Miracle-dz/UniFAR-FTUs huggingface dataset:
On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters

Abstract Computerized texture analysis characterizes spatial patterns of image intensity, which originate in the structure of tissues. However, a number of texture descriptors also depend on local average image intensity and/or contrast. This variations, known as image nonuniformity (inhomogeneity) artefacts often occur, e.g. in MRI. Their presence may lead to errors in tissue description. This unwanted effect is explained in this paper using statistical texture descriptors applied for MRI slices of a normal and fibrotic liver. To reduce the errors, correction of image spatial nonuniformity prior to texture analysis is performed. The issue of sensitivity of popular texture parameters to image nonuniformities is discussed. It is illustrated by classification examples of natural Brodatz textures, digitally modified to account for inhomogeneities – modeled as smooth variations of image intensity and contrast. A set of texture features is identified which represent certain immunity to image inhomogeneities.
Effect of Director’s Tunneling on Assets Utilization: Evidence from Corporate Organizations in Nigeria

This study evaluates the effect of director’s tunnelling on asset utilization of companies in consumer goods sector in Nigeria using a panel data collected from annual financial report of thirty listed consumer goods firm in Nigeria between 2011 and 2016. The study was based on ex-post-facto research design and the data collected were analysed using descriptive statistics, correlation analysis and multiple regression. The study finds that the director’s pay and equity holding varies widely among consumer goods firms. Chairman’s pay and director’s equity holding have a statistically significant effect on asset utilization at 5% level. While the director’s pay policy has no statistically significant effect on asset utilization. The finding shows pay, chairman’s pay and director’s equity holding are three major avenues used for tunnelling as they have a significant effect on tunnelling. The study recommends that policymaker should formulate a policy that will reduce the tunnelling tendency of directors and board chairman.
Cohort racial composition: the impact on student achievement

I examine the effect racial composition of a classroom has on student achievement. Utilizing a first differencing approach to perform adjacent cohort-to-cohort comparisons, I can allow for randomness of births within a given community to produce variation of racial composition, while indirectly controlling for school and community level variables by utilizing cohort-to-cohort comparisons. I find that racial composition does influence student achievement, but many are potentially practically insignificant, although, a high concentration of Black students may be beneficial for their own math scores, and White students may have English scores that are negatively affected if the percentage of Black students increases. Overall Asian students benefit from a higher percentage of students
On metrics and models for multiplex networks

In this thesis, we extend the concept of null models as canonical ensembles of multi-graphs with given constraints and present new metrics able to characterize real-world layered systems based on their correlation patterns. We make extensive use of the maximum-entropy method in order to find the analytical expression of the expectation values of several topological quantities; furthermore, we employ the maximum-likelihood method to fit the models to real datasets. One of the main contributions of the present work is providing models and metrics that can be directly applied to real data. We introduce improved measures of overlap between layers of a multiplex and exploit such quantities to provide a new network reconstruction method applicable to multi-layer graphs. It turns out that this methodology, applicable to a specific class of multi-layer networks, can be successfully employed to reconstruct the World Trade Multiplex. Furthermore, we illustrate that the maximum-entropy models also allow us to find the so-called backbone of a real network, i.e. the information which is irreducible to the single-node properties and is therefore peculiar to the network itself. We conclude the thesis moving our attention to a different dataset, namely the scientific publication system.
Coupling Power and Heat Sectors as a Flexibility Option

The energy transition is the transformation of the energy system and the main energy resources from fossil-based to zero-carbon resources. Decarbonization of the end-use sectors and the integration of the variable renewable energy (VRE) sources are the main challenges of the transition. In the Netherlands, the heat sector depends on natural gas and much more effort is needed for reducing CO2 emissions. This research proposal presents "sector coupling" of power and heat markets in the Netherlands as a promising solution to the above-mentioned challenges. The addressed research question is: “What are the flexibility implications of the increasing sector coupling between heat and power? ”. A flexible heat demand and hybrid technologies for heating will have a significant grid and market implications. In addition, to unlock this potential, incentives and amendments on regulations are necessary. A model-based study by using the power systems model COMPETES reveals the flexibility potential, power sector implications and the effects on end-users: households and industry. Effects of natural gas and electricity price structures are also investigated as one of the important barriers against power-to-heat technologies. Results reveal that flexibility of the heat demand provides a more cost-efficient coupling. The policy instruments such as a tax shift from electricity to natural gas have the potential to make power-to-heat technologies more attractive.
The Role of Innovation in Upgrading in Global Value Chains

ABSTRACT We examine the role of innovative efforts in industries from the perspective of global value chains (GVCs). We set up a simple model and empirically test it using patent and bilateral export decomposition data. The model shows that innovative activities assumed to decrease the cost of conducting high-skilled tasks can upgrade the position of the industry in GVCs. This is also supported by data on patents and GVC-related data from the OECD and the Asian Development Bank. The empirical results reveal that a higher number of patent applications are closely related to more production of intermediate goods rather than assembly.
Trends in Cloud Computing and Big Data

BIG data refers to the large amount of data which is generated and collected in an unstructured way. Big data provides the certain characteristics and technologies to address the challenges. Its Characteristics are often described as volume, velocity and variety. Due to the high volume and velocity of big data it is an effective option to store the big data on cloud because the cloud possess capabilities to store big data and process high volume of user access requests. Cloud Computing is a scalable model for delivering the resources and provides services on dem- -and through the network. It provides three types of services based models namely, Infrastructure a s a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS). Clouds can be public, private, hybrid or community. Amazon web services, Google App Engine are some of the cloud providers. Clouds are used to deal with big data to store and exploit the unstructured data. With this we can access the data anywhere and in a cost effective way. Hadoop, which is a tool provides a way to manage the big data.
Digitalización de la cultura: un fenómeno que resignifica el campo de Comunicación/Educación

La aparicion de nuevas practicas culturales vinculadas al manejo de tecnologias cada vez mas integradas en la vida cotidiana, proponen interpelaciones que desde el campo de Comunicacion/ Educacion deben ser atendidas tanto desde su reconocimiento como desde la reflexion intelectual y estrategica que devuelva su sentido y capacidad de intervencion y transformacion politica.Triunfos inesperados en la politica, cataratas de noticias falsas, Big Data y otros procesos asociados a las formas de interpelacion que logran su reconocimiento fruto del acople en el cual hoy nos encontramos permanentemente vinculados con multipantallas, afectan de manera definitiva nuestra vida cotidiana.La denominada convergencia es, ante todo, un fenomeno de convergencia de medios y tecnologias que produce la digitalizacion de la cultura y con eso, la modifica, desde las (hiper)mediaciones, y genera un proceso de transformacion del campo con su consecuente necesidad de repensarlo.
MgO enables enhanced bioactivity and antimicrobial activity of nano bioglass for bone tissue engineering application

ABSTRACT Nanobioglass is perceived as an effective biomaterial for bone tissue regeneration because of its predominant osteoconductivity since it was developed. The present work reports the synthesis of 58S nanobioglass (nBg) by sol-gel technique and MgO incorporated nBg by adding pure MgO nanoparticles with different concentrations. The prepared MgO, bioglass (Bg) and MgO-bioglass (MBg) nanoparticles were characterized by FESEM which revealed the particle size in the range 25–65nm for MgO, 40–70nm for Bg and 9–35nm for MBg nanoparticles. XRD results suggested that the synthesis of pure form of MgO nanoparticles and amorphous phase of Bg as well as MBg. In-vitro bioactivity and the antimicrobial test revealed that the presence of MgO improves antimicrobial activity and apatite layer formation ability of Bg. The study has demonstrated that the sol-gel derived MBg might be a potential biomaterial platform for bone tissue regeneration.
Una aproximación a las apropiaciones de menores y adopciones irregulares bajo la dictadura militar en el sur de Chile (1978-2016). Memorias de Alejandro

El presente articulo se inscribe en los estudios de memorias sobre los diversos tipos de violencia social ejercidos bajo la dictadura militar en Chile. Nos interesa aproximarnos a partir de una historia de vida a la problematica de las apropiaciones ilegales de menores ocurridas bajo el entramado institucional dictatorial, posibilitadas por el uso del poder sobre los sectores populares. Este trabajo es el avance de una investigacion mayor, sobre las apropiaciones de menores y adopciones internacionales de caracter irregular desarrolladas durante la dictadura militar en Chile. La metodologia de trabajo de esta investigacion se vincula a la historia oral. De ahi que el testimonio es un elemento central para abordar la problematica, junto con el analisis de la documentacion asociada a las causas de menores.
A Social Popularity Based Probabilistic Routing in Delay Tolerant Network

Due to uncertainty in network topology incurred by nodal mobility, designing a routing protocol in Social Delay Tolerant Networks (SDTNs) always faces with challenges. The current research achievements on routing algorithms tend to evaluate the available profit for each prospective relay node separately. In this paper, we not only consider characteristics of each node but also consider characteristics of node’s social group. Then a Social Popularity Based Probabilistic routing algorithm (SPPR) is proposed, which defines a metric called social popularity to evaluate node influence in networks. In this paper, message copies are delivered to more popular nodes until they are brought to social group of destinations. Social group is some nodes who interact more frequently with the given node. And then the probability for delivering a message to its destination within time-to-live is computed. We further propose an optimal message schedule policy implemented in destination’s social group, by modeling the buffer management problem as 0-1 knapsack. Through the backtracking search, the maximum sum of delivery probability of the problem can be achieved. Extensive simulation results show that SPPR significantly outperforms Epidemic, Prophet and NPSW in general. Especially the message delivery of SPPR is 56.7% higher than Epidemic in Infocom 5 Scenario.
Recommender Systems using SVD with Social Network Information

Collaborative Filtering (CF) predicts the focal user’s preference for particular item based on user’s preference rating data and recommends items for the similar users by using them. It is a popular technique for the personalization in e-commerce to reduce information overload. However, it has some limitations including sparsity and scalability problems. In this paper, we use a method to integrate social network information into collaborative filtering in order to mitigate the sparsity and scalability problems which are major limitations of typical collaborative filtering and reflect the user"s qualitative and emotional information in recommendation process. In this paper, we use a novel recommendation algorithm which is integrated with collaborative filtering by using Social SVD++ algorithm which considers social network information in SVD++, an extension algorithm that can reflect implicit information in singular value decomposition (SVD). In particular, this study will evaluate the performance of the model by reflecting the real-world user"s social network information in the recommendation process.
Newsmap

This paper presents the results of an evaluation of three different types of geographical news classification methods: (1) simple keyword matching, a popular method in media and communications research; (2) geographical information extraction systems equipped with named-entity recognition and place name disambiguation mechanisms (Open Calais and Geoparser.io); and (3) a semi-supervised machine learning classifier developed by the author (Newsmap). Newsmap substitutes manual coding of news stories with dictionary-based labelling in the creation of large training sets to extract large numbers of geographical words without human involvement and it also identifies multi-word names to reduce the ambiguity of the geographical traits fully automatically. The evaluation of classification accuracy of the three types of methods against 5000 human-coded news summaries reveals that Newsmap outperforms the geographical information extraction systems in overall accuracy, while the simple keyword matching suffers from ambiguity of place names in countries with ambiguous place names.
Mobile Robot Manipulator System Design for Localization and Mapping in Cluttered Environments

In this thesis, a compact mobile robot has been developed to build real-time 3D maps of hazards and cluttered environments inside damaged buildings for rescue tasks using visual Simultaneous Localization And Mapping (SLAM) algorithms. In order to maximize the survey area in such environments, this mobile robot is designed with four omni-wheels and equipped with a 6 Degree of Freedom (DOF) robotic arm carrying a stereo camera mounted on its end-effector. The aim of using this mobile articulated robotic system is monitor different types of regions within the area of interest, ranging from wide open spaces to smaller and irregular regions behind narrow gaps. In the first part of the thesis, the robot system design is presented in detail, including the kinematic systems of the omni-wheeled mobile platform and the 6-DOF robotic arm, estimation of the biases in parameters of these kinematic systems, the sensors and calibration of their parameters. These parameters are important for the sensor fusion utilized in the next part of the thesis, where two operation modes are proposed to retain the camera pose when the visual SLAM algorithms fail due to variety of the region types. In the second part, an integrated sensor data fusion, odometry and SLAM scheme is developed, where the camera poses are estimated using forward kinematic equations of the robotic arm and fused to the visual SLAM and odometry algorithms. A modified wavefront algorithm with reduced computational complexity is used to find the shortest path to reach the identified goal points. Finally, a dynamic control scheme is developed for path tracking and motion control of the mobile platform and the robot arm, with sub-systems in the form of PD controllers and extended Kalman filters. The overall system design is physically implemented on a prototype integrated mobile robot platform and successfully tested in real-time.
Human activity recognition using symbolic sequences

Human activity recognition research is an active area in an early stage of development. We present two approaches to activity recognition based on symbolic representations of multivariate time series of joint locations in articulated skeletons.One approach uses pairwise alignment and nearest-neighbour classification, and the other uses spectrum kernels and SVMs as classifiers. We tested both approaches on three datasets derived from RGBD cameras (e.g., Microsoft Kinect) as well as ordinary video, and compared our results with those of other researchers.
A high power, high efficiency UMTS amplifier using a novel Doherty configuration

The majority of prior publications on high efficiency amplifiers employing the Doherty configuration have described relatively low peak power circuits. Much of the previously published work has employed Si CMOS, GaAs MESFET or HBT technologies at RF power levels of, at most, a few watts suitable for mobile handset applications. This paper describes a novel Doherty amplifier configuration employing packaged Si LDMOS FETs. The main emphasis has been on providing acceptable gain, flatness, efficiency and linearity over the full UMTS band of 2.11 to 2.17 GHz at single circuit power levels of 90 watts. At 8 dB back-off from P1dB this amplifier configuration has achieved close to 33% drain efficiency under 2 carrier W-CDMA conditions compared to a reference drain efficiency of approximately 20 to 23% under the same ACPR and ACLR conditions for a single, 90 W, discrete transistor amplifier.
Private Equity 4.0: Using ESG to Create More Value with Less Risk

A growing number of private equity firms have responded to the increased focus on climate change, social issues, and technology disruption by broadening their corporate mission to encompass all important stakeholders, as well as their limited partners. And in the process, the management of ESG risks and pursuit of ESG opportunities have become increasingly fundamental to the staying power and value creation potential of PE firms by reducing the risk of their investments, discovering new sources of growth, and increasing their resilience to changes in the political and regulatory environment. This article tells the story of how the Nordic PE firm, Summa Equity, has turned its ESG approach into a core competence and a source of competitive advantage that has enabled the firm to distinguish itself from its competitors and bring about significant improvements in the financial performance of its portfolio companies while providing benefits for their stakeholders. Using the U.N. Sustainable Development Goals to guide them, the firm invests in companies they perceive to be addressing major environmental or social challenges in an innovative and commercially successful way. This has led to investments in significant growth opportunities in areas such as health care, education, waste management, and acqua&#8208;culture. And the firm's returns to its investors have been high enough&#8212;and the perceived social benefits large enough&#8212;that the firm recently closed its second fund (which was significantly oversubscribed) for 650 million euros, and received the ESG award at the 2019 Private Equity Awards in London.
Orientational order parameter of liquid crystalline nanocomposites by Newton’s rings and image analysis methods

Liquid crystalline nanocomposites are prepared by dispersing TiO2, ZnO, Fe2O3 and Fe3O4 nanoparticles separately in 4-Cyano 4’-Propoxy-1, 1’-Biphenyl (3O-CB) liquid crystal in a 1:100 ratio. The characteristic textures exhibited are captured at different liquid crystalline phases by using POM. The phase transition temperatures are measured by both polarizing optical microscope (POM) and differential scanning calorimeter (DSC). The optical textures are analyzed by using MATLAB software to compute birefringence and order parameter of samples. The birefringence and order parameter also measured by conventional Newton’s rings technique, the results are discussed.
ENCOURAGING AND DISCOURAGING FACTORS FOR NETWORKING AMONG TECHNOLOGY FIRMS: A CASE STUDY IN THE TECHNOLOGY, INNOVATION AND KNOWLEDGE FIRMS NETWORK (TIKFN)

ABSTRACT Purpose: The aim of this article is to identify which factors encourage and/or discourage companies to operate as a network, based on the Technology, Innovation and Knowledge Firms Network (TIKFN). Originality/value: Few advances have been made in the literature to understand the technology-based firms’ cooperation networks (TBFs), which have different characteristics, especially in relation to levels of uncertainty and innovation. Design/methodology/approach: It was conducted a Study Case on this paper. Therefore, data were collected through documents, semi structured questionnaires and focal groups to assure data source triangulation. Additionally, data analysis consisted of networks structural analysis, regression analysis and content analysis by frequency and category. Findings: Among the observed results, it is noticeable that congruence of objectives, length of participation and network strengthening represent the encouraging factors for network operation. On the other hand, asymmetric interests, low actors’ engagement and lack of active management are the discouraging factors. In order to assure the credibility of this study, results were evaluated through two focal groups composed by the network managers and participants, thus increasing the authenticity of the study. Results can be considered relevant as they contribute to understand the behavior of technology companies in a network, whose characteristics differ from other interorganizational networks especially regarding uncertainty levels and access to resources and information.
An Iris Recognition System Based on Analysis of Textural Edgeness Descriptors

ABSTRACT A novel iris recognition system is presented in this paper. Based on the studies of different textural edgeness descriptors, a well-discriminate texture feature is employed and investigated for feature computation from iris pattern. Here, the proposed system consists of three components: pre-processing, feature extraction and classification. During pre-processing, an iris portion is segmented out from an input eye image which is then normalized to extract features for segmented iris portion. Classification is the task either to verify or to identify a subject whose iris image is under consideration. Five benchmark iris databases, namely MMU1, UPOL, CASIA-v3-Interval, IITD and UBIRIS.v1, are used to test the performance of the proposed system. An extensive experimental result is demonstrated for each database and the results are compared with the existing methods. The results show the robustness and effectiveness of the proposed approach.
Chinese Term Extraction Based on PAT Tree

A new method of automatic Chinese term extraction is proposed based on Patricia (PAT) tree. Mutual information is calculated based on prefix searching in PAT tree of domain corpus to estimate the internal associative strength between Chinese characters in a string. It can improve the speed of term candidate extraction largely compared with methods based on domain corpus directly. Common collocation suffix, prefix bank are constructed and term part of speech (POS) composing rules are summarized to improve the precision of term extraction. Experiment results show that the F-measure is 74.97%.