Reviewerly/exHarmony huggingface dataset:
Evidence Acquisition in Social Media for Cyber Crime

Social Media forensics a branch of forensics involves in collecting the evidences for the cyber crime. Investigating social media is a complex process which involves the privacy issues for accessing the users, suspects and victims information on social media. Manual processing of social media data is not feasible as it contains large volumes of data. An automated process is needed to incident specification, evidence extraction and for provenance. The need for handling heterogeneity of data as users have accounts with multiple social websites is also explained. This study briefs the existing models and the challenges faced in analyzing with those models. The research goals in this field are also addressed. A pool of tools which can contribute in guarding the solution for cyber crime is also presented.
Security and Performance Comparison using QR Code through Blockchain Technology

Advances and transitions in healthcare system is constantly changing and acquiring fresh forms. The requirement of modern smart health systems is to protect sensitive patient data from attacks by potential adversaries. The secure data access mechanism ensures that only authorized personnel can access patient's health information. Therefore, in distributed method, it protects data in the healthcare system. Here it provides patients and doctors with an efficient and secure access mechanism based on blockchain data collection. In this health system the proposed system can also protect the privacy of patients. Security analysis depends upon architecture to prevent from attacks and maintain integrity of system. In addition, the implementation of system based Ethereum used for feasibility test of proposed system. By hiding private information in the coverage environment in a way that a third party cannot discover this confidential information. In today's era, with the emergence of new technologies, QR codes have become one of the most popular codes. Provide mechanisms for protecting sensitive information QR codes are the most reliable method of hiding data because they do not use error correction levels and because it is difficult to change the encoded information. The process is divided into different categories, and various statistical parameters are used to evaluate its performance.
Three-Stage Salient Object Detection based on Integrated Priors

In this paper, a three-stage model is proposed for salient object detection. In the proposed method, an intuitive and straightforward pre-treatment method is firstly proposed to conduct superpixel segmentation adaptively, then superpixel-based graphs are constructed to express the structure of the image. To make full use of the information of individual images, multiple priors, including background prior, foreground prior, center prior and global contrast prior, are integrated in the three-stage detection model. In the first stage, under the assumption of background prior that the borders of the image are more likely to be the background, the absorbing Markov chain model is constructed to compute the saliency scores based on the absorbed time of each node in random walk. Then in the second stage, the saliency scores computed in the first stage, are taken as the foreground prior to compute the saliency scores via manifold ranking. In the third stage, center-biased global contrast filter combining center prior and global contrast prior is formulated to refine the saliency map. Experimental results demonstrate the effectiveness of the proposed three-stage method.
An Efficient ConvNet for Text-based CAPTCHA Recognition

Text-based CAPTCHA is a widely used security mechanism to protect websites from malicious operations. The CAPTCHA recognition based on deep learning is a representative method to verify the security of CAPTCHAs deployed on the website. The ConvNet is a typical model for a wide variety of vision tasks and proves to be effective when it recognizes characters in different scenarios. However, the ConvNet applied to solve CAPTCHAs is still limited to high computation and complicated processing. In this work, we propose an efficient end-to-end network entirely consisting of standard ConvNet modules. According to the property of CAPTCHA, we reduce the redundant convolution calculation in the previous ConvNet and introduce a novel group convolution operation along the width of the image with improved performance and efficiency. The experiment shows our ConvNet successfully solves the CAPTCHAs from the highly-visited website, Sina.com, with a high accuracy above 90%, and the number of trainable parameters of it is less than 1/5 of the model in prior work based on prominent ConvNets such as ResNet and Inception network.
Sparse Method Towards Temporal Action Detection

Temporal action detection aims to correctly predict the categories and temporal intervals of actions in an untrimmed video by using only video-level labels, which is a basic but challenging task in video understanding. Inspired by the work of Sparse R-CNN object detection, we present a purely sparse method in temporal action detection. In our method, a fixed sparse set of learnable temporal proposals, total length of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{N}$</tex> (e.g.50), are provided to dynamic action interaction head to perform classification and localization. Sparse temporal action detection method completely avoids all efforts related to temporal candidates design and many- to-one label assignment. More importantly, final predictions are directly output without non-maximum suppression post-procedure. Extensive experiments show that our method achieves state-of-the-art performance for both action proposal and localization on THUMOS14 detection benchmark and competitive performance on ActivityNet-l.3challenge.
Deep Learning-Based Demodulation of Radio Signal

M-ary quadrature amplitude modulation (M-QAM) modulated signal is commonly used in digital telecommunication systems for its arbitrarily high spectral efficiencies limited only by the noise level and linearity of the communications channel. Typical demodulation techniques for M-QAM signal utilize variants of coherent demodulation. This paper aims to exploit the robustness of deep learning, specifically by using neural networks to demodulate M-QAM symbols. This is achieved with simulated time-domain baseband M-QAM signals across a range of channel impairments namely additive white Gaussian noise, DC offset and I/Q imbalance. The presented results show an improvement when utilizing deep learning over optimal receiver.
Hybrid Attention Mechanism combined with Peak Sampling for Object Tracking

In recent years, deep learning has created a research boom in the field of computer vision and continues to drive the development of high-level tasks such as object tracking and object detection. However, designing a robust object tracking method remains a challenging topic due to the tracking challenges such as illumination changes, occlusions, deformations, etc. In this paper, we propose a novel object tracking method that combines attention mechanism and peak sampling to address the problems that arise in most existing object trackers when facing the above challenges. First, an effective hybrid attention mechanism is proposed and then introduced into the model to enhance the tracker's attention to foreground targets. Then, the Maximum peak sampling method is adopted to strengthen the highest peak of the feature response map to highlight the foreground target, which effectively suppresses the interference of target analogues. Finally, a combination of offline training and online learning methods is used to further improve the tracking accuracy and robustness. Experimental results on several standard datasets show that the proposed tracker is able to effectively promote the tracking accuracy and robustness.
Single Image Super-Resolution Using Inverted Residual and Channel-Wise Attention

Single-image super-resolution (SISR) is the task of reconstructing a high-resolution image from a low-resolution image. Convolutional neural network (CNN)-based SISR techniques have demonstrated promising results. However, most CNN-based models cannot discriminate between different forms of information and treat them identically, which limits the models' ability to represent information. On the other hand, when a neural network's depth increases, the long-term information from earlier layers is more likely to degrade in later levels, which leads to poor image SR performance. This research presents a single image super-resolution strategy employing inverted residual connection with channel-wise attention (IRCA) to preserve meaningful information and keep long-term features while balancing performance and computational cost. The inverted residual block achieves long-term information persistence with fewer parameters than traditional residual networks. Meanwhile, by explicitly modeling inter-dependencies between channels, the attention block progressively adjusts channel-wise feature responses, enhancing essential information and suppressing unnecessary information. The efficacy of our suggested approach is demonstrated in three publicly accessible datasets. Code is available at https://github.com/mdhosen/SISR_IResBlock
Layered Deep learning for Improved Breast Cancer Detection

Breast cancer is the most serious disease affecting women around the world, and is the fifth leading cause of death in women. The contribution of this work is to help facilitate early diagnosis of the breast cancer. The introduction section highlights the importance of the problem, and gives insight to literature review, where existing research works conducted in this direction are surveyed. The proposed approach presents related dataset chosen to evaluate the approach investigated in this work. A layered deep learning model is investigated, which is trained using a dataset. Several evaluation metrics related to machine learning are employed to evaluate effectiveness of the proposed approach. The results suggest that accuracy of the proposed model is above 96% for both training and validation of the model. The training and validation results are discussed, and sample detection and classification results are presented.
Omnidirectional Image Quality Assessment With a Superpixel-Based Sparse Model

Panoramic interaction represented by omnidirectional images and virtual reality will be an important format of information technology in the future. How to evaluate the quality of omnidirectional images accurately and quickly is essential for the user experience of panoramic interaction. In this paper, we propose a novel superpixel-based sparse model for full reference omnidirectional image quality assessment. First, we segment the omnidirectional images into superpixel regions. The Entropy of Primitives (EoP) is then calculated as image information based on the sparse model. Furthermore, the Kullback-Leibler divergence is exploited to represent the difference of visual information between original and distorted images. The quality score is predicted by an SVR model trained from the visual information features. Experimental results show that the proposed metric achieves high consistency with the subjective evaluation on the OIQA database.
Neural Data-to-Text Generation: An Encoder-Decoder Structure with Multi-Candidate-based Context Module

The data-to-text generation task mainly uses the encoder-decoder architecture, in which the context module provides the information that the decoder wants to observe at the moment. However, there are multiple entities and elements in a single sentence. We conjecture that the architecture has room for improvement to be more suitable for the data-to-text generation task. This paper proposes the Multi-Candidate-based Context Module, using the concept of multiple candidates to simultaneously observe multiple entities and their records. The experiment confirms the effectiveness of our multi-candidate concept and the improvement over the state-of-the-art on the recently released Rotowire dataset.
Information-Based Rule Ranking for Associative Classification

Classification rule mining is a promising approach in data mining to create more interpretable and accurate prediction systems. This approach typically builds on top of well-known association rule mining and classification techniques, which identify a subset of rules known as class association rules (CAR), whose consequents are limited to target class labels. Existing classification rule mining methods have proven to provide better predictive accuracy while improving the interpretability and reasoning of a problem. Nevertheless, the challenges of such methods are mainly on a large number of generated CAR and the ranking and selection of interesting CAR for building classifiers. This paper proposed a hybrid of association rule mining (FP-growth) and neural network (sequential network of dense layers) techniques, focusing on using an information-based approach to rank and select interesting CAR. Preliminary experiments were conducted on nine UCI Machine Learning Repository datasets to examine the effect of the proposed hybrid model on generic datasets. The results show that the proposed approach achieved higher accuracy than other associative classification methods.
Lightweight Scrambled JPEG XS Coding for Privacy Protection

In this paper, we propose lightweight scrambled JPEG XS coding for privacy protection. The proposed method operates as an Encryption-then-Compression (EtC) system. It enables the EtC scheme to be used while maintaining compatibility with JPEG XS. Input images are scrambled and fed to JPEG XS. Despite the scrambling, the rate-distortion performance is only slightly degraded. The proposed method provides almost the same rate-distortion performance as that of JPEG XS without scrambling. We demonstrate its performance via simulation for 8K ultra-high-definition images.
A Temperature Data Denoising Method with Consideration of Noise Variance

In this paper, a temperature data denoising method is presented by considering the noise variance. First, conventional smoothness-based denoising method is briefly reviewed. Then, the minimization of closeness function of conventional method is replaced by the minimization of negative log likelihood function to develop a denoising method in which probability density function of noise is assumed to be a zero-mean Gaussian function with different variances. The optimal denoised temperature data is easily obtained by solving matrix inversion if the noise variance is known in advance. Finally, the temperature data collected from the sensor networks in USA and Taiwan are used to show the effectiveness of the proposed denoising method.
Recurrent Network with Attention for Symbol Detection in Communication Systems

One major challenge for wireless receivers to maintain information fidelity involves the demodulation of faded signals in noisy environments. Typical demodulation techniques for M-ary quadrature amplitude modulated (M-QAM) signal utilize variants of coherent demodulation. This paper explores deep learning (DL), specifically by using a proposed architecture recurrent-attention networks to compliment, or even overcome the limitations of demodulating M-QAM symbols. The proposed model is shown to outperform the benchmark coherent demodulator and other DL-based demodulators such as convolutional neural network (CNN), recurrent neural network (RNN) and the hybrid of both up to 5 dB learning gain at a lower model complexity and requires less memory usage.
Implementation of Pipelined Architecture for Red Channel Compensation Unit for Underwater Image Processing in 45 nm technology

Underwater images obtained from sea is degraded in its quality due to absorption, scattering, bending of light etc. Red channel compensation is a major process in underwater image restoration which helps in avoiding the effect of red artifacts in the image. In this paper pipelined architecture is developed for red channel compensation unit. The architecture is synthesized for 45nm Technology node and simulated for its functionality verification at 100MHz of operating frequency. The IEEE-754 standard is used to store and access the image from memory. A method is proposed in the designed architecture intended for fast computation and power reduction.
No-Reference DIBR-Synthesized Video Quality Assessment based on Spatio-Temporal Texture Inconsistency Measurement

The relevant applications of depth-image-based-rendering (DIBR) exist mainly in the form of video sequences. However, existing studies on the quality assessment of DIBR-synthesized views primarily focused on DIBR-synthesized images. To this end, this paper proposes a DIBR-synthesized video quality evaluation metric based on measuring spatio-temporal texture inconsistency, dubbed STTI. Specifically, STTI first extracts the texture map of each frame in the spatial domain. Then, STTI further employs the histogram of oriented optical flow to extract the dynamic variations of adjacent frames' texture information in the spatio-temporal domain. Finally, STTI calculates the cosine similarity of the histograms of oriented optical flow between the texture maps of adjacent frames to measure spatio-temporal texture inconsistency. Experimental results on the publicly available datasets show that the proposed STTI outperforms the popular image/video quality assessment methods developed for natural scene and DIBR-synthesized views.
A Reversible Image Processing Method with Selectable Functions

This paper proposes an image processing method for color images to reversibly achieve sharpening and smoothing. We have previously proposed a reversible method that enhances brightness contrast and improves saturation. The proposed method has additional functions of sharpening and smoothing without loosing the advantages of our previous method. We designed an effective filter considering area segmentation to ensure reversibility. The experimental results demonstrate that our method can attain the above functions selectively and guarantee reversibility.
Cross-View Integration for Stereoscopic Video Deblurring

Stereoscopic cameras are now often seen in modern technology, including new Cellphones. Numerous elements, such as blur artifacts from camera/object motion, might influence the stereo video's quality. There are various deblurring techniques for monocular content, yet there are not many works for stereo content. A novel encoder-decoder-based stereoscopic video deblurring model presented in this work considers the subsequent left and right video frames. This approach employs the cross-view stereoscopic information to aid in deblurring. The proposed model uses the left and right stereoscopic frames and some nearby left and right frames as inputs to deblur the middle stereo frames. To extract their features, we first apply the stereo batch of frames to the encoder of our model. The left and right features are then fused together after being aggregated using the Parallax Attention Module (PAM). The decoder then extracts the deblurred stereo video frames using the output of PAM features. According to experimental findings on the recently proposed Stereo Blur dataset, the proposed approach effectively deblurs the stereoscopic video frames.
A Method for Integrated Processing of Edge Enhancement and Contrast Enhancement

Image enhancement methods consist of contrast enhancement and edge enhancement, and research on these two enhancements has been done independently. In this paper, we divide the image into two frequency bands, apply contrast enhancement to the low-frequency component and edge enhancement to the high-frequency component, respectively, thereby achieving two enhancement methods at the same time.
Crowd Monitoring of Hajj Pilgrimage using Video Analytics and Deep Learning *

This paper presents developments in crowd analysis, with a specific emphasis on crowd density during the Hajj and Umrah pilgrimages. In recent years, there has been a rising interest in increasing video analytics and video surveillance to enhance the safety and security of pilgrims throughout their stay in Mecca. Crowd surveillance research and development are still in their infancy. Real-time monitoring of crowds is impeded by a lack of distinct crowd scenes or the high intensity of the crowd, which reduces its use. This paper proposed a fully convolutional neural network (FCNN) method for large crowd analysis, and it is able to classify the three classes of crowd density. This is intended to overcome existing technical challenges in video analysis in situations involving the circulation of a large number of pilgrims at a density of seven to eight per square meter. This endeavor intends to build a new dataset based on the Hajj scenario, in part to overcome the aforementioned difficulty. Manual and semi-automatic approaches are now used for video analysis, notably during Hajj. However, it is vital to automatically monitor pilgrim crowd events. Integrating FCNN classification with FCNN regression requires a strategy that is more powerful than the direct approach. Using ResNet50 and Fully Convolutional Neural Networks, a deep learning architecture is constructed to predict the density of certain crowd footage (FCNNs). The new dataset (Hajj-Crowd dataset) and using the recommended strategy, our solution obtained 100 % accuracy in crowd analysis, exceeding the state-of-the-art technology. In addition, the ResNet50 method achieved an accuracy of 98.6% for the developed crowd dataset.
Efficacious Opportunities and Implications of Virtual Reality Features and Techniques

Virtual Reality (VR) is a computer-generated environment that allows the user to interact with the environment virtually. It provides visualization to feel the virtual environment similar to the real world. Augmented reality, virtual reality, and mixed reality are commonly used in extended reality. Virtual reality is completely based on technology, it acts as an intermediator between humans and the real world. There are various input and output devices that are used for generating a virtual reality environment. VR techniques are currently used in numerous fields like education, entertainment, gaming. Primarily, the paper has elaborated on Virtual Reality with its significance. The importance and need for virtual reality are conferred with its impact in the modern world. The framework of Virtual Reality and the steps leading to its development over the years has been elaborated. Various tools and techniques used in VR, the applications of VR in the current world and the implication of VR for the future are emphasized.
Detection For Non-Genuine Identification Documents

Due to the recent pandemic, electronic know-your-customer (e-KYC) technologies have enabled business continuity for various sectors. In the case of Malaysia, the Malaysia National Identity card (MyKad) is often used as the official document for identity-verification purposes. However, when presenting MyKad online, the uploaded MyKad could be an image of the actual MyKad taken a few years back, a printout of a legit MyKad, or a digitally/physically tampered MyKad. This research aims to detect non-genuine MyKad. Specifi-cally, we consider the unique colour count, the surface rough-ness score (i.e., complexity), and local binary patterns (LBP) of the received MyKad image to make inferences. The proposed method achieves an accuracy of 97.71% and an Fl-score of 0.9773.