Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking educational leadership

This chapter argues for a shift in thinking about the nature of school leadership. It starts by questioning the relevance and appropriateness of traditional models of organisational design to schools. In particular it challenges the application of rational-bureaucratic models to schools and then goes on to review the possible implications of this approach for learners and learning. The chapter then moves into the development of a possible alternative approach by considering the concept of community as a possible model for school structure, culture and relationships. The central hypothesis of the chapter is that the theory and practice of community is a more appropriate paradigm for learning and well-being than traditional organisational structures and processes. Criteria for the components of social capital are outlined and these are then considered in detail in terms of their implications for definitions of effective leadership. The chapter closes by considering the implications of the potential tension between bureaucratising the curriculum and creating a learning community.
Enhancement of Lipopolysaccharide-Induced Neutrophil Oxygen Radical Production by Tumor Necrosis Factor Alpha

ABSTRACT Although tissues become exposed to both exogenous and endogenous cell-activating mediators during infection, there is little appreciation of the effects of subjecting cells to multiple mediators. We examined the hypothesis that the response of neutrophils to bacterial lipopolysaccharide (LPS) is significantly altered in the presence of the endogenous mediator tumor necrosis factor alpha (TNF). The data showed that human neutrophils pretreated with TNF for 10 to 30 min, displayed significantly enhanced superoxide production in response to LPS (from eitherEscherichia coli K-235 or E. coli 0127:B8), measured as lucigenin-dependent chemiluminescence (CL), seen as an increase in the initial peak rate as well as the total CL accumulated over the incubation period. TNF amplified the response to LPS at 1 to 100 U of TNF/106 neutrophils and was able to enhance the response to a wide range of concentrations of LPS (0.01 to 1,000 ng/ml). The TNF-induced increase in the LPS response was paralleled by an increase in LPS binding to the neutrophils, which could be abrogated by an anti-CD14 monoclonal antibody. The results demonstrate that TNF significantly increases the LPS-induced release of oxygen radicals in neutrophils through the upregulation of cell surface CD14.
An Intelligent Search Infrastructure for Language Resources on the Web

Language occupies a central role on the web: most content is expressed in a given language, and most access takes place via natural language input and interfaces. Today, investigation of human language in all its forms depends on access to this vast store of language data. In particular, linguists and language technologists annotate and analyze this data and develop new language resources including grammars, dictionaries, and a raft of new technologies for automatic translation, information extraction, question answering, and so forth. As this new documentation is disseminated on the web, and as the new technologies are in turn deployed on the web, a further round of collection and processing is enabled, closing the loop. For instance, a collection of Japanese text with an aligned English translation can be used for translation studies, for adding examples to bilingual dictionaries, and developing translation systems. These resources can then be used for new purposes, e.g. to provide English speakers access to content stored in Japanese text, or to provide Japanese learners of English with more authentic example sentences.
Message from the Organizers

This volume contains the proceedings of the Joint Meeting of the Fourth Workshop on Model-Based Development of Computer-Based Systems (MBD) and the Third International Workshop on Model-Based Methodologies for Pervasive and Embedded Software (MOMPES 2006). This joint event is organized within the 13th IEEE International Conference on Engineering of Computer Based Systems (ECBS 2006) and takes place in the city of Potsdam, located in the Berlin metropolitan area of Germany. The MBD/MOMPES 2006 meeting serves as a forum for disseminating scientific and practical aspects related with the adoption of MDA (OMG’s ModelDriven Architecture) and other MBD (Model-Based Development) methodologies (notation, process, methods, and tools) for supporting the construction of computer-based systems, and more specifically, pervasive and embedded software. Model-Based Development (MBD) comprises approaches to software development separating the development process by constructing an application-oriented model and supporting the transition to implementation-oriented model. Therefore, with Model-Driven Architecture (MDA) as a prominent representative, MBD addresses technical issues like meta-models for architectural descriptions, suitable platforms, and automatic transformations from models to code. For a successful application of MBD, additional methodical aspects—like the definition of domainspecific models or analytic and generative techniques for these models—are needed. The contributions of the workshop—consisting of both invited talks and regular papers—reflect both sides of MBD, providing a balance of theoretical contributions in various relevant areas and case studies showing the benefits of using model-based approaches in a wide range of applications areas. As demonstrated by the invited talks about experiences on the usage of model-based methodologies—one from academia, one from industry—MBD is a thriving field with advances in both theory and practice. There were 24 papers submitted to MBD/MOMPES 2006. The authors represented 13 countries and 4 continents. We feel that this paper submission from such a diverse group of researchers is definitely a positive sign—indicating that this type of event is capturing a truly international audience. The papers were reviewed by the Program Committee; every member received on the average three papers for review. Every paper was evaluated by three different reviewers. A total of 15 regular papers were accepted. The geographic distribution of the accepted papers was (based on the home country of the corresponding author):
Extreme learning machine with initialized hidden weight

The Extreme Learning Machine (ELM) is a recent training method for feedforward neural networks. Its main advantage is a faster and simpler training procedure when it is compared with traditional global search optimization method. It is achieved by using a least square solution for the output layer and random initialization for hidden layer. In this way only one solution is attained. In this sense, a question arises: is the random initialization method really an efficient for ELM? The present work studies the influence of more sophisticated methods of initialization, in terms of performance and complexity.
Revisiting the “Enigma” of Musicians With Dyslexia: Auditory Sequencing and Speech Abilities

Previous research has suggested a link between musical training and auditory processing skills. Musicians have shown enhanced perception of auditory features critical to both music and speech, suggesting that this link extends beyond basic auditory processing. It remains unclear to what extent musicians who also have dyslexia show these specialized abilities, considering often-observed persistent deficits that coincide with reading impairments. The present study evaluated auditory sequencing and speech discrimination in 52 adults comprised of musicians with dyslexia, nonmusicians with dyslexia, and typical musicians. An auditory sequencing task measuring perceptual acuity for tone sequences of increasing length was administered. Furthermore, subjects were asked to discriminate synthesized syllable continua varying in acoustic components of speech necessary for intraphonemic discrimination, which included spectral (formant frequency) and temporal (voice onset time [VOT] and amplitude envelope) features. Results indicate that musicians with dyslexia did not significantly differ from typical musicians and performed better than nonmusicians with dyslexia for auditory sequencing as well as discrimination of spectral and VOT cues within syllable continua. However, typical musicians demonstrated superior performance relative to both groups with dyslexia for discrimination of syllables varying in amplitude information. These findings suggest a distinct profile of speech processing abilities in musicians with dyslexia, with specific weaknesses in discerning amplitude cues within speech. Because these difficulties seem to remain persistent in adults with dyslexia despite musical training, this study only partly supports the potential for musical training to enhance the auditory processing skills known to be crucial for literacy in individuals with dyslexia.
ReSuMe-Proof of convergence

We consider learning convergence in Spiking Neural Networks trained according to the ReSuMe learning rule. We begin with a short introduction of the ReSuMe learning rules and we define the learning scenarios to be considered. Next, we present a formal proof of learning convergen ce for the Spike Response Model (SRM) trained according to the defined scenario s. Finally, we demonstrate that the proof made for the SRM holds for the wide class of the spiking neurons. keywords: Spiking Neural Networks, Supervised Learning, Proof of Converge nce
Analyzing the impact of substrate noise on embedded analog-to-digital converters

This paper presents the analysis and measurements of the impact of digital substrate noise on embedded Analog-to-Digital converters. The impact of substrate noise on analog design is explained, followed by a specific entire impact analysis of the impact on a regenerative comparator and an A/D converter. To confirm the analysis the substrate noise has also been measured on a test chip designed in a 0.35 /spl mu/m heavily-doped-substrate CMOS technology. From the measurements it was deduced that SNR and the effective number of bits are reduced by 20%.
Charting an intent driven network

The strong divide between applications and the network control plane is desirable, but keeps the network in the dark regarding the ultimate purpose of applications and, as a result, is unable to optimize for these. An alternative approach is for applications to declare to the network their abstract desires; e.g. “I require group multicast”, or “I will run within a local domain and am latency sensitive”. Such an enriched semantic has the potential to enable the network to better fulfill application intent, while also helping optimize network resource usage across applications. We refer to this approach as intent driven networking (IDN). We sketch an incrementally-deployable design to serve as a stepping stone towards a practical realization of IDN within today's Internet.
A New Binary Common Subexpression Elimination Method for Implementing Low Complexity FIR Filters

The complexity of finite impulse response (FIR) filters is dominated by the number of adders (subtractors) used to implement the coefficient multipliers. It is well known that common subexpression flimination (CSE) method based on canonic signed digit (CSD) representation considerably reduces the number of adders in coefficient multipliers. Recently, a binary based CSE (BSE) technique was proposed, which produced better reduction of adders compared to the CSD based CSE. In this paper, we propose a new 4-bit Binary based CSE (BCSE) method which employs 4-bit common subexpressions (CSs). Design examples show an average adder reduction of 31.2 % over the conventional CSD based CSE and 15% reduction over BSE.
miR-134 in extracellular vesicles reduces triple-negative breast cancer aggression and increases drug sensitivity

Exosomes (EVs) have relevance in cell-to-cell communication carrying pro-tumorigenic factors that participate in oncogenesis and drug resistance and are proposed to have potential as self-delivery systems. Advancing on our studies of EVs in triple-negative breast cancer, here we more comprehensively analysed isogenic cell line variants and their EV populations, tissues cell line variants and their EV populations, as well as breast tumour and normal tissues. Profiling 384 miRNAs showed EV miRNA content to be highly representative of their cells of origin. miRNAs most substantially down-regulated in aggressive cells and their EVs originated from 14q32. Analysis of miR-134, the most substantially down-regulated miRNA, supported its clinical relevance in breast tumours compared to matched normal breast tissue. Functional studies indicated that miR-134 controls STAT5B which, in turn, controls Hsp90. miR-134 delivered by direct transfection into Hs578Ts(i)8 cells (in which it was greatly down-regulated) reduced STAT5B, Hsp90, and Bcl-2 levels, reduced cellular proliferation, and enhanced cisplatin-induced apoptosis. Delivery via miR-134-enriched EVs also reduced STAT5B and Hsp90, reduced cellular migration and invasion, and enhanced sensitivity to anti-Hsp90 drugs. While the differing effects achieved by transfection or EV delivery are likely to be, at least partly, due to specific amounts of miR-134 delivered by these routes, these EV-based studies identified miRNA-134 as a potential biomarker and therapeutic for breast cancer.
Elimination of Rent Control in the Swedish Rental Housing Market : Why and How

If housing market rent control is completely eliminated, welfare gains may arise from tenant redistribution. The amount of such welfare gains is estimated at approximately SEK 20 billion (approximately USD 3 billion) for inner Stockholm. In addition, welfare gains may arise from the production of new housing. We demonstrate that total deregulation is preferable to partial deregulation limited to new housing. Furthermore, inefficient overproduction of new housing would follow partial deregulation. Tenants facing rent increases if rent control were phased out would suffer welfare losses, and should be compensated to fulfill the Pareto criterion. Various compensation models could be used, as analyzed here. The amounts necessary to fully compensate tenants in attractive submarkets may be substantial. The Pareto criterion is not necessarily a desirable guideline for politicians if it implies huge wealth redistribution; it is still, however, a natural criterion in connection to all welfare economic analysis.
Security of virtual entities

The concepts of basic virtual entity and derived virtual entity are presented. Their quality characteristics are defined in the context of multiple accessing by heterogeneous target group members. The development conditions of derived entities are established. For collections of basic virtual entities and derived entities, are constructed and implemented algorithms to ensure and increase the level of security in the virtual environment. To implement a complete set of virtual entities, measurements of the security level are performed, using a special metric built.
A Cascaded Retrieval Method of Specified Object Based on Fusing Multiple Features

Considering the need of video retrieval and images retrieval, a cascaded retrieval method of specified object based on fusing multiple features has been put forward in this paper. The color and the SURF features should be fused subtly by the algorithms. And then the retrieval object can be extracted gradually from the rough to the subtle by the cascaded retrieval method. It mainly includes the following 3 parts: rough retrieval on the first level, subtle retrieval on the second level, comprehensive judgment on the third level. The rough area containing the suspected vehicles can be recognized by the color histogram with color layered in rough retrieval. Further recognition on the basis of rough retrieval can be done by obtaining SIFT features information in subtle retrieval. The comprehensive confidence of the object vehicles can be obtained by some principles according the independent similarity from the rough retrieval of colors and the subtle retrieval of features in comprehensive judgment. The experiments show the problem that the object features cannot be extracted from rigid object and flexible object at the same time has been solved by the algorithm in this paper. It has a better retrieval precision and a high processing speed, so that it prove to a new method to retrieve the video and image effectively.
Deformable Geometry Model Matching Using Bipartite Graph

In this paper, we present a novel method for efficient 3D model comparison. The method is designed to match highly deformed models through capturing two types of information. First, we propose a feature point extraction algorithm, which is based on “Level Set Diagram”, to reliably capture the topological points of a general 3D model. These topological points represent the skeletal structure of the model. Second, we also capture both spatial and curvature information, which describes the global surface of a 3D model. This is different from traditional topological 3D matching methods that use only low-dimension local features. Our method can accurately distinguish different types of 3D models even if they have similar topology. By applying the bipartite graph matching technique, our method can achieve a high precision of 0.54 even at a recall rate of 1.0 as demonstrated in our experimental results.
Prosodic Characteristics of Feedback Expressions in Distracted and Non-distracted Listeners

In a previous study [1] we investigated properties of communicative feedback produced by attentive and non-attentive listeners in dialogue. Distracted listeners were found to produce less feedback communicating understanding. Here, we assess the role of prosody in differentiating between feedback functions. We find significant differences across all studied prosodic dimensions as well as influences of lexical form and phonetic structure on feedback function categorisation. We also show that differences in prosodic features between attentiveness states exist, e.g., in overall intensity. Index Terms: communicative feedback; prosody; dialogue; distraction; engagement; attention
Accelerating the Detection of Spectral Bands by ANN-ED on a GPU

Spectrum sensing is the most important technique used to implement cognitive radio; this approach allows opportunistic and dynamic allocation of spectral bands. Among the methods used for detection, there are Artificial Neural Networks (ANN) and Energy Detection (ED); those exploit the signals coming from a Fast Fourier Transformed block (FFT). In this work, we focus on improving the performance of these three blocks by performing parallel computing, and considering the fusion of the two detectors ANN and ED. In this context, we implement three algorithms on GPU, which consist on exploiting the large number of cores to perform parallel calculation. The experimental results are compared with those obtained for CPU implementations. Our study presents how calculations distribution on GPU cores influences the global performance, and how to reduce execution time by optimizing data transfer. Furthermore, by exploiting the fine-grained parallel processing, and using a suitable choice of parameters, we find a considerable advantage of GPUs compared to CPUs, specifically for high data volumes.
Third Grade Reading Policies.

In 2012, 14 states passed legislation geared toward improving 3rd-grade literacy through identification, intervention, and/or retention initiatives. Today, a total of 32 states and the District of Columbia have policies in statute aimed at improving 3rd-grade reading proficiency. The majority of these states require early assessment and intervention, often as early as kindergarten. Fourteen states and the District of Columbia require retention of students on the basis of reading proficiency, most which require assessment and remediation for students in all K-3 grades.
INNOVATING MINDFULLY WITH INFORMATION TECHNOLOGY 1 By :

Although organizational innovation with information technology is often carefully considered, bandwagon phenomena indicate that much innovative behavior may nevertheless be of the “me too” variety. In this essay, we explore such differences in innovative behavior. Adopting a perspective that is both institutional and cognitive, we introduce the notion of mindful innovation with IT. A mindful firm attends to an IT innovation with reasoning Jane Webster was the accepting senior editor for this paper. grounded in its own organizational facts and specifics. We contrast this with mindless innovation, where a firm’s actions betray an absence of such attention and grounding. We develop these concepts by drawing on the recent appearance of the idea of mindfulness in the organizational literature, and adapting it for application to IT innovation. We then bring mindfulness and mindlessness together in a larger theoretical synthesis in which these apparent opposites are seen to interact in ways that help to shape the overall landscape of opportunity for organizational innovation with IT. We conclude by suggesting several promising new research directions.
Habitat selection for parasite‐free space by hosts of parasitic cowbirds

Choice of breeding habitat can have a major impact on fitness. Sensitivity of habitat choice to environmental cues predicting reproductive success, such as density of harmful enemy species, should be favored by natural selection. Yet, experimental tests of this idea are in short supply. Brown-headed cowbirds Molothrus ater commonly reduce reproductive success of a wide diversity of birds by parasitizing their nests. We used song playbacks to simulate high cowbird density and tested whether cowbird hosts avoid such areas in habitat selection. Host species that made settlement decisions during manipulations were significantly less abundant in the cowbird treatment as a group. In contrast, hosts that settled before manipulations started and non-host species did not respond to treatments. These results suggest that hosts of cowbirds can use vocal cues to assess parasitism risk among potential habitat patches and avoid high risk habitats. This can affect community structure by affecting habitat choices of species with differential vulnerability.
An Order-Channel Management Framework for Institutional Investors

Efficient Order-Channel Management, i.e. the process of information gathering, evaluation, decision and control regarding the setup of the overall trading infrastructure and the actual order routing implementation plays a crucial role for trading success as well as the competitiveness of Institutional Investors. This article introduces a framework intended to support Institutional Investors in establishing an individual Order-Channel Management (OCM). For this overall goal, OCM is decomposed into its strategic and operational constituents and the involved key entities, parameters, processes and their interdependencies are outlined. Based on the identified properties, a framework is derived that aims at identifying a suitable mapping from order characteristics to execution venues.
Automatic segmentation of speakers in broadcast audio material

In this paper, dimension-reduced, decorrelated spectral features for general sound recognition are applied to segment conversational speech of both broadcast news audio and panel discussion television programs. Without a priori information about number of speakers, the audio stream is segmented by a hybrid metric-based and model-based segmentation algorithm. For the measure of the performance we compare the segmentation results of the hybrid method versus metric-based segmentation with both the MPEG-7 standardized features and Mel-scale Frequency Cepstrum Coefficients (MFCC). Results show that the MFCC features yield better performance compared to MPEG-7 features. The hybrid approach significantly outperforms direct metric based segmentation.