Reviewerly/exHarmony huggingface dataset:
Byzantine Tolerant Algorithms for Federated Learning

In federated learning, workers periodically upload locally computed weights to a federated learning server (FL server). When Byzantine attacks are presented in the system, attacked workers may upload incorrect weights to the parameter server, i.e., the information received by the FL server is not always the true values computed by workers. Previously proposed score-based, median-based, and distance-based defense algorithms made the following assumptions unrealistic in federated learning: (1) the dataset on each worker is independent and identically distributed (i.i.d.), and (2) the majority of all participating workers are honest. In federated learning, however, a worker may keep its non-i.i.d. private dataset and malicious workers may take over the majority in some iterations. In this paper, we focus on model poisoning type Byzantine attack and propose a novel reference dataset based algorithm along with a practical Two-Filter algorithm (ToFi) to defend against Byzantine attacks in federated learning. Our experiments highlight the effectiveness of our algorithm compared with previous algorithms in different settings.
Hand Sign Recognition using Image Processing

The deaf and those with minor difficulty hearing and speaking use sign language to engage with people and communicate within their group. According to a World Health Organization assessment, there are around 9.1 billion dumb people and 466 million persons with hearing impairment worldwide. It's challenging for these folks to interact with other people. An English translation of the sign language is necessary in order to solve their problem. Although there are other additional sign languages, only American Sign Language (ASL) is taken into consideration in this case. IOT sensors, colors, gloves, and other devices may now be used to recognize sign language. These are demanding, costly, time-consuming, and require a lot of effort. The issue could be resolved via image processing. As part of image processing, some procedures will be performed on the image to find the pertinent text. Image processing makes sign language detection adaptable, quick, easy, and accurate. Image - processing supports in image analysis and detection, along with possible pre-processing techniques on image, if necessary. Here, the user's sign will be captured by the camera, anticipated, and given an English translation that reflects the message that the user is striving to get through.
Smart Glove for Bi-lingual Sign Language Recognition using Machine Learning

The deaf community in our society has a right to live a comfortable and respectable life by having communication with normal people without any hurdles or impediments. To address this objective, several research attempts have been made to develop smart gloves to provide a means of converting sign language to speech or text. This research work has attempted to design, implement and test non-visual-based smart glove to improve performance accuracy and reduce implementation complexity. More specifically, five flex sensors and an accelerometer are used to enable sign language recognition and its further conversion into speech and textual information. Further, the prominent Machine Learning (ML) classifiers (LR, SVM, MLP and RF) are used for recognising both American Sign Language (ASL) and Arabic Sign Language (ArSL). Finally, a classification accuracy of 99.7% for ASL and 99.8% for ArSL with Random Forests (RF) classifier has been achieved. By considering the Feature Importance, the accelerometer features are considered as dominant features in recognizing the sign language when compared to the flex sensor features. In order to further advance this research work, the implementation and performance aspects of non-vision and vision-based sign language recognition can be compared.
Multi-Featured Movie Recommendation Using Knowledge Graph

Recommendation systems are crucial in offering items, particularly in streaming services. Recommendation algorithms are essential for streaming movie services like Netflix in aiding consumers in identifying new movies to watch. Provide a Technologically advanced strategy on autoencoders for constructing a movie filter system that anticipates users’ ratings for movies through vast end-users rating data. Examine the application of deep learning to predict customers’ assessments of new movies using the Movie-Lens dataset, providing movie recommendations. The movie recommendation system proposed here uses particle filtering embedded in a knowledge graph. The model enables the user to provide the best suitable recommendation of movies considering the user’s favorite directors and genre. The experimental findings reveal that compared by error, our recommendation system beats a user-based model on projected ratings and even in the survey where people assess between concepts from both systems.
WoS Bibliometric-based Review on Serverless Computing model

The Serverless architecture is a cloud-native development paradigm that allows developers to build and run programs. Servers are still there in serverless, but they are not involved in the creation of apps. A cloud provider often takes care of the server infrastructure’s provisioning, upkeep, and expansion. Developers merely need to bundle their code in containers in order to deploy it. It also aids in the construction of "cloud-naive computing model". The term "cloud-naive" describes the possible advantages for the environment that IT services provided online may present to society. The number of resources used by the IT services is causing a resource deficit. We use the last 6 years of WoS articles, which were published between 2017 and 2022. Overall analysis is performed in a RStudio environment using the "bibliometrix" library. Even so, we use AMSTAR and PRISMA check-lists to evaluate the methodological quality of the systematic review technique. We compute the outcomes based on eight different factors, including publisher, year, citation, sources, technology, country adoption, research impact, and research gaps.
Lightweight Cryptographic Approach to Address the Security Issues in Intelligent Applications: A Survey

With the origin of the Internet of Things (IoT),massive real-time applications are evolving, but in this cyber world, securing the data of those devices or users is to be focussed more on. Intelligent services are the base components of IoT applications, such as intelligent systems, smart homes, smart cities, smart cars, Etc. An unfold challenge is securing the large amount of data generated and transmitted over the network in the IoT ecosystem by resisting cyber-attacks and adopting the efficient cryptographic method for a resource-constrained environment. This paper focuses on the exhaustive work carried out on user/device identity and a lightweight cryptographic viable solution for the IoT ecosystem through ECC (Elliptic-curve cryptography). The study assures the secure transmission of data and authentication of device/user using various security strategies of ECC for real-time IoT applications. A systematic survey of the previous works is conducted to determine the critical aspects of designing ECC-based authenticated and secured transmission solutions for the IoT ecosystem. The outcome of this study is essential components of ECC, requirements, and solution architecture of ECC for various security aspects in the IoT ecosystem to ensure user/device privacy and secured data transmission and its challenges are addressed.
Data Privacy Over Cloud Computing using Multi Party Computation

As a result of the development of several industries and the constant advancement of technology and inventions, as is well known, enormous volumes of data are produced on a daily basis. Every day, more data is produced as a result of progress, and maintaining these enormous volumes of data is a difficult work because they contain many secret informational items that must be kept secure as part of business secrecy. Furthermore, not all data generated will be useful. Therefore, screening data is a crucial duty. Many small-scale firms believe that all of this data preservation is an expensive endeavor that is only feasible for large industries, but the procedure is also crucial to efficient way.
Machine Transliteration for Indian languages: Survey

Transliteration is an important aspect when conversing from one language to another. This paper addresses several models designed for the Transliteration of Indian languages. It deals with the methods and approaches used for the languages like Marathi, Hindi, and Punjabi. A Transliteration model is designed on the basis of grapheme, phoneme, correspondence and hybrid methods. Various models have been designed for top-level languages but for Indian languages, the researchers are yet in an infant state as fewer resources are available for them as compared to other languages. Initial research work was rule-based which means rules had to be created for every language taking into consideration of rules and grammar of that language. Later, technology transfer occurred, and alignment models and hybrid models gained popularity. Technological progress in the last few years have improved Transliteration results manifold. Advances in this area contributed to the advancement of communication technologies immensely. To improve data retrieval processing, research developments and Transliteration results, the future scope and requirements of several models are discussed briefly in this paper. The analysis of various models in this paper can be further used to improve research in this field.
Does Metaverse a Technological Revolution in Artificial Intelligence? A Bibliometric Analysis

Technology is playing a crucial role in the development of any economy and it will aid in the dynamic change process happening across the globe. Today, organizations are prepared to implement any technology to maintain their competitive market position. The fever of metaverse is sweeping the country’s industrial sector and is expected to create and expand its business scope. Basically, Metaverse is a convergence of physical, augmented and virtual shared space where users could interact with other users through avatars. In the metaverse world, artificial intelligence and blockchain technologies along with augmented reality and virtual reality are expected to come up with new applications. This paper presents various applications of metaverse technology as a future of the internet world in the financial sector and also discusses how these companies will use the metaverse to improve their business operations. Along with this, the paper reviewed previous studies on the topic of ‘Metaverse’ and done bibliometric analysis on it. The findings of the study include a comprehensive summary of the most important scientific articles in the subject area.
A modern approach for positional football analysis using computer vision

In this work we aim to construct a robust pipeline for the sports analysis community in order to successfully extract useful information from broadcast football matches. We propose a fast and efficient solution, based on computer vision and machine learning methods and algorithms. Our solution provides a framework suitable not only for detecting, tracking and identify the roles of the players and staff, but also for mapping each player from their position as seen in broadcast images, to their absolute position on the field. In order to achieve this, we designed each module of the pipeline by comparing multiple solutions and choosing the most suitable ones taking into consideration the trade-off between performance and inference time. We managed to provide a system that can be used by anybody in the community by feeding a sequence of frames taken from a broadcast football match.
Comparative Analysis of Techniques for Big-data Performance Testing

Big data has become the primary objective for any area or department on the globe, including government, healthcare, and industrial sectors. Every day, a large amount of big data is generated, and information is added based on the three characteristics of big data. Handling massive amounts of data with traditional methods has become a significant challenge. To test large amount of duplicate data is very challenging task for researchers. Big data performance testing has great promise for obtaining more optimised solutions. Better performance saves time and money while also producing more optimised results. This paper makes an attempt to present several performance testing approaches. Furthermore, we compare the performance testing techniques and analyse the performance improvement factors. Finally, various tools for performance testing of big data, such as Apache Jmeter, Apache Drill, LoadRunner, WebLoad, YCSB etc., are also compared with various parameters such as domain applicability, scripting interface etc., and tools are suggested to improve overall performance.
Sentiment Analysis Approaches, Types, Challenges, and Applications: An Exploratory Analysis

Even though sociolinguistic and psycholinguistic studies demonstrate that the qualities of the speaker and the intended audience have an impact on the message that is transmitted, the majority of NLP models used today assume language as universal. The difficulty of sentiment analysis, often known as opinion mining, in natural language processing (NLP) is one that is currently being faced. It’s also challenging for people to share their ideas on websites like Facebook, Twitter, and Yelp since innovation has grown dramatically. Social media’s widespread use has resulted in a huge volume of data generation, including evaluations, comments, and opinions. Researchers claim that the phrase, document, aspect, and user levels are the foundations for sentiment analysis. This data requires more time to analyse and is challenging to process. In order to categorize or determine positive, negative, and neutral opinions, an intelligent system must be developed.
Containerized AI Framework on Secure Shared Multi-GPU Systems

Nowadays, technology has reached the point where Graphics Processing Unit (GPU) drive Artificial Intelligence (AI) workloads with large datasets to speed up the process of solving more complex real-world problems. The DevOps movement has brought container-based deployment models into the world of software development and application deployment. Data scientists can quickly prototype, build, and deploy a containerized application or service, allowing them to manage AI workloads more effectively with portability. Docker is a widely used containerization platform, requiring root privileges for execution, which has associated security concerns and may not be readily adopted in secure shared multi-GPU systems. Singularity addresses the needs of container-based virtualization in a secure environment, which allows the same Docker containers to be used without any changes or security concerns. This work presents the challenges and approaches for quickly deploying complex AI workloads on multi-GPU systems without compromising security by ensuring that AI frameworks run in an isolated container environment with user credentials. In addition, a comprehensive performance evaluation of containers converted from Docker to Singularity in the context of native is performed on a multi-GPU system to solve more complex real-world problems with AI workflow containerization.
Speed-up Single Shot Detector on GPU with CUDA

Nowadays, most of the current research on object detection is to improve the whole framework, in order to improve the accuracy of detection, but another problem of object detection is the detection speed. The more complex the architecture, the slower the speed. This time, we implemented a Single Shot Multibox Detector(SSD) using GPU with CUDA.We have improved the object detection speed of SSD, which is one of the most regularly used object detection frameworks. The most time-consuming part, the VGG16 network, is rephrased by using cuDNN, which is made faster by about 9%. The second time-consuming part is post-processing, where non-maximum-suppression (NMS) is performed. We accelerated NMS by implementing our new algorithms that are suitable for GPUs, which is about 52% faster than the original PyTorch version [11]. We also ported those parts that were originally executed on the CPU to the GPU. In total, our GPU-accelerated SSD can detect objects 22.5% faster than the original version. We demonstrate that using GPUs to accelerate existing frameworks is a viable approach.
Noise Tolerance of Linear vs Non-Linear LiDAR Based Ego-Motion Drift Correction Methods

We have previously proposed a linear approach for reducing the global drift of a video-based frame-to-frame trajectory estimation method by correcting it at selected points in time based on the alignment of past and current 3D LiDAR measurements (see [7]). In this paper we assess the tolerance to noise of a series of methods derived from the one previously proposed, this time using both linear and non-linear optimization methods to calculate the correction transform. We generate synthetic datasets with various noise pollution levels and assess the performance of each method under investigation in recovering artificially induced odometry estimation errors.
Identification of Malignant Patterns in FNAC Digital Images of Thyroid Nodules through Cascaded Segmentation Stages

In this paper, an optimal computer aided diagnosis system with a cascade combination of two region-based segmentation stage is proposed and evaluated to discriminate benign thyroid nodules from malignant using thyroid Fine Needle Aspiration Cytology (FNAC) microscopic images. Two region-based image segmentation methods, namely, watershed and mathematical morphology are sequentially ensemble, in the first pass, to extract the foreground cell portions from thyroid FNAC images by discarding background staining information. Because of the ensembled sequential application of two segmentation methods, the majority of the unwanted background image pixels are eliminated, and the required foreground cells are retained in the images. In the second pass, the statistical features are taken by Wavelet decomposition and Gabor filter models. Finally, the Support Vector Machine (SVM) classifier is implemented on derived feature set for differentiating benign thyroid nodules from malignant. A promising 93.33% of highest diagnostic accuracy is obtained using SVM model for Gabor features which is 3.33% higher than the previous results. The new configuration suggests that the proposed system with cascade operation of two region-based segmentation methods can improve the performance for classifying multi-stained FNAC thyroid images as benign or malignant nodule.
Lightweight and Efficient Convolutional Neural Network for Road Scene Semantic Segmentation

Recently, Intelligent Transportation Systems (ITS) have become one of the most important fields of research topics, while it provides advanced road scene monitoring. Actually, computer vision is one of the most widely used fields in ITS, while it offers various tasks, such as object detection, image classification, and segmentation. Besides, Convolutional Neural Networks (CNNs) have shown their effectiveness in deep learning tasks, particularly in computer vision. In fact, road scene semantic segmentation task is a critical and fateful issue that can be addressed using CNN which requires effective precision as well as fewer parameters. The majority of related work on road scene segmentation proposes models that focus on one aspect, the precision, or the parameters requirement, which makes it hard to use in real-time applications when the precision is not a priority. To solve this issue, we propose a new network based on an encoder-decoder architecture, which compromises the precision with fewer parameters. Our proposed model is trained from scratch using only 0.64M parameters. The experiments are evaluated on the popular CamVid dataset, and the results show that our proposed CNN achieves better performance with fewer parameter resources.
Survey on Monocular Depth Estimation for Unmanned Aerial Vehicles using Deep Learning

Deep learning-based solutions for the ill-posed problem of Monocular Depth Estimation (MDE) from 2D color images have shown potential in recent years, spurring a very active field of research. Most state-of-the-art proposals focus on solving the problem in the context of automotive advanced driver assistance and/or autonomous driving systems. While presenting their own complexities and challenges, the vast majority of road environments exhibit a number of commonalities amongst themselves. The aerial domain in which modern Unmanned Aerial Vehicles (UAVs) operate is significantly different and features a large variety of possible scenes based on the specific mission carried out. The increasing number of applications for UAVs could benefit from more advanced learning-based MDE solutions for recovering 3D geometric information from the scene. In this paper, we conduct a study of existing research on the topic of MDE specifically tailored for aerial views, as well as presenting the datasets and tools currently supporting such research, high-lighting the challenges that remain. To the best of our knowledge, this is the first survey covering this field.
Intelligent Generation Algorithm for Multi-scene Virtual Images of Dynamic Pictures Based on Metaverse

Beginning from the Facebook changed its name to Meta, the metaverse is then focused by the researchers. This research study follows the trend of intelligent generation algorithm for the multi-scene virtual images of dynamic images based on metaverse. Conditional generative adversarial networks add additional conditional information to the generator and discriminator, extending the structure of standard GANs. Hence, the GAN is considered as the integration algorithm. Through digital twin technology and Internet of Things technology, the expansion of the living environment is realized, and the real world and the virtual world are integrated to construct a comprehensive environment. Hence, the model is applied to the intelligent generation algorithm. The effectiveness has been evaluated through simulation.
An Intelligent Video Surveillance System using Edge Computing based Deep Learning Model

With the rapid increase in global data volume, various factors like low latency, high efficiency video surveillance is impossible to achieve in a centralized cloud computing model. Therefore, this paper proposes a distributed computing model for intelligent video surveillance system. This paper presents a smart video surveillance system which can execute Deep Learning algorithms in low power consumption embedded de vices. The proposed intelligent video surveillance system based on the edge computing consists of multi-camera for smart cities and homes. In general, the sending of original video surveillance data to the centralized computing model is too much time consuming and this will keep us far away to achieve our objective of real time data transmission so through this paper the edge computing technique is proposed, the idea is perform computation locally at the edge devices and then the computed data will be sent to the centralized computing model which is capable of performing the real time video surveillance by using the deep learning algorithm.