Miracle-dz/UniFAR-FTUs huggingface dataset:
ToC-RWG: Explore the Combination of Topic Model and Citation Information for Automatic Related Work Generation

Automatic related work generation is a new challenge in multi-document scientific summarization focusing on refining a related work section for a given scientific paper. In this paper, we propose a brand new framework ToC-RWG for related work generation by incorporating topic model and citation information. We present an unsupervised generative probabilistic model, called QueryTopicSum, which utilizes a LDA-style model to characterize the generative process of both the scientific paper and its reference papers. We also take advantage of citations of reference papers to identify Cited Text Spans (CTS) from reference papers. This approach provides us with a perspective of annotating the importance of the reference papers from the academic community. With QueryTopicSum and the identified CTS as candidate sentences, an optimization framework based on minimizing KL divergence is exerted to select the most representative sentences for related work generation. Our evaluation results on a set of 50 scientific papers along with their corresponding reference papers show that ToC-RWG achieves a considerable improvement over generic multi-document summarization and scientific summarization baselines.
Adaptive Fault Tolerant Control of Active Suspension Systems With Time-Varying Displacement and Velocity Constraints

In this paper, active suspension system is selected as the research object. Back-stepping method is adopted, and virtual fault tolerant controller, main fault tolerant controller and adaptive law are designed by constructing Time-varying Barrier Lyapunov Function (TVBLF), to ensure that the displacement and speed of the vehicle do not violate the constraint boundary and achieve the goal of fast fault tolerance. For the unknown continuous function caused by the uncertain body mass and other factors in the system, Radial Basis Function Neural Network (RBFNN) is used for approximation. At the same time, the suspension space limitation and tire dynamic load are analyzed. Finally, the effectiveness of the proposed fault-tolerant control method is verified by simulation of deviation fault and partial failure fault.
The Extended LM method For Multiple Frequencies Micro-Doppler Radar for Human Heartbeat Sensing

Micro-Doppler radar is attracting attention as a technology to wirelessly sense small movement of human body such as heartbeat measurement. The off-the-shelf 24 GHz micro-Doppler radar can easily detect the human vital signals. To enhance the detection performance, many signal processing algorithms have been proposed. One of key issue for improving the detection performance is the DC-offset estimation of the micro-Doppler radar because the DC-offset has direct impact on the radar’s movement estimation accuracy. This paper proposes a new accurate DC-offset estimation algorithm that is based on state-of-the-art multiple frequencies micro-Doppler radar.
Big Data Research Landscape: A Meta-Analysis and Literature Review from 2009 to 2018

Big data is a relatively new and lively field of research and practice. Researchers investigating a new field of research are interested in themes, trends, and gaps that demonstrate the field in order to make informed and accurate choices. Understanding up to date trends and potential gaps in a research field is only possible through a detailed analysis of the domain-specific literature. Since scientific literature has an exponential growth, it is not easy to manually identify such trends. In this context, probabilistic topic modeling is an effective approach which has recently attracted considerable attention in semantic analysis of large-scale textual collections. In this study, a semantic content analysis based on topic modeling was conducted on big data literature from 2009 to 2018 in order to discover big data research trends and themes.
Reduced-Sidelobe Multibeam Array Antenna Based on SIW Rotman Lens

A multibeam array antenna (MAA) fed by a Rotman lens with a reduced sidelobe level (SLL) is designed using a substrate integrate waveguide (SIW) technology. The designed MAA is composed of a Rotman lens and a 12 × 8 slot array, which functions as the beamforming network and the radiation part, respectively. To reduce the SLL in E-plane, dual-port excitations (DPEs) are applied, instead of single-port excitations (SPEs), at each feeding port of the Rotman lens. By using DPEs, a more tapered amplitude distribution can be obtained on the array elements as compared to using SPEs; therefore, the SLL is reduced from about −11 to −18 dB. The SLL in H-plane is controlled by introducing a Chebyshev distribution to the designed eight-element slot array. Based on the designed MAA, a fabricated prototype is measured to test the discrepancy between simulation and experiment.
Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems

Since their introduction, anti-lock braking systems (ABS) have mostly relied on heuristic, rule-based control strategies. ABS performance, however, can be significantly improved thanks to many recent technological developments. This work presents an extensive review of the state of the art to verify such a statement and quantify the benefits of a new generation of wheel slip control (WSC) systems. Motivated by the state of the art, as a case study, a nonlinear model predictive control (NMPC) design based on a new load-sensing technology was developed. The proposed ABS was tested on Toyota’s high-end vehicle simulator and was benchmarked against currently applied industrial controller. Additionally, a comprehensive set of manoeuvres were deployed to assess the performance and robustness of the proposed NMPC design. The analysis showed substantial reduction of the braking distance and better steerability with the proposed approach. Furthermore, the proposed design showed comparable robustness against external factors to the industrial benchmark.
Head CT Image Convolution Feature Segmentation and Morphological Filtering for Densely Matching Points of IoTs

With the rapid application of medical imaging technology, the number of medical images is increasing and the form is gradually diversified. The management and retrieval of medical images is an urgent problem to solve. Traditional image matching technology has many limitations due to the complexity of its manual labeling. Therefore, content-based medical image retrieval is a new method to solve this problem. The technique uses the visual attributes contained in the image to establish the feature index of the image, adopts Sparse Connectivity and weight sharing of CNN (Convolutional Neural Network) to obtain the image feature, and then perform feature similarity matching between the query image and the image in the image database, and implement retrieval according to the matching result. The intensive matching based on the Internet of Things has great potential in the application of medical image 3D reconstruction. Compared with the traditional algorithm based on convolution feature segmentation and morphological filtering, the biggest advantage of the proposed algorithm is the computational efficiency, aiming at the same image. The efficiency of dense matching based on the Internet of Things has greatly improved. At the same time, the problem of occlusion, moving objects, rotation, and scaling in multi-view images is well handled. Experiments show that the proposed method can extract more dense and reliable matching points for multi-view images, which is beneficial to the subsequent 3D reconstruction.
Android Malicious Code Detection Based on Secondary Pruning Optimization

In this paper, we propose a detection algorithm for Android malicious code based on integrated multi-feature. By decompiling and processing APK files, the multi-class behavior features of Android application are extracted, and the classification is achieved through an integrated learning framework based on quadratic pruning optimization. The prototype system automatically detects the malicious code of the Android platform and analyzes the validity of the algorithm through experimental verification.
Improved PM Method for Neutron Image Denoising

Due to the influence of many physical factors of the neutron imaging system, the resulting neutron images usually suffer from severe noise pollution. Traditional PM-based methods cannot balance the noise removing and details preserving for neutron images. To address this problem, an improved partial differential equation method for neutron image denoising is proposed, in which the anisotropic diffusion properties are considered. The experimental results show that compared with the traditional PM and PM-based methods, the proposed method can effectively remove the Gaussian noise and achieve satisfying visual effect.
Modeling and Analysis for Spectrum Handoff in Cognitive Heterogeneous Wireless Network

In cognitive heterogeneous wireless network where the open wireless network and the cognitive radio network coexist, we use the queuing theory to model the spectrum handoff of secondary user(SU), solving the problem of large secondary user service delay. First, a cognitive heterogeneous wireless network environment is established based on the shared unlicensed spectrum as well as the opportunity utilization authorized spectrum in cognitive radio network. Then, a novel dynamic data adjustment scheme is proposed based on the channel historical state information. Combining with the user classification and the PRP/NPRP M/G/1 queue model, we analyze the secondary users’ extened data transmission delay in spectrum handoff, and propose an adaptive frequency handoff strategy to minimize the delay. Finally, the simulation results show that the proposed cognitive heterogeneous wireless network can realize the network coexistence and improve the spectrum utilization efficiency. Meanwhile, the proposed adaptive spectrum handoff strategy can effectively reduce the transmission delay of delay-sensitive secondary users, while improve the service performance of non-delay-sensitive ones.
Hyperspectral Image Classification With Online Structured Dictionary Learning

In this study, the spectral and spatial redundancies of hyperspectral images are used for designing a sparse representation-based classification approach. The spectral redundancy is used to define spectral blocks and they are used to adaptively recognize the distinctive bands. The most distinctive blocks are identified as active blocks in a block sparse representation approach. Then the sparse coefficients within each spatial group are imposed to share a common subspace. To achieve this hierarchical sparsity pattern a sparse coding algorithm is proposed. This sparse coding is done over a block-structured dictionary, which is learned from the image data using the online dictionary learning algorithm. The obtained sparse coefficients are then classified using a support vector machine classifier. This structured sparsity pattern alleviates the instability of the sparse coefficients. Experiments on two standard datasets namely, Indian Pines and Pavia University, verify the effectiveness of the proposed approach for the classification of hyperspectral images.
Medical entity recognition of Esophageal Carcinoma based on word clustering

Electronic Medical Records (EMRs) is the core of medical information system in hospital. EMRs arises from the medical institution, and large amount of clinical data generated every day. Due to the EMRs contains many medical entities and clinical information of the patient, by analyzing and mining the texts data, medical knowledge which closely related to patients or certain diseases can be obtained. In this paper, we ues the EMRs of patients with Esophageal Carcinoma(EC), which include the clinical symptoms of the patients, tests they have undergone, results of the examinations, and the diagnosis and treatment plan. In this paper, word vector training is carried out for large-scale electronic medical record corpus by means of the skip model of word2vec deep learning tool. Then applying the features data to the k-means clustering algorithm to identifying medical entities about EC by word clustering. As the first step of medical knowledge mining, medical entity recognition is the premise of extracting the semantic relationship implied in medical text and structuring EMRs.
How Trust Pursuing Businesses Play in an Asymmetric Power Network?

This historical, longitudinal, and qualitative study draws upon a contingent view of trust to explain how asymmetric power causes trust structures to move from trust equilibrium to trust disequilibrium and triggers strategic trust-pursuing actions. We conclude that a trustor firm has the dominant and manipulating power in an asymmetric power network to extract maximal value in a systematic way, thereby causing trustee firms to adopt strategic trust-pursuing actions to buffer the trustor's power. Consequently, this study proposes an original theoretical framework to illustrate how trustee firms must be smart trust-pursuers in a trust-based network with asymmetric power.
An End-to-End Future Frame Prediction Method for Vehicle-Centric Driving Videos

In the field of autonomous driving, training an agent to watch and think similar to human drivers is an efficient way to solve self-driving problems. Inspired by NVIDIA’s frame-level command generation task [2] and the discovery of humans memory capacity [13], we propose a future frame prediction method for vehicle-centric driving videos. An end-to-end deep learning architecture called future frame prediction (FFPRE) network is proposed, which can generate a future frame following the input video sequence. In particular, we develop a general memory preserving module to extract meaningful history information from input data. This module consists of two parts, namely, memory recall and memory refine. We train this module to generate the short-term spatiotemporal information of a given video batch, which is a concatenation of history appearance and temporal clues. Thereafter, the two history clues will be transformed into future representations by a long-term prediction module. Thus, humans’ driving prediction progress is mimicked in a completely modular manner. Given the FFPRE network’s effective long-short spatiotemporal feature learning ability, the proposed network can construct an internal representation (content and dynamic) of vehicle-centric driving videos without tracking the trajectory of every pixel. Experimental results on publicly released datasets of NVIDIA and DR(eye)VE indicate that our proposed method is efficient.
The Novel Mobility Models Based on Spiral Line for Aerial Backbone Networks

With the convenience and flexibility, the UAV has received more attention in the search and rescue. Due to inherent mobility of UAV, the aerial backbone network maybe suffers some problems of dynamics of connectivity, such as uncertain delay jitter or occasional connection interruption. However, the existing mobility model could not accurately capture aerial mobility without the smooth trajectory and the capacity of rapid coverage. In this paper, we propose a novel mobility model based on spiral line (SLMM) for aerial backbone network. Firstly, we describe the background and related work. Then, we propose the basic concept and advantages of SLMM. In addition, we investigate the mathematical feature of SLMM. Besides, we propose some novel mobility metrics and analyze quantitatively the mobility feature of SLMM through simulation. Finally, we evaluate the network performance under the SLMM. The result show that the SLMM has the smooth trajectory and the uniform distribution of spatial nodes at steady state. In addition, covering the zone of area of 16pi the aerial node merely take almost 27s under the SLMM. Meanwhile, the convergence time could be adjusted by changing the internal parameters. Moreover, the aerial backbone network could approach the throughput of 0.43Mb/s, the end-to-end delay of 4.42ms and the packet loss rate of nearly 0.00%° under the SLMM, and the best choice of routing strategy is the AODV protocol. We believe that the SLMM as a significant supplement of mobility model can provide the helpful guideline in the design and analysis of aerial backbone network.
Machine Learning for Efficient Integration of Record Systems for Missing US Service Members

More than 16 million Americans served in World War II. Of these service members, over 400,000 were killed in action during the war. Today, more than 72,000 service members remain unaccounted for from World War II. The United States continues to diligently locate, recover, and identify missing personnel from World War II and other past conflicts to provide the fullest possible accounting. This work importantly provides closure and resolution to numerous US families. To fulfill this mission, massive amounts of information must be integrated from historical records, genealogy records, anthropological data, archeological data, odontology data, and DNA. These disparate data sources are produced and maintained by multiple agencies, with different data governance rules and different internal structuring of service member information. Previously, a manual approach had been undertaken to Extract, Transform, Load (ETL) records from these different data sources, which creates the potential for introduced human error. In addition, a large number of person-hours were required to synthesize this data on a biweekly basis. To address this issue, we implemented (i) a regex decision tree to translate genealogical relationships into DNA type availability and (ii) a machine learning approach for record-linkage between disparate data sources. This application is currently in production and greatly reduces person-hours needed and has a very low error rate for record translation and integration.
UCAA: User-Centric User Association and Resource Allocation in Fog Computing Networks

In recent years, with the eruptive popularity of mobile Internet and the emergence of various new IoT applications, fog computing is proposed to shift the cloud computing services towards the edge, making up for its lack of mobility support and high delay. Fog computing is customized for scenarios with scarce resources and unpredictable environments, but there is no user-centric joint optimization fog computing models designed for such scenarios. In this paper, we aim to maximize the user experience and overall system performance by jointly optimizing user association and resource allocation in the scenarios mentioned above, which can be formulated as a mix-integer non-linear programming problem. To solve the NP-hard problem, we propose a low-complexity two-step interactive optimal algorithm, named UCAA algorithm. For the user association problem, we propose a semi-definite programming based algorithm, and then further propose a Kuhn-Munkres algorithm based user association decision approximation algorithm. For the resource allocation problem, we first prove that it can be decoupled into two sub-problems, ie. , transmission power selection problem and computing resource allocation problem, and solve them individually, in addition, we have given a rigorous proof that the optimal solution of the two sub-problems is the optimal solution to the original problem as well. The numerical results show that the proposed UCAA algorithm achieves better performance than conventional algorithms in terms of the value of average user-centric utility, especially in case of more user equipments (UEs), fewer fog nodes, limited computing capacity of fog nodes, lower delay tolerance, lower local computation capacity, etc., which presented to illustrate that the UCAA algorithm can significantly improve user experience and system performance in the considering fog computing scenarios.
Constrained-Space Optimization and Reinforcement Learning for Complex Tasks

Learning from demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human demonstrations, as well as underlying safety constraints. This article presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks. Through interactions within the constrained space, the reinforcement learning agent is trained to optimize the manipulation skills according to a defined reward function. After learning, the optimal policy is derived from the well-trained reinforcement learning agent, which is then implemented to guide the robot to conduct tasks that are similar to the experts’ demonstrations. The effectiveness of the proposed method is verified with a robotic suturing task, demonstrating that the learned policy outperformed the experts’ demonstrations in terms of the smoothness of the joint motion and end-effector trajectories, as well as the overall task completion time.
Effect of 100-nm Al2O3 Particle Inclusions on 100-kA Pulsed Arc Erosion of W–Cu Electrodes

Erosion resistance of electrode materials is one of the key factors affecting the performance and working life of gap switches. In order to explore whether the electrode material with added nanoscale metalloid particles has better erosion resistance under the strong pulse current, a strong current discharge platform was built, and W80/Cu and W80/Cu–Al<sub>2</sub>O<sub>3</sub> were selected as the research objects. The experimental study was carried out under a strong pulse current with a peak value of over 100 kA. By comparing the mass change, the microtopography, and surface roughness of the erosion area, it was found that the addition of nano-Al<sub>2</sub>O<sub>3</sub> particles improved the erosion resistance of the W–Cu alloy. The anode loss was down about 14.7% slightly after adding Al<sub>2</sub>O<sub>3</sub>, and the cathode loss of W80/Cu–Al<sub>2</sub>O<sub>3</sub> was down about half. The addition of Al<sub>2</sub>O<sub>3</sub> will increase the working life by 25.3%. Besides, the microstructure of the erosion area became flatter and the roughness was reduced.
Central Prediction System for Time Series Comparison and Analysis of Water Usage Data

Revenue water flow is defined as the amount of water for which the water rate has been collected, against tap water production, whereas non-revenue water (NRW) is defined as water that has been produced, but for which payment cannot be charged. In South Korea, there are big differences in NRW among the regions, and the NRW ratio in urban areas is higher than in rural regions. To reduce regional differences and effectively manage the water system, a management system to lower the NRW ratio is required. In particular, the NRW ratio can be reduced through an automatic leakage detection and sensor-error automatic checking system for feed water pipes and piping in household, and through leakage detection of water supply and drainage pipes that transport large volumes of water. Therefore, this study develops a system that can generate automatic alarms whenever abnormal usage is predicted via analysis of household water flow rate. Linear regression, ARIMA model, and additive regression model are compared to find the best method with high accuracy. The proposed method can support efficient water system management to lower the NRW ratio.