Reviewerly/exHarmony huggingface dataset:
Intelligent deep analysis of DNA sequences based on FFGM to enhancement the performance and reduce the computation

In an attempt to improve the analysis DNA sequence, a new intelligent deep analysis algorithm called reduce frequency bast on fast frequency graph mining (RF-FFGM) is established; This algorithm at the beginning converts the DNA sequence into RNA sequences after that split these sequence into multi subsequence through determined specific equation for start and end point of each sequence. After that each subsequence represent as subgraph after label to the bonds between each pair of components related to RNA (i.e., A, G, U, C) these bounds include 16 labels used as Knowledge Constructions (KC)) for this work. After that apply the steps of FFGM that select after deep analysis to graph mining techniques (GSpan, FFSM, Hybrid-Tree-Miner, Approximate Frequent Sub-graph, CloGraMi and FFSM) this analysis focus on determined (the main programming steps, main parameters, advantages, disadvantages) for each algorithm. We discovery FFGM finds the frequent in a short time, because it building matrix code for connection edge and transforming matrices into incidence matrix, also; we found FFGM can get all the edges that have the highest contact with the other edges, so from the second stage therefore it avoids us from going through a sequential path to find duplicate edges. RF-FFGM appears as a pragmatic algorithm, it proves their robust to work with DNA sequence to reduce the computation and time.
Evolving Generative Adversarial Networks to improve image steganography

Images have been repeatedly used as the perfect environment to hide information through the use of steganography techniques. Whether messages, documents or even other images, the bitmap of an digital picture provides a place where hidden data can be embedded without human notice. So far, a plethora of steganography methods can be found in the state-of-the-art literature, together with steganalysis techniques, devoted to detect the presence of hidden information in files. Recent steganography techniques rely on Convolutional Neural Networks, trying to embed as information as possible while minimising visual changes in the image. Following this trend, this article tries to demonstrate that a Generative Adversarial Network (GAN) can be used to improve the ability of a spatial domain steganalysis method and to insert secret information with minimal image alteration. Through a training process, the GAN learns how to adapt an image to later introduce a message using the Least Significant Bit steganography algorithm. The results evidence that the approach is successful at avoiding detection by a state-of-the-art Deep Learning steganalysis architecture.
MINERAL ASSEMBLAGES, FLUID INCLUSIONS, PYRITE TRACE ELEMENTS, AND S-O ISOTOPES OF GOLD ORES FROM THE CENOZOIC DAPING DEPOSIT, SW CHINA: IMPLICATIONS FOR THE GENESIS OF COMPLEX OROGENIC LODE GOLD SYSTEMS

Abstract The Cenozoic Daping orogenic gold deposit, on the southeastern margin of the Tibetan Plateau, China, contains four lode types that contain a total of 55 t gold. Pyrite-chalcopyrite–dominated (VA) and galena-dominated polymetallic sulfide veins (VB) hosted by Neoproterozoic diorite are associated with quartz-sericite-chlorite ± epidote (± barite in VB veins) alteration. Pyrite-dominated (VC) and pyrite-pyrrhotite–dominated veins (VD) hosted by Silurian dolostone (intercalated with carbon-bearing argillaceous limestone in VD veins) are related to ankerite-siderite-quartz ± sericite alteration. All have free gold spatially and temporally associated with pyrite, chalcopyrite, galena, pyrrhotite, or quartz. Most VA and VB veins are controlled by steeply SW-dipping ductile-brittle shear zones with jigsaw wall-rock breccias in VB veins, whereas gently SW-dipping faults host VC and VD veins. There are some significant differences between different veins: (1) there were more acidic mineralization conditions for VA and VB veins relative to VC and VD veins, and more oxidized conditions for VB veins relative to other veins; (2) pyrite is rich in Co-Ni in VA and VB veins, compared to enrichment in As-Au for VC and VD veins; (3) sulfide δ34S values of 3.2 to 11.8‰ (median 8.2‰) for VA, VC, and VD veins contrast with –4.6 to +0.9‰ (median 0‰) for VB veins. The contrasting mineral parageneses, pH values, and pyrite geochemistry can be attributed to fluid-rock interaction as evidenced by replacements of amphibole by sericite in diorite and dolomite by ankerite and siderite in dolostone. The lower (~8‰) VB sulfide δ34S values and interpreted fluid oxidation relative to other veins, together with the presence of breccias and barite, can be explained by phase separation due to flash vaporization triggered by extreme hydrofracturing. The consistent NW-trending vein sets, similar median S-O isotope ratios of original ore fluids, and lack of multistage overlap of gold mineralization and alteration zones support a single-source fluid for gold mineralization at Daping. This study is diagnostic rather than just indicative in that it systematically and quantitatively portrays the mineralization diversity in an orogenic gold system formed by a single-source fluid regulated by the external fluid-rock interactions and internal hydrofracturing.
An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species' obligate brood parasitism (laying eggs in other species' nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior.
Use of Blockchain-Based Smart Contracts in Logistics and Supply Chains

Blockchain is a disrupting technology that has the capability to completely alter the design, activities, and product flows in logistics and supply chain networks. It provides assurance of openness, immutability, transparency, security, and neutrality for all supply chain agents and stakeholders. In this paper, we explore the improvements and tradeoffs introduced by using blockchains in logistics management in terms of the sustainability of society, the environment, and economic dimensions of the supply chain. Blockchain technology makes it much more difficult to counterfeit products by providing indisputable and immutable proof of the provenance of the raw materials, products, and sale to the end consumer. This can potentially enhance the trust of the consumer in the product and financially benefit the manufacturer through the protection of their intellectual property rights. This paper explores the benefits, applications, and issues related to the usage of blockchain and smart contracts for logistics and supply-chain management. We focus on the implementation, deployment, audit, and operational aspects of smart contracts in the blockchain applied to terrestrial, maritime, and aerial logistics networks. The paper also discusses opportunities and challenges that arise due to the use of smart contracts in these sectors.
Building a Bridge of Bounding Box Regression Between Oriented and Horizontal Object Detection in Remote Sensing Images

Oriented object detection (OOD) aims to precisely detect the objects with arbitrary orientation in remote sensing images. Up to now, most of bounding box regression (BBR) losses for OOD are transferred from horizontal object detection (HOD) methods, however, the transferring requires lots of professional knowledge and experiences for designers, consequently, many excellent BBR losses for HOD have not been transferred to OOD. To accelerate the research progress of BBR loss for OOD, a unified transferring strategy (UTS) is proposed to facilitate the transferring of BBR loss from HOD to OOD. The UTS proposes that the BBR of oriented bounding box (OBB) can be converted into the joint BBR of its horizontal smallest enclosing rectangle (HSER) and two offsets, so the BBR loss in HOD can be easily transferred to OOD by using HSER as a bridge. Following the UTS, a BBR loss named Rotated-IoU (RIoU) loss is designed for OOD by transferring an advanced BBR loss in HOD, which can be considered as an example to show how to transfer. On the basis of RIoU loss, a focal rotated-IoU (FRIoU) loss is proposed to assign larger weights to hard samples in the BBR. The comparisons with other BBR losses show that the RIoU and FRIoU losses can give better performance. The ablation study shows that giving more attention to hard samples in BBR is effective. The comparisons with many advanced methods demonstrate that the combinations of baseline methods and FRIoU loss achieve state-of-the-art performance on the DOTA and DIOR-R datasets.
Noninferiority of Artificial Intelligence–Assisted Analysis of Ki-67 and Estrogen/Progesterone Receptor in Breast Cancer Routine Diagnostics

Image analysis assistance with artificial intelligence (AI) has become one of the great promises over recent years in pathology, with many scientific studies being published each year. Nonetheless, and perhaps surprisingly, only few image AI systems are already in routine clinical use. A major reason for this is the missing validation of the robustness of many AI systems: beyond a narrow context, the large variability in digital images due to differences in preanalytical laboratory procedures, staining procedures, and scanners can be challenging for the subsequent image analysis. Resulting faulty AI analysis may bias the pathologist and contribute to incorrect diagnoses and, therefore, may lead to inappropriate therapy or prognosis. In this study, a pretrained AI assistance tool for the quantification of Ki-67, estrogen receptor (ER), and progesterone receptor (PR) in breast cancer was evaluated within a realistic study set representative of clinical routine on a total of 204 slides (72 Ki-67, 66 ER, and 66 PR slides). This represents the cohort with the largest image variance for AI tool evaluation to date, including 3 staining systems, 5 whole-slide scanners, and 1 microscope camera. These routine cases were collected without manual preselection and analyzed by 10 participant pathologists from 8 sites. Agreement rates for individual pathologists were found to be 87.6% for Ki-67 and 89.4% for ER/PR, respectively, between scoring with and without the assistance of the AI tool regarding clinical categories. Individual AI analysis results were confirmed by the majority of pathologists in 95.8% of Ki-67 cases and 93.2% of ER/PR cases. The statistical analysis provides evidence for high interobserver variance between pathologists (Krippendorff's α, 0.69) in conventional immunohistochemical quantification. Pathologist agreement increased slightly when using AI support (Krippendorff α, 0.72). Agreement rates of pathologist scores with and without AI assistance provide evidence for the reliability of immunohistochemical scoring with the support of the investigated AI tool under a large number of environmental variables that influence the quality of the diagnosed tissue images.
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP

Abstract NLP has achieved great progress in the past decade through the use of neural models and large labeled datasets. The dependence on abundant data prevents NLP models from being applied to low-resource settings or novel tasks where significant time, money, or expertise is required to label massive amounts of textual data. Recently, data augmentation methods have been explored as a means of improving data efficiency in NLP. To date, there has been no systematic empirical overview of data augmentation for NLP in the limited labeled data setting, making it difficult to understand which methods work in which settings. In this paper, we provide an empirical survey of recent progress on data augmentation for NLP in the limited labeled data setting, summarizing the landscape of methods (including token-level augmentations, sentence-level augmentations, adversarial augmentations, and hidden-space augmentations) and carrying out experiments on 11 datasets covering topics/news classification, inference tasks, paraphrasing tasks, and single-sentence tasks. Based on the results, we draw several conclusions to help practitioners choose appropriate augmentations in different settings and discuss the current challenges and future directions for limited data learning in NLP.
THCANet: Two-layer hop cascaded asymptotic network for robot-driving road-scene semantic segmentation in RGB-D images

In several existing red–green–blue and depth (RGB-D) semantic segmentation algorithms, schemes are used to supplement contextual information through multilayer feature interactions. However, these approaches ignore the complementation of the contextual information and the introduction of noise interfering with the segmentation process. To minimize noise interference during this process, we introduce a two-layer hop cascaded asymptotic network (THCANet) for robot-driving road-scene semantic segmentation in RGB-D images. To exploit the depth map and supervision to strengthen semantic segmentation, we propose an attention cross-fusion module for the interactive combination of RGB-D features through multimodality weighting. Notably, the information of the two modalities reduces noise during fusion. After fusing features from the RGB-D modalities, we also use a novel multiscale context module to fuse features at multiple scales and employ a jump cascade architecture between the modules to recover lost context information and suppress irrelevant noise. Moreover, multiple supervision is performed at different segmentation stages to improve accuracy. The proposed THCANet system demonstrates the best performance on a robot-driving road dataset compared with similar methods, and its generalization ability is demonstrated using the NYU-Depth V2 dataset.
TriHorn-Net: A model for accurate depth-based 3D hand pose estimation

3D hand pose estimation methods have made significant progress recently. However, estimation accuracy is often far from sufficient for specific real-world applications, and thus there is significant room for improvement. This paper proposes TriHorn-Net, a novel model that uses specific innovations to improve hand pose estimation accuracy on depth images. The first innovation is decomposition of the 3D hand pose estimation into the estimation of 2D joint locations in the depth image space (UV), and the estimation of their corresponding depths aided by two complementary attention maps. This decomposition prevents depth estimation, which is a more difficult task, from interfering with the UV estimations at both the prediction and feature levels. The second innovation is PixDropout, which is, to the best of our knowledge, the first appearance-based data augmentation method for hand depth images. Experimental results demonstrate that the proposed model outperforms the state-of-the-art methods on three public benchmark datasets, achieving 5.73 mm, 7.13 mm, and 7.68 mm on the ICVL, MSRA, and NYU datasets, respectively. The proposed model achieves this performance with a relatively fast average inference speed of 9.25 ms per frame on an NVIDIA 1080Ti GPU, which, as discussed in the paper, places the proposed model among the fastest-performing 3D hand pose estimation models. Our implementation is available at https://github.com/mrezaei92/TriHorn-Net.
I am attracted to my Cool Smart Assistant! Analyzing Attachment-Aversion in AI-Human Relationships

The conversation between humans and Artificial Intelligence (AI)-enabled intelligent voice assistants (IVA) can create bonds that go beyond a mere utilitarian purpose. The emotional cues in a Human-AI conversation can lead consumers to feel connected with the AI-agents and even consider such a relationship as cool. Although brand coolness is known to affect consumer behavior, little is known about how consumers perceive a close relationship with IVAs and what the drivers of their use or avoidance are. Therefore, the current paper adds to the literature by analyzing how AI-enabled voice assistant experience affects IVA coolness and customer-brand relationships using the attachment-aversion (A-A) theory. A total of 308 consumers showed that affective, behavioral, and intellectual experiences with the intelligent voice assistant affect IVA coolness. IVA coolness was also found to affect A-A relationships positively, influencing consumers’ motivational strength to adopt, maintain and enhance the relationship in the future.
A Systematic Review on Federated Learning in Medical Image Analysis

Federated Learning (FL) obtained a lot of attention to the academic and industrial stakeholders from the beginning of its invention.The eye-catching feature of FL is handling data in a decentralized manner which creates a privacy preserving environment in Artificial Intelligence (AI) applications.As we know medical data includes marginal private information of patients which demands excessive data protection from disclosure to unexpected destinations.In this paper, we performed a Systematic Literature Review (SLR) of published research articles on FL based medical image analysis.Firstly, we have collected articles from different databases followed by PRISMA guidelines, then synthesized data from the selected articles, and finally we provided a comprehensive overview on the topic.In order to do that we extracted core information associated with the implementation of FL in medical imaging from the articles.In our findings we briefly presented characteristics of federated data and models, performance achieved by the models and exclusively results comparison with traditional ML models.In addition, we discussed the open issues and challenges of implementing FL and mentioned our recommendations for future direction of this particular research field.We believe this SLR has successfully summarized the state-of-the-art FL methods for medical image analysis using deep learning.
Impact of Different Mammography Systems on Artificial Intelligence Performance in Breast Cancer Screening

Artificial intelligence (AI) tools may assist breast screening mammography programs, but limited evidence supports their generalizability to new settings. This retrospective study used a 3-year dataset (April 1, 2016-March 31, 2019) from a U.K. regional screening program. The performance of a commercially available breast screening AI algorithm was assessed with a prespecified and site-specific decision threshold to evaluate whether its performance was transferable to a new clinical site. The dataset consisted of women (aged approximately 50-70 years) who attended routine screening, excluding self-referrals, those with complex physical requirements, those who had undergone a previous mastectomy, and those who underwent screening that had technical recalls or did not have the four standard image views. In total, 55 916 screening attendees (mean age, 60 years ± 6 [SD]) met the inclusion criteria. The prespecified threshold resulted in high recall rates (48.3%, 21 929 of 45 444), which reduced to 13.0% (5896 of 45 444) following threshold calibration, closer to the observed service level (5.0%, 2774 of 55 916). Recall rates also increased approximately threefold following a software upgrade on the mammography equipment, requiring per-software version thresholds. Using software-specific thresholds, the AI algorithm would have recalled 277 of 303 (91.4%) screen-detected cancers and 47 of 138 (34.1%) interval cancers. AI performance and thresholds should be validated for new clinical settings before deployment, while quality assurance systems should monitor AI performance for consistency. Keywords: Breast, Screening, Mammography, Computer Applications-Detection/Diagnosis, Neoplasms-Primary, Technology Assessment Supplemental material is available for this article. © RSNA, 2023.