Reviewerly/exHarmony huggingface dataset:
Unsupervised domain adaptation based on the predictive uncertainty of models

Unsupervised domain adaptation (UDA) aims to improve the prediction performance in the target domain under distribution shifts from the source domain. The key principle of UDA is to minimize the divergence between the source and the target domains. To follow this principle, many methods employ a domain discriminator to match the feature distributions. Some recent methods evaluate the discrepancy between two predictions on target samples to detect those that deviate from the source distribution. However, their performance is limited because they either match the marginal distributions or measure the divergence conservatively. In this paper, we present a novel UDA method that learns domain-invariant features that minimize the domain divergence. We propose model uncertainty as a measure of the domain divergence. Our UDA method based on model uncertainty (MUDA) adopts a Bayesian framework and provides an efficient way to evaluate model uncertainty by means of Monte Carlo dropout sampling. Experiment results on image recognition tasks show that our method is superior to existing state-of-the-art methods. We also extend MUDA to multi-source domain adaptation problems.
Relation-aware attention for video captioning via graph learning

Video captioning often uses an attentive encoder-decoder as the baseline model. However, the conventional attention mechanism still remains two problems. First, the attended visual feature is often irrelevant to the target word state, because the attention process only uses the unidirectional flow from vision to linguistics, while lacking the reverse flow. Second, each attention result is independent, because it is computed only based on the previous word states while not considering the attention information from the past and future. This does not suit the attention habits of human beings. In this paper, we improve the conventional attention mechanism to a relation-aware attention mechanism. To this end, we propose two kinds of graph learning strategies, namely the linguistics-to-vision heterogeneous graph (HTG) and the vision-to-vision homogeneous graph (HMG). The HTG aims to enhance the inter-relation of attention by reversely modeling the relation of each word with respect to every attended visual feature, supporting proper semantic alignment in between. The HMG aims to enhance the intra-relation of attention by capturing the relations among all of the attended visual features, which can leverage the attention information from the past and future to guide the current attention process. Extensive experiments on two public datasets show that our proposed method not only significantly improves the baseline model, but also outperforms state-of-the-art methods.
EIAASG: Emotional Intensive Adaptive Aspect-Specific GCN for sentiment classification

Deep learning techniques and attention schemes are used by many researchers for classifying the sentiments. Retrieval of semantic relationship between the aspects with words of context will improve the classification accuracy. This was done by Aspect-Specific Graph Convolutional Networks (ASGCN) which utilises aspect-specific relationships and attention scheme by researchers. Long-range dependencies and sensitive-important words are missing in these methods. This work proposes two novel approaches for improving the effectiveness of sentiment classification. First, we propose a method, Adaptive Aspect-Specific GCN (AASGCN) for enhancing ASGCN by incorporating adaptive weights into ASGCN to better capturing of the semantic meaning of the opinion target. Second, we introduce an Emotional Intensive Sentiment Reasoning (EISR) that incorporates emotional intensive information into the mechanism. We experiment our proposed work along with many existing work's datasets such as LAP14 (Pontiki et al., 2014), TWITTER (Dong et al., 2014), REST14 (Pontiki et al., 2014), REST15 (Pontiki et al., 2015), and REST16 (Pontiki et al., 2016). The results prove that AASGCN performs well than the range of state-of-the-art models and can be substantially improved by incorporating the two approaches.
Taxonomy of Cyber Threat Intelligence Framework

Cyber threat intelligence is commonly utilised in the cyber security domain. Pyramid of Pain, MITRE ATT&CK framework, Cyber Kill Chain and The Diamond Model of Intrusion Analysis are examples of cyber security framework or concepts that are used for analysing cyber threat intelligence data. However, the cyber security frameworks or concepts do not divulge in the details on activities execution for harnessing cyber threat intelligence data as they are not exclusively developed for cyber threat intelligence purposes. Researchers have developed the cyber threat intelligence framework that delineate cyber threat intelligence activities and manages resources e.g. human and technologies to deal with cyber threats. Thus, this research reviews cyber threat intelligence framework to understand the architecture of the solution. Our observations have identified three main components that are common for the cyber threat intelligence framework. Furthermore, the discussion on this topic is a reference for cyber security scholars and practitioners that plan to understand and design cyber threat intelligence framework that fit their requirements.
Rare disperse elements in epithermal deposit: Insights from LA–ICP–MS study of sphalerite at Dalingkou, South China

Rare disperse elements (RDEs) have received substantial attention due to their intense application in industry. Sphalerite from some Pb–Zn deposits of MVT, skarn, VMS, SEDEX and epithermal type, containing considerable RDEs contents that could be used as by-product. Here, the concentrations of RDEs in sphalerite from the Dalingkou deposit is examined by in situ laser-ablation inductively coupled mass spectroscopy (LA–ICP–MS), to discuss their substitution mechanism in sphalerite, controlling factors, and genesis model in epithermal system. The Dalingkou deposit is the largest epithermal Ag–Zn–Pb deposit in Zhejiang, South China, with proven reserves of 702 t Ag, 0.10 Mt Pb, 0.15 Mt Zn, and potential resources for RDEs. Orebodies are predominately hosted by intermediate–acid Yanshanian volcanics and subvolcanics and controlled by N–S and NW–SE trending faults. The ores are dominated by abundant base metal sulfides with gangue minerals mainly composed of quartz and rhodochrosite. The hydrothermal activities occurred in four stages: pre-ore stage (stage 1), Pb–Zn mineralization stage (stage 2), Ag–Pb–Zn mineralization stage (stage 3) and post-ore stage (stage 4). Two types of sphalerite were observed (i.e. Sp2 from the stage 2, and Sp3 from the stage 3), according to crosscutting and overprinting relationships of ores. LA–ICP–MS analysis shows that, relative to the Sp3, the Sp2 has higher contents of Fe, Mn, Cd, and In, but slightly lower contents of Ga and Ge. Correlation analyses reveal the incorporation of RDE into sphalerite via both simple substitution, such as Zn2+ ↔ (Cd2+), and coupled substitution, such as 3Zn2+ ↔ In3+ + Sn3+ + □ (vacancy), 3Zn2+ ↔ Ge4+ + 2Cu+, and 2Zn2+↔ (Ag, Cu)+ + Ga3+. Geothermometry of impurity elements (Ga, Ge, In, Mn, and Fe) in sphalerite suggests that the sulfides precipitated from an intermediate- to low-temperature hydrothermal system. It further reveals temperature decrease from stage 2 to stage 3, suggesting fluid mixing between magmatic fluids and meteoric water was involved. Deep parental magma may have contributed In and Cd during the mineralization stage, whereas Ga and Ge were released from surrounding rocks due to hydrothermal alteration, despite that they are not enriched in orebodies. The LA–ICP–MS analysis also reveals that the In, Ga and Ge contents in both Sp2 and Sp3 are too low to be economic. However, compared with sphalerite Cd concentrations from different deposit types worldwide, the Sp2 and Sp3 in Dalingkou have enough high Cd grade as an economic by-product, suggesting that the Dalingkou deposit is also a Cd deposit.
GCHA-Net: Global context and hybrid attention network for automatic liver segmentation

Liver segmentation is a critical step in liver cancer diagnosis and surgical planning. The U-Net's architecture is one of the most efficient deep networks for medical image segmentation. However, the continuous downsampling operators in U-Net causes the loss of spatial information. To solve these problems, we propose a global context and hybrid attention network, called GCHA-Net, to adaptive capture the structural and detailed features. To capture the global features, a global attention module (GAM) is designed to model the channel and positional dimensions of the interdependencies. To capture the local features, a feature aggregation module (FAM) is designed, where a local attention module (LAM) is proposed to capture the spatial information. LAM can make our model focus on the local liver regions and suppress irrelevant information. The experimental results on the dataset LiTS2017 show that the dice per case (DPC) value and dice global (DG) value of liver were 96.5% and 96.9%, respectively. Compared with the state-of-the-art models, our model has superior performance in liver segmentation. Meanwhile, we test the experiment results on the 3Dircadb dataset, and it shows our model can obtain the highest accuracy compared with the closely related models. From these results, it can been seen that the proposed model can effectively capture the global context information and build the correlation between different convolutional layers. The code is available at the website: https://github.com/HuaxiangLiu/GCAU-Net.
CIA: A Collaborative Integrity Auditing Scheme for Cloud Data With Multi-Replica on Multi-Cloud Storage Providers

The emergence of cloud storage has solved many pain points of the traditional storage model. However, the issue of cloud storage data integrity - whether the cloud storage provider has kept the data intact - has raised concerns about cloud storage. Integrity auditing of cloud storage data allows users to know the integrity of the outsourced data without downloading it in its entirety. However it is not enough to know its integrity. Multi-replicas, as a common means of redundancy, improves the reliability of cloud storage data. And storing multi-replicas on multi-cloud storage providers (CSPs) enhances this feature. In a multi-replica multi-CSPs scenario, if the independence of CSPs is given full play and auditing is performed among CSPs, not only the introduction of third party auditor (TPA) can be eliminated, but also the tag generation, which is a huge computational overhead, can be removed. Inspired by this, in this paper we propose a new model for remote data integrity auditing: CIA (Collaborative Integrity Auditing). In addition to the reduction in computational overhead, the proposed scheme provides unprecedented support for free data blocking. The proposed scheme employs only hash functions in the calculation, which has a negligible computational overhead compared to the traditional bilinear pairing-based schemes. Theoretical analysis and experimental results show that the proposed scheme provides high efficiency and flexibility with security assurance, and can be used as a lightweight alternative to traditional remote data integrity auditing schemes in multi-replica multi-CSP scenarios.
A disentangled linguistic graph model for explainable aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims to use interactions between aspect terms and their contexts to predict sentiment polarity for given aspects in sentences. Current mainstream approaches use deep neural networks (DNNs) combined with additional linguistic information to improve performance. DNN-based methods, however, lack explanation and transparency to support predictions, and no existing model completely solves the trade-off between explainability and performance. In contrast, most previous studies explain the relationship between input and output by attribution; however, this approach is insufficient to mine hidden semantics from abstract features. To overcome the aforementioned limitations, we propose a disentangled linguistic graph model (DLGM) to enhance transparency and performance by guiding the signal flow. First, we propose a disentangled linguistic representation learning module that extracts a specific linguistic property via neurons to help capture finer feature representations. To further boost explainability, we propose a supervised disentangling module, in which labeled linguistic data help reduce information redundancy. Finally, a cross-linguistic routing mechanism is introduced into the signal propagation of linguistic chunks to overcome the defect of distilling information in an intralinguistic property. Quantitative and qualitative experiments verify the effectiveness and superiority of the proposed DLGM in sentiment polarity classification and explainability.
ReCasNet: Improving consistency within the two-stage mitosis detection framework

Mitotic count (MC) is an important histological parameter for cancer diagnosis and grading, but the manual process for obtaining MC from whole-slide histopathological images is very time-consuming and prone to error. Therefore, deep learning models have been proposed to facilitate this process. Existing approaches utilize a two-stage pipeline: the detection stage for identifying the locations of potential mitotic cells and the classification stage for refining prediction confidences. However, this pipeline formulation can lead to inconsistencies in the classification stage due to the poor prediction quality of the detection stage and the mismatches in training data distributions between the two stages. In this study, we propose a Refine Cascade Network (ReCasNet), an enhanced deep learning pipeline that mitigates the aforementioned problems with three improvements. First, window relocation was used to reduce the number of poor quality false positives generated during the detection stage. Second, object re-cropping was performed with another deep learning model to adjust poorly centered objects. Third, improved data selection strategies were introduced during the classification stage to reduce the mismatches in training data distributions. ReCasNet was evaluated on two large-scale mitotic figure recognition datasets, canine cutaneous mast cell tumor (CCMCT) and canine mammary carcinoma (CMC), which resulted in up to 4.8% percentage point improvements in the F1 scores for mitotic cell detection and 44.1% reductions in mean absolute percentage error (MAPE) for MC prediction. Techniques that underlie ReCasNet can be generalized to other two-stage object detection pipeline and should contribute to improving the performances of deep learning models in broad digital pathology applications.
Domain-Specific Bias Filtering for Single Labeled Domain Generalization

Conventional Domain Generalization (CDG) utilizes multiple labeled source datasets to train a generalizable model for unseen target domains. However, due to expensive annotation costs, the requirements of labeling all the source data are hard to be met in real-world applications. In this paper, we investigate a Single Labeled Domain Generalization (SLDG) task with only one source domain being labeled, which is more practical and challenging than the CDG task. A major obstacle in the SLDG task is the discriminability-generalization bias: the discriminative information in the labeled source dataset may contain domain-specific bias, constraining the generalization of the trained model. To tackle this challenging task, we propose a novel framework called Domain-Specific Bias Filtering (DSBF), which initializes a discriminative model with the labeled source data and then filters out its domain-specific bias with the unlabeled source data for generalization improvement. We divide the filtering process into (1) feature extractor debiasing via k-means clustering-based semantic feature re-extraction and (2) classifier rectification through attention-guided semantic feature projection. DSBF unifies the exploration of the labeled and the unlabeled source data to enhance the discriminability and generalization of the trained model, resulting in a highly generalizable model. We further provide theoretical analysis to verify the proposed domain-specific bias filtering process. Extensive experiments on multiple datasets show the superior performance of DSBF in tackling both the challenging SLDG task and the CDG task.
Blockchain based Securing Medical Records in Big Data Analytics

The patient privacy is danger while medical records and data are transmitted or share beyond secure big data. This is because violations push them to the margins and they begin to avoid fully revealing their stages. This kind of stages contains negative impact in scientific investigate. To overcome this issue, Secure Block Chain System for Managing and Sharing Electronic Medical Records in Big Data Field is proposed. In this manuscript, a Cryptographic Hash Generator (CHG) technique based Secured and Trusted Data storage and transmission using Block Chain (BC) in Hadoop Distributed File System (HDFS). Initially, the Big data collected from the health care center is partitioned into sensitive and insensitive data. Block chain system utilizes an asymmetric cryptography for validating transactions authentication. Here, the user key is created through secured bitwise cryptographic hash generator (CHG) while there is required to fetch the newly record for usage. In block chain system, when a user seeking data from a healthcare application have forward a request to CHG. The message is send back to the user with a secret key for confirmation. The key can be decrypted or even denied access if only a valid user allows the user to link to this cluster. Only sensitive data were selected to the process of encryption for the process of encryption, this CHG technique employs the Discrete Shearlet Transform (DST) for encrypting the data, and the data’s are warehoused in the block chain to upgrade the level of security. And the insensitive data are put directly on the Hadoop Distributed File System. During the verification process, CHG is utilized for creating the request forward through the user. The operator creates the purpose of remote key to create the block (request) and signing the request using transaction private key, then forward to request queuing. To validate a request, the request from the queue is supplied first and an Improved Grey wolf Optimization algorithm (IGWO) is utilized to determine the optimal request that is fetch through the consensus node for initiating the process of validation. After accepting the user’s request, access is given to the user associated with input or requested data, then the verified request is set to broadcast. The proposed method is executed in JAVA and Hadoop platform. Experimental results show that the proposed BC-CHG-DST-IGWOA shows better performances of higher Efficiency 20.14%, 31.25%, 24.33%, 14.69%, lower time 16.12%, 15.09%, 21.36%, 46.26% compared with the existing methods, such as medical records managing and securing blockchain based system supported by genetic algorithm with discrete wavelet transform (BC-SMR-BD-GA-DWT), DQN-based optimization framework to secure shared blockchain systems (BC-SMR-BD-DQNSB), Hyper ledger blockchain enabled secure medical record management along deep learning-based diagnosis model (BC-SMR-BD-HBESDM-DLD), Secure attribute-based signature scheme with multiple authorities for blockchain in electronic health records system (BC-SMR-BD-MA-ABS) respectively.
Manipal-UAV person detection dataset: A step towards benchmarking dataset and algorithms for small object detection

Intelligent UAV video analysis has drawn the attention of many researchers due to the increasing demand for unmanned aerial vehicles (UAVs) in computer vision-related applications. Applications such as search and rescue, the military, and surveillance demand automatic detection of human targets in large-scale UAV images, which is very challenging due to the small size and inadequate feature representation of person objects. Despite the significant advancements in generic object detection tasks, the performance of the state-of-the-art small object detection algorithms falls below the satisfactory level due to the lack of a representative dataset and the limited information available for small objects. To facilitate advancements in UAV and small object detection research, we present a Manipal-UAV person detection dataset1 collected from two UAVs flying at varying altitudes, locations, and weather conditions. The dataset contains 13,462 sampled images from 33 videos having 1,53,112 person object instances. The videos are captured in an unconstrained environment with complex scenes covering small objects of varying scales, poses, illumination, and occlusion, making person detection extremely challenging on this newly created dataset. This article compares the characteristics of the Manipal-UAV dataset with the standard VisDrone and Okutama datasets having aerial view person objects. In addition, it provides baseline evaluation results of the various state-of-the-art object detection algorithms applied to the newly created Manipal-UAV Person detection dataset. The dataset is made publicly available at https://github.com/Akshathakrbhat/Manipal-UAV-Person-Dataset.
The role of digital transformation in improving the efficacy of healthcare: A systematic review

Healthcare is of high importance, and today it has been undergoing a profound change and being reshaped due to the adoption of digital transformations (DT). Nevertheless, considering the quick evolution of these technologies and its massive adoption, the current literature is still incipient on how innovations are translated into practice, with limited empirical evidence of its effectiveness, problems, and applications in digital healthcare. In fact, despite a considerably growing body of research literature on the use of new technologies in healthcare there is no systematic analysis on the practice of DT in healthcare systems. We believe that a literature review to fill this gap is important and pertinent, especially through the lens of applications, benefits, opportunities, and threats to analyze the status of these implementations and its impact in healthcare systems. We chose to perform a systematic literature review because it is a methodology that reviews previous literature and bring the field together, with rigor, concision, and minimal room for subjectivity, offering transparency in data collection and results with a higher level of objectivity and reproducibility. We expect to advance the understanding of the development of technical innovations in healthcare ecosystem and support scholars and practitioners to further explore empirical evidence of the effectiveness of digital applications in healthcare. We also offer a research agenda based on a bibliographic coupling analysis that identifies theoretical trends in the field researched and indicated four clusters with the most relevant themes of literature: IoT and Cloud computing, Data security, Healthcare structure, and Blockchain.