Miracle-dz/UniFAR-FTUs huggingface dataset:
The Scale Measurement of the Indicators of the Stress Resistance Assessment of Insurance Companies in Ukraine

The article analyzes a method for measuring the indicators of the stress resistance of the insurance company by means of microprudential indicators that provide an objective assessment of the sensitivity of insurance companies to various crisis situations. Detailed analysis of existing indicators of the assessment of the stress resistance of insurance companies is given and characteristics of their application during the scale measurement are described. The author was justified the effect of the level of stress resistance of insurance companies on the level of their risks.
A Semantic Metadata Enrichment Software Ecosystem Based on Topic Metadata Enrichment

As existing computer search engines struggle to understand the meaning of natural language, semantically enriched metadata may improve interest-based search engine capabilities and user satisfaction. This paper presents an enhanced version of the ecosystem focusing on semantic topic metadata detection and enrichment. It is based on a previous paper, a semantic metadata enrichment software ecosystem (SMESE). Through text analysis approaches for topic detection and metadata enrichment this paper propose an algorithm to enhance search engines capabilities and consequently help users finding content according to their interests. It presents the design, implementation and evaluation of SATD (Scalable Annotation-based Topic Detection) model and algorithm using metadata from the web, linked open data, concordance rules, and bibliographic record authorities. It includes a prototype of a semantic engine using keyword extraction, classification and concept extraction that allows generating semantic topics by text, and multimedia document analysis using the proposed SATD model and algorithm. The performance of the proposed ecosystem is evaluated using a number of prototype simulations by comparing them to existing enriched metadata techniques (e.g., AlchemyAPI, DBpedia, Wikimeta, Bitext,<br>AIDA, TextRazor). It was noted that SATD algorithm supports more attributes than other algorithms. The results show that the enhanced platform and its algorithm enable greater understanding of documents related to user interests.
Incorporating elements of heuristic research in teaching Anthropology

An examination is presently attempted of whether anthropological teaching may to some extent blend well with elements of heuristic research. Since heuristic research gives emphasis to personal involvement and focuses on queries pertinent to the individual, it could possibly enhance the usefulness of any anthropological knowledge a student receives. Such an approach would perhaps be more fitting for teaching at a university level, providing an interesting add-on to anthropology courses that are not addressed to would-be anthropologists.
A Level Set Method Based on Bayesian Risk for Textured Image Segmentation

Abstract—A level set method based on the Bayesian risk is proposed for textured image segmentation. First, the original texture image is converted into an orientation image. Second, based on Bayesian risk formed by false-positive and false-negative fraction in a hypothesis test, the level set evolution functional is deduced. Third, to prevent the propagating curves from generating excessively irregular shapes and lots of small regions, curvature and gradient of edges in the orientation image are integrated into the functional. Finally, to extract the boundaries of texture targets, the Bayesian level set equation is derived from the functional and then orientation image is regarded as input of the Bayesian level set equation. Compared with other segmentation methods for texture image, the proposed Bayesian level set approach relies on the optimum decision of pixel classification; thus the approach has more reliability in theory and practice. Experiments show that the proposed approach can accurately segment the texture image; moreover, the algorithm is extendable for multiphase segmentation.
Sistema de reconocimiento automático de neuronas en microscopía de epifluorescencia

Durante el desarrollo de esta tesis se habla de la importancia de las investigaciones en neurociencias y de como han sido impulsadas por el avance de la tecnologia. Se definen los problemas que enfrentan los investigadores del Departamento de Neurociencia Cognitiva del IFC de la UNAM para la identificacion manual de neuronas en videos que obtienen mediante microscopia de epifluorescencia. Con el fin de apoyarlos en tal tarea se propone SRANME, un sistema basado en procesamiento de imagenes digitales. Con este sistema se lograron resultados satisfactorios en el sentido de que en menos de 20 minutos y sin la necesidad de altas prestaciones de computo, es capaz de reconocer arriba del 80% de las neuronas identificadas por el experto e incluso aquellas que fueron omitidas por el, siendo reconocido que esto representa una importante mejora en el desempeno de sus actividades.
An evaluation on sustainable tourism demand: The case of Muğla, Turkey

Tourism sector that is one of the most important, driving power of social, economic and cultural development, has a significant role, especially for developing countries. In this sense, the countries aiming to get a bigger share in tourism have to carefully observe the fast progression in this sector and fulfill the changing expectations of the customers. In order to fulfill quality service and customer satisfaction, sustainability is very important in the sector in general. In this study which primarily discusses sustainable tourism, sustainable tourism practices affecting tourism demand will be referred. The ultimate aim of this research is to evaluate the level of the effect of sustainable tourism practices on tourism demand. The sample of the study is a total of 600 persons of foreign tourists who visited Mugla. According to data acquired from the questionnaires; protection of ecological, cultural and natural heritage, slow city, eco-label, concept and the competition power of the enterprise are the affecting factors on the tourism demand. Such results showed that sustainable tourism practices influence tourism demand positively.
Expressive Motion

Since the advent of motion capture animation, attempts have been made to extract the seemingly nebulously defined attributes of 'content' and 'style' from the motion data. Enabling quick access to highly precise data, the benefits of motion capture for animation purposes are abundant. Yet manipulating the expressive attributes of the motion data in a comprehensive manner has proved elusive. This dissertation poses practical solutions that are based on insights from the dance community and learning attributes from the motion data itself. The culminating project is a system which learns the deformations of the human body and reapplies them in exaggerated form for enhanced expressivity. While simultaneously developing efficient and usable tools for animators, the result is a three pronged technique to enhance the expressive qualities of motion capture animation. The key aspect is the creation of a deformable skeleton representation of the human body using a unique machine learning approach. The deformable skeleton is modeled by replicating the actual movements of the human spine. The second step relies on exploiting the subtle aspects of motion, such as hand movement to create an emotional effect visually. Both of these approaches involve exaggerating the movements in the same vein as traditional 2-D animation technique of 'squash and stretch'. Finally, a novel technique for the application of style on a baseline motion capture sequence is developed. All of these approaches are rooted in machine learning techniques. Linear discriminate analysis was initially applied to a single phrase of motion demonstrating various style characteristics in LABAN notation. A variety of methods including nonlinear PCA, and LLE were used to learn the underlying manifold of spine movements. Nonlinear dynamic models were learned in attempts to describe motion segments versus single phrases. In addition, the dissertation focuses on the variety of obstacles in learning with motion data. This includes the correct parameterization of angles, applying statistical analysis to quaternions, and appropriate distance measures between postures.
Factors affecting Attitudes towards Adoption of Mobile Banking: Users and Non-Users Perspectives

The advent and dispersal of technology is an interesting area of study since its success is dependent on the attitude for the adoption of it by customers. Extant literature indicates that mobile banking is the least adopted type of electronic banking when compared to other types of banking like Automated Teller Machine (ATM), despite its being the cheapest and quickest mode of communication. This study empirically examines and tests factors affecting users and non-users’ attitude towards the adoption of mobile banking. Data were collected from 256 participants both from users and non-users of mobile banking. Collected data were analyzed using chi-square, ANOVA, and correlation analysis. Findings indicate that trust, perceived ease of use, relative advantage, and compatibility have strong correlations with both users and non-users’ adoption towards mobile banking. However, perceived risk is found to have no significant relationship with users and non-users’ attitude towards the adoption of mobile banking. Moreover, it is found that there is a difference between users and non-users’ attitude towards the adoption of mobile banking. Furthermore, managerial implications, limitations of the study and future research directions were discussed.
Automatic de-identification of electronic health records : an Australian perspective

We present an approach to automatically de-identify health records. In our approach, personal health information is identified using a Conditional Random Fields machine learning classifier, a large set of linguistic and lexical features, and pattern matching techniques. Identified personal information is then removed from the reports. The de-identification of personal health information is fundamental for the sharing and secondary use of electronic health records, for example for data mining and disease monitoring. The effectiveness of our approach is first evaluated on the 2007 i2b2 Shared Task dataset, a widely adopted dataset for evaluating de-identification techniques. Subsequently, we investigate the robustness of the approach to limited training data; we study its effectiveness on different type and quality of data by evaluating the approach on scanned pathology reports from an Australian institution. This data contains optical character recognition errors, as well as linguistic conventions that differ from those contained in the i2b2 dataset, for example different date formats. The findings suggest that our approach compares to the best approach from the 2007 i2b2 Shared Task; in addition, the approach is found to be robust to variations of training size, data type and quality in presence of sufficient training data.
Determinants of Off-Balance Sheet (OBS) Activities: A Case of Selected Retail Banks in Malaysia

The present paper examines factors influencing the Off-Balance Sheet activities of selected commercial banks in Malaysia for the period 2004- 2014. OBS activities are an integral part of financial institutions in response to the needs of businesses for different types of guarantee that have conflicting implications on the stability of financial institutions. Data collected on selected banks from the Bankscope database was analyzed using the Generalized Method of Moments (GMM) regression. Specifically, the study built its analysis on three main recognized determining factors namely: (1) liquidity motives, (2) credit risk transfer motive, (3) profitability motives, and (4) capital arbitrage motive. The findings thus suggest that the selected banks mainly used OBS instruments for capital arbitrage purpose, enhancing operational efficiency and managing loan portfolio risks. The findings further suggested that its usage for capital arbitrage purposes may undermine the regulatory measures of accurately estimating and monitoring the risk of banks. The findings thus offer significant practical and policy implications that can help to enhance financial stability. Keywords: off-balance sheet, liquidity, credit risk transfer, profitability, capital arbitrage
An Analytical Survey on Diagnosis Algorithm in Generic Defect Large-Scale Failures in Computer Networks

We present a framework and a set of algorithms for determining faults in networks when large scale outages occur. The design principles of our algorithm, netCSI, are motivated by the fact that failures are geographically clustered in such cases. We address the challenge of determining faults with incomplete symptom information due to a limited number of reporting nodes. netCSI consists of two parts: a hypotheses generation algorithm, and a ranking algorithm. When constructing the hypothesis list of potential causes, we make novel use of positive and negative symptoms to improve the precision of the results. In addition, we propose pruning and thresholding along with a dynamic threshold value selector, to reduce the complexity of our algorithm. The ranking algorithm is based on conditional failure probability models that account for the geographic correlation of the network objects in clustered failures. We evaluate the performance of netCSI for networks with both random and realistic topologies. We compare the performance of netCSI with an existing fault diagnosis algorithm, MAX-COVERAGE, and demonstrate an average gain of 128 percent in accuracy for realistic topologies.
Metropolitan financing and development in South Africa: Quo Vadis?

Metropolitan municipalities are largely regarded as the catalysts of economic development. The main finding of the paper globally. Eight metropolitan municipalities were introduced in the then new democratic South African state, post 1994. The article only reviewed the primary and secondary literature impacting on metropolitan financing and development, drawn from the desktop literature review and documentary analysis in the larger study. This article focussed on the three largest metropolitan municipalities, namely eThekwini; Cape Town and Tshwane City Councils. Increasing urbanisation and infrastructural decay has placed considerable pressure on these municipalities to deliver on their service delivery mandate, in the broader context of redistribution/equity. The accumulated developmental backlogs pre and post 1994 in South Africa are interrogated and the required funding for additional growth/development in metropolitan municipalities are explored. Public-private partnerships and alternative service delivery models are also examined as an option for enhancing service delivery. The issue of unfunded mandates has to be given serious consideration as they are currently impacting negatively on the finances of local government. The notion of a “smart city” and public private partnerships in the broader context of local economic development and financial sustainability has to be prioritised in terms of the overall socio – economic impact on local communities. A main finding of the paper was a funding gap in local government South Africa and the development of a model to address such a funding gap. Keywords: Development, Financing, Income, Metropolitan, Municipalities, South Africa
Student Performance Measure by using Different Classification Methods of Data Mining

iii Table of Content . iv List of Figures . vi List of Tables . vii Chapter 1: Introduction . 1 1.1 Prelude . 1 1.2 Motivation . 2 1.3 Objectives . 2 1.4 Types of Educational Environment . 3 1.5 Types and Modes of different Data . 4 1.6 Grading system in education . 6 1.7 Criteria and challenges for evaluating grading system . 6 1.8 Ideal Grading System . 9 1.9 Thesis Organization . 10 Chapter 2: Literature Review . 12 2.1 Approaches of Data Mining in Higher Education . 12 Chapter 3: Problem Statement . 21 3.1 Research Gap Analysis . 21 3.2 Problem Formulation and Approach . 21 3.3 Objectives . 21 3.4 Scope of the Research . 22 Chapter 4: Tools and Techniques Used . 23 4.1 Tool Used . 23 4.2 IBM SPSS Modeler Features . 23
An unsupervised video foreground co-localization and segmentation process by incorporating motion cues and frame features

Video foreground segmentation is one of the key problems in video processing. In this paper, we proposed a novel and fully unsupervised approach for foreground object co-localization and segmentation of unconstrained videos. We firstly compute both the actual edges and motion boundaries of the video frames, and then align them by their HOG feature maps. Then, by filling the occlusions generated by the aligned edges, we obtained more precise masks about the foreground object. Such motion-based masks could be derived as the motion-based likelihood. Moreover, the color-base likelihood is adopted for the segmentation process. Experimental Results show that our approach outperforms most of the State-of-the-art algorithms.
Towards Temporal Saliency Detection: Better Video Understanding for Richer TV Experiences

—More and more popular, Smart TVs and set-top boxes open new ways for richer experiences in our living rooms. But to offer richer and novel functionalities, a better understanding of the multimedia content is crucial. If many works try to automatically annotate videos at object level, or classify them, we think that investigating the emotions through the use of digital analysis and processing techniques will allow great TV experience improvements. With our work, we propose a temporal saliency detection approach capable of deﬁning the most exciting parts of a video that will be of the most interest to the users. To identify the most interesting events, without performing their classiﬁcation (in order to be independent from the video domain), we compute a time series of arousal (excitement level of the content), based on audio-visual features. Our goal is to merge this preliminary work with user emotions analysis, in order to create a multi-modal system, allowing to bridge the gap between users’ needs and multimedia contents.
Towards Flexible Energy Demand – Preferences for Dynamic Contracts, Services and Emissions Reductions

Abstract Households’ preferences for attributes of flexible energy demand are not well understood. This paper evaluates Finnish households’ acceptance of hypothetical contracts and services aimed at increasing demand side flexibility. We conduct a Choice Experiment to analyze households’ willingness to offer flexibility through timing their electricity usage and heating; their interest in dynamic pricing contracts such as real-time pricing, two-rate tariffs, or power-based tariffs; and how emissions reductions affect their choices. The results indicate that households’ sensitivity to restrictions in electricity usage is much stronger than their sensitivity to restrictions in heating. Households also require considerable compensation to choose real-time pricing over fixed fees. Furthermore, other value-creating elements besides monetary compensation could incentivize households to offer demand side flexibility because they value reductions in CO2 emissions at the power system level.
Enabling intelligence with temporal world models

A system for storing knowledge along with the ability to query it, which together we call a world model, provides a central source of relevant information to the various processes that constitute an autonomous agent. We outline the development of a world model that supports ontologies to describe the world, and provides interfaces for processes to populate and query the system, including queries over time. The world model is implemented as a tuple store with structural subsumption query support. We evaluate its effectiveness on an embodied system, and show that it can answer spatiotemporal queries about objects observed in the environment.
Intellectual capital and bank profitability: Evidence from conventional and Islamic bank in Indonesia

This study investigates the effect of intellectual capital on bank profitability. In addition, we also analyze the effect of intellectual capital on bank profitability based on bank types, conventional and Islamic bank. Our data consist of conventional and Islamic banks operated in Indonesia from 2010 to 2016 annually. Since our data are a panel, we employ panel regression. Intellectual capital is measured by using Value Added Intellectual Capital (VAIC). Our result shows that intellectual capital has a positive significant impact on bank profitability. After data classified based on bank types, intellectual capital only has a positive significant effect on conventional bank profitability. We also attempt to estimate the impact of VAIC components, such as Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE) and Capital Employed Efficiency (CEE), on bank profitability. The results show that the impact of HCE is strongly significant in both banks. However, CEE and SCE do not have a significant effect on both types of bank profitability. Our results indicate that conventional banks synergize their intellectual and physical capital in creating profit better than Islamic banks. Thus, this research could be a critique of the Indonesian Islamic banking industry in determining and overcome their weakness. JEL Classification: G21, G32, G32 DOI: https://doi.org/10.26905/jkdp.v23i2. 3028
Soft Error Benchmarking for L2 Cache with PARMA

Low voltage operation and small device sizes reduce the critical charge stored in a SRAM cell making caches more susceptible to soft errors. To counter the problem, a plethora of protection schemes are being proposed to reduce the power and area overheads of cache protection. Accurate methods to measure and compare the effectiveness of such schemes are sorely needed. This paper introduces a unified reliability benchmarking framework with PARMA (Precise Analytical Reliability Model for Architecture). PARMA is a rigorous analytical framework to measure the failure rate in the presence of soft errors under any protection scheme. PARMA continuously and accurately accounts for the increase of failure density due to soft errors affecting program behavior during an execution. We have implemented the PARMA framework on top of a cycle-accurate simulator to benchmark Level-2 cache failure rates for a set of CPU 2000 benchmarks. Three protection schemes are compared: parity, word-level 1-bit ECC and block-level 1-bit ECC.