Reviewerly/exHarmony huggingface dataset:
Real-Time Advanced Computational Intelligence for Deep Fake Video Detection

As digitization is increasing, threats to our data are also increasing at a faster pace. Generating fake videos does not require any particular type of knowledge, hardware, memory, or any computational device; however, its detection is challenging. Several methods in the past have solved the issue, but computation costs are still high and a highly efficient model has yet to be developed. Therefore, we proposed a new model architecture known as DFN (Deep Fake Network), which has the basic blocks of mobNet, a linear stack of separable convolution, max-pooling layers with Swish as an activation function, and XGBoost as a classifier to detect deepfake videos. The proposed model is more accurate compared to Xception, Efficient Net, and other state-of-the-art models. The DFN performance was tested on a DFDC (Deep Fake Detection Challenge) dataset. The proposed method achieved an accuracy of 93.28% and a precision of 91.03% with this dataset. In addition, training and validation loss was 0.14 and 0.17, respectively. Furthermore, we have taken care of all types of facial manipulations, making the model more robust, generalized, and lightweight, with the ability to detect all types of facial manipulations in videos.
A novel Lyapunov-stability-based recurrent-fuzzy system for the Identification and adaptive control of nonlinear systems

The requirement to manage complicated nonlinear systems with high uncertainty is one of the main drivers of progress in the identification and control discipline. Since most of the systems are nonlinear, exact identification and control of them using traditional techniques is a challenging task. This paper proposes a novel Takagi–Sugeno (T-S) type Recurrent Fuzzy System (RFS) for nonlinear system identification and adaptive control. To ensure the system's overall stability, a novel Lyapunov-stability-based learning algorithm is developed to obtain the parameter update equations for the proposed model. The performance of the proposed model is also evaluated with the well-known gradient-descent-based Back Propagation (BP) learning method and compared with the Lyapunov-stability method. To judge the efficacy of the proposed model and the learning algorithm we have performed an extensive comparative study by considering other popular soft-computing models such as Jordan Recurrent Neural Network (JRNN), Feed-Forward Neural Network (FFNN), Radial Basis Function Network (RBFN) and a conventional recurrent fuzzy system. The results obtained from the proposed method (RFS + Lyapunov-stability-based learning algorithm) are compared with those obtained from other models and are found to be superior.
PSO-Based Ensemble Meta-Learning Approach for Cloud Virtual Machine Resource Usage Prediction

To meet the increasing demand for its services, a cloud system should make optimum use of its available resources. Additionally, the high and low oscillations in cloud workload are another significant symmetrical issue that necessitates consideration. A suggested particle swarm optimization (PSO)-based ensemble meta-learning workload forecasting approach uses base models and the PSO-optimized weights of their network inputs. The proposed model employs a blended ensemble learning strategy to merge three recurrent neural networks (RNNs), followed by a dense neural network layer. The CPU utilization of GWA-T-12 and PlanetLab traces is used to assess the method’s efficacy. In terms of RMSE, the approach is compared to the LSTM, GRU, and BiLSTM sub-models.
A-DQRBRL: attention based deep Q reinforcement battle royale learning model for sports video classification

ABSTRACTABSTRACTAnalyzing video content and classification are the major challenges for computer vision applications. Thus, the proposed research develops a new attention-based deep Q reinforcement battle royale learning (A-DQRBRL) model for effective sports video classification. Initially, the required keyframes from the input sports video are extracted by an action template approach. Then, pre-processing is performed to remove unwanted noises through cross-guided bilateral filter (CGBF). From the pre-processed frames, the most discriminative features are extracted by Elman recurrent neural network (ERNN). Then, the needed portions are segmented from the extracted features by utilizing a cascaded random forest (CRF) approach. Finally, the sports video is classified by proposing a new A-DQRBRL method. The experimental setup is done by python tool, and the proposed work attains an accuracy of 99.29% in the SVW dataset and 98.97% in the UCF101 dataset. Thus, the experimental results show that the proposed techniques perform better than previous methods.KEYWORDS: Key frame extractionpre-processingElman recurrent neurak networkcascaded random forestdeep Q-learningsports video classificationcross guided bilateral filterspatial and temporal featuressegmentation Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing is not applicable to this article.Correction StatementThis article has been republished with minor changes. These changes do not impact the academic content of the article.
Semantics-Native Communication via Contextual Reasoning

Recently, machine learning (ML) has shown its effectiveness in improving communication efficiency by reinstating the semantics of bits. To understand its underlying principles, we propose a novel stochastic model of semantic communication, dubbed semantics-native communication (SNC). Inspired from human communication, we consider a point-to-point SNC scenario where a speaker has an intention of referring to an entity, extracts its semantic concepts, and maps these concepts into symbols that are communicated to a target listener through the traditional Shannon communication channel. Next, we recall rational humans who can communicate more efficiently by reasoning about the others’ contexts before communication, referred to as contextual reasoning. This motivates us to propose a novel SNC framework harnessing agent states as side information in a way that the speaker locally and iteratively communicates with a virtual agent having the listener’s state, and vice versa. Theoretically, we prove the convergence of contextual reasoning, at which it minimizes the bit length while guaranteeing a target reliability. Simulation results corroborate that contextual reasoning based SNC can significantly reduce bit lengths, and be a robust solution to imperfect agent states by quantizing the entity-concept-symbol mapping before contextual reasoning.
Lightweight Real-Time Semantic Segmentation Network With Efficient Transformer and CNN

In the past decade, convolutional neural networks (CNNs) have shown prominence for semantic segmentation. Although CNN models have very impressive performance, the ability to capture global representation is still insufficient, which results in suboptimal results. Recently, Transformer achieved huge success in NLP tasks, demonstrating its advantages in modeling long-range dependency. Recently, Transformer has also attracted tremendous attention from computer vision researchers who reformulate the image processing tasks as a sequence-to-sequence prediction but resulted in deteriorating local feature details. In this work, we propose a lightweight real-time semantic segmentation network called LETNet. LETNet combines a U-shaped CNN with Transformer effectively in a capsule embedding style to compensate for respective deficiencies. Meanwhile, the elaborately designed Lightweight Dilated Bottleneck (LDB) module and Feature Enhancement (FE) module cultivate a positive impact on training from scratch simultaneously. Extensive experiments performed on challenging datasets demonstrate that LETNet achieves superior performances in accuracy and efficiency balance. Specifically, It only contains 0.95M parameters and 13.6G FLOPs but yields 72.8% mIoU at 120 FPS on the Cityscapes test set and 70.5% mIoU at 250 FPS on the CamVid test dataset using a single RTX 3090 GPU. Source code will be available at https://github.com/IVIPLab/LETNet.
Recent Advances of Blockchain and Its Applications

Blockchain is an emerging decentralized data collection, sharing, and storage technology, which have provided abundant transparent, secure, tamper-proof, secure, and robust ledger services for various real-world use cases. Recent years have witnessed notable developments of blockchain technology itself as well as blockchain-enabled applications. Most existing surveys limit the scopes on several particular issues of blockchain or applications, which are hard to depict the general picture of current giant blockchain ecosystem. In this paper, we investigate recent advances of both blockchain technology and its most active research topics in real-world applications. We first review the recent developments of consensus and storage mechanisms and communication schema in general blockchain systems. Then extensive literature review is conducted on blockchain-enabled Internet of Things (IoT), edge computing, federated learning, and several emerging applications including healthcare, COVID-19 pandemic, online social network, and supply chain, where detailed specific research topics are discussed in each. Finally, we discuss the future directions, challenges, and opportunities in both academia and industry.
Automated Detection and Classification of Oral Squamous Cell Carcinoma Using Deep Neural Networks

This work aims to classify normal and carcinogenic cells in the oral cavity using two different approaches with an eye towards achieving high accuracy. The first approach extracts local binary patterns and metrics derived from a histogram from the dataset and is fed to several machine-learning models. The second approach uses a combination of neural networks as a backbone feature extractor and a random forest for classification. The results show that information can be learnt effectively from limited training images using these approaches. Some approaches use deep learning algorithms to generate a bounding box that can locate the suspected lesion. Other approaches use handcrafted textural feature extraction techniques and feed the resultant feature vectors to a classification model. The proposed method will extract the features pertaining to the images using pre-trained convolution neural networks (CNN) and train a classification model using the resulting feature vectors. By using the extracted features from a pre-trained CNN model to train a random forest, the problem of requiring a large amount of data to train deep learning models is bypassed. The study selected a dataset consisting of 1224 images, which were divided into two sets with varying resolutions.The performance of the model is calculated based on accuracy, specificity, sensitivity, and the area under curve (AUC). The proposed work is able to produce a highest test accuracy of 96.94% and an AUC of 0.976 using 696 images of 400× magnification and a highest test accuracy of 99.65% and an AUC of 0.9983 using only 528 images of 100× magnification images.
CAML: Contextual augmented meta-learning for cold-start recommendation

The performance of recommendation engines is challenged by the cold-start issues. Classical recommendation techniques have limited capability to address this problem because the underlying machine learning models are data-thirsty. Conversely, the recent success of meta-learning attracted a lot of attention due to few-shot learning capabilities which can be employed to accommodate new tasks even when there are few data samples for training. Most of the meta-learning recommendation models adopt model-agnostic meta-learning to initialize parameters that may lead to stuck into local optima instead of global optima for some users. To leverage the learning process, we propose a Contextually Augmented Meta-Learning recommender system (CAML). The proposed method augments the contextual features into a meta-learning model which considerably improves the tasks adaptation capability. We constructed a Data Augmentation Unit (DAU) that used a hybrid similarity to augment data samples of similar neighbors. The augmented samples are then forwarded to a Meta-Learner (MetaL) to learn user preferences and generate relevant recommendations. Additionally, we highlighted that the proposed method is generalized and can be adapted for various meta-learning-based recommendation models. We validate our proposal on three benchmark datasets for both ranking and rating prediction perspectives. The experimental outcomes prove the significance of our proposal over the state-of-the-art recommendation methods.
Multi-view inter-modality representation with progressive fusion for image-text matching

Recently, image-text matching has been intensively explored to bridge vision and language. Previous methods explore an inter-modality relationship between an image-text pair from the single-view feature. However, it is difficult to discover all the abundant information based on a single inter-modality relationship. In this paper, a novel Multi-View Inter-Modality Representation with Progressive Fusion (MIRPF) is developed to explore inter-modality relationships from multi-view features. The multi-view strategy provides more complementary and global semantic clues than single-view approaches. In particular, the multi-view inter-modality representation network is constructed to generate multiple inter-modality representations, which provide diverse views to discover the latent image-text relationships. Furthermore, the progressive fusion module is performed to fuse inter-modality features stepwise, which fully uses the inherent complementary between different views. Extensive experiments on Flickr30K and MSCOCO verify the superiority of MIRPF compared with several existing approaches. The code is available at: https://github.com/jasscia18/MIRPF.
Tight Remainder-Form Decomposition Functions With Applications to Constrained Reachability and Guaranteed State Estimation

This paper proposes a tractable family of remainder-form mixed-monotone decomposition functions that are useful for over-approximating the image set of nonlinear mappings in reachability and estimation problems. Our approach applies to a new class of nonsmooth, discontinuous nonlinear systems that we call either-sided locally Lipschitz semicontinuous (ELLS) systems, which we show to be a strict superset of locally Lipschitz continuous (LLC) systems, thus expanding the set of systems that are formally known to be mixed-monotone. In addition, we derive lower and upper bounds for the over-approximation error and show that the lower bound is achieved with our proposed approach, i.e., our approach constructs the tightest, tractable remainder-form mixed-monotone decomposition function. Moreover, we introduce a set inversion algorithm that along with the proposed decomposition functions, can be used for constrained reachability analysis and guaranteed state estimation for continuous- and discrete-time systems with bounded noise.
Prominent Sampling Techniques Analysis in Machine Learning: Bibliometric Survey and Performance Evaluation

The issue of whether data collection can use an effective sampling strategy presents a challenge to the researcher. Researchers must be familiar with the distinctions in order to choose the most suitable sampling technique or method for the particular study under consideration, given the vast number and variety of sampling techniques and methods that are available. The bibliometric analysis also done for the sampling technique with computer science according to country wise and year wise which provide better ideas for future research. The scope of keyword and citation for the document describe with results obtained using the VosViewer software and data from the Web of Science and Scopus databases unsupervised ML techniques are used to solve the sampling problem. There are different types of ML techniques that are applied to sampling, i.e., K-Mean clustering, DBSCAN, Fuzzy clustering, etc. This research also examines the fundamental ideas of probability sampling, as well as various probability sampling approaches and their strengths and weaknesses.
Designing a Blockchain-Enabled Methodology for Secure Online Voting System

Blockchain has built-in security features. Basic concepts in blockchain include cryptographic, decentralized, and consensus concepts that guarantee integrity. It's been extremely difficult for a very long time to create a safe and secure electronic voting system that gives the clarity and versatility provided by electronic systems, as well as the transparency and privacy supplied by present voting systems. In this research work, blockchain application is assessed for implementing decentralized electronic voting systems. The study offers a different electronic voting system based on blockchain that tackles some of the drawbacks and limitations of current systems and assesses some of the well-known blockchain frameworks in order to build a blockchain-based e-voting system. Through the analysis of a case study, the possibilities of distributed ledger technology have been specifically evaluated.
Application of Machine Learning in the Classification of Data over Social Media Platform

Data classification operation over social media platform such as twitter, Facebook and what’s app is in demand from last two decades. There have been several approaches that are used for data classification such as machine learning and different soft computing techniques. It has been observed that machine learning has made significant contribution in data classification. There have been several research work that did work on data classification using machine learning but the issue with existing research work was limited performance and lack of accuracy. Present research work has focused on application of machine learning in classification of data over social media platform. The theme of work is to enhance the accuracy of classification model that is making use of machine learning technique. In proposed work user comments over social platform have been considered for classification that would support sentiment analysis along with opinion mining.
Edge-enhanced GAN with Vanishing Points for Image Inpainting

Reconstructing the damaged images with perspective views has an extensive range in the field of image inpainting. However, most existing methods generated inadequately realistic restored images. Accomplishing this problem, we propose an edge-enhanced image generation model considering viewpoints. Our method applies edge map information to guide image generation based on the perspective views of an image using vanishing points detection. Texture synthesis will be presented as post-processing to complete the remaining missing regions. Experiment shows that our approach can generate perspective images with convincing details, such as indoor and outdoor facades.
Quantitative Evaluation of Wavelet Analysis Method for Turbulent Flux Calculation of Non‐Stationary Series

Abstract This study evaluates the uncertainties of turbulent flux calculation using eddy covariance (EC) and wavelet analysis (WA) methods. First, a non‐stationary data set is concocted by adding periodic waves and random perturbations which mimic the large eddies, turbulent intermittency, and asymmetry into an observational stationary data set, and the theoretical “true” fluxes are used to quantitatively evaluate the accuracy of these methods. Results show that EC and Morlet‐wavelet generate biases ranging 50%–100% of the “true” values at different non‐stationarity grades, whereas the Mexican hat (Mexhat) wavelet has a bias of about half of them. Furthermore, there is a high correlation of the Mexhat‐derived fluxes to the benchmark values, the regression slopes of the values of these two can be improved to almost 1 by adding a correction coefficient. The results suggest the potential of using the Mexhat‐wavelet method to calculate turbulent fluxes with high accuracy under non‐stationary conditions.
Securing Critical User Information over the Internet of Medical Things Platforms Using a Hybrid Cryptography Scheme

The application of the Internet of Medical Things (IoMT) in medical systems has brought much ease in discharging healthcare services by medical practitioners. However, the security and privacy preservation of critical user data remain the reason the technology has not yet been fully maximized. Undoubtedly, a secure IoMT model that preserves individual users’ privacy will enhance the wide acceptability of IoMT technology. However, existing works that have attempted to solve these privacy and insecurity problems are not space-conservative, computationally intensive, and also vulnerable to security attacks. In this paper, an IoMT-based model that conserves the privacy of the data, is less computationally intensive, and is resistant to various cryptanalysis attacks is proposed. Specifically, an efficient privacy-preserving technique where an efficient searching algorithm through encrypted data was used and a hybrid cryptography algorithm that combines the modification of the Caesar cipher with the Elliptic Curve Diffie Hellman (ECDH) and Digital Signature Algorithm (DSA) were projected to achieve user data security and privacy preservation of the patient. Furthermore, the modified algorithm can secure messages during transmission, perform key exchanges between clients and healthcare centres, and guarantee user authentication by authorized healthcare centres. The proposed IoMT model, leveraging the hybrid cryptography algorithm, was analysed and compared against different security attacks. The analysis results revealed that the model is secure, preserves the privacy of critical user information, and shows robust resistance against different cryptanalysis attacks.
Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method

An ICU is a critical care unit that provides advanced medical support and continuous monitoring for patients with severe illnesses or injuries. Predicting the mortality rate of ICU patients can not only improve patient outcomes, but also optimize resource allocation. Many studies have attempted to create scoring systems and models that predict the mortality of ICU patients using large amounts of structured clinical data. However, unstructured clinical data recorded during patient admission, such as notes made by physicians, is often overlooked. This study used the MIMIC-III database to predict mortality in ICU patients. In the first part of the study, only eight structured variables were used, including the six basic vital signs, the GCS, and the patient's age at admission. In the second part, unstructured predictor variables were extracted from the initial diagnosis made by physicians when the patients were admitted to the hospital and analyzed using Latent Dirichlet Allocation techniques. The structured and unstructured data were combined using machine learning methods to create a mortality risk prediction model for ICU patients. The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The model achieved an AUROC of 0.88, indicating accurate prediction of patient vital status. Additionally, the model was able to predict patient clinical outcomes over time, successfully identifying important variables. This study demonstrated that a small number of easily collectible structured variables, combined with unstructured data and analyzed using LDA topic modeling, can significantly improve the predictive performance of a mortality risk prediction model for ICU patients. These results suggest that initial clinical observations and diagnoses of ICU patients contain valuable information that can aid ICU medical and nursing staff in making important clinical decisions.