In this panel, the experience adopting and adapting computer science education to the availability of generative AI will be examined from multiple international perspectives spanning varied educational and cultural contexts: the Arab world, India, Latin America, and the US. This will help computer science educators compare notes on what they teach, how they teach, and how students engage with the emerging tools. It will provide an opportunity to discuss how institutions are updating curricula, supporting faculty, and preparing students to use generative AI effectively and responsibly. The premise of the panel is that the similarities among these contexts outweigh the educational and cultural differences. Given how generative AI is dominating the discussion of computer science curricula, the global conversation will be of interest to all computer science educators.

Digital twins (DTs) for tower crane operations support task simulation before execution, prediction of movement trajectories, and hazard alert generation for nearby personnel and surrounding structures. These capabilities improve situational awareness and support operator decision-making in real-time. To implement such DT systems, suitable simulation platforms should balance modeling accuracy, computational requirements, and integration with sensing and control components. This paper benchmarks two widely used platforms—NVIDIA Isaac Sim and Gazebo—focusing on their applicability to resource-constrained, real-time environments. We evaluate GPU resource usage, including processing load, memory consumption, power draw, and overall performance. Results show that while Isaac Sim achieves higher frame rates (82 FPS), it consumes significantly more power (175W), whereas Gazebo, operating at 75 FPS, demonstrates much higher performance per watt, achieving 2.89 FPS/W compared to Isaac Sim’s 0.47 FPS/W. These results suggest Gazebo can have practical advantages for deploying lightweight, energy-efficient DTs in crane operations.

Digital Twin (DT) technology creates virtual replicas of physical systems for monitoring and optimization. This papers investigates the effects of DT simulations on GPU systems, evaluating processing, memory usage, and power consumption. It shows that adjusting rendering quality can improve performance. It also demonstrates the importance of optimizing energy consumption and performance for sustainable deployment, highlighting advancements in GPU technology and energy management.

In today's rapidly evolving industry, new challenges constantly arise that must be addressed quickly to keep up with the growing demand. Industry 5.0 aims to eliminate ob-stacles in production lines and integrate intelligent systems into manufacturing without excluding human operators from their daily tasks. In this context, developing software for controlling and managing data-driven evidence in the production cells becomes essential when considering performance, reliability, and practical aspects, as well as the financial returns it can bring to organizations. This work presents a brief overview of such an approach, proposing the development of a software architecture that manages industrial quality inspection in automotive parts using an Internet of Things publisher/subscriber model for controlling and synchronizing sub-processes. The results obtained demonstrate the benefits of implementing a control and evidence management system, offering production operators online monitoring of failures in manufactured parts and effective visualization of evidence of failures. This helps in decision-making and integrates the operator (user) with the system, which aligns with Industry 5.0 principles. This contribution is supported by creating a database of quality inspection evidence that can be used to train machine learning models to detect failures more accurately in the future.

Demand for ocean-based high-quality and sustainable fish protein soared in the last decade. Unlike precision agriculture, aquaculture remains an under-equipped farming activity. The aquaculture industry has provided remarkable contributions to the Sustainable Development Goal of zero hunger based on providing animal-based protein for human consumption worldwide. The success of the aquaculture industry hinges on appropriate monitoring of key water quality indicators to ensure both animal health and optimal productivity. In this context, the present work presents a cloud-based LoRaWAN system for quasi-real-time tracking of essential water quality parameters by integrating Internet of Things (IoT) sensor devices. The proposed approach harnesses the power of Long Range (LoRa) technology - especially the LoRa Wide Area Network (LoRaWAN) protocol - to facilitate efficient, large-scale monitoring focusing on data security and scalability. With practical insights drawn from IoT system deployment at an industrially relevant aquaculture farm in Brazil, this research provides a comprehensive look into the system's capabilities, drawbacks, and end-user feedback, offering a blueprint for future aquaculture innovations.

Massive garbage patches within all oceanic gyres have garnered global attention, underscoring microplastic pollution as an emerging concern. Various isolated approaches have been proposed for real-time monitoring of microplastics in environmental and industrial settings. However, these fragmented solutions may hinder distributed data and knowledge sharing across applications, limiting the potential for leveraging AI development to enhance early warning systems and decision-making in large-scale industrial operations. To address these challenges, this paper introduces a framework that utilizes Planetary Digital Twins (PDTs) and affordable modular flow cytometry tools. These innovations enable the real-time tracking and sharing of data on microplastic pollution, tracing their origins from polymer-based industrial processes to their presence in the environment. The proposed framework includes a distributed system architecture based on publisher/subscriber for robust and scalable data sharing. By integrating Industry 5.0 principles, which prioritize sustainability and resilient production processes, the digital twin technology enables a dynamic and interconnected monitoring network. The validation results suggest that the proposed system has the potential to expand and enhance environmental risk assessments on a global scale and support the development of mitigation strategies through improved data integration and sharing capabilities for microplastic monitoring.

This paper describes a digital-twin-based approach to dimensional control designed to assist quality inspection of automotive air-conditioning rotors in the vehicle manufacturing industry. It focuses on dimensional quality control within the field of dimensional metrology, aiming to accurately measure the dimensions of parts, subassemblies, and complete equipment systems. The proposed approach has created a prototype, collecting measurement data from a test sample to generate validation results for error profiling. The main goal is to develop a method for improving the precision of sensors used for this type of quality inspection.

Today, more than sixty companies (in the world) are building quantum computers. The natural language of their quantum gates is that of linear algebra in a complex (Hilbert) vector space. Since 2017 it is known that one can replace the linear algebra with some string-rewriting rules no more complicated than the basic rules of arithmetic. The original system was introduced by Terry Rudolph and has been promoted and disseminated in large-scale outreach projects (among others) by Diana Franklin (University of Chicago), Sofia Economou and Ed Barnes (Virginia Tech) and other educators at the high-school level. In this workshop we show how a slightly modified (though still very elementary) system can be used to communicate a visual and entirely operational understanding of key quantum computation concepts such as: superposition, entanglement, phase, interference and unitary state evolution, as they occur in quantum algorithms. Examples include the phase kickback phenomenon, teleportation, and the famous Deutsch-Josza, Bernstein-Vazirani and Grover algorithms along with the GHZ game. We work out concrete examples of proving properties for quantum gates and quantum circuits without resorting at all to complex numbers or matrix multiplication; only simple, abacus-like operations are used, hence the title of the tutorial. We show how this approach can create a genuine bridge to the mathematics of quantum computation, that is, of vector and tensor algebras in complex spaces for students who may have little or no proper mathematical background.

At the time of this writing more than 60 (sixty) companies in the world are building quantum computers. These computers, based on quantum physics principles, are radically different from those that operate according to the more familiar principles of classical physics. A quantum algorithm takes a number of classical bits as its input, manipulates them so as to create a superposition of all their possible states, further manipulates this exponentially large superposition to obtain the final quantum result, and then measures the result to get (with the appropriate probability distribution) the same number of output bits as in its input. For the middle phase, there are elementary operations which count as one step and yet manipulate all the exponentially many amplitudes of the superposition. The natural language of these quantum gates is that of linear algebra in a complex (Hilbert) vector space. Since 2017 it is known that it is possible to replace the linear algebra with some string-rewriting rules which are no more complicated than the basic rules of arithmetic. The original system was introduced by Terry Rudolph and has been promoted and disseminated in large-scale outreach projects (among others) by Diana Franklin (University of Chicago) and Sofia Economou and Ed Barnes (Virginia Tech) as well as several other educators at the high-school level. In this paper we show how a slightly modified (though still very elementary) system can be used to communicate a visual and entirely operational understanding of key quantum computation concepts such as: superposition, probability, entanglement, phase, interference and unitary state evolution, as they occur in well-known quantum algorithms. We give concrete examples of proving properties for quantum gates and quantum circuits without resorting at all to complex numbers or matrix multiplication. Only simple, abacus-like operations are used-hence the title of the paper. The system we present allows a novice learner to actually trace a quantum algorithm as if it were a classical computation, which is a rare (and, frankly, borderline incredible) luxury in the area of quantum computation, where traditional debugging is impossible. Examples include the phase kickback phenomenon and the famous Deutsch-Josza algorithm. We end with a discussion (and more examples) of how this approach can create a genuine bridge to the mathematics of quantum computation, that is, of vector and tensor algebras in complex spaces for students who may have little or no proper mathematical background.

Fisheries and aquaculture industries notably contribute to animal-source protein production worldwide. Climate change is creating environmental conditions suitable for harmful algal blooms (HAB) on a global scale. Some phytoplankton species can also release toxins, which may cause large-scale marine mortality with knock-on effects on coastal economies. Reliable phytoplankton monitoring and early HAB detection are also essential in climate-resilient solutions for aquaculture applications. Currently, phytoplankton monitoring is primarily based on traditional microscopy. However, it is time-consuming and requires an experienced taxonomist. There is a need to expedite and automate phytoplankton monitoring to support aquaculture industries. Analytical instruments based on microscopy coupled with artificial intelligence (AI) models may be vital to monitoring applications. Digital plankton data sets are usually imbalanced and reflect natural environmental differences. The lack of data to represent minority species/genera prevents AI models from understanding some taxa completely. It compromises system reliability for HAB monitoring applications. The present study investigates state-of-the-art models for class imbalance problems tailored for HAB monitoring within multi-trophic aquaculture farms from Brazil, South Africa, and Scotland. A unified benchmark database covering publicly available microscopic image-based datasets supported phytoplankton modelling. AI deep collaborative models and threshold moving techniques provided the best results compared to standard architectures. It prevailed, especially for low-abundant yet toxic organisms.

Continuing a process that began more than 50 years ago with the publication of Curriculum 68 ACM, IEEE-Computer Society and AAAI have sponsored five efforts to establish international curricular guidelines for undergraduate programs in computing on a roughly 10-year cycle. Over the last 15 years significant advances in quantum technologies have led to a new awareness about their impact on computing (QC). There are now 60 companies worldwide that build quantum computers. In the US the Quantum Economic Development Consortium (QED-C) was created in 2018 to accelerate the quantum industry by establishing a robust supply chain and infrastructure, including workforce and standards. But the continued absence of any serious education in quantum mechanics in a large fraction of traditional US engineering programs, including computer engineering and the closely related CS and data science programs, present many BS degree STEM graduates with the daunting problem of how to get trained quickly and efficiently to pursue the new opportunities in Quantum Information Science and Technology (QIST). To address this issue the ACM Board of Education has teamed up with the QED-C Workforce Development TAC and has developed (for the first time ever and over a period of 18 months) a Quantum Architectures (Q-AR) Knowledge Unit (KU) for CS2023. In November 2022 we asked the QED-C members (industry, academia, national labs, and government agencies) to comment on the proposed competency-based curricular plans along with the selected topics and learning outcomes. We present the analysis of the data we collected during that process.

The Computer Science Curricula 2023 (CS2023) effort includes a synergistic endeavor to provide supplemental material to aid in the adoption of the guidelines. This supplemental material takes the form of a series of peer-reviewed articles by experts on the design and delivery of Computer Science programs. The Computing for Social Good (CSG) committee is responsible for providing an international perspective on Computing for Social Good in Education (CSG-Ed), description of models for incorporating CSG into curricula, and a discussion of best practices including examples. There is a wide range of approaches to CSG-Ed both in the classroom and beyond. This BoF is intended to gain input from current instructors about these approaches including a discussion of which approaches work well and which are more problematic. We also intend for the BoF to bolster the community of educators who are interested in CSG-Ed in order to disseminate approaches to a wider audience.

Sixteen years ago, Scott Aaronson remarked (in the presence of Ray Laflamme) that quantum mechanics (QM) resembles an operating system on which the rest of Physics is running its application software (except for general relativity "which has not yet been successfully ported to this particular OS''). Prior to that, it took the insight of an educator and eminent computer scientist (Umesh Vazirani) to realize that a complete and consistent introduction to QM can be given via the language of qubits and quantum gates. Closer to the present, it took the profound intuition of another polymath (Terry Rudolph) to realize that the linear algebra normally at the foundation of such an approach can be replaced with a simple rewriting system accessible to middle school students. Rewriting systems are at the foundation of Computer Science, they are, in fact, the very fabric of it (e.g., Turing machines and lambda calculus), so these are very fortunate developments. Furthermore, a linear algebra prerequisite is now shared firmly in the CS undergraduate curriculum with Machine Learning, a topic that has known a very deep and sudden revival. Quantum Information Science and Technology (QIST) is inherently interdisciplinary and spans physics, computer science, mathematics, engineering, chemistry and materials science. We present three curricular plans for incorporating QIST topics (via Quantum Computing) into the CS undergraduate curriculum. Such plans have been constructed with a preliminary consultation with QED-C members (industry, academia, national labs, and government agencies) asking for comments, suggestions and general input on these three curricular plans.

New trends in computer architecture include non-general purpose architectures, brain-inspired design, agile hardware development and environmentally responsible design. Computer Science students need to understand computer architecture to develop programs that can achieve high performance through a programmer's awareness of parallelism and latency. In a re-visit of curriculum guidelines, students could develop skills and competencies in less mature yet cutting-edge topics, including quantum computing. For example, should students understand and appreciate quantum processor design's components and characteristics? In a task to revise curricular guidelines, one faces the decision of which topics may be obsolete and should be dropped or modified. Equally important is deciding the set of topics that must be included. Such questions are hard to address, particularly for a curriculum intended to remain fresh under a 10-years horizon. For example, the danger of not properly promoting the discussion of quantum processor design in the new ACM/IEEE-CS/AAAI CS202X curricula is the long waiting cycle (15 years+) for any new revision. This BOF will foster the participants' interactions into a debate of whether new curricular guidelines should contain quantum processor design as part of the Architecture and Organization (AR) Knowledge Area.

This Research to Practice Full Paper introduces an approach for the early identification of students at risk of dropping out of their distance learning courses. Students dropout in university courses have long been a major issue leading to social, academic and financial impacts. Predictive modelling analysis has been used as a tool to identify at an early stage probable cases of dropout. In distance learning, this type of analysis is made possible mostly because of the increasing adoption rate of Virtual Learning Environments (VLEs) where data on the student academic activities can be continuously recorded. This paper discusses the design and validation of a Learning Analytics system for early identification of students at risk of dropping out. The case study presented relies on data collected from two postgraduate courses as part of Brazils Open University. The methodology for the system design and validation comprises the steps to build an intelligent data pipeline. Jupyter Notebooks have been d used as the data science analysis environment in order to create data pipelines and have their performance evaluated. Results obtained from the validation of models built out of eight machine learning techniques show an average accuracy of 84% among the set of ML techniques tested. The highest accuracy is delivered by the Extra Trees classifier with 88%. Logistic Regression performed the worst performance with an accuracy of 79%.

This complete research work presents the use of chatbot in the educational context for automation of care to the student of higher education. Due to the evolution of technologies in educational and professional contexts, new teaching/learning methods have been an emerging demand for today's society. The expansion of learning modalities such as face-to-face, combined and distance learning as well as demand anywhere, at any time, has been a premise in these education systems. With the advancement of virtual learning environments (VLEs), successful interaction depends on solutions that enable efficient communication between the student and the educational context. One way to achieve this goal has been to use chatbots to respond to students' questions in the educational field. This has become possible through artificial intelligence and natural language processing embodied in simple service platforms. The purpose of this article is to develop a chatbot that interacts with students through text messages, on subjects in a closed context, to students' doubts about the Higher Education Institution Course. To validate chatbot performance, it will be submitted to a set of questions of the same context that were not correctly answered in the previous interactions. The results of these interactions will be analyzed in order to verify the efficiency and contribution of a chatbot as a support tool for the student. It is hoped that this study contributes to solutions for assistance automation in the learning context.

This abstract refers to a full paper in the research category. This paper presents a Systematic Review (SR) that aims at characterising the current scenario of Educational Recommendation Systems (ERS). ERS has recently attracted the attention of researchers in developing methods that contribute to the educational environment. Recommendation systems capable of searching and selecting relevant contents are one of the core technological resources offered to the education sector. Such systems which were initially designed for commercial applications (e.g. e-commerce) have now been used in educational application-driven research. For instance, a teacher might rethink methodologies and practises tailored to a set of pedagogical needs. These resources are enablers for the tools development that contribute to better educational practices. The goal of this SR is to provide an analysis of the current research efforts in the field of educational recommendation systems. With the string search of ”educational support” AND “recommendation system” used in four academic databases (Brazil and International) 2016 and 2018. The SR returned 439 papers of which 54 were selected for further analysis. The results show that the profile-based filtering technique for recommendation is the most used, occurring 27.78% of the studies. In 44.44% of these studies, the target audience has been the students. Furthermore, the usual evaluation method was experimental achieving 41% of the selected papers. The results demonstrate applications in learning situations as the recommendation of digital resources, related to learning styles and based on ability and performance.

This summary refers to a complete research paper. In 2010, the World Health Organization (WHO) estimated that 19 million children under the age of 15 were visually impaired, with about 39 million blind and 246 million people with severe or moderate vision loss. These numbers suggest the potential size and potential impact of the visually impaired on a day-today basis. The situation has seen some improvement in recent years with increasing access to formal education, from primary school to higher education. As a result, the demand for innovative, technology-based assistance tools that enhance the user experience and the quality of education has increased. This work takes a step towards bridging a technological gap with the design and validation of a system of artificial neural networks, called as Deep Neural Networks (DNNs), capable of identifying the main Cartesian curves of the mathematics curriculum. The Cartesian set comprises 6 degrees of rational algebraic curves, in addition to a total of 42 conic curves, for this paper will be presented two sets of curves, parabolas and ellipses. The development of this gave some methodological steps. First, relevant artificial neural networks were investigated considering the needs of the user and the space of the problem - computer efficiency, loss rate and precision of the generated models used as selection metrics for each neural network tested. The selected network models were InceptionV3, MobileNETV2, VGG16, and VGG19. With average accuracy of 94% and 20% of mean loss, the selected network for the application was VGG16.

This Research to Practice Complete Work presents the category of Innovate Practice. Digital social grouping is now a common practice for exchanging any kind of data between group connections. However, this has posed some key issues for education professionals on the best path to the current educational system to fully embrace this practice and move on. [1] It suggests that “the school model as we know it is over. It makes no sense to students, does not produce expected results, nor prepares the new generations for the challenges of contemporary life.” To fill this gap, an innovative teaching model emerged as an alternative approach centered on the student and reinforced by the pedagogical and technological infrastructures. This work presents a framework as a computational tool to build and provide custom educational interfaces based on the concept of persuasion to push users to the most relevant content (eg, direction). This structure is based on participatory elements from various disciplines that cross the fields of education, computational engineering and psychology. The proposed framework shows that user interactions explored from the persuasion, engagement, and content customization techniques take into account the skills, needs and interests make teaching more attractive and applicable.

Forward-looking sonars (FLS) are perception sensors that are not affected by underwater turbidity. FLS are used in Remotely Operated Vehicles (ROVs) to help them in the tasks of exploration, navigation and region mapping. Besides the advantages of working with acoustic images rather than optical images, the former presents various challenges inherent to their construction. Classic Computer Vision (CV) algorithms do not achieve the same success with acoustic images. Furthermore, data-driven approaches are dictating the state-of-the-art in several tasks that require feature extraction. For example, Convolutional Neural Networks (CNNs) are already been used in several CV problems such as classification, image matching, image retrieval, place recognition and one-shot learning. CNNs are showing promising results for problems with FLS images as well. Unfortunately, there are as not as many public datasets and methods for FLS problems as we have for optical images. Knowing that CNNs are capable of mapping correctly millions of images into thousands of labels, we are proposing a novel framework of feature learning strategy for FLS images. In order to evaluate how well the methods generalize, we selected three different FLS annotated datasets for our experiments. Two of them are real-world FLS images from a harbour environment from different locations. The third is generated from a custom 3D scene integrated with open-source underwater robot simulators. In our experiments, we compared our method with state-of-the-art approaches in an unknown environment achieving superior results.

Smart grids and smart homes improve energy management by coupling communication capabilities to their devices. Due to computational constraints of these devices, employment of simplified communication protocols is necessary. In this paper, we investigate the use of communication protocols based on CoAP and 6LoWPAN in smart home environments. Specifically, we analyze the vulnerabilities a smart home employing CoAP and 6LoWPAN may be susceptible to. We also present a performance analysis of the use of these protocols for ensuring secure communicationsin smart homes.

The accelerometer devices as traditionally used in the epidemiological field for physical activity monitoring (e.g. Actigraph, Actical, and RT3) provide manufacturer-dependent output values called counts that are computed by obscure and proprietary signal processing techniques. This lack of transparency poses a challenge for comparison of historical accelerometer data in counts with data collected using raw accelerometry in S.I. units — m/s2. The purpose of this study was to develop a method that facilitates the compatibility between both methods through conversion of raw accelerometer output data collected with inertial acceleration sensors into Actigraph counts — the most widely used (de facto standard) device brand in epidemiological studies. The basics of the conversion algorithm were captured from the technical specifications of the Actigraph GT1M. Fine-tuning of the algorithm was achieved empirically under controlled conditions using a mechanical shaker device. A pilot evaluation was carried out through physical activity monitoring in free-living scenarios of 19 adult participants (age: 47 ± 11 yrs, BMI: 25.2 ± 4.1 kg-m−2) wearing both devices. The results show that Actigraph counts estimated by the proposed method explain 94.2% of the variation in Actigraph counts (p < 0.001). The concordance correlation coefficient was 0.93 (p < 0.05). The sensitivity for classifying intensity ranged from 93.4% for light physical activity to 70.7% for moderate physical activity.

The "location-aware" construction of overlay networks requires the identification of nodes that are efficient with respect to network delay and available bandwidth. In this short paper, we propose Highways to create clusters of nodes using a novel "location-aware" method, based on a scalable and distributed network coordinate system. This helps to build overlay routing tables to achieve better proximity accuracy, thus, providing a mechanism to boost performance in application overlay routing.

In the near future, billing for network services will not only be concerned with time or volume based accounting but also in ways of measuring the quality of the service provided. Dynamic price schemes, such as congestion-based charging, have been proposed. In some of these models, the charging infrastructure relies on the distribution of electronic tariffs to end-users machines. The tariff structure includes the price information and an algorithm to calculate the charge. Thus, the monitoring of network usage according to this tariff is essential within these frameworks. However, little attention has been given to the security issues associated with Internet metering in these schemes. This has had a great impact on the new models proposed today, since security has become a major concern in open networks. Systems that naturally have incentive to fraud, such as metering systems used for billing purposes, must deal with security threats in large scale environments. The article compiles the security issues of a dynamic networked system where electronic tariffs and service level agreement (SLA) structures are distributed among service providers and customers. To address these issues, a set of security protocols is outlined.

In this paper we give an overview of work in progress to propose a distributed metering model using Active Network concepts. The business model which motivates this work poses accounting requirements between different kind of providers and customers. However, security issues also need to be addressed. We briefly describe the problems being investigated which relate to the business model used. We have split the problem mainly into four subtopics. They are concerned with questions of how to control the system through high level policies, how to translate from such policies (tariff/SLA) to a low level device dependent configuration. Other questions are concerned with what threats a system like this may be exposed to and what the limitations are in terms of system performance when measuring network traffic.