Reviewerly/exHarmony huggingface dataset:
Comprehensive Survey of Cutting-Edge Methods for Software Architecture Evaluation

The crucial responsibility of assessing software architecture is of utmost importance in ensuring that a software system conforms to superior qualities. It is a crucial tool for cutting expenses and labor during the course of the software development lifecycle. The main goal of software architecture evaluation is to provide reliable methods for determining and improving the quality characteristics that are innate in software. This study is deeply devoted to investigating the wide range of techniques used in software architecture assessment. One of the main focuses of these evaluation techniques is scenario-based assessments, which offer a comprehensive picture of the software's behavior under different circumstances. Within the scope of this study, a thorough analysis is carried out on eighteen different methods. Thirteen of these fall into the category of early evaluation techniques, which are positioned to detect and address architectural problems at the outset of development. The other five strategies are classified as late assessment techniques and focus on validating and optimizing the software architecture in the latter stages of the development process.
Pareidolia of Meanings of Digital Axiology

Virtuality with its inherent heterogeneity, diffuseness and fragmentation unfolds before us the space of new challenges of ethical and axiological nature. The topology of the digital is the topology of the eternally ongoing and unfinished, a “non-space” that cannot be subsumed under any particular framework by setting boundaries. The unboundedness of virtual space requires a revision of the essence and form of the classical axiological system. Thus, the article attempts to define the modality of values of modern digital culture, to describe the transformation of values from a hierarchical structure through rhizomatic to the modern, most chaotic and atomic state. Conclusion dwells upon the fact that digital axiology is not the transition of the habitual system of values into a digital format, it is the opening of a new field/dimension of values and meanings that cannot be stated, concretized, but can be represented by the metaphor of a pareidolic illusion.
Recognition of Handwritten Mathematical Expressions Using Systems of Convolutional Neural Networks

Accurate recognition of handwritten mathematical expressions has proven difficult due to their two-dimensional structure. Various machine-learning techniques have previously been employed to transcribe handwritten math, including approaches based on convolutional neural networks (CNNs) and larger encoder/decoder-based models. In this work, we explore a CNN-based method for transcribing handwritten math expressions into the typesetting language known as LaTeX. This approach utilizes machine learning not only for classifying individual characters but also for extracting individual characters from handwritten inputs and determining what forms of two-dimensionality exist within the expression. This approach achieves significant reliability when recognizing common mathematical expressions.
An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels

Due to issues with sample quality, there is an increasing interest in deep learning models that can handle noisy labels. Currently, the optimal way to deal with noisy labels is by combining robust active and passive loss functions. However, the weighting parameters for these functions are typically determined manually or through a large number of experimental iterations, and even the weighting parameters change as the dataset and the noisy rate change. This can lead to suboptimal results and be time-consuming. Therefore, we propose an adaptively weighted method for the combined active passive loss (APL) in remote sensing image retrieval with noisy labels. First, two metrics are selected to measure the noisy samples: the ratio of the entropy to the standard deviation and the difference of the predicted probabilities. Then, an adaptive weighted learning network with a hidden layer is designed to dynamically learn the weighting parameters. The network takes the above two metrics as inputs and is trained concurrently with the feature extraction network in each batch, without significantly increasing the computational complexity. Extensive experiments demonstrate that our improved APL method outperforms the original manually weighted APL method and other state-of-the-art robust loss methods while saving the time on manual parameter selection.
Object Detection for Enhanced Visuals Using Deep Learning

Object detection serves as a foundational aspect in computer vision, finding utility across a spectrum of applications spanning autonomous driving to security surveillance. The primary objective of this endeavor was to engineer a resilient and effective object detection framework leveraging deep learning methodologies, subsequently assessing its efficacy on real-world datasets. The project leveraged contemporary neural network architectures alongside modern tools for data preprocessing and evaluation. Keywords: Single shot Detector (SSD), Pre-Processing, Deep Learning, Image Processing, Classification.
Bridge Construction Risk Assessment Based on Variable Weight Theory and Cloud Model

In order to effectively prevent the occurrence of risky accidents during bridge construction, this study proposes a bridge construction risk assessment method based on variable weight theory and the cloud model theory. Firstly, the fishbone diagram was used to identify risk factors in constructing a bridge construction risk index system. Secondly, according to the cloud model theory, the comment cloud model of each risk index was established by using the forward cloud generator. Finally, the risk factor weights were quantified according to the intuitionistic fuzzy analytic hierarchy process (IFAHP). Combined with the variable weight theory, a zoning variable weight function was constructed and the weights were reallocated. Through the mutual aggregation of the comment cloud model and weights, the risk level of construction bridges was obtained. The method takes full account of the fuzziness and randomness existing in the evaluation process, optimizes the distribution of weight values of indicators, and uses Delphi iteration to effectively eliminate the subjective defects of individuals. A construction bridge in Changchun was used as an example for risk assessment, and the advance of the method was well verified. The results demonstrate that the method is highly feasible and effective after accuracy verification and sensitivity analysis.
A Novel Improved Whale Optimization Algorithm for Global Optimization and Engineering Applications

The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm based on natural heuristics, which has gained considerable attention from researchers and engineers. However, WOA still has some limitations, including limited global search efficiency and a slow convergence rate. To address these issues, this paper presents an improved whale optimization algorithm with multiple strategies, called Dynamic Gain-Sharing Whale Optimization Algorithm (DGSWOA). Specifically, a Sine–Tent–Cosine map is first adopted to more effectively initialize the population, ensuring a more uniform distribution of individuals across the search space. Then, a gaining–sharing knowledge based algorithm is used to enhance global search capability and avoid falling into a local optimum. Finally, to increase the diversity of solutions, Dynamic Opposition-Based Learning is incorporated for population updating. The effectiveness of our approach is evaluated through comparative experiments on blackbox optimization benchmarking and two engineering application problems. The experimental results suggest that the proposed method is competitive in terms of solution quality and convergence speed in most cases.
Metaheuristic Optimization Algorithms: an overview

Metaheuristic optimization algorithms are known for their versatility and adaptability, making them effective tools for solving a wide range of complex optimization problems. They don't rely on specific problem types, gradients, and can explore globally while handling multi-objective optimization. They strike a balance between exploration and exploitation, contributing to advancements in optimization. However, it's important to note their limitations, including the lack of a guaranteed global optimum, varying convergence rates, and their somewhat opaque functioning. In contrast, metaphor-based optimization algorithms, while intuitively appealing, have faced controversy due to potential oversimplification and unrealistic expectations. Despite these considerations, metaheuristic algorithms continue to be widely used for tackling complex problems. This research paper aims to explore the fundamental components and concepts that underlie optimization algorithms, focusing on the use of search references and the delicate balance between exploration and exploitation. Visual representations of the search behavior of selected metaheuristic algorithms will also be provided.
Prescribed intelligent elliptical pursuing by UAVs: A reinforcement learning policy

This paper proposes a prescribed intelligent elliptical pursuing algorithm driven by deep reinforcement learning (DRL) for Unmanned Aerial Vehicles (UAVs), aiming to achieve an optimal performance without violating appointed-time constraints. Specifically, an unknown system dynamics estimator (USDE) with merely one filtering coefficient is employed to counteract uncertainties effectively. Subsequently, an elliptical enclosing controller is devised for UAVs to achieve asymptotic convergence towards the target ellipse orbit. Particularly, a proximal policy optimization (PPO)-based optimal learning term is contrived to achieve control optimality related to consumption and efficiency, where a refined reward function is established to characterize the appointed-time constraints. The salient merit is that an efficient learning paradigm is formulated to fulfill a near optimal appointed-time enclosing free from prescribed performance control. Finally, a plenty of simulations substantiate the feasibility of developed algorithm.
A Study on the Safety Accident Prevention System in conjunction with the Internet of Things

Recently, in an Internet of Things environment, devices (objects) with built-in sensors or communication functions are connected to the Internet by utilizing a physical network that allows objects equipped with IoT sensors and communication chips to automatically exchange real-time data without human intervention. It can collect surrounding information, exchange this information with other devices, and even make appropriate decisions. It can be said to be a huge state system of wired and wireless networking objects based on attached sensors and chips. Here, we provide a practical and systematic approach to overcome the limitations of preventing safety accidents such as safety education, safety equipment, and on-site safety inspections by securing a strong safety net against repeated occurrences of habitual electric shock accidents and to overcome the continuous occurrence of accidents despite multifaceted efforts to achieve zero accidents. Through transformation, the need to develop a system that combines accident prevention and IoT technology is emerging. With the implementation of the Severe Accident Punishment Act, key requirements such as setting safety and health goals and management policies, a dedicated safety and health organization, risk assessment, budget, safety and health management manager, deployment of professional personnel, listening to workers
Resource Hyperlinks

Abstract In this chapter, links to resources in quality improvement are provided from leading journals and websites. Included are the BMJ QI series, the AURUM Institute, journals on learning health systems, and NHS England, NHS Scotland, and Ireland Health Service Executive (HSE) resources. Resources on key focus areas are provided, including driver diagrams, measurement tools, microsystems, systems convening, and person-centred care. Leads to quality improvement organizations and quality improvement videos have been listed.
Extend the Ant Colony Optimization Algorithm for virtualization Technologies to improve the Resources utilization in On-Premises Datacenters

Over the past few decades, there has been an increasing demand for computational power, which has fueled the growth of on-premises data centers. In recent years, virtualization techniques have been introduced to enhance data center resource utilization. These techniques consolidate multiple workloads onto fewer servers, reducing the need for physical devices to support an organization's IT infrastructure. Virtualization technologies have increased IT agility by allowing for quicker deployment of virtual machines (VMs), which in turn facilitates faster application and service rollouts, improves disaster recovery capabilities, and reduces carbon emissions, leading to significant cost savings for organizations. In this paper, we enhance the Ant Colony Optimization Algorithm (ACO) by applying it to virtualization. We simulate the ACO for virtual machine resource management. Our evaluation results demonstrate that the proposed algorithm can further improve resource utilization and reduce carbon emissions. Keywords: Virtualization, Datacenter, Resource utilization, Ant Colony Optimization.
Anti-deepfake rules will be contested despite harms

Headline INT: Anti-deepfake rules are contested despite harms
Analysis of Patient Health Using Arduino and Monitoring System

The proposed project is a health monitoring system using Arduino that measures key health parameters such as heart rate, blood oxygen level, and body temperature. The system consists of sensors such as DHT11 for temperature, MAX30100 for heart rate and blood oxygen level, and an Arduino Nano for processing the data. The measured results are displayed on an LCD screen and a buzzer sounds when the results are ready. The system has been designed with simplicity and ease-of-use in mind, making it accessible for personal use. While the system has limitations such as sensor accuracy and lack of IoT functionality, it still has potential for improving individual health monitoring and wellness. In the future, potential areas for development include the inclusion of more sensors, IoT functionality, and machine learning algorithms for personalized insights and recommendations. Overall, the proposed health monitoring system is a promising step towards empowering individuals to take a more active role in monitoring their health and wellness.
Water Meter Reading Application System Development using Image Processing A Case Study from Sindangsari Village Water Services

PAMDES is a drinking water company managed by local villagers. The water meter data are read and recorded manually without any technology, which is ineffective and inefficient. Digital image processing can be implemented to read and record the water meter data automatically. When implemented in the water meter, it can help PAMDES officers to read the data without the internet, without changing the conventional water meter device, and the water meter data can be read and recorded effectively and efficiently. This research used the agile method, one of the methods used in the Software Development Life Cycle (SDLC). The method is done repetitively within a short period of time. The output of this research is an application with a digital image processing model that can read water meter data up to 82% in normal conditions and still can be improved. This research aims to make the water meter data reading and recording more effective and efficient and to contribute to the transformation of Sindangsari village into a digitalized village.
Lack of CBN reform may fuel more corruption in Nigeria

Significance Cardoso has promised to prosecute any entities involved in making fraudulent claims. The Deloitte audit is one of three concurrent investigations into the bank’s management under the leadership of former Governor Godwin Emefiele (2014-23). Impacts Backdoor fuel subsidies may increase fiscal pressures on the government. There may be little appetite for prosecuting most of the officials indicted by the reports, with Emefiele serving as a scapegoat. When the CBN’s monetary policy committee meets for the first time since July 2023, they are likely to increase rates.
Sustainable Development for Smart Healthcare using Privacy-preserving Blockchain-based FL Framework

Artificial Intelligence (AI) methods need to learn from an adequately large dataset to achieve clinical-grade accuracy and validation, which is vital in the healthcare field. However, sensitive medical data is usually fragmented, and not shared due to security and patient privacy policies. In this context, our work aims at classifying abdominal and chest radiographs by applying Federated Learning (FL) without exchanging patient data. FL framework has been implemented on distributed data across multiple clients. In the framework, a multilayer perceptron is used as a deep learning model for the classification task. FL is a novel approach in which machine learning models are built with the collaboration of multiple clients controlled by a central server or service provider. FL model ensures data privacy and security by retaining the training data decentralized. FL model provides security and privacy for patients by training individual models in distributed clients and sharing merely the model weights.
The Impact of the Dynamicity and Non-dynamicity of Assessment on EFL Learners' Productive Skills: Attitude in Focus

The possible effects of dynamic evaluation (DE) and non-dynamic (non-DE) evaluation on the productive skills of Saudi EFL students were examined in this study. This study also looked at how Saudi EFL students felt about utilizing DE in their writing and speaking sessions. To achieve these objectives, sixty-four Saudi intermediate EFL students were split into two groups and selected using the convenience sample approach. Then, a pre-test was given to both groups for two skills: speaking and writing. After that, one group was taught speaking and writing using dynamic evaluation, while the other group was taught using NDE. Following eighteen training sessions, the groups were given posttests in speaking and writing, and the dynamic evaluation group was also given a perception questionnaire. The speaking and writing posttests for the two groups showed a substantial difference that favored the experimental group. The speaking and writing posttests demonstrated that the DE group fared better than the non-DE group. The results also pointed out that the DE group members had favorable opinions of the evaluation process. It was concluded that one of the best ways to help EFL students advance in their English language learning is to use DE in the classroom. Teachers and course designers may be convinced to incorporate dynamic evaluation into their lesson plans and courses by the consequences of this research.
Big Data Analysis of TikTok in the Education of Traditional Chinese Opera

Opera is the essence of traditional art accumulated in Chinese history and civilization. In China, the inheritance of opera culture has been developed as an educational cause. However, in the era of the Internet, the once glorious and popular opera is also facing an existential crisis of losing a large number of audiences. How to reconstruct the communication ecology of opera with the help of new media has become an urgent problem for opera and media circles. In this paper, through big data of Douyin (Tiktok), to analyze the education of traditional Chinese opera, the ultimate hope as an innovative communication form combining traditional opera and mobile new media, short video of opera can expand the acceptance and influence of opera and promote the popularization and development of opera.
Methodological Aspects of Teachers’ Work in the Electronic Information Educational Environment

Currently, it is becoming more and more relevant not just the use of information technologies in the educational process, but the conduct of the educational process in a specially designed electronic information educational environment. As practice shows, not all teachers use such a practice for conducting the educational process. Many teachers still prefer to teach classes according to the traditional classroom system. And there are good reasons for this. This article discusses the method of activating the interest and motivation of teachers for the use of electronic information educational environment in their professional activities. The method is to organize and conducting a refresher course “The work of a teacher in the electronic information environment of an educational organization of higher education”. The article provides a brief review of the structure and content of this course, an analysis of the effectiveness of the course based on a survey of students who have completed training. According to the results of the study, measures aimed at activating the interest of teachers in using electronic information educational environment are proposed. The study was conducted at Tver State Technical University. The practical significance of the research is to gain experience in the development and implementation of training courses on the use of electronic information educational environment in the educational process. The authors determined the optimal structure of the course, the content and conducted a statistical assessment of its effectiveness. The effectiveness of the course was determined based on the calculation of the proportion of listeners who, after completing the course, practice remote technologies in their teaching activities, in particular, work in an electronic information educational environment. The scientific novelty is the application of the author’s method for developing the course and its application as a special tool for activating the demand for distance learning technologies.
Optimizing neural network training with Genetic Algorithms

In modern society, computer plays an important role among all human beings. Through the increasing development of technology, some problems happened gradually. In order to solve and regenerate the country, individuals should test their strengths. This paper discusses how to use genetic algorithms to optimize neural network training. As an important tool of machine learning, neural networks have made remarkable achievements in dealing with complex tasks. However, the training process of neural networks involves a lot of hyperparameter adjustment and weight optimization, which often requires a lot of time and computing resources. In order to improve the efficiency and performance of neural network training, humans should introduce genetic algorithms as an optimization method. Experiments are conducted on several common datasets to compare the performance of neural network training with Genetic Algorithm optimization against the traditional method. The results indicate that using Genetic Algorithms significantly improves the convergence speed and performance of neural networks while reducing the time and effort spent on hyperparameter tuning. Neural networks optimized using the Genetic Algorithm outperform their counterparts trained under the same time frame.