Reviewerly/exHarmony huggingface dataset:
Image Security using Chaos and Fractal Sorting Matrix

In the modern age, imaging technology has become an integral part of our daily lives, and its importance cannot be overstated. However, with the widespread use of digital images, the need for secure image transmission and storage has increased exponentially. Towards this end, we have proposed a cryptosystem based on enhanced chaotic maps. The developed system involves five phases of operation. In the first phase, a sorting matrix is used to generate the sorting block using a spiral-transform-based fractal sorting matrix (FTSM). In the second phase, the initial governing parameters for two enhanced dynamic systems are estimated. Third phase, corresponds to the generated of key blocks using the imagesensitive pseudo random sequences produced in the previous stage. The fourth phase involves a full-scale confusion operation that is followed by a two-stage diffusion operation in the fifth stage to produce the final cipher image. The results demonstrate that our algorithm is highly efficient, with the encrypted images exhibiting excellent performance in terms of both security and speed that is it can encrypt a 256 x 256 in just 0.02 seconds. In conclusion, our proposed algorithm provides an effective solution for securing digital images, particularly in applications that require high levels of security and efficiency.
Flow2Flow: Audio-visual cross-modality generation for talking face videos with rhythmic head

Audio-visual cross-modality generation refers to the generation of audio or visual content based on input from another modality. One of the key tasks in this field is the generation of realistic talking facial videos from audio and head pose information, which has significant applications in human–computer interaction, virtual reality, and video production. However, previous work has limitations such as the inability to generate natural head poses or interact with audio, which compromises the realism and expressive power of the generated videos. This paper aims to address these issues and improve the state-of-the-art in this field. To this end, we propose an autoregressive generation method called Flow2Flow and collect a large-scale in-the-wild solo-singing-themed audio-visual dataset called AVVS to investigate the rhythmic head movement patterns. The Flow2Flow model involves a multimodal transformer block with cross-attention, which can encode audio features and historical head poses to establish potential audio-visual motion entanglement and uses normalizing flows to generate future facial motion representation sequences. The generated motion representations are identity-independent, allowing the method to be transferred to any face identity. We model the motion of image content using warping flows generated from 3D keypoints based on the facial motion representation sequences, carefully manipulate animation generation, and estimate dense motion fields based on deformation flows using a neural rendering model to present photo-realistic talking facial videos. Experimental results show that our proposed method generates photo-realistic videos with natural head poses and lip-syncing, and we validate the effectiveness of our method compared to state-of-the-art methods on two public datasets.
Propagation Search Algorithm: A Physics-Based Optimizer for Engineering Applications

For process control in engineering applications, the fewer the coding lines of optimization algorithms, the more applications there are. Therefore, this work develops a new straightforward metaheuristic optimization algorithm named the propagation search algorithm (PSA), stirred by the wave propagation of the voltage and current along long transmission lines. The mathematical models of the voltage and current are utilized in modeling the PSA, where the voltage and current are the search agents. The propagation constant of the transmission line is the control parameter for the exploitation and exploration of the PSA. After that, the robustness of the PSA is verified using 23 famous testing functions. The statistical tests, comprising mean, standard deviation, and p-values, for 20 independent optimization experiments are utilized to confirm the robustness of the PSA to find the best result and the significant difference between the outcomes of the PSA and those of the compared algorithms. Finally, the proposed PSA is applied to find the optimum design parameters of four engineering design problems, including a three-bar truss, compression spring, pressure vessel, and welded beam. The outcomes show that the PSA converges to the best solutions very quickly, which can be applied to those applications that require a fast response.
Detecting Images in Two-Operator Series Manipulation: A Novel Approach Using Transposed Convolution and Information Fusion

Digital image forensics is a crucial emerging technique, as image editing tools can modify them easily. Most of the latest methods can determine whether a specific operator has edited an image. These methods are suitable for high-resolution uncompressed images. In practice, more than one operator is used to modify image contents repeatedly. In this paper, a reliable scheme using information fusion and deep network networks is presented to recognize manipulation operators and the operator’s series on two operators. A transposed convolutional layer improves the performance of low-resolution JPEG compressed images. In addition, a bottleneck technique is utilized to extend the number of transposed convolutional layers. One average pooling layer is employed to preserve the optimal information flow and evade the overfitting concern among the layers. Moreover, the presented scheme can detect two operator series with various factors without including them in training. The experimental outcomes of the suggested scheme are encouraging and better than the existing schemes due to the availability of sufficient statistical evidence.
SMHSDVS: A Secure and Mutual Heterogeneous Strong Designated Signature Between PKI and IBC

The homogeneous strong designated verifier signature scheme cannot meet the requirements of heterogeneous cryptography communication. With the idea of Heterogeneous Signcryption and strong designated verifier signature, we present a securely and mutually heterogeneous strong designated verifier signature (SMHSDVS) scheme between Public Key Infrastructure and Identity-based Cryptography, which has correctness, non-transferability, unforgeability, strongness, source hiding and non-delegatability. In addition, we use the Computational Diffie-Hellman model in oracle to analyze unforgeability and use mechanized tool CryptoVerif to analyze non-transferability. Finally, we evaluate the performance of heterogeneous digital signature scheme by compared with related schemes, and the results show that the proposed scheme is secure and efficient.
Enhanced imaging through turbid water based on quadrature lock-in discrimination and retinex aided by adaptive gamma function for illumination correction

This paper presents an improved method for imaging in turbid water by using the individual strengths of the quadrature lock-in discrimination (QLD) method and the retinex method. At first, the high-speed QLD is performed on images, aiming at capturing the ballistic photons. Then, we perform the retinex image enhancement on the QLD-processed images to enhance the contrast of the image. Next, the effect of uneven illumination is suppressed by using the bilateral gamma function for adaptive illumination correction. The experimental results depict that the proposed approach achieves better enhancement than the existing approaches, even in a high-turbidity environment.
3D convolutional long short-term encoder-decoder network for moving object segmentation

Moving object segmentation (MOS) is one of the important and well studied computer vision tasks that is used in a variety of applications, such as video surveillance systems, human tracking, self-driving cars, and video compression. While traditional approaches to MOS rely on hand-crafted features or background modeling, deep learning methods using Convolution Neural Networks (CNNs) have been shown to be more effective in extracting features and achieving better accuracy. However, most deep learning-based methods for MOS offer scene-dependent solutions, leading to reduced performance when tested on previously unseen video content. Because spatial features are insufficient to represent the motion information, the spatial and temporal features should be used together to succeed in un seen videos. To address this issue, we propose the MOS-Net deep framework, an encoder-decoder network that combines spatial and temporal features using the flux tensor algorithm, 3D CNNs, and ConvLSTM in its different variants. MOS-Net 2.0 is an enhanced version of the base MOS-Net structure, where additional ConvL STM modules are added to 3D CNNs for extracting long-term spatiotemporal features. In the final stage of the framework the output of the encoder-decoder network, the foreground probability map, is thresholded for producing a binary mask where moving objects are in the foreground and the rest forms the background. In addition, an ablation study has been conducted to evaluate different combinations as inputs to the proposed network, using the ChangeDetection2014 (CDnet2014) which in cludes challenging videos such as those with dynamic backgrounds, bad weather, and illumination changes. In most approaches, the training and test strategy are not announced, making it difficult to compare the algorithm results. In addition, the pro posed method can be evaluated differently as video-optimized or video-agnostic. In video-optimized approaches, the training and test set is obtained randomly and sep arated from the overall dataset. The results of the proposed method are compared with competitive methods from the literature using the same evaluation strategy. It has been observed that the introduced MOS networks give highly competitive re sults on the CDnet2014 dataset. The source code for the simulations provided in this work is available online.
Prediction of Au-Polymetallic Deposits Based on Spatial Multi-Layer Information Fusion by Random Forest Model in the Central Kunlun Area of Xinjiang, China

In recent years, there has been a growing emphasis on combining intelligent prospecting algorithms, such as random forest, with extensive geological and mineral data for the purpose of quantitatively predicting exploration geochemistry. This approach holds significant importance for enhancing the accuracy of target delineation. The central Kunlun area in Xinjiang possesses highly favorable ore-forming geological conditions, offering excellent prospects for mineral exploration. However, the depletion of shallow deposits coupled with a decade-long gap in geological exploration have presented increasing challenges in the quest to discover substantial metal resources. Consequently, there is now a severe shortage of reserve assets in the region, prompting an urgent need for the implementation of new theories, methods, and technologies in mineral resource investigation and evaluation efforts. The researchers used geological and regional geochemical data to construct a random forest metallogenic discriminant model for predicting the mineralization of gold polymetallic minerals in the central Kunlun area of Xinjiang and delineating the metallogenic target area. Two different sampling methods were compared to quantitatively predict gold polymetallic mineral resources. The results indicate that the selected training samples offer higher prediction accuracy and reliability by fully capturing the complex information of the original data. The random forest model using select training samples has valuable applications in metallogenic prospect prediction and potential division due to its ability to consider the actual exploration cost and identify small areas with high potential and a high proportion of ore. This study significantly improves prediction accuracy, reduces exploration risk, and expands the use of machine learning algorithms in mathematical geology in the central Kunlun area of Xinjiang.
Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature

Abstract Background Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable. Purpose To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design. Methods The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer. A modification of Quality Assessment of Diagnostic Accuracy Studies (mQUADAS-2) tool was developed for this review and was applied to the included studies. Results of reported studies (area under curve [AUC] of receiver operator curve [ROC] curve, sensitivity, specificity) were recorded. Results A total of 12,123 records were screened, of which 107 fit the inclusion criteria. Training and test datasets, key idea behind model architecture, and results were recorded for these studies. Based on mQUADAS-2 assessment, 103 studies had high risk of bias due to nonrepresentative patient selection. Four studies were of adequate quality, of which three trained their own model, and one used a commercial network. Ensemble models were used in two of these. Common strategies used for model training included patch classifiers, image classification networks (ResNet in 67%), and object detection networks (RetinaNet in 67%). The highest reported AUC was 0.927 ± 0.008 on a screening dataset, while it reached 0.945 (0.919–0.968) on an enriched subset. Higher values of AUC (0.955) and specificity (98.5%) were reached when combined radiologist and Artificial Intelligence readings were used than either of them alone. None of the studies provided explainability beyond localization accuracy. None of the studies have studied interaction between AI and radiologist in a real world setting. Conclusion While deep learning holds much promise in mammography interpretation, evaluation in a reproducible clinical setting and explainable networks are the need of the hour.
Blockchain and Machine Learning Based Approach to Prevent Phishing Attacks

Communication using Emails, SMS, and various social media platforms have become an important part of life when dealing professionally and socially. Due to their large uses, these platforms also have gained the attention of hackers to carry out sophisticated attacks by sending out messages that contains Malicious content (Generally URL). Huge numbers of Phishing emails are sent daily. Over the past few years, various Machine Learning based techniques were introduced to identify Phishing Attacks still, the count and loss due to Phishing is increasing daily. Blockchain technology is gaining a lot of attention in Security Domain over the past few years, in this state of art authors have proposed a blockchain-based system integrated with a Machine Learning model to identify and prevent Phishing content that is sent out on various messaging platforms. For implementation purposes Ethereum Based platform and from a Machine Learning perspective Classification based Gradient Boost Algorithm and Support Vector Machine Algorithm was used. The proposed systems talk about, sender while sending out a message to a recipient should pay some ETH (cryptocurrency) and if after content validation through ML models the message is found to be legitimate, the ETH value that the sender had paid will be refunded back, else if ML models identify the content as Phishing, then there will be no refund. Even though after validation through the ML model if the recipient marks the email as SPAM or reports the same as Suspicious then also there will be no refund. This eventually will reduce the traffic and will make hackers rethink when sending Phishing Messages. Authors were able to Create a PoC illustrating the same. API endpoints that were created in this state-of-art executed within 1000 ms & the accuracy obtained from the Machine Learning model was also high.
A New Approach to Sentiment Analysis on Twitter Data with LSTM

Sentiment analysis, also known as opinion mining, aims to categorize subjective information in texts into positive, negative, or neutral sentiments. It involves analyzing words to determine emotions expressed in sentences, paragraphs, or documents. The process includes cleaning the text by removing unnecessary characters, stop words, and punctuation, as well as tokenization to divide the text into separate words while maintaining contextual meaning. Analyzing sentiment in Twitter data requires extensive preprocessing, including the removal of unnecessary characters, handling hashtags, and tokenization. Long Short-Term Memory (LSTM), a type of recurrent neural network, is an effective technique for sentiment analysis as it captures sequential and contextual information from text data. In this study, an LSTM model is proposed to perform sentimental analysis on Twitter data. The proposed model was able to achieve 93% accuracy with 0.93 F1 score, outperforming other models.
CaptionGenX: Advancements in Deep Learning for Automated Image Captioning

The inception of an extraordinary and sophisticated image caption generating system, known as the CaptionGenX, is witnessed in the realm of artificial intelligence. This groundbreaking project embraces the realm of deep learning, an intricate discipline that enables machines to acquire knowledge and decipher complex patterns through neural networks. Within this context, the CaptionGenX embodies a pinnacle of innovation, harnessing the power of advanced deep learning techniques to unravel the intrinsic meanings concealed within images and transform them into contextually rich and linguistically refined captions. By leveraging a meticulously crafted neural network architecture, this cutting-edge system emerges as an epitome of computational prowess, engaging in a profound analysis of visual cues, intricate semantics, and subtle intricacies ingrained within the images. Through a meticulous process, the CaptionGenX seamlessly fuse them into a unified framework capable of generating captions that exhibit an exceptional level of coherence, nuance, and descriptive power. The integration of advanced deep learning methodologies and algorithms empowers the CaptionGenX to transcend the boundaries of conventional image captioning systems. It adeptly discerns and dissects the complex interplay between objects, scenes, and emotions depicted in the images, unraveling their essence with remarkable precision. Furthermore, this state-of-the-art system possesses an inherent ability to adapt and refine its captioning abilities over time, continuously learning and evolving to enhance its linguistic prowess and perceptual acuity. As an emblem of scientific ingenuity, the CaptionGenX stands poised to revolutionize the landscape of image caption generation. Its capacity to synthesize visual and textual domains, coupled with its unprecedented linguistic sophistication, renders it an invaluable tool in various domains, including multimedia analysis, content indexing, and assistive technologies for the visually impaired. With each passing milestone, this pioneering system forges new frontiers in the realm of artificial intelligence, promising a future where machines possess an extraordinary ability to comprehend and articulate the rich tapestry of the visual world.
Waste Management Detection Using Deep Learning

In the modern world waste pollution is the challenging environmental issues. To reduce pollution, we need to recycle the waste. In order to recycle waste, one must know which category the waste belongs to. Waste classification is a social issue related to people’s livelihood, so letting smart systems perform intelligent waste classification has important significance. So, we are here with a waste detection system that uses deep learning and categorizes the waste plastic or non-recyclable or glass or bio or others. The proposed system is trained and tested to detect the classify the waste using the CNN(Convolution Neural Network) i.e. a deep learning algorithm. The image of the waste material is fed into a neural network which determines the class of object. We have considered precision, recall and F1 Score as performance metrics to evaluate our model. The proposed model is compared with existing baseline models like machine learning, basic neural networks and datamining approach. The developed deep learning model has outperformed the existing literature with chosen parameters.
An Optimized Transfer Learning and Deep Convolutional Neural Network Approach for Automated Breast Cancer Detection

One of the most common malignancies in the world is breast cancer, which primarily affects women, its early detection is important to diagnose the unwanted cell growth in the breast region. The survival rate increases to 90% when the growth is detected early. The objective of research is to identify different algorithms having a higher accuracy and is faster in detection of the malignant cell growth at the same time to assist doctors to detect the cancerous growth. The research intends to determine whether the existing Convolutional Neural Network architecture performs the detection accurately or not. In this approach of detection, we implemented various model architectures such as Deep-CNN, VGG16, VGG19, InceptionV3, MobileNetV2 on a vast dataset to learn from it. The various models were compared on the basis of their accuracy. VGG16 performed the best in comparison to every other model and came out to be the best of the implemented, afterwards various optimizers were applied on VGG16 to further increase the accuracy to find its best tuning parameter. The best accuracy achieved by tuning different optimizers of VGG16 was 97% when the optimizer was set to default, and the default optimizer was “Adam”. This accuracy over a real-life dataset is considered to be ideal for classification purposes.
Design and Implementation: Deep Learning-based Intelligent Chatbot

Currently, chatbots face the issues such as inadequate use of multi-modality information, poor feelings of expression competence, and a lack of a model fusion mechanism. Deep learning applications include NLP-based "natural language understanding (NLU)", sentiment analysis, machine translation, and word vector representation. People have started looking into the new capabilities of chatbots and applying deep learning techniques to chatbots. Provided the actual problem that conventional chatbots Seq-2-Seq model training tends to produce quite secure, widely accepted, and repetitive responses to the speech corpus, this article utilizes the understanding of cooperative pieces of evidence to develop the optimization problem of the model in order to generate better and much more versatile response by the model and can provide to the client with a better and expected and quality outcome. Results of the experiment revealed that the model was found to outperform the single model in many indicators in the short text given data experiment.