New Generation Computing最新文献

The development of walking has been extensively studied because it enables infants to move more, carry and manipulate objects, and engage in more frequent interactions with people. Changes in various domains, such as motor and cognitive abilities, can interact with each other. This phenomenon is known as a developmental cascade. This study focuses on infant walking and behavioral changes. After providing an overview of theoretical frameworks, I review recent infant walking studies, including my own, to discuss the relationship between motor and behavioral development. The significance of observations in everyday environments was also discussed.

Electricity procurement of industrial consumers is becoming more and more complicated, involving a combination of various procurement methods due to electricity liberalization and decarbonization trends. This study analyzed and improved power procurement strategies for a factory to achieve carbon neutralization through a multi-agent model simulating the electricity market and introduced a factory agent using various procurement methods including PV, FC, storage batteries (SB), and DR. Firstly, we created a new procurement strategy utilizing all methods. Then, by using the simulation model, we assessed the effectiveness of the existing peak shift DR scenarios in terms of cost reduction efficiency. Results revealed that the introduction of PV has led to a counterproductive outcome for DR. Based on the results, we proposed two methods to improve the effectiveness of DR, namely considering the operation of PV in the DR scenario and expanding the range of optional time periods for DR activation. Finally, we made three new DR scenarios based on our proposal. Through experiment, the new scenarios were confirmed to be effective in cost-effectiveness for decarbonization.

Background

The SARS-CoV-2 virus, which caused the COVID-19 pandemic, emerged in late 2019, leading to significant global health challenges due to the lack of targeted treatments and the need for rapid diagnosis.

Aim/objective

This study aims to develop an AI-based system to accurately detect COVID-19 from CT scans, enhancing the diagnostic process.

Methodology

We employ a faster region-based convolutional neural network (faster R-CNN) for extracting features from pre-processed CT images and use the chaotic satin bowerbird optimization algorithm (CSBOA) for fine-tuning the model parameters.

Results

Our experimental results show high performance in terms of precision, recall, accuracy, and f-measure, effectively identifying COVID-19 affected areas in CT images. The suggested model attained 91.78% F1-score, 91.37% accuracy, 91.87% precision, and 90.3% recall with a learning rate of 0.0001.

Conclusion

This method contributes to the advancement of AI-driven diagnostic tools, providing a pathway for improved early detection and treatment strategies for COVID-19, thus aiding in better clinical management.

This study explores in detail how the art form of dance can be supported from an information processing perspective. Dance has historically served as a medium for conveying emotions and messages by coordinating body movements with music and rhythm. This document centers on the three primary processes intrinsic to dance execution: Creation, Practice, and Performance. Various techniques and tools are introduced for choreographing dance and mastering movement; these methodologies employ advanced technologies, including tactile and auditory feedback, robotics, wearable devices, VR, and AR. The discussion encompasses multiple strategies for developing systems predicated on dance movements and musical compositions, further highlighting potential avenues for research in dance information processing.

Deep learning-powered AI tools offer significant potential to improve COVID-19 lung infection diagnosis. This study proposes a novel AI-based image analysis method for multiclass classification. We analyzed publicly available datasets from Italian Society of Medical and Interventional Radiology (SIRM), Kaggle, and Radiopaedia. However, the relevance, strength, and relationships of static features extracted from these images require further investigation. Bayesian inference approaches have recently emerged as powerful tools for analyzing static features. These approaches can reveal hidden dynamics and relationships between features. Using Analysis of variance (ANOVA) based ranking techniques, we extracted gray level co-occurrence matrix (GLCM) features from images belonging to three classes such as COVID-19, bacterial pneumonia, and normal. To delve deeper into the dynamic behavior and optimize its diagnostic potential, Homogeneity (identified as the most significant feature) was chosen for further analysis using dynamic profiling and optimization methods. This focused investigation aimed to decipher the intricate, non-linear dynamics within GLCM features across all three classes. Our method offers a two-fold benefit. First, it deepens our understanding of the intricate relationships between features extracted from chest X-rays using gray level co-occurrence matrix analysis. Second, it provides a comprehensive examination of these features themselves. This combined analysis sheds light on the hidden dynamics that are crucial for accurate diagnosis and prognosis of various infectious diseases. In addition to the above, we have developed a novel AI-powered imaging analysis method for multiclass classification. This innovative approach has the potential to significantly improve diagnostic accuracy and prognosis of infectious diseases, particularly COVID-19.

Group coordination is defined as interactions with other members to implement a task that is difficult to do alone or to achieve higher performance than an individual. Meanwhile, the adjustment process in coordination is not uniquely determined because each individual has many degrees of freedom. It is more difficult to explain and model complex and dynamic coordination, such as nonverbal behavior of three or more members than pair or verbal interaction. Hence, we previously introduced a coordinated drawing task and conducted the behavioral experiment. The triads operated reels to change the tensions of threads connected to a pen, shared three heterogeneous roles (pulling, relaxing, and adjusting), and moved the pen to draw an equilateral triangle. The results indicated that the adjusting role was related to high task performance by helping resiliently without disturbing the pen’s smooth movement while avoiding great pen deviation. However, this experiment alone cannot explain details of the adjustment process of tension. To supplement these findings, this study formulated the three roles using equations of motion. The multi-agent simulation results showed that the adjusting role might use the degree of pen deviation reflected by the others’ motor information, such as the operating procedures and forces, to change the tension and draw at least three sides. Although it is necessary to consider that we used the experimental task, our study contributes to the fundamental understanding of resilient adjustment in coordination by sharing heterogeneous roles as the first step.

Card-based cryptography is a research field for realizing cryptographic protocols using a deck of physical cards. Shinagawa et al. proposed a regular n-sided polygon card, which can hold a value from 0 to (n-1), and constructed an addition protocol over (mathbb {Z}/nmathbb {Z}) and a voting protocol with v voters and c candidates when (v<n). In this paper, we propose an addition protocol over (mathbb {Z}/mnmathbb {Z}) using regular n-sided polygon cards. Technically, we introduce a cyclic integer encoding and a rot-and-shift shuffle to extend the modulus from n to mn. In addition, we construct two voting protocols with v voters and c candidates using regular n-sided polygon cards. Our first voting protocol requires (c(lceil frac{v+1}{n} rceil + v + 1)) cards and (v+1) shuffles without restriction. Our second voting protocol reduces the number of cards to (lceil frac{v+1}{n} rceil n + v + 1) when (v < n) and (cle n).

A zero-knowledge proof (ZKP) allows a prover to prove to a verifier that it knows some secret, such as a solution to a difficult puzzle, without revealing any information about it. In recent years, ZKP protocols using only a deck of playing cards for solutions to various pencil puzzles have been proposed. The previous work of Lafourcade et al. deals with a famous puzzle called Slitherlink. Their proposed protocol can verify that a solution forms a single loop without revealing anything about the solution, except this fact. Their protocol guarantees that the solution satisfies the single-loop condition, by interactively constructing a solution starting from a state that holds a simple single loop, and proceeding via steps that preserve the invariant of encoding a single loop, until the proper solution is reached. A drawback of their protocol is that it requires additional verifications to guarantee a single loop. In this study, we propose a more efficient ZKP protocol for such a puzzle with fewer additional verifications. For this, we employ the previous work of Robert et al., which addressed the connectivity property in a puzzle. That is, we verify that a solution is connected but not split, to be a single loop. Applying our proposal, we construct a card-based ZKP protocol for Moon-or-Sun, which has its specific rule of alternating pattern in addition to the single-loop condition.

Sumplete is a logic puzzle generated by ChatGPT in March 2023. The puzzle consists of a rectangular grid, with each cell containing an integer. Each row and column also has an integer called target value assigned to it. The objective of this puzzle is to cross out some numbers in the grid such that the sum of uncrossed numbers in each row and column is equal to the corresponding target value. In this paper, we prove that Sumplete is NP-complete. We also propose a physical zero-knowledge proof protocol for the puzzle using physical cards.

Finding the source of rumors in the social network was addressed by researchers with probabilistic models like Maximum Likelihood Estimation in complex network analysis for the past few decades. However, the most promising results could reach up to 2-hop distant neighborhoods on average. With the advent of graph neural networks, the issue was addressed as a classical node classification problem in large networks. Node classification problems achieve the best results when there are appropriate node attributes as features and when node classes are balanced. However, unlike other node classification scenarios, the data collected for source identification usually lacks node attributes because of time limitations. Moreover, the detection of the sources among thousands of users is typically a class imbalance problem. If we could deal with these issues skillfully, then the dominance of the deep learning method compared to other conventional probabilistic methods in multiple rumor source detection will be prominent. We have proposed here a deep learning-based multiple source node classification framework that can predict the sources with promising accuracy. The primary hurdles in non-attributed network classification are navigated by generating feature vectors that capture the structural characteristics of the network and spreading pattern. These features are further solidified with the Graph embedding technique, incorporating the neighborhood features. We have triumphed over the challenge of imbalanced node classes by synthetic node generation with a suitable mathematical model. The concern is further resolved by selective sampling and weighted loss estimation in the deep learning network for classification used in the framework. A study of the Immuno-Diffuse Likelihood parameter in Label propagation based feature construction and its influence on accurate prediction is examined. Our approach demonstrates superior performance compared to existing methods in the datasets available in public repositories, making it a reliable and robust tool for rumor source detection in the complex landscape of social media.