Miracle-dz/UniFAR-FTUs huggingface dataset:
Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images

The region-based convolutional networks have shown their remarkable ability for object detection in optical remote sensing images. However, the standard CNNs are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. To address this, we introduce a new module named deformable convolution that is integrated into the prevailing Faster R-CNN. By adding 2D offsets to the regular sampling grid in the standard convolution, it learns the augmenting spatial sampling locations in the modules from target tasks without additional supervision. In our work, a deformable Faster R-CNN is constructed by substituting the standard convolution layer with a deformable convolution layer in the last network stage. Besides, top-down and skip connections are adopted to produce a single high-level feature map of a fine resolution, on which the predictions are to be made. To make the model robust to occlusion, a simple yet effective data augmentation technique is proposed for training the convolutional neural network. Experimental results show that our deformable Faster R-CNN improves the mean average precision by a large margin on the SORSI and HRRS dataset.
A Tabu Search Optimization Algorithm with 2.5K Null Model

The 2.5K null model has the same joint degree distribution and clustering spectrum with the original network. As a frame of reference, the design of 2.5K null model is extremely significant tool to study the clustering characteristics of the original network. The existing 2.5K null model algorithms are mainly generated based on random scrambling methods, which lead to redundant exchange problems during the exchange of edges and usually can not effectively reach the 2.5K targets for high-clustering networks. To solve these problems, we propose a tabu search optimization algorithm with 2.5K null model, which is constructed by setting weight of edges in initial network-2K null model, selecting edges to be disconnected according to the probability and limiting the newborn edges depended on tabu search. The simulation results show that the null model generated by the proposed algorithm is more accurate and effective than the existing ones.
Multi-algorithm Fusion of RGB and HSV Color Spaces for Image Enhancement

Aiming at the problem of image quality decline caused by insufficient or uneven illumination, we propose a new image enhancement method through multi-algorithm fusion in RGB and HSV color spaces. Firstly, R⌍G and B channels of the image are automatic contrast stretched in the RGB color space respectively, the original image is converted from RGB color space to HSV color space at same time, and then the improved multi-scale Retinex algorithm is adopted to enhance the brightness of channel V in HSV color space, after that the obtained image is converted from the HSV color space back to the RGB color space. Secondly, the two images are weighted fused in the RGB color space. Finally, the fused image is corrected by Gamma algorithm, the image color is recovered and compensated. The experimental results show that the new algorithm can improve the brightness and the contrast of the image compared with MSR and MSRCR algorithm, while it preserve the richer details and effectively prevent the ” halo artifacts” phenomenon.
A Comparative Study of Supervised Learning Techniques for Human Activity Monitoring Using Smart Sensors

In this work various supervised learning techniques such as support vector machine (SVM), perceptron neural network (PNN), recurrent neural network (RNN) and back-propagation neural network (BPNN) has been used for human activity classification using signals collected from smart sensors. Collected features are used as input to the classifiers to recognize different human activity such as Walking, Walking upstairs, Walking Downstairs, Sitting, Standing, Laying Down etc. Highest accuracy obtained for SVM, PNN, RNN and BPNN are 59.11, 94.10, 97.55, and 97.40% respectively. Highest accuracy obtained for activity classification is 97.55% which is for RNN. Hence the method can be used effectively for human activity monitoring.
Extracting chemical–protein relations using attention-based neural networks

Abstract Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical–protein interactions. We investigate multiple deep neural network (DNN) models, including convolutional neural networks, recurrent neural networks (RNNs) and attention-based (ATT-) RNNs (ATT-RNNs) to extract chemical–protein relations. Our experimental results indicate that ATT-RNN models outperform the same models without using attention and the ATT-gated recurrent unit (ATT-GRU) achieves the best performing micro average F1 score of 0.527 on the test set among the tested DNNs. In addition, the result of word-level attention weights also shows that attention mechanism is effective on selecting the most important trigger words when trained with semantic relation labels without the need of semantic parsing and feature engineering. The source code of this work is available at https://github.com/ohnlp/att-chemprot.
Planar Air-Filled Terahertz Antenna Array Based on Channelized Coplanar Waveguide Using Hierarchical Silicon Bulk Micromachining

A planar air-filled terahertz antenna array based on channelized coplanar waveguide (CCPW) is proposed and fabricated using silicon bulk micromachining technology. Planar air-filled four-unit array composed of two silicon layers is verified at 300 GHz. Periodical field blocking stubs are loaded in the gaps of the CCPW alternatively to break the fringing field symmetry, generating an in-phase radiating aperture. The fabrication technology has two features: first, the silicon dielectrics are completely plated with gold, generating a purely air-filled antenna with high performance and second, the hierarchical micromachining method enables a single silicon layer to be divided into two sublayers, resulting in reduced antenna layers. The numerical result shows a simulated −10 dB impedance bandwidth of 71.1 GHz (25.95%, 238.4–309.5 GHz) and stable fan-shaped beams with broadside gains and radiation efficiencies higher than 10.6 dBi and 87.9%, respectively. Experiment of the fabricated prototype exhibits a bandwidth that totally covers the simulation and stable beams with broadside gains higher than 8.6 dBi. To the best of author’s knowledge, this is the first time that a planar air-filled terahertz antenna array is proposed. Moreover, confirmation of the strategy demonstrates the antenna’s distinguished suitability to realize antenna-in-package solution.
A Control System Design for 7-DoF Light-weight Robot based on EtherCAT Bus

In order to increase control frequency and expansibility for 7-degree-of-freedom(DoF) light-weight, a novel robot control system based on EtherCAT bus is designed. The control system consists of arm controller and joint controller. The arm controller is implemented by a core-i5 PC, which achieves motion control, trajectory planning and real-time task planning. The joint controller is designed based on Field Programmable Gate Array(FPGA), which achieves sensor samping and motor control. EtherCAT bus achieves data transmission between joint controller and arm controller. The experiment results show that our control system can achieve higher control frequency and better expansibility.
A low-cost video-based pavement distress screening system for low-volume roads

ABSTRACT This study investigates the use of consumer-grade video camera to develop a low-cost pavement distress screening system, with a particular focus on low-volume roads. Due to the oblique view of video data captured in the car front, existing crack detection algorithms that are tailor-made for nadir view cannot be used directly. Instead, we propose and develop a Video-based PAvement Distress Screening (VPADS) system, which includes an automatic data processing workflow by first defining a Region of Interest (RoI) through robust line fitting of the two side lanes. The pavement condition can be assessed within the RoI by implementing a multi-scale ridge detection filter followed by a boundary contour analysis. Since the proposed VPADS system is designed for preliminary screening purpose, each video image scene is examined if there exists any potential crack or distress feature(s). We tested the proposed system on video data collected for two low-volume roads (Highway 624 and 668) in Northern Ontario region, Canada. The overall accuracy of detecting crack and distress features yielded 80%. Instead of replacing traditional field inspection or high-end multi-sensor pavement evaluation system, the proposed VPADS system aims to provide a computer-aided screening solution for transportation authorities of a vast rural road/highway network and with limited inspection budget. The scope of the work can be further expanded by developing a crowdsensing inspection network built by any regular road user, who can also act as a data provider for the regional/municipal pavement distress screening system.
State Estimation with Multi-packet Transmission Over the Wireless Network

In wireless control, the measurement of a system may be divided into multiple partial observations for transmission purpose, known as “multi-packet transmission”. The state estimation for the discrete linear time-invariant system with the multipacket transmission is considered. We focus on what the filter has received during each time step. For the randomness of the received packets, the classical Kalman filter is revised to the time-varying Kalman filter. With the concept of combination and the supposed delivery rate for each packet, we analyze the stability of the filter in the sense of expectation. To deal with the combination explosion problem, alternative conditions on delivery rates is provided.
Litigants in Person in Northern Ireland: Barriers to Legal Participation - Summary Report

1. The School of Law at Ulster University and the Northern Ireland Human Rights Commission (NIHRC) are collaborating on a research study about people who are involved in legal proceedings in the Northern Ireland civil and family courts without legal representation from a solicitor or barrister. They are known as litigants in person and here are abbreviated to LIP. 2. The study started in April 2016 and will run until September 2018. The study is on-going and this paper presents an outline of the study with some of the main findings and their implications. The study is being guided by an external advisory group who will help to inform the report’s final recommendations. The full report will be presented at a conference in September 2018.
Strategic Improvement for Quality and Satisfaction of Hospital Information Systems

The purpose of our study aimed to identify attributes capable of improving physicians' satisfaction levels with the use of a hospital information system (HIS). A model inclusive of system quality, information quality, and service quality related to an HIS is used to form antecedents of user satisfaction. Survey methodology was used to collect an attributive set representing the system quality, information quality, and service quality made available from 150 physicians at a large health-care system in southern Taiwan. Responses were segmented into low and high satisfaction and analyzed with partial least squares and importance-performance analysis. The results reveal that system quality, information quality, and service quality may be used to significantly predict physicians' satisfaction. Two system quality attributes (reliability and response time) were identified as the highest priorities for intervention by low- and high-satisfaction users. Low-satisfaction users further expect improvement of the HIS service quality to take place. The subject health-care system should produce coping interventions for those high priorities to enhance the satisfaction of physicians.
Control for balance of Legged-Wheel Hybrid Robot (LWHR) by regulating ground reaction force with kinematic coupling

This paper is concerned with a novel control method for a legged-wheel hybrid robot(LWHR). We first derive a contact map from ground reaction forces(GRF) to linear and angular forces on center of mass(CoM). To resolve the lack of rank condition of the contact map due to an existance of reciprocal axis, each of the GRF at the contact points are regulated by a reformed linear invertible contact map while remained forces on the CoM are handled by the kinematic coupling on the conservation of momentum. It would be worthly to remark that there are no singular cases because the reformed contact map has full rank condition kinematically as long as the constraint of each wheels is active. As a result, by this method, we can fully manage all forces on the CoM by which the balance control is accomplished. Finally, we demonstrate the effectiveness of the methods through some simulation results.
Clusterin contributes to early stage of Alzheimer's disease pathogenesis

While clusterin is reportedly involved in Alzheimer's disease (AD) pathogenesis, how clusterin interacts with amyloid‐β (Aß) to cause Aß neurotoxicity remains unclear in vivo. Using 5×FAD transgenic mice, which develop robust AD pathology and memory deficits when very young, we detected interactions between clusterin and Aß in the mouse brains. The two proteins were concurrently upregulated and bound or colocalized with each other in the same complexes or in amyloid plaques. Neuropathology and cognitive performance were assessed in the progeny of clusterin‐null mice crossed with 5×FAD mice, yielding clu−/−;5×FAD and clu+/+;5×FAD. We found far less of the various pools of Aß proteins, most strikingly soluble Aß oligomers and amyloid plaques in clu−/−;5×FAD mice at 5 months of age. At that age, those mice also had higher levels of neuronal and synaptic proteins and better motor coordination, spatial learning and memory than age‐matched clu+/+;5×FAD mice. However, at 10 months of age, these differences disappeared, with Aß and plaque deposition, neuronal and synaptic proteins and impairment of behavioral and cognitive performance similar in both groups. These findings demonstrate that clusterin is necessarily involved in early stages of AD pathogenesis by enhancing toxic Aß pools to cause Aß‐directed neurodegeneration and behavioral and cognitive impairments, but not in late stage.
Image Contrast Enhancement Using a Modified Histogram Equalization

The histogram equalization is the basic technology in image processing, which is widely used to increase the integral contrast of the image and is characterized by high efficiency. However, known techniques of histogram equalization have a number of disadvantages, the major one of which is a decrease in the contrast of small objects in the image. To address this disadvantage, a new technique of modified histogram equalization is proposed based on the assessment of the two-dimensional probability distribution of brightness in the image. A new method of increasing the contrast of the image based on the modified equalization of histogram is also proposed. The proposed technique of modified histogram equalization can be recommended to increase the image contrast in automatic mode in imaging and image processing.
SIW Multibeam Antenna Based on Modified Horn Beam-Forming Network

A simple multibeam antenna fed by a modified horn beam-forming network (BFN) is realized in substrate integrated waveguide technology. Compared with Rotman lens fed multibeam antenna, such an antenna has the merits of a simple architecture and design process. The modified horn BFN applied here is developed from a traditional horn BFN by adding multiple lateral feeds to perform the beam scanning property. The operation principle, design method, and performance of the multibeam antenna are illustrated sequentially. Fabrication and measurement are conducted to test the consistency between simulation and experiment.
Hyper Parameters Selection for Image Classification in Convolutional Neural Networks

Cognitive image processing is made possible by the availability of faster and cheaper memories, increased processing power of multicore processors and the explosive growth in visual data generation. Object classification remains an active research area with rapid advancements. The recent advances in deep neural networks are providing better than expected results for image classification. The process involves training models with large labelled datasets to learn the underlying features from various image classes to support cognitive inferences on the test data. By training on large annotated image datasets contextual and semantic information can be automatically extracted from the images. An important determinant of better results is the choice of hyper parameters which is difficult to get right. In this paper we empirically investigate the effect of selected hyper parameters of a convolutional neural network on CIFAR-10 dataset and provide results to demonstrate their effect and importance for image classification.
Phase- and Amplitude-Control Metasurfaces for Antenna Main-Lobe and Sidelobe Manipulations

The metasurface (MS) antenna is an important microwave component in the communication system due to its unique beam radiation capability. However, current studies mainly pay attention to improve the performance of the main lobe of the MS antenna, leaving the sidelobe unexplored although it is also essential in some practical applications. In this paper, several phase- and amplitude-control reflected MSs have been proposed to simultaneously manipulate the antenna’s main lobe and sidelobes. The MSs consist of modified I-shaped particles which can independently manipulate the phases and amplitudes of the cross-polarization waves by changing the split size and orientation, respectively. A focusing phase distribution and different Taylor amplitude distributions have been fixed on the MSs. By illuminating the MSs with a self-made antenna, we have successfully designed four MS antennas. For the first antenna, we solely pay attention to improve the main lobe and achieve a high gain of 20.7 dB at 10 GHz. For the other three antennas, we also aim to manipulate their sidelobes. The resultant sidelobe levels (SLLs) are about −25 dB in the $xoz$ plane for the second antenna, −29 dB in the $yoz$ plane for the third antenna, and both of the former two characteristics for the fourth antenna. Compared with the first MS antenna, the last three antennas suffer from gain reductions of 2, 1.7, and 3 dB, respectively. These proposed MS antennas provide a new way to manipulate both main-lobe levels and SLLs and also greatly promote the integration of MSs and antennas.
Reaching Consensus in Opportunistic Networks

In large systems with many sensors and devices that collect information, the infrastructure costs can reach extremely high values, since all the nodes in the network need to be connected to a server so they can upload their data and have them processed. However, in order to reduce such costs, the sensors and devices can communicate with each other, exchanging data when in close proximity. Thus, an opportunistic network (ON) is created, where devices follow a store-carry-forward paradigm, deciding upon each contact whether the encountered node is the next hop for the data they are carrying. Besides their many advantages, opportunistic networks present several challenges, one of the most disputed being the achievement of consensus between the nodes of the network. The lack of a central entity which could gather information and validate messages leaves this task to the goodwill and collaboration between nodes. Consensus is required in order for the nodes to depend on and trust the data that they receive in the network. In this paper, we address the problem of reaching consensus in opportunistic networks. We propose an algorithm for consensus in ONs and show that it is able to correctly select several leaders for each community of devices, which then collaborate between each other to reach consensus. Furthermore, we present two scenarios where opportunistic networks can be employed and consensus is required.
Control of Planar Spring–Mass Running Through Virtual Tuning of Radial Leg Damping

Existing research on dynamically capable legged robots, particularly those based on spring–mass models, generally considers improving in isolation either the stability and control accuracy on the rough terrain, or the energetic efficiency in steady state. In this paper, we propose a new method to address both, based on the hierarchical embedding of a simple spring-loaded inverted pendulum (SLIP) template model with a tunable radial damping coefficient into a realistic leg structure with series-elastic actuation. Our approach allows using the entire stance phase to inject/remove energy both for transient steps and in steady state, decreasing the maximum necessary actuator power while eliminating wasteful sources of the negative work. In doing so, we preserve the validity of the existing analytic approximations to the underlying SLIP model, propose improvements to increase the predictive accuracy, and construct accurate, model-based controllers that use the tunable damping coefficient of the template model. We provide extensive comparative simulations to establish the energy and power efficiency advantages of our approach, together with the accuracy of model-based gait control methods.
Evaluation of cell proliferation in cystic lesions associated with impacted third molars

The purpose of this study was to evaluate the metabolism and epithelial cell proliferation of odontogenic keratocyst (OKC), dentigerous cyst (DC), and unicystic ameloblastoma (UA) by quantifying the nucleolar organizing regions (AgNORs) and Ki‐67 protein immunoexpression. Forty‐eight cases (16 OKC, 16 DC, and 16 UA) were evaluated retrospectively. The metabolism and epithelial cell proliferation was measured by the Ki‐67 positive cell percentage index and by the mean AgNOR count in each group. The Ki‐67 and AgNOR counts were significantly higher in OKC comparing to the DC and UA (p < .001). Ki‐67 positive cells were observed higher in suprabasal cell layers of OKC with uniform distribution, a few of them were predominantly observed in basal cell layer in DC and UA. The AgNOR count was significantly higher in the OKC basal cell layers and observed throughout the lining epithelium of DC and UA. Ki‐67 and AgNOR reinforced the aggressive character of OKC, presenting high metabolism and cellular proliferation compared to DC and UA, possibly due to its more aggressive clinical behavior and high recurrence rate.
A low-cost IoT-based health monitoring platform enriched with social networking facilities

The use of distant health monitoring solutions is consciously increasing nowadays exploiting the advancements in modern technologies such as the Internet of Things (IoT), mobile networking and pervasive communication. Already in cases of chronic disease management the regular use of mHealth and telemonitoring approaches is very common facilitating the constant patient monitoring and enabling the use of innovative features for patients, medical experts and caregivers. Remote communication is essential as it allows the prevention or/and the precise treatment of patients. The power efficiency, the computing capabilities and the affordability of modern biosignals sensors, portable computing machines and IoT devices allows for the development of advanced and compelling solutions in the area of mHealth and patient telemonitoring. This work presents a low-cost, IoT enabled, home monitoring solution, as part of a dedicated communication and social networking platform, which provides wireless and automated collection and storage of biosignals received from sensors. The proposed solution provides also remote communication and monitoring via a dedicated social network, which enables real-time videoconferencing and data exchange.