Reviewerly/exHarmony huggingface dataset:
MMEA: Entity Alignment for Multi-modal Knowledge Graph

Entity alignment plays an essential role in the knowledge graph (KG) integration. Though large efforts have been made on exploring the association of relational embeddings between different knowledge graphs, they may fail to effectively describe and integrate the multi-modal knowledge in the real application scenario. To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view. Specifically, we first design a novel multi-modal knowledge embedding method to generate the entity representations of relational, visual and numerical knowledge, respectively. Along this line, multiple representations of different types of knowledge will be integrated via a multi-modal knowledge fusion module. Extensive experiments on two public datasets clearly demonstrate the effectiveness of the MMEA model with a significant margin compared with the state-of-the-art methods.
A novel image encryption method based on fractional discrete Meixner moments

In order to improve the performance of the Meixner moments, we suggest in this article a new set of Fractional Discrete Meixner Polynomials (FrDMPs), a sort of a generalization of the traditional whole order. Firstly, we introduce the necessary algebraic derivatives of FrDMPs using the spectral decomposition of Discrete Meixner Polynomials (DMPs) for any order without numerical fluctuation and always preserving the property of orthogonality thanks to the algorithm of the Gram-Schmidt process. Then, we determine the eigenvalues and the corresponding multiplicity of the Meixner transform matrix. An image encryption and decryption method was proposed based on jigsaw transform and generation of Fractional Discrete Meixner Moments (FrDMMs), in which the image is broken up into bit planes. Each bit plane undergoes a jigsaw transform is divided into blocks and encrypted. The transformed bit planes are combined together and then encrypted using random phase masks and fractional discrete Meixner moments. In addition, we introduce a new encryption and decryption scheme based on the proposed FrDMMs. This scheme has a good encryption effect because the fractional parameters used as key to the encrypted data. Experimental results show that the encryption scheme is sensitive to keys, it is resist a variety of attacks, and decrypted images are almost non-distored, which indicate excellent encryption effect, sufficient security and robustness of the method.
Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer

Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADSUS). Ultrasound images of 159 solid masses were analyzed. Algorithms extracted nine grayscale features and two color Doppler features. These features, along with patient age and BI-RADSUS category, were used to train an AdaBoost ensemble classifier. Though training on computer-extracted grayscale features and color Doppler features each significantly increased performance over that of models trained on clinical features, as measured by the area under the receiver operating characteristic (ROC) curve, training on both color Doppler and grayscale further increased the ROC area, from 0.925 ± 0.022 to 0.958 ± 0.013. Pruning low-confidence cases at 20% improved this to 0.986 ± 0.007 with 100% sensitivity, whereas 64% of the cases had to be pruned to reach this performance without color Doppler. Fewer borderline diagnoses and higher ROC performance were both achieved for diagnostic models of breast cancer on ultrasound by machine learning on color Doppler features.
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them. Although matrix factorization and deep learning based methods have proved effective in user preference modeling, they violate the triangle inequality and fail to capture fine-grained preference information. To tackle this, we develop a distance-based recommendation model with several novel aspects: (i) each user and item are parameterized by Gaussian distributions to capture the learning uncertainties; (ii) an adaptive margin generation scheme is proposed to generate the margins regarding different training triplets; (iii) explicit user-user/item-item similarity modeling is incorporated in the objective function. The Wasserstein distance is employed to determine preferences because it obeys the triangle inequality and can measure the distance between probabilistic distributions. Via a comparison using five real-world datasets with state-of-the-art methods, the proposed model outperforms the best existing models by 4-22% in terms of [email protected] on Top-K recommendation.
Reversible Data Hiding in Encrypted Images Based on Pixel Prediction and Bit-plane Compression

Reversible data hiding in encrypted images (RDHEI) receives growing attention because it protects the content of the original image while the embedded data can be accurately extracted and the original image can be reconstructed lossless. To make full use of the correlation of the adjacent pixels, this paper proposes an RDHEI scheme based on pixel prediction and bit-plane compression. Firstly, to vacate room for data embedding, the prediction error of the original image is calculated and used for bit-plane rearrangement and compression. Then, the image after vacating room is encrypted by a stream cipher. Finally, the additional data is embedded in the vacated room by multi-LSB substitution. Experimental results show that the embedding capacity of the proposed method outperforms the state-of-the-art methods.
Prime Inner Product Encoding for Effective Wildcard-Based Multi-Keyword Fuzzy Search

With the prevalence of cloud computing, a growing number of users are delegating clouds to host their sensitive data. To preserve user privacy, it is suggested that data is encrypted before outsourcing. However, data encryption makes keyword-based searches over ciphertexts extremely difficult. This is even challenging for <i>fuzzy search</i> that allows uncertainties or misspellings of keywords in a query. In this article, we propose a prime inner product encoding (PIPE) scheme, which makes use of the <i>indecomposable</i> property of prime numbers to provide efficient, highly accurate, and flexible multi-keyword fuzzy search. Our main idea is to encode either a query keyword or an index keyword into a vector filled with primes or reciprocals of primes, such that the result of vectors’ inner product is an integer only when two keywords are similar. Specifically, we first construct <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{0}$</tex-math></inline-formula> that is secure in the known ciphertext model. Unlike existing works that have difficulty supporting AND and OR semantics simultaneously, <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{0}$</tex-math></inline-formula> gives users the flexibility to specify different search semantics in their queries. Then, we construct <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{\text{S}}$</tex-math></inline-formula> that subtly adds random noises to a query vector to resist linear analyses. Both theoretical analyses and experiment results demonstrate the effectiveness of our scheme.
Online Mathematics Tutoring During the COVID-19 Pandemic: Recommendations for Best Practices

During the Spring 2020 semester, universities transitioned to online instruction to slow the spread of COVID-19. During this time, mathematics support/tutoring centers also moved their services online. We discuss best practices for online tutoring from the literature and the shared experiences of 28 tutoring center leaders who transitioned to online tutoring during the pandemic. We recommend tutoring centers offer both synchronous video conferencing with shared white boards and an asynchronous or text-based option to increase flexibility and alleviate issues of access. Tutors should be trained to utilize the chosen technology and to adapt their communication to the new environment.
A novel quantum computing model based on entanglement degree

This paper shows a novel quantum computing model that solves quantum computing problems based on the degree of entanglement. We show two main theorems: the first theorem shows the quantum circuit that can be used to quantify the concurrence value between two adjacent qubits. The second theorem shows the quantum circuit of a proposed operator, called [Formula: see text] operator, which can be used to differentiate between the non-orthogonal states in the form [Formula: see text], with arbitrary accuracy, using the concurrence value. Then, the mathematical machinery for implementing the proposed model and its techniques using the circuit model is investigated extensively.
SPORES

Machine learning algorithms are commonly specified in linear algebra (LA). LA expressions can be rewritten into more efficient forms, by taking advantage of input properties such as sparsity , as well as program properties such as common subexpressions and fusible operators. The complex interaction among these properties' impact on the execution cost poses a challenge to optimizing compilers. Existing compilers resort to intricate heuristics that complicate the codebase and add maintenance cost, but fail to search through the large space of equivalent LA expressions to find the cheapest one. We introduce a general optimization technique for LA expressions, by converting the LA expressions into Relational Algebra (RA) expressions, optimizing the latter, then converting the result back to (optimized) LA expressions. The rewrite rules we design in this approach are complete, meaning that any equivalent LA expression is covered in the search space. The challenge is the major size of the search space, and we address this by adopting and extending a technique used in compilers, called equality saturation. Our optimizer, SPORES, uses rule sampling to quickly cover vast portions of the search space; it then uses a constraint solver to extract the optimal plan from the covered space, or alternatively uses a greedy algorithm to shorten compile time. We integrate SPORES into SystemML and validate it empirically across a spectrum of machine learning tasks; SPORES can derive all existing hand-coded optimizations in SystemML, and perform new optimizations that lead to up to 10X speedup.
CAN-GAN: Conditioned-attention normalized GAN for face age synthesis

This work aims to freely translate an input face to an aging face with robust identity preservation, satisfying aging effect and authentic visual appearance. Witnessing the success of GAN in image synthesis, researchers employ GAN to address the problem of face aging synthesis. However, most GAN-based methods hold that the aging changing of all facial regions is equal, which ignores the fact that different facial regions have distinct aging speeds and aging patterns. To this end, we propose a novel Conditioned-Attention Normalization GAN (CAN-GAN) for age synthesis by leveraging the aging difference between two age groups to capture facial aging regions with different attention factors. In particular, a new Conditioned-Attention Normalization (CAN) layer is designed to enhance the aging-relevant information of face, while smoothing the aging-irrelevant information of face by attention map. Since different facial attributes contribute to the discrimination of age groups with divers degrees, we further present a Contribution-Aware Age Classifier (CAAC) that finely measures the importance of face vector's elements in terms of the age classification. Qualitative and quantitative experiments on several commonly-used datasets show the advance of CAN-GAN compared with the other competitive methods.
Phishing website detection based on effective machine learning approach

Phishing a form of cyber-attack, which has an adverse effect on people where the user is directed to fake websites and duped to reveal their sensitive and personal information which includes passwords of accounts, bank details, atm pin-card details etc. Hence protecting sensitive information from malwares or web phishing is difficult. Machine learning is a study of data analysis and scientific study of algorithms, which has shown results in recent times in opposing phishing pages when distinguished with visualization, legal solutions, including awareness workshops and classic anti-phishing approaches. This paper examines the applicability of ML techniques in identifying phishing attacks and report their positives and negatives. In specific, there are many ML algorithms that have been explored to declare the appropriate choice that serve as anti-phishing tools. We have designed a Phishing Classiﬁcation system which extracts features that are meant to defeat common phishing detection approaches. We also make use of numeric representation along with the comparative study of classical machine learning techniques like Random Forest, K nearest neighbours, Decision Tree, Linear SVC classifier, One class SVM classifier and wrapper-based features selection which contains the metadata of URLs and use the information to determine if a website is legitimate or not.
Technological Aspects of Blockchain Application for Vehicle-to-Network

Over the past decade, wireless communication technologies have developed significantly for intelligent applications in road transport. This paper provides an overview of telecommunications-based intelligent transport systems with a focus on ensuring system safety and resilience. In vehicle-to-everything, these problems are extremely acute due to the specifics of the operation of transport networks, which requires the use of special protection mechanisms. In this regard, it was decided to use blockchain as a system platform to support the needs of transport systems for secure information exchange. This paper describes the technological aspects of implementing blockchain technology in vehicle-to-network; the features of such technology are presented, as well as the features of their interaction. The authors considered various network characteristics and identified the parameters that have a primary impact on the operation of the vehicle-to-network (V2N) network when implementing the blockchain. In the paper, an experiment was carried out that showed the numerical characteristics for the allocation of resources on devices involved in organizing V2N communication and conclusions were drawn from the results of the study.
Synthetic minority oversampling of vital statistics data with generative adversarial networks

Abstract Objective Minority oversampling is a standard approach used for adjusting the ratio between the classes on imbalanced data. However, established methods often provide modest improvements in classification performance when applied to data with extremely imbalanced class distribution and to mixed-type data. This is usual for vital statistics data, in which the outcome incidence dictates the amount of positive observations. In this article, we developed a novel neural network-based oversampling method called actGAN (activation-specific generative adversarial network) that can derive useful synthetic observations in terms of increasing prediction performance in this context. Materials and Methods From vital statistics data, the outcome of early stillbirth was chosen to be predicted based on demographics, pregnancy history, and infections. The data contained 363 560 live births and 139 early stillbirths, resulting in class imbalance of 99.96% and 0.04%. The hyperparameters of actGAN and a baseline method SMOTE-NC (Synthetic Minority Over-sampling Technique-Nominal Continuous) were tuned with Bayesian optimization, and both were compared against a cost-sensitive learning-only approach. Results While SMOTE-NC provided mixed results, actGAN was able to improve true positive rate at a clinically significant false positive rate and area under the curve from the receiver-operating characteristic curve consistently. Discussion Including an activation-specific output layer to a generator network of actGAN enables the addition of information about the underlying data structure, which overperforms the nominal mechanism of SMOTE-NC. Conclusions actGAN provides an improvement to the prediction performance for our learning task. Our developed method could be applied to other mixed-type data prediction tasks that are known to be afflicted by class imbalance and limited data availability.
Importance Weighted Gaussian Process Regression for Transferable Driver Behaviour Learning in the Lane Change Scenario

Due to advantages of handling problems with nonlinearity and uncertainty, Gaussian process regression (GPR) has been widely used in the area of driver behaviour modelling. However, traditional GPR lacks the ability of transferring knowledge from one driver to another, which limits the generalisation ability of GPR, especially when sufficient data for driver behaviour modelling are not available. To solve this limitation, in this paper, a novel GPR model, Importance Weighted Gaussian Process Regression (IWGPR) is proposed. The importance weight (IW) represents the probabilistic density ratio between two drivers and the unconstrained least-squares importance fitting (ULSIF) is applied to calculate IW. Meanwhile, an IW-based model selection (IWMS) method is proposed to help the model select optimal parameters. Using IWGPR, sufficient historical data collected from one driver can be used to model another driver with insufficient data, and thus improve the generalisation ability of GPR. To verify the proposed algorithm, a toy regression problem is used to illustrate the working mechanism of IWGPR. With simulated and naturalistic driving data, three experiments for driver behaviour modelling in the lane change scenario, are designed and carried out. Experimental results indicate that IWGPR performs better than GPR when sufficient data are not provided by the new driver, which proves the generalisation ability of IWGPR. Meanwhile, the comparative study between different transferable driver behaviour learning methods is detailed and analysed.
Trust-Based Cloud Machine Learning Model Selection for Industrial IoT and Smart City Services

With machine learning (ML) services now used in a number of mission-critical human-facing domains, ensuring the integrity and trustworthiness of ML models becomes all important. In this work, we consider the paradigm where cloud service providers collect big data from resource-constrained devices for building ML-based prediction models that are then sent back to be run locally on the intermittently connected resource-constrained devices. Our proposed solution comprises an intelligent polynomial-time heuristic that maximizes the level of trust of ML models by selecting and switching between a subset of the ML models from a superset of models in order to maximize the trustworthiness while respecting the given reconfiguration budget/rate and reducing the cloud communication overhead. We evaluate the performance of our proposed heuristic using two case studies. First, we consider Industrial IoT (IIoT) services, and as a proxy for this setting, we use the turbofan engine degradation simulation data set to predict the remaining useful life of an engine. Our results in this setting show that the trust level of the selected models is 0.49%-3.17% less compared to the results obtained using integer linear programming (ILP). Second, we consider smart cities services, and as a proxy of this setting, we use an experimental transportation data set to predict the number of cars. Our results show that the selected model's trust level is 0.7%-2.53% less compared to the results obtained using ILP. We also show that our proposed heuristic achieves an optimal competitive ratio in a polynomial-time approximation scheme for the problem.
Improving data and model quality in crowdsourcing using cross-entropy-based noise correction

Crowdsourcing services provide a fast, efficient, and cost-effective approach to obtaining labeled data, particularly for human-like tasks. In a crowdsourcing scenario, after ground truth inference methods have been employed to obtain integrated instance labels, label noise remains present in the integrated labels. Label noise handling techniques can then be implemented to mitigate the effects of this noise. In this study, we propose a Cross-Entropy-based Noise Correction (CENC) method for crowdsourcing. CENC uses the entropies of the label distributions generated from multiple noisy label sets to filter noisy instances. It then exploits the cross-entropies between each possible true class probability distribution and each predicted class probability distribution to rectify the noisy instances. Using both simulated benchmark data and real-world crowdsourced data, we show that CENC outperforms all other existing state-of-the-art noise correction methods.
Similarities and differences in spatial and non-spatial cognitive maps

Learning and generalization in spatial domains is often thought to rely on a "cognitive map", representing relationships between spatial locations. Recent research suggests that this same neural machinery is also recruited for reasoning about more abstract, conceptual forms of knowledge. Yet, to what extent do spatial and conceptual reasoning share common computational principles, and what are the implications for behavior? Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks. Participant behavior indicated sensitivity to both spatial and conceptual distance, and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel. The same Gaussian Process model best captured human search decisions and judgments in both domains, and could simulate realistic learning curves, where we found equivalent levels of generalization in spatial and conceptual tasks. At the same time, we also find characteristic differences between domains. Relative to the spatial domain, participants showed reduced levels of uncertainty-directed exploration and increased levels of random exploration in the conceptual domain. Participants also displayed a one-directional transfer effect, where experience in the spatial task boosted performance in the conceptual task, but not vice versa. While confidence judgments indicated that participants were sensitive to the uncertainty of their knowledge in both tasks, they did not or could not leverage their estimates of uncertainty to guide exploration in the conceptual task. These results support the notion that value-guided learning and generalization recruit cognitive-map dependent computational mechanisms in spatial and conceptual domains. Yet both behavioral and model-based analyses suggest domain specific differences in how these representations map onto actions.