Reviewerly/exHarmony huggingface dataset:
Internal Resources of Personal Psychological Well-Being

The issue of the ways to preserve and strengthen an individual’s mental health and the effective means of promoting his/her psychological well-being and life satisfaction is a new and promising area of research in modern psychology. The article presents an empirical analysis of personal resources; their development is the basis of an individual’s psychological well-being and effective functioning, when he/she can realize his/her potential, overcome life’s difficulties effectively and work productively. In this context, the correlations of the components of psychological well-being with self-determination abilities, coping strategies and actual abilities were analysed. The inner orientation, which had the greatest impact on the prognostic model of psychological well-being, correlated with awareness of the meaning of one’s own life, understanding of life goals, and the belief in the ability to achieve them. The primary actual capabilities, which determined emotional experiences in relation to oneself and in interpersonal relationships, correlated with almost all examined indicators. The linear regression analysis that was conducted showed that the primary actual capabilities, namely, hope, love, trust, and time are of special importance in an individual’s achieving one’s positive life priorities.
USING PROTOTYPING METHOD FOR ANALYSIS AND DESIGN OF INFORMATION SYSTEMS FOR STUDENT REGISTRATION IN SEKOLAH MASTER

Sekolah Master or Master’s Schools is one of the schools for Education for equality of students for the less fortunate. One of the obstacles that master schools have is the absence of technology in student enrollment in schools. Many students wish to enroll and continue Education in masters schools to get an equivalency Education, so as to have the recognition of such Educational status. So far, schools are still using conventional methods in registration. Where, students and parents or families must come to school to register students or students by bringing the required documents, not to mention the requirements that cannot be completed, even queue time is needed to be served by the operator or staff on duty at the school. This shows that a technological device or system is needed to facilitate student enrollment in schools. One method that can be used to collect the required requirements and make a design is the prototyping method. The prototyping method is one of the methods used as an effort to develop a system with almost finished services and products. This method produces an analysis of functional and non-functional requirements that form the basis of the system that can be developed. This research also produced a design with a navigation structure, use case diagrams with a unified modeling language, relation tables, and interface proposals from end users and admins. This interface is used as a basis for the system to be made as a user-approved interface, so that the development of this system can be carried out properly during implementation and future trials.
On Distributed Cognition While Designing an AI System for Adapted Learning

When analyzing learning, focus has traditionally been on the teacher, but has in the recent decades slightly moved toward the learner. This is also reflected when supporting systems, both computer-based and more practical equipment, has been introduced. Seeing learning as an integration of both an internal psychological process of acquisition and elaboration, and an external interaction process between the learner and the rest of the learning environment though, we see the necessity of expanding the vision and taking on a more holistic view to include the whole learning environment. Specially, when introducing an AI (artificial intelligence) system for adapting the learning process to an individual learner through machine learning, this AI system should take into account both the learner and the other agents and artifacts being part of this extended learning system. This paper outlines some lessons learned in a process of developing an electronic textbook adapting to a single learner through machine learning, to the process of extracting input from and providing feedback both to the learner, the teacher, the learning institution, and the learning resources provider based on a XAI (explainable artificial intelligence) system while also taking into account characteristics with respect to the learner's peers.
Impact of Information Technology on Society in the New Century

As we are aware Information Technology had its cutting-edge lifestyle from the overdue sixties of the remaining century whilst the Arpanet become introduced, funded with the aid of using the branch of protection of the USA. After that, the IT enterprise has come a protracted manner to its cutting-edge form in which its miles gambling a dominant function in each sphere of life. It has made innovative modifications in facts amassing and dissemination and worldwide communication. It is growing into a surely paperless painting environment. Also, we can now ship a message very without difficulty to everywhere withinside the international in seconds. From a schooling factor of view, we can have a digital elegance in which the teacher ought to take a seat down in any part of the arena and his college students scattered in all exceptional elements of the arena via video convention with the presentation of look at substances in addition to query and solution sessions. A health practitioner now sitting in any part of the arena ought to carry out a surgical procedure in which the affected person is mendacity in some other part of the arena. These work examples display where we stand these days compared to what has become 1/2 of a century back. But as we recognize, nothing on this international is only correct as the whole thing has a darkish side. In this paper, we might speak about the deserves and demerits of enforcing IT globally and in which we are heading withinside the future.
GAN Inversion of High-Resolution Images

Image generation is the task of automatically generating an image using an input vector z. In recent years, the quest to understand and manipulate this input vector has gained more and more attention due to potential applications. The previous works have shown promising results in interpreting the latent space of pre-trained Generator G to generate images up to 256 x 256 using supervised and unsupervised techniques. This paper addresses the challenge of interpreting the latent space of pre-trained Generator G to generate high-resolution images, i.e., images with resolution up to 1024x1024. This problem is tackled by proposing a new framework that iterates upon Cyclic Reverse Generator (CRG) by upgrading Encoder E present in CRG to handle high-resolution images. This model can successfully interpret the latent space of the generator in complex generative models like Progressive Growling Generative Adversarial Network (PGGAN) and StyleGAN. The framework then maps input vector zf with image attributes defined in the dataset. Moreover, it gives precise control over the output of generator models. This control over generator output is tremendously helpful in enhancing computer vision applications like photo editing and face manipulation. One downside of this framework is the reliance on a comprehensive dataset, thus limiting the use of it.
Analysis and Prediction of Body Test Results Based on Improved Backpropagation Neural Network Algorithm

The mining and analysis of student achievement data is of great importance to teaching management. Using the relevant data of college students’ physical fitness test and sports performance as the object of research, a BP neural network model is developed to predict performance. Based on BP neural network, an algorithm for predicting students’ endurance performance is proposed, which is applied to the sunshine long-distance intelligent sports testing system at Hangzhou Dianzi University. The nonlinear relationship between students’ performance in sunshine running and endurance performance is determined, and students’ performance in sunshine running is used to predict their endurance performance the following year. Experimental results indicate that the accuracy of the model is above 85%. At the same time, the prediction results are combined with the Internet of things technology to produce a student sports prescription management system, which sets different sunshine running parameters for students with different predicted results and provides personalized sports prescriptions for students with different physical conditions, which has extensive and far-reaching application value.
Deep Learning-Guided Simulated Annealing for Designing Vocational High Educational System

With the rapid development of web technology and the improvement of online purchasing products, the traditional classroom instructing model has been unable to bear the requirements of teachers and students. Aiming at the problems of instructing and correspondence, a deep learning-based educational control system supporting B/S structure is designed and implemented. The system adopts the software engineering model for adjustment, uses Java language as the main programming language of the system, uses SQL Server database to store various intelligences, and realizes online teaching and facing problems, data division, teaching direction and testing, and many other cosecants. Due to its structural features and limitations of algorithmic production, traditional fancy emotional literature models perform poorly in the classification of white-eye dimensional data. In order to improve the simulated annealing prediction of full-dimensional data, a modified brain emotion based on simulated annealing algorithm is proposed. By improving the network structure and using the feign annealing algorithm rules, the training process of conceiving scientific standards is proposed, its data fitting capacity and prediction ability are well refined. And the prediction accuracy of the model for high-dimensional data classification problems is improved. Some data adjustments commonly used for instructing performance of our proposed algorithm are excluded from the experiments.
The Influence of the Development of the Internet of Things Industry on the Optimization of the High- and New-Tech Industry Structure under the Wireless Mobile Network

At present, the pressure on China's economic development is increasing day by day due to the profound changes in the internal and external environment. The global economic pattern is undergoing significant changes in terms of the external environment. Adjusting and optimizing the industrial structure will aid in achieving the goal of facilitating transformation through steady growth in the short term. Meanwhile, it will also accelerate sustainable economic development. In this study, the relevant theories of industrial structure optimization are described based on the impact of wireless mobile networks and the Internet of things (IoT) industry. Based on the gray correlation degree, the high- and new-tech industries under the development of the IoT industry are analyzed and the impact of optimization of the high- and new-tech industry structure is investigated. The results show that the development of the IoT industry has driven the development of the high- and new-tech industry. The gray correlation between the development of the IoT industry and the high- and new-tech industry obtained is 0.64, indicating a strong correlation. The average output share of the electronic computer and office equipment manufacturing industries is 47.09%. The average output ratio of the industrial structure optimization of the electronics and communication manufacturing industry is 42.55%. Moreover, the proportion of the output of medical manufacturing and medical equipment and instrument manufacturing industrial structure optimization is small, 15.63% and 10.54%, respectively. The results have significant value in the research on the impact of the development of the IoT industry on the high- and new-tech industry under the wireless mobile network and the effect of its industrial structure optimization.
Recursive solving of the Problems using the "Divide &amp; conquer" method

The methodology for studying the theme "Recursive solving of the problem using the "divide &amp; conquer" method" in preparing schoolchildren for Olympiads in informatics is illustrated in the article by solving the "Head on shoulders" problem. The solution of this problem includes several algorithms (subtasks) considered sequentially, namely: merge sort, counting the number of inversions in a permutation, the number of pairs of intersecting segments. The study of the method is based on the sequential solution of these subtasks. For each subtask, the following materials are given: the formulation of the subtask, the idea of a solution with a proposal to come up with an implementation on their own, the solution in the Pascal programming language. Distance learning system DL.GSU. BY is the eﬀective technical base for teaching. The system allows to oﬀer for a student a formulation of the task; to submit the solution for review; to get a verdict from the system — a correct or incorrect solution; for incorrect solution, the number of the test on which the solution did not pass is indicated. A student can take a test (input and output data), on which his solution did not pass, fgure out what the error is in his program, correct and send the solution again.
A novel dynamic analysis on multi-scale quantum harmonic oscillator algorithm using double-well function

Based on the double-well function, a novel dynamic analysis is proposed to study the optimization mechanism of the multi-scale quantum harmonic oscillator algorithm. This procedure-oriented method gives new clues for the algorithm's improvement, it can be adapted to study other algorithms' evolution process and complement the prevailing performance-oriented benchmark testing.
Multi-objective recommender system for corporate MOOC

Mandarine Academy is a major MOOC (Massive Open Online Course) operator with more than 100 active e-learning platforms in multiple languages and 550K overall users. The company is providing a wide range of content types to help users in their learning process. With thousands of pedagogical online resources in an everyday growing catalog, users can have a hard time finding relevant content. Mandarine Academy is looking to improve the overall user experience while minimizing both information overload and drop rate levels. This paper details the conception and implementation of a multi-objective e-learning recommender system. The proposed approach takes advantage of multiple known implementations like content-based and collaborative filtering among other techniques to generate an initial population of solutions. A custom genetic operator is proposed and compared to classical operators using multiple algorithms (NSGA-II, NSGA-III, SPEA-2, IBEA, and MOEA/D). Using the best configurations found by the tuning process, we showcase the initial results obtained by each approach when applied to real-world data from a public MOOC provided by the company.
Tag-based module regulation for genetic programming

This Hot-off-the-Press paper summarizes our recently published work, "Tag-based regulation of modules in genetic programming improves context-dependent problem solving," published in Genetic Programming and Evolvable Machines [1]. We introduce and experimentally demonstrate tag-based genetic regulation, a genetic programming (GP) technique that allows programs to dynamically adjust which code modules to express. Tags are evolvable labels that provide a flexible naming scheme for referencing code modules. Tag-based regulation extends tag-based naming schemes to allow programs to "promote" and "repress" code modules to alter module execution patterns. We find that tag-based regulation improves problem-solving success on problems where programs must adjust how they respond to current inputs based on prior inputs; indeed, some of these problems could not be solved until regulation was added. We also identify scenarios where the correct response to an input does not change over time, rendering tag-based regulation an unnecessary functionality that can sometimes impede evolution. Broadly, tag-based regulation adds to our repertoire of techniques for evolving more dynamic computer programs and can easily be incorporated into existing tag-enabled GP systems.
Independent influence of exploration and exploitation for metaheuristic-based recommendations

Exploration and exploitation (E&E) of a search space are two fundamental processes in many fields of artificial intelligence. Indeed, when the search space is vast, it is important to ensure that many of its regions are examined, so as not to get trapped in a local optimum, but also that promising regions are examined more in depth in order to find good local optima. Influencing both processes is thus necessary. The literature has rarely proposed to approach the recommendation task as a vast search space problem. This paper introduces a metaheuristic-based recommendation approach with two contributions: an E&E influence process which is independent from the influenced algorithm and new indicators to represent and explain E&E. Performed on a genetic algorithm (GA) and on a reinforcement algorithm (RA), our experiments confirm that (1) the proposed influence process has a positive impact on evaluation criteria and on E&E which brings better recommendations, (2) proposed indicators contribute to represent E&E from new angles.
Towards evolution-based autonomy in large-scale systems

To achieve truly autonomous behaviour systems need to adapt to not only previously unseen circumstances but also previously unimagined ones. A hierarchical evolutionary system is proposed which is capable of displaying the emergent behaviour necessary to achieve this goal. This paper reports on the practical challenges encountered in implementing this proposed approach in a large-scale open-source system.
Evolutionary training of deep neural networks on heterogeneous computing environments

Deep neural networks are typically trained using gradient-based optimizers such as error backpropagation. This study proposes a framework based on Evolutionary Algorithms (EAs) to train deep neural networks without gradients. The network parameters, which may vary up to millions, are considered optimization variables. We demonstrate the training of an encoder-decoder segmentation network (U-Net) and Long Short-Term Memory (LSTM) model using (μ + λ)-ES, Genetic Algorithm, and Particle Swarm Optimization. The framework can train models with forward propagation on machines with different hardware in a cluster computing environment. We compare prediction results from the two models trained using our framework and backpropagation. We show that the neural networks can be trained in less time on CPUs as compared to the training on specialized compute-intensive GPUs.
On the interaction between lexicase selection, modularity and data subsets

Lexicase selection represents a framework for maintaining population diversity, and has therefore demonstrated a capacity for discovering solutions to a wide range of tasks. We note, however, that the benchmarking studies used to demonstrate these properties also tend assume some form of modularity in the representation. With that in mind, an empirical study using five classification datasets is performed in which we compare: 1) Lexicase and a canonical 'breeder' model of selection, and 2) modularity is explicitly enabled and disabled. A clear preference is demonstrated for including modularity with both forms of selection. However, the effect is most pronounced under Lexicase selection with significantly simpler solutions also resulting when modularity is enabled.
Accelerated pattern search with variable solution size for simultaneous instance selection and generation

The search for the optimum in a mixed continuous-combinatorial space is a challenging task since it requires operators that handle both natures of the search domain. Instance reduction (IR), an important pre-processing technique in data science, is often performed in separated stages, combining instance selection (IS) first, and sub-sequently instance generation (IG). This paper investigates a fast optimisation approach for IR considering the two stages at once. This approach, namely Accelerated Pattern Search with Variable Solution Size (APS-VSS), is characterised by a variable solution size, an accelerated objective function computation, and a single-point memetic structure designed for IG.
Light on the origin of the verdete siltstone, Bambuí group, central Minas Gerais state, Brazil

The Verdete is a variety of a greenish siltstone that occurs occasionally at different stratigraphic levels of the Serra da Saudade Formation, Bambuí Group (Neoproterozoic to Cambrian age). The main rock types related to the Verdete are potassium rich and show the follow mineralogy: quartz, illite/glauconite, orthoclase, albite, ilmenite, fluorapatite and dawsonite. Conventionally, the Verdete has been considered as formed by diagenetic processes in transgressive conditions at calm water environments. However, the present research based on different data considers the Verdete as result of potassic metasomatism in preexisting detritic sedimentary rock (siltstone, fine sandstone and greywacke). The main arguments that support the potassic metasomatic alteration hypothesis are: i) coincidence of the Verdete main occurrences with geophysical anomalies interpreted as alkaline intrusions in depth; ii) presence of dawsonite which is one hydrothermal sodium carbonate; iii) local presence of brecciated facies of phosphorite cross cutting the Verdete facies; iv) lateral discontinuity of the beds and v) microscopic evidence of substitution in coarser facies. The potassic fluids percolated in fracture zone in a northeast trend in the main area of occurrence in central Minas Gerais State (Cedro do Abaeté Town), and besides this region there are also incidence in other areas. The Verdete is considered as a possible source of potassium to be applied to agromineral or industrial supply for fertilization production. In this way, the understanding of its origin must be important to develop new processes or improve the methods to the potassium recover (chemical, biological and thermal routes).
Internet of Things (IoT Devices)-A Literature Review

Moving into the future world, life will be getting more easier and easier because of internet of things (IOT). Nowadays with increased need for surveillance, monitoring and data collection IOT has become more important. Today almost all devices are equipped with sensors and are controlled by controllers such as from cars to vacuum cleaners, rockets to Vernier calipers, air conditioners to water pumps. By embedding intelligence in everyday objects, they turned into smart devices and can be controlled from anywhere in the world. This paper discusses about internet of things and its advantages and disadvantages. It discusses the applications of IOT in various fields such as city, agriculture, remote monitoring, smart street light, etc. This paper also tells about the history of IOT.