Miracle-dz/UniFAR-FTUs huggingface dataset:
Shaping the future of care together

This Green Paper, "Shaping the Future of Care Together (Cm. 7673)", sets out the Government's proposals for reforming the care and support system for adults in England and to establish a National Care Service. Care and support help people stay as independent, active, safe and well as possible, and to participate in and contribute to society throughout the different stages of their lives. Need for care and support can increase as a result of accidents, long-term illnesses, disability and growing older. The proposed National Care Service will provide six basic services: prevention; assessment; a joined-up service; information and advice; personalised care and support; and, fair funding. The Green paper explores ways in which different services can work together, a wider range of services can be developed, and better quality and innovation be achieved. The choices around funding are crucial to the reforms. The cost of care and support is high (a 65-year-old man can expect to need care costing GBP 30,000 during retirement). The Government has ruled out people paying for all of the costs or use of a tax-funded scheme (deemed unfair on those of working age). The options for funding being proposed are: partnership, where the state would pay, for those who qualified for support, a quarter to a third of the basic care and support, with extra help for the less well-off; insurance (either private or a state scheme) to cover costs above the quarter to a third met by the state; and, comprehensive, where everyone over retirement age who could afford it would be required to pay into a state insurance scheme.
Frequency of infectious gastrointestinal illness in Australia, 2002: regional, seasonal and demographic variation

SUMMARY To estimate the frequency of infectious gastroenteritis across Australia, and to identify risk factors, we conducted a national telephone survey of 6087 randomly selected respondents in 2001–2002. The case definition was three or more loose stools and/or two or more vomits in a 24-hour period in the last 4 weeks, with adjustment to exclude non-infectious causes and symptoms secondary to a respiratory infection. Frequency data were weighted to the Australian population. Multivariate logistic regression was used to assess potential risk factors including season, region, demographic and socioeconomic status. Among contacted individuals, 67% responded. The case definition applied to 7% of respondents (450/6087) which extrapolates to 17·2 million (95% CI 14·5–19·9 million) cases of gastroenteritis in Australia in one year, or 0·92 (95% CI 0·77–1·06) cases/person per year. In the multivariate model, the odds of having gastroenteritis were increased in summer and in the warmest state, in young children, females, those with higher socioeconomic status and those without health insurance.
Performance evaluation of vector controlled asymmetrical two-phase induction generator using three-leg voltage source inverter

The objective of this paper is to evaluate the performance of a vector controlled asymmetrical two-phase induction generator (TPIG) using a three-leg voltage source inverter. The proposed method is a decoupling scheme by controlling flux and torque separately. Improved performance is achieved by compensated stator currents. Matlab/Simulink software is applied to the presented system under various conditions. The simulation results of dynamic response for the proposed vector control are compared to those for scalar control with slip frequency regulation leading to conclude that TPIG can be used for wind energy applications.
Automated Synthesis of Run-time Monitors to Enforce Authorization Policies in Business Processes

Run-time monitors are crucial to the development of security-aware workflow management systems, which need to mediate access to their resources by enforcing authorization policies and constraints, such as Separation of Duty. In this paper, we introduce a precise technique to synthesize run-time monitors capable of ensuring the successful termination of workflows while enforcing authorization policies and constraints. An extensive experimental evaluation shows the scalability of our technique on the important class of hierarchically specified security-sensitive workflows with several hundreds of tasks.
On the convergence of reinforcement learning using a decision tree learner

In this paper, we propose conditions under which Q iteration using decision trees for function approximation is guaranteed to converge to the optimal policy in the limit, using only a storage space linear in the size of the decision tree. We analyze different factors that influence the efficiency of the proposed algorithm, and in particular study the efficiency of different concept languages. We illustrate the approach with some preliminary experiments.
Placement of relayers in wireless industrial sensor networks: An approximation algorithm

We consider the joint problem of relay-aware TDMA scheduling and optimal placement of relays to improve transmission reliability under deadline constraints. We build on existing algorithms for TDMA scheduling. We propose an iterative placement and scheduling algorithm and compare its performance (in terms of the average number of packets not successfully transmitted before their deadline) against two baseline algorithms, one using random relay placement, and another algorithm giving the truly optimal relay positions and schedule on a grid. Our results show that the iterative placement algorithm is substantially better than random placement. Furthermore, we have applied the truly optimal scheduling and placement algorithm to a simple scenario with one relay and found that the iterative placement algorithm gives almost the same performance.
Bordersearch: An adaptive identification of failure regions

The reliability and safety of modern analog devices, e.g. in automotives, aircraft or consumer electronics, is influenced by many input parameters like supply voltage, ambient temperature or load resistances. In certain regions of this large parameter space, the device exhibits degraded performance or it fails completely. The validation of such a device has to find the regions of the input parameter space in which the device misbehaves. However, with several parameters, it is a complex task to determine these regions, especially if parameters interact. In this paper, we present the Bordersearch algorithm, which combines adaptive testing with a machine learning classifier to efficiently determine the border between passing and failing regions in the parameter space. Furthermore, this method enables sophisticated post-processing analysis, e.g. better visualizations and automatic ranking of the parameters according to their influence. This algorithm scales well to a high-dimensional parameter space and is robust against outliers and fuzzy borders. We show the effectiveness of this method on an automotive electromechanical system with eleven input parameters.
Predictive control of the heat exchanger using Local Model Network

The paper deals with the problem of modeling and control of nonlinear processes using the Local Model Network (LMN). The idea is based on development of the local linear models for the whole operating range of the controlled process. The nonlinear plant is then approximated by a set of locally valid sub-models, which are smoothly connected using the validity function. For saving the computational load, linear model is obtained by interpolating these linear models at each sample instant and then used in Model Predictive Control (MPC) framework to calculate the future behavior of the process. The approach is verified in a real-time control of Multifunction Process Control Teaching System (MPCTS) - the Armfield PCT 40.
The Tactics and Evolution of Social Entrepreneurial Storytelling

Like all entrepreneurs, social entrepreneurs construct communication to persuade stakeholders to provide resources. However, unlike conventional entrepreneurs, social entrepreneurs must develop a complex message that balances both business and social-good themes. Yet it is unclear precisely how such communication influences entrepreneurs’ success in acquiring resources. To address this topic, this study uses a case-based, partially-inductive design to examine the narratives communicated by social entrepreneurs. Findings reveal that entrepreneurs receiving external investment differed from unfunded entrepreneurs in their narrative tactics, characteristics, and evolution. These insights contribute to work on entrepreneurial resource acquisition and organizational narrative theory and have normative implications for practicing social entrepreneurs.
Comparison of Whole Blood and Peripheral Blood Mononuclear Cell Gene Expression for Evaluation of the Perioperative Inflammatory Response in Patients with Advanced Heart Failure

Background Heart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction (MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition of MOD allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from patients' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined. Methods We collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25–75% IQR 7–14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (≤ 4 points), intermediate (5–11), and high (≥ 12) risk categories correlating with GEP. Results Results indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups. Conclusion GEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD.
VectorH: Taking SQL-on-Hadoop to the Next Level

Actian Vector in Hadoop (VectorH for short) is a new SQL-on-Hadoop system built on top of the fast Vectorwise analytical database system. VectorH achieves fault tolerance and storage scalability by relying on HDFS, and extends the state-of-the-art in SQL-on-Hadoop systems by instrumenting the HDFS replication policy to optimize read locality. VectorH integrates with YARN for workload management, achieving a high degree of elasticity. Even though HDFS is an append-only filesystem, and VectorH supports (update-averse) ordered tables, trickle updates are possible thanks to Positional Delta Trees (PDTs), a differential update structure that can be queried efficiently. We describe the changes made to single-server Vectorwise to turn it into a Hadoop-based MPP system, encompassing workload management, parallel query optimization and execution, HDFS storage, transaction processing and Spark integration. We evaluate VectorH against HAWQ, Impala, SparkSQL and Hive, showing orders of magnitude better performance.
Using an Emotional Intelligent Agent to Support Customers' Searches Interactively in e-Marketplaces

Emotions play a key role in any purchasing process even more when it is done through web portals. In this paper, we present a fuzzy-logic based system aimed to generate emotions for a virtual seller or avatar. The 3D avatar is integrated within the e-Zoco e-commerce portal and its behaviour depends on the product search results. Moreover, the mood of the avatar not only depend on the products that the portal can offer, but also the recommendations that can be used to guide the customer when beginning a new search. The system provides advice to the clients in order to assist them in the specification of constraints and thus obtain more interesting products in a shorter time.
High resolution of ISAR imaging based on enhanced sparse Bayesian learning

The compressive sensing (CS) has been successfully used in inverse synthetic aperture radar (ISAR) imaging. Since the sparse reconstruction based on l1 norm is sensitive to the regularized factor and makes it inconvenient to be used in practice, the sparse Bayesian learning (SBL) is considered in this situation, which retains a preferable property of the l0 norm and has no user parameter. In this paper, we proposed the ISAR imaging approach based on enhanced sparse Bayesian learning (ESBL), the ESBL determines the signal support by applying the statistical thresholding to accept the active components of the model, which is integrated into the SBL and improving its accuracy and reducing convergence time. The simulation result show that an accurate reconstruction of high-resolution ISAR images can be obtained than most its counterparts in low SNR, and resulting in lower mean square error (MSE).
A binarization algorithm for historical manuscripts

Binarization methods are applied to document images for discriminating the text from the background based on pure thresholding and filtering combined with image processing algorithms. The proposed binarization procedure consists of five discrete steps in image processing, for different classes of document images. A refinement technique enhances further the image quality. Results on Byzantine historical manuscripts are discussed and potential applications and further research are proposed. The main contribution of this paper is to propose a simple and robust binarization procedure for pre-filtered historical manuscripts images, and simulation results are also presented.
The Research of Orthogonal Experiment Applied in Mobile Phone's Software Test Case Generation

Aimed at the large amount of test cases in handset’s software testing and tremendous manual work, this paper adopted orthogonal experiment to generate test cases after comparing three algorithms which are genetic algorithm, formal specification and orthogonal experiment. By constructing the experiment and analysing their difficulties, maneuverability, coverage rate and rigour, etc, it is concluded that orthogonal method could not only generate test cases more easily and simplify them more efficiently but also make the reduced cases representative. Finally, take calendar of a simulator for exapmle, we use orthogonal experiment to extract its factors and levels, and generate the cases by Test Case Generation Tool which is prorammed in Visual Studio. Through the analysis of the paired-coverage rate of these cases, it is concluded that orthogonal experiment is suitable for phone’s test case generation.
Evidence Accumulation Clustering using Pairwise Constraints

Recent work on constrained data clustering have shown that the incorporation of pairwise constraints, such as must-link and cannot-link constraints, increases the accuracy of single run data clustering methods. It was also shown that the quality of a consensus partition, resulting from the combination of multiple data partitions, is usually superior than the quality of the partitions produced by single run clustering algorithms. In this paper we test the effectiveness of adding pairwise constraints to the Evidence Accumulation Clustering framework. For this purpose, a new soft-constrained hierarchical clustering algorithm is proposed and is used for the extraction of the consensus partition from the co-association matrix. It is also studied whether there are advantages in selecting the must-link and cannot-link constraints on certain subsets of the data instead of selecting these constraints at random on the entire data set. Experimental results on 7 synthetic and 7 real data sets have shown the use of soft constraints improves the performance of the Evidence Accumulation Clustering.
Diagnostic imaging of the temporomandibular joint: recommendations for use of the various techniques.

The indications for an imaging study of the TMJ are the same for MR, CT, and arthrognaphy; only those for a plain film study differ. In general, an examination is warranted (1) when the diagnosis of an internal derangement is in doubt and must be established before therapy can be instituted; (2) if the diagnosis is not in doubt but confirmation or documentation is required, such as for third-party payers and in medicolegal cases; (3) pneoperatively for disk surgery; and (4) when therapy for a presumed internal derangement has failed and doubt about the diagnosis arises. Note that this set of indications excludes many patients with clicking, painful joints who will have conservative treatment. We believe it is impontant to emphasize that not every patient with an internal derangement needs to have an imaging study. An internal derangement of the TMJ is basically an anterionly displaced disk. Although joint dynamics, capsular tears, adhesions, and disk perforations also can be imaged, they are usually incidental findings that are not treated differently from the accompanying displaced disk. Only when it is decided that an imaging study should be performed is it necessary to decide which type of study is appropriate. Choosing the type of study that is appropriate will depend on what is available and what information the referring clinician desires. Each imaging technique has its strengths and weaknesses (Table 1); however, in most cases the only information required is the disk position.
Personalized Service-Oriented E-Learning Environments

The social component of Web 2.0-related services is providing a new open and personal approach to how we expect things to solve problems in our information-driven world. In particular, students' learning needs require open, personal e-learning systems adapted to life-long learning needs in a rapidly changing environment. It therefore shouldn't be surprising that a new wave of ideas centered on pervasive systems has drawn so much attention. This article analyzes current trends in the evolution of e-learning architectures and describes a new architecture that captures the needs of both formal (instructor-led) and informal (student-led) learning environments.
Aberrant DNA Methylation of P16, MGMT, and hMLH1 Genes in Combination with MTHFR C677T Genetic Polymorphism in gastric cancer

Objective: We aimed to explore the association of P16, MGMT and HMLH1 with gastric cancer and their relation with Methylenetetrahydrofolate reductase (MTHFR). Methods: 322 gastric patients who were confirmed with pathological diagnosis were included in our study. Aberrant DNA methylation of P16, MGMT and HMLH1 and polymorphisms of MTHFR C677T and A1298C were detected using PCR-RFLP. Results: The proportions of DNA hypermethylation in P16, MGMT and hMLH1 genes in gastric cancer tissues were 75.2% (242/322), 27.6% (89/322) and 5.3% (17/322), respectively. In the remote normal-appearing tissues, 29.5% (95/322) and 16.1%(52/322) showed hypermethylation in P16 and MGMT genes, respectively. We found a significantly higher proportion of DNA hypermethylation of P16 in patients with N1 TNM stage in cancer tissues and remote normal-appearing tissues (P<0.05). Similarly, we found DNA hypermethylation of MGMT had significantly higher proportion in N1 and M1 TNM stage (P<0.05). Individuals with homozygotes (TT) of MTHFR C677T had significant risk of DNA hypermethylation of MGMT in cancer tissues [OR (95% CI)=4.27(1.76-7.84)], and a significant risk was also found in those carrying MTHFR 677CT/TT genotype [OR (95% CI)= 3.27(1.21-4.77)]. Conclusion: We found the aberrant hypermethylation of cancer-related genes, such as P16, MGMT and HMLH1, could be predictive biomarkers for detection of gastric cancer.
Blindly Combined Energy Detection for Spectrum Sensing in Cognitive Radio

In this letter, a method is proposed to optimally combine the received signal samples in space and time based on the principle of maximizing the signal-to-noise ratio (SNR). After the combining, energy detection (ED) is used. However, optimal combining needs information of the source signal and channel, which is usually unknown. To overcome this difficulty, a method is proposed to blindly combine the signal samples. Similar to energy detection, blindly combined energy detection (BCED) does not need any information of the source signal and the channel a priori. BCED can be much better than ED for highly correlated signals, and most importantly, it does not need noise power estimation and overcomes ED's susceptibility to noise uncertainty. Also, perfect synchronization is not required. Simulations based on wireless microphone signals and randomly generated signals are presented to verify the methods.
Methods for Evaluating Simulated Learners: Examples from SimStudent

We discuss methods for evaluating simulated learners associated with four different scientific and practical goals for simulated learners. These goals are to develop a precise theory of learning, to provide a formative test of alternative instructional approaches, to automate authoring of intelligent tutoring systems, and to use as a teachable agent for students to learn by teaching. For each goal, we discuss methods for evaluating how well a simulated learner achieves that goal. We use SimStudent, a simulated learner theory and software architecture, to illustrate these evaluation methods. We describe, for example, how SimStudent has been evaluated as a theory of student learning by comparing, across four domains, the cognitive models it learns to the hand-authored models. The SimStudent-acquired models generally yield more accurate predictions of student data. We suggest future research into directly evaluating simulated learner predictions of the process of student learning.
Regional frequency analysis methods for evaluating changes in hydrologic extremes

A common assumption in frequency analysis is that hydrologic extremes (floods or heavy precipitation) are generated by a random process. This implies that natural climatic variability does not change the distribution of extreme events. A regional frequency analysis approach is proposed to test the hypothesis of randomness over secular timescales. Observed regional occurrences of extreme events are compared to those from a random process. Significant departures may indicate nonrandomness due to climatic variability. Application of the approach to a region in the Southern Plains indicates nonrandomness in annual maximum precipitation.
Rule-based service composition and service-oriented business rule management

As business processes change constantly, there is a growing need for adaptive composite services. Unfortunately, existing service composition languages and techniques result in rather brittle and rigid processes, whose services live in a straitjacket. In this paper, we propose a rule-driven approach for service composition that is purely declarative, highly adaptive and integrated in a truly service-oriented approach to business rule management.