Reviewerly/exHarmony huggingface dataset:
A generative image inpainting network based on the attention transfer network across layer mechanism

To solve the problems of blur, artifacts, and semantic inaccuracy of existing deep learning-based image inpainting algorithm when the large-area irregular defect area images are repaired, combining the U-NET architecture and the idea of a generative adversarial network, a generative image inpainting network based on the attention transfer cross layer mechanism is proposed. The network is divided into two parts: generator and discriminator; the generator proposes a attention transfer network cross layer based on the U-NET architecture; after the input image is encoded, the attention transfer network cross layer is used to reconstruct the encoding feature map of each layer. The decoding is performed by fusing the skip connection with the corresponding potential feature in the multi-scale decoder, solving the information loss between the codec network layers. Moreover, each decoding layer of the multi-scale decoder is prompted to generate consistent content, and finally the repaired image is obtained. After the decoding is completed, the decoding feature map of each layer is converted into a multi-scale RGB image, and the reconstruction L1 loss is applied on each scale; by minimizing the adversarial loss, the discriminator can determine the authenticity of the repaired image; the perceptual loss and style loss are introduced to judge the output image, so as to restrict the generator to generate more realistic and reasonable content. A qualitative and quantitative comparative analysis is performed on the Celeba, Façade, and Places2 with the current mainstream image inpainting algorithms. The results show that the repair results in this paper are better than others in terms of mean L1 loss, PSNR, and SSIM, and more in line with human visual characteristics. The attention transfer network cross layer proposed in this paper can effectively reconstruct the more detailed coding feature map, and it can play an active guiding role in the coding process by using skip connection; the image inpainting network can generate content that is highly consistent with the structure and semantics of the real image when repairing large-area irregular defect areas, which is more in line with the human visual experience.
Analysis of COVID-19-Impacted Zone UsingMachine Learning Algorithms

Covid-19, first detected at Wuhan in late 2019, has now spread all over the world among many developed and developing countries. As a result of this, World Health Organization (WHO) declared COVID-19 a pandemic on March 11, 2020. Until now, many people have been infected with this coronavirus, some of them are recovering and others causing death. The concern of this paper will be the comparative study of KNN and Naïve Bayes algorithms via the Weka tool's Explorer and Experimenter interfaces, which will tell algorithm is more articulated to be used to evaluate the accuracy of death and recovery of infected COVID-19 patients, so we could estimate that the region will belong to which zone. The COVID-19 dataset to be used in this paper includes details about people who have visited Wuhan during this pandemic or who are from Wuhan and are affected by COVID-19 are fever, cold, cough, breathing difficulties, and many more. The main goal here will be to help users extract valuable data from the dataset and define a predictive algorithm for it. From the results shown, it can be concluded that KNN would demonstrate better precision than Naïve Bayes.
An Intelligent Self-Service Vending System for Smart Retail

The traditional weighing and selling process of non-barcode items requires manual service, which not only consumes manpower and material resources but is also more prone to errors or omissions of data. This paper proposes an intelligent self-service vending system embedded with a single camera to detect multiple products in real-time performance without any labels, and the system realizes the integration of weighing, identification, and online settlement in the process of non-barcode items. The system includes a self-service vending device and a multi-device data management platform. The flexible configuration of the structure gives the system the possibility of identifying fruits from multiple angles. The height of the system can be adjusted to provide self-service for people of different heights; then, deep learning skill is applied implementing product detection, and real-time multi-object detection technology is utilized in the image-based checkout system. In addition, on the multi-device data management platform, the information docking between embedded devices, WeChat applets, Alipay, and the database platform can be implemented. We conducted experiments to verify the accuracy of the measurement. The experimental results demonstrate that the correlation coefficient R2 between the measured value of the weight and the actual value is 0.99, and the accuracy of non-barcode item prediction is 93.73%. In Yangpu District, Shanghai, a comprehensive application scenario experiment was also conducted, proving that our system can effectively deal with the challenges of various sales situations.
Machine Learning and Deep Learning in Steganography and Steganalysis

Steganography is the way to conceal data inside a normal (non-mystery) record to keep it from discovery. While encryption expects to conceal the substance of data, steganography means to shroud the presence of data. Information security is significant when touchy information is communicated over the internet. Steganography and steganalysis methods can take care of the issue of copyright, possession, and discovery of malevolent information. Steganography is to conceal mystery information without contortion, and steganalysis is to distinguish the presence of shrouded information. In this chapter, steganography and steganalysis methods are depicted along with AI structures to show that AI systems can be utilized to recognize the mystery information covered up in the picture utilizing steganography calculations.
ExGSense

Immersive face-to-virtual-face telecommunication is one unique use case for virtual reality (VR) technologies. Existing camera-based telephony systems cannot be used for such immersive VR video chat, due to the physical occlusions of head-mounted displays (HMDs) and/or unwieldy positioning of cameras. To address these, we present ExGSense, a new VR input modality that can sense and reconstruct both upper and lower facial gestures, by only using lightweight biopotential sensors embedded within the HMDs. We optimize the sensor arrangement based on facial anatomy and employ a multiview classification pipeline to exploit the multiple dimensions of signal features. We thus enable ExGSense to detect whole facial gestures by using a sparse set of biopotential transducers. We prototyped ExGSense and evaluated its performance with 42 facial gestures and across different users. We showed a 93% accuracy for user-specific evaluation, and 77% accuracy for user-independent evaluation with low calibration overhead. We believe ExGSense constitutes a promising input modality for immersive VR interactions.
Social media use and anti-immigrant attitudes: evidence from a survey and automated linguistic analysis of Facebook posts

Social media has a role to play in shaping the dynamic relations between immigrants and citizens. This study examines the effects of threat perceptions, consumptive and expressive use of social media, and political trust on attitudes against immigrants in Singapore. Study 1, based on a survey analysis (N = 310), suggests that symbolic but not realistic threat perception, is positively associated with anti-immigrant attitudes. The consumptive use of social media and political trust is negatively related to anti-immigrant attitudes. Moderation analyses suggest that consumptive social media use has negative consequences for individuals with increased symbolic threat perception and high political trust. But is there a correspondence between consumptive and expressive use of social media in terms of predicting prejudicial attitudes? Study 2 benchmarks the survey findings against participants' opinion expression via Facebook posts (N = 146,332) discussing immigrants. Automated linguistic analyses reveal that self-reported survey measures correlate with the expressive use of social media for discussing immigrants. Higher anti-immigrant attitudes are associated with higher negative sentiment, anger, and swear words in discussing immigrants. The findings highlight the need to pay attention to the combined influence of social media use and individual political beliefs when analyzing intergroup relations.
Texture Enhanced Generative Adversarial Network For Stain Normalisation In Histopathology Images

Digitised histopathology image analysis has drawn researchers’ attention over recent years. However, stain variation due to several factors can be a significant hurdle for the diagnosis process. Stain normalisation can be used as an effective method to address this issue but most existing methods require careful selection of a reference image. In this work, we propose a texture enhanced pix2pix generative adversarial network (TESGAN), which takes higher contrast hematoxylin components as input and includes a novel loss function to guide the generator to produce higher quality images without the need for reference images. We implement our method as a pre-processing approach for an isocitrate dehydrogenase (IDH) mutation status classification task. Evaluated on The Cancer Genome Atlas (TCGA) glioma cohorts, the proposed model achieves Area Under Curve (AUC) of 0.967, which substantially outperforms the current state-of the-art.
Semantic boundary enhancement and position attention network with long-range dependency for semantic segmentation

Some factors hinder advancements in semantic segmentation techniques, including intraclass inconsistency and interclass distinction. In addition, existing methods with self-attention mechanisms disregard semantic boundaries in highest-level feature maps, which hinders further performance improvement. To improve semantic segmentation performance, we propose a semantic boundary enhancement and position network (SBEPNet) that enhances feature maps with semantic boundaries and captures useful contextual information along these boundaries. Specifically, edge enhancement is employed to extract semantic boundaries, which are used to enhance the high-level feature maps. The enhanced feature maps are input into the boundary enhancement attention module to guide the learning of the discriminative long-range dependencies along object boundaries. The resultant feature maps are further refined by fusing the feature maps from the position attention module and the original feature maps. The experimental results verify the effectiveness of SBEPNet, which demonstrates the high potential for improving the generalizability of semantic segmentation. • Semantic boundary detection is embedded in enhancement of high-level feature maps. • Long-range discriminative spatial dependencies is learnt over semantic boundaries. • The experimental results verify that the effectiveness of the new method.
Realizing value from voluntary business-government information sharing through blockchain-enabled infrastructures: The case of importing tires to the Netherlands using TradeLens

Blockchain technology has emerged as new technology and governments are now exploring its potential for realizing value. In recent years, studies have focused on identifying opportunities and barriers of blockchain-based applications for government, and multiple piloting initiatives have been started to experiment and test the potential of this technology. Nevertheless, many of these blockchain initiatives are in the pilot stage. For the initiatives that have reached a large-scale implementation or production stage, these are largely on a local (municipal) or national level. There is limited research on operational blockchain-enabled infrastructures implemented on a global level and how they can be used by government organizations to create value. This is an area that we set out to explore with this paper. In this paper, we take the specific focus on business-government information sharing via a global blockchain-enabled platform (TradeLens) to create value for business and government. As a case study, we focus on examining the case of importing tires from China to the Netherlands and the value creation processes for the tire importer and customs enabled by information sharing via the TradeLens platform. From a theoretical perspective, building on two models that have been published earlier, we propose a framework that explicitly allows to link and reason about business-government information sharing enabled by blockchain, how it enables value creation for both business and government, and the technical blockchain design choices that are linked to this value creation process.
Bayesian mixture modelling for glass refractive index measurement

Glass is a common type of physical evidence in forensic science. Broken glass recovered from a suspect may have similar physical characteristics to glass collected at a crime scene and therefore can be used as evidence. Statistical treatment of this evidence involves computing a measure of the weight of evidence. This may be done in a Bayesian framework that incorporates information from the circumstances of the crime. One of the most crucial quantities in this calculation is the assessment of the relative rarity of the characteristics of the glass, essentially the probability distribution used to model the physical characteristics of recovered glass. Typical characteristics used in casework are the elemental composition of glass and the refractive index measurement. There is a considerable body of scientific literature devoted to the modelling of this information. For example a kernel density estimation has been used to model the background population of glass based on the refractive index measurement and a multivariate Gaussian finite mixture model has been used to model the elemental composition of glass. In this paper, we present an alternative approach, the Dirichlet Process Mixture Model, to model the glass refractive index measurement in a Bayesian methodology. A key advantage is that using this method allows us to model the probability density distribution of refractive index measurements in a more flexible way.
An efficient data flow material model based cloud authentication data security and reduce a cloud storage cost using Index-level Boundary Pattern Convergent Encryption algorithm

The cloud server stores its unique DE-duplicate data replication cloud storage system as outsourced only data copies. Deduplication cloud storage audits can verify the integrity of data stored in the cloud, while the cloud calculates the need to maintain only one copy of the copied file. The existing de-duplication auditing solutions for cloud storage are vulnerable to brute-force dictionary attacks, which is a disclosure leakage of user privacy and more time consumption to find a duplicate files in cloud. Users must check the integrity of lightweight cloud computing data to create data authentication and retrieve files in the cloud. The proposed system uses a secure Array Preserving Triple Data Encryption (AP3DE) algorithm to verify with confidence the expertise of aggregate technology. In this method to analysis the user upload document before store on cloud. The aggregator to provide an authentication access the data access role to cloud user. Cloud protects the privacy, confidentiality, and integrity of data stored in the cloud by generating the code of the authentication file and the encryption key of the file through the proposed system test. The Index-level Boundary Pattern Convergent Encryption (IBPCE) algorithm integrates dynamic data handling and data exclusion capabilities for secure cloud storage privacy security public audits. The cloud data security using a Convergent international data encryption algorithm used to encrypted data. It is a 64 bit plaintext and cipher text blocks is controlled by 128 bit key. Analysis of safety and efficiency shows that the proposed mechanism is highly efficient and safe. In this proposed IBPCE method to manage and reduce the cloud storage compare to other existing method.
Adversarial attacks by attaching noise markers on the face against deep face recognition

Deep neural networks (DNNs) have become increasingly effective in difficult machine learning tasks, such as image classification, speech recognition, and natural language processing. Face recognition (FR) using DNNs shows high performance and is widely used in various domains such as payment systems and immigration inspection. However, DNNs are vulnerable to adversarial examples generated by adding a small amount of noise to an original sample, resulting in misclassification by the DNNs. In this study, we attempt to deceive state-of-the-art FR by attaching noise markers on a face in the real world. To deceive an FR model in the real world, we address challenges in the attack process, including selection of locations of noise markers, the differences between colors of digital noise markers and those of noise markers after printing, the differences between the colors of noise markers that are attached to the face and those of noise markers after a picture is taken, and the differences between the locations of digital noise markers and those of noise markers that are attached to the face. In experiments, we generate noise markers considering these challenges and show that state-of-the-art FR can be deceived by attaching a maximum of 10 noise markers to a face. This can cause a security risk for FR models using DNNs.
A stacking weighted k-Nearest neighbour with thresholding

In Multi-label problems, each instance is associated with a set of predefined labels. Binary Relevance, as a common approach, uses one binary classifier for each label and ignores the probable dependencies between some labels. Stacked-Binary Relevance (SBR) was proposed to consider the label dependencies, by augmenting a second layer of binary models using the predicted labels of the first level binary models as additional features. By reusing all predicted labels, SBR implicitly assumes full dependencies between labels, which is not usually a true assumption in the real world. Moreover, SBR uses a constant threshold in decision functions of the binary models, while adjusting the threshold for each label specially for imbalanced ones can improve the performance. This paper proposes a k-Nearest Neighbor stacking method that adjusts the thresholds in decision functions of the binary classifiers and uses a feature-weighted distance measure to reduce the effect of irrelevant labels in stacking. The method can leverage positive/negative and symmetric/asymmetric label dependencies expressed as feature weights. Also, it can tackle the main shortcomings of SBR (revealed in the existence of irrelevant labels and imbalanced data). Using 22 multi-label datasets, the proposed method is assessed and outperforms state-of-the-art methods presented in the literature.
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation

Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instances with uniform distribution, which, however, will severely affect a model’s convergence, stability, and even recommendation accuracy. A promising solution for this problem is to over-sample the “difficult” (a.k.a. informative) instances that contribute more on training. But this will increase the risk of biasing the model and leading to non-optimal results. Moreover, existing samplers are either heuristic, which require domain knowledge and often fail to capture real “difficult” instances, or rely on a sampler model that suffers from low efficiency. To deal with these problems, we propose CoSam, an efficient and effective collaborative sampling method that consists of (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency, and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling. Correspondingly, we derive a fast reinforced training algorithm of our framework to boost the sampler performance and sampler-recommender collaboration. Extensive experiments on four real-world datasets demonstrate the superiority of the proposed collaborative sampler model and integrated sampler-recommender framework.
A Knowledge Organization System for the United Nations Sustainable Development Goals

This paper presents a formal knowledge organization system (KOS) to represent the United Nations Sustainable Development Goals (SDGs). The SDGs are a set of objectives adopted by all United Nations member states in 2015 to achieve a better and sustainable future. The developed KOS consists of an ontology that models the core elements of the Global SDG indicator framework, which currently includes 17 Goals, 169 Targets and 231 unique indicators, as well as more than 450 related statistical data series maintained by the global statistical community to monitor progress towards the SDGs, and of a dataset containing these elements. In addition to formalizing and establishing unique identifiers for the components of the SDGs and their indicator framework, the ontology includes mappings of each goal, target, indicator and data series to relevant terms and subjects in the United Nations Bibliographic Information System (UNBIS) and the EuroVoc vocabularies, thus facilitating multilingual semantic search and content linking.
Exploring Genetic-histologic Relationships in Breast Cancer

The advent of digital pathology presents opportunities for computer vision for fast, accurate, and objective solutions for histopathological images and aid in knowledge discovery. This work uses deep learning to predict genomic biomarkers- TP53 mutation, PlK3CA mutation, ER status, PR status, HER2 status, and intrinsic subtypes, from breast cancer histopathology images. Furthermore, we attempt to understand the underlying morphology as to how these genomic biomarkers manifest in images. Since gene sequencing is expensive, not always available, or even feasible, predicting these biomarkers from images would help in diagnosis, prognosis, and effective treatment planning. We outperform the existing works with a minimum improvement of 0.02 and a maximum of 0.13 AUROC scores across all tasks. We also gain insights that can serve as hypotheses for further experimentations, including the presence of lymphocytes and karyorrhexis. Moreover, our fully automated workflow can be extended to other tasks across other cancer subtypes.
Toward a quantum repeater protocol based on the coherent state approach

The aim of this paper is to swap the entanglement between two separate long distant locations. The well-known entangled coherent states as two-mode continuous-variable states are very interesting in quantum teleportation and entanglement swapping processes. To make our investigation more realistic, by using such entangled states as the building block of our quantum repeater protocol, the effect of decoherence on the swapped entanglement is also considered. We explicitly establish our model for four locations, moreover, we find that our model can be extended to 2N locations, where N = 3; 4; ... . Consequently, we could introduce this model as a quantum repeater which is helpful for entanglement swapping to enough long distances.