Reviewerly/exHarmony huggingface dataset:
Subspace Clustering in High-Dimensional Data Streams: A Systematic Literature Review

Clustering high dimensional data is challenging as data dimensionality increases the distance between data points, resulting in sparse regions that degrade clustering performance. Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space. Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams. Data streams are not only high-dimensional, but also unbounded and evolving. This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams. Although many articles have contributed to the literature review on data stream clustering, there is currently no specific review on subspace clustering algorithms in high-dimensional data streams. Therefore, this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments. The review follows a systematic methodological approach and includes 18 articles for the final analysis. The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams. The main findings relate to six elements: clustering process, cluster search, subspace search, synopsis structure, cluster maintenance, and evaluation measures. Most algorithms use a two-phase clustering approach consisting of an initialization stage, a refinement stage, a cluster maintenance stage, and a final clustering stage. The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected micro-clusters. Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers. Future work can focus on the clustering framework, parameter optimization, subspace search techniques, memory-efficient synopsis structures, explicit cluster change detection, and intrinsic performance metrics. This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams.
Improved Transient Search Optimization with Machine Learning Based Behavior Recognition on Body Sensor Data

Recently, human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems. Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs, physical human action recognition (HAR) through body sensors gives useful information about the lifestyle and functionality of an individual. This article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based Behavior Recognition (ITSOML-BR) technique using body sensor data. The presented ITSOML-BR technique collects data from different body sensors namely electrocardiography (ECG), accelerometer, and magnetometer. In addition, the ITSOML-BR technique extract features like variance, mean, skewness, and standard deviation. Moreover, the presented ITSOML-BR technique executes a micro neural network (MNN) which can be employed for long term healthcare monitoring and classification. Furthermore, the parameters related to the MNN model are optimally selected via the ITSO algorithm. The experimental result analysis of the ITSOML-BR technique is tested on the MHEALTH dataset. The comprehensive comparison study reported a higher result for the ITSOML-BR approach over other existing approaches with maximum accuracy of 99.60%.
Modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition Model

Traditional indoor human activity recognition (HAR) is a time-series data classification problem and needs feature extraction. Presently, considerable attention has been given to the domain of HAR due to the enormous amount of its real-time uses in real-time applications, namely surveillance by authorities, biometric user identification, and health monitoring of older people. The extensive usage of the Internet of Things (IoT) and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing. The more commonly utilized inference and problem-solving technique in the HAR system have recently been deep learning (DL). The study develops a Modified Wild Horse Optimization with DL Aided Symmetric Human Activity Recognition (MWHODL-SHAR) model. The major intention of the MWHODL-SHAR model lies in recognition of symmetric activities, namely jogging, walking, standing, sitting, etc. In the presented MWHODL-SHAR technique, the human activities data is pre-processed in various stages to make it compatible for further processing. A convolution neural network with an attention-based long short-term memory (CNN-ALSTM) model is applied for activity recognition. The MWHO algorithm is utilized as a hyperparameter tuning strategy to improve the detection rate of the CNN-ALSTM algorithm. The experimental validation of the MWHODL-SHAR technique is simulated using a benchmark dataset. An extensive comparison study revealed the betterment of the MWHODL-SHAR technique over other recent approaches.
An Efficient Color-Image Encryption Method Using DNA Sequence and Chaos Cipher

Nowadays, high-resolution images pose several challenges in the context of image encryption. The encryption of huge images' file sizes requires high computational resources. Traditional encryption techniques like, Data Encryption Standard (DES), and Advanced Encryption Standard (AES) are not only inefficient, but also less secure. Due to characteristics of chaos theory, such as periodicity, sensitivity to initial conditions and control parameters, and unpredictability. Hence, the characteristics of deoxyribonucleic acid (DNA), such as vast parallelism and large storage capacity, make it a promising field. This paper presents an efficient color image encryption method utilizing DNA encoding with two types of hyper-chaotic maps. The proposed encryption method comprises three steps. The first step initializes the conditions for generating Lorenz and Rossler hyper-chaotic maps using a plain image Secure Hash Algorithm (SHA-256/384). The second step performs a confusion procedure by scrambling the three components of the image (red, green, and blue) using Lorenz hyper-chaotic sequences. Finally, the third step combines three approaches to encrypt the scrambled components for diffusion: DNA encoding/decoding, addition operation between components, and XORing with Rossler hyper-chaotic sequences. The simulation results indicate that the suggested encryption algorithm satisfies the requirements of security. The entropy value of confusion and diffusion is 7.997, the key space is 2<sup>200</sup>, and the correlation coefficient is nearly zero. The efficacy of the proposed method has been verified through numerous evaluations, and the results show its resistance and effectiveness against several attacks, like statistical and brute-force attacks. Finally, the devised algorithm vanquishes other relevant color image encryption algorithms.
Smart Fraud Detection in E-Transactions Using Synthetic Minority Oversampling and Binary Harris Hawks Optimization

Fraud Transactions are haunting the economy of many individuals with several factors across the globe. This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions. This research proposes a novel methodology through three stages. Firstly, Synthetic Minority Oversampling Technique (SMOTE) is applied to get balanced data. Secondly, SMOTE is fed to the nature-inspired Meta Heuristic (MH) algorithm, namely Binary Harris Hawks Optimization (BinHHO), Binary Aquila Optimization (BAO), and Binary Grey Wolf Optimization (BGWO), for feature selection. BinHHO has performed well when compared with the other two. Thirdly, features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and non-fraud. The efficiency of BinHHO is analyzed with other popular MH algorithms. The BinHHO has achieved the highest accuracy of 99.95% and demonstrates a more significant positive effect on the performance of the proposed model.
Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions. The number of social media users has been increasing over the last few years, which have allured researchers’ interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a better way. Irony and sarcasm detection is a complex task in Natural Language Processing (NLP). Irony detection has inferences in advertising, sentiment analysis (SA), and opinion mining. For the last few years, irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web content. Therefore, this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification (CLODBN-IRC) model on social media. The presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social media. To attain this, the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature extraction. For irony detection and classification, the DBN model is exploited in this work. At last, the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization (IABC) algorithm. The experimental validation of the presented CLODBN-IRC method can be tested by making use of benchmark dataset. The simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.
The Design &amp; Implementation of a Self-adaptive Hybrid Electric Skateboard

This paper proposes a novel electric skateboard control architecture to hybridize the skater's manual operation and electric motor drive. The proposed scheme does not need a handheld remote controller for steering; hence provides better man-machine coordination and enhances the safety of new skaters. For this purpose, a torque-speed control algorithm is designed to compensate for the manual acceleration force and rolling resistance by sensing the motor's speed, acceleration, and torque outputs. The compensation level is configurable according to the skater's comfortableness. The proposed electric control solution also enhances the battery mileage per charging and can be applied to various electric skateboards since it does not require a dedicated weight/pressure sensor to detect if the skater is on or off the board. The control scheme is simulated in MATLAB/Simulink and experimentally verified by a controller based on C2000 DSP that supports sensorless brushless DC(BLDC) motor drive.
Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities. Several models have been available in the literature for sign language detection and classification for enhanced outcomes. But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks. This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning (ASLGC-DHOML) model. The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures. The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network (DenseNet169) model. For gesture recognition and classification, a multilayer perceptron (MLP) classifier is exploited to recognize and classify the existence of sign language gestures. Lastly, the DHO algorithm is utilized for parameter optimization of the MLP model. The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects. The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.
Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data, but these techniques and algorithms cannot be used to protect data from an attacker. Cloud cryptography is the best way to transmit data in a secure and reliable format. Various researchers have developed various mechanisms to transfer data securely, which can convert data from readable to unreadable, but these algorithms are not sufficient to provide complete data security. Each algorithm has some data security issues. If some effective data protection techniques are used, the attacker will not be able to decipher the encrypted data, and even if the attacker tries to tamper with the data, the attacker will not have access to the original data. In this paper, various data security techniques are developed, which can be used to protect the data from attackers completely. First, a customized American Standard Code for Information Interchange (ASCII) table is developed. The value of each Index is defined in a customized ASCII table. When an attacker tries to decrypt the data, the attacker always tries to apply the predefined ASCII table on the Ciphertext, which in a way, can be helpful for the attacker to decrypt the data. After that, a radix 64-bit encryption mechanism is used, with the help of which the number of cipher data is doubled from the original data. When the number of cipher values is double the original data, the attacker tries to decrypt each value. Instead of getting the original data, the attacker gets such data that has no relation to the original data. After that, a Hill Matrix algorithm is created, with the help of which a key is generated that is used in the exact plain text for which it is created, and this Key cannot be used in any other plain text. The boundaries of each Hill text work up to that text. The techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorithms. Then, the Kasiski test is used to verify the validity of the proposed algorithm and found that, if the proposed algorithm is used for data encryption, so an attacker cannot break the proposed algorithm security using any technique or algorithm.
An Improved Calibration Method of Grating Projection Measurement System

In the traditional fringe projection profilometry system, the projector and the camera light center are both spatially virtual points. The spatial position relationships specified in the model are not easy to obtain, leading to inaccurate system parameters and affecting measurement accuracy. This paper proposes a method for solving the system parameters of the fringe projection profilometry system, and the spatial position of the camera and projector can be adjusted in accordance with the obtained calibration parameters. The steps are as follows: First, in accordance with the conversion relationship of the coordinate system in the calibration process, the calculation formula of the vertical distance from the camera light center to the reference plane and the calculation formula of the distance between the projector and the camera light center are given respectively. Secondly, according to the projector calibration principle, the position of the projector light axis perpendicular to the reference plane is gained by comparing the parallel relationship between the reference plane coordinate system and the projector coordinate system’s Z-axis. Then, in order to fulfill the position restriction that the line between the projector light center and the camera light center must be parallel to the reference plane, the camera’s spatial location is adjusted so that the vertical distance between it and the reference plane tends to that between the projector light center and the reference plane. And finally, the three-dimensional (3D) reconstruction of the target object can be finished using the phase height model’s system parameters once the aforementioned position limitations are put into practice. Experimental results demonstrate that the method improves the measurement accuracy, and verifies that it is effective and available in 3D shape measurement.
Image Splicing Detection Using Generalized Whittaker Function Descriptor

Image forgery is a crucial part of the transmission of misinformation, which may be illegal in some jurisdictions. The powerful image editing software has made it nearly impossible to detect altered images with the naked eye. Images must be protected against attempts to manipulate them. Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications. Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images. Because image tampering detection targets processing techniques such as object removal or addition, identifying altered images remains a major challenge in research. In this study, a novel image texture feature extraction model based on the generalized k-symbol Whittaker function (GKSWF) is proposed for better image forgery detection. The proposed method is divided into two stages. The first stage involves feature extraction using the proposed GKSWF model, followed by classification using the “support vector machine” (SVM) to distinguish between authentic and manipulated images. Each extracted feature from an input image is saved in the features database for use in image splicing detection. The proposed GKSWF as a feature extraction model is intended to extract clues of tampering texture details based on the probability of image pixel. When tested on publicly available image dataset “CASIA” v2.0 (Chinese Academy of Sciences, Institute of Automation), the proposed model had a 98.60% accuracy rate on the YCbCr (luminance (Y), chroma blue (Cb) and chroma red (Cr)) color spaces in image block size of 8 × 8 pixels. The proposed image authentication model shows great accuracy with a relatively modest dimension feature size, supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms.
Mining Fine-Grain Face Forgery Cues with Fusion Modality

Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns. Despite the considerable progress in existing methods, we note that: Previous works overlooked fine-grain forgery cues with high transferability. Such cues positively impact the model’s accuracy and generalizability. Moreover, single-modality often causes overfitting of the model, and Red-Green-Blue (RGB) modal-only is not conducive to extracting the more detailed forgery traces. We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues. First, we propose two functional modules to reveal and locate the deeper forged features. Our method locates deeper forgery cues through a dual-modality progressive fusion module and a noise adaptive enhancement module, which can excavate the association between dual-modal space and channels and enhance the learning of subtle noise features. A sensitive patch branch is introduced on this foundation to enhance the mining of subtle forgery traces under fusion modality. The experimental results demonstrate that our proposed framework can desirably explore the differences between authentic and forged images with supervised learning. Comprehensive evaluations of several mainstream datasets show that our method outperforms the state-of-the-art detection methods with remarkable detection ability and generalizability.
Cognitive problems of philosophical anthropology: Consciousness and Representation

The aim of the study is to identify the main cognitive problems of philosophical anthropology in the context of opposition or interaction between the experimental sciences and the humanities. The paper traces the process of formation of the concept of “consciousness” as an alternative to the concept of “soul”; presents introspection as a traditional method of self-consciousness analysis and development; characterises the concept of “representation” in philosophical anthropology and formulates the main idea of introducing cognitive science into education. The scientific novelty of the study lies in determining the role of consciousness in the system of civilised education in the formation of the social, political, aesthetic, natural science and other spheres of modern reality. As a result of the study, the authors have formulated the ideas that within the framework of cultural neurosciences development, cognitive sciences make it possible to include a philosophical, primarily anthropological, understanding of consciousness and self-consciousness in a comprehensive interdisciplinary analysis. At the same time, it is noted that within the framework of cognitive approaches, the influence of social factors on consciousness, including the ideological influence of specific societies, is not sufficiently taken into account.
Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

Video prediction is the problem of generating future frames by exploiting the spatiotemporal correlation from the past frame sequence. It is one of the crucial issues in computer vision and has many real-world applications, mainly focused on predicting future scenarios to avoid undesirable outcomes. However, modeling future image content and object is challenging due to the dynamic evolution and complexity of the scene, such as occlusions, camera movements, delay and illumination. Direct frame synthesis or optical-flow estimation are common approaches used by researchers. However, researchers mainly focused on video prediction using one of the approaches. Both methods have limitations, such as direct frame synthesis, usually face blurry prediction due to complex pixel distributions in the scene, and optical-flow estimation, usually produce artifacts due to large object displacements or obstructions in the clip. In this paper, we constructed a deep neural network Frame Prediction Network (FPNet-OF) with multiple-branch inputs (optical flow and original frame) to predict the future video frame by adaptively fusing the future object-motion with the future frame generator. The key idea is to jointly optimize direct RGB frame synthesis and dense optical flow estimation to generate a superior video prediction network. Using various real-world datasets, we experimentally verify that our proposed framework can produce high-level video frame compared to other state-of-the-art framework.