: kathrin.fenner@eawag.ch Supplementary information:Supplementary data are available at Bioinformatics online.
Analysis of size interval task assignment policies
Authors: Eitan Bachmat, Hagit Sarfati
Venue: ACM SIGMETRICS Performance Evaluation Review (2008)

We analyze the performance of Size Interval task assignment (SITA) scheduling policies, for multi-host scheduling in a non-preemptive environment. We establish a general duality theory for the performance analysis of SITA policies. When the job size distribution is Bounded Pareto and the range of job sizes tends to infinity. we determine asymptotically optimal cutoff values and provide asymptotic formulas for average waiting time and slowdown. In the case of inhomogeneous hosts we determine their optimal ordering. We also consider TAGS policies. We provide a general formula that describes their load handling capabilities and examine their performance when the job size distribution is Bounded Pareto.
Johnny Appleseed: wardriving to reduce interference in chaotic wireless deployments
Authors: Tim Dasilva, Kevin Eustice, Peter Reiher
Venue: Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems (2008)

Many areas have dense deployments of 802.11 wireless access points, often with little or no planning of the best choices of channel assignments. As a result, there is often very high interference due to poor channel assignments. One contributing factor is that many access points are deployed with the absolute minimum of configuration effort, which means they are assigned to the channel the manufacturer has chosen, as a default. In many cases, such minimal effort deployments also mean that the access point uses the manufacturer-default password. Inspired by Johnny Appleseed, a 19th century American altruist who wandered the wilderness planting apple trees for the use of others, we investigate a method by which an altruistic wardriver moving through a dense wireless deployment could take advantage of such minimally configured access points. Where possible, he could use the default passwords to log into the system and change the channel assignment to better suite the surrounding environment, reducing interference for all. We examine this solution in simulation using real data gathered by wardrivers in several locations. We demonstrate that even with some conservative assumptions on the number of access points our Johnny Appleseed could alter, simple single-pass algorithms can result in a 10% reduction of total interference in a dense wireless deployment. We discuss the legal and ethical implications of the approach.