Reviewerly/exHarmony huggingface dataset:
GP with Ranging-Binding Technique for Symbolic Regression

This paper proposes a model-based genetic programming algorithm for symbolic regression, called the ranging-binding genetic programming algorithm (RBGP). The goal is to allow offspring to retain the superiority of their promising parents during evolution. Inspired by the concept of model building, RBGP makes use of syntactic information and semantics information in a program to capture the hidden patterns. When compared with GP-GOMEA, ellynGP, and gplearn, RBGP outperformed the others on average in the Penn machine learning benchmarks, RBGP achieving statistically significant improvements over all other methods on 44% of the problems.
Improved Multi-Objective Data Stream Clustering with Time and Memory Optimization

Data stream clustering is essential in machine learning and big data analytics. However, clustering this type of data requires some restrictions in time and memory. This paper proposes a multiobjective data stream clustering algorithm (IMOC-Stream) that addresses these challenges by incorporating time and memory optimization into the clustering process. The goal of IMOC-Stream is to 1) reduce computation time by using idle times to apply genetic operations and enhance the solution. 2) reduce memory allocation by introducing a new tree synopsis. 3) find arbitrarily shaped clusters by using a multi-objective framework. We conducted an experimental study with high-dimensional stream datasets and compared them to well-known stream clustering techniques. The experiments show the ability of our method to partition the data stream into arbitrarily shaped, compact, and well-separated clusters while optimizing time and memory. Our approach also outperformed most of the other stream algorithms.
An analysis of training models to evolve heuristics for the travelling salesman problem

Designing heuristics is an arduous task, usually approached with hyper-heuristic methods such as genetic programming (GP). In this setting, the goal of GP is to evolve new heuristics that generalise well, i.e., that work well on a large number of problems. To achieve this, GP must use a good training model to evolve new heuristics and also to evaluate their generalisation ability. For this reason, dozens of training models have been used in the literature. However, there is a lack of comparison between different models to determine their effectiveness, which makes it difficult to choose the right one. Therefore, in this paper, we compare different training models and evaluate their effectiveness. We consider the well-known Travelling Salesman Problem (TSP) as a case study to analyse the performance of different training models and gain insights about training models. Moreover, this research opens new directions for the future application of hyper-heuristics.
The Influence of Probabilistic Grammars on Evolution

Context-Free Grammars (CFGs) are used in Genetic Programming (GP) to encode the structure and syntax of programs, enabling efficient exploration of potential solutions and generation of well-formed and syntactically correct programs. Probabilistic Context-Free Grammars (PCFG) can be used to model the distribution of solutions to help guide the search process. Structured Grammatical Evolution (SGE) is a grammar-based GP algorithm that uses a list of dynamic lists as its genotype, where each list represents the ordered indexes of production rules to expand for each non-terminal in the grammar. Two recent variants incorporate PCFG into the SGE framework, where the probabilities of the production rule change during the evolutionary process, resulting in improved performance.
Geobotanical and Biogeochemical Prospecting Method of Complex Sulphide Ore of Pb-Zn-Cu-Ba in Abuni-Adudu areas of the Middle Benue Trough, Nigeria

This research work focused on two plant species namely, the Anogeissus leiocarpus (DC.) and Dichrostachys cinerea for mineral prospecting. The two plants were sampled based on their occurrence, abundance and outlook. Twelve samples (six each of Anogeissus leiocarpus (DC.) and Dichrostachys cinerea) were collected around Abuni-Adudu mining communities in the Middle Benue Trough, Nigeria. In addition, bulk ore and twelve soil samples were also collected and analysed for correlation. Geologically, the study area is made up compacted shale, baked shale (hornfels), and sandstones of sedimentary origin, which are intruded by tertiary basalts of igneous origin. Samples were analysed for elemental composition, using inductively coupled plasma mass spectrometry method. Analysis revealed that the geobotanical method of prospecting for Pb-Zn-Cu-Ba mineralization in the area is promising as biogeochemical data indicated that the Dichrostachys cinerea are good indicator of Pb-Zn-Cu mineralization while the Anogeissus leiocarpus were indicative of Barium. Given the relatively low concentration of Barium in the ore (3.8 ppm) as against concentration in the soil samples, which ranges from 83 ppm (sample YS6) to 552 ppm (sample YS12), Barium has its source from the shale and sandstone or nearby Barite mineralization and not the ore.
Aerial Object Tracking Using Filtering Methods

Visual object tracking from an aerial vision, raises fundamental challenges, such as the occlusion of objects, small-scale objects, rapid changes of direction, background disorder, and camera movements that make it difficult to correctly detection and tracking of the target, these cause errors in the estimation, that is, a variation between the estimated position and the truth position. Therefore, in the object tracking it seeks to improve the real pose of target and reduce variability. We consider the variations in the tracking as Gauss-Markov Colored Measurement Noise (CMN). To overcome these difficulties, we propose the use of measurement differentiation, UFIR, and Kalman estimators modified for CMN. The management of poor lighting, rapid change of direction, and small-scale objects were improved. We evaluate our approach to video sequences captured from a low-altitude camera. UFIR and KF algorithms are tested with the "RemoteCar3" video using a classic detection and tracking method. It is shown that, in terms of estimation errors and tracking precision under a large disturbance with a coloredness factor, UFIR CMN presents the best tracking performance. On the other hand, the Kalman algorithms, both in their standard version and the one modified for CMN work in a similar way with poor performance under the most common IoU threshold, 0.5. In general, it is concluded that the UFIR CMN filter is more suitable for applications in object tracking from an aerial view than Kalman algorithms.
Rolling Force Prediction Based on PELM

In the process of hot strip rolling, the calculation accuracy of rolling force directly affects the actual thickness of strip steel, which is also the prerequisite of accurate online control. However, because the actual rolling process is affected by many factors, the prediction accuracy using the traditional model is often lower. In order to improve the prediction accuracy of rolling force, this paper proposes a parallel heterogeneous extreme learning machine (PELM) prediction model. Taking the actual production data of 2250 production line for a steel plant in Baotou as the example to predict the rolling force, the results show that the rolling force prediction model trained by the algorithm has good prediction accuracy and is suitable for the rolling force prediction of hot strip rolling process.
The Power BI Report Server

Power BI is not only a cloud-based reporting technology. Due to the demand to have data and reporting solutions on-premises, Power BI also has the option to be deployed on-premises. Power BI on-premises hosting is handled via the Power BI Report Server. This chapter covers using Power BI in a fully on-premises solution with the Power BI Report Server.
5G+ Smart Education

Education is vital to improving the comprehensive quality of the population and promoting the all-round development of individuals, and serves as the cornerstone of national revitalization and social progress. Strengthening education is fundamental to the pursuit of national rejuvenation. Under the background of new development stages, new development concepts, and new development patterns, China must ramp up the modernization of its education system to tackle new changes facing the society.
The politeness of criticism speech acts in Japanese and Minangkabau films

Criticism tends to be a face-threatening speech act that can endanger social relationships if not managed using appropriate strategies, especially in the intercultural context. This study aims to compare and contrast the politeness of criticism speech acts used in Japanese and Minangkabau languages as depicted in film series. The data on criticism speech acts in this study were collected from eleven Japanese film series with a total duration of 538 minutes and eight Minangkabau films with a total duration of 535 minutes. The collected data were formed into a set of data cards, classified based on the criticism speech acts strategies by Nguyen (2005), and analyzed based on Brown and Levinson’s (1987) politeness theory. The data classification in Japanese and Minangkabau was validated through the expert judgment process. The findings indicated that the Japanese and Minangkabau languages employed the same set of criticism speech act strategies but have different tendencies. In Japanese, speakers tend to use indirect strategies as their main criticism speech act strategy by ‘asking/presupposing,’ ‘correction,’ and ‘advice for change.’ Meanwhile, in Minangkabau, speakers tend to use direct strategies by using ‘negative evaluation,’ ‘expression of contradiction,’ and ‘disagreement’ strategies. Regarding the politeness strategy, off-record politeness strategies are more dominantly used among Japanese speakers. In contrast, Minangkabau speakers use bald on-record politeness strategies in criticism speech acts, reflecting their communication culture. The findings of this study provide a deeper understanding of criticism speech act and politeness strategies. The study is expected to contribute to the area of research on intercultural communication and Sociolinguistics.
Aspect-Based Sentiment Analysis Using Fabricius Ringlet-Based Hybrid Deep Learning for Online Reviews

The sentiment analysis relying on the aspect of online reviews is utilized for identifying the polarity of the given review. Nowadays, many methods are introduced for aspect-based sentiment analysis (ABSA) using neural networks, and many methods failed to consider contextual information exploitation to make the performance more accurate. Hence, this research proposed an optimized deep learning method for the detection of the aspect and to identify the polarity. Hence, in this research, an optimized deep learning technique for the ABSA is introduced by considering the online reviews, in which the deep learning classifiers are trained with the proposed Fabricius ringlet optimization (FRO) algorithm to reduce the loss that helps to enhance the accuracy of sentiment polarity prediction. The proposed FRO is developed by the hybridization of the behavioral nature of the Fabricius and the ringlet in feeding for the determination of the global best solution. The tuning of the weights and biases of the classifier enhance the performance of the classifier. The objective behind the tuning is to minimize the loss function while training and to enhance the accuracy of aspect extraction and polarity prediction of sentiment. Based on a study of the existing approach, the suggested FRO-based hybrid deep learning method is significantly improved; its accuracy, sensitivity, and specificity are 87.06%, 90.83%, and 79.37%, respectively, with a training percentage of 40%. The accuracy, sensitivity, and specificity of the existing technique have also been enhanced for aspect restaurant values, which are 87.53%, 96.06%, and 79.88% with a 60% training percentage. Similar to that, Twitter values for accuracy, sensitivity, and specificity are reported to be 89.08%, 99.35%, and 79.70%, respectively, with an 80% training percentage. The proposed method obtained the 90.13%, 99.35%, and 81.10% accuracy, sensitivity, and specificity from the assessment of the FRO-based hybrid deep learning.
Klasifikasi Jenis Mangga Apel Menggunakan Metode K-Means Klustering

Introduction: The rapid development of technology is pushing it further, making people more comfortable in many fields, including industry. Mango can be processed into various types of food. Using sweets, various processed products, and various mangoes. One example of the current impact of technology on the industrial sector is the potential for systems to self-study (automatically) like humans. This is a process known as an artificial neural network. Purpose: Knowing and analyzing the classification of mango species using the K-Means Clustering method. Methods: This study uses the K-means clustering method, in which the system is built by applying an artificial neural network to the modeling and extraction of RGB values and standard RGB matrices, circumference, area, length, width, shape, and slenderness. Results: Based on the experimental results, the computation time for the Mango image required for the feature extraction process for each dataset is on average 0.85 seconds, and the computational time for training data on the test data is an average of 0.006 seconds. Conclusion: In this study, it can be concluded that the average computation time of mango image for each dataset is 0.856 seconds. The use of 1 hidden layer is more effective with the highest accuracy and the fastest time than using 2 hidden layers.
Unraveling the Gordian Knot of Dyadic Alliance Stability

Throughout the strategic alliance literature, successful strategic alliances include a multitude of factors promoting various facets of mutual benefit. There is convergence, however, on the notion that longer-lived, more stable alliances signal success. Thus, duration is consequential as a metric and measure of success. Dyadic alliance stability is conceptualized as the duration of the entity or initial agreement and presumes stability is a desirable aim. This research challenges the assumption that duration is an accurate and effective measure of dyadic alliance stability and success. We contribute to the alliance literature by providing much-needed construct clarity regarding the concept of dyadic alliance stability and a framework for the effects of stability on innovation performance. We conceptualize dyadic alliance stability as the alliance's duration, resilience, and robustness over time. Counterintuitively we posit that high levels of duration and low levels of resilience result in a
PENGOLAHAN DATA BERBASIS WEB DENGAN SISTEM BARCODE MENGGUNAKAN FRAMEWORK LARAVEL 8

This study aims to create a computerized web-based barcode system so that the process of obtaining code information on barcodes becomes more effective and produces reports that are more practical and facilitate work processes at PT Yoji Technology Indonesia. The research method used is observation, interviews, and literature study. The results of this study indicate that the process of classifying a code on a barcode is still done manually, so it takes quite a long time and is inefficient. Therefore, by designing a web-based barcode system, it is hoped that it will make it easier to get information about a code on a barcode. Thus minimizing the errors that occur.
Adventures in Mispronunciation: A Reflection on Mispronouncing Words in a Foreign Language

<p style="font-stretch: normal; line-height: normal; font-family: &quot;Times New Roman&quot;; color: rgb(38, 38, 38);"><span style="font-size:18px;font-kerning: none;">Whenever one endeavors to learn a foreign language that person is bound to mispronounce words and make numerous mistakes. The results that occur when a learner mispronounces or makes mistakes when trying to communicate in a foreign language are various. A learner’s reaction to their mispronunciations and mistakes are just as varied. In some instances, the learner can view the mispronunciation or mistake as comical, and take it as a good learning lesson. In other instances, a learner can be humiliated or embarrassed by the mispronunciation or mistake, and want to stop learning that particular foreign language altogether. Due to the fact that mispronunciations and mistakes can elicit nearly any emotion, it becomes imperative that both the learner and their audience exercise a level of patience and understanding in order to foster a positive learning environment. This presentation will examine some of the mispronunciations and mistakes that I made personally, or witnessed in my journey to learn French, Mohawk, and Seneca. In the end, if the goal of a learner is to communicate effectively in their particular foreign language of choice, then they must maintain their resolve to do so despite the results and emotions of their mispronunciations and mistakes.&nbsp;</span>
The Hidden Influences of Blockchain Governance: A Study of Organizational Imprinting

Blockchain technologies are increasingly being used as algorithmic governance mechanisms for interorganizational transactions. Despite blockchains’ promise of decentralized governance and full automation, the underlying algorithmic protocols are often insufficient to align incentives, regulate behavior, and ensure trust. As a result, organizations typically introduce additional centralized governance structures to fill the gaps in the algorithmic governance layer, but this can lead to serious governance conflicts in complex network constellations with many stakeholders and divergent interests. Using an imprinting lens, we examine such complex governance issues in three interorganizational blockchain networks that differed dramatically in terms of success despite similar goals and founding conditions, with one network shutting down, one stagnating, and another continuing to operate. Drawing on extensive qualitative evidence from 56 interviews and comprehensive internal and external records, we found that the initial imprints from the shared technology provider played a significant role in the initial governance choices, maladaptation problems, and ultimately the success of these blockchain networks. By introducing imprinting theory into governance research, we can explain subconscious, latent, and persistent mechanisms that influence governance decisions at a profound level, often without the actors involved being aware of it.
Improving Students Writing Skills Explanation Text using Animated Video

This study focuses on the influence of an animated video on students' ability to write explanation text. This research uses descriptive qualitative research method. The purpose of this study is to find out whether the animated video used can help students improve their explanation text writing skills and see the differences in students' abilities before and after using animated videos in Indonesian language learning. The subjects in this study came from 20 students of class VII A at MTs Pasundan Cimahi, and the objects in this study were obtained from the results of the pre-test and post-test of students in writing explanation texts before and after using animated videos. The results of the analysis show that based on the output table data 1, there is no decrease from the pre test value to the post test value. Then between the results of the explanation text writing test for the pre-test and post-test there were 20 positive data (N), meaning that 20 students experienced an increase in the results of the explanation text writing test. Furthermore, in output table 2, it shows that there is a difference between the results of the explanation text writing test for pre-test and post-test, meaning that there is an influence of using animated videos on the learning outcomes of writing explanation texts for class VII A students. Animated videos are one of the learning media that can be used to improve the skills of writing explanation text.Â
On the Same Wavelength: The Effects of Shared Reality in the Workplace

Understanding the effects of experiencing a shared reality—or lack thereof—is more pressing than ever in an increasingly remote and fragmented workplace. From teammates laughing at an inside joke to coworkers sharing the same reaction to a new company policy, everyday organizational life is replete with instances of shared reality—i.e., the perception of sharing thoughts, feelings, and beliefs in common with other people about the world (Hardin & Higgins, 1996; Higgins, Rossignac-Milon, & Echterhoff, 2021). Yet, shared reality as a construct has not been directly examined in organizational contexts. We assemble leading shared reality scholars to examine questions like: What are the benefits of experiencing a shared reality in one’s workplace relationships? What are the effects of being surrounded by coworkers who do not share one’s opinions, and how can workers reconcile a lack of shared reality? Finally, how can diverse teams leverage shared reality to unlock their potential? Four empirical papers provide converging evidence that experiencing a sense of shared reality with one’s coworkers plays a key role in social connection, prosocial behavior, trust, work meaningfulness, and the performance of individuals and teams. Shared Reality Predicts Prosocial Behavior, Work Meaningfulness, and Job Performance Author: Maya Rossignac-Milon; IESE Business School Author: Sandra Matz; Columbia Business School Consequences of Self-Censoring Unpopular Opinions Author: Sanaz Talaifar; Imperial College Business School Author: Ashwini Ashokkumar; New York U. Sharing Relevance to Build Common Ground Author: Federica Pinelli; Columbia Business School Author: E. Tory Higgins; Columbia Business School Diverse, but on the Same Page: Shared Reality Moderates the Effect of Team Diversity on Performance Author: Sandra Matz; Columbia Business School Author: Maya Rossignac-Milon; IESE Business School
A CROSS-CULTURAL COMPARISON OF CHINESE AND MALAY ANIMAL WORDS

The Chinese and Malaysian people have evolved an in-depth observation and comprehension of animals, resulting in complex linkages and the formation of a great number of animal-related words. These animal words not only relate to diverse creatures in nature. But they also include the distinct cultural characteristics of both countries. Because of the two countries' distinct cultural environments, mentalities, lifestyles, religious convictions, and historical backgrounds, there are also various interpretations of how animals are perceived and understood, resulting in both similar and disparate cultural connotations of the words related with them. It will not be difficult for native speakers to understand, but it will inevitably be difficult for second-language learners who lack such cultural knowledge. Consider this, this paper compiles animal words in Chinese and Malay. The interdependence of language and culture serves as the foundation for this paper's comparative analysis of frequently used animal words in Chinese and Malay using descriptive explanation and comparative analysis. To accomplish the goal of language cross-cultural communication for second language learners and develop their cross-cultural awareness, the objective of this study is to investigate the similarities and differences in the cultural meanings of animal words in these two languages and discover the factors that impact their cultural similarities and differences.