waashk/dblp huggingface dataset:
a machine learning approach to semi-automating workflow staff assignment. staff assignment is an important aspect of workflow resource management. in many current workflow applications, staff assignment is still performed manually by resource assigners like process initiator or process monitor. in this paper, we present a semi-automated approach intended to ease the burden of staff assigner. our approach applies a machine learning algorithm to workflow event log to learn various kinds of activities each actor undertakes. when a new process is initiated, the classifiers generated by the machine learning technique suggest a suitable actor to undertake the specified activities. with this approach, we have achieved an average prediction accuracy of 85.8% and 80.1% on two car manufacturing enterprises respectively. we report on the result of our experiment and discuss issues and improvement of our approach.
modeling component based embedded systems applications with explicit connectors in uml 2.0. when building a system by connecting components, the connection itself, the connector, becomes a hot-spot of abstraction for any interaction. in contrary to most existing component models, we introduce explicit connectors as first class architectural entities. they materialize detailed contracts regarding composition, deployment and interaction and hence provide fine granular information on composed structures. using explicit connectors results in customtailored and consequently light-weight middleware, as any interaction logic is contained within them. modeling component architectures with explicit connectors allows the usage of off-the-shelf connector libraries. thereby, developing a distributed component based application becomes less complex and more competitive due to reduced costs and increased reliability. we contribute by adopting a model driven development process for the use of explicit connectors by extending the syntax of uml 2.0 and defining a set of required model transformations.
using a knowledge base to disambiguate personal name in web search results. results of queries by personal names often contain documents related to several people because of the namesake problem. in order to differentiate documents related to different people, an effective method is needed to measure document similarities and to find documents related to the same person. some previous researchers have used the vector space model or have tried to extract common named entities for measuring similarities. we propose a new method that uses web directories as a knowledge base to find shared contexts in document pairs and uses the measurement of shared contexts to determine similarities between document pairs. experimental results show that our proposed method outperforms the vector space model method and the named entity recognition method.
personalized ranking: a contextual ranking approach. as data of an unprecedented scale are becoming accessible on the web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. for instance, in the context of text retrieval, in contrast to traditional web search engines retrieving the same results for all users, major commercial search engines are starting to support personalization, improving the search quality by adapting to the user-specific retrieval contexts, e.g., prior search history or other application contexts. this paper studies how to enable such personalization in the context of structured data retrieval. in particular, we adopt context-sensitive ranking model to formalize personalization as a cost-based optimization over context-sensitive rankings collected. with this formalism, personalization is essentially retrieving the context-sensitive ranking matching the specific user's retrieval context and generating a personalized ranking accordingly. in particular, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. our empirical evaluations over real-life data validate both the effectiveness and efficiency of our framework.
an efficient implementation of rc4 cipher for encrypting multimedia files on mobile devices. abstract. in this paper, we implement and evaluate an efficient rc4 stream cipher, called key-pooled rc4, to transfer securely multimedia files in the wireless mobile network. in key-pooled rc4, a 1mb-sized key stream pool, which consists of 2048 or 8192, or 32768 key stream frames, is created uniquely for each client device in the registration step. when a client requests a multimedia file, the server delivers the file after encrypting it using the sequence of key stream frames which are randomly selected from the corresponding key stream pool. our experimental results show that the proposed scheme is more time efficient than the normal rc4. moreover, the key-pooled rc4 scheme is more secure than normal rc4.
on using user query sequence to detect off-topic search. retrieving off-topic documents to a user's pre-defined area of interest via a search engine is potentially a violation of access rights and is a concern to every private, commercial, and governmental organization. we improve content-based off-topic search detection approaches by using a sequence of user queries versus the individual queries. in this approach, we reevaluate how off-topic a query is, based on the sequence of queries that preceded it. our empirical results show that using the information from the queries in a given query window, the false alarm rate is reduced by a statistically significant amount.
finding putative core promoter elements with position-dependent consensuses. so far, within the core promoter region, only five known core promoter elements were found to be involved in the transcription initiation. in this paper, we proposed a framework for finding putative core promoter elements in the core promoter sequences. after implementing the framework, we used epd promoters as the datasets in the experiments, and identified the putative core promoter elements with functional positions. the experimental results demonstrated that our system is feasible and reliable, regardless of whatever species are.
balancing energy consumption and memory usage in sensor data processing. algorithms for data processing in sensor nodes of wireless sensor networks should be able to handle the resource limitations the nodes face (i.e. energy and memory). an important issue to be considered is that the materialization of large amounts of data in sensor nodes may cause memory overflows and consequently data losses. on the other hand, the excessive sending of data packets to other nodes may result in unacceptable energy consumption levels. some alternatives to save energy and also avoid memory overflows involve in-network aggregation and reductions in sensor activities (when sensor nodes experience resource constraints). in this paper we study energy-memory tradeoffs in sensor nodes and how they affect the accuracy of query results in wireless sensor networks. we propose an adaptive data processing strategy to balance energy consumption and memory usage at sensor node level. our goal is to maximize sensor lifetime while also maintaining the accuracy of query results. we implemented our approach by means of an algorithm called adaga (adaptive aggregation algorithm for sensor networks), which process in-network aggregation in sensor nodes. adaga is able to adapt its behavior according to energy and memory availabilities by dynamically adjusting data collection and data sending intervals. the results on the efficiency of our approach are also presented at the end of the paper.
applying a component-based framework to develop multi-agent environments: case study. this paper introduces an infrastructure for engineering environments for multi-agent systems. such an infrastructure is based on a component model specification, named cms, which promotes dynamic unanticipated evolution of software. we propose an agent model specification (ams), focusing on environment engineering issues. moreover, we present a java framework that implements ams and a case study to exemplify the application of our approach.
an efficient indexing structure for content based multimedia retrieval with relevance feedback. this paper proposes an efficient indexing structure for cbmr (content-based multimedia retrieval), called hbi (hierarchical bitmap index), in which each object is represented as a bitmap of size 2 &middot; d &middot; l bits, where d is the number of dimensions of object's feature vector and l is the number of bitmaps. in this bitmap representation, the feature (or attribute) value of object at each dimension is represented with a set of two bits each of which indicates whether it is relatively high ('11'), low ('00'), or neither ('01') compared to the feature values of other objects at a hierarchical organized interval. using these compact representations of feature vectors, a lot of irrelevant objects could be quickly filtered-out by a couple of simple xor operations, and it helps to reduce the filtering process of similarity search in high-dimensional data space. it also presents an optimization algorithm, called fqd (filtering by query difference), for the similarity search with relevance feedback that reuses the previously calculated distances between the original query and all objects in the database when filtering the irrelevant objects in the successive search with modified query. it helps to further reduce the search time of cbmr with relevance feedback. experimental results show that the similarity search using hbi is about 2 ~ 3 times faster than va-file while guaranteeing the exact solutions, and fqd for relevance feedback helps to further reduce the elapsed time of successive similarity search compared to the one for the first search.
reversing guis to ximl descriptions for the adaptation to heterogeneous devices. the spread of personal wireless devices (pwds) has raised the need to migrate existing applications to these new environments. desktop applications often exhibit complex user interfaces and are too large and resource demanding to be executed on devices with limited resources without changing the application code. current research efforts are mainly focused on web applications whose user interfaces are specifically designed for multi-platform environments through platform-independent models. on the contrary, little effort has been made to support the migration of applications with component-based guis towards pwd environments. this paper presents a tool for reverse engineering java guis through their transformations to ximl-based abstract descriptions. the resulting descriptions are used by the tcpte framework to be rendered into different guis, which are dynamically adapted to heterogeneous devices on the basis of their profile communicated at request time.
towards a synthetic analysis of user's information need for more effective personalized filtering services. the consideration of underlying analysis of user's information need is a key requirement in an intelligent filtering environment. however, the majority of current approaches to filtering are relevance-oriented, rather than user-oriented. this is partly because they are issued from fields that have somewhat different perspectives from that of information filtering, but also because of the difficulty of understanding and measuring user's motivations and the way in which the user expects the system to respond. this paper presents an original approach to information analysis and filtering inspired by the novelty detection theory. as well as being able to accurately learn user's information need, the approach has an analytical capacity for better understanding user's need. it provides a new way of looking at user's need in terms of precise, broad, and contradictory profile-contributing criteria. these criteria go on to estimate the relative importance the user might attach to precision and recall. the filtering threshold is then adjusted taking into account this knowledge about user's need. experimental results on the standard reuters-21578 collection prove the effectiveness of the approach and confirm the potential usefulness of adapting the filtering results according to the knowledge acquired about user's need.
a solver for quantified boolean and linear constraints. we make a number of contributions to the understanding and practical resolution of quantified constraints. unlike previous work in the cp literature that was essentially focused on constraints expressed as binary tables, we focus on presburger arithmetics, i.e., boolean combinations of linear constraints. from a theoretical perspective, we clarify the problem of the treatment of universal quantifiers by proposing a "symmetric" version of the notion of quantified consistency. this notion imposes to maintain two constraint stores, which will be used to reason on universal and existential variables, respectively. we then describe a branch & bound algorithm that integrates both forms of propagation. its implementation is, to the best of our knowledge, the first cp solver for this class of quantified constraints.
towards context-aware and resource-driven self-adaptation for mobile handheld applications. mobile handheld computing is gaining momentum as more and more wireless handheld devices are being used to accomplish various computing tasks. context-awareness can help to adapt and personalize applications or to optimize resource usage. however, many existing context infrastructures by themselves impose heavy resource requirements for deployment or highly affect the limited autonomy of the mobile device. in this paper we describe how resource-driven self-adaptation is used in our layered context-driven application middleware in order to enable deployment on a mobile device and optimize resource usage. our evaluation shows that the introspection and intercession capabilities of the self-adapting middleware provide the necessary flexibility to achieve a balanced resource usage between the context-driven application middleware and the context-aware applications, and that the resource-driven self-adaptation is able to more than double the battery lifetime in real life scenarios.
a mof metamodel for the development of context-aware mobile applications. context-aware mobile applications are increasingly attracting interest of the research community. to facilitate the development of this class of applications, it is necessary that both applications and support platforms share a common context metamodel. this paper presents a metamodel defined using the omg meta object facility (mof). this metamodel has been used as basis for the development of context-aware applications and an associated service platform.
a fair scheduling scheme for a time-sensitive traffic over the dual-channel wireless network. this paper proposes a message scheduling scheme capable of improving both throughput and fairness on the dual-channel wireless network. based on the time-slotted access mechanism for each channel within a single cell, the coordinator schedules the transmission of the first channel by edf (earliest deadline first) discipline that can maximize the actual throughput, while gdf (greatest degradation first) discipline is exploited for the second channel to improve the fairness factor. during runtime, with the support of channel estimation mechanism, the coordinator can dynamically switch the channel between the two scheduled streams. the simulation result shows that the proposed scheme improves the actual throughput by 7.2 % compared with edf, while the fairness is improved by 2.4 % compared with gdf.
enhancing traceability using ontologies. traceability refers to the ability to link information in a process chain. this paper proposes the integration of ontologies into the unified process (up) [1] to provide concept-based traceability throughout the software lifecycle. this approach allows the integration of the different models of a software system including business, requirements, analysis and design models in a lower granularity degree than conventional requirements traceability approaches. to assist the designers in creating the ontology and linking concepts to artifacts we provide a tool integrated with an uml modeler.
evolution of iterated prisoner's dilemma strategies with different history lengths in static and cultural environments. we investigate evolutionary approaches to generate well-performing strategies for the iterated prisoner's dilemma (ipd) with different history lengths in static and cultural environments. the length of the history determines the number of the most recent moves of both players taken into account for the current move decision. the static environment constituting the opponents of the evolved players is made up of ten standard strategies known from the literature. the cultural environment starts with the standard strategies and gradually increases by addition of the best evolved players representing a culture. the performance of the various evolved strategies is compared in specific tournaments. also, the behavior of an evolved player is analyzed in more detail by looking at the specific game sequences (and corresponding decisions), which out of all possible sequences are actually utilized in a tournament.
the detection and assessment of possible rna secondary structure using multiple sequence alignment. we here devise a new method for detecting and assessing rna secondary structure by using multiple sequence alignment. the central idea of the method is to first detect conserved stems in the alignment using a special matrix and then assess them by evaluating the ratio of the signal to the noise. we tested the method on data sets composed of pairwise and three-way alignments of known ncrnas. for the pairwise tests, our method has sensitivity 61.42% and specificity 97.05% for structural alignments, and sensitivity 42.05% and specificity 98.15% for blast alignments. for the three-way tests, our method has sensitivity 65.17% and specificity 97.96% for structural alignments, and sensitivity 40.70% and specificity 97.87% for clustalw alignments. our method can detect conserved secondary structures in gapped or ungapped rna alignments.
shared-stack cooperative threads. multithreaded sensor operating systems provide the paradigm of threads which enables programmers to program and maintain their applications more easily. however, a lot of memory space can be wasted due to the fact that a fixed-size stack is allocated for each thread. in this paper, we propose the shared-stack cooperative threads which can combine the simplicity of the multithreaded programming with the performance and scalability of event-driven systems. our experimental results show that shared-stack cooperative threads utilize memory space more efficiently with some affordable context switching overhead for threading while providing programmers with the ease of multithreaded programming.
translation disambiguation in web-based translation extraction for english-chinese clir. dictionary based translation is a traditional approach in use by cross-language information retrieval systems. however, significant performance degradation is often observed when queries contain words that do not appear in the dictionary. this is called the out of vocabulary (oov) problem. in recent years, web-based translation extraction was shown to be one of the more effective approaches to the solution of this problem. previous work focussed on selecting the correct translation from a set of web extracted terms. the common methods for translation selection for web-based translation always rely on word frequency calculation but the results are not always satisfactory. in this paper we present our approach to the selection of terms in a more accurate manner. our experiments show improvement in translation accuracy over other commonly used approaches.
dynamic adaptation of corba component-based applications. an important requirement for pervasive computing systems is the ability to adapt at runtime to handle varying resources, user mobility, and changing user needs. in this paper, we present an innovative approach to adapt the corba component-based applications. this approach proposes to extend the corba deployment model to describe the variability of the architecture of applications and to extend the corba execution model in order to support adaptation at runtime. the originality of this approach is to consider the adaptation activities as non-functional aspects and to integrate them in the corba container.
sim and usim filesystem: a forensics perspective. the main purpose of this paper is to describe the real filesystem of sim and usim cards, enlightening what the official standard reference does not say. by analyzing the full filesystem of such embedded devices, it is possible to find a lot of undocumented files usable to conceal sensitive and arbitrary information that are unrecoverable with the standard tools normally used in a forensic field. in order to understand how it is possible to use a sim/usim for data hiding purposes, the paper will present a tool capable of extracting the entire observable memory of these devices together with the effective filesystem structure. further, some practical examples regarding the data hiding procedure as a proof of concept will be analyzed and discussed.
injection/withdrawal scheduling for natural gas storage facilities. control decisions for gas storage facilities are made in the face of extreme uncertainty over future natural gas prices on world markets. we examine the problem faced by owners of storage contracts of how to manage the injection/withdrawal schedule of gas, given past price behavior and a predictive model of future prices. real options theory provides a framework for making such decisions. we describe the theory behind our model and a software application that seeks to optimize the expected value of the storage facility, given capacity and deliverability constraints, via monte-carlo simulation. our approach also allows us to determine an upper bound on the expected valuation of the remaining storage facility contract and the gas stored therein.
an analytical model for generalized processor sharing scheduling with heterogeneous network traffic. implementation of differentiated quality-of-service (qos) in next-generation computer networks has received increasing research interests from both academia and industry. the generalized processor sharing (gps) scheduling strategy has been widely studied as a promising way to provide differentiated qos due to its service protection feature. most of the previous studies reported in the literature, however, have focused on the analysis of gps under either short range dependent (srd) or long range dependent (lrd) traffic only, neither of which is able to capture the heterogeneous properties of realistic traffic in multi-service networks solely. to fill this gap, this paper develops a new analytical performance model for gps systems subject to both lrd self-similar traffic and srd poisson traffic. more specifically, using an approach based on large deviation principles, this study contributes to performance modelling and evaluation of gps scheduling by deriving the analytical upper and lower bounds of the aggregate and individual queue length distributions of heterogeneous traffic flows. the comparisons between analytical bounds and extensive simulation results validate the accuracy and merits of the analytical model which can be adopted as a practical and cost-effective evaluation tool for investigating the performance behaviour of gps systems under heterogeneous network traffic with various parameter settings.