Reviewerly/exHarmony huggingface dataset:
A Comprehensive Review of Recent Variants and Modifications of Firefly Algorithm

Swarm intelligence (SI) is an emerging field of biologically-inspired artificial intelligence based on the behavioral models of social insects such as ants, bees, wasps, termites etc. Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. In the last two decades, there has been a growing interest of addressing Dynamic Optimization Problems using SI algorithms due to their adaptation capabilities. This paper presents a broad review on two SI algorithms: 1) Firefly Algorithm (FA) 2) Flower Pollination Algorithm (FPA). FA is inspired from bioluminescence characteristic of fireflies. FPA is inspired from the the pollination behavior of flowering plants. This article aims to give a detailed analysis of different variants of FA and FPA developed by parameter adaptations, modification, hybridization as on date. This paper also addresses the applications of these algorithms in various fields. In addition, literatures found that most of the cases that used FA and FPA technique have outperformed compare to other metaheuristic algorithms.
CAMORPH: A TOOLBOX FOR CONVERSION BETWEEN CAMERA PARAMETER CONVENTIONS

Abstract. Defining or estimating camera poses and attributes is a vital part of computer graphics and computer vision. These areas encompass a vast amount of sub-fields like virtual reality, photogrammetry, computer generated imagery and many more. It is desirable to share this camera data between applications of these different areas to leverage their strengths in a workflow. However, a great amount of formats and conventions make it difficult to transport camera parameters from one software package to another. This paper presents an easy to use python library camorph to convert different formats into each other which enables the usage of a multi-software workflow for the best possible results and facilitates the comparison of different methods.
Emotional Intelligence and Empathy in Pre-primary Teachers: A Pilot Test

Today, concepts such as Emotional Education and Emotional Intelligence are recognized by many authors and researchers, emphasizing the importance of their development in all people, but especially in schools, specifically in teachers or educators and children. However, we still find many shortcomings within the schools that are needed to continue working. The aim of this study is to investigate the presence of emotional intelligence and empathy in teachers, through a sample of 42 participants. The research was conducted based on two validated and highly reliable assessment tools, called TMMS-24 and TECA. The results of the TMMS-24 indicate that a large percentage of participants have an adequate score of emotional intelligence. The results of the TECA indicate that most of the participants are in the middle, high or extremely high scores in relation to the level of empathy.
Dynamic Notary Group Election Algorithm Based on Reputation Value

As a distributed ledger technology, blockchain can be used in the fields of information sharing, logistics chain, certificate storage and anti-counterfeiting.However, due to the isolated nature of the blockchain network and the high degree of heterogeneity between chains, the connection between different chains is hindered, which makes each blockchain form a value island and cannot serve the practical applications well.The emergence of cross chain technology realizes the value circulation between different chains and enhances the interoperability and scalability of blockchains.Among them, the cross-chain technology of notary mechanism transforms the trust problem among cross-chain users into the loyalty problem of notary, and is favored for its ability to support different types of underlying blockchain systems in a more flexible manner.However, the introduction of notaries in the notary mechanism also leads to the risk of centralization, and the loyalty of notaries will directly determine the success of cross-chain transactions.In this paper, by introducing the improved PageRank algorithm, we design a dynamic notary group election mechanism based on reputation value, which effectively avoids malicious nodes from becoming notaries and improves the success rate of cross-chain transactions while preventing the over-concentration of rights in a single node.The experimental analysis shows that selecting notary representatives by dynamically adjusting the reputation value ranking of notary nodes increases the selection probability of loyal nodes, which is more reasonable than the method of randomly selecting notaries.
Analisis Faktor-Faktor yang Memengaruhi Angka Partisipasi Kasar SMA/Sederajat di Indonesia Menggunakan Regresi Ridge

One indicator of education in Indonesia is the gross enrollment ratio (GER). Based on data from Statistics Indonesia, the GER at the senior high school level in Indonesia is still low compared to the GER at primary and junior high schools. Summarizing the findings of prior studies, the factors affecting GER include the number of schools, percentage of poor population, education budget, and student-teacher ratio. Therefore, this study aims to examine the variables that affect GER in Indonesian senior high schools. Since multicollinearity between the predictor variables was identified, ridge regression was employed. This study found that the number of senior high schools, the percentage of the poor population, and the education budget simultaneously had a significant effect and contributed 71.39% to the GER at the senior high school level in Indonesia. It is remarkable to observe that, partially, the number of senior high schools, the percentage of poor people, and the education budget had no direct effect on the GER. Additionally, there was a positive correlation between the variables and GER. The number of senior high schools and the education budget have a favorable impact. In contrast, the percentage of poor people has a negative effect on GER. Meanwhile, the student-teacher ratio does not have a linear relationship with the GER at the senior high school level in Indonesia.
Statistical Characteristics Improvement of a Hardware Random Number Generator by a Software Method

As a source of random numbers, hardware random number generators are often used, the operation of which is based on randomly changing parameters of various physical processes. The statistical characteristics of such generators do not always allow their use in the field of information security. To improve the statistical characteristics, various software tools for processing the output data of the generator are used. The purpose of this work is to study the possibility to improve the statistical characteristics of a hardware random number generator by software. The investigated hardware random number generator is based on the ND103L noise diode and has a random digital sequence of binary numbers at the output. To improve the statistical characteristics, the output stream of random numbers was processed using a software method based on the calculation of high-order finite differences. This method would allow one to get a more symmetrical distribution of random numbers, as well as increase the speed of their generation. After processing, the data from the generator under study have better statistical characteristics, which is confirmed by the NIST and Diehard tests, and the generation rate has also increased by more than 5 times. The results of this work may be useful to developers of hardware random number generators who need to improve the performance of the generator.
Deteksi Tingkat Kematangan Tandan Buah Segar Kelapa Sawit dengan Algoritme K-Means

Oil extraction rate (OER) of fresh fruit bunches (FFB) of palm oil is depend on the stage of ripeness. The process of detecting the ripeness of oil palm FFB has difficult by manually. Farmers find it difficult to reach the fruit to detect ripeness with the eye, when the palm tree is tall. So farmers need a system that is able to detect the maturity level of oil palm FFB based on color. The K-Means method is capable of clustering based on the closest mean value to the centroid from a number of objects to cluster k. Data obtained from 2 oil palm plantations in East and North Kalimantan. In this study, the clustering of fresh fruit bunches of oil palm has four levels of maturity based on the calculation of the elbow method. The training data used in this study is 80 data. The test image data used in this study is 40 data. There are 36 appropriate data based on the classification method so the accuracy obtained in grouping using the k-means clustering segmentation method is 90%.
Hybrid libraries and the development of society

Introduction: The distinctive and fundamental libraries’ properties are changed over time, and some of these changes reflected in the nomenclature used to refer to the characteristics of the libraries. This is the case of the traditional and hybrid libraries. This research defends the thesis that hybridity paradigm for libraries stagnated in the 1990s, however, the library management changed, because of their communities’ needs. Objective: It aimed to follow the sociocultural trajectory of the concept of hybrid library. Methodology: The study is qualitative, exploratory, and carried out a theoretical-epistemological and field research. It used the Multimodal Discourse Analysis as research method. The Bibliographic Research was used as a tool to collect data in specific and related Information Science databases. The Field Research was used as a tool to collect data in hybrid library environments placed in the State of Missouri, United States of America. Results: As partial results, was possible to identify that hybridity should be understood beyond its physical infrastructure, as a representative factor to develop communities. The concept of hybridity is inserted in the context of systemic organizations, presupposing an open environment action and administrative junctions, conjecturing micro and macro informational spaces.Conclusion: The hybridity promotes ways to use technology in favor of a collective intelligence, approaching knowledge networks, which can bring new perspectives to the libraries
A Survey of Image Classification Algorithms based on Convolution Neural Network

With the deep learning (DL) sweeping the world. Traditional image classification methods are difficult to process huge image data and cannot meet people's requirements for image classification accuracy and speed. The image classification method based on convolutional neural network (CNN) breaks through the bottle neck of traditional image classification methods and becomes the mainstream algorithm of image classification at present, how to effectively use convolutional neural network to classify images has become a hot research topic in the field of computer vision at home and abroad. Convolutional neural network (CNN) has performed well in image classification and segmentation, target detection and other applications, and its powerful feature learning and feature expression capabilities are increasingly respected by researchers. However, CNN still has a few problems, such as incomplete feature extraction and overfitting of sample training. In view of these problems, after in-depth research on the application of convolutional neural network in image processing, this paper gives the mainstream structure model, advantages and disadvantages, time/space complexity, problems that may be encountered in the model training process and corresponding solutions used in image classification based on convolutional neural network. Through the overview of the research status of CNN model in image classification, it provides suggestions for the further development and research direction of CNN.
Information Communication Technology Devices for Agricultural Information Dissemination among Smallholder Farmers in Uganda (Rubanda, Mayuge Districts, and Mbarara City)

Agriculture continues to be the main source of livelihood in developing countries and smallholder farming is the most common type of agriculture practice supporting more than 70% of the population. On the other hand, information and communication technology (ICT) is quickly growing as its helping to connect easier and faster; thus, a dire need to harness it in promoting the agricultural sector. This study investigated the Information Communication Technology Devices for Agricultural Information Dissemination among Smallholder Farmers in Uganda (Rubanda, Mayuge Districts, And Mbarara City). The study adopted a cross-sectional research design that comprised a mixed methods approach of quantitative and qualitative. The instrument for this study consisted of structured questionnaires and interviews. More so, available reports and records were explored. The questionnaire was subjected to face and content validity and reliability test. Data collected were analysed using descriptive statistics of frequency counts and percentages. Findings show that the leading types of ICT devices for agricultural information dissemination in Rubanda District, Mayuge District, and Mbarara City were mobile phones and radios. The study recommends that the most important information to farmers should always be disseminated via phone conversation, texting, and radio for broadcast agricultural-related programs in respective local languages
Graphic Model of Virtual Teaching Supervision through Fuzzy Logic in Non-University Educational Centers

This research analyzes the supervision of non-university virtual training due to the unexpected non-face-to-face teaching scenario caused by COVID-19 with a graphic model using the SULODITOOL® instrument. It arises as a research line of the Chair of Education and Emerging Technologies, Gamification and Artificial Intelligence of the Pablo de Olavide University (Seville) and is developed under the auspices of other assessment instruments within the framework of the functions and attributions of the Education Inspectorate of Spain. The aforementioned instrument is made up of 10 weighted supervisory indicators using fuzzy logic. The aggregation of linguistic variables of 242 expert judges was performed using the probabilistic OR function and defuzzified using the area centroid method to calculate the aforementioned weights. Based on the innovative analytical and graphic methodology used to analyze the supervision of virtual teaching, both synchronous and asynchronous, it stands out from the results obtained that there are certain supervision indicators, such as the training design and the methodology used, which should be considered as factors key in all the scenarios studied (primary education, compulsory secondary education and post-compulsory education).
LUMDE: Light-Weight Unsupervised Monocular Depth Estimation via Knowledge Distillation

The use of the unsupervised monocular depth estimation network approach has seen rapid progress in recent years, as it avoids the use of ground truth data, and also because monocular cameras are readily available in most autonomous devices. Although some effective monocular depth estimation networks have been reported previously, such as Monodepth2 and SC-SfMLearner, most of these approaches are still computationally expensive for lightweight devices. Therefore, in this paper, we introduced a knowledge-distillation-based approach named LUMDE, to deal with the pixel-by-pixel unsupervised monocular depth estimation task. Specifically, we use a teacher network and lightweight student network to distill the depth information, and further, integrate a pose network into the student module to improve the depth performance. Moreover, referring to the idea of the Generative Adversarial Network (GAN), the outputs of the student network and teacher network are taken as fake and real samples, respectively, and Transformer is introduced as the discriminator of GAN to further improve the depth prediction results. The proposed LUMDE method achieves state-of-the-art (SOTA) results in the knowledge distillation of unsupervised depth estimation and also outperforms the results of some dense networks. The proposed LUMDE model only loses 2.6% on δ1 accuracy on the NYUD-V2 dataset compared with the teacher network but reduces the computational complexity by 95.2%.
The PBC Model: Supporting Positive Behaviours in Smart Environments

Several behavioural problems exist in office environments, including resource use, sedentary behaviour, cognitive/multitasking, and social media. These behavioural problems have been solved through subjective or objective techniques. Within objective techniques, behavioural modelling in smart environments (SEs) can allow the adequate provision of services to users of SEs with inputs from user modelling. The effectiveness of current behavioural models relative to user-specific preferences is unclear. This study introduces a new approach to behavioural modelling in smart environments by illustrating how human behaviours can be effectively modelled from user models in SEs. To achieve this aim, a new behavioural model, the Positive Behaviour Change (PBC) Model, was developed and evaluated based on the guidelines from the Design Science Research Methodology. The PBC Model emphasises the importance of using user-specific information within the user model for behavioural modelling. The PBC model comprised the SE, the user model, the behaviour model, classification, and intervention components. The model was evaluated using a naturalistic-summative evaluation through experimentation using office workers. The study contributed to the knowledge base of behavioural modelling by providing a new dimension to behavioural modelling by incorporating the user model. The results from the experiment revealed that behavioural patterns could be extracted from user models, behaviours can be classified and quantified, and changes can be detected in behaviours, which will aid the proper identification of the intervention to provide for users with or without behavioural problems in smart environments.
Book Recommendation System using Machine learning and Collaborative Filtering

Nowadays the amount of information available on the internet has got a severe raise recently and people need some instruments to find and access appropriate information. One of such tool is called recommendation. Recommendation systems help to navigate quickly and receive the necessary information. Recommendation system are effective software technique to overcome the problem. Recommendation system can be used in various places one of them is Library. So, in this paper we are going to propose a Book Recommendation System using Collaborative filtering (CF)and Content Based Algorithm to recommend the books to the user according to their likes and information of the books ie. Ratings given by the existing users. The proposed system will give its users the ability to view and search the book, publications and genres category wise using the Support Vector Machine (SVM). SVM will list the most top-rated books based on the subject name given as input and give the ratings. It will also make sure user’s privacy to be maintained.
Pemanfaatan Youtube dalam Pembelajaran Bahasa Indonesia di Perguruan Tinggi pada Kurikulum MBKM

The development off the times, the human need for technology cannot be abandoned. One of them is you tube. You Tube is a website where users can watch, upload, and share videos. The existence of you tube is a medium that can help the implementation of education now. However, its effect on students' Indonesian language skills is still a question. Therefore,the researchers tried to find the answer by conducting this research. This research used a questionnaire method with a Likert scale as the answer model. Then the validity test using SPSS and response analysis from the results of filling out the Likert scale answers. After the research, data were obtained from respondents, most of whom agreed with the hypothesis that had been proposed by the researchers. Finally, the researchers concluded that the existence of YouTube has an effect on Indonesian language skills.
Stable Normative Explanations

Modelling the concept of explanation is a central matter in AI systems, as it provides methods for developing eXplainable AI (XAI). When explanation applies to normative reasoning, XAI aims at promoting normative trust in the decisions of AI systems: in fact, such a trust depends on understanding whether systems predictions correspond to legally compliant scenarios. This paper extends to normative reasoning a work by Governatori et al. (2022) on the notion of stable explanations in a non-monotonic setting: when an explanation is stable, it can be used to infer the same normative conclusion independently of other facts that are found afterwards.
A Multi-Step Approach in Translating Natural Language into Logical Formula

Translating often has the meaning of converting from one human language to another. However, in a broader sense, it means transforming a message from one form of communication to another form. Logic is an important form of communication and the ability to translate natural language into logic is important in many different fields, in which logical reasoning and logical arguments are used. In the legal field, for example, judges must often reason from facts and arguments presented in natural language to logical conclusions. In this paper, toward the goal of support for this kind of reasoning with machines, we propose a method for translating natural language into logical representations using a combination of deep learning methods. Our approach contributes methodologies and insights to the development of computational methods for converting natural language into logical representations.
Classically Simulating Quantum Supremacy IQP Circuits through a Random Graph Approach

Quantum Supremacy is a demonstration of a computation by a quantum computer that can not be performed by the best classical computer in a reasonable time. A well-studied approach to demonstrating this on near-term quantum computers is to use random circuit sampling. It has been suggested that a good candidate for demonstrating quantum supremacy with random circuit sampling is to use \emph{IQP circuits}. These are quantum circuits where the unitary it implements is diagonal. In this paper we introduce improved techniques for classically simulating random IQP circuits. We find a simple algorithm to calculate an amplitude of an $n$-qubit IQP circuit with dense random two-qubit interactions in time $O(\frac{\log^2 n}{n} 2^n )$, which for sparse circuits (where each qubit interacts with $O(\log n)$ other qubits) runs in $o(2^n/\text{poly}(n))$ for any given polynomial. Using a more complicated stabiliser decomposition approach we improve the algorithm for dense circuits to $O\left(\frac{(\log n)^{4-\beta}}{n^{2-\beta}} 2^n \right)$ where $\beta \approx 0.396$. We benchmarked our algorithm and found that we can simulate up to 50-qubit circuits in a couple of minutes on a laptop. We estimate that 70-qubit circuits are within reach for a large computing cluster.