Reviewerly/exHarmony huggingface dataset:
The Optimized Deployment of Service Function Chain Based on Reinforcement Learning

Authors:
raw_author_name: Yibo Zhang

Abstract: With the rapid development and application of technologies such as artificial intelligence, the Internet of Things, and cloud computing, data is showing explosive growth. In order to address the rising energy consumption due to the increasing number of devices in the traditional network architecture, software-defined networking and network function virtualization have been proposed. In this paper, we propose a reinforcement learning model based on actor-critic architecture. The service function chain deployment problem is mathematically modeled, and minimizing the total service function chain delay is taken as the optimization objective. The experimental results demonstrate that the service function chain deployment algorithm proposed in this paper is improved in terms of total system latency.
Intelligent Trajectory for Mobile Element in WSNs with Obstacle Avoidance

Authors:
raw_author_name: Ebenezer Jangam
raw_affiliation_string: Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
raw_author_name: Pallavi Joshi
raw_affiliation_string: Department of Computer Science, Amrita Vishwa Vidyapeetham, Bogadi, Mysuru, Karnataka 570026, India

Abstract: In wireless sensor networks (WSNs), mobile sink-driven data acquisition can mitigate hotspot issues, which further increases WSN efficiency, such as throughput, lifetime, and energy efficiency, while reducing delay and packet loss. Recently, most mobile sink algorithms have focused on efficient paths, and few consider obstacles in the network environment. Nevertheless, constructing an obstacle-aware trajectory in a WSN is challenging. In this context, this paper proposes a bug algorithm based on an obstacle-aware intelligent trajectory (CSOBUG) for a mobile sink to acquire data from sensor nodes in WSNs efficiently with the help of cat swarm optimization (CSO). The proposed CSOBUG algorithm has two phases: selecting visiting points and constructing a trajectory. A CSO-based clustering approach is used to select visiting points, and a bug algorithm is used to select a trajectory. Comparing CSOBUG with existing techniques, it is found that CSOBUG is less computationally intensive than the existing techniques. As well as outperforming traditional methods based on multiple performance metrics, the CSOBUG achieves superior results in a variety of scenarios.
Methodology, application and platform for urban security planning and management

Authors:
raw_author_name: Chunlin Liu
raw_affiliation_string: Digital Technology Solutions, AIS Communications Pte Ltd; School of Economic and Management, Fuzhou University; Security by Design, K&C Protective Technologies (Fujian) Co. Ltd; Security by Design, K&C Protective Technologies Pte Ltd

Abstract: With the acceleration of economic development and urban construction, urban security accidents have occurred around the world with alarming frequency, causing serious casualties and economic losses. Urban security planning and management as emerging areas of research have drawn widespread attention. For city development plans, urban security planning and management have become one of major topics. This paper first outlines the principles of urban security planning and management, combined with the construction of a digital and intelligent platform for urban emergency management. This research then analyzes the core technology and equipment support system of urban security management and its practical application. It also presents a new model based on urban security planning and management, followed by examples of its application in some mega infrastructure development for security planning and design (for example, Singapore Changi Airport and Shanghai Hongqiao Airport Transportation Hub). Additionally, a blast protection concept of urban security planning and management is provided.
Deep Learning Based Multi-Camera Person Tracking and Re-identification System

Authors:
raw_author_name: Mudipalli Varun Kumar -
raw_author_name: Mohammad Kaif Ali -

Abstract: This research paper presents a novel Deep Learning Based Multi-Camera Person Tracking and Re-identification System designed to enhance security and surveillance in public spaces. Leveraging advanced technologies including Object Detection through YOLO and Deep SORT, the system offers an efficient solution for monitoring and tracing individuals across multiple camera views. Through object detection, individuals are identified within each camera frame, while Deep SORT ensures seamless tracking as they move across different camera perspectives. Additionally, a person re-identification module enhances tracking accuracy by extracting distinctive features and linking individuals across various camera views using unique identifiers. This system represents a significant advancement in surveillance capabilities, contributing to the development of effective and user-friendly surveillance solutions.
A study of the thermal evolution of permeability and porosity of porous rocks based on fractal geometry theory

Authors:
raw_author_name: Tongjun Miao
raw_author_name: Richeng Liu
raw_author_name: Peng Xu
raw_author_name: Boming Yu

Abstract: FractalsAccepted Papers No AccessA study of the thermal evolution of permeability and porosity of porous rocks based on fractal geometry theoryTongjun Miao, Aimin Chen, Richeng Liu, Peng Xu, and Boming YuTongjun Miao, Aimin Chen Search for more papers by this author , Richeng Liu Search for more papers by this author , Peng Xu Search for more papers by this author , and Boming Yu Search for more papers by this author https://doi.org/10.1142/S0218348X24500518Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out New & Notable Titles in Nonlinear ScienceIncludes authors Leon O Chua, Bruce J West and more " FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 13 December 2023 Accepted 20 February 2024 PDF download
Digitalization of Education of the Future—A Trend or a Requirement of the Time?

Authors:
raw_author_name: Antonina Hurbanska
raw_affiliation_string: Kyiv National University of Culture and Arts

Abstract: The article substantiates the need to introduce innovative digital technologies in the future education system as a natural response to changes in the socio-economic sphere of social being. Particular attention is paid to the source of the active use of digital technology - digitalization, the concept of electronic functionality is formulated. The authors highlighted the main trends in the development of digital education and assumed that these dynamics will only grow in the future. The article reflects the main trends in the development of digital education, analyzes their strengths, and considers reservations about their implementation. The authors managed to present the digital transformation of the educational process as a global trend aimed at creating a new information society functioning in an innovative digital environment.
Optimized deep learning multi-model investigation of images for ground water level detection

Authors:
raw_author_name: Chetan M. Thakar

Abstract: &lt;p&gt;When referring to groundwater in the context of the normal water cycle, the word “reservoir” is acceptable. Compared to the atmosphere or fresh surface water, groundwater has a much greater capacity to hold water. Despite the high degree of variety and complexity of the subsurface ecosystem, there is currently only a small amount of data available. People who depended on reality-based models ran into both of these challenges at some point. Statistical modeling was used in order to get a more accurate calibration of the model throughout the course of time. Because of the expansion in global population, governments in both wealthy and developing nations are increasingly looking to groundwater as an essential resource for satisfying the water requirements of their populations. Because water was preserved in such a large amount, it may be used again, even when there is a drought or other dry time. In this manuscript, a Particle Swarm Optimization (PSO) enabled Visual Geometry Group (VGG) 16 deep learning model is used in order to determine the level of ground water. Accuracy, specificity and sensitivity of PSO enabled VGG is highest among the algorithms used in the experimental study. Accuracy of PSO VGG 16 is 92.5 percent, specificity and sensitivity of PSO VGG 16 is 99 percent.&lt;/p&gt;
Review of Intelligent Ship Path Planning Algorithms

Authors:
raw_author_name: Yixuan Song
raw_affiliation_string: Beijing Jiaotong University, China
raw_author_name: Xi Cao
raw_affiliation_string: China Coast Guard Academy, China

Abstract: Intelligent ship route planning has become an important research direction in the field of shipping in recent years. This paper first provides an overview of the basic theory of intelligent ship route planning. Secondly, various intelligent ship route planning algorithms are introduced, including methods based on A* algorithm, artificial potential field algorithm, RRT algorithm, and reinforcement learning. These algorithms analyze information such as ocean environment, predict sea conditions and traffic conditions, and consider ship dynamics and navigation safety constraints to provide efficient and safe navigation routes for ships. Finally, this paper points out the key issues and future development directions in intelligent ship route planning. The continuous innovation and application of intelligent ship route planning algorithms will provide more intelligent and efficient ship transportation services for the shipping industry, promoting the sustainable development of the shipping industry.
Exploring the Performance of PySpark and Scikit-Learn Libraries in Developing Fall Detection Systems

Authors:
raw_author_name: Erhan KAVUNCUOGLU
raw_affiliation_string: SIVAS CUMHURIYET UNIVERSITY

Abstract: Falls pose a significant risk for the elderly population, often resulting in serious injuries and decreased quality of life. Accurate and effective fall detection systems can play an important role in reducing these risks. This study conducts a comparative analysis of the performance of PySpark and Scikit-Learn libraries in the development of fall detection models. PySpark offers a distributed computing environment specifically designed for big data processing, while Scikit-Learn is a Python-based library widely used for data analysis and machine learning. Five popular machine learning algorithms, including logistic regression, gradient boosting classifier, random forest, support vector machine, and Decision Tree, were used to build fall detection models using both PySpark and Scikit-Learn. Models were evaluated using a comprehensive set of measurements including accuracy, sensitivity, specificity, and confusion matrix. In addition to the five popular machine learning algorithms included in the study, 26 different features were extracted from the Sisfall dataset, one of the most comprehensive fall and daily life activity datasets, in five main categories: basic statistical features, frequency domain features, time series features, motion features, and relational features. These features were incorporated into fall detection models to improve their ability to accurately identify falls. The findings reveal that both PySpark and Scikit-Learn provide strong and effective results in fall detection. Logistic regression emerged as the best performing algorithm in both libraries, achieving the highest accuracy rates. However, PySpark exhibited slightly longer training times compared to Scikit-Learn, which performed better in testing. Ultimately, this study will benefit researchers and practitioners involved in the development of robust and effective fall detection systems, thereby enabling improved safety and well-being for the elderly population. Additionally, it contributes to the literature by presenting a comprehensive feature extraction methodology for fall detection, which has not been explored extensively in previous research works.
Fuzzy Clustering-Based Grouping for Mapping the Distribution of Student Success Data

Authors:
raw_author_name: Mustakim Mustakim
raw_author_name: Delvi Nur Aini
raw_author_name: Moh. Erkamim

Abstract: Learning activities are the main activity in the overall teaching and learning process in schools. This is because whether or not the achievement of educational goals depends on how the learning process is carried out by students. The uneven level of student success in learning is one of the problems in the school's efforts to realize the vision and mission of SMKN 5 Pekanbaru in preparing skilled graduates to be able to work in certain sectors by the public interest and the industrial world. In this study, mapping and grouping student grade data was carried out using the Fuzzy C-Means algorithm to provide information to the school in making the right decisions and optimizing the learning process. Furthermore, clustering was carried out in several experiments K=3 to K=7, and obtained the best validity value tested with the Silhouette Index of 0.4277 located at K=5. Then the distribution of cluster 5 on student score data was obtained with details, namely cluster 1 with a capacity of 1 student, cluster 2 with a capacity of 27 students, cluster 3 with a capacity of 1 student, cluster 4 with a capacity of 10 students, cluster 5 with a capacity of 23 students.
Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm

Authors:
raw_author_name: Yerik Afrianto Singgalen
raw_affiliation_string: Atma Jaya Catholic University of Indonesia, Jakarta

Abstract: Sentiment analysis plays a pivotal role in comprehending public sentiment, notably within digital communication, where copious amounts of textual data are generated daily. This study delves into the efficacy of sentiment classification models, namely the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), within the imbalanced datasets commonly encountered in sentiment analysis tasks. Employing a comparative analysis methodology, a dataset comprising robot hotel reviews from online platforms is the basis for evaluation. Both NBC and SVM models undergo training and assessment, with and without the Synthetic Minority Over-sampling Technique (SMOTE), to rectify the class imbalance. Performance evaluation relies on critical metrics, including accuracy, recall, precision, f-measure, and Area Under Curve (AUC) to gauge model effectiveness. Findings demonstrate SVM's superiority over NBC in terms of accuracy (SVM: 76.88%, NBC: 67.43%), precision (SVM: 92.03%, NBC: 86.87%), recall (SVM: 58.88%, NBC: 41.00%), f-measure (SVM: 71.78%, NBC: 55.63%), and AUC (SVM: 0.907, NBC: 0.961). Incorporating SMOTE significantly enhances both models' performance, particularly in addressing class imbalance concerns. Although NBC exhibits a more balanced performance across precision and recall metrics, SVM demonstrates heightened accuracy and predictive capability in sentiment classification tasks. These findings underscore the pivotal role of algorithm selection and preprocessing techniques in optimizing sentiment analysis performance, thereby providing invaluable insights for practitioners and researchers alike.
Educational Media Of Ancient History Through Interactive, Evocative, And Interactive Display Design For Sangiran Museum Krikilan Cluster

Authors:
raw_author_name: Hening Laksani
raw_affiliation_string: Institut Seni Indonesia Surakarta, Indonesia
raw_author_name: Nur Rahmat Ardi Candra Dwi Atmaja
raw_affiliation_string: Institut Seni Indonesia Surakarta, Indonesia
raw_author_name: Basnendar Herry Prilosadoso
raw_affiliation_string: Institut Seni Indonesia Surakarta, Indonesia
raw_author_name: Asmoro Nurhadi Panindias
raw_affiliation_string: Institut Seni Indonesia Surakarta, Indonesia
raw_author_name: Isnawati Muslihah
raw_affiliation_string: Institut Seni Indonesia Surakarta, Indonesia

Abstract: The Sangiran site is one of the most important prehistoric sites in the world, more than 50% of hominid fossils are found in this area and has extraordinary importance because it is considered a complete ecosystem laboratory consisting of geology, palaeontology, palaeobotany, and archaeology (ICOMOS, 1996). By UNESCO, the Sangiran Ancient Human Site was designated as a World Culture Heritage on 5 December 1996 in Merida (Mexico) by the World Heritage Committee with number C593, since then Sangiran has belonged to the world and has become one of three centres of ancient human evolution in addition to similar sites in Africa and China. Preservation of cultural knowledge through cultural knowledge by designing the Museum as a centre of information and cultural preservation. A museum through the exhibition system (museum display system) to convey information. The use of museum display facilities, tailored to the needs of the museum and the type of collection to be exhibited. The museum display system enables information communication. In the display system, objects are placed in displays with interpretations that can explain their meaning. The use of supporting facilities, also known as museum displays, is tailored to the needs and type of museum collection to be exhibited. To display a collection of artefacts about ancient human history, Sangran Museum Krikilan Cluster uses three presentation approaches: intellectual approach, romantic (evocative) approach, and interactive approach. The aim of this design is to determine an alternative display system in the exhibition space of the Sangiran Museum Krikilan Cluster to attract more visitors, both local and foreign.
Exploration of the Utilization of Foreign Museum Resources in Junior High School World History Contextual Teaching – A Case Study on the Meiji Restoration

Authors:
raw_author_name: Xiuli Ji

Abstract: Addressing common issues in junior high school world history learning, such as unfamiliar content and weak spatial awareness, effectively using foreign museum resources in teaching through contextual methods proves to be a successful strategy. When selecting contextual teaching resources, it is crucial to adhere to principles based on student needs, teaching content, and the correct value orientation. Implementation should be based on students' experiences to achieve significant teaching results.
An Analysis of Xia Nai's Museum Education Thought — A Study Centered on Xia Nai's Diary

Authors:
raw_author_name: Xiuli Ji
raw_affiliation_string: Taishan University, China

Abstract: Based on a systematic study of "Xia Nai's Diary", it was found that the famous archaeologist Xia Nai developed a systematic museum education philosophy based on his rich museum practice and mentor guidance. He believes that museums have important educational functions, serving as centers for professional knowledge learning, academic research materials, and patriotic sentiments. They should be fully utilized through the comparison of physical objects and literature. Based on this, he deeply realized the close relationship between museums and archaeology, providing valuable experience for guiding museum construction and archaeological development after returning to China.
Research on the Training Mode of Innovative Talents in Information and Computing Science from the Perspective of Scientific and Technological Innovation

Authors:
raw_author_name: None Jing Lei
raw_affiliation_string: School of Mathematics and Statistics, Taishan University, Tai'an 271000, China
raw_author_name: None Ji-Yan Wang
raw_affiliation_string: School of Mathematics and Statistics, Taishan University, Tai'an 271000, China

Abstract: This study aims to identify and analyze effective training modes for fostering innovative talents in information and computing science, with a focus on the perspective of scientific and technological innovation. The research method employed includes a comprehensive literature review, interviews with industry experts and academics, and empirical data analysis from existing training programs. The findings indicate that an integrated approach combining project-based learning, interdisciplinary collaboration, and practical experiences can enhance students' innovative capabilities. Recommendations from this research include the need for increased investment in educational facilities and resources, the formation of strategic partnerships between universities and industries, and the development of curricula responsive to the evolving needs of the job market. This research provides a significant contribution to the development of relevant and effective training strategies in response to societal demands for innovative talents in the field of information and computing science
The Neuropsychological Impact of Immersive Experiences in the Metaverse and Virtual and Augmented Reality

Authors:
raw_author_name: Laura De Clara
raw_affiliation_string: METACARE SRL, Italy

Abstract: Recent advances in mental healthcare technology, especially in virtual reality (VR) and augmented reality (AR), have revolutionized psychiatry and psychology. This chapter critically examines these innovations' potential in treating various mental disorders, from anxiety and depression to PTSD and ADHD. It emphasizes the controlled and customizable nature of VR environments, which empower individuals to confront fears within personalized settings. Interdisciplinary collaboration is stressed, showcasing applications in psychology, from assessment to clinical treatment. The chapter explores neuropsychological impacts, detailing how VR modulates emotions, enhances neural plasticity in stroke rehabilitation, and alters neural networks. Despite promise, challenges like treatment standardization and ethical concerns persist, demanding careful consideration. The chapter advocates for ongoing research and global interdisciplinary cooperation to fully exploit these technologies' potential in mental healthcare.
Comparative Overview of Augmented Reality, Virtual Reality, and Mixed Reality

Authors:
raw_author_name: C. Selvan
raw_affiliation_string: REVA University, India
raw_author_name: K. Veningston
raw_affiliation_string: National Institute of Technology, Srinagar, India

Abstract: The virtual and augmented reality is a rapidly growing technology which focuses on visualization and recognition of 2D and 3D images using tools with bonded techniques (features). The virtual reality (VR) delivers only assumed digital world for viewers, but the expectations of viewers is real world with digital world. Thus, augmented reality (AR) has become popular to deliver enhanced view of VR. However, AR fulfilled the expectations of viewers but there is a gap between VR and AR. The intermediate gap is focused on enhanced visualization for controlling and manipulating VR and AR in parallel. In this chapter, the comparative study of VR, AR, and mixed reality (MR) are focused to expose different aspects of applications, performances, and tools requirements.