Miracle-dz/UniFAR-FTUs huggingface dataset:
Care bundles in intensive care

Care bundles were first developed over 20 yr ago. They have been used in a number of different medical and surgical specialities and have been used particularly extensively in cardiology. The care bundle approach to providing medical care forms part of a more general philosophical change in the way that medicine is practised. Important key elements include the development and widespread application of evidence-based medicine (on which bundles are based), the introduction of audit and performance assessment, and the introduction of guideline and protocol-based care to ensure the delivery of a minimum standard. Interest in the application of care bundles to critical care developed in the late 1990s and early years of this century. This was fuelled by the publication of an early goal-directed therapy study in the treatment of severe sepsis and septic shock. Although this study is now considered controversial, it generated much interest at the time because the apparent improvement in outcomes it demonstrated came about as the result of implementation of a protocol involving a number of distinct elements which could be regarded as (or in a more general sense, delivered as) a care bundle. Bundles were further described by Berenholtz and colleagues as a means of assessing quality of care. In their approach, interventions were applied which could be shown to prevent avoidable morbidity and mortality. They could further be viewed as a method of improving the processes involved in clinical care. As can be seen from this approach, care bundles have two main strands, focusing on (i) the interventions themselves and (ii) the processes of care delivery. Consequently, the care bundle is also seen as a valuable tool for audit and quality assurance. As such, it has become popular both as a management and clinical tool. Bundles formed a key part of the ‘100 000 Lives Programme’ (2004) and the ‘5 Million Lives Campaign’ (2006) introduced by the Institute for Healthcare Improvement. These were aimed at increasing patient safety and improving quality of care in a variety of clinical conditions. In critical care medicine, these included sepsis, cardiac, and respiratory failure. On the international scene, bundles have also been strongly promoted in critical care. A key example of this is the Surviving Sepsis campaign. This international collaborative initiative aimed to design and implement a care bundle approach to improve survival from severe sepsis by 25% by the year 2009. The success of all three care bundle programmes described above has led to further enthusiasm and interest in intensive care bundles for the management of a wider range of conditions in the future.
Deep Global-Connected Net With The Generalized Multi-Piecewise ReLU Activation in Deep Learning

Recent Progress has shown that exploitation of hidden layer neurons in convolution neural networks incorporating with a carefully designed activation function can yield better classification results in the field of computer vision. The paper firstly introduces a novel deep learning architecture aiming to mitigate the gradient-vanishing problem, in which the earlier hidden layer neurons could be directly connected with the last hidden layer and feed into the last layer for classification. We then design a generalized linear rectifier function as the activation function that can approximate arbitrary complex functions via training of the parameters. We will show that our design can achieve similar performance in a number of object recognition and video action benchmark tasks, under significantly less number of parameters and shallower network infrastructure, which is not only promising in training in terms of computation burden and memory usage, but is also applicable to low-computation, low-memory mobile scenarios.
Fogernetes: Deployment and management of fog computing applications

Devices used in fog and edge computing are heterogeneous and distributed. These computing environments are unpredictable and their applications are becoming more complex. This leads to challenges regarding deployment and management of fog and edge applications. It is important to ensure that quality of service, availability, reliability and real-time characteristics are guaranteed during deployment to take advantage of fog computing. In this paper, we present Fogernetes, a fog computing platform that enables management and deployment of fog applications with specific requirements on heterogeneous devices with different capabilities. Fogernetes allows matching the requirements of application components with device capabilities by using a labeling system. Based on a case study, we evaluate and test Fogernetes and examine its practical applicability for the deployment and management of fog computing applications. Fodeo serves as an example application. Fodeo analyzes video streams from multiple cameras and optimizes the video delivery. Fogernetes enables the deployment of Fodeo components on appropriate devices by matching requirements and capabilities.
What We Know about Testing Embedded Software

To cost-effectively test embedded software, practitioners and researchers have proposed many test techniques, approaches, tools, and frameworks. However, obtaining an overview of the state of the art and state of the practice in this area is challenging for practitioners or new researchers. In addition, owing to an inadequate overview of what already exists in this area, some companies often reinvent the wheel by designing a test approach that’s new to them but already exists. To address these problems, the authors conducted a systematic literature review of this area that covered the testing topics, testing activities, test artifacts, and industries on which the studies focused. The results can benefit both practitioners and researchers by serving as an index to the vast body of knowledge in this important, fast-growing area.
Protecting location privacy in opportunistic mobile social networks

Users face location-privacy risks when accessing Location-Based Services (LBSs) in an Opportunistic Mobile Social Networks (OMSNs). In order to protect the original requester's identity and location, we propose a location- privacy obfuscation protocol Multi-Hop Location-Privacy Protection (MHLPP) protocol that utilizes social ties between users. To increase chances of completing obfuscation operations, users detect and make contacts with one-hop or multi-hop neighbor friends in social networks. Encrypted obfuscation queries avoid users learning important information except for the original requester who generates queries and trusted users, especially the original requester's identity and location. Simulation results show that our protocol can give a higher query success ratio compared to its existing counterpart.
DDoS defense using MTD and SDN

Distributed large-scale cyber attacks targeting the availability of computing and network resources still remains a serious threat. In order to limit the effects caused by those attacks and to provide a proactive defense, mitigation should move to the networks of Internet Service Providers. In this context, Moving Target Defense (MTD) is a technique that increases uncertainty due to an ever-changing attack surface. In combination with Software Defined Networking (SDN), MTD has the potential to reduce the effects of a large-scale cyber attack. In this paper, we combine the defense techniques moving- target using Software Defined Networking and investigate their effectiveness. We review current moving-target defense strategies and their applicability in context of large-scale cyber attacks and the networks of Internet Service Providers. Further, we enforce the implementation of moving target defense strategies using Software Defined Networks in a collaborative environment. In particular, we focus on ISPs that cooperate among trusted partners. We found that the effects of a large-scale cyber attack can be significantly reduced using the moving-target defense and Software Defined Networking. Moreover, we show that Software Defined Networking is an appropriate approach to enforce implementation of the moving target defense and thus mitigate the effects caused by large-scale cyber attacks.
Gray projection for single image dehazing

Restoration of hazy image is an important issue in outdoor vision system. To accelerate the processing speed, a fast single image dehazing method is proposed based on the atmospheric scattering model and gray projection. In this method the transmission map is estimated roughly using minimum filtering which is based on dark channel prior and refined with fast average filtering. The gray projection method is adopted to obtain the atmospheric light. The subsection mechanism is designed to avoid the high light of sky region in recovered image, and Weber-Fechner Law is employed for color compensation in restored image. Our experimental results show that this algorithm can restore satisfied haze-free image, and keep the balance of quality and speed in real-time system.
FNBtools: A Software to Identify Homozygous Lesions in Deletion Mutant Populations

Deletion mutagenesis such as fast neutron bombardment (FNB) has been widely used for forward and reverse genetics studies in functional genomics. Traditionally, the time-consuming map-based cloning is used to locate causal deletions in deletion mutants. In recent years, comparative genomic hybridization (CGH) has been used to speed up and scale up the lesion identification process in deletion mutants. However, limitations of low accuracy and sensitivity for small deletions in the CGH approach are apparent. With the next generation sequencing (NGS) becoming affordable for most users, NGS-based bioinformatics tools are more appealing. Although several deletion callers are available, these tools are not efficient in detecting small deletions. Population-scale deletion callers that can identify both small and large deletions are rare. We were motivated to create a population-scale deletion detection tool, called FNBtools, to identify homozygous causal deletions in mutant populations by using NGS data. FNBtools is a tool to call deletions at a population-scale and to achieve high accuracy at different levels of coverage. In addition, FNBtools can detect both small and large deletions with the ability to identify unique deletions in a mutant pool by filtering deletions that exist in a wild-type or control pool. Furthermore, FNBtools is also able to visualize all identified deletions in a genome-wide scope by using Circos. From simulated data analysis, FNBtools outperforms four existing popular deletion callers in detecting small deletions at different coverage levels. To test the usefulness of FNBtools in real biological applications, we used it to analyze a salt-tolerant mutant in Medicago truncatula and identified the unique deletion locus that is tightly linked with this trait. The causal deletion in the mutant was confirmed by PCR amplification, sequencing and genetic linkage analyses. FNBtools can be used for homozygous deletion identification in any species with reference genome sequences. FNBtools is publicly available at: https://github.com/noble-research-institute/fnbtools.
A mapping study on microservice architectures of Internet of Things and cloud computing solutions

Internet of Things is a fairly new paradigm adopted by the industry, which offers the connectivity, via wireless systems, of all the devices that surround us. One of the challenges of IoT relates to the required resources to store and compute the huge amount of data resulted from devices' connections. Cloud computing is a solution to the IoT challenges; it provides on-demand resources in an easy-to-access manner. Another trend in the enterprise world is the usage of microservice architectures. Being a newly developed paradigm, and although its principles are defined, it is difficult to have a vision of the existing microservice-based research solutions. This paper, through the mapping study methodology, provides an overview of the current state-of-the-art and -practice regarding the usage of microservice architectures by IoT and cloud computing solutions. More specifically, we synthesize the data from 364 selected studies and describe the research types, number of publications and their main venues.
Image Based Classification Label PredictionsPoint Cloud View Prediction ResNet 50 ResNet 50 View Pool Depth Image Generation

We propose a novel neural network architecture for point cloud classification. Our key idea is to automatically transform the 3D unordered input data into a set of useful 2D depth images, and classify them by exploiting well performing image classification CNNs. We present new differentiable module designs to generate depth images from a point cloud. These modules can be combined with any network architecture for processing point clouds. We utilize them in combination with state-of-the-art classification networks, and get results competitive with the state of the art in point cloud classification. Furthermore, our architecture automatically produces informative images representing the input point cloud, which could be used for further applications such as point cloud visualization.
Improving the accuracy of the SVM classification using the Parzen classifier

In this paper the data classification technique, implying the consistent application of the SVM and Parzen classifiers, has been suggested. The Parzen classifier allows improving the accuracy of the data classification on the basis of the SVM classifier. The Parzen classifier is applied for data of subareas near the hyperplane dividing the classes. These subareas are determined experimentally for the concrete dataset. The recommendations on the use of the Parzen classifier near the hyperplane dividing the classes have been provided. The examples of application of the offered technique for the solving the data classification problem have been given.
Dynamical Hurst analysis identifies EEG channel differences between PTSD and healthy controls

We employ a time-dependent Hurst analysis to identify EEG signals that differentiate between healthy controls and combat-related PTSD subjects. The Hurst exponents, calculated using a rescaled range analysis, demonstrate a significant differential response between healthy and PTSD samples which may lead to diagnostic applications. To overcome the non-stationarity of EEG data, we apply an appropriate window length wherein the EEG data displays stationary behavior. We then use the Hurst exponents for each channel as hypothesis test statistics to identify differences between PTSD cases and controls. Our study included a cohort of 12 subjects with half healthy controls. The Hurst exponent of the PTSD subjects is found to be significantly smaller than the healthy controls in channel F3. Our results indicate that F3 may be a useful channel for diagnostic applications of Hurst exponents in distinguishing PTSD and healthy subjects.
Capacity sizing in the presence of repeated customer behavior: Dimensioning an inbound call center

This research develops a behavioral queuing model with feedback for a call center in which managers determine capacity sizing to maximize profit. In this paper, we explicitly take into account repeated customer behavior, which implies that the satisfied customers might return and have an impact on the arrival rate. For this case, customer satisfaction as perceived in terms of waiting time changes according to the capacity sizing.Then this research defines the staffing optimality of the call center as the number of agents maximizing the profit, including the income from customers and the costs from agents.Using a numerical analysis, this paper makes certain comparisons with the traditional models that do not consider repeated customer behavior. Furthermore, the results indicate how managers allocate the optimal staffing with various customer behavior mechanisms.
Differences in Cortical Structure and Functional MRI Connectivity in High Functioning Autism

Autism spectrum disorders (ASD) represent a complex group of neurodevelopmental conditions characterized by deficits in communication and social behaviors. We examined the functional connectivity (FC) of the default mode network (DMN) and its relation to multimodal morphometry to investigate superregional, system-level alterations in a group of 22 adolescents and young adults with high-functioning autism compared to age-, and intelligence quotient-matched 29 healthy controls. The main findings were that ASD patients had gray matter (GM) reduction, decreased cortical thickness and larger cortical surface areas in several brain regions, including the cingulate, temporal lobes, and amygdala, as well as increased gyrification in regions associated with encoding visual memories and areas of the sensorimotor component of the DMN, more pronounced in the left hemisphere. Moreover, patients with ASD had decreased connectivity between the posterior cingulate cortex, and areas of the executive control component of the DMN and increased FC between the anteromedial prefrontal cortex and areas of the sensorimotor component of the DMN. Reduced cortical thickness in the right inferior frontal lobe correlated with higher social impairment according to the scores of the Autism Diagnostic Interview-Revised (ADI-R). Reduced cortical thickness in left frontal regions, as well as an increased cortical thickness in the right temporal pole and posterior cingulate, were associated with worse scores on the communication domain of the ADI-R. We found no association between scores on the restrictive and repetitive behaviors domain of ADI-R with structural measures or FC. The combination of these structural and connectivity abnormalities may help to explain some of the core behaviors in high-functioning ASD and need to be investigated further.
Infection categorization using deep autoencoder

This paper proposes a framework to cluster the infections according to the form of attacking using user and entity behavior analytics. We integrate outside (open-source) and inside (traffic behavior) intelligence and construct a deep autoencoder to develop infection clustering models. According to the evaluation of real infections inside a tier-1 network, we demonstrate the capability of our framework to categorize infections by their intrusion characteristics.
One-shot Texture Segmentation

We introduce one-shot texture segmentation: the task of segmenting an input image containing multiple textures given a patch of a reference texture. This task is designed to turn the problem of texture-based perceptual grouping into an objective benchmark. We show that it is straight-forward to generate large synthetic data sets for this task from a relatively small number of natural textures. In particular, this task can be cast as a self-supervised problem thereby alleviating the need for massive amounts of manually annotated data necessary for traditional segmentation tasks. In this paper we introduce and study two concrete data sets: a dense collage of textures (CollTex) and a cluttered texturized Omniglot data set. We show that a baseline model trained on these synthesized data is able to generalize to natural images and videos without further fine-tuning, suggesting that the learned image representations are useful for higher-level vision tasks.
Hybrid Trajectory Planning for Autonomous Driving in Highly Constrained Environments

In this paper, we introduce a novel and efficient hybrid trajectory planning method for autonomous driving in highly constrained environments. The contributions of this paper are fourfold. First, we present a trajectory planning framework that is able to handle geometry constraints, nonholonomic constraints, and dynamics constraints of cars in a humanlike and layered fashion and generate curvature-continuous, kinodynamically feasible, smooth, and collision-free trajectories in real time. Second, we present a derivative-free global path modification algorithm to extract high-order state information in free space for state sampling. Third, we extend the regular state-space sampling method widely used in on-road autonomous driving systems to a multi-phase deterministic state-space sampling method that is able to approximate complex maneuvers. Fourth, we improve collision checking accuracy and efficiency by using a different car footprint approximation strategy and a two-phase collision checking routine. A range of challenging simulation experiments show that the proposed method returns high-quality trajectories in real time and outperforms existing planners, such as hybrid A* and conjugate-gradient descent path smoother in terms of path quality, efficiency, and computation resources used.
Application of accurate online support vector regression in atmospheric SO2 concentration prediction

In order to adapt to the characteristics of high nonlinear and time-varying for air pollutants concentration and improve the real-time prediction accuracy of air pollutants concentration, a forecasting model of air pollutants concentration based on accurate online support vector regression (AOSVR) algorithm is established in this paper. According to the hourly SO2 concentration and meteorological parameters from May 2014 to April 2015 in Wanliu Monitoring Station of Beijing in China, the data of 2 months are selected as experimental samples. At the same time, Pearson correlation coefficient method is used to select the exogenous inputs which have strong correlation with the output variable. The results show that the AOSVR algorithm can adjust the prediction model dynamically, and the prediction accuracy is higher than that of the conventional fixed support vector regression (SVR) model.
Fully Scalable Gaussian Processes using Subspace Inducing Inputs

We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional input data. Our key idea is a representation trick over the inducing variables called subspace inducing inputs. This is combined with certain matrix-preconditioning based parametrizations of the variational distributions that lead to simplified and numerically stable variational lower bounds. Our illustrative applications are based on challenging extreme multi-label classification problems with the extra burden of the very large number of class labels. We demonstrate the usefulness of our approach by presenting predictive performances together with low computational times in datasets with extremely large number of instances and input dimensions.
Design of High-Speed and Power-Efficient Ternary Prefix Adders Using CNFETs

Ternary logic circuits have advantage over the corresponding binary counterparts with respect to area and interconnect complexity. CNFET technology is ideal to implement ternary logic circuits because the threshold voltage of CNFETs depends on the physical dimensions of their channel. This paper presents the methodology and implementation of ternary prefix adders using CNFETs. While the concept of carry propagate–generate is widely used in the implementation of binary prefix adders, same, however, cannot be directly applied in the implementation of ternary prefix adders. In this paper, a technique that enables the use of carry Propagate-Generate concept in multidigit ternary adders, is proposed. Multidigit ternary prefix adders, which use binary prefix trees for carry computation, are implemented using CNFETs. Five variants of CNFET-based multidigit ternary adders, which use different prefix networks in carry computation, are implemented using HSPICE. Simulation results show that the proposed ternary prefix adders result in up to <inline-formula><tex-math notation="LaTeX">$58\%$</tex-math></inline-formula> reduction in average power consumption, <inline-formula><tex-math notation="LaTeX">$50\%$</tex-math></inline-formula> reduction in propagation delay and <inline-formula><tex-math notation="LaTeX">$80\%$</tex-math></inline-formula> reduction in power-delay product, when compared to other CNFET-based ternary adders existing in the literature.