Miracle-dz/UniFAR-FTUs huggingface dataset:
A method to identify services using master data and artifact-centric modeling approach

Service identification is one of the biggest challenges in implementing a service-oriented architecture. Current service identification methods (SIMs) rely on business process descriptions to elicit business perspective. However, service identification requires a level of business process documentation only found on organizations mature on business process modeling. In this context, master data (core enterprise information concepts, needed across different business processes, organizational units and applications across the organization) can be used as alternative input to business process. This work proposes a SIM that uses master data and logical data models as inputs. The proposed method also uses artifact-centric modeling technique to detail master data lifecycle and business rules within it.
Regular Algorithm of Adaptive Estimation of the State of a Managed Object with Correlated Noise Object and Interference Measurements

The paper deals with the construction of the regular adaptive estimation algorithms with auto and cross- correlated noise object and interference measurements. In the synthesis of regular estimation algorithms in the case of autocorrelated noise and interference measurements of the object describes how to expand the state vector and difference measurements. As a regular procedure uses statistical form discrepancy principle. When constructing regular estimation algorithms in the case of cross-correlated noise and interference measurements of the object as a regularizing procedures used regularization methods M.M. Lavrentev and V.N. Strahov. These expressions allow for convergence of the estimates and to improve the accuracy of their evaluation.     i i i i i i w i i i i i i i i i i i i i i i i i i i T i i i i i T i i i i
Lognormal-mixture dynamics and calibration to market volatility smiles

We introduce a general class of analytically tractable models for the dynamics of an asset price based on the assumption that the asset-price density is given by the mixture of known basic densities. We consider the lognormal-mixture model as a fundamental example, deriving explicit dynamics, closed form formulas for option prices and analytical approximations for the implied volatility function. We then introduce the asset-price model that is obtained by shifting the previous lognormal-mixture dynamics and investigate its analytical tractability. We finally consider a specific example of calibration to real market option data.
Draft – April 16 , 2013 Observing Progress Properties via Contextual Refinements ( Extended Version )

Implementations of concurrent objects should guarantee linearizability and a progress property such as wait-freedom, lock-freedom, obstruction-freedom, starvation-freedom, or deadlock-freedom. Conventional informal or semi-formal definitions of these progress properties describe conditions under which a method call is guaranteed to complete, but it is unclear how these definitions can be utilized to formally verify system software in a layered and modular way. In this paper, we propose a unified framework based on contextual refinements to show exactly how progress properties affect the behaviors of client programs. We give formal operational definitions of all common progress properties and prove that for linearizable objects, each progress property is equivalent to a specific type of contextual refinement that preserves termination. The equivalence ensures that verification of such a contextual refinement for a concurrent object guarantees both linearizability and the corresponding progress property. Contextual refinement also enables us to verify safety and liveness properties of client programs at a high abstraction level by soundly replacing concrete method implementations with abstract atomic operations.
Uncertainty, entropy and decoherence of the damped harmonic oscillator in the Lindblad theory of open quantum systems

In the framework of the Lindblad theory for open quantum systems, expressions for the density operator, von Neumann entropy and effective temperature of the damped harmonic oscillator are obtained. The entropy for a state characterized by a Wigner distribution function which is Gaussian in form is found to depend only on the variance of the distribution function. We give a series of inequalities, relating uncertainty to von Neumann entropy and linear entropy. We analyze the conditions for purity of states and show that for a special choice of the diffusion coefficients, the correlated coherent states (squeezed coherent states) are the only states which remain pure all the time during the evolution of the considered system. These states are also the most stable under evolution in the presence of the environment and play an important role in the description of environment induced decoherence.
An investigation of planar array system artefacts generated within an electrical impedance mammography system developed for breast cancer detection

An Electrical Impedance Mammography (EIM) planar array imaging system is being developed at the University of Sussex for the detection of breast cancers. Investigations have shown that during data collection, systematic errors and patient artefacts are frequently introduced during signal acquisition from different electrodes pairs. This is caused, in particular, by the large variations in the electrode-skin contact interface conditions occurring between separate electrode positions both with the same and different patients. As a result, the EIM image quality is seriously affected by these errors. Hence, this research aims to experimentally identify, analyse and propose effective methods to reduce the systematic errors at the electrode-skin interface. Experimental studies and subsequent analysis is presented to determine what ratio of electrode blockage seriously affects the acquired raw data which may in turn compromise the reconstruction. This leads to techniques for the fast and accurate detection of any such occurrences. These methodologies can be applied to any planar array based EIM system.
What does Self Rated Mental Health Represent

Background Unlike the widely used self rated health, the self rated mental health was found unsuitable as a proxy for mental illness. This paper analyses the relationships between the self ratings of physical health, mental health and overall health, and their association of with the objective indicators for physical and mental health. Design and methods The study is a secondary analysis of data from a nationwide representative sample of the non-institutionalized adult residents of Israel in 2003 that was collected via computer-assisted personal interview methods [n=4859]. Results The self rated physical health and the self rated mental health were strongly related to each other yet the self rated mental health was not related to chronic physical conditions and the self rated physical health was not related to mental disorders. In a multiple logistic regression analysis, those with positive self rated mental health had 93 times the odds of reporting positive overall health whereas those with positive self rated physical health had 40 times the odds of reporting positive overall health. Conclusions The self rating of mental health presents a qualitatively different dimension from mental illness. The self rated mental health is two times more important than the self rated physical health in predicting the self rated overall health Significance for public health The present study is an original study on the self rated physical, mental and overall health measures. Because of the wide range of associations with other health indicators, and the simplicity with which they are collected, self-rated health measures are widely used in large population surveys. The present study questions the automatic assumption that the self rated mental health functions as a proxy measure of psychiatric morbidity, and suggests that the self rated mental health is more closely related to subjective well-being. The results show that self rated mental health predicts self rated general health better than self rated physical health.
An improved fingerprint algorithm of 3D-DCT for video fingerprinting

In this paper, a new learned basis set algorithm (3D-LBT) based on 3D-DCT (Discrete Cosine Transform) is proposed for video fingerprinting and matching, in which for different video categories an Adaboost-based machine learning method is applied to each category of videos for selecting suitable sets of 3D-DCT coefficients to generate fingerprints, and a weighted distance of fingerprints is also defined for fingerprint matching. Our experimental results have illustrated that the proposed algorithm outperforms the conventional 3D-DCT algorithm and the 3D-RBT (Randomized Basis seT) algorithm in terms of robustness and uniqueness. Moreover, the proposed algorithm has better security performance for copyright applications.
Towards realistic known-item topics for the ClueWeb

Known-item finding is the task of re-finding and re-accessing an item previously seen. Typical examples of known items include accessed Web sites, received emails, or documents on one's personal desktop. Current research on known-item finding heavily relies on corpora of known-item queries and the respective known items. However, many existing corpora are proprietary and not available to the public (in particular those derived from Web query logs), a fact which does not allow for repeatable research. The existing publicly available corpora either contain automatically generated queries or queries that were manually generated while seeing the known item itself. Hence, we consider these public corpora to be rather artificial in nature. In this paper, we propose a methodology to create a known-item topic set that is much more realistic and that is built on top of a large-scale public test corpus. From know-item questions posted on the popular Yahoo! Answers platform we extract queries for known-items in a crowdsourcing setup. Since we ensure that all the known-items correspond to Web pages in the publicly available ClueWeb09 corpus (a large static Web crawl), we provide an environment for repeatable realistic Web-scale known-item searches.
Large-scale multi-robot mapping in MAGIC 2010

We describe a large-scale decentralised multi-robot mapping system that outputs globally optimised metric maps in real-time. The mapping system was used by team WAMbot in the finals of the Multi-Autonomous Ground-robotics International Challenge (MAGIC 2010). Research contributions include a novel large-scale multi-robot graph-based non-linear map optimisation approach, a hybrid decentralised and distributed mapping system and novel graphics processing unit (GPU) based approaches for accelerating intensive map matching and fusion operations. Our mapping system scales linearly with map size and on commodity hardware can easily map a 500m×500m urban area. We demonstrate robust, highly efficient and accurate mapping results from two different fleets of mobile robots. Videos, maps and timing results from the MAGIC 2010 challenge are presented.
Collaborative Multiagent Reinforcement Learning by Payoff Propagation

In this article we describe a set of scalable techniques for learning the behavior of a group of agents in a collaborative multiagent setting. As a basis we use the framework of coordination graphs of Guestrin, Koller, and Parr (2002a) which exploits the dependencies between agents to decompose the global payoff function into a sum of local terms. First, we deal with the single-state case and describe a payoff propagation algorithm that computes the individual actions that approximately maximize the global payoff function. The method can be viewed as the decision-making analogue of belief propagation in Bayesian networks. Second, we focus on learning the behavior of the agents in sequential decision-making tasks. We introduce different model-free reinforcement-learning techniques, unitedly called Sparse Cooperative Q-learning, which approximate the global action-value function based on the topology of a coordination graph, and perform updates using the contribution of the individual agents to the maximal global action value. The combined use of an edge-based decomposition of the action-value function and the payoff propagation algorithm for efficient action selection, result in an approach that scales only linearly in the problem size. We provide experimental evidence that our method outperforms related multiagent reinforcement-learning methods based on temporal differences.
Candesartan Mediated Amelioration of Cisplatin-Induced Testicular Damage Is Associated with Alterations in Expression Patterns of Nephrin and Podocin

Nephrin and podocin are known to be closely related to the pharmacological effects of angiotensin-II receptor blocker (ARB). The objectives of this study were to investigate the role of nephrin and podocin using cisplatin-induced testicular damage and to evaluate the effect of ARB. At first, we evaluated the effects of cisplatin either alone or in combination with ARB candesartan on changes in expression patterns of nephrin and podocin in the rat testes. We then conducted in vitro studies to investigate the effects of angiotensin using cultured Sertoli cells, line TM4. As a result, the expression of nephrin and podocin was shown to localize around the basal membrane of seminiferous tubules. Treatment with cisplatin resulted in a marked decrease in the expression of nephrin and podocin and induced a shift of both proteins from linear to granular expression patterns, accompanying the increased apoptotic index in the testes; these changes were partially restored by the additional administration of candesartan. In vitro studies with TM4 revealed the angiotensin-II mediated expression changes of nephrin and podocin. These findings suggest that candesartan can prevent cisplatin-induced testicular damage by regulating expression patterns of the nephrin-podocin complex in the testes.
Application of CRF and SVM based semi-supervised learning for semantic labeling of environments

Understanding the environment in both geometric and semantic levels enables a robot to perform high-level tasks in complex environments. Therefore in recent years research towards identifying and semantically labeling the environments based on onboard sensors for mobile robots has been gaining popularity. After the era of heuristic and rule-based approaches, supervised learning algorithms like Support Vector Machines (SVM) and AdaBoost have been extensively used for this purpose showing satisfactory performance. With the introduction of graphical models, approaches like Conditional Random Fields (CRF) which take the advantage of connectivity of samples provide more flexibility to capture complex dependencies. In this paper, we focus on a real-world task which challenges the generalization ability of the model, evaluate some graph based features, propose a semi-supervised learning algorithm by iteratively utilizing the results from SVM and CRF, and suggest a solution for CRF parameter estimation with partially labeled training data. Experiments have been conducted on six real-world indoor environments demonstrating the competence of the algorithm.
A Novel Datamining Approach to Determine the Vanished Agricultural Land in Tamilnadu

The presence of wide heterogeneity in the investigational material that is often used in agricultural research, led to the application of data mining tools and as a result many refinements and newer developments in statistics followed. Data mining, in fact, provides scientific tools for representative data collection, appropriate analysis and summarization of data and inferential procedures for drawing conclusions in the face of uncertainty. There is a need to provide remunerative prices for farmers in order to maintain food security and increase income of framers. Farmer finds himself thriftily poor and the most of the grains of rich agriculture have been appropriated by other section of the community. The difference between the engineering industry profit and agriculture sector produce profit tend them to leap over to the other sector. For this they need a capital investment and they find better way to make money is selling away the valuable and cultivable land to non agriculture purpose. So the food producing land is lost to the non-food producing sector, year by year the population of cultivable farmer and crop cultivable land is diminishing in a large chunk. By using clustering techniques this paper examines the current usage and details of agriculture land vanished in the past seven years.
Cross-correlating Sunyaev–Zel’dovich and weak lensing maps

We present novel statistical tools to cross-correlate frequency cleaned thermal Sunyaev-Zel'dovich (tSZ) maps and tomographic weak lensing (wl) convergence maps. Moving beyond the lowest order cross-correlation, we introduce a hierarchy of mixed higher-order statistics, the cumulants and cumulant correlators, to analyze non-Gaussianity in real space, as well as corresponding polyspectra in the harmonic domain. Using these moments, we derive analytical expressions for the joint two-point probability distribution function (2PDF) for smoothed tSZ (y_s) and convergence (\kappa_s) maps. The presence of tomographic information allows us to study the evolution of higher order {\em mixed} tSZ-weak lensing statistics with redshift. We express the joint PDFs p_{\kappa y}(\kappa_s,y_s) in terms of individual one-point PDFs (p_{\kappa}(\kappa_s), p_y(y_s)) and the relevant bias functions (b_{\kappa}(\kappa_s), b_y(y_s)). Analytical results for two different regimes are presented that correspond to the small and large angular smoothing scales. Results are also obtained for corresponding {\em hot spots} in the tSZ and convergence maps. In addition to results based on hierarchical techniques and perturbative methods, we present results of calculations based on the lognormal approximation. The analytical expressions derived here are generic and applicable to cross-correlation studies of arbitrary tracers of large scale structure including e.g. that of tSZ and soft X-ray background.
Automatic and generic mosaicing of satellite images

The CNES (the French Space Agency) has specified and developed a fully automatic mosaicing processing unit, in order to generate satellite image mosaics under operational conditions. This tool, called SIGMA, can automatically put each input image in a common geometry, homogenize the radiometry, and generate orthomosaics using stitching lines.
Self aware actuation for fault repair in sensor networks

Actuation ability introduces a fundamentally new design dimension in wireless ad-hoc sensor networks, allowing the network to adaptively reconfigure and repair itself in response to unpredictable run-time dynamics. One of the key network resources in these systems is energy and several uncontrollable factors lead to situations where a certain segment of the network becomes energy constrained before the remaining network. The performance gets limited due to the constrained sections. We argue that in this scenario, instead of rendering the complete network useless, the remaining energy resources should be reorganized to form a new functional topology in the network. We present methods for the network to be aware of its own integrity and use actuation to improve performance when needed. This capability of the system is referred to as "self aware actuation". In this paper, we consider a network where nodes (or a subset of the nodes) have traction ability. The network uses mobility to repair the coverage loss in the area being monitored by it. We present a completely distributed energy aware algorithm (referred to as CO-Fi) for coordinated coverage fidelity maintenance in sensor networks. The energy overheads of mobility are incorporated in the algorithm, thus leaving no hidden costs. Our preliminary analysis shows that CO-Fi can significantly help improve the usable lifetime of these networks.
RBACS: Rootkit Behavioral Analysis and Classification System

In this paper, we focus on rootkits, a special type of malicious software (malware) that operates in an obfuscated and stealthy mode to evade detection. Categorizing these rootkits will help in detecting future attacks against the business community. We first developed a theoretical framework for classifying rootkits. Based on our theoretical framework, we then proposed a new rootkit classification system and tested our system on a sample of rootkits that use inline function hooking. Our experimental results showed that our system could successfully categorize the sample using unsupervised clustering.
Intravenous Allogeneic Mesenchymal Stem Cells for Nonischemic Cardiomyopathy: Safety and Efficacy Results of a Phase II-A Randomized Trial.

RATIONALE Potential benefits of mesenchymal stem cell (MSC) therapy in heart failure may be related to paracrine properties and systemic effects, including anti-inflammatory activities. If this hypothesis is valid, intravenous administration of MSCs should improve outcomes in heart failure, an entity in which excessive chronic inflammation may play a pivotal role. OBJECTIVE To assess the safety and preliminary efficacy of intravenously administered ischemia-tolerant MSCs (itMSCs) in patients with nonischemic cardiomyopathy. METHODS AND RESULTS This was a single-blind, placebo-controlled, crossover, randomized phase II-a trial of nonischemic cardiomyopathy patients with left ventricular ejection fraction ≤40% and absent hyperenhancement on cardiac magnetic resonance imaging. Patients were randomized to intravenously administered itMSCs (1.5×106 cells/kg) or placebo; at 90 days, each group received the alternative treatment. Overall, 22 patients were randomized to itMSC (n=10) and placebo (n=12) at baseline. After crossover, data were available for 22 itMSC patients. No major differences in death, hospitalization, or serious adverse events were noted between the 2 treatments. Change from baseline in left ventricular ejection fraction and ventricular volumes was not significantly different between therapies. Compared with placebo, itMSC therapy increased 6-minute walk distance (+36.47 m, 95% confidence interval 5.98-66.97; P=0.02) and improved Kansas City Cardiomyopathy clinical summary (+5.22, 95% confidence interval 0.70-9.74; P=0.02) and functional status scores (+5.65, 95% confidence interval -0.11 to 11.41; P=0.06). The data demonstrated MSC-induced immunomodulatory effects, the magnitude of which correlated with improvement in left ventricular ejection fraction. CONCLUSIONS In this pilot study of patients with nonischemic cardiomyopathy, itMSC therapy was safe, caused immunomodulatory effects, and was associated with improvements in health status and functional capacity. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials. gov. Unique identifier: NCT02467387.
Integration of Documentation Task into Medical Treatment Processes in the Hospital

Medical Guidelines capture medical knowledge how to diag- nose and treat certain diseases. To some degree they describe patient data that is required to make treatment decisions. Usually documentation of medical procedures is carried out as an afterthought and produces additional workload for doctors and nurses. In this contribution we study the German guideline for neck pain, translate the guideline into a process model and identify the tasks of this process where documentation needs to take place, as well as the required data.