waashk/dblp huggingface dataset:
checking concurrent contracts with aspects. the applicability of aspects as a means of implementing runtime contract checking has been demonstrated in prior work, where contracts are identified as cross-cutting concerns [12, 13]. checking contracts at runtime encounters a set of challenges within concurrent environments, such as the risk that evaluation will introduce deadlock to code which is otherwise deadlock-free. this paper presents a simple methodology for generating runtime contract checking aspects targeted at concurrent programs. the novel features of this approach allow contracts to depend on active objects without race conditions or deadlock, and addresses issues relating to timing and blame assignment. the cojava language is discussed whose tool-supported aspect generation methodology allows the correct checking of contracts predicated on active objects.
an improved wlan-first access scheme for umts/wlan integrated system. in a tunnel-wlan model of a umts/wlan interworking system, a user always prefers to access the wlan as soon as he/she moves to a wlan hotspot. thus a user never misses the higher bit rate of wlan as long as its bandwidth is available. we have examined that overall dropping probability of a request in a mixed cell (i.e., a umts cell with underlying wlans) improves if and only if the blocking probability of a request in wlan remains lower than that in the umts system. thus dropping probability increases with increasing blocking probability in wlan. we propose a wlan-first access scheme which transfers all blocked requests of wlan to overlaying umts system, thereby preventing compulsory dropping of blocked requests in wlan. this technique improves the request dropping probability in an entire mixed cell.
g-tries: an efficient data structure for discovering network motifs. in this paper we propose a novel specialized data structure that we call g-trie, designed to deal with collections of subgraphs. the main conceptual idea is akin to a prefix tree in the sense that we take advantage of common topology by constructing a multiway tree where the descendants of a node share a common substructure. we give algorithms to construct a g-trie, to list all stored subgraphs, and to find occurrences on another graph of the subgraphs stored in the g-trie. we evaluate the implementation of this structure and its associated algorithms on a set of representative benchmark biological networks in order to find network motifs. to assess the efficiency of our algorithms we compare their performance with other known network motif algorithms also implemented in the same common platform. our results show that indeed, g-tries are a feasible, adequate and very efficient data structure for network motifs discovery, clearly outperforming previous algorithms and data structures.
semantic scene detection system for baseball videos based on the mpeg-7 specification. in this paper, we proposed a content-based multimedia analysis/retrieval system mainly based on the mpeg-7 specification which is capable of handling the high-level content analysis such as the semantic scene detection for baseball broadcast videos. here, eight semantic scene classes were predefined for baseball videos. first, an effective shot boundary detection scheme based on scalable color histogram was proposed to segment a video into many shots. then, various visual features including field color features, skin color features, and camera motion information were extracted to analyze the semantics for each shot. according to the visual properties of different scenes, we developed a two-stage classification strategy for the semantic scene detection. finally, the experimental results showed that the proposed framework identifies eight semantic baseball scenes with 81% of precision rates and 84% of recall rates.
link prediction using probabilistic group models of network structure. modeling of complex networks is a crucial task such as in biology and social sciences. a large number of researches have been conducted for such a problem; however, most of them require explicit, specific prior knowledge on target networks. on the other hand, a few recent works on multinomial mixture models presented that those models do not require such explicit prior knowledge and turned out to be effective for the task of group detection of vertices such as in social networks. this paper focuses on another task, link prediction in such complex networks, using a bayesian multinomial mixture model, which assumes unobservable prior distributions over multinomial mixtures based on network structure and are estimated using bayesian inference via gibbs sampling. we demonstrate that link prediction performance was significantly improved using this method, compared to five conventional methods, through experiments using a metabolic network and a co-authorship network.
a morphologic three-stage approach for detecting exudates in color eye fundus images. an efficient detection of exudate lesions in color eye fundus images is an essential stage for grading the diabetic macular edema automatically. in this work, we present a new automatic method based on mathematical morphology to locate exudate lesions. the proposed method was evaluated using the publicly available diaretdb1 database, and it obtains 72.21% of mean sensitivity and 98.97% of mean specificity. this results indicate that our approach potentially can achieve a better performance than other known methods proposed in the literature.
a network polling solution through a p2p-based distributed management environment. network management involves several activities for operation, administration, and maintenance of network devices. among them, network polling is one of the most cpu and network-intensive tasks, showing serious scalability and fault tolerance drawbacks when executed through traditional management architectures. this way, alternative approaches have to be investigated and analyzed for the execution of such operation. this paper proposes a distributed network polling architecture through a p2p-based distributed management environment. it presents the model followed by the architecture, the polling architecture and services responsible by polling operations.
inductive reasoning and semantic web search. extensive research activities are recently directed towards the semantic web as a future form of the web. consequently, web search as the key technology of the web is evolving towards some novel form of semantic web search. a very promising recent approach to such semantic web search is based on combining standard web search with ontological background knowledge and using standard web search engines as the main inference motor of semantic web search. in this paper, we propose to further enhance this approach to semantic web search by the use of inductive reasoning. this adds the important ability to handle inconsistencies, noise, and incompleteness, which often occur in distributed and heterogeneous environments, such as the web. we report on a prototype implementation of the new approach and extensive experimental results.
an improved algorithm to enumerate all traces that sort a signed permutation by reversals. in 2008, braga et al. proposed an algorithm to perform the enumeration of traces that sort a signed permutation by reversals. this algorithm has exponential complexity in both time and space. the original implementation uses a special structure, to handle the information during the process. however, even with this structure, memory consumption is still a problem. in this work, we propose a stack structure to represent the set of traces that is being enumerated by the algorithm. this new structure consumes less memory and can be kept in the main memory, improving the space and time performance of the algorithm.
query systems in similarity-based databases: logical foundations, expressive power, and completeness. this paper presents logical foundations for an extension of codd's relational model of data which aims at utilizing various aspects of similarity in data processing. a need for development of solid foundations for such extensions, sometimes called similarity-based relational databases, has repeatedly been emphasized by leading database experts. this paper argues that, contrary to what may be perceived from the literature, solid foundations for similarity-based databases can be developed in a conceptually sound way. in this paper, we outline the foundations and propose two query systems for similarity-based databases: a domain relational calculus (drc) and a relational algebra (ra). we compare the expressive power of drc and ra in our model and prove relational completeness of ra over drc. a major implication of the paper is that similarity-based data querying can be made an integral part of an extended, similarity-based, relational model of data which is based on first-order predicate logics using residuated structures of truth values in much the same way as the querying in the codd's model is based on the classic first-order predicate logic.
separation of concerns in service-oriented applications based on pervasive design patterns. service-oriented computing (soc) allows developers to build applications by reusing and invoking web-accessible services. soc promotes loose coupling between applications and services, which has been mostly addressed by using techniques for separation of concerns (soc). contemporary soc development models based on soc either rely on difficult-to-adopt, ad-hoc programming facilities and languages or fail at isolating applications from details of the application-service interaction. we propose di4ws, a soc programming model that combines the well-known adapter and dependency injection patterns. we show that di4ws allows reducing couplings to services, which has a positive effect on application maintenance, without requiring developers to learn such facilities or languages. di4ws follows a contract-last approach to service invocation, whereby developers first code the logic of their applications and then non-invasively "adapt" and "inject" required services. an empirical comparison of di4ws with two related approaches to decouple services is also reported, showing that the di4ws versions of 4 evaluated applications used less memory and ran faster than the others.
distributed data stream classification for wireless sensor networks. it has been established experimentally that in-network processing in wireless sensor networks is the acceptable mode of operation. however, this solution is faced by resource constraints of the sensor nodes, especially when running traditional data mining techniques that tend to consume the resources rapidly. on the other hand, data stream mining algorithms still fall short with the limited computational capabilities of the nodes. these algorithms need real-time adaptation to availability of resources. distributed processing is also essential to produce a global model of the data streams emanated from the network. in this paper, we propose a novel distributed data stream classification technique that is able to adapt to availability of resources in wireless sensor networks.
efficiently detecting clusters of mobile objects in the presence of dense noise. we address the problem of detecting and tracking clusters of moving objects in very noisy environments. monitoring a crowded football stadium for small groups of individuals acting suspiciously is an example of one such environment. in this scenario the vast majority of individuals are not part of suspicious groups and are considered noise. existing spatio-temporal clustering algorithms are either incapable of detecting small clusters in extreme noise, or have high computation and storage requirements that prohibit their use in real-time alerting systems. we propose a technique called dynamic density based clustering (ddbc) that utilizes the relational history of the moving objects to increase the accuracy of the clustering algorithm. the incorporation of this information into the clustering algorithm is done efficiently and implicitly by using a relationship graph. the relationship graph incrementally estimates the strength of the relationships between moving objects. a modified dbscan algorithm is then used to find clusters of highly related objects from the relationship graph. we evaluate the ddbc technique experimentally on a number of data sets of mobile objects. the experiments show that ddbc outperforms both trajectory mining and moving cluster mining techniques in terms of accuracy, memory usage, and computation time as the density of noise increases.
energy-efficient scheduling on homogeneous multiprocessor platforms. low-power and energy-efficient system implementations have become very important design issues to extend operation duration or cut power bills. to balance the energy consumption resulting from the dynamic power consumption and the static power consumption, the concept of critical speed has been adopted widely in the literature. most scheduling algorithms for such systems assume that the critical speed is the lowest speed for scheduling and then perform job/task procrastination to turn the processor(s) to the dormant mode when there is no job for execution. this paper shows that the critical speed might be too optimistic and turning the processor(s) to the dormant mode might be energy-inefficient. by allowing tasks to run at lower speeds than the critical speed, in this paper, a new approximation algorithm is developed for homogeneous multiprocessor systems with a 1.21-approximation factor, which significantly improves the state-of-the-art approximation algorithm with a 1.667-approximation factor. performance evaluation shows the effectiveness of the proposed algorithm with comparison to the state-of-the-art approximation algorithm. our algorithm can reduce the energy consumption by at most 15% in our simulations.
the impact of random samples in ensemble classifiers. the use of ensemble classifiers, e.g., bagging and boosting, is wide spread to machine learning. however, most of studies in this area are based on empirical comparisons that suffer from a lack of care to the randomness of these methods. this paper describes the dangers of experiments with ensemble classifiers by analyzing the efficiency of bagging and boosting methods over 32 different data sets. the experiments show that variations due to randomness are often more relevant than the advantages among methods encountered in the literature. this paper main contribution is the claim, supported by statistical analysis, that no empirical comparison of ensemble classifiers can be scientifically done without paying attention to the random choices taken.
fact-oriented declarative semantic enterprise modeling. in this paper we will investigate the extent to which the extension of the fact-oriented approach with modeling constructs that cover the information-, the process- and behavioural perspectives can provide declarative modeling constructs for declarative semantic enterprise modeling.
generic flow-sensitive optimizing transformations in c++ with concepts. compilers are typically hardwired to attempt optimizations only on expressions involving particular built-in types. ideally, an optimizing compiler can recognize a rewrite opportunity whenever the operands in an expression satisfy the (algebraic) properties that justify the rewrite. this paper applies the principles and techniques of generic programming and the planned "concepts" language feature of c++ to approximate this ideal. concretely, a concept defines the signature and algebraic laws of a class of types. we attach rewrite rules to a concept, so that the rules apply to all types in the class defined by a concept. the annotation burden to a programmer is thus small---the declaration that a type models a particular concept is simultaneously taken as an annotation that enables optimizations. to increase the applicability of generic rewrite rules, we instantiate them to type-specific rules, enabling the use of data-flow information from the compiler's existing analyses, and interleave the application of rewrite rules with function inlining. our prototype is implemented as an extension of the conceptgcc compiler; our experiments show the approach is effective in eliminating abstraction penalties.
an improved k-view algorithm for image texture classification using new characteristic views selection methods. image texture classification plays an important role in the computer processing of images. many methods including the gray level co-occurrence (glco), local binary patterns and k-view algorithms for image texture classification have been proposed. there exist several variations of the k-view algorithms. among them, the k-view algorithm using rotation-invariant features can produce higher classification accuracy; however, the algorithm is not stable. this paper presents an improved k-view algorithm using new characteristic views selection methods. we propose three different methods for selecting characteristic views. the first method chooses characteristic views based on the interval of minimum mean gray level and maximum mean gray level of all views in the primitive set and then randomly select the same number of views in each sub-interval. the second method divides the sorted interval with equal sub-interval and then select one view randomly in each sub-interval. the third method chooses views in an equal distance starting from the minimum gray level in the sorted list. experimental results show that the proposed characteristic views selection methods improve the performance of the k-view algorithms. this improved k-view algorithm is more robust and accurate compared with the results of the previous k-view algorithms.
load forecasting applied to soft real-time web clusters. dynamic configuration techniques such as dvfs (dynamic voltage and frequency scaling) and turning on/off computers are well known ways to promote energy consumption reduction in web server clusters. this paper demonstrates how the application of forecasting methods improves energy savings in a soft real-time application, and compares it with other energy aware methods. instead of a synthetic workload, a real traffic pattern was used to make the experiments more realistic. our system promotes energy reduction while maintaining user's satisfaction with respect to deadlines being met. the results obtained show that prediction capabilities increase the qos of the system, while maintaining or improving the energy savings over state-of-the-art power management mechanisms.
detecting metamorphic malwares using code graphs. malware writers and detectors have been running an endless battle. self-defense is the weapon most malware writers prepare against malware detectors. malware writers have tried to evade the improved detection techniques of anti-virus(av) products. packing and code obfuscation are two popular evasion techniques. when these techniques are applied to malwares, they are able to change their instruction sequence while maintaining their intended function. we propose a detection mechanism defeating these self-defense techniques to improve malware detection. since an obfuscated malware is able to change the syntax of its code while preserving its semantics, the proposed mechanism uses the semantic invariant. we convert the api call sequence of the malware into a graph, commonly known as a call graph, to extract the semantic of the malware. the call graph can be reduced to a code graph used for semantic signatures of the proposed mechanism. we show that the code graph can represent the characteristics of a program exactly and uniquely. next, we evaluate the proposed mechanism by experiment. the mechanism has an 91% detection ratio of real-world malwares and detects 300 metamorphic malwares that can evade av scanners. in this paper, we show how to analyze malwares by extracting program semantics using static analysis. it is shown that the proposed mechanism provides a high possibility of detecting malwares even when they attempt self-protection.
searching for knee regions in multi-objective optimization using mobile reference points. evolutionary algorithms have amply demonstrated their effectiveness and efficiency in approximating the pareto front of different multi-objective optimization problems. fewer attentions have been paid to search for the preferred parts of the pareto front according to the decision maker preferences. knee regions are special portions of the pareto front containing solutions having the maximum marginal rates of return, i.e., solutions for which an improvement in one objective implies a severe degradation in at least another one. such characteristic makes knee regions of particular interest in practical applications from the decision maker perspective. in this paper, we propose a new updating strategy for a reference points based multi-objective evolutionary algorithm which forces this latter to focus on knee regions. the proposed idea uses a set of mobile reference points guiding the search towards knee regions. the extent of the obtained regions could be controlled by the means of a user-defined parameter. the verification of the proposed approach is assessed on two- and three-objective knee-based test problems a priori and interactively. the obtained results are promising.
chemical-inspired self-composition of competing services. this paper aims at pushing the clear relationship between software service composition and chemical dynamics a step forward. we developed a coordination model where services and clients are coordinated via a tuple space handling services as if they were interacting chemical substances: on the one hand, services get equipped with an "activity value" resembling chemical concentration and measuring their reactiveness as imposed by the tuple space; on the other hand, services automatically compose via interaction ports resembling chemical bonding. the tuple space enacts a feedback loop that regulates and balances the activity level of (atomic or composite) services, decreasing it over time as in chemical decay, but reinforcing it each time the service is correctly used. this behaviour promotes service competition: losing (i.e. unused) services literally extinguish. which services or service compositions survive competition is automatically decided solely based on resulting performance, i.e. the rate at which services are actually exploited.
a persistent hy-tree to efficiently support itemset mining on large datasets. this paper presents the hy-tree persistent tree structure that provides a compact representation of a transactional dataset for frequent itemset mining. the hy-tree is characterized by a hybrid structure that easily adapts to different data distributions. the data representation is complete, since no support threshold is enforced during the hy-tree creation process. the hy-tree can be profitably exploited by a variety of itemset mining algorithms (e.g., lcm v.2, nonordfp). it effectively supports the data retrieval step in the itemset mining process by reducing both the i/o cost and the memory requirements for data loading. experiments on large synthetic datasets show the compactness of the hy-tree data representation and the efficiency and scalability on large datasets of the mining algorithms supported by it.