Reviewerly/exHarmony huggingface dataset:
A Survey Paper on Emerging Techniques Used to Translate Audio or Text to Sign Language

Sign language is a form of communication that uses gestures through hand, arm movements and body postures in three dimensions. It is primarily used by hearing or speech impaired people to express themselves. However, the deaf and mute community find it difficult to convey emotions as not all people know the sign language. The translation of speech to sign language by a mediator can tend to be complex due to the grammar rules. Therefore, an Audio Text to Sign Language Converter which is a web-based application is designed to translate the audio or text input to corresponding Indian Sign Language. The front-end is developed by HTML, CSS, and JavaScript. Natural Language Processing performs task of speech recognition and text processing. An animation character is created to enact the sign language. Thus, the system makes communication less challenging for the people having hearing or speech disorder. It can be used as an educational tool that will assist people in learning the Indian Sign Language.
Analysis and Research of Spinal CT Image Segmentation Based on Improved Watershed Algorithm

The traditional watershed is easy to be affected and then produces over-segmentation. In order to reduce the image over-segmentation, it is necessary to reduce the irrelevant information and enhance the foreground and background contrast of the spine CT images by improving the threshold segmentation. After marking the foreground and background, the watershed algorithm works in its restricted area by modifying the original gradient function. By experimental comparative analysis, the proposed method effectively restricts the image over-segmentation and also removes the holes made by the influence of noise. At the same time, the improved algorithm has universal applicability to the spine images to process pictures for saving time and laying a good foundation for the subsequent reconstruction.
Image Caption Generation in Kannada using Deep Learning Frameworks

The official language of the state of Karnataka and one of the most frequently spoken languages in India, Kannada is spoken by more than 57 million people worldwide. In this modern era, one of the most important tasks in the field of Natural Language Processing in the present times is the development of image captions. In order to provide a textual description of an image intended to highlight its key qualities, Image Caption generation has long been a study of significant interest to academics in the fields of Artificial Intelligence and Machine Learning. While a great deal of research is being conducted in English, resources in regional languages like Kannada, are severely lacking. Given that 72% of the Karnataka population are Kannada speakers, Image Caption generation in Kannada is the realization of human-computer interaction for native language speakers, enabling the development of regional language virtual assistants which is a breakthrough in this line of work. Through our research, we present a model that generates semantic, syntactic and structured captions in Kannada.
Comparative Study of Image Processing Neural Networks using Face Mask Dataset

Over the years there has been huge improvements in the performance of Image Processing Algorithms due to increase in computation power of Devices as well as use of Neural Networks. This paper focuses on comparison of size and performance of different Image Classification networks used widely. Each one of the machine learning models studied presents a new technique to process the input images that might be useful in different scenarios. Our main focus is to identify the framework with low latency, high accuracy as they can be used practically in surveillance equipment’s that need to be fast and accurate.
Improving Smart Home Safety with Face Recognition using Machine Learning

In this paper, Machine Learning (ML) Algorithms are used to implement the proposed approach to identify social distance, face masks, drowsiness detection, age-gender detection, and emotion detection. While dealing with social distancing initially, we need to detect humans’ faces, which are available by using COCO (Common Objects in Context) datasets, and later on, polygon-shaped ROI (Rectangular-region of Interest) is warped with a rectangle, which helps to find the distance from each centroid (person). Similarly, we predict the facemask, age-gender, emotion, and drowsiness altogether using frontal-face detection and eye-detection via haarcascade dataset loaded into Convolutional Neural Network (CNN) to train and test the models on colour mapped images. In the proposed model, we are using machine-learning techniques such as linear discriminant Analysis (LDA), Independent Component Analysis (ICA), Principal Component Analysis (PCA) for age-gender detection and emotion detection, K-Nearest Neighbours (KNN) for Social Distancing, and Support Vector Machine (SVM) for facemask detection and drowsiness detection. The accuracy of proposed system depends on frame (i.e., 88.2%, 89.7%, 95.1% and 98.3% in 0~0.2s, 0.2~0.6s, 0.6~1s, >1s time windows respectively). The accuracy even depends upon the distance away from the camera (i.e., 60.4%, 73.9%, 89.3%, 95.2%, and 62.2% in >15, 15~10, 10~6, 6~0.5, <0.5 meters respectively). The resultant average accuracy of all the models is 96.3%, which is capable to predict various tasks as said above. This complete model is made accessible to users via a standalone software/Desktop GUI. The proposed approach is promising for performing all the tasks and activities more accurately and efficiently.
Anomaly Detection for IoT Security: Comprehensive Survey

The increasing adoption of the Internet of Things (IoT) technology in various fields like smart home appliances, smart grids, health care systems etc., is transforming the way we live and work. But this has also called for a robust security mechanism and anomaly detection has been proven to be an effective solution to securing IoT devices. The rapid development in Machine learning, Deep Learning and related studies have brought rise to a lot of advanced methods to solve the anomaly detection problems. The objective of this paper is to discuss the challenges faced by smart devices that make use of IoT technology, in detecting anomalous behaviors in threat and risk analysis domains. This article could help readers better understand the common IoT security problems and countermeasures using anomaly detection techniques, as well as have an insight into the research trends, challenges and limitations in this domain.
Analysis of Data using hybridized K-means clustering with PSO-JAYA algorithm

Despite decades of intensive study on grouping for small to medium-sized data sets, research into huge data clustering is gaining more and more attention from both business and academia. Contrary to clustering algorithms on small and large amounts of data, big data grouping requires more processing power and resources to handle the massive data retention and complex analysis of data. This research suggests an optimal method for data clustering to address this problem. In addition to the combined model of Particle Swarm Optimization and JAYA Approach, this strategy used the idea of K-means clustering to initiate the search for optimal clusters. The simulations are run using a Python program using the Pendigits dataset. The efficacy of the scheme is shown when compared to current state-of-the-art methodologies by analyzing the simulation outcomes in terms of NMI and FMI score.
MICE - Medical Image Contrast Enhancement

Medical images are essential in pathological diagnosis, especially for tumors, osteoporosis, and cerebral hemorrhage. In addition to general medical diagnosis, medical images are used to guide devices during surgery. Most medical images produced by imaging equipment are in black and white. Due to equipment performance and environmental limitations, these images are unclear and dim. In most medical images, medical personnel needs to observe a certain lesion clearly in the region of interest (ROI), when other regions have relatively little impact on diagnosis. Therefore, we propose a Medical Image Contrast Enhancement (MICE) technology to identify the ROI and perform special enhancement on it. The area outside the ROI is compressed as much as possible after evaluation so that the ROI area has more visible space but the details of the non- ROI area are not discarded. This method allows medical staff to select a position in the area to confirm the object to be observed. Then, the grayscale data is analyzed to find out the grayscale area of ROI, stretch the grayscale, distinguish the adjacent brightness, and highlight the desired object. Observation details are used for the organ or tissue to be diagnosed. To verify the effectiveness of this method, we compare MICE with the CLAHE technology that is often used and the representative technology AGCWD of the AGC image contrast enhancement method, and the LIME image enhancement technology that is currently receiving attention. The regional entropy, regional standard deviation (Std), regional signal-to-noise ratio (SNR), and medical image evaluation indicators are measured to prove that the image processed by MICE has an excellent enhancement effect and distortion-free display.
Research on Intelligent Proofreading Algorithm and Implementation for English Translation Based on Improved Phrase Analysis

The traditional English translation proofreading system focuses on the correction of phrases and is unable to overcome the actual problem of poor coherence between Chinese-English translation contexts. Therefore, a new Chinese-English translation proofreading management platform is developed in this study. The main component of the entire management system is composed of search engine templates, Chinese-English translation templates, Chinese-English translation correction templates, and action logs. Due to the use of search engine templates to retrieve the basic meaning and professional content of the words to be proofread and to analyze the target user's action data based on action logs, the management system can be better optimized. The system software department with a correction algorithm promotes the text translation mode to find and replace accurate words in the proofread words, thus achieving intelligent correction in Chinese-English translation. The designed platform shows the increase in the accuracy of Chinese-English translation conclusions by 30.5% and corrects the main issues of contextual discontinuity in Chinese-English translation conclusions. Compared with other operating systems, the proposed system has higher accuracy and better contextual coherence.
Lightweight Attention Mechanism Via Knowledge Distillation For Vein Recognition

Internal biometric identification systems based on convolutional neuron network (CNN) is precise but there are disadvantages of demanding enormous data and endless calculating. Therefore, it is difficult for CNN to be used widely. To solve the problem, we present a light CNN with a channel shuffle that automatically balances channel attention and spatial attention. Then, the teacher-student training strategy is used to solve the insufficient generalization ability of light CNN and enhance the accuracy as the model focused on the program’s information. The experiment results showed that the proposed light CNN model increased the accuracy by 14.35% without additional parameters based on the public dataset FVUSM.
Research on VR Virtual Display Technology of Non-heritage Cultural and Creative Products

With the rise of the current Chinese culture craze, traditional culture and its derived cultural products have received more attention. This not only attaches more importance to intangible cultural heritage, but also provides more opportunities for the promotion of intangible cultural and creative products. However, there are still problems with the current intangible cultural heritage and creative products such as low overall development level, single variety, outdated display methods, and difficulty in reflecting cultural connotations. At the same time, the design innovation of the products is not enough, and the cost-effectiveness is not high, making it difficult to achieve the inheritance and protection of local culture. These problems seriously hinder the development and promotion of intangible cultural heritage and creative products. In order to solve the above problems and promote better display, We combine the current popular VR technology to construct a model and analyze the display strategies of intangible cultural heritage and creative products. Applying VR technology to intangible cultural heritage and creative products shows feasibility. It enhances the integration ability of cultural elements and information, systematically displays the connotation of cultural and creative products, and allows product development. This improves product innovation capabilities, which is of great significance for inheritance, development, and dissemination of traditional culture.
End-to-End PQC Encryption Protocol for GPKI-Based Video Conferencing Systems

Due to the expansion of non-face-to-face activities, security issues in video conferencing systems are becoming more critical. In this study, we investigate the end-to-end encryption (E2EE) function among security services of video conferencing systems. First, the E2EE-related protocols of Zoom and Secure Frame (SFrame), which are representative video conferencing systems, are thoroughly explored, and the two systems are compared and analyzed overall. Next, the E2EE protocol in the Government Public Key Infrastructure (GPKI)-based video conferencing system is considered, in which the user authentication mechanism is fundamentally different from those used in private sector systems, such as Zoom or SFrame. In particular, under E2EE-related protocols, we propose a detailed mechanism in which the post-quantum cryptography (PQC) key encapsulation mechanism (KEM) is applied to the user key exchange process. As the session key is not disclosed to the central server even in futuristic quantum computers, the mechanism to which the PQC KEM method proposed in this study is applied satisfies the E2EE security requirements and can be applied to domestic GPKI-based systems. It is expected to contribute to strengthening the safety of the next-generation video conferencing system.
Research on Recognition Algorithm of English Gerund Collocation Errors Based on Data Classification

The most common verb matching errors that English learners are prone to are found in using gerunds. After analyzing the errors in CLEC, we provide a standard model to correct the errors for Chinese language learners. Firstly, a standard verb language matching library is constructed, and then a way to measure the similarity between target matches is provided. Then, a rough set of similar matches is obtained based on the statistical similarity between target matches and the standard matching library. Finally, a classification method is used to filter out matches that cannot be classified as the correct type in the similarity matching set, resulting in a candidate result set. Finally, the candidate result set is ranked based on the language module, and the final correction opinion is obtained. On the standard detection set constructed using BNC, this method of combining similarity judgment and contextual features has a significant effect on finding and correcting verb-matching errors.
Recent Trends in Solving the 8-Puzzle Problem

The 8-puzzle problem is a well-known artificial intelligence problem consisting of a 3x3 grid with 8 tiles numbered 1-8 and one blank space. The goal is to move the tiles around to achieve a specific goal configuration. This problem is often used as a test case for AI algorithms because of its small size and simple rules. It is a challenging problem because it requires a combination of search, planning, and decision-making skills. In this paper, we review recent trends focussing on techniques that have shown promise in improving the efficiency and effectiveness of the search process. We discuss the use of hybridization approaches, which combine different search or optimization algorithms to improve the search process, as well as the use of parallel and distributed computing approaches, which can potentially speed up the search by dividing the search space into smaller pieces and assigning each piece to a separate processor or thread to be explored concurrently. We also review the use of machine learning techniques, such as reinforcement learning and deep learning, for solving this problem and discuss their potential for future research. Finally, we discuss open research questions and areas for future work in this field highlighting opportunities for further research and development in the field of solving the 8-puzzle problem.
Enhancing Node Fault Tolerance through High-Availability Clusters in Kubernetes

Microservices architecture and containerization technology have become ubiquitous recently. Docker containers have emerged as a standardized software packaging unit, offering rapid deployment, flexible scaling, and cross-platform operability. This enables the industry to focus on innovation and business needs while managing underlying infrastructure effortlessly. With the advent of technologies such as the Internet of Things, big data, and machine learning, there is a growing demand for parallel processing of large amounts of data across multiple hosts. Ensuring system resource availability and stability is essential, especially when services experience unexpected interruptions. As the number of containers increases, docker has introduced a container management platform called Docker Swarm to manage and schedule containers across multiple hosts and adjust their operational scale based on workload. If a container unexpectedly stops operating, the Docker Swarm cluster generates new containers automatically, ensuring the high availability of container services. Meanwhile, Google has introduced a container scheduling system called Kubernetes. The Horizontal Pod Autoscaler in Kubernetes automatically adjusts the number of service Pods based on node target memory usage, improving overall resource utilization. While Kubernetes can simplify application management and deployment, the cluster’s performance after deployment has yet to be effectively evaluated and compared. This study aims to optimize and adjust cluster node resource configuration and parameter settings using tools such as Vertical Pod Autoscaler, Descheduler, Ingress Controller, and Scheduling Framework. The performance of Kubernetes is compared with the Docker Swarm architecture to analyze the average response time, longest response time, connection success rate, success count, and failure count of the overall cluster’s web service traffic workload. The optimization is carried out to ensure the high availability of container services in case of node failures.
Research on English Text Keyword Extraction Algorithm Based on Big Data Analysis

For effective keyword extraction of massive English Web texts, a Hadoop big data platform is proposed in this study. First, the Hadoop big data platform is reconfigured to support the natural language processing process. Secondly, the GATE method is used to implement word and sentence segmentation, personalized tagging, and annotation standardization on web English texts, thereby obtaining candidate keyword sets. Finally, the traditional TF-IDF algorithm is weighted using vocabulary localization and key factors to calculate the correlation between candidate keywords and the internal file. The results show that the keywords in the file can be used to identify file features and the proposed distributed keyword extraction method can quickly and accurately find keywords for web English text.
Motion Navigation Algorithm Based on Deep Reinforcement Learning for Manipulators

In this research, we implement the deep reinforcement learning algorithm for handling the navigation problem of a 6-DOF robotic manipulator, which is a complex problem in robotics and requires real-time decision-making capabilities according to a particular strategy. A motion planning algorithm is developed based on combining the hindsight experience replay with the deep deterministic policy gradient algorithm (i.e., DDPG +HER), in which the robot is trained in the virtual physical environment. According to the experimental results, the developed algorithm can control the 6-DOF robot Nachi-MZ07 to perform a task autonomously and effectively.
Research on Intelligent Allocation and Optimization Algorithm of Higher Education Resources Based on PSO

In order to achieve the rationality and value optimization of the allocation of employees and training resources in science and technology, the university has established a comprehensive grade index system of resource input-output and provided a resource allocation algorithm for management and productivity. The experimental results show that the rate of entrepreneurship and innovation of higher education resources has been improved by 25 and 33% with the most efficient utilization of resources.
Research on Mandarin-English Code-switching Speech Recognition Based on Improved MEDT

The traditional Mandarin-English Code-switching Speech Recognition methods lose more information unique to the language. In addition, the model generalization is not satisfactory, which makes it difficult to perform the Chinese-English Code-switching speech recognition in multivariate scenarios. Given this, we propose a hybrid Chinese-English recognition model based on the MDET model. Based on the MEDT model, the architecture of the model is reconstructed by combining the respective characteristics of Chinese and English, and a hybrid encoder and a joint decoder are introduced. The Conformer model is used as the encoder, while the joint decoder architecture is adopted, using CTC and LSTM-RNNLM models to assist GPT-Decoder to decode the serialized input, The proposed model successfully makes the MER on the TALCS dataset decrease to 13.93% with the relative improvement in model performance of about 6.9%.
Research on Traffic Safety AI Education System Based on User Portrait and Collaborative Filtering

With the continuous development of the operational transportation industry, the continuing education content of drivers in the industry also needs to be improved urgently. In the limited time, how to make drivers learn more useful and suitable traffic safety knowledge has become a new direction for the development of traffic safety training software. To this end, we design a personalized learning recommendation system based on the collaborative filtering of user portraits. Aiming at the problem of cold start of users in the User-CF algorithm, we establish a similarity calculation model based on user portraits. As the difficulty of the problem is different from person to person, we establish a difficulty correction mechanism, too. By taking the series of measures, the result helps drivers acquire more suitable knowledge in a limited time.
Dual-Handed Dynamic Gesture Recognition using Inertial Sensors

Dual-handed gesture recognition is investigated using inertial measurement units (IMUs). A database of 10 types of dual-handed dynamic gestures is collected by five people with 250 samples each. The training set consists of 2,000 inertial data, and the test set consists of 500. A three-layer convolutional neural network (CNN) is used as the main training model to recognize gesture movements. The collected row wave data is directly sent to CNN for deep learning, and the convolutional layer extracts the IMU features of different gestures. Combined with the activation function (ReLU) and efficient gradient descent and backpropagation, the problems of gradient issues during training are avoided. The intermediated output data is compressed by the pooling layer (Max pooling), and the collected data from both hands is merged using the concatenate method. Finally, the fully connected layer is used for feature classification. The final success rate of 97.5% is achieved in the designed system.