Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Based on the Nursing Notes on In-Hospital 28-Day Mortality of Sepsis Patients Utilizing the MIMIC-III Database

In medical visualization, nursing notes contain rich information about a patient's pathological condition. However, they are not widely used in the prediction of clinical outcomes. With advances in the processing of natural language, information begins to be extracted from large-scale unstructured data like nursing notes. This study extracted sentiment information in nursing notes and explored its association with in-hospital 28-day mortality in sepsis patients. The data of patients and nursing notes were extracted from the MIMIC-III database. A COX proportional hazard model was used to analyze the relationship between sentiment scores in nursing notes and in-hospital 28-day mortality. Based on the COX model, the individual prognostic index (PI) was calculated, and then, survival was analyzed. Among eligible 1851 sepsis patients, 580 cases suffered from in-hospital 28-day mortality (dead group), while 1271 survived (survived group). Significant differences were shown between two groups in sentiment polarity, Simplified Acute Physiology Score II (SAPS-II) score, age, and intensive care unit (ICU) type (all P < 0.001). Multivariate COX analysis exhibited that sentiment polarity (HR: 0.499, 95% CI: 0.409-0.610, P < 0.001) and sentiment subjectivity (HR: 0.710, 95% CI: 0.559-0.902, P = 0.005) were inversely associated with in-hospital 28-day mortality, while the SAPS-II score (HR: 1.034, 95% CI: 1.029-1.040, P < 0.001) was positively correlated with in-hospital 28-day mortality. The median death time of patients with PI ≥ 0.561 was significantly earlier than that of patients with PI < 0.561 (13.5 vs. 49.8 days, P < 0.001). In conclusion, sentiments in nursing notes are associated with the in-hospital 28-day mortality and survival of sepsis patients.
Filter Pruning via Measuring Feature Map Information

Neural network pruning, an important method to reduce the computational complexity of deep models, can be well applied to devices with limited resources. However, most current methods focus on some kind of information about the filter itself to prune the network, rarely exploring the relationship between the feature maps and the filters. In this paper, two novel pruning methods are proposed. First, a new pruning method is proposed, which reflects the importance of filters by exploring the information in the feature maps. Based on the premise that the more information there is, more important the feature map is, the information entropy of feature maps is used to measure information, which is used to evaluate the importance of each filter in the current layer. Further, normalization is used to realize cross layer comparison. As a result, based on the method mentioned above, the network structure is efficiently pruned while its performance is well reserved. Second, we proposed a parallel pruning method using the combination of our pruning method above and slimming pruning method which has better results in terms of computational cost. Our methods perform better in terms of accuracy, parameters, and FLOPs compared to most advanced methods. On ImageNet, it is achieved 72.02% top1 accuracy for ResNet50 with merely 11.41M parameters and 1.12B FLOPs.For DenseNet40, it is obtained 94.04% accuracy with only 0.38M parameters and 110.72M FLOPs on CIFAR10, and our parallel pruning method makes the parameters and FLOPs are just 0.37M and 100.12M, respectively, with little loss of accuracy.
Spatial and Temporal Splitting Heuristics for Multi-Robot Motion Planning

In this work, we systematically examine the application of spatio-temporal splitting heuristics to the Multi-Robot Motion Planning (MRMP) problem in a graph-theoretic setting: a problem known to be NP-hard to optimally solve. Following the divide-and-conquer principle, we design multiple spatial and temporal splitting schemes that can be applied to any existing MRMP algorithm, including integer programming solvers and Enhanced Conflict Based Search, in an orthogonal manner. The combination of a good baseline MRMP algorithm with a proper splitting heuristic proves highly effective, allowing the resolution of problems 10+ times than what is possible previously, as corroborated by extensive numerical evaluations. Notably, spatial partition of problem fusing with the temporal splitting heuristic and the enhanced conflict based search (ECBS) algorithm increases the scalability of ECBS on large and challenging DAO maps by 5–15 folds with negligible impact on solution optimality.
Continuous Active Learning for Seismo-Volcanic Monitoring

Deep learning has advanced seismo-volcanic monitoring to unprecedented performance levels. Nevertheless, seismic data labeling still requires substantial annotation efforts, often delayed in time if the eruptive state alters the data conditions. The selective segmentation of which earthquake transients have to be reviewed by an expert can significantly reduce annotation time, speed up algorithmic training, and boost monitoring adaptability to unforeseen situations. In this work, we propose a Bayesian temporal convolutional neural network (B-TCN) to perform continuous detection and classification while extracting the most uncertain events from the continuous data stream. Formulated as an active learning (AL) procedure, our B-TCN outputs an uncertainty map over time, highlighting the class memberships that are needed to be reviewed. We attain a significant improvement in monitoring metrics, with only a fraction of the initial dataset to achieve a recognition performance of 83% for four seismo-volcanic events.
Usability evaluation of augmented reality‐based maintenance instruction system

Abstract This article presents an augmented reality‐based instruction (ARBI) system for maintenance tasks. A traditional manual instruction method and a computer‐assisted instruction method were compared. Three maintenance instruction methods, three task difficulty levels (low, medium, and high), and the user's gender (male and female) were specified as the independent variables in the experimental design. The dependent variables included task completion time and error rate as objective measures, and system usability scale (SUS) and NASA‐task load index (NASA‐TLX) scores as subjective measures. There were 30 participants (15 males and 15 females) in the experiment. The results indicated that the instruction method and task difficulty significantly affected the task completion time, error rate, SUS, and NASA‐TLX. Among the instruction methods, the ARBI method exhibited the highest SUS score, lowest NASA‐TLX score, shortest task completion time, and minimum error rate. In conclusion, the proposed ARBI method was beneficial for assisting iPhone maintenance tasks.
A Nature-Inspired Algorithm to Adaptively Safe Navigation of a Covid-19 Disinfection Robot

Autonomous mobile robots have been extensively used in medical services. During the Covid-19 pandemic, ultraviolet type-C irradiation (UV-C) disinfection robots and spray disinfection robots have been deployed in hospitals and other public open spaces. However, adaptively safe navigation of disinfection robots and spray disinfection robots have not been adequately studied. In this paper, an adaptively safe navigation model of Covid-19 disinfection robots is proposed using a nature-inspired method, cuckoo search algorithm (CSA). A Covid-19 disinfection robot is adaptively navigated to decelerate in the vicinity of objects and obstacles thus it can sufficiently spray and illuminate around objects, which assures objects to be fully disinfected against SARS-CoV-2. In addition, the path smoothing scheme based on the $$\mathcal {B}$$ -spline curve is integrated with adaptive-speed navigation to generate a safer and smoother trajectory at a reasonable distance from the obstacle. Simulation and comparative studies prove the effectiveness of the proposed model, which can plan a reasonable and short trajectory with obstacle avoidance, and show better performance than other meta-heuristic optimization techniques.
The Importance of Security Is in the Eye of the Beholder: Cultural, Organizational, and Personal Factors Affecting the Implementation of Security by Design

Security by design is a recommended approach, addressing end-to-end security and privacy in the design of software systems. To realize this approach, proactive security behavior is required from software developers. This research follows results from previous studies that suggest that personal and organizational characteristics influence security-related behaviors during the software design process. The research is aimed at gaining an in-depth understanding of proactive security behavior and the factors affecting it. Leveraging organization climate theory from organizational psychology, we propose a theoretical model, detailing different factors and their relations with proactive security behavior and test it in empirical settings. The empirical study was conducted in collaboration with an internationally distributed information technology enterprise and included a survey questionnaire completed by 499 software developers working in seven countries. The results of the survey confirm the moderation-mediation relations in the proposed model, revealing that organizational security climate and security self-efficacy are both positively associated with proactive security behavior, organizational security climate is positively associated with security self-efficacy, and cultures promoting individualism moderate the relationship between organizational security climate and security self-efficacy, thus impeding proactive security behavior. The body of knowledge of organizational psychology points to directions that can effectively be activated for improvement.
Digital evidence and the crime scene

Many criminal investigations maintain an element of digital evidence, where it is the role of the first responder in many cases to both identify its presence at any crime scene, and assess its worth. Whilst in some instances the existence and role of a digital device at-scene may be obvious, in others, the first responder will be required to evaluate whether any 'digital opportunities' exist which could support their inquiry, and if so, where these are. This work discusses the potential presence of digital evidence at crime scenes, approaches to identifying it and the contexts in which it may exist, focusing on the investigative opportunities that devices may offer. The concept of digital devices acting as 'digital witnesses' is proposed, followed by an examination of potential 'digital crime scene' scenarios and strategies for processing them.
Evaluation of Feature Selection Techniques for Breast Cancer Risk Prediction

This study evaluates several feature ranking techniques together with some classifiers based on machine learning to identify relevant factors regarding the probability of contracting breast cancer and improve the performance of risk prediction models for breast cancer in a healthy population. The dataset with 919 cases and 946 controls comes from the MCC-Spain study and includes only environmental and genetic features. Breast cancer is a major public health problem. Our aim is to analyze which factors in the cancer risk prediction model are the most important for breast cancer prediction. Likewise, quantifying the stability of feature selection methods becomes essential before trying to gain insight into the data. This paper assesses several feature selection algorithms in terms of performance for a set of predictive models. Furthermore, their robustness is quantified to analyze both the similarity between the feature selection rankings and their own stability. The ranking provided by the SVM-RFE approach leads to the best performance in terms of the area under the ROC curve (AUC) metric. Top-47 ranked features obtained with this approach fed to the Logistic Regression classifier achieve an AUC = 0.616. This means an improvement of 5.8% in comparison with the full feature set. Furthermore, the SVM-RFE ranking technique turned out to be highly stable (as well as Random Forest), whereas relief and the wrapper approaches are quite unstable. This study demonstrates that the stability and performance of the model should be studied together as Random Forest and SVM-RFE turned out to be the most stable algorithms, but in terms of model performance SVM-RFE outperforms Random Forest.
Inferring Bias and Uncertainty in Camera Calibration

Accurate camera calibration is a precondition for many computer vision applications. Calibration errors, such as wrong model assumptions or imprecise parameter estimation, can deteriorate a system’s overall performance, making the reliable detection and quantification of these errors critical. In this work, we introduce an evaluation scheme to capture the fundamental error sources in camera calibration: systematic errors (biases) and uncertainty (variance). The proposed bias detection method uncovers smallest systematic errors and thereby reveals imperfections of the calibration setup and provides the basis for camera model selection. A novel resampling-based uncertainty estimator enables uncertainty estimation under non-ideal conditions and thereby extends the classical covariance estimator. Furthermore, we derive a simple uncertainty metric that is independent of the camera model. In combination, the proposed methods can be used to assess the accuracy of individual calibrations, but also to benchmark new calibration algorithms, camera models, or calibration setups. We evaluate the proposed methods with simulations and real cameras.
Filter-function formalism and software package to compute quantum processes of gate sequences for classical non-Markovian noise

Correlated, non-Markovian noise is present in many solid-state systems employed as hosts for quantum information technologies, significantly complicating the realistic theoretical description of these systems. In this regime, the effects of noise on sequences of quantum gates cannot be described by concatenating isolated quantum operations if the environmental correlation times are on the scale of the typical gate durations. The filter function formalism has been successful in characterizing the decay of coherence under the influence of such classical, non-Markovian environments and here we show it can be applied to describe unital evolution within the quantum operations formalism. We find exact results for the quantum process and a simple composition rule for a sequence of operations. This enables the detailed study of effects of noise correlations on algorithms and periodically driven systems. Moreover, we point out the method's suitability for numerical applications and present the open-source Python software package filter_functions. Amongst other things, it facilitates computing the noise-averaged transfer matrix representation of a unital quantum operation in the presence of universal classical noise for arbitrary control sequences. We apply the presented methods to selected examples.
Image Search with Text Feedback by Deep Hierarchical Attention Mutual Information Maximization

Image retrieval with text feedback is an emerging research topic with the objective of integrating inputs from multiple modalities as queries. In this paper, queries contain a reference image plus text feedback that describes modifications between this image and the desired image. The existing work for this task mainly focuses on designing a new fusion network to compose the image and text. Still, little research pays attention to the modality gap caused by the inconsistent distribution of features from different modalities, which dramatically influences the feature fusion and similarity learning between queries and the desired image. We propose a Distribution-Aligned Text-based Image Retrieval (DATIR) model, which consists of attention mutual information maximization and hierarchical mutual information maximization, to bridge this gap by increasing non-linear statistic dependencies between representations of different modalities. More specifically, attention mutual information maximization narrows the modality gap between different input modalities by maximizing mutual information between the text representation and its semantically consistent representation captured from the reference image and the desired image by the difference transformer. For hierarchical mutual information maximization, it aligns distributions of features from the image modality and the fusion modality by estimating mutual information between a single-layer representation in the fusion network and the multi-level representations in the desired image encoder. Extensive experiments on three large-scale benchmark datasets demonstrate that we can bridge the modality gap between different modalities and achieve state-of-the-art retrieval performance.
K-bMOM: A robust Lloyd-type clustering algorithm based on bootstrap median-of-means

The median-of-means is an estimator of the mean of a random variable that has emerged as an efficient and flexible tool to design robust learning algorithms with optimal theoretical guarantees. However, its use for the clustering task suggests dividing the dataset into blocks, which may provoke the disappearance of some clusters in some blocks and lead to bad performances. To overcome this difficulty, a procedure termed "bootstrap median-of-means" is proposed, where the blocks are generated with a replacement in the dataset. Considering the estimation of the mean of a random variable, the bootstrap median-of-means has a better breakdown point than the median-of-means if enough blocks are generated. A clustering algorithm called K-bMOM is designed, by performing Lloyd-type iterations together with the use of the bootstrap median-of-means strategy. Good performances are obtained on simulated and real-world datasets for color quantization and an emphasis is put on the benefits of our robust intialization procedure. On the theoretical side, K-bMOM is also proven to have a non-trivial probabilistic breakdown point in well-clusterizable situations.
Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs

Graph sampling and random walk operations, capturing the structural properties of graphs, are playing an important role today as we cannot directly adopt computing-intensive algorithms on large-scale graphs. Existing system frameworks for these tasks are not only spatially and temporally inefficient, but many also lead to biased results. This paper presents Skywalker, a high-throughput, quality-preserving random walk and sampling framework based on GPUs. Skywalker makes three key contributions: first, it takes the first step to realize efficient biased sampling with the alias method on a GPU. Second, it introduces well-crafted load-balancing techniques to effectively utilize the massive parallelism of GPUs. Third, it accelerates alias table construction and reduce the GPU memory requirement with efficient memory management scheme. We show that Skywalker greatly outperforms the state-of-the-art CPU-based and GPU-based baselines, in a wide spectrum of workload scenarios.
A Lightweight SE-YOLOv3 Network for Multi-Scale Object Detection in Remote Sensing Imagery

Current state-of-the-art detectors achieved impressive performance in detection accuracy with the use of deep learning. However, most of such detectors cannot detect objects in real time due to heavy computational cost, which limits their wide application. Although some one-stage detectors are designed to accelerate the detection speed, it is still not satisfied for task in high-resolution remote sensing images. To address this problem, a lightweight one-stage approach based on YOLOv3 is proposed in this paper, which is named Squeeze-and-Excitation YOLOv3 (SE-YOLOv3). The proposed algorithm maintains high efficiency and effectiveness simultaneously. With an aim to reduce the number of parameters and increase the ability of feature description, two customized modules, lightweight feature extraction and attention-aware feature augmentation, are embedded by utilizing global information and suppressing redundancy features, respectively. To meet the scale invariance, a spatial pyramid pooling method is used to aggregate local features. The evaluation experiments on two remote sensing image data sets, DOTA and NWPU VHR-10, reveal that the proposed approach achieves more competitive detection effect with less computational consumption.
Real-time 3D-Lidar, MMW Radar and GPS/IMU fusion based vehicle detection and tracking in unstructured environment

To solve the problem of unmanned ground vehicle leader-follower formation transportation in unstructured environment, we propose a novel target detection and tracking method based on multi-sensor fusion perception. Combined with 3D-Lidar, millimeter wave Radar and GPS/IMU, the proposed method can achieve stable target detection and continuous tracking of both static and dynamic vehicles. First, 3D-Lidar is used to detect the geometric model of the leader vehicle to complete the initialization of tracking target and it can also be assisted for target tracking. Then during the movement, the dynamic leader is mainly tracked through millimeter wave Radar as this sensor can keep tracking the same target with a constant index and effectively distinguish dynamic vehicle from other static obstacles according to relative speed estimation. In addition, by using GPS/IMU based integrated navigation, the movement trend of the leader can be derived according to the echo vehicle pose information and the relative position relationship. This is helpful to reduce the region of interest for target tracking and improve the real-time performance. In different unstructured environments, we perform the leader-follower formation transportation experiments for hundreds of kilometers. In rough terrain, the maximum tracking speed can still reach 40km/h and the maximum tracking distance can be up to 100 meters. Experiments show that the proposed method is suitable for vehicle target detection and tracking in unstructured environment. It has good robustness and high real-time performance with an average processing frame rate of 20Hz. The proposed method can be used for the formation transportation of unmanned ground vehicles to reduce labor costs.
Target-targeted Domain Adaptation for Unsupervised Semantic Segmentation

Semantic segmentation has attracted increasing attention due to its important role in self-driving, and it is often realized by supervised learning with large number of well labeled maps. However, the labeled images are hard to be obtained in most circumstances, and the common way for unsupervised semantic segmentation is usually implemented by transferring the knowledge from source supervised domain to target unsupervised domain. Most researches focus on encouraging target predictions to be closer to the source ones through a weight-sharing network, and achieve certain performance. However, these methods often suffer from the domain shift problem that the networks are often trained towards the source domain and lead to performance degradation. In this paper, we propose a target-targeted domain adaptation approach by focusing the training on target domain. Our model consists of two components: the Image-to-image Translation (IIT) module to translate the source image to target domain and the Target-targeted Segmentation Adaptation (TSA) module to focus the semantic segmentation on target domain. The IIT module deals with image space alignment while the TSA module bridges the domain gap at the segmentation map level. In addition, we design a closed-loop learning to promote each other by employing feedback from TSA to IIT. Extensive experiments on GTA5 and SYNTHIA to Cityscapes demonstrate the effectiveness of our method in domain adaptation of unsupervised semantic segmentation.