Reviewerly/exHarmony huggingface dataset:
Classification of Human Activities using CNN with Principal Component Analysis

Human Activities Recognition is the process of automatically identifying a person’s physical activities in order to create a secure environment for everyone, even elderly people, in their daily lives. In this paper, the classification of human activities using Conventional Neural networks with Principal Component Analysis with presented. In the proposed method, Principal Component Analysis is employed for dimensionality reduction and Conventional Neural networks are employed for classification. The Human Activities Recognition dataset from Kaggle is used in the suggested model. The effectiveness of the proposed model is assessed in terms of accuracy. The proposed model achieved an accuracy of about 96.71%.
Safety Helmet Detection: A Comparative Analysis Using YOLOv4, YOLOv5, and YOLOv7

Safety helmets are of utmost importance to workers’ lives as the most fundamental form of protection. However, safety helmets are frequently not worn as a result of a lack of safety awareness. Utilizing outdated manual inspection techniques and video monitoring to check if employees are wearing helmets results in missed inspections and poor punctuality. As object detection technologies advanced, the YOLO family of detection algorithms, which have extremely high speed and precision, were applied in multiple detection segments. In this paper, we compare and analyze the three models of the YOLO family, the YOLOv4, the YOLOv5, and the YOLOv7 for helmet detection. A publicly available dataset of 5000 images was collected and annotated. Our results have shown that the YOLOv7 accomplishes an mAP of 96.4% which is 1.36% better than the YOLOv5 and 3.00% better than the YOLOv4. The results also show that the YOLOv7 has an average detection time of 12.4 ms, outperforming that of the YOLOv4 and the YOLOv5. Both in terms of accuracy and speed, the YOLOv7 exceeds both models, making it possible for even greater real-time object detection.
Data security and Integrity in Cloud Computing

Cloud computing security refers to data protection, while data integrity refers to the reliability of the data. Integrity and security of data are not the same things. Therefore, both of them are very important in the field of cloud computing. A primary concern of users related to Cloud Computing is data security and integrity. The purpose of this paper is to discuss the threats and solutions associated with the problems of data security and data integrity that cloud computing users must deal with. Data protection is concerned with data security, whereas data reliability is concerned with data integrity. A data security strategy focuses on reducing the risk of leaks of trade secrets, emails, medical data, and business documents.Data privacy, security, and integrity are essential in information technology. In contrast, data integrity ensures that digital data is not corrupted and that only authorized individuals can access or edit the data (i.e., data consistency, accuracy, and reliability). Several platforms use more accessible language to make their terms more transparent, and users can trust them. Cloud computing is based on distributing computer resources across several users. Several policies are designed to ensure regulatory compliance while protecting the information, applications, and infrastructure associated with Cloud Computing. The paper provides an overview of Cloud Computing, its concepts, its significance in various applications, and solutions to issues and methods for ensuring its integrity and security. This research also discusses multiple challenges associated with cloud computing. To provide high-quality cloud data security, cloud security solutions, comprehensive security policies, and a security-conscious organizational culture are essential.
Development of an Elephant Detection and Repellent System based on EfficientDet-Lite Models

Human-elephant conflict (HEC) has become a major concern in Sri Lanka that results in many unfortunate human and elephant deaths. Methods that are currently in place to mitigate HEC, such as electrical fences have undesirable consequences resulting in both human and elephant casualties. In this paper, we have proposed a method based on computer vision and deep learning that has a promising potential for detecting and repelling elephants without endangering the lives of elephants or humans. We have used EfficientDet-Lite models that provide a good compromise between accuracy and performance in order to be usable with a resource-constrained device like a Raspberry Pi.
Multi-Lingual Hybrid Chatbot for Empowering Rural Women Self-Help Groups in India

Recently, chatbots are bringing new dynamics to customer service by providing instant services to the users of the chatbots. In this work, we aim to extend the utilization of chatbots for women’s empowerment by bringing chatbot facilities to support the work of women’s self-help groups. To enhance their work, we aim to provide novel means of communication and service response to the rural women’s community. Chatbots are usually created to address customer queries and are conversational. In this study, we discuss the potential societal advantages of chatbots and conversational user interfaces and how helpful this is to self-help groups. Chatbots can increase the availability, affordability, and accessibility of necessary services for this community. This work proposed a hybrid multi-lingual chatbot using two approaches and compared the advantages of both methods.
The Roles of Stakeholders in Internet of Things: A Theoretical Framework

Internet of Things (IoT) technology is one example of a contemporary technology. Since millions of devices are sending data to the cloud quickly and in large quantities, a vast array of data is produced. The IoT is a subset of big data. Control, management, and monitoring of the network on the IoT needs to be improved. This study presents a theoretical framework for enhancing the work of humans as stakeholders in the security IoT domain. It aims to identify who the stakeholders are and then use mining algorithms to provide knowledge to each stakeholder based on their role in the business. A questionnaire was distributed to collect data about stakeholders. The framework uses a semi-supervised approach. A human-driven feature selection concept was used to determine the interrelationship between stakeholders and the attributes in the dataset. The objective of the study is improved decision-making to secure against security incidents in the IoT. The questionnaires were sent to IoT experts, and the responses were used to determine the stakeholders in the field of security.
BowlingDL: A Deep Learning-Based Bowling Players Pose Estimation and Classification

Human Pose Estimation (HPE) is one of the trending areas of research among artificial intelligent research. It has gained a lot of attention due to its versatile potential applications in various domains including transportation, healthcare, gaming, augmented reality, and sports. HPE can be used to build sports analytics, personalized training, and selflearning systems which allow players, athletes, and trainers to improve the training quality by evaluating various human poses detected from images or videos. As far as we know none of the exciting works considered developing a pose estimation and classification framework for bowling players. Therefore, in this paper, we proposed a deep-learning approach for bowling players’ pose estimation and classification. It uses our proposed Bowling Deep-Learning (BowlingDL) model along with the MoveNet model for bowling players’ pose estimation and classification. The MoveNet model detects various key points in human pose and the BowlingDL model classifies the detected bowling player’s poses into five different classes. For model training and evaluation, we collected and labelled our own dataset as no dataset was found for bowling posture. Our proposed model achieved 80% accuracy for the training dataset and 83% accuracy for the testing dataset. In addition, a smart mobile application for bowling players was developed where an edge-friendly version of BowlingDL–generated using TensorFlow Lite–was deployed.
An Enhanced Method for Detecting Attack in Collaborative Recommender System

In recent decades, the advent of digital information services by YouTube, Amazon, Netflix, and many other web services of this kind have made recommendation systems more and more ubiquitous in our lives. rice field. The system suggests and recommends articles to the user that may interest the user in online advertising (recommending and suggesting appropriate content to the user that matches the user’s tastes and activities). Recommendation systems have become an integral part of our daily online journeys. The quality of predictions is degraded by the attackers by injection of fake profiles. therefore, the shilling attacks detection are necessary. thus, various shilling attacks detection techniques proposed. In this paper, we introduce an enhanced technique for detecting shilling attacks in collaborative recommender system using supervised learning techniques. The proposed method results show that getting better accuracy when we employee ensemble learning algorithm.
Human Action Recognition using Computer Vision and Deep Learning Techniques

Activity detection is a significant problem in smart video monitoring. The ability to recognize human movement in surveillance footage is a complicated computer vision task, however, owing to the size of the video as well as the CCTV camera's high resolution, doing so typically takes a long time. Therefore, it's necessary to lower the video clips resolution and identify the subjects' current activities. Using a variety of video processing methods and Convolutional neural networks associated with long-short term memory networks are examples of deep learning methods. We develop a model which uses the above networks for human action recognition on videos. Convolutional LSTM and Long Recurrent Convolutional Networks (LRCN) are the two models used for detecting human actions in videos and those models will utilize both the Convolutional Neural Network (CNN) and Long-Short Term Memory Network (LSTM). Both approaches can be used with Tensor Flow. The best model will then be taken into consideration for making predictions on Y ouTube videos. There are a lot of deep learning systems available right now, however, none of them work well when the video has a lot of details and it becomes challenging to identify the actual activity, Therefore, We select Convolutional Neural Networks (CNN) for picture data and Long-Short Term Memory (LSTM) networks for sequence information. ConvLSTM and LRCN have accuracy rates of 72.34% and 94.89%, respectively. video surveillance, recognizing human activity, patient monitoring, and human-robot interaction are some of the social effects of this research.
A Comparative Analysis and Study of a Fast Parallel CNN Based Deepfake Video Detection Model with Feature Selection (FPC-DFM)

Deep learning is an efficient and practical method that has been widely applied in numerous fields. Videos created with swapped faces in a video, altered facial emotions, changed genders, fraudulent video content generation, and altered facial features are referred to as “DeepFake” videos. These videos are created utilizing deep learning technology called generative adversarial networks. Fake videos are used to stir up political agitation, commit acts of terrorism, and demand money. A fast Parallel CNN-based deepfake video detection model with feature selection is the new model we presented in this project (FPC-DFM). In order to identify Deepfake videos, the FPC DFM architecture uses feature extraction, feature selection, and prediction. The FPC-DFM model extracted features using three convolutional models: EfficientN et, VGG16, and ResNet as well as Principal Component Analysis (PCA)-based feature selection and Support Vector Machine-based classification. We offer a new architecture for capturing the video frame features that will be utilized to determine if the video is real or fake by utilizing deep learning techniques. In comparison to other pre-trained models like VGG16-TL, EfficientNet-TL and Resnet50-TL and our results demonstrated that FPC-DFM has the best performance and the highest accuracy of 96.50%.
A Comprehensive Study on E-learning Environments for Deaf or Hard of Hearing Learners

Quality education is the fundamental right of every individual regardless of the disabilities they have. For the Deaf or Hard of Hearing (d/DHH) people, e-learning is the most promising way to access the educational materials referred to as digital learning objects (LO) at any time and space which increase their autonomous learning skills. This form of instruction delivery was widely accepted during the outbreak of Covid-19. Hence a background study has been conducted to investigate the challenges in teaching the d/DHH learners during the pandemic. This research work aims at providing a personalized e-learning environment to the d/DHH student community belonging to St. Clare Oral Higher Secondary School for The Deaf, situated in Kerala. To build personalized systems, the primary step is to review the existing e-learning solutions available in the literature and the adaptation techniques implemented by them to offer personalization in line with the components of traditional adaptive e-learning systems. The study carried out in this paper illuminates the need of personalized e-learning platforms that adapt the basic needs, abilities and disabilities of deaf learners which will find the “best learning solutions” in the form of learning objects.
Analyzing and Addressing Data-driven Fairness Issues in Machine Learning Models used for Societal Problems

This work aims to systematically analyze and address fairness issues arising in machine learning models because of class imbalances present in data, specifically used for addressing societal problems and providing unique insights.Using a specific data set, spectral analysis is first performed to present evidence and characterize the fairness issues.Subsequently, a series of class imbalance correction techniques are applied before the data is used to generate various machine learning models.The models so generated are then evaluated using multiple metrics.The results are then analyzed to compare the various approaches to determine the relative merits of each.As the experiments described in this paper confirm, not all oversampling techniques help in correcting data-induced model biases.Based on the Kappa statistic, F-1 score, and accuracy measured by the area under the Receiver Operating Characteristic curve, among the approaches evaluated, the Majority Weighted Minority Oversampling Technique, MWMOTE oversampling technique addresses the fairness issues the best and also improves the performance of the models at least for the dataset in consideration.The experiments also demonstrate that some of the oversampling techniques can degrade the models both in terms of performance and fairness.The results are interpreted using the evaluation metrics.
Neural Machine Translation Using Attention

Machine Translation pertains to translation of one natural language to other by using automated computing. The most common method for dealing with the machine translation problem is Statistical machine translation. This method is convenient for language pairs with similar grammatical structures even so it taken vast datasets.N evertheless, the conventional models do not perform well for languages without similar grammar and contextual meaning. Lately this problemhas been resolved by the neural machine translation (NMT) that has proved to be an effective curative. Only a little amount of data is required for training in NMT and it can translate only a small number of training words. A fixed-length vector is used to identify the important words that contribute for the translation of text, and assigns weights to each word in our proposed system. The Encoder-Decoder architecture with Long- Term and Short- Term Memory (LSTM) Neural Network and trained modelsare employed by calling the previous sequences and states. The proposed model ameliorates translation performance with attention vector and by returning the sequences of previous states unlike LSTM.English-Hindi sentences corpus data for implementing a Model with attention and without attention is considered here. By evaluating the results, the proposed solution, overcomes complexity of training a Neural Network and increases translation performance.
Cursive Handwriting Recognition Using CNN with VGG-16

Cursive handwritten text recognition is a process of identifying handwritten text from images. The recognition process is difficult due to person's unique style of writing. Pattern recognition is used to classify the data into various categories. This study used CNN with VGG16 model to identify cursive English alphabets and words in a given scanned text document. The first phase is image acquisition, which involves acquiring the scanned picture, normalizing the image, extracting the features from the image, and applying segmentation. Three different pre-processing techniques like data augmentation, image segmentation and image data generator are implemented in this work. CNN with VGG 16 model is employed for recognition. Three kinds of experiments were done with various combinations of pre-processing techniques combined with CNN model. The experimental results indicates that data augmentation pre-processing technique with CNN produced 98.36% training accuracy and 95.1% testing accuracy which is best than other combinations.
Convolutional Neural Networks

This chapter presents Convolutional Neural Networks (CNNs). The chapter begins with a review of the convolution equation, and a description of the original LeNet series of CNN architectures. It then traces the emergence of Convolutional Networks as a key enabling technology for Computer Vision resulting from the publication of AlexNet at the 2012 ImageNet Large Scale Image Recognition Challenge. This is followed by a description of the VGG architecture and the YOLO Single Shot Detection network for Image Object Detection. Learning Objectives: This chapter presents Convolutional Neural Networks, with a summary of the history, fundamental theory, and a review of popular architectures that have played a key role in the emergence of Deep Learning as an enabling technology for Artificial Intelligence. After reading this chapter, students will be able to understand the basic principles of convolutional neural networks and how such networks can be used to detect patterns in signals. Students will understand the meaning and significance of network hyper-parameters, and be able to select among the commonly used architectures such as VGG and YOLO to solve problems in pattern analysis and signal detection in audio, visual and other forms of multidimensional signals.
A Controllable Agent by Subgoals in Path Planning Using Goal-Conditioned Reinforcement Learning

The aim of path planning is to search for a path from the starting point to the goal.Numerous studies, however, have dealt with a single predefined goal.That is, an agent who has completed learning cannot reach other goals that have not been visited in the training.In the present study, we propose a novel reinforcement learning (RL) framework for an agent reachable to any subgoal as well as the final goal in path planning.To do this, we utilize goal-conditioned RL and propose bidirectional memory editing to obtain various bidirectional trajectories of the agent.Bidirectional memory editing can generate various behavior and subgoals of the agent from the limited trajectory.Then, the generated subgoals and behaviors of the agent are trained on the policy network so that the agent can reach any subgoals from any starting point.In addition, we present reward shaping for the short path of the agent to reach the goal.In the experimental result, the agent was able to reach the various goals that had never been visited by the agent during the training.We confirmed that the agent could perform difficult missions, such as a round trip, and the agent used the shorter route with reward shaping.
A Survey of Various Lightweight Cryptography Block ciphers for IoT devices

Internet of things (IoT) helps in creating connections among dissimilar things present in a heterogeneous environment and provides better services to end users via real-time data processing, communication, and visualization. Rapidly growth in IoT devices, connected to the internet, poses a huge range of challenges like heterogeneity and scalability of devices, security of IoT generated data and so on. Developing new cryptographic algorithms and selecting appropriate ciphers for providing confidentiality, integrity, authenticity and non-repudiation as well as satisfying the device constraints are needed in today's scenario. The characteristics of each block cipher are closely related to a specific setting and cannot be directly transferred to other applications. This paper presents various challenges faced by researchers in finding suitable light weight cryptography (LWC) ciphers for their specific needs and applications. The key contribution of this paper is to provide a detailed overview of existing light weight block ciphers in terms of various parameters. Moreover, this paper describes the basic measures required to combine different ciphers together to form a hybrid cryptosystem for securing IoT devices.
Challenges of Proof-of-Useful-Work (PoUW)

Proof-of-Work is a popular blockchain consensus algorithm that is used in cryptocurrencies like Bitcoin in which hashing operations are repeated until the resulting hash has certain properties. This approach uses lots of computational power and energy for the sole purpose of securing the blockchain. In order to not waste energy on hashing operations that do not have any other purpose than enabling consensus between nodes and therefore securing the blockchain, Proof-of-Useful-Work is an alternative approach which aims to replace excessive usage of hash functions with tasks that bring additional real-world benefit, e.g. supporting scientific experiments that rely on computationally heavy simulations. In this publication theoretical PoUW concepts such as Coinami, CoinAI and the cryptocurrency Primecoin are analyzed with respects to how PoW properties can be retained while doing useful work.
Engaging Museum Visitors with AI-Generated Narration and Gameplay

It has become a challenge in recent years to raise interest in museum visits, especially among younger visitors, as the range of alternative entertainment options has become overwhelming and increasingly attractive, interactive, and playful. To re-engage a wide audience with art and cultural heritage, we propose to use artificial intelligence to make the presented artworks more interesting. By using natural language processing to generate a narrative around a selection of individual exhibits and presenting the story as a scavenger hunt, we connect the individual exhibits and make access more playful. The museum visitors are guided through the story by two characters, who also pose challenges to be solved in mini-games. The two characters were chosen as a living being (a puppy) and an embodied agent (a humanoid robot) to indicate whether an utterance is preformulated and fact-based (puppy) or generated and partially made up. By testing the prototype, we could confirm that the generated stories are plausible and exciting, that the participants became more interested in the presented items through the story and mini-games and that the participants could distinguish if the utterance was fact-based or fictional.