waashk/dblp huggingface dataset:
on the fuzzy bayesian inference of population annoyance level caused by noise exposure. too many people suffer from noise levels that scientists and health experts consider to be unacceptable, where most people become annoyed, where sleep is disturbed and where adverse health effects are to be feared. the present paper centers on inferring individuals annoyance level caused by noise exposure. the starting point of our thoughts is the impossibility of assigning an exact number to observed values. observed sound level values are imprecise. although sound level meters are more and more accurate, they cannot assign yet an exact number to an observed value. really, we assume that actually, no exact number can be assigned to a sound level observed value. if our interest lies in maximum effects of the propagation of this imprecision, an interval-based approach seems to be adequate. anyway, although in a nearly future we could fill these gaps, it seems better to think about a time interval and its associated data-interval corresponding to the observed sound level range. although we revised a maximum likelihood solution, the aim of this paper is to discuss how imprecision of observation values is propagated when fuzzy bayesian inference for these non-precise observations is carried out.
grid resource discovery based on semantic p2p communities. grid technologies enable the sharing of a wide variety of resources. the full use of these resources requires effective resource discovery mechanisms. however, the complicated and dynamic characteristics of grid resources make sharing and discovering them a challenge. in this paper, we propose a semantic community approach to enable efficient resource discovery in grids. the system clusters nodes into communities according to their semantic properties. the community construction and maintenance is fully decentralized and self-organizing. this structure helps prune the searching space and reduce the cost of searching. the system exhibits many desirable properties: it supports complex queries and is fully decentralized, scalable, and efficient. our simulation results show how searching the grids can take advantage of semantic communities to reduce searching costs and improve the quality of results.
mobile real-time read-only transaction processing in data broadcast environments. data broadcast is a widely accepted data dissemination method for mobile computing systems. when data broadcast is used to deliver frequently updated data to mobile read-only transactions, we call it updates dissemination. existed updates dissemination protocols are unsuitable for mobile real-time read-only transaction processing since they neglect the time constraints on both data and transactions. in this paper a new updates dissemination protocol called hybrid forward multi-version data broadcast (hfmvb) is proposed. hfmvb not only guarantees the consistency of read-only transactions, it also provides higher data currency and lower miss rate by shortening the consistency interval, instantly broadcasting updates and making use of broadcast on-demand.
editorial message: special track on mobile computing and applications. riding on the success of the 2003 and 2004 mobile computing and applications track, we are pleased to present the 2005 mobile computing and applications track that features research papers drawn from a highly diversified spectrum of mobile computing. we have continued to receive a significant number of submissions this year. the papers collected in this track cover three different and yet complementary areas: mobile data management that realizes the applications of the mobile computing paradigm, under the system integration efforts provided by middleware to support mobile computing, that necessarily depends on the concrete mobile data communication research work. in particular, special attention was dedicated to draw upon research efforts and expertise from different areas of research, so as to promote better synergy and to bring forth not only core communication and sensor technologies that lay the research foundation, but also important research applications to realize the benefits of anywhere, any place and anytime pervasive and ubiquitous computing.
using recursive classification to discover predictive features. finding most predictive features for statistical classification is a challenging problem and has important applications. support vector machines (svms), for example, have been found successful with a recursive procedure in selecting most important genes for cancer prediction. it is not well understood, however, how much the success depends on the choice of the classifier, and how much on the recursive procedure. we answer this question by examining multiple classifiers (svm, ridge regression and rocchio) with feature selection in recursive and non-recursive settings, on a dna microarray dataset (amlall) and a text categorization benchmark (reuters-21578). we found recursive ridge regression most effective: its best classification performance (zero error) on the amlall dataset was obtained when using only 3 genes (selected from over 7000), which is more impressive than the best published result on the same benchmark - zero error of recursive svm using 8 genes. on reuters-21578, recursive ridge regression also achieves the best result ever published (the improvement was verified in a significance test). an in-depth analysis of the experimental results shows that the choice of classifier heavily influences the recursive feature selection process: the ridge regression classifier tends to penalize redundant features to a much larger extent than the svm does.
editorial message: special track on mobile computing and applications. riding on the success of the previous mobile computing and applications track in 2003 to 2005, we are delighted to present the 2006 mobile computing and applications track that features research papers drawn from a highly diversified spectrum of mobile computing. we have continued to receive a significant number of submissions this year. the papers collected in this track cover three different and yet complementary areas: mobile communications that forms the basis for higher level applications, inclusive of mobile data management as a support for practical mobile applications. in particular, special attention was dedicated to draw upon research efforts and expertise from different areas of research, so as to promote better synergy and to bring forth not only core communications and modeling that support data management policies to lay the foundation for application development, but also important research applications to realize the benefits of anywhere, any place and anytime pervasive and ubiquitous computing.
improving the performances of proxy cache replacement policies by considering infrequent objects. in this paper, we perform a careful study of the effect of infrequent objects on the performance of many well-known web proxy cache replacement policies including lru, lfu, gdsf, and lfd (an offline policy). using a "frequency-aware" version of these policy (one that is aware of these infrequent objects), we show that significant improvement in the performance (hit rates and byte hit rates) can potentially be achieved. we also present lru-pred, which is a modified lru replacement policy that attempts to predict single access objects that it will not cache. the algorithm, though simple, achieve better performance than lru. the results are encouraging and point to more research on designing more sophisticated replacement policies that can predict infrequent objects.
mining maximal frequent intervals. many real world data are associated with intervals of time or distance. mining frequent intervals from such data allows the users to group transactions with similar behavior together. previous work only focuses on the problem of mining frequent intervals in a discrete domain. this paper first proposes the notion of maximal frequent intervals, which are superior to frequent intervals in many perspectives, and then provides a method for mining maximal frequent intervals in either a discrete domain or a continuous domain. experimental results indicate that our method outperforms previous method for mining frequent intervals, and improves the runtime by two orders of magnitude for databases with a large number of distinct endpoints.
a new distributed data mining model based on similarity. distributed data mining (ddm) has been very active and enjoying a growing amount attention since its inception. current ddm techniques regard the distributed data sets as a single virtual table and assume there exists a global model which could be generated if the data were combined/centralized. this paper proposes a similarity-based distributed data mining(sbddm) framework which explicitly take the differences among distributed sources into consideration. a new similarity measure is introduced and its effectiveness is then evaluated and validated. this paper also illustrates the limitations of current ddm techniques through three concrete case studies. finally distributed clustering within the sbddm framework is also discussed.
adaptive data dissemination and caching for edge service architectures built with the j2ee. the deployment of distributed enterprise applications and e-business solutions, that leverage edge service architectures across wide area networks, require flexible and adaptable models for data dissemination and caching. in this paper we present the design of an architecture that streamlines the integration of proactive data dissemination and caching into e-commerce solutions built with the java 2 enterprise edition. the utilization of an adaptive push and pull approach combined with the flexible and transparent design leads to an infrastructure that facilitates the development of efficient communication strategies for business participants. well defined interfaces significantly simplify the evaluation and analysis of adaptive data dissemination and caching strategies that utilize application specific semantics to minimize network traffic and maximize temporal coherency.
using a genetic algorithm to optimize the gape of a snake jaw. ga's have more success with optimizing a single configuration than with optimizing multiple configurations with connectivity constraints tying them together, i.e., variable geometry problems such as designing the variable geometry wings of a plane. this paper describes a ga based approach to solve variable geometry optimization problems where (a) the connectivity requirement cannot be easily specified or tested, (b) the space of configurations is made up of multiple disconnected spaces, thus making it likely that a ga would find sets of configurations that are not connected and (c) the cost of testing for connectivity, while examining each pair of configurations, is prohibitive. the approach has been tested and evaluated on a problem from computational biology, modeling the bones of a snake jaw.
manpower scheduling with time windows. in this paper, we propose a manpower allocation model with time windows which is of practical interest to serviceman scheduling operations. specifically, this problem originates from peculiar port yard scheduling needs where demand is generated from locations in the yard for servicemen who are dispatched from a central point and where the objectives are to minimize the number of servicemen scheduled, travel distances, travel times and waiting times at each location. although closely related to the well-known vehicle routing problem, this problem is different while its solution could provide insight to the latter. we develop solutions using metaheuristic methods, and in particular provide tabu-embedded simulated annealing and squeaky wheel optimization with local search algorithms for this problem. we apply these newly-developed metaheuristics with adaptations for solutions to the manpower allocation problem, while our analysis throws light on how these work. computational results are reported which show the effectiveness of our approach when applied to the manpower allocation problem.
the container loading problem. this paper addresses single and multiple container loading problems. we propose to use dynamic prioritization to handle awkward box types. the box type with a higher priority will be packed onto lower surfaces of the container, or packed in earlier containers. the solution found in one iteration of the algorithm is analyzed, and the priorities are updated to be used in the next iteration. our algorithm outperformed all previous methods using standard benchmark data sets. we found the existing test data for the multiple container loading problem to be deficient and supplemented them by generating new test data consisting of 2800 test cases. the results from our algorithm using this data set are excellent.
heuristic methods for graph coloring problems. in this work, the graph coloring problem and its generalizations - the bandwidth coloring problem, the multicoloring problem and the bandwidth multicoloring problem - are studied. a squeaky wheel optimization with tabu search heuristic is developed and experiments using benchmark geometric test cases show that the algorithm performs well for these problems and achieves results for the bandwidth multicoloring problem which improve on results obtained by other researchers.
a back-end for ghc based on categorical multi-combinators. &mu;tcmc is an abstract graph reduction machine for the implementation of lazy functional languages. categorical multi-combinators served as a basis for the evaluation model of &mu;tcmc. this paper presents the implementation of a haskell compiler, using the front-end of the glasgow haskell compiler (ghc) and a new back-end based on the &mu;tcmc abstract machine. a number of code optimisations are introduced to &mu;tcmc. the performance of our implementation is benchmarked against the glasgow haskell compiler, one of the most efficient haskell compilers available.
maximum likelihood based classification for the microstructure of human sleep. in this paper a classifier for the microstructure paradigm of human sleep, the cyclic alternating pattern sequence (caps), is presented. sleep electroencephalogram (eeg) is the signal used for the scoring. an eeg model makes the feature extraction (pre-processing). the caps phases are then detected using maximum likelihood (ml) estimation. a final processing block checks caps context rules. this system was tested with good results on the record of 8 hours sleep of a normal adult subject.
web services: separation of concerns: computation coordination communication. the purpose of this paper is to investigate the use of a new concept in component communication, expressed by the channel based coordination language called &rho;ε&omega; in the coordination of web services. the role of &rho;ε&omega; is to construct and manage connectors. connectors are patterns of connected channel communicators. the communication and coordination of components lying over a distributed address space has been dealt so far with stream or datagram connections created and controlled by the participating calculation and coordination components. web services can take advantage of the &rho;ε&omega; channel system that separates the communication issue using components that have independent sink and source ports that can be attached to web services components, thus overcoming the problem of compatibility in distributed systems. the flow of information is entirely regulated by the channel interconnections.
editorial message: special track on handheld computing. handheld computing is an emerging mobile computing paradigm that promotes using handheld wireless devices (or mobile devices) such as cellular phones and personal digital assistants (pdas) to accomplish various computing tasks. as handheld devices continue to appear in many forms with diverse functionalities, handheld computing will become the dominant computing paradigm in many fields including education, enterprises, and healthcare.
special track on handheld computing. handheld computing is an emerging mobile computing paradigm that promotes using handheld wireless devices (or mobile devices) such as cellular phones and personal digital assistants (pdas) to accomplish various computing tasks. as handheld devices continue to appear in many forms with diverse functionalities, handheld computing will become the dominant computing paradigm in many fields including education, enterprises, and healthcare.
supporting efficient query processing on compressed xml files. xml has been widely accepted as the de facto format for data representation and exchange. however, it is also known for the excessive information redundancy in its representation. while various compression schemes have been proposed and some of them can support query processing over compressed files, it is usually inevitable to perform partial (or full) data decompression which is expensive and in some cases may dominate the query processing time.in this paper, we propose a new xml compression scheme based on the sequitur compression algorithm. by organizing the compression result as a set of context free grammar rules, the scheme supports efficient processing of xpath queries without decompression. the experimental results show that this scheme achieves comparable compression ratio as gzip while its query processing time is among the best of existing algorithms.
network traffic anomaly detection based on packet bytes. hostile network traffic is often "different" from benign traffic in ways that can be distinguished without knowing the nature of the attack. we describe a two stage anomaly detection system for identifying suspicious traffic. first, we filter traffic to pass only the packets of most interest, e.g. the first few packets of incoming server requests. second, we model the most common protocols (ip, tcp, telnet, ftp, smtp, http) at the packet byte level to flag events (byte values) that have not been observed for a long time. this simple system detects 132 of 185 attacks in the 1999 darpa ids evaluation data set [5] with 100 false alarms, after training on one week of attack-free traffic.
on handling conflicts between rules with numerical features. rule conflicts can arise in machine learning systems that utilise unordered rule sets. a rule conflict is when two or more rules cover the same example but differ in their majority classes. this conflict must be solved before a classification can be made. the standard methods for solving this type of problem are to use naive bayes to solve the conflict or using the most frequent class (cn2). this paper studies the problem of rule conflicts in the area of numerical features. a novel family of methods, called distance based methods, for solving rule conflicts in continuous domains is presented. an empirical evaluation between a distance based method, cn2 and naive bayes is made. it is shown that the distance based method significantly outperforms both naive bayes and cn2.
editorial message: special track on document engineering. document engineering is a discipline within computer science that investigates systems for documents in any form and in all media. document engineering is concerned with principles, tools and processes that improve our ability to create, manage, store, compact, access and maintain documents. the fields of document recognition and retrieval have grown rapidly in recent years. this development has been fueled by the emergence of new application areas such as the world wide web (www), digital libraries, and video- and camera-based ocr. the use of ocr is spreading from high-volume, niche domains to more general tasks, including the processing of noisy "real-world" documents, photocopies, and faxes.
a process for separation of crosscutting grid concerns. this paper describes how to explicitly separate crosscutting grid concerns in a parallel java application. this process, named gridaspecting, uses a restricted subset of the java threads model for application decomposition, and aspect-oriented programming for allowing parallel execution of the application's threads as grid tasks. as a result of the process, all grid-related code is encapsulated in aspects, thus improving the application's modularity. in addition, by relying on java's native concurrency abstractions the process simplifies the grid programming model and makes it possible to test a grid application even without the grid.
dynamic structuring of web information for access visualization. the internet has led to the formation of a global information infrastructure. to explore a web site, a site map would be useful as a short cut for a user to locate for the target information in a structured and efficient manner, rather than drilling into the web site following hyperlinks, reading possibly irrelevant information. useless information impacts a mobile web environment, where mobile clients are only connected with unreliable wireless channels of limited bandwidth. structured web page organization at the web server proxy is an important issue to resolve to provide efficient browsing experience for web clients, while minimizing the browsing of unrelated pages or sites. in this paper, we adopt the document information extraction mechanism to construct a document cluster dynamically and intelligently with respect to a requested root web page. the document cluster works like a dynamic site map, spanning across several web sites. the clusters are generated and stored in xml format at proxy server so that it can potentially benefit a large number of mobile clients. clients process the xml clusters and transform them to be visualized through vrml or dom. for vrml, a transformer is built at the client-side to support a three-dimensional modeling view. for dom, javascript is used for accessing the parsed xml data to produce a two-dimensional tree output.
automatic language identification of written texts. language identification is one of the search keys of most widespread use in the internet. this article describes efficient and easily extensible solutions to the problem of identifying the language of written texts based on closed grammatical classes. an identification tool was developed for recognizing texts written in portuguese, spanish, french and english.
comparisons of file formats for image transmission through networks. this paper presents a comparative analysis of the three most important progressive file formats: png, jpeg and jpeg2000.
a small-world model of the human mind. we propose a model of the human mind from first principles and introspection. we then try to represent that model as a semantic network. finally, we hypothesize that the key for the intelligent processing of information might be to consider that the semantic network graph to be a kind of small-world.
supporting transparent evolution of component interfaces. component-oriented programming facilitates the development of reusable application parts encapsulated by well-defined interfaces. there is however a tension between compatibility and evolution, since the interface of a component may constrain refactoring or require manual development of multiple, ad-hoc adaptation layers when an interface is evolved. we here present the declarative language vidl for specifying component interface evolution. vidl allows evolution of components with automatic generation of efficient adapter code that statically guarantees interface compatibility with other components that rely on anterior versions of the interface.