Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Semantic Labeling of Photogrammetry Meshes Based on Active Learning

As different urban scenes are similar but still not completely consistent, coupled with the complexity of labeling directly in 3D, high-level understanding of 3D scenes has always been a tricky problem. In this paper, we propose a procedural approach for 3D semantic expression of urban scenes based on active learning. We first start with a small labeled image set to fine-tune a semantic segmentation network and then project its probability map onto a 3D mesh model for fusion, finally outputs a 3D semantic mesh model in which each facet has a semantic label and a heat model showing each facet's confidence. Our key observation is that our algorithm is iterative, in each iteration, we use the output semantic model as a supervision to select several valuable images for annotation to co-participate in the fine-tuning for overall improvement. In this way, we reduce the workload of labeling but not the quality of 3D semantic model. Using urban areas from two different cities, we show the potential of our method and demonstrate its effectiveness.
Deep Learning Based Sepsis Intervention: The Modelling and Prediction of Severe Sepsis Onset

Sepsis presents a significant challenge to healthcare providers during critical care scenarios such as within an intensive care unit. The prognosis of the onset of severe septic shock results in significant increases in mortality rate, length of stay and readmission rates. Continual advancements in health informatics data allows for applications within the machine learning field to predict sepsis onset in a timely manner, allowing for effective preventative intervention of severe septic shock. A novel deep learning application is proposed to provide effective prediction of sepsis onset by up to six hours prior, involving the use of novel concepts such as a boosted cascading training methodology and adjustable margin hinge loss function. The proposed methodology provides statistically significant improvements to that of current machine learning based modelling applications based off the Physionet Computing in Cardiology 2019 challenge. Results show test F1 scores of 0.420, a significant improvement of 0.281 as compared to the next best challenger results.
Programmable Metasurface Based on Substrate-Integrated Waveguide for Compact Dynamic-Pattern Antenna

A programmable metasurface based on a substrate-integrated waveguide (SIW) is proposed to realize a dynamic-pattern antenna, which satisfies the critical needs of low cost, ease of manufacturing, and easy integration with planar circuits in real-life applications. The proposed metasurface consists of 16 elements of complementary electric-LC resonators that are etched on the top layer of the SIW. Each element is installed with two p-i-n diodes to reach different states, which are controlled by a dc bias signal through a via from the backside of a printed circuit board (PCB). Both the element parameters and their positions along the SIW are carefully designed for better performance as an antenna. By utilizing different diodes’ tuning schemes, the phase difference between the radiating elements can be dynamically changed, thereby realizing antenna-pattern generation and reconfiguration in real time. Experimental results show that the proposed metasurface can generate not only narrow-to-wide steerable directive beams but also complex multibeam patterns by programming different digital codes. A prototype is fabricated with low-cost PCBs, and good agreements among the simulated and measured patterns are observed. The proposed SIW-based metasurface has a great potential for applications in dynamic-pattern-enhanced microwave imaging, radar, and communication systems.
Experimental Validation of a Metasurface Luneburg Lens Antenna Implemented With Glide-Symmetric Substrate-Integrated Holes

In this letter, we present the experimental validation of a <inline-formula><tex-math notation="LaTeX">$K_{a}$</tex-math></inline-formula>-band Luneburg lens antenna based on a novel cost-effective metasurface. The metasurface is composed of a parallel plate waveguide (PPW) loaded with quasi-periodic inclusions in both conductors. The inclusions are square holes printed on a substrate, with vias placed around the holes. The vias connect the printed layer of the substrate to the ground. This configuration is named substrate-integrated hole (SIH). It is demonstrated that the SIH metasurface can obtain a higher effective refractive index, compared to the conventional holey metasurface. To further increase the effective refractive index, the SIHs in the two conductors of the PPW are glide-symmetrically arranged. The refractive index distribution of the Luneburg lens is realized by locally tuning the dimensions of the SIHs. The lens is fed with 11 waveguide feeds with an angular separation of 10<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>. Thus, the antenna can steer its radiation in a 100° angular range. A flare is integrated with the PPW to match the antenna to the free-space impedance. Since the wave propagates mainly in the PPW air gap, the dielectric losses are low. The measured radiation efficiency of the antenna is roughly 80%.
Face Anti-Spoofing Using Spatial Pyramid Pooling

Face recognition system is vulnerable to many kinds of presentation attacks, so how to effectively detect whether the image is from the real face is particularly important. At present, many deep learning-based anti-spoofing methods have been proposed. But these approaches have some limitations, for example, global average pooling (GAP) easily loses local information of faces, single-scale features easily ignore information differences in different scales, while a complex network is prone to be overfitting. In this paper, we propose a face anti-spoofing approach using spatial pyramid pooling (SPP). Firstly, we use ResNet-18 with a small amount of parameter as the basic model to avoid overfitting. Further, we use spatial pyramid pooling module in the single model to enhance local features while fusing multi-scale information. The effectiveness of the proposed method is evaluated on three databases, CASIA-FASD, Replay-Attack and CASIA-SURF. The experimental results show that the proposed approach can achieve state-of-the-art performance.
Trajectory-User Link with Attention Recurrent Networks

The prevalent adoptions of GPS-enabled devices have witnessed an explosion of various location-based services which produces a huge amount of trajectories monitoring the individuals' movements. In this paper, we tackle Trajectory-User Link (TUL) problem, which identifies humans' movement patterns and links trajectories to the users who generated them. Existing solutions on TUL problem employ recurrent neural networks and variational autoencoder methods, which face the bottlenecks in the case of excessively long trajectories and fragmentary users' movements. However, these are common characteristics of trajectory data in reality, leading to performance degradation of the existing models. In this paper, we propose an end-to-end attention recurrent neural learning framework, called TULAR (Trajectory-User Link with Attention Recurrent Networks), which focus on selected parts of the source trajectories when linking. TULAR introduce the Trajectory Semantic Vector (TSV) via unsupervised location representation learning and recurrent neural networks, by which to reckon the weight of parts of source trajectory. Further, we employ three attention scores for the weight measurements. Experiments are conducted on two real world datasets and compared with several existing methods, and the results show that TULAR yields a new state-of-the-art performance. Source code is public available at GitHub: https://github.com/taos123/TULAR.
Construction worker hardhat-wearing detection based on an improved BiFPN

Work in the construction site is considered to be one of the occupations with the highest safety risk factor. Therefore, safety plays an important role in construction site. One of the most fundamental safety rules in construction site is to wear a hardhat. To strengthen the safety of the construction site, most of the current methods use multi-stage method for hardhat-wearing detection. These methods have limitations in terms of adaptability and generalizability. In this paper, we propose a one-stage object detection method based on convolutional neural network. We present a multi-scale strategy that selects the high-resolution feature maps of DarkNet-53 to effectively identify small-scale hardhats. In addition, we propose an improved weighted bi-directional feature pyramid network (BiFPN), which could fuse more semantic features from more scales. The proposed method can not only detect hardhat-wearing, but also identify the color of the hardhat. Experimental results show that the proposed method achieves a mAP of 87.04%, which outperforms several state-of-the-art methods on a public dataset.
Real-Time Multi-Focus Biomedical Microscopic Image Fusion Based on m-SegNet

Activity level measurement and fusion rules are the two key factors of image fusion. In the fusion method based on neural networks, the activity level measurements are realized by dividing the image into small blocks and predicting the sharpness of each block; then, the global decision graph guiding fusion is generated according to the predicted results. However, these two tasks are serial in nature, which makes it difficult to complete them simultaneously while achieving satisfactory fusion performance. Therefore, a new multi-focus microscopic image fusion method is proposed in this paper to quickly fuse multiple histological microscopic images from different focusing planes to generate full-focus images. The improved SegNet network was used to detect the unfocused regions. Considering that two or more images are needed for fusion, a parallel fusion strategy is proposed herein to generate clear fusion images based on multiple images instead of pairwise decision graphs. Compared with the convolutional neural network, the proposed network has better representation ability and can extract and fuse the most ideal features to provide a more accurate fusion decision. Compared with the traditional Segnet network, it is lightweight, which greatly improves computing speed and achieves real-time fusion.
Cluster-size constrained network partitioning

In this paper we consider a graph clustering problem with a given number of clusters and approximate desired sizes of the clusters. One possible motivation for such task could be the problem of databases or servers allocation within several given large computational clusters, where we want related objects to share the same cluster in order to minimize latency and transaction costs. This task differs from the original community detection problem. To solve this task, we adopt some ideas from Glauber Dynamics and Label Propagation Algorithm. At the same time we consider no additional information about node labels, so the task has the nature of unsupervised learning. We propose an algorithm for the problem, show that it works well for a large set of parameters of Stochastic Block Model (SBM) and theoretically show that its running time complexity for achieving almost exact recovery is of O(n.d−.ω) for the mean-field SBM with d− being the average degree and $w$ tending to infinity arbitrary slow. Other significant advantage of the proposed approach is its local nature, which means it can be efficiently distributed with no scheduling or synchronization.
Vacant Parking Space Detection based on Task Consistency and Reinforcement Learning

In this paper, we proposed a novel task-consistency learning method that allows training a vacant space detection network (target task) based on the logic consistency with the semantic outcomes from a flow-based motion behavior classifier (source task) in a parking lot. By well designing the reward mechanism upon semantic consistency, we show the possibility to train the target network in a reinforcement learning setting. Compared with conventional supervised detection methods, this work's main contribution is to learn a vacant space detector via semantic consistency rather than supervised labels. The dynamic learning property may make the proposed detector been deployed and updated in different lots easily without heavy human loads. The experiments show that based on the task consistency rewards from the motion behavior classifier, the vacant space detector can be trained successfully.
Backward-to-Forward Wide-Angle Fast Beam-Scanning Leaky-Wave Antenna With Consistent Gain

A planar periodic leaky-wave antenna (PLWA) with continuous backward-to-forward beam-scanning capability and consistent gain is presented. The PLWA is made of a new type of periodically arranging meander line structures, in which the capacitive coupling between neighboring units plays the key role in achieving a high scanning rate. In detail, the beam-scanning rate of the proposed PLWA is efficiently tuned by varying the gap between two adjacent units, without considerably changing the working frequency band. To mitigate the open stopband (OSB) and to improve the impedance matching, several inductive open stubs are introduced into a single meander line unit cell to enhance the radiation. In the experiment, a prototype of the proposed PLWA was fabricated and measured. The simulated and measured beams show good agreement in terms of scanning range and radiation performance. A continuous beam scanning from −60° to +58° through broadside in the frequency band of 5.95–7.1 GHz is observed, and hence, a 102.6°/GHz scanning rate is realized in practice. Besides, the PLWA shows an average gain level of 11.96 dBi with a variation lower than 2 dB and a sidelobe below −10 dB at all the measured frequencies. The proposed PLWA may have potential applications in radar, microwave imaging, and wireless communication due to its compact structure, easy to fabricate, and dispersionless performance.
Multi - Direction Convolution for Semantic Segmentation

Context is known to be one of crucial factors effecting the performance improvement of semantic segmentation. However, state-of-the-art segmentation models built upon fully convolutional networks are inherently weak in encoding contextual information because of stacked local operations such as convolution and pooling. Failing to capture context leads to inferior segmentation performance. Despite many context modules have been proposed to relieve this problem, they still operate in a local manner or use the same contextual information in different positions (due to upsampling). In this paper, we introduce the idea of Multi-Direction Convolution (MDC)-a novel operator capable of encoding rich contextual information. This operator is inspired by an observation that the standard convolution only slides along the spatial dimension $(x,y \ \text{direction})$ where the channel dimension $(z \quad \text{direction})$ is fixed, which renders slow growth of the receptive field (RF). If considering the channel-fixed convolution to be one-direction, MDC is multi-direction in the sense that MDC slides along both spatial and channel dimensions, i.e., it slides along $x,y$ when $z$ is fixed, along $x,z$ when $y$ is fixed, and along $y, z$ when $x$ is fixed. In this way, MDC is able to encode rich contextual information with the fast increase of the RF. Compared to existing context modules, the encoded context is position-sensitive because no upsampling is required. MDC is also efficient and easy to implement. It can be implemented with few standard convolution layers with permutation. We show through extensive experiments that MDC effectively and selectively enlarges the RF and outperforms existing contextual modules on two standard benchmarks, including Cityscapes and PASCAL VOC2012.
FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation

Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large user-item rating matrix. However, when the rating matrix sparseness increases its performance deteriorates. Expanding MF to include side-information of users and items has been shown by many researchers both to improve general recommendation performance and to help alleviate the data-sparsity and cold-start issues in CF. In regard to item feature side-information, most schemes incorporate this information through a two stage process: intermediate results (e.g., on item similarity) are first computed based on item attributes; these are then combined with MF. In this paper, focussing on item side-information, we propose a model that directly incorporates item features into the MF framework in a single step process. The model, which we name FeatureMF, does this by projecting every available attribute datum in each of the item features into the same latent factor space with users and items, thereby in effect enriching item representation in MF. Results are presented of comparative performance experiments of the model against three state-of-the-art item information enriched models, as well as against four reference benchmark models, using two public real-world datasets, Douban and Yelp, with four training:test ratio scenarios each. It is shown to yield the best recommendation performance over all these models across all contexts including data-sparsity situations, in particular, achieving over 0.9% to over 6.5% MAE recommendation performance improvement over the next best model, HERec. FeatureMF is also found to alleviate cold start and to scale well, almost linearly, in regard to computational time, as a function of dataset size.
A Deep Semantic Alignment Network for the Cross-Modal Image-Text Retrieval in Remote Sensing

Because of the rapid growth of multimodal data from the internet and social media, a cross-modal retrieval has become an important and valuable task in recent years.The purpose of the cross-modal retrieval is to obtain the result data in one modality (e.g., image), which is semantically similar to the query data in another modality (e.g., text). In the field of remote sensing, despite a great number of existing works on image retrieval, there has only been a small amount of research on the cross-modal image-text retrieval, due to the scarcity of datasets and the complicated characteristics of remote sensing image data. In this article, we introduce a novel cross-modal image-text retrieval network to establish the direct relationship between remote sensing images and their paired text data. Specifically, in our framework, we designed a semantic alignment module to fully explore the latent correspondence between images and text, in which we used the attention and gate mechanisms to filter and optimize data features so that more discriminative feature representations can be obtained. Experimental results on four benchmark remote sensing datasets, including UCMerced-LandUse-Captions, Sydney-Captions, RSICD, and NWPU-RESISC45-Captions, well showed that our proposed method outperformed other baselines and achieved the state-of-the-art performance in remote sensing image-text retrieval tasks.
Array of Arrays: Optimizing Phased Array Tiles

This letter discusses some of the challenges for realizing large millimeter-wave phased array antennas using multiple scalable arrays (tiles). The letter uses the radiation pattern multiplication and the array of arrays concepts to analyze the effect of the tiling gap on the large array radiation pattern. Different techniques to mitigate the performance deterioration due to the tiling gap are proposed and analyzed. The proposed methods use either the elements’ spacing, or the amplitude excitation to minimize the tiling impact on the array performance.
Merged 1D-2D Deep Convolutional Neural Networks for Nerve Detection in Ultrasound Images

Ultrasound-Guided Regional Anesthesia (UGRA) becomes a standard procedure in surgical operations and contributes to pain management. It offers the advantages of the targeted nerve detection and provides the visualization of regions of interest such as anatomical structures. However, nerve detection is one of the most challenging tasks that anesthetists can encounter in the UGRA procedure. A computer-aided system that can detect automatically the nerve region would facilitate the anesthetist's daily routine and allow them to concentrate more on the anesthetic delivery. In this paper, we propose a new method based on merging deep learning models from different data to detect the median nerve. The merged architecture consists of two branches, one being one-dimensional (1D) convolutional neural networks (CNN) branch and another 2D CNN branch. The merged architecture aims to learn the high-level features from 1D handcrafted noise-robust features and 2D ultrasound images. The obtained results show the validity and high accuracy of the proposed approach and its robustness.
Aging: The Two Faces of Janus in Digital Inclusion?

Does aging present the two faces of Janus when it comes to digital inclusion, influencing older people’s digital inclusion in some cases, but not in others? This article presents qualitative research and finds that older people—namely, men and women at least 65 years of age—are highly selective in the digital domain, and diversity marks their attitudes toward and uses and critiques of digital technologies. Thus, older people’s digital inclusion is marked by intragenerational diversity, in which a complex interplay of structural/external and individual/internal factors exists and aging plays a context-and life-experience–driven role. Thus, this article supports a psychosocial approach that views age identity as formed not only by biology
Examining Avoidance of Ongoing Political Issues in the News: A Longitudinal Study of the Impact of Audience Issue Fatigue

This study examines relations between news users’ fatigue from ongoing political news issues and different forms of avoiding these issues. When it comes to political issues, avoidance would be detrimental to an informed citizenry and problematic against the backdrop of citizens increasingly tuning out news media in general, as observed in recent years. The results from fixed effects regressions based on a three-wave panel survey show that increasing issue fatigue leads to increasing avoidance of the issue when selecting news media content and during exposure to news media content on the issue, although it does not affect avoidance of interpersonal discussions on the issue. Issue fatigue is a stronger predictor of avoidance during media use than other issue predispositions, such as issue importance and attitude. Thus, it emerges as a new explanatory approach to avoidance of media content on an issue level.