A Business Intelligence Framework for Analyzing Educational Data

Currently, universities are being forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on students’ learning. These factors have forced universities to look for a solution that allows them to extract data from different information systems and convert them into the knowledge necessary to make decisions that improve learning outcomes. The information systems administered by the universities store a large volume of data on the socioeconomic and academic variables of the students. In the university field, these data are generally not used to generate knowledge about their students, unlike in the business field, where the data are intensively analyzed in business intelligence to gain a competitive advantage. These success stories in the business field can be replicated by universities through an analysis of educational data. This document presents a method that combines models and techniques of data mining within an architecture of business intelligence to make decisions about variables that can influence the development of learning. In order to test the proposed method, a case study is presented, in which students are identified and classified according to the data they generate in the different information systems of a university

Analysis of Educational Data in the Current State of University Learning for the Transition to a Hybrid Education Model

Currently, the 2019 Coronavirus Disease pandemic has caused serious damage to health throughout the world. Its contagious capacity has forced the governments of the world to decree isolation and quarantine to try to control the pandemic. The consequences that it leaves in all sectors of society have been disastrous. However, technological advances have allowed people to continue their different activities to some extent while maintaining isolation. Universities have great penetration in the use of technology, but they have also been severely affected. To give continuity to education, universities have been forced to move to an educational model based on synchronous encounters, but they have maintained the methodology of a face-to-face educational model, what has caused several problems in the learning of students. This work proposes the transition to a hybrid educational model, provided that this transition is supported by data analysis to identify the new needs of students. The knowledge obtained is contrasted with the performance presented by the students in the face-to-face modality and the necessary parameters for the transition to this modality are clearly established. In addition, the guidelines and methodology of online education are considered in order to take advantage of the best of both modalities and guarantee learning

Integrating Plant Science and Crop Modeling: Assessment of the Impact of Climate Change on Soybean and Maize Production

Increasing global CO₂ emissions have profound consequences for plant biology, not least because of direct influences on carbon gain. However, much remains uncertain regarding how our major crops will respond to a future high CO₂ world. Crop model inter-comparison studies have identified large uncertainties and biases associated with climate change. The need to quantify uncertainty has drawn the fields of plant molecular physiology, crop breeding and biology, and climate change modeling closer together. Comparing data from different models that have been used to assess the potential climate change impacts on soybean and maize production, future yield losses have been predicted for both major crops. When CO2 fertilization effects are taken into account significant yield gains are predicted for soybean, together with a shift in global production from the Southern to the Northern hemisphere. Maize production is also forecast to shift northwards. However, unless plant breeders are able to produce new hybrids with improved traits, the forecasted yield losses for maize will only be mitigated by agro-management adaptations. In addition, the increasing demands of a growing world population will require larger areas of marginal land to be used for maize and soybean production. We summarize the outputs of crop models, together with mitigation options for decreasing the negative impacts of climate on the global maize and soybean production, providing an overview of projected land-use change as a major determining factor for future global crop production

Application of a Big Data Framework for Data Monitoring on a Smart Campus

At present, university campuses integrate technologies such as the internet of things, cloud computing, and big data, among others, which provide support to the campus to improve their resource management processes and learning models. Integrating these technologies into a centralized environment allows for the creation of a controlled environment and, subsequently, an intelligent environment. These environments are ideal for generating new management methods that can solve problems of global interest, such as resource consumption. The integration of new technologies also allows for the focusing of its efforts on improving the quality of life of its inhabitants. However, the comfort and benefits of technology must be developed in a sustainable environment where there is harmony between people and nature. For this, it is necessary to improve the energy consumption of the smart campus, which is possible by constantly monitoring and analyzing the data to detect any anomaly in the system. This work integrates a big data framework capable of analyzing the data, regardless of its format, providing effective and efficient responses to each process. The method developed is generic, which allows for its application to be adequate in addressing the needs of any smart campus

Application of a Smart City Model to a Traditional University Campus with a Big Data Architecture: A Sustainable Smart Campus

Currently, the integration of technologies such as the Internet of Things and big data seeks to cover the needs of an increasingly demanding society that consumes more resources. The massification of these technologies fosters the transformation of cities into smart cities. Smart cities improve the comfort of people in areas such as security, mobility, energy consumption and so forth. However, this transformation requires a high investment in both socioeconomic and technical resources. To make the most of the resources, it is important to make prototypes capable of simulating urban environments and for the results to set the standard for implementation in real environments. The search for an environment that represents the socioeconomic organization of a city led us to consider universities as a perfect environment for small-scale testing. The proposal integrates these technologies in a traditional university campus, mainly through the acquisition of data through the Internet of Things, the centralization of data in proprietary infrastructure and the use of big data for the management and analysis of data. The mechanisms of distributed and multilevel analysis proposed here could be a powerful starting point to find a reliable and efficient solution for the implementation of an intelligent environment based on sustainability

Gate-controlled Guiding of Electrons in Graphene

Ballistic semiconductor structures have allowed the realization of optics-like phenomena in electronics, including magnetic focusing and lensing. An extension that appears unique to graphene is to use both n and p carrier types to create electronic analogs of optical devices having both positive and negative indices of refraction. Here, we use gate-controlled density with both p and n carrier types to demonstrate the analog of the fiber-optic guiding in graphene. Two basic effects are investigated: (1) bipolar p-n junction guiding, based on the principle of angle-selective transmission though the graphene p-n interface, and (2) unipolar fiber-optic guiding, using total internal reflection controlled by carrier density. Modulation of guiding efficiency through gating is demonstrated and compared to numerical simulations, which indicates that interface roughness limits guiding performance, with few-nanometer effective roughness extracted. The development of p-n and fiber-optic guiding in graphene may lead to electrically reconfigurable wiring in high-mobility devices.Comment: supplementary materal at http://marcuslab.harvard.edu/papers/OG_SI.pd

Face Video Competition

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-01793-3_73Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realise facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents the first known benchmarking effort of person identity verification using facial video data. The evaluation involves 18 systems submitted by seven academic institutes.The work of NPoh is supported by the advanced researcher fellowship PA0022121477of the Swiss NSF; NPoh, CHC and JK by the EU-funded Mobio project grant IST-214324; NPC and HF by the EPSRC grants EP/D056942 and EP/D054818; VS andNP by the Slovenian national research program P2-0250(C) Metrology and Biomet-ric System, the COST Action 2101 and FP7-217762 HIDE; and, AAS by the Dutch BRICKS/BSIK project.Poh, N.; Chan, C.; Kittler, J.; Marcel, S.; Mc Cool, C.; Rua, E.; Alba Castro, J.... (2009). Face Video Competition. En Advances in Biometrics: Third International Conference, ICB 2009, Alghero, Italy, June 2-5, 2009. Proceedings. 715-724. https://doi.org/10.1007/978-3-642-01793-3_73S715724Messer, K., Kittler, J., Sadeghi, M., Hamouz, M., Kostyn, A., Marcel, S., Bengio, S., Cardinaux, F., Sanderson, C., Poh, N., Rodriguez, Y., Kryszczuk, K., Czyz, J., Vandendorpe, L., Ng, J., Cheung, H., Tang, B.: Face authentication competition on the BANCA database. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 8–15. Springer, Heidelberg (2004)Messer, K., Kittler, J., Sadeghi, M., Hamouz, M., Kostin, A., Cardinaux, F., Marcel, S., Bengio, S., Sanderson, C., Poh, N., Rodriguez, Y., Czyz, J., Vandendorpe, L., McCool, C., Lowther, S., Sridharan, S., Chandran, V., Palacios, R.P., Vidal, E., Bai, L., Shen, L.-L., Wang, Y., Yueh-Hsuan, C., Liu, H.-C., Hung, Y.-P., Heinrichs, A., Muller, M., Tewes, A., vd Malsburg, C., Wurtz, R., Wang, Z., Xue, F., Ma, Y., Yang, Q., Fang, C., Ding, X., Lucey, S., Goss, R., Schneiderman, H.: Face authentication test on the BANCA database. In: Int’l. Conf. Pattern Recognition (ICPR), vol. 4, pp. 523–532 (2004)Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 947–954 (2005)Bailly-Baillière, E., Bengio, S., Bimbot, F., Hamouz, M., Kittler, J., Marithoz, J., Matas, J., Messer, K., Popovici, V., Porée, F., Ruiz, B., Thiran, J.-P.: The BANCA Database and Evaluation Protocol. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688. Springer, Heidelberg (2003)Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)Martin, A., Doddington, G., Kamm, T., Ordowsk, M., Przybocki, M.: The DET Curve in Assessment of Detection Task Performance. In: Proc. Eurospeech 1997, Rhodes, pp. 1895–1898 (1997)Bengio, S., Marithoz, J.: The Expected Performance Curve: a New Assessment Measure for Person Authentication. In: The Speaker and Language Recognition Workshop (Odyssey), Toledo, pp. 279–284 (2004)Poh, N., Bengio, S.: Database, Protocol and Tools for Evaluating Score-Level Fusion Algorithms in Biometric Authentication. Pattern Recognition 39(2), 223–233 (2005)Martin, A., Przybocki, M., Campbell, J.P.: The NIST Speaker Recognition Evaluation Program, ch. 8. Springer, Heidelberg (2005

Wheat-barley hybridization – the last forty years

Abstract Several useful alien gene transfers have been reported from related species into wheat (Triticum aestivum), but very few publications have dealt with the development of wheat/barley (Hordeum vulgare) introgression lines. An overview is given here of wheat 9 barley hybridization over the last forty years, including the development of wheat 9 barley hybrids, and of addition and translocation lines with various barley cultivars. A short summary is also given of the wheat 9 barley hybrids produced with other Hordeum species. The meiotic pairing behaviour of wheat 9 barley hybrids is presented, with special regard to the detection of wheat– barley homoeologous pairing using the molecular cytogenetic technique GISH. The effect of in vitro multiplication on the genome composition of intergeneric hybrids is discussed, and the production and characterization of the latest wheat/barley translocation lines are presented. An overview of the agronomical traits (b-glucan content, earliness, salt tolerance, sprouting resistance, etc.) of the newly developed introgression lines is given. The exploitation and possible use of wheat/barley introgression lines for the most up-to-date molecular genetic studies (transcriptome analysis, sequencing of flow-sorted chromosomes) are also discussed

Three-dimensional studies of pathogenic peptides from the c-terminal of Trypanosoma cruzi ribosomal P proteins and their interaction with a monoclonal antibody structural model

The acidic C-terminal peptides from Trypanosoma cruzi ribosomal P proteins are the major target of the antibody response in patients suffering Chagas chronic heart disease. It has been proposed that the disease is triggered by the cross-reaction of these antibodies with the second extra cellular loop of the β1-adrenoreceptor, brought about by the molecular mimicry between the acidic C-terminal peptides and the receptor's loop. To improve the understanding of the structural basis of the autoimmune response against heart receptors, the 3-dimensional structure of the C-terminal peptides of Trypanosoma cruzi ribosomal proteins P0 (EDDDDDFGMGALF) and P2β (EEEDDDMGFGLFD) were solved using the Electrostaticaly Driven MonteCarlo method. Their structures were compared with the second extra-cellular loop of our homology model of human rhodopsin and the existing experimental NMR structures of the C-terminal peptides from human P0 (EESDDDMGFGLFD) and from Leishmania braziliensis P0 (EEADDDMGFGLFD). Docking of Trypanosoma cruzi peptides P0, P2β and human rhodopsin loop into our anti-P2β monoclonal antibody homology model allowed to explore their interactions