Perception of e-Learning Among Hungarian Engineering Students

The success of developing e-learning is determined by curriculum quality, the availability of technological requirements and, to an even greater extent, by user response to the technology introduced in the process. Digital enhancement of education through e-learning solutions should also take the unique attributes of the targeted local region, institution, target group and field of expertise into consideration prior to implementation. The paper reports on research conducted to understand the approach of engineering students (n=94) to e-learning supported by Moodle in Hungary, including computer use, evaluation of e-learning materials, systems and online exams. The research used an online questionnaire for exploring the motivation and restrictive factors of using e-learning. Survey findings confirm that e-learning functions primarily in a complementary way to traditional learning. 87.2% of the students in the sample just download the learning materials and use those offline often or regularly. 58% of them find the usefulness of e-learning materials good, but structure or aesthetics is evaluated weak or moderate by more than half of the respondents. Considering the exams, 38% of the students with previous experience in online exams prefer the traditional exams, while 25% prefer the online format. Since access to technological tools and services required for effective e-learning is available, continuous training of the teachers and tutors is necessary both for developing their everyday skills and recognizing the LCMS opportunities.

Antivirus Applied to Jar Malware Detection based on Runtime Behaviors

Background and Objective: Java vulnerabilities correspond to 91% of all exploits observed on the World Wide Web. Then, this present work aims to create an antivirus software with machine learning and artificial intelligence, master in Java malwares detection.. Methods: Within the proposed methodology, the suspect Jar sample is executed in order to infect, intentionally, Windows OS monitored in a controlled environment. In all, our antivirus monitors and ponders, statistically, 6,824 actions that the suspected Jar file can do when executed. Results: Our antivirus achieves an average performance of 91.58% in the distinction between benign and malwares Jar files. Different initial conditions, learning functions and architectures of our antivirus are investigated in order to maximize their accuracy.Conclusions: The limitations of commercial antiviruses can be supplied by intelligent antiviruses.Instead of blacklist-based models, our antivirus allows Jar malware detection in a preventive way and not in a reactive manner as Oracle's Java and traditional antivirus modus operandi.

Online Learning at SD Muhammadiiyah 1 Krian during the Covid-19 Pandemic

The development of education today is influenced by the rapid progress of information and communication technology. One of the information technologies that play a role in the world of education in online learning. Online learning functions as a link between edeucators and students with an internet network that can be accesed anytime and anywhere. The online media used by the research subjects were various (WhatsApp, Google Classroom, Website). In online learning, teachers have the challenge of creating creative, smart (smart teaching), ), and fun learning (joyful learning). A teacher must pay attention to the context of students, supporting facilities and infrastructure, as well as the demands of the curriculum so that it can create effective and enjoyable learning. The purpose of this study was to determine the learning strategies at SD Muhammadiiyah 1 Krian online. The method collected in data collection was a question and answer question to the resource person, namely one of the teachers at SD Muhammadiiyah 1 Krian. The methods used during online learning in the midst of this pandemic are numerous.

Towards Understanding the Spectral Bias of Deep Learning

An intriguing phenomenon observed during training neural networks is the spectral bias, which states that neural networks are biased towards learning less complex functions. The priority of learning functions with low complexity might be at the core of explaining the generalization ability of neural networks, and certain efforts have been made to provide a theoretical explanation for spectral bias. However, there is still no satisfying theoretical result justifying the underlying mechanism of spectral bias. In this paper, we give a comprehensive and rigorous explanation for spectral bias and relate it with the neural tangent kernel function proposed in recent work. We prove that the training process of neural networks can be decomposed along different directions defined by the eigenfunctions of the neural tangent kernel, where each direction has its own convergence rate and the rate is determined by the corresponding eigenvalue. We then provide a case study when the input data is uniformly distributed over the unit sphere, and show that lower degree spherical harmonics are easier to be learned by over-parameterized neural networks. Finally, we provide numerical experiments to demonstrate the correctness of our theory. Our experimental results also show that our theory can tolerate certain model misspecification in terms of the input data distribution.

Comparison of library website function access tools 2014–2018

PurposeFour years after the release of a responsive design website that provides a dynamic screen layout across three access devices (computer, tablet and smartphone), a repeat study was conducted to determine changes in the way that library website functions are and are not accessed.Design/methodology/approachA survey, similar to the original study, was used to determine the rate of access of 20 website functions by three access devices (computer, tablet and smartphone) and a “did not use” (DNU) category. A two-proportion Z-test was used to determine significant differences in the rate of access for each website function and the “did not use” (DNU) category by each access device from 2014 to 2018.FindingsThe computer is still the primary tool used to access website functions, but its rate of use is declining, while access via the smartphone is increasing, including research and for learning functions traditionally accessed only by computer. Access by tablet, with poor website function access rates in 2014, declined in use, with some rates approaching zero. Increases in the DNU category for website functions occurred, but reasons why were not determined. The increases raised questions about the relevance of some of the website functions and if other actions are needed to increase use.Originality/valueRepeating the 2014 study provided insights into the changing landscape of device preferences for accessing library website functions. The number of significant changes identified demonstrates the importance of recurring studies to determine how online access to library website functions evolves over time.

A review on consequences of English language while writing a research paper

Balla, E. (2016)Now a days English is most important language around the world. there is no study can be started without using English and its learning. It is widely used in number of states and official language of united nations, widely spread language. Author says that continuous learning towards foreign language it's better to communicate easily and it is going to be easier to remind. Learning is the continuous process to focus on modern English learning functions at school level for better improvement in early stage of students.

Implementation of E-Learning Functions With the Use of Information Systems Architecture

E-learning utilizing information systems framework has been booming in information systems. This paper covers the systematic performance evaluation results process, and understanding how e-learning utilizing information system implementation was undertaken for education in the context of universities. The aim of this paper is to analyse the framework of improving e-learning utilizing information system adoption utilizing information system, implementation, and development in higher education. Although e-learning utilizing information systems have been widely used at various education sectors, several fixed outcomes have been calculated. Recent e-learning utilizing information systems structure was implemented utilizing current method and managed for the drawbacks of recent academic technique. There are many things that are not flexible and effective for supporting the real era of this world referring to teaching and learning.

Kebijakan Sekolah terhadap Penggunaan Gadget dalam Pembelajaran IPS Terpadu

This study aims to determine the use of gadgets in Integrated Social Studies learning in class VII C of SMP Ma'arif 1 Ponorogo and to determine the implementation of school policies in the use of gadgets in SMP Ma'arif 1 Ponorogo. This study uses a qualitative research method in which the data collected in the form of words, pictures and not numbers like quantitative research. The data analysis technique in this study uses inductive analysis and the type of research is case study. Data was collected by using interview, observation and documentation techniques. The technique of checking the validity of the data is done by triangulation of sources and triangulation of techniques. The data analysis technique uses the Miles and Huberman model analysis technique by using the steps of reducing data, presenting data, and drawing conclusions. The results of this study indicate that the use of gadgets in class VII C SMP Ma'arif 1 Ponorogo in integrated social studies learning functions as a learning facility that is used to work on questions, look for material that has not been understood and look for integrated social science material that is not in the book, gadgets at the time of writing. Lesson hours may not be used but are still under the supervision of the teacher.

Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure

Developing multifunctional and diversified artificial neural systems to integrate multimodal plasticity, memory, and supervised learning functions is an important task toward the emulation of neuromorphic computation. Here, we present a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The artificial synapse is composed of an optoelectronic transistor based on graphene/MoS2 heterostructure and an integrated triboelectric nanogenerator. By controlling the charge transfer/exchange in the heterostructure with triboelectric potential, the optoelectronic synaptic behaviors can be readily modulated, including postsynaptic photocurrents, persistent photoconductivity, and photosensitivity. The photonic synaptic plasticity is elaborately investigated under the synergistic effect of mechanical displacement and the light pulses embodying different spatiotemporal information. Furthermore, artificial neural networks are simulated to demonstrate the improved image recognition accuracy up to 92% assisted with mechanical plasticization. The mechano-photonic artificial synapse is highly promising for implementing mixed-modal interaction, emulating complex biological nervous system, and promoting the development of interactive artificial intelligence.

Deep Residual Network in Network

Deep network in network (DNIN) model is an efficient instance and an important extension of the convolutional neural network (CNN) consisting of alternating convolutional layers and pooling layers. In this model, a multilayer perceptron (MLP), a nonlinear function, is exploited to replace the linear filter for convolution. Increasing the depth of DNIN can also help improve classification accuracy while its formation becomes more difficult, learning time gets slower, and accuracy becomes saturated and then degrades. This paper presents a new deep residual network in network (DrNIN) model that represents a deeper model of DNIN. This model represents an interesting architecture for on-chip implementations on FPGAs. In fact, it can be applied to a variety of image recognition applications. This model has a homogeneous and multilength architecture with the hyperparameter “L” (“L” defines the model length). In this paper, we will apply the residual learning framework to DNIN and we will explicitly reformulate convolutional layers as residual learning functions to solve the vanishing gradient problem and facilitate and speed up the learning process. We will provide a comprehensive study showing that DrNIN models can gain accuracy from a significantly increased depth. On the CIFAR-10 dataset, we evaluate the proposed models with a depth of up to L = 5 DrMLPconv layers, 1.66x deeper than DNIN. The experimental results demonstrate the efficiency of the proposed method and its role in providing the model with a greater capacity to represent features and thus leading to better recognition performance.