Concept Of Computing Research Articles

Computational Locked and Unlocked Strategies and Dissipative Brains The Abductive Eco-Cognitive Perspective

<p dir="ltr"><span>Eco-cognitive computationalism is a cognitive science perspective that views computing in context, focusing on embodied, situated, and distributed cognition. It emphasizes the role of Turing in the development of the Logical Universal Machine and the concept of machines as “domesticated ignorant entities”. This perspective explains how machines can be dynamically active in distributed physical entities, allowing data to be encoded and decoded for appropriate results. In this perspective, we can clearly see that the concept of computation evolves over time due to historical and contextual factors, and it allows for the emergence of new types of computations that exploit new substrates. Taking advantage of this eco-cognitive framework I will also illustrate the concepts of “locked and unlocked strategies in deep learning systems, indicating different inference routines for creative results. Locked abductive strategies are characterized by poor hypothetical creative cognition due to the lack of what I call eco-cognitive openness, while unlocked human cognition involves higher kinds of creative abductive reasoning. This special kind of “openness” is physically rooted in the fundamental character of the human brain as an open system constantly coupled with the environment (that is an “open” or dissipative system): its activity is the uninterrupted attempt to achieve the equilibrium with the environment in which it is embedded, and this interplay can never be switched off without producing severe damage to the brain. Brain cannot be conceived as deprived of its physical quintessence which is its openness. In the brain, contrary to the computational case, ordering is not derived from the outside thanks to what I have called in a recent book “computational domestication of ignorant entities”, but it is the direct product of an “internal” open dynamical process of the system.</span></p><div><span><br /></span></div>

Mobile Cloud Computing Paradigm: A Survey of Operational Concerns, Challenges and Open Issues

ABSTRACTThe Mobile Cloud Computing paradigm has revolutionized the concepts of mobile computing and the Internet of Things (IoT). This paradigm allows outsourcing the workload of mobile devices, or other connected “things,” to be computed in the Cloud. Currently, outsourcing possibilities have been widely developed making available computing platforms at different network layers. In a consequence of that, a virtual increasing of the performance and a homogenization of the computing capabilities of the devices are produced. The research described in this work presents a review of the state of the art about recent works, the main operational concerns, challenges, and open issues of this paradigm in order to update the border of knowledge on this topic. As a result, a critical analysis is conducted, and new research directions are discussed. The findings provide value‐added to the scientific community and, therefore it could be helpful for other researches in these topics, especially given the rising popularity of IoT platforms.

Curriculum Framework and Assessment Approach for Computational Thinking in the Early Years

In light of current developments, there is an increasing effort to integrate computing-oriented activities into the education of children as young as two years old. Although the computing strand is not officially addressed in the Early Years Foundation Stage Statutory Framework (DfES, 2024), a small number of early years teachers in England implement computing-oriented activities to ensure that young children progress from early years to Key Stage 1. A particular gap in the field is that previous research on computational thinking concepts never or rarely establish curriculum links in a way that teachers can utilise in their practices. This theoretical article therefore proposes a curriculum-based framework for both teaching and assessing computational thinking (CT) in early years education as assessment is not possible without pedagogic guidelines. Offering a sample lesson plan with links to the Early Learning Goals, this framework aims to encourage teachers, including those without specific computing training, to integrate CT concepts more explicitly into their teaching and enables them to monitor and assess their pupils' progress in relevant skills.

Using Card Sorting Activity as a Strategy for Evaluating Students’ Learning of Computational Thinking Concepts

This study investigated the effectiveness of the ‘Match it’ card sorting activity for evaluating the prospective teachers’ knowledge and understanding of computational thinking (CT) concepts. 146 primary prospective teachers were asked to sort 26 scenarios and words alongside nine images under five main computational concepts: algorithmic thinking, abstraction, decomposition, patterns & generalisation, and evaluation. The study found that the card sorting activity, as a method for assessment was useful, however, the issues around the design and the content of the current card sorting activity were reported by students which suggests that further revisions should be made to improve the effectiveness of the tool.

Guest Editorial: Quantum Computing: A Beacon of Transformation for the Oil and Gas Industry

This past June, the United Nations proclaimed 2025 as the International Year of Quantum Science and Technology to recognize the potential of quantum science to drive innovations in sustainable development and global communications. It is also a recognition of how far the theoretical concept of quantum computing has come to become a reality, and the race to build a powerful quantum computer is accelerating every year. According to a recent report from Global Quantum Intelligence, the impact of quantum tech across the oil and gas industry could equate to $2.6 trillion by 2035. As the first chief data scientist in the oil and gas industry, I am acutely aware of the myriad challenges that our industry faces. I firmly believe that quantum computing and related quantum technologies are on the brink of revolutionizing our industry in ways we have yet to fully comprehend. The quantum revolution demands that the oil and gas and the broader energy industry rethink their approach to exponential technology and sustainability. The industry's history of adopting emerging technologies, as evidenced by the slow uptake of digital transformation, underscores the urgency of this shift. Even 30 years after the introduction of the digital oilfield concept, the industry lags in data quality, addressing data silos, and data democratization, as compared with other complex and highly regulated industries. The urgency of an energy transition towards a sustainable future necessitates the oil and gas industry to confront its complex challenges, which are beyond the scope of current computing power. Quantum computing, with its unparalleled computational power, emerges as one of the most promising technologies to optimize and accelerate solutions to the industry's intricate problems. As we discuss quantum computing in detail, we will explore how related quantum interconnected areas—quantum sensing, quantum networking, and quantum simulations—will enable unique applications for the oil and gas industry. In simple words, quantum computing is the field of computing that uses the principles of quantum mechanics to solve complex problems that are intractable for classical computers. The three main principles of quantum mechanics that are the cornerstone of quantum computing are superposition, entanglement, and quantum interference. Unlike traditional computing, where a bit (0 or 1) is the basic unit, a qubit is the unit of quantum computing. A qubit can exist in a state of 0, 1, or both simultaneously due to superposition, enabling parallel computations and the exploration of multiple solutions at once. Entanglement allows for correlated states, enabling operations on multiple qubits simultaneously for enhanced computational power. Quantum interference allows quantum states to interfere constructively or destructively with each other, amplifying correct solutions and suppressing incorrect ones, thereby empowering efficient computations. Thus, a significant quantum advantage is gained by using quantum computing for certain computationally challenging problems compared with traditional computing. Quantum computing will be performed on quantum computers, which are developed today using various technologies such as photonics, light particles, trapped ions, neutral atom traps, superconducting qubits, and controlling nitrogen vacancy centers in imperfect diamonds.

TEACHING COMPUTATIONAL THINKING IN SAUDI ARABIAN SCHOOLS USING SMARTPHONE APPS

This study investigates the efficacy of smartphone applications in teaching computational thinking to students in Saudi Arabian primary and secondary schools. Computational thinking, encompassing problem-solving, computational logic, and algorithmic thinking, is an essential skill in the modern world. The research assesses various educational apps that support the development of computational thinking and examines their integration into the Saudi curriculum. This study incorporates qualitative and quantitative research techniques to provide an inclusive perspective. Participants included primary and secondary school students from various schools in Saudi Arabia. The research employed pre- and post-tests for quantitative analysis and interviews and questionnaires for qualitative data. Quantitative results include pre- and post-tests of the students in computational thinking with the aid of co-varying with Computational thinking, while interviews and questionnaires with teachers and students offer qualitative data. Best-selling educational applications for ScratchJr, Lightbot, Cargo-Bot, and Kodable are compared concerning how efficiently they introduce programming ideas and encourage rational thinking. Results have suggested that smartphone applications have a positive influence on students’ computational skills. An enhanced standard is also noticed regarding the students’ aspects that involve problem analysis, identification of pattern, and formation of algorithm. The use of applications such as ScratchJr and Lightbot is justified regarding an application’s capacity to improve students’ motivation and willingness to learn computational concepts. Teachers indicate that their students have shown more concern and engagement mainly when the teacher integrates these apps. Hence, based on the findings of the study, it is evident that incorporating Smartphone applications into the curriculum will lead to a reforming change in the teaching approaches towards efficiency as well as feasibility of the learners. These findings are valuable for the Saudi context for policy and teaching enhancement, signaling the need to provide more resources in digital tools and invest in the professional development of teachers to capture the virtues of advanced technology in improving learners’ performance.

Hands-on parallel & distributed computing with Raspberry Pi devices and clusters

Parallel and distributed computing (PDC) concepts are now required topics for accredited undergraduate computer science programs. However, introducing PDC into the CS curriculum is challenging for several reasons, including an instructor's lack of PDC knowledge and difficulties in accessing PDC hardware. This paper addresses both of these challenges by presenting free, interactive, web-based PDC teaching modules using inexpensive Raspberry Pi single board computers (SBCs). Our materials include a free disk image that makes it possible for instructors to build Raspberry Pi clusters in minutes and use our software in a variety of curricular contexts. Our multi-year assessment of these materials on students and faculty members indicates that: (i) our materials increased students' confidence regarding important PDC concepts and motivated them to study PDC further; and (ii) our materials increased faculty members' confidence and preparedness in teaching key PDC concepts at their own institutions.

Dynamic state awareness for distribution network based on robust adaptive cubature kalman filter

With the rapid development of the economy and society, the demand for power quality is constantly increasing. As a crucial part of grid situational awareness, distribution network state estimation plays a vital role in providing critical data support for other advanced applications, which is significant for ensuring the safe and reliable operation of the distribution network. Therefore, this paper proposes a dynamic state awareness method for distribution networks based on the robust adaptive cubature Kalman filter (RACKF). The proposed solution, grounded on the core computational concept of the cubature Kalman filter (CKF), constructs a robust noise statistical estimator (NSE) composed of a biased NSE and an unbiased NSE to adapt to the unknown and time-varying process noise parameters in the dynamic state estimation process. The proposed solution can ensure that the calculated process noise parameters always meet the constraints and guarantee the robustness of the algorithm. In addition, an estimation strategy for the fusion of multi-time scale measurement data is developed according to the RACKF-based dynamic state estimation features in order to realize system state corrections and updates. The results of simulation experiments and comparisons with traditional CKF methods demonstrate the accuracy and superiority of the proposed solution.

Computational Concepts and their Assessment in Preschool Students: An Empirical Study

Computational thinking (CT) is a multidimensional term that encompasses a wide variety of problem-solving skills related to the field of computer science. Unfortunately, standardized, valid, and reliable methods to assess CT skills in preschool children are lacking, compromising the reliability of the results reported in CT interventions. To surpass this limitation, we validated in a sample of 700 preschool students (5–6 years old) the Beginners Computational Thinking test Short-Form (BCTt-SF), an unplugged 12-item instrument that measures three of the most common computational concepts assessed in preschool research: sequences, loops, and conditionals. The theoretical model underpinning the BCTt-SF was supported by dimensionality assessment, which suggested that preschool students can be distinguished in terms of four specific abilities (i.e., sequences, simple loops, nested loops, and conditionals) and that all of these abilities were related by a general factor. We modeled this hierarchical structure with a bi-factor model that presented excellent psychometric properties, from good statistical fit indices to adequate reliability of the general ability. To take full advantage of this model, we created an online application in the Shiny platform (https://computationalthinkingtests.shinyapps.io/SF-BCTt/) for the seamless scoring of examinees by any teacher or researcher who uses the BCTt-SF to assess CT skills in preschool children. Finally, we demonstrated how the BCTt-SF can be used to test the impact of educational interventions for improving CT skills in preschoolers.

The impact of quantum computing on the development of algorithms and software

Introduction: There is a great potential that the quantum computing can change the way of algorithms and software development more than classical computers. Thus, this article will try to focus on how algorithm design and software development can be affected by quantum computing as well as what possibilities could appear when quantum principles are implemented into traditional paradigms. This paper aims at identifying the impact of quantum computing on algorithm and software advancement, through a discussion of essential quantum algorithms, quantum languages, as well as the opportunities and challenges of quantum technologies. Method: An extensive literature review and theoretical investigation was also performed to investigate the foundational concepts of quantum computing and subsequent effects on algorithm and software engineering. Some of the research questions included exploring the contrast between classical and quantum algorithms, reviewing current literature on quantum programming languages, and delving into examples of real-life deployments of quantum algorithms cross numerous domains. Results: This paper shows that quantum computing brings qualitatively new paradigms in the algorithm design and function while the quantum algorithms such as Shor’s and Grover’s perform exponentially faster certain problems. Software development for quantum has brought the need to devise new frameworks of coding in light of probability in quantum circuit. It is also comforting to note that there is still effort being made although in its most embryonic form to create quantum programming languages like Qiskit and Cirq. Some of challenges include quantum decoherence; limited number of quantum hardware; and need for strong error correction processes.Conclusion: While there are currently relatively few quantum algorithms it is believed that the findings in this field have the ability to revolutionize algorithm and software design and subjects like cryptography, optimization and AI. However, trends in quantum computing show that the constraints to computational capabilities are likely to be lifted to allow creativity to develop the most powerful software solutions

EQID: Entangled quantum image descriptor an approach for early plant disease detection

In present day agriculture, early and accurate identification of plant diseases is essential for prompt response, which protects crop quality and output. This paper presents the Entangled Quantum-Inspired Deep learning model (EQID), a unique method that improves feature representation and classification in plant disease prediction by utilizing the concepts of quantum computing. Two different datasets with images of potatoes and tomatoes as leaves were used to test the EQID model, which performed better than traditional models. EQID obtained 98.96% accuracy, 98.98% precision, 98.96% recall, and 98.90% F1 score on images of potato leaves. For tomato leaves, comparable outcomes were noted, with accuracy, precision, recall, and F1 score all above 99.61%. The accuracy of disease prediction is greatly increased by the efficient and effective feature representation made possible by the EQID model's inclusion of quantum computing techniques. Additionally, the model outperformed other cutting-edge models such as DenseNet-121, VGGNet 16, and Xception Net, illustrating the potentially revolutionary effects of quantum-inspired models in agriculture. Future work will focus on applying the EQID model to a broader range of crops and plant diseases, as well as incorporating additional data sources to further enhance the model's predictive capabilities.

Web-платформа для організації хмарних обчислень у нейрофізіологічних дослідженнях на основі даних айтрекінгу

The purpose of the work is to develop the architecture and web version of the software complex based on the proposed new concept of cloud computing organization, which allows to expand the diagnostic capabilities of the tools of model-oriented information technology for assessing the neurophysiological state of a person using methods of nonlinear dynamic identification of the oculomotor system based on eye tracking data. The concept of cloud computing is proposed, which is based on the combination of PaaS and SaaS services as part of the developed software complex, due to which the cross-platform nature of cloud computing is ensured, the productivity and efficiency of scientific research increases. The developed architecture allows you to easily expand the functionality of the software complex and adapt it to different application conditions. The key feature of the complex is its undemanding hardware on the client side thanks to the use of cloud computing and its modular structure, which allows for easy scaling of the complex, as well as the isolation of the script code execution process in the cloud computing environment to increase the level of security when interpreting the script code on the server . Compared to other similar services, the complex has several advantages: it provides effective work in research and educational applications, supports Python and JavaScript programming languages, and also allows the use of software-implemented identification methods through specially developed GUI interfaces. In addition, the complex offers social opportunities and a high level of abstraction, which allows to optimize the research process.

Improving patient’s medical history classification using a feature construction approach based on situation awareness and granular computing

Healthcare decision support systems aid physicians in disease classification by analyzing patients’ medical histories to suggest preliminary diagnoses. As physicians largely base their analysis on anamnesis, integrating this process into an automated recommendation system can expedite decision-making and transition to relevant clinical investigations, thus enhancing efficiency in diagnosing potential pathologies. In this research, an innovative method for feature construction is introduced, drawing on the concepts of Situation Awareness and Granular Computing. The aim of this method is to enhance the performance of out-of-the-box classification algorithms used in machine learning. The approach is specifically tailored to mimic physicians’ cognitive processes when analyzing a patient’s medical history, resulting in the generation of new, information-dense features that can be used for classification tasks. By employing this strategy, a deeper comprehension of the data can be achieved, as well as a more precise categorization of anamneses in relation to possible medical conditions. To authenticate the efficacy of the proposed technique, three major disease categories, namely cardiac, gastrointestinal, and thyroid, were considered. The dataset comprised 1213 medical histories. The experimental results indicate that the study’s six classifiers attained a balanced accuracy exceeding 90%. Among these, the SVM classifier demonstrated the highest balanced accuracy at 93%. Overall, the proposed approach resulted in an average increase of 16 percentage points in balanced accuracy, representing an improvement over the traditional methods commonly employed in machine learning. This approach could be integrated into a clinical decision support system, aiding physicians in accurately identifying necessary investigations and expediting diagnosis.

Variational approach to learning photonic unitary operators

Structured light, light tailored in its internal degrees of freedom, has become topical in numerous quantum and classical information processing protocols. In this work, we harness the high dimensional nature of structured light modulated in the transverse spatial degree of freedom to realize an adaptable scheme for learning unitary operations. Our approach borrows from concepts in variational quantum computing, where a search or optimization problem is mapped onto the task of finding a minimum ground state energy for a given energy/goal function. We achieve this by a pseudo-random walk procedure over the parameter space of the unitary operation, implemented with optical matrix-vector multiplication enacted on arrays of Gaussian modes by exploiting the partial Fourier transforming capabilities of a cylindrical lens in the transverse degree of freedom for the measurement. We outline the concept theoretically, and experimentally demonstrate that we are able to learn optical unitary matrices for dimensions d = 2, 4, 8, and 16 with average fidelities of >90%. Our work advances high dimensional information processing and can be adapted to both process and quantum state tomography of unknown states and channels.

Quantum Machine Learning: Leveraging Quantum Computing for Enhanced Learning Algorithms

The paper "Quantum Machine Learning: Leveraging Quantum Computing for Enhanced Learning Algorithms" explores the integration of quantum computing principles into classical machine learning techniques, aiming to address limitations such as scalability and computational inefficiency. It presents the foundational concepts of quantum computing, including superposition and entanglement, and their application in accelerating machine learning processes. The study emphasizes the potential for quantum algorithms to significantly improve the performance of machine learning tasks by processing large datasets more efficiently and exploring larger hypothesis spaces. Key quantum machine learning algorithms discussed include Quantum Support Vector Machines (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum Neural Networks (QNN), each of which leverages quantum mechanics to overcome the computational barriers faced by classical algorithms. The Quantum Approximate Optimization Algorithm (QAOA) is also highlighted for its ability to optimize machine learning models more effectively. While the theoretical benefits of Quantum Machine Learning (QML) are promising, the practical application of these techniques is currently limited by the constraints of existing quantum hardware. This research contributes to the emerging field of QML by examining its potential advantages and future implications in addressing complex data processing challenges.

Empowering Latine elementary school students with disabilities: computer programming through culturally sustaining curriculum

ABSTRACT Background and Context The study was conducted in a special education classroom in an elementary school with multilingual Latine students, utilizing a computer science curriculum focused on community-based environmental literacy. Objective This study explores the experiences of diverse elementary students with disabilities in learning computer programming and identifies instructional strategies that enhance their learning within a culturally sustaining curriculum. Method An exploratory case study approach was used to examine students’ learning experiences and teachers’ instructional strategies during curriculum implementation. Findings Students who typically did not engage with peers collaborated effectively, and those with behavioral and performance difficulties exhibited heightened engagement. Instructional strategies included multisensory engagement and connecting environmental and computational concepts to real-life situations. Implications The result underscore how a culturally sustaining computer science curriculum can empower diverse students, foster inclusivity, and leverage their strengths through effective teaching practices.

Development and Evaluation of an Internet of Things Project for Preservice Elementary School Teachers

Programming education is gaining more attention in elementary schools, especially through the use of visual programming tools and development boards. However, the elementary school teacher education in Taiwan allows only limited time periods for training in visual programming and the use of development boards. Development boards are ideal for creating internet of things (IoT) projects, and project-based learning (PBL) is an effective teaching strategy in programming education to promote active learning. Therefore, to sustainably cultivate 21st-century teachers, this study developed an IoT PBL activity for preservice elementary school teachers in Taiwan, so as to improve their attitude toward programming and to enhance their content knowledge of programming teaching, including in computational thinking concepts and knowledge concerning the micro:bit and IoT. This project aimed to engage preservice elementary school teachers in using the micro:bit and electronic modules to create cardboard games with IoT features. A preliminary evaluation was conducted within a teacher education course to verify the project’s feasibility. Consequently, the preservice elementary school teachers participating in this project could develop various IoT cardboard games through teamwork. Their scores on the assessments of computational thinking concepts, development board knowledge, and attitude toward programming showed significant improvement after the project activity. They also expressed highly positive feedback on the project. These findings verify that the proposed PBL activity could be feasible for elementary teacher education.

Airborne fog computing framework: instant primers and design choices

This article focuses on the challenge of providing services and applications by a swarm of mobile flying devices, which may belong to the same tenant or not. Starting by introducing the concepts of mobile edge computing and fog computing, this work shows how the latter can be leveraged to create a novel framework of airborne fog computing. This article defines the applicability, design principles, and challenges inherent to such a framework, starting by showing how the technical challenges can be tackled based on the combination of three different networking paradigms: opportunistic networking, information-centric networking, and software-defined networking. This article shows how these three networking paradigms can be combined to define an airborne fog computing framework based on the notion of in-network computing dynamically programming the data plane to support the execution of services and the integration of the paradigms, in which services are defined as chains of computational functions that are dynamically installed and executed in different flying devices.

Enhancing computational thinking in students with autism spectrum disorder and intellectual disabilities: a robot programming approach

Objectives Research examining computational thinking skills development via programming education using robotic teaching tools for students with Autism Spectrum Disorder and Intellectual Disabilities remains limited. This study aims to analyze the advancements in computational thinking skills among middle school students with Autism Spectrum Disorder and Intellectual Disabilities through participation in robot programming classes. Methods Problem-solving proficiency in computational thinking was assessed utilizing Brennan and Resnick’s three dimensions framework. A tailored teaching and learning approach was implemented for robot programming education, accounting for participants’ cognitive abilities and required levels of support. Four middle school students, comprising one with Autism Spectrum Disorder and three with Intellectual Disabilities, were enrolled. Results Significant enhancements were observed in all participants’ computational thinking skills across conceptual understanding, practical application, and perspective. Notably, all participants successfully resolved the final project problem at their individual proficiency levels. The acquisition of computational concepts was found to correlate positively with improved practical computational skills. Additionally, all participants demonstrated keen interest in and positive attitudes toward both robots and programming. Conclusions This study underscores the efficacy of robotic teaching tools in enhancing computational thinking skills through programming education. Additionally, it highlights considerations for enhancing students’ accessibility in future programming class design.

From theory to practice: Truchet tile as a computational visual coding approach for facade visualization

The study presents unique teaching approach for generating a parametric modular facade fabrication for first-year students in visual design module using a computational visual coding method based on Truchet Tiles. The method followed four main phases: exploring tiling, testing connectivity, fabrication, and assembly. Shape grammar was considered during the design principles implementation to respond to climatic behavior. Space-filling shapes method known as the “Generalized Abeille Tiles” was followed to ensure tiling connectivity. The experimentation involves manual folding techniques without any software. A survey was conducted among first-year and fourth-year students post-introduction to computational software. The results showed a wide range of alternative geometries with different self-shading patterns generated. It highlighted that facade fabrication aided students in comprehending computational logic, easing the understanding of computational software complexity. This pilot study emphasizes the effectiveness of teaching computational concepts in early curricula.