Reviewerly/exHarmony huggingface dataset:
Bidirectional Posture-Appearance Interaction Network for Driver Behavior Recognition

Driver behavior recognition has become one of the most important tasks for intelligent vehicles. This task, however, is very challenging since the background contents in real-world driving scenarios are often very complex. More critically, the difference between driving behaviors is often very minor, making it extremely difficult to distinguish them. Existing methods often rely only on RGB frames (or skeleton data), which may fail to capture the minor differences between behaviors and appearance information of objects simultaneously and thus fail to achieve promising performance. To address the above issues, in this paper, we propose a bidirectional posture-appearance interaction network (BPAI-Net), which simultaneously considers RGB frames and skeleton ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , posture) data for driver behavior recognition. Specifically, we propose a posture-guided convolutional neural network (PG-CNN) and an appearance-guided graph convolutional network (AG-GCN) to extract appearance and posture features, respectively. To exploit the complementary information between appearance and posture, we use the appearance features from PG-CNN for guiding AG-GCN to exploit the contextual information ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , nearby objects) to enhance posture features. Then, we use the enhanced posture features from AG-GCN to help PG-CNN focus on critical local areas of video frames that are related to driver behaviors. In this sense, we are able to use the interaction between two modalities to extract more discriminative features and thus improve the recognition accuracy. Experimental results on Drive&Act dataset show that our method outperforms state-of-the-art methods by a large margin (67.83% vs. 63.64%). Furthermore, we collect a bus driver behavior recognition dataset and yield consistent performance gain against baseline methods, demonstrating the effectiveness of our method in real-world applications. The source code and trained models are available at github.com/SCUT-AILab/BPAI-Net/.
Signed Bipartite Graph Neural Networks

Signed networks are such social networks having both positive and negative links. A lot of theories and algorithms have been developed to model such networks (e.g., balance theory). However, previous work mainly focuses on the unipartite signed networks where the nodes have the same type. Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets. Signed bipartite networks can be commonly found in many fields including business, politics, and academics, but have been less studied. In this work, we firstly define the signed relationship of the same set of nodes and provide a new perspective for analyzing signed bipartite networks. Then we do some comprehensive analysis of balance theory from two perspectives on several real-world datasets. Specifically, in the peer review dataset, we find that the ratio of balanced isomorphism in signed bipartite networks increased after rebuttal phases. Guided by these two perspectives, we propose a novel Signed Bipartite Graph Neural Networks (SBGNNs) to learn node embeddings for signed bipartite networks. SBGNNs follow most GNNs message-passing scheme, but we design new message functions, aggregation functions, and update functions for signed bipartite networks. We validate the effectiveness of our model on four real-world datasets on Link Sign Prediction task, which is the main machine learning task for signed networks. Experimental results show that our SBGNN model achieves significant improvement compared with strong baseline methods, including feature-based methods and network embedding methods.
Template-guided Clarifying Question Generation for Web Search Clarification

Clarification has attracted much attention because of its many potential applications especially in Web search. Since search queries are very short, the underlying user intents are often ambiguous. This makes it challenging for search engines to return the appropriate results that pertain to the users' actual information needs. To address this issue, asking clarifying questions has been recognized as a critical technique. Although previous studies have analyzed the importance of asking to clarify, generating clarifying questions for Web search remains under-explored. In this paper, we tackle this problem in a template-guided manner. Our objective is jointly learning to select question templates and fill question slots, using Transformer-based networks. We conduct experiments on MIMICS, a collection of datasets containing real Web search queries sampled from Bing's search logs. Our method is demonstrated to achieve significant improvements over various competitive baselines.
Spatial Cognition-Driven Deep Learning for Car Detection in Unmanned Aerial Vehicle Imagery

Small object detection is the main challenge for image detection of unmanned aerial vehicles (UAVs), especially with small pixel ratios and blurred boundaries. In this article, a one-stage detector (SF-SSD) is proposed with a new spatial cognition algorithm. The deconvolution operation is introduced to a feature fusion module, which enhances the representation of shallow features. These more representative features prove effective for small-scale object detection. Empowered by a spatial cognition method, the deep model can redetect objects with less-reliable confidence scores. This enables the detector to improve detection accuracy significantly. Both between-class similarity and within-class similarity are fully exploited to suppress useless background information. This motivates the proposed model to take full use of semantic features in the detection process of multiclass small objects. A simplified network structure can improve the speed of object detection. The experiments are conducted on a newly collected dataset (SY-UAV) and the benchmark datasets (CARPK and PUCPR+). To further demonstrate the effectiveness of the spatial cognition module, a multiclass object detection experiment is conducted on the Stanford Drone dataset (SDD). The results show that the proposed model achieves high frame rates and better detection accuracies than the state-of-the-art methods, which are 90.1% (CAPPK), 90.8% (PUCPR+), and 91.2% (SDD).
DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange

Intelligent vehicles clearly benefit from the expanded Field of View (FoV) of the 360° sensors, but the vast majority of available semantic segmentation training images are captured with pinhole cameras. In this work, we look at this problem through the lens of domain adaptation and bring panoramic semantic segmentation to a setting, where labelled training data originates from a different distribution of conventional pinhole camera images. First, we formalize the task of unsupervised domain adaptation for panoramic semantic segmentation, where a network trained on labelled examples from the source domain of pinhole camera data is deployed in a different target domain of panoramic images, for which no labels are available. To validate this idea, we collect and publicly release Densepass - a novel densely annotated dataset for panoramic segmentation under cross-domain conditions, specifically built to study the Pinhole→panoramictransfer and accompanied with pinhole camera training examples obtained from Cityscapes. Densepass covers both, labelled- and unlabelled 360° images, with the labelled data comprising 19 classes which explicitly fit the categories available in the source domain (i.e. pinhole) data. To meet the challenge of domain shift, we leverage the current progress of attention-based mechanisms and build a generic framework for cross-domain panoramic semantic segmentation based on different variants of attention-augmented domain adaptation modules. Our framework facilitates information exchange at local- and global levels when learning the domain correspondences and improves the domain adaptation performance of two standard segmentation networks by 6.05% and 11.26% in Mean IoU.
Head pose estimation using facial-landmarks classification for children rehabilitation games

During the last decade, there has been an increasing interest in developing Head-Pose Estimation (HPE) methods for different applications. Among these, there is the possibility to track a children’s head pose during rehabilitation sessions and to use such information to control a virtual avatar, so to increase the engagement and the effectiveness of the exercises. This requires the ability to perform such tracking in real-time, with high precision, and considering wide set of tracking angles. HPE methods can be generally categorised either as appearance-based or model-based methods, while, in this paper, we propose a novel, simple but effective, hybrid method for estimating the Head-Pose. It starts by detecting the face followed by detecting robust feature points on it (facial landmarks). The second part consists of applying a classification mechanism to assign facial landmarks characterising a face to a predefined range of angles representing the face orientation. The obtained results allow using the proposed approach in real-time and showed the efficiency of this approach to get significant improvement compared to the state of the art.
FMCW Radar-Based Hand Gesture Recognition Using Spatiotemporal Deformable and Context-Aware Convolutional 5-D Feature Representation

Recently, frequency-modulated continuous-wave (FMCW) radar-based hand gesture recognition (HGR) using deep learning has achieved favorable performance. However, many existing methods use extracted features separately, i.e., using one of the range, Doppler, azimuth, or elevation angle information, or a combination of any two, to train convolutional neural networks (CNNs), which ignore the interrelation among the 5-D time-varying-range-Doppler-azimuth-elevation feature space. Although there have been methods using the 5-D information, their mining of the interrelation among the 5-D feature space is not sufficient, and there is still room for improvements. This article proposes a new processing scheme of HGR based on 5-D feature cubes that are jointly encoded by a 3-D fast Fourier transform (3-D-FFT)-based method. Then, a CNN is proposed by building two novel blocks, i.e., the spatiotemporal deformable convolution (STDC) block and the adaptive spatiotemporal context-aware convolution (ASTCAC) block. Concretely, STDC is designed to cope with hand gestures’ large spatiotemporal geometric transformations in the 5-D feature space. Moreover, ASTCAC is designed for modeling long-distance global relationships, e.g., relationships between pixels of the feature at the upper left corner and lower right corner, and exploring the global spatiotemporal context, in order to enhance the target feature representation and suppress interference. Finally, our presented method is verified on a large radar dataset, including 19 760 sets of 16 common hand gestures, collected by 19 subjects. Our method obtains a recognition rate of 99.53% on the validation dataset and that of 97.22% on the test dataset, which is significantly better than state-of-the-art methods.
The Impact of Academic Freedom on Democracy in Africa

Shared experiences but diversified developments make Africa an interesting region in which to investigate the impact of education on democracy, and the role of academic freedom in this impact. As building expertise takes time, we focus on the causality between past experience of academic freedom and electoral democracy. Our theory is that independent experts advocating free and fair elections are acclaimed, which makes rigging elections a costly strategy for rulers. Using the Varieties of Democracy (V-Dem) index of Academic Freedom, and the Generalized method of moments (GMM) technique to estimate a dynamic panel model, we find a positive impact of preceding academic freedom on the quality of elections after the Post-Cold war democratic transitions. The result is robust when we check reverse causality and country-specific effects such as the initial level of democracy or dependence on oil exportation. The analysis is robust to indicators measuring democracy by accountability of the executive and Polity2 and Freedom House Indices as alternatives to V-Dem measures of democracy. We discuss the observed heterogeneity of countries showing a counterintuitive relationship. The study highlights the significance of scholars as a channel through which education supports democracy.
cuTS

Subgraph isomorphism is a pattern-matching algorithm widely used in many domains such as chem-informatics, bioinformatics, databases, and social network analysis. It is computationally expensive and is a proven NP-hard problem. The massive parallelism in GPUs is well suited for solving subgraph isomorphism. However, current GPU implementations are far from the achievable performance. Moreover, the enormous memory requirement of current approaches limits the problem size that can be handled. This work analyzes the fundamental challenges associated with processing subgraph isomorphism on GPUs and develops an efficient GPU implementation. We also develop a GPU-friendly trie-based data structure to drastically reduce the intermediate storage space requirement, enabling large benchmarks to be processed. We also develop the first distributed sub-graph isomorphism algorithm for GPUs. Our experimental evaluation demonstrates the efficacy of our approach by comparing the execution time and number of cases that can be handled against the state-of-the-art GPU implementations.
Transformer in Computer Vision

Transformer is widely used in Natural Language Processing (NLP), in which numerous papers have been proposed. Recently, the transformer has been borrowed for many computer vision tasks. However, there are few papers to give a comprehensive survey on the vision-based transformer. To this end, we give an in-depth review of the vision-based transformer. We conclude 15 articles covering transformers on image object detection, multiple object tracking, action classification, and visual segmentation. Furthermore, we summarize 6 related datasets for corresponding tasks as well as their metrics. We also provide a comprehensive experimental comparison to validate the strength of transformer-based methods. We provide a brief introduction to the transformer and its applications on computer vision tasks, which can help beginners to recognize it.
Distant emitters in ultrastrong waveguide QED: Ground-state properties and non-Markovian dynamics

Starting from the paradigmatic spin-boson model (SBM), we investigate the static and dynamical properties of a system of two distant two-level emitters coupled to a one-dimensional Ohmic waveguide beyond the rotating wave approximation. Employing static and dynamical polaron Ans\"atze we study the effects of finite separation distance on the behavior of the photon-mediated Ising-like interaction, qubit frequency renormalization, ground-state magnetization, and entanglement entropy of the two-qubit system. Based on previous works we derive an effective approximate Hamiltonian for the two-impurity SBM that preserves the excitation-number and thus facilitates the analytical treatment. In particular, it allows us to introduce non-Markovianity arising from delay-feedback effects in two distant emitters in the so-called ultrastrong coupling (USC) regime. We test our results with numerical simulations performed over a discretized circuit-QED model, finding perfect agreement with previous results, and showing interesting dynamical effects arising in ultrastrong waveguide QED with distant emitters. In particular, we revisit the Fermi two-atom problem showing that, in the USC regime, initial correlations yield two different evolutions for symmetric and antisymmetric states even before the emitters become causally connected. Finally, we demonstrate that the collective dynamics, e.g., superradiance or subradiance, are affected not only by the distance between emitters, but also by the coupling, due to significant frequency renormalization. This constitutes another dynamical consequence of the USC regime.
Serverless Containers – Rising Viable Approach to Scientific Workflows

The increasing popularity of the serverless computing approach has led to the emergence of new cloud infrastructures working in Container-as-a-Service (CaaS) model like AWS Fargate, Google Cloud Run, or Azure Container Instances. New infrastructures facilitate an innovative approach to running cloud containers where developers are freed from managing underlying resources. In this paper, we focus on evaluating the capabilities of elastic containers and their usefulness for scientific computing in the scientific workflow paradigm using AWS Fargate and Google Cloud Run infrastructures. For the experimental evaluation of our approach, we extended the HyperFlow engine to support these CaaS platforms, together with adapting four scientific workflows composed of several dozen to hundreds of tasks organized into a dependency graph. Studied applications are used to create cost-performance benchmarks and flow execution plots, delay, elasticity, and scalability measurements. Results show that serverless containers can be successfully utilized for running scientific workflows. Moreover, the results allow for gaining insight into the specific advantages and limits of the studied platforms.
GL-GAN: Adaptive global and local bilevel optimization for generative adversarial network

Although Generative Adversarial Networks (GAN) have shown remarkable performance in image generation, there exist some challenges in instability and convergence speed. During the training, the results of some models display the imbalances of quality within a generated image, in which some defective parts appear compared with other regions. Different from general single global optimization methods, we introduce an adaptive global and local bilevel optimization model (GL-GAN). The model achieves the generation of high-resolution images in a complementary and promoting way, where global optimization is to optimize the whole images and local is only to optimize the low-quality areas. Based on DCGAN, GL-GAN is able to effectively avoid the nature of imbalance by local bilevel optimization, which is accomplished by first locating low-quality areas and then optimizing them. Moreover, through feature map cues from discriminator output, we propose the adaptive local and global optimization method (Ada-OP) for interactive optimization and observe that it boosts the convergence speed. Compared with the current GAN methods, our model has shown impressive performance on CelebA, Oxford Flowers, CelebA-HQ and LSUN datasets.
Deep residual learning-based cognitive model for detection and classification of transmitted signal patterns in 5G smart city networks

Primary user (PU) signal detection or classification is a critical component of cognitive radio (CR) related wireless communication applications. In CR, the PU detection methods are mostly based on statistical models, and their detection performance heavily relies on the accuracy of assumed models. In this paper, we design a novel detector, dubbed as PU-Net, that dynamically learns the PU activity patterns in a cognitive 5G smart city, where a network of unmanned aerial vehicles (UAVs) is deployed as flying base stations to serve the Internet-of-Things (IoT) users. Unlike the traditional schemes, the PU-Net is free from signal-noise model assumptions and is leveraged through deep residual learning integrated with atrous spatial pyramid pooling (ASPP) to sense the PU's transmitted signal patterns in the network. The PU-Net detects and classifies the active and idle PU states by exploiting the multilevel spatial-temporal features in the signal and noise frames. The proposed model is trained using locally synthesized Rayleigh channel-impaired data with large variability of modulated signals and different noise floor regimes. Additionally, the PU-Net model is blind-tested and evaluated on real-world over-the-air signals and with variable-length frames and varying channel effects at secondary users (SUs). With extensive experiments, it is shown that PU-Net outperforms other benchmark detectors, obtaining an accuracy of 0.9974, with 0.9978 recall and 0.9970 precision in detecting and classifying the PU transmitted signal patterns. Correspondingly, the proposed PU-Net can be adopted for IoT/UAV-assisted communication systems in optimizing spectrum efficiency and resolving the coexistence issues in 5G and beyond networks.
Reduction in Gas Cost for Blockchain Enabled Smart Contract

Smart contract is the heart of the Blockchain based decentralized applications. Smart contracts are the programs that get executed automatically on the Blockchain when the pre-set conditions are met. Ethereum is one of the world's largest platforms for creating decentralized applications. In Ethereum, gas (in Ether, Ethereum cryptocurrency) is used to pay miners for using their resources and running the smart contracts. It has been observed that if a smart contract is not optimized, it costs more than the required gas and therefore, the user is overcharged. Hence, optimization techniques are required to apply on smart contracts before deploying it onto the main chain. The work is broken into three modules. (1.) Designing a smart contract for storing an educational certificate on Blockchain, (2.) Writing the smart contract in Solidity using Sublime Text 3 and Remix Ethereum IDE for storing an educational certificate and then finally, (3.) Analyzing the smart contract for calculation of the gas cost which has been optimized based on the following three parameters:(a.) data types - when applied bytes32 data type is used in place of string data type, gas cost is reduced to 0.18%, (b) packaging variables into a single slot through the SOLC-using structure for packing reduces the gas cost up to the 256 bits (32 bytes) length of packed variables. Here, packed variables' length is more than 256 bits (32 bytes), hence it did not reduce the gas cost and, (c) error handling functions - no change in the cost of gas while using revert () in place of throw. So, optimization of gas cost has been considerably achieved and it can be further enhanced by increasing the number of transactions.
A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth

Although melanoma occurs more rarely than several other skin cancers, patients’ long term survival rate is extremely low if the diagnosis is missed. Diagnosis is complicated by a high discordance rate among pathologists when distinguishing between melanoma and benign melanocytic lesions. A tool that allows pathology labs to sort and prioritize melanoma cases in their workflow could improve turnaround time by prioritizing challenging cases and routing them directly to the appropriate sub-specialist. We present a pathology deep learning system (PDLS) that performs hierarchical classification of digitized whole slide image (WSI) specimens into six classes defined by their morphological characteristics, including classification of "Melanocytic Suspect" specimens likely representing melanoma or severe dysplastic nevi . We trained the system on 7,685 images from a single lab (the reference lab), including the the largest set of triple-concordant melanocytic specimens compiled to date, and tested the sys-tem on 5,099 images from two distinct validation labs. We achieved Area Underneath the ROC Curve (AUC) values of 0.93 classifying Melanocytic Suspect specimens on the reference lab, 0.95 on the first validation lab, and 0.82 on the second validation lab. We demonstrate that the PDLS is capable of automatically sorting and triaging skin specimens with high sensitivity to Melanocytic Suspect cases and that a pathologist would only need between 30% and 60% of the caseload to address all melanoma specimens.