Reviewerly/exHarmony huggingface dataset:
An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism

The Harris hawks optimization (HHO) algorithm is a new swarm-based natural heuristic algorithm that has previously shown excellent performance. However, HHO still has some shortcomings, which are premature convergence and falling into local optima due to an imbalance of the exploration and exploitation capabilities. To overcome these shortcomings, a new HHO variant algorithm based on a chaotic sequence and an opposite elite learning mechanism (HHO-CS-OELM) is proposed in this paper. The chaotic sequence can improve the global search ability of the HHO algorithm due to enhancing the diversity of the population, and the opposite elite learning can enhance the local search ability of the HHO algorithm by maintaining the optimal individual. Meanwhile, it also overcomes the shortcoming that the exploration cannot be carried out at the late iteration in the HHO algorithm and balances the exploration and exploitation capabilities of the HHO algorithm. The performance of the HHO-CS-OELM algorithm is verified by comparison with 14 optimization algorithms on 23 benchmark functions and an engineering problem. Experimental results show that the HHO-CS-OELM algorithm performs better than the state-of-the-art swarm intelligence optimization algorithms.
Optical precursors in waveguide quantum electrodynamics

When a broadband signal propagates through a dispersive medium, some frequency components move faster than the center of the pulse. This leads to the appearance of precursors, transient signals that emerge from the medium earlier than the main part of the pulse and seem to propagate superluminally. Here, we investigate the microscopic origin of precursors in a minimal setup: an array of qubits coupled to a waveguide. The linear transmission function only converges to that of a continuous medium for large qubit numbers. Nevertheless, the dispersion produced by only two qubits is enough to produce oscillatory transients. Precursors are best observed under conditions of electromagnetically induced transparency, as the center of the pulse is significantly delayed. Under these conditions, just a single qutrit is enough to generate a precursor. Our results pave the way towards dispersion engineering of light with just a few qubits and can be realized with superconducting qubits coupled to transmission lines or atoms coupled to optical waveguides.
A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location

In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis.
Professional competences of a higher school teacher

Since the education system acts as a bridge that should ensure a confident transition to the digital era associated with new types of work and a sharp increase in the creative capabilities of a person, his productivity growth, the educational process is based on a competency-based approach. In this regard, the reflection of the professional competences of a higher school teacher is gaining great relevance within the research field. The purpose of this article is to conduct a socio-pedagogical analysis of the personality traits of a higher school teacher and study his priority areas of activity within the framework of the modern educational process. The analyzed material made it possible to conclude that teachers of higher education should have competences that characterize their personal qualities - conscientiousness, self-criticism, objectivity, responsibility, and focus on health preservation as well as competences that reflect their communicative qualities and competences that ensure professional pedagogical activity.
Idiosyncratic frequency as a measure of derivation vs. inflection

There is ongoing discussion about how to conceptualize the nature of the distinction between inflection and derivation. A common approach relies on qualitative differences in the semantic relationship between inflectionally versus derivationally related words: inflection yields ways to discuss the same concept in different syntactic contexts, while derivation gives rise to words for related concepts. This differential can be expected to manifest in the predictability of word frequency between words that are related derivationally or inflectionally: predicting the token frequency of a word based on information about its base form or about related words should be easier when the two words are in an inflectional relationship, rather than a derivational one. We compare prediction error magnitude for statistical models of token frequency based on distributional and frequency information of inflectionally or derivationally related words in French. The results conform to expectations: it is easier to predict the frequency of a word from properties of an inflectionally related word than from those of a derivationally related word. Prediction error provides a quantitative, continuous method to explore differences between individual processes and differences yielded by employing different predicting information, which in turn can be used to draw conclusions about the nature and manifestation of the inflection–derivation distinction.
Learning what to memorize: Using intrinsic motivation to form useful memory in partially observable reinforcement learning

Reinforcement Learning faces an important challenge in partially observable environments with long-term dependencies. In order to learn in an ambiguous environment, an agent has to keep previous perceptions in a memory. Earlier memory-based approaches use a fixed method to determine what to keep in the memory, which limits them to certain problems. In this study, we follow the idea of giving the control of the memory to the agent by allowing it to take memory-changing actions. Thus, the agent becomes more adaptive to the dynamics of an environment. Further, we formalize an intrinsic motivation to support this learning mechanism, which guides the agent to memorize distinctive events and enable it to disambiguate its state in the environment. Our overall approach is tested and analyzed on several partial observable tasks with long-term dependencies. The experiments show a clear improvement in terms of learning performance compared to other memory based methods.
A Novel Noise Injection-based Training Scheme for Better Model Robustness

Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work. In this work, we propose a novel noise injection-based training scheme for better model robustness. Specifically, we first develop a likelihood ratio method to estimate the gradient with respect to both synaptic weights and noise levels for stochastic gradient descent training. Then, we design an approximation for the vanilla noise injection-based training method to reduce memory and improve computational efficiency. Next, we apply our proposed scheme to spiking neural networks and evaluate the performance of classification accuracy and robustness on MNIST and Fashion-MNIST datasets. Experiment results show that our proposed method achieves a much better performance on adversarial robustness and slightly better performance on original accuracy, compared with the conventional gradient-based training method.
Blockchain Governance: To Govern, or Not to Govern?

Disruptive innovation at the helm of digital technologies is the defining characteristic of our time. Blockchain, along with artificial intelligence, big data, robotics, quantum computers, and many other up-and-coming technologies will be a key component of creating automated, decentralized, and human-machine hybrid systems, which will highly likely define in a not-so-distant future our systems of transactions in business, societal, and government domains.
Precise Recommendation Method of Suitable Planting Areas of Maize Varieties Based on Knowledge Graph

The rapid increase in the number of new maize varieties and the intensification of market competition have raised the need to precisely promote new maize varieties to suitable planting areas and fully exploit the variety potential and win the market competition. This paper proposes a precise recommendation method for suitable planting areas of maize varieties based on a knowledge graph. The meteorology knowledge graph of maize ecological regions is constructed at county-scale and a RippleNet recommendation model is used to mine the potential spatial correlation of maize variety suitability in different meteorological environments. The county-scale precise recommendation for suitable planting areas is then realized. In total, 331 maize varieties and agricultural meteorological data of 59 experimental areas in the Huang-Huai-Hai ecological region are used for model training and testing (accuracy 76.3%). Through experimental comparison, the recommendation accuracy of this method is 24.3% higher than that of six traditional machine learning methods, 11.2% higher than that of graph attention networks, and 5.8% higher than that of graph convolution neural networks. This study provides a data-driven solution for the precise recommendation and market positioning of maize varieties, enhances the scientificity of variety recommendation and helps to fully exploit their planting potential.
Information Technologies that Help Improve Academic Performance, A Review of the Literature

Currently in the world the use of technology is increasing and even more so in the time of Covid-19, when digitization is vital, essentially in education, more technological tools are being used to improve academic performance and learning. The study that was carried out is a review of the scientific literature, 127 articles were collected from the different databases; IEEE Xplore, ScienceDirect, EBSCO, Scielo, and Scopus. Based on the inclusion and exclusion criteria of the systematization used, only 53 have been used, allowing the identification of technologies that improve the academic performance of different students around the world.
THE RELATIONSHIP BETWEEN SALARY AND RECOGNITION ON JOB SATISFACTION: A STUDY OF PAKTIA UNIVERSITY LECTURERS

The study highlights the importance of understanding the relationship between salary and recognition on job satisfaction for academic staff, specifically for lecturers at Paktia University. This information can be valuable for organizations in evaluating the impact of compensation and recognition on employee satisfaction and can inform future HR policies and practices. data of this study were obtained with a specific goal in mind constituting the primary data. A convenience sample technique was used to conduct a survey of 60 lecturers in the Afghan province of Paktia utilizing an online Google Form. The study found that the implementation of the payment system at Paktia University has had a positive impact on the academic staff. The improved payment system has led to an increase in job satisfaction and recognition, which in turn has improved job satisfaction among the university's lecturers.
Enhancing religious education through emotional and spiritual intelligence

In the context of the changes and challenges of the 21st century, the main focus of education, especially religious education, is to prepare students to live purposeful and meaningful lives with well-developed analytic, emotional and spiritual abilities to assist them in achieving a life perspective that allows them to face the larger world with greater self-confidence and self-awareness. Therefore, the main objectives of the study are: to bring forward the concepts of religious education, emotional intelligence and spiritual intelligence; to discuss the importance of emotional and spiritual literacy in children’s education; and to discuss the main strategies to enhance the educational process through emotional and spiritual intelligence. This article is a conceptual framework based on a theoretical review. The author reviews literature from books, scientific journals and research reports to develop relevant concepts.Contribution: This study provides information about two important intelligence in Christian Orthodox religious education. Recognising emotional and spiritual intelligence has allowed teachers to engage with students and help them perform at their best. Emotional intelligence and spiritual intelligence promote self-efficacy, self-awareness, self-regulation, motivation, empathy and social skills, which help students perform management tasks in their future occupations but also help them to improve relationships with parents, colleagues and God.
Active Listening: Its Impact on Language Learning and Understanding of Education Students

In learning a new language, there are four macro skills that a learner must develop and use: listening, reading, speaking, and writing. In academic environments such as schools, these macro skills are taught and improved every day to suit the needs of learners. Language learners are often trained when or how they should talk; however, learners are seldom taught how they should listen. This descriptive quantitative study revolved around the utility of listening, how it affects upon the acquired macro skill, specifically, active listening to education students specializing in English from a state institution in Panay Island, Western Visayas, Philippines, as well as its impact on their language learning and understanding. Findings revealed that active listening is vital in the language learning and acquisition with its effects found on multiple aspects of the learning process. It had an underlying impact on phonology, morphology, and pragmatics. It is implied that active listening as an underrated skill should be attended to by language teachers who take charge in teaching macro skills in communication.
A Novel Parameter Estimation Method for PMSM by Using Chaotic Particle Swarm Optimization With Dynamic Self-Optimization

In this study, a novel parameter estimation method for permanent magnet synchronous motor (PMSM) of chaotic particle swarm optimization with dynamic self-optimization (DSCPSO) is proposed, where the voltage source inverter (VSI) nonlinearity is estimated simultaneously with the parameters to achieve real-time compensation of VSI nonlinearity. In DSCPSO, the tent chaos theory is introduced into the updating of particle swarm algorithm (PSO) populations, inertia weights and learning factors to enhance its ability to explore potentially better regions. Moreover, a memory tempering annealing (MTA) strategy is employed to guarantee particle pluralistic learning, which combines the superior robustness of the simulated annealing algorithm (SA) while enhancing population diversity. Furthermore, to achieve a reasonable tradeoff between exploration and exploitation, a dynamic lens imaging opposition-based learning (DLIOBL) and domain optimization strategy based on evolutionary information is designed, i.e., DLIOBL in the pre-evolutionary stage guarantees the depth of the exploration learning, while the domain optimization strategy is performed in the post-evolutionary stage accelerates the exploitation operation and avoids the problem of slow convergence in the late stages of PSO. The proposed method is applied to the parameter estimation of PMSM and the experimental results show that, the proposed method can track the VSI nonlinearity and variable parameter better than the conventional method under different working conditions.
Perspective Aware Road Obstacle Detection

While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. In this letter, we account for this by computing a scale map encoding the apparent size of a hypothetical object at every image location. We then leverage this perspective map to (i) generate training data by injecting onto the road synthetic objects whose size corresponds to the perspective foreshortening; and (ii) incorporate perspective information in the decoding part of the detection network to guide the obstacle detector. Our results on standard benchmarks show that, together, these two strategies significantly boost the obstacle detection performance, allowing our approach to consistently outperform state-of-the-art methods in terms of instance-level obstacle detection.
Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.
Development of E-Commerce Website Recommender System Using Collaborative Filtering and Deep Learning Techniques

Recommender system or recommendation system is becoming an increasingly important technology on e-commerce websites to help users find products that suit their preferences. However, the growing number of users and products makes finding the right product difficult. Therefore, this study aims to develop a recommender system on e-commerce websites using collaborative filtering and deep learning techniques. Collaborative filtering is used to find similarities between users based on their preferences, while deep learning is used to improve the performance of the recommender system in generating more accurate recommendations. The test method is carried out by comparing the performance of the recommender system developed with the recommender system that already exists on the e-commerce website. The results of the test show that the recommender system developed is able to provide recommendations that are more accurate and more in line with user preferences compared to the existing recommender system.
The Development of 5G Technology and Its Implications For The Industry

The development of 5G technology is very important in today's technological world. These technologies are bringing about significant changes in the way we interact with the digital world and are changing the way we use technology. The speed offered by 5G technology makes it possible to carry out data transfers very quickly, opening up opportunities for new applications and services in various sectors. The implications of the development of 5G technology on industry are huge and can affect various aspects, such as productivity, efficiency, and innovation. In the production sector, 5G technology makes it possible to speed up the production process and increase efficiency by using industrial technology 4.0. Within the service sector, 5G technology makes it possible to provide faster and high-quality services, such as more stable and faster video streaming services. On the other hand, the development of 5G technology also carries implications that must be considered, such as security and privacy issues. The speed offered by 5G technology makes it easier to carry out attacks and access personal data. Therefore, it is important for the industry to ensure that the 5G technology used meets the established security standards. The development of 5G technology brings significant changes in the industry and opens up new opportunities in various sectors. The implications of the development of 5G technology are huge and affect productivity, efficiency, and innovation. However, it is important for the industry to ensure that the 5G technology used meets established security standards to avoid security and privacy concerns.