Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of real-time data analysis system based on Impala

With the continuous development of Internet technology, from a mass of data real-time, efficient analysis and dig out the valuable information, especially important for enterprises. At present, relatively common practice is built up data analysis system in the Hadoop environment based on Hive. But it is more suitable for the batch processing in large data of clusters, and is not suitable for the real-time processing of large data requirements brought about by the development of the business adjustment. This paper presents a real-time data analysis system based on Impala. It can be used as a good supplement scheme. This paper will explain the thought and method of the construction of the real-time data analysis system based on Impala, from the system selection, system architecture, and practical.
Continuous software process improvement through statistical process control

Measurement based software process improvement is nowadays a mandatory activity. This implies continuous process monitoring in order to predict its behaviour, highlight its performance variations and, if necessary, quickly react to it. Process variations are due to common causes or assignable ones. The former are part of the process itself while the latter are due to exceptional events that result in an unstable process behaviour and thus in less predictability. Statistical process control (SPC) is a statistical based approach able to determine whether a process is stable or not by discriminating between the presence of common cause variation and assignable cause variation. It is a well-established technique, which has shown to be effective in manufacturing processes but not yet in software process contexts. Experience in using SPC is not mature yet. Therefore a clear understanding of the SPC outcomes still lacks. Although many authors have used it in software, they have often not considered the primary differences between manufacturing and software process characteristics. Due to such differences SPC cannot be adopted "as is" but it must be tailored. In this sense, the author proposes an SPC-based approach that reinterprets SPC, and applies it from a software process point of view.
Semantic heterogeneity in distributed geographic databases

This paper considers the special problems of semantic heterogeneity in a distributed system of databases containing spatially referenced information. Two forms of semantic heterogeneity are defined. Generic semantic heterogeneity arises when nodes are using different generic conceptual models of the spatial information. Contextual semantic heterogeneity is caused by the particular local environmental conditions at nodes. It is contextual heterogeneity which is especially the consideration with geographic databases and to which the paper devotes most attention. Two possible solutions are proposed, one founded on transforming processors between models and a second using a canonical model which is a generalization of existing generic spatial models.
Bridging College and Careers: Using Dual Enrollment to Enhance Career and Technical Education Pathways. An NCPR Working Paper.

The National Center for Postsecondary Research is a partnership of the Community College Research Center, Teachers College, Columbia University; MDRC; the Curry School of Education at the University of Virginia; and faculty at Harvard University. The Concurrent Courses Initiative (CCI), supported by The James Irvine Foundation from 2008 to 2011, funded a selection of secondary–postsecondary partnerships in California to implement or enhance career-focused dual enrollment programs. These programs specifically targeted youth who were low-income, struggling academically, or within populations historically underrepresented in higher education, aiming to improve their high school and college outcomes.
Automatic Generation of Object Models for Process Planning and Control Purposes using an International standard for Information Exchange

In this paper a formal mapping between static information models and dynamic models is presented. The static information models are given according to an international standard for product, process and resource information exchange, (ISO 10303-214). The dynamic models are described as Discrete Event Systems. The product, process and resource information is automatically converted into product routes and used for simulation, controller synthesis and verification. A high level language, combining Petri nets and process algebra, is presented and used for speci- fication of desired routes. A main implication of the presented method is that it enables the reuse of process information when creating dynamic models for process control. This method also enables simulation and verification to be conducted early in the development chain.
Outcome of idiopathic membranous nephropathy using targeted stepwise immunosuppressive treatment strategy.

BACKGROUND The natural course of idiopathic membranous nephropathy (IMN) is variable and the role of immunosuppressive therapy is controversial. In our centre, the strategy has been conservative: the immunomodulating treatment (glucocorticoids and/or cyclosporine A) has been targeted to patients at high risk of developing progressive renal disease and the cytotoxic drugs have been used cautiously. The aim of this retrospective observational study was to evaluate the efficacy of this strategy. METHODS We evaluated the clinical course and outcome of IMN patients diagnosed between 1993 and 2003. Risk assessment was done during an observation period of ≥6 months after the initial renal biopsy. Patients were followed up until death, the development of end-stage renal disease (ESRD) or the last clinical visit (before December 2006). Treatments and their side effects were recorded. RESULTS One hundred and forty-two patients with membranous nephropathy were diagnosed of which 81 were idiopathic. The clinical course of 76 IMN patients (38 high risk and 38 low risk) were followed up [mean duration 66 ± 40 (median 59) months]. Thirty-five patients were treated with immunosuppressive drugs, and at last follow-up, 71% of them were in complete or partial remission. The overall response rate of this therapy was 83%. 11% of the high-risk patients had reached ESRD. For the high-risk patients, 10-year survival (alive with glomerular filtration rate >10 mL/min/1.73 m(2)) was 79%. No major side effects were observed. CONCLUSIONS This study suggests that targeted, stepwise, cytotoxic drug-sparing immunosuppressive treatment in IMN was associated with favourable renal, as well as overall survival among patient at risk of developing ESRD.
Distributed event stream processing with non-deterministic finite automata

Efficient matching of incoming events to persistent queries is fundamental to event pattern matching, complex event processing, and publish/subscribe systems. Recent processing engines based on non-deterministic finite automata (NFAs) have demonstrated scalability in the number of queries that can be efficiently executed on a single machine. However, existing NFA based systems are limited to processing events on a single machine. Consequently, their event processing capacity cannot be increased by adding more machines. In this paper, we present an experimental evaluation of different methods for distributing an event processing system that is based on NFAs across multiple machines in a cluster. Our results show that careful input stream partitioning gives close to linear performance scaleup for CPU bound workloads.
Hardware Acceleration for Constraint Solving for Random Simulation

Constrained random simulation has been widely adopted in contemporary hardware verification flows. In this methodology, a set of user-specified declarative constraints describe valid input stimuli for the design under test (DUT). A constraint solver produces the simulation input vectors; their generation is interleaved with the actual simulation of the design for these vectors. Besides its distribution, the solver's performance is one of the most critical characteristics that determines the overall verification efficiency. There are no general approaches to hardware acceleration for solving declarative constraints. Current setups for hardware acceleration-based verification combine a software constraint solver running on a general-purpose processor with the hardware-accelerated DUT. This approach suffers from a major efficiency bottleneck caused by the significant performance mismatch between the solver executed in software and the DUT running on an accelerator. In this paper, we present a hardware constraint solver that uses a set of parallel solving units executing Markov chain Monte Carlo sampling. We propose to combine this solver and the DUT on the same device and run both entities hardware-accelerated in order to eliminate the performance mismatch. We discuss the details of the solver architecture and its implementation and report comprehensive results on performance and distribution characteristics as well as experience obtained from our case study where we used our solver to verify a real-world hardware design.
The Best Leadership Model for Organizational Change Management: Transformational Leadership

Internet and information technology are creating a new opportunity for consumers to share their product evaluations online (Avery, Resnick and Zeckhauser 1999). Amazon.com started to offer consumers an option to post their comments on products on its website in 1995. Currently, Amazon.com has about 10 millions consumer reviews on all its product categories, and these reviews are regarded as one of the most popular and successful features of Amazon (New York Times, Feb. 24, 2004). In recent years, an increasing number of online sellers (e.g., BevMo.com, BN.com, cduniverse.com, circuitcity.com, GameStop.com, computer4sure.com, c-source.com, half.com, goodguys.com, wine.com) are adopting the same strategy. These online sellers invite users of their products to post personal product evaluations on the sellers’ websites or provide their customers consumer review information offered by some third-party sources such as Epinions.com and CNET.com. Online consumer reviews are common for many product categories such as apparel, books, electronics, games, videos, music, beverages, games, and wine. Small business is often regarded as entrepreneurship-driven. There are many reasons for this, e.g.: (1) The entrepreneur and leader as a person plays a much more vital role than in large firms and, (2) entrepreneurial and leadership spirit are not weakened by considerable hierarchies and can more easily pervade the firm. Welsh & White's (1981) statement on the differences between small and big business seems to apply. Management research, in particular in the area of small business, is by now not sufficiently linked with entrepreneurship theory. However, entrepreneurship theory offers a lot regarding the evolutionary management of SMEs. It is the intention of this conceptual and theory-based paper to scrutinize this linkage and to identify useful insights for the leadership management of SMEs. Leader itself can be identified as the person who leads or commands a group, organization, or country. Even though defining a leader by the existence of followers has a long tradition, but it is not useful in attempting to improve the leadership of organizations. However, we define “leader” as an ordinary human being with both a commitment to produce a result whose realization would be extraordinary given the current circumstances as seen by the participants, and the integrity to see this commitment through to its realization. In this study, we investigated one mechanism of action, exploring how the consistency of transformational leadership effectiveness across hierarchical levels influenced the implementation of a strategic initiative in organizational change management. The research concludes that a multi-functional approach of transformational leadership that fits the managerial challenges of the small business best.
An improved energy detection algorithm based on signal correlation in cognitive radio

Energy detection is one of the spectrum sensing techniques in the cognitive radio system that facilitate to detect vacancies in spectrum frequency. In order to improve the detection performance of the primary user and overcome the shortcomings of the traditional energy detection algorithm, this paper proposes an improved energy detection algorithm based on signal correlation (SCED). This algorithm introduces the signal correlation factor to the traditional energy detection threshold, then carries on the signal correlation judgment based on the traditional energy detection, and finally gives the further decision result. Monte Carlo simulation results show that compared with the traditional energy detection algorithm, the detection performance of the system is improved, and the detection probability is higher than that of the traditional energy detection algorithm.
Affective recommender systems: The role of emotions in recommender systems

Recommender systems have traditionally relied on data-centric descriptors for content and user modeling. In recent years we have witnessed an increasing number of attempts to use emotions in different ways to improve the quality of recommender systems. In this paper we introduce a unifying framework that positions the research work, that has been done so far in a scattered manner, in a three stage model. We provide examples of research that cover various aspects of the detection of emotions and the inclusion of emotions into recommender systems.
The Language Environment and Syntactic Word-Class Acquisition.

Syntactic categories of words in the lexicon are the building blocks from which any type of grammar must be built, and knowledge of these building blocks is an essential part of human language processing. An important research question in Cognitive Science is the question about the nature and origins of this knowledge. So far, a mostly nativist stance on this issue has been taken. The categories are assumed to be prewired in the child’s cognitive system at birth. Since this cannot be a full explanation of how children learn to master the syntactic categories in their particular language, this assumption has the consequence that we must shift our attention to the process of language acquisition, regardless of whether we agree with this nativist assumption or not. Namely, if the syntactic categories are indeed innate, we must go on to answer the question of which steps the child must take in order to hook up its internal representations to the actual language it is exposed to. If they are not innate, we must explain how it is possible that they emerge by exposure to language. In this paper we will take as a starting point one of the most explicit accounts of category acquisition, a theory with a nativist flavor proposed by Pinker (1984). In discussing his proposal, we will try to convince the reader that Pinker’s Semantic Bootstrapping Hypothesis is by no means the only possible solution to the acquisition problem. To support this claim, we will argue that a form of distributional bootstrapping, as put forward by e.g. Maratsos & Chalkley (1981), might also be viable contrary to Pinker’s claim. For this purpose we will present data about the distributional analysis of a large corpus of text with the help of a method which we call the Lexical Space. We will propose an alternative theory of category acquisition based on the concept of Lexical Space. According to this theory the child exploits multiple sources of cues—be they distributional, semantic or other—that are readily available to it in an information-rich environment, to acquire syntactic categories of words. Our research investigates how much information the child has available in this language environment. If there is indeed enough information ready at hand to bootstrap into a primitive classification of syntactic categories it can also be questioned whether children need a prewired representational system for syntactic categories.
JNDSLAM: A SLAM extension for speech synthesis

Pitch movement is a large component of speech prosody, and despite being directly modelled in statistical parametric speech synthesis systems very ﬂat intonation contours are still produced. We present an open-source fully data-driven approach to pitch contour stylisation suitable for speech synthesis based on the SLAM approach. Modiﬁcations are proposed based on the Just Noticeable Difference in pitch and tailored to the need of speech synthesis for describing the movement of the pitch. In an anchored Mean Opinion Score (MOS) test using oracle labels the proposed method shows an improvement over standard synthesis. Long Short-Term Memory Neural Networks were then used to predict the contour labels, but initial experiments achieved low prediction rates. We conclude that using current linguistic features for pitch stylisation label mapping is not feasible unless additional features are added. Furthermore an open-source implementation is released.
A Trustworthy Component Selection Mechanism

Grid is the next generation information infrastructure of Internet and software running support platform. At present, there are many resource discovery mechanisms in Grid, which can find probably useful resources, including software component. Furthermore, this paper solves the problem of searching trusty component by providing a trust evaluation and component selection mechanism. The simulation experiment denotes the mechanism is rational, valid and reliable. Keywords-Grid; Resource Discovery; Trust Evaluation; Component
Financial Forecasting using Neural Networks

Inspired by the human brain structure and functions, artificial neural networks (ANNs) have a large application area in forecasting the economic phenomena. In the actual context, when the economic crises affect the entire business world, it is necessary that financial tools and application software cooperate in order to forecast the market trends with the aim to reduce the insecurity and provide solid growth for the companies in the future.The aim of this paper is the theoretical presentation of a forecasting model based on neural networks and a study case regarding the evolution of the company OMV PETROM S.A. , one of the largest companies in Southern and Eastern Europe. For the experiment we used various implementations of the neural network algorithm offered by GMDH Shell software and the financial consolidate statements of the company during 2005-2011, in order to obtain a forecasting of financial indicators for 2012. Keywords: Corporate management, Financial indicators, Forecasting software, Neural networks.
Roles of Fatty Acid Oversupply and Impaired Oxidation in Lipid Accumulation in Tissues of Obese Rats

To test the roles of lipid oversupply versus oxidation in causing tissue lipid accumulation associated with insulin resistance/obesity, we studied in vivo fatty acid (FA) metabolism in obese (Obese) and lean (Lean) Zucker rats. Indices of local FA utilization and storage were calculated using the partially metabolizable [9,10-3H]-(R)-2-bromopalmitate (3H-R-BrP) and [U-14C]-palmitate (14C-P) FA tracers, respectively. Whole-body FA appearance (R a) was estimated from plasma 14C-P kinetics. Whole-body FA oxidation rate (R ox) was assessed using 3H2O production from 3H-palmitate infusion, and tissue FA oxidative capacity was evaluated ex vivo. In the basal fasting state Obese had markedly elevated FA levels and R a, associated with elevated FA utilization and storage in most tissues. Estimated rates of muscle FA oxidation were not lower in obese rats and were similarly enhanced by contraction in both lean and obese groups. At comparable levels of FA availability, achieved by nicotinic acid, R ox was lower in Obese than Lean. In Obese rats, FA oxidative capacity was 35% higher than that in Lean in skeletal muscle, 67% lower in brown fat and comparable in other organs. In conclusion, lipid accumulation in non-adipose tissues of obese Zucker rats appears to result largely from systemic FA oversupply.
SEA LEVEL DETERMINATION EXPERIENCES AT CAPE OF BEGUR USING ALTIMETRY AND TIDE GAUGES

Three Begur Cape experiences on radar altimeter calibration and marine geoid mapping made on 1999, 2000 and 2002 are overviewed. The marine geoid has been used to relate the coastal tide gauge data from l’Estartit harbour to off-shore altimetric data. The necessity to validate and calibrate the satellite’s altimeter due to increasing needs in accuracy and long term integrity implies establishing calibration sites with enhanced ground based methods for sea level monitoring. A technical Spanish contribution to the calibration experience has been the design of GPS buoys and GPS catamaran taking in account the University of Colorado at Boulder and Senetosa/Capraia designs. Altimeter calibration is essential to obtain an absolute measure of sea level, as are knowing the instrument’s drifts and bias. Specially designed tidegauges are necessary to improve the quality of altimetric data, preferably near the satellite track. Further, due to systematic differences a month instruments onboard different satellites, several in-situ calibrations are essentials to tie their systematic differences. L’Estartit tide gauge is a classical floating tide gauge set up in l’Estartit harbour (NE Spain) in 1990. Data are taken in graphics registers from which each two hours the mean value is recorded in an electronic support. In the framework of a Spanish Space Project, the instrumentation of sea level measurements has been improved by providing this site with a radar tide gauge and with a continuous GPS station nearby. This will have a significant incidence in the satellite altimeter calibration activities. The radar tide gauge with data recorder and transmitter is a Datamar 3000C with 26 GHz frequency, 1mm resolution, 8o beam width incorporating a GPS receiver for automatic clock synchronization and a Thales Navigation Internet-Enabled GPS Continuous Geodetic Reference Station (iCGRS) with a choke ring antenna. It is intended that the overall system will constitute a CGPS Station of the ESEAS (European Sea Level) and TIGA (GPS Tide Gauge Benchmark Monitoring) networks. We present a synthesis of the sea level results results obtained from Topex/Poseidon and Jason-1 altimeter calibration campaigns using the direct measurements from GPS buoys and the derived marine geoid
Over-expression of histone H3K4 demethylase gene JMJ15 enhances salt tolerance in Arabidopsis

Histone H3 lysine 4 trimethylation (H3K4me3) has been shown to be involved in stress-responsive gene expression and gene priming in plants. However, the role of H3K4me3 resetting in the processes is not clear. In this work we studied the expression and function of Arabidopsis H3K4 demethylase gene JMJ15. We show that the expression of JMJ15 was relatively low and was limited to a number of tissues during vegetative growth but was higher in young floral organs. Over-expression of the gene in gain-of-function mutants reduced the plant height with accumulation of lignin in stems, while the loss-of-function mutation did not produce any visible phenotype. The gain-of-function mutants showed enhanced salt tolerance, whereas the loss-of-function mutant was more sensitive to salt compared to the wild type. Transcriptomic analysis revealed that over-expression of JMJ15 down-regulated many genes which are preferentially marked by H3K4me3 and H3K4me2. Many of the down-regulated genes encode transcription regulators involved in stress responses. The data suggest that increased JMJ15 levels may regulate the gene expression program that enhances stress tolerance.