Reviewerly/exHarmony huggingface dataset:
Visual Gesture-Based Home Automation

In recent years, there has been an increase in the use of IoT devices for home automation, shopping malls, and other public places. However, for individuals who are mute or bedridden, accessing these devices can be difficult, especially when they are voice-activated. To address this issue, hand gesture recognition technology has been developed to allow individuals to control these devices through simple hand movements. Image processing and pattern recognition are crucial for accurately detecting these hand gestures, and platforms such as Open CV, Python, PyCharm, and Media Pipe are commonly used in software development to achieve this. This technology has the potential to help people with physical, sensory, or intellectual disabilities to participate fully in all activities in society and enjoy equal opportunities. By using hand gestures to communicate with IoT devices, individuals who are deaf can also benefit from this technology. Ultimately, this technology has the potential to create a human-computer interaction that is accessible to all, making it a valuable addition to the field of assistive technology. Furthermore, hand gesture recognition technology is an excellent example of the potential of IoT devices to facilitate a more connected and automated world. However, it is important to note that with any new technology, there are also concerns around data privacy and security. As such, it is essential that developers prioritize ethical considerations and robust security protocols when designing these systems. Moreover, hand gesture recognition technology can be further improved through the use of artificial intelligence and machine learning. These technologies can help improve the accuracy of the recognition system and provide a more personalized experience for users. This system is highly reliable and user-friendly, and does not require any physical contact, which makes it highly suitable for disabled people. Furthermore, the development of new sensor technologies can also help increase the reliability and efficiency of the hand gesture recognition system. Overall, the development of hand gesture recognition technology is an exciting and innovative area of research that has the potential to improve the lives of many individuals, particularly those with physical or sensory disabilities. With continued advancements in technology, it can expect to see more sophisticated and accessible hand gesture recognition systems that will help create a more inclusive and accessible society.
Comparison of Machine Learning Techniques for Sentiment Analysis

Sentiment analysis is the process of categorizing and locating the emotions represented in a textual source. Sentiment analysis can be used widely in different areas, such as customer review data, feedback data classification, survey responses, and social media comments. Tweets on Twitter contain a variety of sentiments reflecting the perception, thinking, and working background of the user. With the help of the sentiment analyzer, it can define the response of others on any matter or subject of interest. Here, we used machine learning-based NLP (natural language processing) and text analysis technology to define an automated model that can classify the sentiment of a large dataset. Here we used the following three machine-learning classifiers: logistic regression, SVM, and Bernoulli Naïve Bayes. The effectiveness and performance of these classifiers are assessed using F1 scores and accuracy. The accuracy of these models is 83%(LR), 81%(SVM), and 80%(BNB) So logistic regression model provides the best result among these.
Exploring Methods for Dealing with Class Imbalances in Supervised Machine Learning Structured Datasets

The class imbalanced datasets are major challenge for classification techniques. In this paper, the role and possibilities of handling of imbalanced classes in structured and tabular dataset have been experimentally discussed. In methodology, diverse over sampling and under sampling techniques were applied and analyzed on basis of parameters viz., accuracy, precision, recall, and f1-score. Haberman Breast Cancer, Pima Indian diabetes and synthetic datasets were considered for experimental study, unbalanced datasets were considered. All three are unbalanced datasets were analyzed through classification algorithms. Further, class balancing techniques were applied through over sampling and under sampling methods and then supervised classification algorithms were applied and analyzed on basis of metrics. The results reflected with best fit metrics for both under and over sampling methods. In conclusion a best technique out of implemented methods were identified and proposed for future use.
An Enhanced Image Loading Framework for Social Media Applications

Online Social network (OSN) is the most popular platform where users prefer to share images and videos. Image loading time in social media applications is time-consuming due to significantly less internet bandwidth. Uploading an image on a social media platform demands accurate size, highest quality, format, and resolution. Often, duplicates of images may be uploaded by the user accidentally. Uploading images or videos by individual users on platforms like Facebook or Instagram is Content loading. In this article, we suggest a suitable method for reducing the content loading time by finding the duplicate images and replacing those images with the original image that is already loaded using ANNOY (Artificial Neural Network Oh Yeah). In the methodology we could successfully reduce the image loading time by checking the duplication.
Experimental study of a novel technique of data hiding with high PSNR

The task of concealment of information is an important challenge in the context of an advance infrastructure of open and uncontrolled Internet users' communication through computer networks. Various steganographic methods have been developed to solve the problem of the protection of transmitted information. The aim of the present paper is the experimental study of the robustness of the algorithm developed by authors for the concealment of information. The reliability of the algorithm for stego analytics attacks has been investigated by the RS method of steganalysis. In addition, the computational complexity of the algorithm is investigated which in turn should not exceed a certain threshold.The results of the experimental studies demonstrate the algorithm’s high robustness and minimal computational time.
Assessing the Effect of Pre-processing Techniques on Classification of Breast Cancer using Histopathological Images

Over past few decades Breast cancer (BC) has become more common and affecting females in early age, which is an alarming and challenging situation for researchers to provide methods to identify the disease in their early stage. This is the deadliest cancer among women and is alarming female fraternity becoming second leading cause of deaths. If the disease gets identified in their early stage it may leads to reduction in mortality rate. It may occur in cells that produce milk (lobules) or in the passages responsible for carrying milk (milk ducts). This paper presents the performance comparison of various pre-processing techniques based on the BreakHis dataset. The dataset used contains 1980 breast histopathological images including 625 benign and 1355 malignant cases. Initially the histopathological images have been pre-processed using techniques including contrast limited adaptive histogram equalization (CLAHE), contrast stretching (CS), histogram equalization (HE), and unsharp masking (UM) followed by feature extraction using 2D Gabor Wavelet Transform to obtain texture feature from both the categories like original and preprocessed images. Finally, support vector machine (SVM) classifies the images in two categories namely benign and malignant. The experiments results show that texture features computed using UM as pre-processing tool outperformed for making difference between benign and malignant images using breast histopathological images with a classification accuracy of 84.1 %.
Hybrid Model for Sentiment Analysis of Whatsapp Data

Sentiment analysis, also called opinion mining (OM), is an approach to analyzing the sentiments expressed in data. This approach uses Natural Language Processing to classify the data based on emotions in different classes. Various Social media data are analyzed to determine sentiment, and Machine Learning (ML) techniques classify the data. This study utilizes ML models to analyze sentiment in WhatsApp data. The sentiment analysis process includes some steps, such as to pre-process the data, extract the features, and classify the data. The initial stage contributes to the clean-up of raw data and transforms it to make it suitable for analysis. Feature extraction is a stage to retrieve a relevant feature from the pre-processed data that contribute to determining sentiment. Finally, machine learning algorithms classify data to determine the sentiments expressed in the text. This work proposes a voting classifier which is hybrid architecture comprising SVM, KNN, and a Decision tree. Python is executed to simulate the suggested algorithm, and its performance is evaluated based on accuracy, precision, and recall metrics. These parameters are useful in measuring the efficiency of the algorithm in accurately classifying the sentiments existing in the data.
Parallel Model to Detect Attacks Using Evolutionary Based Technique

Evolutionary-based algorithms emerged due to their flexibility and effectiveness in solving different varieties of problems. Optimization-based techniques are used in finding solutions that involve multiple conflicting objectives. Parallel evolutionary-based algorithms are used to overcome the time-consuming job of finding solutions to these types of problems. In this paper, we present a parallel genetic programming-based model that runs parallelly and obtains solutions in a minimal amount of time. The model also allows the user to select the best set of objectives based on the requirements of the users. An island model is used which runs the operations on different islands parallelly. This not only decreases the execution time of the process but also increases the diversity of the population. The results obtained in different islands are fed to an ensemble classifier to get the required result. The model was trained and tested using the state-of-the-art ISCX-2012 and CICIDS2017 datasets. In our work, we have mainly focused on detecting the attacks in a system in a short duration of time. The model developed gave significant performance improvement compared to the results obtained using the normal CPU implementation.
Integrated Intelligent Surveillance System Using Deep Learning

Nowadays the surveillance systems are widely used to find out the suspicious events that have occurred. In conventional systems, there are a lot of limitations such as storage, bandwidth, cost, the short lifespan of hardware devices, loading issues, etc. We developed an intelligent surveillance system using deep learning in which the video footage of suspicious events is extracted. Transfer learning, a part of machine learning, is used for face detection which involves the reuse of a pre-trained model on new data. The abnormal activity detection is done using a multi person MoveNet Light model and the face detection is done using VGG16. The suspicious objects found in the frame (gun, mask) are identified using corner detection. This system offers less bandwidth, high security, effective storage, and reduced load-balancing issues. In this paper, we detailed the face detection, object detection and anomaly detection used in our system.
Protecting Contactless Credit Card Payments from Fraud through Ambient Authentication and Machine Learning

Credit card fraud (CCF) is a persistent issue in the financial sector with serious consequences. Data mining has proven to be extremely useful in detecting fraud in online transactions. However, detecting CCF through data mining is quite a difficult task because of two causes: constant changes in the profiles of normal and fraudulent behaviour, and the highly skewed nature of the data sets. The outcome of fraud detection in credit card transactions depends on the sampling approach, detection techniques, and variable selection. This work studies the performance of K-Nearest Neighbor, Naive Bayes, Logistic Regression and Random Forest algorithms on a highly skewed dataset. The dataset contains 2,84,807 transactions and has been collected from European cardholder transactions. A hybrid of under-sampling and oversampling techniques has been used on the skewed data. The four techniques were utilized on both data namely preprocessed and raw, and the results are evaluated using specificity, accuracy, sensitivity, and F1-score. The outcomes show that the optimal accuracy for Naive Bayes, Logistic Regression, K-Nearest Neighbor and Random Forest classifiers are 98.72%, 52.34%, 96.89%, 91.67%, respectively. The comparative results indicate that K-Nearest Neighbor performs better than Logistic Regression, Random Forest and Naive Bayes techniques.
Parasitical Disease Prediction Model – a Deep Learning Based Approach

Female anopheles mosquitoes transmit the highly contagious parasitical diseases. Animals as well as humans are harmed by this sickness. In the worst-case scenario, this illness could result in the patient's death if it is not adequately diagnosed in the early stages. It is exceedingly difficultly in confirming 0 the presence of ailment in industry owing towards a deficiency of exceedingly methodological competence. Cutting-edge this situation, data retrieval assistance is required for accurate and quick disease identification. With the aid of IT division buzzword know-hows like Machine Learning, Deep Learning, and Non-natural Acumen, modern IT sectors are working tirelessly to combat this sickness. If appropriately applied, these technologies will continue to be the backbone of healthcare as they have been in recent years. In order to determine if an organism is infected with a parasite or not, this study applies the Convolutional Neural Network (CNN) algorithm to a minuscule carbon copy of the contaminated blood cells. 15 out of 16 random photos can be accurately predicted by our suggested model, which achieved an accuracy of 95.23 percent.
ICE: Implicit Coordinate Encoder for Multiple Image Neural Representation

In recent years, implicit neural representations (INR) have shown their great potential to solve many computer graphics and computer vision problems. With this technique, signals such as 2D images or 3D shapes can be fit by training multi-layer perceptrons (MLP) on continuous functions, providing many advantages over conventional discrete representations. Despite being considered a promising approach to 2D image encoding and compression, the application of INR to image collections remains a challenge, since the number of parameters needed rapidly grow with the number of images. In this paper, we propose a fully implicit approach to INR which drastically reduces the size of MLP models in multiple image representation tasks. We introduce the concept of implicit coordinate encoder (ICE) and show it can be used to scale INR with the image number; specifically, by learning a common feature space between images. Furthermore, we show that our method is valid not only for image collections but also for large (gigapixel) images by applying a "divide-and-conquer" strategy. We propose an auto-encoder deep neural network architecture, with a single ICE (encoder) and multiple MLP (decoders), which are jointly trained following a multi-task learning strategy. We demonstrate the benefits coming from ICE when it is implemented as a one-dimensional convolutional encoder, including a better performance of the downstream MLP models with an order of magnitude fewer parameters. Our method is the first one to make use of convolutional blocks in INR networks, unlike the conventional approach of using MLP architectures only. We show the benefits of ICE in two experimental scenarios: a collection of twenty-four small ( 768×512 ) images (Kodak dataset), and a single large ( 3072×3072 ) image (dwarf planet Pluto), achieving better quality than previous fully-implicit methods, using up to 50% fewer parameters.
Chinese Face Dataset for Face Recognition in an Uncontrolled Classroom Environment

Since the position of the classroom surveillance camera is not fixed, the angle of the face captured through the surveillance video is also different. The deep learning-based face verification model has achieved good results in controlled environments, but there is still much room for improvement in the face verification ability in uncontrolled environments. The performance of the model depends not only on the results of the network but also on the quality and diversity of the dataset. The current Asian face dataset in an uncontrolled environment is insufficient; for this reason, this paper constructs a Chinese face dataset (UCEC-Face) in an uncontrolled classroom environment, which is collected by 35 real classroom surveillance videos. The UCEU dataset contains 7395 images of 130 subjects, including 44 males and 86 females. To verify that there is still room for improving the performance of existing face verification models for Asian face verification, we further utilize four models such as OpenFace and ArcFace for face verification, as well as the VGG-Face model for gender, expression, and age recognition on the UCEC-Face. The experimental results show that the UCEC-Face constructed in this paper is more challenging and difficult to verify in face verification tasks because it is closer to the real environment, and the best results obtained on the existing models only reach 69.7%, which is largely below the average accuracy of the identification results of other datasets.
Self-Supervised and Few-Shot Contrastive Learning Frameworks for Text Clustering

Contrastive learning is a promising approach to unsupervised learning, as it inherits the advantages of well-studied deep models without a dedicated and complex model design. In this paper, based on bidirectional encoder representations from transformers (BERT) and long-short term memory (LSTM) neural networks, we propose self-supervised contrastive learning (SCL) as well as few-shot contrastive learning (FCL) with unsupervised data augmentation (UDA) for text clustering. BERT-SCL outperforms state-of-the-art unsupervised clustering approaches for short texts and for long texts in terms of several clustering evaluation measures. LSTM-SCL also shows good performance for short text clustering. BERT-FCL achieves performance close to supervised learning, and BERT-FCL with UDA further improves the performance for short texts. LSTM-FCL outperforms the supervised model in terms of several clustering evaluation measures. Our experiment results suggest that both SCL and FCL are effective for text clustering.
Twitter Bot Detection Using Neural Networks and Linguistic Embeddings

Twitter is a web application playing the dual role of online social networking and micro-blogging. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots. In this article, we propose a Twitter bot detection model using recurrent neural networks, specifically bidirectional lightweight gated recurrent unit (BiLGRU), and linguistic embeddings. To the best of our knowledge, our Twitter bot detection model is the first that does not require any handcrafted features, or prior knowledge or assumptions about account profiles, friendship networks or historical behavior. The proposed model uses only textual content of tweets and linguistic embeddings to classify bot and human accounts on Twitter. Experimental results show that the proposed model performs better or comparably to state-of-the-art Twitter bot detection models while requiring no feature engineering, making it faster and easier to train and deploy in a real network. We also present experimental results that show the performance and computational costs of different types of linguistic embeddings and recurrence network variants for the task of Twitter bot detection. The results will potentially help researchers design high-performance deep-learning models for similar tasks.
Specific Emitter Identification Based on Differential Constellation

The modulator distortion feature of the existing specific emitter identification (SEI) based on constellation contains the inherent carrier frequency offset of the receiver and transmitter, and the influence of frequency offset on the feature distribution cannot be completely eliminated even by using high-precision frequency synchronization technology. Therefore, this paper presents a novel SEI method based on a differential constellation. On the basis of constructing the modulator distortion signal model, the demodulated signal is differentially processed to form a differential constellation, by comprehensively comparing the difference between the differential demodulation constellation and the ideal constellation, the maximum likelihood method is used to separate the frequency offset in the baseband signal from the modulator distortion feature vector, and a new modulator distortion feature representation is designed, which completely eliminates the influence of carrier frequency offset on the distortion feature distribution of the modulator. Then, a random forest classifier based on decision tree was constructed to learn the individual difference in the distortion features. Compared with the existing identification methods, the fingerprint features extracted in this method are completely independent of the frequency offset of the signal examples, and the influence of frequency offset on the recognition is eliminated. Our results show that the mean and variance of the feature vector distribution proposed in the method do not change with the frequency offset, and the stable and high-precision identification of eight sources can be achieved under different carrier frequency offset conditions, and the accuracy can reach more than 90%.
Attribute-Based Location Sharing for Cloud Computing Environments

The rapid advancement of mobile internet technology and the ubiquitous availability of GPS have made mobile online social networks (mOSNs) more common than conventional online social networks (OSNs) during the last few years. They provide fundamental social services such as location sharing, establishing friendships, and other location-based services. In a mOSN, mobile devices transmit their location data to a server-side (e.g., a cloud service provider) for sharing it with intended users. However, as the location data usually is considered as information with a high level of secrecy, providing confidentiality and fine-grained access control is necessary to preserve the privacy of users. In this paper, we propose a fine-grained attribute-based access control location-sharing scheme addressing these problems effectively. Our scheme enables mobile devices to encrypt their location data under an access structure such that only the users whose attributes satisfies the access structure can trace its location. We define the security of our proposed primitive and prove that it is secure under the hardness assumption of the decisional bilinear Diffie-Hellman (DBDH) problem and in the standard model.