Reviewerly/exHarmony huggingface dataset:
Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things (IoT) systems.Multivariate time series timestamp anomaly detection (TSAD) can identify timestamps of attacks and malfunctions.However, it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis, a process referred to as fine-grained anomaly detection (FGAD).Although further FGAD can be extended based on TSAD methods, existing works do not provide a quantitative evaluation, and the performance is unknown.Therefore, to tackle the FGAD problem, this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship's dynamic changes between indicators.Accordingly, this paper proposes a multivariate time series fine-grained anomaly detection (MFGAD) framework.To avoid excessive fusion of features, MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly.Based on this framework, an algorithm based on Graph Attention Neural Network (GAT) and Attention Convolutional Long-Short Term Memory (A-ConvLSTM) is proposed, in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators.Extensive simulations on a realworld dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.
APST-Flow: A Reversible Network-Based Artistic Painting Style Transfer Method

In recent years, deep generative models have been successfully applied to perform artistic painting style transfer (APST). The difficulties might lie in the loss of reconstructing spatial details and the inefficiency of model convergence caused by the irreversible en-decoder methodology of the existing models. Aiming to this, this paper proposes a Flow-based architecture with both the en-decoder sharing a reversible network configuration. The proposed APST-Flow can efficiently reduce model uncertainty via a compact analysis-synthesis methodology, thereby the generalization performance and the convergence stability are improved. For the generator, a Flow-based network using Wavelet additive coupling (WAC) layers is implemented to extract multi-scale content features. Also, a style checker is used to enhance the global style consistency by minimizing the error between the reconstructed and the input images. To enhance the generated salient details, a loss of adaptive stroke edge is applied in both the global and local model training. The experimental results show that the proposed method improves PSNR by 5%, SSIM by 6.2%, and decreases Style Error by 29.4% over the existing models on the ChipPhi set. The competitive results verify that APST-Flow achieves high-quality generation with less content deviation and enhanced generalization, thereby can be further applied to more APST scenes.
PNG-Stega: Progressive Non-Autoregressive Generative Linguistic Steganography

The autoregressive-based model with the left-to-right generation order has been a predominant paradigm for generative linguistic steganography. However, such steganography does not perform well on semantic control and content planning, which is forced by the secret message during the generation process. To mitigate this issue and efficiently produce high-quality steganographic texts (stegotexts), we present a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> rogressive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> on-autoregressive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> enerative linguistic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stega</i> nography (PNG-Stega), which encodes secret messages and extends the context to generate stegotexts in a multi-round insertion manner. Each round continuously refines the generated steganographic sequences on the premise of the global information of the previous round, while striving to decline the adverse effects of steganographic encoding on text quality. Moreover, for enhancing the semantic internal dependency of stegotexts, we utilize a constraint word sequences extraction scheme to obtain keywords to initialize the skeleton of targeted stegotexts, then expand the existing keywords with insertion operations. Experimental results demonstrate that PNG-Stega outperforms compared methods in terms of imperceptibility and anti-steganalysis ability. In particular, PNG-Stega provides high information hiding efficiency, even exceeding the autoregressive methods by around 2 times.
Learning a High Fidelity Identity Representation for Face Frontalization

This paper considers the problem of face frontalization in the wild, which transforms a face image with profile views into a frontal face. Face frontalization provides an effective solution to the face recognition problem in uncontrolled scenes. However, the existing methods either focus on deep learning techniques as an end-to-end framework or combine other explicit facial prior estimation tasks, such as 3D representation, optical flow estimation and so on, where computation is highly redundant and facial identity cannot be well represented. In this paper, we focus on how to maximise the potential of the model for identity learning and representation, and propose an accurate and lightweight face frontalization approach, named identity-preserving model (IPM). IPM has a well-designed encoder-decoder architecture which restores input face to a frontal counterpart. The encoder is constructed to extract representation from the input face, where a contrastive loss function is applied that encourages representations to form compact clusters, while preserving their relationships across the corpora. Then a cross-domain rectification module is proposed to eliminate the representation differences between the recognition and reconstruction domains, thus improving the accuracy of the reconstructed face. Extensive experiments on benchmark datasets show that the proposed IPM approach not only outperforms the state-of-the-art on public datasets but also can cope with images in the uncontrolled scenes.
Semi-Fragile Reversible Watermarking for 3D Models Using Spherical Crown Volume Division

Aiming at the large distortion and low tampering localization accuracy of the existing semi-fragile reversible watermarking for 3D mesh models, a novel semi-fragile reversible watermarking for 3D models using spherical crown volume division is proposed. The crown volume of a sphere is divided to reduce the embedding distortion. The possible geometric and topological transformations are separately considered in the watermark generation, and the vertices of the one-ring neighbourhood are grouped to improve the tampering localization accuracy. Experimental results show that the proposed scheme can achieve better localization accuracy and lower embedding distortion than some state-of-the-art algorithms. It has good potential for the applications in integrity authentication for 3D mesh models.
Identification of Characteristics of Mewari Poems

Poetry is a type of literature that is used to convey a man’s feeling, sentiments and imaginations. It is formed by a beautiful amalgamation of thoughts and notions in the form of words and tones. Mewari in particular is a major dialect of the Rajasthani language and is spoken by a vast majority of population in the Mewar region. It possess a great importance because many literature works created in Mewari glorifies about the great kings like Maharana Pratap, Maharana Kumbha, etc. Thus the cultural significance of this language increases exponentially. Despite having this huge significance very little work has been done in the field of Mewari language in the field of Natural Language Processing, be it identification, classification or extraction. Like Hindi, the Mewari language is written in Devanagari lipi. This paper aims to identify various elements of a Mewari literature like rhymic extent, negative tone, mood and number of a poem. The idea is to extract the data and match it with various parameters to find the hidden elements. Although, decent amount of work has been done in the field of poetry classification and analysis in different languages but this will be the first work being done on Mewari dialect. Various preprocessing operations were performed and it successfully predicted the Rhyme, Negative Tone, Mood and Number with accuracy of 83.75, 90,70 and 70.7 percentage respectively.
Optimising Feature Selection: A Comparative Study of mRMR-Boruta/RFE Hybrid Approach

Feature selection is an essential component in the data preprocessing pipeline, particularly when dealing with datasets that possess a vast array of dimensions. In this paper, we present a time efficient wrapper technique Boruta to improve the overall complexity of our feature selection process. We have combined this wrapper technique with the filter class Minimum Redundancy Maximum Relevance (mRMR) to enhance the selection of relevant features. Additionally, our scope includes refining a previously proposed hybrid model that combines filter class Minimum Redundancy Maximum Relevance (mRMR) known for faster processing speed with wrapper class Recursive Feature Elimination (RFE) known for its high classification accuracy. We demonstrated the effectiveness of our approach on a variety of datasets and showed that our model is able to identify a smaller and more interpretable subset of features while generating better performance. Our results suggest that the combination of preprocessing and hybrid feature selection model is a promising approach to process a dataset with high dimensions.
VISMA: A Machine Learning Approach to Image Manipulation

Powerful tools for semantic picture modification have recently been developed thanks to recent improvements in image production. Existing methods, however, may need a large amount of supplementary data. They are unable to handle all editing actions, including the insertion, modification, or deletion of semantic attributes of the image. The proposed method, a new Image Semantic Manipulator pair for Adding, Moving, or Erasing Objects in Scenes during Semantic Editing, to overcome these restrictions. In our arrangement, the generator generates the relevant pixels and edits the image when the end user gives the semantic identifiers of the regions to be changed. The paper aims to present results for two jobs: (a) picture editing and (b) image production with semantic label constraint using General Adversarial Networks (GAN) machine learning approach. A pre-trained neural network can also adjust to images of various sizes. Making the image fit the needs of the user by altering the overall aesthetic and emotional tone also helps. When given appropriate training input/output pairings, our technique may be expanded to provide a range of outputs when label mappings are changed, enabling interactive image modification that enables users to interact with changing an object’s appearance in real time. Last but not least, our suggested approach will raise resolution while establishing a new standard for realistic photos.
Human Detection in Surveillance Video using Deep Learning Approach

Object detection (OD) is a key challenge in computer vision and received a lot of importance from last decade. The main goal of object identification is to determine location of specific target class object with good accuracy and label object in an image with appropriate class. Basically, deep learning-based OD techniques are splits up into two parts:-1) two-stage identifier and 2) one-stage identifier. In this article, we give a short and vital review of current developments in deep learning-based OD. The purpose of our research work is to identify humans in a surveillance video and divide the video into active and non-active frames to save the time of watching the entire video. We implemented two novel methods of object detection, YOLOv4 and Mask-RCNN because of their good and promising performance. After extensive experiments, it is noted that Mask-RCNN performs better than YOLOv4 on a developed dataset. Mask-RCNN achieves 85% accuracy and exhibit better performance as compared to YOLOv4 which achieve 65% accuracy only.
An Efficient Image Encryption Reversible Data Hiding Technique to Improve Payload and High Security in Cloud Platforms

Researchers are becoming more intrigued by images (RDHEI) since of its potential applications in areas like privacy protection and cloud storage. If you need to include more information in the encrypted domain, RDHEI is a useful method that won’t compromise your security. An RDHEI technique is proposed in this paper, which is both fully reversible and capable of embedding a large payload. Once the content owner has encrypted the original picture via homomorphism encryption, the hider may insert the secret statistics into the encrypted portion of the image. Privacy may be protected via the use of data concealing in images. It employs a slew of deceptive techniques to conceal sensitive information. Heavily altering a histogram may make hidden information much harder to find. Additionally, the data is not expanded during homomorphism encryption, and the payload is still capable of being enhanced. The recipient of the encrypted watermarked picture needs just the data concealing key in order to access the hidden information. To decode a picture, all the recipient has to know is the encryption key. Having both the data concealing key and the encryption allows the receiver to decrypt the picture and restore the original data without any quality loss. The receiver will be able to decipher the secret communication and restore the unique picture with zero quality loss. The experimental results demonstrate not only an enhancement of the embedding payload, but also an exact extraction of the concealed data and a complete retrieval of the cover picture.
Leveraging Blockchain Technology for Improving the Quality of Corporate Governance

Blockchain technology allows secured exchange (peer to peers) with the consensus-based algorithm. Corporate governance focuses on the governance of corporate data through a secure mechanism. This paper aims to interpret the possibility of a robust corporate governance mechanism through blockchain technology to eliminate the chances of corporate fraud. There are multiple uses of Blockchain in cooperate governance. Authors have discussed how shared information consensus prevents possible losses to fraud embezzlement. Boardroom practices to disclosures, blockchain technology, and distributed ledger technology hold a strong possibility of implementing it. Past studies have stressed the impact of technology on corporate governance and the positive relationship between blockchain technology and governance. Taking the lead from antecedents, the authors have elaborated on leveraging blockchain technology for improved corporate governance in the present study. The paper uniquely explores the application of blockchain technology for sound corporate governance which provides potential for direct application by organizations globally.
Cloud Scheduling Heuristic Approaches for Load Balancing in Cloud Computing

Cloud computing provides a framework for remote users to access diverse resources in DC (Data Centers) to compute High-Performance Computing workloads. In the cloud, physical resources are virtualized to provide user services via VMs (Virtual Machines). Task scheduling is a crucial component of the cloud, and successful VM usage by CSPs (Cloud Service Providers) necessitates an optimum task-scheduling algorithm. An ideal scheduling heuristic must be efficient, fair, & starvation-free to provide a shorter timeline with better resource use. However, static heuristics frequently result in inefficient and ineffective resource use on the Cloud. The fundamental issue in the CC environment is task scheduling, which is critical in optimizing overall execution time. This paper compares scheduling heuristic algorithms such as DRALBA, RALBA, DLBA, Min-min, Max-min & Round Robin depending upon makespan, throughput, and ARUR using workflows on synthetic workload and Google a realistic workload called GoCJ datasets. According to this study, the existing DRALBA approach outperforms in terms of performance parameters than the other approaches over both synthetic and GoCJ data.
Software Fault Prediction using Wrapper based Ant Colony Optimization Algorithm for Feature Selection

Feature selection is both important and difficult part in classification technology. It is used to reduce the dimensionality of dataset and remove unwanted features. In this paper, we are going to discuss about a supervised feature selection method based on ant colony optimization for software fault prediction. Software fault prediction helps to identify software fault at an early stage. We have used KNN, Naive Bayes and Decision Tree classifiers. We designed a fitness function and a two-step pheromone update rule is applied for effective elimination of duplicate features. This algorithm is inspired from real ants that search for the shortest path to the food source depending upon the concentration of the pheromone. We have used 12 different datasets and compared them using fitness plots. Each and every graph represents the effectiveness of ant colony optimization along with different classifiers. We have also made a table that represents the accuracy of the prediction for all the different classifiers while using the algorithm.
Modified Caesar Cipher with Image Steganography

In Data Security, Cryptography refers to encryption (at sender side) and decryption(at receiver side) of data and Steganography refers to the method of hiding a data in another form of data. Using only Cryptography is not as much secure as it was earlier as the hackers have become more skilled now-a-days. Combining Steganography with Cryptography is a secure method for transmission of data over the internet. In this paper, we have used Caesar Cipher cryptographic technique and combined it with “Image Steganography” to derive a secure method for data security.
Feature Selection using Ant Colony Optimization for Microarray Data Classification

Microarray datasets have very high dimensions and contain noise and redundancy. The issue with the microarray dataset is that it includes more characteristics than the samples, which reduces the algorithm’s effectiveness. To put it another way, compared to rows, there are more columns. To precisely extract data from the microarray dataset, we need a powerful approach. Microarray datasets are essential for diagnosing cancer, tumours, and other disorders. So here is the feature selection strategy in action. Recently, feature selection (FS) has gained in importance as a data preparation strategy, particularly for high-dimensional data. We favour classification issues with fewer characteristics and high accuracy since we know that all attributes are not required to attain high accuracy. The main objective of feature selection is to identify the best subset of features. In order to accomplish this goal, we are utilizing the Ant Colony optimization (ACO) method, which is a technique that takes inspiration from the foraging habits of ant colonies. We will be using ACO combined with Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF), and. We will compare the accuracy of every algorithmic model and every dataset, as well as fitness error. Random Forest and Logistic Regression has performed well for both datasets.
A Lightweight and Efficient Scheme for e-Health Care System using Blockchain Technology

We are witnessed to the huge amount of health care information is processed via Internet and uploaded to the servers. Hence, the network content suffers from various challenges in handling this wide range of information such as privacy-preservation, message requests, and sharing of information with the entities. Protection of health care information with less communication overhead and avoid signaling congestion among entities is the biggest concern. We suggest a simple and effective blockchain-based system for e-health care in order to address these problems. We attempt to show the collective trust between receiver/ users and key generation center (KGC). Due to the characteristics such as tamper-resistant of transactions and responsiveness, the blockchain technology maintains the data access policy and privacy of each receiver/ users. In addition, we provide the public transactions apparently with the help of miners. Also, the performance analysis shows that the protocol is free from all the security attacks and avoid the signaling congestion with less overhead.
Music Recommendation System through Hand Gestures and Facial Emotions

Music can be a powerful tool to describe the human mood. Hand Gestures and Facial emotions are forms of fast non-linguistic communication. The current research on Music recommendation either using a hand gesture music controller (that only controls the operations for playing music) or an emotion based music player but not both. In this work, a new and hybrid approach for playing music both using hand gestures and facial emotions is proposed that can help the user to recommend and play music. In this research facial expression recognizer(FER) algorithm is used that extract the features from the image for emotion detection and the MediaPipe framework and Tensorflow library are used for hand detection and gesture recognition respectively. The music will play based on the most recent gesture and emotion by using a pygame. First, priority is given to hand gestures and then to facial emotions. The accuracy of the proposed work is also compared with existing approaches to music recommendation.
Two Stage of Histogram Matching Augmentation for Domain Generalization: Application to Left Atrial Segmentation

Convolutions neural networks have obtained promising results in various medical image segmentation tasks. However, these methods ignore the problem of domain shift, which will lead to a model trained in a source domain performing poorly when applied to different target domains. In this work, we propose a two-stage segmentation network, and utilize histogram matching to eliminate domain shift. Specifically, the first stage obtains the region of interest by performing coarsely segmentation on down-sample images. Then the second stage segments the left atrium (LA) based on the region of interest. The method is evaluated on LAScarQS 2022 data-set, acquiring average Dice of 0.87790 for LA segmentation. Besides, the two-stage network is about four times faster against a single-stage network in the test phase.