Reviewerly/exHarmony huggingface dataset:
Enhanced Human Action Recognition with Ensembled DTW Loss Function in CNN LSTM Architecture

Human action recognition is a concept that involves acquiring information based on the sequence of movements by the target. This recognition model is used to recognise elderly people's actions to monitor their anomalies and provide appropriate guidance as soon as possible to avoid further disaster. This algorithm involves refining HAR and provides precise decisions to predict any emergencies while handling elderly people. This algorithm uses Dynamic Time Warping as a loss function ensembled with mean absolute error in CNN LSTM neural network. DTW is used in the loss function to get a relationship between two sequences of actions considered as waves. This results in performing better at any speed of action as long as the sequence of movements for action remains the same. The variations in the change of the sequence of movements for action are further countered by mean absolute error to perform better in any variation of an action. Thus, it provides an accuracy of 75% for the taken dataset which is way more than the accuracy produced by DTW or MAE as a separate loss function.
On distinguishing labelling of groups for the conjugation action

In this paper, the conjugation action of various classes of groups on themselves is studied to obtain theirdistinguishing numbers along with a distinguishing labeling for the said action. An equivalent condition concerning the existence of a 2-distinguishing labeling for the action of a group G on a G-set X and a partition of X into two subsets is established. Also, the distinguishing number for the conjugation action of a group acting on itself is completely characterized.
Predicting Useful Information From Typing Patterns Using a Bootstrapped-Based Homogeneous Ensemble Approach

Nowadays, the way a user connects with computing devices is being analysed with the goal of extracting useful information for some interesting applications beyond user authentication. It enables the development of next-generation intelligent human-computer interaction, auto-profiling, and soft biometrics. In this paper, a bootstrapped-based homogeneous ensemble model has been proposed without wasting rare samples in each bootstrapped training set to overcome the uneven distribution of classes (common in keystroke dynamics) for predicting users’ traits, fine motor skills, and cognitive deficiency automatically using the user’s daily typing habit. This model is lightweight, faster, and could be implemented in low-configured devices like smartphones. The proposed model has been verified with a more realistic evaluation and several shared and authentic keystroke dynamics (KD) datasets and achieved 93.21% of accuracy in predicting age group, 65.35% in identifying gender, 87.14% for handedness, 77.14% in the case of hand(s) used determination, 91.25% in predicting qualification, 74.44% in recognising typing skill, 58.45% in observing lie, 84.12% in the determination of Parkinson’s disease (PD), and 99.14% in predicting emotional stress (ES). The proposed model might be used for a wide range of more interesting applications, including automatic user profiling in social networking, age-restricted access control to protect kids from Internet threats, age- and gender-specific product recommendations in e-commerce, medical diagnostics at-home environment for better treatment and therapy management, soft biometric traits in improving biometric models, learner’s cognitive deficiency in effective online teaching and learning, cognitive deficiency marker in a competitive examination, unbiased online feedback collection, and online inquiry correctness measurement, to mention a few.
MBLEACH: Modified Blockchain-Based LEACH Protocol

One of the primary clustering-based routing protocols in the wireless sensor network is the LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol. In the LEACH protocol, cluster head plays an important role as entire data transmission is performed through cluster head nodes, and if any one of the cluster heads is corrupted, the entire network will be affected. Hence, the cluster head dysfunction must be discarded and addressed. To handle the above-mentioned issue this paper proposes a secure blockchain-based security mechanism for the detection and removal of malicious cluster head nodes in LEACH protocol. In the proposed work, a hybrid blockchain model is designed which is consisting of public and private blockchains. The base station joins the public blockchain as a miner node and monitors the cluster head nodes which join the local blockchain and are known as validators. Whenever the Base station monitors that some cluster head nodes are behaving maliciously (i.e., does not mine transactions and does not produce new blocks or does not sign the transactions), a smart contract on the public blockchain is triggered and stores the node information of the malicious cluster head node. After that, the Base station triggers an event to notify the cluster head nodes in the local blockchain and that cluster head node validates the information among themselves through PoA (Proof-of-Authority) consensus to remove these malicious nodes from the network.
Fallacies of Meta-argumentation

ABSTRACT This article argues that the theoretical concept of meta-argumentative fallacy is useful. The authors argue for this along two lines. The first is that with the concept, the authors may clarify the concept of meta-argumentation. That is, by theorizing where meta-argument goes wrong, the authors may capture the norms of this level of argumentation. The second is that the concept of meta-argumentative fallacies provides an explanatory model for a variety of errors in argument otherwise difficult to theorize. The authors take three as exemplary: the straw man, both sides, and free speech fallacies.
A Framework For Single Image Dehazing Using DWT Based Cross Bilateral Filter Fusion of Generative and ASM Models

Due to the latest advancements in technologies like artificial intelligence, machine learning, and deep learning in image processing, computer vision, and their uses in various applications, there has been a lot of interest in the restoration of murky images. This paper gives a novel single-image dehazing (SID) framework for the restoration of single hazy image (SHI). The framework has three parts. The first part of the framework comprises three networks. A white-balanced auto-encoder network in the front, followed by a pair of sequential auto-encoder networks at the end for image dehazing (ID). A white balance auto-encoder network is used for correcting the white balance error in the input hazy image (HI). Pair of sequential auto- encoder networks gives haze-reduced output by taking corrected white balanced error image as input. The second part comprises an ASM-based model Dark Channel Prior (DCP) for ID. DCP produce haze-free images (HFI) by taking HI as input. The third part presents the fusion model for integrating the first and second parts of the framework. DWT-based cross bilateral filter model is the fusion model (FM) to get the final HFI. The first part of the framework is trained by using a hybrid loss function (perceptual loss function combined with MSE loss function) on O- HAZE, I-HAZE, and MRFID data sets. The proposed framework got considerably good result in terms of Structural Similarity Index (SSIM), Peak signal-to-noise ratio (PSNR), compared with cutting-edge methods.
A Taxonomy towards Blockchain computing for Resource security

In the least tough of terms, blockchain can be portrayed as documents you can solely add to, proper now not reduce, or change. Subsequently, the time body is "chain" due to the reality you are making a chain of data. Not being there for the talent to replacement the principal blocks make it so secure. Moreover, blockchains are understanding-driven, so no one issue can take manipulate the data. With blockchain, you do not be given that a depended upon untouchable ought to oversee or assist trades. Blockchain development achieves decentralized safety and have faith in extra approaches than one. Regardless, new blocks are always taken care of straightly and successively. That is, they are constantly introduced to the "end" of the blockchain.
Blockchain based Security Solution for Internet of Drones

Nowadays, Drone usage is increasing faster than before. They have frequently been employed in civil services. In a number of sectors, including transportation, agriculture, building, and emergency response, drones have grown in significance. Drones can operate in places that are inaccessible to humans, collect data more effectively than conventional techniques, and carry out tasks more quickly. With the development of technology and laws, their use is anticipated to increase in the future. Therefore, it is imperative that drones must become smarter, more private, and more secure as they offer data transfer secrecy and reliability. In this paper, we have proposed a blockchain-based security solution model for the Internet of Drone environment. The proposed method is evaluated on a limited number of pretend drones. Here we may easily authenticate the administrator, add new drones, authenticate a drone, delete drones, get data from drones, save it to a blockchain-based secure system, and securely retrieve that information.
RFID Based System for Effortless Shopping and Fast Billing in Supermarket

Advancement in electronic technology leading to single chip integration of millions of transistors fosters the people to use modern electronic devices and thereby fulfilling the aim of making their lives easier. Nowadays Shopping malls are a place where people get the products of daily need. The proposed system gives RFID based alternate solution to the time consuming and manpower dependent process of barcode reading employed in purchasing products from supermarkets. The system also facilitates easy movement of the shopping trolley with the use of joystick and DC motor in transmitter and receiver end respectively. Fast and error-free scanning of product information and addition of product in purchase list are enabled by using RFID reader and tags. User friendly display of purchase information is ensured by using LCD. GSM module enables the exchange of messages related to the purchase made. The proposed system limits the human interaction with surroundings and with each other thereby facilitating the concept of personal hygiene. Also, the use of such technology promises to make shopping easier for elderly and old people by limiting the physical effort. Core of the proposed system is ATMEGA328 microcontroller. C programming language is used for writing the code and Proteus Design Suite is used for simulation purposes.
Sentiment Analysis Model using Text and Emoticons for Pharmaceutical &amp; Healthcare Industries

Background: Nowadays, pharmaceutical and healthcare industries apply sentiment analysis (SA) to analyze customers' feedback on pharmaceutical products. SA uses natural language processing (NLP) to recognize, assess and analyze the pharmaceutical data to extract valuable insights about the sentiment it conveys. Over the past two decades, in addition to text, emoticons have become a conventional way of expressing one's sentiments. The researchers utilizing textual data for SA have largely ignored emoticons due to their complexity and insufficient resources. Aim of the study: Necessary to include emoticons in SA to capture the emotion expressed by pharmaceutical data. However, this research develops an algorithm that performs SA on text and emoticons. Methods: To highlight the significance of emoticons, the analysis has been conducted on text-only and text-and-emoticon data, using deep learning (DL) and machine learning (ML) methods on multiple datasets. The features – term frequency-inverse document frequency (TF–IDF), emoticon lexicons, and a bag of words (BoW) are taken for simulation. Results: Simulation of text features using BiLSTM provides better accuracy, between 85% and 90%, compared to other methods. The precision lies between 70% and 90%. The recall is lowered to 64%. The sentiment the emoticon conveys outweighs that of the text associated with it. In addition, the DL algorithms outperform the ML methods. The overall results indicate that considering emoticons along with text positively impacts SA.Further, it is noted that the DL model marginally outperforms all of the ML algorithms used. Conclusions: This research analyzed the augmentations to the implementation, including multiple enhancements during the pre-processing stage, the inclusion of emojis, and the usage of a bi-long short-term memory network (BiLSTM) in the DL model. The experimental results conclude that better measures are obtained using the DL method.
A Near-Optimal Deep Detector for BER Minimization in an OFDM AF Relay System

This paper investigates a Near-Optimal Deep Learning (DL)-based detector for an Orthogonal Frequency Division Multiplexing (OFDM) Amplify and Forward (AF) relay system. The main goal of the proposed DL detector is to achieve the best Bit Error Rate (BER) performance by a low-complexity Deep Neural Network (DNN). A pre-processing stage based on the frequency domain is applied before entering the input data into DNN. Since the performance of the model is very sensitive to tuning the parameters, simulation results compare the BER performance for different scenarios to obtain an accurate model. Finally, we show that the desired model is close to the Maximum Likelihood (ML) as an optimal detector.
Abnormal Behavior Detection in Electronic-Exam Videos Using BeatGAN

Abnormal Behavior Detection is a core ability to recognize cheating in electronic exams (e-exams), especially in the scenes where a fraudulent candidate hides unauthorized resources from the view of the proctor. This work utilizes the BeatGAN algorithm to detect behavioral abnormalities in the skeleton-based motion information of candidates during remote e-exams. We examine the algorithm on the previously published dataset of skeleton features extracted from e-exam videos. The results show that BeatGAN outperforms previously investigated algorithms in sequence anomaly detection using the raw joint-coordinate, implicit bone-length-and-angle and explicit bone-length feature types. But, in event anomaly detection, BeatGAN does not improve the state-of-the-art results on the dataset.
Radio Frequency Fingerprint Identification of Drones Based on Variational Mode Decomposition

Recent technological advances have made drones one of the most popular unmanned vehicles for commercial and noncommercial uses. However, there are challenging concerns about their malicious uses. This issue shows the necessity of using a drone detection and identification system with high accuracy and range. In this research, we introduce a drone detection and identification system using fingerprint Identification of radio frequency (RF) signals of drones. Firstly, the drone signal is detected using an energy detector. Then, the RF signal variational mode decomposition (VMD) is calculated. In the following, highorder statistical features are extracted from VMD band-limited modes. The classification process has been applied using directed acyclic graph SVM (DAGSVM). According to the results, the proposed method can detect and identify different drone types with 93% and 86% accuracy for standard and practical datasets, respectively.
Video-based Person Re-Identification Using Attention Based on Separation Index

Re-identifying people from a video is a biometric task for identifying an individual from a long distance. RGB images are used to represent a person in each frame in appearance-based approaches. Furthermore, RGB images contain gait features and other visual clues which can be recognized. Hence, these approaches are not invariant to changes in clothing and baggage. Skeleton-based approaches are alternatives to appearance-based methods. These methods are invariant to view changes. In this experiment, a skeleton-based method is developed to employ pose information in situations where videos of individuals vary in view. First, extracted features by an EfficientNet-b7 were divided into 1, 2 and 4 parts. By utilizing pose information, a Graph Neural Network (GNN) has been constructed. The GNN captures the intrinsic relationship between these regional features. A new attention mechanism by Separation Index (SI) proposed to weight feature maps. This method is evaluated and achieved 96.63% and 88.67% top-1 accuracy on the most frequently used datasets, PRID2011 and iLIDS-VID.
Crime Analysis Using Linear Regression

The crime rate is frequently increasing day by day in our society, also crime goes unreported, because people don’t have enough substantiation to help police with the disquisition. Also police didn't know the correct statistical report of once times of crime. Crime analysis and forestallment is a technical way of finding and analysing similar pattern occurred in crime. By adding the arrival of motorized systems, available data judgement can help the judicialsystem tofasten the process of recent an emergent crimes. With the conception of data mining and machine learning, the proposed system can prioritize preliminarily unknown, known from an unshaped data. By using the crime analysis technic, the levels of the crime are reduced and thus leads the Country’s reputation and growth. We can also reduce the cost of investment in the crime investigation. Our Moto of this paper is to mitigate the crime rate using Machine Learning algorithms.
Evaluating Keyphrase Extraction Methods for Clustering Influenza-Related Scientific Papers

The number of annual scientific publications is growing year by year, which has led to the accumulation and formation of large databases. This increases the complexity of the search for relevant articles. Modern search engines leverage keyphrases to improve the performance of search results. Keyphrases (or keywords) are a set of single or multi-word expressions which provide a very compact summary of contents and describe the overall topic of a document. Implementation of keyphrase extraction can be varied for specific-domain databases. This motivated us to evaluate these methods on a database of influenza scientific literature. In this work, we considered 9 well known methods for extracting keyphrases. Our preliminary results show txhat graph-based methods which employ topics outperform others using our database. We also determined that influenza-related papers can be grouped into three general topics: public health & medical care; molecular biology & immunology; and phylogenetic & epidemiological studies.
An Automatic Method for Image Steganography Using Jensen Similarity Criterion

Steganography, an important component of security systems, plays an essential role in the information security mechanisms. Among various types of steganography, image steganography has attracted lots of attention in the academic community. Despite the increasing development of the image steganography solutions, there are still some concerns about their level of security. In this paper, we propose a new image steganography scheme which is based on Least-Significant-Bit (LSB) substitution and Jensen Similarity Criterion. The proposed method first divides both the LSBs of the input image and the bitstream of the secret message into several blocks. Then, our method employs the Jensen similarity criterion over each block of the message and the reversed message to choose its closest block in the image. We repeat these operations by changing of scroll type of bits in the image until the highest PSNR is obtainedt Finally, each secret message block is hidden into its corresponding image block selected in the previous step. Our experimental results show that our approach achieves a very high level of hiding capacity. Moreover, the results demonstrate that our approach provides around 4.5% higher visual quality compared to the state of the art methods and measured by the Peak Signal-to-Noise-Ratio (PSNR).
Handwritten Gujarati Numeral Recognition using Deep Learning

India is the second most populous nation in the world, making up around 17.74% of the world’s population. It is a country with a diversified linguistic population since it has 22 official languages. Indians are accustomed to favoring regional languages for their daily activities. The identification of handwritten Gujarati characters or numbers has lately piqued the interest of researchers due to its many potential applications. Due to the wide range of writing styles, it is more difficult to identify handwritten data from physical documents than printed data. This paper deals with the Optical Character Recognition (OCR) method for the ’Gujarati’ language originating from the Devanagari script using Deep Learning approach. The data set of more than 18,000 images is used for developing prediction models. Convolutional Neural Networks (CNN) architectures like AlexNet, GoogLeNet, VGG16 (Visual Geometry Group) and LeNet-5 have been used for classification models. Owing to the diversity of dataset, we propose a custom CNN architecture that resulted in highest accuracy for predictions.
Tracking scientific Authenticated Computing Simulation

The motive for cloud-based affirmation is to defend agencies from programmers trying to take personal data. Cloud affirmation approves accredit-ed customers throughout businesses and mainland to safely get to records put away in the cloud with validation given via cloud-based administrations. Cloud computing verification is the approach worried in determining the personality of a client. The subtleties of verification fluctuate contingent upon how you are getting to Cloud Storage but fall into two standard sorts: A server-driven movement approves a software to straightforwardly keep the skills of an assist report to end confirmation. Most of the investigation frameworks proposed truly make them suppose in everyday that we supply a location as hostile to specific headings of the area of the client. Furthermore, the cloud resources are given to the end users with the help of IoT applications. The qualities are being maintained based on authentications of the cloud services to the end users.
Implementation of Data Mining Techniques in Healthcare

Healthcare sector is having lots of data about various disease and newly discovered viruses and bacteria’s and so on. The data has been resided for a long at various data warehouses of each country’s healthcare department but due to lack of collaboration and employment of advanced machine learning algorithms the meaningful insights which may conclude from data research and association, remain hidden from world. Hence this paper proposes the application of various machine algorithms like linear regression and multi-layer perceptron on a target dataset which is having the attributes of related to healthcare industry. With implementation of algorithm the detection of diabetes in various areas covered in 80 attributes of selected dataset.
An Accelerated Coordinate Descent Method for Support Vector Machine

Support vector machines (SVMs) are famous and important classifiers in classification researches. SVM and its application have been used in an enormous amount of research in various scientific domains in recent years. There are different methods to solve SVM problems. Coordinate descent methods are one of the main categories of methods for solving these problems. In this paper, using the generalization of Aitken’s $\Delta 2$ process to the vector case, a new accelerated coordinate descent algorithm is proposed. The new algorithm is tested with three different datasets. The numerical results indicate the efficiency of the new algorithm in the concept of increasing the speed of convergence.