Reviewerly/exHarmony huggingface dataset:
SUPPORTING BUSINESS MODEL INNOVATION BASED ON DEEP LEARNING SCENE SEMANTIC SEGMENTATION

The capacity to create innovative Business Models (BM) has become the foundation for numerous businesses. Business Model Innovation (BMI) grows more significant as digitalization influences our everyday lives and prompts the development of better approaches for working, imparting and collaborating in this computerized universe of Industry 4.0. In this paper we present a conceptual architecture which can be applied in the modern video-conference systems with the help of semantic segmentation. The scene represents an environment, intended for discussion of ideas in business modeling. The semantic segmentation allows each pixel of an image (or video) from the scene to be related or classified to a specific type of object. In this way it is possible to interpret the description of a scene by the machine. Thus, with the help of the proposed architecture, the processes taking place between objects and people in the surrounding environment can be analyzed for the purpose of digitization of BMI by modelling human behavior and cognitive processes into logical expressions that can be digitized and automated. The semantic segmentation is considered as a basic element in this type of interaction. We demonstrate the effectiveness of our algorithm in with real data examples.
Automated Snake Bite Prevention System

Snakebite is nt an illness without a treatment, anyway a substantial number of grievous setbacks go untreated each year. Many governments basically put this issue in the difficult to handle basket. Hence we have made a gadget which will recognize snakes using cameras and spurn the distinguished snake using a snake repeller. This should provoke an uncommon decrease in the number of snakebites and in like manner in the number of deaths. We are planning a basic, easy, convenient gadget which will automate the procedure of snake repeller.
Sooner Lightweight Cryptosystem: Towards Privacy Preservation of Resource-Constrained Devices

The use of cryptosystem became popular because of the increased need for exchanges across untrusted medium especially Internet-enabled networks. On the basis of application several forms of cryptosystems have been developed for purpose of authentication, confidentiality, integrity, and non-repudiation. Cryptosystems make use of encryption schemes that convert plaintext to ciphertext in diverse areas of applications. The vast progressions in the Internet of Things (IoT) technology and resource-constrained devices have given rise to massive deployment of sensor devices and growth of services targeted at lightweight devices. Though, these devices support a number of services, they require strong lightweight encryption approaches for privacy protection of data. Existing lightweight cryptosystems fall short on the expected privacy levels and applicability in emerging resource-constrained environment. This paper develops a mathematical model for a Sooner lightweight cryptographic scheme based on reduced and hardened ciphertext block sizes, hash sizes and key sizes of traditional cryptosystems and Public Blockchain technology for ubiquitous systems. Thereafter, the hardening procedure offered by the RSA homomorphic encryption was applied for the purpose of generating stronger, secure and lightweight AES, RSA and SHA-3 in order to deal with untrusted channels exchanges. The proposed Sooner is recommended for adoption in public Blockchain-based smart systems and applications for the purpose of data privacy.
Peramalan Data Indeks Harga Konsumen Berbasis Time Series Multivariate Menggunakan Deep Learning

Multivariate Time Series based forecasting is a type of forecasting that has more than one criterion changes from time to time that it can forecast based on historical patterns of data sequences. The Consumer Price Index (CPI) issued regularly every month by the Statistics Indonesia calculated based on data observations. This study is a development of previous research that only used on type of algorithm to predict CPI value resulting poor of accuracy due to lack of architecture variations testing. This study developed a CPI forecasting model with a new approach about using several types of deep learning algorithms, namely LSTM, Bidirectional LSTM, and Multilayer Perceptron with architectural variations of the number of neurons and epochs. Furthermore, this study adapt ADDIE model of Research and Development method. Based on the results, the best accuracy is obtained from the LSTM Bidirectional with 10 neurons and 2000 epoch resulting 3,519 of RMSE value. Meanwhile, based on the average RMSE value for the whole test, LSTM gets the smallest average of RMSE followed Bidirectional LSTM and Multilayer Perceptron with the RMSE value 4,334, 5,630, 6,304 respectively.&#x0D;
LINEARLY DEPENDENT AND CONCISE SUBSETS OF A SEGRE VARIETY DEPENDING ON k FACTORS

We study linearly dependent subsets with prescribed cardinality, $s$, of a multiprojective space. If the set $S$ is a circuit, we give an upper bound on the number of factors of the minimal multiprojective space containing $S$, while if $S$ has higher dependency this may be not true without strong assumptions. We describe the dependent subsets $S$ with $\#S=6$.
Early Stopping by Gradient Disparity

Validation-based early-stopping methods are one of the most popular techniques used to avoid over-training deep neural networks. They require to set aside a reliable unbiased validation set, which can be expensive in applications offering limited amounts of data. In this paper, we propose to use \emph{gradient disparity}, which we define as the l2 norm distance between the gradient vectors of two batches drawn from the training set. It comes from a probabilistic upper bound on the difference between the classification errors over a given batch, when the network is trained on this batch and when the network is trained on another batch of points sampled from the same dataset. We empirically show that gradient disparity is a very promising early-stopping criterion when data is limited, because it uses all the training samples during training. Furthermore, we show in a wide range of experimental settings that gradient disparity is not only strongly related to the usual generalization error between the training and test sets, but that it is also much more informative about the level of label noise.
Maximum Power Point Tracking u sing Light Dependent Resistor and DC motor for Solar Photovoltaic System in Kuwait

In this paper a Maximum Power point (MPP) tracking system is developed using dual-axis DC motor feedback tracking control system. An efficient and accurate DC motor system is used to increase the system efficiency and reduces the solar cell system coast. The suggested automated DC motor control system based on the photovoltaic ( PV ) modules operated with the μ-microcontroller. This servo system will track the sun rays in order to get MPP during the day using direct radiation. A photometric cell is used to sensor the direct sun radiation and to feed a signal to the μ microcontroller and then select the DC motor mechanism to deliver optimum energy. The proposed system is demonstrated through simulation results. Finally, using the proposed system based on microcontroller, the system will be more efficient, minimum cost, and maximum power transfer is obtained.
Unsupervised and scalable low train pathology detection system based on neural networks

<h2>Abstract</h2> Currently, there exist different technologies applied in the world of medicine dedicated to the detection of health problems such as cancer, heart diseases, etc. However, these technologies are not applied to the detection of lower body pathologies. In this article, a Neural Network (NN)-based system capable of classifying pathologies of the lower train by the way of walking in a non-controlled scenario, with the ability to add new users without retraining the system is presented. All the signals are filtered and processed in order to extract the Gait Cycles (GCs), and those cycles are used as input for the NN. To optimize the network a random search optimization process has been performed. To test the system a database with 51 users and 3 visits per user has been collected. After some improvements, the algorithm can correctly classify the 92% of the cases with 60% of training data. This algorithm is a first approach of creating a system to make a first stage pathology detection without the requirement to move to a specific place.
Daydriex: Translating Nighttime Scenes towards Daytime Driving Experience at Night

What if the window of our cars is a magic window, which transforms dark views outside of the window at night into bright ones as we can see in the daytime? To realize such a window, one of important requirements is that the stream of transformed images displayed on the window should be of high quality so that users perceive it as real scenes in the day. Although image-to-image translation techniques based on Generative Adversarial Networks (GANs) have been widely studied, night-to-day image translation is still a challenging task. In this paper, we propose Daydriex, a processing pipeline to generate enhanced daytime translation focusing on road views. Our key idea is to supplement the missing information in dark areas of input image frames by using existing daytime images corresponding to the input images from street view services. We present a detailed processing flow and address several issues to realize our idea. Our evaluation shows that the results by Daydriex achieves lower Fréchet Inception Distance (FID) scores and higher user perception scores compared to those by CycleGAN only.
Towards Higher Software Quality in Very Small Entities

This paper focuses on software and systems process improvement in small companies, for which the ISO/IEC 29110 series systems and software lifecycle profiles for very small entities (VSEs) has been developed and started to be implemented worldwide. The ISO/IEC 29110 series consists of VSE profiles. The most implemented profile worldwide has been the software basic profile. Published experience gained from the pilot projects and software basic profile implementations showed that software quality and testing are not sufficiently covered. In order to improve the effects of the implementation of the ISO/IEC 29110 software basic profile and increase the quality of a developed software product, the aim of this research is to provide process mapping between the ISO/IEC 29110 software basic profile and selected test improvement approaches, particularly the ISO/IEC/IEEE 29119-2 and ISO/IEC 20246 testing standards, and TMMi. The results of the mapping are presented in the form of the mapping tables and the ArchiMate model.
Governing ID: Principles of Evaluation

As governments across the globe implement new and foundational digital identification systems (Digital ID), or modernize existing ID programs, there is an urgent need for more research and discussion about appropriate uses of Digital ID systems. Using examples from India, Kenya, Estonia, and Jamaica, this framework provides tests, which can be used to evaluate the governance of Digital ID across jurisdictions, as well as determine whether a particular use of Digital ID is legitimate. Through three kinds of checks - Rule of Law tests, Rights based tests, and Risks based tests - this scheme is a ready guide for evaluation of Digital ID.
Teaching the Effect of Limestone Particle Fixation on Batik Natural Color Density in Inclusive Classroom

The purpose of this study is to teach how the effect amount of limestone particles on batik color density on students in inclusive-setting classrooms. Experiments are carried out on two groups of students. Nine students from vocational school and five students from secondary high school from the science program. The research used single-subject research with multiple baseline designs between research subjects by comparing two groups in understanding the content through applying in functional activities by giving the same activities starting with a pretest, giving material, and posttest. The experimental procedure is started by sifting the limestone with coconut milk and tea sieve and soaking the lime solution for one night. Then, taking the solution and mixed it with turmeric powder. In obtaining other color variations, the sample is diluted into water to get 1:1 ratio. The experimental results show that the finer the lime particles, the darker the color and the stronger the lime concentration produce. It shows that the concentration of the solution greatly affects the color density. This learning method is capable of cultivating the curiosity of all students including students with special needs. However, for students with double disabilities, they require direct practice and concise guidance.
On the memory benefits of repeated study with variable tasks.

Encoding variability refers to the situation in which repeated items are processed in different ways on each presentation. Superior memory performance resulting from encoding variability is sometimes argued to underlie important phenomena in human memory such as the spacing effect. However, the memory benefits of encoding variability are often elusive. Here we investigated encoding variability in ten experiments in which participants studied words with the same or different orienting tasks across repetitions. We have found the benefits of variable encoding to depend on the number of learning cycles and the retrieval demands at test. These results are interpreted in light of a distinction between different components of memory representations established at study, suggesting that encoding variability promoted via different orienting tasks-as implemented in the present study-fosters more elaborate encoding of semantic features. This augmented semantic component benefits memory performance only when a memory test is used that taps predominantly semantic features of memory representations, minimizing the role of contextual and relational factors. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
A new definition of the concept of "innovative education"

It should be noted that, in principle, the natural conservatism of education may not be an obstacle to the advancement of science. The education system is developing and improving the structure. Experts are working to improve theoretical approaches to the development of new educational technologies, changing the estimated learning system in schools and other educational institutions. Innovative activity includes all structures and control of educational processes. However, there are fundamental differences in the understanding of innovative approaches between the scientific community and practical education. Let's try to examine some of the definitions of these concepts. Vocabulary Teacher Defines the Concept of "Innovative Education" - Innovation in educational processes, focused, able to make a lasting impact on the educational development of new elements that can improve some parts of the educational process and lead to change of the whole process Education.
Distribution of absorbed photons in the tunneling ionization process

Abstract We describe a procedure that allows us to solve the three-dimensional time-dependent Schrödinger equation for an atom interacting with a quantized one-mode electromagnetic field. Atom-field interaction is treated in an ab initio way prescribed by quantum electrodynamics. We use the procedure to calculate probability distributions of absorbed photons in the regime of tunneling ionization. We analyze evolution of the reduced photon density matrix describing the state of the field. We show that non-diagonal density matrix elements decay quickly, as a result of the decoherence process. A stochastic model, viewing ionization as a Markovian birth-death process, reproduces the main features of the calculated photon distributions.
Prediction with Gaussian Process Dynamical Models

The modeling and simulation of dynamical systems is a necessary step for many control approaches. Using classical, parameter-based techniques for modeling of modern systems, e.g., soft robotics or human-robot interaction, is often challenging or even infeasible due to the complexity of the system dynamics. In contrast, data-driven approaches need only a minimum of prior knowledge and scale with the complexity of the system. In particular, Gaussian process dynamical models (GPDMs) provide very promising results for the modeling of complex dynamics. However, the control properties of these GP models are just sparsely researched, which leads to a blackbox treatment in modeling and control scenarios. In addition, the sampling of GPDMs for prediction purpose respecting their non-parametric nature results in non-Markovian dynamics making the theoretical analysis challenging. In this article, we present the relation of the sampling procedure and non-Markovian dynamics for two types of GPDMs and analyze the control theoretical properties focusing on the transfer between common sampling approximations. The outcomes are illustrated with numerical examples and discussed with respect to the application in control settings.
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice

The recent theoretical investigation (Li et al., 2020) on the upper bound of generalization error of deep neural networks (DNNs) demonstrates the potential of using the gradient norm as a measure that complements validation accuracy for model selection in practice. In this work, we carry out empirical studies using several commonly-used neural network architectures and benchmark datasets to understand the effectiveness and efficiency of using gradient norm as the model selection criterion, especially in the settings of hyper-parameter optimization. While strong correlations between the generalization error and the gradient norm measures have been observed, we find the computation of gradient norm is time consuming due to the high gradient complexity. To balance the trade-off between efficiency and effectiveness, we propose to use an accelerated approximation (Goodfellow, 2015) of gradient norm that only computes the loss gradient in the Fully-Connected Layer (FC Layer) of DNNs with significantly reduced computation cost (200~20,000 times faster). Our empirical studies clearly find that the use of approximated gradient norm, as one of the hyper-parameter search objectives, can select the models with lower generalization error, but the efficiency is still low (marginal accuracy improvement but with high computation overhead). Our results also show that the bandit-based or population-based algorithms, such as BOHB, perform poorer with gradient norm objectives, since the correlation between gradient norm and generalization error is not always consistent across phases of the training process. Finally, gradient norm also fails to predict the generalization performance of models based on different architectures, in comparison with state of the art algorithms and metrics.
Exam System

Nowadays education requires modern approaches. For effective teaching, teachers need effective tools which help them lead education process while they do not have to waste time by administration. By using information system where all of administration processes are grouped together, teacher can save valuable time which can be invested into making higher quality teaching. As well as teachers, students using information system are relieved of carrying many not directly necessary stuffs required for learning process. This article analysis new Exam system which is meant to be a system for the subject administration, the administration of lessons, exams, tests. The Exam system should be used by teachers and students at the University of Zilina. In this paper the reader can find information about a concept of the exam system, its functions, and details about them.
Practical Real Time Recurrent Learning with a Sparse Approximation

Recurrent neural networks are usually trained with backpropagation through time, which requires storing a complete history of network states, and prohibits updating the weights (after every timestep). Real Time Recurrent Learning (RTRL) eliminates the need for history storage and allows for online weight updates, but does so at the expense of computational costs that are quartic in the state size. This renders RTRL training intractable for all but the smallest networks, even ones that are made highly sparse. We introduce the Sparse n-step Approximation (SnAp) to the RTRL influence matrix. SnAp only tracks the influence of a parameter on hidden units that are reached by the computation graph within n timesteps of the recurrent core. SnAp with n=1 is no more expensive than backpropagation but allows training on arbitrarily long sequences. We find that it substantially outperforms other RTRL approximations with comparable costs such as Unbiased Online Recurrent Optimization. For highly sparse networks, SnAp with n=2 remains tractable and can outperform backpropagation through time in terms of learning speed when updates are done online.
Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data

Summary For large-scale testing with graph-associated data, we present an empirical Bayes mixture technique to score local false-discovery rates (FDRs). Compared to procedures that ignore the graph, the proposed Graph-based Mixture Model (GraphMM) method gains power in settings where non-null cases form connected subgraphs, and it does so by regularizing parameter contrasts between testing units. Simulations show that GraphMM controls the FDR in a variety of settings, though it may lose control with excessive regularization. On magnetic resonance imaging data from a study of brain changes associated with the onset of Alzheimer’s disease, GraphMM produces greater yield than conventional large-scale testing procedures.
Performance of Naïve Bayes in Sentiment Analysis of User Reviews Online

Both sellers and buyers heavily depend on the opinions of customers in purchasing and selling products online. When it comes to text-based data, sentiment analysis of user reviews has become a prominent facet of machine learning. Text data is generally unstructured which makes opinion mining very challenging. A wide array of pre-processing and post-processing techniques need to be applied. But the major challenge is selecting the right classifier for the job. Naïve Bayes algorithm is a commonly used machine learning classifier when it comes to opinion mining and sentiment analysis. The focus of this survey is to observe and analyze the performance of Naïve Bayes algorithm in sentiment analysis of user reviews online. Recent research from a wide array of use-cases such as sentiment analysis of movie reviews, product reviews, book reviews, blog posts, microblogs and other sources of data have been taken into account. The results show that Naïve Bayes algorithm performs exceptionally well with accuracies between 75% to 99% across the board.