Reviewerly/exHarmony huggingface dataset:
Opposition‐based ideal gas molecular movement algorithm with Cauchy mutation, velocity clamping, and mirror operator

Abstract The ideal gas molecular movement (IGMM) as a metaheuristic optimization method is a prominent option for solving optimization problems. However, in some complex cases, IGMM may possess premature convergence or get trapped in local optima. Therefore, to tackle these issues, this paper indicates a new modified IGMM algorithm named opposition‐based IGMM, which has been incorporated with opposition based learning (OBL), Cauchy mutation (CM), velocity clamping (VC), and mirror operator (MO) to enhance its performance. OBL, VC, and MO improve the convergence of IGMM, whereas CM assists IGMM to escape local optima. The effect of each strategy, OBL, CM, VC, and MO, on IGMM, is confirmed through 30 low and high‐dimensional benchmarks, including 23 well‐known mathematical problems and CEC2017 as complex test functions and three engineering problems. Analysis results represent that integration IGMM with OBL, CM, VC, and MO has the best performance among other IGMM variants and eventually improved IGMM in exploration, exploitation, accelerating convergence, and local optima avoidance.
Review and Trends on Hand Gesture Recognition of Sign Language based on Deep Learning Approaches

Hand gestures are observed as an effective tool for making the interaction in the community with individuals having intellectual disabilities. It is highly essential for communicating the computers and people. Therefore, it is aimed to design an automatic hand gesture recognition approach that is utilized for repeatedly performing human-computer interaction. Sign languages are considered the natural languages utilized by hearing-impaired people that involve some expressional way of communication in routine life. It reveals the sentences, words, and letters present in the spoken language for performing the gesticulations that enable communication between them. The deaf community makes communicates with normal people using an automation system that relates the signs with the words of speech. The hand gesture recognition system is implemented independently of requiring any unique hardware rather than using the webcam. Thus, it is highly significant to make a short review of hand gesture recognition based on deep learning techniques considering the Indian sign language. Hence, this paper discusses and clarifies existing research work based on hand gesture recognition in Indian sign language with algorithmic classification. This survey also compares different performance measures, datasets utilized, and also different tools used for the implementation. Then, upcoming research and also current research gaps in hand gesture recognition in Indian sign language are analyzed. This review on state-of-the-art hand gesture recognition for Indian sign language tools has shown their potential for providing the right solution in different real-life situations. It is hoped that the contents and illustrations in this paper assist researchers in laying a good foundation to inform their studies.
Novel Method for Recognizing Sign Language using Regularized Extreme Learning Machine

The ability of humans to effectively communicate through the use of hand signs has a wide range of practical applications. People with speech problems around the world have embraced them due to their intuitive design. Around 1% of Indians are in this group, which is a quite high percentage. It is for this reason that the incorporation of a framework familiar with Indian Sign Language would have such a profoundly positive effect on the lives of the people of India. A median filter is used to an input image to remove unnecessary details and improve clarity. Feature extraction is performed using principal component analysis (PCA), and the YCbCr color space is used for hand segmentation. The model is then trained through Regularized Extreme Learning. Using regularization to achieve peak structural performance for precise prediction, RELMs are a subclass of ELMs. This method exceeds popular alternatives like the support vector machine (SVM), Extreme Learning Machine (ELM), and CNN in terms of accuracy (around 97.8%).
Chatease: A Blockchain based Chat Application

Security problems have traditionally arisen when messages are exchanged through unsecured channels. Despite the fact that there are various strategies for encrypting messages, attempts on gathering Information continues to be sent. Furthermore, the fact that the vast majority of applications store their data in centralized databases is grounds for worry. Blockchain technology offers new ways to ensure data privacy, censorship resistance, immutability, and decentralization. Each node in the network receives a copy of the data sent directly into the Blockchain by users. Using their private key, only approved persons may access the data on the blockchain. It eliminates the need for trustworthy intermediates. Because the system is totally decentralized, users may send messages with confidence. This software shows how to hash messages and mine them in the blockchain. To provide decentralized storage and quick lookup, we'll use a distributed hash table (DHT) and blockchain. This paper describes how to build a decentralized chat program.
A Polarization-Containing Ethics of Campaign Advertising

Abstract This paper establishes moral duties for intermediaries of political advertising in election campaigns. First, I argue for a collective duty to maintain the democratic quality of elections which entails a duty to contain some forms of political polarization. Second, I show that the focus of campaign ethics on candidates, parties and voters—ignoring the mediators of campaigns—yields mistaken conclusions about how the burdens of the latter collective duty should be distributed. Third, I show why it is fair to require intermediaries to contribute to fulfilling this duty: they have an ultimate filtering position in the campaign communication process and typically benefit from political advertising and polarization. Finally, I argue that a transparency-based ethics of campaign advertising cannot properly accommodate a concern with objectionable polarization. By contrast, I outline the polarization-containing implications of my account, including a prohibition on online targeted advertising, and intermediaries’ duties to block hateful political advertising.
Explainability to Business: Demystify Transformer Models with Attention-based Explanations

Recently, many companies are relying on Natural Language Processing (NLP) techniques to understand the text data generated daily. It has become very critical to deal with this data because finding the sentiments of text and summarizing them will help the company understand the pain points of the customers posting reviews on social media or understand the experience of the customer. These requirements have increasingly demanded many advanced algorithms to deal the text data. The introduction of Transformers led to businesses adopting NLP methods more and more to keep up with their needs. Models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT), state-of-the-art results were achieved with billions of parameters learned. Although these advancements improved the accuracy and expanded the use of algorithms to a wide range of NLP tasks like language translation, text summarization, and language modeling. Businesses are more interested in the Explainability of the model compared to its accuracy. Explainable Artificial Intelligence (XAI) plays an important role to comprehend the complexities of the model as well as the influence of weights on predictions. In this paper, the complexities of the transformer model are unraveled by presenting a straightforward method for computing explainable predictions. The DistilBERT model is chosen as an example to implement the explainable system due to its lighter nature. Combining the strengths of a Posthoc expla-nation with those of a self-learning neural network, the method makes it simple to scale it to other algorithms to implement. With technologies like python, PyTorch, and Hugging Face, a detailed step-by-step algorithmic computation is demonstrated to explain the predictions from the attention-based explanations.
Image Synthesis from Themes Captured in Poems using Latent Diffusion Models

Due to the presence of complex literary devices such as metaphors and imagery, poetry can be difficult to comprehend. This is especially true for literary works of classical poets like Kaalidasa that employ intricate, often conflicting themes which tend to be particularly tedious to interpret and decipher. The beauty of these works of art tends to get lost in translation. A visual representation in the form of images corresponding to the various themes in the poetry, greatly aids in providing a clearer understanding of the meaning and imagery described. The main aim here is to make classical poetry more accessible by generating detailed images that capture and depict the metaphors and themes used in various works of literature. The core task in this paper is to employ novel machine learning (NLP) techniques to detect and extract the central themes and keywords from the poems that encapsulate the essence of the literary work. This is done using transformer models fine-tuned specifically on a summarization dataset, that generate an abstractive summary of the segment of input text. Maintaining context while doing so is essential to the accuracy of the images being generated. Further, this summary is then provided as an input to a Latent Diffusion Model to generate detailed images corresponding to the poetry. The goal of the project is to make it easier to consume and enjoy classical works of literature by providing additional context and information in the form of images complementing the poetry.
Gaussian Process Regression for Learning Environment Impacts on Localization Accuracy of a UAV with Respect to UGV for Search Planning

In this article, we present a path planning algorithm for a team of an Unmanned Ground Vehicle and an Unmanned Aerial Vehicle (UAV) that leverages Gaussian process regression to plan a path that meets information gathering objectives while reducing the UAV's localization uncertainty by learning to compensate for outlier measurements or missed expected sensor measurements over the trajectory. Simulation results are compared to approach that also compensates for belief space planning but is incapable of handling outliers or unexpected degradation from the environment <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home Sensors

Nowadays, one of the most important objectives in health-related research is the improvement of the living condition and well-being of people. Smart home systems can provide health protection for residents based on the results of daily activity recognition. Recent advances and developments in sensor technology have increased the need for sensor-compatible goods and services in smart homes. Consequently, the ever-increasing volume of data requires the field of deep learning (DL) for auto-matic human motion recognition. Recent research has modeled spatiotemporal sequences gathered by smart home sensors using long short-term memory networks. In this work, ResNeXt-based models that learn to classify human activities in smart homes were proposed to improve recognition performance. Experiments conducted on Center for Advanced Studies in Adaptive Systems (CASAS) data, a publicly available benchmark dataset, shows that the proposed ResNeXt-based techniques are significantly superior to the existing DL methods and provide better results compared to the existing literature. The ResNeXt model achieved the averaged accuracy over the benchmark method to 84.81%, 93.57%, and 90.38% for the CASAS_Cairo, CASAS_Milan and CASAS_Kyoto3 datasets, respectively.
Deep Learning Approaches for HAR of Daily Living Activities Using IMU Sensors in Smart Glasses

In the latest days, study into the development of intelligent technologies has proven valuable, contributing to attempts to improve the quality of human existence. Smart glass is one of the intelligent wearable devices that can be used for various purposes, including healthcare monitoring, fall detection, sleep tracking, and human activity recognition (HAR). Smart-phones and smartwatches are the primary wearables utilized in sensor-based HAR to collect human motions for training recognition models based on physical movement. These wearable tools, nevertheless, are more intrusive than smart glasses. Using IMU sensor data acquired via smart glasses, we investigate deep learning algorithms for detecting people's activities of daily living (ADL). This work proposes a hybrid deep neural network that automatically extracts spatial-temporal information from raw data to enhance identification <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{p}$</tex> erformance. We performed tests to evaluate deep learning models using a publically available benchmark dataset, UCA-EHAR, which included IMU sensor data from multiple ADL from smart eyewear. The recommended CNN-LSTM model achieved the best effectiveness with the highest F1-score of 93.20%, as determined by experimental findings.
Netrahitha: Currency Detector Jacket for visually impaired people

Many people in India are subjected to visual impairment and blindness which are not curable. Most of such people are poor and cannot afford digital equipment for assistance. Visually Impaired or visually challenged people face a lot of difficulty in recognizing the paper currencies during their financial transactions due to the similar texture of paper and dimensions of currency notes. This makes it time-consuming, complex and erroneous at times. The proposed framework is to help visually impaired or blind people by providing them with digital assistance, which will take a photo of the note and notify the user the denomination via speech medium.
Q-Learning Model Covariance Adaptation of Rao-Blackwellized Particle Filtering in Airborne Geomagnetic Navigation

Over the last decades the Geomagnetic navigation problem has been on the scope as a possible candidate for alternatives to Global Positioning System (GPS) in cases when the GPS has interference or cannot be accessed. Furthermore, navigation methodologies based on magnetic fields still faces development challenges due to its complex characterisation, resulting yet in poor localization performance compared to its GPS counterpart. The following research effort the introduces a machine learning techniques as support for a proposed geomagnetic navigation algorithm for it estimation enhancement specifically for GPS denied environments or dead-reckogning applications. The estimation architecture described in this document uses the magnetic information provided by the airborne surveys of the concentrations of geomagnetic anomalies in the Earth surface and establishes a probability correlation of magnetic measurements and the area by means of a Rao-Blackwellized Particle Filter (RBPF). Furthermore, this research focuses its analysis over the error correction of the Inertial Navigation System (INS) when an adaptive state covariance parameter is obtained and used by means of Reinforcement learning techniques as Q-learning.
Question Answering System for Healthcare Information based on BERT and GPT

Recently, demand for telemedicine counseling has been on the rise. Although telemedicine is legally prohibited in Korea, the number of healthcare-related questions on online question and answer platforms is steadily increasing. However, doctors have to manually write answers to similar questions, which is time-consuming and inefficient. This study implemented a healthcare information provision question and answer system using an algorithm that combines BERT and GPT-2 structures. Intellectual Q&A pairs were used as training data, and the BERT-GPT algorithm experimented with creating sentences that provide healthcare information such as related medical subjects and suspected diseases as answers to healthcare-related questions. As a result of BERT-GPT2 algorithm gets worse PPL score and loss than the baseline model, but better score in the qualitative assessment. This result means BERT-GPT2 understands the conversation intention better.
Performance Comparison of Enhanced Deep Residual Networks for Single-image Super-Resolution Model with Change in Loss Function

Super-resolution technology is receiving a lot of attention to restore low-resolution images to high-resolution. Among several models, we use an enhanced deep super-resolution network model that outperforms a single image. EDSR maximizes the efficiency of learning by using residual blocks with batch normalization removed from residual blocks of ResNet, enabling fast and good accuracy learning. We compared loss functions such as mean absolute error (MAE), mean square error (MSE), and binary cross-entropy (BCE) for resolution x4 bicubic down sampling images using Enhanced Deep Super-Resolution Network (EDSR). The mean absolute error (MAE) showed the best performance, and the mean square error (MSE) also showed compliance performance that could not be distinguished by the human eye. On the other hand, binary cross-entropy (BCE) showed significantly lower performance. In future studies, it is judged that a performance comparative experiment according to the optimal hyper-parameters for each scale is necessary. It is also an applicable technique for fast transmission of high-resolution images.
The Application Of Quick Response Code Technology To Promote Cultural Tourism: Case Study of San Pu Loie

Cultural tourism is a type of tourism that aims to learn about the culture and way of life of indigenous peoples who have their own culture. Tambon San Pu Loei in Doi Saket District, Chiang Mai is also known as cultural tourism destination where tourist gain information and experience about historical and local people way of life. However, the report from local governments mentioned that tourists had difficulty acquiring information. As a result, the community lacked the ability to communicate the local knowledge in a variety of disciplines since improper storing of data sources and met the problem of coordinates not matching the actual location of the place. In this study, the two-dimensional bar code technology (QR Code) was improved and employed on location-based services for promoting the cultural tourism and utilizing to tackle the problem of tourists accessing information. 83 travelers were involved in this study. Satisfaction level is measured based on 3 parts: content, design, and useful. The overall result of tourist satisfaction who scanned the QR code and visited the website (www.dekdoi-travel.com) is also presented in this paper.
Experimental Platform Measuring Semantic Information and Semantic Heterogeneous Data Compression

The use of semantic information necessitates a quantitative assessment of the information in general. This article will provide an overview and analysis of the present state of semantic information measurement as methods and algorithms for semantic data reduction. We propose an algorithm combining initial information classification, knowledge enrichment through keywords, identification of potential semantic correlations between them, and semantic information extraction.
Person and Face Re-Identification Using Semantic Information and Single Shot Face Identification

Person re-identification is a contemporary problem involving the identification o f single person a cross multiple camera views, usually non-overlapping. The modern solutions of this problem involve deep learning based solutions, implicitly providing feature and metric learning. In this work we propose a novel approach for person and face re-identification guided by semantic information. Face re-identification i s approached using single shot face recognition for initialization using facial semantic parts. Person re-identification i s a lso guided b y semantic information represented by hands, legs, etc. It is further supported by the decision of face re-identification, if available. The proposed approach is tested on popular databases for person re-identification and demonstrate competitive results.
A Mixed Cloud-and-Embedded-based Approach with Machine Learning Towards the Development of a Fall Monitoring System

The ability to monitor falls, especially for the elderly, deems to be a crucial task to provide quality and timely healthcare response. However, there have been minimal efforts in centralizing such activity for efficient hospital management. This paper presents the development of a full-stack fall monitoring system with edge computing and machine learning technologies. Using a 3-axis accelerometer of a smartphone, motion data is collected and directly sent to an edge computing platform wherein a shallow neural network is directly trained to classify the motion data into positional states: stable, falling sidewards, falling flat, and standing up. A confusion matrix is presented to evaluate the performance of the neural network model, both in training and in real time. A cloud-based approach using ReactJS for front-end integration and Firebase's Cloud Firestore with NodeJS embedded capabilities for real-time data storage and embedded classification is implemented.
Virtual Collaboration in Mixed Reality for Enhancing Business Model Innovation

In this paper we demonstrate how using Mixed Reality can add one more layer of interactivity thus extending and enhancing the capabilities of the unique Business Model Innovation tool - the BeeCube. The participants in the Business Model Innovation process can utilize the physical structure of the BeeCubee, collaborate using the virtual Bee tools, or work within a virtual BeeCube without necessarily being in the real physical environment. While brainstorming and working together on a new or existing product, the collaborators in the BeeCube can import different 3D Bee tools, add spatial drawings, and annotations to visualize their ideas helping to improve the mutual understanding of the Business Model Innovation process and enhance the collaboration. The developed User Interface is based on intuitive interactions based on hand tracking, gesture, and speech recognition.
The Creation of Digital image in Thai Cultural using AI generated

The purpose of this study is to create Digital images in the experimental creation with the support from Artificial Intelligence (AI) that is used to assist in the creation of artwork with frameworks newly designed to reduce time of the creation of digital art works without the decrease of quality of the works but will lever the works without destroying the personalities of the artists and the styles of works. Concerning the contents of the works, Thai arts and culture are used as the contents of contemporary works so that people nowadays still recognize applied arts in new forms. Therefore, there is the cooperation between AI and the artist in the new form of Digital Image, which has been publicized in NFT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Non-fungible token (NFT) is a unique digital identify in a blockchain used to certify authenticity and ownership. In this case is digital art image type. [7](Non-fungible Token) art market in order to get feedback which will be applied for further development. From the creation of the work, it has been discovered that AI generated Art is faster and fits standards. However, there still are many arguments in several issues in the art field. Thus, I, as the Artist, have designed the Frameworks to correct such arguments until this work is accepted and auctioned in NFT market eventually.