Miracle-dz/UniFAR-FTUs huggingface dataset:
Seeing Humans in the Data: Ethical Blind Spots of Taiwan Academic Researchers in the Era of Behavioral Big Data

The advent of Behavioral Big Data (BBD) has profoundly impacted research ethics. At the same time, academic disciplines with no experience in human subjects research increasingly make use of BBD datasets. In this first-of-its-kind study, we evaluate Taiwan academic researchers’ knowledge and awareness of data ethics using a series of four BBD-based hypothetical research scenarios. We uncover several data ethics blind spots affecting academic researchers. Through the results of this research we hope to strengthen academic researchers’ data ethics awareness and knowledge in the context of BBD, and provide suggestions for improving the ethics training of academic researchers conducting BBD studies. We also contribute a re-conceptualization of data ethics encompassing both traditional human subjects research ethics and new paradigms for the regulation of personal data, such as the General Data Protection Regulation (GDPR).
Machine Learning for Software Engineering: a Bibliometric Analysis from 2015 to 2019

The increase of computer processor speed and the ubiquitous availability of data coming from a diversity of sources (e.g., version control systems, software developers forums, operating system logs, etc.) have boosted the interest in applying machine learning to software engineering. Accordingly, the research literature on this topic has increased rapidly. This paper provides a comprehensive overview of that literature for the last ﬁve years. To do so, it examines 1,312 records gathered from Elsevier Scopus, identifying (i) the most productive authors and their collaboration networks, (ii) the countries and institutions that are leading research, (iii) the journals that are publishing the most papers, and (iv) the most important research themes and the highest impacted articles for those themes.
Improving News Popularity Estimation via Weak Supervision and Meta-active Learning

Social news has fundamentally changed the mechanisms of public perception, education, and even dis-information. Apprising the popularity of social news articles can have significant impact through a diversity of information redistribution techniques. In this article, an improved prediction algorithm is proposed to predict the long-time popularity of social news articles without the need for ground-truth observations. The proposed framework applies a novel active learning selection policy to obtain the optimal volume of observations and achieve superior predictive performance. To assess the proposed framework, a large set of experiments are undertaken; these indicate that the new solution can improve prediction performance by 28% (precision) while reducing the volume of required ground truth by 32%.
Identification of Linear Power System Models Using Probing Signals

This paper compares the accuracy of two methods to identify a linear representation of a power system: the traditional Eigensystem Realization Algorithm (ERA) and the Loewner Interpolation Method (LIM). ERA is based on time domain data obtained using exponential chirp probing signals and LIM system identification method is based on frequency domain data obtained using sinusoidal probing signals. The ERA and LIM methods are evaluated with the noise produced by the nonlinear characteristics of the system, these characteristics are caused by increasing the amplitude of the applied probing signal. The test systems used are: the two-area Kundur system and a reduced order representation of the Northeastern portion of the North American Eastern Interconnection. The results show that the LIM method provides a more accurate identification than the ERA method.
Software as a Medical Device (SaMD): Useful or Useless Term?

Software as a medical device is a relatively new and expanding field in which patient safety must be a key concern. Regulation and standards regarding software as a medical device (subsequently referred to as “SaMD”) must incorporate all components that could potentially influence SaMD, both in its development and implementation. However, SaMD has been varyingly defined by organisations and individuals within the literature, therefore there is no clear boundary as to what is or is not SaMD, consequently, no clear definition of SaMD exists. Without a clear definition it therefore becomes impossible to create standards to regulate SaMD. Ultimately, this results in increased risks to patient safety. The purpose of this study was to identify SaMD concepts through a Scoping Review to establish the boundaries of SaMD. This has significant impact on new technology applications to support healthcare monitoring and healthcare service delivery. This will ultimately affect how new technology can be regulated in healthcare and will impact innovation and design in this field.
Exploring multilevel social determinants of depressive symptoms for Tanzanian adolescents: evidence from a cross-sectional study

Background Depression has substantial and enduring impacts for adolescents, particularly those living in poverty. Yet, evidence on its determinants in low-income countries remains scarce. We examined the social determinants of depressive symptoms for Tanzanian adolescents. Methods We used cross-sectional data for 2458 adolescents (aged 14–19), to describe associations with depressive symptoms within and across five domains—demographic, economic, neighbourhood, environmental and social-cultural—using linear mixed models. We estimated depressive symptoms using the 10-item Centre for Epidemiological Studies Depression Scale, which ranges from 0 to 30 and increases with additional symptoms. Results Factors associated with depressive symptoms in the fully adjusted models included experiencing five or more household economic shocks (β=2.40; 95% CI 1.48 to 3.32), experiencing droughts/floods (β=0.76; 95% CI 0.36 to 1.17), being in a relationship (β=1.82; 95% CI 1.30 to 2.33), and having moderate (β=1.26; 95% CI 0.80 to 1.71) or low (β=2.27; 95% CI 1.81 to 2.74) social support. Exclusive schooling was protective compared with being engaged in both school and paid work (β=1.07; 95% CI 0.05 to 2.61) and not engaged in either (β=0.73; 95% CI 0.24 to 1.22). Household size and relationship status were more important factors for girls, while employment status, and extreme precipitation were more important for boys. Conclusion Mental health is associated with determinants from multiple domains. Results suggest that environmental shocks related to climate change contribute to poor mental health in adolescents, highlighting an important area for intervention and research.
Composite Silica Spheres with Magnetic and Luminescent Functionalities

A new class of highly fluorescent, photostable, and magnetic core/shell nanoparticles in the submicrometer size range has been synthesized from a modified Stöber method combined with the layer‐by‐layer (LbL) assembly technique. Luminescent magnetic nanoparticles are prepared via two main steps. The first step involves controlled addition of tetraethoxysilane to a dispersion of Fe3O4/γ‐Fe2O3 nanoparticles, which are thereby homogeneously incorporated as cores into monodisperse silica spheres. The second step involves the LbL assembly of polyelectrolytes and luminescent CdTe quantum dots onto the surfaces of the silica‐coated magnetite/maghemite particles, which are finally covered with an outer shell of silica. These spherical particles have a typical diameter of 220 ± 10 nm and a saturation magnetization of 1.34 emu g–1 at room temperature, and exhibit strong excitonic photoluminescence. Nanoparticles with such a core/shell architecture have the added benefit of providing a robust platform (the outer silica shell) for incorporating diverse functionalities into a single nanoparticle.
Degradation of particulate organic matter — A comparison of different model concepts

From the results of investigations at full-scale and pilot plants, it has been found that with ‘usual’ parameters the ASM1 is only valid for a small range of SRT as well as of oxic reactor section (VOX/V). To make ASM1 applicable to a wider range of SRT the very slowly biodegradable particulate organic matter has to be taken into consideration. This fraction can be interpreted as part of the ‘heterotrophic biomass’ XB,H which requires modification of the values for the ‘heterotrophic decay rate’ bH and for the fraction of XI in the influent. In order to distinguish between ‘active heterotrophic biomass’ (XB,H) and the very slowly biodegradable substrate it seems to be useful to inkoduce the latter mass fraction as a new model component (XV) into the ASM1. This extended simulation model has given reasonable results for different activated sludge systems.
Citizen participation in climate protection – how to get everyone on board Integration of citizens in climate protection measures of German Municipalities in Euregio

Aim – This thesis analyzes how German municipalities in the Cross-Border Region Euregio integrate citizens in their climate protection. It applies the institutional capacity and the adherence to good regional governance of municipalities to explore how they facilitate citizen participation. Method – The thesis selects five municipalities through the criteria of population size and evidence of climate protection activity. The subsequent case study analyzes strategic papers and interviews with relevant local actors. The data is utilized to understand what climate protection measures exist and how municipalities integrate citizens in them. Results – All municipalities agree on the importance of citizen participation for successful climate protection. Despite differences in their background, all have similar institutional capacities and adhere to good regional governance. Several climate protection measures dependent on citizens appear in all municipalities. Additionally, some municipalities offer notable unique measures. Conclusion – Local climate protection depends on the integration of all local actors – citizens included. The municipalities attempt this by providing information and offering an infrastructure for communication between citizens and other actors. Transparency and meeting citizens on eye-level improve the chances of success. Additionally, municipalities have a role-model function towards citizens. By taking action, they can increase the support of citizens in climate protection. Table of Content
Possible Involvement of Innate Lymphoid Cells in the Development of Chronic Inflammatory Pancreatic Diseases

Introduction Type 1 autoimmune pancreatitis is a chronic fibro-inflammatory disorder. We previously reported the involvement of M2 macrophages and basophils in autoimmune pancreatitis. It is reported that Group 2 innate lymphoid cells (ILC2s) and basophils play an important role in asthma. Thus, this study investigated the roles of innate lymphoid cells in autoimmune pancreatitis. Subjects and methods We recruited 28 patients with autoimmune pancreatitis (25 men and 3 women; mean age, 68.4 years) who were not receiving steroid therapy, 10 patients with chronic pancreatitis (CP; 2 women and 3 men with idiopathic CP and 5 men with alcoholic CP; mean age, 65.9 years), and 30 healthy controls (HCs; 5 women and 25 men; mean age, 66.9 years). Peripheral ILCs were analyzed using flow cytometry. We also analyzed two types of ILC2s (lineage− CD127+ CD161+ c-Kit+/− CRTH2+ and lineage− CD25+ IL-33R+ cells). Results The proportions of ILC2s and ILC3s were significantly higher in the autoimmune pancreatitis (0.140% ± 0.083% and 0.055% ± 0.043%, respectively) and CP groups (0.119% ± 0.055% and 0.051% ± 0.040%, respectively) than in the HC group (0.054% ± 0.039% and 0.018% ± 0.017%, respectively). The proportion of ILC1s did not differ among three groups. There was no significant correlation between the counts of the two ILC2 types. Conclusion Elevated ILC2 and ILC3 counts may be involved in the development of chronic pancreatic inflammation.
A Survey on Sign Language Recognition Systems

: Sign language is used by speech impaired people for communication but normal people do not know sign language which causes a communication gap between them. This gap can be filled using today’s advanced technologies. Technologies such as image processing and machine learning can be used to build a system which converts sign language to text or speech. These systems can be of great help to dumb people as they can easily communicate with anyone using such system. This paper provides a brief survey of various research works carried out so far in this field
Design space exploration for super-resolution using convolutional autoencoders

—Nowadays, in the context of digital image processing, there is a need for obtaining high-resolution images from low-resolution versions, to improve visualization and feature extraction from these images. This deﬁnes the open super-resolution problem. There are several methods that have been designed to solve this problem, among which there is deep learning (DL), which is one of the most promising in terms of results, partly due to the big amount of hardware resources available nowadays for their processing. Several DL algorithms have been used in a number of tasks; however, in this work we decided to explore the possibilities of comparing, in the super-resolution task, the models generated by the convolutional autoencoders (CAE), given their behavior of compressing and then reconstructing the input, which seems to be an interesting feature with potential for its use in super-resolution. In this work, we present quantitative and qualitative analysis that allow showing the behavior of these models based on AE. For the performance evaluation of these models in the super-resolution task, we use the DIV2K and Flickr2K image sets, comparing our results with respect to other methods, such as Nearest Neighbors, among others, showing that there is an improvement in terms of some metrics for some of the proposed architectures and that, although the performance of other state-of-the-art DL methods is not improved, there is a considerable reduction of hardware resources needed for processing these architectures.
Dividend Growth Does Not Help Predict Returns Compared To Likelihood-Based Tests: An Anatomy of the Dog

The dividend-growth based test of return predictability, proposed by Cochrane [2008, Review of Financial Studies 21, 1533-1575], is similar to a likelihood-based test of the standard return-predictability model, treating the autoregressive parameter of the dividend-price ratio as known. In comparison to standard OLS-based inference, both tests can achieve power gains by using restrictions or prior information on the value of the autoregressive parameter. When compared to the likelihoodbased test, there are no power advantages for the dividend-growth based test. In common implementations, with the autoregressive parameter set equal to the corresponding OLS estimate, Cochrane’s test suffers from severe size distortions. JEL classification: C22, G1.
Improved prediction of protein-small organic ligand binding sites via consensus-based ranking with linear regression

Prediction of binding of small organic ligands to proteins based on the knowledge of protein structures finds applications in rational drug discovery and elucidation of various cellular-level processes. Recent work shows that predictive quality of computational predictors of these binding events can be improved with the use of a consensus-based approach that combines predictions from several base predictors. We designed a novel type of a consensus, called ConSitePred, which uses a regression-based meta-predictor to (re)rank predictions from four well-performing base methods. The regression uses a vector of six custom-designed and empirically selected features that quantify atomic composition of the protein nearby the predicted binding site and presence and quality of other binding site predictions that are close to the predicted site. We empirically show that ConSitePred’s predictions improve over the predictions of a comprehensive set of ten existing predictors, including its four base methods. Our method provides an alternative to other consensuses-based approaches that are based on clustering predictions from the base methods.
Investigation of a transgenic model of Alzheimer's disease, the TASTPM mouse, using magnetic resonance spectroscopy and matrix assisted laser desorption imaging

AbstractThe University of ManchesterDuncan Matthew ForsterDoctor of Philosophy (PhD)Investigation of a transgenic model of Alzheimer's Disease, theTASTPM mouse, using magnetic resonance spectroscopy andmatrix assisted laser desorption imaging2010There is currently no definitive biomarker for Alzheimer? s Disease (AD),confirmation of diagnosis is only possible post-mortem. Magnetic resonancespectroscopy (MRS) has potential in aiding diagnosis, an MRS scan can beperformed during an MRI scan, only adding around 10 minutes to scan time.Use of data from the two scans may allow more accurate diagnosis of AD. Thisthesis investigates a transgenic mouse model of AD, the TASTPM mouse usingin vitro and in vivo MRS as well as matrix assisted laser desorption ionisationmass spectrometry imaging (MALDI MS Imaging). The first aim of the studywas to search for a biomarker of AD that may allow better diagnosis or furtherour understanding of the pathology of the disease. The second aim was toevaluate the TASTPM mouse as a model of AD for use in preclinical testing ofamyloid lowering agents. The third aim was to investigate a thalamic pathologyin the TASTPM mice using MALDI MS Imaging. Metabolically, we founddifferences between the brains of TASTPM mice and their wild type base strainin both in vitro and in vivo scans. These differences may be exploited in thepreclinical testing of novel amyloid lowering therapies. We also foundsimilarities with human AD and other mouse models, lower N-acetylaspartate,lower glutamate and higher myo-inositol are all observed in human AD, as wellas the TASTPM mice in vivo. We also found further evidence of impairedneuronal energy metabolism in TASTPM mice, such as lower succinate.Cerebral hypometabolism is a symptom of human AD. The TASTPM mouseseems to be a fairly good approximation of the human disease, sharing severaltraits. In our investigation of the thalamic pathology, we discovered a peptidewhich was strongly localised to the regions of the pathology and isolated it, butwere unable to identify it, the work in this area will continue.
Näıvely Dialectic Belief Formation

I model a person who interprets the world by adjudicating between the best case and worst case explanations of observable evidence. The model has a parameter, interpreted as skepticism or credulity, to capture how much the person discounts less likely explanations. I apply the model to belief formation, subjective assessment of probabilities, and susceptibility to political spin. In all three cases, someone who is excessively credulous towards far-fetched explanations displays well-known behavioral anomalies. The calibrated model can therefore simultaneously rationalize things like stubbornness in the face of new evidence, systematically biased beliefs, the overweighting of small probabilities, and the existence of maximally extreme yet effective political messaging. ∗Email: jcampb10@providence.edu. I am grateful to Bernhard Ganglmair and Stephanie Tilden for extremely helpful discussions.
Deep Learning for Fast Identification of Bacterial Strains in Resource Constrained Devices

This papers presents a proposal of a deep learning based system for classification of bacterial species, this system is able to work in resource constrained devices by fine tuning the state of the art mobile network called MobileNetV2, which drastically reduces the memory footprint without sacrificing accuracy, this is achieved by using depth wise separable convolutions and a novel layer module, called inverted residual with linear bottleneck. This resource constrained approach aims to provide complementary information to the traditional bacterial identification tests by being a suitable option for deployment in mobile or embedded systems. We measured the performance of several architectures over the original Digital Image of Bacterial Species (DIBaS) dataset and an augmented version of it, the best models achieved 95.53% and 94.22% of accuracy, respectively. We provide comparative tables between the original and augmented data sets as well as the results obtained by different configurations of the architecture, we also made publicly available the code and the pre-trained models with higher scores.
MACHINE LEARNING APPROACH TO INDUSTRIAL CONTROL SYSTEM HEALTH MONITORING AND CYBER SECURITY: SIMILARITIES, CONFLICTS AND LIMITATIONS

Most of the reported cyber-attacks carried out on industrial control systems usually affect the process measurement. Similarly, system failure or component fault is mostly indicated by process change, and these two “abnormal occurrences” could sometimes have a similar signature. However, the distinction is not evident in the recent approach that utilizes data-driven algorithms for cyber intrusion detection and fault diagnosis. This study reviews machine learning-based abnormal occurrence detection tools and techniques, and their application in complex systems. In this work, we identified and discuss the issues in the development of the machine learning-based intrusion detection and protection systems and the inherent limitation that affects their implementation. As a case study, the nuclear plant control system, its operating states and typical process changes that are similar to those obtainable during random system fault and cyber-attacks are discussed. Finally, recommendations on the future direction for efficient identification and classification of abnormal occurrences to enhance process monitoring and malicious attack detection in nuclear power plants are presented.
SPES 2020 Deliverable 1.4.B-3 Concepts for an Integrated Tool Architecture

Syntax Structure (concepts and properties) Context sensitive constraints Configurable units Repository Common Model Repository Complex constraint checking Configuration management Language evolution Concrete Syntax Types: diagram / text / table Based on canonical syntaxes Combination of syntax types Editors Generic Editor Framework Searching and browsing Editing and refactoring Comparing and merging Process Definition Roles and access rights Artifact as constrained concept Activity as in-place transformation Workflow Workflow Engine Task distribution and to-do lists Automated change management Quality gates and access control Semantics Refactoring transformations Translational semantics Operational semantics Analysis / Synthesis Model Interpretation Engine In-place M2M transformation Out-of-place M2M and M2T transf. Generic Simulation Framework Figure 6: Decomposing development tools. To assess the ratio of the business functionality and the management infrastructure, we performed an empirical study on the functionality provided by engineering tools. Our assumption is that by investigating the menus or toolbars of a tool and by classifying the functionality that can be accessed from there, we can estimate the ratio between the functionality of tools that is related to their business domain and the functionality that is horizontal. We performed our empirical study based on the description of menus and toolbars command buttons as given in the user documentation of the following tools: Esterel Scade v6.0, Telelogic Rhapsody v7.4 and Telelogic Doors v9.1. In Table 1 we present an overview of the functionality of COTS as resulted from our classification. We notice in this figure that most of the functionality accessible to tool users is related to the editing of the models (e. g., creation, modification or deletion of model parts), navigation (e. g., searching) and layout (e. g., colors, fonts, zooming).