Miracle-dz/UniFAR-FTUs huggingface dataset:
ON THE ROOTED TUTTE POLYNOMIAL ( * ) by

Consider a finite graphG with vertex set V and edge setE. A spanning subgraph G′(S) ⊆ G is a subgraph of G containing all members of V and an edge set S ⊆ E. Let C be a set of q distinct colors. A q-coloring of G is a coloring of the vertices in V such that two vertices connected by an edge bear different colors. It is well-known that the number of q-colorings of G is given by the chromatic polynomial (see [1])
INSTAGRAM MARKETING: ATTRACTIVENESS OF BRANDS

In this study, the consumer’s perspective of Instagram is examined. Using qualitative research methods, the underlying motives to use Instagram, the ways in which Instagram is used, and the drivers of brand attractiveness on Instagram are identified. The results show that consumers follow brands on Instagram because of five important motives: positive attitude, gathering latest information, experience, inspiration and identification. These motives help to establish two user groups, buyers and recreational users, to which the consumers belong. Most consumers use Instagram daily, which consists of several short usage moments. This behavior is mostly applicable to young adults. With new insights into Instagram usage, two additional user groups are established: heavy users and occasional users. Furthermore, three tiers of drivers of brand attractiveness are distinguished, which have a different amount of importance for determining the brand attractiveness. This study contributes to the current stream of literature on social media marketing by laying a foundation for the research on Instagram, which is currently lacking.
Is a Simple Spinal Sprain From Whiplash Injury Really That Simple

The answer of course is a resounding NO. I want to educate you on what a spinal sprain is and WHY is it critical that you get under the care of a doctor who understands how to test for, document, and effectively treat the spinal ligament injuries. Unless you think a life or chronic pain, or poor health is ideal for you, a loved one or anyone else that you may know, we really hope you take this to heart. First you need to understand that a spinal sprain is an injury to the ligaments, which are the tough straps that hold the spinal motion units (2 or more vertebra) together. This is what they look like (Figure 1):
Influences of Melamine Flow Rate on the Growth Behavior of Nanocrystalline Diamond Films by Direct Current Plasma Chemical Vapor Deposition Method

InCH4/H2 atmosphere, Nitrogen-doped nanocrystalline diamond films were prepared by direct current hot-cathode plasma chemical vapor deposition (PCVD) method on the conditions of different flow rate of Melamine. While the flow rate of Melamine is 2sccm, the grain refining effect on the diamond films is obvious and the nanocrystalline grains protrude from the basis of the film which shape rough surfaces. The films are incompact, due to its large gaps and holes as well as defects, however the conductivity of these films are increased. The increasing flow rate of Melamine promotes the growth of (111) oriented.
Energy efficiency forecast of the Krasnoyarsk region

The energy efficiency of the Russian economy lags far behind the developed countries of the world. Increasing energy efficiency and, above all, energy saving, is the source that can provide additional economic growth through organizational and technical measures. The object of research is the economy of the Krasnoyarsk Territory, the increase in energy efficiency of which is of particular relevance due to its high energy intensity. The purpose of the study is to carry out an energy-economic analysis of the Krasnoyarsk Territory and to fulfill the forecast of the growth of the energy efficiency of the economy. The methodical approach developed by the authors is based on balance and statistical methods. In the course of the study, retrospective fuel and energy balances (FEB) of the Krasnoyarsk Territory (for the period 2005-2017) were developed, energy efficiency indicators were calculated and a regression equation was compiled for the forecast of the energy intensity of the gross regional product (GRP). The forecast for the development of the economy and the fuel and energy complex (FEC) of the Krasnoyarsk Territory has been made, the forecast TEBs have been developed (for the period up to 2050), the energy intensity of the regional GRP has been calculated for the long term, and measures have been proposed to increase energy efficiency.
Filtered forms of prokaryotes and bacteriophages in soil concretions

In Albic Retisols, Stagnic Fluvisols and Gleyic Fluvisols processes of ferriferous and manganese transformation follows rather active, that create new ecological niches for soil microorganisms.The study of concretions in the Albic Retisols, Stagnic Fluvisols and Gleyic Fluvisols showed, that the most characteristic feature of the prokaryotic community was the higher number and diversity of recoverable bacteria in comparison with the same indexes in the host horizons. The representatives of phylum Proteobacteria (class Gammaproteobacteria and Deltaproteobacteria) were dominant in the microbial communities in studied concretions. For the first time, the bacteriophages were found in concretions among them tailed phages were dominant. The method of the high-throughput sequencing of the 16S rRNA gene allowed to reveal the variability of the prokaryotic community in concretions of the Albic Retisol (Cutanic, Siltic), which was characterized predominantly by 7 bacterial phyla and 2 archaea phyla Both groups of organisms typical of soils and prokaryotes capable of transformation of iron and manganese under aerobic and anaerobic conditions were revealed in soil concretions. The specific characteristic of the studied soil concretions was a widespread occurrence of filtered bacterial forms, which may be considered as specific survival forms in soil loci.
Time Series Data Prediction Based on Sequence to Sequence Model

Home appliance test is an essential part of the R&D process. However, since home appliance test must be carried out under certain circumstances, it makes the development cycle longer. If it is possible to predicting the home appliance test data, it will improve the efficiency of home appliance development. Sequence to sequence model has been proved that it has a strong ability to map the input sequence and output sequence and it has been widely used in machine translation tasks. In order to solve the home appliance test data prediction problem, we firstly tried to apply sequence to sequence model to predict numerically continuous time series data. Our experiments proved that our model can predict the data of relatively long-time steps when inputting data of relatively short time steps. In our experiment, the mean absolute percentage error of the prediction of our model is about 0.1%. Our approach has a strong generalization ability and performs well in different experimental scenarios. Finally, we believe that the sequence to sequence method performs satisfactorily on prediction problems for continuous time series data. With this model, we can obtain more accurate results than traditional methods and it is meaningful for the R&D of home appliances.
An RSS-Based Classification of User Equipment Usage in Indoor Millimeter Wave Wireless Networks Using Machine Learning

The use of millimeter wave technologies offer a promising solution for dense small cell networks, despite having to contend with challenging propagation characteristics. In particular, user-induced effects can lead to significant channel variations depending on the user equipment (UE) usage mode which in turn, can impact the quality of service. Estimation of UE operating conditions is therefore critical for optimal radio resource management. We propose a new approach to user activity recognition which makes use of both supervised and unsupervised machine learning. In particular, using information extracted from the received signal strength (RSS), a common metric readily available from many receiver chipsets, we perform a classification of user state (static or mobile relative to an access point) and UE mode of operation (voice call, using an app or in pocket). To develop and then train our classification system, measured RSS data was obtained using a custom 60 GHz measurement system for a range of indoor office scenarios which considered various UE to ceiling mounted access point configurations. In our approach, differentiation between static and mobile states is performed in preprocessing using a k-means algorithm. Small-scale fading features are then estimated from the RSS data and, using different feature scaling mechanisms, various supervised learning approaches are applied to investigate the optimal classification accuracy for the considered use cases in this work. We compare the classification performance of various window sizes and types, and show that a sliding window length of 1s without overlap performs best for time series segmentation at 60 GHz for the activities considered in this study. Among the different supervised learning approaches, the Decision Tree (DT) classifier performs best for both the user static and mobile cases with an accuracy of 100% and 98.0%, respectively. For static cases, user orientation, i.e., line-of-sight (LOS), quasi-LOS, and non-LOS, can also be classified and here the DT classifier also performs best with an accuracy of 98.2%, 97.6% and 100% for the voice call, using an app or in pocket use cases. Additionally, a feature ranking algorithm, called ReliefF, is adopted to determine the small-scale fading features that have the most significant influences on the classification accuracy and three different feature sets, namely Full, Reduced and Constrained sets, are then proposed based on feature ranking results. This allows the proposed techniques to be deployed on wireless platforms with different levels of processing capability.
Maize Carotenoid Gene Locus Mining Based on Conditional Gaussian Bayesian Network

How to mine the gene locus for maize carotenoid components is an important research problem in biology study. Along with the rapid development of high-throughput biotechnologies, we have produced a large number of maize multi-omics data, including genome, transcriptome, metabolome, phenotype, etc. How to conjointly analyze these continuous and discrete data, and thus to mine the genetic loci that control the maize carotenoid components have a very important biological significance. In this work, we use the conditional Gaussian Bayesian network learning method to construct the network of maize gene, SNP locus and carotenoid components, aim to get the possible significant loci about four reported genes for the carotenoid component traits. The method is validated using the multi-omics data of maize global germplasm collection with 368 elite inbred lines. Four algorithms are used to do the comparison, and experiment results show the method can mine the effective locus for the phenotype traits. It is concluded that the conditional Gaussian Bayesian network learning method is an effective way of analyzing multi-omics data conjointly, mining the possible gene locus for maize carotenoid component traits, and thus to provide genetic resources and useful information for molecular breeding of maize.
Articulated Multi-Perspective Cameras and Their Application to Truck Motion Estimation

While monocular and stereo camera based motion estimation has reached a level of maturity that enables industrial use, the community keeps exploring novel multi-sensor solutions to meet the high robustness and accuracy requirements of certain applications such as autonomous vehicles. The present paper focuses on motion estimation with multi-perspective camera systems. In particular, we look into the intricate case in which the cameras are distributed over an articulated body, a scenario that occurs in truck motion estimation where additional cameras are installed on the trailer. The resulting articulated multi-perspective camera is analyzed in theory and practice, and we show that— by taking the non-holonomic constraints of the vehicle into account— a single point correspondence measured from the trailer is sufficient to render the additional unknown parameters given by the internal joint configuration before and after a relative displacement fully observable. optimizing over all parameters enhances the accuracy of the motion estimation of the entire system with respect to using the cameras on each rigid part alone. Results are confirmed on both simulated and real data.
Classification of LiDAR Data Combined Octave Convolution With Capsule Network

Light Detection and Ranging (LiDAR) data are widely used for high-resolution land cover mapping, which can provide very valuable information about the height of the surveyed area for the discrimination of classes. In order to utilize the advantages of deep models for the classification of LiDAR-derived features, a new classification algorithm combined Octave Convolution (OctConv) with Capsule Network (CapsNet), is proposed here to hierarchically extract robust and discriminant features of the input data, called as OctConv-CapsNet. In the proposed approach, CapsNet captures the spatial information of the data, and OctConv processes separately for high- and low-frequency feature. OctConv is embedded in the primary capsule layer of CapsNet so that the proposed approach can make the most of both the spatial information and the high- and low-frequency information simultaneously. The proposed framework performs experiments on two LiDAR-DSM datasets (i.e. Bayview Park and Recology datasets). The results show that, compared with the traditional deep convolution model, OctConv-CapsNet can improve the classification accuracy of LiDAR-DSM data, and when the number of training samples of the experiment is 800, the classification accuracies reached 96.12% and 96.79% on Bayview Park and Recology datasets, respectively.
The Security of Autonomous Driving: Threats, Defenses, and Future Directions

Autonomous vehicles (AVs) have promised to drastically improve the convenience of driving by releasing the burden of drivers and reducing traffic accidents with more precise control. With the fast development of artificial intelligence and significant advancements of the Internet of Things technologies, we have witnessed the steady progress of autonomous driving over the recent years. As promising as it is, the march of autonomous driving technologies also faces new challenges, among which security is the top concern. In this article, we give a systematic study on the security threats surrounding autonomous driving, from the angles of perception, navigation, and control. In addition to the in-depth overview of these threats, we also summarize the corresponding defense strategies. Furthermore, we discuss future research directions about the new security threats, especially those related to deep-learning-based self-driving vehicles. By providing the security guidelines at this early stage, we aim to promote new techniques and designs related to AVs from both academia and industry and boost the development of secure autonomous driving.
Small-Scale Compliant Dual Arm with Tail for Winged Aerial Robots

Winged aerial robots represent an evolution of aerial manipulation robots, replacing the multirotor vehicles by fixed or flapping wing platforms. The development of this morphology is motivated in terms of efficiency, endurance and safety in some inspection operations where multirotor platforms may not be suitable. This paper presents a first prototype of compliant dual arm as preliminary step towards the realization of a winged aerial robot capable of perching and manipulating with the wings folded. The dual arm provides 6 DOF (degrees of freedom) for end effector positioning in a human-like kinematic configuration, with a reach of 25 cm (half-scale w.r.t. the human arm), and 0.2 kg weight. The prototype is built with micro metal gear motors, measuring the joint angles and the deflection with small potentiometers. The paper covers the design, electronics, modeling and control of the arms. Experimental results in test-bench validate the developed prototype and its functionalities, including joint position and torque control, bimanual grasping, the dynamic equilibrium with the tail, and the generation of 3D maps with laser sensors attached at the arms.
A Novel Simple Miniaturization Technique for Double Rat-race coupler

The miniaturization of a double ring microstrip coupler was investigated. The coupler has a central frequency of 1 GHz. The substrate material was chosen to be FR-4 with a permittivity $\varepsilon =$ 4.4, loss tangent $\delta =$ 0.02, thickness h = 1 mm. The stages of miniaturization are based on the implementation of microstrip low-pass filters, which have compact dimensions compared to the transmission line, are presented. After the numerical simulation, coupler design was obtained, which was created using a standard method of PCB manufacturing and its parameters were measured. The obtained coupler retains the parameters of a conventional device in a wide frequency band. The proposed method allowed reducing the coupler size by 86% related to the straight design.
Multilevel Feature Fusion With 3D Convolutional Neural Network for EEG-Based Workload Estimation

Mental workload is defined as the proportion of the information processing capability used to perform a task. High cognitive load requires additional resources to process information; this demand for additional resources may reduce the processing efficiency and performance. Therefore, the technique of workload estimation can ensure a proper working environment to promote the working efficiency of each person. In this paper, we propose a three-dimensional convolutional neural network (3D CNN) employing a multilevel feature fusion algorithm for mental workload estimation using electroencephalogram (EEG) signals. The 1D EEG signals are converted to 3D EEG images to enable the 3D CNN to learn the spectral and spatial information over the scalp. The multilevel feature fusion framework integrates local and global neuronal activities by workload tasks in the 3D CNN algorithm. Multilevel features are extracted in each layer of the 3D convolution operation and each multilevel feature is multiplied by a weighting factor, which determines the importance of the feature. The weighting factor is adaptively estimated for each EEG image by a backpropagation process. Furthermore, we generate subframes from each EEG image and propose a temporal attention technique based on the long short-term memory model (LSTM) to extract a significant subframe at each multilevel feature that is strongly correlated with task difficulty. To verify the performance of our network, we performed the Sternberg task to measure the mental workload of the participant, which was classified according to its difficulty as low or high workload condition. We showed that the difficulty of the workload was well designed, which was reflected in the behavior of the participant. Our network is trained on this dataset and the accuracy of our network is 90.8 %, which is better than that of conventional algorithms. We also evaluated our method using the public EEG dataset and achieved 93.9 % accuracy.
The Prediction of Alcohol Use Disorder: A Scoping Review

The prediction of Alcohol Use Disorder (AUD) may help to alleviate the number of deaths caused by alcohol related diseases, which had amounted to 3.3 million in 2014, worldwide. This article reports on the results of a scoping review of literature which focused on the prediction of AUD. A search in the academic databases including Medline, Web of Science and EBSCOhost had identified 28 articles which were published from 1980 to 2018, and which fulfilled our inclusion criteria related to the prediction of AUD. The findings suggest that research focusing on the prediction of AUD has been solid, with majority of the investigations focusing on genetics and family history, and psychological factors. It was observed that no study had tried to extract the predictor variables of AUD from their collected samples. Further, while a few studies had applied the machine learning approach in this domain, most investigations were based on statistical methods. Our review also suggests that compared to other regions where the rate of harmful drinking and mortality caused by alcoholism is high, Denmark is a country that has been less explored.
Geometry Augmented SURF with Modified Sobel for Improved Affine Invariance in Image Matching

Reliable image matching and alignment is a key issue in difference extraction of aerial images. This paper presents an affine, scale and rotation-invariant method for aligning images taken at different timelines. SURF feature points index pair polling is used to detect best candidate match from an image library against a reference image. SURF is used to ensure speedy match detection as a large library is being scanned. The two images are then coarse-aligned using a statistical model. A modified sobel operator is used to ensure complete edge detection along six orientations. Since SURF is not satisfied for affine invariance, a geometry based approach is used to discard undesired differences. The resulting difference helps locating new structures/ buildings. This integrated approach allows difference extraction in affine environments while satisfying robustness and low computational complexity. The results show upto 90% increase in correlation after alignment between the reference and matched image. The augmented approach increased the probability of detecting valid differences while suppressing the false detections upto 99%.
Bayesian Optimization for Adaptive Experimental Design: A Review

Bayesian optimisation is a statistical method that efficiently models and optimises expensive “black-box” functions. This review considers the application of Bayesian optimisation to experimental design, in comparison to existing Design of Experiments (DOE) methods. Solutions are surveyed for a range of core issues in experimental design including: the incorporation of prior knowledge, high dimensional optimisation, constraints, batch evaluation, multiple objectives, multi-fidelity data, and mixed variable types.
Normal Distribution Mixture Matching based Model Free Object Tracking Using 2D LIDAR

In this paper, a novel normal distribution mixture matching based model free object tracking algorithm using 2D LIDAR is proposed. Each target object is modeled as a normal distribution mixture that captures the distribution of the points scanned from the surface of the object. This novel representation enables normal distribution transform (NDT) to accurately estimate the motion of objects, even if the shape of the points differs depending on where it is observed. Our evaluation of the proposed algorithm shows good performance in practical applications. In addition, we provides an alternative way of segmentation and data association using occupancy grid map to avoid a problem that defines a distance metric between the mixture and the point cloud. As a result, the proposed algorithm works in real time in our experiments.
Integrated Functional Safety and Security Diagnosis Mechanism of CPS Based on Blockchain

This paper proposes a functional safety and information security protection mechanism based on blockchain technology. The design of the basic level and integration level blockchain structure of CPS distributed architecture and related functional safety and information security measures are introduced. An effective communication judgment mechanism based on functional safety error threshold is proposed, which is stored and judged by smart contract. And a refund transaction with a clock is proposed to ensure the effective execution of the functional safety error threshold mechanism. The article takes cyber physical machine tool system as an example to describe the SIL judgment method of CPS physical equipment and functional safety loop and combines SIL with fault diagnosis and risk protection. Finally, the rationality of our proposed mechanism is proved by information security, functional security, real-time and maintainability.