Reviewerly/exHarmony huggingface dataset:
A hybrid data-level ensemble to enable learning from highly imbalanced dataset

Highly imbalanced class distribution has been well-recognized as a major cause of performance degradation for most supervised learning algorithms. Unfortunately, such detrimental distribution inherently occurs in various real-world applications. In this work, we developed a hybrid data-level ensemble (HD-Ensemble), which integrates ensemble learning with the union of a margin-based undersampling and diversity-enhancing oversampling. The proposed undersampling method filters out certain number of unrepresentative majority instances based on an unsupervised margin definition, while the proposed oversampling method generates diverse minority instances according to the behavior of ensemble learning. The combination of the two data-level approaches serves a twofold purpose of balancing the data distribution, and optimizing the fundamental properties (e.g., margin distribution and diversity) of the ensemble, therefore, the inferior performance caused by adopting single data-level approach can be better addressed. Targeting on binary classification task, we evaluated the HD-Ensemble on 42 highly imbalanced datasets, which exhibited a considerable variety in sample number (ranging from 129 to 20,034), feature number (ranging from 3 to 5,000) and imbalance ratio (ranging from 9.08 to 970.6). Experimental results demonstrated the performance advantages of proposed HD-Ensemble over ten other ensemble solutions.
Facial Expression Recognition in the Wild Using Multi-Level Features and Attention Mechanisms

Learning discriminative features is of vital importance for automatic facial expression recognition (FER) in the wild. In this article, we propose a novel Slide-Patch and Whole-Face Attention model with SE blocks (SPWFA-SE), which jointly perceives the discriminative locality characteristics and informative global features of the face for effective FER. Specifically, the well-designed slide patches are proposed to extract local features. Different from the existing methods, our slide patches not only can maintain the information at the edge area of patches, but also do not need to detect facial landmarks. Moreover, to make the model adaptively focus on the distinguishable regions, an attention module is proposed in the patch level to learn the weight of each patch. Furthermore, squeeze-and-excitation blocks are explored in the channel level to learn the weight of each channel. As such, the proposed multi-level feature extraction and attention mechanisms can enhance the representative ability of the learned features. Extensive experiments on five challenging datasets demonstrate that our method can achieve state-of-the-art performance. Cross database experiments on another three databases show the superior generalization performance of our model. Furthermore, complexity analysis results show that our model contains fewer parameters with fast training advantages than other competing models.
Edge-based auditing method for data security in resource-constrained Internet of Things

The explosive generation of Internet of Things (IoT) data calls for cloud service providers (CSP) to further provide more secure and reliable transmission, storage, and management services. This requirement, however, goes against the honest and curious nature of CSP, to the extent that existing methods introduce the third-party audit (TPA) to check data security in the cloud. TPA solves the problem of unreliable CSP but puts a heavy burden on lightweight users because of the sheer amount of the pre-audit data processing work. In this paper, we establish an audit model based on a designed binary tree assisted by edge computing, which provides computing capability for the resource-constrained terminals. The data pre-processing task is offloaded to the edge, which reduces computing load and improves the efficiency of task processing. We propose an improved correlation mechanism between data blocks and nodes on the binary tree so that all nodes on the binary tree can be fully utilized while existing methods use only leaf nodes and thus are required to establish multiple binary trees. Moreover, to improve audit efficiency, the binary tree in the audit process is designed to be self-balanced. In experiments, we compare our methods with the traditional method and experimental results show that the proposed mechanism is more effective to store and manage big data, which is conducive to providing users with more secure IoT services.
uPort Open-Source Identity Management System: An Assessment of Self-Sovereign Identity and User-Centric Data Platform Built on Blockchain

Managing identity across an ever-growing digital services landscape has become one of the most challenging tasks for security experts. Over the years, several Identity Management (IDM) systems were introduced and adopted to tackle with the growing demand of an identity. In this series, a recently emerging IDM system is Self-Sovereign Identity (SSI) which offers greater control and access to users regarding their identity. This distinctive feature of the SSI IDM system represents a major development towards the availability of sovereign identity to users. uPort is an emerging open-source identity management system providing sovereign identity to users, organisations, and other entities. As an emerging identity management system, it requires meticulous analysis of its architecture, working, operational services, efficiency, advantages and limitations. Therefore, this paper contributes towards achieving all of these objectives. Firstly, it presents the architecture and working of the uPort identity management system. Secondly, it develops a Decentralized Application (DApp) to demonstrate and evaluate its operational services and efficiency. Finally, based on the developed DApp and experimental analysis, it presents the advantages and limitations of the uPort identity management system.
Bi-Directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification

RGB-Infrared person re-identification (RGB-IR Re-ID) is a cross-modality matching problem, where the modality discrepancy is a big challenge. Most existing works use Euclidean metric based constraints to resolve the discrepancy between features of images from different modalities. However, these methods are incapable of learning angularly discriminative feature embedding because Euclidean distance cannot measure the included angle between embedding vectors effectively. As an angularly discriminative feature space is important for classifying the human images based on their embedding vectors, in this paper, we propose a novel ranking loss function, named Bi-directional Exponential Angular Triplet Loss, to help learn an angularly separable common feature space by explicitly constraining the included angles between embedding vectors. Moreover, to help stabilize and learn the magnitudes of embedding vectors, we adopt a common space batch normalization layer. The quantitative and qualitative experiments on the SYSU-MM01 and RegDB dataset support our analysis. On SYSU-MM01 dataset, the performance is improved from 7.40% / 11.46% to 38.57% / 38.61% for rank-1 accuracy / mAP compared with the baseline. The proposed method can be generalized to the task of single-modality Re-ID and improves the rank-1 accuracy / mAP from 92.0% / 81.7% to 94.7% / 86.6% on the Market-1501 dataset, from 82.6% / 70.6% to 87.6% / 77.1% on the DukeMTMC-reID dataset.
Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation

Cold-start problem is a fundamental challenge for recommendation tasks. Despite the recent advances on Graph Neural Networks (GNNs) incorporate the high-order collaborative signal to alleviate the problem, the embeddings of the cold-start users and items aren't explicitly optimized, and the cold-start neighbors are not dealt with during the graph convolution in GNNs. This paper proposes to pre-train a GNN model before applying it for recommendation. Unlike the goal of recommendation, the pre-training GNN simulates the cold-start scenarios from the users/items with sufficient interactions and takes the embedding reconstruction as the pretext task, such that it can directly improve the embedding quality and can be easily adapted to the new cold-start users/items. To further reduce the impact from the cold-start neighbors, we incorporate a self-attention-based meta aggregator to enhance the aggregation ability of each graph convolution step, and an adaptive neighbor sampler to select the effective neighbors according to the feedbacks from the pre-training GNN model. Experiments on three public recommendation datasets show the superiority of our pre-training GNN model against the original GNN models on user/item embedding inference and the recommendation task.
Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation

We address the problem of semantic nighttime image segmentation and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night through progressively darker times of day, exploiting cross-time-of-day correspondences between daytime images from a reference map and dark images to guide the label inference in the dark domains; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, comprising 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 201 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark for our novel evaluation. Experiments show that our map-guided curriculum adaptation significantly outperforms state-of-the-art methods on nighttime sets both for standard metrics and our uncertainty-aware metric. Furthermore, our uncertainty-aware evaluation reveals that selective invalidation of predictions can improve results on data with ambiguous content such as our benchmark and profit safety-oriented applications involving invalid inputs.
Graph Attention Auto-Encoders

Auto-encoders have emerged as a successful framework for unsupervised learning. However, conventional auto-encoders are incapable of utilizing explicit relations in structured data. To take advantage of relations in graph-structured data, several graph auto-encoders have recently been proposed, but they neglect to reconstruct either the graph structure or node attributes. In this paper, we present the graph attention auto-encoder (GATE), a neural network architecture for unsupervised representation learning on graph-structured data. Our architecture is able to reconstruct graph-structured inputs, including both node attributes and the graph structure, through stacked encoder/decoder layers equipped with self-attention mechanisms. In the encoder, by considering node attributes as initial node representations, each layer generates new representations of nodes by attending over their neighbors' representations. In the decoder, we attempt to reverse the encoding process to reconstruct node attributes. Moreover, node representations are regularized to reconstruct the graph structure. Our proposed architecture does not need to know the graph structure upfront, and thus it can be applied to inductive learning. Our experiments demonstrate competitive performance on several node classification benchmark datasets for transductive and inductive tasks, even exceeding the performance of supervised learning baselines in most cases.
Collaborative Training Between Region Proposal Localization and Classification for Domain Adaptive Object Detection

Object detectors are usually trained with large amount of labeled data, which is expensive and labor-intensive. Pre-trained detectors applied to unlabeled dataset always suffer from the difference of dataset distribution, also called domain shift. Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance. In this paper, we are the first to reveal that the region proposal network (RPN) and region proposal classifier (RPC) in the endemic two-stage detectors (e.g., Faster RCNN) demonstrate significantly different transferability when facing large domain gap. The region classifier shows preferable performance but is limited without RPN’s high-quality proposals while simple alignment in the backbone network is not effective enough for RPN adaptation. We delve into the consistency and the difference of RPN and RPC, treat them individually and leverage high-confidence output of one as mutual guidance to train the other. Moreover, the samples with low-confidence are used for discrepancy calculation between RPN and RPC and minimax optimization. Extensive experimental results on various scenarios have demonstrated the effectiveness of our proposed method in both domain-adaptive region proposal generation and object detection. Code is available at https://github.com/GanlongZhao/CST_DA_detection .
A novel progressively undersampling method based on the density peaks sequence for imbalanced data

Undersampling is a widely used resampling technique for imbalanced data. As traditional undersampling techniques, typically making majority and minority classes in imbalanced data into the same scale, tend to miss valuable information, many strategies like clustering have been developed. However, two essential problems still remain and require more efforts to be put; that is, which and how many instances should be extracted in undersampling. To alleviate these two problems, in this paper we propose a novel undersampling method for imbalanced data. It exploits a sequence of density peaks to progressively extract instances from the majority classes of the imbalanced data. Specifically, two factors are introduced to measure the importance degree of each instance in the majority classes. With these two factors, we generate a sampling sequence based on the importance of instances for classification. Furthermore, the optimal undersampling size of the majority classes is automatically determined by progressively extracting the important instances from the sequence. To evaluate the effectiveness of the proposed method, a series of experiments comparing to six popular undersampling methods were conducted on 40 public benchmark datasets. The experimental results show that the performance of the proposed undersampling method is superior to the state-of-the-art undersampling methods.
On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks

The conventional architecture of vehicular ad hoc networks (VANETs) with a centralized approach has difficulty overcoming the increasing complexity of intelligent transportation system (ITS) applications as well as challenges in providing large amounts of data storage, trust management, and information security. Therefore, vehicular edge computing networks (VECNets) have emerged to provide massive storage resources with powerful computing on network edges. However, a centralized server in VECNets is insufficient due to potential data leakage and security risks as it can still allow a single point of failure (SPoF). We propose consortium blockchain and smart contracts to ensure a trustworthy environment for secure data storage and sharing in the system to address these challenges. Practical byzantine fault tolerance (PBFT) is utilized because it is suitable for consortium blockchain to audit publicly, store data sharing, and records the whole consensus process. It can defend against system failures with or without symptoms to reach an agreement among consensus participants. Furthermore, we use an incentive mechanism to motivate the vehicle to contribute and honestly share their data. The simulation results satisfy the proposed model’s design goals by increasing vehicular networks’ performance in general.
TMD-Unet: Triple-Unet with Multi-Scale Input Features and Dense Skip Connection for Medical Image Segmentation

Deep learning is one of the most effective approaches to medical image processing applications. Network models are being studied more and more for medical image segmentation challenges. The encoder-decoder structure is achieving great success, in particular the Unet architecture, which is used as a baseline architecture for the medical image segmentation networks. Traditional Unet and Unet-based networks still have a limitation that is not able to fully exploit the output features of the convolutional units in the node. In this study, we proposed a new network model named TMD-Unet, which had three main enhancements in comparison with Unet: (1) modifying the interconnection of the network node, (2) using dilated convolution instead of the standard convolution, and (3) integrating the multi-scale input features on the input side of the model and applying a dense skip connection instead of a regular skip connection. Our experiments were performed on seven datasets, including many different medical image modalities such as colonoscopy, electron microscopy (EM), dermoscopy, computed tomography (CT), and magnetic resonance imaging (MRI). The segmentation applications implemented in the paper include EM, nuclei, polyp, skin lesion, left atrium, spleen, and liver segmentation. The dice score of our proposed models achieved 96.43% for liver segmentation, 95.51% for spleen segmentation, 92.65% for polyp segmentation, 94.11% for EM segmentation, 92.49% for nuclei segmentation, 91.81% for left atrium segmentation, and 87.27% for skin lesion segmentation. The experimental results showed that the proposed model was superior to the popular models for all seven applications, which demonstrates the high generality of the proposed model.
Attention-based context aggregation network for monocular depth estimation

Depth estimation is a traditional computer vision task, which plays a crucial role in understanding 3D scene geometry. Recently, algorithms that combine the multi-scale features extracted by the dilated convolution based block (atrous spatial pyramid pooling, ASPP) have gained significant improvements in depth estimation. However, the discretized and predefined dilation kernels cannot capture the continuous context information that differs in diverse scenes and easily introduce the grid artifacts. This paper proposes a novel algorithm, called attention-based context aggregation network (ACAN) for depth estimation. A supervised self-attention model is designed and utilized to adaptively learn the task-specific similarities between different pixels to model the continuous context information. Moreover, a soft ordinal inference is proposed to transform the predicted probabilities to continuous depth values which reduce the discretization error (about 1% decrease in RMSE). ACAN achieves state-of-the-art performance on public monocular depth-estimation benchmark datasets. The source code of ACAN can be found in https://github.com/miraiaroha/ACAN .
Revisiting Shadow Detection: A New Benchmark Dataset for Complex World

Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on various benchmark data, their performance is still limited for general real-world situations. In this work, we collected shadow images for multiple scenarios and compiled a new dataset of 10,500 shadow images, each with labeled ground-truth mask, for supporting shadow detection in the complex world. Our dataset covers a rich variety of scene categories, with diverse shadow sizes, locations, contrasts, and types. Further, we comprehensively analyze the complexity of the dataset, present a fast shadow detection network with a detail enhancement module to harvest shadow details, and demonstrate the effectiveness of our method to detect shadows in general situations.
Techno-economic assessment of concentrated solar thermal power generation and potential barriers in its deployment in Pakistan.

In this study, the current situation and future prospects of cleaner energy production through concentrated solar power (CSP) plants in Pakistan are analyzed. The assessment of required resources, evaluation of techno-economic feasibility, analysis of existing policy framework, and potential barriers in adopting the concentrated solar thermal technologies in Pakistan are presented. Six potential sites for installing solar thermal power plants are selected on the basis of solar resource assessment, land availability, and feasible infrastructure. A case study of a 100 MW parabolic trough collector (PTC) solar thermal power plant is simulated for these potential sites by using the System Advisor Model (SAM) software. The optimization of solar field size and thermal energy storage is performed to obtain the minimum Levelized cost of electricity (LCOE) for each potential site. Results indicate that the LCOE under the optimized configuration of the PTC solar thermal power plant in Pishin and Quetta can be minimized to 14.7 cents/kWh and 15.3 cents/kWh, respectively. Although the LCOE is the lowest in Pishin, the better availability of the desired infrastructure including transmission lines, water facility, and grid stations in Quetta makes it a more favorable site for installing the PTC solar thermal power plant. Furthermore, a discussion of government policies, along with major barriers (financial, political, infrastructural, technical, and social) that hinder the development of concentrated solar thermal technologies in Pakistan, is presented in detail. In order to overcome these barriers, several recommendations, including the introduction of financial incentives to investors, promoting private stakeholders, the establishment of research facilities in the country, and educating the general population regarding the benefits of clean energy are also outlined.
Generation of non-classical states of light and their application in deterministic quantum teleportation

Non-classical states of light, which include squeezed and entangled states of light, are the cornerstone of quantum mechanics and quantum information sciences. To date, non-classical states of light with much higher quality than before are required to develop high-fidelity quantum information processing and high-precision quantum metrology. Squeezed and entangled states with approximately 10 dB noise below the corresponding shot noise limit have been generated using a series of methods, which means that the noise variance reaches a few percent of the vacuum noise. Quantum teleportation, which means transferring an unknown quantum state from a sending station to a distant receiving station supported by entangled states, is the foundation of quantum computation and quantum communication networks. Quantum teleportation in continuous variable regions is unconditional because the entangled states used are always deterministic. The quantum teleportation distance was recently extended to the order of kilometers, which paves the way for constructing a practical quantum information network.