Reviewerly/exHarmony huggingface dataset:
Collaboration of Mixed Reality for Interactive Visualization of Ocean Mapping

Technological advancements in recent times have led to the combination of Artificial Intelligence (AI) and Mixed Reality (MR), making it a powerful tool to address major challenges in a variety of fields. Ocean mapping includes a wide network of research associations which collect ocean data for analysis. This information about ocean floors is vital for various industrial and education fields. A few machine learning algorithms and data pre-processing techniques help to collect, clean, and manipulate data faster and at a lower cost due to the increased speed and efficiency it offers. This allows us to create MR infographics which can be used by research associates to visualize and view the data in a better way. This immersive technology enhances the method of visualization and makes it easier for researchers to conclude a final decision from the analysis. This paper includes an introduction of key terminologies, followed by the current ocean mapping methods i.e., Bathymetry and Multibeam Mapping, along with their respective visualization techniques, and how MR can be used for visualizing the collected data for further study purposes. This paper also briefs about how the implementation of MR in ocean mapping can be advantageous to a wide range of industries.
A Case Study Using Zoom Touch Gestures: How Does the Size of a Training Dataset Impact User’s Age Estimation Accuracy in Smartphones?

In this paper, we focus on improving the age estimation accuracy on smartphones. Estimating a smartphone user’s age has several applications such as protecting our children online by filtering age-inappropriate contents, providing a customized e-commerce experience, etc. However, accuracy of the the state-of-the-art age estimation techniques that use touch behavior on smartphones is still limited because of the lack of sufficient amount of training data. We perform rigorous experiments using zoom gestures on smartphones and demonstrate that increasing the amount of training data can significantly improve the age estimation accuracy. Based on the findings in this study, we recommend creating a large touch dynamics-based age estimation data set so that more accurate age estimation models can be built and in turn, can be used more confidently.
A Prototype for QKD-secure Serverless Computing with ETSI MEC

In this demonstration, we showcase the realization of a prototype of an edge computing network, where the client and edge domains both host simulated Quantum Key Distribution devices, for a hospital use case. In particular, digital health applications using the Function-as-a-Service (FaaS) paradigm will invoke remote functions provided by an Apache OpenWhisk cluster deployed in the edge infrastructure, where the arguments and return value are encrypted using keys generated through an underlying simulated QKD point-to-point network. All the interactions in the control/management plane are handled through standard interfaces defined by the ETSI MEC and QKD industry study groups.
3D Printing and Blockchains for an Emergency Response Supply Chain

The COVID-19 pandemic has emphasized the importance of responsive, efficient, and cost-effective production networks to manufacture essential goods rapidly. The Air Factories 2.0 project is a research-driven initiative designed to withstand the pandemic by leveraging advanced technologies, such as 3D printing, blockchains, and distributed manufacturing, to summon the maker community’s expertise and resources. However, this project’s potential extends beyond the medical field and can be actualized in several domains. This paper comprehensively analyzes the Air Factories 2.0 platform, its technological and scientific contexts, and its potential implications for the future of manufacturing, emergency response, and 3D printing-enabled decentralized supply chains.The study outlines the Air Factories 2.0 project’s operational and organizational structure, stakeholders and roles, participation and tokenization mechanisms, algorithms used to manage production and distribution, blockchains, and smart contracts employed to ensure transparency, security, and efficiency. Furthermore, this paper examines the advantages and limitations of the Air Factories 2.0 platform for the future of advanced manufacturing across various domains.
Real-World Community-in-the-Loop Smart Video Surveillance System

In recent years, smart video surveillance (SVS) systems have become essential in maintaining public safety and security, particularly in smart city environments. We propose an SVS system that uses advanced technologies such as artificial intelligence and computer vision to ensure the timely detection of anomalous behaviors and suspicious objects. The system’s performance is demonstrated through a smartphone application and real-world scenario videos, highlighting its effectiveness in enhancing citizen security with low latency. This paper represents a demonstration of such a system for implementing community-in-the-loop smart video surveillance systems and emphasizes their practicality in improving public safety in various settings. The study adds to the growing research on deploying smart video surveillance systems and underscores the importance of engaging local communities in these projects.
Image Encryption Through Aperiodic Josephus Permutation And Novel Cyclic Shift Operation

In recent years, chaos based cryptography has become a prevalent and efficient way to secure digital images because of the similarities between chaotic properties and the traits needed for encryption. This paper proposes an image encryption algorithm combining a chaotic map, Josephus problem, cyclic shift operation, and XOR operation. The proposed encryption procedure follows the traditional permutation-substitution or confusion-diffusion structure. Here, the Henon map and the novel key generation phase generate image-sensitive chaotic streams, which are used for permutation and diffusion. The aperiodic Josephus permutation and bit-level chaotic cycle shift method accomplish the permutation stage by altering the position and value of each pixel. And this proposed permutation thwarts statistical cryptanalysis by dropping the correlation between neighboring pixels to approximately equal zero. The substitution stage is achieved with modulus and XOR operations on the scrambled image and the chaotic matrix. We compare the proposed method with other recent image encryption algorithms in the simulation experiment and security analysis, and the results confirm that the proposed method has better performance and higher security.
A Hybrid Digital and Optical Double Color Image Encryption Scheme Using a Nine-Dimensional Chaotic System

A nine-dimensional (9D) chaotic-based hybrid digital/optical Encryption (HDOE) scheme is proposed for a double color image. The 9D chaotic sequences are grouped into three independent sets, each responsible for the encryption of one of the RGB channels. Thus, the three color channels are encrypted separately, enhancing the security level and robustness of the encryption process. The digital encryption (DE) part uses fusion and scrambling and is interrupted by a chaotic color image controlled by three of the chaotic sequences, one from each set. The optical encryption (OE) part is implemented in the optical Fourier transform (FT) domain and assisted by two chaotic phase masks (CPMs) for phase-encoding operation. The color CPM is constructed by three chaotic sequences, one from each chaotic sequences sets. The proposed HDOE scheme yields very-high entropy (7.9988), which is very close to the ideal case (8).
Point-based Gesture Recognition Techniques

Gesture recognition is a computing process that attempts to recognize and interpret human gestures through the use of mathematical algorithms. In this paper, we describe Point Based Gesture Recognition and Point Clouds nearest neighbors and sampling. Also, we explore these techniques with previous studies.
An Efficient Approach for Resilience and Reliability Against Cascading Failure

Cloud computing becoming popular now day as, the world is moving towards vitalization and it provide resource to the users depending on their needs by using different resource allocation technique. Resilience and reliability is one of the major issue while dealing with cloud computing. Mitigation failure and migration failure are the issues in cloud resilience and reliability services which cause many service level objective violation. Many work have been done to improve the quality of resilience and reliability. The aim of this paper is to provide a better technique to avoid and recover from mitigation failure and a reliable resource allocation approach at minimum possible cost, for this purpose we used Cascading Failure Resilience System (CSFR) technique. Comparative analyses done to validate our approach and the results shows that our approach handle mitigation failure in an efficient way as well as it provide reliability while providing resources to the users at a comparative low cost.
Proposed Deep Learning System for Arabic Text Detection and Recognition

Building a system to recognize Arabic words or texts has been challenging. It's harder when the text is in various sizes and fonts, regardless of font complexities. This work built a smart system to recognize Arabic words and texts by creating a dataset and training it by using deep learning techniques. This system can scan text into a computer texts. Each of the 1,000 words in the dataset was written out 24 different ways, using 24 different Arabic fonts. Words in images were identified and deduced with the use of image processing methods. Finally, the deep learning (Convolution Neural Network CNN) algorithm takes over, extracting features from the truncated word and retrieving text words that are visually similar to the ones that were cut. In experiments, the system achieved 99% accuracy in words detection and 96% accuracy in recognition.
The Role and Potential Applications of Cloud Computing in the Banking Industry

The introduction of cloud computing has altered how IT demands are satisfied. Every chief information officer is focused on cloud computing because it has ushered in a new era in IT. Many banks are now utilising cloud technologies to accomplish their ever-demanding goals. Cloud computing offers business models for providing cutting-edge customer experiences, successful teamwork, improved IT efficiency and faster speed to market. Banks can react to changing business requirements rapidly by using cloud computing. The paper offers a valuable perspective on the potential applications of cloud computing in the banking sector and its various business models, cloud service providers, and adoption challenges.
COVID QA Network: A Specific Case of Biomedical Question Answering

COVID-19 crisis has led to an outburst of information that needs to be organized, validated, and made available to the seekers. Despite the rapid growth and success of BERT models in the last 3 years, COVID QA is a difficult task due to the lack of applicable datasets and a relevant language representation. Therefore, this study proposes a transformer-based Question Answering (QA) model for COVID-19 questions from the biomedical domain. Further, explored several datasets, and models required for question type prediction, no-answer prediction, and answer extraction and transfer learning strategies. It has been demonstrated that the exact match score can be significantly improved with limited amounts of training data from the biomedical domain. Finally, the findings of the study have been summarized as Factoid QA Finetuning Framework (FQFF), which can provide initial direction for domain-specific QA tasks with a limited amount of data.
An Intelligent Routing Approach for Multimedia Traffic Transmission Over SDN

Multimedia applications such as video streaming services have become popular, especially with the rapid growth of users, devices, increased availability and diversity of these services over the internet. In this case, service providers and network administrators have difficulties ensuring end-user satisfaction because the traffic generated by such services is more exposed to multiple network quality of service impairments, including bandwidth, delay, jitter, and loss ratio. This paper proposes an intelligent-based multimedia traffic routing framework that exploits the integration of a reinforcement learning technique with software-defined networking to explore, learn and find potential routes for video streaming traffic. Simulation results through a realistic network and under various traffic loads, demonstrate the proposed scheme's effectiveness in providing improved end-user viewing quality, higher throughput and lower video quality switches when compared to the existing techniques.
iCan: Psychological Look in Future Using Augmented Reality Technology

Looking to the future is always something people have been interested in from the old days. What people can do using their current ability is ambiguous, but this can be achieved psychologically. In this study, a systematic Augmented Reality (AR) system named iCan has been proposed to reveal the ability of what we can do and which way we should choose when facing any junction of choices. iCan has been developed in four steps: basic AR development implementation, building 3D-Models, making the best interaction UX(User-Experience) and test-debug-publish. In the first step, booklet pages have been chosen for AR development instruction. Next, 3D models have made and have been presented. Next, interaction methods are under development. Some individuals have tested the system, and the results show the robustness of the presented system, which there is a hope to change people's future soon.
Gesture Recognition Techniques

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. In this paper, we describe some of Gesture recognition techniques such as Vision based gesture recognition and Graph based gesture recognition. Also, we explore these techniques with previous studies.
Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs

In this paper we address the problem of trajectory following in an unknown environment with an unmanned aerial vehicle (UAV). The main goal is to safely follow the planned trajectory by avoiding obstacles. The proposed approach is suitable for aerial vehicles equipped with 2D or 3D sensors, such as LiDARs. We present a novel algorithm based on the conventional Artificial Potential Field (APF) called Augmented Artificial Potential Field (AAPF) that corrects the planned path to avoid obstacles. Our proposed algorithm uses a combination of two attractive forces and both normal and rotational repulsive forces to avoid obstacles and handle local minima problems. The smooth trajectory following achieved with the MPC tracker allows us to quickly change and re-plan the UAV path. Comparative simulation experiments have shown that our approach solves local minima problems in trajectory following and generates more efficient paths to avoid potential collisions with static obstacles compared to our previously developed algorithm for obstacle avoidance. The laboratory experimental evaluation results indicate that the algorithm can be deployed on a real UAV with limited computational power and real-time processing requirements.
InfPose: Real-time Infrared Multi-human Pose Estimation for Edge devices based on Encoder-Decoder CNN Architecture

Despite its remarkable performance, RGB-based Multi-human Pose Estimation (MPE) technology has many practical limitations, such as nighttime and smoggy environments. Infrared imaging is a valid substitution in these scenarios but needs an efficient and fast method for MPE. This paper aims to design an infrared MPE model based on the Encoder-Decoder CNN architecture, InfPose, which can perform real-time on edge devices. We first built a lightweight Encoder-Decoder CNN backbone based on hardware-friendly inverted residual blocks. Secondly, we utilized three methods to improve the capability of InfPose, including decoupling associative embedding head, multi-scale supervision, and cross-modal knowledge distillation. In addition, we gathered a wild infrared human pose dataset to train and evaluate our methods. Experiment results show that the proposed model is more robust and has less latency when inference on edge GPU platforms in comparison to the prevailing mainstream models. The inference time for InfPose on Xavier NX was recorded as 27.7ms ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 37 fps) and maintained sufficient accuracy for use. This research can be applied to human-machine interaction of autonomous vehicles or intelligent robots in nighttime or other scenes with poor visibility.
Letter from the editor

Robots need to exhibit proactive behavior if they are to be accepted in human-centered environments. A proactive robot must reason about the actions it can perform, the state of the environment, the state and the intentions of its users, and what the users deem desirable. This paper proposes a computational framework for proactive robot behavior that formalizes the above ingredients. The framework is grounded on the notion of Equilibrium Maintenance: current and future states are continuously evaluated to identify opportunities for acting that steer the system into more desirable states. We show that this process leads a robot to proactively generate its own goals and enact them, and that the obtained behavior depends on a model of user intentions, preferences, and the temporal horizon used in prediction. A number of examples show that our framework accounts for even slight variations in user preference models and perceived user intentions. We also show how the level of informedness of the system is easily customizable.
Cyclic Generative Attention-Adversarial Network for Low-Light Image Enhancement

Images captured under complex conditions frequently have low quality, and image performance obtained under low-light conditions is poor and does not satisfy subsequent engineering processing. The goal of low-light image enhancement is to restore low-light images to normal illumination levels. Although many methods have emerged in this field, they are inadequate for dealing with noise, color deviation, and exposure issues. To address these issues, we present CGAAN, a new unsupervised generative adversarial network that combines a new attention module and a new normalization function based on cycle generative adversarial networks and employs a global–local discriminator trained with unpaired low-light and normal-light images and stylized region loss. Our attention generates feature maps via global and average pooling, and the weights of different feature maps are calculated by multiplying learnable parameters and feature maps in the appropriate order. These weights indicate the significance of corresponding features. Specifically, our attention is a feature map attention mechanism that improves the network’s feature-extraction ability by distinguishing the normal light domain from the low-light domain to obtain an attention map to solve the color bias and exposure problems. The style region loss guides the network to more effectively eliminate the effects of noise. The new normalization function we present preserves more semantic information while normalizing the image, which can guide the model to recover more details and improve image quality even further. The experimental results demonstrate that the proposed method can produce good results that are useful for practical applications.
Discussion on the Role of Basic Music Education in Cultivating Aesthetic Personality

Human beings are eternal in their pursuit of beauty.Aesthetic education is indispensable at every stage of human growth.The Chinese people's pursuit of "truth, goodness and beauty" is a dialectical and unified aesthetic pursuit.Universal basic music education plays a vital role in the early development and shaping of the aesthetic personality.Aesthetic education is realm education not bottom line education.Basic music education is the starting point, not the end point, of training highly qualified people.A person's aesthetic standards are ultimately reflected in his or her cultivation as a person.If knowledge is emphasized over aesthetic education, society will be "illiterate, but aesthetically illiterate".People's moral thinking will increasingly degenerate into bottom-line thinking, and the pursuit of a better life will stagnate and lack momentum, or even lose its direction.It is the duty of the society to cultivate aesthetic personality in basic music education from the "children".Victor Hugo once said that music, writing, and mathematics are the three keys to human intelligence, of which music education plays the most intuitive and enlightening role.It is necessary to return to the nurturing nature of basic music education by adopting the approach of teaching and learning with fun.By summarizing the development rules of classical music education in music history, using interdisciplinary knowledge integration and introducing music technology and other factors, enhancing the ability to use aesthetics, comprehending music philosophy and connotation, continuously guiding students to recognize beauty, discover beauty, comprehend beauty and pursue beauty, nurturing "beautiful hearts" and inspiring "intelligent minds", so as to better play the long-term role of basic music education in cultivating aesthetic personality and serving the development of the information technology era and make the world a better and more splendid place.
Development of Canva-Based Digital Scrapbook Learning Media Using The ADDIE Model in Indonesian Language Learning Material Writing Short Story Text

Writing skills in Indonesian language learning include many texts. One of them is short story text. However, sometimes, learning to write short stories becomes boring. This happens if the teacher does not provide innovation and the right strategy in the learning process so that it runs effectively in accordance with the learning objectives. The development of science and technology requires teachers to be able to develop skills in making learning media. This research uses the Research and Development method and the model used is ADDIE. This research method is used to produce certain products and test the effectiveness of these products. Based on the assessment of design experts, a score of 85.71% was obtained, an assessment by media experts obtained a percentage score of 85.71%, and a material expert assessment with a percentage score of 90%. Based on the results of validation conducted by design experts and media experts, the results obtained are included in the "Very Feasible" category. Then from the results of the student response questionnaire obtained a score of 92%. The results of the assessment by media experts are 85.71%, so it can be concluded that the feasibility of Digital Scrapbook learning media on the material of writing short story text is included in the "Very Feasible" category, meaning that the development of Digital Scrapbook learning media on the material of writing short story text for students of class XI MP 1 SMK Negeri 1 Probolinggo is "Very Feasible" to be used in Indonesian language learning.&#x0D;