Miracle-dz/UniFAR-FTUs huggingface dataset:
Touch-based Smartphone Authentication Using Import Vector Domain Description

The security of smartphones is vital as much privacy information is stored on them. In this paper, we propose a touch-based authentication system by exploiting a novel one-class classification algorithm import vector domain description (IVDD). We record each complete stroke by a 32-dimensional attribute vector and select features with high discriminability by employing the correlation analysis and the conditional mutual information maximization. With the selected features, the trained IVDD classifier generates a probabilistic result for each user, which is further used for authentication based on a predefined threshold. We evaluate the classifier IVDD in terms of the impact of the number of touch behaviors, computation time, individual effect, and accuracy, and the experimental results show that the IVDD reaches the lower BER of 2.5% under 15 touches and lower mean FRR of 2.14%, comparing with the support vector domain description (SVDD).
Highly Energy-Efficient Resource Allocation in Power Telecommunication Network

The Orthogonal Frequency Division Multiplexing (OFDM) has been widely used in the next generation networks. With the increasing of the wireless equipment, the problem of energy consumption for the wireless network has become a big challenge. Power control is the key of the network management, while power allocations and channel assignments have been investigated for maximizing energy efficiency in each cell in the OFDM-based cellular network. The optimal problem of maximizing energy efficiency of networks has been formulated as a non-linear fractional program. The dual decomposition and sub-gradient iteration have been used to solve it. Furthermore, a numerical simulation has been proposed to verify the algorithm proposed in this paper. The simulation results show that the maximum energy efficiency in each cell can be obtained.
Mismatch in elevational shifts between satellite observed vegetation greenness and temperature isolines during 2000–2016 on the Tibetan Plateau

Climate warming on the Tibetan Plateau tends to induce an uphill shift of temperature isolines. Observations and process‐based models have both shown that climate warming has resulted in an increase in vegetation greenness on the Tibetan Plateau in recent decades. However, it is unclear whether the uphill shift of temperature isolines has caused greenness isolines to shift upward and whether the two shifts match each other. Our analysis of satellite observed vegetation greenness during the growing season (May–Sep) and gridded climate data for 2000–2016 documented a substantial mismatch between the elevational shifts of greenness and temperature isolines. This mismatch is probably associated with a lagging response of greenness to temperature change and with the elevational gradient of greenness. The lagging response of greenness may be associated with water limitation, resources availability, and acclimation. This lag may weaken carbon sequestration by Tibetan ecosystems, given that greenness is closely related to primary carbon uptake and ecosystem respiration increases exponentially with temperature. We also found that differences in terrain slope angle accounted for large spatial variations in the elevational gradient of greenness and thus the velocity of elevational shifts of greenness isolines and the sensitivity of elevational shifts of greenness isolines to temperature, highlighting the role of terrain effects on the elevational shifts of greenness isolines. The mismatches and the terrain effect found in this study suggest that there is potentially large micro‐topographical difference in response and acclimation/adaptation of greenness to temperature changes in plants. More widespread in situ measurements and fine‐resolution remote sensing observations and fine‐gridded climate data are required to attribute the mismatch to specific environmental drivers and ecological processes such as vertical changes in community structure, plant physiology, and distribution of species.
Assessing Perceptions of Teamwork Quality Among Perioperative Team Members

Successful surgery depends on collaboration and mutual trust among interdisciplinary team members. We compared teamwork quality as perceived by surgeons, anesthesia care providers, and perioperative nurses using two surveys in the same hospital. The general survey sent to the homes of the OR personnel revealed teamwork climate scores in the medium to high range. Attending surgeons were significantly more satisfied than perioperative nurses and resident surgeons; anesthesiologists were significantly more satisfied than perioperative nurses. A second single-item survey administered immediately after elective open abdominal surgical procedures also showed relatively high satisfaction with teamwork. Results of the second survey, however, showed that attending surgeons were significantly less satisfied than the members of all the other professions, and perioperative nurses were significantly more satisfied than the members of all the other professions. We conclude that general surveys about teamwork quality among members of surgical teams may not necessarily reflect teamwork quality during actual surgical procedures.
A Real-Time Learning-Based Super-Resolution System Using Direct Simple Functions

This paper proposes a real-time super-resolution (SR) system. The proposed system performs a fast SR algorithm that generates a high-resolution image from a low-resolution image using direct regression functions. The system implemented on a Xilinx Virtex 7 field programmable gate array achieves output resolution of 3840 × 2160 (UHD) at 200 fps and 2000Mpixels/s throughput. Experimental results show that the proposed system provides high image quality for real-time applications.
Electromagnetic Characterization of Coarse-Grained Soils with a One Port Large Coaxial Cell

This paper reports the design, the calibration and the application of a one port large coaxial cell. The objective of the device is to characterize the electromagnetic properties of compacted and partly saturated coarse grained soil under controlled boundary conditions. The dielectric spectra were obtained by means of fitting the measured scattering function using a Transvers electromagnetic mode (TEM) propagation model considering the frequency dependent complex permittivity. The method was applied on a construction site material with aggregate up to 15 mm. 6 samples were characterized, the results showing a good agreement in terms of Root Mean square error.
Physical Activity Stage of Change and Its Related Factors in Secondary School Students of Sarableh City: A Perspective from Iran

BACKGROUND: Physical activity is highly beneficial to health. These benefits are so important and indispensable for adolescents. AIM: The aim of this study was to investigate the Physical activity stage of change and its related factors in the male secondary School students of Sarableh city. MATERIAL AND METHODS: In a cross-sectional study, 261 male secondary School students possessing the eligibility criteria were selected using the simple random sampling technique. After giving their informed consent, the students filled the stage of change questionnaire and the structures of the transtheoretical model in a self-reporting manner. Using SPSS.21, the data were analysed through One-way ANOVA and Pearson correlation test with a 0.05 level of significance. RESULTS: The mean age of the students was 16.16±0.89 years. According to the stage of change, 26.8 per cent (n = 70) were in preaction stages (precontemplation, contemplation, and preparation) and 73.2 per cent (n = 191) were in action and maintenance stages. The one-way ANOVA revealed that awareness is raising, self-reevaluation, counter conditioning and reinforcing management differed significantly across stages (P < 0.05). However, this difference is not significant for other cognitive and behavioural processes (P > 0.05). According to the findings, increase in the self-efficacy, pros and decrease in cons was found by students’ progress in the Physical activity stage of change (P < 0.05). CONCLUSION: Self-efficacy and processes of change are warranted when designing Physical activity stage interventions in the adolescents.
A New Protection Scheme for Two-terminal DC Grid

DC grid enables power system more flexible and efficient, and increases absorptive capacity of the renewable energy, which will be an important part of the future smart grid. However, higher requirement of protection is a significant barrier to more widespread adoption of dc grid. To solve the drawbacks of the existing protection methods, a new protection scheme which delivers fast operation for a two-terminal DC network is presented in this paper. The proposed scheme comprises a communication-assisted fault detection and an isolation method through differences of fault current directions. The scheme uses multiple intelligent electronic devices (IEDs) to exchange digital signals of zone current direction, which can distinguish line and bus fault, then combines with circuit breakers to cut-off DC faults within sub-millisecond timescales. Converter and battery protection are also presented to perfect grid protection. The effectiveness and practicability of the proposed protection strategy is validated through PSCAD.
Capitalizing on Hierarchical Graph Decomposition for Scalable Network Analysis

Processing large graphs has become commonplace across many academic and industrial applications. We address the computational challenge of analyzing large networks on a single consumer-grade machine. Our strategy involves arranging networks into layers of smaller, increasingly cohesive subgraphs, which is motivated by the observation that real-world networks exhibit a hierarchical organization. We decompose large networks to reveal the underlying hierarchy and extract signals from this hierarchical topology to solve three network analysis problems viz., network comparison, determining influential spreaders, and centrality computation. Empirical investigation reveals that our approach is effective and faster than state-of-the-art competing algorithms.
Automatic Semantic Parsing of the Ground Plane in Scenarios Recorded With Multiple Moving Cameras

Nowadays, video surveillance scenarios usually rely on manually annotated focus areas to constrain automatic video analysis tasks. Although manual annotation simplifies several stages of the analysis, its use hinders the scalability of the developed solutions and might induce operational problems in scenarios recorded with multiple moving cameras (MMCs). To tackle these problems, an automatic method for the cooperative extraction of areas of interest (AoIs) is proposed. Each captured frame is segmented into regions with semantic roles using a state-of-the-art method. Semantic evidences from different junctures, cameras, and points-of-view are, then, spatio-temporally aligned on a common ground plane. Experimental results on widely used datasets recorded with multiple but static cameras suggest that this process provides broader and more accurate AoIs than those manually defined in the datasets. Moreover, the proposed method naturally determines the projection of obstacles and functional objects in the scene, paving the road towards systems focused on the automatic analysis of human behavior. To our knowledge, this is the first study dealing with this problem, as evidenced by the lack of publicly available MMC benchmarks. To also cope with this issue, we provide a new MMC dataset with associated semantic scene annotations.
Relationship between serum estrogen concentration and propofol consumption: A prospective observational study of patients undergoing oocyte retrieval

Controlled ovarian hyperstimulation results in elevated levels of estrogen during in vitro fertilization (IVF). Although serum ovarian steroid hormones were found to influence central nervous system and anesthetic requirements, the relationship between propofol requirements and serum estrogen concentration in women undergoing oocyte retrieval has not been studied. The aim of this study was to determine the relationship between serum estrogen and progesterone concentrations and the required propofol dose for loss of consciousness (LOC).
Maternal experience and adult neurogenesis in mammals: Implications for maternal care, cognition, and mental health

The transition to motherhood encompasses physiological and behavioral adaptations essential for the initiation and maintenance of offspring care and feeding and includes widespread changes throughout the brain. The growth of new neurons occurs across the lifespan in distinct regions of mammalian brains and changes dynamically across reproductive events in female mammals. The subventricular zone (SVZ) and dentate gyrus (DG) of the hippocampus undergo high rates of neurogenesis in adulthood and are sensitive to hormonal fluctuations. Pregnancy and the postpartum period are associated with increased cell proliferation in the SVZ and interneuron survival in the olfactory bulb. In mice, peripartum prolactin signaling mediates SVZ neurogenesis and is important for enhanced olfactory recognition of offspring and maternal care. In contrast, cell proliferation and immature neuron survival decrease in the DG during the postpartum period. High baseline glucocorticoid concentrations suppress hippocampal neurogenesis, potentially representing an energetic trade‐off accompanying a reduced need for spatial navigation early postpartum. In women, hippocampal volume decline during pregnancy and partial recovery during the postpartum period could contribute to the risk of psychiatric illness. New evidence indicates that the dorsal raphe nucleus (DR) is an additional site for adult neurogenesis sensitive to reproductive experience and offspring contact. In this review, we discuss the initial and lasting impact of maternal experience on adult neurogenesis. Because neurogenesis has been implicated in a variety of psychiatric and neurodegenerative illnesses, understanding how reproductive experience alters new neuron production in maternal mammals has far‐reaching implications for women's health and wellness across the lifespan.
A Method for Improving the Measurement Accuracy of Vehicle FMCW Radar*

According to the problem that the center frequency of the signal is not accurate, the result is inaccuracy. In view of this situation, the principle of measuring the range and velocity of the target using FMCW radars is studied, and then the beat signal is processed to improve the accuracy of the frequency estimation. This paper uses the window function for signal filtering in order to reduce the effect of leakage and fence effect; then FFT is used to find out the rough location of the target frequency; finally one-dimensional Gauss function is used to refine spectrum of FFT in order to get the estimated frequency. Matlab is used for simulation. Compared with different methods, the results show that the method of this paper can get a higher accuracy of the distance and speed values.
Vulnerability Assessment Model of Power Grid Cascading Failures Based on Fault Chain and Dynamic Fault Tree

Cyber-physical security of the smart grid obtains increasing attention. Critical transmission lines have a major impact on large-scale cascading failures in modern power grids. In this paper, we proposed a predicting model of cascading failures based on the fault chain theory and Fuzzy C-Means. The development process of cascading failures in a power grid is described based on the fault chain and dynamic fault tree theories. Loading rate, coupling relationship of sequent tripped lines and power flow variation are considered in the proposed method to predictive the fault chains comprehensively. A novel branch vulnerability assessment method is proposed based on the fault chain and dynamic fault tree theories to identify critical branches which have a significant impact on cascading failure expansion. The effectiveness of the proposed assessment model is tested based on IEEE 39-bus system.
Deep Multiscale Spectral-Spatial Feature Fusion for Hyperspectral Images Classification

Learning representative and discriminative feature that make full use of spectral-spatial information is of cardinal significance for hyperspectral imagery (HSI) interpretation. In this paper, we present a novel unsupervised feature extraction method, deep multiscale spectral-spatial feature fusion (DMS$^3$ F$^2$), for hyperspectral images classification (HSIC). First, pretrained filter banks in VGG16 are transferred to extract deep multiscale spatial structural information about HSI. Second, we propose a new unsupervised cooperative sparse autoencoder method to fuse together the deep spatial feature and the raw spectral information. It achieves both dimensionality reduction and feature fusion, and cooperates on different features to fulfil self-supervised projective learning, which is beneficial in detecting important structure and retaining significant amount of information about the input. In the end, fused spectral-spatial features from multiple scales in VGG16 are further weighted fused and upsampled to obtain the final discriminative feature. Qualitative classification experiments by support vector machines are reported to show the effectiveness of the proposed algorithm. Compared with existing feature extraction techniques, the proposed method is much more effective for HSIC, especially for datasets with less training samples and scenes with complex semantic information and great heterogeneity in terms of geometrical shape.
Implementation of a DC current sensing transformer with H-bridge MOSFET rectifier in a resonant LLC converter

This paper presents experimental implementation and tests the efficacy of a current feedback circuit for an LLC resonant convertor, working as a battery charger. The circuit is based on a current transformer and a second H-bridge which is synchronised with the first side H-bridge of the converter. The proposed solution, along with all mandatory requirements such as galvanic isolation, stability, fast response, offers intermittent synchronisation between the full bridge of the converter and the feedback circuit. The paper clarifies the application of the proposed current sense circuit as a current feedback loop, which is part of a digital control system. The implementation of the current measurement circuit is shown and proved experimentally.
Diagnosis of Interturn Short-Circuit Fault in PMSM by Residual Voltage Analysis

This paper proposes a method that is based on a model to diagnose interturn short-circuit fault (ISCF) in a permanent magnet synchronous machine (PMSM). The fault diagnosis is accomplished with residual voltage components (RV Cs) that represent the difference between the measured stator voltages and the estimated stator voltages. The fault indicators based on RVCs are proposed to estimate the fault severity; these can distinguish the severity of the ISCF and can also detect the phase where the ISCF occurred. Simulation results from a finite element analysis tool validate the proposed method diagnoses the ISCF in different fault cases. This method can be used for early diagnosis of ISCFs in PMSMs.
2X Super-Resolution Hardware Using Edge-Orientation-Based Linear Mapping for Real-Time 4K UHD 60 fps Video Applications

Based on our previous super-interpolation method, we propose a novel hardware-friendly super-resolution (SR) algorithm, called HSI method, and its dedicated hardware architecture for up-scaling full-high-definition (FHD) video streams to 4K ultra-high-definition (UHD) video streams in real-time. Our proposed HSI method involves training and up-scaling steps. In the training step, an edge-orientation-based clustering is applied for low-resolution (LR) training patches to obtain a training patch set for each class, and a linear mapping kernel is learned from LR to high-resolution (HR) based on the training patch set for each class. In the up-scaling step, each LR input patch is transformed to an HR patch by applying the linear mapping kernel for its class. We implemented the up-scaling step of our HSI method by a dedicated hardware (HW) with the pre-trained linear mapping kernels stored in a look-up table. Our HW implementation, called HSI HW, contains 159K gate counts and achieves about 880 Mpixels/s throughput by using the TSMC 0.13-um CMOS process, and thus performing the SR operation from FHD to 4K UHD in real-time. Compared with conventional SR methods, our HW implementation of HSI reconstructs HR images of higher peak signal to noise ratio values and better visual quality.
A Survey of Artificial Bee Colony Algorithm

Artificial bee colony algorithm (ABC) is a novel biological heuristic optimization paradigm inspired by the honeybee foraging behaviors. And it has received a surge of attention due to its merits of a simplified model, fewer control parameters, and strong robustness. This paper firstly introduces the basic biological principles and mathematical operators of ABC, and then comprehensively presents and analyzes the recent significant research on ABC with aspects of algorithm improvements and engineering applications. Specifically, several representative improvement models and strategies are summarized, and its extended versions including discrete ABC and multi-objective ABC are introduced in detail. Furthermore, its typical applications on neural network training, wireless network, image processing, wireless network, and scheduling are summarized. Finally, we propose our views about the challenges and further research highlights of ABC.
Maps of subjective feelings

Significance Subjective feelings are a central feature of human life, yet their relative organization has remained elusive. We mapped the “human feeling space” for 100 core feelings ranging from cognitive and affective processes to somatic sensations; in the analysis, we combined basic dimension rating, similarity mapping, bodily sensation mapping, and neuroimaging meta-analysis. All feelings were emotionally loaded, and saliencies of bodily and mental experiences were correlated. Feelings formed five groups: positive emotions, negative emotions, cognitive processes, somatic states, and homeostatic states. Feeling space was best explained by emotionality, mental experience, and bodily sensation topographies. Subjectively felt similarity of feelings was associated with basic feeling dimensions and the bodily sensation maps. This shows that subjective feelings are categorical, emotional, and embodied. Subjective feelings are a central feature of human life. We defined the organization and determinants of a feeling space involving 100 core feelings that ranged from cognitive and affective processes to somatic sensations and common illnesses. The feeling space was determined by a combination of basic dimension rating, similarity mapping, bodily sensation mapping, and neuroimaging meta-analysis. A total of 1,026 participants took part in online surveys where we assessed (i) for each feeling, the intensity of four hypothesized basic dimensions (mental experience, bodily sensation, emotion, and controllability), (ii) subjectively experienced similarity of the 100 feelings, and (iii) topography of bodily sensations associated with each feeling. Neural similarity between a subset of the feeling states was derived from the NeuroSynth meta-analysis database based on the data from 9,821 brain-imaging studies. All feelings were emotionally valenced and the saliency of bodily sensations correlated with the saliency of mental experiences associated with each feeling. Nonlinear dimensionality reduction revealed five feeling clusters: positive emotions, negative emotions, cognitive processes, somatic states and illnesses, and homeostatic states. Organization of the feeling space was best explained by basic dimensions of emotional valence, mental experiences, and bodily sensations. Subjectively felt similarity of feelings was associated with basic feeling dimensions and the topography of the corresponding bodily sensations. These findings reveal a map of subjective feelings that are categorical, emotional, and embodied.