Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperspectral Remote Sensing Image Classification Based on Convolutional Neural Network

Remote sensing hyperspectral imaging can obtain rich spectral information of terrestrial objects, which allows the indistinguishable matter in the traditional wideband remote sensing to be distinguished in hyperspectral remote sensing. Hyperspectral image has the characteristics of “combining image with spectrum”. Making full use of spectral information and spatial information in hyperspectral image is the premise of obtaining accurate classification results. At present, most of hyperspectral data feature extraction algorithms mainly utilize local spatial information in the same channel and spectral information in the same spatial location of different channels. However, these methods require a large amount of prior knowledge, it is difficult to fully grasp the hyperspectral data of all spatial and spectral information, and the model generalization ability is poor. With the development of deep learning, convolutional neural network shows superior performance in all kinds of visual tasks, especially in the two-dimensional image classification, and could get a high classification accuracy. In this paper, an image classification method based on three-dimensional convolution neural network is proposed based on the structural properties of hyperspectral data. In the proposed method, first the stereo image blocks of hyperspectral data are intercepted, then multi-layer convolution and pooling operation of extracted blocks by convolutional neural network are implemented to obtain the essential information of hyperspectral data, finally the classification of hyperspectral data is completed. The experimental results show the proposed method could provide better feature expression and classification accuracy for hyperspectral image.
Robust Visual Tracking Based on Adversarial Fusion Networks

Recent advances in visual tracking showed that deep Convolutional Neural Networks(CNN) trained for image classification can be strong feature extractors for discriminative trackers. However, due to the long-tail of categories, occlusions, deformations and some other attributes are so rare that they will hardly happen. Yet we want to learn a model invariant to such occurrences for fear that we could handle complex attributes in tracking procedure. In this paper, we propose an alternative solution. We propose to learn an Adversarial Fusion Networks(AFN) that generates examples with occlusions and deformation based on the internal structure of Region Proposal Network (RPN). We discovered that the internal structure of Adversarial Fusion Networks(AFN)'s top layer feature can be utilized for robust visual tracking. We illustrated that such networks can be more robust when the tracking object suffering from occlusion and deformation. Without ensemble and any extra treatment on feature maps, our proposed method achieved state-of-the-art results on several large scale benchmarks including OTB50, OTB100 and VOT2016.
Secondary Learning and Kernel Initialization on Auto-Tagging of Music Events Using Convolutional Neural Networks

In this paper, we show a deep secondary learning method using convolutional neural networks (CNN) that takes into account the prediction results of a previous tagging method. In particular, we initialize the values of the kernel functions by sampling from some existing instrument signal patterns. We evaluate the tagging on male, female and no-vocal events in 100 popular songs. The performance increases 14.8%, 24.96%, 2.97% in precision, recall and accuracy respectively in comparison with the previous method.
Texture Analysis of Brain Mr Images

This proposed work is the method to perform texture analysis of human brain MR image and to provide a method for decision making using MATLAB tool in medical field. Hospitals throughout the world are witnessing huge volumes of digital medical images and associated clinical data on a daily basis. The number of people available in the hospitals to analyze and take decisions are very less when compared to images that need to be analyzed by these experts and hence automation of this process is necessary. We have implemented a system for automatic segmentation and extraction of different texture features of human brain images. SVM is used for classification and K-fold validation is performed.
Voice-Based User Interaction System for Call-Centers, Using a Small Vocabulary for Romanian

This paper presents a voice-based user interaction system for call centers, in Romanian. A proof of concept for the banking domain was implemented. The system consists of an automatic speech recognition engine, a text-to-speech engine, a dialogue management subsystem and an interface with the bank database. The banking services and operations were simulated for a small number of services. Since Romanian is still an under resourced language, we choose to build a small vocabulary automatic speech recognizer and develop a dialogue model using only digits as user input. The text-to speech engine can generate any Romanian utterance.
Dynamic Defense Strategy Against DoS Attacks Over Vehicular Ad Hoc Networks Based on Port Hopping

To fight against denial of services (DoS) attacks on vehicular ad hoc networks, which can cause congestion over networks and degrading the user’s experience, a lot of detective techniques and schemes have been proposed. However, the complex ones cannot keep pace with the growth of vehicle networks. In this paper, we propose a simple but effective defense strategy scheme inspired by the port-hopping mechanism, which advantage is manifested in that the detection and filtering off of malicious packets launched by attackers can be achieved without any change in existing protocol. First, we design a dynamic defense strategy scheme to puzzle a DoS attacker, where the specific defense strategy will change according to a scheme of time. To mitigate the losses caused by an attacker whose goal is to probe the vulnerable services’ ports contained in the UDP/TCP headers between vehicle-to-vehicle or vehicle-to-infrastructure, we add some security services’ ports that are valueless to attackers. Second, we give the specific construction of such a defense strategy scheme reflected as a matrix and a security analysis with respect to detecting the probed ports. At last, in comparison with the non-strategy defense scheme, simulations considering some parameters are conducted, which can show that our scheme is an effective defense scheme used for protecting VANETs.
Representation Learning for Sensor-based Device Pairing

The emergence of on-body gadgets has introduced a novel research direction: unobtrusive and continuous device pairing. Existing approaches leveraged contextual information collected by sensors to generate secure communication keys. The secret information is represented throught hand-engineered features. In this paper, we propose a learning method based on Siamese neural networks to extract features that signify on-body context while separating off-body devices.
Drought-Induced Reduction in Net Primary Productivity across Mainland China from 1982 to 2015

Terrestrial net primary productivity (NPP) plays an essential role in the global carbon cycle as well as for climate change. However, in the past three decades, terrestrial ecosystems across mainland China suffered from frequent drought and, to date, the adverse impacts on NPP remain uncertain. This study explored the spatiotemporal features of NPP and discussed the influences of drought on NPP across mainland China from 1982 to 2015 using the Carnegie Ames Stanford Application (CASA) model and the standardized precipitation evapotranspiration index (SPEI). The obtained results indicate that: (1) The total annual NPP across mainland China showed an non-significantly increasing trend from 1982 to 2015, with annual increase of 0.025 Pg C; the spring NPP exhibited a significant increasing trend (0.031 Pg C year−1, p < 0.05) while the summer NPP showed a higher decreasing trend (0.019 Pg C year−1). (2) Most areas of mainland China were spatially dominated by a positive correlation between annual NPP and SPEI and a significant positive correlation was mainly observed for Northern China; specific to the nine sub-regions, annual NPP and SPEI shared similar temporal patterns with a significant positive relation in Northeastern China, Huang-Huai-Hai, Inner Mongolia, and the Gan-Xin Region. (3) During the five typical drought events, more than 23% areas of mainland China experienced drought ravage; the drought events generally caused about 30% of the NPP reduction in most of the sub-regions while the NPP in the Qinghai-Tibet Plateau Region generally decreased by about 10%.
Managing Collaborative Strategy in 3D Bioprinting Technology

The potential of a three-dimensional bioprinting (3DBP) technology in core applications of the healthcare sector has been paid attention for managing global aging. Currently this technology area is situated in the embryonic stage. Therefore, it is essential to recognize the crucial factors in early stage of industry life cycle which allow firms to increase the rate of survival and the capabilities. Due to the scares resources in young and inexperienced firms, establishing collaborative strategy plays a vital role in supplement such shortcomings. However, as the technological development process has been complicated by converging among heterogeneous technologies, it is difficult to find the potential strategic partners for new entrants. In this study, we suggest the framework to search the technologically suited partners readily and provide detail information to manage relationships among participants. In addition, it is the first exploration to provide insights from the strategic perspective rather than technological one for the entire 3DBP technology industry.
A precise information extraction algorithm for lane lines

Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model (GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform (PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.
Reconceptualizing Self-Affirmation With the Trigger and Channel Framework: Lessons From the Health Domain

Self-affirmation—a theory-based technique to affirm the adaptive adequacy of the self—can promote positive behavior change and adaptive outcomes, although effects are variable. We extend a novel framework (Trigger and Channel), proposing three conditions that facilitate self-affirmation-induced behavior change: (a) presence of psychological threat, (b) presence of resources to foster change, and (c) timeliness of the self-affirmation with respect to threat and resources. Using health behavior as a focus, we present meta-analytic evidence demonstrating that when these conditions are met, self-affirmation acts as a psychological trigger into a positive channel of resources that facilitate behavior change. The presence of a timely threat and the availability of timely resources independently predicted larger self-affirmation effects on behavior change, and the two interacted synergistically to predict still larger effects. The results illustrate the conditionality of self-affirmation effects and offer guidelines for when, where, and for whom self-affirmation will be most effective.
Low Temperature Reaction Dynamics for the CH 3 OH + OH Collisions on a New Full Dimensional Potential Energy Surface

In Table 1 the stationary points of the ROH and RCH reactions are compared with several methods. Note that for the methods MRCIF12 and MRCI-F12 + Q the geometries correspond to those calculated with the CCSD-F12a method and cc-pVDZ-F12 basis set. These results indicate that the CCSD-F12a method yields to an accurate description of the PES near the transition state region. Indeed, if we analyze the multi-reference wave function of the MRCIF12 method, we see that two configurations dominate, that correspond to the configurations · · ·13a214a1 and · · ·13a114a2. Note that these valence orbitals correspond to the degenerate π orbitals of the OH reactant in the 2Π state, that are mixed when OH reacts with methanol. Because the second configuration corresponds to an excitation of one electron from one valence orbital to other valence orbital of the Hartree-Fock configuration, the CCSD-F12a method takes into account the main excitations of these configurations and, for this reason, correctly describes this region. As shown in Fig. 1, the energy of the transition state of the CCSD(T)-F12a method is between the MRCI-F12 values with and without Davidson correction. On the other hand, it is interesting to analyze the energies of the excited states along the IRC. The two first electronic states, 12A and 22A, are degenerated in the reactants asymptote. In the transition state region of the ground electronic state, the excited electronic states 22A and 32A show an avoided crossing at about 1.5-2.0 eV. The excitation from one valence orbital to the 15a virtual orbital have only a weight less than 5% in the CASSCF reference configurations, and it is only important in the 42A electronic state, that shows a ionic character for s > 1 Å.
Transfer Learning via Unsupervised Task Discovery for Visual Question Answering

We study how to leverage off-the-shelf visual and linguistic data to cope with out-of-vocabulary answers in visual question answering task. Existing large-scale visual datasets with annotations such as image class labels, bounding boxes and region descriptions are good sources for learning rich and diverse visual concepts. However, it is not straightforward how the visual concepts can be captured and transferred to visual question answering models due to missing link between question dependent answering models and visual data without question. We tackle this problem in two steps: 1) learning a task conditional visual classifier, which is capable of solving diverse question-specific visual recognition tasks, based on unsupervised task discovery and 2) transferring the task conditional visual classifier to visual question answering models. Specifically, we employ linguistic knowledge sources such as structured lexical database (e.g. WordNet) and visual descriptions for unsupervised task discovery, and transfer a learned task conditional visual classifier as an answering unit in a visual question answering model. We empirically show that the proposed algorithm generalizes to out-of-vocabulary answers successfully using the knowledge transferred from the visual dataset.
Broadband Substrate Integrated Waveguide Slotted Array Antenna at mm-Wave Bands

In this paper, a novel substrate integrated waveguide (SIW) based on a longitudinal slot array is designed with the help of a Genetic Algorithm (GA). The GA is used to find the optimum location of the slots on the surface of SIW. The antenna is radiating towards broad side with a side lobe level (SLL) of at least 10 dB. The antenna is optimized to provide a 3 GHz of BW ranging from 27 GHz to 30 GHz covering future 5G bands. The maximum gain of the antenna is 9.18 dB with a size of 62×7.23 mm3.
Cost-Efficient Deployment of Fog Computing Systems at Logistics Centers in Industry 4.0

In Industry 4.0, the factories become increasingly smart and efficient through intelligent cyber-physical systems based on the deployment of Internet of Things (IoT), mobile devices, and cloud computing systems. In practice, the cloud computing system in a factory is managed in a centralized way, and hence may not afford heavy computing loads from thousands of IoT devices in the factory. An approach to address this issue is to deploy fog/edge computing resources nearby IoT devices in a distributed way to provide real-time computing responses on sites. This paper investigates deployment of an intelligent computing system consisting of a cloud center, gateways, fog devices, edge devices, and sensors attached to facilities in a logistics center. Except for locations of the cloud center and sensors that have been determined based on the factory layout, this paper establishes an integer programing model for deploying gateways, fog devices, edge devices in their respective potential sites, so that the total installation cost is minimized, under the constraints of maximal demand capacity, maximal latency time, coverage, and maximal capacity of devices. This paper further solves this NP-hard facility location problem by a metaheuristic algorithm that incorporates discrete monkey algorithm to search for good quality solutions and genetic algorithm to increase computational efficiency. Simulation verifies high performance of the proposed algorithm in deployment of intelligent computing systems in moderate-scale instances of intelligent logistics centers.
Real Time Robot Path Planning Method Based on Improved Artificial Potential Field Method

Artificial potential field method is a common method to solve real time path planning problems of robots, but this method has inherent problems: local minima traps and oscillations in the presence of obstacles and narrow passages. To overcome these two problems, this paper proposes an improved artificial potential method. A new form of repulsive force is established, which includes radial repulsive force and tangential repulsive force. The movement of the robot is controlled by velocity command where the direction of the resultant force on the robot denotes the desired direction of the robot's velocity. The parameters of the attractive force and the repulsive force are analyzed and the range of the ratio of the repulsive coefficient to the attractive coefficient is determined. The method was tested by three stages: tests by a 6DOF Simulink model of quadrotor in MAT LAB environment, tests by a 6DOF C++ model of quadrotor in gazebo and ROS environment, and tests by a DJ M100 quadrotor in experiment environment. The results show that the proposed method can avoid local minima traps and eliminate oscillations compared with traditional artificial potential field method.
Adding insult to injury: Perceived inequity modulates pain perception

Inequity and pain typically co-appear during disasters or illnesses and have significant effects on health. However, the relationship between inequity and pain perception is poorly understood. Four studies investigated whether perceived inequity modulates pain perception using different priming paradigms and pain measurements. Studies 1–3 consistently revealed that participants perceived pain more intensely and rapidly after inequity priming. Study 4 demonstrated that inequity in cancer patients predicts pain perception after controlling for individual differences in equity sensitivity and pain sensitivity. The findings suggest that equity may help manage adversity in achieving mental and physical health.
Contact Force Prediction of Teleoperation System Based on Kalman Filter Algorithm

We target the problem of providing 5G network connectivity in rural zones by means of Base Stations (BSs) carried by Unmanned Aerial Vehicles (UAVs). Our goal is to schedule the UAVs missions to: i) limit the amount of energy consumed by each UAV, ii) ensure the coverage of selected zones over the territory, ii) decide where and when each UAV has to be recharged in a ground site, iii) deal with the amount of energy provided by Solar Panels (SPs) and batteries installed in each ground site. We then formulate the RURALPLAN optimization problem, a variant of the unsplittable multicommodity flow problem defined on a multiperiod graph. After detailing the objective function and the constraints, we solve RURALPLAN in a realistic scenario. Results show that RURALPLAN is able to outperform a solution ensuring coverage but not considering the energy management of the UAVs.
TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data

Travel behavior understanding is a long-standing and critically important topic in the area of smart cities. Big volumes of various GPS-based travel data can be easily collected, among which the taxi GPS trajectory data is a typical example. However, in GPS trajectory data, there is usually little information on travelers’ activities, thereby they can only support limited applications. Quite a few studies have been focused on enriching the semantic meaning for raw data, such as travel mode/purpose inferring. Unfortunately, trip purpose imputation receives relatively less attention and requires no real-time response. To narrow the gap, we propose a probabilistic two-phase framework named TripImputor, for making the real-time taxi trip purpose imputation and recommending services to passengers at their dropoff points. Specifically, in the first phase, we propose a two-stage clustering algorithm to identify candidate activity areas (CAAs) in the urban space. Then, we extract fine-granularity spatial and temporal patterns of human behaviors inside the CAAs from foursquare check-in data to approximate the priori probability for each activity, and compute the posterior probabilities (i.e., infer the trip purposes) using Bayes’ theorem. In the second phase, we take a sophisticated procedure that clusters historical dropoff points and matches the dropoff clusters and CAAs to immerse the real-time response. Finally, we evaluate the effectiveness and efficiency of the proposed two-phase framework using real-world data sets, which consist of road network, check-in data generated by over 38 000 users in one year, and the large-scale taxi trip data generated by over 19 000 taxis in a month in Manhattan, New York City, USA. Experimental results demonstrate that the system is able to infer the trip purpose accurately, and can provide recommendation results to passengers within 1.6 s in Manhattan on average, just using a single normal PC.
Detection of Candidate Nodules in Lung Tomography by Image Processing Techniques

In this project, the data set generated from the data of the National Cancer Institute (NCI) derived from DICOMformatted lung cancer data has been converted to PNG format to make it suitable for the operation of image processing algorithms. The converted data is filtered by a second-order median filter to prevent noise. A laplacian filter was applied to clarify the boundaries of the candidate nodules. The threshold must be determined according to the histogram value of each view. To select a different threshold for each image was subjected to the determination threshold Otsu method. In Otsu threshold selection method determines the point at which the minimum-class variance and between-class variance is a maximum, and determines the threshold value. In the availability of candidate nodules, nodule diameter is intended for detection of the large nodules than 2 mm. To meet this requirement, the Otsu thresholding method was applied on the MATLAB platform to sieve nodules smaller than 2 mm in diameter and candidate nodules were detected. By determining the morphological characteristics of the identified candidate nodules in MATLAB platform, candidate nodule; Major features such as area, major axis length, minor axis length, and perimeters are extracted. The boundaries of the identified candidate nodules are bounded by the edge detection algorithm and numerically ordered.