Reviewerly/exHarmony huggingface dataset:
Identifying Topics and Trends in DevOps: A Study of Stack Overflow Posts

DevOps (i.e., Development and Operations) is a growing concept, which aims to make building, testing, and releasing software faster, more frequent, and more reliable by automating the software delivery and architectural change process. Since DevOps is a relatively new concept, we seek to understand the hot topics in DevOps and the challenges encountered so far. We use data from Stack Overflow (SO), the largest developer question-and-answer platform, to understand the interests and difficulties of DevOps. First, we collected all DevOps-related posts from 2009 to 2022 based on SO tags. Then we used the Latent Dirichlet Allocation model to identify topics, and manually analyzed time trends and hot topics. The results indicate that: (1) DevOps-related issues can be divided into four categories: Container, Pipeline, Configuration, and Deployment; (2) The issues that get more attention are DevOps foundation issues, cloud deployment failure issues, Kubernetes technology usage issues, and DevOps complex environment issues, while the most difficult issues to solve are CI/CD-related issues; (3) DevOps has been introduced since 2009 and the number of questions on the SO platform is growing, especially between 2014 and 2020; (4) The percentage of DevOps-related questions with no accepted answers on the SO platform reached 56.3%. Our results show that problems are evident in terms of complex environments and the lack of experts. Also, developers need to pay more attention to basic theoretical knowledge when they are new to DevOps.
Toward a Secure Educational Metaverse: A Tale of Blockchain Design for Educational Environments

In the era of social distancing, distance learning represents a crucial educational challenge. Several 2D information technologies have been provided, yet these share multiple limitations and have negative social, educational, and psychological implications for learners. Metaverse promises to revolutionize education as we know it: this is a persistent, virtual, three-dimensional environment that is supposed to address most of the limitations of 2D information technologies. Nonetheless, there are still software engineering challenges to face to enable such a metaverse, especially when turning to software security and privacy. In this paper, we aim at performing the first steps toward an improved understanding of the security perspective of educational metaverse, by analyzing how blockchain can be employed within educational environments and how applications may be designed. Our ultimate goal is to provide insights into how blockchain can be further tailored in the context of educational metaverse. We conduct a systematic literature review, which targets 20 primary studies. The key findings of the study showcase the use of blockchain in 3 educational tasks, other than describing the blockchain design approaches, which protocol they commonly use and the associated limitations. We conclude by developing a conceptualization of a blockchain-based educational metaverse.
Hybrid Cloud Service Portability Platform

The software industry is heading towards cloud solutions which give them better scalability, security, and availability all at the same time. The companies deploy their important assets to various cloud platforms which offer the required solutions. Many times these requirements change according to the industry's demand-supply chain and hence companies try to migrate between different cloud platforms taking those same assets, but they face many difficulties with data security and sanctity as there are hardly any flexible ways of transfer. We desire to create a ‘Hybrid Cloud Service Portability Platform’ which would offer a seamless migration between cloud solutions overcoming the impediments like vendor lock-in, uncountable hours of transfer and security checks, bad mobility, etc. We try to build a zone that helps relocation from ‘any cloud to any cloud’ with minimal time and effort and irrespective of the size of the data. This project has the potential to add a new perspective to hybrid cloud technology and solve many concerned problems. Currently, the scope of the solution is limited to migrating a Load Balancer from AWS to GCP. We have explicitly defined AWS as the source cloud platform and GCP as the target cloud platform.
Evaluating Optimization Approaches for Deep-Reinforcement-Learning-based Navigation Agents

In recent years, Deep Reinforcement learning has made remarkable progress in various application areas such as control of robots and vehicles, simulation, and natural language processing. In recent years, various research works applied DRL to conduct different kinds of tasks for autonomous navigation of vehicles and robots such as lane changing, cruise control, or obstacle avoidance. However, DRL training is still a tedious and difficult process due to long training times, catastrophic forgetfulness and the myopic nature of DRL agents. In this paper, we integrate and explore the effect of a variety of state-of-the-art optimization approaches for DRL agents such as imitation learning, behavior cloning, frame stacking, and hindsight replay for autonomous obstacle avoidance. We evaluate the effect of each of these changes to the DRL agent in terms of training- as well as navigational performance. The resulting agents are compared against baseline approaches without those optimizations and we found an increase in navigational performance for some of these methods while other optimization approaches surprisingly resulted in decreased performing. The findings of this paper should aid in development of DRL approaches for autonomous navigation approaches.
PyramidEnsemble: Joining Large and Small Models

In this paper, we aim to improve segmentation performance and uncertainty calibration within a fixed computational budget. We propose PyramidEnsembles, which contain members ranging from small over medium and large, to over-come one major problem in applying neural networks to the automotive domain: the tradeoff between model performance and overconfidence in the uncertainty predictions. PyramidEn-sembles use multiple models of different sizes from the same family in order to combine their strengths: good segmentation performance and well-calibrated uncertainties. We focus our experiments on EfficientNet-based segmentation models applied to the Cityscapes dataset, which is widely used in the field of autonomous driving. We evaluate single models, uniform ensembles (one architecture repeated) and PyramidEnsembles (combination of different model capacities) composed of the EfficientNet model family. Our evaluations show that within the same computational budget, PyramidEnsembles can outperform a single model in terms of segmentation performance while providing better calibrated uncertainties. Scaling over different computational budgets shows that this performance gap increases further. Different uniform ensembles offer a comparable segmentation or uncertainty calibration performance: 3 copies of the EfficientNet-B3 achieve an IoU of 0.7195 while an ensemble of 7 EfficientNet-B0 models yields an Expected Calibration Error (ECE) of 0.0667. One PyramidEnsemble containing an instance of EfficientNet-B0 through B3 is a close second on either metric at 0.7188 IoU and 0.0698 ECE and offers a better tradeoff between segmentation performance and uncertainty calibration in this computational budget,
Deep Learning for Histopathological Image Analysis in Uterine Cancer Diagnosis

The proposed work presents a novel deep learning-based method for improving the diagnostic precision of histopathology image interpretation for uterine cancer. To enhance patient outcomes, the need for precise and early detection of uterine cancer persists as a significant public health concern. The proposed method can precisely identify and classify uterine cancer cells in histopathology images using advanced deep-learning algorithms. This study employs cutting-edge data preprocessing and augmentation techniques to train and validate the models on a dataset of uterine tissue samples. The deep learning model had remarkable diagnostic accuracy and outperformed state-of-the-art methods by a wide margin, demonstrating the immense potential of the findings. This strategy has the potential to revolutionize the detection of uterine cancer and expedite clinicians' options, both of which have far-reaching implications for medicine.
Semblance Unmasker: Hidden Emotion Recognition Using Deep Learning Approach

Emotion-aware systems have emerged as critical tools in the field of e-learning, aiming to address prevalent challenges such as low student self-esteem, high dropout rates, lack of motivation and engagement, self-regulation, and task performance. Customizing the educational experience for each learner requires the use of machine learning and deep learning techniques. The development of a machine learning model that can predict the mood or attitude of a group of students or a single student is recommended in order to assist teachers in responding properly. By adding emotional intelligence with educational technology, it is feasible to run cost-effective training programs while simultaneously significantly raising the quality of remote learning. It has been noted that emotion-aware systems have the potential to increase the effectiveness of online learning, and it has been recommended that further research and development be done on these systems.
Automation and Development Effort in Continuous AI Development: A Practitioners’ Survey

The widespread adoption of AI-enabled systems and their required continuous development and deployment (MLOps) sparks research interest due to the added intricacy of automatically handling data, code, and the model itself. A better understanding of the stages for the continuous development of AI, namely Data Handling, Model Learning, Software Development, and System Operations, and the respective tasks can help to optimize and improve their effectiveness.Thus, this paper explores the degree of automation, development effort, importance, utilization of computing resources, and factors contributing to automation throughout these stages and tasks. We conducted a questionnaire-based global survey to explore these topics by analyzing 150 responses from experienced AI, data, and MLOps engineers.The results determined that the stage System Operations is mainly automated. Whereas several tasks from the other three stages (e.g., data cleaning, data quality assurance, model design, model improvement, and system level quality assurance) are more often partially automated than automated, and documentation-related tasks are mostly not automated or developed. Participants required the highest development effort for the stage Data Handling. Furthermore, the study reveals a negative correlation between automation and the perceived development effort, whereas the importance of the tasks does not seem to affect automation. 93% of participants consider the availability of computing resources, with model training, data transformation, and data cleaning ranked as the most resource-intensive tasks.
Neural-Based Cross-Modal Search and Retrieval of Artwork

Creating an intelligent search and retrieval system for artwork images, particularly paintings, is crucial for documenting cultural heritage, fostering wider public engagement, and advancing artistic analysis and interpretation. Visual-Semantic Embedding (VSE) networks are deep learning models used for information retrieval, which learn joint representations of textual and visual data, enabling 1) cross-modal search and retrieval tasks, such as image-to-text and text-to-image retrieval; and 2) relation-focused retrieval to capture entity relationships and provide more contextually relevant search results. Although VSE networks have played a significant role in cross-modal information retrieval, their application to painting datasets, such as ArtUK, remains unexplored. This paper introduces BoonArt, a VSE-based cross-modal search engine that allows users to search for images using textual queries, and to obtain textual descriptions along with the corresponding images when using image queries. The performance of BoonArt was evaluated using the ArtUK dataset. Experimental evaluations revealed that BoonArt achieved 97 % Recall@10 for image-to-text retrieval, and 97.4 % Recall@10 for text-to-image Retrieval. By bridging the gap between textual and visual modalities, BoonArt provides a much-improved search performance compared to traditional search engines, such as the one provided by the ArtUK website. BoonArt can be utilised to work with other artwork datasets.
Synergistic Resource Allocation in Space Division Multiplexed Data Center Optical Networks Secured with Quantum Key Distribution

In this article, we propose two novel core prioritization and crosstalk aware (CP-XT-Aware) algorithms, including CP-XT-Aware with wavelength grouping (CPWG-XT-Aware) and CP-XT-Aware with wavelength reservation (CPWR-XT-Aware), aiming to realize the synergistic transmission of classical and quantum signals in quantum key distribution (QKD)-enabled optical data center networks based on wavelength-division-multiplexing and space-division-multiplexing, For comparison, we introduce the core and wavelength random allocation without XT-Aware, called the CWRA. Analytical simulations show that the both proposed algorithms can simultaneously improve the average secure key rate and reduce blocking probabilities under dynamic traffic scenarios in different network topologies, compared to the CWRA. Particularly, the CPWG-XT-Aware performs exceptionally well in QKD under the low traffic load, while the CPWR-XT-Aware excels under the high traffic load.
Improving Feature Vector for Extractive Text Summarization of Legal Judgement

Extractive Text summarization in legal judgment has the key task of finding important information from the legal text and representing it in concise summaries. Large amounts of legal documents can be effectively produced into summaries through the use of extractive text summarization. Finding the most important and informative details for summarizing might be difficult, though, due to the volume of material in legal papers. The task of feature selection in legal documents for extractive text summarization is covered in this article. The goal of this study is to examine and evaluate the value of feature selection methods for enhancing the accuracy and efficiency of legal text summaries. Features like sentence length, sentence position, Term frequency and inverse sentence frequency (TF-ISF), degree of similarity, keywords, and citation are used. To extract the most important details from legal papers, this paper provides a methodology that incorporates both generic text elements and domain-specific legal features. This feature selection method will enable us to find the most important sentences in the judgment. Using a large Indian-Extractive dataset (IN-Ext) of legal documents, the proposed approach is examined.
Test-Driven Development and Embedded Systems: An Exploratory Investigation

We present the results of an exploratory investigation to obtain preliminary evidence on the use of Test-Driven Development (TDD), an incremental approach to software development where tests are written before production code, to develop Embedded Systems (ESs). Specifically, we conducted two experiments in which we compared TDD with a non-TDD approach in terms of the external quality of ESs and developers’ productivity. In the experiments, we also gathered qualitative data to better understand the investigated phenomenon. We found that the external quality of the implemented solutions increases when using TDD as compared to a non-TDD approach, while there is not a substantial difference with respect to developers’ productivity. However, TDD is perceived as more difficult to apply than a non-TDD approach, and the development task is deemed more challenging with TDD.
Image Classification Improvement: Text-to-Image AI for Synthetic Dataset Approach

In this paper, we share our experience in using synthetic image generation through text-to-image AI models with the aim of improving the performance of image classifiers models.The procedure for generating high-quality synthetic images with text-to-image AI models is described, utilizing generic prompts based on proximity in hypernymys to generate 200 images of samples (lions, cats, and monkeys). Two image classifiers were built using the synthetic images alongside with classical image augmentation techniques. Each classifier was evaluated using three different datasets. Stable Diffusion 2.1 was employed as the image generator, and the results demonstrate that our proposed technique of generating synthetic images can indeed enhance image classifier accuracy. The results rely on the proficiency of the AI and the appropriateness of utilized prompts. We think our results can be improved by advances on the Latent Diffusion models.
Sentiment Analysis using Deep Learning based Emotion Extraction from Social Media Texts

Text message sentiment analysis is becoming increasingly essential for comprehending user emotions, perspectives, and social media trends. The purpose of this study is to improve the accuracy and specificity of sentiment analysis by investigating the use of deep learning techniques to extract sentiment from social media posts. To sanitize and prepare the text data, this employs cutting-edge natural language processing preprocessing techniques for an enormous and diverse set of social media posts. The proposed deep learning model, which employs both convolutional and recurrent neural networks, performs significantly better than conventional methods. The results indicate that the model can identify not only positive, negative, and impartial sentiments but also more nuanced emotional nuances, providing a more complete picture of how social media users truly feel. This research contributes to an understanding of sentiment analysis and has applications in marketing, public opinion research, and brand management, among others. Understanding and interpreting user sentiments using deep learning techniques is crucial in the swiftly evolving digital environment of today, as social media continues to influence public discourse.
QuaFedAsync: Quality-based Asynchronous Federated Learning for the Embedded Systems

In recent years, Federated Learning, as an approach to distributed learning, has shown its potential with the increasing number of devices on the edge and the development of computing power. The method enables large-scale training on the device that creates the data but with the sensitive data remaining within the data’s owner. In reality, however, the vast majority of enterprises have the problem of low data volume and poor model quality to support the implementation of Federated Learning methods. Learning quality assurance for edge devices is still the major issue which prevents Federated Learning to be applied in industrial contexts, especially in safety-critical applications. In this paper, we propose a quality-based asynchronous Federated Learning algorithm (QuaFedAsync) to address these challenges. We report on a study in which we used two well-known data sets, i.e., DDAD and KITTI datasets, and validate the proposed algorithm on an industrial use case concerned with monocular depth estimation in the automotive domain. Our results show that the proposed algorithm significantly improves the prediction performance compared to the commonly applied aggregation protocols while maintaining the same level of accuracy as centralized machine learning. Based on the results, we prove the learning efficiency and robustness when applying the algorithm to industrial scenarios.
Investigating Writing Anxiety of Iraqi EFL Senior Secondary School Students

The research aims to determine the level of writing anxiety among Iraqi EFL senior secondary school students and whether there is a statistically significant difference between male and female students experiencing writing anxiety. Furthermore, the research aims to identify if there is a significant difference between low and high-proficient students in terms of writing anxiety, as well as to identify the type of correlation between students' proficiency and writing anxiety. A group of (160) male and female sixth-grade students were selected randomly from four public secondary schools as the study sample. A 3-point Likert scale of (20) items designed by Cheng (2004) ranging from “true of me” to never true of me” was employed as an instrument to collect the required data. S.P.S.S. software version (23) was used to analyze the collected data: One-Sample Test, An independent sample t-test, and a Pearson product-moment correlation. The findings of the study revealed that Iraqi EFL senior secondary school students experience writing anxiety positively with a moderate level through the process of learning English. There is no statistically significant difference between male and female students in experiencing writing anxiety. There is no significant difference between low and high-proficient students in terms of writing anxiety. Finally, the findings disclosed a negative correlation (r= -.012-) between students' proficiency and writing anxiety; it implies that the less proficiency a student has in a foreign language, the more writing anxiety a student experiences.
Application of Fuzzy Tsukamoto Method to Rainfall Prediction in Sleman Regency

The context of this research is that erratic rainfall can disrupt community activities, especially for traders who want to make sales. In addition, information about rainfall is also needed by farmers in determining planting patterns in order to get maximum yields. The purpose of this research is to be able to help farmers predict rainfall to get maximum crop yields. One method to be able to predict rainfall is Fuzzy Logic. This research will use the Tsukamoto Fuzzy method. In the research conducted this time, the author conducted monthly rainfall forecasting in Sleman Regency. Rainfall data in Sleman Regency from 2015 to 2022 will be used in this research. This research succeeded in getting a MAPE value of 49.31%. The result of this research is the highest monthly rainfall prediction in November, with a rainfall of 713.78 mm. At the same time, the lowest occurred in August, which amounted to 36.47 mm. This research only gets a MAPE value of 49.31%. So it can be concluded that the Tsukamoto fuzzy method cannot predict rainfall well.
Naive Bayes and Decision Tree Algorithms for BRI Life Sharia Insurance Product Classification

Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are eligible for KIP scholarships, it is necessary to classify scholarship recipients correctly using data mining classification techniques. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.