Reviewerly/exHarmony huggingface dataset:
Safe medicine recommendation via star interactive enhanced-based transformer model

With the rapid development of electronic medical records (EMRs), most existing medicine recommendation systems based on EMRs explore knowledge from the diagnosis history to help doctors prescribe medication correctly. However, due to the limitations of the EMRs' content, recommendation systems cannot explicitly reflect relevant medical data, such as drug interactions. In recent years, medicine recommendation approaches based on medical knowledge graphs and graph neural networks have been proposed, and the methods based on the Transformer model have been widely used in medicine recommendation systems. Transformer-based medicine recommendation approaches are readily applicable to inductive problems. Unfortunately, traditional Transformer-based medicine recommendation approaches require complex computing power and suffer information loss among the multi-heads in Transformer model, which causes poor performance. At the same time, these approaches have rarely considered the side effects of drug interaction in traditional medical recommendation approaches. To overcome the drawbacks of the current medicine recommendation approaches, we propose a Star Interactive Enhanced-based Transformer (SIET) model. It first constructs a high-quality heterogeneous graph by bridging EMR (MIMIC-III) and a medical knowledge graph (ICD-9 ontology and DrugBank). Then, based on the constructed heterogeneous graph, it extracts a disease homogeneous graph, a medicine homogeneous graph, and a negative factors homogeneous graph to get auxiliary information of disease or drug (named enhanced neighbors). These are fed into the SIET model in conjunction with the relevant information in the EMRs to obtain representations of diseases and drugs. It finally generates the recommended drug list by calculating the cosine similarity between disease combination representations and drug combination representations. Extensive experiments on the MIMIC-III, DrugBank, and ICD-9 ontology datasets demonstrate the outstanding performance of our proposed model. Meanwhile, we show that our SIET model outperforms strong baselines on an inductive medicine recommendation task.
Supervised Service Classification using Downlink Control Indicator in LTE Physical Downlink Control Channel

Service classification for mobile traffic is an essential task for traffic management and service improvements. This paper proposed a supervised model based on multi-modal deep neural networks to classify mobile traffic into their services. Specifically, the proposed model is specialized to handle Downlink Control Indicator (DCI) obtained from Long Term Evolution (LTE) Physical Downlink Control CHannel (PDCCH). DCI contains control information such as Radio Network Temporary Identifier (RNTI), Resource Block (RB) assignment, and so on. Thus, it can observe which RNTI used an LTE Cell and the corresponding device used how many RBs. It is natural to regard the information in DCI as a sequential vector, the proposed model is designed with Recurrent Neural Networks (RNNs). Furthermore, dual modalities in DCI (downlink and uplink control information) are efficiently co-working by the hierarchical structure of the proposed model. With evaluations of the proposed model, we proved the efficiency of the model in the real-world data manually gathered during sixteen hours. The analysis of the experimental results suggested more problems to handle DCIs for service classification as well.
Chinese Painting Style Transfer System Based on Machine Learning

The paper first briefly summarizes the development of image style transfer technology and the current mainstream technology route, and then focuses on the analysis of the types and characteristics of Chinese painting. On this basis, it describes the migration implementation algorithm of six typical Chinese painting styles based on the deep learning network, and designs the application software framework to realize the program functions. Finally, the results of the implementation of the program were analyzed and discussed. The proposed algorithm and software have certain application value. With the software, ordinary digital pictures or paintings can be transfer the ones with different Chinese painting styles, which has played a positive role in the promotion of Chinese painting and Chinese culture.
Patient Identification based on Physical Rehabilitation Movements using Skeleton Data

Human activity recognition (HAR) is used to observe human movement in healthcare. Physical disability evaluation requires the help of experts, and with the development of artificial intelligence and sensors, we want to observe diseases and conditions without experts in everyday life. In this paper, we propose an algorithm for identifying patients through physical rehabilitation movements. Algorithm evaluation was performed using a dataset of physical rehabilitation movements acquired with a Kinect sensor. The dataset includes skeleton data of 15 patients and 14 normal subjects for 9 gestures. The proposed algorithm obtains heatmaps from skeleton joints and detects features using the ResNet backbone. HAR was pre-trained for 9 gestures because the dataset for patient identification was insufficient to learn ResNet. Pre-trained ResNet with 50% of layers frozen was additionally trained for the patient and normal subjects. As a result, an accuracy of 98.59% was obtained for the shoulder flexion left gesture.
High-Quality and Low-Memory-Footprint Progressive Decoding of Large-Scale Particle Data

Particle representations are used often in large-scale simulations and observations, frequently creating datasets containing several millions of particles or more. Due to their sheer size, such datasets are difficult to store, transfer, and analyze efficiently. Data compression is a promising solution; however, effective approaches to compress particle data are lacking and no community-standard and accepted techniques exist. Current techniques are designed either to compress small data very well but require high computational resources when applied to large data, or to work with large data but without a focus on compression, resulting in low reconstruction quality per bit stored. In this paper, we present innovations targeting tree-based particle compression approaches that improve the tradeoff between high quality and low memory-footprint for compression and decompression of large particle datasets. Inspired by the lazy wavelet transform, we introduce a new way of partitioning space, which allows a low-cost depth-first traversal of a particle hierarchy to cover the space broadly. We also devise novel data-adaptive traversal orders that significantly reduce reconstruction error compared to traditional data-agnostic orders such as breadth-first and depth-first traversals. The new partitioning and traversal schemes are used to build novel particle hierarchies that can be traversed with asymptotically constant memory footprint while incurring low reconstruction error. Our solution to encoding and (lossy) decoding of large particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error heuristics can be supplied by the user. Finally, through extensive experimentation, we demonstrate the efficacy and the flexibility of the proposed techniques when combined as well as when used independently with existing approaches on a wide range of scientific particle datasets.
VR social copresence with light field displays

As virtual reality (VR) devices become increasingly commonplace, asymmetric interactions between people with and without headsets are becoming more frequent. Existing video pass-through VR headsets solve one side of these asymmetric interactions by showing the user a live reconstruction of the outside world. This paper further advocates for reverse pass-through VR , wherein a three-dimensional view of the user's face and eyes is presented to any number of outside viewers in a perspective-correct manner using a light field display. Tying together research in social telepresence and copresence, autostereoscopic displays, and facial capture, reverse pass-through VR enables natural eye contact and other important non-verbal cues in a wider range of interaction scenarios, providing a path to potentially increase the utility and social acceptability of VR headsets in shared and public spaces.
Data Augmentation for JPEG Steganalysis

Deep Convolutional Neural Networks (CNNs) have performed remarkably well in JPEG steganalysis. However, they heavily rely on large datasets to avoid overfitting. Data augmentation is a popular technique to inflate the datasets available without collecting new images. For JPEG steganalysis, the augmentations predominantly used by researchers are limited to rotations and flips (D4 augmentations). This is due to the fact that the stego signal is erased by most augmentations used in computer vision. In this paper, we systematically survey a large number of other augmentation techniques and assess their benefit in JPEG steganalysis.
A hybrid CPU/GPU Scheme for Optimizing ChaCha20 Stream Cipher

The secure transmission of large-scale data has attracted more and more attention. In the widely recognized security protocol TLSv1.3, the only algorithms that support large-scale data en-/decryption are ChaCha20 and the Advanced Encryption Standard (AES). Although AES has a higher usage rate, ChaCha20 still has the advantage of speed and security on many platforms, and has a better performance against post-quantum attacks. However, for a CPU/GPU platform, compared to the AES algorithm, no work has fully described the application scheme of ChaCha. This paper proposes an optimization scheme to optimize the performance of the ChaCha20 algorithm on a CPU/GPU platform. On a CPU platform, we provide a parallelization implementation that is better than that of OpenSSL. On a single GPU, our implementation of ChaCha20 achieves peak throughput of 211.41GB/s, which is better than any previous implementation of ChaCha20 and AES algorithms on GPU. More importantly, we are the first to detail the optimization of ChaCha on GPU. When considering the interconnection between CPU and GPU, we use the 87.76% peak bidirectional bandwidth of a PCIe channel. Finally, we also provide a scheme for the application of ChaCha20 on a CPU/GPU platform.
Measuring fault tolerance in IoT mesh networks using Voronoi diagram

In an IoT network, a gateway plays a pivotal role that enables it to connect to the cloud services or external networks. In a fog/edge infrastructure, multiple gateways work simultaneously to prevent bottleneck for both inbound and outbound traffic. While gateway failure is inevitable, some diversion strategies to minimize the impact of network outage are needed. In this paper, we propose a generalization model of order-k hops Voronoi diagram to provide precomputed k backup gateways and evaluate several gateway diversion strategies to redirect a router from multiple gateway failures to an alternate gateway with the closest hops. Our simulations show that device blackout during multiple gateway failure can be minimized by using at least 4 precomputed gateways. Furthermore, our model is able to identify the maximum x-chain failure that can be tolerated during multiple gateway failure.
A Multiclass Nonparallel Parametric-Margin Support Vector Machine

The twin parametric-margin support vector machine (TPMSVM) is an excellent kernel-based nonparallel classifier. However, TPMSVM was originally designed for binary classification, which is unsuitable for real-world multiclass applications. Therefore, this paper extends TPMSVM for multiclass classification and proposes a novel K multiclass nonparallel parametric-margin support vector machine (MNP-KSVC). Specifically, our MNP-KSVC enjoys the following characteristics. (1) Under the “one-versus-one-versus-rest” multiclass framework, MNP-KSVC encodes the complicated multiclass learning task into a series of subproblems with the ternary output {−1,0,+1}. In contrast to the “one-versus-one” or “one-versus-rest” strategy, each subproblem not only focuses on separating the two selected class instances but also considers the side information of the remaining class instances. (2) MNP-KSVC aims to find a pair of nonparallel parametric-margin hyperplanes for each subproblem. As a result, these hyperplanes are closer to their corresponding class and at least one distance away from the other class. At the same time, they attempt to bound the remaining class instances into an insensitive region. (3) MNP-KSVC utilizes a hybrid classification and regression loss joined with the regularization to formulate its optimization model. Then, the optimal solutions are derived from the corresponding dual problems. Finally, we conduct numerical experiments to compare the proposed method with four state-of-the-art multiclass models: Multi-SVM, MBSVM, MTPMSVM, and Twin-KSVC. Experimental results demonstrate the feasibility and effectiveness of MNP-KSVC in terms of multiclass accuracy and learning time.
Wildcarded identity-based encryption from lattices

Wildcard identity-based encryption (WIBE) is a generalization of hierarchical identity-based encryption (HIBE) where a sender can simultaneously encrypt messages to a group of users matching a certain pattern, defined as a sequence of identities and wildcards. This special feature makes WIBE a promising solution to distributed IoT data security. In this paper, we propose the first construction of a post-quantum WIBE scheme. Our scheme is proven secure in the standard model, and it enjoys the security in the future quantum computer era.
End-to-End Multi-View Fusion for Enhanced Perception and Motion Prediction

Perception and motion prediction are indispensable components to the smooth operation of autonomous vehicles and the safety of the surrounding environment. Strengthening the accuracy of perception and motion prediction in autonomous vehicles remains of paramount importance. Therefore, we propose an end-to-end multi-view fusion methodology applied to MotionNet backbone network to enhance the sharpness of both perception and motion prediction. MotionNet is a state-of-the-art real-time model designed for joint perception and motion prediction. Our multi-view input is based on a single LIDAR sensor and formed by the fusion of range view features with bird's eye view. We evaluate our proposed work on nuScenes dataset and demonstrate through experiments that our proposed extension to MotionNet using the multi-view fusion technique outperforms MotionNet in both perception and motion prediction, especially for small and distant objects.
Joint Resource Allocation and User Scheduling Scheme for Federated Learning

This paper investigates the impact of communication factors on the convergence performance of federated learning (FL) in wireless networks. Considering the limited communication resources in wireless networks, it is difficult to schedule all users to participate in a comprehensive training and the convergence performance of training model relies much on the user scheduling scheme. To minimize the maximum update delay of user training, we propose a joint resource allocation and user scheduling scheme in this paper. Particularly, the user communication delay and user training results are jointly considered to dynamically schedule users and allocate communication resources. Simulation results show that the convergence time can be reduced by 41.6% compared with the random scheduling allocation scheme.
Research on named entity recognition of chinese electronic medical records based on multi-head attention mechanism and character-word information fusion

In the medical field, Named Entity Recognition (NER) plays a crucial role in the process of information extraction through electronic medical records and medical texts. To address the problems of long distance entity, entity confusion, and difficulty in boundary division in the Chinese electronic medical record NER task, we propose a Chinese electronic medical record NER method based on the multi-head attention mechanism and character-word fusion. This method uses a new character-word joint feature representation based on the pre-training model BERT and self-constructed domain dictionary, which can accurately divide the entity boundary and solve the impact of unregistered words. Subsequently, on the basis of the BiLSTM-CRF model, a multi-head attention mechanism is introduced to learn the dependency relationship between remote entities and entity information in different semantic spaces, which effectively improves the performance of the model. Experiments show that our models have better performance and achieves significant improvement compared to baselines. The specific performance is that the F1 value on the Chinese electronic medical record data set reaches 95.22%, which is 2.67%higher than the F1 value of the baseline model.
Cervical Cell Image Classification Based On Multiple Attention Fusion

Accurate classification of cervical cells is of great significance to the detection and treatment of cervical cancer. Over the years, convolutional neural network (CNN) has been successfully applied to cervical cell classification. Recently, the attention mechanism has been the research focus, which can learn local discriminant features. To further improve the performance of cervical cell image classification, we propose a novel cervical cell image classification method based on multiple attention fusion in this paper. Specifically, the Squeeze and Excitation (SE) and Spatial Attention Module (SAM) blocks are fused to learn the dependency between features from the channel and spatial directions respectively. In order to capture the long-range dependencies between features, the features embedded with SE and SAM are further fused with Disentangled Non-Local block (DNL). Experimental results on the publicly available cervical cell dataset SIPaKMeD show the effectiveness of our method.
Event‐Based Training in Label‐Limited Regimes

Abstract The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.
On Missingness Features in Machine Learning Models for Critical Care: Observational Study

Missing data in electronic health records is inevitable and considered to be nonrandom. Several studies have found that features indicating missing patterns (missingness) encode useful information about a patient's health and advocate for their inclusion in clinical prediction models. But their effectiveness has not been comprehensively evaluated.The goal of the research is to study the effect of including informative missingness features in machine learning models for various clinically relevant outcomes and explore robustness of these features across patient subgroups and task settings.A total of 48,336 electronic health records from the 2012 and 2019 PhysioNet Challenges were used, and mortality, length of stay, and sepsis outcomes were chosen. The latter dataset was multicenter, allowing external validation. Gated recurrent units were used to learn sequential patterns in the data and classify or predict labels of interest. Models were evaluated on various criteria and across population subgroups evaluating discriminative ability and calibration.Generally improved model performance in retrospective tasks was observed on including missingness features. Extent of improvement depended on the outcome of interest (area under the curve of the receiver operating characteristic [AUROC] improved from 1.2% to 7.7%) and even patient subgroup. However, missingness features did not display utility in a simulated prospective setting, being outperformed (0.9% difference in AUROC) by the model relying only on pathological features. This was despite leading to earlier detection of disease (true positives), since including these features led to a concomitant rise in false positive detections.This study comprehensively evaluated effectiveness of missingness features on machine learning models. A detailed understanding of how these features affect model performance may lead to their informed use in clinical settings especially for administrative tasks like length of stay prediction where they present the greatest benefit. While missingness features, representative of health care processes, vary greatly due to intra- and interhospital factors, they may still be used in prediction models for clinically relevant outcomes. However, their use in prospective models producing frequent predictions needs to be explored further.
Deep Reinforcement Learning Based Collaborative Optimization of Communication Resource and Route for UAV Cluster

To solve the communication service quality reduction and real-time route planning difficulty issues caused by inter-node interference in typical logistics environments, this paper proposes a deep reinforcement learning based method to realize the collaborative optimization of communication resource allocation and route planning for the UAV cluster. Simulation experiments show that the communication agents among UAV nodes can effectively learn to minimize the communication transmission interference while ensuring the latency constraint. The system communication capacity obtained by the proposed approach is about 2.15 times of the random resource allocation method. Meanwhile, a reasonable route is planned for each UAV node to facilitate the completion rate of logistics distribution tasks to reach 81.25%.
Multi-agent Collaborative Learning with Relational Graph Reasoning in Adversarial Environments

This paper proposes a collaborative policy framework via relational graph reasoning for multi-agent systems to accomplish adversarial tasks. A relational graph reasoning module consisting of an agent graph reasoning module and an opponent graph module, is designed to enable each agent to learn mixture state representation to enhance the effectiveness of the policy. In particular, for each agent, the agent graph reasoning module is designed to infer different underlying influences from different opponents and generate agent-level state representation. The opponent graph reasoning module is creatively designed for the opponents to reason relations from their surrounding objects including the agents and the opponents based on their latent features and then predict the future state of the opponents. It forms an opponent-level state representation. Besides, in order to effectively predict the state of the opponents, an intrinsic reward based on prediction error is designed to motivate the policy learning. Furthermore, interactions among agents are utilized to transmit messages and fuse information to promote the cooperative behaviors among the agents. Finally, various representative simulations on two multi-agent adversarial tasks are conducted to demonstrate the superiority and effectiveness of the proposed framework by comparison with existing methods.