Miracle-dz/UniFAR-FTUs huggingface dataset:
Service Management Beyond UDDI - A Design Science Approach

The organisational change that comes along with the evolution of enterprise architectures (EA) towards service-oriented architectures (SOA) requires a consolidation of the enterprise’s information system. Though, the SOA paradigm is not restricted to application system functionality, but takes also semiautomatic and manual services of an organization into consideration. In order to reach the interorganizational transparency over the shared resources and processes and to facilitate the reuse of services, a consistent SOA management is recommended. A catalogue system was developed enabling access to both technical and organisational services. Benefits of this approach are illustrated in a pilot study implemented by the MIRO project at the University of Muenster.
Introduction : The idea and the purpose

PageRank algorithm is one of the most commonly used algorithms that determine the global importance of web pages. Due to the size of web graph which contains billions of nodes, computing a PageRank vector is very computational intensive and it may takes any time between months to hours depending on the efficiency of the algorithm. This promoted many researchers to propose techniques to enhance the PageRank algorithm. The researchers investigated all aspects of PageRank algorithm that covers stability, convergence speed, memory consumption, I/O efficiency, and the connectivity matrix properties [2-7, 11]. However, some aspects of PageRank algorithm are left unstudied. In addition, very few techniques are building on the results of the others. In this work we investigate some PageRank properties and report our findings.
A Novel Spectral Conversion Based Approach for Noisy Speech Enhancement

noisy speech enhancements algorithms are efficiently used for additive noise but not very good for convolutive noise as reverberation. And even for additive noise, the estimation of noise, when only one microphone source is provided, is based on the assumption of a slowly varying noise environment, commonly assumed as stationary noise. However, real noise is non-stationary noise, which difficult to be efficiently estimated. Spectral conversion can be used for predicting the vocal tract (spectral envelope) parameters of noisy speech without estimating the parameters of the noise source. Therefore, it can be applied to a general speech enhancement model, for both stationary and non-stationary additive noise environment, as well as convolutive noise environment, when only one microphone source is provided. In this paper, we propose a spectral conversion based speech enhancement method. The experimental results show that our method outperforms traditional methods.
The Waiting Game: the Role of Predicted Value, Wait Disconfirmation, and Providers' Actions in Consumers' Service Evaluations

Management of consumer waiting experiences is critical for practitioners in that unpleasant waiting experiences may result in negative service evaluations. This paper develops a conceptual framework in which predicted value of service, wait expectation (conceptualized as “consumer zone of wait tolerance” derived from the service literatures), wait disconfirmation (consumers’ compari-sons between wait expectations and perceptions), and affective response to waiting are proposed to directly or indirectly affect service experience evaluation. In addition, this paper proposes that actions of the service provider moderate the relationship between affective response to waiting and service experience evaluation. Conclusions and contributions are discussed.
Distribution Free Learning with Local Queries

The model of learning with \emph{local membership queries} interpolates between the PAC model and the membership queries model by allowing the learner to query the label of any example that is similar to an example in the training set. This model, recently proposed and studied by Awasthi, Feldman and Kanade, aims to facilitate practical use of membership queries. We continue this line of work, proving both positive and negative results in the {\em distribution free} setting. We restrict to the boolean cube $\{-1, 1\}^n$, and say that a query is $q$-local if it is of a hamming distance $\le q$ from some training example. On the positive side, we show that $1$-local queries already give an additional strength, and allow to learn a certain type of DNF formulas. On the negative side, we show that even $\left(n^{0.99}\right)$-local queries cannot help to learn various classes including Automata, DNFs and more. Likewise, $q$-local queries for any constant $q$ cannot help to learn Juntas, Decision Trees, Sparse Polynomials and more. Moreover, for these classes, an algorithm that uses $\left(\log^{0.99}(n)\right)$-local queries would lead to a breakthrough in the best known running times.
COMPUTER AIDED DIAGNOSIS FOR LIVER CANCER USING STATISTICAL MODEL

Liver Cancer is one of the most difficult cancer to cure and the number of deaths that it causes gener ally increasing. The signs and the symptoms of the liver cancer are not known, till th e cancer is in its advanced stage. So, early detect ion is the main problem. If it is detected earlier then it can be helpful for the Med ical treatment to limit the danger, but it is a challenging task due to the Cancer cell structure. Interpretation of Medical image is often difficult and time consuming, even for the experie nced Physicians. Most traditional medical diagnosis systems founded needs huge quantity of training data and takes long proc essing time. Focused on the solution to these problems, a Medical Diagnosis System based on Hidden Markov Model (HMM) is prese nted. This paperdescribes a computer aided diagnosis system for liver cancer that detects the liver tumor at an early stage from the chest CT images. This automation process reduces the time complexity and increases the diagnosis confidence.
Program locality analysis using reuse distance

On modern computer systems, the memory performance of an application depends on its locality. For a single execution, locality-correlated measures like average miss rate or working-set size have long been analyzed using reuse distance—the number of distinct locations accessed between consecutive accesses to a given location. This article addresses the analysis problem at the program level, where the size of data and the locality of execution may change significantly depending on the input. The article presents two techniques that predict how the locality of a program changes with its input. The first is approximate reuse-distance measurement, which is asymptotically faster than exact methods while providing a guaranteed precision. The second is statistical prediction of locality in all executions of a program based on the analysis of a few executions. The prediction process has three steps: dividing data accesses into groups, finding the access patterns in each group, and building parameterized models. The resulting prediction may be used on-line with the help of distance-based sampling. When evaluated on fifteen benchmark applications, the new techniques predicted program locality with good accuracy, even for test executions that are orders of magnitude larger than the training executions. The two techniques are among the first to enable quantitative analysis of whole-program locality in general sequential code. These findings form the basis for a unified understanding of program locality and its many facets. Concluding sections of the article present a taxonomy of related literature along five dimensions of locality and discuss the role of reuse distance in performance modeling, program optimization, cache and virtual memory management, and network traffic analysis.
Social gaming: what attracts the most attention?: an investigation using an improved diary method

The twenty first century has seen leaps in quality of integration of computing into entertainment systems involving human-computer interaction (HCI). Various games have attracted their masses of audiences, some hitting international records of 6 million subscribers, as in World of Warcraft by Blizzard [1]. This study hopes to further develop the preferred game model by developing and testing a new methodology for HCI assessment.
Driver behavior modeling based on database of personal mobility driving in urban area

This paper proposes the modeling of longitudinal driving behavior based on urban driving data of one-person micro-scale electric vehicle for developing the personalized driver adaptive assistance system. The system enables us to realize a practical cooperative safety system that suits individual maneuvering behavior in real-world traffic situation. First, the urban driving data is collected by on-road experiments using continuous sensing drive recorder equipped in the micro-scale electric vehicle. Next, the human pedal operation for longitudinal vehicle control is divided into several modes. Consequently, the driver model is formulated by setting up the input-output relationship of each operation mode and the mode switching rules based on the collected urban driving data. It is proved that the proposed driver model is effective to express driver pedal operation. Finally, the proposed driver model is applied to detect hurry driving by comparison between the model-based estimated pedal operation and the measured value in hurry driving situation.
Using Topic Hierarchies with Privileged Information to Improve Context-Aware Recommender Systems

Recommender systems are designed to assist individuals to identify items of interest in a set of options. A context-aware recommender system makes recommendations by incorporating available contextual information into the recommendation process. One of the major challenges in context-aware recommender systems research is the lack of automatic methods to obtain contextual information for these systems. Considering this scenario, in this paper, we propose to use contextual information from topic hierarchies to improve the performance of context-aware recommender systems. Three different types of topic hierarchies are constructed by using the LUPI-based Incremental Hierarchical Clustering method: a topic hierarchy using only a traditional bag-of-words, a second topic hierarchy using a bag-of-words of named entities and a third topic hierarchy using both information. We evaluate the contextual information in four context-aware recommender systems. The empirical results demonstrate that by using topic hierarchies we can provide better recommendations.
Massive modularity? The relationship between context-relevance, information encapsulation and functional specialization

In this article, I discuss the debate between domain-specificity and content-generality in regard to the human mind. My main objective is to argue that the human mind can both be understood as a content-dependent machinery, as well as a general-purpose system that encapsulates information and manifests it accordingly. In evolutionary psychology a strong case is made for the mind’s domain-specific architecture but assumptions of domain-general importance could also be taken into account. In evolutionary terms, it is argued that the mind is composed of content-dependent and specialized functions that have evolved in order to help humans deal with adaptive problems. The mind’s evolved functionality of domain-specific systems has helped humans not only to survive but also to be creative in their relationships to others and the environment. However, advocates of behaviourist models and learning theories have proposed that the mind is a content-independent device which encapsulates information of a general nature. In this paper, domain specificity will be explained in terms of functional specialization, and content-generality in terms of informational encapsulation. My main aim will be to argue that both assumptions can be viewed in context-relevance to each other.
The pro‐ or antiangiogenic effect of plasminogen activator inhibitor 1 is dose dependent

Plasminogen activator inhibitor 1 (PAI‐1) is believed to control proteolytic activity and cell migration during angiogenesis. We previously demonstrated in vivo that this inhibitor is necessary for optimal tumor invasion and vascularization. We also showed that PAI‐1 angiogenic activity is associated with its control of plasminogen activation but not with the regulation of cell‐matrix interaction. To dissect the role of the various components of the plasminogen activation system during angiogenesis, we have adapted the aortic ring assay to use vessels from gene‐inactivated mice. The single deficiency of tPA, uPA, or uPAR, as well as combined deficiencies of uPA and tPA, did not dramatically affect microvessel formation. Deficiency of plasminogen delayed microves‐sel outgrowth. Lack of PAI‐1 completely abolished angio‐genesis, demonstrating its importance in the control of plasmin‐mediated proteolysis. Microvessel outgrowth from PAI‐1‐/‐ aortic rings could be restored by adding exogenous PAI‐1 (wild‐type serum or purified recombi‐nant PAI‐1). Addition of recombinant PAI‐1 led to a bell‐shaped angiogenic response clearly showing that PAI‐1 is proangiogenic at physiological concentrations and antiangiogenic at higher levels. Using specific PAI‐1 mutants, we could demonstrate that PAI‐1 promotes an‐giogenesis at physiological (nanomolar) concentrations through its antiproteolytic activity rather than by interacting with vitronectin. —Devy, L. , Blacher, S. , Grignet‐Debrus, C. , Bajou, K. , Masson, V. , Gerard, R. D. , Gils, A. , Carmeliet, G. , Carmeliet, P., Declerck, P. J., Noèl, A. , Foidart, J. M. The pro‐ or antiangiogenic effect of plasminogen activator inhibitor 1 is dose dependent. FASEB J. 16, 147–154 (2002)
The Network Architecture Designed for an Adaptable IoT-based Smart Office Solution

The paper presents a prototype of the Smart Office system, which was developed as one of pilot applications of the FP7 EU project ELLIOT, http://www.elliot-project.eu. The described solution is based on the LinkSmart semantic middleware, http://sourceforge.net/projects/linksmart/, an open source technology enabling a construction of distributed enterprise Internet of Things applications. LinkSmart was enhanced on features of seamless networking and interoperability of connected devices within the another FP7 EU project EBBITS, http://www.ebbits-project.eu. Principles of the system architecture and service-based networking are discussed and demonstrated on the Smart Office solution, employing the approach of user experience monitoring and continuous evaluation for adjusting the application to desired knowledge, social, and business parameters.
Object Recognition Using Support Vector Machine Augmented by RST Invariants

In this paper the support vector machine is utilized to recognize the object from the given image. The proposed method for object recognition is associated with the reduction of feature vector by Kernel Principal Component Analysis (KPCA) and recognition using the Support Vector Machine (SVM) classifier. Also in this paper the feature extraction method extracts features from global descriptors of the image. In the feature extraction process for an image, global features are extracted and formed as feature vector. For the entire training image the feature vector is generated and dimension reduction is done using KPCA. The reduced feature vector is used to train the SVM classifier. Later test images are given as input and tested the performance of the Classifier. To prove the efficiency of the SVM Classifier, Back Propagation Neural Network is used for the object recognition. From the comparison, SVM classifier outperforms.
Alkbh8 Regulates Selenocysteine-Protein Expression to Protect against Reactive Oxygen Species Damage

Environmental and metabolic sources of reactive oxygen species (ROS) can damage DNA, proteins and lipids to promote disease. Regulation of gene expression can prevent this damage and can include increased transcription, translation and post translational modification. Cellular responses to ROS play important roles in disease prevention, with deficiencies linked to cancer, neurodegeneration and ageing. Here we detail basal and damage-induced translational regulation of a group of oxidative-stress response enzymes by the tRNA methyltransferase Alkbh8. Using a new gene targeted knockout mouse cell system, we show that Alkbh8-/- embryonic fibroblasts (MEFs) display elevated ROS levels, increased DNA and lipid damage and hallmarks of cellular stress. We demonstrate that Alkbh8 is induced in response to ROS and is required for the efficient expression of selenocysteine-containing ROS detoxification enzymes belonging to the glutathione peroxidase (Gpx1, Gpx3, Gpx6 and likely Gpx4) and thioredoxin reductase (TrxR1) families. We also show that, in response to oxidative stress, the tRNA modification 5-methoxycarbonylmethyl-2′-O-methyluridine (mcm5Um) increases in normal MEFs to drive the expression of ROS detoxification enzymes, with this damage-induced reprogramming of tRNA and stop-codon recoding corrupted in Alkbh8-/- MEFS. These studies define Alkbh8 and tRNA modifications as central regulators of cellular oxidative stress responses in mammalian systems. In addition they highlight a new animal model for use in environmental and cancer studies and link translational regulation to the prevention of DNA and lipid damage.
Utilization of Method Graphs to Measure Cohesion in Object Oriented Software

The primarily used technique in measuring cohesion in an object oriented software is analyzing the relations between methods and data members of the software classes. However, extraction of such relations is rather problematic since the usage of data members can vary heavily in program code. Object code analysis is a widely used technique to solve this problem which requires the compilation of the source code. In this paper, we analyze the relations between various method graphs (including call graphs) which can be easily obtained by static source code analysis to obtain software-wide cohesion measures. We perform our analysis to discover the relationship between a widely used cohesion metric, namely LCOM, and the relation among method graphs. This way, we provide cohesion measures that can be extracted rapidly and easily compared to conventional techniques. Proposed measures can be used to obtain information about overall degree of cohesion which in turn can let the designer infer about the quality concerns like modularity and reusability of an object oriented software. Our results show that using different kind of relations in such analysis provides significantly different results in evaluating software-wide average class cohesion. Using graphs involving cooperating methods provide the most correlated results with the LCOM metric.
A New Corpus Resource for Studies in the Syntactic Characteristics of Terminologies in Contemporary English

In this paper, we present a new corpus resource that has been constructed specially for the study of the syntactic characteristics of terminologies. The corpus is based on the British component of the International Corpus of English (ICE-GB), comprising four parallel subject domains from two text categories (i.e. academic vs. popular prose) with a total of about 200,000 running word tokens. The resource is richly annotated at lexical, grammatical, syntactic, and terminological levels. It is also parameterized according to both text categories and subject domains. The corpus resource is expected to contribute towards a linguistically motivated description of terms and their internal structures. It is also expected to provide an analytical framework for the study of relations between terminological use and text categories as well as subject domains.
RNA world – the dark matter of evolutionary genomics

For a long time, molecular evolutionary biologists have been focused on DNA and proteins, whereas RNA has lived in the shadow of its famous chemical cousins as a mere intermediary. Although this perspective has begun to change since genome‐wide transcriptional profiling was successfully extended to evolutionary biology, it still echoes in evolutionary literature. In this mini‐review, new developments of RNA biochemistry and transcriptomics are brought to the attention of evolutionary biologists. In particular, the unexpected abundance and functional significance of noncoding RNAs is briefly reviewed. Noncoding RNAs control a remarkable range of biological pathways and processes, all with obvious fitness consequences, such as initiation of translation, mRNA abundance, transposon jumping, chromosome architecture, stem cell maintenance, development of brain and muscles, insulin secretion, cancerogenesis and plant resistance to viral infections.
XMM-Newton observations of High Mass X-ray Binaries in the SMC

Based on XMM-Newton EPIC data of four pointings towards the Small Magellanic Cloud (SMC), results on timing and spectral analyses of 16 known high mass X-ray binaries (HMXBs) and HMXB candidates in the SMC are presented. We confirm the pulse periods of four sources which were known to show pulsations. In addition, two new X-ray pulsars are discovered: XMMUJ005605.2-722200 with P p u l s e = 140.1 ′ 0.3 s and RX J0057.8-7207 with P p u l s e = 152.34 ′ 0.05 s. Due to the low Galactic foreground absorption, X-ray binary systems in the Magellanic Clouds are well suited for studies of the soft component in their X-ray spectrum. Spectral analysis reveals soft emission besides a power law component in the spectra of three sources. The existence of emission lines in at least one of them corroborates the thermal nature of this emission with temperatures of 0.2-0.3 keV and heavy element abundances lower than solar. For the HMXB SMC X-2 which was in a low luminosity state, we determine a flux upper limit of 1.5 x 10 - 1 4 erg cm - 2 s - 1 (0.3-10.0 keV). Furthermore, two new sources (XMMUJ005735.7-721932 and XMMU J010030.2-722035) with hard spectrum and emission line objects as likely optical counterparts are proposed as new X-ray binary candidates.
Rare Events , Splitting , and Quasi-Monte Carlo

In the context of rare-event simulation, splitting and importance sampling (IS) are the primary approaches to make important rare events happen more frequently in a simulation and yet recover an unbiased estimator of the target performance measure, with much smaller variance than a straightforward Monte Carlo (MC) estimator. Randomized quasi-Monte Carlo (RQMC) is another class of methods for reducing the noise of simulation estimators, by sampling more evenly than with standard MC. It typically works well for simulations that depend mostly on very few random numbers. In splitting and IS, on the other hand, we often simulate Markov chains whose sample paths are a function of a long sequence of independent random numbers generated during the simulation. In this paper, we show that RQMC can be used jointly with splitting and/or IS to construct better estimators than those obtained by either of these methods alone. We do that in a setting where the goal is to estimate the probability of reaching B before reaching (or returning to) A when starting from a distinguished state not in B, where A and B are two disjoint subsets of the state space and B is very rarely reached. This problem has several practical applications. The paper is in fact a two-in-one: the first part provides a guided tour of splitting techniques, introducing along the way some improvements in the implementation of the multilevel splitting. At the end of the paper, we also give examples of situations where splitting is not effective. For these examples, we compare different ways of applying IS and combining it with RQMC.