Reviewerly/exHarmony huggingface dataset:
Noise Reduction Learning based on XLNet-CRF for Biomedical Named Entity Recognition

In recent years, Biomedical Named Entity Recognition (BioNER) systems have mainly been based on deep neural networks, which are used to extract information from the rapidly expanding biomedical literature. Long-distance context autoencoding language models based on transformers have recently been employed for BioNER with great success. However, noise interference exists in the process of pre-training and fine-tuning, and there is no effective decoder for label dependency. Current models have many aspects in need of improvement for better performance. We propose two kinds of noise reduction models, Shared Labels and Dynamic Splicing, based on XLNet encoding which is a permutation language pre-training model and decoding by Conditional Random Field (CRF). By testing 15 biomedical named entity recognition datasets, the two models improved the average F1-score by 1.504 and 1.48, respectively, and state-of-the-art performance was achieved on 7 of them. Further analysis proves the effectiveness of the two models and the improvement of the recognition effect of CRF, and suggests the applicable scope of the models according to different data characteristics.
Cloudlet and Virtual Machine Performance Enhancement With CLARA and Evolutionary Paradigm

The standardised IT paradigm pools services together as an internet network is cloud computing. So, management of the load by cloud providers at this point is difficult and hence manifests the existence of load balancing concept. The aim of proposed algorithm is to enhance the performance by minimizing results, which includes Execution time, Makespan time, and Processing Cost, and maximizing throughput, using ABC Optimization. R code is used to execute the algorithm, and dataset is processed using Microsoft Excel 2007. In the dataset, the MIPS of VMs range from 2000-9000 and bandwidth range from 10000-50000. Finally, it is concluded that, for 3 clusters, the efficiency rate of execution time, makespan time, and processing cost lies between 18%-20% and throughput and degree of imbalance are approximately 16% and 6%, respectively, when compared with the previous work; and for 10 clusters, the efficiency rate of execution time and makespan time raises to approximately 50% with processing cost, throughput, and degree of imbalance as approximately 72%, 33%, and 4%, respectively.
Incorporating rich syntax information in Grammatical Error Correction

Syntax parse trees are a method of representing sentence structure and are often used to provide models with syntax information and enhance downstream task performance. Because grammar and syntax are inherently linked, the incorporation of syntax parse trees in GEC is a natural solution. In this work, we present a method of incorporating syntax parse trees for Grammatical Error Correction (GEC). Building off a strong sequence-to-sequence Transformer baseline, we present a unified parse integration method for GEC that allows for the use of both dependency and constituency parse trees, as well as their combination - a syntactic graph. Specifically, on the sentence encoder, we propose a graph encoder that can encode dependency trees and constituent trees at the same time, yielding two representations for terminal nodes (i.e., the token of the sentence) and non-terminal nodes. We next use two cross-attentions (NT-Cross-Attention and T-Cross-Attention) to aggregate these source syntactic representations to the target side for final corrections prediction. In addition to evaluating our models on the popular CoNLL-2014 Shared Task and JFLEG GEC benchmarks, we affirm the effectiveness of our proposed method by testing both varying levels of parsing quality and exploring the use of both parsing formalisms. With further empirical exploration and analysis to identify the source of improvement, we found that rich syntax information provided clear clues for GEC; a syntactic graph composed of multiple syntactic parse trees can effectively compensate for the limited quality and insufficient error correction capability of a single syntactic parse tree.
TRACE: Travel Reinforcement Recommendation Based on Location-Aware Context Extraction

As the popularity of online travel platforms increases, users tend to make ad-hoc decisions on places to visit rather than preparing the detailed tour plans in advance. Under the situation of timeliness and uncertainty of users’ demand, how to integrate real-time context into dynamic and personalized recommendations have become a key issue in travel recommender system. In this article, by integrating the users’ historical preferences and real-time context, a location-aware recommender system called TRACE ( T ravel R einforcement Recommendations Based on Location- A ware C ontext E xtraction) is proposed. It captures users’ features based on location-aware context learning model, and makes dynamic recommendations based on reinforcement learning. Specifically, this research: (1) designs a travel reinforcing recommender system based on an Actor-Critic framework, which can dynamically track the user preference shifts and optimize the recommender system performance; (2) proposes a location-aware context learning model, which aims at extracting user context from real-time location and then calculating the impacts of nearby attractions on users’ preferences; and (3) conducts both offline and online experiments. Our proposed model achieves the best performance in both of the two experiments, which demonstrates that tracking the users’ preference shifts based on real-time location is valuable for improving the recommendation results.
Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study

Tumor purity is the percentage of cancer cells within a tissue section. Pathologists estimate tumor purity to select samples for genomic analysis by manually reading hematoxylin-eosin (H&E)-stained slides, which is tedious, time consuming, and prone to inter-observer variability. Besides, pathologists' estimates do not correlate well with genomic tumor purity values, which are inferred from genomic data and accepted as accurate for downstream analysis. We developed a deep multiple instance learning model predicting tumor purity from H&E-stained digital histopathology slides. Our model successfully predicted tumor purity in eight The Cancer Genome Atlas (TCGA) cohorts and a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values. Thus, our model can be utilized to select samples for genomic analysis, which will help reduce pathologists' workload and decrease inter-observer variability. Furthermore, our model provided tumor purity maps showing the spatial variation within sections. They can help better understand the tumor microenvironment.
Image-Based Visual Servoing System for Components Alignment Using Point and Line Features

The design of visual servoing system for robotic high-precision assembly is of great challenge. For the difficulty of insufficient accuracy of target object feature extraction, a deep neural network combined with feature pyramid network (FPN) structure is proposed. This lightweight network requires only a small amount of labeled data to achieve significant segmentation results. The control laws of translation and orientation for component alignment are separately designed. The translation is controlled based on the interaction matrix of point features. The orientation is controlled based on the interaction matrix of line features. The relations between the cameras’ motion and the end-effector of the manipulator are calibrated via the manipulator’s active movements, which are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \times $ </tex-math></inline-formula> 3 transformation matrices. The depth information of feature points is integrated into the transformation matrix. The alignment pose error estimation is realized with the interaction matrices, transformation matrices, and point and line features. A robotic assembly system is developed to assemble two aviation circular connectors with six degree-of-freedoms (DOFs) in high precision in 3-D space. The experimental results verify the effectiveness of the proposed method.
Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma

Mouse models are highly effective for studying the pathophysiology of lung adenocarcinoma and evaluating new treatment strategies. Treatment efficacy is primarily determined by the total tumor burden measured on excised tumor specimens. The measurement process is time-consuming and prone to human errors. To address this issue, we developed a novel deep learning model to segment lung tumor foci on digitally scanned hematoxylin and eosin (H&E) histology slides.Digital slides of 239 mice from 9 experimental cohorts were split into training (n=137), validation (n=37), and testing cohorts (n=65). Image patches of 500×500 pixels were extracted from 5× and 10× magnifications, along with binary masks of expert annotations representing ground-truth tumor regions. Deep learning models utilizing DeepLabV3+ and UNet architectures were trained for binary segmentation of tumor foci under varying stain normalization conditions. The performance of algorithm segmentation was assessed by Dice Coefficient, and detection was evaluated by sensitivity and positive-predictive value (PPV).The best model on patch-based validation was DeepLabV3+ using a Resnet-50 backbone, which achieved Dice 0.890 and 0.873 on validation and testing cohort, respectively. This result corresponded to 91.3 Sensitivity and 51.0 PPV in the validation cohort and 93.7 Sensitivity and 51.4 PPV in the testing cohort. False positives could be reduced 10-fold with thresholding artificial intelligence (AI) predicted output by area, without negative impact on Dice Coefficient. Evaluation at various stain normalization strategies did not demonstrate improvement from the baseline model.A robust AI-based algorithm for detecting and segmenting lung tumor foci in the pre-clinical mouse models was developed. The output of this algorithm is compatible with open-source software that researchers commonly use.
Coupling Two Laser-Cooled Ions via a Room-Temperature Conductor

We demonstrate coupling between the motions of two independently trapped ions with a separation distance of 620 μm. The ion-ion interaction is enhanced via a room-temperature electrically floating metallic wire which connects two surface traps. Tuning the motion of both ions into resonance, we show flow of energy with a coupling rate of 11 Hz. Quantum-coherent coupling is hindered by strong surface electric-field noise in our device. Our ion-wire-ion system demonstrates that room-temperature conductors can be used to mediate and tune interactions between independently trapped charges over distances beyond those achievable with free-space dipole-dipole coupling. This technology may be used to sympathetically cool or entangle remotely trapped charges and enable coupling between disparate physical systems.
Turbo Detection Aided Autoencoder for Multicarrier Wireless Systems: Integrating Deep Learning Into Channel Coded Systems

A variety of deep learning schemes have endeavoured to integrate deep neural networks (DNNs) into channel coded systems by jointly designing DNN and the channel coding scheme in specific channels. However, this leads to limitations concerning the choice of both the channel coding scheme and the channel paramters. We circumvent these impediments and conceive a turbo-style multi-carrier auto-encoder (MC-AE) for orthogonal frequency-division multiplexing (OFDM) systems, which is the first one that achieves the flexible integration of DNN into any given channel coded systems while achieving an iteration gain. More explicitly, first of all, we design the MC-AE independently of both the channel coding arrangement and of the channel model, where the output layer of the MC-AE decoder is designed for both accepting and producing reliable soft-bit decisions. Owing to the fact that bit-dependency is imposed by the MC-AE mapping, our bespoke MC-AE decoder becomes capable of achieving a beneficial iteration gain, when the extrinsic information is exchanged between the soft-decision MC-AE decoder and the soft-decision channel decoder. Secondly, in order to be able to interpret the performance advantages of our MC-AE over the conventional OFDM, we map the MC-AE’s input-output relationship to an equivalent model-based representation. The associated theoretical analysis verifies the fact that during the process of data-driven signal reconstruction across OFDM’s subcarriers, a beneficial frequency diversity gain is achieved by the proposed MC-AE design. Finally, our simulation results demonstrate that the MC-AE is capable of achieving substantial performance advantages over both conventional OFDM and OFDM based index modulation (OFDM-IM) in channel coded systems.
Learnable Multi-View Matrix Factorization With Graph Embedding and Flexible Loss

The goal of multi-view learning is to learn latent patterns from various data sources. Most of previous research focused on fitting feature embedding in target tasks. There is very limited research on the connection between feature representations with hidden layers of neural networks. In this paper, a multi-view deep matrix factorization model is proposed to learn a shared feature representation. The proposed model automatically explores the most discriminative features of multi-view data and makes these features meet the requirements of specific applications. Here we explore the connection between deep learning and feature representations. First, the model constructs a scalable neural network with shared hidden layers for exploring a low-dimensional representations of all views. Second, the quality of representation matrix is evaluated via relaxed graph regularization and evaluators to improve the feature representation capability of matrix factorization. Finally, the effectiveness of the proposed method is verified through comparative experiments with eight state-of-the-art multi-view clustering algorithms on eight real-world datasets.
SRRV: A Novel Document Object Detector Based on Spatial-Related Relation and Vision

Document object detection is a challenging task due to layout complexity and object diversity. Most of existing methods mainly focus on vision information, neglecting representative inherent spatial-related relationship among document objects. To capture structural information and contextual dependencies, we propose a novel document object detector based on spatial-related relation and vision (SRRV). It consists of three parts: vision feature extraction network, relation feature aggregation network and result refinement network. Vision feature extraction network enhances information propagation of hierarchical feature pyramid by adopting feature augmentation paths. Then, relation feature aggregation network combines graph construction module and graph learning module. Specifically, graph construction module calculates spatial information from geometric attributes of region proposals to encode relation information, while graph learning module stacks Graph Convolutional Network (GCN) layers to aggregate relation information at global scale. Both the vision and relation features are fed into result refinement network for feature fusion and relational reasoning. Experiments on the PubLayNet, POD and Article Regions datasets demonstrate that spatial relation information improves the performance with better accuracy and more precise bounding box prediction.
An Investigation into Inconsistency of Software Vulnerability Severity across Data Sources

Software Vulnerability (SV) severity assessment is a vital task for informing SV remediation and triage. Ranking of SV severity scores is often used to advise prioritization of patching efforts. However, severity assessment is a difficult and subjective manual task that relies on expertise, knowledge, and standardized reporting schemes. Consequently, different data sources that perform independent analysis may provide conflicting severity rankings. Inconsistency across these data sources affects the reliability of severity assessment data, and can consequently impact SV prioritization and fixing. In this study, we investigate severity ranking inconsistencies over the SV reporting lifecycle. Our analysis helps characterize the nature of this problem, identify correlated factors, and determine the impacts of inconsistency on downstream tasks. Our findings observe that SV severity often lacks consideration or is underestimated during initial reporting, and such SVs consequently receive lower prioritization. We identify six potential attributes that are correlated to this misjudgment, and show that inconsistency in severity reporting schemes can severely degrade the performance of downstream severity prediction by up to 77%. Our findings help raise awareness of SV severity data inconsistencies and draw attention to this data quality problem. These insights can help developers better consider SV severity data sources, and improve the reliability of consequent SV prioritization. Furthermore, we encourage researchers to provide more attention to SV severity data selection.
CARAN: A Context-Aware Recency-Based Attention Network for Point-of-Interest Recommendation

Point-of-interest (POI) recommendation system that tries to anticipate user’s next visiting location has attracted a plentiful research interest due to its ability in generating personalized suggestions. Since user’s historical check-ins are sequential in nature, Recurrent neural network (RNN) based models with context embedding shows promising result for modeling user’s mobility. However, such models cannot provide a correlation between non-consecutive and non-adjacent visits for understanding user’s behavior. To mitigate data sparsity problem, many models use hierarchical gridding of the map which cannot represent spatial distance smoothly. Another important factor while providing POI recommendation is the impact of weather conditions which has rarely been considered in the literature. To address the above shortcomings, we propose a Context-Aware Recency based Attention Network (CARAN) that incorporates weather conditions with spatiotemporal context and gives focus on recently visited locations using the attention mechanism. It allows interaction between non-adjacent check-ins by using spatiotemporal matrices and uses linear interpolation for smooth representation of spatial distance. Moreover, we use positional encoding of the check-in sequence in order to maintain relative position of the visited locations. We evaluate our proposed model on three real world datasets and the result shows that CARAN surpasses the existing state-of-the art models by 7–14%.
An advanced multi-camera system for automatic, high-precision and efficient tube profile measurement

In this paper, an advanced multi-camera system based on stereo vision is developed for automatic, highly accurate and efficient tube three-dimensional profile measurement of tubes, which can be applied to tube pre-assembly or in-process tube profile inspection. Multiple cameras are designed to acquire the tube images from different angles to help avoid image understanding disaster in case of tube overlap of complex tubes. A high precision calibration method based on photography is adopted to provide basic accuracy guarantee of the developed system. Then importantly, a new approach to tube model reconstruction is proposed, including a creative and adaptive tube seed growing method for accurate tube axis reconstruction, and a direct tube end reconstruction method. Furthermore, an automatic method of tube features calculation to help improve the efficiency of tube measurement. Finally, several experiments are performed to verify the reliability, efficiency and high-precision of the developed system. According to the experimental results, the tube measurement process can be finished in about 1.5 s/m. The average deviation of the radial deviation and axial deviation is within 0.062 mm respect to its reference value with the maximum standard deviation of 0.025 mm. For the bending angle and rotate angle, the average deviations from the reference values are within 0.025° and 0.031° with the maximum standard deviation of 0.011° and 0.012°, respectively.
AdaFed: Optimizing Participation-Aware Federated Learning With Adaptive Aggregation Weights

Federated learning (FL) has become one of the mainstream paradigms for multi-party collaborative learning with privacy protection. As it is difficult to guarantee all FL devices to be active simultaneously, a common approach is to only use a partial set of devices to participate in each round of model training. However, such partial device participation may introduce significant bias on the trained model. In this paper, we first conduct a theoretical analysis to investigate the negative impact of biased device participation and derive the convergence rate of FedAvg, the most well-known FL algorithm, under biased device participation. We further propose an optimized participation-aware federated learning algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdaFed</i> , which can adaptively tune the aggregation weight of each device based on its historical participation records and remove the bias introduced by partial device participation. To be more rigorous, we formally prove the convergence guarantee of AdaFed. Finally, we conduct trace-driven experiments to validate the effectiveness of our proposed algorithm. The experimental results are consistent with our theoretical analysis and show that AdaFed improves the global model accuracy and converges much faster than the state-of-the-art FL algorithms by eliminating the negative effect of biased device participation.
Cycle-reconstructive subspace learning with class discriminability for unsupervised domain adaptation

Unsupervised domain adaptation is used to effectively learn a classifier for data of the unlabeled target domain by utilizing the data of the source domain with sufficient labels but different distributions. In general, a transformation matrix is employed to acquire a common subspace where the distributions of the two domains are aligned, which is easy to lose lots of unique information of the two domains. To better preserve useful information during the transformation process, we propose a novel Cycle-Reconstructive Subspace Learning with Class Discriminability (CRSL) approach that uses two reconstructive matrixes through an iterative strategy to cycle-reconstruct data matrixes and update the common subspace. In this way, we learn the invariant features in the common subspace while better preserving global and local structures of the two original domains. Finally, we implement additional discriminative constraints such as intra-class aggregation and inter-class diffusion on the transformed features to ensure the class discriminability of data of the two domains. Extensive experiment results show that our conventional method outperforms state-of-the-art conventional methods and is comparable with advanced deep methods on four current domain adaptation datasets.