Miracle-dz/UniFAR-FTUs huggingface dataset:
An iterative technique to retrieve the planar wide-mesh scanning near-field data from those affected by an inaccurate positioning

In this work, an effective near-field to far-field transformation (NFFFT) with planar wide-mesh scanning (PWMS) from NF data affected by known probe-positioning errors is experimentally validated. The related NFFFT from correctly positioned samples allows a remarkable measurement time saving, since the NF data needed by the classical plane-rectangular NFFFT are accurately reconstructed from a reduced number of PWMS samples via a non-redundant sampling representation of the probe voltage developed by shaping the antenna with an oblate ellipsoid. The voltage values at the sampling points specified from the representation are unknown and are effectively recovered by the inaccurately positioned ones by an iterative approach. Experimental results assessing the validity of such an approach are reported.
An accurate and robust environment sensing algorithm for enhancing indoor localization

With the development of Internet of Thing (IoT), positioning is of a significant importance in indoor environments. Thus, recent years, localization technology has advanced greatly aimed at localizing the target by utilizing existing infrastructure such as WiFi. However, in a complex indoor environment, the performance of localization system tend to fluctuates when there is Non-Line-of-Sight (NLoS) propagation. Specially, for the high accuracy localization system using Angle of Arrival (AoA) or Time of Flight (ToF) of the direct signal path, the robustness is a challenge since the propagations of signal are not always in Line-of-Sight (LoS) due to the complicated indoor environment. Environment sensing is of significant importance for improving the accuracy and robustness of high accuracy localization system working in NLoS environment. In this paper, we propose a context-aware algorithm by using the spatial properties of multipath signal to aid indoor localization. Firstly, we investigate the influence of Signal-Noise-Ratio (SNR) on the distribution of signal spatial parameters, i.e., AoA and ToF, by deriving a mathematical model. Then, we creatively employ the method of clustering tendency analysis to construct a LoS/NLoS identification collaborating with non-parametric statistical method. Finally, we deploy a localization system with the proposed context-aware algorithm. The extensive experimental result shows that the proposed algorithm can sense environment well and further improve the accuracy and robustness of localization system.
Teacher Collaboration in German Schools: Do Gender and School Type Influence the Frequency of Collaboration Among Teachers?

According to the literature, collaboration among teachers can be regarded as a subject that can positively influence numerous aspects of the school. However, factors influencing the extent to which collaboration takes place have received less attention. For example, gender differences are usually not examined. We carry out a secondary analysis of the representative PISA 2012 German teacher sample to measure the state of teacher collaboration through three different collaboration forms namely instruction-, project- and organization related. Because of the stratification of the German school system in secondary education, variations by school type should be taken into account. We conducted a MANCOVA to investigate the differences and effects that gender and school type of the lower secondary education have on the frequency with which teachers collaborate. Age and experience (in years) were used as covariates. Results show that women collaborate minimally more than men, that the higher the formal education of a school, the less frequently collaboration occurs and that the overall level of collaboration is weak. There is no interaction effect between these two variables. Suggestions for future research as well as implications are presented.
A two layer model of malware propagation in a search engine context

Malware, which spreads among hosts, brings potential damage to computer systems. Its propagation will even be facilitated when search engine exists. In this paper, we try to dig deeper into the infected time of communities and the malware behaviour in a search engine context. In order to address these problems, a two layer model is proposed for malware propagation via search engine. Derived from this model, we have found that when search engine exists, the reciprocal of infected time in different communities follows power law distribution and the number of malware increases exponentially at the early stage. Both of these hypothesis are validated later by simulations on a real-world dataset.
Silencing NIK potentiates anti-VEGF therapy in a novel 3D model of colorectal cancer angiogenesis

Angiogenesis is essential for colorectal cancer (CRC) progression, as demonstrated by the beneficial clinical effects of therapeutics inhibiting VEGF signaling. However, alternative mechanisms of neovascularization can develop, resulting in treatment failure. Previously we demonstrated NF-κB-inducing kinase (NIK) contributes to pathological angiogenesis. Here, we investigate NIK as a therapeutic target in endothelial cells (EC) in CRC. To determine NIK expression levels in CRC tissues, we immunostained both primary colorectal tumors and tumors metastasized to the liver. Additionally, a 3D tumor-stromal cell interaction model was developed including EC, fibroblasts and CRC cells to study tumor angiogenesis. This model tested efficacy of NIK-targeting siRNA (siNIK) in EC alone or in combination with the anti-VEGF antibody, bevacizumab. Both primary CRC and liver metastases contained blood vessels expressing NIK. In patients receiving chemotherapy plus bevacizumab, immature NIK+ vessels (p < 0.05) were increased as compared to chemotherapy alone. Activation of NIK by lymphotoxin-beta receptor (LTβR) induced increases in pro-angiogenic mediators, including interleukin (IL)-6, IL-8, chemokine (C-X-C motif) ligand (CXCL)1 and CXCL5 in EC and fibroblasts, accompanied by sprouting in the 3D model, which was blocked by siNIK in EC. Treatment with bevacizumab plus siNIK in EC resulted in a synergistic effect and reduced VEGF and bFGF-induced sprouting (p < 0.05). Here, we demonstrate a role for NIK in CRC-associated angiogenesis. Targeting NIK in EC in combination with anti-VEGF antibody bevacizumab may hold therapeutic potential to increase efficiency in blocking tumor neovascularization, either to prevent treatment failure due to activation of accessory pathways such as NF-κB signaling or as a rescue treatment.
Shifting the Balance Between Goals and Habits: Five Failures in Experimental Habit Induction

Habits are repetitive behaviors that become ingrained with practice, routine, and repetition. The more we repeat an action, the stronger our habits become. Behavioral and clinical neuroscientists have become increasingly interested in this topic because habits may contribute to aspects of maladaptive human behavior, such as compulsive behavior in psychiatry. Numerous studies have demonstrated that habits can be induced in otherwise healthy rats by simply overtraining stimulus–response behaviors. However, despite growing interest in this topic and its application to psychiatry, a similar body of work in humans is absent. Only a single study has been published in humans that shows the effect of extensive training on habit expression. Here, we report five failed attempts to demonstrate that overtraining instrumental behavior leads to the development of inflexible habits in humans, using variants of four previously published outcome devaluation paradigms. Extensive training did not lead to greater habits in two versions of an avoidance learning task, in an appetitive slips-of-action task, or in two independent attempts to replicate the original demonstration. The finding that these outcome devaluation procedures may be insensitive to duration of stimulus-response training in humans has implications for prior work in psychiatric populations. Specifically, it converges with the suggestion that the failures in outcome devaluation in compulsive individuals reflect dysfunction in goal-directed control, rather than overactive habit learning. We discuss why habits are difficult to experimentally induce in healthy humans, and the implications of this for future research in healthy and disordered populations.
Review of artificial neural network application in nanotechnology

Nanotechnology has shown its great potential in different fields of science such as medicine and pharmacy. This paper presents a review on artificial neural networks used in nanotechnology based on information gathered from different research. It is important to understand applications of artificial neural networks so that they can be used even more efficiently in future applications. Research papers summarized and compared here show different results in two fields of science. Artificial neural networks were made and proven to be useful in diagnostics and tracing diseases. The pharmaceutical industry has also shown to be a good candidate for the development of ANNs on the nanotechnology level. Regression analysis was used as a statistical method for presenting the best results from both fields observed. Root mean square error and mean error were calculated to measure the differences between values predicted by a model and the values actually observed from the environment that was being modelled. Based on individual results, each of the ANNs made were accurate enough to be considered as a diagnostic tool in fields of medicine and pharmacy. Performance is greater than 90% 10 out of 12 times, which is viewed in this paper. The multilayer perceptron ANN is mostly used. Based on the latest results, in upcoming years, one can expect better understanding and more research in the field of ANN applications in nanotechnology.
Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients

Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between “unsupervised” and “supervised” network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.
Standardized Uptake Values Derived from 18F-FDG PET May Predict Lung Cancer Microvessel Density and Expression of KI 67, VEGF, and HIF-1α but Not Expression of Cyclin D1, PCNA, EGFR, PD L1, and p53

Background Our purpose was to provide data regarding relationships between 18F-FDG PET and histopathological parameters in lung cancer. Methods MEDLINE library was screened for associations between PET parameters and histopathological features in lung cancer up to December 2017. Only papers containing correlation coefficients between PET parameters and histopathological findings were acquired for the analysis. Overall, 40 publications were identified. Results Associations between SUV and KI 67 were reported in 23 studies (1362 patients). The pooled correlation coefficient was 0.44. In 2 studies (180 patients), relationships between SUV and expression of cyclin D1 were analyzed (pooled correlation coefficient = 0.05). Correlation between SUV and HIF-1α was investigated in 3 studies (288 patients), and the pooled correlation coefficient was 0.42. In 5 studies (310 patients), associations between SUV and MVD were investigated (pooled correlation coefficient = 0.54). In 6 studies (305 patients), relationships between SUV and p53 were analyzed (pooled correlation coefficient = 0.30). In 6 studies (415 patients), associations between SUV and VEGF expression were investigated (pooled correlation coefficient = 0.44). In 5 studies (202 patients), associations between SUV and PCNA were investigated (pooled correlation coefficient = 0.32). In 3 studies (718 patients), associations between SUV and expression of PD L1 were analyzed (pooled correlation coefficient = 0.36). Finally, in 5 studies (409 patients), associations between SUV and EGFR were investigated (pooled correlation coefficient = 0.38). Conclusion SUV may predict microvessel density and expression of VEGF, KI 67, and HIF-1α in lung cancer.
An IMU-based turn prediction system

Many fatal car accidents are rear-ended collisions. To help alleviate this issue, a lightweight vehicle turn prediction system is developed, which identifies the forthcoming turn event at the early stage so that the neighboring vehicles can be notified in advance to prevent traffic accidents. The system uses smartphone sensors and digital maps. The smartphone sensors (a.k.a Inertial measurement unit) collect position information of the vehicle and predict the future position using particle filters. These positions are used to compute the curvature of the vehicle trace. From digital maps, the curvature of the road that the vehicle is currently traveling is also computed. By examining the difference of these two curvatures as well as the speed of the vehicle, the system determines if the vehicle is making a turn at the early stage. The experimental results show that the proposed system can correctly identify all the turns on the road. In addition, the proposed system does not require extra hardware, which allows it for inexpensive large-scale deployment.
FP -TESTER : Automated Testing of Browser Fingerprint Resilience

Despite recent regulations and growing user awareness, undesired browser tracking is increasing. In addition to cookies, browser fingerprinting is a stateless technique that exploits a device's configuration for tracking purposes. In particular, browser fingerprinting builds on attributes made available from Javascript and HTTP headers to create a unique and stable fingerprint. For example, browser plugins have been heavily exploited by state-of-the-art browser fingerprinters as a rich source of entropy. However, as browser vendors abandon plugins in favor of extensions, fingerprinters will adapt. We present FP-TESTER, an approach to automatically test the effectiveness of browser fingerprinting countermeasure extensions. We implement a testing toolkit to be used by developers to reduce browser fingerprintability. While countermeasures aim to hinder tracking by changing or blocking attributes, they may easily introduce subtle side-effects that make browsers more identifiable, rendering the extensions counterproductive. FP-TESTER reports on the side-effects introduced by the countermeasure, as well as how they impact tracking duration from a fingerprinter's point-of-view. To the best of our knowledge, FP-TESTER is the first tool to assist developers in fighting browser fingerprinting and reducing the exposure of end-users to such privacy leaks.
SPARK detection with thermal camera

Motion detection can be defined as the problem of detecting changes in the motion of objects depending on the environment with various cameras (RGB, thermal etc.). This problem has been studied by researchers for many years, taking into consideration the state of the camera, its capabilities and the physical structure of the objects to be detected. In this research, SPARK (i.e. sudden motion) changes are studied and focused on temperature change using thermal camera. The experimental results show that the proposed method achieves promising results in the proposed dataset.
Development and investigation of algorithms for determining relative navigation and orientation based on distance measurements

In research the problems of nanosatellite relative navigation and attitude determination at formation flight are considered. For relative navigation and attitude determination the algorithms based on methods of range radionavigation have been developed. The algorithm field of applicability is formulated. Estimates of the algorithm method errors are obtained and recommendations about their practical use are formulated.
Towards practical personalized recommendation with multi-level differential privacy controls

Recommender systems have been widely applied in many scenarios such as shopping websites, online learning systems and video sharing clients, etc, which need to collect user's historical data for improving the accuracy of recommended results. However, user's private data inadvertently left behind on the internet or clients also will be gathered by service providers, and the risk of user's privacy being leaked may greatly increase. While the problem of private recommended algorithm has been studied in recent years, the latest research findings are still inadequate in security or utility. In this paper, we propose a novel multi-level differential privacy (Multi-DP)scheme under untrusted personalized recommendation system. Specifically, we firstly design a multi-level differential privacy algorithm employing Laplace mechanism to protect both overall privacy towards service provider and each data attributes privacy. Then, randomized response is introduced to sanitize user's historical data for generating useful perturbed data. In this way, the error between raw historical data and perturbed data will be further reduced. Finally, theoretical analysis and extensive experiments demonstrate the high security and data utility of our proposed model compared with existing methods.
Homophily and Controversy: On the Role of Public Opinion in Online Viral Diffusion

—It is critical in social network analysis to understand the underlying mechanisms of online information diffusion. Although there has been much progress on the influential factors that lead to online viral diffusion, little is known about the impact by public opinion. In this paper, we examine the relations between the public opinion among information propagators and the virality of online diffusion based on a large-scale real-world dataset. We propose a set of new metrics for public opinion in online diffusion to reveal their correlation with diffusion structural virality, and further apply our understanding to predict diffusion virality based on public opinion. The experimental results show the effectiveness of the proposed analysis in the prediction of viral diffusion events.
Management preferences of deep caries in permanent teeth among dentists in Saudi Arabia

ABSTRACT Objective: To report the management preferences of deep caries in permanent teeth among dentists. Materials and Methods: This observational cross-sectional study included a convenience sample of 177 dentists selected from private and public dental clinics in different cities of the Eastern Province of Saudi Arabia. Four clinical scenarios about the absence or presence of pain/symptoms and the risk of pulp exposure in deep caries were presented to the dentists in a pilot-tested questionnaire. Results: Most dentists (82.5%) preferred complete caries excavation when there was no risk of pulp exposure and no pain/symptom was associated with deep caries in permanent teeth. The stepwise technique was the procedure of choice reported by 57.8% of dentists when there was no pain but a risk of exposure was present. In case of no exposure risk but the presence of nonspontaneous pain (pain on thermal testing), complete caries removal was chosen by 55.9% of the respondents. Root canal treatment and stepwise caries removal were preferred by 42.4% and 38.4%, respectively, when there was a risk of exposure and nonspontaneous pain. The risk of exposure (81.4%), the progression of caries (73.4%), and treatment failure (58.8%) were the most common concerns with different caries removal techniques. The hardness was the most commonly used criterion (85.3%) followed by the color of caries (50.3%) during caries excavation. Conclusions: The majority of dentists preferred to completely remove caries in case of no risk of pulp exposure. The dentists should update their knowledge and adopt latest concepts about minimally invasive caries removal approaches.
A maneuver-prediction method based on dynamic bayesian network in highway scenarios

The accurate maneuver prediction for dynamic vehicles can enhance driving safety in complex environments. This paper presents a maneuver prediction method for dynamic vehicles in highway scenarios. The method effectively combines multi-frame vehicle states, road structures and interactions among vehicles. With a novel extraction algorithm of environment feature, the method infers the probability of each driving maneuver by using a Dynamic Bayesian Net­work. The experimental results demonstrate that our method can predict lane-change maneuvers at least 2 seconds before they occur in real environments with an accuracy of 84.9%.
Chamber dimension optimization of a bellow-type soft actuator for food material handling

Soft robots demonstrate interesting possibilities of handling fragile and highly deformable objects without complex and accurate control. So far, the design of soft robots mostly rely on designer's intuition and trail-and-error method which is not efficient and therefore hinders the wide applications of soft robots. In this paper, we presented a way to investigate the optimal chamber design of a bellow-type soft actuator using Abaqus and Isight software. The finite element (FE) model of the actuator was developed in Abaqus with two design variables. The model was then imported into Isight and two objective functions of maximizing bending deformation and contact force were implemented. The optimal parameters were found to be at the boundaries of the predetermined parameter sets. Four kinds of actuators, having parameters of the initial guesses, optimal values, and two other optional sets, were fabricated and experimentally tested. Good agreements were achieved. Two-fingered grippers were constructed using different actuators and grasping tests were performed on defrozen broccolis. Results showed that using the optimized actuators required less air pressure to handle the same targets.
Face recognition algorithm based on cascading BGP feature fusion

BGP feature is a simple and efficient face recognition descriptor which is robust to light, expression and occlusion. Normally, the feature extraction of BGP is performed once on the face, and the texture information obtained is not rich enough. In this paper, a multi-level cascaded BGP extraction method is applied to obtain richer facial texture information. Firstly, feature extraction is performed on the original face using BGP. Then, feature extraction is performed on the obtained face feature images in the same way to obtain multi-level concatenated features. Finally, the histograms of the feature images are spliced and combined on the face features. The algorithm is fully validated in Yale and extended YaleB, and the recognition accuracy is significantly improved compared with the original BGP algorithm.