Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of an early childhood program in rural areas of Zambia

In 2004, Plan introduced the Early Childhood Care and Development (ECCD) program in rural areas of Zambia. The goal of this program was to ensure that all children receive quality care and to prepare young children for going to primary school. This study will provide a description of the quality of the ECCD program in practice and indicate how the quality of the program can be strengthened. In order to examine the quality of the program in practice, observations were carried out at ten ECCD centres. Since the program is community-based, the views of different stakeholders were included in this study as well. Interviews were conducted with caregivers, committee members, grade 1 teachers, head teachers and Plan staff members. The results of this study showed that there is a lack of quality within the ECCD program because several aspects that are relevant to achieve a quality early childhood program are not realized in practice. According to the participants, there is a lack of motivated and trained caregivers. Furthermore, the participants indicated that there are insufficient materials and facilities at the centres. The lack of these elements was also observed during the field visits. If more attention is given to caregivers‟ training and the monitoring process the quality of the program can be improved. However, it is important that the community is not dependent on the resources of Plan Zambia and that they are able to manage the centre by themselves. This study shows that early child care programs learning and social-emotional development. The program provides a place get for Issa,
Grass-weed challenges, herbicide resistance status and weed control practices across crop establishment systems in Ireland’s mild Atlantic climate

The challenges of grass-weed control in a mild Atlantic climate, added to by the use of non-inversion tillage and limited herbicide options necessitated on-farm knowledge acquisition on grass weeds in Ireland. We surveyed 103 farms from 2020 to 2021 of which, 62 were plough-based and 41 non-inversion tillage. The survey comprised a questionnaire to determine grower demographics, grass-weed challenges, and the adoption of integrated grass-weed control methods (IWM); a grid-square assessment of key grass weeds encountered in one or more cereal fields in both years using weed scores from 0 (absent) to 10 (total weed cover); with samples collected for herbicide-resistance testing. Across the 103 farms, Bromus spp. , (62%) and Avena fatua (56%) were the most prevalent but with moderate resistance (8% acetolactate synthase (ALS)-resistant Bromus, 10% acetyl-CoA carboxylase (ACCase)-resistant A. fatua); Lolium multiflorum (13%) and Alopecurus myosuroides (16%) were found on fewer farms but with higher resistance levels (56% ACCase and/or ALS-resistant L. multiflorum, 43% ACCase/ALS-resistant A. myosuroides). Of those who adopted non-inversion tillage, a higher proportion of growers practiced crop rotation (88% cereal/non-cereal break or 83% alternate spring/winter-sown) and used cover crops (71%) than those ploughing (52-66% crop rotation or 19% cover crops), but herbicide use was similar in both systems. Despite higher levels of IWM practices, non-inversion tillage farms had higher weed scores (2.2 ± 0.2 or 3.8 ± 0.7) of B. sterilis and L. multiflorum than ploughing (1.2 ± 0.2 or 2.0 ± 0.7). Considering the complex resistance profile of high resistance-risk species, there is a need for urgent determination of and adoption of effective IWM across systems, specifically, non-inversion tillage. Most of the growers were aware of herbicide-resistant grass weeds (>80%) and most (>90%) used IWM practices (4 or more) to some extent. In our survey, non-inversion tillage farms tended to have younger growers with more formal education and larger farms. This is the first multi-component survey in Ireland establishing long-term monitoring of grass-weed challenges, grower characteristics and IWM practices.
Development of a new craving index for anticipating quitting smoking in patients who undergo the Japanese smoking cessation therapy

INTRODUCTION The 10-item version of the Questionnaire of Smoking Urges (QSU-brief) has demonstrated excellent reliability. However, the QSU-brief may be too long to use in clinical settings. We developed a new craving index called the Tobacco Craving Index (TCI) and investigated how closely the TCI grade is associated with success of quitting smoking in Japanese smoking cessation therapy (SCT) patients. METHODS The TCI questionnaire consists of two items: the first question asks about the strength of tobacco craving on a 4-point scale, and the second question asks about the frequency of tobacco craving per day on a 4-point scale. We conducted a prospective cohort study of 85 participants who underwent the Japanese SCT at a Japanese smoking cessation clinic. We administered the QSU-brief and TCI at each of the 5 sessions during the 12-week SCT. RESULTS Significant correlations were observed between the TCI grade and QSU-brief score (r=0.27, 0.55, 0.72, 0.58 and 0.68, at the five sessions). The change in mean TCI grade showed a similar trend as the change in mean QSU-brief score among the 43 patients who succeeded in quitting smoking and also among the 7 patients who failed to quit smoking by the last session. Both TCI and QSU-brief assessed after the second session were significantly associated with the smoking status at the last session. The area under the receiver operating characteristic curve for the success of quitting smoking in TCI grade was 0.615–0.881 at the 5 sessions, whereas it was 0.536–0.849 in QSU-brief score. CONCLUSIONS The TCI can be used as a predictive tool for success of quitting smoking in the Japanese SCT. As the TCI consists of two questionnaire items, it can be easily administered in smoking cessation interventions.
Relaxamento de demanda em processos de seleção de fornecedores

1/11 Resumo: Os processos de seleção de fornecedores geralmente avaliam um conjunto de critérios afim de melhor atender uma demanda. Mas e se a própria demanda pudesse ser repensada, expandindo as possibilidades de aquisição? Quais os impactos em custo e utilidade de se comprar objetivos ao invés de materiais pré-determinados? Este trabalho introduz essa discussão por meio de uma abordagem em programação matemática para seleção de fornecedores com relaxamento da demanda. Os pré-requisitos para seu desenvolvimento e aplicação em um departamento de suprimentos também são apontados. A adoção do modelo proposto em associação com conceitos de leilões combinatórios em um caso hipotético, mas realista, sugere um alto potencial para redução de custos sem redução significativa na utilidade. Palavras-chave: Seleção de fornecedores; Compras; Relaxamento da demanda. Abstract: Supplier selection processes generally evaluate several criteria in order to fulfill a demand. But, what if the demand itself could be rethought, spreading the possibilities of acquisition? What are the impacts in cost and utility of buying objectives instead of pre-determined materials? This work tries to expand this discussion with a mathematical programming approach to selecting suppliers with demand relaxation. The requirements for the framework development and application in purchasing departments are also pointed out. Its adoption in association with combinatorial auctions concepts in a hypothetical but realistic context suggests a high potential to cost reduction with no significant utility decrease.
Smartphone based Improved Floor Determination Technique for Multi-Floor Buildings

In recent years, most of the existing Indoor Localization Systems (ILSs) mainly used the Wi-Fi based fingerprinting approach to determine the floor position in multi-floor buildings. However, this method suffers from large fluctuation on the Wi-Fi signals which reduces the accuracy of floor estimation. Besides, it is also computationally extensive due to its heavy database size. For resolving these issues, we propose an improved floor determination technique using the atmospheric pressure sensor built in a smartphone. In this paper, the statistical features are extracted from the atmospheric pressure data, then the Fuzzy C-Mean (FCM) clustering algorithm is applied on them to improve the floor determination technique. The proposed work has been evaluated on the “Indoor Positioning Indoor Navigation” (IPIN) 2016 competition dataset. Experimental results are compared with three other well known methods and the participants of IPIN 2016. The proposed method exhibited better results for floor estimation with the accuracy of 98.68%, and the time complexity of proposed method also improved to O(1).
MODELING OF THE PRESSING FORCES USING GENETIC PROGRAMMING

In this paper, for the research of the models of genetically programed pressing forces, experimentally given values of pressing forces of axial symmetric stratified wood were used, given by the procedure of bending and simultaneous sticking while being warmed up by high frequency electricity. Namely, during the experiment the pressing forces were measured as output values, with variable parameters which determine the conditions of the experiment that are defined by independent varying input process variables. Those are: contact pressure, tangent voltage and strains determined by the thickness of the workpiece. For research purposes, special tools were made by using necessary measurement devices, and for the production of the models a software package for genetic programing called GPdotNET was used. This program was developed for modeling and optimization by using genetic programing. Based on the results given by the GP model, the coefficient of multiple regression was calculated thus implying that the GP model exquisitely describes the interpretation process.
Phase transitions and optimal algorithms in the semi-supervised classfications in graphs: from belief propagation to convolution neural networks

By analyzing Bayesian inference of generative model for random networks with both relations (edges) and node features (discrete labels), we perform an asymptotically exact analysis of the semi-supervised classfication problems on graph-structured data using the cavity method of statistical physics. We unveil detectability phase transitions which put fundamental limit on ability of classfications for all possible algorithms. Our theory naturally converts to a message passing algorithm which works all the way down to the phase transition in the underlying generative model, and can be translated to a graph convolution neural network algorithm which greatly outperforms existing algorithms including popular graph neural networks in synthetic networks. When applied to real-world datasets, our algorithm achieves comparable performance with the state-of-the art algorithms. Our approach provides benchmark datasets with continuously tunable parameters and optimal results, which can be used to evaluate performance of exiting graph neural networks, and to find and understand their strengths and limitations. In particular, we observe that popular GCNs have sparsity issue and ovefitting issue on large synthetic benchmarks, we also show how to overcome the issues by combining strengths of our approach.
Aerosol-induced changes in sky polarization pattern: potential hint on applications in polarimetric remote sensing

ABSTRACT Polarization is the intrinsic property of electromagnetic wave and has not been fully investigated in remote sensing field. The incident solar radiation is polarized by the components in the atmosphere and shows stable patterns. Therefore, the sky polarization could be served as an important information source for navigation. However, how different factors affecting the variations of sky polarization pattern is still a complex question, making it difficult to apply sky polarization pattern in navigation. Aerosol is one of the major sources for this uncertainty. Current studies mainly neglect aerosol or just consider aerosol optical depth (AOD), without considering microphysical properties of aerosol. In this study, we perform the influences of both aerosol mode and AOD on the trends of sky polarization pattern. The results indicate that scattering aerosol mode and absorbing aerosol mode have different effects on sky polarization pattern. Over ocean surface, scattering aerosol tends to decrease the magnitude of sky polarization pattern, while absorbing aerosol tends to increase that. Over land surface, both aerosol modes tend to decrease the magnitude of sky polarization, and scattering aerosol mode is more effectively. The discoveries in our study suggest that to utilize sky polarization pattern information, it is necessary to get detailed aerosol information especially the aerosol mode information.
Cryptographic Attribute-Based Access Control (ABAC) for Secure Decision Making of Dynamic Policy With Multiauthority Attribute Tokens

This article aims to establish a cryptographic solution to improve security and reliability of the National Institute of Standards and Technology's attribute-based access control (ABAC) model. By breaking down the existing structure of attribute-based encryption, we propose a new cryptographic ABAC (C-ABAC) framework with dynamic policy authorization and real-time attribute credentials. Moreover, a practical C-ABAC construction is proposed to support provable policy decision making and verifiable attribute Tokens among multiple distributed authorities. In this construction, we develop a concrete approach of generating a cryptographic policy from access control markup language. We also prove that attribute Token has existential unforgeability under chosen-attribute and chosen-nonce attacks, and the cryptographic policy is existentially unforgeable under chosen-object attack. In addition, our C-ABAC construction provides semantic security against chosen-plaintext attack with Token and policy queries under the extended general Diffie–Hellman exponent assumption. Finally, we evaluate the performance of the C-ABAC system according to complexity analysis and experimental results. The results show that the C-ABAC system is reliable and easy to implement.
Towards Multilingual Sign Language Recognition

Sign language recognition involves modeling of multichannel information such as, hand shapes, hand movements. This requires also sufficient sign language specific data. This is a challenge as sign languages are inherently under-resourced. In the literature, it has been shown that hand shape information can be estimated by pooling resources from multiple sign languages. Such a capability does not exist yet for modeling hand movement information. In this paper, we develop a multilingual sign language approach, where hand movement modeling is also done with target sign language independent data by derivation of hand movement subunits. We validate the proposed approach through an investigation on Swiss German Sign Language, German Sign Language and Turkish Sign Language, and demonstrate that sign language recognition systems can be effectively developed by using multilingual sign language resources.
Giant cell myocarditis in the explanted heart of an orthotopic heart transplant recipient - a case report

Giant cell myocarditis (GCM) is an unusual cause of cardio- myopathy. Because of its fulminant clinical course and specific treatment, it should be taken into consideration in patients with a new onset of left ventricular heart failure, which is difficult to treat with standard therapy. Giant cell myocarditis may present as an atrioventricular block as well. The outcome is fatal, unless the patient undergoes a heart transplantation. Mean survival time from the first symptoms to end-point (transplantation or death) is 5.5 months. We would like to present a case of a patient who was successfully treated with heart transplantation for cardiomyopathy caused by GCM,
Understanding How to Use Static Analysis Tools for Detecting Cryptography Misuse in Software

The use of cryptography is nowadays common in software systems, with cryptographic libraries widely available to software developers. As such, the likely weakest link in sensitive software has moved from cryptographic function implementations to the application code surrounding such functions. Ordinary developers usually lack knowledge in practical cryptography, and support from specialists is rare. Frequently, these difficulties are addressed by running static analysis tools to automatically detect cryptography misuse during coding and reviews. However, the effectiveness of such tools is not yet well understood. This article studies how well programmatic misuse of cryptography is detected by free static code analysis tools. The performance of such tools in detecting misuse is correlated to coding tasks and use cases commonly found in development efforts; also, cryptography misuse is classified in comprehensive categories, easily recognizable by software security practitioners. Our research shows that the coverage of public-key cryptography by static code analysis tools is full of blind spots, because tools prioritize only those misuses related to the most frequent coding tasks and use cases, while neglecting infrequent use cases. We found that, in addition to a relatively low recall in our tests, evaluated tools also have a small overlap regarding the misuses detected by all the evaluated tools, as well as an intersection of false alarms, suggesting lack of discrimination between specific misuses and corresponding good uses of cryptography. In spite of that, well-selected tools can be useful when developing cryptographic software, but support of experts is still required for solving complex cases.
Challenges and Control in Performance Management System Implementation: A Case Study

Change in business environment requires organizations to strive to maintain performance. Performance Management System (PMS) coupled with control mechanisms can be used to improve performance. This study aims to explain the challenges encountered during the implementation of PMS in a Government Linked Company (GLC). The study also examined how controls can be used to mitigate the challenges. An explanatory case study method is used and data is collected through semi-structured interviews, document reviews, informal conversations and observations. The results indicate that challenges to implement PMS include employee readiness, PMS objectives and direction; PMS measurements; and management commitment. Proposed control systems include belief, boundary, diagnostic and interactive. Findings from this study can be used as guidelines by other organizations implementing change in PMS. Findings can also be used by policy makers to improve policies regarding PMS in GLCs. Theoretically this study contributes to line of literature in PMS using institutional isomorphism theory.
Stratifying Lie Strata of Hilbert Modular Varieties

In this survey we explain a stratiﬁcation of a Hilbert modular variety M E in characteristic p > 0 attached to a totally real number ﬁeld E . This stratiﬁcation reﬁnes the stratiﬁcation of M E by Lie type, and has the property that many strata are central leaves in M E , called distinguished central leaves . In the case when the totally real ﬁeld E is unramiﬁed above p , this stratiﬁcation reduces to the stratiﬁcation of M E by α -type ﬁrst introduced by Goren and Oort and studied by Yu, and coincides with the EO stratiﬁcation on M E . Moreover it is known that every non-supersingular α -stratum of M E is irreducible. To treat the general case where E may be ramiﬁed above p , a key ingredient is the notion of congruity , a p -adic numerical invariant for abelian varieties with real multiplication by O E in characteristic p . For every Lie stratum N e on M E , this new invariant deﬁnes a ﬁnite number of locally closed subsets Q c ( N e ), and N e is the disjoint union of these Lie-congruity strata Q c ( N e ) in N e . The The Hecke transitivity implies, according to the method of prime-to- p monodromy of Hecke invariant subvarieties, every non-supersingular distinguished central leaf in a Hilbert modular variety M E is irreducible. The last irreducibility result is a key ingredient of the proof the Hecke orbit conjecture for Siegel modular varieties. dimensions, irreducible components and smoothness of strata of f ∗ l S EO • and the incidence relation between strata.
A Comprehensive Analysis of Semantic Compositionality in Text-to-Image Generation

In this study, we analyze the structure of a feature representation space in a deep neural text-to-image generative model in order to explore the possibility that the model implicitly acquires the semantic compositionality while generating images from captions as an explicit task. This is a fundamental approach toward concept acquisition by grounding between linguistic expressions and images. We analyze the semantic compositionality in a text embedding space in a generative model. Our experimental result suggests that the semantic compositionality appears among words indicating positions. This study is the ﬁrst attempt to explore the semantic compositionality in text-to-image generation.
Behavioural Biases of Overconfidence and Disposition Effect and their Impact on Investment Decision in the Indonesian Capital Market

The Odean methodology revealed that investors are more susceptible to realizing profits than losses as indicated by disposition effects. This study intended to analyze the effect of overconfidence bias and the disposition effect on investment decisions in Indonesia. The sampling method was purposive sampling, with the respondents being 40 young investors who were members of Club Investors. Data collection was done by distributing questionnaires. The analysis method in this study was quantitative analysis. The analytical tool used was binomial regression analysis and multiple linear regression. Binomial regression was used to discover investor behavior and whether it tended to be biased or rational in its investment decisions. After discovering the attitude of the investors, be it biased or rational, the effect of the bias was analyzed using multiple linear regression. A multiple linear test was used to predict the probability of influence of each investment decision variable. The results showed that overconfidence had an effect on investment decisions. Some investors tend to experience an overconfidence bias in decision making, which shows that the investors feel very confident in their experience and ability to choose stocks. The investors believe that the results will be in line with their expectations. Keywords—disposition effect, investment decision,