Reviewerly/exHarmony huggingface dataset:
Sign Language Interpreter

Sign language is a form of communication for those who are deaf or hard of hearing. Since it is difficult for non-deaf individuals "to communicate with deaf people, this" technique is beneficial in supporting them. A hand gesture recognition system can allow deaf people to communicate with people who can hear them without the need for an interpreter or intermediary. The goal of this project is to create computer vision that can take a user’s sign and translate it into text in real time. The OpenCV python library and Keras are used to capture and process sign motions. To achieve high accuracy prediction, the captured motion is scaled, turned to a grayscale image, and the noise is filtered. Our project is an innovative way to assisting persons with speech and voice difficulties, so that they can communicate with each other as well as with others with ease. People with hearing and speaking disabilities have difficulties in schools and colleges, they are not able to keep it up with other students. This project also aims to help those students so that they can also get equal opportunity to build a great future.
An Improved Chatbot for Medical Assistance using Machine Learning

In most cases, users are unaware of all treatment options or symptoms related to a specific illness. For small difficulties, the user must go straight to the hospital for a checkup, which takes much longer. In addition, you will be in charge of the telephonic conversations for the complaints. A problem like this can be extremely difficult to solve medical chatbot was used to fix the problem by providing appropriate information.The technological advancement in machine learning algorithms demand interactive health assistance to the public with its significant classification performance. In this study, the support vector machine (SVM) algorithm is used to predict the health status. Using the Google API to convert speech to text and text to voice. The input is sent to a chatbot, which responds with relevant information and displays it on the standalone app.
An Implementation and Evaluation of Blockchain-based Digital Health Passports

While several electronic apps help with contact tracing of COVID patients, and those apps do not prevent patients from getting in contact with healthy international travelers or audiences attending large gatherings. Medical tests are getting expensive, and individuals may falsify their test results when traveling. This paper aims to implement and evaluate a secure Digital Health Passport (DHP) using blockchain technology. Users (issuers, holders, verifiers) register in BigchainDB to create or view digital health certificates. The issuer has read-write access to upload test results and read-only access to holders and verifiers to guarantee tamper-free. The holders can share the test results using QR codes to verifiers. In this implementation, This research study use BigchainDB as the blockchain database. Then, DHP is evaluated based on the smart healthcare requirements and the key features of blockchain technology. The evaluation and discussions present that the DHP is secured, immutable and traceable. DHP use cases will be helpful as health identification cards in international tourism, attending nationwide events, workplaces and educational institutions, and the entertainment industry.
Multi-Lingual Meta Embeddings in LinCE data set

Multilingual Meta-Embeddings (MME) leverage information from pre-trained mono-lingual embeddings. The exploited multi-lingual information can be effectively used in the code-mixed domain. This paper maneuvers MME to propose a language identification mechanism for code-mixed text. Empirical evaluation of conventional deep learning models in discrete, and permutated forms, GRU, LSTM, CRF, GRU with CRF, LSTM with CRF, LSTM with CNN, and GRU with CNN. Multi-Task Learning approach and evaluated our models on the Lince Hindi- English Code Mixed Language Identification data set.
Online Training Platform for Foreign Language in Colleges based on Computer-Aided Clustering Algorithm of Embodied-driven Efficacy Data

Online training platform for foreign language in colleges based on computer-aided clustering algorithm of embodied-driven efficacy data is designed and implemented in the paper. For the efficient clustering model, the mixed attribute data is considered in two parts: numerical attribute data and categorical attribute data. According to the influence degree of the sample on the clustering results and the calculation method of the classification attribute weight γ in the K-prototypes algorithm, the corresponding weights of each attribute are calculated respectively, and also the dissimilarity calculation formula is improved, thereby improving the clustering accuracy. The network security virtual core simulation experiment platform established by using general cloud computing and virtualization technology allows students to carry out remote experiments through the network and with these ideas, the designed models are then simulated with the comparison analysis.
Blockchain Solutions for Covid - policy Enforcement versus Food Security: A Demonstration of Customizable Benefits of Blockchain for e-Governance and e-Business

Blockchain, AI and robotics are the top three disruptive technologies that are likely to consume a major share of the supply chain (SC) technology investments in this decade. As in the past, each technology comes with inherent elements that have the potential to unlock governance and business capabilities, but not all the technological elements/ features of each are equally facilitative to the objectives of an organization. However, due to the lack of understanding of the capabilities of a technology in a specific context, governments and organizations make major investments in technologies that then result in suboptimal return on investment (ROI). This research study identifies the most facilitative features of the blockchain technologies and demonstrate their differential effects on achieving specific outcomes. Our case-based research identifies how each BCT feature contributes to specific performance benefits. Particularly this study focuses on two different use cases to show how transparency, immutability, smart contracts and consensus enablement in blockchain-based systems can help enforce Covid-19 rules and assure food safety. Finally, this research study has concluded that matching the features of a technology, specifically blockchains to the expected project outcomes can increase adoption of the technology with better return on technology investment.
Posture Correction of College Students' English Reading Mobile Learning Habits based on Smart Phone Camera Image-assisted Distance Measurement Algorithm

Posture correction of college students' English reading mobile learning habits based on smart phone camera image-assisted distance measurement algorithm is discussed in the paper. Based on the related study, the speed of video image acquisition and the resolution of the collected image will directly affect the industrial production efficiency. Hence, this study considers the camera image-assisted distance measurement algorithm to improve the information transmission efficiency. Considering that the line synchronization signal of the video stream is aligned with the output data, this module will operate the line synchronization timing sequence, and the purpose is to synchronize the line synchronization signal with the output data during the working time of the camera. Furthermore, the posture correction of college students' English reading mobile learning habits is combined as the application testing. The experimental analysis is conducted to verify the performance.
Realization and Optimization of English Smart Teaching Environment based on Real-Time Action Data Computer Acquisition Algorithm

Realization and optimization of English smart teaching environment based on real-time action data computer acquisition algorithm is studied in the paper. Data preprocessing is needed to improve the proportion of useful data before action recognition. In this paper, the method of looking for standardized modulus is used to reduce the deviation caused by absolute data. The vector extracted from the data of human inherent limb structure can be used as a standard vector because it is less affected by the change of human action. The core idea of the algorithm is: firstly initialize the weights of each training sample in the sample set, then train and update the weights to obtain different weak classifiers, and then arrange the weak classifiers according to their classification ability from low to high. With this integration, the designed model is then implemented through the analysis of the smart systems. Through the systematci test and verification, the performance is validated.
Java Implementation of Teacher Information Ability Measurement and Analysis Intelligent System (AMAIS) Based on ICT-TPACK and Recurrent Neural Network

This study discusses about the implementation of Java in the process of teacher information ability measurement and analysis based on ICT-TPACK and recurrent neural network. From the research study it is evident that when using the lock cache mechanism in the Java virtual machine to improve its runtime cache performance, the dynamic lock cache method can achieve an optimization effect that is not weaker than the static lock cache method. At this point, if other memory accesses in the system conflict with the compiled method, the compiled method may be replaced by the cache. The proposed architecture assist in finding the best solution for the teacher information ability measurement and intelligent system analysis. To apply the system into various model, the ICT-TPACK and recurrent neural network are combined. The performance of the proposed model is then validated by performing an experimental testing.
A Deep Learning-based Approach onVehicle Detection, Tracking, and Counting System

Transportation is one of the major features of civilization. Ever since the human race evolved, the need to move from one place to another has become mandatory. Currently, there are a lot of ways of transportation available. Yet, most of the population prefer roadways as it is simpler and more convenient. Due to the alarming increase in population, the number of vehicles on the road has also increased. Thus, it becomes tougher for the traffic police and the government to monitor the movement of all vehicles at different places. To resolve this issue, this study aims in finding the best Deep Learning (DL) algorithm that can be used to detect, track and count the number of vehicles from a video. For this process, a dataset consisting of 47 mp3 videos of the road with or without vehicles in every frame is collected from Kaggle. The dataset is then preprocessed. The preprocessing includes the conversion of video to frame-by-frame images. Two DL models will be developed using two different algorithms. The algorithms include the faster RCNN algorithm and the YOLO algorithm. The models are then trained using the images taken from the videos. The trained models are tested by analyzing various factors. The factors include precision, recall and the average IoU. The values are also compared in the form of a graph for better results. The performance of both the models is recorded and examined to find that the YOLO algorithm is a better algorithm than the faster RCNN when it comes to vehicle detection and tracking.
Energy Efficient Cloud Task Scheduling Policy Using Virtual Machine Concept and VMRRU Technique

Cloud computing and its components make use of their combine efforts to process input request to any cloud architecture. Cloud components such as virtual machine (Vm), Cloud Data center (DC), Broker request (BR) and their configurations combined configure a cloud scenario. Cloud computing component makes use of request, resource, and its utilization analysis to process any of the algorithm. It makes usage of resource in proper optimization manner. To provide better quality of services and proper cloud component scheduling, many algorithms have been proposed. Algorithms such as round robin, throttle, Genetic algorithm etc. were proposed by the existing research work. Limitations in such monitoring single level of utilization. They are either concentrating on resource utilization or in energy consumption by their resources for that request process. Further the internal process doesn’t comply with parallel process of monitoring such utilization. In this research, An Advance Algorithm named VMERRU (Virtual machine energy resource request utilization) is proposed. The approach also makes use of utilizing monitoring of energy, resource usage count, input request requirement and matching requirement of assigning DC, and VM to it. Thus, an optimal request handling algorithm with parallel computation is proposed. An implementation is performed using CloudSim API cloud analyst simulator and further computation shows the efficiency of the proposed algorithm.
Cloud Computing Elastic Storage System for Storage Optimization

Cloud environments are becoming increasingly important for businesses, research laboratories, and even ordinary customers. Because of their many offerings, such a technology has been able to attract a large number of users all over the world. One of the most intriguing cloud computing services is file storage, which entails uploading files to the cloud's data centers and accessing them from anywhere at any time. Because of the large number of users, it is necessary to improve the management of storage space surrounding data centers. Many studies have been conducted to optimise storage space; academics are attempting to develop some complicated compression algorithms without taking into account their execution time or the user's behaviour with a sluggish system. For large amounts of information applications, this article introduces E2FS, an elastic and efficient file storage system. E2FS can scale and proportion up and down the storage system in response to the real-time needs of big data applications. In order to better balance cloud storage performance and progress, cloud storage architecture and the E2FS system are explored. Furthermore, it is compared against DeDu to demonstrate that E2FS outperforms DeDu.
Key Agreement Protocol Based on Chaotic Authenticated Encryption Scheme

This paper proposes a shared secret key exchange based on a lightweight chaotic authenticated encryption. The Standard map is utilized for a keyed-hash function and the hash function is used to construct an Encrypt-then-MAC using the counter mode of the lightweight block cipher. Then, the Chebyshev polynomial is employed for the public key encryption in the key agreement scheme. Security analysis shows the results that the integration of the two chaotic maps enhances confidentiality, integrity, and authentication for the key agreement protocol. Hardware design of the proposed authenticated encryption scheme with the Lightweight Counter Mode shows that it is suitable for embedded systems due to low resource utilization.
Privacy Preserving Human Activity Recognition Using Microaggregated Generative Deep Learning

Internet of Things (IoT) allow data collection and sharing of many aspects of our daily life. With the huge amount of data transferred via IoT devices, privacy concerns need to be addressed while preserving the usefulness of the shared data. This research proposes a microaggregation-generative privacy preserving model for human activity recognition in IoT data. While generative deep neural networks have been used for data perturbation, data leakage and disclosing private information of the training sample is still an issue when applying the traditional deep learning generative models. We proposed a novel approach to perturb IoT data using Generative Adversarial Networks and Microaggregation while preserving the utility of its features. Our approach reduces the size of the original dataset by employing an entropy-preserving measure to discard noisy records after anonymization. The performance of the proposed approach was measured using several criteria such as classification accuracy, precision, recall, and F-score before and after anonymization. As a result, the proposed GAN-Microaggregation privacy preserving technique showed a remarkable performance in term of accuracy. Moreover, the privacy was measured, showing the benefits of the proposed approach to share IoT datasets with minimal privacy attacking surface.
An Efficient Masking Method for AES Using Tower Fields

A combination of the Boolean masking with the multiplicative masking for AES S-box is secured against side-channel attacks, particularly power analysis attacks. However, it is paid for by significantly increasing the complexity of the S-box implementation in hardware. This paper proposes a masking method based on the inversion in the tower field to cope with that problem. The experimental results show that the proposed method assures security against CPA up to 30,000 traces with the AES-TFM-2 scheme and more than 12,660 traces with the AES-TFM-1 one. There is a trade-off between the security level and the hardware implementation cost of the two proposed schemes. However, the technique reduces that cost considerably compared to existing approaches, and it is also secured against zero-value attacks.
Spatial Ensemble Anomaly Detection Method for Exhaustive Map-Based Datasets

Spatial anomaly detection is an essential part of subsurface data quality control and modeling for resources, e.g., groundwater aquifer, hydrocarbon resources, and mining mineral grades, along with environmental remediation. Efficiently identifying spatial local anomalous regions is important for detecting changes in subsurface population and data acquisition artifacts. Advanced data analytics and machine learning methods may be applied, but often omit essential spatial context with the additional cost of reduced interpretability. Considering the high cost of the risk in subsurface resource exploration, it is essential to maximize the integration of domain expertise. Our proposed method is an ensemble anomaly detection method that calculates the local anomaly probability based on the joint probability density space derived from the semivariogram model. The ensemble approach of our proposed method utilizes multiple local anomaly classifications calculated over a moving search window around each grid of a 2D map or 3D model. The anomalous regions are eventually decided based on the majority rule of the ensemble anomaly classifiers. We demonstrate the proposed method with 3 synthetic exhaustive, map-based, spatial datasets to cover different scenarios for spatial anomaly applications. The proposed ensemble anomaly detection method integrates domain expertise, spatial continuity, and scale of interest. We suggest using the proposed method as an automated tool to effectively identify the spatial local anomalies, based on the rigorous use of spatial continuity and volume variance from geostatistics, to focus professional time.
Requirements for a System Architecture for the Analysis of Scientific Texts

The creation of scientific research proposals and publications leads to a high effort for scientists, which has increased significantly in recent years. At the same time, technologies from the fields of information retrieval, machine learning, and semantic technologies offer potentials to relieve scientists and to increase the quality of scientific texts. Taking these technologies into account, this paper focuses on the determination of requirements for a generalistic system that enables the automated assessment of the text quality of scientific texts. First, a criterion-based requirements analysis was performed. As a result, ten specific criteria were identified and enriched by quality-dependent criteria. These formed the basis for the derivation of a total of 26 non-functional and qualitative requirements. These were analyzed, and dependencies were checked and transferred into a total of four requirement clusters. The requirement clusters comprise the requirements for data quality, text analysis, text quality assessment, and other qualitative requirements. Thus, a basis for the creation of a system architecture was created, which annotates, trains and evaluates texts, and contributes to the assessment of the text quality of scientific texts.
ASL Recognition by the Layered Learning Model Using Clustered Groups

American Sign Language (ASL) images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly, ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons, because sign language is their only channel of expression.Representative ASL recognition methods primarily adopt images, sensors, and pose-based recognition techniques, and employ various gestures together with hand-shapes.This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers.In the proposed model, the collected ASL images were clustered based on similarities in shape, and clustered group classification was first performed, followed by reclassification within the group.The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition.After selecting the optimized group, we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing.The proposed model exhibited an improved performance compared with the general classification model.
Algorithm metadata vocabulary: A representational model and metadata vocabulary for describing and maintaining algorithms

Metadata vocabularies are used in various domains of study. It provides an in-depth description of the resources. In this work, we develop algorithm metadata vocabulary (AMV), a vocabulary for capturing and storing the metadata about the algorithms (a procedure or a set of rules that is followed step-by-step to solve a problem, especially by a computer). The snag faced by the researchers in the current time is the failure of getting relevant results when searching for algorithms in any search engine. The designed vocabulary can be used by the algorithm repository developers, managers, and application developers. Besides, AMV is represented as a semantic model and produced OWL file, and it can be directly used by anyone interested to create and publish algorithm metadata as a knowledge graph, or to provide metadata service through the SPARQL endpoint. To design the vocabulary, we propose a well-defined methodology, which considers factual issues faced by the algorithm users and the practitioners. The evaluation shows promising results.
Mitigating conflict situations in spectrum sharing: A localized and decentralized governance approach

As spectrum sharing matures, usage conflict situations (e.g., harmful interference) remain a common concern. To deal with the challenges associated with conflict situations, multiple solutions have been proposed. Examples of approaches to mitigate conflict situations include the creation of exclusion and coordination zones, the development of spectrum coordinators (e.g., SAS), or the implementation of sensing-based mechanisms. Nonetheless, most of these solutions are designed as ``one-size-fits-all'' or global solutions. In this paper, we propose a local analysis of conflict situations in spectrum sharing settings through the design, development, and deployment of polycentric and self-governance systems. We have created a multi-tiered spectrum sharing model to analyze such systems using Agent-Based Modeling, Common-Pool Resource frameworks, and the core definitions of Radio Environment Maps (REMs).