Reviewerly/exHarmony huggingface dataset:
Thermal modeling and simulation for engineering education using Simulink

This article deals with an approach to simulation of thermal processes using MATLAB&Simulink. The article briefly describes the current state of educational processes during pandemia, issues of moving lectures, practices and labs online. Using MATLAB and Simulink for virtual labs is proposed as a solution which does not demand real complicated lab equipment with remote access. Some examples of the model creation process are described. Simulink models of typical thermal processes such as natural and forced convection, steady-state and transient thermal conductivity, mass transfer are given. The results of this work are mainly intended for modeling thermal processes in the educational process in the fields of energy and electrical engineering, chemical engineering, metallurgy, civil engineering, mechanical engineering etc. The models can be useful for virtual laboratory work arrangement. The results can be used for scientific and engineering calculations. Full version of the described model's package is available in the public repository.
An Improved Flower Pollination Algorithm for Global and Local Optimization

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.
Augmenting Alzheimer’s Research: Schol‐AR

Abstract Background The multi‐disciplinary field of Alzheimer’s disease research relies on many modern forms of data, from neuroimaging volumes and cellular image‐stacks to population and genetic information. Unfortunately, much of this information is difficult to communicate through the standard communication of scientific articles and static images, often resulting in incomplete data reporting and suboptimal comprehension for many readers. Method We introduce an augmented reality technique to layer modern digital information ontop of printable scientific figures, enabling full datasets to be attached to scientific articles. This system is additionally paired with an uploading framework that allows authors to create and disseminate augmentations from their own data. Result The capabilities provided through both the augmented viewing and augmentation creating systems facilitate communication and comprehension of the complex information underlying Alzheimer’s research. Conclusion Augmented reality technologies can improve and modernize the state of scientific communication in Alzheimer’s research, bridging the gap between modern digital data and journal article communications.
Using 3D Scanning to Study Composite Slotted Tools from the Early Holocene of Eastern Siberia (the Case of Kazachka-1 Site)

The article demonstrates the potential applications of 3D scanning of composite slotted tools and the use of models as a basis for further scientific research. The scanning was carried out with high-precision equipment (Solutionix D700 scanner). The scanner provides high precision data acquisition of the geometry and surface of an artefact. As examples of archaeological artefacts we used composite slotted tools from the site of Kazachka-I. There were two bone tools from cultural layers 19 and 11. As a result of three-dimensional scanning application, a series of high-resolution scaled models was created. The series of cross-sections and metric measurements were made on the obtained models with the use of special software. Through analysis of cross-sectional shapes and comparison with experimental modelling data, conclusions on the basic process steps in slotted tools manufacture have been drawn. V-, U- and W-shaped cross-sections have been identified. Slots of bone tool from layer 19 has V- and U-shaped cross-sections. Slots of bone tool from layer 11 have U- and W-shaped cross-sections. The variety of cross-sectional shapes may indicate different techniques in the manufacture of the slots. The U-shaped cross-section may have been created by cutting the slot with a stone tool, the edge of which has been broken in the process. The W-shape may indicate that the direction of motion of the tool in this section was changed during operation. The V-shape was obtained by using a sharp tool throughout the slotting process. These conclusions are preliminary and need to be verified by a series of experiments and use-wear analysis.
User Interface Adaptation through Ontology Models and Code Generation

The development of adaptive user interfaces becomes increasingly complex due to the rapid growth of interaction devices. This has promoted a great interest towards the study of adaptive user interface in the Human Computer Interaction (HCI) field. Consequently, various model-driven approaches have been introduced to further such fields. However, these approaches did not make use of semantic models and thus did not support knowledge reuse for representing user interfaces and adaptation rules. To tackle these issues, we present in this work the user interface generation component that concerns the development of adaptive user interfaces from ontology models. In this regard, a model driven approach along with ontology models represent the main artifact of the proposed component to allow knowledge reuse and to improve the semantics of the generated user interfaces. To illustrate the feasibility of the present component, we provide a concrete case study that highlights the adaptation of the generated user interfaces in different contexts of use.
IMPLEMENTATION POSSIBILITY OF A LIKE DEEP LEARNING ALGORITHMS ON NON-DEEP NETWORKS WITH HIDDEN LAYERS

Learning deep neural networks using the backpropagation algorithm is considered implausible from a biological point of view. Numerous recent publications offer sophisticated models for biologically plausible deep learning options that typically define success as achieving a test accuracy of around 98 % in the MNIST dataset. Here we examine how far we can go in the classification of numbers (MNIST) with biologically plausible rules for learning in a network with one hidden layer and one reading layer. The weights of the hidden layer are either fixed (random or random Gabor filters), or are trained by uncontrolled methods (analysis of main/independent components or sparse coding), which can be implemented in accordance with local training rules. The paper shows that high dimensionality of hidden layers is more important for high performance than global functions retrieved by PCA, ICA, or SC. Tests on the CIFAR10 object recognition problem lead to the same conclusion, indicating that this observation is not entirely problem specific. Unlike biologically plausible deep learning algorithms that are derived from the backpropagation algorithm approximations, we have focused here on shallow networks with only one hidden layer. Globally applied, randomly initialized filters with fixed weights/Gabor coefficients (RP/RGs) of large hidden layers result in better classification performance than training them with unsupervised methods such as principal/independent analysis (PCA/ICA) or sparse coding (SC). Therefore, the conclusion is that uncontrolled training does not lead to better performance than fixed random projections or Gabor filters for large hidden layers.
Macchiato

Since caches are shared and coherent, a memory access of one process may evict from the cache another process' memory block with an address mapped to the same cache line. This property is exploited by several attacks to form side channels. We show that MAC learning in Software Defined Networks (SDNs) has a similar property in the sense that a MAC address discovered by one network device may be revoked by the discovery of the same address at another switch. This allows us to implement Macchiato, a covert channel for SDNs between any two network devices (including hosts); prior SDN covert channels required at least one malicious switch. We evaluate a prototype implementation of Macchiato and discuss how methods to improve the performance of cache side channels (such as deep neural networks) can also be used in Macchiato.
Localized Tamper Detection in Digital Images Using Three Phase Reversible Watermarking

The prime requirement for industries dealing with sensitive data is to ensure authentication and integrity of the received data. Tampering in received digital image may occur due to different types of corruption and attacks affecting complete or part of the image. In this paper, we present a localization of tampered regions in received image using three phase reversible watermarking which allows the receiver to reject only the selective tampered regions. In this scheme, the image is divided into blocks, the integrity value is then computed taking into account all the pixels within block and the corresponding block number, subsequently this value is reversibly embedded in the block. Further, the current and next blocks are combined dynamically when embedding capacity of a current block is not sufficient. The experimental results on some test images indicate that the watermarked image has high PSNR and SSIM value with relatively low false rejection rate also the results under various attacks on watermarked image demonstrates the effectiveness of the algorithm.
NeuralDPS: Neural Deterministic Plus Stochastic Model With Multiband Excitation for Noise-Controllable Waveform Generation

The traditional vocoders have the advantages of high synthesis efficiency, strong interpretability, and speech editability, while the neural vocoders have the advantage of high synthesis quality. To combine the advantages of two vocoders, inspired by the traditional deterministic plus stochastic model, this paper proposes a novel neural vocoder named NeuralDPS which can retain high speech quality and acquire high synthesis efficiency and noise controllability. Firstly, this framework contains four modules: a deterministic source module, a stochastic source module, a neural V/UV decision module and a neural filter module. The input required by the vocoder is just the spectral parameter, which avoids the error caused by estimating additional parameters, such as F0. Secondly, to solve the problem that different frequency bands may have different proportions of deterministic components and stochastic components, a multiband excitation strategy is used to generate a more accurate excitation signal and reduce the neural filter's burden. Thirdly, a method to control noise components of speech is proposed. In this way, the signal-to-noise ratio (SNR) of speech can be adjusted easily. Objective and subjective experimental results show that our proposed NeuralDPS vocoder can obtain similar performance with the WaveNet and it generates waveforms at least 280 times faster than the WaveNet vocoder. It is also 28% faster than WaveGAN's synthesis efficiency on a single CPU core. We have also verified through experiments that this method can effectively control the noise components in the predicted speech and adjust the SNR of speech. Examples of generated speech can be found at https://hairuo55.github.io/NeuralDPS.
Algorithms for linear time reconstruction by discrete tomography II

The reconstruction of an unknown function f from its line sums is the aim of discrete tomography. However, two main aspects prevent reconstruction from being an easy task. In general, many solutions are allowed due to the presence of the switching functions. Even when uniqueness conditions are available, results about the NP-hardness of reconstruction algorithms make their implementation inefficient when the values of f are in certain sets. We show that this is not the case when f takes values in a field or a unique factorization domain, such as R or Z. We present a linear time reconstruction algorithm (in the number of directions and in the size of the grid), which outputs the original function values for all points outside of the switching domains. Freely chosen values are assigned to the other points, namely, those with ambiguities. Examples are provided.
Homography Estimation Network Based on Dense Correspondence

Reliable homography estimation is essential for plenty of computer vision tasks. However, the ground truth of homography is difficult to obtain. Moreover, it is relatively hard for ordinary convolutional neural networks to model correspondence, which leads to the difficulty for optimization. In this paper, we propose a novel framework based on unsupervised dense correspondence. We extract feature pyramid for each image and estimate the dense correspondence map in a coarse-to-fine manner. Relying on correlation layers and photometric loss, we can get rid of the deficiency of ordinary networks and estimate reliable dense correspondence for each image pair, which facilitates the later prediction of homography matrix. To achieve the homography training on both synthetic dataset and realistic dataset, we apply different losses for different tasks respectively being supervised loss and triplet loss, to regulate the training procedure. Qualitative and quantitative evaluations illustrate the effectiveness of the dense correspondence map, and shows the competitive performance of our method compared with traditional feature-based algorithms and deep learning methods.
The Integration of Problem Based Learning Model on Indonesian Language E-Modules

There are still many teachers who have difficulty using technology to develop teaching materials that can facilitate students' learning. This has an impact on student learning outcomes are low. This study aims to develop E-module with problem-based learning model. This type of research is development using the ADDIE model. The subject of the expert test consisted of 3 people, namely 1 subject expert, 1 learning design expert, and 1 learning media expert. The product trial subjects consisted of 9 students. The methods used in collecting data are observation, interviews, and questionnaires. The instrument used in collecting data is a questionnaire. The technique used to analyze the data is descriptive qualitative and quantitative analysis. The results of the study were the assessment given by the subject matter expert, which was 96.47 (very good). The assessment of learning design experts is 93.33% (very good). The assessment of learning media experts is 97.5% (very good).Individual trial results, that is 95.53% (very good), and the result of small group trial is 95.53% (very good). It can be concluded that E-modules with problem-based learning models are valid and feasible to be applied in the learning process. The implication of this research is that the E-module with the PBL model can be used by teachers in learning.
Exploring the factors making online learning attractive and enjoyable

The pandemic forced the universities to go online and brought in front the online lectures as focal activities.A lot of effort has been put to move the teaching and learning resources on the online platform which became the main interaction space between students and between students and teachers.An exclusive online learning environment has both benefits and disadvantages, especially as regards the students' engagement with the online lectures.Maintaining students' interest is difficult and depends on several factors.The purpose of the paper is to explore the factors that make online learning attractive and enjoyable.A formatively measured model having as focal construct the intrinsic motivation has been tested on a sample of 354 Romanian university students.The results highlight the students' need for self-presentation and show that social learning features are key factors that make online learning attractive and enjoyable.
Room and Laboratory Equipment Lending Based On Smartphone Application Using QR Code Technology

The main problem in this research is dependency lab activities on the presence of a technician and complicated administrative process which take five procedure stages of lending at D3 and D4 Electronic Engineering polban. This research aims to create an equipment that is able to eliminate the dependency of the presence of technician, and to reduce the complicated administrative process. The QR code technology is the method used in this research to control the opening of lab doors and equipment cupboards by scanning the QR code with a mobile application. The result of this research show that the procedure borrowing of equipment facilities can be shortened from five procedurals to three procedurals. The QR Code information is able open the door of the room and laboratory cabinets by inputing password that been set at the database and able to borrow equipment that is integrated with the automatic recording of borrowing equipment.
Robust Dual Polarization Encryption Scheme based on a Tailored Pixelated Polarizer

We propose a Tailored DPES based on a tailored pixelated polarizer to encrypt target images into a predefined uniformly distributed noisy image. Relying on Mueller–Stokes formalism, it provides more robustness and flexibility than DPES.
Ontological Foundations of Cognition in Quantum Physics

The aim of the article is to analyze the ontological foundations of quantum mechanics and the relationship of various interpretations of quantum mechanics with the question of truth in scientific knowledge. The theoretical material (various interpretations of quantum mechanics) was analyzed. The research methods were analysis, comparative-historical and institutional approaches. On the basis of the study, it can be concluded that physicists are only able to predict the probabilities of physical phenomena or processes, without any knowledge of the structure of entity in itself. The study helps to complement the theoretical picture of the ontology of quantum mechanics and to get an idea about the development prospects of the methodology of physical sciences. We should not ascribe properties that are unobservable in experience to the entity because science can truly interpret only the representation of entity in itself in experience and the ways of dealing with it, not future events or possible states of the entity. Otherwise, science “slides” down to the level of dialectical (rhetorical) exercises based not on true premises, but on generally accepted or “authoritative” biases, which gives room to extreme forms of relativism and takes natural scientists away from studying nature to inventing empirically unsupported hypotheses. When, in accordance with the Cartesian ontology, the scientist assumes that the entity is as the “subject” observes it (that is, the “reflection” of the entity in consciousness is identified with entity in itself), this assumption leads the scientist to attempts to “scientifically” describe the future events or the possible states of the entity. The criterion for the truth of a scientific theory put forward by Einstein in his dispute with Bohr remains a fundamentally unattainable ideal. Cognition of nature is probabilistically and fundamentally incomplete since a person can consciously comprehend natural phenomena only within the framework of their representation in the existing being. The probabilistic nature of quantum mechanics is due to the impossibility of knowing things as they exist by themselves, that is, as they exist outside of representation in the mind of the observer. This does not mean that physical entities did not exist before their knowledge: they existed potentially, but were not valid for the observer, that is, conscious. Quantum physics is as incomplete as any other physical theory when compared with an unattainable ideal, but this fact does not negate its truth for modern scientists since quantum theory is based on the results of verified experiments.
Application and Research of Folk Music Composition Based on Artificial Intelligence Technology

Music is one of the important forms of human emotion expression. Using artificial methods and technologies to allow the machine to quickly identify optical scores and real-time music sounds, through the inference and optimization of music emotion models, obtain human music emotion expression patterns, and actively complete human-machine collaboration with intelligent composition, and other related services. The development of multi-source perception of affective artificial intelligence has important research value and practical significance. This article mainly integrates multiple models of existing artificial intelligence models (typed music models, Markov chain models, genetic algorithm models, neural network models, etc.) to improve the rule knowledge to form a practical and effective hybrid model. Its innovation lies in combining formulas and matrix data combinations to intelligently compose non-note units (reproducing structures) in automatic composition, so as to achieve harmony, audibility and intelligence of music.
Classifying regencies and cities on human development index dimensions: Application of K-Means cluster analysis

The Human Development Index (HDI) is a measurement that analyzes a region's development in improving human development. The government's development plan aims to create a successful and peaceful life. The unbalanced development in every regency and city in Indonesia is a typical issue during the development process. It may also be shown that the HDI level changes across regencies and cities in Indonesia. This research aims to identify Indonesian regencies and cities based on HDI indices. K-Means clustering algorithm is the clustering method adopted. The results of the analysis formed 4 clusters. The first cluster consisted of 20 regencies with a low average HDI indicator. The second cluster consisted of 148 regencies and cities with an average HDI indicator is medium. The third cluster consisted of 88 regencies and cities with an average HDI indicator. The fourth cluster consists of 258 regencies and cities with high HDI indicators.
Impact of Blockchain Technology on Changing Role of Human Resource Management Operations

Human resource management is an area where blockchain can be leveraged to manage and sign smart contracts and audit. Human resource operations may benefit from blockchain by enhancing data gathering, verification, and validation along with strengthening security of sensitive data. It has the potential to provide value to both the HR function and the whole business in the long run by reinforcing the overall performance management system. At the moment, third-party vendors are using blockchain technology to offer services such as recruiting, payroll and grievance handing.