Reviewerly/exHarmony huggingface dataset:
A Stable Cloud Storage Algorithm for Online Interaction Effect Data based on HarmonyOS

At present, the skyrocketing amount of effective and redundant information makes the demand for storage more and more high, which provides an opportunity for the recent development of cloud storage and makes it one of the fastest accepted forms of cloud services. Under this background, this paper proposes a stable cloud storage algorithm for online interaction effect data based on the HarmonyOS. The designed model firstly optimizes the traditional Chord algorithm by introducing the genetic algorithm into the processing of data. The designed strategy allows the system to choose a relatively good path in each path selection, the system can choose a relatively good path. Then, the HarmonyOS is selected as the multi-core scenario and the online interaction effect data is selected as the sample application. Through the test, this study compares the performance of the original Scalable Chord routing protocol and the improved routing protocol in terms of resource location by using PeerSim. It can be seen that the designed model is robust.
HOG and Cloud Computing based Face Recognition for Attendance Monitoring

Participation plays a key role in the classroom culture. When calling out the students' names at the beginning and end of a lecture, lecturers may miss a student, or someone may respond several times to substitute their peers. Face identification can be difficult when using low-definition video and other information systems with the assistance of facial recognition technologies. This necessitates the need to develop a novel device to quickly and accurately identify human faces from images and videos. Many algorithms and techniques have been recently developed to improve the facial recognition accuracy. Deep learning has been recently implemented in various real-time applications to identify and perceive multiple faces at the same time. When it comes to computers, however, it is unrealistic to attempt any of the human identity. The identification of the human identity is a critical aspect of biometrics. The biometric results are consistent with human characteristics. Remote and applied facial expressions can be used to enhance the results. Computers that identify and recognize faces can be used for a variety of purposes, including criminal recognition, network surveillance, biometric authentication, and so on. In this paper, an automated system for attendance recognition is proposed.
Performance Analysis of Object Detection Algorithms for Waste Segregation

The amount of waste generated in urban cities is increasing at an alarming rate and its effective and efficient management has become nearly impossible. To combat this issue, this study has compared different object detection algorithms applied on data containing trash with no cluttered background, so as to first evaluate the different algorithms only on the basis of their inherent performance. The algorithms used for training are YOLOv5, EfficientDet and Faster RCNN. Using mAP(Mean Average Precision) as the evaluating metric, this study has found that YOLOv5 with mAP of 0.92 has outperformed EfficientDet and Faster RCNN, whose mAP was 0.846 and 0.836 respectively.
Labeled Hands in Wild

Hand pose estimation has been playing a major role in many applications such as in Augmented/Virtual reality that is the human-computer interaction and gesture recognition. Among the existing hand datasets, some of them are synthetically generated which do not provide information about the background considering the various lighting conditions where the hand skin tone information would be lost.Hence, the proposed Labelled Hand Dataset in the Wild provides this additional information and also solves a major problem ie occlusion. Since manually annotating a large dataset is a tedious task,hence we propose a novel approach to automate the generation of large dataset using the triangulation method which is also known as multiview Annotation.In this approach two best frames are labelled with the 2D points(21 keypoints) which are then triangulated in 3D space using multiview geometry with the use of fiducial markers.These triangulated points in a 3D space are reprojected onto all other images of a particular pose and this process is repeated for all the other poses thus automating the generation of large labeled dataset in wild.
Indic Language Question Answering: A Survey

Over the past few years, research interest in the sub-domain of question answering has tremendously increased. Yet, most of the work on QA and more generally, on natural language processing has been predominantly limited to the English language. In contrast, with each passing year, the number of people with access to the internet is exponentially increasing, especially those residing in South Asian countries whose primary language is not English. With this in mind, the survey’s aim is to recognize, review and analyze the various question-answering datasets that exist for resource-scare Indic languages such as Hindi, Urdu, Tamil, and Marathi. It also intends to shed light on the state-of-the-art of Indic question-answering itself, in terms of methods used, best-performing models, and evaluation metrics. The review also includes multilingual benchmarks which have been recently published.
Generate Secure Transactions between Clients using a Three-Phase Password Generation Technique

Text-based password mechanisms protect documents and many applications today. Hackers can easily break this type of text-based password. To overcome this problem today most systems suggest the user set the alphanumeric combination text or lengthy text combination of some special characters. This will make it challenging for the hackers to guess the password. The primary problem with this approach is that it is hard to recall those passwords. Set simple or easy-to-remember passwords are the choice of the majority of users. But this type of password is easy to crack or identify by hackers. The other type of password is set either by using image-based or biometric passwords, but both techniques have their own disadvantages. The proposed three-phase password system uses images and different types of colors to protect the system. This technique removes all the drawbacks of the existing technique. To secure the authentication against shoulder surfing, dictionary attacks, and other hacking systems. The result of the finding showed that every case is successfully handled by the suggested strategy.
Machine Learning Pose Detection Kit Implementation in Taspen Android Application

The purpose of this paper is to determine that a machine learning kit for live detection is able to minimize the time consumed or time efficiency from offline pension live detection to activate the monthly pension recipient bank account compared to online methods that are available now. Time efficiency measured by time needed to complete the task to check the lives of pension recipients offline and online. The research case application for this paper is live detection in Taspen authentication android application that is available on google play store for online, and old offline process that obliged the pension recipient to come to PT. Taspen office directly. The methodology to measure time efficiency is very efficient. In broad outline, the results of time efficiency are very satisfied compared to online live check. The result is making the conclusion that online live detection is more efficient than the old offline method to detect the lives of pension recipients. Note that this paper is a conceptual paper without any data to collect and also analyze, but just to determine that machine learning has a significant role and is productive and also useful in financial technology.
Design of Two -Wheeler Hybrid Electric Vehicle using Series Parallel Configuration

In India two wheelers are most common mode of transport to a majority of people because of its’s affordability. Though they are also a major cause of pollution almost 20% of total pollution (which is 10% more than the pollution by cars) and increase in demand and supply gap of oil, which was 6 million barrel per day back in 2020 which is estimated to increase by 14.4 million barrels. All of this can be minimized by simply replacing IC engine vehicles by pure electric or Hybrid electric vehicles for two wheelers. This paper discusses the idea of simulation of HEV two-wheeler using power split to interchange power between the IC engine and motor and provide a balanced output to the
Realization of Halcon Image Segmentation Algorithm in Machine Vision for Complex Scenarios

In the complex real world, with the application and popularization of the intelligent systems and information platforms, complex image recognition and segmentation need to be paid attention to. Therefore, this paper studies the realization of Halcon image segmentation algorithm in machine vision for complex scenarios. Firstly, the image complexity is analyzed. The core reason for firstly analyzing the image complex modelling is that for different images with the segmentation task, the training sets are different. Through the classification of the image complexity, different training and experimental sets can be targeted for performing the real-time tasks, and then, a novel complexity level model is defined. Then, a 2-step segmentation algorithm is proposed. For the simple and complex images, the segmentation models are different to make the comprehensive model efficient. For the complex image, the Selection of Cluster Number algorithm is applied. The proposed experiment compares the proposed model with the FCM, KFCM and SVM and the results have shown that the designed model is efficient considering different factors.
Analysis of Face Feature Recognition using MATLAB

One of the fastest and simplest ways to distinguish between two persons who seem identical is to look at their face. Face recognition is a kind of personal identification system that uses an individual's physical traits to determine their identity. Face detection, which occurs quickly in people (unless in situations when the item is nearby), and face recognition are the main steps in the process of identifying a face. The second phase, dubbed "introduction," is proving that a certain person is connected to a specific face. Then, using a duplicate of the scene, a model for facial image recognition—also known as face recognition—is created. One of the biometrics technologies that has attracted the most research focus is facial recognition. The Fisherface technique and the Eigenface method are two examples of the several techniques utilized in the established facial recognition patterns of today. The conceptual basis of the technique is the Principal Component Analysis (PCA), which is used to minimize the face-dimensional space occupied by facial features. Finding the eigenvector that corresponds to the face image's biggest eigenvalue was the main goal of principal component analysis (PCA) when it came to face recognition using Eigen faces (facial space). Image processing will be a major component of the face detection and identification system that is being developed for this project. Software called Matlab is necessary for this task.
Email Spam: A New Strategy of Screening Spam Emails using Natural Language Processing

Due to its cost-effectiveness, email has become the primary method of commercial and general communication. These emails can be accessed by everyone quickly with an internet connection. For these reasons, despite the existence of other communication tools, the usage of email keeps growing. The rapid increase in technology has caused many applications to be automated. Considering this example: Email spamming increased as a result of the automation of email classification. This has become a significant problem for email communication. Before artificial intelligence and machine learning techniques, various solutions included manual detection by identifying the specific keywords in the email but this method is not possible to solve the current problem. Since the number of emails generated nowadays is vast, then the picture of artificial intelligence and machine learning algorithms comes into focus. Emails can be classified into various categories very easily based on the requirements of a particular user or a company. This email spam classification model is implemented by using Python & classification algorithms like Logistic Regression, Naive Bayes, and Support Vector Machine.
Guided Cost Learning for Lunar Lander Environment Using Human Demonstrated Expert Trajectories

Inverse Reinforcement Learning is a subset of Imitation learning, where the goal is to generate a reward function that captures an expert’s behavior using a set of demonstrations by the expert. Guided Cost Learning (GCL) is a recent approach to finding a neural network reward function. In this paper the GCL algorithm is explored and applied to the Lunar Lander environment of the OpenAI gym. We generated our own set of expert demonstrations and implemented the GCL algorithm. We successfully demonstrate that Guided Cost Learning can generate a reward that completely encapsulates desired behavior depicted in the expert demonstrations, even for high dimensional state space environments such as the lunar lander environment. Reward and policy evaluations between the actual reward function and the GCL generated rewards function are compared and the results are presented.
Knowledge Blended Open Domain Visual Question Answering using Transformer

Interacting with an image in the form of dialog is one of the challenging applications of the vision-language model. Image question answering allows us to interact with an image in form of question and answer. Ask any question about the image and the machine will generate an answer in a natural language. Not all questions are image-dependent; some of the questions may require external knowledge. Integrating external knowledge in an image question-answering model has been an open research area. A novel knowledge-incorporated image question-answering model based on a transformer using deep co-attention has been proposed. The model leverages the structured knowledge present in the ConceptNet. Important objects from the image and important keywords from the question are extracted. Using these extracted objects and text keywords, related concepts from the ConceptNet have been extracted. The top five most related concepts have been considered for further processing. A novel attention mechanism using a transformer has been introduced to combine this external knowledge with the Visual question answering model. The proposed model is evaluated based on VQA 2.0 dataset. The experimental results show that the incorporation of the external knowledge base in the VQA model allows the model to answer more complex open-domain questions and achieves the accuracy of 67.97% on VQA validation set.
Blockchain based Secure Erlang Server for Request based Group Communication over XMPP

Many real world activities in computer science scenarios are linked with concurrency and security related issues and have to handle large number of processes to be executed in parallel with false safe security solutions. There are many traditional methods in programming languages to handle concurrency. Concurrency is one of the major issues that need to be addressed by most of the servers when dealing with the group communication operations. Security of the data as well as the credibility of the users are the other aspects when a group of users involve in real-time communication. Many light-weighted servers are designed to carryout elementary operations of request handling, file sharing etc. In design of such servers having large number of clients, the request service handling will be based on the individual server programs. Keeping track of individual credibility and establishing concurrency solutions in server design is challenging. The whole work describes the significance and implementation of an Erlang based XMPP server in comparison with a Python based XMPP server with a view to service the client request handling operations for sending messages, group chatting, buddy-list creation, presence identification integrated with XML messaging pattern as per the XMPP protocol. We also accomplish the security and credibility of the users using a blockchain based interface that keep track of user activities during group communication. The security analysis is also performed for blockchain based interface.
Sarcasm Detection followed by Sentiment Analysis for Bengali Language: Neural Network &amp; Supervised Approach

As www data grows, so do opinions, views, visitors, news, and comments. Using opinions, perspectives, and remarks, Natural Language Processing (NLP) professionals may classify emotions. Classifying and evaluating Bengali text emotions is becoming significant in e-commerce, journalism, movies, OTT, and security applications. The lack of Bengali corpus makes developing a Sentiment Analysis system difficult. Sarcasm is another popular social media trend. Positive words are often used to indicate hatred. Thus, it’s hard to tell what these sentences mean. This study presents a method for identifying and analysing sarcasm. GloVe is used to represent words while LSTM is trained and tested on the represented characteristics. Experiments show 91.94% accuracy. Predicted sarcastic sentences are labelled as negative and added to Sentiment Analysis corpora (SA). Logistic Regression (LR), K-Nearest Neighbor (K-NN), Linear Support Vector Machine (SVM), and Random Forest (RF) are used to feature matrices for sentiment analysis. For Unigram, Bi-gram, and Tri-gram models, Linear SVM has the highest precision (92.5%), whereas LR model approaches greater accuracy (72.04%) and F1-score (68.15%).
Detecting the Human Activities of Aging People using Restricted Boltzmann Machines with Deep Learning Technique in IoT

Approximately 962,000,000 persons worldwide are 60 or older. Despite the widespread adoption of techniques for human activity detection, there has been a dearth of study into the specific challenge of identifying the tasks performed by the elderly. Given the proliferation of wearable and mobile devices, the Internet of Healthcare Things is assuming a larger role in Human Activity Recognition (HAR). This article focuses on assisting the elderly by tracking their movements in both indoor and outdoor settings. The dataset includes human actions like sitting, walking, ascending and descending stairs, standing, and lying down. Here, how deep learning might improve HAR in Internet of Home Things settings has been examined. For better HAR results, a semi-supervised deep learning framework has been developed that makes effective use of the imperfectly labelled sensor data in order to fine-tune the classifier learning model. An intelligent auto-labelling system is built on top of Deep Q-Network to enhance learning performance in Internet of Things (IoT) environment and address the issue of insufficient labelled samples. Finally, real-world data is used in trial and evaluation to prove the method’s utility and efficacy.
Review of Artificial Intelligence methods for detecting cancer in medical image processing

Cancer is the unchecked spread of aberrant cells throughout the body. Cancer is a general word for a set of diseases brought on by the growth of abnormal cells in various bodily parts. Lung cancer, breast cancer, skin cancer, oral cancer, colon cancer, and prostate cancer are just a few of the more than a hundred different forms of cancer. Delays in treatment might result in major health problems and even death. This research reviews strategies for detecting liver, brain, and lung cancer using image processing. Automated and computer-aided detection systems (CAD) with artificial intelligence are the approaches utilized for detection, and these systems are good at processing big datasets to produce accurate and effective results in the detection of cancer. To be compatible with AI, these processing systems must overcome numerous obstacles, including picture capture, pre-processing, data management, segmentation, and classification algorithms. The numerous picture acquisition and segmentation approaches are reviewed in this work. These methods are now essential to meet the needs of the expanding patient population and to improve the healthcare system.
Hybrid Perception Analysis of World Leaders in Reddit using Sentiment Analysis

Natural Language Processing (NLP) is the branch of Artificial Intelligence that deals with the interpretation of human speech. NLP is a vast area of study that is continually being developed each day, with active research happening worldwide. The development of NLP algorithms is of utmost importance to the advancements in the field of Artificial Intelligence. With the increasing popularity of social media and the number of hours spent on social media multiplying, people share their opinion on a wide range of topics and issues. This sudden burst of data generated by social media platforms contains a massive potential when combined with state-of-the-art NLP models, it can be leveraged to our advantage. This work builds a dataset of user comments on the top posts about political leaders on Reddit, using Reddit’s API. On Reddit, extensive discussions on various topics occur daily. The end goal of this work is to rank the chosen world leaders based on their likability. To find the general likability of a public personality, we try to classify the comments collected from Reddit using Sentiment Analysis. This paper employs state-of-the-art NLP algorithms such as Flair, DistilBERT, and Text Blob Analysis, and combines the results to get a better final rank of the world leaders.
Impact of Stain Normalisation Technique on Deep Learning based Nuclei Segmentation in Histopathological Image

Cell nuclei count and morphology are the key parameters in the histopathological image for evaluating various pathological conditions. However, the manual extraction of these parameters is a tedious and time-consuming task. Automated nuclei segmentation is the practical solution. Deep learning-based approaches have recently become popular for automated nuclei segmentation tasks in histopathological images. Stain colour variability frequently occurs in Hematoxylin and Eosin (H&E)-stained histopathological images because of differences in the staining process and digitisation medium. A deep learning-based approach is susceptible to data variability; therefore, data augmentation and normalisation are crucial pre-processing steps to improve the model's generalisation. In the present work, we performed the comparative analysis of the colour augmentation and stain normalisation techniques, namely Reinhard, Macenko and Vahadane, for deep learning-based nuclei segmentation tasks in H&E stained histopathological images. We have used three different datasets and performed within-dataset and cross- dataset analysis to evaluate the trained model's generalisation capabilities. Stain normalisation methods, e.g. Reinhard and Vahadane, showed better performance in various datasets than colour augmentation techniques.
Opinion Detection in Hinglish News Reporting

News bulletins play an important role in people’s daily lives. As humans evolved, so did our ability to form opinions. In the domain of journalism and news reporting, it is desirable that those reporting news do not add their personal opinions. However, we often observe biases in news reporting, and therefore the task of assessing the opinion of news reporters has become a significant issue. In this work, we study the performance of classical machine learning and vectorization techniques on opinion detection in Hinglish code-mixed news debates related to political and religious issues aired on Indian news channels. We were able to achieve the best accuracy of 87% using Logistic Regression algorithm with Bag of Words (BoW) vectorization technique.