Miracle-dz/UniFAR-FTUs huggingface dataset:
MSPPNet: A Lightweight Network for Real-time Semantic Image Segmentation

Real-time semantic segmentation is widely applied in many fields. However, current state-of-the-art methods ignore the inference speed, while some other models that have short run-times produce coarse segmentation results. To balance the inference speed and segmentation accuracy, we propose a Multi-scale Spatial Pyramid Pooling Network (MSPPNet), a lightweight and efficient network for real-time semantic segmentation. Here, we adopt modified Xception to obtain high-level and low-level feature maps, which fundamentally reduces computational complexity and the number of parameters. Besides, we design the Multi-scale Spatial Pyramid Pooling module (MSPP) to aggregate context information from high-level feature maps, which effectively improves segmentation accuracy. Furthermore, the spatial attention mechanism is employed to enrich the details of segmentation and recover object boundaries. Experiments on the Cityscapes dataset show that MSPPNet has less than 1M parameters, and achieves 64.55% mean IoU with a speed of 121 fps. It is demonstrated that MSPPNet achieves a balance between speed and accuracy.
Persuasive Factors and Weakness for Security Vulnerabilities in BIG IOT Data in Healthcare Solution

In the present scenario, public health is a global challenge. In view of COVID-19 pandemic, interventions of emerging technologies hasbeen highly increased and post-pandemica big technological shift is expected for providing information and communication Technology-enabled solutions to healthcare as well a meeting other social challenges. Internet of Things or IoT and Big data are the technologies prominently being used in healthcare applications. In smart city visualization to provide ubiquitous computing environment, urge of smart, small but powerful sensor devices or IoT technology-enabled healthcare solutions deployments done over open networked infrastructure and underlying architecture. Such highly dynamic and heterogeneous environment with rapid digital transformation enforcing trusted security resource-restrictions and performance implication. In this paper, firstly, we explore the existing security, privacy and authentication weakness in reference to IoT or IoMT and big data enabled healthcare applications. Secondly, scaling the low to high security risks done based on the major weaknesses. In this work primarily we focus on most challenging attacks like Denial of Services (DoS), Man in the middle and dynamic intrusions. In winding-up machine learning based intelligent adaptive approach proposed for underlying deficiencies and insufficiencies in IoT enabled Healthcare application security. The key driving forces for the imprecision of trust and security with emerging Big IoT also presented as future scope.
Li-ion battery state of health Prediction based on Long Short-Term Memory Recurrent Neural Network

The state of health (SOH) prediction of lithium-ion battery is essential for the health management of batteries. At present, the prediction method combined with the extraction of health indicators in charge-discharge process has received extensive attention, however, many studies ignored that the extraction of battery discharge data will be affected by the actual operating conditions, which will affect the effectiveness of health indicators extraction. In this work, a type of recurrent neural network (RNN), which is long short-term memory-RNN(LSTM-RNN), is proposed to prediction the SOH of Li-ion batteries through the data of charging process and capacity. Because the different choice of network parameters will also affect the performance of the model, particle swarm optimization (PSO) algorithm is used to optimize LSTM model. The test results show that this method can effectively predict SOH of battery, and the maximum RMSE is less than 0.01. Compared with the traditional LSTM algorithm, it has higher accuracy.
Measurement of gas-density variation in pulsed streamer discharge at atmospheric pressure by Mach–Zehnder interferometry

In this study, gas-density variations occurring in pulsed streamer discharge at atmospheric pressure were quantitatively measured using a Mach–Zehnder interferometer. Experiments were performed using different gas compositions involving N2 or air in humid or dry conditions. Spatiotemporal variations in the gas density after the discharge were characterised in terms of the energy relaxation process of the molecules. The measured gas-density variations were well reproduced by a zero-dimensional simulation model of vibrational-to-translational energy transfer in humid-air and humid-N2 conditions. This indicated that the vibrational energy relaxation of N2 through water molecules has a major effect on the gas density. However, the gas-density variations in dry-air and dry-N2 conditions were not reproduced by the model; the corresponding etiology is outlined and discussed.
An intelligent routing optimization strategy based on deep reinforcement learning

Finding the optimal strategy in network routing has always been a NP hard problem. Due to the complexity and dynamics of network traffic, the existing intelligent routing algorithms have poor generalization ability. Therefore, this paper proposes an intelligent routing strategy based on deep reinforcement learning, and with the help of SDN control can dynamically collect network traffic distribution information, can dynamically adjust the routing strategy. Compared with traffic engineering algorithms such as TCMP and DRL-TE, the end-to-end delay is optimized under different throughput.
Design and Implementation of an Attention-Based Interpretable Model for Document Classification

With the rapid development of artificial intelligence and machine learning, deep learning technology has been successfully applied to various tasks in the field of natural language processing. However, the erroneous decisions produced by the deep learning model and the characteristics of its black box cause users to doubt its decisions. We urgently need the model to have the ability to make rationalized explanations of its decisions, that is, interpretability. In this paper, we propose an attention-based interpretable model for document classification, which aims to have a better model performance and at the same time have better interpretability. Experiments show that our classification model is close to the performance of other mainstream models. Also, the attribution score explanation provided by the interpretation method are effective.
Poly(vinylidene fluoride)-based hybrid gel polymer electrolytes for additive-free lithium sulfur batteries

The permeation of dissolved lithium polysulfides across the porous polyolefin-based commercial separator is a major hindrance for using lithium sulfur batteries (LSBs). In this work, the poly(vinylidene fluoride) (PVDF)-based gel polymer electrolyte (GPE) with a compact morphology to block polysulfide penetration is prepared using a simple solution-casting method, and the strategy of incorporating poly(ethylene oxide) and nano zirconium dioxide is applied to guarantee electrolyte uptake and Li+ mobility. Superior to the commercial separator with liquid electrolyte, the LSB assembled with additive-free GPE exhibits a high initial capacity of 1429 mA h g−1, coulombic efficiency of 96% at 0.2C and improved rate performance. After 500 cycles at 1C, the LSB could still deliver a capacity of 847.2 mA h g−1, with a low fading rate of 0.05%. The LSB with high sulfur loading (5.2 mg cm−2) could attain a high areal capacity of 4.6 mA h cm−2. Results of scanning electron microscopy suggest that such a hybrid GPE could effectively protect the lithium anode from polysulfide corrosion. Therefore, this novel membrane of hybrid PVDF-based GPE provides a simple and effective method to establish high-performance LSBs.
Complex Document Layout Segmentation Based on An Encoder-Decoder Architecture

In our work, we propose an end-to-end encoder-decoder network for complex document layout segmentation. The proposed multi-scale feature extraction network with two parallel branches is applied to further process the feature maps, where one branch enriches the multi-scale information of the feature maps by building feature pyramids, another branch is introduced to capture the dependencies between different locations and integrate long-range context information. Moreover, we merge the outputs of the two branches to enhance the feature representation so as to further improve segmentation accuracy. Experimental results on datasets of PubLayNet and DSSE-200 demonstrate the effectiveness of our proposed method, which yields pixel-wise accuracy of above 99%.
The research of changes in biochemical parameters extracts of sprouting soybean and rapeseed seeds

At different periods of swelling and incubation (12, 24, 36, 48, 60 h) soybean seeds were selected: swollen (after reaching the critical humidity of soy 58.6 %; rapeseed 43.6 %); sprouted "spouts"; with sprouts ≥1 cm. It was found that the protein content in seedlings gradually decreases from 37.0-41.3% in dormant seeds, to 18.6-20.3% in sprouts after 48 hours in rapeseed and 60 hours of incubation in soybeans. The developed method of differentiation of germinating seeds by physiological state made it possible to register step-by-step changes in extracts according to pH and buffer capacity (BC). This rapid analysis is applicable in the development of technology for protein complex extraction and preparation of protein preparations from non-traditional sources.
An Auxiliary Identification System for Destructive Pests

This work takes this Asian Hornet’s invasion of Washington in 2020 as an example to build an auxiliary identification system for Destructive Pests to help the government’s rescue work. To help the government to prioritize the resource to the most possible reports and improve the investigation efficiency, we built a binary classification model to classify the report that contains images and text. For image classification, we design a VGG16-based convolutional neural network pretraining with ImageNet, which achieved superior performance with a high F1 score (95.08%) on our dataset. For text classification, we use the Latent Dirichlet Allocation (LDA) model to cluster words into different themes and adopt cosine similarity to calculate the similarity between the new report and the benchmark positive/negative sets. The results demonstrated that our approach can identify target pests efficiently and help the government to prioritize the resource to the most possible reports, improving the investigation efficiency.
The security function dynamic calling scheme for software-defined security

With the rapid development of cloud computing, various network attack methods are becoming more advanced, and the attack speed is getting faster. Traditional security protection solutions can no longer adapt to new network attacks, bringing significant challenges to security protection in virtualized network environments such as cloud computing. Based on these problems, this paper proposes a dynamic calling mechanism of security functions for software-defined security based on software-defined network technology. This mechanism defined the strategies of dynamically scheduling of virtual security service nodes in the virtualized network environment. The strategies describe the network’s data flow and virtual security devices by extending the attribute-based access control policy model. It can dynamically construct the mapping relationship between network flow and virtual security devices according to users’ security requirements, thus forming a personalized security service chain for specific network flows. This mechanism can protect the “north-south” network flow at the IoT edge agent according to the user security business requirements.
Visible and infrared missile-borne image registration based on improved SIFT and joint features

For the acquired visible and infrared missile-borne images, in order to provide accurate and reliable image data for the subsequent processing of images to achieve complementary information, we carried out visible and infrared missile-borne image registration research. Firstly, the shape context is used to extract the rough edge features of the image. Then we improve the SIFT gradient definition to overcome the difference in image gray value, and combine with the coherent point drift algorithm to increase the number of correct matching points to achieve global image registration. Experimental results show that, compared with the existing registration methods, the proposed method has a good visual effect of registration, greatly improves the correct registration points and registration accuracy, and can better solve the problem of visible infrared missile-borne image registration.
MULTI-SMALL TARGET DETECTION AND TRACKING BASED ON IMPROVED YOLO AND SIFT FOR DRONES

Multi-target tracking algorithms applied to drones often face more detection algorithm, while achieving active tracking of small targets and fast-moving targets. In response to these problems, this paper proposes a multi-small target detection and tracking method based on improved YOLO and SIFT for drones. First, we prune the network of the YOLO algorithm. Through sparse training, channel pruning, and network adjustment, the drone can deploy a target detection network with a smaller model. Secondly, we propose a method of adaptive threshold and minimum distance and optimize SIFT feature extraction, achieve eﬀective detection of small targets, and improve tracking accuracy and success rate. Finally, we consider the needs of the UAV to track fast-moving targets, we use scale and position information to complete the data association, and the UAV can eﬀectively track fast-moving targets. The algorithm of this paper is evaluated by the VisDrone 2019 MOT benchmark data set. The leading evaluation indicators MOTA and MOTP are 38.7 and 75.7. The secondary evaluation indicators IDF1, ML, and FN, etc. all perform better. The algorithm meets the complex task requirements of multi-target tracking for UAVs.
CCIDE_A_231670 233..240

*These authors contributed equally to this work Abstract: Posterior open bite (POB) is one of the most severe malocclusions that can impair patients’ masticatory functions, yet it is also a condition that is poorly understood and not well studied. Most reported cases are either sporadic or idiosyncratic with a diverse yet poorly understood etiology. Although primary failure of eruption (PFE), lateral tongue thrust, and certain medical syndromes or pathology of the temporomandibular joints have all been shown to cause POB, the complex interplay of environmental and genetic factors makes its etiopathogenesis a difficult subject to understand and investigate. Here, we provide a comprehensive review of the etiology of posterior open bite. Additionally, a genetic cause for POB is proposed through a report of an apparently non-syndromic familial case series with high POB penetrance across two generations. Further investigations of the gene(s) and mechanism(s) involved can not only provide a unique opportunity to better understand POB and the intricate muscular-occlusal relationship, but also offer powerful insight into the most effective approaches to clinical management of these (and potentially other) malocclusions.
Seizure pathways and seizure durations can vary independently within individual patients with focal epilepsy

A seizure's electrographic dynamics are characterised by its spatiotemporal evolution, also termed dynamical"pathway"and the time it takes to complete that pathway, which results in the seizure's duration. Both seizure pathways and durations can vary within the same patient, producing seizures with different dynamics, severity, and clinical implications. However, it is unclear whether seizures following the same pathway will have the same duration or if these features can vary independently. We compared within-subject variability in these seizure features using 1) epilepsy monitoring unit intracranial EEG (iEEG) recordings of 31 patients (mean 6.7 days, 16.5 seizures/subject), 2) NeuroVista chronic iEEG recordings of 10 patients (mean 521.2 days, 252.6 seizures/subject), and 3) chronic iEEG recordings of 3 dogs with focal-onset seizures (mean 324.4 days, 62.3 seizures/subject). While the strength of the relationship between seizure pathways and durations was highly subject-specific, in most subjects, changes in seizure pathways were only weakly to moderately associated with differences in seizure durations. The relationship between seizure pathways and durations was weakened by seizures that 1) had a common pathway, but different durations ("elastic pathways"), or 2) had similar durations, but followed different pathways ("duplicate durations"). Even in subjects with distinct populations of short and long seizures, seizure durations were not a reliable indicator of different seizure pathways. These findings suggest that seizure pathways and durations are modulated by different processes. Uncovering such modulators may reveal novel therapeutic targets for reducing seizure duration and severity.
Why Existing Machine Learning Methods Fails At Extracting the Information of Future Returns Out of Historical Sctock Prices : the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes

The financial time series analysis is important access to touch the complex laws of financial markets. Among many goals of the financial time series analysis, one is to construct a model that can extract the information of the future return out of the known historical stock data, such as stock price, financial news, and e.t. c. To design such a model, prior knowledge on how the future return is correlated with the historical stock prices is needed. In this work, we focus on the issue: in what mode the future return is correlated with the historical stock prices. We manually design several financial time series where the future return is correlated with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future return can be extracted from the curve shapes of the historical stock prices. By applying various kinds of existing algorithms on those pre-designed time series and real financial time series, we show that: (1) the major information of the future return is not contained in the curve-shape features of historical stock prices. That is, the future return is not mainly correlated with the historical stock prices in the CSF mode. (2) Various kinds of existing machine learning algorithms are good at extracting the curveshape features in the historical stock prices and thus are inappropriate for financial time series analysis although they are successful in the image recognition and natural language processing. That is, new models handling the NCSF series are needed in the financial time series analysis.
Innovation Interdependence and Investment Choices: An Experimental Approach to Decision Making in Ecosystems

We explore how decision makers perceive and assess the level of risk arising in interdependent settings that characterize innovation ecosystems. In a series of four stylized experiments, we examine the way in which the presence of required partner co-innovations impact how individuals set expectations for their own innovation investments. We find that opportunities characterized by greater interdependence – e.g., a greater number of participating partners – leave individuals subjectively more confident and optimistic than equivalent situations with less interdependence. Our study elaborates on the biased assessment of conjunctive events (Bar-Hillel, 1973) and explores the effects of the presentation of interdependence on a number of key decisions: project valuation and “green-lighting” decisions; project composition, and choice of the number of partners. We also consider how task structure may improve judgment by separating the assessment of risk from the assessment of value. We conduct our study with a wide range of participant samples ranging from undergraduates to senior executives. Collectively our findings hold important implications for the ways in which individuals, organizations, and policymakers approach and assess their innovation choices.
Safety climate, nursing organizational culture and the intention to report medication errors: A cross-sectional study of hospital nurses

Background & Aim: Medication errors are the second most common accident after the fall accident in hospitals. Medication errors are a threat to patient safety. It is important to find the cause of such errors so that we can reduce them. However, the rate of medication error reporting is low. This study aimed to explore the factors associated with the intention to report medication errors among general hospital nurses. Methods & Materials: A cross-sectional study design was used. The participants were 171 registered nurses working in 4 general hospitals in South Korea. Convenient random sampling was used to select participants. Data were collected using mobile self-report structured questionnaires that covered general characteristics, medication errors and the intention to report them, the safety climate, and the nursing organizational culture. The data collection period was from March 14 to April 6, 2018. The data were analyzed by descriptive statistics, the Pearson correlation coefficients, and multiple regression analysis. Results: The mean scores of the intention to report medication errors and safety climate were 5.20±1.40 and 3.04±0.47, respectively. Each mean score of nursing organizational culture types was hierarch-oriented culture 3.63±0.51, relation-oriented culture 3.13±0.69, task-oriented-culture2.99±0.56, and innovation-oriented culture 2.85±0.67. Significant factors associated with the intention to report medication errors were the safety climate (β=.26, p=.001), a task-oriented culture (β=.16, p=.023), educational level (β=-.19, p=.006), the experience of medication errors (β=.19, p=.006), and male (β=-.18, p=.011). They accounted for 25% of the intention to report medication errors. Conclusion: This study found that the safety climate of the hospital, task-oriented culture of the nursing organization, education level, experience of medication errors, and gender associated with the intention to report medication errors. It is necessary to find ways to improve the safety climate of the hospital and the task-oriented culture of the nursing organization and establish a strategy for improving the intention to report medication errors for male nurses and nurses with medication errors.
Interplay between Calcite, Amorphous Calcium Carbonate, and Intracrystalline Organics in Sea Urchin Skeletal Elements

Biomineralization processes in living organisms result in the formation of skeletal elements with complex ultrastructures. Although the formation pathways in sea urchin larvae are relatively well known, the interrelation between calcite, amorphous calcium carbonate (ACC), and intracrystalline organics in adult sea urchin biominerals is less clear. Here, we study this interplay in the spines and test plates of the Paracentrotus lividus sea urchins. Thermogravimetric analysis coupled with differential scanning calorimetry or mass spectrometry measurements, nuclear magnetic resonance technique, and high-resolution powder X-ray diffraction show that pristine spines and test plates are composed of Mg-rich calcite and comprise about 1.2 to 1.6 wt % organics, 10 wt % of anhydrous ACC and less than 0.2 wt % of water. Anhydrous ACC originates from incomplete crystallization of a precursor ACC phase during biomineralization and is associated with intracrystalline organics at the molecular level. Molecular interacti.