Reviewerly/exHarmony huggingface dataset:
Classification of yoga pose using machine learning techniques

Nowadays, yoga is that the part of existence during a number of the people. The human pose estimation is that the deep rooted trouble in computer vision. That has exposed in many challenges inside the beyond. They have many fields to capture the posture like video surveillance, biometric, webcam, sort of the equipment, etc. The pose detection techniques have observed and it'll be used to identify the posture and thus the accuracy of the yoga posture in machine learning techniques. To classify the yoga asana for Sun salutations set of postures in four machine learning models and pose estimation algorithm for a person's body is used for skeleton drawing in the real-time. Sun salutations set of posture are often collected the important time and used for the estimation of a pose algorithm for the accuracy result of yoga poses. We have used for various parameters. The find the results of classify of four machine learning technique during a sun salutation set of posture. Find the accuracy of knowledge in machine learning technique. This paper to classify the sun salutation yoga poses and which machine learning technique get the currency results of the pose. We have collected the info in one male participant. Their age, weight, height is often regarded. To detect the yoga pose supported the angle draw out from the skeleton joint of the estimation of a pose algorithm.
Domain-Adaptive Object Detection via Uncertainty-Aware Distribution Alignment

Domain adaptation aims to transfer knowledge from the source data with annotations to scarcely-labeled data in the target domain, which has attracted a lot of attention in recent years and facilitated many multimedia applications. Recent approaches have shown the effectiveness of using adversarial learning to reduce the distribution discrepancy between the source and target images by aligning distribution between source and target images at both image and instance levels. However, this remains challenging since two domains may have distinct background scenes and different objects. Moreover, complex combinations of objects and a variety of image styles deteriorate the unsupervised cross-domain distribution alignment. To address these challenges, in this paper, we design an end-to-end approach for unsupervised domain adaptation of object detector. Specifically, we propose a Multi-level Entropy Attention Alignment (MEAA) method that consists of two main components: (1) Local Uncertainty Attentional Alignment (LUAA) module to accelerate the model better perceiving structure-invariant objects of interest by utilizing information theory to measure the uncertainty of each local region via the entropy of the pixel-wise domain classifier and (2) Multi-level Uncertainty-Aware Context Alignment (MUCA) module to enrich domain-invariant information of relevant objects based on the entropy of multi-level domain classifiers. The proposed MEAA is evaluated in four domain-shift object detection scenarios. Experiment results demonstrate state-of-the-art performance on three challenging scenarios and competitive performance on one benchmark dataset.
PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution

Generative Adversarial Networks (GANs) have been employed for face super resolution but they bring distorted facial details easily and still have weakness on recovering realistic texture. To further improve the performance of GAN-based models on super-resolving face images, we propose PCA-SRGAN which pays attention to the cumulative discrimination in the orthogonal projection space spanned by PCA projection matrix of face data. By feeding the principal component projections ranging from structure to details into the discriminator, the discrimination difficulty will be greatly alleviated and the generator can be enhanced to reconstruct clearer contour and finer texture, helpful to achieve the high perception and low distortion eventually. This incremental orthogonal projection discrimination has ensured a precise optimization procedure from coarse to fine and avoids the dependence on the perceptual regularization. We conduct experiments on CelebA and FFHQ face datasets. The qualitative visual effect and quantitative evaluation have demonstrated the overwhelming performance of our model over related works.
Meta Parsing Networks: Towards Generalized Few-shot Scene Parsing with Adaptive Metric Learning

Recent progress in few-shot segmentation usually aims at performing novel object segmentation using a few annotated examples as guidance. In this work, we advance this few-shot segmentation paradigm towards a more challenging yet general scenario, i.e., Generalized Few-shot Scene Parsing (GFSP). In this task, we take a fully annotated image as guidance to segment all pixels in a query image. Our mission is to study a generalizable and robust segmentation network from the meta-learning perspective so that both seen and unseen categories can be correctly recognized. Different from previous practices, this task performs segmentation on a joint label space consisting of both previously seen and novel categories. Moreover, pixels from these multiple categories need to be simultaneously taken into account, which is actually not well explored before. Accordingly, we present Meta Parsing Networks (MPNet) to better exploit the guidance information in the support set. Our MPNet contains two basic modules, i.e., the Adaptive Deep Metric Learning (ADML) module and the Contrastive Inter-class Distraction (CID) module. Specially, the ADML takes the annotated pixels from the support image as the guidance and adaptively produces high-quality prototypes for learning a deep comparison metric. In addition, MPNet further introduces the CID module learning to enlarge the feature discrepancy of different categories in the embedding space, leading the MPNet to generate more discriminative feature embeddings. We conduct experiments on two newly constructed benchmarks, i.e., GFSP-Cityscapes and GFSP-Pascal-Context. Extensive ablation studies well demonstrate the effectiveness and generalization ability of our MPNet.
A Social Network Analysis and Comparison of Six Dark Web Forums

With increasing monitoring and regulation by platforms, communities with criminal interests are moving to the dark web, which hosts content ranging from whistle-blowing and privacy, to drugs, terrorism, and hacking. Using post discussion data from six dark web forums we construct six interaction graphs and use social network analysis tools to study these underground communities. We observe the structure of each network to highlight structural patterns and identify nodes of importance through network centrality analysis. Our findings suggest that in the majority of the forums some members are highly connected and form hubs, while most members have a lower number of connections. When examining the posting activities of central nodes we found that most of the central nodes post in sub-forums with broader topics, such as general discussions and tutorials. These members play different roles in the different forums, and within each forum we identified diverse user profiles.
High capacity reversible data hiding in encrypted images using SIBRW and GCC

In this paper, a reversible data hiding in encrypted images (RDHEI) method combining GCC (group classification encoding) and SIBRW containing sixteen image-based rearrangement ways is proposed to achieve high-capacity data embedding in encrypted images. Each way of SIBRW aims at bringing strongly-correlated bits of each higher bit-plane together by rearranging each higher bit-plane. For each higher bit-plane, the optimal way achieving the most concentrated aggregation performance is selected from SIBRW to rearrange this bit-plane, and then, GCC compresses the rearranged bit-plane in group-by-group manner. By making full use of strong-correlation between adjacent groups, GCC can compress not only consecutive several groups whose bits are valued 1 (or 0) but also a single group so that a large embedding space is provided. The encryption method including the bit-level XOR-encryption and scrambling operations enhances the security. The experimental results show that the proposed scheme can achieve large embedding capacity and high security.
Gated Graph Neural Attention Networks for abstractive summarization

Sequence to sequence (Seq2Seq) model for abstractive summarization have aroused widely attention due to their powerful ability to represent sequence. However, the sequence structured data is a simple format, which cannot describe the complexity of graphs and may lead to ambiguous, and hurt the performance of summarization. In this paper, we propose a Gated Graph Neural Attention Networks (GGNANs) for abstractive summarization. The proposed GGNANs unified graph neural network and the celebrated Seq2seq for better encoding the full graph-structured information. We propose a graph transform method based on PMI, self-connection, forward-connection and backward-connection to better combine graph-structured information and the sequence-structured information. Extensive experimental results on the LCSTS and Gigaword show that our proposed model outperforms most of strong baseline models.
The 19 Unifying Questionnaire Constructs of Artificial Social Agents

In this paper, we report on the multi-year Intelligent Virtual Agents (IVA) community effort, involving more than 80 researchers worldwide, researching the IVA community interests and practises in evaluating human interaction with an artificial social agent (ASA). The effort is driven by previous IVA workshops and plenary IVA discussions related to the methodological crisis on the evaluation of ASAs. A previous literature review showed a continuous practise of creating new questionnaires instead of reusing validated questionnaires. We address this issue by examining questionnaire measurement constructs used in empirical studies between 2013 to 2018 published in the IVA conference. We identified 189 constructs used in 89 questionnaires that are reported across 81 studies. Although these constructs have different names, they often measure the same thing. In this paper, we, therefore, present a unifying set of 19 constructs that captures more than 80% of the 189 constructs initially identified. We established this set in two steps. First, 49 researchers classified the constructs in broad theoretically based categories. Next, 23 researchers grouped the constructs in each category on their similarity. The resulting 19 groups form a unifying set of constructs, which will be the basis for the future questionnaire instrument of human-ASA interaction.
An intelligent healthcare system for optimized breast cancer diagnosis using harmony search and simulated annealing (HS-SA) algorithm

The paper offers a crossbreed streamlining algorithm combining harmony search (HS) and simulated annealing (SA) known as harmony search and simulated annealing (HS-SA) for precise and accurate breast malignancy. Additionally, an improved wavelet-based contourlet transform (WBCT) system for feature extraction explores to get the highlights of the region of interest (ROI), permitting performance improvement over other standard methodologies. In the mined feature space, the projected HS-SA algorithm intends to diminish the feature dimensions and congregate at the unprecedented feature set. The SVM classifier backed with diverse kernel functions is used for classification, which is fed by the chosen features, and its exhibition contrasts with the conventional machine learning classification and optimization techniques. The actualized computer-aided diagnosis (CAD) learning mechanism is challenged by evaluating its findings. It examines two different breast mammographic datasets i) benchmark BCDR-F03 dataset and ii) local mammographic dataset. Trial reproductions, empirical outcomes, and measurable examinations likewise indicate that the proposed model is practical and advantageous for the arrangement of malignant breast growth. The findings show that the proposed CAD framework (HS-SA + kernel SVM) is better than different characterization accuracy procedures (with an accuracy of 99.89% for the local mammographic dataset and 99.76% for benchmark BCDR-F03 dataset, AUC of 99.41% for the local mammographic dataset and 99.21% for reference BCDR-F03 dataset), while keeping the feature space limited to just seven feature subsets and computational prerequisites as low as is prudent.
Blockchain as a solution to Drone Cybersecurity

Industry 4.0 has revolutionized industrial processes. The use of Unmanned Aerial Vehicles (UAVs) has increased due to the need to facilitate automation. Initially, drone usage was confined to military usage and other institutions that required high-level government clearance. Back then, the number of operating drones was few who made it easier to regulate and secure. However, with the emergence of Industry 4.0, the need for drone usage has increased in various industries. These industries include insurance, health, e-commerce, learning institutions, conducting and coordinating military airstrikes, and performing weather monitoring, among other crucial functionalities. The increased use and adoption of drones has resulted in the Internet of Drones. As more industries continue to adopt drone usage, the issue of securing them arises. The need for organizations to ensure and coordinate thousands of drones has necessitated the adoption of a security mechanism that guarantees the security of these devices. We explore the use of Blockchain technology as a means of enhancing drone cybersecurity. We research how industrial players perceive Blockchain and its contribution to cybersecurity.
NumClaim

The goal of claim detection in argument mining is to sort out the key points from a long narrative. In this paper, we design a novel task for argument mining in the financial domain, and provide an expert-annotated dataset, NumClaim, for the proposed task. Based on the statistics, we discuss the differences between the claims in other datasets and the claims of the investors in NumClaim. With the ablation analysis, we show that encoding numeral and co-training with the auxiliary task of the numeral understanding, i.e., the category classification task, can improve the performance of the proposed task under different neural network architectures. The annotations in the NumClaim is published for academic usage under the CC BY-NC-SA 4.0 license.
RANTT: A RISC-V Architecture Extension for the Number Theoretic Transform

Lattice-based cryptography has been growing in demand due to their quantum attack resiliency. Polynomial multiplication is a major computational bottleneck of lattice cryptosystems. To address the challenge, lattice-based cryptosystems use the Number Theoretic Transform (NTT). Although NTT reduces complexity, it is still a well-known computational bottleneck. At the same time, NTT arithmetic needs vary for different algorithms, motivating flexible solutions. Although there are prior hardware and software NTT designs, they do not simultaneously offer flexibility and efficiency. This work provides an efficient and flexible NTT solution through domain-specific architectural support on RISC-V. Rather than using instruction-set extensions with compiler modifications or loosely coupling a RISC-V core with an NTT co-processor, our proposal uses application-specific dynamic instruction scheduling, memory dependence prediction, and datapath optimizations. This allows achieving a direct translation of C code to optimized NTT executions. We demonstrate the flexibility of our approach by implementing the NTT used in several lattice-based cryptography protocols: NewHope, qTESLA, CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon. The results on the FPGA technology show that the proposed design is respectively 6x, 40x, and 3x more efficient than the baseline solution, Berkeley Out-of-Order Machine, and a prior HW/SW co-design, while providing the needed flexibility.
Face illumination recovery for the deep learning feature under severe illumination variations

The deep learning feature is the best for face recognition nowadays, but its performance exhibits unsatisfactorily under severe illumination variations. The main reason is that the deep learning feature was trained by the internet face images with variations of large pose/expression and slight/moderate illumination, which cannot well tackle severe illumination variations. Inspired by the fact that the deep learning feature can cope well with slight/moderate varying illumination, this paper proposes an illumination recovery model to transform severe varying illumination to slight/moderate varying illumination. The illumination recovery model enables the illumination of the severe illumination variation image close to that of the reference image with slight/moderate varying illumination. The reference image generated from the severe illumination variation image is termed as the generated reference image (GRI), which is obtained by normalizing singular values of the logarithm version of the severe illumination variation image to have unit L2-norm. The gradient descent algorithm is employed to address the proposed illumination recovery model, to obtain the generated reference image based illumination recovery image (GRIR). GRIR preserves better face inherent information than GRI such as the face color. Experimental results indicate that the proposed GRIR can efficiently improve the performance of the deep learning feature under severe illumination variations.
Nonparametric variational learning of multivariate beta mixture models in medical applications

Abstract Clustering as an essential technique has matured into a capable solution to address the gap between the growing availability of data and deriving the knowledge from them. In this paper, we propose a novel clustering method “variational learning of infinite multivariate Beta mixture models.” The motivation behind proposing this technique is the flexibility of mixture models to fit the data. This approach has the capability to infer the model complexity and estimate model parameters from the observed data automatically. Moreover, as a label‐free method, it could also address the problem of high costs of medical data labeling, which can be undertaken just by medical experts. The performance of the model is evaluated on real medical applications and compared with other similar alternatives. We demonstrate the ability of our proposed method to outperform widely used methods in the field as it has been shown in experimental results.
Deep Learning Based Approach for Multiple Myeloma Detection

Multiple myeloma cancer is caused by the abnormal growth of plasma cells in the bone marrow. The most commonly used method for diagnosis of multiple myeloma is Bone marrow aspiration, where the aspirate slide images are either observed visually or passed onto existing digital image processing software for the detection of myeloma cells. The current work explores the effectiveness of deep learning based object detection/segmentation algorithms such as Mask-RCNN and unet for the detection of multiple myeloma. The manual polygon annotation of the current dataset is performed using VGG image annotation software. The deep learning models were trained by monitoring the train and validation loss per epoch and the best model was selected based on the minimal loss for the validation data. From the comparison results obtained for both the models, it is observed that Mask-RCNN has competing results than unet and it addresses most of the challenges existing in multiple myeloma segmentation.
Hornbill: A Self-Evaluating Hydro-Blasting Reconfigurable Robot for Ship Hull Maintenance

Maintenance of ship hull involves routine tasks during dry-docking that includes inspection, paint stripping, and re-painting. Among those, paint stripping is always seen as harmful for human operators and a time-consuming task. To reduce human risk, cost, and environmental cleanliness, the shipping maintenance industries started using robotic solutions. However, most of such robotic systems cannot operate fully autonomous since it requires human in the loop to monitor the cleaning efficiency. To this end, a novel autonomous self-evaluating hull cleaning robot called Hornbill is presented in this paper. The proposed robot is capable of navigating autonomously on the hull surface and perform water jet blasting to strip off the paint coating. The robot is also enabled with a Deep Convolutional Neural Network (DCNN) based self-evaluating scheme that benchmarks cleaning efficiency. We evaluated the proposed robot's performance by conducting experimental trials on a metal plate under three different paint coatings. While performing the paint stripping task in every experimental trial, the self-evaluating scheme would generate the heat map that depicts the plate's cleanliness. The results indicate that the proposed self-evaluating system could successfully generate high accurate cleanliness heat maps in all considered scenarios, which simplifies the checkup process for paint inspectors.
Automatic Classification of Text Complexity

This work introduces an automatic classification system for measuring the complexity level of a given Italian text under a linguistic point-of-view. The task of measuring the complexity of a text is cast to a supervised classification problem by exploiting a dataset of texts purposely produced by linguistic experts for second language teaching and assessment purposes. The commonly adopted Common European Framework of Reference for Languages (CEFR) levels were used as target classification classes, texts were elaborated by considering a large set of numeric linguistic features, and an experimental comparison among ten widely used machine learning models was conducted. The results show that the proposed approach is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features that influenced the predictions.
HazDesNet: An End-to-End Network for Haze Density Prediction

Vision-based intelligent systems such as driver assistance systems and transportation systems should take into account weather conditions. The presence of haze in images can be a critical threat to driving scenarios. Haze density measures the visibility and usability of hazy images captured in real-world conditions. The prediction of haze density can be valuable in various vision-based intelligent systems, especially in those systems deployed in outdoor environments. Haze density prediction is a challenging task since the haze and many scene contents have a lot in common in appearance. Existing methods generally utilize different priors and design complex handcrafted features to predict the visibility or haze density of the image. In this article, we propose a novel end-to-end convolutional neural network (CNN) based method to predict haze density, named as HazDesNet. Our HazDesNet takes a hazy image as input and predicts a pixel-level haze density map. The density map is then refined and smoothed, and the average of the refined map is calculated as the global haze density of the image. To verify the performance of HazDesNet, a subjective human study is performed to build a Human Perceptual Haze Density (HPHD) database, which includes 500 real-world hazy images and 100 synthetic hazy images, and the corresponding human-rated perceptual haze density scores. Experimental results show that our method achieves the best haze density prediction performance on our built HPHD database and existing databases. Besides the global quantitative results, our HazDesNet is capable of predicting a continuous, stable, fine, and high-resolution haze density map. We will make the database and code publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JiaheZhang/HazDesNet</uri> .