Miracle-dz/UniFAR-FTUs huggingface dataset:
Requirements Engineering Methodology in Agile Environment

This paper provides a better understanding of the architecturerelated issues in agile projects and proposes a methodology to guide and assist practitioners adopting agile requirements engineering. The methodology was motivated by the lack of structure to the agile requirements engineering process with minimal impact on agility. It describes in detail the phases in the agile requirements engineering process and suggests techniques that can be used to perform these phases. As the length of the development lifecycle is taken into account, the methodology describes not only the requirements engineering process activities but the complete development process as well. It reflects the agile principles such as direct stakeholder involvement, evolutionary requirements, refactoring, no BRUF, just-in-time gathering of details and minimal documentation.
Herd-Level Risk Factors for Bovine Tuberculosis: A Literature Review

Bovine tuberculosis (TB), caused by Mycobacterium bovis, is one of the most challenging endemic diseases currently facing government, the veterinary profession, and the farming industry in the United Kingdom and Ireland and in several other countries. The disease has a notoriously complex epidemiology; the scientific evidence supports both cattle-cattle and wildlife-cattle transmission routes. To produce more effective ways of reducing such transmission, it is important to understand those risk factors which influence the presence or absence of bovine TB in cattle herds. Here we review the literature on herd-level risk factor studies. Whilst risk factors operate at different scales and may vary across regions, epidemiological studies have identified a number of risk factors associated with bovine TB herd breakdowns, including the purchase of cattle, the occurrence of bovine TB in contiguous herds, and/or the surrounding area as well as herd size. Other factors identified in some studies include farm and herd management practices, such as, the spreading of slurry, the use of certain housing types, farms having multiple premises, and the use of silage clamps. In general, the most consistently identified risk factors are biologically plausible and consistent with known transmission routes involving cattle-cattle and wildlife-cattle pathways.
Group Profiling for Understanding Social Structures

The prolific use of participatory Web and social networking sites is reshaping the ways in which people interact with one another. It has become a vital part of human social life in both the developed and developing world. People sharing certain similarities or affiliates tend to form communities within social media. At the same time, they participate in various online activities: content sharing, tagging, posting status updates, etc. These diverse activities leave behind traces of their social life, providing clues to understand changing social structures. A large body of existing work focuses on extracting cohesive groups based on network topology. But little attention is paid to understanding the changing social structures. In order to help explain the formation of a group, we explore different group-profiling strategies to construct descriptions of a group. This research can assist network navigation, visualization, and analysis, as well as monitoring and tracking the ebbs and tides of different groups in evolving networks. By exploiting information collected from real-world social media sites, extensive experiments are conducted to evaluate group-profiling results. The pros and cons of different group-profiling strategies are analyzed with concrete examples. We also show some potential applications based on group profiling. Interesting findings with discussions are reported.
Semantic Data Integration for Ubiquitous Logistics - An Approach Supporting Autonomous Logistics in Urban Environments

This paper introduces an approach for seamless plug & play data integration in a novel urban logistics concept. The logistics concept is called ubiquitous logistics and contains an agent-based perspective on shared logistics resources. Each private and public vehicle participating in the concept as well as packages features an intelligent agent that may request (or offer) information services to other agents or legacy systems. The technical approach of this paper suggests that each of the heterogeneous data sources delivers additional information that is used to virtually integrate the data in an automated way. This additional information concerns, the authentication, data structure and sequence â information that have to be provided manually nowadays. The technical approach is explained using a typical situation from the future urban logistics concept. This situation represents an intelligent agent trying to deliver small goods along a stream of urban commuters.
Efficient Identification Using a Prime-Feature-Based Technique

Identification of authorized train drivers through biometrics is a growing area of interest in lo- comotive radio remote control systems. The existing technique of password authentication is not very reli- able and potentially unauthorized personnel may also operate the system on behalf of the operator. Finger- print identification system, implemented on PC/104 based real-time systems, can accurately identify the operator. Traditionally, the uniqueness of a finger- print is determined by the overall pattern of ridges and valleys as well as the local ridge anomalies e.g., a ridge bifurcation or a ridge ending, which are called minutiae points. Designing a reliable automatic fin- gerprint matching algorithm for minimal platform is quite challenging. In real-time systems, efficiency of the matching algorithm is of utmost importance. To achieve this goal, a prime-feature-based indexing al- gorithm is proposed in this paper. The technique in- volves identifying the most prominent feature of the fingerprint and searching only for that feature in the database to expedite the search process. The pro-
TGF-β: An Important Mediator of Allergic Disease and a Molecule with Dual Activity in Cancer Development

The transforming growth factor-β (TGF-β) superfamily is a family of structurally related proteins that includes TGF-β, activins/inhibins, and bone morphogenic proteins (BMPs). Members of the TGF-β superfamily regulate cellular functions such as proliferation, apoptosis, differentiation, and migration and thus play key roles in organismal development. TGF-β is involved in several human diseases, including autoimmune disorders and vascular diseases. Activation of the TGF-β receptor induces phosphorylation of serine/threonine residues and triggers phosphorylation of intracellular effectors (Smads). Once activated, Smad proteins translocate to the nucleus and induce transcription of their target genes, regulating various processes and cellular functions. Recently, there has been an attempt to correlate the effect of TGF-β with various pathological entities such as allergic diseases and cancer, yielding a new area of research known as “allergooncology," which investigates the mechanisms by which allergic diseases may influence the progression of certain cancers. This knowledge could generate new therapeutic strategies aimed at correcting the pathologies in which TGF-β is involved. Here, we review recent studies that suggest an important role for TGF-β in both allergic disease and cancer progression.
Assessing Progress in Reducing the At-Risk Population after 13 Years of the Global Programme to Eliminate Lymphatic Filariasis

Background In 1997, the World Health Assembly adopted Resolution 50.29, committing to the elimination of lymphatic filariasis (LF) as a public health problem, subsequently targeted for 2020. The initial estimates were that 1.2 billion people were at-risk for LF infection globally. Now, 13 years after the Global Programme to Eliminate Lymphatic Filariasis (GPELF) began implementing mass drug administration (MDA) against LF in 2000—during which over 4.4 billion treatments have been distributed in 56 endemic countries—it is most appropriate to estimate the impact that the MDA has had on reducing the population at risk of LF. Methodology/Principal Findings To assess GPELF progress in reducing the population at-risk for LF, we developed a model based on defining reductions in risk of infection among cohorts of treated populations following each round of MDA. The model estimates that the number of people currently at risk of infection decreased by 46% to 789 million through 2012. Conclusions/Significance Important progress has been made in the global efforts to eliminate LF, but significant scale-up is required over the next 8 years to reach the 2020 elimination goal.
Discriminative adaptation based on fast combination of DMAP and dfMLLR

This paper investigates the combination of discriminative adaptation techniques. The discriminative Maximum A-Posteriori (DMAP) adaptation and discriminative feature Maximum Likelihood Linear Regression (DfMLLR) are examined. Since each of the methods is proposed for distinct amount of adaptation data it is useful to combine them in order to preserve the systems performance in situations with varying amount of adaptation data. Generally, DfMLLR and DMAP are executed subsequently (DMAP preceded by DfMLLR) demanding to approach the data twice. Since both methods address the data through the same statistics an one-pass-combination was proposed in order to decrease the time consumption. The one-pass-combination utilizes the advantage of DfMLLR method to transform directly the feature vectors. However, instead of feature vectors the statistics are transformed, what allows to use already computed statistics for the DMAP pass without the need to process the data once again. All the approaches are compared also to their non-discriminative alternatives.
Seeds of Life in Space (SOLIS). III. Zooming Into the Methanol Peak of the Prestellar Core L1544

Toward the prestellar core L1544, the methanol (CH3OH) emission forms an asymmetric ring around the core center, where CH3OH is mostly in solid form, with a clear peak at 4000 au to the northeast of the dust continuum peak. As part of the NOEMA Large Project SOLIS (Seeds of Life in Space), the CH3OH peak has been spatially resolved to study its kinematics and physical structure and to investigate the cause behind the local enhancement. We find that methanol emission is distributed in a ridge parallel to the main axis of the dense core. The centroid velocity increases by about 0.2 km s−1 and the velocity dispersion increases from subsonic to transonic toward the central zone of the core, where the velocity field also shows complex structure. This could be an indication of gentle accretion of material onto the core or the interaction of two filaments, producing a slow shock. We measure the rotational temperature and show that methanol is in local thermodynamic equilibrium (LTE) only close to the dust peak, where it is significantly depleted. The CH3OH column density, Ntot(CH3OH), profile has been derived with non-LTE radiative transfer modeling and compared with chemical models of a static core. The measured Ntot(CH3OH) profile is consistent with model predictions, but the total column densities are one order of magnitude lower than those predicted by models, suggesting that the efficiency of reactive desorption or atomic hydrogen tunneling adopted in the model may be overestimated; or that an evolutionary model is needed to better reproduce methanol abundance.
Reclaiming thinking : dialectic , dialogic and learning in the digital age

This paper examines and reflects on a selection of approaches to technology enhanced learning that focus on the promotion of contemporary interactions and thinking. These processes and practices are articulated in terms of dialectic and dialogic dimensions of learning dialogue. We hold that these dialectic and dialogic processes (interpreted broadly) are a primary 'engine' for stimulating and supporting the collaborative development of knowledge, meaning and understanding in ways that promote deep learning in most educational contexts. The argument presented in this article includes reviews of the authors' previous work which encompasses philosophical foundations, sociocultural psychology, empirical studies and design-based research methods. After presenting related perspectives and examples that substantiate our 'dialectical and dialogical stance', we will summarise the implications for designing interactions that stimulate thinking, conceptual development and engaging learning. One major implication is that we need to improve our theorising about the key features of the anatomy of effective and meaningful learning interaction and dialogue.
Exact error and outage probability formulas for Alamouti space time code 2 × J

Alamouti space-time code (STC) is a part of the UMTS-WCDMA standard. However, up to the best of our knowledge no exact closed-form outage and bit error probability (BEP) formulas for this famous code exists. Evaluating its performance through simulations is time-consuming and therefore, there should be analytical performance graphs to serve as a reference which are derived in this paper for coherently MPSK-modulated data. Additionally, analytical results take into account different channel fading levels from transmit antennas to receive ones.
Clustering over High-Dimensional Data Streams Based on Grid Density and Effective Dimension

Clustering algorithm based on grid and density has many excellent features. But for the highdimensional data stream, the number of grids will be increased sharply as the space dimensionality grows. To solve the defect, we propose GDH-Stream, a clustering method based on the effective dimension and grid density for high-dimensional data stream, which consists of an online component and an offline component. First, we define the effective value of dimension and give the partition method of the projection intervals for each dimension. Then the effective values of dimensions, which will be ranked in decreasing order, can be calculated. After that, the subset of dimensions will be chosen to generate subspace. On the online component, with the arrival of the data stream, GDHStream will map each data to the original grid structure. On the offline component, when a clustering request arrives, the subspace will be generated by the effective values of dimensions. Then the original grid structure is projected to the subspace and the new grid structure will be formed. Moreover, the clustering will be performed on the new grid structure according to the connection of the density grids. Experimental results show that the GDH-Stream has better clustering quality and efficiency. Meanwhile, GDH-Stream has strong scalability for clustering data stream.
Three Kinds of Hierarchies in Confucian tradition:Illustrated by example of Xunzi

Unlike Mengzi,Xunzi focused on the topic of"Waiwang"rather than"Neisheng". The institution of Li was the center of Waiwang,consisting of Qinqin,Zunzun and Xianxian.Qinqin and Zunzun were social facts in patriarchal society.During the transition period to imperial society,Xianxian became more and more important.Among the three,Zunzun was the most important while Qinqin and Xianxian offered the reasonable grounds for Zunzun.Xunzi's ideal society was not grounded on the ideal of equality but the society in which humanity was"well-developed"and social roles were"well-fulfilled".
Reduction of Bitstream Transfer Time in FPGA

In reconfigurable system, the size of bitstream and memory is reduced using bitstream compression. By reducing the reconfiguration time, the system can improve the communication bandwidth. Existing system shows that, at the cost of compression efficiency effective compression is achieved with slow or fast decompression rate. To improve both compression and decompression efficiencies, this paper proposes a new technique i.e., decode aware compression technique. Different approaches for the proposed system are i) A large set of matching pattern is created by using effective bitmask selection technique. ii) Using the bitmask and dictionary selection technique, a bitmask based compression is achieved because of which there is an efficient reduction in memory requirement. iii) Repetative patterns are generated by grouping bitmask based compression and run length encoding. Finally, using decompression engine the original bitstream can be generated.
Face sketch synthesis using non-local means and patch-based seaming

This paper proposed a face sketch synthesis method by using non-local means (NL-Means), which takes the advantage of the non-local self-similarity of face photo and sketch patches. With a learning database of individuals described by one face photo and one face sketch, we assume that, for a given individual, the NL-Means coefficient of a given face photo patch is the same as its corresponding sketch patch. In order to handle the visible seam due to intensity difference of neighbor overlapping patches, we use patch based optimal seam to enforce the consistency of synthesized overlapping sketch patches. Experimental results on CUHK Face Sketch Database illustrate that our method has the advantage of easy implementation and much less required training samples, meanwhile our method can achieve fairly competitive synthesis results.
Unifying Blind Separation and Clustering for Resting-State EEG/MEG Functional Connectivity Analysis

Unsupervised analysis of the dynamics (nonstationarity) of functional brain connectivity during rest has recently received a lot of attention in the neuroimaging and neuroengineering communities. Most studies have used functional magnetic resonance imaging, but electroencephalography (EEG) and magnetoencephalography (MEG) also hold great promise for analyzing nonstationary functional connectivity with high temporal resolution. Previous EEG/MEG analyses divided the problem into two consecutive stages: the separation of neural sources and then the connectivity analysis of the separated sources. Such nonoptimal division into two stages may bias the result because of the different prior assumptions made about the data in the two stages. We propose a unified method for separating EEG/MEG sources and learning their functional connectivity (coactivation) patterns. We combine blind source separation (BSS) with unsupervised clustering of the activity levels of the sources in a single probabilistic model. A BSS is performed on the Hilbert transforms of band-limited EEG/MEG signals, and coactivation patterns are learned by a mixture model of source envelopes. Simulation studies show that the unified approach often outperforms conventional two-stage methods, indicating further the benefit of using Hilbert transforms to deal with oscillatory sources. Experiments on resting-state EEG data, acquired in conjunction with a cued motor imagery or nonimagery task, also show that the states (clusters) obtained by the proposed method often correlate better with physiologically meaningful quantities than those obtained by a two-stage method.
PAROLE/SIMPLE 'lemon' ontology and lexicons

The PAROLE/SIMPLE 'lemon’ Ontology and Lexicon are the OWL/RDF version of the PAROLE/SIMPLE lexicons (defined during the PAROLE (LE2-4017) and SIMPLE (LE4-8346) IV FP EU projects) once mapped onto lemon model and LexInfo ontology. Original PAROLE/SIMPLE lexicons contain morphological, syntactic and semantic information, organized according to a common model and to common linguistic specifications for 12 European languages. The data set we describe includes the PAROLE/SIMPLE model mapped to lemon and LexInfo ontology and the Spanish & Catalan lexicons. All data are published in the Data Hub and are distributed under CC Attribution 3.0 Unported license. The Spanish lexicon contains 199466 triples and 7572 lexical entries fully annotated with syntactic and semantic information. The Catalan lexicon contains 343714 triples and 20545 lexical entries annotated with syntactic information half of which are also annotated with semantic information. In this paper we describe the resulting data, the mapping process and the benefits obtained. We demonstrate that the Linked Open Data principles prove essential for datasets such as original PAROLE/SIMPLE lexicons where harmonization and interoperability were crucial. The resulting data is lighter and better suited for exploitation. In addition, it facilitates further extensions and linking to external resources such as WordNet, lemonUby, DBpedia etc.
A double-layer Boussinesq-type model for highly nonlinear and dispersive waves

We derive and analyse, in the framework of the mild-slope approximation, a new double-layer Boussinesq-type model that is linearly and nonlinearly accurate up to deep water. Assuming the flow to be irrotational, we formulate the problem in terms of the velocity potential, thereby lowering the number of unknowns. The model derivation combines two approaches, namely the method proposed by Agnon et al. (Agnon et al. 1999J. Fluid Mech. 399, 319–333) and enhanced by Madsen et al. (Madsen et al. 2003Proc. R. Soc. Lond. A 459, 1075–1104), which consists of constructing infinite-series Taylor solutions to the Laplace equation, to truncate them at a finite order and to use Padé approximants, and the double-layer approach of Lynett & Liu (Lynett & Liu 2004aProc. R. Soc. Lond. A 460, 2637–2669), which allows lowering the order of derivatives. We formulate the model in terms of a static Dirichlet–Neumann operator translated from the free surface to the still-water level, and we derive an approximate inverse of this operator that can be built once and for all. The final model consists of only four equations both in one and two horizontal dimensions, and includes only second-order derivatives, which is a major improvement in comparison with so-called high-order Boussinesq models. A linear analysis of the model is performed, and its properties are optimized using a free parameter determining the position of the interface between the two layers. Excellent dispersion and shoaling properties are obtained, allowing the model to be applied up to the deep-water value kh=10. Finally, numerical simulations are performed to quantify the nonlinear behaviour of the model, and the results exhibit a nonlinear range of validity reaching at least kh=3π.
Reducing Peak Power Consumption inMulti-Core Systems without ViolatingReal-Time Constraints

The potential of multi-core chips for high performance and reliability at low cost has made them ideal computing platforms for embedded real-time systems. As a result, power management of a multi-core chip has become an important issue in the design of embedded real-time systems. Most existing approaches have been designed to regulate the behavior of average power consumption, such as minimizing the total energy consumption or the chip temperature. However, little attention has been paid to the worst-case behavior of instantaneous power consumption on a chip, called chip-level peak power consumption, an important design parameter that determines the cost and/or size of chip design/packaging and the underlying power supply. We address this problem by reducing the chip-level peak power consumption at design time without violating any real-time constraints. We achieve this by carefully scheduling real-time tasks, without relying on any additional hardware implementation for power management, such as dynamic voltage and frequency scaling. Specifically, we propose a new scheduling algorithm FPΘ that restricts the concurrent execution of tasks assigned on different cores, and perform its schedulability analysis. Using this analysis, we develop a method that finds a set of concurrent executable tasks, such that the design-time chip-level peak power consumption is minimized and all timing requirements are met. We demonstrate via simulation that the proposed method not only keeps the design-time chip-level peak power consumption as low as the theoretical lower bound for trivial cases, but also reduces the peak power consumption for non-trivial cases by up to 12.9 percent compared to the case of no restriction on concurrent task execution.