Reviewerly/exHarmony huggingface dataset:
Dealing Big Data using Improved Fuzzy C Means Based Improved Redundant Particle Swarm Optimization with Map Reduction

There is a lot of interest in big data analysis from both the academic and commercial worlds. A number of algorithms have been implemented to improve the analysis process. Improved Fuzzy C-means clustering has been used to improve the MapReduce model in this study. &#x0027;In this work, Redundant Particle Swarm Optimization with Multi-Objective Optimization (MOO-MR-RPSO) based Improved Fuzzy C Means (IFCM) clustering mechanism is used along with MapReduce model. Each data element is mapped together and forms the data heterogeneity attributes. Due to unbalanced data in the large datasets, it is necessity to extract the features of the data; it is performed by using the Principle Component Analysis (PCA) based optimal feature extraction and feature selection respectively. Finally, MOO-MR-RPSO based optimization mechanism is developed for selecting the both appropriate clusters with optimal centroid(s) in the IFCM by using PCA features based objective function and the method named as the MOO-IFCM-RPSO with map reduce. The simulation results shows that the proposed MapReduce approach gives Maximum clustering accuracy compared to the state of art approaches.
Semantic-refined spatial pyramid network for crowd counting

In this paper, we propose a novel encoder-decoder model called Semantic-refined Spatial Pyramid Network (SSPNet) for generating high-quality density maps, which aims to build a scale-aware counting network to estimate the number of crowds accurately. The SSPNet consists of the front-end based on VGG-16, spatial pyramid multi-scale module (SPMM), and semantic enhancement module (SEM). First, a series of convolutional neural layers are utilized as the front-end to get deeper features without the extra computational cost. Moreover, the SPMM, which has a spatial pyramid structure with multiple receptive fields, is employed to capture multi-scale features. Furthermore, the SEM is designed to refine the features captured by SPMM, which uses deep semantic information to better integrate multi-scale features. Finally, the shallow texture information is adopted to compensate for the detail of the feature map to enhance the quality of the density map. Extensive experiments and comparisons on three challenge datasets, including ShanghaiTech Part_A & Part_B, UCF_CC_50, and UCF-QNRF, illustrate the superiority of our method.
EM emission modeling for secure IC design

For some decades now, electromagnetic radiations have not only generated electromagnetic compatibility problems but are a critical threat also for secure IC designers. In fact, these radiations can be collected, with an EM probe, to perform electromagnetic side-channel attacks (EM SCA), in order to retrieve secure data from ICs. Within this context, this paper aims to propose an industrial simulation flow able to reproduce the EM emissions of ICs during the design stage. It also proposes a technique allowing to correctly identify EM leakages prior to fabrication, considering the absence of noise in simulated traces. Finally, attacks performed with horizontal and vertical probes are compared, as well as front-side and back-side analyses, in order to demonstrate that only upper metal layers need to be considered to model the magnetic field captured by an EM probe placed close to the IC surface.
A Method for Dehazing Based on CycleGAN

Abstract With the development of artificial intelligence, computer vision has been widely applicated. The performance of these applications is closely related to the image quality, which is, however, affected by various lighting and weather conditions, such as rain and haze. The traditional methods focus on the dehazing problems under specific and idealize conditions, leading to the consequence that these methods can only be used in certain scenario on account of that indexes or hyperparameters need to be adapted according to the scene. This paper proposed an unsupervised learning algorithm for dehazing. We used the CycleGAN architecture to avoid difficulties when obtaining hazy and clear images in pairs. Besides, the identity loss was introduced for improving stability of the training procedure, and we used the reuse loss for sake of the steadiness of output hue. The experiments on public datasets such as RESIDE and I-HAZE show the effectiveness of the proposed method, which achieves comparable results to the state-of-the-art algorithms.
Machine Learning based U - Net Algorithm for SAR Image Classification

Synthetic Aperture Radar (SAR) images are non-intuitive, and has a side-looking geometry which makes the interpretation of SAR images not very straight forward. Using automatic image annotation techniques, it is possible to annotate large quantity of images in less time with reliable outcome. Hence, the requirement of labelled SAR images is essential to improve the performance of Machine Learning (ML) algorithms. This effectively decreases the manual effort required. In this paper, we develop an unsupervised and semi - supervised algorithms for SAR image classification. A set of SAR images are downloaded from TerraSAR - X through open source repository and manually annotated using APEER annotation tool. The unsupervised learning is implemented using the traditional way of clustering and classification. It involves 48-D feature extraction using Gabor filters. The feature vector is labelled using clustering algorithms. K - Means, Gaussian Mixture Model (GMM), and Hierarchical Clustering methods are simulated. We propose a semi supervised learning that involves the implementation of a U-Net architecture for classification that involves clustering and segmentation. It is observed that the proposed methodology using U-Net is better than the conventional clustering. The U-Net model has an increase of approximately 64.224% and 56.43% in training and validation accuracy compared to the conventional clustering and classification.
Proceed with Care

To address the challenge of limited training data for machine learning models in the healthcare domain, we advocate for human-in-the-loop machine learning, which involves domain experts in an inter- active process of developing predictive models. Interpretability offers a promising way to facilitate this interaction. We describe an approach that offers a simple decision tree interpretation for any complex blackbox machine learning model. In a case study with physicians, we find that they were able to use the interpretation to discover an unexpected causal issue in a personalized patient risk score trained on electronic medical record data. To account for dynamics in disease progression, we advocate for building decision models that integrate predictions of the disease progression at the individual patient level with system models capturing the dynamic operational environments. We describe a case study on hospital inpatient management, showing how to build a Markov decision framework that leverages predictive analytics on patient readmission risk and prescribes the optimal set of patients to be discharged each day.
Application of Discrete Mathematical Model in Edge Distortion Correction of Moving Image

The existing edge distortion correction methods of moving images ignore the consistency of human subjective vision in the restoration process. Therefore, this study proposes an edge distortion correction method of moving images using a discrete mathematical model. According to the image binarization, the moving target region is segmented, the motion fuzzy degradation model is constructed by using the discrete mathematical model, the global structure information of the image is considered by the structure similarity, the image gradient is transformed from the rectangular coordinate system to the polar coordinate system, the image deconvolution algorithm of the edge distribution of the natural image is used to enhance the image edge features, and the edge distortion correction of the moving image is realized. The experimental results show that the astringency of the experimental scattergram is obviously improved, the fitting line is almost a straight line, and it has good recognition of the jump of the gray level of noise points, which can meet the requirements of the consistency of the subjective vision of human eyes.
Penetration Multilayer Overload Signal Generation Based on TransGAN

Abstract With the booming development of modern electronic technology, artificial intelligence in the military field is showing a fast-rising trend, but at the same time, it is also facing the dilemma of a serious shortage of data. To address the current problems of low number and unbalanced categories of penetration multi-layer overload signals, a TransGAN-based penetration multi-layer overload signal generation method is proposed in combination with the fruitful neural network model of deep learning. Firstly, the dataset is built using overload signals that have been tested in practice at the range. Secondly, generator and discriminator are constructed. Generator borrowing the model structure of TransGAN, which consists of Transformer Encoder for learning the feature mapping of the overload dataset, and discriminator using a simple attention mechanism to reduce the complexity of the model. Finally, the generator and discriminator are trained and optimized using a generative adversarial network to achieve the penetration multilayer overload signal. Finally, the generative adversarial network is utilized to train and optimize the generator and discriminator to achieve penetration multilayer overload data generation. Experimental results show that the method can generate effective overload data with a different number of layers, which can address the issue of the lack of penetration multilayer overload signals to a certain extent.
A Novel Semifragile Consensus Algorithm Based on Credit Space for Consortium Blockchain

Nowadays, blockchain is known as a new generation of secure information technologies for realizing business and industrial sustainability, and consensus algorithm is the key technology of blockchain. In order to solve the problem of “oligarchy” nodes and excessive punishment for nodes in existing credit consensus algorithms, a novel semifragile consensus algorithm based on the credit space for consortium blockchain is proposed in this paper. Firstly, the accounting node selection mechanism based on credit space is proposed. The credit value of the node is calculated according to a novel credit evaluation model, and then the credit space of the node is allocated according to the size of the credit value. Afterward, a random algorithm is used to select the accounting node in the credit space. This mechanism effectively inhibits the generation of “oligarchy” nodes and maintains the enthusiasm of nodes. Secondly, this paper proposes a semifragile hierarchical punishment mechanism, which punishes the malicious nodes with severe measures and gives the nonmalicious nodes the opportunity to continue participating in the consensus. So, this semifragile punishment mechanism solves the problem of excessive punishment of nodes. Experimental simulation results demonstrate that the proposed consensus algorithm has randomness while maintaining the credit incentive among nodes. In addition, the node’s punishment mechanism is more reasonable. This algorithm has better security and can be well applied to consortium blockchain scenarios.
Designing Sound Synthesis Interfaces for Head-mounted Augmented Reality

We report on designing a sound synthesis interface for a head-mounted augmented reality environment. The increased accessibility of augmented reality (AR) devices has incentivised the exploration of sound applications for music performance in computer music and other relevant communities. However, interaction affordances vary based on the specific AR device and thus implies different design considerations. In this poster, we present different interface prototypes for a frequency modulation synthesis system in the Microsoft HoloLens 2 and report our insights during the process of their developments.
A Deep Unified Pedestrian Detection Framework

Pedestrian detection is one of the important applications of computer vision with key contributions in various fields of human life such as intelligent vehicles, surveillance, and advanced robotics. Considerable research has taken place in protecting vulnerable road users particularly pedestrians due to the high risk of accidents to ensure safety on roads. The process of pedestrian detection is often marred by significant intra-class variance due to varied postures and appearances, exhibited by humans on roads. Other crucial issues like occlusion, background complexity, lead to loss of accuracy in final detection results. Detecting overlapping, occluded, and small objects in a complex dataset like pedestrians, with acceptable accuracy, has always been a challenging task. The proposed work unifies two families of detectors, the first is one stage detector responsible for precise bounding boxes but low recall value while the other one is a family of two-stage detectors, giving a high recall value but an imprecise number of bounding boxes. Adaptive fusion of two generates enhanced detection accuracy and decreases the overall Log Average Miss Rate (LAMR). The performance of the proposed work has been evaluated and assessed on three publicly available datasets: ETH, INRIA, and Central Pedestrian crossing sequence, which exhibits superior pedestrian detection performance over the existing state-of-the-art.
A Cardboard-Based Virtual Reality Study on Self-Avatar Appearance and Breathing

Cardboard-based virtual reality is an affordable solution for experiencing virtual reality content. Particularly during the COVID-19 pandemic, several studies used cardboard-based virtual reality remotely to minimize viral spread. We conducted a study to explore the potentials of low-cost virtual reality on participants&#x0027; sense of presence and body ownership illusion in our research lab, thereby providing a controlled research setting. Our 2 (Avatar: realistic vs. mannequin self-avatar) &#x00D7; 2 (Breathing; breathing vs. no breathing motion) study investigated presence and body ownership when participants were instructed to observe a virtual environment passively through a cardboard-based virtual reality application while being embodied as a self-avatar. Our study&#x0027;s results indicated that: (1) the mannequin self-avatar exerted a stronger effect on participants&#x0027; presence; (2) younger participants who experienced the mannequin avatar reported stronger body ownership compared with older participants; and (3) while experiencing a mannequin avatar with no breathing motion, participants with prior VR experience reported higher body ownership illusion compared with participants with no prior VR experience. In this paper, we discuss our findings, as well as the study&#x0027;s limitations and future research directions.
Comparison of Different Machine Learning Approaches to Text Classification

Naive Bayes, logistic regression, linear support vector machine, and deep neural networks are estimated and compared from the point of fulfilling text classification. The naive Bayes approach can be used in real-time with big datasets, but it assumes that each feature makes an independent and equal contribution to the outcome. Logistic regression is a linear model that is easy to implement in classification problems but has poor performance on nonlinear data. The support vector machine algorithm is a linear algorithm that performs well in higher dimensions, but it requires significant time to process. The main advantage of the deep neural network model over the other techniques is that it reduces the need for extensive feature extraction and selection and the distributed representation of words as features input into the network.
Improving Comparative Opinion Mining Through Detection of Support Sentences

Comparative opinion contains contrasting views of products (e.g., which aspect of a product is better or worse). Most existing works for comparative opinion mining focus on single comparative sentences but have yet explored the benefits of additional comparative details in neighbouring sentences of a comparative sentence. Motivated by the needs to exploit these supporting details, this chapter proposes an approach to identify the link between a comparative sentence and its neighbouring sentences. As contextual similarity between comparative sentence and neighbouring sentence is crucial to determine their relatedness, contextual features of these two sentences are exploited to measure the similarity between them. Then, linear-chain conditional random field (CRF) is used to identify neighbouring sentence that is related to comparative sentence. Detection of supporting neighbouring sentence using linear-chain CRF with optimized contextual features (cosine similarity, Wordnet similarity, and comparative keywords) outperforms sentence similarity baseline by 4% in accuracy and 0.06 in F-score.
Light VR Client for Point Cloud Navigation with 360<sup>∘</sup> Images

Since point clouds require a large amount of data to be visually pleasing, they tend to be voluminous. Hence, hardware with limited computational and memory capabilities may not be able to handle such large data structures. Here, we propose a light VR client to explore a static point cloud, stored in a remote server, through 360<sup>&#x2218;</sup> images. The client visualizes in an HMD the omnidirectional rendering of the point cloud and moves to another position with a teleportation metaphor. The main advantage of our proposition is the ability to work on modest hardware without a continuous high bandwidth.
Towards Scalable and Real-time Markerless Motion Capture

Human motion capture and perception without the need for complex systems with specialized cameras or wearable equipment is the holy grail for many human-centric applications. Here, we present a scalable markerless motion capture method that estimates 3D human poses in real-time using low-cost hardware. We do so by replacing the inefficient 3D joint reconstruction techniques, such as learnable triangulation and feature splatting, with a novel uncertainty-driven approach that exploits the available depth information and the edge sensors’ spatial alignment to fuse the per viewpoint estimates into final 3D joint positions.
Augmented Reality and Surgery: Human Factors, Challenges, and Future Steps

Augmented reality (AR) has shown much potential when applied in surgical settings, which can help guide surgeons through complex procedures, train students, and provide heads- up and hands-free spatial information. In this position paper, we discuss some of the current use cases of AR in surgical practice, evaluation measures, challenges and potential directions for future research. The aim of this paper is to start important discussion to improve future research and outcomes for system implementations for surgery.
“What a Mess!”: Traces of Use to Increase Asynchronous Social Presence in Shared Virtual Environments

Shared virtual environments (VEs) are challenged conveying and triggering users&#x0027; feelings of social presence. Traces of use are implicit evidence of prior interactions that support social awareness in the real environment (RE). However, they have not been explored in VEs so far. We investigate the traces&#x0027; effect on users&#x0027; perception of asynchronous social presences in a within-subject study (<tex>$\mathrm{N}=26$</tex>) by comparing the users&#x0027; experience with and without traces. The traces significantly increased the feeling of social presence. We contribute an initial exploration of the traces of use concept in VE to design shared social spaces for long-term use.
Local Free-View Neural 3D Head Synthesis for Virtual Group Meetings

Virtual group meetings provide enormous potential for remote group communication. However, today&#x0027;s video conferences incur numer-ous challenges compared to face-to-face meetings. For instance, perception of correct gaze, deictic relations, or eye-to-eye contact is impeded due to the fact that the camera is offset from the eyes of the other users&#x0027; avatars and that the gallery view is different for each group member. In this paper, we describe how 3D neural heads can be synthesized to overcome these limitations. Therefore, we generate different head poses using a generative adversarial network for a given source image frame using state-of-the-art technology. These head poses can then be viewed in a local space to freely control the gaze of the head poses. We introduce and discuss five use cases for these synthesized head poses that aim to improve intelligent agents and virtual avatar representations in regular video group meetings.
Deer hunting optimization technique for clustering unsupervised data in data mining

Unsupervised data clustering investigation is a standout among the most valuable tools and is an informative task in data mining that looks to characterize similar articles’ gatherings. One of the eminent algorithms for the clustering field is K-means clustering. Scholars recommended enhancing the nature of K-means, and optimization algorithms were hybridized. In this study, a heuristic calculation, deer hunting optimization algorithm (DHOA), was adjusted for K-means data clustering by altering the fundamental parameters of DHOA calculation, which are propelled from the characteristic enlivened calculations. During this work, a new human-based descriptive DHOA has been developed following a human deer hunting strategy. In order to attack the fawn, hunters update their positions based on the movement of the leader and backward movement while also considering the angle of the deer. In this work, the DHOA was hybridized with K-means clustering and the performance of the proposed approach is tested against UCI repository data with different algorithms.
Augmenting Sculpture with Immersive Sonification

We present an artistic Mixed Reality (MR) system that remixes a sculptural element of a building and its aesthetic context to provide an on-site augmented art experience. Mainstream MR systems, particularly art-related, focus on the use of visuals in presenting additional information, whereas the use of audio as the main information channel has rarely been considered. In this work, we explore two different versions of a sonic experience for walking through an artistic staircase to enhance its public&#x0027;s engagement. Our user evaluation reveals the effectiveness of sonic design for a rewarding MR experience. With this, we emphasise the importance of sonic design in MR applications.
Text local error detection and correction based on N-gram for Chinese homophone replacement

With the continuous development of the Internet, more and more information enter people's lives. However, the information is mixed. It is difficult to guarantee the correctness of the text. For errors caused by homophone replacement, an automatic Chinese text local error detection and correction solution based on n-gram model is proposed. A method of local error detection based on the combined model of 2-gram and 3-gram is proposed, and a method of local error correction based on 3-gram model is proposed. Experiments show that the error detection recall rate is 83.1%, the error detection accuracy rate is 41.5%, the F-score is 55.4%; the error correction rate is 78.1%. The method is compared with the 2-gram model and the 3-gram model. The accuracy of error detection is increased by 7.2% and 8.2% respectively. The F-score is increased by 6.3% and 8.2% respectively.