Miracle-dz/UniFAR-FTUs huggingface dataset:
UML-based Rule Modeling with Fujaba

In this paper we describe visual rule modeling tool Strelka, which is implemented as a Fujaba plugin. The modeling tool supports a UML-Based Rule Modeling Language (URML). It extends standard UML metamodel with a concept of a rule. We discuss an issue of a UML-based rule modeling, present rule metamodel, describe implementation of a Fujaba plugin and give examples of business rules, modeled using Strelka.
Optimization for Simulation : Theory vs . Practice

Probably one of the most successful interfaces between operations research and computer science has been the development of discrete-event simulation software. The recent integration of optimization techniques into simulation practice, specifically into commercial software, has become nearly ubiquitous, as most discrete-event simulation packages now include some form of “optimization” routine. The main thesis of this article, however, is that there is a disconnect between research in simulation optimization—which has addressed the stochastic nature of discrete-event simulation by concentrating on theoretical results of convergence and specialized algorithms that are mathematically elegant—and the recent software developments, which implement very general algorithms adopted from techniques in the deterministic optimization metaheuristic literature (e.g., genetic algorithms, tabu search, artificial neural networks). A tutorial exposition that summarizes the approaches found in the research literature is included, as well as a discussion contrasting these approaches with the algorithms implemented in commercial software. The article concludes with the author’s speculations on promising research areas and possible future directions in practice. (Simulation Optimization; Simulation Software; Stochastic Approximation; Metaheuristics)
Controlled Language for Geographical Information System Queries

Natural language interface to spatial databases have not received a lot of attention in computational linguistics, in spite of the potential value of such systems for users of Geographical Information Systems (GISs). This paper presents a controlled language for GIS queries, solves some of the semantic problems for spatial inference in this language, and introduces a system that implements this controlled language as a novel interface for GIS.
Estimating the potential of signal and interlocutor-track information for language modeling

Although today most language models treat language purely as word sequences, there is recurring interest in tapping new sources of information, such as disfluencies, prosody, the interlocutor’s dialog act, and the interlocutor’s recent words. In order to estimate the potential value of such sources of information, we extend Shannon’s guessing-game method for estimating entropy to work for spoken dialog. Four teams of two subjects each predicted the next word in a dialog using various amounts of context: one word, two words, all the words spoken so far, or the full dialog audio so far. The entropy benefit in the full-audio condition over the full text condition was substantial, . 64 bits per word, greater than the . 54 bit benefit of full text context over trigrams. This suggests that language models may be improved by use of the prosody of the speaker and context from the interlocutor. Index Terms: entropy, perplexity, Shannon’s guessing game, prediction, context, prosody
Parikh's theorem in commutative Kleene algebra

Parikh's theorem says that, the commutative image of every context free language is the commutative image of some regular set. Pilling has shown that this theorem is essentially a statement about least solutions of polynomial inequalities. We prove the following general theorem of commutative Kleene algebra, of which Parikh's and Pilling's theorems are special cases: Every finite system of polynomial inequalities f/sub i/(x/sub 1/,.,x/sub n/)/spl les/x/sub i/, 1/spl les/i/spl les/n, over a commutative Kleene algebra K has a unique least solution in K/sup n/; moreover, the components of the solution are given by polynomials in the coefficients of the f/sub i/. We also give a closed-form solution in terms of the Jacobian matrix of the system.
Intelligent fault diagnosis of plunger pump in truck crane based on a hybrid fault diagnosis scheme

At the initial stage of the mechanism, the collected samples are always in actual state, and the signals in fault conditions are gathered after a certain running time, so the general fault diagnosis model cannot be trained effectively. In this paper, a hybrid fault diagnosis scheme for pump in truck crane was proposed based on particle swarm optimization (PSO) SVDD and DBI K-Cluster method. Firstly, the SVDD procedure was constructed with the data in actual state, and the model parameters were optimized with PSO algorithm. Secondly, when the total number of novelty samples reached a given threshold, the K-Cluster method was utilized to classify the collected samples and the labels were allocated. In this procedure, the number of the class was determined with the Davies Bouldin index (DBI). Finally, each class data was trained with SVDD, and a whole diagnosis model was constructed with all the two-class classifiers. For the multi-fault mode samples of the pump in truck crane, experiments show that a promising classification performance is achieved.
Bond Return Predictability: An Investigation for the European Market

The predictability of security returns has received considerable attention in the finance literature. Notwithstanding, the predictability of bond returns, in particular outside the US, has been far less explored. In this paper we analyse the ability of several predetermined information variables in predicting bond returns in the European market. We test if variables, commonly used for that matter in the context of other markets (such as inverse relative wealth, term spread, real bond yield and a January dummy) are also useful predictors of European bond returns. Due to some particularities of the sample period of analysis, characterised by the EMU convergence, we also make another contribution by including the yield spread in relation to German bonds. Furthermore, we analyse the return predictability across different bond maturities: 1-3, 3-5 and 5 or more years to maturity. The results indicate that variables like the term spread, IRW and a January dummy represent useful information in order to predict bond returns for different maturities. The other two variables add little in terms of explanatory power. Surprisingly, the DM yield spread does not seem to have any predictive ability for the countries expected to participate in the EMU. However, a puzzling result was obtained: this variable appears to be significant for the UK market! Additionally, we find that investors, using simple trading strategies that exploit this information, may obtain higher returns. This outperformance is observed for different maturities, being more evident for long-term Government bonds. These findings may have important implications on other related issues such as market efficiency, asset pricing and portfolio performance evaluation.
Inconsistency Resolution in Merging Versions of Architectural Models

State-of-the-art optimistic model versioning systems, which are critical to enable efficient team-based development of architectural models, are able to detect and help resolve basic conflicts arising during the merging of model versions. However, it is often overlooked that model merging may also cause severe syntactical and semantic inconsistencies. In this paper, we propose an approach to guide the resolution of inconsistencies detected in a merged architectural model. Our approach automatically finds and presents to the software architects all solutions for resolving all inconsistencies arisen during the merging of model versions. For inconsistencies that pre-exist in the model, our approach is able to suggest exactly which model elements should be changed to resolve them. Our approach is built upon a repair generation which can quickly derive resolutions for an inconsistency by examining its static and dynamic structure and forming concrete repair actions from changes in the versions to be merged. An empirical validation on a range of industrial models has demonstrated that our approach is scalable to both large models and large differences between model versions.
Data dependent power use in multipliers

Recent research has demonstrated the vulnerability of certain smart card architectures to power and electromagnetic analysis when multiplier operations are insufficiently shielded from external monitoring. In this paper several standard multipliers are investigated in more detail in order to provide the foundation for understanding potential weaknesses and enabling the subsequent successful repair of those systems. A model is built which accurately predicts power use as a function of the Hamming weights of inputs without the combinatorial explosion of exhaustive simulation. This confirms that power use is indeed data dependent at least for those multipliers. Laboratory experiments confirm that EMR also corresponds closely to these power predictions over a wide range of frequencies.
Post-Modern Portfolio Theory Comes of Age

is president o f Sponsor-Software Systems, Inc. in New York, which he founded in 1987. Prior to this, he was primpal and New York region manager of investment consulting at Mercer-Meidinger-Hansen, Inc., and a senior manager in Peat Manvick’s Investment Consulting Group. MK Rom holds M.B.A. degrees from Columbia University and the University o f Cape Town in South Afica, as well as an M.S. in computer science and mathematics.
Automatic recognition of major language families in India

India is a vast country with a large number of languages. Among these some languages descend from a single mother language giving rise to a language family. The major official languages in India fall under two language families namely Indo-European and Dravidian. In this paper, we have discussed about a system which takes speech file as input and identifies the language family to which it belongs. We also used this system to find out the influence of Dravidian family on Indo-European family. The system uses a combination of Mel Frequency Cepstral Coefficients (MFCC) and Shifted Delta Coefficients (SDC) as language specific features. Presently, SDC is the most popular feature for language identification. It captures temporal information of speech over a broad range of time. Gaussian Mixture Model based approach is used to effectively model the language families where the distribution of feature vector of a class is approximated using sum of Gaussians. The results give interesting insights of certain Indian languages and applicability of machine learning process in this domain.
Power-Aware Variable Partitioning for DSPs With Hybrid PRAM and DRAM Main Memory

Phase change random access memory (PRAM) is one kind of nonvolatile memory, which is desirable to be used for DSP systems as main memory, as it consumes less power than DRAM and is much denser than DRAM. In this paper, we utilize a hybrid main memory composed of DRAM and PRAM, which leverages the low power consumption of PRAM while minimizing the performance and lifetime degradation caused by PRAM write. To make full use of different advantages of DRAM and PRAM, especially for the application-specific DSP systems, we reconsider the variable partitioning and instruction scheduling problems on the hybrid main memory. Different optimization objectives, for example power consumption, schedule length, and the number of writes on PRAM, are considered. At the same time, different kinds of hybrid architectures are analyzed. Graph models, ILP model, and algorithms are proposed for different settings. Experiments show that the proposed techniques reduce up to 49% power consumption and 88% the number of writes on PRAM on average.
Rethinking Encounter: intercultural interactions between parents in Australia’s culturally diverse primary schools

ABSTRACT This paper critically engages with the idea that everyday spaces of association can be seen as key sites in the development of intercultural understanding for diverse individuals. Drawing on research undertaken in two culturally diverse primary schools in metropolitan Melbourne, it explores the primary school as a key site for intercultural encounters between parents in Australia’s culturally diverse suburbs. This paper identifies a number of barriers limiting productive intercultural encounters between parents within the primary school setting. Specifically, it shows that such encounters are limited by national narratives of diversity, the complex demands of urban life, and the social and emotional benefits of avoiding uncertain interactions with difference. The paper builds on understandings of encounter as a contingent achievement and underscores the complexity of the ‘everyday’ as a space of political possibility.
Fast human detection using Gaussian Particle Swarm Optimization

Human detection is a challenging task in many fields because it is difficult to detect humans due to their varying appearance and posture. The evaluation speed of the method is important as well as its accuracy. In this paper, we propose a novel method using Gaussian Particle Swarm Optimization (Gaussian-PSO) for human detection with the Histograms of Oriented Gradients (HOG) feature to achieve a fast and accurate performance. Keeping the robustness of HOG feature on human detection, we raise the process speed in detection process so that it can be used for real-time applications. These advantages are given by a simple process which needs only one linear-SVM classifier with HOG features and Gaussian-PSO procedure.
Optimizing simulations with CSIM18/OptQuest: finding the best configuration

In many cases, a simulation model of a system is used to evaluate alternative configurations of that system, with the goal of finding the configuration which maximizes (or minimizes) the value of an objective while meeting all of the stated requirements. The CSIM18/OptQuest package automates this kind of search for the best configuration by combining a powerful simulation engine, CSIM18, and a state-of-the-art optimization package, OptQuest. This paper describes this integrated package for doing optimization and simulation. The paper concludes with two examples: finding the best configuration for a job-shop, and finding the best configuration for a web server.
Skeleton extraction of color cerbrovascular image based on topological nodes

In this paper, a new continuous skeleton extraction method on color image was proposed. This method uses a new level set model in color space to extract the topology node of an object using a skeleton point as source-point which spreading a moderate wave-front, and then using these topology nodes as new source nodes, it spreads the second high-speed transmission wave front in order to identify skeleton points. All the parameters are obtained from the analysis, avoiding human intervention. Experiment and analysis can verify the validity and the robustness of this method, and it is insensitive to color gradient and boundary noise.
Sheffield Systems for the English-Romanian WMT Translation Task

This paper provides an overview of the submissions the University of Shefﬁeld for the English-Romanian Translation Task of the ACL 2016 First Conference on Machine Translation (WMT16). The submitted translations were produced with a phrase-based system trained using the Moses toolkit, in two variants: (i) n-best rescoring using additional features from Quality Estimation (primary submission), and (ii) a novel weighted ranking optimisation approach (secondary submission).
Checking Consistency of Pedigree Information is NP-complete

Consistency checking is a fundamental computational problem in genetics. Given a pedigree and information on the genotypes of some of the individuals in it, the aim of consistency checking is to determine whether these data are consistent with the classic Mendelian laws of inheritance. This problem arose originally from the geneticists' need to filter their input data from erroneous information, and is well motivated from both a biological and a sociological viewpoint. This paper shows that consistency checking is NP-complete, even in the presence of three alleles. Several other results on the computational complexity of problems from genetics that are related to consistency checking are also offered. In particular, it is shown that checking the consistency of pedigrees over two alleles can be done in polynomial time.
Exploiting Semantic Constraints for Estimating Supersenses with CRFs

The annotation of words and phrases by ontology concepts is extremely helpful for semantic interpretation. However many ontologies, e.g. WordNet, are too fine-grained and even human annotators often have disagreements about the precise word sense. Therefore we use coarse-grained supersenses of WordNet. We employ conditional random fields (CRFs) to predict these supersenses taking into account the interaction of neigboring words. As the annotation of training data is costly we modify the CRF algorithm to process lumped labels, i.e. a set of possible labels for each training example, one of which is the correct label. This information can be derived automatically from WordNet. By this and the use of new features we are able to increase the f-value for about 8% compared to previous results. If we use training data without annotations it turns out that the resulting F-value is only slightly lower than for a fully labeled training data. Therefore unlabeled data may be employed in an unsupervised way to increase the quality of predicted supersenses.
Individual Differences in the Ability to Recognise Facial Identity Are Associated with Social Anxiety

Previous research has been concerned with the relationship between social anxiety and the recognition of face expression but the question of whether there is a relationship between social anxiety and the recognition of face identity has been neglected. Here, we report the first evidence that social anxiety is associated with recognition of face identity, across the population range of individual differences in recognition abilities. Results showed poorer face identity recognition (on the Cambridge Face Memory Test) was correlated with a small but significant increase in social anxiety (Social Interaction Anxiety Scale) but not general anxiety (State-Trait Anxiety Inventory). The correlation was also independent of general visual memory (Cambridge Car Memory Test) and IQ. Theoretically, the correlation could arise because correct identification of people, typically achieved via faces, is important for successful social interactions, extending evidence that individuals with clinical-level deficits in face identity recognition (prosopagnosia) often report social stress due to their inability to recognise others. Equally, the relationship could arise if social anxiety causes reduced exposure or attention to people's faces, and thus to poor development of face recognition mechanisms.
Resilient decision making in steam network investments

Steam is a key energy vector for industrial sites, used for process heating, direct injection and stripping, tracing and cogeneration of mechanical power. Steam networks transport steam from producers to consumers and across different pressure levels. The steam production equipments (boilers, cogeneration units and heat exchangers) should be dimensioned to always supply key consumers as well as to deal with extreme demand caused by exceptional events such as unit startups or extreme weather. An important issue to be dealt with is that of unexpected boiler shutdowns, which can take significant amounts of time to bring back online. In cases where demand surpasses the available production of steam, load shedding is necessary in order to keep the network operable. A penalty cost can be associated to load shedding. A well dimensioned steam network is one which is resilient to such events, being able to overcome extreme demand and unexpected boiler shutdowns at minimum cost. This paper proposes a methodology for evaluating the operability of a steam network when facing unexpected boiler shutdowns. A Monte-Carlo simulation is carried out on a multi-period steam network problem, randomly shutting down boilers according to their failure properties (probability of failure and duration of failure). The aim of this method is to evaluate how resilient a steam network is to boiler shutdowns. The Monte-Carlo simulation is applied to a steam network model built using a Mixed Integer Linear Programming (MILP) formulation, whose objective function is to minimise the operational costs of the steam network and therefore also to minimise the penalty costs associated to load shedding. A case study based on anonymised industrial data is used to demonstrate the method. Two investment propositions are evaluated and compared using the proposed method.
Handling Class Imbalance Problem in Cultural Modeling

Cultural modeling is an emergent and promising research area in social computing. It aims at developing behavioral models of groups and analyzing the impact of culture factors on group behavior using computational methods. Machine learning methods in particular classification, play a central role in such applications. In cultural modeling, it is expected that classifiers yield good performance. However, the performance of standard classifiers is often severely hindered in practice due to the imbalanced distribution of class in cultural data. In this paper, we identify class imbalance problem in cultural modeling domain. To handle the problem, we propose a user involved solution employing the receiver operating characteristic (ROC) analysis for classification algorithms with sampling approaches. Finally, we conduct experiment to verify the effectiveness of the proposed solution.
Model-checking concurrent systems with unbounded integer variables: symbolic representations, approximations, and experimental results

Model checking is a powerful technique for analyzing large, finite-state systems. In an infinite state system, however, many basic properties are undecidable. In this article, we present a new symbolic model checker which conservatively evaluates safety and liveness properties on programs with unbounded integer variables. We use Presburger formulas to symbolically encode a program's transition system, as well as its model-checking computations. All fixpoint calculations are executed symbolically, and their convergence is guaranteed by using approximation techniques. We demonstrate the promise of this technology on some well-known infinite-state concurrency problems.
Grouping-Based Fine-Grained Job Scheduling in Grid Computing

Grid computing is the technology for building Internet-wide computing environment in which distributed and heterogeneous resources are integrated. However, in Grid environment, job scheduling is confronted with a great challenge. This paper focuses on lightweight jobs scheduling in Grid Computing. An Adaptive Fine-grained Job Scheduling (AFJS) algorithm is proposed. Compared with other fine-grained job scheduling algorithms based on grouping, AFJS can outperform them according to the experimental results.
Quantitative , cognitive and computational aspects of the Romanian syllables

In this paper we argue for the need to construct a data base of Romanian syllables. We explain how we have constructed the data base, the corpus which we have used, the computational aspects of the syllabification and we present the main cognitive and quantitative aspects which we have extracted from our research. The results are compared with other researches realized for different languages.