Reviewerly/exHarmony huggingface dataset:
Knowledge modeling via contextualized representations for LSTM-based personalized exercise recommendation

Intelligent education systems have enabled personalized learning (PL). In PL, students are presented with educational contents that are consistent with their personal knowledge states (KS), and the critical task is accurately estimating these states through data. Knowledge tracing (KT) infers KS (latent) through historical student interactions (observed) with the knowledge components (KCs). A wide variety of KT techniques have been developed, from Bayesian Knowledge Tracing (BKT) to Deep Knowledge Tracing (DKT). However, in most of these methods, the KCs are represented as stand-alone entities, and the effect of representing KCs using contexts such as learning-related factors has been under-investigated. Also, KT needs to generate personalized results to facilitate tasks such as exercise recommendation. In this paper, we propose two approaches that use a contextualized representation of KCs, one with a content-based approach and another with a Long Short Term Memory (LSTM) network plus a personalization mechanism. By performing extensive experiments on two real-world datasets, results show not only a tangible improvement in prediction accuracy in the KT task compared to existing methods, but also its effectiveness in improving the recommendation precision.
Automatic segmentation of whole-slide H&amp;E stained breast histopathology images using a deep convolutional neural network architecture

The segmentation of malignant breast tissue from histological images represents a crucial task for the diagnosis of breast cancer (BC). This is a time-consuming process that could be alleviated with the help of computerized segmentation methods, leading to elevated precision and reproducibility results. However, this automated segmentation poses a challenge due to the large size of histological whole-slide images and the significant variability, heterogeneity and complexity of features in them. In this research, we propose a processing pipeline for the automatic segmentation of stained BC images presenting different types of histopathological patterns. To deal with the gigantic size of whole-slide images, the digital preparations were processed in a tile-wise manner: a large part of the image is split into patches. Then, the segmentation of each tile was accomplished by applying a deep convolutional neural network (DCNN) along with an encoder-decoder with separable atrous convolution architecture, which, once successfully validated, has revealed to be a promising method to segment pathological image patches. Next, in order to combine the local segmentation results (segmented tiles), while avoiding discontinuities and inconsistencies, an improved merging strategy based on an efficient fully connected Conditional Random Field (CRF) was applied. Experimental results on a collection of patches of breast cancer images demonstrate how the designed processing pipeline performs properly regardless the size, texture or any other colour-shape features typical of the malignant carcinomas considered in this study. The estimated segmentation accuracy and frequency weighted intersection over union (FWIoU) were 95.62%, 92.52%, respectively. Additionally, in order to facilitate the collaboration between pathologists and researchers to extract the specialist knowledge in form of training datasets that allows the training of new algorithms, a web-based platform which includes a slide-viewer and an annotation tool was developed. The automatic segmentation method proposed in this work was integrated into this platform and currently, it is being used as a decision support tool by pathologists.
Histogram of Oriented Gradients Feature Extraction From Raw Bayer Pattern Images

This brief studies the redundancy in the image processing pipeline for histogram of oriented gradients (HOG) feature extraction. The impact of demosaicing on the extracted HOG features is analyzed and experimented. It is shown that by taking advantage of the inter-channel correlation of natural images, the HOG features can be directly extracted from the Bayer pattern images with proper gamma compression. Due to the elimination of the image processing pipeline, the power consumption and computational complexity of the detection system can be significantly reduced. Experimental results show that the Bayer pattern image-based HOG features can be used in pedestrian detection systems with little performance degradation.
BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks

Estimating the parameters of mathematical models is a common problem in almost all branches of science. However, this problem can prove notably difficult when processes and model descriptions become increasingly complex and an explicit likelihood function is not available. With this work, we propose a novel method for globally amortized Bayesian inference based on invertible neural networks that we call BayesFlow. The method uses simulations to learn a global estimator for the probabilistic mapping from observed data to underlying model parameters. A neural network pretrained in this way can then, without additional training or optimization, infer full posteriors on arbitrarily many real data sets involving the same model family. In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics. Learning summary statistics from data makes the method applicable to modeling scenarios where standard inference techniques with handcrafted summary statistics fail. We demonstrate the utility of BayesFlow on challenging intractable models from population dynamics, epidemiology, cognitive science, and ecology. We argue that BayesFlow provides a general framework for building amortized Bayesian parameter estimation machines for any forward model from which data can be simulated.
ASER: A Large-scale Eventuality Knowledge Graph

Understanding human’s language requires complex world knowledge. However, existing large-scale knowledge graphs mainly focus on knowledge about entities while ignoring knowledge about activities, states, or events, which are used to describe how entities or things act in the real world. To fill this gap, we develop ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data. ASER contains 15 relation types belonging to five categories, 194-million unique eventualities, and 64-million unique edges among them. Both intrinsic and extrinsic evaluations demonstrate the quality and effectiveness of ASER.
Spatially-Constrained Fisher Representation for Brain Disease Identification With Incomplete Multi-Modal Neuroimages

Multi-modal neuroimages, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), can provide complementary structural and functional information of the brain, thus facilitating automated brain disease identification. Incomplete data problem is unavoidable in multi-modal neuroimage studies due to patient dropouts and/or poor data quality. Conventional methods usually discard data-missing subjects, thus significantly reducing the number of training samples. Even though several deep learning methods have been proposed, they usually rely on pre-defined regions-of-interest in neuroimages, requiring disease-specific expert knowledge. To this end, we propose a spatially-constrained Fisher representation framework for brain disease diagnosis with incomplete multi-modal neuroimages. We first impute missing PET images based on their corresponding MRI scans using a hybrid generative adversarial network. With the complete (after imputation) MRI and PET data, we then develop a spatially-constrained Fisher representation network to extract statistical descriptors of neuroimages for disease diagnosis, assuming that these descriptors follow a Gaussian mixture model with a strong spatial constraint (i.e., images from different subjects have similar anatomical structures). Experimental results on three databases suggest that our method can synthesize reasonable neuroimages and achieve promising results in brain disease identification, compared with several state-of-the-art methods.
A Novel Ant Colony Optimization Algorithm With Levy Flight

Ant Colony Optimization (ACO) is a widely applied meta-heuristic algorithm. Little researches focused on the candidate selection mechanism, which was developed based on the simple uniform distribution. This paper employs the Levy flight mechanism based on Levy distribution to the candidate selection process and takes advantage of Levy flight that not only guarantees the search speed but also extends the searching space to improve the performance of ACO. Levy ACO incorporating with Levy flight developed on the top of Max-min ACO. According to the computational experiments, the performance of Levy ACO is significantly better than the original Max-min ACO and some latest Traveling Salesman Problem (TSP) solvers.
Hashing Nets for Hashing: A Quantized Deep Learning to Hash Framework for Remote Sensing Image Retrieval

Fast and accurate remote sensing image retrieval from large data archives has been an important research topic in the remote sensing research literature. Recently, hashing-based remote sensing image retrieval has attracted extreme attention because of its efficient search capabilities. Especially, deep remote sensing image hashing algorithms have been developed based on convolutional neural networks (CNNs) and have shown effective retrieval performance. However, implementing a deep hashing network tends to be highly expensive in terms of storage space and computing resources to be suitable for on-orbit remote sensing image retrieval, which usually operates on resource-limited devices such as satellites and unmanned aerial vehicles (UAVs). To address this limitation, we propose to hash a deep network that in turn hashes remote sensing images. Specifically, we develop a quantized deep learning to hash (QDLH) framework for large-scale remote sensing image retrieval. The weights and activation functions in the QDLH framework are binarized to low-bit representations, which require comparatively much less storage space and computing resources. The QDLH results in a lightweight deep neural network for effective remote sensing image hashing. We conduct extensive experiments on two public remote sensing image data sets by incorporating several state-of-the-art network architectures into our QDLH methodology for remote sensing image hashing. The experimental results demonstrate that the proposed QDLH is effective in saving hardware resources in terms of both storage and computation. Moreover, superior remote sensing image retrieval performance is also achieved by our QDLH, compared with state-of-the-art deep remote sensing image hashing methods.
Y-Net: Multi-Scale Feature Aggregation Network With Wavelet Structure Similarity Loss Function For Single Image Dehazing

Single image dehazing is the ill-posed two-dimensional signal reconstruction problem. Recently, deep convolutional neural networks (CNN) have been successfully used in many computer vision problems. In this paper, we propose a Y-net that is named for its structure. This network reconstructs clear images by aggregating multi-scale features maps. Additionally, we propose a Wavelet Structure SIMilarity (W-SSIM) loss function in the training step. In the proposed loss function, discrete wavelet transforms are applied repeatedly to divide the image into differently sized patches with different frequencies and scales. The proposed loss function is the accumulation of SSIM loss of various patches with respective ratios. Extensive experimental results demonstrate that the proposed Y-net with the W-SSIM loss function restores high-quality clear images and outperforms state-of-the-art algorithms. Code and models are available at https://github.com/dectrfov/Y-net.
Multi-source knowledge fusion: a survey

Abstract Multi-source knowledge fusion is one of the important research topics in the fields of artificial intelligence, natural language processing, and so on. The research results of multi-source knowledge fusion can help computer to better understand human intelligence, human language and human thinking, effectively promote the Big Search in Cyberspace, effectively promote the construction of domain knowledge graphs (KGs), and bring enormous social and economic benefits. Due to the uncertainty of knowledge acquisition, the reliability and confidence of KG based on entity recognition and relationship extraction technology need to be evaluated. On the one hand, the process of multi-source knowledge reasoning can detect conflicts and provide help for knowledge evaluation and verification; on the other hand, the new knowledge acquired by knowledge reasoning is also uncertain and needs to be evaluated and verified. Collaborative reasoning of multi-source knowledge includes not only inferring new knowledge from multi-source knowledge, but also conflict detection, i.e. identifying erroneous knowledge or conflicts between knowledges. Starting from several related concepts of multi-source knowledge fusion, this paper comprehensively introduces the latest research progress of open-source knowledge fusion, multi-knowledge graphs fusion, information fusion within KGs, multi-modal knowledge fusion and multi-source knowledge collaborative reasoning. On this basis, the challenges and future research directions of multi-source knowledge fusion in a large-scale knowledge base environment are discussed.
Explainable AI for Healthcare: From Black Box to Interpretable Models

As artificial intelligence penetrates deeper into work and personal life, it raises questions about trust and transparency. These questions are of greater consequence in healthcare where decisions are literally a matter of life and death. In this paper, we reflect on recent investigations about the interpretability and explainability of artificial intelligence methods and discuss their impact on medicine and healthcare.
Improving Interorganizational Information Sharing for Vendor Managed Inventory: Toward a Decentralized Information Hub Using Blockchain Technology

Supply chain literature has long recognized the benefits of information sharing. Researchers and practitioners see high potential for improving processes, enabling new replenishment policies, and enhancing supply chain integration. Even though information technology represents a vital role as a driver and a success factor for implementing information sharing, established technologies still do not reflect the highly decentralized and fragmented characteristics of complex and often global supply chains. Early research shows that blockchain might be an enabler for the supply chain management. However, little is known about how blockchain might improve information sharing for complex, multitier supply chains. Therefore, this article aims at analyzing how and to what extent blockchain can facilitate information sharing for vendor managed inventory by designing a software prototype based on Hyperledger Fabric using the design science research approach. This research took place in close cooperation with a leading German healthcare technology manufacturer, which allowed us to base our study both on literature and practical insights. With our results, we contribute to the supply chain knowledge base by introducing the decentralized information hub model, describing how companies utilize decentralized technologies, such as blockchain, to facilitate information sharing.
GASOL: Gas Analysis and Optimization for Ethereum Smart Contracts

Abstract We present the main concepts, components, and usage of G asol , a Gas AnalysiS and Optimization tooL for Ethereum smart contracts. G asol offers a wide variety of cost models that allow inferring the gas consumption associated to selected types of EVM instructions and/or inferring the number of times that such types of bytecode instructions are executed. Among others, we have cost models to measure only storage opcodes, to measure a selected family of gas-consumption opcodes following the Ethereum’s classification, to estimate the cost of a selected program line, etc. After choosing the desired cost model and the function of interest, G asol returns to the user an upper bound of the cost for this function. As the gas consumption is often dominated by the instructions that access the storage, G asol uses the gas analysis to detect under-optimized storage patterns, and includes an (optional) automatic optimization of the selected function. Our tool can be used within an Eclipse plugin for which displays the gas and instructions bounds and, when applicable, the gas-optimized function.
Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation

Supervised deep learning with pixel-wise training labels has great successes on multi-person part segmentation. However, data labeling at pixel-level is very expensive. To solve the problem, people have been exploring to use synthetic data to avoid the data labeling. Although it is easy to generate labels for synthetic data, the results are much worse compared to those using real data and manual labeling. The degradation of the performance is mainly due to the domain gap, i.e., the discrepancy of the pixel value statistics between real and synthetic data. In this paper, we observe that real and synthetic humans both have a skeleton (pose) representation. We found that the skeletons can effectively bridge the synthetic and real domains during the training. Our proposed approach takes advantage of the rich and realistic variations of the real data and the easily obtainable labels of the synthetic data to learn multi-person part segmentation on real images without any human-annotated labels. Through experiments, we show that without any human labeling, our method performs comparably to several state-of-the-art approaches which require human labeling on Pascal-Person-Parts and COCO-DensePose datasets. On the other hand, if part labels are also available in the real-images during training, our method outperforms the supervised state-of-the-art methods by a large margin. We further demonstrate the generalizability of our method on predicting novel keypoints in real images where no real data labels are available for the novel keypoints detection. Code and pre-trained models are available at https://github.com/kevinlin311tw/CDCL-human-part-segmentation
Polarimetric underwater image recovery via deep learning

Polarimetric imaging is an effective way for clear vision in water, in which deducing the object radiance in clear water from the obtained polarimetric information in turbid water is essential. In this letter, we propose, for the first time to our knowledge, a learning-based method for polarimetric underwater image recovery. It is based on the dense network and can learn well the relation between the object radiance and the polarization information. The experimental results demonstrate that additionally introducing the polarization information is beneficial for improving the image quality. Moreover, the proposed learning-based method can effectively remove the veiling light and outperforms other existing methods, even in dense turbid water.
Proof of X-repute blockchain consensus protocol for IoT systems

Recently, blockchain technology has been used to address the security issues of Internet of things (IoT) applications. However, some issues particular to blockchain should be solved to meet the security requirements of IoT systems. The core of blockchain technology is distributed computing, along with the collaboration mechanism of group trust under the distributed computing environment, which can solve the scalability, collaboration ability, trust relationship, and security protection challenges faced by the IoT. The existing blockchain consensus protocol can only solve the trust cooperation problem with limited credibility. Although distributed trust relationship management has advantages over centralized trust relationship management, several risks exist. Here, the most critical issue is the credibility of the blockchain consensus protocol. Based on the characteristics of the traditional public chain, we propose a repute-based consensus protocol for blockchain-enabled IoT systems. In the protocol, two methods are designed to enable the blockchain system to reach a consensus rapidly and safely. The repute rewards and punishments method settles the repute values of nodes; nodes with satisfactory behavior receive repute rewards. The repute mining method proposes repute requirements for consensus nodes; nodes with high repute value produce blocks more easily. Security analysis has been conducted using a theory model and experimental evaluation. The establishment of our repute system can improve the consensus protocol, resisting stronger attacks, and giving several users with lower computing power a greater opportunity to participate in consensus. The experimental results show that the repute-based consensus protocol has advantages in terms of security and that its resistance capability against attacks is improved.
An efficient image encryption using deep neural network and chaotic map

Inspite of progressive growth of cryptography, encrypting sensitive information of an image is still a computationally complex task. After reviewing existing literature, it is now known that security problems are yet not solved and there is an open scope of further research. In most recent times, it has been noticed that neural network has proven cost effective optimization mechanism in offering security towards images. However, such implementation are computationally expensive process and do not solve various diversified attacks on image. Hence, the prime purpose of proposed system is to introduce an analytical research methodology for presenting a sophisticated framework where deep neural network has been used for optimizing the performance of simple encryption approaches. The robustness of optimization principle is further added with chaotic map concept for enhanced security performance. The study outcome shows that proposed implementation offers much better security performance without any negative effect on image quality.