Reviewerly/exHarmony huggingface dataset:
Modeling software processes from different domains using SPEM and BPMN notations: An experience report of teaching software processes

In a current application development scenario in different environments, technologies and contexts, such as IoT, Blockchain, Machine Learning and Cloud Computing, there is a need for particular solutions for domain-specific software development processes. The proper definition of software processes requires the understanding for the involved teams and organization’s particularities and specialized technical knowledge in Software Engineering. Although it is an essential part of Software Engineering, many university curricula do not dedicate as much effort to teaching software processes, focusing more on the basic principles of Software Engineering, such as requirements, architecture and programming languages. Another important aspect of software processes is modeling. The modeling of a software process provides a basis for managing, automating and supporting the software process improvement. In this context, teaching software process modeling becomes challenging, mainly due to the great emphasis on theory and few practices. This work presents an experience report teaching the definition and modeling of software processes in different domains. In the discipline of software processes, we applied a practice for defining and modeling processes in various application domains, such as: IoT, cloud, mobile, critical systems, self-adaptive systems, machine learning, blockchain and games. The processes were modeled in the Software Systems Process Engineering Metamodel (SPEM) and Business Process Model and Notation (BPMN) notations based on references from the literature for each domain. We evaluated the process modeling practice with the SPEM and BPMN in two classes of the software processes discipline, and we had discussions about the use of the two notations applied to the different domains. In general, students reported good experiences in defining processes, highlighting the importance of practical modeling applications for professional life. As the main results of the study in teaching process modeling, we have that: (i) students accepted HEFLO tool better than EPF Composer tool; (ii) most students are not aware of specific domains and that anticipating the study of these domains in the discipline is a good strategy; and, (iii) the students also highlighted the need for more support for the two notation tools.
The Role of Aggregation Functions on Transformers and ViTs Self-Attention for Classification

Aggregation functions are mathematical operations that combine or summarize a set of values into a single representative value. They play a crucial role in the attention mechanisms of Transformer neural networks. However, Transformers' default aggregation functions, based on matrix multiplication, may have limitations in certain classification scenarios. This function may struggle with the complexity of information present in the input data, resulting in lower accuracy and efficiency. Considering this issue, the present work aims to replace the traditional matrix multiplication operation used in the classical attention mechanism with alternative and more general aggregation functions. To validate the new aggregation methods on the attention mechanism, we conducted experiments on two datasets, the recently propose Google American Sign Language (ASL) Fingerspelling Recognition and the well-known CIFAR-10, performing time series and image classification, respectively. Results shed light on the role of aggregation functions for classification with Transformers, demonstrating promising outcomes and potential for further improvements.
Test smell refactoring revisited: What can internal quality attributes and developers’ experience tell us?

Test smells represent a set of poorly designed tests, which can harm test code maintainability. Although fundamental steps to understand test smells have been taken, there is still an evident lack of studies evaluating the impact of test smell refactoring from the perspective of internal quality attributes, such as size, cohesion, coupling, and complexity. In addition, the literature still lacks research that addresses the difficulties developers face during test smell refactoring. This article investigates the impact of test smell refactoring from a developer’s perspective, considering the internal quality attributes and the background experience. We investigated the perceptions and difficulties encountered by 20 developers while removing five types of test smells in four open-source projects over two months. Through this study, we analyzed: (i) the impact that test smell refactoring has on internal quality attributes; (ii) the developers’ perception of test smells as actual problems within software systems; (iii) the main difficulties developers face during test smell refactoring; (iv) the influence of developers’ experience on assertiveness and refactoring time of test smells, and (v) the effects of refactoring on the test smell density. Our findings can help developers design a prioritization scheme for test smell refactoring and make them aware of the real benefits of test smell refactoring.
Perbandingan Algoritme Dijkstra dan Algoritme A* (A-Star) dalam Penentuan Lintasan Terpendek dari Dinas Pendidikan Provinsi Lampung ke Beberapa Sekolah Menengah Atas (SMA) Negeri di Provinsi Lampung

In this research, we aim to find the most efficient route from the Lampung Provincial Education Office to various State Senior High Schools in Lampung Province, employing both the Dijkstra Algorithm and the A* (A-Star) Algorithm. To address this problem, we model it as a weighted graph, where the vertices represent the locations of the Lampung Provincial Education Office and the Public High Schools, and the edges' weights represent the distances. Furthermore, we implement the solution using the Python programming language. The outcomes demonstrate that both algorithms yield optimal results with the same distance and route. However, when considering the execution time of the program, the A* (A-Star) algorithm outperforms Dijkstra's Algorithm because it quickly identifies the shortest path from the starting point to the destination, whereas Dijkstra's Algorithm explores the shortest paths from neighboring points first.
Multitenant Containers as a Service (CaaS) for Clouds and Edge Clouds

In recent years, along with containers, the cloud community has rapidly taken up Kubernetes, the de facto industry standard container orchestration system. All major cloud providers currently offer Kubernetes-based Containers as a Service (CaaS). However, when CaaS is offered to multiple independent consumers, or tenants, a multi-instance approach is used, in which each tenant receives its own separate cluster, which imposes significant overhead due to employing virtual machines for isolation. If CaaS is to be offered not only in the cloud, but also in the edge cloud, where resources are limited, another solution is required. In this paper, drawing upon the scientific literature, we provide a novel classification of Kubernetes multitenancy into three approaches: multi-instance through multiple clusters, multi-instance through multiple control planes, and single-instance native. We propose a single-instance multitenancy framework, meaning tenants are served out of a shared control plane in a single cluster. Our empirical findings show that the single-instance approach imposes a markedly decreased overhead than the other two. However, it entails a tradeoff in workload isolation owing to tenants sharing the compute nodes. There are still means to compensate for such weakened isolation, and we describe how our framework does it. The framework is publicly available as liberally-licensed, free, open-source software that extends Kubernetes. It is in production use within the EdgeNet testbed for researchers.
3DHR-Co: A Collaborative Test-time Refinement Framework for In-the-Wild 3D Human-Body Reconstruction Task

The field of 3D human-body reconstruction (3DHR) that utilizes parametric pose and shape representations has witnessed significant advancements in recent years. However, the application of 3DHR techniques to handle real-world, diverse scenes, known as in-the-wild data, still faces limitations. The primary challenge arises as curating accurate 3D human pose ground truth (GT) for in-the-wild scenes is still difficult to obtain due to various factors. Recent test-time refinement approaches on 3DHR leverage initial 2D off-the-shelf human keypoints information to support the lack of 3D supervision on in-the-wild data. However, we observed that additional 2D supervision alone could cause the overfitting issue on common 3DHR backbones, making the 3DHR test-time refinement task seem intractable. We answer this challenge by proposing a strategy that complements 3DHR test-time refinement work under a collaborative approach. Specifically, we initially apply a pre-adaptation approach that works by collaborating various 3DHR models in a single framework to directly improve their initial outputs. This approach is then further combined with the test-time adaptation work under specific settings that minimize the overfitting issue to further boost the 3DHR performance. The whole framework is termed as 3DHR-Co, and on the experiment sides, we showed that the proposed work can significantly enhance the scores of common classic 3DHR backbones up to -34 mm pose error suppression, putting them among the top list on the in-the-wild benchmark data. Such achievement shows that our approach helps unveil the true potential of the common classic 3DHR backbones. Based on these findings, we further investigate various settings on the proposed framework to better elaborate the capability of our collaborative approach in the 3DHR task.
Comprehensive IoT-driven Fleet Management System for Industrial Vehicles

In the context of fleet management, various challenges, including equipment breakdowns, rising maintenance costs, inefficient resource utilization, and outdated telematics systems, necessitate a transformative approach. Traditional telematics systems encounter limitations such as closed compatibility with specific brands, unique network protocols, and insufficient data analysis and decision-making support. Therefore, these challenges can be addressed by using IoT-driven solutions. This paper introduces an IoT-driven system called the "intelligent dispatching and health monitoring system" (IDHMS), which is designed to enhance fleet operations in industries such as mining, construction, and agriculture while addressing issues related to legacy telematics systems. The architecture of IDHMS is a comprehensive IoT-driven framework that integrates embedded hardware, cloud-based software, and flexible network infrastructure. This system facilitates real-time communication, gathering and analysis of data, and decision-making for fleet management in such industries. The IDHMS addresses the limitations of conventional telematics systems by offering standardized protocols, open APIs, robust security measures, comprehensive data analytics capabilities, and a multipurpose ecosystem. Its data-driven insights empower informed decision-making, drive continuous improvement, and enable strategic resource allocation for cost reduction, increased productivity, and competitiveness in dynamic industries. The practical implementation of IDHMS in significant Middle Eastern mines underscores its compatibility with multiple brands, scalability, and potential adoption in various industries.
Thermal Analysis of Al and Cu Metals Heat Sinks with Different Geometries at Raspberry Pi Control Cards Used for Image Analysis-Based Drone Control in Smart Agriculture Drones

A heat sink is a tool for dissipating the heat generated by electronic parts. The equipment's specific operating conditions necessitate the equipment's extra heat dissipation. This research compared and optimized the temperature and heat flux parameters based on the results of the design of a heat sink for CPU, RAM, and PCLe to a USB 3.0 bridge. It is aimed at an examination of the advantages and disadvantages of using square, rectangular, and circular shapes in the design of a heat sink. Copper and aluminum (Al) are the most common heat sink materials (Cu). Autodesk Inventor Pro software with Nastran module is used for design and thermal analysis.&#x0D; According to Inventor Nastran's thermal analysis results it is found that there is no significant difference between Al and Cu materials based on cooling capacity at designed models. Also, it is found that the geometry of the heat sinks directly affects the cooling capacity of a heat sink.&#x0D; The application results show that the cooling achievement is directly related to the correct heatsink design and enough surface area.&#x0D; According to Inventor Nastran's thermal analysis results it is found that there is no important difference between Al and Cu materials based on cooling capacity at designed models. Also, it is found that the geometry of the heat sinks directly affects the cooling capacity of a heat sink.&#x0D; The application results show that the cooling achievement is directly related to the correct heatsink design and enough surface area.
Les études italiennes en Pologne à la lumière d’une analyse bibliométrique des publications des années 2000 à 2020

The paper off ers an analysis of the complete body of research writings produced between 2000 and 2020 by all Polish Italian Studies scholars currently affiliated with university faculties and departments of the two major disciplines of modern philology: linguistics and literary studies. The argument is based on the bibliometric data compiled as part of the research project “Bibliography of Italian Studies in Poland: Digital Repository, Text Digitization, Bibliometrics,” under which 2716 publications authored by 139 scholars from 19 Polish research institutions were analysed. The statistics presented in the paper cover the type of research publication, language, place of publication, the author’s affiliation relative to the place of publication and the number of authors. Our findings indicate that Polish Italian Studies scholars mainly choose to publish chapters in collected volumes (mostly conference proceedings), with their contributions being single-authored and written in Italian (meaning that their research is largely geared to an Italian-speaking readership, and collaborative research projects are very rare). Their research papers were as a rule published in Polish journals, often those published by university faculties, and the internationalisation of research (publishing in languages other than Polish) was only apparent. Slow changes in publishing practices have been observable in recent years as a consequence of the guidelines of the Ministry of Education and Science on the assessment of research performance and the promotion of publication in foreign journals and publishing houses.
Sistem Informasi Manajemen Outbound Call (OBC) Subunit Payment Collection Pada Witel Lampung Berbasis Web

The regional division of PT. Telekomunikasi Indonesia (Telkom) in Lampung, known as Witel Lampung, is responsible for managing telecommunication services for customers in the area. However, the current manual process of recording outbound call data in Microsoft Excel spreadsheets poses challenges in terms of accuracy, reliability, and efficiency. This affects the decision-making process of finance officers who rely on this data to calculate various metrics and indicators. To address these challenges, Witel Lampung requires an information system that can automate the process of collecting, storing, and analyzing outbound call data. The main objective of this research is to design and develop a user-friendly front-end system for finance officers to automatically calculate outbound call data. The implementation of this system will improve data accuracy and reliability, reduce time and effort required for data management, and enhance the overall efficiency of Witel Lampung's operations, resulting in better customer service and satisfaction. Investing in this system will help Witel Lampung improve its data management capabilities, decision-making process, and overall competitiveness in the telecommunications industry.
Poster Session I: Digital dual-Purkinje-image eye tracking enables precise determination of visual receptive fields in fixating macaques

Understanding the relationship between visual stimuli and neural activity is a fundamental goal in visual neuroscience. However, the study of visual neurophysiology in awake primates is complicated by the constant occurrence of eye movements, even during periods of nominal fixation. To address this challenge, we adapted a recently developed high-resolution digital dual-Purkinje-image (dDPI) eye tracker (Wu et al., 2023) for use with macaque monkeys. In addition to tracking the Purkinje images, we simultaneously estimate the pupil center and size: a first for video eye tracking. We then sought to evaluate the efficacy of dDPI eye tracking for studying visual processing in fixating macaques by recording single neurons from the lateral geniculate nucleus while a spatially-correlated noise stimulus was displayed. Our analyses show that, as a result of properly accounting for eye movements post-hoc, the predictive performance of generalized linear models improves and the estimates center radii contract to values equal to or smaller than those reported in the literature. Notably, correcting for eye movements using the locations of the pupil center and corneal reflection-the standard method in video eye tracking-yielded worse model fits and larger receptive field sizes. This finding implies that the pupil center is an inaccurate reporter of small eye movements, while the Purkinje images may be veridical during fixations.
An IoMT-based Federated Learning Survey in Smart Transportation

Abstract: Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency response and reducing the impact of accidents on victims. Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine Learning techniques have enabled Intelligent Transportation systems by performing centralized vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed machine learning approach called Federated Learning (FL) is used. Here only model updates are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can predict traffic accurately without compromising privacy. We first present the overview of XAI and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss the applications of using FL in transportation and open-source projects. Finally, we highlight several research challenges and their possible directions in FL
Detecting Outliers Using Modified Recursive PCA Algorithm For Dynamic Streaming Data

Outlier analysis has been widely studied and has produced many methods. However, there is still rare a method to detect outliers for dynamically streaming batch data (online learning). In the present research, a novel online algorithm to detect outliers in such dataset is proposed. Data points are proceeded by applying a modified recursive PCA to predict sequentially parameters of the model; eigenvalues and eigenvectors of the statistical detection model are recursively updated using approximate values by perturbation methods. More specifically, the recursive eigenstructure is obtained from the derivation of the covariance matrix using the first-order perturbation technique. The Mahalanobis distance is then used as an outlier score. Our algorithm performances are evaluated using some metrics, namely accuration, precision, recall, F1-score, AUC-PR, and the execution time. Results show that the proposed online outlier detection is computationally efficient in time and the algorithm's performance effectiveness is comparable to that of the offline outlier detection algorithm via classical PCA.
3-D breast nodule detection on automated breast ultrasound using faster region-based convolutional neural networks and U-Net

Abstract Mammography is currently the most commonly used modality for breast cancer screening. However, its sensitivity is relatively low in women with dense breasts. Dense breast tissues show a relatively high rate of interval cancers and are at high risk for developing breast cancer. As a supplemental screening tool, ultrasonography is a widely adopted imaging modality to standard mammography, especially for dense breasts. Lately, automated breast ultrasound imaging has gained attention due to its advantages over hand-held ultrasound imaging. However, automated breast ultrasound imaging requires considerable time and effort for reading because of the lengthy data. Hence, developing a computer-aided nodule detection system for automated breast ultrasound is invaluable and impactful practically. This study proposes a three-dimensional breast nodule detection system based on a simple two-dimensional deep-learning model exploiting automated breast ultrasound. Additionally, we provide several postprocessing steps to reduce false positives. In our experiments using the in-house automated breast ultrasound datasets, a sensitivity of $$93.65\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>93.65</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> with 8.6 false positives is achieved on unseen test data at best.
Simple2In1: A Simple Method for Fusing Two Sequences from Different Captioning Systems into One Sequence for a Small-scale Thai Dataset

The increasing number of Deaf and Hard of Hearing (DHH) individuals has amplified the need for quality captions, especially in real-time. Previous studies have successfully explored various methods to enhance caption quality, including alignment-based and neural network approaches. While alignment-based methods represent a conventional approach, neural network models are inherently more complex yet offer superior performance. However, these neural models demand large datasets, posing challenges for languages such as Thai with limited datasets available. In this paper, we propose a simple but effective method to improve the quality of Thai captions on a small-scale Thai dataset. Our method utilizes a pre-trained mT5 model to generate a single sequence from two sequences from two different captioning systems. Despite a small dataset and limited input sequences, our method shows potential in improving six evaluation metrics, surpassing all baseline models.
«Diamonds and Mirages»

Vadim Valeryevich Vasilyev is Doctor of Philosophy Sciences, Professor, Corresponding Member of Russian Academy of Sciences, and cofounder and member of the Academic Council of the Moscow Center for Consciousness Research at the Philosophy Department of the Lomonosov Moscow State University. The talk is dedicated to the specifics of analytic philosophy in Russia.