Reviewerly/exHarmony huggingface dataset:
Discriminant Cross modal Hash Retrieval Algorithm with Multilevel Semantics

In view of the fact that most of the cross modal hash methods use binary matrices to represent the degree of correlation, they cannot capture the deeper semantic information between multi label data, and they ignore the problems of maintaining the discriminability of semantic structure and data characteristics, so a discriminant cross modal hash retrieval algorithm based on multi-level semantics, ML-SDH, is proposed. The proposed algorithm uses multi-level semantic similarity matrix to find the deep correlation information in the cross modal data, and uses the equal guidance cross modal hash to represent the correlation in the semantic structure and discrimination classification, which realizes the purpose of encoding the multi tag data containing high-level semantic information. Moreover, the structure that preserves multi-level semantics can ensure that the final learned hash code is discriminative while maintaining semantic similarity. On the NUSWIDE dataset, when the hash code length is 32bit, the average accuracy (mAP) of the proposed algorithm in the two retrieval tasks is 19.48, 14.50, 1.95 percentage points higher than that of the deep cross-modal hashing (DCMH), paired associative hash (PRDH), and equality guided discriminant hash (EGDH) algorithms, respectively, and 16.32, 11.82, 2.08 percentage points higher than that of the proposed algorithm.
Unsupervised Domain Adaptation for the Semantic Segmentation of Remote Sensing Images via One-Shot Image-to-Image Translation

Domain adaptation is one of the prominent strategies for handling both the scarcity of pixel-level ground truth and the domain shift, that is widely encountered in large-scale land use/cover map calculation. Studies focusing on adversarial domain adaptation via re-styling source domain samples, commonly through generative adversarial networks (GANs), have reported varying levels of success, yet they suffer from semantic inconsistencies, visual corruptions, and often require a large number of target domain samples. In this letter, we propose a new lightweight unsupervised domain adaptation (UDA) method for the semantic segmentation of very high-resolution remote sensing images, based on an image-to-image translation (I2IT) approach, via an encoder–decoder strategy where latent content representations are mixed across domains, and a perceptual network module and loss function enforce visual semantic consistency. We show through cross-domain comparative experiments that it: 1) leads to semantically consistent images; 2) can operate with a single target domain sample (i.e., one-shot); and 3) at a fraction of the number of parameters required from the state-of-the-art methods, while still outperforming them. Code is available at github.com/Sarmadfismael/RSOS_I2I.
Block chain based IAS protocol to enhance security and privacy in cloud computing

Cloud computing has become increasingly popular for data storage and processing, but security and privacy remain major concerns due to the potential for malicious attacks on wireless and mobile communication networks. In order to address these issues, researchers have proposed an IAS protocol that incorporates identity, access control, and secure authentication. To ensure the authenticity and security of data transmission in cloud computing, the proposed IAS protocol is implemented on top of blockchain technology. This allows for decentralized key management for recovery and revocation, as well as identity verification. To evaluate the effectiveness of this approach, the proposed model using Block Chain based Identity Management, Access Control and Secure Sharing (BC-IAS) is simulated on the cloud environment. The simulation assesses primary performance key metrics, including Data Access Rate, Message Delivery Ratio, End to End Delay and Energy consumption. Overall, the proposed BC-IAS protocol on top of blockchain technology shows promise for enhancing security and privacy in cloud computing. The proposed BC-IAS protocol on top of blockchain technology presents a promising solution for enhancing the security and privacy of cloud computing. The decentralized nature of blockchain technology ensures that data is stored in a tamper-proof and transparent manner, while the use of smart contracts allows for automated enforcement of access control policies. Moreover, the incorporation of identity verification and secure authentication ensures that only authorized users can access sensitive data, reducing the risk of data breaches and cyber-attacks.
A high-fidelity quantum state transfer algorithm on the complete bipartite graph

High-fidelity quantum state transfer is critical for quantum communication and scalable quantum computation. Current quantum state transfer algorithms on the complete bipartite graph, which are based on discrete-time quantum walk search algorithms, suffer from low fidelity in some cases. To solve this problem, in this paper we propose a two-stage quantum state transfer algorithm on the complete bipartite graph. The algorithm is achieved by the generalized Grover walk with one marked vertex. The generalized Grover walk’s coin operators and the query oracles are both parametric unitary matrices, which are designed flexibly based on the positions of the sender and receiver and the size of the complete bipartite graph. We prove that the fidelity of the algorithm is greater than $$1-2\epsilon _{1}-\epsilon _{2}-2\sqrt{2}\sqrt{\epsilon _{1}\epsilon _{2}}$$ or $$1-\left( 2+2\sqrt{2}\right) \epsilon _{1}-\epsilon _{2}-\left( 2+2\sqrt{2}\right) \sqrt{\epsilon _{1}\epsilon _{2}}$$ for any adjustable parameters $$\epsilon _{1}$$ and $$\epsilon _{2}$$ when the sender and receiver are in the same partition or different partitions of the complete bipartite graph. The algorithm provides a novel approach to achieve high-fidelity quantum state transfer on the complete bipartite graph in any case, which will offer potential applications for quantum information processing.
A generalization of Bernstein–Vazirani algorithm with multiple secret keys and a probabilistic oracle

A probabilistic version of the Bernstein–Vazirani problem (which is a generalization of the original Bernstein–Vazirani problem) and a quantum algorithm to solve it are proposed. The problem involves finding one or more secret keys from a set of multiple secret keys (encoded in binary form) using a quantum oracle. From a set of multiple unknown keys, the proposed quantum algorithm is capable of (a) obtaining any key (with certainty) using a single query to the probabilistic oracle and (b) finding all keys with a high probability (approaching 1 in the limiting case). In contrast, a classical algorithm will be unable to find even a single bit of a secret key with certainty (in the general case). Owing to the probabilistic nature of the oracle, a classical algorithm can only be useful in obtaining limiting probability distributions of 0 and 1 for each bit-position of secret keys (based on multiple oracle calls), and this information can further be used to infer some estimates on the distribution of secret keys based on combinatorial considerations. For comparison, it is worth noting that a classical algorithm can be used to exactly solve the original Bernstein–Vazirani problem (involving a deterministic oracle and a single hidden key containing n bits) with a query complexity of $$\mathcal {O}(n)$$ . An interesting class of problems similar to the probabilistic version of the Bernstein–Vazirani problem can be construed, where quantum algorithms can provide efficient solutions with certainty or with a high degree of confidence and classical algorithms would fail to do so.
The Contribution of Social Entrepreneurship to the Development of Human Capital of Students

The article presents the author’s vision of the place and role of social entrepreneurship in the development of the human capital of students. The authors note that the ongoing changes in society and the economy pose the most important task - the study of a phenomenon such as human capital. Training in social entrepreneurship of students can somehow contribute to the formation of the individual human capital of a student to more successfully adapt to the modern labor market. The authors proposed a model of human capital consisting of seven components, which served as the basis for the development and implementation of a modular curriculum in the work of several universities. The article presents a pedagogical experiment on testing a modular program at universities in the South and North Caucasus Federal Districts and evaluates its results. Suggestions were made for the development of an urgent and state-important strategic direction for universities - a focus on the formation of positive human capital for students.
Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing

Natural computation, motivated by the organic game arrangement, is a knowledge base field that formalizes the measurements seen in living organic entities to plan machine techniques to tackle complicated issues or to plan artificial structures with additional traditional behaviors. Seeable corporations disconnected from natural wonders, reminiscent of mind demonstration, self-association, self-redundancy, Darwinian resistance, self-evaluation, discernment, and granulation, nature is crammed as a supply of motivation to advance competition. Computational devices or frameworks accustomed solve complex problems. The ideal, nature-motivated primary computation models used for such sweetening incorporate artificial neural organizations, spongy reasoning, arduous set, biological process calculations, shape mathematics, DNA registration, artificial life, And granular or insight-based processing. The granulation of information within the granular register is an innate attribute of human thought and therefore the life of thought acted call at regular daily existence. This paper illustrates the importance of normal recording in terms of granulation-based data preparation models, for example, neural organizations, soft and ugly sets, and their hybridization. we have a tendency to emphasize the bio-sensitive inspiration, designing standards, application zones, open scan problems, and testing issues for these models.
Decentralized Learning and Model Averaging Based Automatic Modulation Classification in Drone Communication Systems

Automatic modulation classification (AMC) is a promising technology to identify the modulation mode of the received signal in drone communication systems. Recently, benefiting from the outstanding classification performance of deep learning (DL), various deep neural networks (DNNs) have been introduced into AMC methods. Most current AMC methods are based on a local framework (LocalAMC) where there is only one device, or a centralized framework (CentAMC) where multiple local devices (LDs) upload their data to only one central server (CS). LocalAMC may not achieve ideal results due to insufficient data and finite computational power. CentAMC carries a significant risk of privacy leakage and the final data for training model in CS are quite massive. In this paper, we propose a practical and light AMC method based on decentralized learning with residual network (ResNet) in drone communication systems. Simulation results show that the ResNet-based decentralized AMC (DecentAMC) method achieves similar classification performance to CentAMC while improving training efficiency and protecting data privacy.
An adversarially consensus model of augmented unlabeled data for cardiac image segmentation (CAU&lt;sup&gt;+&lt;/sup&gt;)

High quality medical images play an important role in intelligent medical analyses. However, the difficulty of acquiring medical images with professional annotation makes the required medical image datasets, very expensive and time-consuming. In this paper, we propose a semi-supervised method, $ {\mathrm{C}\mathrm{A}\mathrm{U}}^{+} $, which is a consensus model of augmented unlabeled data for cardiac image segmentation. First, the whole is divided into two parts: the segmentation network and the discriminator network. The segmentation network is based on the teacher student model. A labeled image is sent to the student model, while an unlabeled image is processed by CTAugment. The strongly augmented samples are sent to the student model and the weakly augmented samples are sent to the teacher model. Second, $ {\mathrm{C}\mathrm{A}\mathrm{U}}^{+} $ adopts a hybrid loss function, which mixes the supervised loss for labeled data with the unsupervised loss for unlabeled data. Third, an adversarial learning is introduced to facilitate the semi-supervised learning of unlabeled images by using the confidence map generated by the discriminator as a supervised signal. After evaluating on an automated cardiac diagnosis challenge (ACDC), our proposed method $ {\mathrm{C}\mathrm{A}\mathrm{U}}^{+} $ has good effectiveness and generality and $ {\mathrm{C}\mathrm{A}\mathrm{U}}^{+} $ is confirmed to have a improves dice coefficient (DSC) by up to 18.01, Jaccard coefficient (JC) by up to 16.72, relative absolute volume difference (RAVD) by up to 0.8, average surface distance (ASD) and 95% Hausdorff distance ($ {HD}_{95} $) reduced by over 50% than the latest semi-supervised learning methods.
Temporal-spatial binary encoding method based on dynamic threshold optimization for 3D shape measurement

The binary encoding method has been widely used for three-dimensional (3D) shape measurement due to the high-speed projection characteristics of its digital mirror device (DMD)-based projector. However, traditional binary encoding methods require a larger defocus to achieve a good sinusoidality, leading to a reduction in the measurement depth of field and signal-to-noise ratio (SNR) of captured images, which can adversely affect the accuracy of phase extraction, particularly high-frequency fringes for 3D reconstruction. This paper proposes a spatial-temporal binary encoding method based on dynamic threshold optimization for 3D shape measurement. The proposed method decomposes an 8-bit sinusoidal fringe pattern into multiple( K ) binary patterns, which can be outlined into two steps: determining the dynamic threshold and then performing temporal-spatial error diffusion encoding. By using an integral imaging strategy, approximate sinusoidal patterns can be obtained under nearly focused projection, which can then be subjected to absolute phase unwrapping and 3D reconstruction. The experiments show that compared to the three comparative algorithms under the same experimental conditions, this proposed method improves the reconstruction error of measuring a plane and an object by at least 13.66% and 12.57% when K =2. The dynamic experimental result on the palm confirms that the proposed method can reliably reconstruct the 3D shape of the moving object.
Real-time Object Detection Using Deep Learning

As technology improved, object detection, which is connected to video and image analysis, caught researchers' interest. Earlier object recognition techniques are based on hand-crafted features and imprecise architectures and trainable algorithms. One of the main issues with many object detection systems is that they rely on other computer vision methods to support their deep learning-based methodology, which leads to slow and subpar performance. In this article, we present an end-to-end solution to the object detection problem using a deep learning based method. The single shot detector (SSD) technique is the quickest method for object detection from an image using a single layer of a convolution network. Our research's primary goal is to enhance accuracy of SSD method.
Multivariate time-series anomaly detection

Anomalies are rare items that differ significantly from the majority of the data and raise suspicion. Time series anomaly detection is of great significance in industrial applications, data mining and other fields. In this paper, we propose an autoencoder-based anomaly detection model. In the encoder part, the dependency relationship between different time series is mined through the graph attention network, the time domain data features are extracted through the GRU, and the frequency domain features are extracted through the convolutional neural network. The temporal data is reconstructed using a decoder consisting of a recurrent neural network in the encoder part. The residual between the reconstructed data and the original data is used to further judge anomalies. Data cleaning is performed in the preprocessing part to improve model performance. The effectiveness of our method is demonstrated on three publicly available datasets, and our method is found to outperform four other common anomaly detection methods.
Investigating user behavior in slideshows and scrollytelling as narrative genres in medical visualization

In this study, we explore the impact of genre and navigation on user comprehension, preferences, and behaviors when experiencing data-driven disease stories. Our between-subject study (n=85) evaluated these aspects in-the-wild, with results pointing towards some general design considerations to keep in mind when authoring data-driven disease stories. Combining storytelling with interactive new media techniques, narrative medical visualization is a promising approach to communicating topics in medicine to a general audience in an accessible manner. For patients, visual storytelling may help them to better understand medical procedures and treatment options for more informed decision-making, boost their confidence and alleviate anxiety, and promote stronger personal health advocacy. Narrative medical visualization provides the building blocks for producing data-driven disease stories, which may be presented in several visual styles. These different styles correspond to different narrative genres, e.g., a Slideshow. Narrative genres can employ different navigational approaches. For instance, a Slideshow may rely on click interactions to advance through a story, while Scrollytelling typically uses vertical scrolling for navigation. While a common goal of a narrative medical visualization is to encourage a particular behavior, e.g., quitting smoking, it is unclear to what extent the choice of genre influences subsequent user behavior. Our study opens a new research direction into choice of narrative genre on user preferences and behavior in data-driven disease stories.
CNN-based battlefield classification and camouflage texture generation for real environment

It is critical to understand the environment in which the military forces are deployed. For self-defence and greater concealment, they should camouflage themselves. Camouflage is being used by the defence system to hide its personnel and equipment. The industry demands an intelligent system that can categorise the battlefield before generating texture for camouflaging their assets and objects, allowing them to adopt the conspicuous features of the scene. In this study, a CNN-based battlefield classification model has been developed to learn background information and classify the terrain. The study also intended to develop the texture for specific terrain by matching its salient features and boosting the effectiveness of the camouflage. Saliency maps have been used to measure the effectiveness of blending a camouflaged object into an environment.
Two-stream coupling network with bidirectional interaction between structure and texture for image inpainting

Since neural networks possess universal approximation property and the property can be a theoretical guarantee for image inpainting, the research, which uses deep learning technology to complete images, has attracted more attention from researchers in recent years. Given that structure and texture are two factors in the image, some existing studies have shown that modeling structure and texture can improve the effect of image inpainting. However, there exist close relationships between structure and texture, and they can contribute to each other, which is not considered in the existing works. This paper not only models the structure and texture of the image, but also constructs a bidirectional interactive relationship between them. A two-stream coupling network for image inpainting, which can effectively perform the bidirectional interaction between structure and texture, is proposed. Specifically, two parallel encoder–decoder networks are constructed to be responsible for structure reconstruction and texture synthesis respectively. Meanwhile, two interaction mechanisms called gated interaction unit (GIU) and spatial-channel attention (SCIM), are embedded between them, which perform bidirectional interaction to structure and texture information. GIU weighs the interaction between structure and texture features through gating mechanisms, and SCIM achieves a deeper interaction to the distant contexts of structure and texture through spatial-channel attention mechanisms. In this way, the network promotes both structure-guided texture synthesis and texture-facilitated structure reconstruction. Extensive experiments illustrate the superiority of the proposed method.
Sequential Active Contour Based on Morphological-Driven Thresholding for Ultrasound Image Segmentation of Ascites

Paracentesis is a high-demanding and routine operation, which has great potentials and benefits if semi-autonomous procedures can be developed. One of the most important techniques that facilitate semi-autonomous paracentesis is to segment the ascites from ultrasound images accurately and efficiently. The ascites, however, is usually with significantly different shapes and noise among different patients, and its shape/size changes dynamically during the paracentesis. This makes most of existing image segmentation methods either time consuming or inaccurate for segmenting ascites from its background. In this article, we propose a two-stage active contour method to facilitate accurate and efficient segmentation of ascites. First, a morphological-driven thresholding method is developed to locate the initial contour of the ascites automatically. Then, the identified initial contour is fed into a novel sequential active contour algorithm to segment the ascites from background accurately. The proposed method is tested and compared with state-of-the-art active contour methods on over 100 real ultrasound images of ascites, and the results show the superiority of our method in both accuracy and time efficiency.
Demand Analysis of the Cybersecurity Knowledge Areas and Skills for the Nurses: Preliminary Findings

The purpose of this paper is to present a preliminary analysis of the cybersecurity market demand in the nursing and health sector. Currently, the market demand study is ongoing under the Digital Europe Programme CyberSecPro project, which strengthens the role of higher education institutions as a provider of practical and working life skills. The project promotes reliable digital transformation in critical sectors, such as healthcare. The rapid development of e-health emphasizes the central position of cybersecurity in healthcare organizations that are increasingly the targets of cyber-attacks. This descriptive literature review explores what a nurse needs to know about cybersecurity. Our results show that awareness of cyber risks is weak in the healthcare sector. Understanding cyber risks and recognizing the effects of one's own activities increases the cybersecurity of the entire organization, therefore cybersecurity training for nurses should be increased. Our study suggested that nurses’ most important cyber skills are their own cyber-safe way of operations, identifying cyber threats related to equipment, identifying the effects of cyber disruptions, and acting in a cyber disruption situation. Future nurse training programs should be updated to include these skills. Additionally, the teaching of nurses must be developed so that it meets these competence needs.