ppxscal/embedding_network huggingface dataset:
Multimedia retrieval at INEX 2007
Authors: Theodora Tsikrika, Thijs Westerveld
Venue: ACM SIGIR Forum (2008)

Structured document retrieval from XML documents allows for the retrieval of XML document fragments, i.e., XML elements or passages, that contain relevant information. The main INEX Ad Hoc task focuses on text-based XML retrieval. The INEX Multimedia track considers types of media other than text and its objective is to exploit the XML structure that provides a logical level at which multimedia objects are connected, in order to improve the retrieval performance of an XML-driven multimedia information retrieval system. To this end, it provides an evaluation platform for the retrieval of multimedia documents and document fragments. In addition, it creates a discussion forum where the participating groups can exchange their ideas on different aspects of the multimedia XML retrieval task.
Multiple instance ranking
Authors: Charles Bergeron, Jed Zaretzki, Curt Breneman, Kristin P. Bennett
Venue: Proceedings of the 25th international conference on Machine learning (2008)

This paper introduces a novel machine learning model called multiple instance ranking (MIRank) that enables ranking to be performed in a multiple instance learning setting. The motivation for MIRank stems from the hydrogen abstraction problem in computational chemistry, that of predicting the group of hydrogen atoms from which a hydrogen is abstracted (removed) during metabolism. The model predicts the preferred hydrogen group within a molecule by ranking the groups, with the ambiguity of not knowing which hydrogen atom within the preferred group is actually abstracted. This paper formulates MIRank in its general context and proposes an algorithm for solving MIRank problems using successive linear programming. The method outperforms multiple instance classification models on several real and synthetic datasets.
Lossless image compression via predictive coding of discrete Radon projections
Authors: Andrew Kingston, Florent Autrusseau
Venue: Image Communication (2008)

This paper investigates predictive coding methods to compress images represented in the Radon domain as a set of projections. Both the correlation within and between discrete Radon projections at similar angles can be exploited to achieve lossless compression. The discrete Radon projections investigated here are those used to define the Mojette transform first presented by Guedon et al. [Psychovisual image coding via an exact discrete Radon transform, in: T.W. Lance (Ed.), Proceedings of the Visual Communications AND Image Processing (VCIP), May 1995, Taipei, Taiwan, pp. 562-572]. This work is further to the preliminary investigation presented by Autrusseau et al. [Lossless compression based on a discrete and exact radon transform: a preliminary study, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. II, May 2006, Toulouse, France, pp. 425-428]. The 1D Mojette projections are re-arranged as two dimensional images, thus allowing the use of 2D image compression techniques onto the projections. Besides the compression capabilities, the Mojette transforms brings an interesting property: a tunable redundancy. As the Mojette transform is able to both compress and add redundancy, the proposed method can be viewed as a joint lossless source-channel coding technique for images. We present here the evolution of the compression ratio depending on the chosen redundancy.
Limits of opinion-finding baseline systems
Authors: Craig Macdonald, Ben He, Iadh Ounis, Ian Soboroff
Venue: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (2008)

In opinion-finding, the retrieval system is tasked with retrieving not just relevant documents, but which also express an opinion towards the query target entity. Most opinion-finding systems are based on a two-stage approach, where initially the system aims to retrieve relevant documents, which are then re-ranked according to the extent to which they are detected to be of an opinionated nature. In this work, we investigate how the underlying 'baseline' retrieval system performance affects the overall opinion-finding performance. We apply two effective opinion-finding techniques to all the baseline runs submitted to the TREC 2007 Blog track, and draw new insights and conclusions.
Data mining research for customer relationship management systems: a framework and analysis
Authors: Sarika Sharma, D. P. Goyal, R. K. Mittal
Venue: International Journal of Business Information Systems (2008)

Data mining is a new technology that helps businesses to predict future trends and behaviours, allowing them to make proactive, knowledge-driven decisions. When data mining tools and techniques are applied on the data warehouse based on customer records, they search for the hidden patterns and trends. These can be further used to improve customer understanding and acquisition. Customer Relationship Management (CRM) systems are adopted by the organisations in order to achieve success in the business and also to formulate business strategies, which can be formulated based on the predictions given by the data mining tools. Basically three major areas of data mining research are identified: implementation of CRM systems, evaluation criteria for data mining software and CRM systems and methods to improve data quality for data mining. The paper is concluded with a proposed integrated model for the CRM systems evaluation and implementation. This paper focuses on these areas, where there is need for more explorations, and will provide a framework for analysis of the data mining research for CRM systems.
Object-based classification of residential land use within Accra, Ghana based on QuickBird satellite data
Authors: D. Stow, A. Lopez, C. Lippitt, S. Hinton, J. Weeks
Venue: International Journal of Remote Sensing (2007)

A segmentation and hierarchical classification approach applied to QuickBird multispectral satellite data was implemented, with the goal of delineating residential land use polygons and identifying low and high socio-economic status of neighbourhoods within Accra, Ghana. Two types of object-based classification strategies were tested, one based on spatial frequency characteristics of multispectral data, and the other based on proportions of Vegetation-Impervious-Soil sub-objects. Both approaches yielded residential land-use maps with similar overall percentage accuracy (75%) and kappa index of agreement (0.62) values, based on test objects from visual interpretation of QuickBird panchromatic imagery.
Innovations in science teacher education: Effects of integrating technology and team-teaching strategies
Authors: Syh-Jong Jang
Venue: Computers Education (2008)

The purpose of this study was to integrate technology and team-teaching techniques into science teacher education method courses in order to explore the effects of such integration on preservice teachers. The participants included one instructor and a total of 42 preservice teachers. A technology team-teaching model (TTT) was designed in this study to restructure science method courses with technology. This study used a mixed-method design, incorporating both quantitative and qualitative techniques. The results revealed that there were significant differences in ''designing an appropriate science topic to be taught with technology'' and ''integrating computer activities with appropriate pedagogy in classroom instruction'' (F=5.260, p
Classification Model Learning for Bulletin Board Site Analysis Based on Unbalanced Textual Examples
Authors: Shigeaki Sakurai, Ryohei Orihara
Venue: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications (2008)

This paper proposes a method that acquires a more appropriate classification model for label extraction. The model can extract specific labels from articles included in bulletin board sites. The labels represent the contents of the articles and are used to characterize the articles. The method selects two kinds of important examples not including a specific label by using expressions related to the label. The method inductively acquires the classification model from the selected examples and examples including the label. The paper applies the method to articles collected from three bulletin board sites and verifies its effect through comparative experiments
Camera based vehicle detection and tracking using shadows and adaptive template matching
Authors: Marco Krips, Andreas Teuner, Jörg Velten, Anton Kummert
Venue: Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing (2003)

Enhancement of road traffic safety means that the human component as well as the technical components have to work at highest level. Together they build a complex system. Due to rising road traffic, mental and physical stress, driver's concentration is deteriorating and is the main reason for traffic accidents. Improvement of the human factor is very difficult, but the introduction of driver assistance systems offers a wide range of applications that help to compensate or correct human faults. Typical accidents, caused by missing driver's attention, are accidents during lane change. Drivers forget to assure that no other vehicle is alongside; especially they forget to check the blind spot. This paper presents our first approaches to detect vehicles in the blind spot of a driving car.
Joint transmit power and physical carrier sensing adaptation based on loss differentiation for high density IEEE 802.11 WLAN
Authors: Hui Ma, Jing Zhu, Sumit Roy, Soo Young Shin
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2008)

In high density (HD) IEEE 802.11 WLAN, packet loss can occur due to co-channel interference (asynchronous interference) or collisions (synchronous interference). In order to effectively mitigate the interference for spatial reuse, the causes of packet loss should be differentiated and corresponding network parameters (physical carrier sensing (PCS) threshold, transmit power (TXPW) and contention windows size) tuned accordingly. Such loss differentiation ability is not supported by current IEEE 802.11 networks; we devised a novel, zero over-the-air overhead, robust yet accurate method of estimating the probability of collision and interference, respectively. In this work, we investigate how to use differentiated packet error rate (PER) to mitigate asynchronous interference for increasing spatial reuse. Motivated by analysis, a joint transmit power and PCS threshold self-adaptation algorithm based on loss differentiation is proposed. Heuristics that address node starvation and fairness are also incorporated within the above (aggregate throughput maximization) framework to allow a flexible suite of approaches to network tuning. Elaborate simulations show that such joint adaptation algorithm can increase both total throughput and worst link throughput in HD WLAN greatly compared with PCS only adaptation without loss differentiation.
Similarity based smoothing in language modeling
Authors: Zoltán Szamonek, István Biró
Venue: Acta Cybernetica (2007)

In this paper, we improve our previously proposed Similarity Based Smoothing (SBS) algorithm. The idea of the SBS is to map words or part of sentences to an Euclidean space, and approximate the language model in that space. The bottleneck of the original algorithm was to train a regularized logistic regression model, which was incapable to deal with real world data. We replace the logistic regression by regularized maximum entropy estimation and a Gaussian mixture approach to model the language in the Euclidean space, showing other possibilities to use the main idea of SBS. We show that the regularized maximum entropy model is flexible enough to handle conditional probability density estimation, thus enable parallel computation tasks with significantly decreased iteration steps. The experimental results demonstrate the success of our method, we achieve 14% improvement on a real world corpus.
Expressive and safe static reflection with MorphJ
Authors: Shan Shan Huang, Yannis Smaragdakis
Venue: Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation (2008)

Recently, language extensions have been proposed for Java and C# to support pattern-based reflective declaration. These extensions introduce a disciplined form of meta-programming and aspect-oriented programming to mainstream languages: They allow members of a class (i.e., fields and methods) to be declared by statically iterating over and pattern-matching on members of other classes. Such techniques, however, have been unable to safely express simple, but common, idioms such as declaring getter and setter methods for fields. In this paper, we present a mechanism that addresses the lack of expressiveness in past work without sacrificing safety. Our technique is based on the idea of nested patterns that elaborate the outer-most pattern with blocking or enabling conditions. We implemented this mechanism in a language, MorphJ. We demonstrate the expressiveness of MorphJ with real-world applications. In particular, the MorphJ reimplementation of DSTM2, a software transactional memory library, reduces 1,107 lines of Java reflection and bytecode engineering library calls to just 374 lines of MorphJ code. At the same time, the MorphJ solution is both high level and safer, as MorphJ can separately type check generic classes and catch errors early. We present and formalize the MorphJ type system, and offer a type-checking algorithm.
Combining Rule-based and Machine Learning Approaches for Shape Recognition
Authors: Glenn Becker
Venue: Proceedings of the 36th Applied Imagery Pattern Recognition Workshop (2007)

Shape recognition applications are multi-step processes that include capture, preprocessing, feature extraction, recognition, and a decision process. Each step can use approaches in the continuum from rule-based to machine learning. This paper presents example approaches, the pros and cons of these rule-based and adaptive approaches, and performance differences in biometric applications. Rule-based and machine learning approaches represent the extremes in a continuum of approaches. Each step in the shape recognition process may include algorithms that fall anywhere on this continuum or fusion that combines the results of multple algorithms. Determining an appropriate approach for each step is generally determined by the availability of ground-truthed data for training and testing, and how much of the analysis can be automated. If analysis is based on experience and a sufficient number of ground-truthed exemplars are available then a machine learning approach is indicated. Conversely, a rule-based approach may be best if the analysis uses predictions or adequate training data is not available.
A semantic nomenclature for the pharmaceutical sector based on networked ontologies
Authors: Tomás Pariente Lobo, Germán Herrero Cárcel
Venue: Proceedings of the 2007 Euro American conference on Telematics and information systems (2007)

Knowledge intensive sectors, such as the pharmaceutical, have typically to face the problem of dealing with heterogeneous and vast amounts of information. In these scenarios discovery, integration and easy access to the knowledge are the most important factors. The use of semantics to classify meaningfully the information and to bridge the gap between the different representations that different stakeholders have is widely accepted [1]. The problem arises when the ontologies used to model the domain become too large and unmanageable. The current status of the technology does not allow to easily working with this type of ontologies. In this paper we propose the use of networked ontologies to solve these problems for the particular case scenario of the pharmaceutical sector in Spain. Instead of using a single ontology, the idea is to break the model in several meaningful pieces and bind them together using a networked ontology model for representing and managing relations between multiple ontologies. The Semantic Nomenclature is a case study that is currently under development in the NeOn1 EC funded FP6 project [2]. Our approach is that of the integration of the large quantity of information about pharmaceutical products provided by a wide range of actors in the Spanish pharmaceutical sector using networked ontologies. Cover the profound lack of systematization for creating, maintaining and updating drug-related information, the creation of a common reference ontology about drugs, the provision of easy mechanisms for discovery, model and mapping of new sources of information in a collaborative way, and the ability to reason on the context of user and ontologies in order to facilitate the mapping and retrieving processes, are among the main goal of this case study that will be further developed in the present paper.
An Interference-aware and Power Efficient Topology Control Algorithm for Wireless Multi-hop Networks
Authors: Huang Chuanhe, Cheng Yong, Li Yuan, Shi Wenming, Zhou Hao
Venue: Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (2008)

This paper investigates topology control and seeks to find a distributed solution with low interference, high performance. The paper proposes a distributed topology control algorithm called CBDTG by using the method of adjusting transmission power and the achievements of computational geometry, especially Delaunay Triangulation (DT). Each node in the network invokes the neighbor discovery process by using an initial power and adds the discovered neighbors into its neighbor set. Each node finds DT neighbors in order to generate a triangulation with good properties such as planarity and regularity. The experimental result shows that the average node degree is between that of basic CBTC and the condensed CBTC with several edges removed, and the resulting topology will not suffer from high interference due to high node degree, nor will it suffer from low connectivity because of extremely low node degree. CBDTG also outperforms the basic CBTC in throughput.
Class-of-service in ip backbones: informing the network neutrality debate
Authors: Murat Yuksel, Kadangode K. Ramakrishnan, Shivkumar Kalyanaraman, Joseph D. Houle, Rita Sadhvani
Venue: Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems (2008)

The benefit of Class-of-Service (CoS) is an important topic in the "Network Neutrality" debate. Proponents of network neutrality suggest that over-provisioning is a viable alternative to CoS. We quantify the extra capacity requirement for an over-provisioned classless (i.e., best-effort) network compared to a CoS network providing the same delay or loss performance for premium traffic. We first develop a link model that quantifies this Required Extra Capacity (REC). For bursty and realistic traffic distributions, we find the REC using ns-2 simulation comparisons of the CoS and classless link cases. We use these link models to quantify the REC for realistic network topologies. We show that REC can be significant even when the proportion of premium traffic is small, a situation often considered benign for the over-provisioning alternative.
A Library for Locally Weighted Projection Regression
Authors: Stefan Klanke, Sethu Vijayakumar, Stefan Schaal
Venue: The Journal of Machine Learning Research (2008)

In this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the key features, our code supports multi-threading, is available for multiple platforms, and provides wrappers for several programming languages.
Program result-checking: a theory of testing meets a test of theory
Authors: M. Blum, H. Wasserman
Venue: Proceedings of the 35th Annual Symposium on Foundations of Computer Science (1994)

We review the field of result-checking, discussing simple checkers and self-correctors. We argue that such checkers could profitably be incorporated in software as an aid to efficient debugging and reliable functionality. We consider how to modify traditional checking methodologies to make them more appropriate for use in real-time, real-number computer systems. In particular, we suggest that checkers should be allowed to use stored randomness: i.e., that they should be allowed to generate, pre-process, and store random bits prior to run-time, and then to use this information repeatedly in a series of run-time checks. In a case study of checking a general real-number linear transformation (for example, a Fourier Transform), we present a simple checker which uses stored randomness, and a self-corrector which is particularly efficient if stored randomness is allowed.
Mental Models: Aligning design strategy with human behavior
Authors: Indi Young
Venue: Ubiquity (2008)

I ndi got her BS in Computer Science from Cal Poly and began her master's at Colorado State. She then worked as a software engineer, later managing Web applications that focused on the user. Her concepts in mental models derive from attempting to bridge the developer-user gap. Her expertise ranges from structuring crossfunctional teams, to managing participant recruiting, and conducting user interviews, thereby creating effective tools for exchanging results.
Preferred extensions as stable models*
Authors: Juan carlos Nieves, Ulises CortÉs, Mauricio Osorio
Venue: Theory and Practice of Logic Programming (2008)

Given an argumentation framework AF, we introduce a mapping function that constructs a disjunctive logic program P, such that the preferred extensions of AF correspond to the stable models of P, after intersecting each stable model with the relevant atoms. The given mapping function is of polynomial size w.r.t. AF. In particular, we identify that there is a direct relationship between the minimal models of a propositional formula and the preferred extensions of an argumentation framework by working on representing the defeated arguments. Then we show how to infer the preferred extensions of an argumentation framework by using UNSAT algorithms and disjunctive stable model solvers. The relevance of this result is that we define a direct relationship between one of the most satisfactory argumentation semantics and one of the most successful approach of nonmonotonic reasoning i.e., logic programming with the stable model semantics.
Implementation of Turing Machines with the Scufl Data-Flow Language
Authors: Tristan Glatard, Johan Montagnat
Venue: Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (2008)

In this paper, the expressiveness of the simple Scufl data-flow language is studied by showing how it can be used to implement Turing machines. To do that, several non trivial Scufl patterns such as self-looping or sub-workflows are required and we precisely explicit them. The main result of this work is to show how a complex workflow can be implemented using a very simple data-flow language. Beyond that, it shows that Scufl is a Turing complete language, given some restrictions that we discuss.