waashk/dblp huggingface dataset:
probabilistic constraints for reliability problems. reliability quantifies the ability of a system to perform its required function under stated conditions. the reliability of a decision is usually represented as the probability of an adequate functioning of the system where both the decision and uncontrollable variables are subject to uncertainty. in this paper we extend previous work on probabilistic constraint programming to compute such reliability, assuming probability distributions for the uncertain values. usually this computation is very hard and requires a number of approximations, thus the computed value may be far from the exact one. traditional methods do not provide any guarantees with respect to correctness of the results provided. we guarantee the computation of safe bounds for the reliability of a decision, which is of major relevance for problems dealing with non-linear constraints.
accelerating multi-core simulators. simulation is an important means of evaluating new microarchitectures. with the invention of multi-core (cmp) platforms, simulators are becoming larger and more complex. however, with the availability of cmps with larger caches and higher operating frequency, the wall clock time required for simulating an application has become comparatively shorter. reducing this simulation time further is a great challenge, especially in the case of multi-threaded workload due to indeterminacy introduced due to simultaneously executing various threads. in this paper, we propose a technique for speeding multi-core simulation. the model of the processor core and cache are replaced with functional models, to achieve speedup. a timed petri net model is used to estimate the execution time of the processor and the memory access latencies are estimated using hit/miss information obtained from the functional model of the cache. this model can be used to predict performance of data parallel applications or multiprogramming workload on cmp platform with various cache hierarchies and shared bus interconnect. the error in estimation of the execution time of an application is within 6%. the speedup achieved ranges between an average of 2x--4x over the cycle accurate simulator.
harnessing input redundancy in a mapreduce framework. the proliferation of data parallel programming on large clusters has set a new research avenue: accommodating numerous types of data-intensive applications with a feasible plan. behind the many research efforts, we can observe that there exists a nontrivial amount of redundant i/o in the execution of data-intensive applications. even the locality-aware scheduling policy in a mapreduce framework is not effective in a cluster environment where storage nodes cannot provide a computation service. in this paper, we introduce split-cache to improve the performance of data-intensive olap-style applications by reducing redundant i/o in a mapreduce framework. the key strategy to achieve the goal is to cut down the i/o redundancy of reading common input data among applications. splitcache caches the first input stream in the computing nodes and reuses them for future demand. in execution of the tpc-h benchmark, we achieved 65.5% faster execution and 87% reduction in network traffic in average.
hypothesis generation and ranking based on event similarities. accelerated by the technological advances in the domain, the size of the biomedical literature has been growing rapidly. as a result, it is not feasible for individual researchers to comprehend and synthesize all the information related to their interests. therefore, it is conceivable to discover hidden knowledge, or hypotheses, by linking fragments of information independently described in the literature. in fact, such hypotheses have been reported in the literature mining community; some of which have even been corroborated by experiments. this paper mainly focuses on hypothesis ranking and investigates an approach to identifying reasonable ones based on semantic similarities between events which lead to respective hypotheses. our assumption is that hypotheses generated from semantically similar events are more reasonable. the validity of our approach is demonstrated in comparison with those based on term frequencies, often adopted in the related work.
fast, flexible, and highly resilient genuine fifo and causal multicast algorithms. we study the fifo and causal multicast problem, two group-communication abstractions that deliver messages in an order consistent with their context. with fifo multicast, the context of a message m at a process p is all messages that were previously multicast by m's sender and addressed to p. causal multicast extends the notion of context to all messages that are causally linked to m by a chain of multicast and delivery events. we propose multicast algorithms for systems composed of a set of disjoint groups of processes: server racks or data centers. these algorithms offer several desirable properties: (i) the protocols are latency-optimal, (ii) to deliver a message m only m's sender and addressees communicate, (iii) messages can be addressed to any subset of groups, and (iv) these algorithms are highly resilient: an arbitrary number of process failures is tolerated and we only require the network to be quasi-reliable, i.e., a message m is guaranteed to be received only if the sender and receiver of m are always up. to the best of our knowledge, these are the first multicast protocols to offer all of these properties at the same time.
an operating system abstraction layer for portable applications in wireless sensor networks. portability of software modules is a major concern in application development for wireless sensor networks (wsn), stressed by the typical lack of resources in embedded systems. abstractions of the hardware platform which are introduced by the operating system (os) allow the development of modules which can be reused in new applications. however, the lack of standards in this domain, restricts the chances to achieve efficient portability to those systems running on very similar platforms (e.g. same os). in this paper, we present an operating system abstraction layer (osal), which unifies the os architecture and establishes a common api across multiple os. portability of applications is effectively granted thanks to a common set of primitives, which are independent of the underlaying os and its particular architecture. we highlight the efficiency of the osal as well as detailed description of its main features and design considerations. we have implemented the osal on top of two well known os and performed extensive evaluations, which show that it effectively reduces portability efforts at the expenses of minimal run-time overhead as well as negligible increase of memory footprint.
a metrics suite for evaluating agent-oriented architectures. the multi-agent systems (mass) paradigm continues to consolidate itself as a new branch of software engineering. traditional software engineering strongly recommends to apply metrics in software developments. however, several research groups of experts in agent-oriented software engineering agree that classical software metrics and object-oriented metrics cannot directly measure the quality of mas architectures. for this reason, this work proposes a suite of metrics to measure certain quality attributes of mas architectures, considering agents and their organization. most of these metrics are inspired by object-oriented metrics but they are adapted to agent-oriented concepts. proposed metrics are validated by the application to four problem domains and eight architectures.
standardized interoperable image retrieval. digital still images are generated, distributed and stored worldwide at an ever increasing rate. yet, the large number of available metadata description formats prevents consistent and efficient access to image repositories. standardized solutions for unifying the access and the retrieval of image repositories are therefore strongly needed. the iso/iec sc29 wg1 (more commonly known as jpeg) established the jpsearch project, which aims to standardize interfaces of an abstract image retrieval framework. in this context, the paper introduces our contribution consisting of a reference metadata model, which in combination with the jpeg query format improves interoperable image retrieval for users. second, a transformation rule declaration language for query reformulation is presented, which contributes to interoperability at the repository side.
a multi-view model-driven approach for packaging software components. tool support in chemical process modeling evolved towards using more complex and more integrated systems, often made by combining smaller sub-systems, coming from different vendors. the increasingly growing heterogeneity of these systems led to the emergence of interoperability standards such as cape-open. however, the component-based architecture imposed by this standard made the development and maintenance of process modelling components more complex. indeed, it requires accurate knowledge about three interconnected domains: the process itself, the standard specification, and the middleware (e.g. com or .net). consequently, both development and maintenance tasks require the collaboration of several experts throughout the entire component lifecycle. tools that assist experts in performing these tasks are thus required. this paper presents an iterative model-driven approach that allows expressing changes through three separated views, each of which is associated to an expert domain, and automatically propagating the effects of these changes in order to generate compliant code. the approach is based on domain-specific modeling and model transformations, and is illustrated through a prototype that has been validated with respect to expected changes impacting the three domains.
external sorting for index construction of large semantic web databases. today's semantic web datasets become increasingly larger containing up to several hundred million triples. the performance of index construction is a crucial factor for the success of large semantic web databases. in this paper, we propose two new approaches for rdf index construction: external chunks merge sort and distribution sort for rdf. the former stores and retrieves chunks from a special chunks heap to speed up replacement selection. the latter leverages the rdf-specific properties to construct rdf indices and significantly improves the performance. our experimental results show that our approaches significantly speed up rdf index construction, and are important techniques for large semantic web databases.
inter-dsl coordination support by combining megamodeling and model weaving. model-driven engineering (mde) advocates the use of models at every step of the software development process. within this context, a team of engineers collectively and collaboratively contribute to a large set of interrelated models. even if the main focus can be on a single model (e.g. a class diagram model), related elements in other models (e.g. a requirement model) often have to be considered and/or accessed. moreover, all the involved models do not necessarily conform to the same metamodel and thus may have been built using different independent domain-specific languages (dsls). such a situation has already been frequently observed in many large-scale industrial deployments of mde. manually coordinating all the involved models, i.e. being able to both manage and use the links existing between them, can become a cumbersome and difficult task. as a proposal to solve this inter-dsl coordination issue, we introduce in this paper a generic and extensible inter-model traceability and navigation environment based on the complementary use of megamodeling and model weaving. we illustrate our solution with a concrete working example.
ontomo: web-based ontology building system: ---instance recommendation using bootstrapping---. in the web research, ontologies are widely used these days. however, several difficulties in ontology building have been pointed out so far and its costs are raising problems currently. in this paper, we propose ontomo that enables internet users to take part in building ontologies as a part of collective intelligence. after introducing an overview of ontomo, an instance and property recommendation mechanism based on the editing history of multiple users will be presented together with experimental evaluations.
advanced motion detection for intelligent video surveillance systems. in this paper, we propose a novel background subtraction method that makes use of spectral, spatial, and temporal features extracted from the video sequence in determination of the best background candidates for background modeling. as the final step of our process, the binary moving object detection mask is computed using prompt background subtraction with our proposed background model. the overall results of these analyses thus demonstrate that our proposed method substantially outperforms existing methods by an f1 metric accuracy rate increase of up to 79%.
background knowledge in formal concept analysis: constraints via closure operators. the aim of this short paper is to present a general method of using background knowledge to impose constraints in conceptual clustering of object-attribute relational data. the proposed method uses the background knowledge to extract only particular clusters from the input data---those which are compatible with the background knowledge and thus satisfy the constraint. as a result, the method allows for extracting less clusters in a shorter time which are in addition more interesting. the paper presents the idea of constraints formalized by means of closure operators and introduces such constraints to a particular clustering technique, namely to formal concept analysis. among the benefits of the presented approach are its versatility (the approach covers several examples studied before, e.g. extraction of closed frequent itemsets in generation of non-redundant association rules) and computational efficiency (polynomial time-delay algorithm for computing constrained clusters). due to scope limitations, we present the main ideas only. details will be available in a full version of this paper.
a two stage yard crane workload partitioning and job sequencing algorithm for container terminals. we propose a new yc workload partitioning scheme which employs dynamic data driven simulations to conduct what-if experiments in container terminals. both the optimal partition of the workload in a row of yard blocks and the optimal dispatching sequences for individual ycs are achieved. the practical consideration of the safety constraints is included. a dynamic programming (dp) approach is used to avoid re-computation. an efficient two stage workload partition algorithm (tswp) is proposed which successfully reduces the number of full what-if simulations while maintaining solution optimality. an effective lower bound (lb) generator with adjustable lb accuracy is designed in supporting the tswp algorithm. experimental results show that the tswp algorithm outperforms the pure dp approach in all tested scenarios and takes less than 1 part per thousand computational time of the dp approach.
feature-based 3d morphing based on geometrically constrained sphere mapping optimization. current trends in free form editing suggest the development of a new novel editing paradigm for cad models beyond traditional cad editing of mechanical parts. to this end we wish to develop accurate, robust and efficient 3d mesh deformation techniques such as 3d structural morphing. in this paper, we present a feature-based approach to 3d morphing of arbitrary genus-0 polyhedral objects that is appropriate for cad editing. the technique is based on a sphere mapping process built on an optimization technique that uses a target function to maintain the correspondence among the initial polygons and the mapped ones while preserving topology and connectivity through a system of geometric constraints. finally, we introduce a fully automated feature-based technique that matches surface areas (feature regions) with similar morphological characteristics between the two morphed objects and performs morphing according to this feature correspondence list. alignment is obtained without user intervention and is based on pattern matching between the feature graphs of the two morphed objects.
swap-before-hibernate: a time efficient method to suspend an os to a flash drive. unlike a magnetic disk, a flash drive needs no seek time when performing random access. in addition, the read speed of a flash drive is faster than the write speed, and the write speed depends on the i/o request size: the bigger the request size, the faster the write speed. system hibernation stores the machine status completely in non-volatile memory. when the system reboots, the data will be reloaded to the machine, and the system status will be recovered completely. the user can continue immediately where they stopped the last time. we use the characteristic that the flash drives requires no seek time when performing random access to write the data in the memory to swap space or a hibernation file, or simply discard it without any i/o. the write process will try to combine random small writes into continuous large writes, if possible, to optimize the write speed. when the system resumes, it only reloads the data that the user needs, and the resume technique is based on paging on demand. paging on demand is actually a kind of random access; however, since the flash drive requires no seek time, it does not drag down the system response time due to reducing the number of i/o.
improving cost and accuracy of dpi traffic classifiers. traffic classification through deep packet inspection (dpi) is considered extremely expensive in terms of processing costs, leading to the conclusion that this technique is not suitable for dpi analysis on high speed networks. however, we believe that performance can be improved by exploiting some common characteristics of the network traffic. in this paper we present and evaluate some optimizations that can definitely decrease the processing cost and can even improve the classification precision.
convex onion peeling genetic algorithm: an efficient solution to map labeling of point-feature. map labeling of point-feature is the problem of placing text labels to corresponding point features on a map in a way that minimizes overlaps while satisfying basic rules for the quality. this problem is a critical problem in the applications of cartography and geographical information systems (gis). in this paper we study the fundamental issues related to map labeling of point-feature and develop a new genetic algorithm to solve this problem. we adopt a data structure called convex onion peeling and utilize it in our proposed convex onion peeling genetic algorithm (copga) to efficiently manage point features. we evaluated the performance of the proposed algorithm through extensive experiments on both synthetic and real datasets. the experimental results show that our genetic algorithm based on the convex onion peeling structure is an efficient, robust and extensible algorithm for automated map labeling of point-feature.
understanding meta-communication in an inclusive scenario. meta-communication can be defined as all exchanges of information that are related to the conceptual model of the system, including the communication that takes place in order to clarify or overcome problems during the communication process. the scenario of having users with low levels of literacy and familiarity with technology has not yet been addressed in help systems. this paper examines previous literature on meta-communication and other support frameworks, and draws on semio-participatory practices conducted within an inclusive scenario to extend the concept of help systems, aiming at a broader scaffolding instrument to let users climb up in the process of learning with the use of inclusive systems. preliminary findings of the investigation inform the design of support systems instantiated in the context of online social networks meant to be inclusive.
network-adaptive management of computation energy in wireless sensor networks. today's sensor nodes can be equipped with powerful microcontrollers to address the increasing need of real-time processing of sensed data. for instance, body sensor networks for gesture recognition require filtering of acceleration values at line rate. this requirement imposes a paradigm shift with regard to more traditional sensor networks characterized by low activity duty cycles. therefore, energy conservation strategies applied to wireless sensor nodes to increase their lifetime must take into account computation power rather than focusing only on communication power. in this paper we present a novel approach which aims at exploiting the knowledge of network status to optimize the power consumption of the node microcontroller. the proposed approach is tested in various network conditions, both synthetic and realistic, in the context of ieee 802.15.4 standard. experimental results demonstrate that the proposed approach allows to achieve power savings of up to 70% with minimum performance penalty.
dual analysis for proving safety and finding bugs. program bugs remain a major challenge for software developers and various tools have been proposed to help with their localization and elimination. most present-day tools are based either on over-approximating techniques that can prove safety but may report false positives, or on under-approximating techniques that can find real bugs but with possible false negatives. in this paper, we propose a dual static analysis that is based on only over-approximation. its main novelty is to concurrently derive conditions that lead to either success or failure outcomes and thus we provide a comprehensive solution for both proving safety and finding real program bugs. we have proven the soundness of our approach and have implemented a prototype system that is validated by a set of experiments.
subjective video quality assessment applied to scalable video coding and transmission instability. this paper presents a study of subjective quality of scalable video sequences, coded using the scalable extension of the h.264 standard (svc). a group of experiments was performed to measure, primarily, the effects that the transmission instability has in the video quality and the relationship between the three scalability methods (spatial, temporal and quality) in terms of subjective quality. the decisions taken to model the tests were based on layered transmission systems that use protocols for adaptability and congestion control. to run the subjective assessments we used the acrhrr methodology and recommendations given by itu-r rec. bt.500 and itu-t rec. p.910. the results show that the instability modeled does not cause signifficant alterations on the overall video quality when compared to a stable video and that temporal scalability usually produces videos with worse quality than spatial and quality scalabilities, the latter being the one with the better quality.
business modeling for service engineering: a case study in the it outsourcing domain. service engineering for both, service-oriented architectures and evolving service market places in the internet, is still a challenge due to dynamic environments, high uncertainties, and increasing coopetition of market participants. against this context, this paper introduces the integrated service engineering framework, presents a conducted case study in the it outsourcing domain, and discusses the framework's performance. additionally, it reviews work related to business modeling. the case study shows that the ise framework is particularly strong in process and data modeling and their derivation from one perspective into the other.