Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Method of Citation Sequence Labeling Based on Conditional Random Fields

Citation sequence labeling is an essential phase in citation entity resolution and other applications on citations. Scholars proposed many methods and models. In all the statistical learning models, the conditional random fields (CRFs) is the best one which is studied and used extensively. Most of the papers which study applications based on conditional random fields focus on the three basic questions and pay less attention to feature selection, granularity choosing and structure learning. This paper has discussed the use of text features in citation sequence labeling based on conditional random fields model. According to this, this paper made some differences in structure learning and feature selection. Experimental results show that our algorithm make a further improvement in the precision of citation sequence labeling.
On Atomicity and Software Development

This paper shows how the concept of atomicity can ease the development of concurrent software. It illustrates by means of a case study how atomicity is used to reduce the complexity of concurrency by presenting simplified models or views of the system at certain stages of the development cycle. As the development process goes on, the atomic views from the early stages are refined - broken up into smaller pieces - to slowly introduce concurrency back into the system. Finally, at the design stage, low-level concepts that provide atomicity, such as transaction or monitors, are used to ensure consistent concurrent updating of the application state.
The Viewer's Reception and Processing of Sponsorship Information in Sport Telecasts

Although enormous sums are spent on sport sponsorships, knowledge of sponsorship information processing is still limited. For a continuing growth of sponsorship as a field significant improvements in our understanding of sponsoring effectiveness are required. Whereas the direct effect of sponsor signage exposure on sponsor recall has been identified in several studies, attention to sponsor signage as the mediator of sponsorship information has not been investigated thoroughly. Based on spotlight theory and the associative network model of memory, the present paper addresses this research gap and investigates the viewer’s visual attention to sponsorship information by applying eye tracking methodology. Regression models have been estimated to analyze information reception and processing in sport telecasts. The results reveal that the capture of attention is determined by the placement of sponsor signage and by exposure variables. Furthermore, sponsor recall is found to be a function of attention and brand-.
Origin and early development of the chicken adenohypophysis

The adenohypophysis (ADH) is an important endocrine organ involved in the regulation of many physiological processes. The late morphogenesis of this organ at neural tube stages is well known: the epithelial ADH primordium is recognized as an invagination of the stomodeal roof (Rathke’s pouch), whose walls later thicken and differentiate as the primordium becomes pediculated, and then fully separated from the stomodeum. The primordium attaches to the pial surface of the basal hypothalamus, next to the neurohypophyseal field (NH; future posterior pituitary), from which it was previously separated by migrating prechordal plate (pp) cells. Once the NH evaginates, the ADH surrounds it and jointly forms with it the pituitary gland. In contrast, little is known about the precise origin of the ADH precursors at neural plate stages and how the primordium reaches the stomodeum. For that reason, we produced in the chicken a specific ADH fate map at early neural plate stages, which was amplified with gene markers. By means of experiments labeling the mapped presumptive ADH, we were able to follow the initial anlage into its transformation into Rathke’s pouch. The ADH origin was corroborated to be strictly extraneural, i.e., to lie at stage HH4/5 outside of the anterior neural plate (anp) within the pre-placodal field. The ADH primordium is fully segregated from the anterior neural border cells and the neighboring olfactory placodes both in terms of precursor cells and molecular profile from head fold stages onwards. The placode becomes visible as a molecularly characteristic ectodermal thickening from stage HH10 onwards. The onset of ADH genoarchitectonic regionalization into intermediate and anterior lobes occurs at closed neural tube stages.
Diretrizes para Pesquisas Qualitativas em Estudos Organizacionais: Controvérsias e Possibilidades

The aim of the present article is to tackle the controversy of establishing guidelines for qualitative research in Organization and Management Theory (OMT) and to present a summary of suggestions on how to conduct good qualitative research given by methodologists on top-tier international publications. In order to do so, the article discusses: general guidelines for qualitative research; how to achieve coherence and transparency in a qualitative empirical study; the meaning and importance of the concept of reflexivity; and, finally how to establish a theoretical contribution and transferability of findings in such context. The work presents a valuable contribution because such guidelines, concepts, and approaches can be adopted by students and researchers when conducting a qualitative research proposal, and by periodic reviewers to evaluate the quality of existing empirical studies.
Distributed multistage alert correlation architecture based on Hadoop

There are three main approaches to design when implementing an alert correlation architecture; these are centralised, hierarchical, and decentralised. Centralised approaches benefit from simplicity of implementation and high algorithm expressiveness, but suffer in terms of scalability. The scalability issue is alleviated with hierarchical and decentralised approaches, but this comes at a cost of additional implementation complexity and lower algorithm quality. Introduced is a new alert correlation architecture based on Hadoop. The developed architecture allows for greater scalability whilst maintaining algorithm expressiveness and design simplicity. It incorporates alert aggregation, verification, and correlation components, which together provide for a clear and succinct view of potentially malicious activity. Each component was tested against a series of datasets that represent potential real world scenarios across a cluster of varying size. The results demonstrate that all components in the architecture have the ability to scale across many nodes in a cluster, allowing for the processing of large and complex attack scenarios in a timely manner.
AKURASI PREDIKSI FINANCIAL DISTRESS: PERBANDINGAN MODEL ALTMAN DAN OHLSON

Prediction of financial distress accurately becomes very crucial for every company. This study is want to know which financial distress prediction models (Altman and Ohlson) is the most suitable to be used in Indonesia. To decide the best model, an analysis will be conducted based on accuracy and error rates of Altman Z-Score and Ohlson O-Score model (original model, sensitivity analysis by modifying cut-off score and modifying the whole model). Finally, this study will make accuracy prediction for companies listed in BEI in 2010 by using the best known model. Used data from manufacturing companies with matched-pair sampling, this study investigated whether Z-score and O-Score models can predict bankruptcies for a period up to three years earlier. The research show that the Ohlson model is the best original model, but after modifying the cut-off score, Altman model is the best. Finally, after modifying the whole model, Ohlson model is proven to the best model that can application in Indonesia because having superior accuracy and minimum error rates of all model.
The policy gradient estimation of continuous-time hidden Markov decision processes

Recently, gradient based methods have received much attention to optimize some dynamic systems with hidden information, such as routing problems of robotic systems. In this paper, we presented a process - continuous time hidden Markov decision process (CTHMDP), which can be used to model the robotic systems. For this process, the problem of policy gradient estimation is studied. Firstly, an approximation formula to the gradient is presented, then by using the uniformization method, we introduce an algorithm, which can be considered as an extension of gradient of partially observable Markov decision process (GPOMDP) algorithm to the continue time model. Finally, the convergence and error bound of the algorithm are considered.
Detecting Community Structures in Networks by Label Propagation with Prediction of Percolation Transition

Though label propagation algorithm (LPA) is one of the fastest algorithms for community detection in complex networks, the problem of trivial solutions frequently occurring in the algorithm affects its performance. We propose a label propagation algorithm with prediction of percolation transition (LPAp). After analyzing the reason for multiple solutions of LPA, by transforming the process of community detection into network construction process, a trivial solution in label propagation is considered as a giant component in the percolation transition. We add a prediction process of percolation transition in label propagation to delay the occurrence of trivial solutions, which makes small communities easier to be found. We also give an incomplete update condition which considers both neighbor purity and the contribution of small degree vertices to community detection to reduce the computation time of LPAp. Numerical tests are conducted. Experimental results on synthetic networks and real-world networks show that the LPAp is more accurate, more sensitive to small community, and has the ability to identify a single community structure. Moreover, LPAp with the incomplete update process can use less computation time than LPA, nearly without modularity loss.
Vehicle operation safety monitoring using context based metrics: A case study

This paper presents results of the deployment of vehicle safety systems developed by the Intelligent Vehicles and Safety Systems Group at the Australian Centre for Field Robotics. The technology was deployed within an active mine site in Australia undergoing standard open-cut mining operations. Data were collected from the vehicles and analysed in order to assess the overall safety and performance of the vehicle operations within the mine. Metrics calculated include distributions of vehicle paths along stretches of road for driving line analysis, and statistics around vehicle events such as overspeed and proximity to other vehicles.
Enhancing the Scalability of Consistency-based Progressive Multiple Sequences Alignment Applications

Multiple Sequence Alignment (MSA) is an extremely powerful tool for important biological applications, such as phylogenetic analysis, identification of conserved motifs and domains and structure prediction. In this paper we propose a new approach to reduce the computational requirements of TCoffee, a memory demanding MSA tool that uses a consistency based scheme to produce more accurate alignments. Our goal is to minimize the memory constraints in order to increase the performance and scalability of the application. The experimental results show that our approach is able to reduce the memory consumption and increase both the time performance and the number and the length of sequences that the method can align. In summary, it is able to reduce the memory requirements by between 72% and 58% depending on the optimization level, improving the alignment scalability as a whole. Also, the library reduction yields a further improvement: the alignment execution time can be reduced by up to 92%. These results are obtained without a significant impact on the final alignment quality, that declines by less than 3%.
An Application of Semantic Annotations to Design Errors

As current engineered systems (e.g. aviation systems) have been equipped with automated and computer-based artefacts, human-system interaction (e.g. human computer interaction) has been an important issue. Design errors that are attributable to human-system interaction failures are not pure engineering design issues, but a multidisciplinary subject with related other areas such as management, psychology, physiology or ergonomics. To identify such design errors (called design-induced errors) in accident reports is important for designing more reliable systems. However, the lack of precise definitions of the concept of design-induced error and the diversity of expression of such failures make it difficult to retrieve relevant documents from accident reports. This paper describes how an ontology and annotation scheme can help to overcome such limitations. Engineering designers can be assisted by the developed ontology and annotation scheme to reason on the issues of design induced error
Exercise protects against obesity induced semen abnormalities via downregulating stem cell factor, upregulating Ghrelin and normalizing oxidative stress

Increased oxidative stress and hormonal imbalance have been hypothesized to underlie infertility in obese animals. However, recent evidence suggests that Ghrelin and Stem Cell Factor (SCF) play an important role in fertility, in lean individuals. Therefore, this study aimed at investigating whether changes in the levels of Ghrelin and SCF in rat testes underlie semen abnormal parameters observed in obese rats, and secondly, whether endurance exercise or Orlistat can protect against changes in Ghrelin, SCF, and/or semen parameters in diet induced obese rats. Obesity was modelled in male Wistar rats using High Fat Diet (HFD) 12-week protocol. Eight week-old rats (n=40) were divided into four groups, namely, Group I: fed with a standard diet (12 % of calories as fat); Group II: fed HFD (40 % of calories as fat); Group III: fed the HFD with a concomitant dose of Orlistat (200 mg/kg); and Group IV: fed the HFD and underwent 30 min daily swimming exercise. The model was validated by measuring the levels of testosterone, FSH, LH, estradiol, leptin, triglycerides, total, HDL, and LDL cholesterol, and final change in body weight. Levels were consistent with published obesity models (see Results). As predicted, the HFD group had a 76.8 % decrease in sperm count, 44.72 % decrease in sperm motility, as well as 47.09 % increase in abnormal sperm morphology. Unlike the control group, in the HFD group (i.e. obese rats) Ghrelin mRNA and protein were elevated, while SCF mRNA and protein were diminished in the testes. Furthermore, in the HFD group, SOD and GPx activities were significantly reduced, 48.5±5.8 % (P=0.0012) and 45.6±4.6 % (P=0.0019), respectively, while TBARS levels were significantly increased (112.7±8.9 %, P=0.0001). Finally, endurance exercise training and Orlistat administration individually and differentially protected semen parameters in obese rats. The mechanism includes, but is not limited to, normalizing the levels of Ghrelin, SCF, SOD, GPx and TBARS. In rat testes, diet induced obesity down regulates SCF expression, upregulates Ghrelin expression, and deteriorate oxidative stress levels, which are collectively detrimental to semen parameters. Exercise, and to a lesser extent Orlistat administration, protected effectively against this detrimental effect.
Area and Power Optimization of High-Order Gain Calibration in Digitally-Enhanced Pipelined ADCs

Digital calibration techniques are widely utilized to linearize pipelined analog-to-digital converters (ADCs). However, their power dissipation can be prohibitively high, particularly when high-order gain calibration is needed. This paper demonstrates the need for high-order gain calibration in pipelined ADCs designed using low-gain opamps in scaled digital CMOS. For high-order gain calibration, this paper then proposes a design methodology to optimize the data precision (number of bits) within the digital calibration unit. Thus, the power dissipation and chip area of the calibration unit can be minimized, without affecting the ADC linearity. A 90-nm field-programmable gate array synthesis of a second-order gain calibration unit shows that the proposed optimization methodology results in 53% and 30% reductions in digital power dissipation and chip area, respectively.
THE COSMOLOGICAL CONSTANT PROBLEM AND INFLATION IN THE STRING LANDSCAPE

An earlier paper points out that a quantum treatment of the string landscape is necessary. It suggests that the wave function of the universe is mobile in the landscape until the universe reaches a meta-stable site with its cosmological constant Λ0 smaller than the critical value Λc, where Λc is estimated to be exponentially small compared to the Planck scale. Since this site has an exponentially long lifetime, it may well be today's universe. We investigate specific scenarios based on this quantum diffusion property of the cosmic landscape and find a plausible scenario for the early universe. In the last fast tunneling to the Λ0(<Λc) site in this scenario, all energies are stored in the nucleation bubble walls, which are released to radiation only after bubble collisions and thermalization. So the Λ0 site is chosen even if Λ0 plus radiation is larger than Λc, as long as the radiation does not destabilize the Λ0 vacuum. A consequence is that inflation must happen before this last fast tunneling, so the inflationary scenario that emerges naturally is extended brane inflation, where the brane motion includes a combination of rolling, fast tunnelings, slow-roll, hopping and percolation in the landscape. We point out that, in the brane world, radiation during nucleosynthesis are mostly on the standard model branes (brane radiation, as opposed to radiation in the bulk). This distinction may lead to interesting dynamics. We consider this paper as a road map for future investigations.
Entity Based Sentiment Analysis on Twitter

Its fair to say that when it comes to global entities that matter to us, we are quite obsessed with finding out what other people think about them. In the past, querying people’s opinions with respect to a global entity such as Michael Jackson, Microsoft or Cuba was done through user polls funded by organizations and only a small percentage of these questionable polls trickled to the average person. With the onset of real-time information networking service Twitter [5] there is an unparalleled public collection of opinions about every global entity that matters. Opinion creation has never been so democratized. Even though Twitter provides a great outlet for opinion creation, the information cycle is incomplete without clever tools for analyzing those opinions to facilitate their consumption.
Primary School Learners’ Conceptions of the Role and Function of the Brain

Abstract In many developing countries formal schooling has tended to propagate a Western view of mainstream school science (MSS), almost oblivious to the worldviews learners bring to the classroom. This study reports grade six Zulu children’s conceptions of the role and functions of the brain. The survey involved 197 girls and boys from KwaZulu-Natal, South Africa. Data was collected using a paper-pencil instrument consisting of one open-ended task given to the children as homework to enable them to tap into their parents’ understanding of the topic being investigated. The study revealed that context was a very strong frame of reference for the children in the way they answered the question. They interpreted the question entirely within the intrigues of their own life worlds-a world dominated by Zulu traditions and culture. The findings reported in this study have far-reaching implications, including triggering national-wide curriculum revisions and reorientations.
Determinants of early withdrawal and of early withdrawal by reason of disability from the Irish labour force in the third age

Background Studies suggest a higher prevalence of early retirement through disability among older people with lower educational attainment. There have been no national studies in Ireland on the factors that affect early withdrawal from the labour force through disability or long-term illness. Aims To identify and analyse potential impacts of education on early retirement through disability in the over 50 age cohort of the Irish Labour force. Methods We analysed the educational attainment of participants using The Irish Longitudinal Study of Ageing (TILDA). The group of interest were those aged 50–75 who had retired early. The sample was dichotomized on disability. Examination of interviewer-recorded information on background influences determining early retirement decisions included the following factors: age, gender, education, family and socio-economic circumstances, including parental education. Results A total of 334 of 1179 study subjects (28%) retired early through disability. Comparison of those retired early with and without disability showed a significantly higher frequency of lower educational attainment both personally and for parents. Men with lower educational attainment and from a non-professional background were more likely to retire early through disability. Nonprofessional disabled respondents with less well-educated parents had lower educational attainment than non-disabled respondents. Conclusions Among TILDA participants, educational attainment appears to influence early retirement through disability. The sector of previous employment was also a significant factor. Behaviour, lifestyle and employment choice are influenced by educational level, which may affect cognitive ability to process health information. Factors affecting the education–disability relationship could include parental education, employment status and socio-economic characteristics.
User-guided independent vector analysis with source activity tuning

In this paper, user-guided source separation based on independent vector analysis is presented. In this framework, temporal power variations of sources can be tuned by a user. The information is exploited as prior distributions of source activities in independent vector analysis with time-varying Gaussian model, and source signals are separated by maximum a posteriori (MAP) estimation. Experimental evaluations show the source activity tuning is much effective to improve the separation performance in hard mixing conditions such as long reverberation or level mismatch of sources.
Optimization of Association Rule Mining through Genetic Algorithm

:- Strong rule generation is an important area of data mining. In this paper we design a novel method for generation of strong rule. In which a general Apriori algorithm is used to generate the rules after that we use the optimization techniques. Genetic algorithm is one of the best ways to optimize the rules . In this direction for the optimization of the rule set we design a new fitness function that uses the concept of supervised learning then the GA will be able to generate the stronger rule set