Reviewerly/exHarmony huggingface dataset:
CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images

Objective.Breast cancer is the major cause of cancer death among women worldwide. Deep learning-based computer-aided diagnosis (CAD) systems for classifying lesions in breast ultrasound images can help materialise the early detection of breast cancer and enhance survival chances.Approach.This paper presents a completely automated BUS diagnosis system with modular convolutional neural networks tuned with novel loss functions. The proposed network comprises a dynamic channel input enhancement network, an attention-guided InceptionV3-based feature extraction network, a classification network, and a parallel feature transformation network to map deep features into quantitative ultrasound (QUS) feature space. These networks function together to improve classification accuracy by increasing the separation of benign and malignant class-specific features and enriching them simultaneously. Unlike the categorical crossentropy (CCE) loss-based traditional approaches, our method uses two additional novel losses: class activation mapping (CAM)-based and QUS feature-based losses, to capacitate the overall network learn the extraction of clinically valued lesion shape and texture-related properties focusing primarily the lesion area for explainable AI (XAI).Main results.Experiments on four public, one private, and a combined breast ultrasound dataset are used to validate our strategy. The suggested technique obtains an accuracy of 97.28%, sensitivity of 93.87%, F1-score of 95.42% on dataset 1 (BUSI), and an accuracy of 91.50%, sensitivity of 89.38%, and F1-score of 89.31% on the combined dataset, consisting of 1494 images collected from hospitals in five demographic locations using four ultrasound systems of different manufacturers. These results outperform techniques reported in the literature by a considerable margin.Significance.The proposed CAD system provides diagnosis from the auto-focused lesion area of B-mode BUS images, avoiding the explicit requirement of any segmentation or region of interest extraction, and thus can be a handy tool for making accurate and reliable diagnoses even in unspecialized healthcare centers.
ONTOLOGY DEVELOPMENT FOR GREEN BUILDING BY USING A SEMI-AUTOMATIC METHOD

ABSTRACT Green building has been deemed an important endeavor to promote sustainable building development. However, knowledge from different standards, different companies, and different software in the green building domain is difficult to share and reuse since different terminologies, measurement indicators, and criteria are adopted. Therefore, there is a need to create a consistent knowledge representation model in the green building domain. This study proposes a green building ontology (GB-Onto) which is an abstract conceptualization of the knowledge in the green building domain. To build the ontology more effectively, this study adopts the ontology learning method which is based on NLP and machine learning techniques. An improved TF-IDF method is introduced to extract concepts in the green building domain. Concept inclusion and semantic networks method are integrated to extract taxonomic relations. The associate rule method is used for extracting non-taxonomic relations. Finally, all these methods are implemented by adopting software and Python programming. The GB-Onto is evaluated through consistency checking and criteria-based evaluation. The GB-Onto fills the knowledge gap by providing a formal and shared vocabulary for the green building domain which promotes knowledge reuse and sharing among different stakeholders.
Enhancements in Circle Rendering: An Improved Approach to the Midpoint Circle Drawing Algorithm

Abstract Rendering circles on digital platforms is essential in computer graphics. The widely adopted midpoint circle algorithm is a premier tool for this task. This article dives deep into the foundational aspects of this algorithm, highlighting its prowess in enabling accurate and effortless circle depiction on digital displays. It further contrasts its merits with alternative circle drawing techniques. Importantly, our research presents a refined technique aimed at minimizing inaccuracies inherent in the standard cartesian-based Midpoint Circle Drawing Algorithm (MPCDA). By incorporating an error-reduction strategy, we ensure greater accuracy in depicting circles. Comparative tests on diverse circle sizes and placements confirmed the efficacy of our enhanced method. When set against results from the traditional MPCDA, our approach showcases significant improvements.
Ticket to Ride: A Journey Through the Most In-Demand Soft Skills for QA Professionals in Brazil

Background: Software quality assurance (QA) is carried out in tandem with software development. Essentially, QA activities are conducted in hopes of assessing the extent to which a software product aligns with predefined requirements. Although QA includes highly technical tasks, much like software development, it remains largely a human-centered endeavor. Consequently, soft skills can play a significant role in contributing to the success of a project and product quality, as well as boosting the productivity of QA professionals. Aim: Our objective is to investigate: (i) the most sought-after soft skills for QA professionals, (ii) potential correlations among these in-demand soft skills, (iii) variations in soft skill requirements based on the seniority level of positions, and (iv) potential differences in soft skills expectations according to the size of the hiring companies. Our analysis is centered around organizations in Brazil. Method: We looked for soft skills in 253 job advertisements from Brazilian companies. Our data extraction process followed an inductive, data-driven approach that included both manual and automated steps. Results: approximately 72% of the job advertisements list at least one soft skill. We identified 35 soft skills, among which communication-related soft skills appear to be the most sought-after. We found that companies of all sizes highly value communication skills for QA professionals at every career stage. Conclusions: Our findings underscore the critical role of communication skills. The results of our study hold potential value for QA professionals in Brazil seeking to enhance their employability and provide insights to those tasked with staffing, curriculum design, and professional development.
The use of Design Thinking in creating projects and improving customer value perception: an experience report on software projects at a Research and Development Institute in Amazonas

Context:The measure of success for a product is the extent to which it meets users' needs. To meet these needs, it's necessary to understand what they are. In this context, where the user and their needs are central to the success of a product, Design Thinking (DT) techniques are essential. Objective: This study presents the application of Design Thinking techniques in innovation projects at a significant Research and Development Institute in Manaus, aiming to enhance customer value perception and satisfaction with the deliverables. Method: The research strategy employed in this study was field research, the methods were exploratory case study and archival research, with quantitative and qualitative analyses conducted on the data. Results: It was possible to notice that there was an increase in customer satisfaction indicators through the utilization of Design Thinking (DT) techniques in the process of uncovering user needs. The CVP (Customer Value Perception), which assesses customer satisfaction concerning intermediate project deliverables, rose from 3.3 in 2016 to 4.0 in 2022, where score 1 represents the minimum and 5 the maximum rating. The NPS (Net Promoter Score), gauging customer value perception across all organization sectors, surged from -50 (Critical Zone) in 2016 to 71.4 (Quality Zone) in 2022, with -100 as the lowest and 100 as the highest achievable score. These data were collected considering the CVP and NPS surveys conducted between 2016 and 2022. Conclusion: The results demonstrate that implementing DT techniques assisted the Institute in achieving greater precision in its deliverables. In response to this insight, the Institute has established a laboratory to facilitate collaborative meetings and co-creation sessions to enhance customer engagement and foster project innovation.
Digital Competencies in Ukrainian Education of the Future: Teaching and Assessment

The acquirement of digital competencies in the times of global digitalisation of society is an urgent task for the educational system. Being a part of the globalised world Ukraine needs to further improve the learning process based on digital technologies. The aim of the study was to determine the state of digital competencies in Ukrainian education by analysing the learning and assessing the skills of students. Quantitative and qualitative analysis were used in this paper. 120 students from different educational institutions of Ukraine were involved in the study, representing different professional fields. The students were selected based on pre-developed criteria: a student of a higher education institution, a combination of participants from different specialities and different educational levels, and experience in using digital technologies. A random sample was used in the study. Subsequently, it was stratified based on education levels and age groups in order to ensure proportional representation. The results indicated a high level of awareness of the importance of developing digital competencies for professional growth among modern students. The results showed that modern digitalisation processes have become a factor in the introduction and development of innovative teaching methods. For this reason, it is important to introduce methods of individualised learning using adaptive technologies, the use of virtual and augmented reality to improve learning. Such tools as a PC, laptop, smartphone, interactive whiteboards that have become commonplace for Ukrainian students. The conclusions noted that more than half of the respondents stated that their educational institutions created necessary conditions for digital competencies development. Forty-two percent considered that such conditions were not sufficient, which may be due to insufficient funding, lack of developed technical infrastructure. The implications influence the development of curricula in Ukrainian education, prompting the integration of digital competencies as a core component.
Study Analysis: Learning Pattern of Blind Children Using Aid Applications for Science Learning

Use of technology for learning for blind children. One of the main gaps is the availability and accessibility of this technology in the school environment. Not all schools have adequate infrastructure or financial support to implement technology effectively. This research uses a descriptive qualitative approach with triangulation techniques in data collection to provide an in-depth description of the learning process evaluation for blind students at SLB 1 Makassar. The data collection technique involved four blind students and four teachers who were also blind, selected through purposive sampling. The Context, Input, Process, Product (CIPP) approach developed by Stufflebeam was used as the framework. This research focuses specifically on evaluating the learning process. Unique dynamics in the blind class at SLB 1 Makassar, where teachers and students face blind disabilities. The presence of teachers who are also blind, especially those with total blindness, creates a learning environment full of empathy and mutual understanding. The Be My Eyes, Voice Dream Reader, and Blind Square applications contribute significantly to the inclusive education of blind children at SLB 1 Makassar. With the help of this application, it is possible to increase the independence of blind children in learning, especially science learning.
Virtual Tourism in Berastagi Based on Roblox Metaverse Post-Covid-19 Pandemic

The limited mobility and tightening of social restrictions have resulted in most tourist destinations becoming devoid of visitors. However, amid these challenges, an innovative concept has emerged that has captured attention, namely, the Virtual Tourism of Berastagi based on the Roblox Metaverse. The aim of this research is to provide a safe and innovative alternative for tourists, especially in the post-Covid-19 situation. Additionally, it is also expected to enhance the potential for future tourism visits to Berastagi. One of the platforms utilized is Roblox, a popular gaming and metaverse platform among children and teenagers. Through this platform, tourists can access Berastagi virtually with greater ease and interactivity.
Bald Eagle Search Optimization Algorithm Combined with Spherical Random Shrinkage Mechanism and Its Application

Abstract Over the last two decades, stochastic optimization algorithms have proved to be a very promising approach to solving a variety of complex optimization problems. Bald eagle search optimization (BES) as a new stochastic optimization algorithm with fast convergence speed has the ability of prominent optimization and the defect of collapsing in the local best. To avoid BES collapse at local optima, inspired by the fact that the volume of the sphere is the largest when the surface area is certain, an improved bald eagle search optimization algorithm (INMBES) integrating the random shrinkage mechanism of the sphere is proposed. Firstly, the INMBES embeds spherical coordinates to design a more accurate parameter update method to modify the coverage and dispersion of the population. Secondly, the population splits into elite and non-elite groups and the Bernoulli chaos is applied to elite group to tap around potential solutions of the INMBES. The non-elite group is redistributed again and the Nelder-Mead simplex strategy is applied to each group to accelerate the evolution of the worst individual and the convergence process of the INMBES. The results of Friedman and Wilcoxon rank sum tests of CEC2017 in 10, 30, 50, and 100 dimensions numerical optimization confirm that the INMBES has superior performance in convergence accuracy and avoiding falling into local optimization compared with other potential improved algorithms but inferior to the champion algorithm and ranking third. The three engineering constraint optimization problems and 26 real world problems and the problem of extracting the best feature subset by encapsulated feature selection method verify that the INMBES’s performance ranks first and has achieved satisfactory accuracy in solving practical problems.
Quantifying the diversity of multiple time series with an ordinal symbolic approach

The main motivation of this paper is to introduce the ordinal diversity, a symbolic tool able to quantify the degree of diversity of multiple time series. Analytical, numerical, and experimental analyses illustrate the utility of this measure to quantify how diverse, from an ordinal perspective, a set of many time series is. We have shown that ordinal diversity is able to characterize dynamical richness and dynamical transitions in stochastic processes and deterministic systems, including chaotic regimes. This ordinal tool also serves to identify optimal operating conditions in the machine learning approach of reservoir computing. These results allow us to envision potential applications for the handling and characterization of large amounts of data, paving the way for addressing some of the most pressing issues facing the current big data paradigm.
Throughput Optimization for Blockchain System with Dynamic Sharding

Sharding technology, which divides a network into multiple disjoint groups so that transactions can be processed in parallel, is applied to blockchain systems as a promising solution to improve Transactions Per Second (TPS). This paper considers the Optimal Blockchain Sharding (OBCS) problem as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL)-based methods, where the action space must be discretized to increase processability. However, the discretization degrades the quality of the solution since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and provide dynamic sharding strategies based on them. The Branching Dueling Q-Network Blockchain Sharding (BDQBS) algorithm is designed for discrete action spaces. Compared with traditional DRL algorithms, the BDQBS overcomes the drawbacks of high action space dimensions and difficulty in training neural networks. And it improves the performance of the blockchain system by 1.25 times. We also propose a sharding control algorithm based on the Parameterized Deep Q-Networks (P-DQN) algorithm, i.e., the Parameterized Deep Q-Networks Blockchain Sharding (P-DQNBS) algorithm, to efficiently handle the discrete–continuous hybrid action space without the scalability issues. Also, the method can effectively improve the TPS by up to 28%.
Populismo e linguaggio della politica: I discorsi populisti in Italia negli anni Novanta

This paper aims at conducting an analysis on populist discourses in the 1990s in Italy by personalities that have been referred to as populist. The analysis focuses on the seduction strategies and rhetorical means used in these speeches, as well as on the main emerging themes: this study is realized with the help of Sketch Engine and Leximancer, software for content analysis. The aim of the paper is to individuate whether these texts are characterized by traits d’union that go back to populism.
A Session-Based Recommendation Model That Integrates the Temporal Sequence of Session Interactions and the Global Distance-Awareness of Items with Graph Neural Networks

In the session-based recommendation algorithm, a better class of methods is to model the complex interaction relationship within the session as a graph structure, and then use the graph neural network to capture the deep features of the item from it. However, most models do not deeply mine the effective information contained in the sequence temporal relationship, nor do they pay attention to the auxiliary contribution of the global distance in different sessions to the item representation. This paper proposes a session-based recommendation model that integrates the temporal sequence of session interactions and the global distance-awareness of items with graph neural networks (TSDA-GNN). First, according to all session sequences, a global graph, a session graph, and a feature graph are constructed. In the global graph, the average “spacing” between items is introduced to represent the association between two items in the global graph. In the session graph, the interaction timing of the item is combined as the weight information in the relationship matrix, and the connection matrix of the session graph is constructed, which can more accurately capture the association relationship between items. Finally, by fusing the feature information of the item, using a graph neural network can deeply mine user preferences hidden in the feature information of the item. The model can not only model the information transmission relationship between items in different sessions, but also further deeply mine the hidden information of global domain and item features. Multiple experiments on two benchmark datasets show that the model proposed in this paper achieves the best results in various indicators.
Sketch to Image Translation using Generative Adversarial Network

Using a Generative Adversarial Network (GAN) has proven its ability to successfully implement realistic images in image translation fields. It has its successful implementation in the sketch-to-image translation, too. Generative adversarial networks are widely used for the purpose of image translation. Most discriminators in generative adversarial networks use encoder or decoder blocks for image segmentation and classification tasks. U-net-based architecture is mostly used in the generator but rarely in the discriminator. If used in the discriminator, it is used for image resolution increment and segmentation tasks. In this research, a U-net-based discriminator is used for image translation tasks. U-net-based discriminator uses local and global differences between the real and fake images, which helps maintain global and local data representation. Resnet-9, used in the generator, uses skip connections, shortcuts, and concatenations, enabling information to flow from earlier to later layers. This helps preserve the original image features and solves the vanishing gradient problems in normal generators. The use of a strong discriminator and effective generator helps in the improvement system's performance. The available dataset was unpaired at the same time. Datasets from various sources were combined and formed a sketch-image pair. The input is a 512x256 human sketch and a corresponding real image pair. The image pair is split into sketch and image with dimensions 256x256. The system's output is the human face image of the corresponding sketch.
Curriculum Development of EdTech Class Using 3D Modeling Software for University Students in the Republic of Korea

This study discusses the development of a software-centered EdTech (Education Technology) class model via the implementation of a course titled “3D Time Machine” at a 4-year university in the Republic of Korea over two semesters. The course focused on teaching the 3D modeling software Blender within the history department. The primary objective of offering this course was to equip students from the digital generation with the capability to manipulate digital technology effectively for their sustainable lives and individual development. By studying historical materials and accumulating domain knowledge, students could construct their narratives from their unique perspectives. This aimed to foster their proficiency in digital technology operation, preparing them for a sustainable education environment increasingly centered around virtual worlds. As the use of virtual worlds gains prominence in educational settings, there is a growing need to incorporate curricula that prepare students to thrive in a “leaving no one behind” society as well-prepared citizens. Assessing the digital competencies of contemporary university students and designing instructional models with particular attention to their needs is becoming increasingly important. This research draws insights from interviews, conducted in both face-to-face and written formats, with students who participated in the “3D Time Machine” course. The interviews revealed valuable insights that can be actively incorporated into the development of software-centered EdTech instructional models. They reported that they expanded their perceptions as they learned how to make their ideas tangible. The course helped students overcome their fear caused by the vagueness of digital technology. These opinions significantly contribute to the development of practical digital educational courses that can be easily and rapidly acquired and applied within virtual educational environments. In conclusion, this kind of course effectively employs 3D modeling technology, a software-centered EdTech, as a core element in helping students develop their narratives rapidly and diversely, thereby playing a crucial role in their ability to articulate their unique perspectives.