Reviewerly/exHarmony huggingface dataset:
Weight Optimization Based on Firefly Algorithm for Analogy-based Effort Estimation

Proper cost estimation is one of the vital tasks that must be achieved for software project development.Owing to the complexity and uncertainties of the software development process, this task is ambiguous and difficult.Recently, analogybased estimation (ABE) has become one of the popular approaches in this field due to its effectiveness and practicability in comparing completed projects and new projects in estimating the development effort.However, in spite of its many achievements, this method is not capable to guarantee accurate estimation confronting the complex relation between independent features and software effort.In such a case, the performance of the ABE can be improved by efficient feature weighting.This study introduces an enhanced software estimation method by integrating the firefly algorithm (FA) with the ABE method for improving software development effort estimation (SDEE).The proposed model can provide accurate identification of similar projects by optimising the performances of the similarity function in the estimation process in which the most relevant weights are assigned to project features for obtaining the more accurate estimates.A series of experiments were carried out using six real-world datasets.The results based on the statistical analysis showed that the integration of the FA and ABE significantly outperformed the existing analogy-based approaches especially for the ISBSG dataset.
DAILYMOTION APPLICATION AS TEACHING MEDIA TO INCREASE STUDENTS’ LISTENING SKILL

This study aimed to know the effect of the flipped classroom model on students' writing skill in class XI SMA Negeri 18 Makassar. This study focused on pre-experimental research one group pre-test and post-test. The population in this study were students of class XI SMAN 18 Makassar. The writer took 270 students as the population. Random sampling technique was used to take one class as the research sample, totaling 30 students. Data obtained from writing test. The results of this study used inferential analysis of the t-test by SPSS v.16, which showed a significant effect on the students’ mean score writing skill between the pretest and post-test. The mean score of the students’ writing enhanced from 52.33 in the pretest to 82.03 in the posttest. The significance value calculated 0.000 is smaller than 0.05 (P-value = 0.000 &lt; 0.05). This study aims to determine whether the use of the Dailymotion Application as a learning media can improve students listening skill of eleven grade at SMA Negeri 4 Makassar. This study uses a pre-experimental research method in one class pre-test and post-test. The population of this research is the eleven grade students of SMA Negeri 4 Makassar in the academic year 2022/2023. The number of samples 30 students from class XI-8. Data obtained from listening test. The results were analyzed using the inferential t-test of the SPSS 22.0 application. And after doing research, the researcher concludes that the use of the Dailymotion Application as a learning media can improve students' listening skill. This can be proven by obtaining scores based on the students' pretest and post-test. The mean score increased from 60.83 on the pre-test to 88.30 on the post-test. This shows that the H? hypothesis is accepted and of course the H? hypothesis is rejected. This means that the Dailymotion application can improve the listening skill of students in the eleven grade of SMA Negeri 4 Makassar
MALARIA DISEASE DETECTION USING DEEP LEARNING

Artificial intelligence technology is starting to have a bigger influence in certain industries, like healthcare [1]. There is a lot of effort being done to increase the effectiveness of diagnosing malaria early on, when medical imaging is essential. A parasite that frequently infects a particular kind of mosquito that feeds on people can cause malaria, a dangerous and occasionally deadly disease. A contagious illness An viral disease called malaria kills more than 500,000 people annually, estimated 95% of deaths throughout the world. The majority of these fatalities are brought on by a delayed or inaccurate diagnosis. The manual microscope is now thought to be the best technology for diagnosing malaria. On the other hand, it takes time and is subject to human mistake. It is crucial that the evaluation procedure be automated because it is such a significant issue for world health. Female anopheles mosquitoes transmit the highly contagious disease malaria. Animals as well as humans are harmed by this sickness. In the worst case scenario, this illness could result in the patient's death if it is not adequately diagnosed in the early stages. because of a lack of highly technical knowledge It becomes quite challenging to confirm the existence of sickness in the workplace. In this situation, IT assistance is required for accurate and quick disease identification.. In this paper, a deep learning based technique to detect malaria from several modalities.
Design and Development of an Information Kiosk with Log Monitoring for Leonor M. Bautista National High School

People were advised to avoid physical contact due to the pandemic. Although it is encouraged for parents to obtain their children's learning modules, the Leonor M. Bautista National High School (LMBNHS) cannot avoid interactions with other visitors who are there for school-related purposes. Visitors can fill out information sheets for the log records while adhering to strict health protocols. Due to the school's limited communication options, such as bulletin board postings, it was also difficult to provide and disseminate important information. The dissemination of information is slowed as a result, and the news is frequently outdated. Receiving false information is likely given the dearth of first-hand knowledge.&#x0D; As a public institution, LMBNHS must provide relevant and up-to-date information to its organization, and stakeholders. With the advancement of technology, it is possible to have this virtual assistant. With this reality, the team’s primary goal is to provide accurate and relevant information and collect data from students and visitors of LMBNHS. This is possible through the employment of Information Kiosk that serves as Automated Information Assistant to everyone.&#x0D; The developed system was evaluated by the beneficiary and graded Excellent for overall satisfaction rating and Very Satisfactory in Client satisfaction category.
Improved GWO and its application in parameter optimization of Elman neural network

Traditional neural networks used gradient descent methods to train the network structure, which cannot handle complex optimization problems. We proposed an improved grey wolf optimizer (SGWO) to explore a better network structure. GWO was improved by using circle population initialization, information interaction mechanism and adaptive position update to enhance the search performance of the algorithm. SGWO was applied to optimize Elman network structure, and a new prediction method (SGWO-Elman) was proposed. The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.
Philosophical and educational traditions of the future

Aims: The purpose of the research is to highlight the philosophical dichotomy of traditional and innovative ways of building educational strategies. Philosophy is designed to highlight the positive and negative aspects of the educational paradigm. In contrast to the educational understanding, philosophical analysis is in constant search of optimising the strategy of educational progress. The difference between educational and philosophical-educational approaches lies in the scale of theoretical and methodological understanding and practical implementation of projects in the education system. While the education is focused on achieving results in real-time or in the short term, the philosophy offers strategic guidelines for the development of education in the context of global socio-cultural development. In this context, the aim of the study is to highlight the axiological aspects of the philosophy of education of the future. Methodology: The methodological basis was a combination of general scientific, pedagogical, and philosophical methods. In order to achieve the set goals, two main methodological approaches of the modern scientific and philosophical paradigm were used: dialectical and synergistic. By contrasting the fundamentality and dynamism of the functioning of the educational system, the realisation of the prospects of the model of educational development is achieved. Results: The results of the study indicate that the synergistic interaction of traditions and innovations ensures the stability of the educational system and the ability to progress in the context of civilizational progress. In the modern scientific and ideological paradigm, there is a balance of traditional and innovative dimensions, but the dynamism of modern civilizational development can ensure the process of the dominance of the innovative component associated with the functioning of information and digital technologies. Scientific Novelty: The scientific novelty of the study is an attempt to determine the priority format of interaction between the traditional and innovative components of the educational system. The philosophy is intended to complement the scientific and pedagogical and organisational and educational clusters of this interaction by providing value guidelines for the educational space. Conclusion: Any educational activity is correlated within the educational system (demand, expediency, effectiveness) and is consistent with the worldview principles of the modern worldview. Thanks to philosophical and educational guidelines of an axiological nature, the status of traditions and innovations in education is balanced.
Prospects and Problems of Digital Currency

Digital currency is money managed, stored, and exchanged through digital devices such as computers and smartphones. There are two types of electronic currency:Virtual and cryptocurrencies that have no physical form and exist only digitally. Digital currency, also known as digital money or cyber cash, can be used to purchase goods and services, but may be restricted to certain online communities such as games or social networks. The data required for the study were collected using secondary data collection methods. This paper explores the importance of digital currency in India and the modern world.
MINERALOGY, GEOCHEMISTRY AND ORIGIN OF CRETACEOUS CHERT FROM WADI AL-WALA, CENTRAL JORDAN

This research is aimed at detailed investigation of the origin chert rocks in Wadi Al-Wala area in central Jordan. The chert in the area is of Upper Cretaceous in age and belongs to the widely distributed chert on the western margin of the Arabian Plate. Field characteristic structures of these cherts indicate that they occur in three forms: bedded, concretion and brecciated structure. Nine representative samples were selected for the present study. The mineralogical study shows that the chert is mainly consisted of microcrystalline quartz, calcite microcrystalline quartz, fossils and phosphatic microcrystalline quartz. The bedded chert are composed almost entirely of microcrystalline quartz and secreting organism. Geochemical investigation of Wadi Al-Wala chert shows that the content of trace elements is considerably variable, and most trace elements are low. This may indicate that the chert in the study area is resulted from the leaching of country rocks by hydrothermal solution and the evidence from submarine hydrothermal origin of silica forming this chert. This is further supported by the low MnO/Fe2O3t ratio, Cr/Zn and Ni/Zn ratios.
Unsupervised Video Anomaly Detection Based on Similarity with Predefined Text Descriptions

Research on video anomaly detection has mainly been based on video data. However, many real-world cases involve users who can conceive potential normal and abnormal situations within the anomaly detection domain. This domain knowledge can be conveniently expressed as text descriptions, such as “walking” or “people fighting”, which can be easily obtained, customized for specific applications, and applied to unseen abnormal videos not included in the training dataset. We explore the potential of using these text descriptions with unlabeled video datasets. We use large language models to obtain text descriptions and leverage them to detect abnormal frames by calculating the cosine similarity between the input frame and text descriptions using the CLIP visual language model. To enhance the performance, we refined the CLIP-derived cosine similarity using an unlabeled dataset and the proposed text-conditional similarity, which is a similarity measure between two vectors based on additional learnable parameters and a triplet loss. The proposed method has a simple training and inference process that avoids the computationally intensive analyses of optical flow or multiple frames. The experimental results demonstrate that the proposed method outperforms unsupervised methods by showing 8% and 13% better AUC scores for the ShanghaiTech and UCFcrime datasets, respectively. Although the proposed method shows −6% and −5% than weakly supervised methods for those datasets, in abnormal videos, the proposed method shows 17% and 5% better AUC scores, which means that the proposed method shows comparable results with weakly supervised methods that require resource-intensive dataset labeling. These outcomes validate the potential of using text descriptions in unsupervised video anomaly detection.
Improving Visual Defect Detection and Localization in Industrial Thermal Images Using Autoencoders

Reliable functionality in anomaly detection in thermal image datasets is crucial for defect detection of industrial products. Nevertheless, achieving reliable functionality is challenging, especially when datasets are image sequences captured during equipment runtime with a smooth transition from healthy to defective images. This causes contamination of healthy training data with defective samples. Anomaly detection methods based on autoencoders are susceptible to a slight violation of a clean training dataset and lead to challenging threshold determination for sample classification. This paper indicates that combining anomaly scores leads to better threshold determination that effectively separates healthy and defective data. Our research results show that our approach helps to overcome these challenges. The autoencoder models in our research are trained with healthy images optimizing two loss functions: mean squared error (MSE) and structural similarity index measure (SSIM). Anomaly score outputs are used for classification. Three anomaly scores are applied: MSE, SSIM, and kernel density estimation (KDE). The proposed method is trained and tested on the 32 × 32-sized thermal images, including one contaminated dataset. The model achieved the following average accuracies across the datasets: MSE, 95.33%; SSIM, 88.37%; and KDE, 92.81%. Using a combination of anomaly scores could assist in solving a low classification accuracy. The use of KDE improves performance when healthy training data are contaminated. The MSE+ and SSIM+ methods, as well as two parameters to control quantitative anomaly localization using SSIM, are introduced.
On a regime switching illiquid high volatile prediction model for cryptocurrencies

Purpose The current paper proposes a prediction model for a cryptocurrency that encompasses three properties observed in the markets for cryptocurrencies—namely high volatility, illiquidity, and regime shifts. As far as the authors’ knowledge extends, this paper is the first attempt to introduce a stochastic differential equation (SDE) for pricing cryptocurrencies while explicitly integrating the mentioned three significant stylized facts. Design/methodology/approach Cryptocurrencies are increasingly utilized by investors and financial institutions worldwide as an alternative means of exchange. To the authors’ best knowledge, there is no SDE in the literature that can be used for representing and evaluating the data-generating process for the price of a cryptocurrency. Findings By using Ito calculus, the authors provide a solution for the suggested SDE along with mathematical proof. Numerical simulations are performed and compared to the real data, which seems to capture the dynamics of the price path of two main cryptocurrencies in the real markets. Originality/value The stochastic differential model that is introduced and solved in this article is expected to be useful for the pricing of cryptocurrencies in situations of high volatility combined with structural changes and illiquidity. These attributes are apparent in the real markets for cryptocurrencies; therefore, accounting explicitly for these underlying characteristics is a necessary condition for accurate evaluation of cryptocurrencies.
Study on Bearing Capacity of Honeycomb Sandwich Structure Embedded Parts

In the composite structure of spacecraft, the honeycomb sandwich structure is the basic bearing component used to bear and transmit loads. To explore the influencing factors on the bearing capacity of honeycomb sandwich structures, this study combines local tests and speckle measurement systems to conduct tensile tests on 10 test specimens with different parameters. Firstly, a comprehensive assessment was conducted on the accuracy of the loading and measurement system, the rationality of the testing method, and the mechanical properties of the test piece. It was found that the maximum measurement error of the speckle measurement system did not exceed 0.01 mm, and the differences between the yield load and failure load measured using different inner diameters of the compression ring were 0.15% and 3.84%, respectively. This indicates that the measurement system is accurate and that the influence of the inner diameter of the compression ring can be ignored. Moreover, it was found that considering the accuracy retention ability of the structure under load, the allowable load of the embedded parts is about 90% of the yield load. Finally, the data of specimens with different parameters were compared and it was found that the strength of the honeycomb sandwich structure is directly proportional to the thickness of the skin, the density of the honeycomb core cells, and the size of the embedded parts.
A critical perspective on generative AI and learning futures. An interview with Stefan Popenici

We present a wide-ranging interview with Stefan Popenici, a distinguished scholar and public speaker with extensive experience in higher education. Popenici's research focuses on the impact of artificial intelligence (AI) on teaching, learning, quality assurance, and student engagement in higher education. The interview delves into the themes of his book, Artificial Intelligence and learning futures: Critical narratives of technology and imagination in higher education (2023), exploring the intersection of AI, intelligence, and societal issues such as eugenics and racism. Popenici critiques the power of tech titans and the belief in technology as a panacea, especially in higher education. The discussion also addresses the identity crisis in higher education, the potential of revisiting Humboldt's 19th-century vision of the university, and the challenges and opportunities presented by the AI revolution. Popenici's insights into the role of AI in assessment, graduate and academic employment, and the future of academic work are particularly illuminating. The interview concludes with Popenici's reflections on his own educational journey and future plans.
Are camera, projector, and camera–projector calibrations different?

Structured light projection systems have become a referent in three-dimensional optical metrology. Calibration of the cameras and projectors of these systems is one of the most critical procedures to achieve high-accuracy measurements. However, the calibration process requires some clarifications for adequate experimental implementation. For instance, it is typically assumed that the calibration of a camera-projector pair differs from calibrating a camera, and the calibration of a projector is possible only with an attached auxiliary camera. This paper presents a unified methodology for camera, projector, and camera-projector calibrations. Experimental results are discussed, providing practical insights into how structured light systems are calibrated. The MATLAB code and data employed in this study are available.
Continual Learning Approach for Continuous Data Stream Analysis in Dynamic Environments

Continuous data stream analysis primarily focuses on the unanticipated changes in the transmission of data distribution over time. Conceptual change is defined as the signal distribution changes over the transmission of continuous data streams. A drift detection scenario is set forth to develop methods and strategies for detecting, interpreting, and adapting to conceptual changes over data streams. Machine learning approaches can produce poor learning outcomes in the conceptual change environment if the sudden change is not addressed. Furthermore, due to developments in concept drift, learning methodologies have been significantly systematic in recent years. The research introduces a novel approach using the fully connected committee machine (FCM) and different activation functions to address conceptual changes in continuous data streams. It explores scenarios of continual learning and investigates the effects of over-learning and weight decay on concept drift. The findings demonstrate the effectiveness of the FCM framework and provide insights into improving machine learning approaches for continuous data stream analysis. We used a layered neural network framework to experiment with different scenarios of continual learning on continuous data streams in the presence of change in the data distribution using a fully connected committee machine (FCM). In this research, we conduct experiments in various scenarios using a layered neural network framework, specifically the fully connected committee machine (FCM), to address conceptual changes in continuous data streams for continual learning under a conceptual change in the data distribution. Sigmoidal and ReLU (Rectified Linear Unit) activation functions are considered for learning regression in layered neural networks. When the layered framework is trained from the input data stream, the regression scheme changes consciously in all scenarios. A fully connected committee machine (FCM) is trained to perform the tasks described in continual learning with M hidden units on dynamically generated inputs. In this method, we run Monte Carlo simulations with the same number of units on both sides, K and M, to define the advancement of intersections between several hidden units and the calculation of generalization error. This is applied to over-learnability as a method of over-forgetting, integrating weight decay, and examining its effects when a concept drift is presented.