Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic computer vision systems for aquatic research

Recently, there has been an increase in biological research interest in fish, and zebrafish, as an efficient model in the investigation of a broad range of human diseases and genetic studies. Economically, the enormous number, low price and limited maintenance requirements of this fish species encouraged the researchers to use it extensively. The larva of this animal is also considered to be promising subjects for research that is not subject to same strict legal requirements as the adult fish. The importance of this animal in research has increased the demand for developing new computer vision tools and methods that could help researchers to perform more related investigations as well as understand behaviour for different experimental tests. Computer vision is an efficient, economical and non-intrusive tool that can be applied to research in aquatic laboratories and aquaculture environments. However, in marine applications, this technology is still facing big challenges due to the free-swimming nature and unpredictable behaviour of the fish. This thesis presents a suite of novel and cost-effective tools for fish tracking and behavioural analysis, sizing, and identification of individual zebrafish. These main contributions this work is outlined briefly as follows. The first part of this work deals with stimulation and physical activity analysis for fish larvae, a novel robust and automated multiple fish larva tracking system is proposed. The system is capable of tracking twenty-five fish larvae simultaneously and extracting all physical activity parameters such as; speed, acceleration, path, moved distance and active time. The system is used for further studies throughout local occurrence behaviour recognition and studying the behavioural of the fish larvae following electrical, chemical and thermal stimulation. The proposed tracking system has been adopted in the biologists' aquatic laboratory to be used as a robust tool for fish behaviour analysis when fish are exposed to several types of stimulation. In the second part of the work, two novel practical and cost-effective models; orthogonal and stereo systems are designed and implemented to estimate the length of small free-swimming fish using off-the-shelf-components. The designed models are accurate and easy to adapt use for small experimental tanks in laboratory settings. The models have been thoroughly tested and validated experimentally. The third part of this thesis offers novel non-contact methods for recognition of individual free-swimming fish. Such systems can significantly reduce experts efforts and time required for fish tagging process and also offer a real-time recognition technique that can be alternative to the existing tagging methods used in this field. Through the purposes of this suite of novel computer vision tools and models, this thesis has provided successful solutions for behavioural analysis, fish sizing, individual fish recognition related research problems. The proposed solutions addressed major research problems and provided novel and cost-effective solutions for these problems.
"Doing" Agile versus "Being" Agile. Empirical Results from 250+ Projects

In numerous occasions Agile practitioners have warned about the negative aspects of adopting Agile tools and techniques, without implementing the primary practices of Agile. They have coined this observation as "doing" Agile, but not "being" Agile. However such warnings are opinion-based, as Agile practitioners have provided little to no empirical evidence that supports their recommendations. We mine 150 open source software (OSS) and 123 proprietary projects to investigate if empirical evidence exists for the phenomenon: "doing" Agile, but not "being" Agile. In particular, we investigate if the Agile technique of continuous integration (CI) influences bug and issue resolution, as well as commit patterns. According to our empirical analysis, for OSS projects, we observe the expected benefits after CI adoption, i.e., more bugs are resolved, and more issues are resolved. However, for the proprietary projects, we cannot make similar observations. Furthermore, we observe proprietary projects to "do" Agile, but not "be" Agile, as these projects use CI tools, without implementing the primary Agile practices for example, making frequent commits. We recommend practitioners not to use Agile techniques such as CI, without adopting the necessary practices.
Data-driven Modelling of Structured Populations: A Practical Guide to the Integral Projection Model

This book is a How To guide for modeling population dynamics using Integral Projection Models (IPM) starting from observational data. It is written by a leading research team in this area and includes code in the R language (in the text and online) to carry out all computations. The intended audience are ecologists, evolutionary biologists, and mathematical biologists interested in developing data-driven models for animal and plant populations. IPMs may seem hard as they involve integrals. The aim of this book is to demystify IPMs, so they become the model of choice for populations structured by size or other continuously varying traits. The book uses real examples of increasing complexity to show how the life-cycle of the study organism naturally leads to the appropriate statistical analysis, which leads directly to the IPM itself. A wide range of model types and analyses are presented, including model construction, computational methods, and the underlying theory, with the more technical material in Boxes and Appendices. Self-contained R code which replicates all of the figures and calculations within the text is available to readers on GitHub. Stephen P. Ellner is Horace White Professor of Ecology and Evolutionary Biology at Cornell University, USA; Dylan Z. Childs is Lecturer and NERC Postdoctoral Fellow in the Department of Animal and Plant Sciences at The University of Sheffield, UK; Mark Rees is Professor in the Department of Animal and Plant Sciences at The University of Sheffield, UK
Model selection for sparse high-dimensional learning

The numerical surge that characterizes the modern scientific era led to the rise of new kinds of data united in one common immoderation: the simultaneous acquisition of a large number of measurable quantities. Whether coming from DNA microarrays, mass spectrometers, or nuclear magnetic resonance, these data, usually called high-dimensional, are now ubiquitous in scientific and technological worlds. Processing these data calls for an important renewal of the traditional statistical toolset, unfit for such frameworks that involve a large number of variables. Indeed, when the number of variables exceeds the number of observations, most traditional statistical techniques become inefficient. First, we give a brief overview of the statistical issues that arise with high-dimensional data. Several popular solutions are presented, and we present some arguments in favor of the method utilized and advocated in this thesis: Bayesian model uncertainty. This chosen framework is the subject of a detailed review that insists on several recent developments. After these surveys come three original contributions to high-dimensional model selection. A new algorithm for high-dimensional sparse regression called SpinyReg is presented. It compares favorably to state-of-the-art methods on both real and synthetic data sets. A new data set for high-dimensional regression is also described: it involves predicting the number of visitors in the Orsay museum in Paris using bike-sharing data. We focus next on model selection for high-dimensional principal component analysis (PCA). Using a new theoretical result, we derive the first closed-form expression of the marginal likelihood of a PCA model. This allows us to propose two algorithms for model selection in PCA. A first one called globally sparse probabilistic PCA (GSPPCA) that allows to perform scalable variable selection, and a second one called normal-gamma probabilistic PCA (NGPPCA) that estimates the intrinsic dimensionality of a high-dimensional data set. Both methods are competitive with other popular approaches. In particular, using unlabelled DNA microarray data, GSPPCA is able to select genes that are more biologically relevant than several popular approaches.
Effects of Elasticity and Structure of Trabecular Bone on the Primary Stability of Dental Implants

This study evaluated the effects of elastic modulus and cellular characteristics of trabecular bone on the primary stabilities of dental implants. Artificial jawbone models with six values of elastic modulus (137, 123, 47.5, 22, 12.4, and 6.5MPa) comprising three kinds of trabecular structure (solid rigid, and cellular rigid with closed-cell and open-cell characteristics) were investigated in terms of the peak insertion torque value (ITV), implant stability quotient (ISQ), and Periotest value (PTV) as measured using a torque meter, Osstell(superscript TM) resonance frequency analyzer, and Periotest(superscript TM) electronic device, respectively. All specimens adhered to 2-mm-thick short-fiber-filled epoxy sheets to mimic the cortical shell, and screw-type implants were placed in the models for measuring the implant stability. In general, the ITV, PTV, and ISQ increased with the elastic modulus of cellular trabecular bone. The differences in the primary stabilities of the implants between closed-cell and open-cell bone specimens (12.4 versus 6.5MPa) were 52% for ITV, 2-fold for PTV, and 8% for ISQ. The regression correlation coefficient (R2) values between the elasticity of trabecular bone with a cellular (closed-cell) structure and ITV, PTV, and ISQ were 0.73 (with linear regression model), 0.93 (with linear regression model), and 0.89 (with logarithmic regression model), respectively. These linear and logarithmic models demonstrate the strong correlations of the elastic modulus of trabecular bone with the obtained ITV, PTV, and ISQ values. Trabecular bone with an osteoporotic structure decreases the primary implant stability. The use of cellular artificial jawbones as experimental models revealed that the elastic modulus and osteoporotic trabecular bone both influence the ITV, PTV, and ISQ primary implant stability parameters. Due to a similarity of trabecular structure, cellular characteristic of artificial bone might be more appropriate for obtaining accurate values of the primary implant stability than solid-bone blocks.
Nonlinear programming approach to biaffine matrix inequality problems in multiobjective and structured control

Some nonlinear minimization problems are proposed for obtaining a solution of the biaffine matrix inequality (BMI) problem. An algorithm is also proposed for solving the nonlinear minimization problems. The algorithm can be easily implemented with the existing convex optimization codes. This nonlinear programming approach can be applied to all multiobjective and structured control problems such as the simultaneous stabilization by static or dynamic output feedback, the mixed H/sub 2//H/sub /spl infin// control, and the decentralized control which can be reduced to BMI problems. The effectiveness is illustrated by numerical examples.
Effects of HRM Practices on it Usage

Since IT plays a critical role in leveraging or exploiting human and business resources, it is likely that HRM practices may have different effects on IT usage under the influence of different sources of IT capabilities. This study examines the moderating effects of the source of IT capability (Internal vs. External) on the relationship between HRM practices and IT usage. The results indicated that organizations with internal IT capability, HRM practices such as employee participation, clearly defined jobs and extensive formal training were significant in predicting IT usage. However for organizations that used external IT capability, only internal career opportunities was significant in predicting IT usage.
How Can I Get the Answer If I Don't Understand the Question?: Teaching Mathematics to English Language Learners

English Language Learners are challenged to be successful in content-area classes at the same time that they are learning English; in addition, the State of Ohio requires that these students pass the Ohio Graduation Test (OGT) in order to receive a high school diploma. Many high school English Language Learners lack the academic English needed to achieve success on the mathematics portion of this test. The purpose of this study was to determine whether or not explicit instruction in the vocabulary and language of mathematics for these students would result in their achieving proficiency on the OGT. During the seven weeks prior to the October 2008 test administration, I incorporated specific vocabulary and language strategies into mathematics instruction for a group of English Language Learners; I compared their scores on the October 2008 test with their scores on the March 2008 test. Although my students did not achieve proficiency on the October test, results of the study indicated that explicit vocabulary and language instruction did result in higher test scores for those students.
Results of Fitted Neural Network Models on Malaysian Aggregate Dataset

This result-based paper presents the best results of both fitted BPNN-NAR and BPNN-NARMA on MCCI Aggregate dataset with respect to different error measures. This section discusses on the results in terms of the performance of the fitted forecasting models by each set of input lags and error lags used, the performance of the fitted forecasting models by the different hidden nodes used, the performance of the fitted forecasting models when combining both inputs and hidden nodes, the consistency of error measures used for the fitted forecasting models, as well as the overall best fitted forecasting models for Malaysian aggregate cost indices dataset.
Privacy concerns and benefits in SaaS adoption by individual users

Software-as-a-Service, a new model of accessing software, presents individual users with tremendous opportunities to work and live conveniently and efficiently. Existing research has put much focus on understanding SaaS potential and risks for organizations and not much effort has been invested in understanding individual users, despite their market significance. This study aims at closing this gap by analyzing the cognitive mechanism through which individual users trade off privacy against the benefits that SaaS offers and identify salient benefits that trigger such trade-off. Based on the Theory of Reasoned Action, the Privacy-Trust-Intention model and cost-benefit theories, we developed a research framework that capture the trade-off between benefits and privacy and further analyzed data collected from an online survey of SaaS users in Korea with 290 respondents. Our findings indicate that individual users’ intention to use SaaS is negatively affected by privacy concerns and positively affected by overall perceived benefits in a relationship that is mediated by trust towards SaaS providers. Surprisingly, no direct, significant effect of privacy concerns on intention to use SaaS was found, whereas overall perceived benefits were found to have a direct effect on users’ intention to adopt SaaS. We found convenience to be the most salient benefit that users expect from adopting SaaS. Our study contributes to existing research by developing and empirically verifying a benefits-versus-privacy framework in a rather neglected segment of SaaS consumers in previous research. The findings also guide SaaS providers in mitigating privacy concerns; while efforts to minimize privacy will enhance users’ trust, it is of paramount importance to maximize salient benefits such as convenience that outweigh privacy concerns during users’ decision making to adopt SaaS.
Deforestation monitoring in tropical regions using multitemporal ERS/JERS SAR and INSAR data

The paper presents a study to assess the use of SAR data to map and monitor deforestation in Sumatra, a region characterized by rapid depletion of the rain forest. Two approaches, for forest/non-forest mapping, are compared: 1$Temporal change approach using ERS and JERS data, 2- Interferometric approach using coherence images from the ERS interferometric pairs. The results obtained from both approaches and both sensors are determined by comparing the results with SPOT HRV data.
Drug Abuse and Academic Performance: A Study of Gender Variation among Adolescent Students

As elsewhere in the world, youth in Nigeria engage in behaviours that are potentially dangerous. The extent of drug abuse among the young is a source of considerable concern. This article presents the results of a study, which sought to determine whether drug abuse among young Nigerian women and men affected their academic performance. Three hundred and seventy students (male=227 and female= 143, mean age; 16.34±.23) participated in the survey. Of this number, 141 students were identified as current drug abusers. Results from current users showed a 38.1% prevalence rate of drug abuse. Reported drug abuse was found to influence students' academic performance negatively, χ 2 (3) = 41.21, p < 0.001. Gender was found to be a significant predictor of reported drug abuse, χ (3) = 33.76, p < 0.001 with males reporting a greater degree of drug abuse. The study concludes that drug abuse predicts poor academic performance and calls for increased efforts to control this epidemic in young Nigerian men and women. Original Research Article Victor et al.; AIR, 14(4): 1-7, 2018; Article no.AIR. 40208 2
4-Hydroxy-3-[(2E)-3-(3,4,5-trimethoxyphenyl)prop-2-enoyl]-2H-chromen-2-one

A new chalcone of the coumarin, C21H18O7, containing an annulated α-pyrone ring, was obtained by condensation of the borate complex of acyl(hydroxy)coumarin with trimethoxybenzaldehyde. The structure exhibits intramolecular hydrogen bonding between the hydroxyl oxygen and the ketonic oxygen in the coumarin group. The bicyclic coumarin fragment and the benzene ring form a dihedral angle of 17.1 (4)°. The crystal packing involves dimers interconnected by C—H⋯O hydrogen bonding.
Recepción de la ficción televisiva norteamericana en España. El caso de Friends

Within the reception studies, this article is about the viewers of the situation comedy Friends in Spain. Taking into account a situation comedy, pleasure is regarded as a light viewing to laugh and forget about the problems. First of all, I will look into consume of this TV show. Why does the Spanish audience want to watch this comedy? Which kind of gratification do they get? Secondly, Spanish viewer’s attitude towards this “American universe” will be described. This research uses a combination of quantitative and qualitative methods to study the distinctive viewing of the Spanish fans of this TV sitcom. In the first phase, existing data comes from 2.494 online questionnaires. This analysis provides a context for the qualitative data that is based on two focus groups and ten in-depth interviews.
Optimization Algorithms for the Shelf Space Allocation Problem

In retail stores, shelves have a limited capacity to place products. Due to this reason, it is extremely important to manage them carefully. According to experimental studies, there are many in-store factors that can influence costumer purchases. Among these are the number of facings of a product (ie. visible items), its position on shelves (horizontal and vertical), the price and product adjacencies. To improve sales it is relevant for retailers to capitalize on these factors by optimizing the allocation of products on shelves. Although there are software applications available to help in shelf space planning, these still have a number of limitations and require significant human interaction. On the other hand, this problem has also been tackled in the literature under the name Shelf Space Allocation Problem. However, models and solution approaches can hardly be adapted to reality as they usually lack key practical constraints and require parameters hard to estimate. Inspired by the case of a Portuguese Supermarket Chain, two novel Biased Random-key Genetic Algorithms are proposed to tackle the Shelf Space Allocation Problem. The algorithms aim to allocate the products on shelves bearing in mind the best location and number of facings to boost their sales. Additionally, products are allocated within families that should form rectangular shapes on the shelves. The main difference between both algorithms lies in the way these families of products are placed on the shelves. The algorithms are tested with real case study instances. Among the key benefits of both approaches is their applicability in practice, both in terms of constraints and execution times.
Automating Text Processing Using Analytics - Automating text classifications and financial news parsing

Automating repetitive processes and replacing manual tasks with automated systems is an area of research that will greatly impact and transform our lives during the 21st century. Automation comes in many forms and we are now at the start of an era, after which repetitive non-creative tasks will be handled mainly by machines. In this thesis, two analytics approaches are presented that can be used to automate text processing tasks. The first is an automation approach using machine learning in which we show how we can improve text classification performance, and how we, through these improvements, can reach practically acceptable performance levels even in certain abstract classification problems. We test the developed methods on problematic web content categories, such as violence, racism, and hate. The second is an automation approach that uses network analytics to automatically process texts. We use this approach to automate processing of financial news and to automatically extract new information. We show that through automating the process, we can extract company specific sentimentrisks that a person would not identify simply by reading the news articles. Lastly, we show that the risks we have extracted can be used to identify companies that are at higher risk of stock price decrease.
Reverse Channel Training in Multiple Antenna Time Division Duplex Systems

Multiple-Input Multiple-Output (MIMO) communication using multiple antennas has received significant attention in recent years, both in the academia and industry, as they offer additional spatial dimensions for high-rate and reliable communication, without expending valuable bandwidth. However, exploiting these promised benefits of MIMO systems critically depends on fast and accurate acquisition of Channel State Information (CSI) at the Receiver (CSIR) and the Transmitter (CSIT). In Time Division Duplex (TDD) MIMO systems, where the forward channel and the reverse channel are the same, it is possible to exploit this reciprocity to reduce the overhead involved in acquiring CSI, both in terms of training duration and power. Further, many popular and efficient transmission schemes such as beamforming, spatial multiplexing over dominant channel modes, etc. do not require full CSI at the transmitter. In such cases, it is possible to reduce the Reverse Channel Training (RCT) overhead by only learning the part of the channel that is required for data transmission at the transmitter. In this thesis, we propose and analyze several novel channel-dependent RCT schemes for MIMO systems and analyze their performance in terms of (a) the mean-square error in the channel estimate, (b) lower bounds on the capacity, and (c) the diversitymultiplexing gain tradeoff. We show that the proposed training schemes offer significant performance improvement relative to conventional channel-agnostic RCT schemes. The main take-home messages from this thesis are as follows: • Exploiting CSI while designing the RCT sequence improves the performance. • The training sequence should be designed so as to convey only the part of the CSI required for data transmission by the transmitter. • Power-controlled RCT, when feasible, significantly outperforms fixed power RCT.