Reviewerly/exHarmony huggingface dataset:
An Efficient Dual-image Reversible Data Hiding Scheme Based on Exploiting Modification Direction

Reversible data hiding (RDH) subtly alter the cover image for embedding the secret data, after extracting the secret data, the cover image can also be completely restored. In this work, we propose a novel RDH scheme based on Exploiting Modification Direction (EMD) with dual images. The characteristics of EMD matrix is exploited, one secret bit and a base-5 digit can be concealed in a cover pixel to generate a pixel pair which will be assigned to two stego images. Without any overhead message, the reversibility can be achieved. In the experiment, the proposed scheme outperforms some state-of-the-art methods in terms of measuring in embedding ratio (ER) or peak-signal-to-noise ratio (PSNR). In addition, the proposed scheme has good performance on resisting static attacks on pixel-value differencing histogram (PDH) and regular singular (RS) analysis.
Generation of Werner-like states via a two-qubit system plunged in a thermal reservoir and their application in solving binary classification problems

Abstract We present a theoretical scheme for the generation of stationary entangled states. To achieve the purpose we consider an open quantum system consisting of a two-qubit plunged in a thermal bath, as the source of dissipation, and then analytically solve the corresponding quantum master equation. We generate two classes of stationary entangled states including the Werner-like and maximally entangled mixed states. In this regard, since the solution of the system depends on its initial state, we can manipulate it and construct robust Bell-like state. In the continuation, we analytically obtain the population and coherence of the considered two-qubit system and show that the residual coherence can be maintained even in the equilibrium condition. Finally, we successfully encode our two-qubit system to solve a binary classification problem. We demonstrate that, the introduced classifiers present high accuracy without requiring any iterative method. In addition, we show that the quantum based classifiers beat the classical ones.
Giant Panda Identification

The lack of automatic tools to identify giant panda makes it hard to keep track of and manage giant pandas in wildlife conservation missions. In this paper, we introduce a new Giant Panda Identification (GPID) task, which aims to identify each individual panda based on an image. Though related to the human re-identification and animal classification problem, GPID is extraordinarily challenging due to subtle visual differences between pandas and cluttered global information. In this paper, we propose a new benchmark dataset iPanda-50 for GPID. The iPanda-50 consists of 6, 874 images from 50 giant panda individuals, and is collected from panda streaming videos. We also introduce a new Feature-Fusion Network with Patch Detector (FFN-PD) for GPID. The proposed FFN-PD exploits the patch detector to detect discriminative local patches without using any part annotations or extra location sub-networks, and builds a hierarchical representation by fusing both global and local features to enhance the inter-layer patch feature interactions. Specifically, an attentional cross-channel pooling is embedded in the proposed FFN-PD to improve the identify-specific patch detectors. Experiments performed on the iPanda-50 datasets demonstrate the proposed FFN-PD significantly outperforms competing methods. Besides, experiments on other fine-grained recognition datasets (i.e., CUB-200-2011, Stanford Cars, and FGVC-Aircraft) demonstrate that the proposed FFN-PD outperforms existing state-of-the-art methods.
HERisk: An improved spatio-temporal human health risks assessment software

Risk assessment is considered an essential tool to assist in the management and mitigation of polluted areas, especially those associated with economic activities that significantly degrade the environment, such as mining. However, most of the methodologies of risk assessment adopt the deterministic approach of using a fixed value for ascertaining the hazards derived from exposure to chemical pollutants. However, this is not the case of the Human, Ecological and Radiological Risk (HERisk) code, which allows space-time assessments of ecological, radiological, and human health risks. Indeed, this work aims to describe this new software (enhanced version of HHRISK), which not only improves the performance of the code but also increases its applicability and versatility. To showcase its usefulness in evaluating ecological pollution and human health risk were studied the contents of potentially toxic elements (Co, Cu, Fe, Mn, Ni, Pb, and Zn) in soils and surface waters from the nickel mining area in the municipality of Itagibá (Bahia, Brazil). The obtained results showed that metals are non-homogeneous distributed, suggesting the presence of local enrichment sources, mainly related to human activities. The statistical analyses carried out revealed that mining and agricultural activities are possibly responsible for the contents of these pollutants in both soils and surface waters. The calculated ecological indices of pollution confirmed anthropogenic pollution around the mining area, especially in the locations closest to sterile waste piles. The results of the human health risk assessment revealed that the ingestion of meat and contaminated water are the main routes for entering the potentially toxic elements to the human body and that Co is the chemical specie that poses the highest risk in the entire region. The hazard index (HI) values indicated that the whole area around the mine should be considered as a high risk for human health.
Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer

Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice. However, the shortage of annotation and the variance of the data among different vendors and medical centers restrict the performance of advanced deep learning methods. In this work, we present a fully automatic method to segment cardiac structures including the left (LV) and right ventricle (RV) blood pools, as well as for the left ventricular myocardium (MYO) in MRI volumes. Specifically, we design a semi-supervised learning method to leverage unlabelled MRI sequence timeframes by label propagation. Then we exploit style transfer to reduce the variance among different centers and vendors for more robust cardiac image segmentation. We evaluate our method in the M&Ms challenge ( https://www.ub.edu/mnms/ ), ranking 2nd place among 14 competitive teams.
Semantic-Driven Face Hallucination Based on Residual Network

State-of-the-art face hallucination methods utilize convolutional neural networks to restore high-quality high-resolution (HR) face images from low-resolution (LR) face images by employing appearance knowledge. However, most of these solutions only deploy the appearance knowledge (such as face attributes) to modulate the first layer of convolution feature with channel-wise shifting and ignore the face-identity information while emphasizing the vision quality of the generated result. To address the above issues, we propose a semantic-driven residual network based on a generative adversarial network to restore the HR face image with proper identity from the LR face image. Firstly, precise semantic information is explored based on improved U-net. Secondly, the semantic information is concatenated into the residual blocks of the reconstruction module, which is exceptionally efficient in modulating the extracted feature and guiding the generation of HR face images. Finally, the proposed methods can obtain the pleasuring SR face image with a similar identity of LR face image, after adversarially optimizing the designed loss functions. Extensive experiments demonstrate that the proposed semantic-driven residual network performs against the state-of-the-arts both quantitatively and qualitatively.
TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes

The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of the elderly. In this work, we investigate the use of sensor data and deep learning to recognize those patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduce novel visual feature extraction methods for locomotion data. Our solution relies on locomotion trace segmentation, image-based extraction of salient features from locomotion segments, and vision-based deep learning. We carried out extensive experiments with a large dataset acquired in a smart-home test bed from 153 seniors, including people with cognitive diseases. Results show that our system can accurately recognize the cognitive status of the senior, reaching a macro- F1 score of 0.873 for the three categories that we target: cognitive health, mild cognitive impairment, and dementia. Moreover, an experimental comparison shows that our system outperforms state-of-the-art methods.
Multi-label Sentiment Analysis Base on BERT with modified TF-IDF

With the evolution of web technologies, sentiment analysis, especially aspect-based sentiment analysis (ABSA), as a technology to identify and extract user opinions, has attracted widespread attention. According to the characteristics of user comments, we convert ABSA problem into a multi-label classification problem, and propose a classification model based on BERT with modified TF-IDF for ABSA. In the feature extraction phase, a modified TF-IDF method is proposed to better reflect the words' importance in multi-label classification problem by calculating the different weights in individual classes. And a new feature is generated by combining the BERT embedding with modified TF-IDF for classification task. Then the feature is input into a fully connected layer to fine-tune BERT model for the multi-label classification task. The effectiveness of supervised TF-IDF and proposed model is validated by experiments of multilabel classification on a restaurant reviews dataset.
Progressive Hybrid Classifier Ensemble for Imbalanced Data

The class imbalance problem has posed a leading challenge in real-world applications. Traditional methods focus on either the data level or algorithm level to solve the binary classification problem on imbalanced data, and seldom consider searching an effective transformation for classification. Besides, the undersampling process adopted in them is always subjective and unilateral. To address the above issues, we first propose a hybrid classifier ensemble (HCE) framework to conduct binary imbalanced data classification, which mainly includes a metric-based data space transformation (MDST) and an adaptive two-stage undersampling process (ATUP). The MDST aims to find a more appropriate embedding space for original imbalance data sets, and the ATUP considers both informative and representative samples to generate balanced data sets. Furthermore, we design a progressive HCE (PHCE) framework to improve the performance of HCE by utilizing a progressive mechanism with local and global evaluation criteria to select ensemble members. Extensive comparative experiments conducted on 28 real-world data sets exhibit that our method PHCE outperforms the majority of imbalance ensemble classification approaches.
Interpersonalizing Intimate Museum Experiences

We reflect on two museum visiting experiences that adopted the strategy of interpersonalization in which one visitor creates an experience for another. In the Gift app, visitors create personal mini-tours for specific others. In Never let me go, one visitor controls the experience of another by sending them remote instructions as they follow them around the museum. By reflecting on the design of these experiences and their deployment in museums we show how interpersonalization can deliver engaging social visits in which visitors make their own interpretations. We contrast the approach to previous research in customization and algorithmic personalization. We reveal how these experiences relied on intimacy between pairs of visitors but also between visitors and the museum. We propose that interpersonalization requires museums to step-back to make space for interpretation, but that this then raises the challenge of how to reintroduce the museum’s own perspective. Finally, we articulate strategies and challenges for applying this approach.
Ensemble Learning for Spectral Clustering

Ensemble clustering has attracted much attention in machine learning and data mining for the high performance in the task of clustering. Spectral clustering is one of the most popular clustering methods and has superior performance compared with the traditional clustering methods. Existing ensemble clustering methods usually directly use the clustering results of the base clustering algorithms for ensemble learning, which cannot make good use of the intrinsic data structures explored by the graph Laplacians in spectral clustering, thus cannot obtain the desired clustering result. In this paper, we propose a new ensemble learning method for spectral clustering-based clustering algorithms. Instead of directly using the clustering results obtained from each base spectral clustering algorithm, the proposed method learns a robust presentation of graph Laplacian by ensemble learning from the spectral embedding of each base spectral clustering algorithm. Finally, the proposed method applies k-means on the spectral embedding obtain from the learned graph Laplacian to get clusters. Experimental results on both synthetic and real-world datasets show that the proposed method outperforms other existing ensemble clustering methods.
A Data Selection Methodology to Train Linear Regression Model to Predict Bitcoin Price

Bitcoin is the most popular and valuable cryptocurrency in the financial market which attracts traders for investment and opens new research opportunities for researchers. Many research works have been done on bitcoin price prediction with different machine learning prediction algorithms. Researchers take relevant features from the dataset which have strong correalation with bitcoin prices and select random data chunks to train and test their model. Randomly selected data to train the model, may cause inappropriate results and reduce the accuracy of price prediction. In this paper, we investigate a proper data selection method to train a prediction model. We apply our proposed methodology to train a simple linear regression prediction algorithm. We predict bitcoin price for 7 days with the linear regression model. When we train the linear regression model with an appropriate data chunk identified by our methodologies, we find acceptable results for the prediction. The percentage error method is applied for error calculation which finds the accuracy is 96.97%. In the end of this manuscript, we conclude our work with future improvements.
Personalized Recommendation Method for English Teaching Resources Based on Artificial Intelligence Technology

Abstract Traditional recommendation methods decompose keywords and key sentences less frequently, resulting in low accuracy of recommended content. Therefore, a personalized recommendation method for English teaching resources based on artificial intelligence technology is proposed. Web mining technology is used to collect user personalized data and mine teaching resource rules and patterns that can represent user characteristics. Artificial intelligence technology is used to simulate the intelligent behavior of users searching for resources, to obtain words with a higher frequency. The words with higher relative contribution are selected as keywords, thereby calculating the similarity between keywords and user characteristics, then the teaching resources with high similarity are taken as the recommendation target, and the recommended format is selected to implement resource recommendation. Experimental results show that compared with traditional methods, this method improves the accuracy of recommended content and enables teaching resources to better meet the individual needs of users.
PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing

Reconstructing RGB image from RAW data obtained with a mobile device is related to a number of image signal processing (ISP) tasks, such as demosaicing, denoising, etc. Deep neural networks have shown promising results over hand-crafted ISP algorithms on solving these tasks separately, or even replacing the whole reconstruction process with one model. Here, we propose PyNET-CA, an end-to-end mobile ISP deep learning algorithm for RAW to RGB reconstruction. The model enhances PyNET, a recently proposed state-of-the-art model for mobile ISP, and improve its performance with channel attention and subpixel reconstruction module. We demonstrate the performance of the proposed method with comparative experiments and results from the AIM 2020 learned smartphone ISP challenge. The source code of our implementation is available at https://github.com/egyptdj/skyb-aim2020-public .
To what extent is researchers' data-sharing motivated by formal mechanisms of recognition and credit?

Data sharing by researchers is a centerpiece of Open Science principles and scientific progress. For a sample of 6019 researchers, we analyze the extent/frequency of their data sharing. Specifically, the relationship with the following four variables: how much they value data citations, the extent to which their data-sharing activities are formally recognized, their perceptions of whether sufficient credit is awarded for data sharing, and the reported extent to which data citations motivate their data sharing. In addition, we analyze the extent to which researchers have reused openly accessible data, as well as how data sharing varies by professional age-cohort, and its relationship to the value they place on data citations. Furthermore, we consider most of the explanatory variables simultaneously by estimating a multiple linear regression that predicts the extent/frequency of their data sharing. We use the dataset of the State of Open Data Survey 2019 by Springer Nature and Digital Science. Results do allow us to conclude that a desire for recognition/credit is a major incentive for data sharing. Thus, the possibility of receiving data citations is highly valued when sharing data, especially among younger researchers, irrespective of the frequency with which it is practiced. Finally, the practice of data sharing was found to be more prevalent at late research career stages, despite this being when citations are less valued and have a lower motivational impact. This could be due to the fact that later-career researchers may benefit less from keeping their data private.
Symbolic annihilation through design: Pregnancy loss in pregnancy-related mobile apps

Pregnancy is a significant life event for many. Using mobile apps to manage pregnancies is common. Pregnancy loss is a common complication associated with stigma that impacts one’s wellbeing, relationships, sense of self, and more. While pregnancy loss is important to many experiencing it, it is unclear to what extent pregnancy-related mobile apps account for it and consider associated users’ needs in their designs. We conducted a feature analysis of 166 pregnancy-related apps. We found that their main features focus on information, tracking, reminders, and social contact, and that 72% of these apps do not account for loss, 18% explicitly account for it, and 10% passively do so. We theorize this lack of consideration as symbolic annihilation through design. We argue that such annihilation is partly shaped by intensified mothering ideologies, further stigmatizes pregnancy loss, and perpetuates a normative, linear portrait of the pregnancy experience which is harmful and marginalizing.
Certificateless Designated Verifier Anonymous Aggregate Signature Scheme for Healthcare Wireless Sensor Networks

The contradiction between the growing demand for healthcare and the lack of professional medical staff urgently needs to be resolved. Healthcare wireless sensor technology is an important option to resolve this contradiction. Patients use wearable devices to collect various health indicators and send them to doctors for diagnosis in a timely manner. This breaks the time and space constraints in traditional medical methods. Patient's data is transmitted over a public network. Therefore, protecting data security and patient privacy has become an urgent issue for healthcare wireless sensor networks (HWSNs). In this article, we first presented a system model for certificateless designated verifier anonymous aggregate signature (CL-DVAAS) scheme for HWSNs, and investigated the security attributes. We then gave a concrete construction and showed the security proofs. We finally compared the new scheme with several other schemes, and the results show that the new scheme is more computationally efficient.
Deep similarity learning for disease prediction

Deep learning is helping many medical experts as well as researchers to discover important insights from healthcare data and provide better medical facilities. Patients' risk assessment of developing a certain disease and providing personalized healthcare is an important research area. The current study presents a comprehensive review of deep learning architectures and how they can be used in learning representation and similarity between a pair of patients for disease prediction. We call this model a deep similarity learning model for disease prediction. As a demonstration of functionality, the deep learning architectures are trained on electronic health records to perform disease prediction. The experimental results obtained encourage us to use one of the suitable models in calculating similarity as future work.
Deep learning based data-adaptive descriptor for non-rigid multi-modal medical image registration

Non-rigid multi-modal medical image registration is a challenging task in the field of medical image processing and analysis because of the unpredictable complicated deformations and the nonfunctional intensity relation between images. The structural representation based registration (SRR) method can address these factors to some extent by transforming this challenging problem into the mono-modal image registration. However, the existing SSR algorithms generally rely on handcrafted features for structural representation, which tend to lead to inaccurate registered results. To address this problem, this paper has proposed a novel Laplacian Eigenmaps based deep learning network for 2D medical image registration. The proposed network is firstly used to extract the intrinsic features of different modal medical images. Then the self-similarity of extracted features is explored to construct the learning based data-adaptive descriptor (LDAD) for structural representations. The sum of squared differences (SSD) between the structural representations of the reference and floating images is used as the similarity metric to construct the objective function for image registration. The experimental results show that the LDAD based registration method can produce visually better registered results and provide higher registration accuracy for different test images than several state-of-the-art registration methods in terms of target registration error.