Miracle-dz/UniFAR-FTUs huggingface dataset:
Expansion of the Beamforming Coverage Area in an Elevation Plane for 60 GHz Band 3-D Beamforming

This letter proposes a novel antenna structure for 60 GHz band 3-D coverage beamforming. By using the proposed antenna structure, the coverage area can be expanded in an elevation plane. Dipole and patch subarray antennas were packaged into one structure by using the copper balls interconnection technique. A simple idea to utilize dipole and patch elements enables endfire and broadside radiations resulting in the coverage area expansion in the elevation plane. Two 2 × 4 array antennas were made up of eight multilayered substrate. Six metal layers were used for each multilayered substrate.
Effect of nitrogen and lime on Massai grass subjected to intensive cutting

Soil acidity and lack of nitrogen fertilization limit the yield of forage grasses. This study aimed at evaluating the effects of lime (0 kg ha -1 ; 2,000 kg ha -1 ; 4,000 kg ha -1 ; and 8,000 kg ha -1 ) and nitrogen (0 kg ha -1 , 20 kg ha -1 , 40 kg ha -1 , 80 kg ha -1 and 160 kg ha -1 ) doses on forage accumulation, soil chemical properties and macronutrient concentrations in leaf blades of Massai grass ( Panicum maximum cv. Massai). A randomized blocks design in a 4 x 5 factorial scheme, with four replications, was used. Lime and nitrogen doses positively affected the accumulation of green dry mass in the Massai grass as well as the base saturation in the soil. The maximum yield was obtained at nitrogen doses close to 587 kg ha -1 year -1 for nitrogen and 5,796 kg ha -1 for lime, which produced the greatest accumulation of green dry mass (15,267 kg ha -1 year -1 ) in the first year of assessment. The demand for lime increased from the first to the second year. Lime increased the base saturation, whereas nitrogen reduced it. The nitrogen applied to the soil raised the nitrogen, magnesium and calcium contents (g kg -1 ) in the leaf blades of Massai grass. Thus, Massai grass reacted positively to nitrogen and lime doses, with significant effects on the accumulation of green dry mass and soil characteristics assessed.
Deep Multiple Instance Learning with Spatial Attention for ROP Case Classification, Instance Selection and Abnormality Localization

This paper tackles automated screening of Retinopathy of Prematurity (ROP), one of the most common causes of visual loss in childhood. Clinically, ROP screening per case requires multiple color fundus image instances that capture different zones of the (premature) retina. A desirable model shall not only make a decision at the case level, but also pinpoint which instances and what part of the instances are responsible for the decision. This paper makes the first attempt to accomplish three tasks, i.e. ROP case classification, instance selection and abnormality localization in a unified framework. To that end, we propose a new model that effectively combines instance-attention based deep multiple instance learning (MIL) and spatial attention (SA). The propose model, which we term MIL-SA, identifies positive instances in light of their contributions to case-level decision. Meanwhile, abnormal regions in the identified instances are automatically localized by the SA mechanism. Moreover, MIL-SA is learned from case-level binary labels exclusively, and in an end-to-end manner. Experiments on a large clinical dataset of 2,186 cases with 11,053 fundus images show the viability of the proposed model for all the three tasks.
Unknown Input Observer Based Approach for Distributed Tube-Based Model Predictive Control of Heterogeneous Vehicle Platoons

This paper addresses the control problem of heterogeneous vehicle platoons subject to disturbances and modeling errors. The objective is to guarantee spatial-geometry constraints of vehicles in a platoon. We deal with the case where a predecessor-leader following (PLF) communication topology is used and heterogeneous vehicle dynamics is subject to disturbances. To estimate the lumped disturbance, the technique of unknown input proportional multiple-integral (PMI) observer is employed such that both the state and the disturbance are simultaneously estimated. Moreover, tube-based model predictive control (TMPC) is used and the corresponding control law is composed of a feed-forward term, a feedback term, and a disturbance compensation term. The gains in the integrated control strategy are optimized by utilizing the particle swarm optimization (PSO) algorithm with an $\mathscr H_{\infty }$ performance index of an augmented error system. It is proved that the deviations between the actual system and the nominal system are bounded in a robustly positively invariant (RPI) set, that is, the main objective is guaranteed. With the proposed control strategy, simulations and comparisons are carried out. We can see that the control performance of the proposed strategy is significantly improved while the computational time is reduced compared with existing methods.
CSpA-DN: Channel and Spatial Attention Dense Network for Fusing PET and MRI Images

In this paper, we propose a novel fusion framework based on a dense network with channel and spatial attention (CSpA-DN) for PET and MR images. In our approach, an encoder composed of the densely connected neural network is constructed to extract features from source images, and a decoder network is leveraged to yield the fused image from these features. Simultaneously, a self-attention mechanism is introduced in the encoder and decoder to further integrate local features along with their global dependencies adaptively. The extracted feature of each spatial position is synthesized by a weighted summation of those features at the same row and column with this position via a spatial attention module. Meanwhile, the interdependent relationship of all feature maps is integrated by a channel attention module. The summation of the outputs of these two attention modules is fed into the decoder and the fused image is generated. Experimental results illustrate the superiorities of our proposed CSpA-DN model compared with state-of-the-art methods in PET and MR images fusion according to both visual perception and objective assessment.
Online Vehicle Velocity Prediction Using an Adaptive Radial Basis Function Neural Network

In order to improve the performance of predictive energy management strategies (PEMS), a novel neural network based vehicle velocity prediction strategy (NN-VVP) was proposed. First, an online trained radial basis function neural network (RBF-NN) with a fixed structure was adopted to build online vehicle velocity prediction (VVP) model. The influence of order and width of RBF-NN on the online prediction accuracy was studied in depth, it was found that RBF-NN with a fixed structure was not always suitable for the overall online prediction process. Then, by introducing a neural network structure determination method (SDM) with the Akaike Information Criterion (AIC), an adaptive RBF-NN which adjust structure in real time was designed to perform online VVP to further improve the prediction accuracy. Simulation results indicate that, the VVP strategy proposed in this paper predicts the future vehicle velocity with acceptable accuracy. Compared with the fixed structure, the RBF-NN with an adaptive structure significantly improve the prediction accuracy by approximately 63.2%, 70.4%, and 71.1%.
Automatic Semantic Segmentation of Structural Elements related to the Spinal Cord in the Lumbar Region by using Convolutional Neural Networks

This work addresses the problem of automatically segmenting the MR images corresponding to the lumbar spine. The purpose is to detect and delimit the different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, etc. This task is known as semantic segmentation. The approach proposed in this work is based on convolutional neural networks whose output is a mask where each pixel from the input image is classified into one of the possible classes. Classes were defined by radiologists and correspond to structural elements and tissues. The proposed network architectures are variants of the U-Net. Several complementary blocks were used to define the variants: spatial attention models, deep supervision and multi-kernels at input, this last block type is based on the idea of inception. Those architectures which got the best results are described in this paper, and their results are discussed. Two of the proposed architectures outperform the standard U-Net used as baseline. Those architectures which got the best results are described in this paper, and their results are discussed. Two of the proposed architectures outperform the standard U-Net used as baseline.
Estimation of Clinical Tremor using Spatio-Temporal Adversarial AutoEncoder

Collecting sufficient well-labeled training data is a challenging task in many clinical applications. Besides the tremendous efforts required for data collection, clinical assessments are also impacted by raters' variabilities, which may be significant even among experienced clinicians. The high demands of reproducible and scalable data-driven approaches in these areas necessitates relevant research on learning with limited data. In this work, we propose a spatio-temporal adversarial autoencoder (ST-AAE) for clinical assessment of hand tremor frequency and severity. The ST-AAE integrates spatial and temporal information simultaneously into the original AAE, taking optical flows as inputs. Using only optical flows, irrelevant background or static objects from RGB frames are largely eliminated, so that the AAE is directed to effectively learn key feature representations of the latent space from tremor movements. The ST-AAE was evaluated with both volunteer and clinical data. The volunteer results showed that the ST-AAE improved model performance significantly by 15% increase on accuracy. Leave-one-out (on subjects) cross validation was used to evaluate the accuracy for all the 3068 video segments from 28 volunteers. The weighted average of the AUCs of ROCs is 0.97. The results demonstrated that the ST-AAE model, trained with a small number of subjects, can be generalized well to different subjects. In addition, the model trained only by volunteer data was also evaluated with 32 clinical videos from 9 essential tremor patients, the model predictions correlate well with the clinical ratings: correlation coefficient $\mathrm{r}=0.91$ and 0.98 for in-person ratings and video watching ratings, respectively.
SFPN: Semantic Feature Pyramid Network for Object Detection

Feature Pyramid Network(FPN) employs a top-down path to enhance low level feature by utilizing high level feature. However, further improvement of detector is greatly hindered by the inner defect of FPN. The dilution issue in FPN is analyzed in this paper, and a new architecture named Semantic Feature Pyramid Network(SFPN) is introduced to address the information imbalance problem caused by information dilution. The proposed method consists of two simple and effective components: Semantic Pyramid Module(SPM) and Semantic Feature Fusion Module(SFFM). To compensate for the weaknesses of FPN, the semantic segmentation result is utilized as an extra information source in our architecture. By constructing a semantic pyramid based on the segmentation result and fusing it with FPN, feature maps at each level can obtain the necessary information without suffering from the dilution issue. The proposed architecture could be applied on many detectors, and non-negligible improvement could be achieved. Although this method is designed for object detection, other tasks such as instance segmentation can also largely benefit from it. The proposed method brings Faster R-CNN and Mask R-CNN with ResNet-50 as backbone both 1.8 AP improvements respectively. Furthermore, SFPN improves Cascade R-CNN with backbone ResNet-101 from 42.4 AP to 43.5 AP. 11This work was partially financially supported by the National Natural Science Foundation of China under grants 61533012, 52041502 and 91748120
Face Anti-Spoofing Based on Dynamic Color Texture Analysis Using Local Directional Number Pattern

Face anti-spoofing is becoming increasingly indispensable for face recognition systems, which are vulnerable to various spoofing attacks performed using fake photos and videos. In this paper, a novel “LDN-TOP representation followed by ProCRC classification” pipeline for face anti-spoofing is proposed. We use local directional number pattern (LDN) with the derivative-Gaussian mask to capture detailed appearance information resisting illumination variations and noises, which can influence the texture pattern distribution. To further capture motion information, we extend LDN to a spatial-temporal variant named local directional number pattern from three orthogonal planes (LDN- TOP). The multi-scale LDN- TOP capturing complete information is extracted from color images to generate the feature vector with powerful representation capacity. Finally, the feature vector is fed into the probabilistic collaborative representation based classifier (ProCRC) for face anti-spoofing. Our method is evaluated on three challenging public datasets, namely CASIA FASD, Replay-Attack database, and UVAD database using sequence-based evaluation protocol. The experimental results show that our method can achieve promising performance with 0.37% EER on CASIA and 5.73% HTER on UVAD. The performance on Replay-Attack database is also competitive.
Audio-Visual Speech Recognition Using A Two-Step Feature Fusion Strategy

Lip-reading methods and fusion strategy are crucial for audio-visual speech recognition. In recent years, most approaches involve two separate audio and visual streams with early or late fusion strategies. Such a single-stage fusion method may fail to guarantee the integrity and representativeness of fusion information simultaneously. This paper extends a traditional single-stage fusion network to a two-step feature fusion network by adding an audio-visual early feature fusion (AV-EFF) stream to the baseline model. This method can learn the fusion information of different stages, preserving the original features as much as possible and ensuring the independence of different features. Besides, to capture long-range dependencies of video information, a non-local block is added to the feature extraction part of the visual stream (NL-Visual) to obtain the long-term spatio-temporal features. Experimental results on the two largest public datasets in English (LRW) and Mandarin (LRW-1000) demonstrate our method is superior to other state-of-the-art methods.
Distress Image Retrieval for Infrastructure Maintenance via Self-Trained Deep Metric Learning Using Experts’ Knowledge

Distress image retrieval for infrastructure maintenance via self-trained deep metric learning using experts’ knowledge is proposed in this paper. Since engineers take multiple images of a single distress part for inspection of road structures, it is necessary to construct a similar distress image retrieval method considering the input of multiple images to support determination of the level of deterioration. Thus, the construction of an image retrieval method while selecting an effective input from multiple images is described in this paper. The proposed method performs deep metric learning by using a small number of effective images labeled by experts’ knowledge with information about their effectiveness and a large number of unlabeled images via a self-training approach. Specifically, an end-to-end learning approach that performs retraining of the model by assigning pseudo-labels to these unlabeled images according to the output confidence of the model is achieved. Thus, the proposed method can select an effective image from multiple images that are input at the retrieval as a query image. This is the main contribution of this paper. As a result, the proposed method realizes highly accurate retrieval of similar distress images considering the actual situation of inspection in which multiple images of a distress part are input.
Uncertainty-Aware Data Augmentation for Food Recognition

Food recognition has recently attracted attention of many researchers. However, high food ambiguity, inter-class variability and intra-class similarity define a real challenge for the Deep learning and Computer Vision algorithms. In order to improve their performance, it is necessary to better understand what the model learns and, from this, to determine the type of data that should be additionally included for being the most beneficial to the training procedure. In this paper, we propose a new data augmentation strategy that estimates and uses the epistemic uncertainty to guide the model training. The method follows an active learning framework, where the new synthetic images are generated from the hard to classify real ones present in the training data based on the epistemic uncertainty. Hence, it allows the food recognition algorithm to focus on difficult images in order to learn their discriminatives features. On the other hand, avoiding data generation from images that do not contribute to the recognition makes it faster and more efficient. We show that the proposed method allows to improve food recognition and provides a better trade-off between micro- and macro-recall measures.
CD-Net: Comprehensive Domain Network With Spectral Complementary for DECT Sparse-View Reconstruction

Dual-energy computed tomography (DECT) is of great clinical significance because of its material identification and quantification capacity. Although DECT measures attenuation using two different spectra, the anatomical structure of the low- and high-energy CT images are consistent with each other and the images are also correlated in the energy domain, resulting in significant information redundancy. Here this redundancy has been exploited by the proposed CD-Net (Comprehensive Domain Network) with spectral complementarity to improve the image quality and reduce the radiation dose for DECT imaging. CD-Net restores accurate anatomical information from both projection and image domains. It encompasses a projection domain neural network (PD-Net), an analytical reconstruction operator (ARO) and an image domain neural network (ID-Net). Embedding ARO into deep learning framework, the proposed one-step DECT sparse reconstruction method can directly produce high-quality DECT images from projection data acquired with spectral complementarity scheme. Qualitative and quantitative analyses demonstrate the competitive performance of CD-Net in terms of CT number accuracy, detail preservation and artifact removal. Two popular DECT applications, virtual non-contrast (VNC) imaging and iodine contrast agent quantification, reveal that the images reconstructed by CD-Net are promising for clinical applications.
A Bayesian Deep CNN Framework for Reconstructing k-t-Undersampled Resting-fMRI

Undersampled reconstruction in resting functional magnetic resonance imaging (R-fMRI) holds the potential to enable higher spatial resolution in brain R-fMRI without increasing scan duration. We propose a novel approach to reconstruct kt undersampled R-fMRI relying on a deep convolutional neural network (CNN) framework. The architecture of our CNN framework comprises a novel scheme for R-fMRI reconstruction that jointly learns two multilayer CNN components for (i) explicitly filling in missing k-space data, using acquired data in frequency-temporal neighborhoods, and (ii) image quality enhancement in the spatiotemporal domain. The architecture sandwiches the Fourier transformation from the frequency domain to the spatial domain between the two aforementioned CNN components, during, both, CNN learning and inference. We propose four methods within our framework, including a Bayesian CNN that produces uncertainty maps indicating the per-voxel (and per-timepoint) confidence in the blood oxygenation level dependent (BOLD) time-series reconstruction. Results on brain R-fMRI show that our CNN framework improves over the state of the art, quantitatively and qualitatively, in terms of the connectivity maps for three cerebral functional networks.
Why student engagement in the accounting classroom matters

This study aims to investigate the theory of student engagement and its impact on student learning. A student-centric pedagogy (inter-teaching) was introduced to create better engagement for students studying an accounting course at an offshore campus of an Australian University, in Vietnam. Surveys were conducted to collect data. The quantitative approach was implemented to test the theory of engagement for improving student learning of the respondents, by comparing two treatments, a teacher-centred class (lecture model) and student-centred class (inter-teaching model). The findings of this study propose that student engagement is an encouraging development for student learning in accounting courses. The findings contribute towards the current body of knowledge of accounting education, effectiveness of student learning, and overall greater student engagement in accounting courses.
In vitro antibacterial, antioxidant, and cytotoxicity evaluations of Musa paradisiaca cv. Sekaki florets from Sarawak, Malaysia

Muhammad Mirza Ariffin1, Heng Yen Khong1*, Nyotia Nyokat1, Gee Moi Liew1, Ahmad Sazali Hamzah2, Korawinwich Boonpisuttinant3 1Faculty of Applied Sciences, Universiti Teknologi MARA, Sarawak, Malaysia. 2Centre for Synthesis and Polymer Technology, Institute of Science, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia. 3Thai Traditional Medicine College, Rajamangala University of Technology Thanyaburi, Thanyaburi, Thailand.
Effects of Indoor Environmental Quality on Concentration

There is a lack of long-term research on the effects of indoor environmental quality on concentration in real environments using objective measurements. Concentration is an essential part of learning and processing information. Since pupils and students spend large parts of their days indoors, it can have major effects on their concentration. This paper presents an environmental monitoring system for collecting continuous indoor environmental quality data and studies the relationship between indoor environmental quality and pupil concentration in a real-life environment. An 18-week pilot was conducted in one school in Northern Finland during the autumn in 2018. Pupils (n=83) and teachers (n=4) in four classrooms participated in the study. Data on pupil concentration was collected twice a week via paper-format concentration tests. Additionally, teachers evaluated pupil concentration on a daily basis. A non-parametric correlation analysis (Spearman) was performed to investigate the associations between indoor environmental quality and pupil concentration. A statistically significant inverse association was found between pupil concentration and the relative humidity in all four classrooms, but no consistent associations with other indoor environmental quality parameters were found. Keywordsindoor air quality; indoor environmental quality; concentration; school; pupil.
EVALUATION OF MNCS LEARNING THROUGH THE LENS OF ‘LEARNING ORGANIZATION’ LITERATURE

The concept of Learning Organization is emerging since past three decades in the field of International Human Resource Management especially after the seminal work of Peter Senge in year 1990. The idea of Learning Organization is to develop the reality of capabilities-based-competition which help the managers and other practitioners of organizations operating domestically and internationally to gain organizational competencies and knowledge to analyze their strengths and weaknesses; which in result also enhances organizational self-transformational capability. Thus, this transformational capability assists organizations to survive and sustain in turbulent time to achieve competitive advantage. This paper through secondary qualitative data discusses the role of learning organization and its implications in multinational organizations.