Reviewerly/exHarmony huggingface dataset:
Enhanced Steganography for High Dynamic Range Images with Improved Security and Capacity

High-dynamic-range (HDR) images are widely regarded as the ideal format for digital images due to their ability to accurately render a wider range of luminance values. Recently, research has focused on introducing data-hiding techniques to HDR images, but these studies often suffer from a low hiding capacity. In 2011, a steganography scheme was proposed, which utilizes homogeneity in RGBE (red, green, blue, and exponent) format, a popular HDR format, and results in cover images with only slight and ignorable distortions after embedding. However, the capacity of the scheme is limited, and their steganography process may raise suspicions due to the abnormal distribution of pixel values caused by the multiplication and division in the embedding process. There is no denying that security is always the main concern for steganography. A major potential problem became clear after a careful revisiting of the scheme. This paper presents an enhanced steganography scheme that improves embedding capacity by modifying non-embeddable pixels to become embeddable in cover images and avoids potential security weaknesses by using additional random numbers to alter pixel values. The proposed scheme improves the embedding capacity of HDR images while maintaining their visual quality and security against statistical analysis attacks. The experimental result shows that the capacity increases 10 times without visual distortion.
Camera-based interactive wall display using hand gesture recognition

the recognition of Hand gestures has become a critical point as it is widely used in everyday applications. the challenge in this is to improve the recognition effect and develop a fast recognition method. Glove and led-based methods involve external devices in detecting and interpreting hand gestures, making human-computer interaction less natural. So, different approaches have been used previously that use purely hand gestures in many systems based on human-computer interaction. This system provides a more natural human-computer interaction; it must be made efficient processing speed of classifying the test data (images) from among the training data (database stored for gestures recognition). This speed makes gesture recognition more effective and reliable to use as compared to previously proposed methods. In this research paper, a proposed system based on a camera-based interactive wall display using bare hand gestures with efficient processing speed for controlling the speed of the mouse and other functions. This system has three modules: one uses Genetic Algorithm and Otsu thresholding to identify the query images as the right or wrong gesture and perform the correct action in case of the proper motion, another module controls functions outside of PowerPoint files or Word documents, e.g., to open folders and go through drives, and the third module uses the convexity hull method for finding the number of fingers open in the user's gesture and operates accordingly.
Chatbots in Digital Marketing

Artificial intelligence software called chatbots are designed to imitate human conversation. They utilize natural language processing technology to comprehend and interpret user input and produce responses based on pre-programmed rules or machine learning algorithms. Chatbots are widely utilized in sales and customer service domains. Customers appreciate the convenience of chatbots' instant responses and ability to quickly provide needed information without human intervention. Chatbots benefit businesses by handling multiple inquiries of customers and reducing the need for additional staff, and can also be used for internal purposes such as responding to employee queries and assisting with various tasks. This chapter examines the use of chatbots in marketing, customer service, and sales, covering their classification and common types, impact on customer experience and service costs, and ethical considerations. This chapter will be useful for scholars researching chatbots and professionals looking to integrate them into their marketing and customer service strategies.
Accessibility and Inclusiveness of New Information and Communication Technologies for Disabled Users and Content Creators in the Metaverse

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Traffic Anomaly Alert Model to Assist ADAS Feature based on Road Sign Detection in Edge Devices

Gazing into the future of driver assistance systems such as Advance Driving Assistance System (ADAS), Radar, Automatic Emergency Braking (AEB), Lane-Keeping Assist (LKA), Traffic Jam Assist (TJA), and much more systems in affordable prices, which are to increase and improve the safety and security of the driver and the passengers, yet still they cause certain new problems due to some of the short comings of the current systems, when there is anomaly such as renovation work in the freeway. Therefore to overcome those situations, an system with low latency that must be able in to run in edge devices with less computational power and be highly precise, and affordable must be developed to alert the driver if the road has some anomaly when the vehicle is in ADAS mode. In this paper, a system that works in complimentary with ADAS will be able to detect road sign using the real-time capturing of he connected webcam and alert the driver using buzzer and red LED light to take control of the vehicle. To facilitate the road sign detection in edge devices with low latency, less size and high precision EfficientDet-Lite2 model architecture and TensorFlow Lite Model Maker is used to train the dataset, which uses transfer learning to reduce the amount of data for training, combining all that with Raspberry Pi 4 model b as the processor, the system is able to reliably detection and produce a output with up-to 90% average precision and low latency.
MPC Based Motion Planning For Mobile Robots Using Velocity Obstacle Paradigm

Model Predictive Control (MPC) has been increasingly adopted in robotics in recent years for several tasks including the real-time control of mobile robots. The main advantage of using the MPC is the possibility of adopting an optimal control policy under a set of constraints which can be used to enforce safety, like collision-free manoeuvres. In literature a well-known approach to ensure real-time collision avoidance for multi-agent systems is the Velocity Obstacle (VO) paradigm which considers the robot as a point-mass, neglecting its dynamic and kinematic constraints. In this paper, we propose an MPC-based motion planning solution that directly considers the dynamics and the kinematics of the mobile robots, exploiting the VO as a constraint on the configuration space of the controlled system. We evaluated the proposed solution in simulation using holonomic and non-holonomic kinematic and dynamic models.
Data Security in Healthcare: Enhancing the Safety of Data with CyberSecurity

Cyberattacks are used to steal money, data, or intellectual property, but the goal is increasingly to produce overt disruption or political influence. Healthcare is more vulnerable to cyberattacks than other industries due to inherent weaknesses in its security posture. In addition to medical equipment and other systems connected to IT networks, cybersecurity threats and vulnerabilities can pose a threat to the confidentiality, resilience, and veracity of those systems. As a result of the rich supply of valuable data, Healthcare makes a good target for cybercriminals. Additionally, while Cybersecurity is critical for patient safety, it has an unreliable track record. Breach of infrastructure has resulted in millions of health records being stolen, potentially putting patients' lives at risk. This necessitates the integration of Cybersecurity into patient safety. Before these attacks, many security experts struggled to persuade corporate executives of the necessity of cyber security; significantly, a great deal can be gained, in the long run, from risk mitigation, through both cost savings and reputation protection. A holistic solution to prioritizing Cybersecurity in the healthcare business necessitates cultural transformations, enhanced leadership communication, and changes in how practitioners conduct their roles in the clinical setting.
A Multi Stage Approach for Object and Face Detection using CNN

Object and face detection are important tasks in computer vision that have numerous applications, such as surveillance, image recognition, and autonomous driving. Artificial intelligence (AI) has transformed the field of image recognition, enabling machines to interpret and analyze visual data with remarkable accuracy and speed. AI algorithms use deep learning techniques to automatically recognize patterns, shapes, and features within images, allowing them to identify objects, people, and even emotions. Image recognition has numerous practical applications, from facial recognition in security systems to medical imaging for diagnosis. The approach for object detection, face recognition, and celebrity identification proposed in this research uses algorithms such the Convolution Neural Network (CNN), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbor (KNN). The CNN model is proven to be more accurate than other models due to their ability to learn features from images. The multi-stage approaches for object and face detection using CNNs have shown to be effective in achieving high accuracy of 93.2% and real-time performance
QoE Optimization in DASH-Based Multiview Video Streaming

DASH-based multiview video allows users to freely navigate between available viewpoints.However, achieving seamless transition during viewpoint switching is still challenging, especially in freeviewpoint video streaming, where user navigation is unpredictable.When bandwidth is limited, delivering all viewpoints in high quality is nearly impossible.The DASH system needs to prioritize high video quality for the selected viewpoint.The problem arises when the switching event occurs because the new video may not yet be available in the buffer.This paper introduces multiview video quality of experience (QoE) then discusses how viewpoint transition delay and viewpoint quality delay affect the overall user experience.We propose quality-based adaptive bitrate (ABR) for multiview video by specifying viewpoint priority and a target quality of the main viewpoint, namely multiview video priority (MVP).Although it select a lower bitrate level than rate-based ABR, it performs better in perceptual video quality, especially when the viewpoint changes frequently.The simulation results show that the MVP algorithm effectively allocates available bandwidth to the required viewpoints.Hence, it can eliminate transition delay and decrease viewpoint quality delay.As a result, the user gets better video quality, smoothness, continuity, and seamless transition during video switching.
Scale Decoupled Pyramid for Object Detection in Aerial Images

Object detection in aerial images is a challenging task for two main reasons: small object and scale variation. Existing methods utilize multi-level features to solve the scale variation problem, but ignore the scale confusion problem of shallow features, limiting the small object detection performance. To solve this issue, we propose a scale decoupling module to emphasizes small object features by eliminating large object features in shallow layers. Moreover, a sparse non-local attention (SNLA) and an adaptive anchor matching strategy (AAMS) are proposed to further improve the small object detection performance. The SNLA only aggregates contextual information of specific sparse positions, which not only refines small object features but also is computationally friendly. The AAMS is suitable for the measurement of small objects, and it can assign more positive samples to small objects. Extensive experiments on 3 challenging aerial datasets, VisDrone-DET2019, UAVDT and DIOR, demonstrate the effectiveness and adaptivity of our method. Code will be available online (https://github.com/MaYou1997/SDP).
Complex-Valued Depthwise Separable Convolutional Neural Network for Automatic Modulation Classification

Automatic Modulation Classification (AMC) is a critical task in industrial cognitive communication systems. Existing state-of-the-art methods, typified by real-valued convolutional neural networks, have introduced innovative solutions for AMC. However, such models viewed the two constituent components of complex-valued modulated signals as discrete real-valued inputs, causing structural phase damage to original signals and reduced interpretability of the model. In this article, a novel end-to-end AMC model called a complex-valued depth-wise separable convolutional neural network (CDSCNN) is proposed, which adopts complex-valued operation units to enable automatic complex-valued feature learning specifically tailored for AMC. Considering the limited hardware resources available in industrial scenarios, complex-valued depth-wise separable convolution (CDSC) is designed to strike a balance between classification accuracy and model complexity. With an overall accuracy of 62.63% on the RadioML2016.10a dataset, CDSCNN outperforms its counterparts by 1% to 11%. After fine-tuning on the RadioML2016.10b dataset, the overall accuracy reaches 63.15%, demonstrating the robust recognition and generalization capability of CDSCNN. Moreover, the CDSCNN exhibits lower model complexity compared to other methods.
Hand Gesture Recognition for Characters Understanding Using Convex Hull Landmarks and Geometric Features

With the latest advancements, hand gesture recognition is becoming an effective way of communication and gaining popularity from a research point of view. Deaf people around the world need assistance, while sign language is only understood by a few people around the globe. It becomes challenging for untrained people to communicate easily, research community has tried to train systems with a variety of models to facilitate communication with deaf people and also human-computer interaction. Researchers have detected gestures with numerous recognition rates; however, the recognition rate still needs improvement. As the images captured via cameras possess multiple issues, the light intensity variation makes it a challenging task to extract gestures from such images, extra information in captured images, such as noise hinders the computation time, and complex backgrounds make the extraction of gestures difficult. A novel approach is proposed in this paper for character detection and recognition. The proposed system is divided into five steps for hand gesture recognition. Firstly, images are pre-processed to reduce noise and intensity is adjusted. The pre-processed images region of interest is detected via directional images. After hand extraction, landmarks are extracted via a convex hull. Each gesture is used to extract geometric features for the proposed hand gesture recognition (HGR) system. The extracted features helped in gesture detection and recognition via the Convolutional Neural Network (CNN) classifier. The proposed approach experimentation result demonstrated over the MNIST dataset achieved a gesture recognition rate of 93.2% and 90.2% with one-third and two-third training validation systems, respectively. Also, the proposed system performance is validated on the ASL dataset, giving accuracy of 91.6% and 88.14% with one-third and two-third training validation systems, respectively. The proposed system is also compared with other conventional systems. Different emerging domains such as human-computer interaction (HCI), human-robot interaction (HRI), and virtual reality (VR) are applicable to the proposed system to fill the communication gap.
Multitask Sparse Representation Model-Inspired Network for Hyperspectral Image Denoising

Hyperspectral images (HSIs) are prone to noise because of the imaging mechanism and environment. This paper proposes a multitask sparse representation (SR) model inspired neural network for HSI denoising. Unlike other deep learning-based methods, our network is interpretable, whose network architecture is induced by unfolding the iterative optimization of a multitask sparse representation model. On the one hand, the model globally represents the common structure among bands, such as image edges, with the shared sparse coefficients. On the other hand, it separately encodes the unique structure of individual bands with unshared ones to capture image details. Accordingly, our network has three modules: the shared SR module, the unshared SR module, and the image reconstruction (IR) module. All the modules are connected with a specific operation of the iterative optimization algorithm, equipping the network with clear physical interpretation. Experimental results on both synthetic and real-world datasets demonstrate the superior performance of our method, visually and quantitatively. The codes will be publicly available at https://github.com/bearshng/mtsrnn for reproducible research.
Federated Domain Adaptation via Transformer for Multi-Site Alzheimer’s Disease Diagnosis

In multi-site studies of Alzheimer's disease (AD), the difference of data in multi-site datasets leads to the degraded performance of models in the target sites. The traditional domain adaptation method requires sharing data from both source and target domains, which will lead to data privacy issue. To solve it, federated learning is adopted as it can allow models to be trained with multi-site data in a privacy-protected manner. In this paper, we propose a multi-site federated domain adaptation framework via Transformer (FedDAvT), which not only protects data privacy, but also eliminates data heterogeneity. The Transformer network is used as the backbone network to extract the correlation between the multi-template region of interest features, which can capture the brain abundant information. The self-attention maps in the source and target domains are aligned by applying mean squared error for subdomain adaptation. Finally, we evaluate our method on the multi-site databases based on three AD datasets. The experimental results show that the proposed FedDAvT is quite effective, achieving accuracy rates of 88.75%, 69.51%, and 69.88% on the AD vs. NC, MCI vs. NC, and AD vs. MCI two-way classification tasks, respectively.
Open-Pit Mining Area Extraction from High-Resolution Remote Sensing Images Based on EMANet and FC-CRF

Rapid and accurate identification of open-pit mining areas is essential for guiding production planning and assessing environmental impact. Remote sensing technology provides an effective means for open-pit mine boundary identification. In this study, an effective method for delineating an open-pit mining area from remote sensing images is proposed, which is based on the deep learning model of the Expectation-Maximizing Attention Network (EMANet) and the fully connected conditional random field (FC-CRF) algorithm. First, ResNet-34 was applied as the backbone network to obtain preliminary features. Second, the EMA mechanism was used to enhance the learning of important information and details in the image. Finally, a postprocessing program based on FC-CRF was introduced to optimize the initial prediction results. Meanwhile, the extraction effect of MobileNetV3, U-Net, fully convolutional network (FCN), and our method were compared on the same data set for the open-pit mining areas. The advantage of the model is verified by the visual graph results, and the accuracy evaluation index based on the confusion matrix calculation. pixel accuracy (PA), mean intersection over union (MIoU), and kappa were 98.09%, 89.48%, and 88.48%, respectively. The evaluation results show that this method effectively identifies open-pit mining areas. It is of practical significance to complete the extraction task of open-pit mining areas accurately and comprehensively, which can be used for production management and environmental protection of open-pit mines.
ARE WE THERE OR DO WE HAVE MORE TO DO? METAVERSE IN FACILITY MANAGEMENT AND FUTURE PROSPECTS

Businesses are eager to adopt new technologies such as augmented reality, blockchain, extended reality, and artificial intelligence, among others, as they see beneficial applications for strategic empowerment. As one of the technological disruptions, metaverse has caught the attention of the larger business community and facility management (FM) sector. In a world where various virtual realities converge, the hyper-connected digital universe known as the metaverse has the potential to fundamentally alter how FM and business interactions are conducted in the future. More people are spending time conducting business online, increasing the metaverse’s potential. Businesses are also making significant investments in the development of metaverse techniques. This study reviews the current literature to comprehend how metaverse techniques have shaped the building blocks of FM by looking at 206 papers. There is currently a dearth of functional metaverse research. This article aims to provide a thorough analysis of the integration of metaverse techniques for facility management. The review reveals several opportunities for further research into the FM-metaverse ecosystem, including improving data interoperability, enhancing the precision of point cloud data for as-built models of existing facilities, and producing efficient virtual property database integration.
Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavor Tagging

Abstract The recent physical realization of quantum computers with hundreds of noisy qubits has given birth to an intense search for useful applications of their unique capabilities. One area that has received particular attention is quantum machine learning (QML), the study of machine learning algorithms running natively on quantum computers. In this work, QML methods are developed and applied to B meson flavor tagging, an important component of experiments which probe CP violation in order to better understand the matter‐antimatter asymmetry of the universe. One simulate boosted ensembles of quantum support vector machines (QSVMs) based on both conventional qubit‐based and continuous variable architectures, attaining effective tagging efficiencies of 28.0% and 29.2%, respectively, comparable with the leading published result of 30.0% using classical machine learning algorithms. The ensemble nature of the classifier is of particular importance, doubling the effective tagging efficiency of a single QSVM, which is find to be highly prone to overfitting. These results are obtained despite the constraint of working with QSVM architectures that are classically simulable, and it finds evidence that QSVMs beyond the simulable regime may be able to realize even higher performance, when sufficiently powerful quantum hardware is developed to execute them.