Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging crowdsourcing – activationsupporting components for IT-based idea competitions

Ideas competitions appear to be a promising tool for crowdsourcing and open innovation processes, especially for business-to-business software companies. Active participation of potential lead users is the key to success. Yet a look at existing ideas competitions in the software field leads to the conclusion that many information technology (IT)–based ideas competitions fail to meet requirements upon which active participation is established. The paper describes how activation-enabling functionalities can be systematically designed and implemented in an IT-based ideas competition for enterprise resource planning software. We proceeded to evaluate the outcomes of these design measures and found that participation can be supported using a two-step model. The components of the model support incentives and motives of users. Incentives and motives of the users then support the process of activation and consequently participation throughout the ideas competition. This contributes to the successful implementation and maintenance of the ideas competition, thereby providing support for the development of promising innovative ideas. The paper concludes with a discussion of further activation-supporting components yet to be implemented and points to rich possibilities for future research in these areas.
Corequisite model: An effective strategy for remediation in freshmen level mathematics courses

The purpose of this study was to compare students’ performances in a freshmen level quantitative reasoning course (QR) under three different instructional models. A cohort of 155 freshmen students was placed in one of the three models: needing a prerequisite course, corequisite (students enroll simultaneously in QR course and a course that provides remediation) or ready to take the QR course alone. A chi-square test revealed a statistically significant relationship between the students’ final course grades and course models. Furthermore, an ANOVA test indicated that students’ grades in QR course were higher when they completed the course under corequisite model compared to prerequisite model.
Effect of water availability on physiological performance and lettuce crop yield (Lactuca sativa)

M.A. Molina-Montenegro, A. Zurita-Silva, and R. Oses. 2011. Effect of water availability on physiological performance and lettuce crop yield (Lactuca sativa). Cien. Inv. Agr. 38(1): 65-74. Water is essential for plants, affecting the majority of the physiological processes related to growth and productivity. Water shortage is one of the most common factors that limit crop productivity worldwide. Many cultivars have elevated water requirements, nevertheless in some countries due to global climate change effects, the availability of water for irrigation is becoming limited. In fact, current models of climate changes predict that central Chile will be a sensitive zone where precipitation will drastically decrease. In this study, the variation of gas exchange and production of fresh biomass in a lettuce cultivar, grown under different water availability regimes, was evaluated. Additionally, the concentration of total soluble sugar and water use efficiency (WUE) as mechanisms related to water shortage responses were also evaluated. Overall, individuals with the lowest water availability (50%) showed lower gas exchange and fresh biomass values than their conspecifics grown in optimal irrigated conditions. On the other hand, those individuals with moderate water shortage showed the highest concentration of total soluble sugars and WUE. Our results suggested that cultures exposed to extensive or intense drought events, could be negatively affected in both physiological performance and productivity. Nevertheless, slight decreases in water availability can enable lettuce plants to exhibit a high WUE, maintaining high levels of physiological performance and productivity.
Methodological Flaws: A Review of Sample Masters Theses

This paper reports results of a review of Masters theses from four academic units at the College of Education and Behavioral Studies, Addis Ababa University, Ethiopia. The purpose of this review was to explore and reflect on the appropriateness of research designs of Masters theses across four academic units. The review focused on the research designs, tools, methods of data analyses, and sampling used in the theses. A total of 121 Masters theses were randomly selected. Thematic analysis and descriptive statistics were used to analyse the data. The review found similar research designs adopted by theses across each academic unit. Findings common to the theses under review include: qualitative data analysis was hardly explained using appropriate methods of qualitative data analysis. In addition, in most of the theses, the sample size was not determined and justified using the proper sample size calculation formula or justification. Therefore, there is a need for the college and academic units on how the research course instructors and supervisors support students to craft their research designs properly. Finally, the researchers suggest that more studies of this kind need to be conducted in the broader context in other higher education institutions in order to build up a more coherent picture of the area.
Deep learning for fine-grained visual recognition

Fine-grained object recognition is an important task in computer vision. The cross-convolutional-layer pooling method is one of the significant milestones in the development of this field in recent years. Based on the method, we conducted a number of experiments on a new fine-grained car dataset CompCars. The corresponding experiments illustrate its applicability and effectiveness on this newlydesigned dataset. Meanwhile, based on the experiments, we found out that pooling the most distinguishable regions like car logos and headlights areas in the indicator maps, which usually have higher activations, with the local features in the same regions can achieve better results than those by pooling the whole indicator maps with the corresponding local features. Therefore, we conjecture that better performance may be achieved if we have more powerful indicator maps or pooling channels that can better highlight these distinguishable regions. Based on the above hypothesis and inspired by the cross-convolutionallayer pooling, next we propose the Spatially Weighted Pooling (SWP) method, which is a simple yet effective pooling strategy to improve fine-grained classification performance. SWP learns a dozen of pooling channels or spatial encoding masks that aggregate local convolutional feature maps with learned spatial importance information and produce more discriminative features. It can be seamlessly integrated into existing convolutional neural network (CNN) architectures such as the deep residual network. It also allows end-to-end training. SWP has few parameters to learn, usually in several hundreds, therefore does not introduce much computational overhead. SWP has shown significant capability to improve fine-grained visual recognition performance by simply adding it before fully-connected layers in off-the-shelf deep convolutional networks. We have conducted comprehensive experiments on a number of widely-used finegrained datasets with a variety of deep CNN architectures such as Alex networks (AlexNet), VGG networks (VGGNet) and the deep residual networks (ResNet). By integrating SWP into ResNet (ResNet-SWP), we achieve state-of-the-art results on three fine-grained datasets and the MIT67 indoor scene recognition dataset. With ResNet152-SWP models, we obtain 85.2% on the bird dataset CUB-200-2011 without bounding-box annotations and 87.4% with bounding-box, 91.2% on FGVC-aircraft, 94.1% on Stanford-cars with bounding-box information and 82.5% on the MIT67 dataset.
Automatic Analysis of Lens Distortions in Image Registration

Geometric image registration by estimating homographies is an important processing step in a wide variety of computer vision appli- cations. The 2D registration of two images does not require an explicit reconstruction of intrinsic or extrinsic camera parameters. However, cor- recting images for non-linear lens distortions is highly recommended. Unfortunately, standard calibration techniques are sometimes dicult to apply and reliable estimations of lens distortions can only rarely be obtained. In this paper we present a new technique for automatically de- tecting and categorising lens distortions in pairs of images by analysing registration results. The approach is based on a new metric for registra- tion quality assessment and facilitates a PCA-based statistical model for classifying distortion eects. In doing so the overall importance for lens calibration and image corrections can be checked, and a measure for the eciency of accordant correction steps is given.
EFFECT OF DIFFERENT LEVELS OF PHOSPHORUS AND SULPHUR ON YIELD AND AVAILABILITY OF N P K, PROTEIN AND OIL CONTENT IN TORIA (Brassica sp.) VAR. P.T.-303

A field experiment was conducted during Rabi season 2008 to Study the effect of different levels of phosphorus and sulphur on availability of N, P, K, protein and oil content in Toria (Brassica Sp.)Var.P.T. -303 on crop research farm Department of Soil Science and Agricultural Chemistry, Allahabad Agricultural Institute- Deemed University, Allahabad. The area is situated on the right bank adjacent to Yamuna river in south of Allahabad city, which is located at 25 0 . 80' N Latitude and 81 0 . 50' E Longitude and 98 meter above the mean sea level having the mean temperature during the growing period was 4.7 to 37.2 0 C. The soil of experimental area falls in order Inceptisol and the experimental field is alluvial in nature. The design applied for statistical analysis was carried out with 3 2 factorial randomized block design having two factors with three levels of Phosphorus 0, 25, and 50 kg ha -1 , and three levels of Sulphur 0, 20, and 40 kg ha -1 , respectively. Treatments were T0 - 0 kg P2O5 + 0 kg S ha -1 -1
Touch Crossing-Based Selection and the Pin-and-Cross Technique

This thesis focuses on the evaluation, exploration and demonstration of crossing paradigm with touch modalities. Under the scenario of crossing selection, the target is selected by stroking through a boundary “goal” instead of tapping or clicking on a rectangular graphical target. Crossing has been widely studied on stylus and mouse input, however, there is no generalizable, controlled empirical support for the application of crossing to touch input. In this work we present fundamental performance results for crossingbased selection tasks with direct touch input. Our study reveals that: touch crossing is faster than or equivalent to touch pointing; continuous selection of large orthogonal crossing targets is the most effective, and continuous selection of small collinear targets is least effective. We model touch crossing with Fitts’ law in order to characterize its performance. Unlike indirect stylus and mouse crossing, not every kind of direct touch pointing performance is modeled accurately with the standard Fitts’ law. Instead, “FFitts” law, used previously for touch pointing with small targets, is employed to more accurately model discrete touch crossing with a directionally constrained target. In addition, visual touch feedback is shown to have a profound effect on task accuracy. With multi-touch input, crossing semantics could become even richer than with pen or mouse. Inspired by the positive results from our study on touch crossing selection, we develop, evaluate and demonstrate a new multi-touch interaction space called “pinand-cross”, where one or more static touches (“pins”) are combined with a cross stroke on a radial target to complete a selection, and all performed with one hand. A formative study and a comparative study are used to evaluate pin-and-cross, and an Android photo app is created to demonstrate four potential usages of pin-and-cross. The formative study is conducted to reveal the kinematic characteristics of pin-andcross, and to evaluate its fundamental performance, as well as subjective preference for target angles. These results are used to form design guidelines and a set of recognition heuristics for pin-and-cross menus invoked with one and two pin fingers on first touch or after a drag. The controlled experiment compares a one-finger pin-and-cross contextual menu with a functionally equivalent Marking Menu and a partial Pie Menu. Results of this study suggest that pin-and-cross is just as accurate and even 27% faster than existed techniques when invoked on a draggable object. Guidelines produced from the formative study are also used in the implementation of the photo app, which is created to demonstrate more pin-and-cross variations such as two-finger accelerated scrolling, drawing modes or attributes changing while drawing, two-finger constrained transformations, and pin-and-cross combined with a Marking Menu. Lastly, with touch crossing being empirically validated as a practical and efficient selection technique, and pin-and-cross being demonstrated an faster menu selection
Towards photoplethysmographic imaging : modeling, experiments and applications

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Discrete Weibull regression for modelling football outcomes

We propose the use of the discrete Weibull distribution for modelling football match results, as an alternative to existing Poisson and generalised Poisson models. The number of goals scored by the two teams playing a football match are regarded as a pairwise observation and are modelled first through two independent discrete Weibull variables, and then through two dependent discrete Weibull variables, using a copula approach that accommodates non-null correlation. The parameters of the bivariate discrete Weibull distributions are assumed to depend on covariates such as the attack and defence abilities of the two teams and the 'home effect'. Several discrete Weibull regression models are proposed and then applied to the 2015-2016 Italian Serie A. Even if the interpretation of parameters is less immediate than in the case of bivariate Poisson models, nevertheless these models represent a suitable alternative, which can be applied also in other fields than sport data analysis.
Analysis of regional disparities and wage convergence in Alabama

This paper examines the degree to which real wages have converged in Alabama over the last thirtyseven years. The increase in government transfers, improvement in information technology and possibly, other government assistance programs would suggest that, wages in spatially dispersed counties within nation-state should become similar over time. However, the interrelation between business cycles, migration, employment structure and changes in per capita earnings over time reduces this possibility. To test the convergence hypothesis, comparable county-level real wage data are obtained from the U.S. Bureau of Economic Analysis and analyzed using cross-section and time series techniques. Particularly, two hypotheses are tested: (1) whether real wages in poor economies (rural counties) are catching up with real wages in rich economies (urban counties), and (2) whether adjacency to urban areas has an effect on the transition from low wages to high wages for rural workers in Alabama. The results differ across the different measurement techniques, but in general, the findings do not confirm the convergence hypothesis within the different sub-periods, but rather patterns of fluctuating coherence. Similarly, the proximity hypothesis is rejected suggesting that, rural workers residing in counties that are contiguous to urban areas have not benefited from the potential spillover effect.
PEMBELAJARAN BERBASIS STEM DALAM PERKULIAHAN KALKULUS DI PERGURUAN TINGGI

Calculus is a must course of many major in higher education. It caused by calculus has a role as basic foundation of many theories and its application is used in many aspects of various majors. However, many students had difficulty to undersatand calculus course. Its difficulty was caused by calculus course could not been integrated with topics that was learned in each of majors. Solution of that problem is to integrated topics from various majors in calculus course and it can be done with STEM learning. STEM learning integrates science aspect (S), technology aspect (T), engineering aspect (E), and mathematics aspect (M) in learning process so it can makes calculus course to be more realistic so students can easily understand it.
Learner-content interaction in distance education: The weakest link in interaction research

Abstract Interaction is a recurrent theme in the literature on distance education. Much of the research along these lines is dedicated to reciprocal interpersonal interaction, that is, learner–learner and learner–instructor interaction. But there is far less research interest in learner–content interaction despite its fundamental and critical role in ensuring the effectiveness of the distance learning experience and education more generally. This article reflects on the interrelationship and interplay between learner–content, learner–instructor and learner–learner interactions by drawing on the three-types-of-interaction framework, equivalency theorem and other interaction research literature. It concludes by calling for more effort to be made to understand how distance learners study course materials or content, arguing that distance learning course materials may not be able to cater for distance learners and achieve their intended learning outcomes unless their design and production are informed by empirical research on learner–content interaction.
Concerning nearly metrizable spaces

The purpose of this paper is to introduce the notion of near metrizability for topological spaces, which is strictly weaker than the concept of metrizability. A number of characterizations of nearly metrizable spaces is achieved here as analogues of the corresponding ones for metrizable spaces. It is seen that near metrizability is a natural idea vis-a-vis near paracompactness, playing the similar role as played by paracompactness with regard to metrizability
A performance based study on gender recognition in large datasets

Gender recognition has achieved impressive results based on the face appearance in controlled datasets. Its application in the wild and large datasets is still a challenging task for researchers. In this paper, we make use of classical techniques to analyze their performance in controlled and uncontrolled condition respectively with the LFW and MORPH datasets. For both sets the benchmarking protocol follows the 5-fold cross-validation proposed by the BEFIT challenge.
Financial Development, Corporate Governance and Cost of Equity Capital

This paper explicitly examines the interactive impact of country level legal and financial development, and firm level governance attributes on the cost of equity capital. Using a comprehensive sample of 7380 firm years drawn from 22 developed countries, we show that firm level governance attributes affect the cost of equity capital only in the Common Law countries with high level of financial development. Our study is the first to highlight the complementary effects of legal origin, financial development and firm level governance attributes in influencing the cost of equity capital.
An Open-Source Framework For Time-Domain Simulations

In scientiﬁc research simulation of new or existing acoustical models is typically implemented using commercial numerical programming environments like Simulink/Matlab or expensive simulation packages like COMSOL or FLU-ENT. In this paper a new version of the open-source simulation library ART (Acoustic Research Tool) is presented where time-domain simulation capabilities have now been added to existing frequency domain models. The concept allows mixing of modeling elements belonging to different levels of abstraction and it relieves the user from tricky implementation details like scheduling, data dependencies and memory allocation. Starting with an equation in the z-Domain, signals can be described recursively as a function of other current or previous signal samples and local or global simulation parameters. Alternatively signals can also be generated by specifying a certain topology of pre-deﬁned elements with certain input and output ports. The library can be called from any programming environment running on Microsoft Windows or on Linux which allows it to be integrated in any application software project. The examples shown here have been written in the open-source high-level programming language Python. They can be downloaded together with the library and documentation from the project site http://artool.sourceforge.
Synaptic Time-Dependent Plasticity Leads to Efficient Coding of Predictions.

Latency reduction of postsynaptic spikes is a well-known effect of Synaptic Time-Dependent Plasticity. We expand this notion for long postsynaptic spike trains, showing that, for a fixed input spike train, STDP reduces the number of postsynaptic spikes and concentrates the remaining ones. Then we study the consequences of this phenomena in terms of coding, finding that this mechanism improves the neural code by increasing the signal-to-noise ratio and lowering the metabolic costs of frequent stimuli. Finally, we illustrate that the reduction of postsynaptic latencies can lead to the emergence of predictions.
Unbiased deep solvers for parametric PDEs

We develop several deep learning algorithms for approximating families of parametric PDE solutions. The proposed algorithms approximate solutions together with their gradients, which in the context of mathematical finance means that the derivative prices and hedging strategies are computed simulatenously. Having approximated the gradient of the solution one can combine it with a Monte-Carlo simulation to remove the bias in the deep network approximation of the PDE solution (derivative price). This is achieved by leveraging the Martingale Representation Theorem and combining the Monte Carlo simulation with the neural network. The resulting algorithm is robust with respect to quality of the neural network approximation and consequently can be used as a black-box in case only limited a priori information about the underlying problem is available. We believe this is important as neural network based algorithms often require fair amount of tuning to produce satisfactory results. The methods are empirically shown to work for high-dimensional problems (e.g. 100 dimensions). We provide diagnostics that shed light on appropriate network architectures.