ppxscal/embedding_network huggingface dataset:
Energy efficient wireless networks
Authors: Xiangyang Li
Venue:  (2005)

Wireless ad hoc networks have been undergoing a revolution that promises to have a significant impact throughout society, one that could quite possibly dwarf milestones in the information revolution. In these wireless networks, there are no wired infrastructures and two nodes can communicate through multi-hop wireless links by using intermediate nodes to relay the message. Energy conservation and network performance are probably the two most critical issues in ad hoc wireless network, because wireless devices are usually powered by batteries only and have limited computing power and memory. Network topology control technique is to let each wireless node select some neighbors to communicate and conserve its transmission power through proper power assignment, while maintaining a structure that supports energy efficient routing and improves the overall network performance. In this report, we focus on discussing the localized algorithms to construct energy efficient topology for homogeneous and heterogeneous wireless ad hoc networks, where the nodes have same and different maximum transmission ranges respectively. We propose several novel localized algorithms to build energy efficient topology for unicast and broadcast applications, where each node selects its transmission range and logical neighbors only according to its 1-hop or 2-hop neighborhood information, hence the topology is self-adaptive to any dynamic change at any time. Extensive experiments are conducted to evaluate the network performance for our proposed topology. Our theoretical results are corroborated in the simulations: these structures are more efficient in practice, compared with other known structures used in wireless ad hoc networks and are easier to construct. In addition, the power assignment based on our new structures shows low energy cost and small interference at each wireless node. We conclude the report by pointing out some open problems.
Probability models for information processing and machine perception
Authors: Christopher J. Pal
Venue:  (2005)

Probability models and their graphical descriptions provide a principled underlying formalism for constructing and describing models for data. Such models can be constructed for many important problems involving the processing and analysis of information in the form of: matrices of numbers, images, and time series of vectors. I look at four problems with important real world applications in general data processing, image, video and audio processing. I use probability models of joint distributions for which the graphical descriptions of the models contain repeating sub-structures and non-linearities. I derive optimization algorithms which take advantage of the structure of the problems and models. I give a brief introduction to graphical probability models and methods for optimizing such models. I discuss variational methods and their relationship to other inference and optimization approaches. This thesis makes a number of contributions. First, I present a model for jointly clustering the rows and columns of a matrix and permuting a matrix into block constant form. I develop a new algorithm for model optimization based on sequentially considering the data. This algorithm proves to be superior to many other alternatives based on synthetic data experiments and results on &ldquo;DNA chip&rdquo; experiments. Secondly, I present a new method for combining images taken with different camera settings into a higher dynamic range image using only pixel values. I derive priors for imaging functions using a generative model of the derivative structure of functions. This derivation is novel in that it ties together Generative Models, Smoothness Regularization and Gaussian Processes. I derive a new optimization technique to estimate both functions and high dynamic range irradiance estimates. Thirdly, I present a new appearance model for visual objects called a Probabilistic Montage. I present variations of the model and derive optimization algorithms. Finally, I present a new method for noise robust speech recognition and show how it is possible to approximate likelihoods in a non-linear model for this problem using parameterized Gaussian basis functions. This leads to an efficient algorithm in a principled model for cleaning noisy speech and produces highly competitive results.
SOM-based algorithms for qualitative variables
Authors: Marie Cottrell, Smail Ibbou, Patrick Letrémy
Venue: Neural Networks (2004)

It is well known that the SOM algorithm achieves a clustering of data which can be interpreted as an extension of Principal Component Analysis, because of its topology-preserving property. But the SOM algorithm can only process real-valued data. in previous papers, we have proposed several methods based on the SOM algorithm to analyze categorical data, which is the case in survey data. In this paper, we present these methods in a unified manner. The first one (Kohonen Multiple Correspondence Analysis, KMCA) deals only with the modalities, while the two others (Kohonen Multiple Correspondence Analysis with individuals, KMCA_ind, Kohonen algorithm on DISJonctive table, KDISJ) can take into account the individuals, and the modalities simultaneously.
LRRM: A Randomized Reliable Multicast Protocol for Optimizing Recovery Latency and Buffer Utilization
Authors: Nipoon Malhotra, Shrish Ranjan, Saurabh Bagchi
Venue: Proceedings of the 24th IEEE Symposium on Reliable Distributed Systems (2005)

An efficient recovery protocol for lost messages is crucial for supporting reliable multicasting. The treebased recovery protocols group nodes into recovery regions and designate a recovery node per region for buffering and retransmitting lost messages. In these protocols, the recovery host may get overloaded during periods of large message losses and costly remote recovery may be initiated even though a peer node has the lost message. To address these drawbacks, the Randomized Reliable Multicast Protocol (RRMP) was proposed which distributes the responsibility of error recovery among all members in a group. The pressure on the buffer and computational resources on the intermediate nodes is increasing due to the wide distribution of multicast participants with widely varying reception rates and periodic disconnections. In this paper, we propose the Lightweight Randomized Reliable Multicast (LRRM) protocol that optimizes the amount of buffer space by providing an efficient mechanism based on best-effort multicast for retrieving a lost message. A theoretical analysis and a simulation based study of two realistic topologies indicate that LRRM provides comparable recovery latency to RRMP for lower buffer space usage. While presented in the context of RRMP, LRRM can also benefit other treebased reliable multicast protocols.
Model-Based Development of Automotive Embedded Systems: A Case of Continuously Variable Transmission (CVT)
Authors: Won Hyun Oh, Jung Hee Lee, Hyoung Geun Kwon, Hyoung Jin Yoon
Venue: Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2005)

Due to the increasing demand for quick time-to-market and diverse functionalities in the development of powertrain controllers, the traditional vehicle development process must be replaced with a model-based process. This type of process offers many advantages in testing, evaluation, and documentation that result in reduced development time and cost. However, it is only feasible on the condition of a proper simulation model and a suitably adapted application product interface. In this paper, we realize a specific model-based development process and demonstrate it for a continuously variable transmission (CVT). We present the main steps of the modeling process and show its implementation, functionality, and reliability.
Making Subsequence Time Series Clustering Meaningful
Authors: Jason R. Chen
Venue: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)

Recently, the startling claim was made that sequential time series clustering is meaningless. This has important consequences for a significant amount of work in the literature, since such a claim invalidates this work's contribution. In this paper, we show that sequential time series clustering is not meaningless, and that the problem highlighted in these works stem from their use of the Euclidean distance metric as the distance measure in the subsequence vector space. As a solution, we consider quite a general class of time series, and propose a regime based on two types of similarity that can exist between subsequence vectors, which give rise naturally to an alternative distance measure to Euclidean distance in the subsequence vector space. We show that, using this alternative distance measure, sequential time series clustering can indeed be meaningful. We repeat a key experiment in the work on which the "meaningless" claim was based, and show that our method leads to a successful clustering outcome.
Cost-Based Scheduling of Scientific Workflow Application on Utility Grids
Authors: Jia Yu, Rajkumar Buyya, Chen Khong Tham
Venue: Proceedings of the First International Conference on e-Science and Grid Computing (2005)

Over the last few years, Grid technologies have progressed towards a service-oriented paradigm that enables a new way of service provisioning based on utility computing models. Users consume these services based on their QoS (Quality of Service) requirements. In such "pay-per-use" Grids, workflow execution cost must be considered during scheduling based on users' QoS constraints. In this paper, we propose a cost-based workflow scheduling algorithm that minimizes execution cost while meeting the deadline for delivering results. It can also adapt to the delays of service executions by rescheduling unexecuted tasks. We also attempt to optimally solve the task scheduling problem in branches with several sequential tasks by modeling the branch as a Markov Decision Process and using the value iteration method.
Prediction of the Running Time of Tasks Based on Load
Authors: Jingbo Yuan, Jiubin Ju, Liang Hu, Shunli Ding
Venue: Proceedings of the IEEE International Conference on e-Business Engineering (2005)

The computing grid is becoming the platform of choice for large-scale distributed data-intensive applications. Performance measurement, analysis and prediction have become increasingly important in a grid environment, mainly due to resource's geographic distribution, heterogeneity, dynamic, distributed ownership with different policies and priorities, varying loads, reliability, and availability conditions. Resource management and scheduling based on performance prediction can allocate more availably resources and schedule tasks to meet user's performance requirement. Resource Performance includes many factors. A performance prediction model is put forwards to deal with task running times .The model is based on host load and can on-line predict the running time of tasks on candidate hosts. The system is evaluated using over 3000 randomized test cases. The experimental result shows that the model is practical and effective and its precision is preferable.
Document Image Retrieval Based on Density Distribution Feature and Key Block Feature
Authors: Hong Liu, Suoqian Feng, Hongbin Zha, Xueping Liu
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

Document image retrieval is an important part of many document image processing systems such as paperless office systems, digital libraries and so on. Its task is to help users find out the most similar document images from a document image database. For developing a system of document image retrieval among different resolutions, different formats document images with hybrid characters of multiple languages, a new retrieval method based on document image density distribution features and key block features is proposed in this paper. Firstly, the density distribution and key block features of a document image are defined and extracted based on documents' print-core. Secondly, the candidate document images are attained based on the density distribution features. Thirdly, to improve reliability of the retrieval results, a confirmation procedure using key block features is applied to those candidates. Experimental results on a large scale document image database, which contains 10385 document images, show that the proposed method is efficient and robust to retrieve different kinds of document images in real time.
CoCA: A Composition-Centric Approach to Requirements Engineering
Authors: Ruzanna Chitchyan, Ian Sommerville, Awais Rashid
Venue: Proceedings of the 13th IEEE International Conference on Requirements Engineering (2005)

In this paper we discuss how mutual influences (e.g. conflicts) of different stakeholder concerns can be detected and reasoned about through composition and stepwise refinement. Some concepts from the Aspect- Oriented Software Development paradigm are used to support the composition-centric approach.
Using a PDA for Mobile Learning
Authors: Moira J. McAlister, Peng Hui Xie
Venue: Proceedings of the IEEE International Workshop on Wireless and Mobile Technologies in Education (2005)

Mobile technology has offered an opportunity to provide a new generation of people with the means to interact with activities irrespective of location. As a consequence it will become more common to utilize its potential within curriculum activities. The feasibility of using mobile technology is directly influenced by its scope. This paper outlines an educational digital game and discusses the implementation with regard to using Personal Digital Assistants as potential mobile learning devices. The paper outlines the considerations which must be made to use such mobile devices and the recommendations on the type of educational software which should be developed. The educational digital game is then assessed with reference to future dynamic environments which will encompass a range of mobile and non-mobile devices.
Novel DBMS Extensions for Functional Brain Image Analysis
Authors: Kristin R. Munch, John V. Carlis, Jose V. Pardo, Joel T. Lee
Venue: Proceedings of the 18th International Conference on Systems Engineering (2005)

Using computerized analysis of neuroimages can potentially enhance sensitivity and specificity in the diagnosis of psychiatric diseases, such as Alzheimer's disease (AD). However, quantitative analysis of functional brain images remains a manual, tedious task. To address this problem, we are building an extended neuropsychiatric image database system to advance the study of brain images of psychiatric disease toward more comprehensive analyses. We report on our progress so far, and show the design and implementation of a suite of DBMS extensions, as well as the interface used by clinicians to compare metabolism results for single and multiple patients in specific brain regions, a task that was previously insurmountable. We are working toward a first in clinical applications of neuroimaging that will bear directly upon patient assessment, diagnosis, and treatment.
Dis/integrating animals: ethical dimensions of the genetic engineering of animals for human consumption
Authors: Traci Warkentin
Venue: AI Society (2005)

Research at the intersections of feminism, biology and philosophy provides dynamic starting grounds for this discussion of genetic technologies and animals. With a focus on animal bodies, I will examine moral implications of the genetic engineering of &#x201c;domesticated&#x201d; animals&#x2014;primarily pigs and chickens&#x2014;for the purposes of human consumption. Concepts of natural and artificial, contamination and purity, integrity and fragmentation and mind and body will feature in the discussion. In this respect, Margaret Atwood&#x2019;s novel, Oryx and Crake, serves as a cogent medium for exploring these highly contentious practices and ideas as it provides hypothetical narratives of possibility. Moreover, it is used to highlight contemporary hegemonic assumptions and values in ways that make them visible. Particular attention is paid to issues of growing human organs in pigs for xenotransplantation (resulting, for Atwood, in &#x201c;pigoons&#x201d;) and the ultimate end of the intensive factory farming of chickens through the genetic engineering of &#x2018;mindless&#x2019; chicken tumours (or, as Atwood calls them, &#x201c;ChickieNobs&#x201d;). Integral to these philosophical considerations is the provocative question of the genetic modification of animal bodies as a means to end the suffering of domestic food animals. The ultimate implications of this question include an ongoing sensory and moral deprivation of human experience, potentially resulting in a future mechanomophosis, the extreme manifestation of an existing mechanomorphism.
Bibliometric maps of field of science
Authors: Irina Marshakova-Shaikevich
Venue: Information Processing and Management: an International Journal (2005)

The present paper is devoted to two directions in algorithmic classificatory procedures: the journal co-citation analysis as an example of citation networks and lexical analysis of keywords in the titles and texts. What is common to those approaches is the general idea of normalization of deviations of the observed data from the mathematical expectation. The application of the same formula leads to discovery of statistically significant links between objects (journals in one case, keywords--in the other). The results of the journal co-citation analysis are reflected in tables and map for field "Women's Studies" and for field "Information Science and Library Science". An experimental attempt at establishing textual links between words was carried out on two samples from SSCI Data base: (1) EDUCATION and (2) ETHICS. The EDUCATION file included 2180 documents (of which 751 had abstracts); the ETHICS file included 807 documents (289 abstracts). Some examples of the results of this pilot study are given in tabular form. The binary links between words discovered in this way may form triplets or other groups with more than two member words.
Modeling landscapes with ridges and rivers
Authors: Farès Belhadj, Pierre Audibert
Venue: Proceedings of the ACM symposium on Virtual reality software and technology (2005)

Generating realistic models of landscapes with drainage network is a major field in computer graphics. In this paper, we present a fractal based method which generates natural terrains using ridges and rivers information. As opposed to methods that compute water erosion for a given terrain model, our terrain mesh is generated constrained by a predefined set of ridge lines and rivers network. A new extension of the midpoint displacement model is used in this context. The resulting landscapes meshes leads to realistic rendering and our method seems to be very promising.
Architecture d'agent sensible au contexte pour la prise de d&eacute;cisions
Authors: Oana Bucur, Philippe Beaune, Olivier Boissier
Venue: Proceedings of the 2nd French-speaking conference on Mobility and ubiquity computing (2005)

Sensing, managing and representing context are necessary capacities for ubiquitous computing applications, moreover, their components must also be able to reason and adapt their behavior to the evolution of their physical and social environment. This paper proposes a multi-agent architecture consisting of context aware agents able to learn how to discern relevant from non relevant context on one hand, and to make appropriate decisions based on it on the other hand. This multi-agent system interacts with a context manager layer, which is able to answer context-related queries by using an ontological representation of context. The use of this architecture is illustrated on a test application for agenda management, using the JADE-LEAP platform on PCs and PocketPCs.
Inside Doubt: On the Non-Identity of the Theory of Mind and Propositional Attitude Psychology
Authors: David Landy
Venue: Minds and Machines (2005)

Eliminative materialism is a popular view of the mind which holds that propositional attitudes, the typical units of our traditional understanding, are unsupported by modern connectionist psychology and neuroscience, and consequently that propositional attitudes are a poor scientific postulate, and do not exist. Since our traditional folk psychology employs propositional attitudes, the usual argument runs, it too represents a poor theory, and may in the future be replaced by a more successful neurologically grounded theory, resulting in a drastic improvement in our interpersonal relationships. I contend that these eliminativist arguments typically run together two distinct capacities: the folk psychological mechanisms which we use to understand one another, and scientific and philosophical guesses about the structure of those understandings. Both capacities are ontologically committed and therefore empirical. However, the commitments whose prospects look so dismal to the eliminativist, in particular the causal and logical image of propositional attitudes, belong to the guesses, and not necessarily to the underlying mechanisms. It is the commitments of traditional philosophical perspectives about the operation of our folk psychology which are contradicted by¿new evidence and modeling methods in connectionist psychology. Our actual folk psychology was not clearly committed to causal, sentential propositional attitudes, and thus is not directly threatened by connectionist psychology.
Constraint-based test data generation in the presence of stack-directed pointers
Authors: Arnaud Gotlieb, Tristan Denmat, Bernard Botella
Venue: Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering (2005)

Constraint-Based Test data generation (CBT) exploits constraint satisfaction techniques to generate test data able to kill a given mutant or to reach a selected branch in a program. When pointer variables are present in the program, aliasing problems may arise and may lead to the failure of current CBT approaches. In our work, we propose an overall CBT method that exploits the results of an intraprocedural points-to analysis and provides two specific constraint combinators for automatically generating test data able to reach a selected branch. Our approach correctly handles multi-levels stack-directed pointers that are mainly used in real-time control systems. The method has been fully implemented in the test data generation tool INKA and first experiences in applying it to a variety of existing programs tend to show the interest of the approach.
Improving Lotos Simulation Using Constraint Propagation
Authors: Malek Mouhoub, Samira Sadaoui
Venue: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (2005)

Lotos is the ISO formal specification language for describing and verifying concurrent and distributed systems. The simulation or execution of complex Lotos specifications is, however, not always efficient due to the space explosion problem of their corresponding transition systems. To overcome this difficulty in practice, we propose in this paper the integration of constraint propagation techniques into the Lotos simulation. Indeed, constraint propagation techniques are very powerful for solving hard discrete combinatorial problems. Experimental tests, we have conducted on the simulation of several specified combinatorial problems, demonstrate the efficiency of integrating constraint propagation into Lotos simulation.