Miracle-dz/UniFAR-FTUs huggingface dataset:
Diffusion Logarithm-Correntropy Algorithm for Parameter Estimation in Non-Stationary Environments over Sensor Networks

This paper considers the parameter estimation problem under non-stationary environments in sensor networks. The unknown parameter vector is considered to be a time-varying sequence. To further promote estimation performance, this paper suggests a novel diffusion logarithm-correntropy algorithm for each node in the network. Such an algorithm can adopt both the logarithm operation and correntropy criterion to the estimation error. Moreover, if the error gets larger due to the non-stationary environments, the algorithm can respond immediately by taking relatively steeper steps. Thus, the proposed algorithm achieves smaller error in time. The tracking performance of the proposed logarithm-correntropy algorithm is analyzed. Finally, experiments verify the validity of the proposed algorithmic schemes, which are compared to other recent algorithms that have been proposed for parameter estimation.
Visual Feedback for Control Using Haar-Like Classifier to Identify the Quadcopter Position

In order to precise control of unmanned aerial vehicles (UAV), there is a need to get information about UAV's attitude. In the paper a visual feedback is used in order to determine an altitude of the quadcopter. In a case of measure in range of meters or hundreds of meters, typical methods based on barometric sensor, or radar altimeter are used. However, in a case of range of centimeters or single meters those methods may fail. Visual feedback allows control in range of centimeters or decimeters, depend of setup of visual control. The Haar-like classifier was created in order to find in a streaming video the quadcopter image. Experimental verification of the visual feedback for control of a quadcopter is presented.
Data-Driven Design of a Cascaded Observer for Battery State of Health Estimation

The design of a combined observer for battery state of charge (SoC) and state of health (SoH) using data-driven models is addressed. Knowledge of both SoC and SoH is an important topic in battery management system design for (hybrid) electrical vehicles. The proposed cascaded observer is designed for estimating SoC and SoH during normal vehicle operation based on measured signals, which are directly available in the car. For an SoC estimation, that is accurate in the entire battery lifespan, the underlying battery model has to be adapted based on the estimated SoH. For that purpose, ageing data analysis using data-driven models and the associated model adaptation is discussed. A cascaded observer structure for combined estimation of SoC and SoH is presented and the performance of the proposed concepts is demonstrated by means of battery ageing data covering the entire lifetime of a lithium-ion cell.
Performance of the SteatoTest, ActiTest, NashTest and FibroTest in a multiethnic cohort of patients with type 2 diabetes mellitus

Fibromax is a diagnostic tool composed of the combination of 4 non-invasive biomarker panels for the diagnosis of steatosis (SteatoTest), necrosis and inflammation (ActiTest and NashTest-2) and fibrosis (FibroTest). The purpose of this study was to assess the performance of these biomarker panels in patients with type 2 diabetes mellitus (T2DM). All patients underwent routine labs, a 75 g oral glucose tolerance test, a liver proton magnetic resonance spectroscopy (1H-MRS) to measure intrahepatic triglyceride content, and a percutaneous liver biopsy to establish the diagnosis of non-alcoholic steatohepatitis (NASH) and to grade and stage the disease in those patients with non-alcoholic fatty liver disease (NAFLD) by 1H-MRS. For determination of the scores, plasma samples were blindly provided to establish the SteatoTest, ActiTest, NashTest-2 and FibroTest scores. A total of 220 patients with T2DM were included in this study. When the ability of the SteatoTest to identify patients with T2DM with NAFLD by 1H-MRS was assessed, the overall performance expressed as the area under the receiver operating characteristic curve was 0.73 (95% CI 0.65 to 0.81). The performance of the ActiTest and NashTest-2 to diagnose definite NASH among patients with T2DM was 0.70 (95% CI 0.63 to 0.77) and 0.69 (95% CI 0.62 to 0.76), respectively. Regarding the FibroTest score, its performance to identify patients with moderate or advanced fibrosis was 0.67 (95% CI 0.58 to 0.76) and 0.72 (95% CI 0.61 to 0.83), respectively. Non-invasive panels for the diagnosis of steatosis, NASH and/or fibrosis, which were developed and validated in non-diabetic cohorts, underperformed when applied to a large cohort of patients with T2DM. Results from non-diabetic populations should not be extrapolated to patients with T2DM.
Solving a class of Hamilton-Jacobi-Bellman equations using pseudospectral methods

This paper presents a numerical approach to solve the Hamilton-Jacobi-Bellman (HJB) problem which appears in feedback solution of the optimal control problems. In this method, first, by using Chebyshev pseudospectral spatial discretization, the HJB problem is converted to a system of ordinary differential equations with terminal conditions. Second, the time-marching Runge-Kutta method is used to solve the corresponding system of differential equations. Then, an approximate solution for the HJB problem is computed. In addition, to get more efficient and accurate method, the domain decomposition strategy is proposed with the pseudospectral spatial discretization. Five numerical examples are presented to demonstrate the efficiency and accuracy of the proposed hybrid method.
Discriminative Training of Sum-Product Networks by Extended Baum-Welch

We present a discriminative learning algorithm for Sum-Product Networks (SPNs) (Poon and Domingos, 2011) based on the Extended Baum-Welch (EBW) algorithm (Baum et al., 1970). We formulate the conditional data likelihood in the SPN framework as a rational function, and we use EBW to monotonically maximize it. We derive the algorithm for SPNs with both discrete and continuous variables. The experiments show that this algorithm performs better than both generative Expectation-Maximization, and discriminative gradient descent on a wide variety of applications. We also demonstrate the robustness of the algorithm in the case of missing features by comparing its performance to Support Vector Machines and Neural Networks.
Achieve Efficient and Privacy-Preserving Medical Primary Diagnosis Based on kNN

Online medical primary diagnosis system, which can provide the pre-diagnosis service anywhere anytime, has attracted considerable interest. However, the flourish of online medical primary diagnosis system still faces many serious challenges since the sensitivity of personal health information and service provider''s diagnosis model. In this paper, we propose an efficient and privacy-preserving medical primary diagnosis scheme based on k-nearest-neighbors classification (kNN), called EPDK. With EPDK, medical users can ensure that their sensitive health information are not compromised during the online medical diagnosis process, and service provider can provide high-accuracy service without revealing its diagnosis model. Specifically, based on lightweight multiparty random masking and polynomial aggregation techniques, a medical user preprocesses her/his query vector before sending out and the preprocessed vector is directly operated in the service provider without obtaining original data, meanwhile, the primary diagnosis result cannot be achieved by anyone except the medical user. Through extensive analysis, we show that EPDK can resist multifarious known security threats, and has significantly lower computation complexity than existing schemes. Moreover, performance evaluations via implementing EPDK in the real environment demonstrate that EPDK is highly efficient in terms of computation overhead.
Efficient Density Estimation Algorithm for Ultra Dense Wireless Networks

Extremely dense wireless network topologies gradually become a reality, especially through wireless sensors networks and more recently nanonetworks. Electromagnetic nanonetworks are expected to allow a very large amount of extremely small and capability-limited devices to communicate with each others. In nanonetworks, even in a communication range limited to tens of centimeters, thousands of neighbors can be found. Information diffusion and routing protocols would greatly benefit from having an accurate estimation of the density of nodes. However, in this context, most traditional wireless communication protocols are not suited. We propose Density Estimator for Dense Networks (DEDeN), a distributed algorithm able to provide the required density estimation. It allows confidence tuning and can cope with an extreme range of local densities. A formal analysis of DEDeN is provided and corroborated by extensive simulations. DEDeN interest is then demonstrated through application to two information diffusion protocols tailored for very dense networks, and also to a routing protocol specific to nanonetworks.
High Glucose Promotes Epithelial-Mesenchymal Transition of Uterus Endometrial Cancer Cells by Increasing ER/GLUT4-Mediated VEGF Secretion

Background/Aims: Uterus endometrial cancer (UEC) is the common malignancy among gynecologic cancers, and most of them are type I estrogen-dependent UEC. Diabetes is well-known risk factor for the development of UEC. However, the underlying link between high glucose (HG) and the estrogen receptor in UEC remains unclear. Epithelial-mesenchymal transition (EMT) has also been shown to occur during the initiation of metastasis in cancer progression. The aim of this study was to determine the relationships and roles of HG, estrogen receptor and EMT in the growth and migration of UEC. Methods: The expression of glucose transport protein 4 (GLUT4) in the control endometrium and UEC tissues was detected by immunohistochemistry (IHC); the cell viability and invasion were analyzed through CCK-8 and Matrigel invasion assays; the transcriptional level of EMT-related genes was evaluated through real-time PCR; and the effect of HG and / or GLUT4 on estrogen receptors, vascular endothelial growth factor (VEGF) and its receptor VEGFR was analyzed through western blotting, ELISA and flow cytometry (FCM) assay, respectively. In addition, Ishikawa-xenografted nude mice were constructed and were used to analyze the effect of estrogen and GLUT4 on the growth of UEC in vivo. Results: Here, we found that exposure to HG led to a high level of viability and invasion of UEC cell lines (UECC, Ishikawa and RL95-2 cells). Compared with the normal endometrium, a higher level of GLUT4 was observed in UEC tissues. Silencing GLUT4 obviously inhibited the HG-promoted viability, invasion and expression of EMT-related genes (TWIST, SNAIL and CTNNB1) of UECC promoted by HG. Further analysis showed that HG and GLUT4 promoted the secretion of VEGF and expression of VEGFR in UECC. Treatment with HG led to the increase of estrogen receptor α (ERα) and β (ERβ) in UECC, blocking ERα or ERβ resulted in the decreases in GLUT4 expression, TWIST, SNAIL and CTNNB1 transcription, and VEGF and VEGFR expression in UECC. Treatment with anti-human VEGF neutralizing antibody restricted the viability and invasion of UECC that was induced by HG and estrogen. Exposure to estrogen accelerated growth, VEGF production, and TWIST and CTNNB1 expression in UEC in Ishikawa-xenografted nude mice, and silencing GLUT4 restricted these effects. Conclusion: These data suggest that HG increases GLUT4 and VEGF/VEGFR expression, further promotes EMT process and accelerates the development of UEC by up-regulating ER
Considering Mixing Process of Rocket Solid Propellant Using Mixing Transport Device Simulating Peristaltic Movement of Intestinal Tract

In recent years, because the development of space technology has been increasing for the purpose of improving the social infrastructure, the expansion of space transportation systems based on low-cost and high-frequency rockets is important. Solid propellants used in solid-fuel rockets have properties of the compactness, inexpensiveness, and easy-handling. However, solid propellants are highly viscous slurries and highly explosive. As there is no device capable of continuously and safely transporting solid propellant, the process of manufacturing solid propellant is a batch process. We focused on the movement of human intestines that knead and transport with a small force as part of the development process. In this report, we design a mechanism for the mixing process by using a peristaltic mixing transporting device for efficiency and by automating the equipment. Specifically, we conduct two experiments with samples using an adjusted fluid ratio: a comparison experiment on fluidity and a pressure response experiment. We investigate the possibility of automatic control and efficiency by using the factors of pressure and flow rate.
The Theory of Planned Behavior to Predict Protective Behavioral Intentions against PM2.5 in Parents of Young Children from Urban and Rural Beijing, China

Smog and air pollution have fast become significant environmental problems and are attributed to rapid global industrialization and urbanization. Emissions of fine particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5) increase smog and air pollution, with strong impacts on human health. Children are particularly vulnerable. While increasing studies are being conducted on the behaviors leading to PM2.5 toxicity from the perspective of environmental toxicants, there is a lack of research on factors influencing anti-PM2.5 behavioral intentions. Thus, this study aims to narrow this gap by adapting the theory of planned behavior framework to investigate the effects of attitude, subjective norms, and perceived behavioral control on protective behavioral intentions against PM2.5. In total, 1277 online questionnaires were collected from parents of young children living in urban and rural areas of Beijing, and the data was analyzed using correlation, regression, and path analyses. Results revealed that there were significant differences between parents from urban and rural areas in terms of attitude (t = 4.727 > 1.96, p < 0.001), subjective norms (t = 5.529 > 1.96, p < 0.001), perceived behavioral control (t = 6.155 > 1.96, p < 0.001), and anti-PM2.5 behavioral intentions (t = 6.838 > 1.96, p < 0.001). Path analysis revealed that parents from urban and rural areas had different behavioral intention paths. For urban parents, the findings indicated that subjective norms (β = 0.73, t = 21.84 > 3.29) and perceived behavioral control (γ = 0.22, t = 6.12 > 3.29) had direct impacts on anti-PM2.5 behavioral intentions. In contrast, the attitudes (γ = 0.39, t = 3.74 > 3.29) and subjective norms (β = 0.60, t = 8.55 > 3.29) of rural parents were found to directly influence anti-PM2.5 behavioral intentions.
Discrete Graph Hashing via Affine Transformation

In unsupervised graph-based hashing for large-scale image retrieval, many efforts have been made to bridge the gap between the learned graph embedding and the corresponding binary codes. Relatively, few studies focus on the issue of the discrimination of graph embedding. In this paper, we firstly devise a discrete graph hashing model (DGH) that smooths graph embedding and simultaneously solving binary codes under the balanced discrete constraint, which equals a novel method of jointly learning graph embedding and spectral rotation, theoretically. To further induce discriminant graph embedding, we substitute affine transformation for spectral rotation in our DGH (abbreviated as ADGH). This is because affine transformation can accommodate both rotational angle and distance of graph embedding, while respecting the neighborhood structure among most samples. Besides, each subproblem of ADGH can yield the closed-form solution. Experiments of image retrieval on three benchmark datasets show that ADGH outperforms the representative hashing methods in quantity.
Analysis and Design of Contactless Energy Transfer System for e-Scooter

Contactless energy transfer (CET) systems transfer electric energy without any wired connection. General CET system mainly consists of three parts: coil system, compensation system and power electronics system. This Paper describes design procedure of CET systems for e-scooter. Theoretical findings are validated through computer assisted softwares i.e. Finite Element Method and Power Simulation. Further, it is observed that the magnetic properties of the coils i.e. self-inductance, mutual inductance and coupling coefficient are improving while using different types of cores.
CAFFE: Congestion-Aware Fast Failure Recovery in Software Defined Networks

A logically centralized controller of Software Defined Networking (SDN) is responsible for the management of routing planning and monitoring of network status. With this central control and global visibility, in this paper, we propose a congestion- aware fast failure recovery scheme (CAFFE), which can not only recover the affected flows fast from a wide range of failure scenarios but also avoid potential congestion in the post-recovery network. CAFFE achieves fast recovery by detouring the traffic from neighbour switches as soon as they detect failures and avoids potential congestion by protected paths constructed with the combined consideration of the knowledge of network topology, failure states and network load distribution. We formulate and solve the protected path planning problems where all potential network failures should be protected and the maximum link load in the post- recovery network should be minimized. The resulting protected paths ensure high traffic reachability and low link load even when network failure happens. All these paths are set up a priori by CAFFE. This allows switches to handle failures automatically without the need on waiting for the response from the controller. Experimental results show that CAFFE achieves fast recovery with relatively low overheads on either switches (in terms of backup rule number to configure) or controller (in terms of computational time). It also shows that CAFFE is able to better load-balance the post-recovery network comparing to shortest path re-calculation and OpenFlow-based Segment Protection (OSP).
Predicting the effect of home Wi-Fi quality on QoE

Poor Wi-Fi quality can disrupt home users' internet experience, or the Quality of Experience (QoE). Detecting when Wi-Fi degrades QoE is extremely valuable for residential Internet Service Providers (ISPs) as home users often hold the ISP responsible whenever QoE degrades. Yet, ISPs have little visibility within the home to assist users. Our goal is to develop a system that runs on commodity access points (APs) to assist ISPs in detecting when Wi-Fi degrades QoE. Our first contribution is to develop a method to detect instances of poor QoE based on the passive observation of Wi-Fi quality metrics available in commodity APs (e.g., PHY rate). We use support vector regression to build predictors of QoE given Wi-Fi quality for popular internet applications. We then use K-means clustering to combine per-application predictors to identify regions of Wi-Fi quality where QoE is poor across applications. We call samples in these regions as poor QoE samples. Our second contribution is to apply our predictors to Wi-Fi metrics collected over one month from 3479 APs of customers of a large residential ISP. Our results show that QoE is good most of the time, still we find 11.6% of poor QoE samples. Worse, approximately 21% of stations have more than 25% poor QoE samples. In some cases, we estimate that Wi-Fi quality causes poor QoE for many hours, though in most cases poor QoE events are short.
Delay Reduction in Multi-Hop Device-to-Device Communication Using Network Coding

This paper considers the problem of reducing the broadcast decoding delay of wireless networks using instantly decodable network coding- based device-to-device communications. In contrast with the previous works that assume a fully connected network, this paper investigates a partially connected configuration in which multiple devices are allowed to transmit simultaneously. To that end, different events occurring at each device are identified so as to derive an expression for the probability distribution of the decoding delay. Afterward, the joint optimization problem over the set of transmitting devices and packet combination of each is formulated. The optimal solution of the joint optimization problem is derived using a graph-theoretic approach by introducing the cooperation graph in which each vertex represents a transmitting device with a weight translating its contribution to the network. This paper solves the problem by reformulating it as a maximum weight clique problem which can efficiently be solved. Numerical results suggest that the proposed solution outperforms state-of-the-art schemes and provides significant gain, especially for poorly connected networks.
Active Probing-Based Schemes and Data Analytics for Investigating Malicious Fast-Flux Web-Cloaking Based Domains

There have been increasing levels of sophistication in the continuous battles between cyber criminals and law enforcement/cybersecurity practitioners. For example, Darknet operators often take advantage of the evasion techniques to hide their criminal activities, such as hosting illegal content, selling illegal materials, or terror-information exchanges. Web cloaking and fast-fluxing are the two common ones. Fast-fluxing constantly changes the host IP addresses and offers higher degree availability & robustness for malicious domain users. Web cloaking allows some dynamic web contents be sent at ordinary times, but different contents may be triggered by specific keywords on search engines or other geo-locations. Both contribute to the great challenges to cybersecurity and law enforcement practitioners, due to the fact that at the time of evidence collection, evidential data from the source may be simply not the same as that the evidence generated earlier for malicious purposes. In this paper, we will present new active probing-based schemes for detecting cloaking fast-flux malicious domains. In our prototype platform, we have integrated our schemes with the Tor system in order that our query and evidence collection are anonymous and distributed, to avoid the detection of malicious domain hosting servers. During the last 10 months, we have used this system to collect evidence data using the six of top ten worldwide search engines (e.g., Bing, Baidu, Ask, AoL, Lycos and Search). With the collected data, we developed algorithmic data analytic solutions to extract and classify the malicious fast-fluxing and web-cloaking domains. The effective evidence collection and analytic solutions will help law enforcement practitioners in their case work handling such malicious domains.
Understanding Housing Market Behaviour from a Microscopic Perspective

This paper discusses the usage of micro-level be- havioural data for understanding macro-level housing market behavior. We use sentiment analysis to examine local newspaper articles discussing real estate at a suburb level in inner-west Sydney, Australia. We calculate the media sentiment index by using two different methods, and compare them with each other and the housing price index. The use of media sentiment index can serve as a finer-grained guiding tool to facilitate decision making for home buyers, investors, researchers and policy makers.