Reviewerly/exHarmony huggingface dataset:
Secure Image Transmission Using 2D ECT and Latin Square Algorithm for IoUT Systems

The Internet of Underwater Things (IoUT) is a new communication system that has been designed to link undersea things in marine and underwater settings. Intelligent surveillance and cybersecurity, smart coastlines and seas, autonomous maritime transportation, location and navigation, underwater exploration, and catastrophe prevention and prediction are all closely related to IoUT technology. The IoUT has an impact on a variety of sizes, including a scientific observatory, a medium-sized port, and the whole marine traffic. IoUT's network architecture is inherently diverse and ought to be sufficiently robust to function under challenging conditions. These days, underwater images are utilised to locate vital resources such as items, minerals, and precious metals. We can send underwater images across a network since the Internet is so widely accessible. We are able to send underwater images across an IoUT network since the Internet is so widely accessible. There is a need for transmitting underwater images securely over the IoUT since they carry crucial information. The IoUT network requires security methods and algorithms to maintain data integrity and confidentiality. In this article, we provide a safe transmission technique for underwater images via IoUTs. This approach is built on a confusion-diffusion framework that employs the 2D Exponential-Cosine transform and the Latin square algorithm. The Security Study demonstrates its high degree of security and its ability to encrypt various colour images into cipher-images that are impossible to decipher.
Data division using Fuzzy Logic and Blockchain for data security in cyber space

The Internet has become a powerful way to connect with people in the modern world. The Cloud provides a great computing platform that enables people and organizations to do variety levels of tasks. Recently, the number of people using cloud services has radically greater than before and plenty of data has been stored in cloud computing storage. Data breaks in cloud services are also increasing every year due to hackers. To initiate cyber-attacks, cybercriminals use a variety of ways. Security becomes one of the most critical issues, and stringent security measures are required when keeping data on the cloud. In this proposed work, the fuzzy logic is used to analyze the trust level of data entering the Blockchain network and also guide cloud manager to divide the file to provide more security. In addition, the divided file is hashed using Blockchain and decrypted at the receiving end.
A Resource-optimized and Accelerated Sentiment Analysis Method using Serverless Computing

Serverless computing is being touted as one of the promising and potential computing paradigms for the future. It clearly represents a deeper automation in information technology (IT) operations. Application developers can get server resources easily and quickly to run their applications and services without bothering about setting up server infrastructure and its sustenance. Application scalability is being met through the infrastructure elasticity. The faster maturity and stability of the containerization aspect has led to the success of the serverless paradigm. With serverless flourishing, the adoption of function as a service (FaaS) is catching up fast. A user request or any noteworthy event can trigger a request to this function to be astutely serviced through the serverless phenomenon. In this paper, we have explained how we are able to optimally use the power of serverless computing to bring forth a new resource-constrained and accelerated sentiment analysis method.
Document Management System Empowered by Effective Amalgam of Blockchain and IPFS

The world had started digitization, even before the global pandemic had struck, which led to various processes, like verification and application of documents, going online. This also leads to an increase in the amount of data and traffic on the internet, causing an increase in cyber fraud, such as document (digital) forgery. All this has led to the need for a secure digital document management platform. With the help of Blockchain Technology, a safe alternative for the same can be developed. In this paper, we have studied the use cases where digital forms of documents are involved, and based on Blockchain Technology, we have created a secure web application for all the processing of the documents. The web application generates new certificates as requested for pre-defined templates and stores their identifier securely in the distributed Blockchain network (Polygon) and the document on an IPFS (InterPlanetary File System). Further, it provides the facility to verify the authenticity of the document. It also allows users to share access to their documents securely for a specific time.
preTrained Convolutional Neural Networks Performance Assessment under Different Color Variability Indexes

Convolutional Neural Networks (CNN) are currently widely used in image classification, so efficiency is key at the time of their implementation. Actually there are several pretrainned CNNs (ptCNN) for image classification e.g. Vgg19, ResNet50, InceptionV3, Xception, DenseNet, EfficientNet-B0, and so on. This document shows that the performance in the classification of images using ptCNN can be affected if images with different levels of color variability, as well as the brightness and standard deviation in the images are used. However, even that some changes in efficiency were found, we can conclude that the ptCNN are stable under changes in those indexes.
Scalable Blockchain Architecture using off-chain IPFS for Marks Card Validation

Blockchain and Distributed Ledger Technology are evolving in today's digital world for faster processing and secure transactions. Most IT industries are migrating their applications to blockchain frameworks because of high security and faster processing operations. Cryptocurrencies and bitcoin applications use Blockchain and distributed ledger technology to perform decentralized transactions. With the current blockchain technology, there is a limitation in storing and processing a large volume of transactions and data, affecting memory and throughput. To overcome this limitation, we propose a methodology to integrate Blockchain with IPFS to enhance the throughput and optimize the memory. As a case study marks card validation application is being developed on the Ethereum framework considering Geth and Parity clients. Blockchain consensus protocol, i.e., proof of work (POW) and proof of stake (POS), are used to build smart contracts and process transactions. InterPlanetary File System (IPFS) is integrated with Blockchain to enhance the throughput and performance of the application. The results indicate that Blockchain integrated with IPFS performs better by optimizing the memory and reducing the transaction delay. The comparative analysis shows that the proposed approach reduces the transaction processing cost by 10% and enhances the throughput by 8% compared to the Fileshare and BlockIPFS techniques.
Physical Layer Security Using Cross Layer Authentication for AES-ECDSA Algorithm

With the increasing use of portable devices, whenever information is sent through broadcasting channel, security re-lated challenges may occur so that the attackers can listen the information shared in the broadcasting channels such as Radio, TV and Wireless Adhoc Networks (WAN). To prevent such attacks, many cyber security-based algo-rithms are developed to enhance the security between the transmitter and receivers, but still the privacy attacks like man-in-the-middle attack, brute force attack etc occur. In this paper a technology called cross layer authentication is proposed where it uses the third party called server to authenticate between transmitter and receiver. The cross-layer authentication generates a group key by using the users (subscribers) identity information and channel (publisher) information and broadcasts the information to channels and users. The group key will continuously update and broadcasts the information to all the users in the channel whenever a user is added or deleted. Since the group key changes frequently, it will be quite challenging for the attackers to track the shared information. Hence, the proposed method avoids the attacks between transmitter and receiver by generating a group key using ECDSA algorithm and the information is encrypted by AES-128 bit.
Automatic Detection of Breast Cancer through Mammogram Images

Breast Cancer (BC) is a primary cause of cancer death among women in which breast cells expand out of control. The chances of survival increase by urging patients to seek treatment as soon as possible. Several researches have been proposed to detect breast cancer. However, they possess a few drawbacks and are inefficient in detecting the disease accurately. In order to save the patient's life, a transfer learning (TL) based model is proposed for the diagnoses and classification of the sus-picious breast area. In the presented model, learning parameters from pre-trained models VGG-19, VGG-16, and Inception-V3 networks are fine-tuned to enhance the performance of malignant lesions classification. The key objectives of the research are to employ segmentation to automatically locate the impacted breast tumor region, reduce training time, and enhance classification accuracy. The Mammographic Image Analysis Society (MIAS) dataset extracts breast tumor features in the proposed model. Three evaluation metrics are used to analyze the performance of the proposed model: accuracy, sensitivity, and specificity. The trials revealed that transferring parameters from the VGG-16 model is more powerful than VGG-19 and Inception V3 for BC classification, with overall specificity, accuracy, and sensitivity of 97.12%, 97.80%, and 96.43%, respectively.
Generalized Label Grouping for Scalable Trajectory Estimation

Multi-Object Tracking (MOT) is concerned with estimating trajectories from sensor measurements. MOT using the Random Finite Set (RFS) framework has been gaining popularity due to its rigorous mathematical foundation and versatility in applications. Notably, large-scale trajectory estimation can be successfully achieved by the label-partitioned Generalized Labeled Multi-Bernoulli (GLMB) filter framework. In this work, we propose an efficient method for grouping object labels in scalable GLMB filtering. Specifically, the label grouping problem for parallel computation is generalized by considering the intersection of predicted measurements, i.e., uncertainty regions. The proposed approach provides a flexible criterion to construct label graphs, whereupon a large number of object labels can be rapidly determined whether to be grouped or not. We demonstrate the performance of our method via large-scale data sets.
iGYM: Implementation of Image Recognition Using Silhouette Extraction and Artificial Neural Network as Gym Instructor

The researchers aimed at creating an App-Based Gym Workout Instructor using Image Recognition via Artificial Neural Network which can recognize the body type of a male person using images and show the workout for the body type. The input is a whole-body image of a male person and the output is the workout for the detected body type. Using MATLAB, the researchers created an Artificial Neural Network that is trained to recognize body types and C# platform to implement the ANN. The results of the study showed that the developed system was able to determine the body type of the user. In terms of the over-all accuracy of the developed igym instructor for all of the body type defined, it was fairly moderate with an average of 64.38%. The effectivity and accuracy of the iGYM does not only depend on the number of training data but also with the quality of the data set.
A Buffered Semi-Asynchronous Mechanism with MAB for Efficient Federated Learning

Federated learning can leverage abundant data generated by scattered client devices without impairing their data privacy. However, heterogeneous properties of devices may cause a long single round duration in synchronous federated learning and stale gradient of clients in asynchronous federated learning. To solve these issues, we propose a buffered semi-asynchronous federated learning with efficient client selection strategy, named BSACS-FL. Specifically, we introduce a buffer mechanism which is used to store models that have not yet been aggregated on client side to improve clients computing resource utilization. In addition, we adopt Multi-Armed Bandit (MAB) method to select a proper set of clients to upload new local models based on the waiting time of local model in the buffer and the learned training capability of clients. The novel BSACS-FL algorithm boosts time efficiency and improves client computing resource utilization, which achieves more effective model aggregations, leading to a faster convergence with high model accuracy. Extensive experiments based on both image classification and human activity recognition tasks demonstrate the superior performance of BSACS-FL, e.g., improving the model accuracy by 19% compared to the FedAvg.
Target Motion Analysis via Hard and Soft Data Fusion

Target Motion Analysis (TMA) requires the online fusion of multiple hard and soft data sources for target tracking. This paper proposes a Bayesian filtering solution for multisource fusion with hard and soft data. Appropriate models for various types of hard and soft data are developed so that they can be fused in a consistent manner under the Bayesian framework. The resulting Bayes filter is highly non-linear and non-Gaussian. Hence, a parallel particle filter is developed to facilitate a user adjustable trade-off between computation time and tracking accuracy. Numerical studies on realistic scenarios are also presented.
Multi-class Multi-target Tracking with the Poisson Labeled Multi-Bernoulli filter

A Random Finite Set (RFS) based multi-target multi-class filter is proposed, which utilizes a labeled Multi-Bernoulli distribution to model the multi-target state, together with a Poisson RFS distribution to model potential new targets. The Poisson distribution is advantageous in modelling any number of new targets with different classes. This is because it allows birth targets with different expected numbers per class to be modelled, where each class of targets could be modelled to appear in different locations of the map. Using class information is expected to improve overall tracking performance, when sensors provide class information, such as video camera image classifiers. The proposed filter is referred to as the Class Poisson Labeled Multi-Bernoulli (CPLMB) filter. Results show that augmenting the target state with class, by using classification information in the measurements, increases PLMB filter performance and, after few iterations, each target class converges to a single value with probability close to or equal to unity.
Developing Novel T-Swish Activation Function in Deep Learning

Activation functions are an essential component of neural network construction. It is used in the hidden layer to achieve the regulation that lies in the learning capability of the model from the training dataset. Activation functions employed in the output layer play a significant part to determine the type of predictions that the model can produce. It is a significant area that has to be studied further to increase the efficacy of a deep neural network. As a result, our research in this area has grown. Several activation functions were established throughout time to remedy the inadequacies of the earlier functions. Over 40 activation functions, some of which are well-known, have been postulated, such as the Tanh, Mish, sigmoid, swish, and Rectified Linear Unit, however not entirely. Using datasets such as CIFAR-100, CIFAR-10, MNIST, and Fashion-MNIST, we developed a new activation function Tunable Swish (T-Swish) that increases enactment on object classification tasks. Then, to validate its applicability, we compared it to well-known activation functions like ReLU, sigmoid, Tanh, and Swish using Network In-Network, ResNet-56 version 1, LeNet-5, and U-Net architecture. The T-Swish function, in particular, enables quick network learning, which is excellent in the Transfer Learning technique.
Blockchain Technology and Analysis of Supply Chain Application Scenario

Blockchain provides a feasible technical route to build a trusted society. In the absence of a trusted environment, without the participation of trusted third parties (institutions), two or more parties that do not trust or even know one another can still deal with and complete the related transactions with the help of the blockchain technology. By creating a new form of value delivery, it not only changes the traditional trading mode and financial system, but also the original mode of public service and social governance, which protect personal privacy and data security to the utmost. This paper illustrates and analyzes what is the block chain, its core technology, the supply chain application scenarios as well as the far-reaching and profound changes brought about by blockchain, so as to offer some valuable theoretical and empirical references to further research and its large-scale implementation and application.
A Multi-Frame Joint Tracking and Classification Method for Weak Target in Radar System

This paper solves the problem of joint tracking and classification of weak targets using multi-frame joint processing technology. Weak targets are easily submerged in background noise, and single-frame threshold detection makes many of their feature information lost, making it challenging to track and classify them effectively. Aiming at solving these problems, a multi-frame joint tracking and classification (MF-JTC) method is proposed. The method achieves the accurate estimation of the motion trajectory and target class by jointly processing the radar measurement data in multi-frames. Finally, the results show that compared with the traditional single-frame joint tracking and classification (SF-JTC) method, the proposed method has better tracking and classification performance for weak targets.
Perspective-guided Point Supervision Network for Crowd Counting

Crowd counting is critical for video surveillance and public safety. However, due to the impact of perspective effects, large-scale variations have become one of the main challenges affecting the counting performance. In this paper, we propose perspective-guided point supervision network (PPSNet), which embeds perspective information into a point-supervised network to better handle the scaling problem. First, we construct a point-supervised framework, which directly applies point annotations as learning targets to predict a set of candidate points and achieve accurate positioning of individuals. Then, PPSNet is constructed by stacking multiple perspective-guided fusion modules, which extract multi-scale features and fuse them guided by perspective attention based on perspective information. Perspective attention combines spatial attention and channel attention to learn long-range dependencies from different distance and capture the most important features among the scales. Experimental results on ShanghaiTech Part A and Part B, and WorldExpo'10 demonstrate that our proposed PPSNet outperforms the state-of-the-art methods.
Riemannian Kernel-based Hashing for Fast Image Set Classification

Image set classification has attracted extensive attention in recent years. Most existing image set classification methods depend on computationally complicated optimization, and the computational complexity and memory usage can be prohibitively expensive when dealing with large-scale datasets. Hashing is a widely-used technique in approximate nearest neighbour search due to its superiority in computation and storage. To take advantage of hashing, in this paper, we propose Riemannian Kernel-based Hashing for efficient image set classification. The proposed SPD-based Iterative Quantization (SPD-ITQ) and Grassmann-based Iterative Quantization (GM-ITQ) first model image set by SPD and Grassmann manifolds to capture nonlinear structure, respectively. The proposed methods map the points in Riemannian space into Hilbert space, and further learn high-quality hash code by learning a rotation matrix to minimize quantization error. Extensive experiments on four image set datasets verify efficiency and competitive performance of the proposed methods compared with the state-of-the-art methods.
High-Fidelity Pluralistic Image Completion with PLSA-VQGAN

The process of image completion is essentially conditional image generation, and as such it accepts diverse results, as long as reasonably coherent semantic information and realistic textures can be achieved. Pluralistic Image Completion(PIC) pioneered the generation of diverse results for the image completion task, but it suffers from the inductive bias of Convolutional Neural Networks(CNNs) and performs poorly in understanding the global structure, resulting in slight semantic variation in completion results. More recently, some hybrid architectures that combine the transformer's ability to acquire long-range dependencies with the CNNs's powerful texture modeling capabilities have performed well. However, these methods use auto encoder to acquire token representations of images and still suffer from texture distortion and poor articulation quality of filled regions due to image reconstruction losses, such as High-Fidelity Pluralistic Image Completion with Transformers(ICt). In this paper, focusing on this problem, we propose an image inpainting network based on VQGAN and transformer to obtain high-quality multiple results. In the first stage, the vector quantization tokenization process, we introduce path-wise local spatial attention (PLSA) to reduce the fine-grained reconstruction losses, followed by a transformer network to generate the missing parts in the next second stage. Experiments on FFHQ and Places2 datasets show that our approach performs well in terms of image fidelity.
A Chaotic Pseudo Orthogonal Covert Communication System

A Chaotic Pseudo Orthogonal Covert (CPOC) communication system is proposed. The Chaotic Pseudo Orthogonal (CPO) signals is proposed to improve the Low Probability of Detection (LPD) performance in the covert communication system. The LPD performance of CPOC system is verified by the cepstrum and Spectral Correlation Function (SCF). The Bit Error Ratio (BER) performance of the proposed CPOC system is compared to the conventional Differential Chaos Shift Keying (DCSK) communication system in Additive Gaussian White Noise (AWGN) and multipath channels. The experiment test has been carried out based on a Software Defined Electronical (SDE) platform to show the feasibility of the covert communication system based on CPOC.
Contrastive-Siamese Collaborative Network for Visual Object Tracking

The siamese network-based tracking framework aims to use the features extracted by the convolutional network to discriminate the similarity between the search image and the target template at the pixel level. This approach prevents the tracker from accurately tracking targets when dealing with tracking challenges such as analogue interference, fast movement, and occlusion. In this paper, we propose a novel tracking framework based on contrastive learning and the collaboration of siamese networks. In this framework, a kind of multi-task learning is formed by combining training with comparison learning task and tracking task. In the training process, the tracker can not only distinguish the target from the background but also learn the essential characteristics of the target without paying attention to the details of the pixel level through the contrastive learning method, to effectively distinguish the target from similar objects in complex scenes. Compared with other methods on three standard datasets OTBI00, VOT2015 and TColor128, our algorithm achieves better performance.