Reviewerly/exHarmony huggingface dataset:
An imConvNet-based deep learning model for Chinese medical named entity recognition

With the development of current medical technology, information management becomes perfect in the medical field. Medical big data analysis is based on a large amount of medical and health data stored in the electronic medical system, such as electronic medical records and medical reports. How to fully exploit the resources of information included in these medical data has always been the subject of research by many scholars. The basis for text mining is named entity recognition (NER), which has its particularities in the medical field, where issues such as inadequate text resources and a large number of professional domain terms continue to face significant challenges in medical NER.We improved the convolutional neural network model (imConvNet) to obtain additional text features. Concurrently, we continue to use the classical Bert pre-training model and BiLSTM model for named entity recognition. We use imConvNet model to extract additional word vector features and improve named entity recognition accuracy. The proposed model, named BERT-imConvNet-BiLSTM-CRF, is composed of four layers: BERT embedding layer-getting word embedding vector; imConvNet layer-capturing the context feature of each character; BiLSTM (Bidirectional Long Short-Term Memory) layer-capturing the long-distance dependencies; CRF (Conditional Random Field) layer-labeling characters based on their features and transfer rules.The average F1 score on the public medical data set yidu-s4k reached 91.38% when combined with the classical model; when real electronic medical record text in impacted wisdom teeth is used as the experimental object, the model's F1 score is 93.89%. They all show better results than classical models.The suggested novel model (imConvNet) significantly improves the recognition accuracy of Chinese medical named entities and applies to various medical corpora.
Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour

Automatic liver segmentation from computed tomography (CT) images is an essential preprocessing step for computer-aided diagnosis of liver diseases. However, due to the large differences in liver shapes, low-contrast to adjacent tissues, and existence of tumors or other abnormalities, liver segmentation has been very challenging. This study presents an accurate and fast liver segmentation method based on a novel probabilistic active contour (PAC) model and its fast global minimization scheme (3D-FGMPAC), which is explainable as compared with deep learning methods.The proposed method first constructs a slice-indexed-histogram to localize the volume of interest (VOI) and estimate the probability that a voxel belongs to the liver according its intensity. The probabilistic image would be used to initialize the 3D PAC model. Secondly, a new contour indicator function, which is a component of the model, is produced by combining the gradient-based edge detection and Hessian-matrix-based surface detection. Then, a fast numerical scheme derived for the 3D PAC model is performed to evolve the initial probabilistic image into the global minimizer of the model, which is a smoothed probabilistic image showing a distinctly highlighted liver. Next, a simple region-growing strategy is applied to extract the whole liver mask from the smoothed probabilistic image. Finally, a B-spline surface is constructed to fit the patch of the rib cage to prevent possible leakage into adjacent intercostal tissues.The proposed method is evaluated on two public datasets. The average Dice score, volume overlap error, volume difference, symmetric surface distance and volume processing time are 0.96, 7.35%, 0.02%, 1.17 mm and 19.8 s for the Sliver07 dataset, and 0.95, 8.89%, -0.02%$-0.02\%$ , 1.45 mm and 23.08 s for the 3Dircadb dataset, respectively.The proposed fully-automatic approach can effectively segment the liver from low-contrast and complex backgrounds. The quantitative and qualitative results demonstrate that the proposed segmentation method outperforms state-of-the-art traditional automatic liver segmentation algorithms and achieves very competitive performance compared with recent deep leaning-based methods.
Covariate dependent Beta-GOS process

Covariate-dependent processes have been widely used in Bayesian nonparametric statistics thanks to their flexibility to incorporate covariate information and correlation among process realizations. Unlike most of the existing work that focuses on extensions of exchangeable species sampling processes such as Dirichlet process, a new class of covariate-dependent nonexchangeable priors is proposed by considering the generalization of an nonexchangeable sequence, namely the Beta-GOS model. The proposed prior has an equivalent formulation under a continuous kernel mixture. It also has a latent variable representation that leads to a natural nonexchangeable parallel with the classical dependent Dirichlet process formulation. This prior is further applied in regression and autoregressive models and it is shown that its posterior sampling algorithm enjoys the same computational complexity with that of the Beta-GOS. The excellent numerical performance of the method is demonstrated via simulation and two real data examples.
A Multi-scale Disperse Dynamic Routing Capsule Network Knowledge Graph Embedding Model Based on Relational Memory

Knowledge graphs use triples containing head entity h, tail entity t, and relation r to represent real-world entities and their intrinsic relationships. In order to predict the actual missing triples in the knowledge graph, combine the strong triple representation ability of relational memory network with the powerful feature processing ability of capsule network, and use disperse dynamic routing that can improve the performance of capsule network, we propose a knowledge graph embedding model named RDMCapsE. First, the embedding vector is formed by encoding potential dependencies between entities and relations; then, different feature maps are generated using convolutional kernels with different window sizes and then reorganized into corresponding capsules; finally, the connection from the parent capsule to the child capsule is specified by the squash function and the disperse dynamic routing, and the credibility of the current triple is judged based on the score of the inner product of the child capsule and the weights. The experimental results show that compared with other models, the model in this paper can effectively improve the effect of knowledge graph completion and the classification accuracy of triples on WN18RR, FB15K-237, WN11, and FB13 datasets.
State Definition for Conflict Analysis with Four-valued Logic

We examined a four-valued logic method for state settings in conflict resolution models. Decision-making models of conflict resolution, such as game theory and graph model for conflict resolution (GMCR), assume the description of a state to be the outcome of a combination of strategies or the consequence of option selection by the decision-makers. However, for a framework to function as a decision-making system, unless a clear definition of the task of placing information out of an infinite world exists, logical consistency cannot be ensured, and thus, the function may be incomputable. The introduction of paraconsistent four-valued logic can prevent incorrect state setting and analysis with insufficient information and provide logical validity to analytical methods that vary the analysis resolution depending on the degree of coarseness of the available information. This study proposes a GMCR stability analysis with state configuration based on Belnap's four-valued logic.
Educational Chatbot to Support Question Answering on Slack

Educational chatbots unlock new possibilities to support teaching activities. In recent years, the progress made in natural language processing and understanding allowed the development of virtual assistants that understand questions written as unstructured data. They generate a proper answer based on knowledge bases. This work proposes an accurate solution easy to test in real world scenarios like university courses. In fact, the use of Slack and RASA technologies reduces the difficulties to train and install the educational chatbot on already existing digital workplaces. We evaluates the effectiveness of our solution reaching 0.93 accuracy in intent recognition building a stacked neural network.
Automatic Educational Concept Extraction Using NLP

Educational concepts are the core of teaching and learning. From the perspective of educational technology, concepts are essential meta-data, representative terms that can connect different learning materials, and are the foundation for many downstream tasks. Some studies on automatic concept extraction have been conducted, but there are no studies looking at the K-12 level and focused on the Swedish language. In this paper, we use a state-of-the-art Swedish BERT model to build an automatic concept extractor for the Biology subject using fine-annotated digital textbook data that cover all content for K-12. The model gives a recall measure of 72% and has the potential to be used in real-world settings for use cases that require high recall. Meanwhile, we investigate how input data features influence model performance and provide guidance on how to effectively use text data to achieve the optimal results when building a named entity recognition (NER) model.
An Unsupervised Learning Method to improve Legal Document Retrieval task at ALQAC 2022

Document retrieval for domain-specific has been an important and challenging research in NLP, particularly legal documents. The main challenge in the legal domain is the close combination of specialized knowledge from experts, which makes the entire data collecting and evaluation procedure complex and time consuming. In this study, we propose a training data augmentation procedure and an unsupervised embedding learning method and apply it to the Legal Document Retrieval task at the Automated Legal Question Answering Competition 2022 (ALQAC 2022). In this task, our method outperformed current standard models and achieved competitive results at ALQAC 2022.
Virtual Reality Revolution: Strategies for treating mental and emotional disorders

Virtual Reality (VR) is increasingly gaining recognition in healthcare, especially as a treatment tool for psychological interventions. This paper reviews current advances in immersive VR-based therapies to explore different strategies designed to treat mental and emotional disorders with Virtual Reality Therapy (VRT). The study contributes to the VR community by exemplifying the application of various psychological treatment strategies in designing VR therapies, such as Cognitive-Behavioral, Distraction, Perspective-Taking and Exposure. For higher adoption of VR by clinicians, greater quality control of these strategies and well-defined user experiences are required, followed by clinical validation.
Dual Modular Redundancy Unit of Convolutional Layer for Low-cost and Reliable CNNs

In mission-critical systems such as self-driving, medical, and infrastructure systems, hardware faults can lead to serious accidents. Therefore, we need a method to detect hardware faults of artificial intelligence (AI) with high accuracy. A low-cost fault detection method with less computation is required to reduce AI’s chip area and/or energy consumption. In this paper, we propose an approximate Dual Modular Redundancy (DMR) unit using a Random Forest approximation method, which can significantly reduce the computation for inference in the convolutional neural networks (CNNs). We assume various scenarios of faults and evaluate the fault effects. In our experiments, we demonstrate that the proposed approximate DMR unit achieves high fault detection for three types of fault models. In addition, we report a 42.8% to 48.3% reduction in the computation for inference compared to the conventional method.
Policy Transfer in POMDP Models for Safety-Critical Autonomous Vehicles Applications

Developing models of complex systems, such as Autonomous Vehicles (AVs) that operate in uncertain, reactive environments is challenging because of complexity and uncertainty in their operational environments. Partially Observable Markov Decision Process (POMDP) is a mathematically principled framework that is suitable for developing such models. A POMDP model is typically defined with respect to a specific task performed in the environment and the resulting policy is obtained based on the defined task and information available about the environment. This implies that if changes occur in the task and/or environment, the model and policy need to be updated. One efficient way to achieve this end is to reuse the experience from the initial task(s) to perform similar task(s) instead of designing new models from scratch. This technique is referred to as Transfer Learning (TL). In this paper, we propose a novel TL technique that uses a customized Q-learning algorithm for policy transfer in POMDPs. We use this technique to transfer policies from a POMDP model, initially designed for a simple AV lane-keeping task within a freeway environment, to a similar task in a more complex environment, where random and risky behaviors from neighboring vehicles in the environment can be expected.
Interpretable Convolutional Learning Classifier System (C-LCS) for higher dimensional datasets

The purpose of this paper is to devise an interpretable hybrid classification model for Convolutional Neural Networks (CNN) and a Learning Classifier System (LCS). The presented hybrid system integrates the fundamental attributes from both types of these classifiers. In the proposed hybrid model CNN works as an automatic feature extractor, and LCS works to provide interpretable rule-based classification results. Although LCS has limitations working on higher dimensional datasets, we resolve this limitation by using CNN as a feature extractor. The other concept of the non-interpretability of CNN is addressed by using the LCS rule. Furthermore, our experiment with higher dimensional datasets like CIFAR-10 and Fashion-MNIST shows that extended LCS provides comparable performance to the standard neural network model while also providing interpretable results. We named this extended LCS method Convolutional Learning Classifier Cystem (C-LCS).
Reinforcement Learning based Autoscaling for Kafka-centric Microservices in Kubernetes

Microservices and Kafka have become a perfect match for enabling the Event-driven Architecture and this encourages microservices integration with various opensource platforms in the world of Cloud Native applications. Kubernetes is an opensource container orchestration platform, that can enable high availability, and scalability for Kafkacentric microservices. Kubernetes supports diverse autoscaling mechanisms like Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA) and Cluster Autoscaler (CA). Among others, HPA automatically scales the number of pods based on the default Resource Metrics, which includes CPU and memory usage. With Prometheus integration, custom metrics for an application can be monitored. In a Kafkacentric microservices, processing time and speed depends on the number of messages published. There is a need for auto scaling policy which can be based on the number of messages processed. This paper proposes a new autoscaling policy, which scales Kafka-centric microservices deployed in an eventdriven deployment architecture, using a Reinforcement Learning model.
Experimental Evaluation of Homomorphic Encryption in Cloud and Edge Machine Learning

Machine Learning (ML)-based intelligent services are gradually becoming the leading service design and delivery model in edge computing, where user and device data is outsourced to take part of large-scale BigData analytics. This paradigm however entails challenging security and privacy concerns, which require rethinking the fundamental concepts behind performing ML. For instance, the encryption of sensitive data provides a straightforward solution that ensures data security and privacy. In particular, Homomorphic encryption allows arbitrary computation on encrypted data and has gained a lot of attention recently. However, it has not been fully adopted by edge computing-based ML due to its potential impact on classification accuracy and model performance. This paper conducts an experimental evaluation of different types of Homomorphic encryption techniques, namely, Partial, Somewhat, and Fully Homomorphic encryption over several ML models, which train on encrypted data and produce classification predictions based on encrypted input data. The results demonstrate two potential directions in the context of ML privacy at the network edge: privacy-preserving training and privacy-preserving classification. The performance of encryption-driven ML models is compared using different metrics such as accuracy and computation time for plaintext vs. encrypted text. This evaluation will guide future research in investigating which ML models perform better over encrypted data.
Joint Water-Filling Algorithm with Adaptive Chroma Adjustment for Shadow Removal From Text Document Images

With smart portable devices such as smartphones and tablets in usage and popularity, people are more willing to use these devices to scan and save digitized documents. However, when capturing document images, shadows are inevitable and influence clarity and readability. How to remove the shadows of document images is an important and meaningful task. In this paper, we propose a water-filling method using chroma adjustment for shadow removal. Firstly, a global and local jointly water-filling approach is designed to estimate the shading map. Then, we design an adaptive global brightness adjustment strategy to optimize the global luminance of the output image. Since only adjusting brightness can cause color distortion of output images, we propose an adaptive chroma adjustment strategy to ensure color consistency across all areas of output images. A series of experiments show that our method can remove shadows of digitized documents, outperforming some state-of the-art methods. Moreover, the proposed method can keep the brightness and color as consistent as possible with the non-shadow area.
Swing: Providing Long-Range Lossless RDMA via PFC-Relay

Remote Direct Memory Access (RDMA) has been widely deployed in datacenters for its high performance. Large-scale high performance cloud services built on geographically distributed datacenters require long-range RDMA for performance requirements. However, existing RDMA solutions can hardly satisfy the stringent requirements of the emerging large-scale high-performance cloud services built on geo-distributed datacenters in terms of throughput and delay. On the one hand, lossless RDMA suffers from a deep buffer and potential suboptimal throughput for inter-datacenter traffic due to delayed response to Priority Flow Control (PFC) messages. On the other hand, lossy RDMA with selective retransmissions suffers from poor performance when multiple flows with different round-trip times (RTTs) coexist in cross-datacenter scenarios. This article proposes <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Swing</small> , which expands the high-performance lossless RDMA to long-distance links through PFC-Relay. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Swing</small> ensures the throughput of long-distance links while minimizing the buffer requirement for long-range RDMA. It enables long-range RDMA without making any modifications to existing in-datacenter networks. The evaluation shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Swing</small> can reduce the average flow completion time (FCT) by 14%-66% in a variety of traffic scenarios.
Learning to Schedule Multi-Server Jobs With Fluctuated Processing Speeds

Multi-server jobs are imperative in modern cloud computing systems. A noteworthy feature of multi-server jobs is that, they usually request multiple computing devices simultaneously for their execution. How to schedule multi-server jobs online with a high system efficiency is a topic of great concern. First, the scheduling decisions have to satisfy the service locality constraints. Second, the scheduling decisions needs to be made online without the knowledge of future job arrivals. Third, and most importantly, the actual service rate experienced by a job is usually in fluctuation because of the dynamic voltage and frequency scaling (DVFS) and power oversubscription techniques when multiple types of jobs co-locate. A majority of online algorithms with theoretical performance guarantees are proposed. However, most of them require the processing speeds to be knowable, thereby the job completion times can be exactly calculated. To present a theoretically guaranteed online scheduling algorithm for multi-server jobs without knowing actual processing speeds apriori, in this article, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Esdp</small> (Efficient Sampling-based Dynamic Programming), which learns the distribution of the fluctuated processing speeds over time and simultaneously seeks to maximize the cumulative overall utility. The cumulative overall utility is formulated as the sum of the utilities of successfully serving each multi-server job minus the penalty on the operating, maintaining, and energy cost. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Esdp</small> is proved to have a polynomial complexity and a logarithmic regret, which is a State-of-the-Art result. We also validate it with extensive simulations and the results show that the proposed algorithm outperforms several benchmark policies with improvements by up to 73%, 36%, and 28%, respectively.
Compact suffix automata representations for searching long patterns

Automata based algorithms have always dominated the history of string matching, thanks to their intrinsic nature of recognizers. Among them, those based on the suffix automaton deserve a special mention, because its use has always led to elegant and very efficient solutions in practice. In this paper, we present two novel string matching algorithms based on the suffix automaton which are particularly suitable for very long strings. The first algorithm simulates the suffix automaton through the well known bit-parallelism approach, while the second makes use of an alternative non-standard simulation. We mainly focus on the second solution, which turns out to be more flexible in practice, and present some extensions of it. Experimental results suggest that both our solutions are very efficient in practical cases scaling much better when the length of the pattern increases. Moreover, our second approach is also competitive with the most effective solutions available for the exact string matching problem.
Conversational Agent Design for Algebra Tutoring

Conversational Intelligent Tutoring Systems (CITS) in learning environments are capable of providing personalized instruction to students in different domains, to improve the learning process. This interaction between the Intelligent Tutoring System (ITS) and the user is carried out through dialogues in natural language. In this study, we use an open source framework called Rasa to adapt the original button-based user interface of an algebraic/arithmetic word problem-solving ITS to one based primarily on the use of natural language. We conducted an empirical study showing that once properly trained, our conversational agent was able to recognize the intent related to the content of the student’s message with an average accuracy above 0.95.