Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Control of Sonification Models with an Audio-Haptic Interface

This paper presents a new interface for controlling sonification models. A haptic controller interface is developed which allows both to manipulate a sonification model, e.g. by interacting with it and to provide a haptic data representation. A variety of input types are supported with a hand-sized interface, including shaking, squeezing, hammering, moving, rotating and accelerating. The paper presents details on the interface under development and demonstrates application of the device for controlling a sonification model. For this purpose, the Data-Solid Sonification Model is introduced, which provides an acoustic representation of the local neighborhood relations in high-dimensional datasets for binary classification problems. The model is parameterized by a reduced data representation obtained from a growing neural gas network. Sound examples are given to demonstrate the device and the sonification model.
An Efficient Hybrid Technique Of Feature Extraction For Facial Expression Recognition Using Adaboost Classifier

Facial Expression Recognition is widely used for designing of human-machine interface. The research issue of Facial Expression Recognition is to select the features which are required to represent a Facial Expression. In this paper we proposed a hybrid method of feature extraction using Discrete Cosine Transform, Wavelet Transform, Gabor Filter and Gaussian distribution to select the distinguished feature for improving the recognition rate of facial expression. JAFFE dataset are used for recognition of different seven expressions: anger, disgust, fear, happiness, sadness, surprise, neutral in Experiments and the result of proposed hybrid technique is compared with results of individual Feature Extraction Techniques as DCT based technique, Wavelet transform based technique, Gabor filter based technique & Gaussian Derivatives based technique which shows that Recognition Rate can be improved by combining distinguished optimum features of DCT, Gabor Filter, Wavelet Transform and Gaussian Distribution in a feature vector for facial expression recognition. KeywordsHybrid, Facial Expression Recognition, Gesture, DCT, Wavelet, Gabor Filter, Gaussian distribution.
The representation and use of focus in dialogue understanding.

Abstract : This report develops a representation of focus of attention thatcircumscribes discourse contexts within a general representation ofknowledge. Focus of attention is essential to any comprehension processbecause what and how a person understands is strongly influenced bywhere his attention is directed at a given moment. To formalize thenotion of focus, the need for and the use of focus mechanisms areconsidered from the standpoint of building a computer system that canparticipate in a natural language dialogue with a ser, Two ranges offocus, global and immediate, are investigated, and representations forincorporating them in a computer system are developed.The global focus in which an utterance is interpreted is determinedby the total discourse and situational setting of the utterance. Itinfluences what is talked about, how different concepts are introduced,and how concepts are referenced. To encode global focuscomputationally, a representation is developed that highlights thoseitems that are relevant at a given place in a dialogue. The underlyingknowledge representation is segmented into subunits, called focusspaces, that contain those items that are in the focus of attention of adialogue participant during a particular part of the dialogue.Mechanisms are required for updating the focus representation,because, as a dialogue progresses, the objects and actions that arerelevant to the conversation, and therefore in the participants' focusof attention, change. Procedures are described for deciding when andhow to shift focus in task-oriented dialogues, i.e., in dialogues inwhich the participants are cooperating in a shared task. Theseprocedures are guided by a representation of the task being performed.The ability to represent focus of attention in a languageunderstanding system results in a new approach to an important problemin discourse comprehension -- the identification of the referents ofdefinite noun phrases.
A Survey of Mobile Cloud Computing Advantages, Challenges and Approaches

Despite explosive growth of mobile computing and its popularity, full exploiting from it, is difficult due to lack of sufficient processing power, storage, also problems such as mobility, frequent disconnections and so on. With exploiting cloud computing along mobile computing a new technology appears called mobile cloud computing. It can overcome these obstacles by executing mobile applications on the cloud instead of mobile devices. Mobile cloud is a new and applicable technology that covers many fields such as: healthcare, vehicular, gaming, commerce and so on. The ABI Research believes that the number of mobile cloud computing users is expected to grow from 43 million (1.1% of total mobile users) in 2008 to 998 million (19% of total mobile users) in 2015. This paper presents a comprehensive survey of mobile cloud computing and explains its advantages, challenges and approaches. Also the future research directions are given.
A study on video performance of multipoint‐to‐point video streaming with multiple description coding over ad hoc networks

Supporting multimedia applications over wireless networks has become a very active research topic recently because video applications are forecast to receive high demand in future wireless networks, such as ad hoc networks. However, enabling video transmission over wireless ad hoc networks is more challenging than over other wireless networks due to node mobility, interference, and the absence of a supporting infrastructure. This paper addresses the problem of video transmission over ad hoc networks. We propose to combine Multiple Description Coding (MDC) with multiple source transport (Multipoint‐to‐Point transfer: MP2P) in order to maintain smooth video streaming over wireless ad hoc networks. In this paper, we describe a comprehensive simulation and test bed experiment designed to show the effect of the proposed MP2P video transfer with MDC for two description cases of video streaming over wireless ad hoc networks. The results show that the proposed mechanism successfully improves video transfer performance in terms of video packet reachability and throughput. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 170(4): 43–50, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20999
Adaptive Signal Processing Algorithms: Stability and Performance

PART I. 1. Introduction. 2. Offline Analysis. 3. Iterative Minimization. 4. Algorithm Construction. 5. Algorithm Analysis: Gaussian White Noise Setting. 6. Algorithm Analysis: Deterministic Global Theory. PART II. 7. Deterministic Averaging: Single Time Scale. 8. Deterministic Averaging: Mixed Time Scale. PART III. 9. Stochastic Averaging: Single Time Scale. 10. Stochastic Averaging: Mixed Time Scale. APPENDICES. A. Matrix Analysis Review. B. Stochastic Signals and Systems Review. C. Deterministic Signals and Systems Review. D. Mathematical Analysis Review. E. Probability Review. Bibliography. Index.
Application of Machine Learning Techniques for Yield Prediction on Delineated Zones in Precision Agriculture

Precision agriculture is the implementation of the recent technology in agriculture. Huge amount of data is collected in agriculture and various techniques of data mining are used to make efficient use of it. In this paper, we have discussed how with the help of both, clustering and classification algorithms, the crop suitable for a particular piece of land can be determined. Management zone delineation is a key task in this. From a data-mining point of view this comes down to variant of spatial clustering which has a constraint of keeping the resulting clusters spatially mostly contiguous. We analyze the need to discretize and normalize the data set and the various techniques that are used for the same. Further, a comparative analysis of the algorithm is shown where it can be seen which algorithm is best suited. We also talk about the future scope of the same and how these could actually be implemented in the real life scenarios.
Taking the right course navigating the ERC universe

We study the equal risk contribution (ERC) investment strategies exploring how these portfolios perform relative to traditional risk-only (minimum variance portfolio), risk–return (Markowitz portfolio) and naïve (1/n) investment schemes when expanding the asset class universe. We propose a combinatorial mechanism to expand the investment universe of all feasible ERC portfolios and then combine them, thus realizing an ERC efficient frontier consistent with the mutual fund separation theorem (Merton in J Financ Quant Anal 7:1851–1872, 1972). In doing this, we mitigate the out-of-sample estimation error in portfolio weights and better diversify asset allocation against unpredictable extreme events. Simulation, bootstrapping and empirical experiments indicate that the corresponding tangency portfolio, computed through the regression-based approach introduced in Britten-Jones (J Finance 54:655–671, 1999), offers better risk-adjusted performance relative to risk-only, risk–return-based competitors as well as the 1/n strategy.
Observing users to validate models

User models serve two purposes: to help us understand users, and hence determine how to supply them with effective search services; and as a framework against which to evaluate the quality of those services once they have been developed. In this extended abstract we describe an experiment we have undertaken in which we observe user behaviours, and try to determine whether these behaviours can be connected to search quality metrics via an existing or novel user model. We provide summary evidence that suggests that the answer is a qualified "yes".
Real World Research: A Resource for Social Scientists and Practitioner-Researchers

This book advice looks at carrying out real world research. The emphasis is on rigour and trustworthiness utilising a systematic procedures appropriate to the task. The author explores the multi-method case study as a serious strategy alongside field experimentation and the survey, with an even-handed coverage of qualitative and quantitative approaches. A final section covers issues in 'making an impact' including different approaches to reporting, the place of enquiry in promoting change, and the relative roles of practitioners and researchers.
Seabed segmentation in synthetic aperture sonar images

A synthetic aperture sonar (SAS) image segmentation algorithm using features from a parameterized intensity image autocorrelation function (ACF) is presented. A modification over previous parameterized ACF models that better characterizes periodic or rippled seabed textures is presented and discussed. An unsupervised multiclass k-means segmentation algorithm is proposed and tested against a set of labeled SAS images. Segmentation results using the various models are compared against sand, rock, and rippled seabed environments.
Yaşam Temelli ARCS Öğretim Modeliyle 9. Sınıf Kimya Dersi “Hayatımızda Kimya” Ünitesinin Öğretimi

Bu calismanin amaci Yasam Temelli ARCS [Attention, Relevance, Confidence, Satisfaction] Ogretim Modelinin ortaogretim kimya ogretiminde uygulanabilirligini incelemektir. Bu amacla modelin edinilen bilginin kaliciligina, ogrencilerin kimyaya karsi tutum ve motivasyonlari uzerine etkisi incelenmistir. Aracsal (instrumental) durum calismasi arastirma yonteminin kullanildigi calismada ortaogretim 9.sinif kimya ogretim programinin “Hayatimizda Kimya” unitesi Yasam Temelli ARCS Ogretim Modeline gore islenmistir. Calisma grubunu, Erzurum sehir merkezinde kolay ulasilabilir ornekleme yontemi ile secilmis bir lisenin, iki farkli 9. sinif subesinde ogrenim goren toplam 60 ogrenci olusturmaktadir. Veriler “Kimya Dersine Yonelik Tutum Olcegi [KDYTO]” “Ogretim Materyalleri Motivasyon Anketi [OMMA]”, “Yapilandirmaci Ogrenme Ortami Anketi [YOOA]”, arastirmaci tarafindan gelistirilen “Basari Testi [BT]” ve yari yapilandirilmis mulakat yoluyla toplanmistir. OMMA, YOOA ve BT verileri ortalama, standart sapma ve grafikle gosterim gibi betimsel yollarla analiz edilmistir. On test-son test KDYTO puan ortalamalari arasinda anlamli bir fark olup olmadigi bagimli gruplar t-testi ile analiz edilerek bakilmistir. Yari yapilandirilmis mulakat verileri icerik analizi yontemiyle analiz edilmistir. Sonuclar kullanilan yontemin ogrenmenin kaliciligini ve ogrencilerin motivasyonlarini artirdigini fakat ogrencilerin kimyaya karsi tutumlari uzerinde istatistiksel olarak anlamli sayilabilecek duzeyde bir etkisinin olmadigini gostermistir. Ayrica ogrencilerin icinde bulunduklari ogrenme ortamini yapilandirmaci bir ogrenme ortami olarak algiladiklari gorulmustur.
Fuzzy logic based controller for induction motor drives

The paper presents a speed control system based on a fuzzy logic approach for an inverter-fed indirect vector-controlled induction motor drive. The analysis and design of the fuzzy logic is investigated. The integral of time by absolute error (ITAE) criterion is then used to evaluate the performance of the fuzzy speed controller for different operating conditions.
How to Measure Significance of Community Structure in Complex Networks

Community structure analysis is a powerful tool for complex networks, which can simplify their functional analysis considerably. Recently, many approaches were proposed to community structure detection, but few works were focused on the significance of community structure. Since real networks obtained from complex systems always contain error links, and most of the community detection algorithms have random factors, evaluate the significance of community structure is important and urgent. In this paper, we use the eigenvectors' stability to characterize the significance of community structures. By employing the eigenvalues of Laplacian matrix of a given network, we can evaluate the significance of its community structure and obtain the optimal number of communities, which are always hard for community detection algorithms. We apply our method to many real networks. We find that significant community structures exist in many social networks and C.elegans neural network, and that less significant community structures appear in protein-interaction networks and metabolic networks. Our method can be applied to broad clustering problems in data mining due to its solid mathematical basis and efficiency.
Electric fields induced in magnetic stimulation of the nervous system. Influence of volume conductor boundaries

Knowledge of the electric fields that are induced inside the brain and the limbs is of importance in magnetic stimulation of the nervous system. Here, an analytical model based on the reciprocity theorem is applied to compare the induced fields in unbounded and semi-infinite media, sphere and cylinder-like prolate spheroids. Typical stimulating coil arrangements are considered, including butterfly coil and different orientations of a single coil. The results can be used to determine when the influence of the boundaries is negligible and simplified geometries can be used. <<ETX>>
PCB inspection based on a variant of the n-tuple technique

Proposes an alternative approach based on a variant of the n-tuple technique to detect small defects in electronic printed circuit boards. The technique operates on a list of nine attributes formed in a 3 by 3 kernel. The comparison of corresponding frequencies of occurrence of these selected attributes recorded in the faulty and the fault-free images, provides an adequate means to categorize the defect. The results obtained during the study, show that the frequency distribution of the nine attributes gives an estimation of the size and shape of the faults. This suggests the use of this rapid technique in a preprocessing stage within a complete autonomous PCB inspection system.
Large-Vocabulary Continuous Speech Recognition of Lhasa Tibetan

The framework of auto speech recognition of Lhasa dialect was established in this paper. Phoneme was chosen as the basic unit for modeling. Then, phonemes set of Lhasa dialect and their Latin transliteration were designed. There were 5568 frequently used monosyllables in the vocabulary. Hidden Markov Models of triphones were established and trained by use of HTK. Word error rate (WER) was 21.81% under the optimal situation.
A systematic determination approach of a models' base for uncertain systems: experimental validation

The increasing complexity of industrial processes and the higher performance requirements, make the multimodel approach necessary. This approach is often confronted to the determination of the useful models' base problem. An approach for a systematic determination of a models' base for the representation of uncertain linear systems is suggested in this article. The application of this approach requires two main steps. We are firstly interested in the classification of the data by using Kohonen self-adapting artificial neural network and in the determination of the necessary number of models. Secondly, we exploit the obtained data relative to the clusters for the structural and parametric identification of different base-models. An experimental validation of the suggested modelling approach is carried out on an olive oil esterification reactor. The obtained results are satisfactory and show a very good precision relatively to the case in which the classical modelling, based on a unique model, is adopted.
Beyond the grammar wars : a resource for teachers and students on developing language knowledge in the English/literacy classroom

Contents Preface * Introduction: "Grammar Wars" and beyond Terry Locke Part I: The "Grammar Wars" in context * Blowin' in the wind: English grammar in United States schools. Martha Kolln and Craig Hancock * The problematics of prescribing grammatical knowledge: The case in England Urszula Clark * The Grammar Wars in Australia Frances Christie * Knowledge about language in the English classroom: A Scottish perspective Graeme Trousdale Part II: The effectiveness of grammar teaching: The research record * Teaching sentence-level grammar for writing: The evidence so far Richard Andrews * Explicit teaching of grammar: Does it help students to become better writers? Amos van Gelderen * Ways of knowing: Writing with grammar in mind Debra Myhill Part III: Into the classroom: Integrating knowledge about language with learning *"Language as a system of meaning potential": The reading and design of verbal texts Hilary Janks * Discovering a metalanguage for all seasons: Bringing literary language in from the cold Terry Locke * Scaffolding grammar instruction for writers and writing Constance Weaver * Primary school children learning grammar: Rethinking the possibilities Ruth French Section IV: Beyond print: A metalanguage for multimodal texts * A grammar for meaning-making Gunther Kress * Schemas for meaning-making and multimodal texts Anne Cloonan, Mary Kalantzis and Bill Cope * Resourcing multimodal literacy pedagogy: Towards a description of the meaning-making resources of language-image interaction Len Unsworth * Rules of grammar, rules of play: Computer games, literacy, and literature Andrew Burn List of Contributors
Media and Journalism: New Approaches to Theory and Practice

An introductory text that provides an overview of the 'mediasphere' to demonstrate how a knowledge of media can inform a knowledge of journalism and vice versa; and to show how such knowledge is best acquired through a combination of theory and practice. It integrates media theory with journalistic practice by drawing on current theories of the media as well as providing practical instruction on how to write journalistic pieces that put these theories into practice.
Identification of fuzzy models for a glass furnace process

In this paper a study is described for several approaches to the identification of models for the temperature within the melter portion of a glass furnace. The focus is on developing models from the gas input to the throat (melter outlet) temperature. Conventional linear techniques for system identification proved to be inadequate for this problem, but proved useful as baseline comparisons for further studies involving nonlinear techniques from intelligent control for model building. Various combinations of input and output variables in a variety of model structures using fuzzy and neuro-fuzzy system modeling approaches are developed, and comparisons are drawn. Approaches reported on here investigate nonlinear Takagi-Sugeno (TS) fuzzy model formulations, where a linear-in-the-parameter identification problem is formulated for various combinations of measured variables and system delays. A fuzzy-neuro formulation is then discussed for parameter selection in the TS model structure while simultaneously optimizing the membership functions associated with the inputs of the TS fuzzy system. Simulation results for data collected from an operating glass furnace process are presented.
Fusion of RADARSAT fine-beam SAR and QuickBird data for land-cover mapping and change detection

The objective of this research is to evaluate multitemporal RADARSAT Fine-Beam C-HH SAR data, QuickBird MS data, and fusion of SAR and MS for urban land-cover mapping and change detection One scene of QuickBird imagery was acquired on July 18, 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. Landsat TM imagery from 1988 was used for change detection. QucikBird images were classified using an object-based and rule-based approach. RADARSAR SAR texture images were classified using a hybrid approach. The results demonstrated that, for identifying 19 land-cover classes, object-based and rule-based classification of Quickbird data yielded an overall classification accuracy of 86.7% (kappa 0.857). For identifying 11 land-cover classes, ANN classification of the combined Mean, Standard Deviation and Correlation texture images yielded an overall accuracy: 71.4%, (Kappa: 0.69). The hybrid classification of RADARSAT fine-beam SAR data improved the ANN classification accuracy to 83.56% (kappa: 0.803). Decision level fusion of RADARSAT SAR and QuickBird data improved the classification accuracy of several land cover classes. The post-classification change detection was able to identify the areas of significant change, for example, major new roads, new low-density and high-density builtup areas and golf courses, even though the change detection results contained large amount of noise due to classification errors of individual images. QuickBrid classification result was able add detailed change information to the major changes identified.
Systematic approach to cosite analysis and mitigation techniques

Describes the development of an analysis tool used to determine a radio's operational performance in a multi-radio cosite environment. Using a Monte-Carlo simulation, the cosite analysis platform simulation program (CAPS) evaluates a receiver's performance as a function of radio-related parameters such as receiver/transmitter characteristics, individual operator message statistics, hop rates, frequency management techniques, as well as platform-related parameters such as antenna configuration, isolation, 'rusty bolts' effects, and characteristics of other support equipment including filters, couplers and amplifiers. The radio and equipment databases in CAPS are based on extensive laboratory measurements. CAPS identifies the major sources of interference (i.e. receiver desensitization/crossmodulation, transmitter broadband noise, intermodulation distortion products, etc.) and helps to facilitate the development of cost-effective solutions. <<ETX>>