Miracle-dz/UniFAR-FTUs huggingface dataset:
Immigrant Sanctuary Policies and Crime-Reporting Behavior: A Multilevel Analysis of Reports of Crime Victimization to Law Enforcement, 1980 to 2004

Sanctuary jurisdictions have existed in the United States since the 1980s. They have recently reentered U.S. politics and engendered contentious debates regarding their legality and influence on public safety. Critics argue that sanctuary jurisdictions create conditions that threaten local communities by impeding federal immigration enforcement efforts. Proponents maintain that the policies improve public safety by fostering institutional trust among immigrant communities and by increasing the willingness of immigrant community members to notify the police after they are victimized. In this study, we situate expectations from the immigrant sanctuary literature within a multilevel, contextualized help-seeking framework to assess how crime-reporting behavior varies across immigrant sanctuary contexts. We find that Latinos are more likely to report violent crime victimization to law enforcement after sanctuary policies have been adopted within their metropolitan areas of residence. We argue that social policy contexts can shift the nature of help-seeking experiences and eliminate barriers that undermine crime victims’ willingness to mobilize the law. Overall, this study highlights the unique role social policy contexts can serve in structuring victims’ help-seeking decisions.
Zimbabwean Non-Uptake of Protective Point-of-Sale Behaviours: Is this a Risk Homeostasis Response?

In a world which is increasingly relying on debit and credit cards to effect transactions, people are entering PINs in a wide range of situations and contexts. We all know we ought to shield PIN entry, and check for skimmers if we are using magnetic stripe cards. Yet previous studies have found that a minority of card users shield their PINs at Points of Sale (PoS). Previous studies into the incidence of PIN shielding have taken place in Europe, with stable currencies and relative wealth. Zimbabwe, in 2019, presented us with a unique opportunity to carry out a replication study that is essentially a “natural experiment” i.e. we can study behaviours in interesting contexts which happen by chance, not by design. The context of interest is one where the country's currency is devaluing steeply, and creating a great deal of uncertainty and hardship. This occurred because Zimbabwe introduced a number of currency reforms in a short period of time. Protection Motivation Theory (PMT) suggests that people engage in a calculus based on their threat and coping appraisals. The devaluing currency ought to heighten threat appraisals (loss being much harder to bear) and the protective action's cost is relatively low (using a hand to shield a PIN). We ought, therefore, to see a higher incidence of protective behaviours in Zimbabwe. Our observation and interview study surprisingly found lower levels of PIN shielding at Points of Sale (PoS) than in previous European studies. We also found that those participants who did not take protective behaviours tended to know how to recover from card fraud. The low incidence we observed contradicted our PMT-based predictions. A possible explanation is that we are observing a risk homeostasis response, which suggests that having a “safety net” (being able to get your money back) might make people decide not to make the effort to take protective actions.
Adaption of RoboSTEAM Project to the Pandemic Situation

COVID pandemic has changed the way in which we carry out our daily life and also have affected educational processes. Teaching and learning have changed from a most common face to face context to a blended or online context. This implies changes in the way to carry out the activates and have an impact in research projects such as RoboSTEAM. Such project, that applies Challenge Based Learning methodologies with application of Robotics and Mechatronics, requires to change its approach to show how it is possible to succeed in the new situation. This paper describes how the project has evolved, how it has been affected by COVID and the possible changes to carry out. Regarding this last issue remote labs and online tools are presented as solutions to support changes in the application of challenge-based learning methodology.
Thermography as a method of acquiring competences in Physiology. Application case for hand blood flow control

The present work proposes a methodological structure as part of the learning of the circulatory system. For this, the application of thermal stress is used, by immersing the hands in cold water to visualize the reperfusion of the hands. Learning, based on the visualization and analysis of thermographic images, allows the acquisition of specific competences at the university level. In graduate studies such as physiotherapy and nursing, the use of virtual tools and materials that allow the acquisition of skills and technical knowledge is essential for the job performance of future professionals. The application of this methodology is proposed in practical sessions of subjects in the area of knowledge of Physiology, to demonstrate and facilitate the understanding of the circulatory system. This approach is framed within the discipline of virtual laboratories since the virtual materials generated can be used for the acquisition of skills and practical competencies, as well as for the evaluation of competencies in e-learning courses. In this way, by recording a pedagogical video that shows a short practice, 5 minutes long, it is possible to establish the necessary knowledge bases to expand them later. This material is easily implementable in any learning management system.
Attractive Educational Robotics Motivates Younger Students to Learn Programming and Computational Thinking

Educational robotics and physical computing have proved to be good sources of motivation for students of all ages and school levels. We conducted a series of workshops in the primary schools of city of Joensuu in eastern Finland, focusing on training the fundamental computational thinking (CT) skills by using a programmable and interactive Teddy Bear toy. Educational robotics and physical computing devices have proved to be an efficient way to teach these skills regardless the students’ age group or previous background. To assess the students’ intrinsic motivation towards Teddy Bear programming, we devised a survey for workshop participants and conducted a statistical analysis to compare differences between the genders and age groups. The results of the large-scale empirical study (n=1440) show that the students at the age of 9-10 years (Grades 3-4) are significantly more motivated towards such a learning tool than the students of age 11-12 years (Grades 5-6). Furthermore, we show that especially young girls find the Teddy Bear programming motivating and they are eager to learn more. This indicates that appealing tools play a key role when teaching programming and CT concepts to young school children.
SAperI: approaching gender gap using Spatial Ability training week in high-school context

The purpose of this paper is to describe the structure of a girls summer school, “SAperI – Spatial Ability per l'Ingegneria” (in English, “Knowledge – Spatial Ability for Engineering”), and to illustrate its impact on spatial ability development and future career preferences on those who participated in the week long summer school compared to a control group that did not participate. The 5 days school, organized by Politecnico di Torino (Italy), was included in a larger project addressing 17 years old high-school students. Thirtyseven girls actively took part in a summer school, while 167 students (both males and females) were tested as a controlled group. For those who attended the summer school, significant gains were observed in four measures of spatial ability - mental rotation, spatial visualization, mental cutting and paper folding. For a minority of participants, scores on one of these tests, paper folding, were lower when measured at the end of the summer school but this was an exception. Furthermore, when tested several months after the summer school, the gains in spatial ability that were made during the course were maintained indicating stability over time with regard to the improvement in spatial ability. In terms of the experience of taking the course, the feedback provided was very positive and all but one participant would recommend the summer school to other girls at this stage of high school.
The Evolution from the Environmental Education Model to the Education for Sustainable Development Model. Case Study of Environmental Engineering at Santo Tomas University, Colombia

Since the beginning of the 21st century, Environmental Education (EE) is being transformed into a more complex model, where different environmental, social, and economic factors are taken into account. This model is called Education for Sustainable Development (ESD), which seeks to create the foundations to achieve sustainable development. In Higher Education, there are some examples of the transition process from EE to ESD, especially in Europe, but in some places from Latin America, until now the ESD model is being explored in universities and its acceptance is reviewed. This is further enhanced in engineering programs where ESD is still not very visible. This research presents an analysis of the transition from EE to ESD in an Environmental Engineering program at Santo Tomas University in Colombia, Latin America. A survey was conducted to three groups of students, two groups formed under the EE model, and another group for which an ESD course was created. From the survey and a curricular analysis, it was possible to identify the inclusion status and the student's acceptance of this undergraduate program for the ESD model. As a global result, it is identified that the curriculum is still anchored to EE, but students find the ESD model more attractive and up-to-dated.
Intelligent Tutoring Systems approach to Introductory Programming Courses

Programming is difficult and requires a lot of work and dedication from the students and teachers. Programming is part of the curriculum of many courses, but especially in computer science, and most teaching and learning is problematic. Despite all the efforts of the researchers, it seems to be difficult to find an effective method of teaching that is suitable for all students. This paper describes a set of possible instructional strategies for teaching and learning and its application to an introductory programming course. The goal of utilizing a smart learning system was to increase student scores, pass rate, and increase efficiency for both students and teachers. The set of instructional strategies based on technology was implemented in an introductory programming course over several academic years. Data were collected and the results are analyzed. The results show that there are significant improvements in the grade distributions, the pass/fail rate, in the interest and participation of the students in the different activities developed throughout the course, greater motivation and passion in solving problems, and the more efficient use of teacher time and effort.
Mapping Textual Feedback to Process Model Elements

In this paper, we have proposed novel concept of mapping natural language customer feedback text to relevant business process model elements. Customer feedback mapped over business process model will provide augmented business process having customer perception. More specifically, in this work, we have proposed systematic approach for mapping feedback comment to relevant process model elements which comprises a)process model generation, b) preparation of real-world customer feedback corpus, c) BPRI framework based mapping guidelines and d) first novel human annotated customer feedback process model element mapping dataset. We have evaluated the effectiveness of six traditional text similarity measures for automatic mapping of customer feedback to process model elements. Based on the results, we concluded that automatic mapping identification is challenging task as six traditional similarity measures resulted zero recall score.
Personalized gamification: A literature review of outcomes, experiments, and approaches

Personalized gamification has gained substantial interest due to the expectation that it can improve gamification's success. Considering some secondary studies on this topic, they lack to present the characteristics of empirical studies and some aspects on how personalization approaches were designed. In this paper, we present a literature review based on previous research to address these gaps. Based on our analysis, our results provide: insights on how experiments to compare personalized gamification and non-personalized gamification are designed and evaluated; evidence on the effectiveness of personalized gamification found in primary studies; and an overview of how personalization approaches were designed. Our analysis converged in possible guidelines and a research agenda revealing five main needs: i) empirical studies comparing one size fits all and personalized gamification; ii) qualitative user studies; iii) personalization approaches that consider contextual characteristics as well as iv) rely on a broader, unambiguous set of game elements; and v) a benchmark of established resources to increase research reproducibility.
Thermodynamic Properties of Aqueous (NH4)2SO4 to High Supersaturation as a Function of Temperature

Ammonium sulfate is an important component of aqueous atmospheric aerosols in both remote and urban regions. Equilibrium water vapor pressures over supersaturated aqueous ammonium sulfate have been determined from 278.15 to 313.15 K using an electrodynamic balance. Partial molar enthalpies of water (L{sub 1} to {approximately}25 mol/kg) and partial molar heat capacities (J{sub 1}, to {approximately}8 mol/kg) have been calculated from the measurements, together with enthalpies of solution, heat capacities, and boiling point elevations. The results have been used to parameterize a mole-fraction-based activity coefficient model. Equilibrium constants for the reaction (NH{sub 4}){sub 2}SO{sub 4}{sub (cr)} {l_reversible} 2NH{sub 4}{sup +} (aq) + SO{sub 4}{sup 2{minus}} aq have been evaluated from 373.15 K to the eutectic point (5.00 mol/kg, 254.21 K). The model satisfactorily represent water vapor pressures and saturations with respect to solid phases ice and ammonium sulfate from 253 to 373.15K and up to 8--10 mol/kg, and water vapor pressures from 278.15 to 313.15 K and up to {approximately}25 mol/kg.
The preparation of alkyltrimethylammonium-kanemite complexes and their conversion to microporous materials.

Single layered polysilicate kanemite (NaHSi 2 O 5 •3H 2 O) was allowed to react with alkyltrimethylammonium chloride solutions to form alkyltrimethylammonium-kanemite complexes. During the organic intercalation, the SiO 2 layers in the complexes were condensed to form three-dimensional SiO 2 networks. The calcined products of the complexes had micro pores 2-4 nm in diameter, and the surface areas were ca. 900 m 2 g −1 . The pore size of the calcined products was altered with the variation in the alkyl-chain length of the alkyltrimethylammonium ions employed
UvA-DARE (Digital Academic Repository) Are psychotic-like experiences related to a discontinuation of cannabis consumption in young adults?

Objective: To assess changes in cannabis use in young adults as a function of psychotic-like experiences. Method: Participantswereinitiallyrecruitedatage14inhighschoolsforthelongitudinalIMAGENstudy.Allmea-surespresented here wereassessed atfollow-upsat age19andatage22,respectively. Perceivedstress was only assessed once at age 22. Ever users of cannabis ( N = 552) gave qualitative and quantitative information on cannabis use and psychotic-like experiences using the Community Assessment of Psychic Experiences (CAPE). Of those, nearly all n = 549 reported to have experienced at least one psychotic experience of any form at age 19. Results: Mean cannabis use increased from age 19 to 22 and age of ﬁ rst use of cannabis was positively associated with a change in cannabis use between the two time points. Change in cannabis use was not signi ﬁ cantly
Utilizing Text Mining and Feature-Sentiment-Pairs to Support Data-Driven Design Automation Massive Open Online Course

This study aimed to develop a case-based design framework to analyze online us-er reviews and understanding the user preferences in a Massive Open Online Course (MOOC) content-related design. Another purpose was to identify the fu-ture trends of MOOC content-related design. Thus, it was an effort to achieve da-ta-driven design automation. This research extracts pairs of keywords which are later called Feature-Sentiment-Pairs (FSPs) using text mining to identify user preferences. Then the user preferences were used as features of an MOOC content-related design. An MOOC case study is used to implement the proposed framework. The online reviews are collected from www.coursera. org as the MOOC case study. The framework aims to use these large scale online review data as qualitative data and converts them into quantitative meaningful infor-mation, especially on content-related design so that the MOOC designer can de-cide better content based on the data. The framework combines the online re-views, text mining, and data analytics to reveal new information about users’ preference of MOOC content-related design. This study has applied text mining and specifically utilizes FSPs to identify user preferences in the MOOC content-related design. This framework can avoid the unwanted features on the MOOC content-related design and also speed up the identification of user preference.
Automated Socket Anomaly Detection through Deep Learning

The paper will demonstrate the application of Deep Learning (DL) for the detection of defective tester sockets. The proposed methodology relies on images like those used for manual or rule-based inspection, commonly collected using Automated Optical Inspection (AOI) equipment. This work represents a practical example of the use of Machine Learning for achieving improved inspection-quality outcomes at a lower cost. The experimental evaluation of the proposed methodology was performed on production set of collected socket images.
Case-By-Case Meta-Analysis from A Bayesian Perspective

Due to the advancement of science and the tendency to use the smallest of humans in needless clinical trials in many situations, the role of meta-analysis to summarize published study with the same objective, using statistics is becoming more and more fundamental. The integration of the knowledge available makes it easier to understand and allows strong conclusions. For example, increasing the number of clinical trials encourages a rising need to conduct meta-analyses, making complexity possible. The purpose of the research was therefore to suggest a Bayesian meta-analytical methodology. Bayesian method was proposed to carry out a case-by-case meta-analysis. The procedure was based on the mixture of the posterior distributions of the parameter in question from each study that is a percentage of the meta-analysis, thus producing a combined posterior distribution as a meta-analytical measure. In addition, a brief comparison is made between the proposed method and some methods used in meta-analysis. Some examples with simulated and real data are presented and discussed.
Detecting Hate Speech in Social Media Articles in Romanized Sinhala

The main aim of this research is to automatically identify the hate content of social media comments and documents written by the Romanized Sinhala Language. Also most of researched done the hate speech recognition study in English or their language but here identify the Sinhala words that’s written in English letters that means Romanized Sinhala language. Hate words and other hated texts are growing issue, and to combat this they turn to machine learning and computer science. In this research compare the several features extraction method and four machine learning algorithms and difference N-gram values unigram, bigram and trigram and used the Min-Df value 3. This study will investigate and compare different features for the different classifier when classifying hate speech comments on Facebook. We have achieved a data set of nearly 2500 comments, some containing hate speech, and trained and tested our classifier with different features.
Model Predictive Control of 2-Degree of Freedom Robotic Manipulator with Backlash

This paper presents the design of model predictive control of 2-degree of freedom robotic manipulator with backlash at the input of a stable linearized system under control constraints. This system is a two-axis serial-robotic manipulator with a nonlinear dynamic model and due to its non-linear nature, it is very useful in teaching environments and research. Backlash is a very important non-linearity that limits the performance of speed and position control in industrial, and in many other applications. The control objective is output tracking and disturbance rejection, we have formulated a multi-objective optimization problem with a cost/objective function having terms representing closed-loop performance. Our main goal is to track output in the presence of nonlinearities. The resulting optimal control problem is implemented online via a Model Predictive Control (MPC) scheme. In this context, MPC arises a powerful technique for achieving multiple objectives for the MIMO system. The reason for the use of MPC is that it can easily handle the multivariable case, system constraint, and non-linearities in a very simple way.
Precomputed Radiance Transfer for Reflectance and Lighting Estimation

Decomposing scenes into reflectance and lighting is an important task for applications such as relighting, image matching or content creation. Advanced light transport effects like occlusion and indirect lighting are often ignored, leading to subpar decompositions in which the albedo needs to compensate for insufficiencies in the estimated shading. We show how to account for these advanced lighting effects by utilizing precomputed radiance transfer to estimate reflectance and lighting. Given the geometry of an object and one or multiple images, our method reconstructs the object’s surface reflectance properties—such as its albedo and glossiness—as well as a colored lighting environment map. Evaluation on synthetic and real data shows that incorporation of indirect light leads to qualitatively and quantitatively improved results.
Dynamic Semiempirical PEMFC Model for Prognostics and Fault Diagnosis

This article introduces a dynamic semiempirical model that predicts the degradation of a proton exchange membrane fuel cell (PEMFC) by introducing time-based terms in the model. The concentration voltage drop is calculated using a new statistical equation based on the load current and working time, whereas the ohmic and activation voltage drops are updated using time-based equations borrowed from the existing literature. Furthermore, the developed model calculates the membrane water content in the PEMFC, which indicates the membrane hydration state and indirectly diagnoses the flooding and drying faults. Moreover, the model parameters are optimized using a recently developed butterfly optimization algorithm. The model is simple and has a short runtime; therefore, it is suitable for monitoring. Voltage degradation under various loading currents was observed for long working hours. The obtained results indicate a significant degradation in PEMFC performance. Therefore, the proposed model is also useful for prognostics and fault diagnosis.