Miracle-dz/UniFAR-FTUs huggingface dataset:
Political stability and tourism sector development in Mediterranean countries: a panel cointegration and causality analysis

Tourism sector has become an important source of domestic income for a great number of countries across the world with the contribution of diminishing and removing the barriers over the international mobility of people and considerable developments and cost reductions in transport sector. As a consequence, many countries have designed and implemented various policies to draw tourists and in turn raise their tourism receipts. In this regard, this study investigates the impact of political stability on the development of tourism sector in selected countries from Mediterranean region over the period 2002-2015 with Westerlund (2007) error-correction-based panel cointegration test and Dumitrescu and Hurlin (2012) panel causality test. The findings of the study revealed that political stability had a positive impact on the development of tourism sector in the long run. Furthermore, a two-way causality between tourism sector development and political stability was discovered.
Multivariate Online Kernel Density Estimation

We propose an approach for online kernel den- sity estimation (KDE) which enables building probability density functions from data by observing only a single data- point at a time. The method maintains a non-parametric model of the data itself and uses this model to calculate the corresponding KDE. We propose an new automatic band- width selection rule, which can be computed directly from the non-parametric model of the data. Low complexity of the modelismaintainedthroughanovelcompressionandrefine- ment scheme. We compare the online KDE to some state-of- the-art batch KDEs on examples of estimating distributions and on an example of classification. The results show that the online KDE generally achieves comparable performance to the batch approaches, while producing models with lower complexity and allowing online updating using only a single observation at a time.
Design thinking for business and entrepreneurship: accelerating innovation in higher education in Sri Lanka

“Design thinking” is a popular concept in today’s business and in many transdisciplinary educational settings which are focussed on innovation. Globally, many universities facilitate design thinking for students both in design and non-design contexts. This concept paper put forward the possibility of using design thinking as a catalyst to support innovation in higher education: particularly in the context of business and multi-disciplinary educational settings in Sri Lanka. Sri Lankan universities have lack of engagement with facilitating design thinking for students. However, there are few public universities obsessed with innovation, entrepreneurship and business design in Sri Lanka. This article reviews extant literature relevant to design thinking in higher education context while providing a few examples of design thinking courses. Then the paper discusses the current state of the design, innovation and entrepreneurial education in public universities across Sri Lanka by selecting a few Masters of Business Administration (MBA) courses. Finally, the evidence of the study highlights that there are possibilities of facilitating design thinking in Sri Lankan higher education.
Human Action Recognition Algorithm Based on Improved Dense Trajectories

Objective Human action recognition is an interesting but challenging task for unconstrained videos with complex background, illumination variation and camera motion. In this paper, we present an improved dense trajectory-based algorithm to improve the accuracy of human action recognition. First, dense optical flow is utilized to track the scale invariant feature transform key-points at multiple spatial scales. The histogram of oriented gradient,histogram of optical flow, and motion boundary histogram are used to depict the trajectory efficiently. Second, eliminating the influence of camera motion based on trajectory direction consistency. The purpose of this operation is to improve the robustness of trajectories. Finally, the Fisher vector model is utilized to compute one Fisher vector for each descriptor separately, and then the linear support vector machine is employed for classification.Experimental results on KTH and YouTube datasets demonstrate that the proposed algorithm can effectively recognize human actions.
Broad-coverage hierarchical word sense disambiguation

In naturally occurring language, hearers and readers are faced with large numbers of “ambiguous” words, i.e., words with multiple senses, and “unknown” words, i.e., words they are encountering for the first time and could not be in their lexicon. Ambiguous and unknown words seem to cause little difficulty for humans, who infer their syntactic and semantic properties on the fly to resolve ambiguities, and incorporate unknown words into their lexicons. Ambiguous and unknown words also pose problems for dictionary-based approaches in natural language processing applications. To use the information contained in the dictionary it is necessary to associate each word in the text that is being processed with one of the senses or concepts defined in the dictionary. If the word is ambiguous, it is necessary to identify the intended sense among the possible senses of the word, if it is unknown it is necessary to assign the word to one among all possible senses defined by the dictionary. The acquisition of unknown words can be seen as a disambiguation task in which the possible senses are all senses listed in the dictionary. In this thesis we formulate a single unified approach for learning unknown words, and performing word sense disambiguation. We focus on nouns but our method can be generalized to verbs and other syntactic categories. We propose a broad-coverage method which can be applied to any kind of text. We frame this problem as a pattern classification task. Each ambiguous or unknown word is classified as belonging to one of the existing concepts on the basis of morphological, syntactic and semantic properties of the contexts in which it appears. Our system takes as input an existing dictionary, which defines a hierarchy of concepts, and a corpus of textual data, and disambiguates all nouns in the corpus. We demonstrate this by disambiguating all nouns in a 40 million words collection of newspaper articles. We present empirical results from experiments carried out also with novel multi-level classification techniques, which exploit generalizations that hold at different levels of the concept hierarchy.
Local finance in Catalonia: Recent evolution, current situation and main challenges

This paper describes and analyses the current situation surrounding local finances in Catalonia, placing special emphasis on municipalities, in order to identify the main challenges in need of a solution today. The realities currently faced by city councils are, in many respects, quite different from when the main laws determining both their jurisdictional framework and their financing model were approved. This paper provides reflections on the role local government should play within the entirety of the Catalan public sector, as well as on the type of competences municipalities could assume, their degree of financial autonomy and the need to adhere to a certain principle of inter-municipal equality, while taking into account aspects such as municipalities’ jurisdictional framework and their size, and the existence of a large number of supra-municipal entities.
Managing industrial operations by lean thinking using value stream mapping and six sigma in manufacturing unit

Purpose The purpose of this paper is to cover the significance of lean thinking using value stream mapping and six sigma methodology in managing industrial operations. Design/methodology/approach Lean manufacturing is an efficient approach for identifying and eliminating waste through a continuous improvement via flow of the product/service at the pull of customer in chase of exactness. This study has been carried out in a manufacturing unit of Northern India that was suffering from high production lead time and work in progress (WIP) inventory. Findings The current state and future state maps for the critical product have been prepared. On comparison of both current and future state maps, it was observed that lead time has been reduced by 14.88 percent, processing time by 14.71 percent and wastage of material movement by 37.97 percent. As proposed in model, the WIP inventories have been reduced by 17.76 percent and workforce by 17.64 percent. Further it would lead to the profit of Rs161,800 per annum. Six sigma projects have been carried out to reduce rejections of the critical product P-19 under study. The total length of the product was undersize of the critical product. Root cause analysis technique has been used through strategic DMAIC implementation. Results of investigation demonstrated net savings of rupees 145,560 annually. Originality/value The paper demonstrates the practical application of lean thinking, showing how it can bring real breakthroughs in saving cost in the manufacturing industry.
Design and Implementation of Interworking Gateway with QoS Adaptation

To support multimedia services between network domains with different environments, it is required to map the functionalities in many aspects. In this paper, we implemented interworking gateway which provides protocol conversion and QoS(Quality of Service) adaptation to interwork DAVIC services based on ATM(Asynchronous TRansfer Model )network and Internet AV services. The interworking gateway converts RTSP(Real-Time Streaming Protocol ) message into DSM-CC(Digital Storage Media Command & Control) messages to control the stream that is served in ATM network, and transmits data stream by using RTP(Real-Time Transport Protocol) The interworking gateway provides QoS adaptation functionalities by QoS monitoring and MPEG filtering to meet the variation of network bandwidth.
Short Notes on Unsupervised Learning Method with Clustering Approach forTumor Identification and Tissue Segmentation in Magnetic Resonance BrainImages

Malignant and benign types of tumor, infiltrated in human brain are diagnosed with the help of an MRI scanner. Using the slice images obtained using an MRI scanner; certain image processing techniques are implemented to have a clear anatomy of brain tissues. One such image processing technique is hybrid Self Organizing Map (SOM) with Fuzzy K Means (FKM) algorithm, which offers a possible identification of tumor region penetration in the tissues of brain. The proposed algorithm is efficient in terms of Jaccard Index, Dice Overlap Index (DOI), Sensitivity, Specificity, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Computational time and memory requirement for processing the Magnetic Resonance (MR) brain images. Automatic detection of tumor region in MR (Magnetic Resonance) brain images has a good impact in helping the radio surgeons to identify the exact topographical location of tumor region. In this paper, the proposed hybrid SOM – FKM algorithm supports the radio surgeon by providing tissue segmentation and an automated tumor identification.
A New Algorithm of Backward Cloud

Cloud model is an effective tool in uncertain transforming between qualitative concepts and their quantitative expressions. Backward cloud generator can transform quantitative values into qualitative concepts. In this paper, a new algorithm of backward cloud is given, which is more simple and precise than the old one. Then we have error analysis and comparison between the two algorithms. At last an application is given, which demonstrates the effective and correctness of the new algorithm.
A K-means Algorithm Based on Optimized Initial Center Points

Aiming at the problems of K-means algorithm,a method is proposed to optimize the initial center points through computing the density of objects.Thus,the initial center of the samples can be built in a heuristic way.Then,a new evaluation function is proposed,namely equalization function,and consequently the cluster number is generated automatically.Compared with the traditional algorithms,the proposed algorithm can get initial centers with higher quality and steadier cluster results.Experimental results show the effectiveness and feasibility of the proposed algorithm.
The Orgasm Answer Guide

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Investor sentiment in the theoretical field of behavioural finance

Abstract Investor sentiment is a research area in the theoretical field of behavioural finance that analyses the sentiment of investors and the way it influences stock market activity. Recently, there has been an increase in the number of publications in this area, which indicates its incremental relevance. To date, there is no consensus on the theoretical structure of behavioural finance nor on the investor sentiment research area. We have used co-citation, bibliographic coupling and co-occurrence analysis to provide an overview of the structure of investor sentiment. Therefore, this study contributes to defining the theoretical structure of investor sentiment by identifying the foundations of the research area and main journals, references, authors, or keywords, which represent the core of knowledge of this research area. The results obtained suggest that investor sentiment is related to efficient market theory and behavioural finance theories. Furthermore, investor sentiment is a relevant research field, especially since 2014. Advances in computer science or theories based on physics or mathematics can help to better define the influence of investor sentiment on stock markets. This study advances research on investor sentiment within the field of behavioural finance, thus showing its relevance.
New regional filling algorithm based on seed

Traditional seed filling algorithm needs a great deal of time and space for a large of I/O stack operations.In order to avoid these shortcomings completely,this algorithm adopts new view.The experiment filled 100 oil region images indicates that the algorithm can completely fill region with a success rate of 100 percent,regardless of the shape,the size and the location.Compared with other filling algorithms,the algorithm has many advantages,such as simple processes,high computing speed,accurate and reliable.So it is a worthy promotion algorithm.
Offline Handwritten Arabic Character Recognition Using Features Extracted from Curvelet and Spatial Domains

Arabic character recognition is a challenging problem in several artificial intelligence applications, especially when recognizing connected cursive letters. Another dimension of complexity is that Arabic characters may form various shapes depending on their positions in the word. As a result, unconstrained handwritten Arabic character recognition has not been well explored. In this study, we propose an efficient algorithm for Arabic character recognition. The new algorithm combines features extracted from curvelet and spatial domains. The curvelet domain is multiscale and multidirectional. Therefore, curvelet domain is efficient in representing edges and curves. Meanwhile, the spatial domain preserves original aspects of the characters. This feature vector is then trained using the back propagation neural network for the recognition task. The proposed algorithm is evaluated using a database containing 5,600 handwritten characters from 50 different writers. A promising average success rate of 90.3% has been achieved. Therefore, the proposed algorithm is suitable for the unconstrained handwritten Arabic character recognition applications.
Some Generalized Linear Models for the Estimation of the Mean Frequency of Claims in Motor Insurance

One of the most important problems that a non-life actuary faces is constructing a fair pricing. In particular, claim counts modeling is one of the components of motor insurance ratemaking. This paper aims to describe the econometric modeling of the mean frequency of claims in a motor insurance portfolio using generalized linear models. The main frequency distributions of count data are presented together with the generalized linear models. Numerical illustration presents and compares the different proposed regression models, using annual CASCO insurance data from a Romanian insurance company. The main findings are that the Negative Binomial regression model performs better than the Poisson model and quantifies overdispersion. The figures, the estimations and the tests are done in the open source soft R.
A Novel Feature Extraction Algorithm of Thyroid Tumor Ultrasound Image

A novel feature extraction algorithm of ultrasound image combined with the texture,shape and the attenuation characteristics information was proposed,which could be used to identify the benign or malignant thyroid tumors.This paper focused on improving the texture feature extraction algorithm of thyroid tumors.On the basis of the traditional Local Binary Pattern(LBP) algorithm,we extended the neighborhood distribution in the elongated manner,which was more conducive to describe thyroid tumors and extract anisotropic properties of tumor effectively.Also,we used fuzzy logic to encode distance,which overcame the uncertainty of speckle noise in the ultrasound image.Furthermore,we extracted tumor circularity,standard deviation of the normalized radial length,area ratio,roughness index and the attenuation coefficient,which formed a feature vector to characterize thyroid tumors.Finally,Support Vector Machine(SVM) was used to classify and identify the thyroid nodules.Compared with other methods of feature extraction,the proposed feature fusion algorithm has a high accuracy of description,which can achieve higher classification accuracy,and the reasonableness and effectiveness of the proposed method is verified by experiments.
Broker Imposed Precarity of Indian Technical Immigrants

The primary purpose of this article is to investigate the labour supply chain of Indian technical immigrants in the United States. The two important findings that emerge are as follows: First, the study unearths a latent phenomenon of broker&#8208;induced precarity that results from the labour supply chain and explores how aspirational jobs are becoming precarious ones. Second, the study provides a foundation for devising policies and interventions to make the process of the labour supply chain less detrimental to the Indian technical immigrants. This article draws on a seven&#8208;month&#8208;long field&#8208;based qualitative study and ensures a meticulous triangulation of the findings through an analysis of archival data and actual artefacts related to manpower movement across the labour supply chain.
Price Discrimination Against Retail Investors: Evidence from Mini Options

This paper studies the rise and fall of “Mini” options that are especially catered to retail investors for popular but high-priced securities. Using transaction-level data, we find that transaction costs of Mini options are much higher than those of standard options and the difference cannot be fully explained by cost-related determinants. Furthermore, we find evidence of price discrimination against retail investors from analyses of price elasticities of option traders, an event-study of changes in bid-ask spreads around earnings announcements, and comparisons of trade prices paid by Mini and standard option traders for the same security at approximately the same time.
A Statistical Query Expansion Model Based on Query Logs

Ambiguity of query terms has been a long-standing problem in information retrieval field, which becomes more serious in Web searching. A method for automatic query expansion based on query logs obtained from users' daily usage is suggested. This model establishes probabilistic relationship between terms in documents and in user queries through statistical learning from the log, and selects high-related expansion terms based on Bayesian theory. These expansion terms are added into the original query to formulate a new one in order to improve the effectiveness of retrieval. Experimental results show that this technique is more adaptive to Web searching, and can improve the precision of document retrieval markedly compared with conventional ones.
Constrained control allocation for systems with redundant control effectors

The Null-Space Intersection method was derived from the same principles used to compute for a generalized inverse. Specifically, the principles involved in finding the intersection of a subspace with a higher dimensional polytope are used. When calculating , the subspace represents directions in which the controls should be deflected to generate specified moments. The Null-Space Intersection method involves the use of a subspace which represents directions in which the controls can be deflected without changing the moment generated.
Research on Knowledge Management Performance Evaluation Based on 4P Model

Performance evaluation is an important way to evaluate and improve the effect of knowledge management.The paper quotes the KeyLogic learning ability 4P assessment model,considering the characteristics of knowledge management strategy and implementation process.Purpose、person、process、performance are introduced into knowledge management performance evaluation.The establishment of knowledge management performance evaluation index system is on the basis of the four dimensions-purpose,person,process and performance.Meanwhile,it makes a careful description of the application of 4P assessment model,which provides the quantifiable scientific method for the evaluation of the performance of knowledge management.
An Improved Algorithm of Data Mining Based on Association Rule of Frequent Itemstes

The Apriori algorithm is one of the most frequently used algorithms.Its inherent defects lead to inefficient performance.In this paper,an improved method is proposed. ,only needs to scan whole database once during extracting the frequent itemsets from large database.Therefore,this algorithm can efficiently reduce the I/O time,and rapidly extract association rules which can meet requirements of users.Finally,the experiment results show:the proposed method is effective.