Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Decomposition of Multi-Author Documents Using Grammar Analysis

The task of text segmentation is to automatically split a text document into individual subparts, which differ according to specific measures. In this paper, an approach is presented that attempts to separate text sections of a collaboratively written document based on the grammar syntax of authors. The main idea is thereby to quantify differences of the grammatical writing style of authors and to use this information to build paragraph clusters, whereby each cluster is assigned to a different author. In order to analyze the style of a writer, text is split into single sentences, and for each sentence a full parse tree is calculated. Using the latter, a profile is computed subsequently that represents the main characteristics for each paragraph. Finally, the profiles serve as input for common clustering algorithms. An extensive evaluation using different English data sets reveals promising results, whereby a supplementary analysis indicates that in general common classification algorithms perform better than clustering approaches.
Pacific Asia Conference on Information Systems ( PACIS ) 2014 DESIGN OF ORGANISATIONAL UBIQUITOUS INFORMATION SYSTEMS : A FRAMEWORK FOR DIGITAL NATIVE AND DIGITAL IMMIGRANT USERS

This paper focuses on the design and implementation of Organisational Ubiquitous Information Systems for Digital Native and Digital Immigrant users. Organisational Ubiquitous Information Systems refer to information systems available through many devices such as smart phones that are used by employees of an organisation anytime and anywhere to achieve specific work related goal(s). A multi-methodological approach was used to explore the design requirements for Organisational Ubiquitous Information Systems design as compared with Traditional Information Systems design. The findings suggest that Organisational Ubiquitous Information Systems are part of a large organisational digital ecosystem, and hence a design and implementation framework for these systems needs to reflect this. This paper proposes a conceptual framework for the Organisational Ubiquitous Information Systems design process.
Hybrid Term Indexing: an Evaluation

Retrieval effectiveness depends on how terms are extracted and indexed. For Chinese text (and others like Japanese and Korean), there are no space to delimit words. Indexing using hybrid terms (i.e. words and bigrams) were able to achieve the best precision amongst homogenous terms at a lower storage cost than indexing with bigrams. However, this was tested with conjunctive queries, using a small test data set. Here, we extended the vector space model using the cosine measure, for processing hybrid terms. We also introduced weighting based on the length of the term. Our evaluation shows that the averaged precision of hybrid term indexing is about the same as the best precision, achieved using bigram indexing, but incurring less storage (about 61% of the storage for bigram indexing). The precision performance of hybrid term indexing is consistently better than that of word indexing, even though their storage cost is about the same. Ranking based on the length of the query terms slightly improves the retrieval effectiveness of hybrid term indexing but degrades the retrieval effectiveness of word indexing. Even though our best performance is the worst compared with that of the other participants, this may be due to some common factors across different indexing strategies (e.g. stop words, term weights and query term processing), and may not be due to the indexing strategies that we are evaluating.
A Spoken Dialog System for Coordinating Information Consumption and Exploration

Passive consumption of information is boring in most cases and even painful in some cases, especially when the information content is delivered by employing speech media. The user of a speech-based information delivery system, for example a text-to-speech system, usually cannot interrupt the ongoing information flow, inhibiting her/him to confirm some part of the content, or to pose an inquiry for further information exploration. We argue that a carefully designed spoken dialog system could remedy these undesirable situations, and further enable an enjoyable conversation with the users. The key technologies to realize such an attractive dialog system are: (1) pre-compilation of a dialog plan based on the analysis of a source content, and (2) the dynamic recognition of user's state of understanding and interests. This paper illustrates technical views to implement these functionalities, and discusses a dialog example to exemplify the technical merits of the proposed system.
Estimating Neospora caninum prevalence in wildlife populations using Bayesian inference

Abstract Prevalence of disease in wildlife populations, which is necessary for developing disease models and conducting epidemiologic analyses, is often understudied. Laboratory tests used to screen for diseases in wildlife populations often are validated only for domestic animals. Consequently, the use of these tests for wildlife populations may lead to inaccurate estimates of disease prevalence. We demonstrate the use of Bayesian latent class analysis (LCA) in determining the specificity and sensitivity of a competitive enzyme‐linked immunosorbent assay (cELISA; VMRD ®, Inc.) serologic test used to identify exposure to Neospora caninum (hereafter N. caninum) in three wildlife populations in southeastern Ohio, USA. True prevalence of N. caninum exposure in these populations was estimated to range from 0.1% to 3.1% in American bison (Bison bison), 51.0% to 53.8% in Père David's deer (Elaphurus davidianus), and 40.0% to 45.9% in white‐tailed deer (Odocoileus virginianus). The accuracy of the cELISA in American bison and Père David's deer was estimated to be close to the 96% sensitivity and 99% specificity reported by the manufacturer. Sensitivity in white‐tailed deer, however, ranged from 78.9% to 99.9%. Apparent prevalence of N. caninum from the test results is not equal to the true prevalence in white‐tailed deer and Père David's deer populations. Even when these species inhabit the same community, the true prevalence in the two deer populations differed from the true prevalence in the American bison population. Variances in prevalence for some species suggest differences in the epidemiology of N. caninum for these colocated populations. Bayesian LCA methods could be used as in this example to overcome some of the constraints on validating tests in wildlife species. The ability to accurately evaluate disease status and prevalence in a population improves our understanding of the epidemiology of multihost pathogen systems at the community level.
Modeling and Control of Six-Phase Symmetrical Induction Machine Under Fault Condition Due to Open Phases

This paper introduces a new fault-tolerant operation method for a symmetrical six-phase induction machine (6PIM) when one or several phases are lost. A general decoupled model of the induction machine with up to three open phases is given. This model illustrates the existence of a pulsating torque when phases are opened. Then, a new control method reducing the pulsating torque and the motor losses is proposed in order to improve the drive performances. The proposed method is compared to two other existing techniques. The simulation and experimental results obtained on a dedicated test-rig confirm the validity and the efficiency of the proposed method for a fault-tolerant symmetrical 6PIM drive.
High-Frequency Mutual Impedance Extraction of VLSI Interconnects In the Presence of a Multi-layer Conducting Substrate

We propose a computationally efficient method to calculate, with high accuracy, the mutual impedance between two wires in the presence of multilayer substrates, as needed for high frequency CAD applications. The resulting accuracy (errors smaller than 2%) and CPU time reduction (factors of seven) emerge from three different ingredients: a two dimensional Green's function approach with the correct quasi-static limit, a modified discrete complex image approximation to the Green's function, and a novel discrete dipole approximation to evaluate the magnetic vector potential. This approach permits the evaluation of the mutual impedance between two loops in terms of easily computable analytical expressions that involve the relative separations and the electromagnetic parameters of the multi-layer substrate. The results are valid for long wires, for any separation, and for frequencies up to 100 GHz.
From Lightweight, Proactive Information Delivery to Business Process-Oriented Knowledge Management

Knowledge work processes consist of interleaved agile, weakly-structured processes and strictly-structured processes. Knowledge management approaches for weakly-structured, ad-hoc knowledge work processes need to be lightweight, i.e., they cannot rely on high upfront modeling effort. However, approaches for business process- oriented knowledge management require intensive modeling activities. In this paper, we introduce a bottom-up strategy for proactive information delivery to cover the complete spectrum of knowledge work processes in different phases, and present a series of prototypes realizing selected phases of this strategy. Among these is a novel prototype for supporting weakly-structured processes by integrating a standard to-do list application with a state-of-the-art document classification system. The resulting system allows for a task-oriented view on an office worker's personal knowledge space in order to realize a proactive and context-sensitive information support during her daily, knowledge-intensive tasks.
A variable selection approach based on the Delta Test for Extreme Learning Machine models

Extreme Learning Machine, ELM, is a newly available learning algorithm for single layer feedforward neural networks (SLFNs), and it has proved to show the best compromise between learning speed and accuracy of the estimations. In this paper, a methodology based on Optimal-Pruned ELM (OP-ELM) for function approximation enhanced with variable selection using the Delta Test is introduced. The least angle regression (LARS) algorithm is used after variable selection to rank the input variables, and scaling is also introduced as a way to estimate the influence of each input in the output value. The performance is assessed on a dataset related to anthropometric measurements for children weight prediction. The accurate results show that this combination of techniques is very promising to solve real world problems and represents a good alternative to classic backpropagation methods.
Efficient Horizontal Wellbore Placement and Completion with Geosteering and Zonal Isolation in a Reservoir on Waterflood: A Case Study of Western Offshore, India

Infill drilling is an integral part of brown field management for exploiting un-drained areas with good oil saturation. In a matured field on water-flood, the primary objective is optimized wellbore placement of infill wells in areas with better petro-physical characteristics, bypassing flooded region. It is also important to design a robust completion strategy to safeguard the longevity of these wells by curtailing produced water. This approach assists in dramatic increase in production by isolating water charged sections and thereby restricting rise in water production. The use of advanced Logging-While-Drilling techniques during horizontal drilling provides an opportunity for effective well planning. Real-time Logging-While-Drilling instruments during directional drilling gives us the opportunity to acquire information pertaining to the reservoir in a single run. Interpretation from the real-time data acquisition boosts the planning during wellbore drilling. This paper discusses a case study of a field in western offshore, India, which focuses on the applications of geosteering and the use of swell packers for zonal isolation to augment oil production. In this study, two wells have been deliberated where the real-time information has been extracted and included in the decision making process. The bottom-hole assembly used in this case, comprised standard Logging-While-Drilling services such as gamma ray, resistivity, neutron porosity, density and density imaging services and also formation pressure testing. Since the field under study is a carbonate reservoir that has been on waterflood for the last twenty eight years, chances of early breakthrough of water in the infill wells has posed a high risk in spite of the presence of good bypassed oil saturation. Geosteering has enabled to restrict the horizontal section safely within the desired zone of better oil saturation and geological features, as interpreted from the Logging-While-Drilling data. Further isolation of suspected water bearing zones with swell packers have assisted in healthy well completion by diminishing chances of sharp rise in water cut in the infill wells.
Convergence analysis of LMS filters with uncorrelated Gaussian data

Statistical analysis of the least mean-squares (LMS) adaptive algorithm with uncorrelated Gaussian data is presented. Exact analytical expressions for the steady-state mean-square error (mse) and the performance degradation due to weight vector misadjustment are derived. Necessary and sufficient conditions for the convergence of the algorithm to the optimal (Wiener) solution within a finite variance are derived. It is found that the adaptive coefficient μ, which controls the rate of convergence of the algorithm, must be restricted to an interval significantly smaller than the domain commonly stated in the literature. The outcome of this paper, therefore, places fundamental limitations on the mse performance and rate of convergence of the LMS adaptive scheme.
HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions

Visual metrics can play an important role in the evaluation of novel lighting, rendering, and imaging algorithms. Unfortunately, current metrics only work well for narrow intensity ranges, and do not correlate well with experimental data outside these ranges. To address these issues, we propose a visual metric for predicting visibility (discrimination) and quality (mean-opinion-score). The metric is based on a new visual model for all luminance conditions, which has been derived from new contrast sensitivity measurements. The model is calibrated and validated against several contrast discrimination data sets, and image quality databases (LIVE and TID2008). The visibility metric is shown to provide much improved predictions as compared to the original HDR-VDP and VDP metrics, especially for low luminance conditions. The image quality predictions are comparable to or better than for the MS-SSIM, which is considered one of the most successful quality metrics. The code of the proposed metric is available on-line.
Resistance and virulence factors of Escherichia coli isolated from chicken

Chicken meat has become an important part of the human diet and besides contamination by pathogenic Escherichia coli there is a risk of antibiotic resistance spreading via the food chain. The purpose of this study was to examine the prevalence of resistance against eight antibiotics and the presence of 14 virulence factors among 75 Escherichia coli strains isolated from chicken meat in the Czech Republic after classification into phylogenetic groups by the multiplex PCR method. More than half of strains belonged to A phylogroup, next frequently represented was B1 phylogroup, which suggests the commensal strains. The other strains were classified into phylogroups B2 and D, which had more virulence factors. Almost half of all E. coli strains were resistant to at least one of eight-tested antibiotics. A multidrug resistance was observed in 13% of strains. The most prevalent virulence genes were iucD, iss and tsh. None of genes encoding toxins was detected. Most of E. coli strains isolated from chicken meat can be considered as nonpathogenic on the basis of analysis of virulence factors, antibiotic resistance and phylogroups assignment. It can provide a useful tool for prediction of a potential risk from food contaminated by E. coli.
The impact of a pharmacist-led educational interview on medication adherence of Saudi patients with epilepsy

Purpose To evaluate the effectiveness of a pharmacist-led educational interview in terms of adherence to antiepileptic drug administration among adult patients with epilepsy. Method Sixty adult patients with epilepsy who fulfilled the inclusion criteria were recruited. A pharmacist-led educational interview was conducted with the intervention group (n=30). Patients in the control group (n=30) were interviewed and contacted 6 weeks after the initial visit without receiving any intervention. Antiepileptic drug adherence was measured during clinic visits, and 6 weeks afterwards using the 8-item Morisky Medication Adherence Scale. This prospective interventional study was conducted between September and December 2013. Results Only 29 control patients and 27 intervention patients completed the 6 weeks post-intervention adherence measurement. The adherence score average in the intervention group was 5.26±0.98 at baseline and improved to 6.7±0.823 (P<0.0001) after intervention. In the control group, the adherence score average was 5.76±1.806 at baseline and 5.83±1.627 at 6 weeks (P=0.792). While there was no statistically significant difference in adherence score between intervention and control groups at baseline, the post-intervention difference was significant (P=0.024). Conclusion Our study suggests that pharmacist-led educational interviews had a positive impact on medication adherence in patients with epilepsy.
Governing the Governors: A Clinical Study of Central Banks

Recent research on central bank governance has focused mainly on their monetary policy task. As the sub-prime loan market turmoil reminded us - central banks play a crucial role in financial markets not only in setting monetary policy, but also in ensuring their soundness and stability. In this paper we study the specific corporate governance structures of a number of central banks in light of their complex role of inflation guardians, bankers’ banks, financial industry regulators/supervisors and, in some cases, competition authorities and deposit insurance agencies. We review their current institutional arrangements, e.g. formal objectives, ownership, board and governor appointment rules, term limits and compensation, using both existing surveys and newly collected information; and we contrast them with the structures suggested in the corporate and public governance literatures, where present. Our analysis highlights a striking variety in central bank governance structures and a number of specific issues that appear unsatisfactorily addressed by existing research, including the incentive structure for governor and board members, the balance between central banks’ multiple objectives, and the need for term limits or post-employment restrictions.
Universal simulation of Hamiltonians using a finite set of control operations

Any quantum system with a non-trivial Hamiltonian is able to simulate any other Hamiltonian evolution provided that a sufficiently large group of unitary control operations is available. We show that there exist finite groups with this property and present a sufficient condition in terms of group characters. We give examples of such groups in dimension 2 and 3. Furthermore, we show that it is possible to simulate an arbitrary bipartite interaction by a given one using such groups acting locally on the subsystems.
Emergence and equilibration of jets in beta-plane turbulence

AbstractStochastic structural stability theory (S3T) provides analytical methods for understanding the emergence and equilibration of jets from the turbulence in planetary atmospheres based on the dynamics of the statistical mean state of the turbulence closed at second order. Predictions for formation and equilibration of turbulent jets made using S3T are critically compared with results of simulations made using the associated quasi-linear and nonlinear models. S3T predicts the observed bifurcation behavior associated with the emergence of jets, their equilibration, and their breakdown as a function of parameters. Quantitative differences in bifurcation parameter values between predictions of S3T and results of nonlinear simulations are traced to modification of the eddy spectrum which results from two processes: nonlinear eddy–eddy interactions and formation of discrete nonzonal structures. Remarkably, these nonzonal structures, which substantially modify the turbulence spectrum, are found to arise fro.
The Observed Impacts of the Two Types of El Niño on the North Equatorial Countercurrent in the Pacific Ocean

This study investigates the interannual variations in the North Equatorial Counter Current (NECC) associated with the eastern Pacific and the central Pacific types of El Niño. Using observational analysis and ocean simulations, we show that the wind stress anomalies during the two El Niño types are of comparable amplitude but have different spatial structures, which results in significant and distinct variations in the NECC. The NECC shifts southward and intensifies during the developing phase of El Niño, but the variations are confined in the central eastern Pacific for the eastern Pacific type and the western central Pacific for the central Pacific type. These differences can be attributed to modulations in equatorial Kelvin wave and tropical Rossby wave propagation as well as Ekman pumping.
Test Case Prioritization Using Adaptive Random Sequence with Category-Partition-Based Distance

Test case prioritization schedules test cases in a certain order aiming to improve the effectiveness of regression testing. Random sequence is a basic and simple prioritization technique, while Adaptive Random Sequence (ARS) makes use of extra information to improve the diversity of random sequence. Some researchers have proposed prioritization techniques using ARS with white-box information, such as code coverage information, or with black-box information, such as string distances of the input data. In this paper, we propose new black-box test case prioritization techniques using ARS, and the diversity of test cases is assessed by category-partition-based distance. Our experimental studies show that these new techniques deliver higher fault-detection effectiveness than random prioritization, especially in the case of smaller ratio of failed test cases. In addition, in the comparison of different distance metrics, techniques with category-partition-based distance generally deliver better fault-detection effectiveness and efficiency, meanwhile in the comparison of different ordering algorithms, our ARS-based ordering algorithms usually have comparable fault-detection effectiveness but much lower computation overhead, and thus are much more cost-effective.