Reviewerly/exHarmony huggingface dataset:
Personalized recommendation by matrix co-factorization with multiple implicit feedback on pairwise comparison

Recommendation systems have been tremendously important to assist users to find relevant items. With the information-overloaded problem, it becomes crucial to understand users' behavior by learning their preferences during the interaction to construct a profile for exploitation in selecting relevant items. Relevant feedback to capture the users' behavior may not only explicitly exist but also implicitly available. In the real world, it is common that explicit feedback may be unavailable, and the recommender systems rely only on implicit feedback. When only implicit feedback exists, there are interpretability issues on performing recommender systems. In addition, multiple implicit feedback may cause diverse interpretability due to different characteristics and distributions. This study aims to propose a decomposition approach by incorporating joint information rating to improve recommender systems. In prior to the development of the decomposition approach, we develop a framework to explore the proper rating transformation on multiple implicit feedback. The best rating transformation approach is evaluated using the traditional recommender systems and is used as the input for the joint information rating in the decomposition approach using matrix co-factorization. The proposed matrix co-factorization incorporates multiple implicit feedbacks (i.e., frequency and duration). The result proves that incorporating multiple implicit feedbacks with matrix co-factorization improves the recommendation quality.
Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction

This study proposes a crime prediction model according to communes (areas or districts in which the city of Buenos Aires is divided). For this, the Python programming language is used, due to its versatility and wide availability of libraries oriented to Machine Learning. The crimes reported (period 2016–2019) that occurred in the city of Buenos Aires selected to test the model are: homicides, theft, injuries, and robberies. With this, it is possible to generate a crime prediction model according to the city area based on the SEMMA (Sample, Explore, Modify, Model, and Assess) model and after data manipulation, standardization and cleaning; clustering is performed using K-means and subsequently the neural network is generated. For prediction, it is necessary to provide the model with the information corresponding to the predictive characteristics (predict); these characteristics being according to the developed neural network model: year, month, day, time zone, commune, and type of crime.
An Efficient Pairing-Free Certificateless Searchable Public Key Encryption for Cloud-Based IIoT

The Industrial Internet of Things (IIoT), as a special form of Internet of Things (IoT), has great potential in realizing intelligent transformation and industrial resource utilization. However, there are security and privacy concerns about industrial data, which is shared on an open channel via sensor devices. To address these issues, many searchable encryption schemes have been presented to provide both data privacy-protection and data searchability. However, due to the use of expensive pairing operations, most previous schemes were inefficient. Recently, a certificateless searchable public-key encryption (CLSPE) scheme was designed by Lu et al. to remove the pairing operation. Unfortunately, we find that Lu et al.’s scheme is vulnerable to user impersonation attacks. To enhance the security, a new pairing-free dual-server CLSPE (DS-CLSPE) scheme for cloud-based IIoT deployment is designed in this paper. In addition, we provide security and efficiency analysis for DS-CLSPE. The analysis results show that DS-CLSPE can resist chosen keyword attacks (CKA) and has better efficiency than other related schemes.
Privacy and digital ethics after the pandemic

The increasingly prominent — and inescapable — role of digital technologies during the coronavirus pandemic has been accompanied by concerning trends in privacy and digital ethics. But more robust protection of our rights in the digital realm is possible in the future.
Security Privacy and Policy for Cryptographic Based Electronic Medical Information System

With the development of the internet, applications have become complicated, and the relevant technology has diversified. Compared with medical applications, the significance of information technology has been expanding to include clinical auxiliary functions of medical information. This includes electronic medical records, electronic prescriptions, medical information systems, etc. Although research on the data processing structure and format of various related systems is becoming mature, the integration is insufficient. An integrated medical information system with security policy and privacy protection, which combines e-patient records, e-prescriptions, modified smart cards, and fingerprint identification systems, and applies proxy signature and group signature, is proposed in this study. This system effectively applies and saves medical resources—satisfying the mobility of medical records, presenting the function, and security of medicine collection, and avoiding medical conflicts and profiteering to further acquire the maximum effectiveness with the least resources. In this way, this medical information system may be developed into a comprehensive function that eliminates the transmission of manual documents and maintains the safety of patient medical information. It can improve the quality of medical care and indispensable infrastructure for medical management.
Medical image super-resolution using a relativistic average generative adversarial network

The medical imaging technique, e.g., positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance imaging (MRI) is essential for clinical diagnosis and nuclear medicine. However, due to the hardware limitations of scanners, it is always clinically challenging to obtain high-resolution (HR) medical images. With the development of artificial intelligence, image super-resolution has been an effective technique to enhance the spatial resolution of medical images. In this paper, we propose a novel medical image super-resolution method using a relativistic average generative adversarial network (GAN), which consists of a generator and a discriminator for enhancing medical imaging quality in terms of both numerical criteria and visual results. The generator is trained to reconstruct HR images according to low-resolution (LR) counterparts. In contrast, the discriminator is trained to discriminate the probability of whether real HR images are more realistic than reconstructed images, further enhancing visual results. We apply our proposed method to two different public medical datasets, and experimental results show that our proposed method outperforms in terms of visual results, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), model complexity and an additional non-reference image quality assessment metric, compared with other state-of-the-art medical image super-resolution methods.
A Financial data security sharing solution based on blockchain technology and proxy re-encryption technology

For the difficulties in sharing sensitive data among financial institutions, hidden dangers in data security, and high financial risk control costs, the paper proposed a secure data sharing solution based on blockchain technology with proxy re-encryption technology. The solution consists of data sharing model and data sharing protocol. Firstly, using the distributed storage, decentralized management and non-tampering characteristics of the blockchain, we designed a data security sharing model. The model sets access control strategies on the blockchain platform, and uses the blockchain platform and distributed databases to store encrypted data together to prevent sensitive data from being tampered with and leaked. The data sharing protocol uses identity-based proxy re-encryption technology and distributed key generation technology. The protocol selects proxy nodes by the proof-of-stake algorithm, and realizes data sharing among users by re-encrypting sensitive data. The analysis of the solution discusses the correctness and security of the proposed scheme in terms of key generation.
On the Bottleneck of Graph Neural Networks and its Practical Implications

Since the proposal of the graph neural network (GNN) by Gori et al. (2005) and Scarselli et al. (2008), one of the major problems in training GNNs was their struggle to propagate information between distant nodes in the graph. We propose a new explanation for this problem: GNNs are susceptible to a bottleneck when aggregating messages across a long path. This bottleneck causes the over-squashing of exponentially growing information into fixed-size vectors. As a result, GNNs fail to propagate messages originating from distant nodes and perform poorly when the prediction task depends on long-range interaction. In this paper, we highlight the inherent problem of over-squashing in GNNs. We demonstrate that the bottleneck hinders popular GNNs from fitting long-range signals in the training data. We further show that GNNs that absorb incoming edges equally, such as GCN and GIN, are more susceptible to over-squashing than GAT and GGNN. Finally, we show that prior work, which extensively tuned GNN models of long-range problems, suffer from over-squashing, and that breaking the bottleneck improves their state-of-the-art results without any tuning or additional weights. Our code is available at https://github.com/tech-srl/bottleneck/ .
Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning

Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods on a pilot dataset. We present a simple and practical approach to perform segmentation in a 2D, slice-by-slice manner, based on region of interest (ROI) localization, applying an optimized training regime to improve segmentation performance from regions of interest. We start from two popular segmentation networks, the preferred model for medical segmentation, U-Net, and a general-purpose model, DeepLabV3+. Furthermore, we show that ensembling of these two fundamentally different architectures brings constant benefits by testing our approach on two different datasets, the publicly available ACDC challenge, and the imATFIB dataset from our in-house conducted clinical study. Results on the imATFIB dataset show that the proposed approach performs well with the provided training volumes, achieving an average Dice Similarity Coefficient of the whole heart of 89.89% on the validation set. Moreover, our algorithm achieved a mean Dice value of 91.87% on the ACDC validation, being comparable to the second best-performing approach on the challenge. Our approach provides an opportunity to serve as a building block of a computer-aided diagnostic system in a clinical setting.
Fast C&amp;W: A Fast Adversarial Attack Algorithm to Fool SAR Target Recognition With Deep Convolutional Neural Networks

In recent years, deep convolutional neural networks (CNNs) pose superior synthetic aperture radar target recognition (SAR-TR) performance. However, CNN-based SAR classifiers would be vulnerable to adversarial attack (AA) when strong nonlinearity of CNN is contrapuntally utilized by AA. The AA can cause a CNN classifier to produce erroneous predictions with extremely high confidence by injecting a tiny adversarial perturbation to the input SAR images. In this letter, an accelerated SAR-TR AA algorithm is proposed named Fast C&W. We introduce a well-trained deep encoder network to replace the process of searching for the optimal perturbation of the input SAR image iteratively in the vanilla C&W algorithm. In this way, an adversarial perturbation can be generated much faster through the rapid forward mapping during an attack. Meanwhile, as a feature extraction network, the encoder network can learn the separable data region by optimizing the attack loss function. Through the encoder network, the added perturbation energy can be mainly concentrated on a region of target instead of background clutter area. This property would be of advantages in the perturbation location control in an SAR image. In the experiments, we use the proposed AA algorithm to interfere with the deep CNN-based high-accuracy SAR-TR model trained on the moving and stationary target acquisition and recognition (MSTAR) data set, which demonstrates its excellent effectiveness and thousands of times of efficiency improvement.
WITHDRAWN: An effective approach to protect social media account from spam mail – A machine learning approach

The internet users in the world are rising rapidly. This technology given the opportunities to grow in the field of business, education, sports, entertainment and social media. With a huge amount of person getting connected to Internet, the security threats which leads massive harms are increasing also. In the last few years, we have been observing a quick development in information being produced and shared in social media. The usage of social media increased exponentially, and rapid growth of users are unexpected in the history of technology insurgency. The uninterrupted growth in social media and connected technologies has managed to an ecosystem where users with different culture and geolocation are connected on various social media platforms. While the explosive growth of internet users in Social Media results a high risk for user’s data due to cyber security breaches and data theft. Now a days, hackers use spam emails to data theft of social media’s profile. They send the spam mails with malicious links, after clicking on the link, details of the profiles are sent to hackers. In this Paper, we discuss the various steps taken to protect the social media profile and propose spam filter using decision tree model. We compare the testing and training accuracy of various algorithms on the emails_small and emails_full datasets and try to explain which one is better with different dataset. We evaluate the performance of Decision Tree (DT) based classifier with Information gain criterion which achieve accuracy 90% and Gini impurity criterion which achieve accuracy 89%. Evaluate the ROC curves for DT and K-Nearest Neighbors (KNN) classifier over emails_full dataset to test the idea of the classifier quality and can be used to quantify the area under the curve.
Accounting for health inequities in the design of contact tracing interventions: A rapid review

BackgroundContact tracing has been a central control measure for coronavirus disease 2019 (COVID-19) transmission. However, without consideration of the needs of specific populations, public health interventions can exacerbate health inequities.AimThe purpose of this rapid review was to determine if and how health inequities were included in the design of contact tracing interventions in epidemic settings.MethodsA search of the electronic databases MEDLINE and Web of Science was conducted. The following inclusion criteria were applied for article selection: (1) described the design of contact tracing interventions, (2) published between 2013 and 2020 in English, French, Spanish, Chinese, or Portuguese, (3) and included at least 50% of empiricism, according to the Automated Classifier of Texts on Scientific Studies (ATCER) tool. Various tools were used to extract data.ResultsFollowing screening of the titles and abstracts of 230 articles, 39 met the inclusion criteria. Only seven references were retained after full text review. None of the selected studies considered health inequities in the design of contact tracing interventions.ConclusionsThe use of tools/concepts for incorporating health inequities, such as the REFLEX-ISS tool, and ‘proportionate universalism’ when designing contact tracing interventions, would enable practitioners, decision-makers, and researchers to better consider health inequities.
A Multistage Hybrid Model ICEEMDAN-SE-VMD-RDPG for a Multivariate Solar Irradiance Forecasting

Accuracy of solar irradiance forecasting is imperative for the effective utilization and integration of solar energy into the power system.To forecast global horizontal irradiance based on a multivariate meteorological data; this study first evaluates five standalone models, including recurrent deterministic policy gradient (RDPG), long short term memory (LSTM) neural network, extreme gradient boosting (XGB), Gaussian process regression (GPR), and support vector regression (SVR).The RDPG model outperforms the standalone counterparts by demonstrating 2.485 W/m 2 , 20.591 W/m 2 , 18.316 W/m 2 , and 23.176 W/m 2 reductions in RMSE compared to the LSTM, XGB, GPR, and SVR models.Afterward, the performance of the RDPG model is further enhanced, by developing two-stage hybrid models, including improved complete ensemble empirical mode decomposition with additive noise-RDPG (ICEEMDAN-RDPG) and variational mode decomposition-RDPG (VMD-RDPG).The subsequent construction of the hybrid model ICEEMDAN-SE-VMD-RDPG (ISVR) results in the further improvement of the two-stage hybrid models.The ISVR surpasses all the established models including VMD-RDPG, ICEEMDAN-RDPG, RDPG, LSTM, XGB, GPR, and SVR by displaying 18.401 W/m 2 , 13.908 W/m 2 , 33.223 W/m 2 , 53.111 W/m 2 , 67.704 W/m 2 , 67.502 W/m 2 and 69.943 W/m 2 , respectively, decrease in RMSE.The overall results are enlightening and reveal the extension feasibility of the IVSR model for the other forecasting tasks, e.g., renewable energy integration and electrical load forecasting.
Defensive functions provoke similar psychophysiological reactions in reaching and comfort spaces

Abstract The space around the body crucially serves a variety of functions, first and foremost, preserving one’s own safety and avoiding injury. Recent research has shown that emotional information, in particular threatening facial expressions, affects the regulation of peripersonal-reaching space (PPS, for action with objects) and interpersonal-comfort space (IPS, for social interaction). Here we explored if emotional facial expressions may similarly or differently affect both spaces in terms of psychophysiological reactions (cardiac inter-beat intervals: IBIs, i.e. inverse of heart rate; Skin Conductance Response amplitude: SCR amplitude) and spatial distance. Through Immersive Virtual Reality technology, participants determined reaching-distance (PPS) and comfort-distance (IPS) from virtual confederates exhibiting happy/angry/neutral facial expressions while being approached by them. During these interactions, spatial distance and psychophysiological reactions were recorded. Results revealed that when interacting with angry virtual confederates the distance increased similarly in both comfort-social and reaching-action spaces. Moreover, interacting with virtual confederates exhibiting angry rather than happy or neutral expressions provoked similar psychophysiological activations (SCR amplitude, IBIs) in both spaces. Regression analyses showed that psychophysiological activations, particularly SCR amplitude in response to virtual confederates approaching with angry expressions, were able to predict the increase of PPS and IPS. These findings suggest that self-protection functions could be the expression of a common defensive mechanism shared by social and action spaces.
Assessment of breast positioning criteria in mammographic screening: Agreement between artificial intelligence software and radiographers

To determine the agreement between artificial intelligence software (AI) and radiographers in assessing breast positioning criteria for mammograms from standard digital mammography and digital breast tomosynthesis.Assessment of breast positioning was performed by AI and by four radiographers in pairs of two on 156 examinations of women screened in Bergen, April to September 2019, as part of BreastScreen Norway. Ten criteria were used; three for craniocaudal and seven for mediolateral-oblique view. The criteria evaluated the appearance of the nipple, breast rotation, pectoral muscle, inframammary fold and pectoral nipple line. Intraclass correlation and Cohen's kappa coefficient (κ) were used to investigate the correlation and agreement between the radiographer's assessments and AI.The intraclass correlation for the pectoral nipple line between the radiographers and AI was >0.92. A substantial to almost perfect agreement (κ > 0.69) was observed between the radiographers and AI on the nipple in profile criterion. We observed a slight to moderate agreement for the other criteria (κ = 0.06-0.52) and generally a higher agreement between the two pairs of radiographers (mean κ = 0.70) than between the radiographers and AI (mean κ = 0.41).AI has great potential in evaluating breast position criteria in mammography by reducing subjectivity. However, varying agreement between radiographers and AI was observed. Standardized and evidence-based criteria for definitions, understandings and assessment methods are needed to reach optimal image quality in mammography.
The Red Colobuses Monkey: A New Nature–Inspired Metaheuristic Optimization Algorithm

The presented study suggests a new nature-inspired metaheuristic optimization algorithm referred to as Red Colobuses Monkey (RCM) that can be used for optimization problems; this algorithm mimics the behavior related to red monkeys in nature.In preparation for proving the suggested algorithm's advantages, a set of standard unconstrained and constrained test functions is employed, sixty-four of identified test functions utilized in optimization were applied as benchmarks for checking the RCM performance.The solutions have also been upgrading their positions based on the optimal solution, which was reached thus far.Also, RCM can replace the worst red monkey by the best child found so far to give an extra enhancement to the solutions.Also, comparative performance checks with Biogeography-Based Optimizer (BBO), Artificial-Bee-Colony (ABC), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA) were done.The acquired results showed that RCM is competitive in comparison to the chosen metaheuristic algorithms.
Comprehensive Analysis of Secure Data Aggregation Scheme for Industrial Wireless Sensor Network

As an Industrial Wireless Sensor Network (IWSN) is usually deployed in a harsh or unattended environment, the privacy security of data aggregation is facing more and more challenges. Currently, the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating, alternately, the aim at enhancing the security of data. The performances of the secure data aggregation protocols are the trade-off of several metrics, which involves the transmission/fusion, the energy efficiency and the security in Wireless Sensor Network (WSN). Unfortunately, there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether in IWSN or in WSN. In consideration of IWSN, we firstly review the security requirements and techniques in WSN data aggregation in this paper. Then, we give a holistic overview of the classical secure data aggregation protocols, which are divided into three categories: hop-by-hop encrypted data aggregation, end-to-end encrypted data aggregation and unencrypted secure data aggregation. Along this way, combining with the characteristics of industrial applications, we analyze the pros and cons of the existing security schemes in each category qualitatively, and realize that the security and the energy efficiency are suitable for IWSN. Finally, we make the conclusion about the techniques and approach in these categories, and highlight the future research directions of privacy preserving data aggregation in IWSN.