Reviewerly/exHarmony huggingface dataset:
An Experimental Study With Imbalanced Classification Approaches for Credit Card Fraud Detection

Credit card fraud is a criminal offense.It causes severe damage to financial institutions and individuals.Therefore, the detection and prevention of fraudulent activities are critically important to financial institutions.Fraud detection and prevention are costly, time-consuming, and labor-intensive tasks.A number of significant research works have been dedicated to developing innovative solutions to detect different types of fraud.However, these solutions have been proved ineffective.According to Cifa, 33 305 cases of credit card identity fraud were reported between January and June in 2018. 1 Various weaknesses of existing solutions have been reported in the literature.Among them all, the imbalance classification is the most critical and well-known problem.Imbalance classification consists of having a small number of observations of the minority class compared with the majority in the data set.In this problem, the ratio fraud: legitimate is very small, which makes it extremely difficult for the classification algorithm to detect fraud cases.In this paper, we will conduct a rigorous experimental study with the solutions that tackle the imbalance classification problem.We explored these solutions along with the machine learning algorithms used for fraud detection.We identified their weaknesses and summarized the results that we obtained using a credit card fraud labeled dataset.According to this paper, imbalanced classification approaches are ineffective, especially when the data are highly imbalanced.This paper reveals that the existing approaches result in a large number of false alarms, which are costly to financial institutions.This may lead to inaccurate detection as well as increasing the occurrence of fraud cases.
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling

Zhifang Fan, Zhen Wu, Xin-Yu Dai, Shujian Huang, Jiajun Chen. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
AI in Health: State of the Art, Challenges, and Future Directions

Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health.The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future.Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed.The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed.Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.
Cloud-Assisted EHR Sharing With Security and Privacy Preservation via Consortium Blockchain

The sharing of electronic health records (EHRs) has great positive significance for research of disease and doctors' diagnosis. In recent years, cloud-based electronic medical record sharing scheme has brought a lot of conveniences, but the centralization of cloud exposes threats inevitably to data security and privacy preservation. Blockchain technology can be seen as a promising solution to address these problems on account of its unique propertis of decentration, anonymity, unforgeability and verifiability. In this paper, we propose a blockchain based secure and privacy-preserving EHR sharing protocol. Data requester can search desired keyword from data provider to find relevant EHRs on the EHR consortium blockchain and get the re-encryption ciphertext from cloud server after getting the data owner's authorization. The scheme mainly uses searchable encryption and conditional proxy re-encryption to realize data security, privacy preservation, and access control. Furthermore, proof of authorization is designed as the consensus mechanism for consortium blockchain to guarantee system's availability. Security analysis demonstrates that the proposed protocol can achieve security goals. Besides, we emulate the cryptographic primitives and implement the proposed scheme on Ethereum platform. Performance evaluation shows that the proposed scheme has high computational efficiency.
Privacy-Preserving Image Retrieval for Medical IoT Systems: A Blockchain-Based Approach

With the advent of medical IoT devices, the types and volumes of medical images have significantly increased. Retrieving of medical images is of great importance to facilitate disease diagnosis and improve treatment efficiency. However, it may raise privacy concerns from individuals, since medical images contain patients' sensitive and private information. Existing studies on retrieval of medical data either fail to protect sensitive information of medical images or are limited to a single image data provider. In this article, we propose a blockchain-based system for medical image retrieval with privacy protection. We first describe the typical scenarios of medical image retrieval and summarize the corresponding requirements in system design. Using the emerging blockchain techniques, we present the layered architecture and threat model of the proposed system. In order to accommodate large-size images with storage-constrained blocks, we capture a carefully selected feature vector from each medical image and design a customized transaction structure, which protects the privacy of medical images and image features. We also discuss the challenges and opportunities of future research.
Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing

The goal of Knowledge Tracing (KT) is to estimate how well students have mastered a concept based on their historical learning of related exercises. The benefit of knowledge tracing is that students’ learning plans can be better organised and adjusted, and interventions can be made when necessary. With the recent rise of deep learning, Deep Knowledge Tracing (DKT) has utilised Recurrent Neural Networks (RNNs) to accomplish this task with some success. Other works have attempted to introduce Graph Neural Networks (GNNs) and redefine the task accordingly to achieve significant improvements. However, these efforts suffer from at least one of the following drawbacks: (1) they pay too much attention to details of the nodes rather than to high-level semantic information; (2) they struggle to effectively establish spatial associations and complex structures of the nodes; and (3) they represent either concepts or exercises only, without integrating them. Inspired by recent advances in self-supervised learning, we propose a Bi-Graph Contrastive Learning based Knowledge Tracing (Bi-CLKT) to address these limitations. Specifically, we design a two-layer comparative learning scheme based on an “exercise-to-exercise” (E2E) relational subgraph. It involves node-level contrastive learning of subgraphs to obtain discriminative representations of exercises, and graph-level contrastive learning to obtain discriminative representations of concepts. Moreover, we designed a joint contrastive loss to obtain better representations and hence better prediction performance. Also, we explored two different variants, using RNN and memory-augmented neural networks as the prediction layer for comparison to obtain better representations of exercises and concepts respectively. Extensive experiments on four real-world datasets show that the proposed Bi-CLKT and its variants outperform other baseline models.
Evaluating reinforcement learning agents for anatomical landmark detection

Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. An artificial RL agent learns to identify the optimal path to the landmark by interacting with an environment, in our case 3D images. Furthermore, we investigate the use of fixed- and multi-scale search strategies with novel hierarchical action steps in a coarse-to-fine manner. Several deep Q-network (DQN) architectures are evaluated for detecting multiple landmarks using three different medical imaging datasets: fetal head ultrasound (US), adult brain and cardiac magnetic resonance imaging (MRI). The performance of our agents surpasses state-of-the-art supervised and RL methods. Our experiments also show that multi-scale search strategies perform significantly better than fixed-scale agents in images with large field of view and noisy background such as in cardiac MRI. Moreover, the novel hierarchical steps can significantly speed up the searching process by a factor of 4-5 times.
Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation

In this paper, we present a novel convolutional neural network architecture to segment images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed model is an extension of the U-net that embeds a cardiac shape prior and involves a loss function tailored to the cardiac anatomy. Since the shape prior is computed offline only once, the execution of our model is not limited by its calculation. Our system takes as input raw magnetic resonance images, requires no manual preprocessing or image cropping and is trained to segment the endocardium and epicardium of the left ventricle, the endocardium of the right ventricle, as well as the center of the left ventricle. With its multiresolution grid architecture, the network learns both high and low-level features useful to register the shape prior as well as accurately localize the borders of the cardiac regions. Experimental results obtained on the Automatic Cardiac Diagnostic Challenge - Medical Image Computing and Computer Assisted Intervention (ACDC-MICCAI) 2017 dataset show that our model segments multislices CMRI (left and right ventricle contours) in 0.18 s with an average Dice coefficient of 0.91 and an average 3-D Hausdorff distance of 9.5 mm.
Interpolation consistency training for semi-supervised learning

We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT encourages the prediction at an interpolation of unlabeled points to be consistent with the interpolation of the predictions at those points. In classification problems, ICT moves the decision boundary to low-density regions of the data distribution. Our experiments show that ICT achieves state-of-the-art performance when applied to standard neural network architectures on the CIFAR-10 and SVHN benchmark datasets. Our theoretical analysis shows that ICT corresponds to a certain type of data-adaptive regularization with unlabeled points which reduces overfitting to labeled points under high confidence values.
Light Field Spatial Super-Resolution Using Deep Efficient Spatial-Angular Separable Convolution

Light field (LF) photography is an emerging paradigm for capturing more immersive representations of the real-world. However, arising from the inherent trade-off between the angular and spatial dimensions, the spatial resolution of LF images captured by commercial micro-lens based LF cameras are significantly constrained. In this paper, we propose effective and efficient end-to-end convolutional neural network models for spatially super-resolving LF images. Specifically, the proposed models have an hourglass shape, which allows feature extraction to be performed at the low resolution level to save both computational and memory costs. To fully make use of the four-dimensional (4-D) structure information of LF data in both spatial and angular domains, we propose to use 4-D convolution to characterize the relationship among pixels. Moreover, as an approximation of 4-D convolution, we also propose to use spatialangular separable (SAS) convolutions for more computationallyand memory-efficient extraction of spatial-angular joint features. Extensive experimental results on 57 test LF images with various challenging natural scenes show significant advantages from the proposed models over state-of-the-art methods. That is, an average PSNR gain of more than 3.0 dB and better visual quality are achieved, and our methods preserve the LF structure of the super-resolved LF images better, which is highly desirable for subsequent applications. In addition, the SAS convolutionbased model can achieve 3× speed up with only negligible reconstruction quality decrease when compared with the 4-D convolution-based one. The source code of our method is online available at https://github.com/spatialsr/DeepLightFieldSSR.
Shaping the Metaverse into Reality: A Holistic Multidisciplinary Understanding of Opportunities, Challenges, and Avenues for Future Investigation

The term metaverse is described as the next iteration of the Internet. Metaverse is a virtual platform that uses extended reality technologies, i.e. augmented reality, virtual reality, mixed reality, 3D graphics, and other emerging technologies to allow real-time interactions and experiences in ways that are not possible in the physical world. Companies have begun to notice the impact of the metaverse and how it may help maximize profits. The purpose of this paper is to offer perspectives on several important areas, i.e. marketing, tourism, manufacturing, operations management, education, the retailing industry, banking services, healthcare, and human resource management that are likely to be impacted by the adoption and use of a metaverse. Each includes an overview, opportunities, challenges, and a potential research agenda.
Cyclical Annealing Schedule: A Simple Approach to Mitigating

Hao Fu, Chunyuan Li, Xiaodong Liu, Jianfeng Gao, Asli Celikyilmaz, Lawrence Carin. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

Abstract Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies—amyloid plaques and cerebral amyloid angiopathy—in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate &gt; 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist’s ability suggests a route to neuropathologic deep phenotyping.
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes

There is a previously identified equivalence between wide fully connected neural networks (FCNs) and Gaussian processes (GPs). This equivalence enables, for instance, test set predictions that would have resulted from a fully Bayesian, infinitely wide trained FCN to be computed without ever instantiating the FCN, but by instead evaluating the corresponding GP. In this work, we derive an analogous equivalence for multi-layer convolutional neural networks (CNNs) both with and without pooling layers, and achieve state of the art results on CIFAR10 for GPs without trainable kernels. We also introduce a Monte Carlo method to estimate the GP corresponding to a given neural network architecture, even in cases where the analytic form has too many terms to be computationally feasible. Surprisingly, in the absence of pooling layers, the GPs corresponding to CNNs with and without weight sharing are identical. As a consequence, translation equivariance, beneficial in finite channel CNNs trained with stochastic gradient descent (SGD), is guaranteed to play no role in the Bayesian treatment of the infinite channel limit - a qualitative difference between the two regimes that is not present in the FCN case. We confirm experimentally, that while in some scenarios the performance of SGD-trained finite CNNs approaches that of the corresponding GPs as the channel count increases, with careful tuning SGD-trained CNNs can significantly outperform their corresponding GPs, suggesting advantages from SGD training compared to fully Bayesian parameter estimation.
Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method

This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A* algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A* algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster.
CHET: an optimizing compiler for fully-homomorphic neural-network inferencing

Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations on encrypted data without requiring a secret key. Recent cryptographic advances have pushed FHE into the realm of practical applications. However, programming these applications remains a huge challenge, as it requires cryptographic domain expertise to ensure correctness, security, and performance.
MODERATION ANALYSIS: ISSUES AND GUIDELINES

This editorial is dedicated to moderation analysis. Similar to what we did with the earlier editorial about mediation analysis, this editorial addresses seven key issues related to moderation and provides guidelines to justify the inclusion of moderator(s) and perform the analysis. Specifically, it discusses identification, conceptualization, usage, analysis, and reporting of moderating variables. Additionally, it also explains several approaches pertaining to moderation analysis and highlights the key differences between a simple moderation analysis and a multi-group analysis. We hope that this editorial will be useful to academics and research students to conduct moderation analysis with rigor.
Asymmetric nexus between COVID-19 outbreak in the world and cryptocurrency market

In the wake of recent pandemic of COVID-19, we explore its unprecedented impact on the cryptocurrencies' market. Specifically, we check how the changing intensity of the COVID-19 represented by the daily addition in new infections worldwide affects the daily returns of the top 10 cryptocurrencies according to the market capitalization. The results from Quantile-on-Quantile Regression (QQR) approach reveal that the changing intensity levels of the COVID-19 affect the Bearish and the Bullish market scenarios of cryptocurrencies differently (asymmetric impact). Additionally, there are differences between these currencies in their responses to the changing levels of this pandemic's intensity. Most of the currencies absorbed the small shocks of COVID-19 by registering positive gains but failed to resist against the huge changes except Bitcoin, ADA, CRO, and up to some extent Ethereum. Our results reveal new and asymmetric dynamics of this emerging asset class against an extremely stressful and unpredictable event (COVID-19). Moreover, these results are robust to the use of alternative proxy (COVID-19 deaths) for pandemic intensity. Our findings help to improve investors and policymakers' understanding of the cryptocurrencies' market dynamics, especially in the times of extremely stressful and unseen events.