Hasil untuk "Electronic computers. Computer science"

Hod Lipson, M. Kurman

C. Pfleeger

Andy Wai Kan Yeung, Anela Tosevska, E. Klager et al.

Background Virtual reality (VR) and augmented reality (AR) have recently become popular research themes. However, there are no published bibliometric reports that have analyzed the corresponding scientific literature in relation to the application of these technologies in medicine. Objective We used a bibliometric approach to identify and analyze the scientific literature on VR and AR research in medicine, revealing the popular research topics, key authors, scientific institutions, countries, and journals. We further aimed to capture and describe the themes and medical conditions most commonly investigated by VR and AR research. Methods The Web of Science electronic database was searched to identify relevant papers on VR research in medicine. Basic publication and citation data were acquired using the “Analyze” and “Create Citation Report” functions of the database. Complete bibliographic data were exported to VOSviewer and Bibliometrix, dedicated bibliometric software packages, for further analyses. Visualization maps were generated to illustrate the recurring keywords and words mentioned in the titles and abstracts. Results The analysis was based on data from 8399 papers. Major research themes were diagnostic and surgical procedures, as well as rehabilitation. Commonly studied medical conditions were pain, stroke, anxiety, depression, fear, cancer, and neurodegenerative disorders. Overall, contributions to the literature were globally distributed with heaviest contributions from the United States and United Kingdom. Studies from more clinically related research areas such as surgery, psychology, neurosciences, and rehabilitation had higher average numbers of citations than studies from computer sciences and engineering. Conclusions The conducted bibliometric analysis unequivocally reveals the versatile emerging applications of VR and AR in medicine. With the further maturation of the technology and improved accessibility in countries where VR and AR research is strong, we expect it to have a marked impact on clinical practice and in the life of patients.

Timothy Proctor, Robin Blume-Kohout, Andrew Baczewski

Building a useful quantum computer is a grand science and engineering challenge, currently pursued intensely by teams around the world. In the 1980s, Richard Feynman and Yuri Manin observed independently that computers based on quantum mechanics might enable better simulations of quantum phenomena. Their vision remained an intellectual curiosity until Peter Shor published his famous quantum algorithm for integer factoring, and shortly thereafter a proof that errors in quantum computations can be corrected. Since then, quantum computing R&D has progressed rapidly, from small-scale experiments in university physics laboratories to well-funded industrial efforts and prototypes. Hype notwithstanding, quantum computers have yet to solve scientifically or practically important problems -- a target often called quantum utility. In this article, we describe the capabilities of contemporary quantum computers, compare them to the requirements of quantum utility, and illustrate how to track progress from today to utility. We highlight key science and engineering challenges on the road to quantum utility, touching on relevant aspects of our own research.

Lin Zheng, Jinlong Li, Zhanbo Zhu et al.

Abstract In recent years, with the popularization of online education, real-time monitoring of learning engagement has become a key challenge for scholars. Existing studies mainly rely on questionnaires and physiological signal detection, which have limitations such as high subjectivity, poor real-time performance, and expensive equipment. Previous research has shown that head pose is closely related to cognitive state. However, current estimation models require substantial computational resources, making real-time deployment on mobile devices challenging. In this study, we validate the significant correlation between head pose and learning engagement based on the DAiSEE dataset (8,925 video clips) and propose a lightweight head pose estimation method. The LightNet proposed in this paper uses an improved feature extraction module (MG-Net) and an Attention-based multi-scale fusion model (AMF). Experiments conducted on the 300W-LP and BIWI benchmark datasets demonstrate that, compared with existing state-of-the-art methods, LightNet substantially reduces model complexity by decreasing the number of parameters to just 0.45 $$\times 10^6$$ × 10 6 , representing over 90% reduction in model size. Despite this significant compression, LightNet maintains a high level of accuracy, with the mean absolute error (MAE) increasing by only 0.15°, indicating a minimal loss in prediction precision. Moreover, the model achieves a notable improvement in processing speed, exceeding 50% increase relative to baseline approaches. This combination of a lightweight architecture, competitive accuracy, and accelerated inference speed underscores LightNet’s effectiveness and its potential suitability for real-time applications. This study not only expands the application of head pose in education but also provides a feasible solution for real-time engagement monitoring on resource-constrained devices.

Muhammad Yusuf Halim, Ahmad Luthfi

Unmanned Aerial Vehicles (UAV) have become vital tools in industrial sectors such as coal mining for site inspections and operational monitoring. However, unauthorized UAV flights present security risks that necessitate forensic investigation. This study examines a forensic case involving a DJI Mini 3 UAV suspected of crossing company boundaries. Using the Conceptual Digital Forensics Model for the Drone Forensic Field, both static and dynamic forensic acquisition methods were applied. Static acquisition recovered 53 photographs, 11 videos, 11 audio files, 10 deleted photos, 4 deleted videos, and 3 unidentified log files. Dynamic acquisition yielded 64 media files including 63 photographs (.JPG and .jpg) with 10 deleted, 14 videos (.MP4, .MOV, .SWF) with 6 deleted, 11 audio files, 4 plain text files, 31 deleted files, 3 EXIF metadata records containing GPS coordinates, and 3 unidentified log files. The GPS data from EXIF metadata was visualized in Google Earth to map flight paths and confirm boundary violations. These findings demonstrate that dynamic acquisition retrieves a more comprehensive artifact set than static acquisition. This study highlights the importance of UAV digital forensics in supporting security investigations and ensuring compliance with industrial UAV policies.

Francisco-Jose Alvarado-Alcon, Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez et al.

The Internet of Things (IoT) is gaining significant attention for its ability to digitally transform various sectors by enabling seamless connectivity and data exchange. However, deploying these networks is challenging due to the need to tailor configurations to diverse application requirements. To date, there has been limited focus on examining and enhancing the carbon footprint (CF) associated with these network deployments. In this study, we present an optimization framework leveraging machine learning techniques to minimize the CF associated with IoT multi-hop network deployments by varying the placement of the required gateways. Additionally, we establish a direct comparison between our proposed machine learning method and the integer linear program (ILP) approach. Our findings reveal that placing gateways using neural networks can achieve a 14% reduction in the CF for simple networks compared to those not using optimization for gateway placement. The ILP method could reduce the CF by 16.6% for identical networks, although it incurs a computational cost more than 250 times higher, which has its own environmental impact. Furthermore, we highlight the superior scalability of machine learning techniques, particularly advantageous for larger networks, as discussed in our concluding remarks.

Ebrahim Rahimi, Clara Maathuis

Computational Thinking (CT) is a key skill set for students in higher education to thrive and adapt to an increasingly technology-driven future and workplace. While research on CT education has gained remarkable momentum in K12 over the past decade, it has remained under-explored in higher education, leaving higher education teachers with an insufficient overview, knowledge, and support regarding CT education. The proliferation and adoption of artificial intelligence (AI) by educational institutions have demonstrated promising potential to support instructional activities across many disciplines, including CT education. However, a comprehensive overview outlining the various aspects of integrating AI in CT education in higher education is lacking. To mitigate this gap, we conducted this systematic literature review study. The focus of our study is to identify initiatives applying AI in CT education within higher education and to explore various educational aspects of these initiatives, including the benefits and challenges of AI in CT education, instructional strategies employed, CT components covered, and AI techniques and models utilized. This study provides practical and scientific contributions to the CT education community, including an inventory of AI-based initiatives for CT education useful to educators, an overview of various aspects of integrating AI into CT education such as its benefits and challenges (e.g., AI potential to reshape CT education versus its potential to diminish students creativity) and insights into new and expanded perspectives on CT in light of AI (e.g., the decoding approach alongside the coding approach to CT).

Maria Frasca, Davide La Torre, Gabriella Pravettoni et al.

Abstract This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box algorithms. While machine learning algorithms are increasingly employed for medical analysis and diagnosis, their complexity underscores the importance of understanding how these algorithms explain and interpret data to take informed decisions. This review comprehensively analyzes challenges and solutions presented in the literature, offering an overview of the most recent techniques utilized in this field. It also provides precise definitions of interpretability and explainability, aiming to clarify the distinctions between these concepts and their implications for the decision-making process. Our analysis, based on 448 articles and addressing seven research questions, reveals an exponential growth in this field over the last decade. The psychological dimensions of public perception underscore the necessity for effective communication regarding the capabilities and limitations of artificial intelligence. Researchers are actively developing techniques to enhance interpretability, employing visualization methods and reducing model complexity. However, the persistent challenge lies in finding the delicate balance between achieving high performance and maintaining interpretability. Acknowledging the growing significance of artificial intelligence in aiding medical diagnosis and therapy, and the creation of interpretable artificial intelligence models is considered essential. In this dynamic context, an unwavering commitment to transparency, ethical considerations, and interdisciplinary collaboration is imperative to ensure the responsible use of artificial intelligence. This collective commitment is vital for establishing enduring trust between clinicians and patients, addressing emerging challenges, and facilitating the informed adoption of these advanced technologies in medicine.

Xinjing Qi, Huan Wang, Yubo Ji et al.

As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have important implications for gas dispatch and pipeline safety. The incorporation of intelligent algorithms into prediction methodologies has resulted in notable progress in recent times. Nevertheless, certain limitations persist. However, there exist certain limitations, including the tendency to easily fall into local optimization and inadequate search capability. To address the challenge of accurately predicting daily natural gas loads, we propose a novel methodology that integrates the adaptive particle swarm optimization algorithm, attention mechanism, and bidirectional long short-term memory (BiLSTM) neural networks. The initial step involves utilizing the BiLSTM network to conduct bidirectional data learning. Following this, the attention mechanism is employed to calculate the weights of the hidden layer in the BiLSTM, with a specific focus on weight distribution. Lastly, the adaptive particle swarm optimization algorithm is utilized to comprehensively optimize and design the network structure, initial learning rate, and learning rounds of the BiLSTM network model, thereby enhancing the accuracy of the model. The findings revealed that the combined model achieved a mean absolute percentage error (MAPE) of 0.90% and a coefficient of determination (R2) of 0.99. These results surpassed those of the other comparative models, demonstrating superior prediction accuracy, as well as exhibiting favorable generalization and prediction stability.

Michael James Martin, Aaron Andersen, Charles Tripp et al.

NREL's computational sciences center hosts the largest high-performance computing (HPC) capabilities dedicated to energy research while functioning as a living laboratory for energy-efficient computing. NREL's HPC capabilities support the research needs of the Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). In ten years of operation, HPC use in EERE-sponsored research has grown by a factor of 30, including work in electricity generation, energy efficiency, transportation, and energy system modeling. This paper analyzes this research portfolio, providing examples of individual use cases. The paper documents NREL's history of operating one of the world's most energy-efficient data centers while examining pathways to reduce economic and environmental impact beyond reduction of Power Usage Efficiency (PUE). This paper concludes by examining the unique opportunities created for accelerating improvements in data center efficiency created by combining an HPC system dedicated to energy research and a research program in energy-efficient computing.

Michael Holly, Jannik Hildebrandt, Johanna Pirker

Skills in the field of computer science (CS) are increasingly in demand. Often traditional teaching approaches are not sufficient to teach complex computational concepts. Interactive and digital learning experiences have been shown as valuable tools to support learners in understanding. However, the missing social interaction affects the quality of the learning experience. Adding collaborative and competitive elements can make the virtual learning environment even more social, engaging, and motivating for learners. In this paper, we explore the potential of collaborative and competitive elements in an interactive virtual laboratory environment with a focus on computer science education. In an AB study with 35 CS students, we investigated the effectiveness of collaborative and competitive elements in a virtual laboratory using interactive visualizations of sorting algorithms.

D. Bassett, B. Ainsworth, A. Swartz et al.

SHEN Xueli, HAN Qianwen

Click-Through Rate(CTR) prediction is one of the most important tools for ad placement.Predicting the CTR of an ad and making recommendations to users can increase ad revenue.Field-aware click-through rate prediction models are superior to other click-through rate prediction models because they consider the field information; however, they generate a large amount of redundant information during feature interaction, which results in a low prediction accuracy.A Field-aware Attention Embedding Neural Network(FAENN) model is herein proposed.This model uses a Self-Attentive Mechanism(SAM) to distribute weights to the input vectors of the embedding layer.This helps to clearly identify the importance of the field-aware embedded features, speeding up the training process.The lower-order feature interaction layer focuses on the explicit first-order information of the features and the second-order interaction feature information and outputs the effective features to the higher-order interaction layer.The higher-order feature interaction layer combines the learned interaction vectors with the deep neural network to capture higher-order feature interactions to improve prediction accuracy.The experimental results show that the FAENN model has a higher prediction accuracy than the FM, FFM, and AFM models.

Anton Nijholt

Minan Tang, Kai Liang, Jiandong Qiu

Abstract The proportion of insulators in aerial power patrol images is small and the background of overhead lines is complex, often leading to incomplete and inaccurate detection of insulators. Therefore, an algorithm for detecting insulator targets based on multi‐feature fusion is developed in this study. Firstly, a dynamic threshold oriented fast and rotated brief algorithm is proposed, which uses the bag‐of‐words dictionary model to determine local shape features of the image, applies gradient weighting to the global texture feature vector extracted by the histogram of oriented gradients algorithm and performs radial gradient transformations to get the improved HOG of features. Secondly, the feature vectors are fused serially, the learning machine is trained and the parameters of the support vector machine are optimized using the quantum particle swarm optimization algorithm. Finally, the target area is pre‐divided by the selective search algorithm, and the area is classified by the learning machine. The experimental results show that the proposed feature extraction method can describe the image details more accurately than the existing methods, and the average accuracy of the feature extraction classifier can reach 93.7%, which helps to overcome the incomplete detection problem of insulator detection at the aerial work site.

Tiziana Catarci, Luca Podo, Daniel Raffini et al.

It has been observed in many studies that female students in general are unwilling to undertake a course of study in ICT. Recent literature has also pointed out that undermining the prejudices of girls with respect to these disciplines is very difficult in adolescence, suggesting that, to be effective, awareness programs on computer disciplines should be offered in pre-school or lower school age. On the other hand, even assuming that large-scale computer literacy programs can be immediately activated in lower schools and kindergartens, we can't wait for >15-20 years before we can appreciate the effectiveness of these programs. The scarcity of women in ICT has a tangible negative impact on countries' technological innovation, which requires immediate action. In this paper, we describe a strategy, and the details of a number of programs coordinated by the Engineering and Computer Science Departments at Sapienza University, to make high school girl students aware of the importance of new technologies and ICT. In addition to describing the theoretical approach, the paper offers some project examples.

J. Walker Orr

This study examines the adaptation of the problem-solving studio to computer science education by combining it with pair programming. Pair programming is a software engineering practice in industry, but has seen mixed results in the classroom. Recent research suggests that pair programming has promise and potential to be an effective pedagogical tool, however what constitutes good instructional design and implementation for pair programming in the classroom is not clear. We developed a framework for instructional design for pair programming by adapting the problem-solving studio (PSS), a pedagogy originally from biomedical engineering. PSS involves teams of students solving open-ended problems with real-time feedback given by the instructor. Notably, PSS uses problems of adjustable difficulty to keep students of all levels engaged and functioning within the zone of proximal development. The course structure has three stages, first starting with demonstration, followed by a PSS session, then finishing with a debrief. We studied the combination of PSS and pair programming in a CS1 class over three years. Surveys of the students report a high level of engagement, learning, and motivation.

A. Singh, R. Kilambi

J. Dietz, Hans B. F. Mulder