Reviewerly/exHarmony huggingface dataset:
Methodological Approaches to the Information and Communication Competence Formation of Educational Institutions Students in Agro-Industrial Profile

The research purpose is to study the problem of the information and communication competence formation of future employees in the agro-industrial complex using a competence-based approach against the system-technological one. The methodological features of each approach in the formation of competencies are considered as well as the study of the interaction possibility of the competence approach against the background of system-technological one, the definition of internal methodological links between them. The question of the importance of the information and communication competence formation of educational institutions students in agro-industrial profile is investigated, since the agro-industrial complex as the main addressee of the development of agricultural education has a powerful economic basis and the potential for development. Information and communication competence, which provides the ability not only to receive, interpret, process information, but also to transmit it with full preservation of content and meaning, is currently becoming the most important factor in the effectiveness of professional activity. The formation of information and communication competence in the learning process will make it possible to prepare a competitive graduate who is able to orientate himself in an environment aimed at the qualified use of information and communication technologies in their practical professional activities.
Application of Game Technologies in a Technical University: Example of a Business Game «Determination of Thermal Resistance of an Enclosing Structure»

A brief review of the development of game technologies in higher education is given. The complexity of development and implementation of game technologies for technical disciplines which have at their core a large volume of calculation methods, clear design, calculation proof base is highlighted. Authors offer an interactive way of learning in the form of business game “Expert” for application in higher education system for technical disciplines. Development of business game “Expert” on example of “Construction physics” discipline is presented, methodology of business game is described, aims of game for students and educational aims of teacher are defined, positive and negative sides of this development are presented. The universality of this method is noted, the possibility to analyze different variants of tasks and study different ways of solution, deep mastering of the material through multiple repetitions of the checking process, and immersion in the structure of the task and calculation methodology. The disadvantages of this method and other game technologies are the significant labor costs for their development, preparation, analysis. The conclusion highlights some points of the planned research on the implementation of this technology. The experience of large-scale studies of the effectiveness of game technologies by other authors reveals a range of problems, the solution of which should be given special attention.
SA-HPMHT for Maritime Dim Targets Tracking With Sensor Location Uncertainty

To address the problem of tracking low signal-to-clutter ratio (SCR) maritime targets with sensor location uncertainty, an effective track-before-detect (TBD) method, called the state augmentation histogram probabilistic multihypothesis tracking (SA-HPMHT) algorithm, is proposed in this article. In this algorithm, the initialization of new tracks is realized based on the constant false alarm rate (CFAR) detector with a low threshold and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means clustering algorithm. The target and sensor states are then simultaneously estimated in an iterative manner via a synthetic histogram and state augmentation (SA) method. In each iteration, we update the estimated sensor state and yield more accurate measured images, allowing for improved performance of detection and tracking targets. Finally, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}/{N}$ </tex-math></inline-formula> logic technology is invoked to confirm or terminate the tracks. The results of simulation and real dataset verification show that the SA-HPMHT algorithm has a better performance compared with the histogram probabilistic multihypothesis tracking (H-PMHT) algorithm and the sequential Monte Carlo probability hypothesis density (SMC-PHD) algorithm.
Constrained Path Planning for Reconfiguration of Cable-Driven Parallel Robots

Obstacle avoidance is a challenging problem in cable-driven parallel robots (CDPRs) due to the cross-distribution of cables in the workspace. To address this challenge, a novel constrained path planning method is proposed in this article to realize the reconfiguration of CDPRs, where the cable anchor positions and cable lengths are adjusted simultaneously in the reconfiguration to achieve obstacle avoidance. To realize such reconfiguration, the loop-closure constraints are first formulated and the constrained configuration space is established in terms of the implicitly represented manifold. Then, combined with the designed wrench feasible detection and collision detection that are responsible for the positive cable tension guarantee and obstacle avoidance, the bidirectional rapidly exploring random trees are utilized to generate branches in the tangent spaces of the manifold to finally discover a feasible path in the whole constrained configuration space. Finally, simulations and experiments are implemented on the 3-DOF reconfigurable CDPR and the results indicate that the proposed method can coordinate the changes of the cable anchor positions and cable lengths by using the loop-closure constraints, and a collision-free path during the reconfiguration can be successfully found in the presence of regular or irregular obstacles.
Strategies of clausal postmodification in learner English

Abstract The difficulty of automatically extracting syntactic structures from authentic learner data has previously limited the kinds of questions addressed by means of Contrastive Interlanguage Analysis (CIA; Granger, 2015 ), or has forced researchers to resort to manual analysis of smaller corpora. This study responds to the call for greater use of parsed corpora by using automated parsing and refined corpus analysis software to investigate advanced EFL learners’ use of relative and participial clauses for nominal postmodification. The use of clausal postmodifiers inside the noun phrase may be seen as a marker of proficiency and syntactic maturity in academic writing, but the proportional use of relative and participial clauses for nominal postmodification may also be subject to L1 transfer. To investigate the role of transfer and syntactic maturity in the use of nominal postmodification, we compare texts written by Dutch EFL learners with those produced by learners from typologically distinct L1s, i.e. Czech and French, and by native speakers of English. Use of participial clauses and relative clauses in Dutch learners’ EFL texts is then compared with their use in L1 Dutch. The high degree of L1-interlanguage congruity suggests that transfer plays an important role in the Dutch learners’ strong preference for relative clauses for nominal postmodification. Based on the comparison with both other learner groups, we hypothesize that such transfer effects in the use of clausal postmodification can only emerge once the learner has reached a sufficient level of syntactic maturity in the target language.
ESPE-Chat: An Inclusive Application for People with Hearing Impairment

This project is oriented to the development of a mobile application for people with hearing disabilities, carried out at the Universidad de las Fuerzas Armadas ESPE Sede Santo Domingo de los Tsáchilas. This application breaks the communication barrier between deaf and hearing people, through messages that are translated from text to audio and vice versa. The API used generates text in Spanish from messages sent in real time, a functionality that facilitates communication for people with hearing difficulties, who require visual support to access information, communicate and learn. The Agile SCRUM development methodology was used for the organization of work in blocks of time and the management of software development. In addition, open source development tools were used. Likert scale surveys were used, where the results obtained were that the development of the created mobile application has usability and inclusive accessibility. For the operation of the hybrid application (mobile and web), it is essential to have technological factors such as hardware and software, for its projection on screens of mobile devices with Android operating system or multiplatform computers with access to the web.
Designing smart clothing and wearable technology for the body: Anatomical and physiological considerations

This chapter looks at the relationship between design and the body, addressing the implications of anatomical features ‘Structures and shape’, physiology ‘How the body works’ and psychological ‘Our love/hate relationship with clothing’, and the impact these have when designing for the end user’s needs. For a design methodology to claim that it is inclusive or universal, it must address the real needs of the body from the outset not design for the technology and then place it in a softer shell. Smart clothing and wearable technology must add to the functionality of the human body not decrease it through poor or ill-considered design. The chapter then moves on to look at the natural processes of ageing placing an emphasis on those that impact most directly on clothing design avoiding where possible changesfur as a consequence of disease processes.
Machine learning approach for seismic assessment

Machine learning (ML) is a term that is used to refer to the ability of computer to learn from the data, explore patterns, and repeatedly and reliably generate new data or make decisions without the need to provide a computational model. ML is considered as part of artificial intelligence, which is aimed to mimic human intelligence in process optimizing and decision-making. It can also extend to mimic the intelligence behind natural or biological processes. In short, the main concept of ML is based on algorithms that work iteratively to find patterns in data to make predictions. ML is gaining more widespread appeal in scientific and engineering applications given its suitability as a computational tool to make predictions in this data-driven era.
Bio-inspired Flower Pollination Algorithm for the Optimization of a Monolithic Neural Network

The Flower Pollination Algorithm (FPA) is a metaheuristic that is inspired by nature and is based on the process of pollination of flowering plants and the constancy of flowers associated with pollinating agents that can be insects, water or the wind. This paper provides a study of the optimization of a multilayer monolithic neural network for pattern recognition with the ORL face dataset, comparisons are also made with other studies that used the ORL dataset and performed optimization of the neural network with other metaheuristics such as PSO (Particle Swarm Optimization) and GA (Genetic Algorithms).
A Comparative Study Between Bird Swarm Algorithm and Artificial Gorilla Troops Optimizer

Nowadays, bio-inspired algorithms are one of the most widely used existing soft computing techniques. Thanks to these methods we can help different problems in improving the results provided. Because new metaheuristics are being proposed more rapidly every day, it is in our interest to meet analysts with these methodologies in order to identify and apply them according to the problem we face. In this research, a new metaheuristic is analyzed, which is called Artificial Gorilla Troops Optimizer, and compare the results obtained with a well-known algorithm that we have used for different optimizations, this is known as Bird Swarm Algorithm. For the present analysis, 10 mathematical benchmak functions were used in order to study the results obtained. After the statistical study carried out, we were able to conclude that according to the dimensions handled, the Artificial Gorilla Troops Optimizer algorithm shows a significant improvement against the Bird Swarm Algorithm.
Introduction to Maple

Throughout this book, we will make use of the Algebra package Maple by Maplesoft (www.maplesoft.com). Maple is a great tool for the occasional differential equation which needs solving. It will also be very helpful for visualizing some of the functions we derive throughout this book. This chapter is intended to give a small introduction to Maple and some of its most rudimentary functions. If the reader already has some understanding of similar Algebra such as, e.g., Mathematica it should not be too difficult to follow the code presented in the following chapters.
Conservation of momentum: the Navier-Stokes equation

Having derived the continuity equation, we will now derive the famous Navier-Stokes equation. This equation may easily be one of the most widely known differential equation in engineering mechanics. The Navier-Stokes equation is the second important conservative equation in fluid mechanics. It represents the conservation of momentum and will give rise to three additional equations for determining the six field variables.
Design and fabrication of an autonomous smart golf caddie

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Amir Asyraaf bin Aziz, Zunaidi Ibrahim, Md Asri Muhammad, S. Ramesh, Seri Rahayu Ya’akub, Mohammad Yeakub Ali; Design and fabrication of an autonomous smart golf caddie. AIP Conference Proceedings 10 January 2023; 2643 (1): 050006. https://doi.org/10.1063/5.0110427 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Solar water heater – Solar thermal energy utilization

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Vanessa Deba Anak Duwat, Mohammad Nurul Islam; Solar water heater – Solar thermal energy utilization. AIP Conference Proceedings 10 January 2023; 2643 (1): 050046. https://doi.org/10.1063/5.0124096 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency

Head pose estimation plays a vital role in biometric systems related to facial and human behavior analysis.Typically, neural networks are trained on head pose datasets.Unfortunately, manual or sensor-based annotation of head pose is impractical.A solution is synthetic training data generated from 3D face models, which can provide an infinite number of perfect labels.However, computer generated images only provide an approximation of real-world images, leading to a performance gap between training and application domain.Therefore, there is a need for strategies that allow simultaneous learning on labeled synthetic data and unlabeled real-world data to overcome the domain gap.In this work we propose relative pose consistency, a semi-supervised learning strategy for head pose estimation based on consistency regularization.Consistency regularization enforces consistent network predictions under random image augmentations, including pose-preserving and pose-altering augmentations.We propose a strategy to exploit the relative pose introduced by pose-altering augmentations between augmented image pairs, to allow the network to benefit from relative pose labels during training on unlabeled data.We evaluate our approach in a domain-adaptation scenario and in a commonly used cross-dataset scenario.Furthermore, we reproduce related works to enforce consistent evaluation protocols and show that for both scenarios we outperform SOTA.
Enhancing Style-Guided Image-to-Image Translation via Self-Supervised Metric Learning

There has been significant success in recent image-to-image translation (I2I) approaches in translating the source image into the style of the target image. Existing techniques rely on the disentanglement of content and style representations, requiring a two-stage style mapping process: Reference images are used to extract style vectors, which are subsequently remapped into the translated images. However, when the target domain contains a variety of styles, such a two-stage style mapping cannot guarantee the translated image be style consistent with its guided reference image. In this work, we propose to explicitly employ metric learning to enhance the two-stage style mapping in style-guided image translation. The distance between deep features Gram matrices is utilized to construct the visual style metric as self-supervised similarity labels, guiding the embedding of style vectors using triplet loss with adaptive margins in the first stage. Furthermore, in the second stage, we consider generated images and their corresponding reference images as positive samples and anchors for each other, while the nearest negative sample is used to construct the triplet loss in the proposed metric space. The proposed learning algorithms can be applied to any I2I framework that uses disentangled representations without modifying the original network architectures. We evaluate the proposed method on three representative I2I translation baselines. Both qualitative and quantitative results demonstrate that the proposed approach enhances style alignment in style-guided translation compared to the baselines.
Examples

This chapter includes several examples of how the mind mapping technique can be applied to artificial intelligence and of how artificial intelligence can be applied to the creation of mind maps.
How to work with mind mapping software

This chapter is an introduction to the history of the mind mapping software MindManager and to its ecosystem. It does also include a simple introduction to the work with MindManager in the process of creation and edition of digital mind maps. MindManager is one of the most powerful mind mapping software management systems in desktop and Cloud environments since its introduction in 1994. It is used by professionals and academics individually and in groups. After introducing MindManager, the application of mind mapping automation is also presented in order for readers to understand the possible future use and the way in which mind mapping can be used in combination with artificial intelligence (AI) applications. Through automation, mind mapping can be used to visualize complex information generated by AI applications. This use is very relevant in order to diminish the problems created by disorientation created by the visualization of complex information as a series of web pages or screens hyperlinked between themselves. AI can also be used to generate mind maps with little human intervention.
Non-Meat Analogs—A Mind Genomics Cartography of their perceived health benefits

Nonmeat analogs are in scientific interest due their possible solution to several world-wide problems, including environmental issues, malnutrition etc. They are meant to offer a healthy and more sustainable alternative for meat products. In spite of the ever-growing interest in nonmeat analogs, many consumers have only a limited or no information on their positive health effect. In order to be able to target these consumer groups, efficient messaging is needed. In the present study, we aim to introduce a methodology, called BimiLeap, that offers an excellent tool to understand the mind of people regarding the health effects on nonmeat analogs. Results suggested that there are three distinct mind-sets present in the population, named critical, lifestyle, and weight loss. Respondent falling into the critical mind-set, are critical of information and need further insights to assess the health benefits of nonmeat analogs, while those falling into the lifestyle mind-set are ready to change their lifestyle to enjoy the health benefits of nonmeat analogs. The third mind-set consisted of people who would connect nonmeat analogs with a healthier lifestyle that helps them lose weight. The identified mind-sets have a unique way of thinking, requiring personalized messaging defined by the used methodology.
Why mind mapping is a vital software tool for the modern worker?

The reasons why mind mapping software is an essential software are multiple and diverse. Some of them are attributable to both pen and paper and digital mind mapping. Others are due to the advantages of the mind mapping software used. The most obvious factors are the use of hierarchical tree diagrams, chunking, and the combining of images and text. Additional are the automatic adaptation of mind maps to the content entered or modified by users of mind mapping software; the ease of communication of mind maps; the fact that mind maps are single compressed files; the possibility of building complex maps composed of mind maps, flowcharts, concept maps, maps built with background objects, SmartShapes, and Smart Rules; the integration of external software; and the possibilities of collaboration in the construction of mind maps—a fairly impressive list.
A Novel Hesitant Fuzzy Association Rule Mining Model

In this paper, a new Hesitant Fuzzy Association Rule model is proposed for the first time to mine the Frequent itemset and generate the rule by hesitant transaction matrix instead of the Boolean database. One fast FFI miner algorithm is used to find the frequent itemset without candidate generation. Also, pruning strategies are used to decrease the algorithm's run time and ignore the unnecessary itemsets. Another strength of our study is calculating the support and confidence metrics based on Hesitant Maximum Scalar Cardinality. A numerical example is conducted to show the performance of the proposed FFI-Miner algorithm compared to the Apriori-based approaches in terms of execution time and the number of evaluated itemsets.
Applications of convolutional neural networks for spectral analysis

Starting from the basic mathematical principle of a convolutional neural network (CNN), this chapter introduces the unique advantages of CNNs in spectral analysis as well as data training strategy and sampling principles. Applications of spectral prediction and ultrafast signal analysis are first introduced. Then, generative models for reverse design are reviewed. Finally, from the perspective of feature extraction, we discuss the applications of an autoencoder in spectral feature extraction and assisting researchers in analysis. These data-driven CNN algorithms can achieve spectral analysis, prediction, and reverse optimization while also helping researchers focus on innovations rather than laborious processes. They are promising for future high-throughput applications.