Miracle-dz/UniFAR-FTUs huggingface dataset:
Misuse of Police Information Systems: Predicting Perceived Likelihood of Misuse among Unsworn Police Employees

Police information systems (ISs) contain highly sensitive data. Misuse of these systems poses a significant risk to agency integrity and personal privacy. In order to prevent this form of misconduct, police agencies must implement strategies that address factors associated with employee misuse. The aim of this research was to determine factors associated with the perceived likelihood of engaging in IS misuse among a sample of employees from an Australian police agency. Two hundred and eighty-seven unsworn police staff completed an online survey that investigated a number of factors, shown in previous research, to be relevant to IS misuse. Findings demonstrated that access to the IS, perceived appropriateness of and concern regarding IS misuse, and perceived certainty of punishment were relevant in explaining the perceived likelihood of IS misuse. Implications are discussed in relation to the focus of agency prevention strategies and the need for future research in the area.
Medidas de potência e energia em atividades físicas por meio de sensores inerciais

Nowaday at gyms, some physical exercises are performed in such a way that is not possible to obtain precise datas of power and energy expenditures, in this way limiting the evaluation and control of the physical activity. Taking this problem into consideration, this work aims to introduce a cheap solution based in the use of inertial sensors and personal computers to obtain real time feedbacks in some physical activities. Due the inertia sensors limitations, it is needed the search for a sensor fusion algorithms that is proper to be used in real time application and to limit the exercises for those that can be modelated as cyclic semicircular mouvements. So its also necessary the biomechanical modeling from those activities and the use of goniometric parameters to obtain the desired estimations. Key-words: IMU. Sensor Fusion. Eletrogoniometer.
De Facto Shareholder Primacy

For generations, scholars have debated the purpose of corporations. Should they maximize shareholder value or balance shareholder interests against the corporation’s broader social and economic impact? A longstanding and fundamental premise of this debate is that, ultimately, it is up to corporations to decide. But this understanding is obsolete. Securities law robs corporations of this choice. Once corporations go public, the securities laws effectively require that they maximize share price at the expense of all other goals. This Article is the first to identify the profound impact that the securities laws have on the purpose of public firms — a phenomenon that it calls “de facto shareholder primacy. ” The Article makes three primary contributions to the literature. First, it provides a rich and layered account of de facto shareholder primacy. The phenomenon is not the result of considered legislation and regulatory decision. Rather, hedge-fund activists leverage the transparency that the securities laws afford to identify, and force companies to adopt, strategies that increase share prices. Their activities cast a shadow over the public market. Because firms must maximize share prices or face costly, disruptive, and protracted battles with activist hedge funds, they preemptively focus solely on stock values. The activists’ novel and opportunistic use of the securities laws has transformed the regulatory apparatus into a powerful lever of shareholder primacy. Second, this Article shows how this distortion of the regulations causes harm. The activities of activists bring the laws into conflict with principles of federalism and private ordering, which hurts entrepreneurs, investors, and equity markets. Finally, to address these concerns, the Article recommends a small change to the securities laws that would end hedge-fund activism and thereby disentangle the securities laws from corporate purpose.
Impact of a flipped classroom on academic achievement and perception among first year nursing students

Background: Flipped classroom has generated significant concern in nursing education in recent years, particularly in higher education. It can provide an innovative solution to the unmet challenges of traditional classroom. Using video helps students to learn content at their own pace and use their time in the classroom to attack difficult problems. Aim of the study: to study the impact of a flipped classroom on academic achievement and perception among first year nursing students.Methods: A quasi-experimental design was utilized. Setting: The study was conducted at Faculty of Nursing of Menoufia University. Subjects: A convenient sample of all first year undergraduate students (360) who were admitted to Faculty of Nursing and were included in fundamental of nursing course of the academic year 2017-2018 throughout second term. They were divided alternatively and randomly into two groups, 180 for each, group I: Exposed to the flipped classroom and group II: Exposed to the traditional lecture classroom. Tools: Three tools were used for data collection. Tool I: Structural interviewing questionnaire, tool II: student perception of flipped classroom and tool III: student achievement tool.Results: The results revealed that the study group achieved higher level of perception towards flipped classroom after the first aid unit and the majority of them (87.2%) did not have any prior experience of flipped classroom. 58.9% of the study group were strongly agree to appreciate learning with video and about two thirds of them (63.9%) strongly agree that it was easier and more effective to learn using flipped classroom. 65% of the study group were strongly agree that video made them learn more and about half of them (57.2%) strongly agree that video could replace traditional lectures completely. The means total score of students’ achievement were 22.22 ± 5.09 and 19.77 ± 5.50 among study and control group respectively.Conclusions: Students shared in the flipped classroom (study group) achieved higher level of perception. The study group had higher level of achievement scores than those in the control group.Recommendations: Similar studies should be carried out with a bigger sample in various courses and at various educational stages to generalize the findings.
Is this risky? Accessing Digital Literacy Level among Young Adult's in TRNC

Young adulthood is an important development time period for humans where majority of them spend most of their time with technology. Which constitute all the millennials of whom are digital natives. Most of the people in this category pride themselves as tech-savvy and are so immersed in technology to the extent that drawing a boundary between offline and online is difficult if not impossible. This overconfidence most often than not leads this young people into committing risky behaviors online. Global research institute interested with children are now paying a very close attention when it comes to children’s right in the digital world. Whereas internet policy was design to suit adult internet users. leaving the young adult vulnerable. This research work explores the digital world of this young adults by conducting a mixed method research where open-ended questionnaires were given to 512 randomly selected undergraduate students of Near East University of the Turkish Republic of Northern Cyprus and an interview was conducted to 50 randomly selected students among the 512 for the purpose of data collection. With the aim of associate evidence with the continued global discourse regarding rights and wellbeing of people in the digital world with special focus on internet access, level of digital literacy, technological usage and applications, risk, vulnerabilities, mediation and other online habits. The data collected was analyzed using SPSS software version 20. The questionnaire shows high reliability with Cronbach alpha at 0.89. The result shows that young adults of the Turkish Republic of Northern Cyprus are conscious of their online behavior although majority confessed to not undergoing digital literacy classes. It was also noted that majority of the young adults accept friendship requests from everyone but will really agree to meet social media friends in real life. Majority of the respondents say they are conscious of what they share online and the data they gave but a significant number of them confess to vulgar chats.
Classification of Lung Nodules Using Deep Learning

Deep learning methods such as the Convolutional Neural Network and the Residual Network were applied to CT scan images in order to classify whether lung nodules become cancerous or not. Especially, the effect of changing the number of layers in the Residual Network was. Experiment were carried out using several models having these two network architectures and consisting of different numbers of layers and parameters.
A unified framework for dissecting the effects of common signals on functional and effective connectivity analyses: power, coherence, and Granger causality

When analyzing neural data it is important to consider the limitations of the particular experimental setup. An enduring issue in the context of electrophysiology is the presence of common signals. For example a non-silent reference electrode adds a common signal across all recorded data and this adversely affects functional and effective connectivity analysis. To address the common signals problem, a number of methods have been proposed, but relatively few detailed investigations have been carried out. We address this gap by analyzing local field potentials recorded from the small brains of fruit flies. We conduct our analysis following a solid mathematical framework that allows us to make precise predictions regarding the nature of the common signals. We demonstrate how a framework that jointly analyzes power, coherence and quantities from the Granger causality framework allows us to detect and assess the nature of the common signals. Our analysis revealed substantial common signals in our data, in part due to a non-silent reference electrode. We further show that subtracting spatially adjacent signals (bipolar rereferencing) largely removes the effects of the common signals. However, in some special cases this operation itself introduces a common signal. The mathematical framework and analysis pipeline we present can readily be used by others to detect and assess the nature of the common signals in their data, thereby reducing the chance of misinterpreting the results of functional and effective connectivity analysis.
Design of Silent Actuators using Shape Memory Alloy

In this paper we present a design for an actuator using a shape memory alloy called flexinol. This actuator can be used to mimic the movements of various muscle groups for robotic applications. This design takes advantage of the properties of shape memory alloys, especially flexinol and its high contraction force. The use of flexinol enables the device to be compact and be highly reliable. We also present an efficient way of controlling the movement of the shape memory alloy using pulse width modulation. The designs are to be embedded inside an artificial facial skin of a robot to expresses human like emotions in robots, in particular, as artificial muscle design for human cheek muscles and the muscles which are used only for linear movement of the human skin.
Mobility Prediction for Multimedia Services

Advances in technology have enabled a proliferation of mobile devices and a broad spectrum of novel and out breaking solutions for new applications and services. In the present, more and more people and companies are demanding mobile access to multimedia services such as real-time rich media. Today, it is necessary to be able to predict adaptation behaviour which concerns and addresses not only the mobile usage or the infrastructure availability, but the service quality especially the continuity of service. Our chapter provides insight to new challenges of mobile multimedia services and applications: Wifi indoor positioning system adapted to heterogeneous building, static and learning mobility prediction, predictive handover policy for multimedia cache management, mobile multimedia guide (such as museum), and network scalability. Mobility Prediction for Multimedia Services
Can topic models be used in research evaluations? Reproducibility, validity, and reliability when compared with semantic maps

We replicate and analyze the topic model which was commissioned to King’s College and Digital Science for the Research Evaluation Framework (REF 2014) in the United Kingdom: 6,638 case descriptions of societal impact were submitted by 154 higher-education institutes. We compare the Latent Dirichlet Allocation (LDA) model with Principal Component Analysis (PCA) of document-term matrices using the same data. Since topic models are almost by definition applied to text corpora which are too large to read, validation of the results of these models is hardly possible; furthermore the models are irreproducible for a number of reasons. However, removing a small fraction of the documents from the sample—a test for reliability—has on average a larger impact in terms of decay on LDA than on PCA-based models. The semantic coherence of LDA models outperforms PCA-based models. In our opinion, results of the topic models are statistical and should not be used for grant selections and micro decision-making about research without follow-up using domain-specific semantic maps.
Investigation of Critical Factors that Perturb Business-IT Alignment in Organizations

Business executives around the globe have recognised the significance of information technology (IT) and started adopting IT in their business processes. Firms always invest in adopting latest technologies in order to comply with the customer requirements despite of heavy investment companies are unable to avail optimum benefits from the underpinning technologies. Consequently IT does not support business the way it should have been and hence a misalignment between business and IT is created. In the current study various factors in a Saudi financial institution have been discussed and assessed that perturb alignments in both the entities. In the research study questionnaire approach has been used which is an effective tool to collect qualitative data. Finally, some recommendations have been suggested to bring business and IT into an alignment.
Adjektiva dan Adverbia Dalam Bahasa Indonesia

Penelitian ini bertujuan mengisi kerumpangan deskripsi adjektiva dan adverbia yang terdapat dalam buku Tala Bahasa Baku Bahasa Indonesiaedisi ketiga sehingga hasilnya diharapkan dapat dipertimbangkan sebagai masukan agar buku itu semakin lengkap uraiannya dan sempurna dalam penyajiannya. Penelitian ini akan membatasi diri pada telaah adjektiva dan adverbiadalam bahasa Indonesia, terutama berkenaan dengan hal yang belum dibahas dalam Tala Bahasa Baku Bahasa Indonesia secara tuntas. Misalnya, pengelompokan adjektiva dan adverbia bukan hanya dari segi semantis saja, melainkan juga perlu dilihat dari sudut bentuk. Selain itu, perilaku sintaktis adjektiva dan fungsi adverbia juga akan diamati lebih mendalam.
Brief Survey on Cross-Language Opinion Mining using Target Extraction

: Opinion target extraction is a subtask of opinion mining which is very useful in many applications. The problem has usually been solved by training a sequence labeler on manually labeled data. In this study, the problem in a cross-language scenario in which, can be take decisions the upscale labeled data (Rich labeled) in a source language for opinion target extraction in a different target language. The English labeled compilation is used as training set. The scenarios generate two Hindi training datasets with different features. Two labeling models for Hindi opinion target extraction are learned based on Conditional Random Fields (CRF). Most existing work on multi lingual sentiment analysis has focused on methods to adapt sentiment resources from resource-rich languages to resource-poor languages. The scenario have expressed a novel approach for joint bilingual sentiment classification at the sentence level that augments available labeled data in each and every language with unlabeled parallel data. The lack of Hindi sentiment collection limits the research progress on Hindi sentiment classification. However, there are many freely available English sentiment collections on the Web.
Active Learning With Unreliable Annotations

With the proliferation of social media, gathering data has became cheaper and easier than before. However, this data can not be used for supervised machine learning without labels. Asking experts to annotate sufficient data for training is both expensive and time-consuming. Current techniques provide two solutions to reducing the cost and providing sufficient labels: crowdsourcing and active learning. Crowdsourcing, which outsources tasks to a distributed group of people, can be used to provide a large quantity of labels but controlling the quality of labels is hard. Active learning, which requires experts to annotate a subset of the most informative or uncertain data, is very sensitive to the annotation errors. Though these two techniques can be used independently of one another, by using them in combination they can complement each other’s weakness. In this thesis, I investigate the development of active learning Support Vector Machines (SVMs) and expand this model to sequential data. Then I discuss the weakness of combining active learning and crowdsourcing, since the active learning is very sensitive to low quality annotations which are unavoidable for labels collected from crowdsourcing. In this thesis, I propose three possible strategies, incremental relabeling, importance-weighted label prediction and active Bayesian Networks. The incremental relabeling strategy requires workers to devote more annotations to uncertain samples, compared to majority voting which allocates different samples the same number of labels. Importance-weighted label prediction employs an ensemble of classifiers to guide the label requests from a pool of unlabeled training data. An active learning version of Bayesian Networks is used to model the difficulty of samples and the expertise of workers simultaneously to evaluate the relative weight of workers’ labels during the active learning process. All three strategies apply different techniques with the same expectation – identifying the optimal solution for applying an active learning model with mixed label quality to
Dynamic analysis of survival models and related processes

This thesis presents new methods of analysis of survival data based on a Dynamic Bayesian approach. The models allow the parameters to change with time. The analysis is tractable and emphasises predictive aspects of the models. The survival problems covered include linear and non-linear regression, analysis of random samples, time-dependent covariates, life tables and competing risks. The analysis is also extended to a number of point processes. Numerical applications are provided and the microcomputer software to perform them is described.
Most common injuries of the musculoskeletal system among children of elementary school age who engage in gymnastic sports (aerobics, artistic or rhythmic gymnastics) at an elite level

Background. The main objective was to find out which musculoskeletal system problems (pain and/or injuries) are the most common among girls of elementary school age who engage in gymnastic sports at an elite level. We were also interested in the influence of the success of girls in gymnastics competitions on problems of the musculoskeletal system. In connection with the pain and injuries of the musculoskeletal system, we asked whether the gymnasts were monitored professionally on the medical side, whether they regularly engaged in primary or secondary physiotherapeutic care. Methods. The data were obtained by combining qualitative and quantitative research in the form of a structured interview and a questionnaire. Retrospective data were collected. The research group consisted of 58 girls (average age 13.5 years) engaging in gymnastic sports at an elite level. Results. A musculoskeletal system problem occurred in 98% female gymnasts during their elementary school age. Their lower limbs, specifically the knees and ankles, appeared to be the most susceptible parts of the musculoskeletal system. Multiple musculoskeletal system problems were more frequent than single isolated problems. As far as the correlation between the incidence of pain/injury and the girls’ individual performance level is concerned, gymnasts assuming non-medal positions had a higher incidence of pain and injuries than the medal-winning gymnasts. The higher incidence of injuries in non-medal gymnasts compared to medal gymnasts was confirmed by statistical analysis. Physiotherapeutic prevention, whether primary or secondary, was given a minimum number of gymnasts. AUC_Kinanthr_1_2019_6644. indd 10 20.06. 19 12:55 11 Most common injuries of the musculoskeletal system among children of elementary school age Conclusion. The results of this study demonstrate a high susceptibility to painful conditions and injuries especially of lower limbs of elite level gymnasts at their younger school age. It also proved statistically significant that gymnasts who are worse off with performance always have a higher incidence of problems with the musculoskeletal system. These results are also probably due to the fact that most of the interviewed gymnasts are not in physiotherapy or other professional care.
PREDIKSI BISNIS FOREX MENGGUNAKAN MODEL NEURAL NETWORK BERBASIS ADA BOOST MENGGUNAKAN 2047 DATA

After conducting research and experiments then showed the first research that has been done using Algorithm Neural Network Backpropagatioan using data from as many as 268 menunjungkan accuracy rate error prediction when the prediction per 5 minutes by 0.758619403, when using the data as much as 2047 showed levels of accuracy error prediction for 0.500161212 and the results of a second study was done using Algorithm Optimization Adaboost in the process of trainning and coupled Neural Network Backpropagation in the learning process indicates the level of accuracy error prediction when the prediction per 5 minutes uses as much data as 268 by 0.397014925, when using the data as much as 2047 showed levels of accuracy error prediction by 0.099951148. Early stage in conducting this study to test using the calculation value prediction accuracy error using the formula MSE (Mean Sequare Error) using optimization algorithms adaboost to provide answers to the problems that the accuracy error Algorithm Neural Network Backpropagation need to be humbled in order of prediction accuracy increases and the second stage conducted trials using more data compared with phase one. Based on the research that has been done, it can be concluded that the Algorithm Neural Network has lower accuracy than the accuracy of using the optimization method adaboost in the process of trainning coupled with Neural Network, this can be seen with the low level of error MSE method adaboost + neural network and can disimpukan also that by using a more amount of data it can lower the level of accuracy error MSE thus succeeded in improving the accuracy of predictions in forex trading business.
Entretiens sur les Chrysomelidae de Belgique et des régions limitrophes 8 : Les Megalopodidae et Orsodacnidae (anciennement Chrysomelidae) de la faune belge (Coleoptera), catalogue et atlas

Dans le cadre de la realisation du catalogue des Chrysomelidae de Belgique, sont presentes ici, les catalogues et atlas des nouvelles familles Megalopodidae et Orsodacnidae, mis a jour a la date du 1er janvier 2018. Un commentaire accompagne chaque espece. Il est adapte a la nomenclature recente et complete par une carte des occurrences connues de nous dans le pays.