Miracle-dz/UniFAR-FTUs huggingface dataset:
Interdisziplinäres Teamteaching Eine empirische Studie über die Einstellung zur Zusammenarbeit von Lehrkräften unterschiedlicher Professionen

Im Rahmen der Inklusionsforschung wird die Kooperation zwischen Regel- und Speziallehrkraften als wichtiger Faktor fur die Umsetzung inklusiven Unterrichts benannt. Der vorliegende Beitrag stellt eine selbstkonstruierte Skala zur Erfassung der Einstellung zum interdisziplinaren Teamteaching (EiTT) vor. Zudem wird untersucht, welche Variablen signifikant zur Vorhersage der Einstellung beitragen konnen. Insgesamt nahmen 545 Personen aus der Schweiz (Lehramtsstudierende, Regellehrkrafte, Speziallehrkrafte und SchulleiterInnen) an einer Onlinebefragung teil. Die Ergebnisse zeigen, dass die Skala den gegebenen Anforderungen an psychometrische Gutekriterien entspricht. Studierende haben im Vergleich zu Regellehrkraften eine positivere Einstellung zum interdisziplinaren Teamteaching (iTT). Daruber hinaus lasst sich erkennen, dass Erfahrung im iTT zu einer positiveren Einstellung gegenuber Teamteaching fuhrt. Sowohl bei Studierenden als auch bei Regellehrkraften geht eine positivere Einstellung gegenuber dem iTT mit dem Wunsch auf eine langere iTT-Zeit einher.
On the outage probability in DF relay selection cooperative wireless networks over Nakagami-m fading channels

This paper proposes a method for transmitting data through a best relay selection technique in a cooperative network over Nakagami-m fading channels. The selection of the best relay among multiple decode and forward relays is based on the minimum outage probability criterion. Therefore, only the relay with the lowest outage probability will transfer the data to the destination. A closed-form expression is derived for the outage probability of the best relay selection with the decode and forward technique. Numerical examples are presented here for demonstration.
Membrane Chemical Cleaning : From Art to Science

Membrane chemical cleaning is an integral part of operation for microand ultrafiltration systems in the water industry and has significant impact on process operations. However, this issue has not been adequately addressed primarily for two reasons: 1) cleaning protocols are typically recommended from membrane manufacturers and some cleaners are proprietary; 2) the issues of membrane fouling are poorly understood and related to site-specific water quality issues. As advances in aquatic chemistries and the analytical tools of membrane fouling occur, a broad understanding of the interactions between fouling materials and membranes, among fouling materials, and between fouling materials and cleaning chemicals becomes possible, although there still are many gaps in the understanding of the basic chemistry of fouling. This paper will present the basic chemistry of membrane fouling and cleaning, physical and chemical conditions that have significant impacts on cleaning efficiency, and the compatibility of membrane materials with different cleaning alternatives. Three case studies will be used as examples. Membrane fouling is a complicated phenomenon and typically results from multiple causes. In spite of its complexity, electrostatic and hydrophobic/hydrophilic interactions that involve both membrane and fouling materials are recognized to have significant bearing, especially for the more-difficult-to-clean membrane fouling dominated by natural organic matter (NOM) and microbial activities. Electrostatic interactions occur among functional groups of membranes, fouling materials, and water primarily through dissociation, which strongly depend on the pH, ionic strength, and concentrations of multivalent cations in the solution. Hydrophobic/ hydrophilic interactions are functions of structure similarities between membranes and fouling materials, the types and density of functional groups on both membrane surfaces and fouling materials, and solubility of molecules of fouling materials. As analytical techniques and knowledge of structural details of NOM progress, the structure-solubility correlations for synthetic compounds can be extended to natural organic matter with modifications to provide a rough assessment on the hydrophobic nature of NOM.
Parameter exploration for response time reduction in accelerometer-based activity recognition

In activity recognition techniques, existing wearable sensors have a problem performing the recognition process. Because existing wearable sensors perform recognition process by dividing sensor data into partial sequences, there is lag between the changes in action and the output of the recognition result. Therefore, we focused on the point activities change and have proposed a method to reduce the response time of the activity recognition technique. However, parameters such as window size immediately after the activity changing point and the activity changing point detection in the proposed method have not been studied well. Thus, in this paper, we conducted experiments using the HASC Corpus, which contains large-scale data of human activity. We report results of examining various parameters in the proposed method and features of the proposed method revealed by comparison with a conventional method. To give a concrete example, for IIR band-pass filter bank to be used for activity changing point detection, we clarified the frequency and the appropriate number of filters. In addition, we clarified the relationship between identification accuracy and the size of a special window that is set after activity changing point detection. The proposed method reduced the response time to the 2035ms on average from 2773ms, the of average of the conventional method. In addition, the proposed method can reduce the amount of calculation, achieve both high recognition accuracy and short response time, and output the recognition results in consistent times to reduce the jitter of response time.
Using Aspect-Orientation to Add Persistency to Applications

This paper presents a comfortable and customizable persistence framework that supports the access of relational databases from Java applications. In order to keep the effort for implementation low, we show how to benefit from aspect-orientation. The framework has advantages over existing mapping tools: Flexibility is higher as the functionality can be freely designed. We demonstrate flexibility by putting emphasis on flexible mapping strategies.
Types of value and cost in consumer–green brands relationship and loyalty behaviour

Recent literature has proposed the dynamic construct of customer value (CV) as an alternative approach to studying proenvironmental consumer purchase and loyalty behaviour. This study empirically tests and validates a model pertaining to the factors that motivate and/or hinder the development of the relationship between consumers and green brands. Findings from a survey on green brands in the detergent category indicate that a set of value dimensions and (purchase) costs have a significant influence on perceptions of CV, which, as an overall assessment construct, mediate the relationship between value dimensions and costs and loyalty to the green brand. This research also examines the moderating effect of involvement on the relationships between the 3 antecedents (value dimensions, purchase costs, and switching costs) and perceived CV. Overall, the current study offers a multiattribute understanding of consumers' relational behaviour, concluding with several value-enhancing and cost-minimising implications for green marketing practitioners.
Understanding the Linux Kernel

In order to thoroughly understand what makes Linux tick and why it works so well on a wide variety of systems, you need to delve deep into the heart of the kernel. The kernel handles all interactions between the CPU and the external world, and determines which programs will share processor time, in what order. It manages limited memory so well that hundreds of processes can share the system efficiently, and expertly organizes data transfers so that the CPU isn't kept waiting any longer than necessary for the relatively slow disks. The third edition of Understanding the Linux Kernel takes you on a guided tour of the most significant data structures, algorithms, and programming tricks used in the kernel. Probing beyond superficial features, the authors offer valuable insights to people who want to know how things really work inside their machine. Important Intel-specific features are discussed. Relevant segments of code are dissected line by line. But the book covers more than just the functioning of the code; it explains the theoretical underpinnings of why Linux does things the way it does.This edition of the book covers Version 2.6, which has seen significant changes to nearly every kernel subsystem, particularly in the areas of memory management and block devices. The book focuses on the following topics:Memory management, including file buffering, process swapping, and Direct memory Access (DMA)The Virtual Filesystem layer and the Second and Third Extended FilesystemsProcess creation and schedulingSignals, interrupts, and the essential interfaces to device driversTimingSynchronization within the kernelInterprocess Communication (IPC)Program executionUnderstanding the Linux Kernel will acquaint you with all the inner workings of Linux, but it's more than just an academic exercise. You'll learn what conditions bring out Linux's best performance, and you'll see how it meets the challenge of providing good system response during process scheduling, file access, and memory management in a wide variety of environments. This book will help you make the most of your Linux system.
DNA sampling from eggshell swabbing is widely applicable in wild bird populations as demonstrated in 23 species

There is increasing interest in noninvasive DNA sampling techniques. In birds, there are several methods proposed for sampling DNA, and of these, the use of eggshell swabbing is potentially applicable to a wide range of species. We estimated the effectiveness of this method in the wild by sampling the eggs of 23 bird species. Sampling of eggs was performed twice per nest, soon after the clutch was laid and again at the end of egg incubation. We genotyped DNA samples using a set of five conserved microsatellite markers, which included a Z‐linked locus and a sex‐typing marker. We successfully collected avian DNA from the eggs of all species tested and from 88.48% of the samples. In most of the cases, the DNA concentration was low (ca. 10 ng/μL). The number of microsatellite loci amplified per sample (0–5) was used as a measure of the genotyping success of the sample. On average, we genotyped 3.01 ± 0.12 loci per sample (mean ± SE), and time of sampling did not seem to have an effect; however, genotyping success differed among species and was greater in those species that used feather material for lining their nest cups. We also checked for the occurrence of possible genotyping errors derived from using samples with very low DNA quantities (i.e. allelic dropout or false alleles) and for DNA contamination from individuals other than the mother, which appeared at a moderate rate (in 44% of the PCR replicates and in 17.36% of samples, respectively). Additionally, we investigated whether the DNA on eggshells corresponded to maternal DNA by comparing the genotypes obtained from the eggshells to those obtained from blood samples of all the nestlings for six nests of magpies. In five of the six magpie nests, we found evidence that the swab genotypes were a mixture of genotypes from both parents and this finding was independent of the time of incubation. Thus, our results broadly confirm that the swabbing of eggshells can be used as a noninvasive method for obtaining DNA and is applicable across a wide range of bird species. Nonetheless, genotyping errors should be properly estimated for each species by using a suite of highly polymorphic loci. These errors may be resolved by sampling only recently laid eggs (to avoid non‐maternal DNA contamination) or by performing several PCR replicates per sample (to avoid allelic dropout and false alleles) and/or by increasing the amount of DNA used in the PCR through increasing the volume of the PCR or increasing the concentration of template DNA.
Implementing Project-Based Learning in Pre-Service Teacher Education

Increasingly, teachers at all levels are being asked to adopt pedagogies that promote collaborative learning and independent problem–solving. Project-based learning, aimed at incorporating these elements, is a new requirement of the Singapore MOE English Language Syllabus (2000). Teachers and teacher-trainees face a double challenge – of implementing change, and adopting pedagogies they have not themselves experienced as learners. This paper reports on a project-based module devised for English language teacher-trainees. The module called for group research and peer teaching in examining different models of integrated, projectbased learning in English, and in critically applying the notions and practices to the Singaporean context. The paper incorporates trainees’ reflections on aspects of the process, together with extracts from their presentations.
Automatic classification of discourse markers on the basis of their co-occurrences

A long-standing linguistic hypothesis asserts that the meanings of words are related to the contexts in which they appear (Miller and Charles 1991). This paper explores this hypothesis by showing that co-occurrences of discourse markers reflect the meanings of the discourse markers themselves. An experiment in classifying discourse markers by their semantic class, e.g. temporal or causal, was carried out, achieving an accuracy level of approximately 75%. Analysis shows that performance differs widely across classes: temporal and negative polarity markers are classified most accurately, while additive and hypothetical markers are classified poorly.
Mixed criteria control design with finite-time boundedness and H∞ property for a class of discrete-time nonlinear systems

A feedback controller design which guarantees both finite-time boundedness and H∞ attenuation for a class of nonlinear systems with conic type nonlinearities and additive disturbances is presented. Conditions which guarantee the existence of a robust state-feedback controller for maintaining a bound on the transient response and satisfying an H∞ bound in the steady state for this class of systems are derived. A solution for the controller gain is obtained through the application of linear matrix inequality techniques. The controller developed is robust for all nonlinearities satisfying the conic inequality and all admissible disturbances. We conclude the paper with a numerical example illustrating the applicability of the controller design.
Smart Multitask Bregman Clustering and Multitask Kernel Clustering

Traditional clustering algorithms deal with a single clustering task on a single dataset. However, there are many related tasks in the real world, which motivates multitask clustering. Recently some multitask clustering algorithms have been proposed, and among them multitask Bregman clustering (MBC) is a very applicable method. MBC alternatively updates clusters and learns relationships between clusters of different tasks, and the two phases boost each other. However, the boosting does not always have positive effects on improving the clustering performance, it may also cause negative effects. Another issue of MBC is that it cannot deal with nonlinear separable data. In this article, we show that in MBC, the process of using cluster relationship to boost the cluster updating phase may cause negative effects, that is, cluster centroids may be skewed under some conditions. We propose a smart multitask Bregman clustering (S-MBC) algorithm which can identify the negative effects of the boosting and avoid the negative effects if they occur. We then propose a multitask kernel clustering (MKC) framework for nonlinear separable data by using a similar framework like MBC in the kernel space. We also propose a specific optimization method, which is quite different from that of MBC, to implement the MKC framework. Since MKC can also cause negative effects like MBC, we further extend the framework of MKC to a smart multitask kernel clustering (S-MKC) framework in a similar way that S-MBC is extended from MBC. We conduct experiments on 10 real world multitask clustering datasets to evaluate the performance of S-MBC and S-MKC. The results on clustering accuracy show that: (1) compared with the original MBC algorithm MBC, S-MBC and S-MKC perform much better; (2) compared with the convex discriminative multitask relationship clustering (DMTRC) algorithms DMTRC-L and DMTRC-R which also avoid negative transfer, S-MBC and S-MKC perform worse in the (ideal) case in which different tasks have the same cluster number and the empirical label marginal distribution in each task distributes evenly, but better or comparable in other (more general) cases. Moreover, S-MBC and S-MKC can work on the datasets in which different tasks have different number of clusters, violating the assumptions of DMTRC-L and DMTRC-R. The results on efficiency show that S-MBC and S-MKC consume more computational time than MBC and less computational time than DMTRC-L and DMTRC-R. Overall S-MBC and S-MKC are competitive compared with the state-of-the-art multitask clustering algorithms in synthetical terms of accuracy, efficiency and applicability.
Meta Path-Based Classification in Heterogeneous Information Networks

Collective classification approaches exploit the dependencies of a group of linked objects whose class labels are correlated and need to be predicted simultaneously. In this paper, we focus on studying the collective classification problem in heterogeneous networks, which involves multiple types of data objects interconnected by multiple types of links. Intuitively, two objects are correlated if they are linked by many paths in the network. By considering di! erent linkage paths in the network, one can capture the subtlety of di! erent types of dependencies among objects. We introduce the concept of meta-path based dependencies among objects, where a meta path is a path consisting a certain sequence of linke types. We show that the quality of collective classification results strongly depends upon the meta paths used. To accommodate the large network size, a novel solution, called Hcc (meta-path based Heterogenous Collective Classification), is developed to e! ectively assign lab els to a group of instances that are interconnected through different meta-paths. The proposed Hcc model can capture di! erent types of dependencies among objects with respect to di! erent meta paths. Empirical studies on real-world net works demonstrate that e! ectiveness of the proposed meta path-based collective classification approach.
Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals

This work studies the frequency behavior of a least-square method to estimate the power spectral density of unevenly sampled signals. When the uneven sampling can be modeled as uniform sampling plus a stationary random deviation, this spectrum results in a periodic repetition of the original continuous time spectrum at the mean Nyquist frequency, with a low-pass effect affecting upper frequency bands that depends on the sampling dispersion. If the dispersion is small compared with the mean sampling period, the estimation at the base band is unbiased with practically no dispersion. When uneven sampling is modeled by a deterministic sinusoidal variation respect to the uniform sampling the obtained results are in agreement with those obtained for small random deviation. This approximation is usually well satisfied in signals like heart rate (HR) series. The theoretically predicted performance has been tested and corroborated with simulated and real HR signals. The Lomb method has been compared with the classical power spectral density (PSD) estimators that include resampling to get uniform sampling. The authors have found that the Lomb method avoids the major problem of classical methods: the low-pass effect of the resampling. Also only frequencies up to the mean Nyquist frequency should be considered (lower than 0.5 Hz if the HR is lower than 60 bpm). It is concluded that for PSD estimation of unevenly sampled signals the Lomb method is more suitable than fast Fourier transform or autoregressive estimate with linear or cubic interpolation. In extreme situations (low-HR or high-frequency components) the Lomb estimate still introduces high-frequency contamination that suggest further studies of superior performance interpolators. In the case of HR signals the authors have also marked the convenience of selecting a stationary heart rate period to carry out a heart rate variability analysis.
Intelligent approaches in locomotion

In this paper we review more than 50 publications and try to not only give a snap shot of the current state of the art research in the area, but also to critically analyse and compare different methodologies used in this research field. Among the investigated intelligent approaches for solving locomotion problems are Neural Networks, Hidden Markov models, Rule Based and Fuzzy Logic systems, as well as Analytical concepts. We try to compare those methods based on the quality of the produced solutions in terms of time, stability, correctness and the expense and cost for achieving them. At the end of each section we list the advantages and disadvantages of the reviewed methods and scrutinise them considering the complexity of the approaches, their applicability to the investigated locomotion tasks and the constraints of the produced solutions.
Efficient Minimum Error Rate Training and Minimum Bayes-Risk Decoding for Translation Hypergraphs and Lattices

Minimum Error Rate Training (MERT) and Minimum Bayes-Risk (MBR) decoding are used in most current state-of-the-art Statistical Machine Translation (SMT) systems. The algorithms were originally developed to work with N-best lists of translations, and recently extended to lattices that encode many more hypotheses than typical N-best lists. We here extend lattice-based MERT and MBR algorithms to work with hypergraphs that encode a vast number of translations produced by MT systems based on Synchronous Context Free Grammars. These algorithms are more efficient than the lattice-based versions presented earlier. We show how MERT can be employed to optimize parameters for MBR decoding. Our experiments show speedups from MERT and MBR as well as performance improvements from MBR decoding on several language pairs.