Miracle-dz/UniFAR-FTUs huggingface dataset:
International Cultural and Event Tourism Alanya, Turkey, 5 'hold Your Nose-you're Going Underwater': an Integrated Experiential Approach to Marketing in the Heritage Sector " Hold Your Nose-you're Going Underwater " 2 2

This paper explores experiential marketing from the perspective of cultural heritage attractions operating in a highly competitive leisure and tourism marketplace. The paper explores the rise of the heritage industry in the UK and the urgent need for sites and attractions to find a competitive edge in a complex and over supplied marketplace. As public funding has been eroded and whole sectors of the cultural industries have been propelled into competing in the open market alongside sophisticated visitor attractions that operate along clearly defined commercial lines, the need for relevant and responsible marketing of our heritage has become imperative and experiential marketing may provide the means to attract and retain an increasingly discerning visitor. The paper goes on to explore heritage as a consumption based experience and cites evidence of the adoption of the experiential paradigm by a number of sites and attractions. Barriers to the adoption of the experiential paradigm are also identified and explored. An integrated approach to experiential marketing is proposed, whereby visitor environments, interpretive planning and marketing communications are utilized in combination to achieve sustainable competitive advantage. The analytical basis for the evaluation of an integrated experiential approach is provided through a case study of an award winning visitor attraction in Bristol, UK, Brunel's ss Great Britain. The paper concludes by proposing a diagnostic model for practitioners taking forward an integrated experiential approach as a potentially timely and effective marketing strategy. Introduction As tourism, leisure and hospitality have become major economic activities cultural heritage attractions have sought to develop marketing strategies that will ensure their capacity to attract visitors and to continue to thrive both now and in the long term. This is a significant challenge for cultural heritage attractions for whom successful marketing is both is a unique privilege but at the same time an enormous responsibility. If successful our physical patrimony is preserved for future generations, ensuring social inclusion and access for all; if unsuccessful we risk the alienation or even destruction of irreplaceable assets. Given this operating scenario, it is essential that marketing in this sector is relevant, responsible and above all sustainable.
Query-Biased Text Summarization as a Question-Answering Technique

(these are discussed in Section 3). Segments with strong Text summarization may soon become a competitive method of answering queries asked of large text corpora. A query brings up a set of documents. These documents are then filtered by a summarizer: it constructs brief summaries from document fragments conceptually close to the query terms. We present the implementation of such a summarization system, based on lexical semantics, and discuss its operation. It is a configurable system, in the sense that the user will be able to choose among two or more implementations of every major step.
Called to Do Meaningful Work: A Blessing or a Curse?.

ABSTRACTTwo groups of people are particularly inclined to mention a calling when talking about their work motivation: those who are spiritual (because the concept of calling originated in the religious realm) and those in serving occupations (such as hospitals, schools, and nongovernmental organizations). Because Christian professors are in both groups, the concept of calling is likely to emerge. In this article, I trace the development of calling as a concept of work motivation from its traditional religious origin to its daily use in organizational scholarship. Several positive sides of callings are described, as well as the potential downsides that come with high aspirations. At the end of this exploration, I conclude with three practical suggestions for Christian professors and educators who live out a calling.
Crowdsourcing annotation: Modelling keywords using low level features

Tagging large collections is often prohibitive and manual tags are known to be imprecise, ambiguous, inconsistent and subject to many variations. A possible way to alleviate these problems and improve the annotation quality is to obtain multiple annotations per image by assigning several annotators into the task. In the current work we present an approach to model the view of several annotators using four MPEG-7 descriptors and a well known data classifier. We apply keywords modelling to the annotation data collected in the framework of Commandaria project where sixteen non-expert users annotated a set of a hundred images using a predefined set of keywords. The images sharing a common keyword are grouped together and used for the creation of the visual model corresponds to this keyword. Finally, the created models used to classify the images into the keyword classes in terms of 2-classes combinations using the 10-fold cross-validation technique. The experimental results are examined under two perspectives: First, in terms of the separation ability of the various keyword classes and second, in terms of the efficiency of the four visual descriptors as far as the image classification task is concerned.
A versatile, non-linear and elasto-static stiffness model of articulated industrial robots

Current challenges in robotics are human-robot interaction, light-weight design, and energy-efficient use of robots. Safety for human-robot interaction can be fundamentally established throughout mechanical design using variable stiffness actuators and/or light-weight robotic link design. Also, light-weight design enables energy-efficient use of robots which becomes increasingly important for manufacturing industries. However, decreasing mass of robotic links increases flexibility and, thus, has a negative effect on a robot's accuracy. In view of absolute position accuracy, an elasto-static robot stiffness model is presented considering mechanical flexibilities of both joints and links. Its intended purpose is the implementation of an analytical stiffness model in the design stage of an articulated robot with six revolute joints. Robot stiffness is modelled based on the concepts of structural analysis and is evaluated and verified with finite element analysis (FEA). Joints exhibit a non-linear and pose-variant stiffness behaviour while links are modelled as soft and flexible structures. The presented method performs accurately, compared to FEA, with an error less than 0.03% while showing the potential of real-time computation suitable for enhanced robot control. Furthermore, a stiffness performance index, the energy of deformation, is adopted. It provides physical correctness contrary to mathematical operators applied to the commonly used Cartesian stiffness matrix and is thus promoted as a suitable metric for robot stiffness assessment. Furthermore, applications are discussed where stiffness modelling might be of great benefit such as robot calibration and various on-site tasks.
Achieving Flatness: Selecting the Honeywords from Existing User Passwords

Recently, Juels and Rivest proposed honeywords (decoy passwords) to detect attacks against hashed password databases. For each user account, the legitimate password is stored with several honeywords in order to sense impersonation. If honeywords are selected properly, a cyber-attacker who steals a file of hashed passwords cannot be sure if it is the real password or a honeyword for any account. Moreover, entering with a honeyword to login will trigger an alarm notifying the administrator about a password file breach. At the expense of increasing the storage requirement by 20 times, the authors introduce a simple and effective solution to the detection of password file disclosure events. In this study, we scrutinize the honeyword system and present some remarks to highlight possible weak points. Also, we suggest an alternative approach that selects the honeywords from existing user passwords in the system in order to provide realistic honeywords-a perfectly flat honeyword generation method-and also to reduce storage cost of the honeyword scheme.
ADAPTATION AND NONLINEAR PARAMETRIZATION: NONLINEAR DYNAMICS PROSPECTIVE

Abstract We consider adaptive control problems in the presence of nonlinear parametrization of model uncertainties. An approach that foregoes on the need for domination in the control loop during adaptation is proposed. Our approach is based on the notions of attractivity, limit sets, equilibria, and multistability from the theory of nonlinear dynamical systems rather than on the conventional method of Lyapunov functions. As a result of this, our algorithms are applicable to general smooth non-monotonic parametrization and do not require any damping or domination in control inputs.
ROSMAN: ROugh Set approach for clustering Supplier base MANagement( SOFT COMPUTING METHODOLOGIES AND ITS APPLICATIONS)

Supply chain and industrial cluster are the results of competition economy. Clustering analysis enable more efficient supply chain management practices. In this paper, we focus our dis- cussion on the rough set theory for clustering supplier chain management. We propose ROSMAN (ROugh Set approach for clustering Supplier chain MANagement), an alternative algorithm for clustering supplier base management based on rough set theory taking into account maximal attributes dependencies in an information system. Experimental result on a supplier data set shows that ROSMAN technique is better with the baseline supplier base management clustering algorithm with respect to computational complexity and clusters purity. Keywords Supplier base management; Clustering; Information system; Rough set theory; Attributes dependencies.
Aplikasi Operational Customer Relationship Management Pemasaran Keripik Jamur Bu Sayom

Customer Relationship Management (CRM) adalah sebuah konsep yang membantu perusahaan mengelola semua aspek hubungan pelanggan, termasuk pemasaran, penjualan, purna jual, dan program pelanggan retainment. Tujuan dari pelaksanaan CRM adalah untuk meningkatkan kinerja perusahaan dalam rangka mempertahankan loyalitas pelanggan kepada perusahaan. Saat ini Keripik Jamur Bu Sayom tidak memiliki sistem yang mendukung pemasaran online, pemasaran yang saat ini dilakukan hanya melalui pemasaran offline dan belum adanya upaya pemasaran produk yang lebih jauh dan lebih luas padahal tingkat persaingan pemasaran offline yang tinggi menyebabkan kesulitan dalam memasarkan produk sehingga menyebabkan penjualan produk jamur stagnan dan tidak ada peningkatan penjualan yang signifikan. Penerapan konsep CRM dilakukan dengan merancang sistem untuk Keripik Jamur Bu Sayom. Konsep CRM yang diterapkan berfokus pada Operasional CRM berbasis website dengan menggunakan aplikasi google map yaitu pelanggan dengan mudah mendapatkan produk keripik jamur dan mendapatkan informasi akurat mengenai tersedianya stok yang tersedia pada masing-masing toko. Sistem ini diuji menggunakan pengujian fungsional secara black-box dan user testing. Berdasarkan pengujian fungsional secara black-box, sistem sudah dapat berjalan dengan baik, dengan ditandai bahwa tiap-tiap fungsi sistem dapat dijalankan tanpa kesalahan. Sedangkan hasil pengujian user testing dari sudut pandang pelanggan cenderung positif, mayoritas berpendapat setuju dalam kemudahan sistem maupun dalam manfaat sistem. Ini menunjukkan bahwa sistem telah mampu menjalankan fungsi utamanya serta memberikan kemudahan dan potensi manfaat bagi pelanggan Keripik Jamur Bu Sayom.
Representing the International Classification of Diseases Version 10 in OWL

Current efforts in the biomedical ontology community focus on establishing interoperability and data integration. In covering human diseases, one of the major international standards in clinical practice is the International Classification for Diseases (ICD), maintained by the World Health Organization (WHO). Several countryand language-specific adaptations exist which share the general structure of the WHO version but differ in certain details. This complicates the exchange of patient records and hampers data integration across language borders. We present our approach for modeling the hierarchy of the ICD-10 using the Web Ontology Language (OWL). Our model captures the hierarchical information of the ICD-10 as well as comprehensive class labels for English and German. Specialties such as “Exclusion” statements, which make statements about the disjointness of certain ICD-10 categories, are modeled in a formal way. For properties which exceed the expressivity of OWL-DL, we provide a separate OWL-Full component which allows us to use the hierarchical knowledge and class labels with existing OWL-DL reasoners and capture the additional information in a machine-interpretable way.
Multimodal Biometrics Based on Near‐Infrared Face Recognition

Biometric identification makes use of the physiological or behavioral characteristics of people, such as fingerprint, iris, face, palmprint, gait, and voice, for personal identification [1], which provides advantages over nonbiometric methods such as password, PIN, and ID cards. Its promising applications as well as the theoretical challenges have gotten its heated attraction from the last decade. Face recognition is a natural, nonintrusive and easy way for biometrics and has been one of the most popular techniques. However, most current face recognition systems are based on face images captured in visible light spectrum, which are compromised in accuracy by changes in environmental illumination. The near-infrared (NIR) face image-based recognition method [2–4] overcomes this problem. It is shown to be invariant to the changes of the visible lighting and hence is accurate and robust for face recognition. Recent research [5–9] has pointed out that multimodal biometric fusion can significantly improve the performance of the system due to the complementary information from different modalities helpful for classification. There exists various methods for multimodal fusion. Brunelli and Falavigna [10] proposed a person identification system based on voice and face, using a HyperBF network as the best performing
A trust-based distributed data fault detection algorithm for wireless sensor networks

Fault detection is a difficult and complex task in WSN because of there are many factors that influence data and could cause faults. Large-scale sensor networks impose energy and communication constraints, thus it is difficult to collect data from each individual sensor node and process it at the sink to detect faulty sensors. The proposed approach saves energy and improves network lifetime by detecting data faults locally in cluster head and therefore reducing the number of transmissions required to convey relevant information to the sink. This paper presents a novel approach for detecting sensors which produce faulty data in a distributed way as well as identifying the type of data faults using trust concepts to gain a high degree of confidence. We validate our method with simulations results.
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination

Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some fixed standard underlying measure. They have therefore not been available for application to Bayesian model determination, where the dimensionality of the parameter vector is typically not fixed. This paper proposes a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of differing dimensionality, which is flexible and entirely constructive. It should therefore have wide applicability in model determination problems. The methodology is illustrated with applications to multiple change-point analysis in one and two dimensions, and to a Bayesian comparison of binomial experiments.
Implementation and Comparison of Different Segmentation Techniques for Medical Images

is an important concept in image processing with an objective of dividing the image into regions and characterizes the structures with some input features, so that the output image is meaningful and easier to analyze. A large number of algorithms have been proposed in various application areas. In medical field the segmentation plays an important role helping doctors to take appropriate decisions. Identifying the region of interest is an important task in the medical field. Soft tissue of human bodies can be produced using the Magnetic Resonance Images. In this paper brain image segmentation is done using the K-Means, Fuzzy C- Means, Otsu Thresholding and morphological closing and reconstruction. Performance measuring parameters such as Structural content, mean square value, peak to signal ratio, Average difference Results obtained are satisfactory
Towards a Formal Behavioral Model for Rich Internet Applications

Rich Internet Applications (RIAs) seek to combine the best of traditional desktop applications with the best of the Web. However, due to the complexity of these applications, traditional Web Application methodologies and techniques are proving inadequate to properly model and implement them in a systematic way. In this paper we outline an equivalent transformation-based method for constructing a model for RIAs. This model provides some of the essential behavioral semantics of RIAs with a view towards facilitating simple and intuitive mapping of appropriate technologies to the model. Keywordsrich internet applications; web applications; equivalent transformation; formal methods; modelling;
Toward a characterization of human activities using smart devices: A micro/macro approach

The emergence of new connected devices has opened up new opportunities and allowed to imagine concepts that bring computer sciences and social sciences closer together. In particular, today's increasingly sophisticated miniature sensors allow to track and understand human activities and behavior with a great precision. Taking different approaches and perspectives, we use in this paper smartwatches and smartglasses to explore these behaviors and show that these objects, considered by many as gadgets, have an important role to play in understanding the lives of individuals. The main objective of this work is to introduce two new scales of activity detection, which lacks a formal and consistent definition in the literature. First, we propose a model that precisely detects and interprets movements made by a person wearing smart devices. Then, we use this model to show different interactions between those micro-activities and bigger chunks of behaviors we call macro-activities. Using a new concept based on 3D visualization, we finally show that combining those two scales and using a limited dataset, it is possible to distinguish between different individuals when they are performing very similar activities. The findings of this study lead the way to enhanced user profiling.
Parallelizing a Network Simulator

In the context of the current multicore revolution, the need to design an effective interconnect network has become increasingly vital. Computer architects make use of network simulators in order to determine the best network designs and configurations. The process by which this is done however is often computationally intensive and requires significant run time. The fact that most simulators are single threaded only adds to the computational bottleneck and renders them unable to take advantage of the new multicore processors. This paper demonstrates how an interconnect network simulator can be parallelized into a multithreaded implementation. The additional performance gain from this parallel simulator ultimately can lead to decreased development time and better designs as architects can sweep additional parameter spaces and consider a wider range of network configurations.
QUANTITATIVE GENETIC ANALYSIS OF MULTIVARIATE EVOLUTION, APPLIED TO BRAIN:BODY SIZE ALLOMETRY

Darwin (1859, pp. 11-14, 143-150) stressed the evolutionary importance of covariation between characters in populations and its "most imperfectly understood" connection with correlated responses to artificial and natural selection. After the turn of the century the discoveries of pervasive pleiotropy and linkage of Mendelian factors revealed the underlying genetic mechanisms. Existing theory on the dynamics of correlated characters has been developed in the limited framework of practical plant and animal breeding. Methods of multivariate analysis, functional analysis and optimality criteria popular among evolutionists, do not account for dynamical constraints imposed by the pattern of genetic variation within populations. Consideration of phenotypic variation often does not suggest any clear mechanism connecting growth patterns or adult variation to interspecific evolution. When there is individual variation in development, no necessary correspondence exists between ontogenetic and adult variation in a population (Cock, 1966, pp. 148-15 1). It is also common for the pattern of adult variation within a species to differ from that at higher taxonomic levels (Simpson, 1953, pp. 25-29). An example which will be investigated here is the brain weight:body weight relationship. At various taxonomic levels, brain and body weights tend to follow the allometric equation
Question Classification using syntactic and rule based approach

Question Classification is a vital component of Question Answering System. In this paper we have proposed a compact and effective method for question classification. Here rather than using a two layered taxonomy of 6 course grain and 50 fine grained categories developed by Li and Roth, 2002, we have classified the questions into three broad categories. We have also studied the syntactic structure of the question and suggest the syntactic patterns and expected answer type for particular category of questions. Using these question Patterns we have also suggested an algorithm for classifying the question into particular category. For experiment purpose we have used Li and Roth data set of 2000 questions. The experimental output shows that even with small set o question categories we can classify the questions with more satisfactory and better result. Hence in brief various contributions through this paper are: - Achieve state of art of question classification using less number of question categories - Communicate an Question classification algorithm which classifies the question into proposed categories which aids to embed the appropriate Answer Extraction Algorithm discussed in our previous work [23]. - Suggest more generic syntactic patterns for Wh questions.
Utilizing Expressive Arts to Explore Educational Goals among Girls in Haiti

ABSTRACT In Haiti, deeply held familial beliefs about gender roles, as well as systemic barriers, block opportunities for women and girls, making the attainment of education a near impossibility for many. This research study utilized expressive arts as a therapeutic tool to facilitate group work with children to explore their aspirations to better understand the collective dreams and hopes of a group that has been marginalized by cultural beliefs and systemic barriers. The youth were clearly shaped by context and articulated the traditional expectations associated with being a Haitian woman. Implications for social work practice with groups are discussed.