Miracle-dz/UniFAR-FTUs huggingface dataset:
Matching evaluation based on image content discriminative features for different image types

ABSTRACT Search by image has taken the place of the prior wordy approach, considering spatial image comparison in the feature space. Several query image forms are available. Sketch-to-real image matching is very challenging problem, which is commonly based on edge feature detectors to extract features from sketches. However, they are inconvenient, and it is crucial to select different methods. Thus, methods assessment is conducted for similarity matching between sketched and real images for different drawing contents and clarity levels. The assessment approach includes comparing feature keypoints gathered using the Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) methods. In addition, the comparison includes matching of objects inside High-Deﬁnition (HD) images and 3D images based on their generated features. The textual-Hcontent image search is another search type that is frequently used in brand logo cases. Hence, it is presented for text-logo image search based on visual features. Besides, the motives and downsides of the considered methods are declared, including the space and time complexities within test cases.
The Effect of Citizenship Education and Family Life Climate Learning on the Responsibility of Students in Distance Learning (

The COVID-19 pandemic has provided a fairly heavy experience for the world of education in Indonesia. This is a challenge for the education sector in Indonesia, which of course must implement the distance learning method which is always a fairly new thing for all elements of implementing education at the school level. Due to this, there are many obstacles faced in the implementation of distance learning which are especially related to the attitude of responsibility of students. The purpose of this research in general is to reveal and provide an overview of the effect Citizenship Education and the climate of family life on the attitude of responsibility of students in distance learning. This study uses a quantitative approach with a cross-sectional survey method. The research instrument used was a questionnaire which was measured using a Likert scale. This study describes the results of the analysis of the effect Citizenship Education and the climate of family life on the responsibility of students in distance learning. The results of data analysis showed that Citizenship Education had a strong and positive inﬂuence on the attitude of responsibility of students in distance learning as well as for the climate of family life had a strong and positive inﬂuence on the attitude of responsibility of students in distance learning. Citizenship Education and the climate of family life have a positive and signiﬁcant inﬂuence on the attitude of responsibility of students in the implementation of distance learning.
Active contour model based on variable exponent p-Laplace equation for image segmentation

ABSTRACT In this paper, we propose a region-based active contour model for image segmentation. By combining the region fitting energy based on coefficient of variation with the variable exponent p-Laplace energy, the proposed method can perform well in segmenting complex images. The region fitting energy conducts the evolving curve to reach the boundaries of the objects, and the p-Laplace energy can handle the topological changes and extract the boundaries accurately. In order to eliminate the re-initialization step, an augmented Lagrangian method is employed to solve the optimization problem. The results of experiments on synthetic and real images demonstrate that our method can successfully segment complex object boundaries, and it is robust to noise and not sensitive to the initial position of contours.
The Impact of Air Fences Geometry on Air Flow around an ICE3 High Speed Train on a Double Line Railway Track with Exposure to Crosswinds

Reduction in weight adjoined with the increase in the railway vehicle speed of travel added to the deteriorating effects of the crosswinds on the running behavior of high speed trains. During the past decade, many researchers have concentrated on examining the aerodynamic force and moment coefficients for the trains. Varieties of studies regarding the effects of crosswinds on the trains are accomplished. However, the need to restrain strong winds from disturbing trains running safety is not completed. This research is concerned with finding a proper solution for attenuating the worrying effects of the winds that hit the trains. Installation of air fences on the sides of the railway tracks is investigated. To serve the purpose, a variety of air fences with different heights, with and without the edges on top of the fences, at a variety of the edge lengths and angles are studied. The study covers double routed railway track while the air fences are installed on either side of the track. The train can be on the leeward or windward line. The problem solving is based on the Lattice-Boltzmann method. This research pioneers in using this method for the said purpose. It is found that by inserting the fences and increasing their heights for up to 1m, the drag forces decrease to 40 percent and the rolling moment coefficients decrease to 15 percent. The presence of the edge can also decrease the drag force for about 55 to 120 percent and decrease the rolling moment coefficients for about 30 to 115 percent in some cases. Variations in percentages of reduction are due to the different angles and the lengths of the edges.
Measuring students' generic skills through national assessment

This study aims to measure the generic skills (GS) of Madrasah Aliyah (MA) students in Indonesia. Respondents who participated in this study were madrasah students scattered in East Java and Central Java as representatives of MAs in Indonesia. The method used in this research is a survey distributed via Google Form. Three dimensions are mentioned in the research instrument for measuring GS: religious moderation, critical and creative thinking skills, and interpersonal skills. Using SPPS version 16 software, a descriptive analysis was employed as the data analysis technique. The results of this study showed that the GS of students with religious moderation indicators obtained the highest average of 54.03% on the Likert scale 4. Critical and creative thinking skills indicators obtained the highest average results, 67.99%, on the Likert scale 4. The indicator of interpersonal skill obtained the highest average of 55.88% on the Likert 4 scale. Hence, it is expected of educational institutions to implement policies to enhance the GS of MA students, particularly in creating a more student-oriented learning model, also known as student-centered learning.
Coloniality of Brown Booby (Sula leucogaster) in Gorgona National Natural Park, Eastern Tropical Pacific

Resumen. – Colonialidad del Piquero Cafe ( Sula leucogaster ) en el Parque Nacional Natural Gor-gona, Pacifico Oriental Tropical. – Se han planteado muchas hipotesis para explicar como la cria colo-nial puede beneficiar a los individuos que la practican, pero existe debil soporte para estas y ninguna pareceser una explicacion generalizable a todas las colonias. De octubre de 2002 a octubre de 2003 se intento pre-cisar cual de las hipotesis para explicar la colonialidad (centro-periferia, centro-satelite o al azar) se ajustabamas al Piquero Cafe en Isla Gorgona (Colombia). Se encontro una poblacion reproductiva de 300 indivi-duos criando asincronicamente. Los nidos presentaron un patron de disposicion espacial agregado condistancia promedio al nido mas proximo de 1,6 m. El exito de eclosion y el exito reproductivo anual fueronde 28,2% y 17,3% respectivamente. El modelo centro–periferia, donde la variacion en supervivencia enuna colonia se origina porque los individuos centrales son menos accesibles a depredadores, son de mejorcondicion fisica y tienen mayor exito reproductivo, no se ajusto a las colonias del Piquero Cafe en Gor-gona. La disposicion espacial de la calidad de pareja mostro una clara autocorrelacion espacial negativa enel primer intervalo de distancias (0 a 1,5 m), lo cual indica que hubo atraccion de parejas de alta calidadsobre parejas de baja calidad, ajustandose esta prediccion al modelo centro–satelite y a un patron de dispo-sicion de nidos agrupado. Este modelo, que predice la atraccion de individuos de baja calidad alrededor delos mejores sitios de crianza ya ocupados por individuos de alta calidad, fue consistente durante todo elano. En consecuencia, la hipotesis de reduccion de la depredacion y el modelo centro–periferia derivadode la misma no son generalizables a todas las colonias de aves marinas.
Futurizing the Intellectual Capital Theory

Intellectual capital theory (IC) theory, which asserts that organisational value is for the most part created with intangible, knowledge-based resources, has become a prevalent way to approach the notion of business viability. Most IC research leans on the classical tripod of IC components, laid down by the first-generation researchers in the field. This tripod divides the value-generating knowledge assets into human capital, structural capital and relational capital; or, more simply put the value vested in an organisation’s personnel, internal structures and processes, and relationships. Even though this conceptualization has been challenged by some, it still remains the cornerstone of the IC-based view of the firm, and it is astutely followed by most researchers in this field. However, various large-scale technological, socio-political, and institutional changes have fundamentally changed the business environment and worklife in recent years. In this paper we argue that these changes call for a critical examination of the relevance of the classical conceptualizations of IC. It may be necessary to update the understanding concerning not only the most essential knowledge resources, but also the structure of IC and the way in which IC is related with new relevant organisational capabilities and aspects of organisational performance. The aim of this theoretical paper is to provide an overview of some of the most essential recent worklife trends that are likely to impact how IC should be understood: digitalization, remote work, gig work, open innovation, crowdsourcing, strive towards sustainability and resilience. Based on these, the paper puts forth a set of propositions concerning the needed novel perspectives for IC. In addition to constructing a novel theoretical framework and propositions for IC research, the paper functions as an introduction to the mini track on Futurizing the Intellectual Capital Theory and provides a common framework on which mini track participants can base their dialogue.
Outdoor recreation participation in the United States - projections to 2060: a technical document supporting the Forest Service 2010 RPA Assessment

Please use the following links to download the 4 appendices associated with this publication: Web Appendix A Web Appendix B Web Appendix C Web Appendix D We developed national projections through 2060 of participation for 17 outdoor recreation activities. The projections were made under futures that vary by population growth, socioeconomic conditions, land use changes, and climate. We used a two-step approach to project the number of participants and the days of participation. The estimation step yielded national level statistical models of adult participation rate and days of participation by activity. The simulation step combined the models with external projections of explanatory variables at 10-year intervals to 2060. Per capita estimates for participation and days were then combined with population projections to derive estimates of participants and days of participation by activity. Results were derived across three 2010 Resources Planning Act Assessment scenarios that each feature three associated climate futures. Findings indicated that outdoor recreation will remain a key part of the social and economic fabric of the United States. In the absence of climate change, the number of participants in the 17 recreation activities is projected to increase over the next 5 decades. In some cases, the participation rate will decline, but population growth will ensure that the number of participants increases. Some climate futures led to projected declines in participants, e.g., snowmobiling and undeveloped skiing showed declines in participant numbers up to 25 percent, despite population growth. Climate was also shown to have disparate effects on projections of annual days of participation, particularly for snowmobiling, undeveloped skiing, and hunting.
Impact of Machine Learning in Music Genre Classification using CNN

The rapid expansion of available music tracks has made the classification of music genres increasingly crucial. To achieve accurate genre predictions, machine learning techniques have been widely employed and proven highly efficient. In this study, we focused on enhancing the accuracy of music genre classification systems by training a Convolutional Neural Network (CNN) model using Keras. Music genre classification serves as a valuable tool for music learning, offering a reliable method of categorization. Given the immense amount of data and the inherent complexity in identifying music genres, we leveraged the power of deep neural networks to classify ten distinct genres. To evaluate the effectiveness of our approach, we compared the performance of the CNN model to other existing models using the GITZAN dataset. Our results demonstrated the success of the CNN model in accurately classifying music genres, surpassing previous studies while requiring less data. This research contributes to the field of music genre classification by providing an advanced and efficient solution using deep learning techniques.
A review of machine learning models applied to genomic prediction in animal breeding

The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there’s no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.
Bootstrapping compositional generalization with cache-and-reuse

People effectively reuse previously learned concepts to construct more complex concepts, and sometimes this can lead to systematically different beliefs when the same evidence is processed in different orders. We model these phenomena with a novel Bayesian concept learning framework that incorporates adaptor grammars to enable a dynamic concept library that is enriched over time, allowing for caching and later reusing elements of earlier insights in a principled way. Our model accounts for unique curriculum-order and conceptual garden-pathing effects in compositional causal generalization that alternative models fail to capture: While people can successfully acquire a complex causal concept when they have an opportunity to cache a key sub-concept, simply reversing the presentation order of the same learning examples induces dramatic failures, and leads people to complex and ad hoc concepts. This work provides an explanation for why information selection alone is not enough to teach complex concepts, and offers a computational account of how past experiences shape future conceptual discoveries.
Exploring Proteus mirabilis Methionine tRNA Synthetase Active Site: Homology Model Construction, Molecular Dynamics, Pharmacophore and Docking Validation

Currently, the treatment of Proteus mirabilis infections is considered to be complicated as the organism has become resistant to numerous antibiotic classes. Therefore, new inhibitors should be developed, targeting bacterial molecular functions. Methionine tRNA synthetase (MetRS), a member of the aminoacyl-tRNA synthetase family, is essential for protein biosynthesis offering a promising target for novel antibiotics discovery. In the context of computer-aided drug design (CADD), the current research presents the construction and analysis of a comparative homology model for P. mirabilis MetRS, enabling development of novel inhibitors with greater selectivity. Molecular Operating Environment (MOE) software was used to build a homology model for P. mirabilis MetRS using Escherichia coli MetRS as a template. The model was evaluated, and the active site of the target protein predicted from its sequence using conservation analysis. Molecular dynamic simulations were performed to evaluate the stability of the modeled protein structure. In order to evaluate the predicted active site interactions, methionine (the natural substrate of MetRS) and several inhibitors of bacterial MetRS were docked into the constructed model using MOE. After validation of the model, pharmacophore-based virtual screening for a systemically prepared dataset of compounds was performed to prove the feasibility of the proposed model, identifying possible parent compounds for further development of MetRS inhibitors against P. mirabilis.
110-GHz PLL Frequency Synthesizer With High-Efficiency Voltage-Controlled Oscillator

A high-efficiency frequency synthesizer operating at 110 GHz with an optimized voltage-controlled oscillator (VCO) is proposed in this article. The proposed synthesizer adopts a subsampling loop (SSL) to reduce the in-band phase noise under steady-state conditions. A new VCO design method is proposed to solve the low output efficiency problem in high-frequency oscillators. First, the maximum efficiency operation condition for a transistor in an oscillator is analyzed, and the method to construct the peripheral passive network with ideal devices is presented. Second, the requirement of the multiple power outputs for the VCO in a synthesizer and the loss of the passive devices are considered in the optimization process. Finally, quantitative analysis is performed with consideration of the real physical implementation and the parasitic influence of the VCO. Based on the above VCO design method, a 110-GHz frequency synthesizer with low phase noise and high efficiency is designed. The frequency synthesizer is implemented using 65-nm CMOS technology. It consumes only 14 mW of dc power to generate −6 dBm of output power. The experimental phase noise of the frequency synthesizer is t102.01 dBc/Hz at a 10-MHz offset.
Why don’t Americans trust university researchers and why it matters for climate change

Scientists have developed a strong consensus that Earth’s climate is changing and that human activities play an important role in these changes. However, current research shows that in the United States, there is significant partisan polarization on climate change and its causes, leading to climate denialism. In this paper, we shed light on the political and social determinants of climate action. Using a May 2022 nationally representative survey of American registered voters (n = 2,096), we examine the multivariate correlates of trust in university research and opinions about climate change. Our results confirm that segments of the American electorate do not believe climate change is a problem for the United States and that climate change is not a consequence of human activities. But we also show that part of the problem regarding climate denialism is a lack of trust in university research. We argue for a comprehensive four-stage research strategy based on the empirical results. First, more research must be done to understand who trusts or distrusts university research on climate change and who is persuadable. Second, more research is needed on climate communication framing and messaging. Third, additional research on appropriate messaging is necessary. Finally, we need to develop a culture of trust in climate research and how it is communicated across society.
How Can the Irish Construction Industry Become Less Fragmented and More Productive?

Globally the Construction Industry is in a fragmented state and the Irish Construction Industry (ICI) is no exception. The ICI has a history of delivering National infrastructure projects over time and over budget. Irish Government publications have identified that low productivity within the ICI is a prevalent issue. Lean construction (LC) is proposed in this research as an antidote to assist the ICI in becoming less fragmented and more productive. Research indicates that successful incorporation of lean methodologies can cut completion times by 30% and costs by 15%. Further research demonstrates that Construction Industries (CI) are lacking the education required to implement lean methodologies. There is a lack of awareness within the ICI regarding the principles and benefits of lean. Lean Construction Ireland (LCI) membership numbers are increasing at a glacial pace. Traditional project management techniques that are autocratic in nature and focus on critical path items and milestones are extensively used in the ICI. These techniques prioritize measuring metrics such as commercial performance which means measuring other performance related metrics such as workflows slip down the order of seniority. This approach conflicts with the principles of lean and limits the opportunity for continual improvement. There is therefore a requirement for government, management, leaders and all project stakeholders to change their way of thinking regarding their project management techniques and embrace change. Aligning these stakeholders will strengthen the entire Irish Construction Supply Chain and only then will the benefits of lean be fully realized. Building Information Modelling (BIM), whilst not technically a lean tool shares multiple characteristics of lean and is further vehicle that can assist in this transition. The results suggest that with enhanced education in lean, alignment of project stakeholders, an overhaul of traditional project management techniques and heavier incorporation of BIM that a shift towards lean and higher productivity is possible for the ICI.