Reviewerly/exHarmony huggingface dataset:
DCT based Enhanced Tchebichef Moment using Huffman Encoding Algorithm (ETMH)

Data Compression includes enhancing a flood of images and dynamically rearranging codes. The consequent arrangement of compressed codes will be greater and simpler than the initial set of images. The choice is to produce a clear-cut code for a definite image or group of images. The proposed algorithm is close to an assortment of data and rules that are consumed to be shared with the input images and the numeric value to Figure out which repeated code(s) can be sustained. The current Huffman coding upholds for double picture and neglects to give the sensible pressure proportion of the genuine yield of the encoder, which is controlled by a bunch of probabilities. While utilizing this kind of coding, an image that has an extremely high likelihood of event, produces a code with few pieces. An image with a low likelihood produces a code with a larger number of pieces. The source images are then diminished by applying improved Huffman encoding calculation to get a high pressure proportion. The proposed Enhanced Tchebichef Moment utilizing Huffman Encoding (ETMH) technique obtains the first picture and its remodel into a grid design. The maximum number of network sizes is separated into various non-covering small sized block lattices. The lesser the number of pixel size, grouping blocks are done to accomplish the pressure proportion. The proposed ETMH calculation is very useful for distinct picture organizations to discover strategies to provide enhanced outcomes and quality. The proposed ETMH calculation is actualized for dim scale picture and consequently, the pressure proportion table is produced. The proposed ETMH calculation is tried and actualized through different boundaries, like MSE, SNR and PSNR by utilizing MATLAB.
Single Object Tracking in Satellite Videos: Deep Siamese Network Incorporating an Interframe Difference Centroid Inertia Motion Model

Satellite video single object tracking has attracted wide attention. The development of remote sensing platforms for earth observation technologies makes it increasingly convenient to acquire high-resolution satellite videos, which greatly accelerates ground target tracking. However, overlarge images with small object size, high similarity among multiple moving targets, and poor distinguishability between the objects and the background make this task most challenging. To solve these problems, a deep Siamese network (DSN) incorporating an interframe difference centroid inertia motion (ID-CIM) model is proposed in this paper. In object tracking tasks, the DSN inherently includes a template branch and a search branch; it extracts the features from these two branches and employs a Siamese region proposal network to obtain the position of the target in the search branch. The ID-CIM mechanism was proposed to alleviate model drift. These two modules build the ID-DSN framework and mutually reinforce the final tracking results. In addition, we also adopted existing object detection datasets for remotely sensed images to generate training datasets suitable for satellite video single object tracking. Ablation experiments were performed on six high-resolution satellite videos acquired from the International Space Station and “Jilin-1” satellites. We compared the proposed ID-DSN results with other 11 state-of-the-art trackers, including different networks and backbones. The comparison results show that our ID-DSN obtained a precision criterion of 0.927 and a success criterion of 0.694 with a frames per second (FPS) value of 32.117 implemented on a single NVIDIA GTX1070Ti GPU.
Integrating information theory and adversarial learning for cross-modal retrieval

Accurately matching visual and textual data in cross-modal retrieval has been widely studied in the multimedia community. To address these challenges posited by the heterogeneity gap and the semantic gap, we propose integrating Shannon information theory and adversarial learning. In terms of the heterogeneity gap, we integrate modality classification and information entropy maximization adversarially. For this purpose, a modality classifier (as a discriminator) is built to distinguish the text and image modalities according to their different statistical properties. This discriminator uses its output probabilities to compute Shannon information entropy, which measures the uncertainty of the modality classification it performs. Moreover, feature encoders (as a generator) project uni-modal features into a commonly shared space and attempt to fool the discriminator by maximizing its output information entropy. Thus, maximizing information entropy gradually reduces the distribution discrepancy of cross-modal features, thereby achieving a domain confusion state where the discriminator cannot classify two modalities confidently. To reduce the semantic gap, Kullback-Leibler (KL) divergence and bi-directional triplet loss are used to associate the intra- and inter-modality similarity between features in the shared space. Furthermore, a regularization term based on KL-divergence with temperature scaling is used to calibrate the biased label classifier caused by the data imbalance issue. Extensive experiments with four deep models on four benchmarks are conducted to demonstrate the effectiveness of the proposed approach.
IoT based Smart Rationing System

In developing countries like India, the poor people meet their fundamental needs through subsidy provided by the government for basic domestic commodities. The prevailing public distribution structure in Ration shops involves labor intensive measurement of quantity. This current system also requires transaction record maintenance. Numerous difficulties are met through this prevailing system. Some of the major issues are ration distribution to unauthorized card holders, excess time spent by the public in the queue for collecting the ration, involving in the transgressions like hoarding, overcharging and black marketing by the authorities and human interference in transaction updating and ledger maintain process. In order to overcome such issues, Internet of Things based smart card system is proposed in which automatically dispenses the basic commodities to genuine card holders after verifying the card holder details. This system also helps in maintaining the transaction details in a separate database in order to prevent any transgression.
Face Recognition System for Financial Identity Theft Protection

The paper propose a part of image processing for applying on security and authentication. This system looks to be about image processing techniques applied to work in security and data access authentication information through application on mobile. In regard to financial transactions by using Eigen face for face recognition to process and verify its validity. The paper is an idea for prototype applications, the system may not be complete and correct, it can be used practically. However, our work will provide the technical know-only.
Quantum variational optimization: The role of entanglement and problem hardness

The authors investigate the ability of variational quantum algorithms to solve a combinatorial optimization problem, and demonstrate an advantage when the entanglement structure in the algorithm is chosen to mimic the structure of the problem. Notably, they find that when a certain cost function is used the depth of variational circuits is only moderately relevant, which suggests that new classical methods using product states may outperform existing quantum architectures.
Arabic Named Entity Recognition: A BERT-BGRU Approach

Named Entity Recognition (NER) is one of the fundamental tasks in Natural Language Processing (NLP), which aims to locate, extract, and classify named entities into a predefined category such as person, organization and location. Most of the earlier research for identifying named entities relied on using handcrafted features and very large knowledge resources, which is time consuming and not adequate for resource-scarce languages such as Arabic. Recently, deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted features. In addition, transfer learning has also proven its efficiency in several NLP tasks by exploiting pretrained language models that are used to transfer knowledge learned from large-scale datasets to domain-specific tasks. Bidirectional Encoder Representation from Transformer (BERT) is a contextual language model that generates the semantic vectors dynamically according to the context of the words. BERT architecture relay on multi-head attention that allows it to capture global dependencies between words. In this paper, we propose a deep learning-based model by fine-tuning BERT model to recognize and classify Arabic named entities. The pre-trained BERT context embeddings were used as input features to a Bidirectional Gated Recurrent Unit (BGRU) and were fine-tuned using two annotated Arabic Named Entity Recognition (ANER) datasets. Experimental results demonstrate that the proposed model outperformed state-of-the-art ANER models achieving 92.28% and 90.68% F-measure values on the ANERCorp dataset and the merged ANERCorp and AQMAR dataset, respectively.
Towards Measuring Fairness in AI: The Casual Conversations Dataset

This paper introduces a novel dataset to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and ambient lighting conditions. Our dataset is composed of 3,011 subjects and contains over 45,000 videos, with an average of 15 videos per person. The videos were recorded in multiple U.S. states with a diverse set of adults in various age, gender and apparent skin tone groups. A key feature is that each subject agreed to participate for their likenesses to be used. Additionally, our age and gender annotations are provided by the subjects themselves. A group of trained annotators labeled the subjects’ apparent skin tone using the Fitzpatrick skin type scale. Moreover, annotations for videos recorded in low ambient lighting are also provided. As an application to measure robustness of predictions across certain attributes, we provide a comprehensive study on the top five winners of the DeepFake Detection Challenge (DFDC). Experimental evaluation shows that the winning models are less performant on some specific groups of people, such as subjects with darker skin tones and thus may not generalize to all people. In addition, we also evaluate the state-of-the-art apparent age and gender classification methods. Our experiments provides a thorough analysis on these models in terms of fair treatment of people from various backgrounds.
Multilevel thresholding using a modified ant lion optimizer with opposition-based learning for color image segmentation

<abstract> Multilevel thresholding has important research value in image segmentation and can effectively solve region analysis problems of complex images. In this paper, Otsu and Kapur's entropy are adopted among thresholding segmentation methods. They are used as the objective functions. When the number of threshold increases, the time complexity increases exponentially. In order to overcome this drawback, a modified ant lion optimizer algorithm based on opposition-based learning (MALO) is proposed to determine the optimum threshold values by the maximization of the objective functions. By introducing the opposition-based learning strategy, the search accuracy and convergence performance are increased. In addition to IEEE CEC 2017 benchmark functions validation, 11 state-of-the-art algorithms are selected for comparison. A series of experiments are conducted to evaluate the segmentation performance of the algorithm. The evaluation metrics include: fitness value, peak signal-to-noise ratio, structural similarity index, feature similarity index, and computational time. The experimental data are analyzed and discussed in details. The experimental results significantly demonstrate that the proposed method is superior over others, which can be considered as a powerful and efficient thresholding technique. </abstract>
VMAN: A Virtual Mainstay Alignment Network for Transductive Zero-Shot Learning

Transductive zero-shot learning (TZSL) extends conventional ZSL by leveraging (unlabeled) unseen images for model training. A typical method for ZSL involves learning embedding weights from the feature space to the semantic space. However, the learned weights in most existing methods are dominated by seen images, and can thus not be adapted to unseen images very well. In this paper, to align the (embedding) weights for better knowledge transfer between seen/unseen classes, we propose the virtual mainstay alignment network (VMAN), which is tailored for the transductive ZSL task. Specifically, VMAN is casted as a tied encoder-decoder net, thus only one linear mapping weights need to be learned. To explicitly learn the weights in VMAN, for the first time in ZSL, we propose to generate virtual mainstay (VM) samples for each seen class, which serve as new training data and can prevent the weights from being shifted to seen images, to some extent. Moreover, a weighted reconstruction scheme is proposed and incorporated into the model training phase, in both the semantic/feature spaces. In this way, the manifold relationships of the VM samples are well preserved. To further align the weights to adapt to more unseen images, a novel instance-category matching regularization is proposed for model re-training. VMAN is thus modeled as a nested minimization problem and is solved by a Taylor approximate optimization paradigm. In comprehensive evaluations on four benchmark datasets, VMAN achieves superior performances under the (Generalized) TZSL setting.
Impact of 360° vs 2D Videos on Engagement in Anatomy Education

Medical education is constantly evolving, especially as students were forced to study from home during the coronavirus disease 2019 (COVID-19) pandemic, and new technologies have driven the rapid development of supplemental online educational resources.In this study, we examine if 360° videos can promote increased engagement over standard two-dimensional (2D) videos among medical students learning anatomy.We enrolled 39 fourth-year medical students to watch two four-minute videos of anatomy lab exercises in a 360° three-dimensional format using an immersive headset or in a 2D format on a laptop computer.Every two minutes, students were asked to rate their engagement from 0-100.Following the videos, they reported their degree of agreement with 14 statements related to engagement, practicality, and interest in the technology.While watching the videos, the average engagement reported by the 360° video group was higher at each time point than the engagement reported by the two-dimensional group.Further, the engagement remained high in the 360° group through the six-and eight-minute timepoints.In the postvideo survey, the 360° group reported a statistically significantly higher average engagement in seven of eight measures on the assessment.A 360° video was rated as more practical and interesting than a twodimensional video.No significant difference existed in the perceived ease of learning.Overall, the use of 360° video may improve engagement for short videos used in medical education.However, developing a better understanding of its impact on learning outcomes will be critical for determining the overall value and effectiveness of this tool.
Feature selection via max-independent ratio and min-redundant ratio based on adaptive weighted kernel density estimation

Feature selection based on entropy structure can be roughly divided into two types according to whether they are related to kernel density estimation (KDE). The first type is feature selection based on non-KDE entropy. The second type is feature selection based on KDE entropy. Compared with the first type, the second type avoids discretization and obtains more accurate mutual information when handling continuous data. However, existing feature selection methods based on KDE entropy neglect the fact that samples with noise have negative impacts on KDE. Besides, the feature evaluation functions don’t effectively assess relevance and redundancy of features. Thus, a feature selection method via maximizing independent and minimizing redundant classification information ratio is constructed based on adaptive weighted kernel density estimation. Firstly, an adaptive weighted kernel density estimation model is designed. Secondly, the entropy structure is defined by the adaptive weighted kernel density estimation model, and their theoretical properties are explored. Thirdly, a feature selection algorithm via maximizing independent and minimizing redundant classification information ratio is designed from the viewpoint of adaptive weighted kernel density estimation. Finally, comprehensive experiments are performed. The results illustrate that our approach has certain robustness, validity and superiority compared with other representative feature selection approaches.
Face Detection System for Public Transport Service Based on Scale-Invariant Feature Transform

The following topics are dealt with: learning (artificial intelligence); natural language processing; pattern classification; feature extraction; diseases; patient diagnosis; regression analysis; image classification; language translation; and social networking (online).
Spatial Pyramid Attention for Deep Convolutional Neural Networks

Attention mechanisms have shown great success in computer vision. However, the commonly used global average pooling in some implementations aggregates a three-dimensional feature map to a one-dimensional attention map, leading a significant loss of structural information in the attention learning. In this article, we present a novel Spatial Pyramid Attention Network (SPANet), which exploits the structural information and channel relationships for better feature representation. SPANet enhances a base network by adding Spatial Pyramid Attention (SPA) blocks laterally. By rethinking the self-attention mechanism design, we further present three topology structures of attention path connection for our SPANet. They can be flexibly applied to various CNN architectures. SPANet is conceptually simple but practically powerful. It uses both structural regularization and structural information to achieve better learning capability. We have comprehensively evaluated the performance of SPANet on four benchmark datasets for different visual tasks. The experimental results show that SPANet significantly improves the recognition accuracy without adding much computation overhead. Using SPANet, we achieve an improvement of 1.6% top-1 classification accuracy on the ImageNet 2012 benchmark based on ResNet50, and SPANet outperforms SENet and other attention methods. SPANet also significantly improves the object detection performance by a clear margin with negligible additional computation overhead. When applying SPANet to RetinaNet based on the ResNet50 backbone, we improve the performance of the baseline model by 2.3 mAP and the enhanced model outperforms SENet and GCNet by 1.1 mAP and 1.7 mAP respectively. The code of SPANet is made publicly available. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> [Online]. Available: <uri>https://github.com/13952522076/SPANet_TMM</uri> </fn>
Learning Barrier Functions With Memory for Robust Safe Navigation

Control barrier functions are widely used to enforce safety properties in robot motion planning and control. However, the problem of constructing barrier functions online and synthesizing safe controllers that can deal with the associated uncertainty has received little attention. This letter investigates safe navigation in unknown environments, using on-board range sensing to construct control barrier functions online. To represent different objects in the environment, we use the distance measurements to train neural network approximations of the signed distance functions incrementally with replay memory. This allows us to formulate a novel robust control barrier safety constraint which takes into account the error in the estimated distance fields and its gradient. Our formulation leads to a second-order cone program, enabling safe and stable control synthesis in a prior unknown environments.
Using vis-NIR and pXRF data to distinguish soil parent materials – An example using 136 pedons from Wisconsin, USA

Many soils have two or more parent materials. It is not always possible to distinguish these materials in a soil pit or in the field. Here we present a study in the Driftless Area of Wisconsin, where soils were developed in loess covering the weathered impurities of dolostone, termed terra rossa. We evaluated the geochemical signature of 136 pedons with loess (A and Bt) and/or terra rossa (2Bt) horizons and developed decision tree models to distinguish the Bt and 2Bt horizons from different geochemical indicators. In total 409 samples were collected from A, Bt, and 2Bt horizons and the samples were scanned using vis-NIR and pXRF spectrometers to obtain the color information, elemental data, and to calculate weathering indices. The Munsell color, texture, total carbon (TC), total nitrogen (TN), and pH were measured in the laboratory. Twenty one types of input data were compared in decision tree models. It was found that the 2Bt horizons had higher clay, pH, Mg, Al, Fe, Ca, Mn, Zn, Rb, CTR, and Ti/Zr than the loess A and Bt horizons. The Ruxten index (R), clay, horizon thickness (HT), CaO/TiO2 (CTR), and Ti/Zr were robust variables to distinguish loess Bt and terra rossa 2Bt using a decision tree model. However, similarities in the values of some elements and weathering indices compromised the characterizations of Bt and 2Bt horizons, which is the result of mixing of parent materials through bioturbation over time. It can be concluded that pXRF and vis-NIR spectra can be used to distinguish parent materials in soil profiles, and pXRF spectra had slightly better overall accuracy than vis-NIR spectra.
A Conceptual Architecture in Decentralizing Computing, Storage, and Networking Aspect of IoT Infrastructure

Since the inception of the Internet of Things (IoT), we have adopted centralized architecture for decades. With the vastly growing number of IoT devices and gateways, this architecture struggles to cope with the high demands of state-of-the-art IoT services, which require scalable and responsive infrastructure. In response, decentralization becomes a considerable interest among IoT adopters. Following a similar trajectory, this paper introduces an IoT architecture re-work that enables three spheres of IoT workflows (i.e., computing, storage, and networking) to be run in a distributed manner. In particular, we employ the blockchain and smart contract to provide a secure computing platform. The distributed storage network maintains the saving of IoT raw data and application data. The software-defined networking (SDN) controllers and SDN switches exist in the architecture to provide connectivity across multiple IoT domains. We envision all of those services in the form of separate yet integrated peer-to-peer (P2P) overlay networks, which IoT actors such as IoT domain owners, IoT users, Internet Service Provider (ISP), and government can cultivate. We also present several IoT workflow examples showing how IoT developers can adapt to this new proposed architecture. Based on the presented workflows, the IoT computing can be performed in a trusted and privacy-preserving manner, the IoT storage can be made robust and verifiable, and finally, we can react to the network events automatically and quickly. Our discussions in this paper can be beneficial for many people ranging from academia, industries, and investors that are interested in the future of IoT in general.