Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender differences in attitudes towards trauma/abuse disclosure

Research has indicated there may be gender differences in responses to trauma disclosure. Currently, the literature is dominated by the sexual abuse of female survivors, largely neglecting male survivors. The present study compared men and women’s responses towards written hypothetical disclosures of sexual, physical, and emotional abuse from male and female survivors. Thirty-two psychology undergraduates (26 females, six males) aged 18-26 years (M = 19.84, SD = 1.54) volunteered to participate. The study utilised a mixed-methods design; 28 participants completed a survey, with four completing it during a semi-structured, think aloud interview. Attitudes towards disclosure were measured with concepts such as victim blame and believing the survivor. Survey and interview data were analysed by within-subjects ANOVA and thematic analysis, respectively. Notably, men were more likely to victim blame and less likely to believe, sympathise and recommend help-seeking. However, a Bayesian ANOVA gave insufficient evidence that gender affected these responses; interestingly, abuse type had a stronger effect on victim blame and help-seeking scores. Contrary to the literature, female interviewees engaged in victim blame, but the male interviewee did not. As expected, participants more frequently imagined men as attackers. Implications include that women are more likely to victim blame than previously thought. Moreover, people may not adequately support men because they are not seen as typical survivors. Limitations are the lack of male participants, missing emotional abuse data and inconsistency in vignette severity. Future work should improve on these limitations and further investigate the impact of abuse type on responses to disclosure.
PSDoodle: Fast App Screen Search via Partial Screen Doodle

Searching through existing repositories for a specific mobile app screen design is currently either slow or tedious. Such searches are either limited to basic keyword searches (Google Image Search) or require as input a complete query screen image (SWIRE). A promising alternative is interactive partial sketching, which is more structured than keyword search and faster than complete-screen queries. PSDoodle is the first system to allow interactive search of screens via interactive sketching. PSDoodle is built on top of a combination of the Rico repository of some 58k Android app screens, the Google QuickDraw dataset of icon-level doodles, and DoodleUINet, a curated corpus of some 10k app icon doodles collected from hundreds of individuals. In our evaluation with third-party software developers, PSDoodle provided similar top-10 screen retrieval accuracy as the state of the art from the SWIRE line of work, while cutting the average time required about in half.
COVID-19 School Closures in Uganda and their Impact on the Well-being of Teachers in Private Institutions in Semi-urban Districts

Objective: This article explored how teachers were supported for continued professional growth and on their general well-being before and after the onset of the COVID-19 pandemic. Experiences of teachers of private schools in Semi-Urban districts during COVID-19 Wave 1&2 lockdown in Uganda are explored. It assess professional growth aspects and their well-being before and during closure of education institutions. The study illustrates challenges experienced by the teachers as well as coping strategies adopted. Methods: In-depth individual interviews, and key informant interviews were employed. At the time of reaching saturation, one hundred and three private secondary school teachers had been interviewed; eight key informants that were head teachers of selected private schools. Data was analyzed using the Conservation of Resources (COR) theory. Results: Teachers’ professional growth is in limbo after suffocation during the COVID-19 given longtime school closures with inability of private schools to earn from their clientele. The well-being of teachers is wanting. Many teachers ought not return to teaching and are engaged in various trades for a living. Conclusion: Teachers of private academic institutions contribute to government’s effort in providing quality education. Exploring their experiences during emergencies such as the current COVID-19 pandemic informs stakeholders in education on how to deal with their professional growth and general well-being. Conclusion: Teachers of private academic institutions contribute to government’s effort in providing quality education. Exploring their experiences during emergencies such as the current COVID-19 pandemic informs stakeholders in education on how to deal with their professional growth and general well-being.
TransER: Homogeneous Transfer Learning for Entity Resolution

Entity resolution (ER) is the process of linking records that refer to the same entity across one or more databases. While recent advances in supervised learning can provide high quality results for ER, these often come with large efforts to obtain labelled training data. Transfer learning (TL) can overcome this expensive labelling task by utilising existing training data from a semantically re-lated source domain to classify instances in a target domain where no training data are available. However, most existing TL solutions for ER involve deep learning models that have shown to be mostly useful for long textual and unstructured attributes. These models are less successful for short structured attributes such as personal data that are known to contain variations and typo-graphical errors. In this paper, we propose a novel TL framework for resolving entities in structured data. We assume homogeneous domains that have the same feature space (same attribute types and similarity functions) for transferring. As records are sourced from different domains, there however can be three key challenges. The marginal probability distributions of the data in the two domains can be different, there can be feature vectors that have contradicting labels in the two domains resulting in different class conditional probability distributions, and the class imbalance and bi-modal data distributions common in ER make it challenging to apply existing TL methods. We address these challenges with three contributions: an instance selector to choose source instances with a high confidence and a similar local structure to the target domain, a label generator that creates pseudo labels for target instances, and a final classifier that labels target instances using only high confidence pseudo labels. On seven data sets we show that our framework outperforms the best of several state-of-the-art methods by up to 13% in
Growing Like India—the Unequal Effects of Service‐Led Growth

Structural transformation in most currently developing countries takes the form of a rapid rise in services but limited industrialization. In this paper, we propose a new methodology to structurally estimate productivity growth in service industries that circumvents the notorious difficulties in measuring quality improvements. In our theory, the expansion of the service sector is both a consequence—due to income effects—and a cause—due to productivity growth—of the development process. We estimate the model using Indian household data. We find that productivity growth in nontradable consumer services such as retail, restaurants, or residential real estate was an important driver of structural transformation and rising living standards between 1987 and 2011. However, the welfare gains were heavily skewed toward high‐income urban dwellers.
Computational anatomy strategies for characterization of brain patterns associated with Alzheimer’s disease

Computational anatomy strategies for characterization of brain patterns associated with Alzheimer’s disease Alzheimer’s disease (AD) is one of the most complex systematic malfunctions of the nervous system that are known. The clinical symptoms of this neurodegenerative disease are alterations in cognition and behaviour that can lead to the onset of a dementia syndrome. Disease mechanisms that lead to neurodegeneration and cognitive impairment in sporadic AD are not well understood yet, making it difficult to predict the clinical progression of patients at the early stages of the AD continuum. Currently, no single biomarker or exam is sufficient to diagnose AD and existing standard instruments are not sensitive enough to detect subtle changes, predict the clinical course, and recognize heterogeneous forms of AD. This thesis presents two computational anatomy strategies aiming to identify and quantify neurodegeneration patterns associated with different clinical stages along the AD continuum using two different modalities of magnetic resonance imaging. A third contribution consists of a data-driven strategy to develop a set of domain-specific scores that result useful to estimate the risk of and predict the progression from mild cognitive impairment to dementia. Evaluation of these strategies with machine-learning and statistical inference methods demonstrate the potential of the proposed quantitative tools to help patients’ clinical management and monitoring and could be used to improve the evaluation of potential disease-modifying interventions.
Phytochemical, Elemental, physico-chemical, and Anticancer investigations of Gynura lycopersicifolia DC

Phytochemical constituents are responsible for medicinal activity of plant species. Hence in the present study Qualitative and quantitative phytochemical screening, Physicochemical, elemental analysis and anticancer activity of Gynura lycopersicifolia a medicinal plant was carried out. Qualitative and quantitative phytochemical analysis of whole plant extract confirm the presence of various secondary metabolites like saponins, triterpenoids, steroids, tannins, alkaloids, flavonoids and phenols. The results suggest that the phytochemical properties for curing various ailments and possess potential antiinflammatory, antimicrobial and antioxidant activities leads to the isolation of new and novel compounds. Physicochemical reveals that dry matter 96.4% followed by Cellulose 25.34%. Elemental analysis reveals Potassium 8.92% followed by Calcium 2.82%. Along with this, macros, micro elements which are essential for maintaining the animal body were also determined quantitatively in whole plant found. Anticancer activity of G. lycopersicifolia exhibited potential anticancer activity towards HeLa (Human Cervix Adenocarcinoma) cancer cell lines, shows 74.39% cell death with cell viability 25.61% at 200 μg/ml. The presence of various bioactive compounds confirms the application of Gynura lycopersicifolia for various ailments by traditional practitioners.
EVALUASI KEBERHASILAN IMPLEMENTASI SISTEM INFORMASI DENGAN PENDEKATAN HOT FIT MODEL (Studi Kasus : Perpustakaan STMIK AMIKOM Purwokerto)

Penelitan ini bertujuan untuk mengevaluasi dan mengetahui tingkat keberhasilan implementasi sistem informasi perpustakaan di STMIK AMIKOM Purwokerto dengan mengacu pada model evaluasi Human Organization Technology (HOT - Fit Model). Objek penelitian ini adalah SLiMS (Senayan Library Manajemen Systems) yang bersifat open source yang dikeluarkan oleh Kemendiknas. Metode analisa data penelitian ini menggunakan SEM PLS untuk pengujian hipotesis dan uji validitas dan Realibilitas instrumen dalam penelitian. Hasil penelitian menunjukkan bahwa penerapan SLiMS belum sepenuhnya berhasil karena ditemukan beberapa fitur dalam SLiMS yang tidak sesuai dengan kebutuhan petugas perpustakaan oleh karena itu perlu diadakan perbaikan dan pengembangan sistem agar sesuai dengan kebutuhan sistem pada STMIK AMIKOM Purwokerto. Kualitas sistem, kualitas informasi dan kualitas sistem berpengaruh positif terhadap penggunaan sistem dan kepuasan pengguna sedangkan kepuasan pengguna dan struktur organisasi berpengaruh terhadap Net benefit ( tingkat signifikansi 0,05).
The design of Indonesian SARS‐CoV‐2 primers based on phylogenomic analysis of the SARS‐CoV‐2 clades

Molecular detection needs to be augmented for COVID‐19 detection in Indonesia using the PCR method with primer‐based gene analysis. This is necessary because the RNA of the SARS‐CoV‐2 virus, the causative infectious agent of the pandemic, has been mutated. Therefore, this study aimed to develop a primer design for determining SARS‐CoV‐2 clades in Indonesia using phylogenomic analysis. Data were obtained from 38 GISAID (Global Initiative on Sharing All Influenza Data) viruses and the relationships were analyzed using maximum likelihood (ML) phylogenomic analysis with a substitution model of generalized time‐reversible (GTR) to construct the tree topology. The results showed that the five types of SARS‐CoVs‐2 clades in Indonesia were L, G, GH, GR, and O. It also indicated that the GH region had the highest rate of clade at 50%, with the S clade affecting its formation. Furthermore, the genome sequences of the GH type used to design its primer were based on three genes, namely RdRp, S, and N. The RdRp and N genes were found to be conserved and hardy mutants, while the S gene occurred repeatedly. Several previous studies have stated that the designed primers produced missense mutations compared to another in silico. Therefore, three sets of primers were achieved from the GC contents and clamps, Tm range, and structural secondary indicator standards.
Design and modelling of a fixturing system for an optimal balancing of a part family

Dimensional errors of the parts of a part family cause the initial misplacing of the workpiece on the fixture affecting the final product quality. Even if the part is positioned correctly, the external machining forces and clamping load cause the part to deviate from this initial position depending upon that external load and the stiffness of the fixture. In this thesis, a comprehensive analytical model, consist of a kinematic and a mechanical model, of a 3-2-1 fixturing system is proposed. The kinematic model relocates the initially misplaced workpiece in the machine reference by the axial advancements of the six locators considering all the fixturing elements to be rigid. This repositioned part again displaces from the corrected position under the clamping and machining forces. The mechanical model calculates this displacement of the part considering the locators and clamps to be elastic. The rigid cuboid baseplate, used to precisely relocate the workpiece, is also considered elastic at the contacts with the locators. The non-linear behavior of the contact deformation is linearized by the converging the deformation of locators till the required precision is attained. Using small displacement hypothesis with zero friction at contacts, Lagrangian formulation enables us to calculate the rigid body displacement of the workpiece, deformation of each locator following minimum energy, and stiffness matrix and mechanical behavior of the fixturing system. This displacement of the workpiece is again compensated by the advancement of the six axial locators calculated through the kinematic model.
Creating Low-cost Intrinsic Motivation Course Conversions in a Large Required Engineering Course

When attempting to create education reform, reformers often point to the unsupportive reward structures and significant time investments for training of faculty as two major impediments to change. In this paper, we present our efforts to minimize these “costs” and develop low-cost, intrinsic motivation course conversions. This intrinsic motivation course conversion aims to lower faculty costs and promote students’ intrinsic motivation to learn in order to create sustainable reform and life-long learners. We describe our design process to create such an IM course conversion, and present our evaluation of the conversion. The results indicate that we can create a shift towards intrinsically-motivated students who experience positive learning experiences at low cost to the faculty.
Childhood apraxia of speech in cases with comorbidities Apraxia de fala Infantil em quadros com comorbidades Apraxia de fala Infantil com comorbidades

Introduction: Childhood apraxia of speech (CAS) can result from neurological impairments associated with complex neurobehavioral disorders or have an unknown nosological entity. Objective: To relate the comorbidities combined with CAS and its clinical manifestations. Data sources: This systematic review was conducted according to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The search for scientific articles was carried out in the Medline database (via Pubmed), LILACS, Scopus and SciELO. The search terms used in English were: Comorbidity, Apraxias, Childhood apraxia of speech, Dyspraxia, Speech, and the terms in between. All studies whose subjects had some diagnosed syndrome and / or disorder and apraxia of speech were included. Initially, titles and abstracts of the articles were analyzed. Three independent reviewers evaluated the full articles and made their selections according to the eligibility criteria. At all stages, disagreements were sorted out by consensus. The main data collected was in relation with the occurrence of comorbidities in subjects with CAS. Summary of the findings: The most common comorbidities connected with CAS were Autism Spectrum Disorder, Rolandic Epilepsy and Down Syndrome. Genetic causes come as a predictor of the association between CAS and these comorbidities. Conclusions: Since CAS is a disorder of heterogeneous manifestation, it can come along with several comorbidities, which generates a late diagnosis, for CAS and the secondary disorder, hindering the process of assessment and early intervention, compromising the development of important speech skills.
The Expectations Gap

The output gap hinges on estimates of potential GDP, which can vary widely and are ultimately unobservable. Beckworth (2020) circumvents this using historical surveys of professional forecasters’ nominal GDP projections to construct an “expectations gap,” which shifts the benchmark for economic performance to what GDP was expected to be. However, this approach has some limitations, which we address as follows: (1) we assess the sensitivity of the expectations gap to alternative surveys of professional forecasters and different forecast horizons; (2) we use forecaster disagreement to capture the uncertainty surrounding the expectations gap; (3) we estimate both the nominal and real expectations gaps and compare them against other conventional output gap measures; (4) we produce real-time forward-looking estimates of the expectations gap and assess their performance near business cycle turning points. Though the expectations gap is closely correlated with existing output gap measures, we find that several historical episodes suggest that it contains additional useful information about the state of the economy.
A Supervised Skyline-Based Algorithm for Spatial Entity Linkage

The ease of publishing data on the web has contributed to larger and more diverse types of data. Entities that refer to a physical place and are characterized by a location and different attributes are named spatial entities . Even though the amount of spatial entity data from multiple sources keeps increasing, facilitating the development of richer, more accurate and more comprehensive geospatial applications and services, there is unavoidable redundancy and ambiguity. We address the problem of spatial entity linkage with SkylineExplore-Trained ( SkyEx-T ), a skyline-based algorithm that can label an entity pair as being the same physical entity or not. We introduce LinkGeoML-eXtended ( LGM-X ), a meta-similarity function that computes similarity features specifically tailored to the specificities of spatial entities. The skylines of SkyEx-T are created using a preference function, which ranks the pairs based on the likelihood of referring to the same entity. We propose deriving the preference function using a tiny training set (down to 0.05% of the dataset). Additionally, we provide a theoretical guarantee for the cut-off that can best separate the classes, and we show experimentally that it results in a near-optimal F-measure (on average only 2% loss). SkyEx-T yields an F-measure of 0.71-0.74 and beats the existing non-skyline-based baselines with a margin of 0.11-0.39 in F-measure. When compared to machine learning techniques, SkyEx-T is able to achieve a similar accuracy (sometimes slightly better one in very small training sets) and more importantly, having no-parameters to tune and a model that is already explainable (no need for further actions to achieve explainability).
Investment feasibility analysis of rainwater harvesting in the city of Itapiranga, Brazil

This study presents an investment feasibility analysis of rainwater harvesting in the city of Itapiranga, southern Brazil. The analysis was performed using the Netuno computer programme and daily rainfall data. Different catchment areas, daily potable water consumption, number of residents, coefficient of utilization and rainwater demand were simulated. By combining such variables, the potential for potable water savings as well as ideal tank capacities were obtained for 144 cases. For the investment feasibility analysis, costs of system components were obtained from local stores. Water and electricity tariffs were obtained from the local utilities; monthly potable water savings and electricity costs due to the use of a motor pump were estimated. The net present value, the corrected payback and internal rate of return were then estimated. Only 45 cases were feasible; all of them for high potable water consumption and large catchment areas. Based on this study it can be concluded that rainwater harvesting, although not financially feasible for all cases, would contribute to high potable water savings. This would benefit society as residents would have a lower potable water consumption, and the public water utility would need to treat lower amounts of water. Therefore, there would be a lower environmental impact. Also, rainwater harvesting in Itapiranga would allow the creation of new jobs and need of experts on this field.
EFFECTIVENESS DECOCTION OF RED BETEL LEAVES (PIPER CROCANTUM) AGAINST DECREASED SYMPTOMS OF FLUOR ALBUS IN HIGH SCHOOL GIRLS

Background: Fluor albus is excessive fluid from vagina that is not menstruation. To reduce the symptoms of fluor albus, non-pharmacological treatment from the decoction water of red betel leaves (Piper c rocantum) was used in the present study. These red betel leaves (Piper c rocantum) contain alkaloids that the green betel leaves do not have, and the alkaloids play a role as an antimicrobial agent. Besides, the red betel leaves have antiseptic power that is twice higher than the green betel leaves’. The red betel leaves (Piper c rocantum) also contain carvakrol which is anti-fungal and disinfectant, and it that can be used as an antiseptic medicine to maintain oral cavity health, cure fluor albus disease, and alleviate bad odor. Purpose: To discover the effectiveness of the decoction of red betel leaves in reducing the fluor albus symptoms. Method: This study uses quasi-experimental research design with one group undertaking pre- and post-tests. It involved 20 adolescent girls of Al-Adzkia Sentul Boarding School in which the sample was taken using purposive sampling techniques. Further, a closed ended questionnaire is used to collect data, then analyzed using paired T -test. Result: The results showed a meaningful difference before being given treatment for 13.40. After treatment the value became 8.27 with a p-value < of α (0.005)