Reviewerly/exHarmony huggingface dataset:
Peer-to-Peer Contact Tracing: Development of a Privacy-Preserving Smartphone App

Background The novel coronavirus disease 2019 (COVID-19) pandemic is an urgent public health crisis, with epidemiologic models predicting severe consequences, including high death rates, if the virus is permitted to run its course without any intervention or response. Contact tracing using smartphone technology is a powerful tool that may be employed to limit disease transmission during an epidemic or pandemic; yet, contact tracing apps present significant privacy concerns regarding the collection of personal data such as location. Objective The aim of this study is to develop an effective contact tracing smartphone app that respects user privacy by not collecting location information or other personal data. Methods We propose the use of an anonymized graph of interpersonal interactions to conduct a novel form of contact tracing and have developed a proof-of-concept smartphone app that implements this approach. Additionally, we developed a computer simulation model that demonstrates the impact of our proposal on epidemic or pandemic outbreak trajectories across multiple rates of adoption. Results Our proof-of-concept smartphone app allows users to create “checkpoints” for contact tracing, check their risk level based on their past interactions, and anonymously self-report a positive status to their peer network. Our simulation results suggest that higher adoption rates of such an app may result in a better controlled epidemic or pandemic outbreak. Conclusions Our proposed smartphone-based contact tracing method presents a novel solution that preserves privacy while demonstrating the potential to suppress an epidemic or pandemic outbreak. This app could potentially be applied to the current COVID-19 pandemic as well as other epidemics or pandemics in the future to achieve a middle ground between drastic isolation measures and unmitigated disease spread.
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation

We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work. State-of-the-art approaches prove that performing semantic-level alignment is helpful in tackling the domain shift issue. Based on the observation that stuff categories usually share similar appearances across images of different domains while things (i.e. object instances) have much larger differences, we propose to improve the semantic-level alignment with different strategies for stuff regions and for things: 1) for the stuff categories, we generate feature representation for each class and conduct the alignment operation from the target domain to the source domain; 2) for the thing categories, we generate feature representation for each individual instance and encourage the instance in the target domain to align with the most similar one in the source domain. In this way, the individual differences within thing categories will also be considered to alleviate over-alignment. In addition to our proposed method, we further reveal the reason why the current adversarial loss is often unstable in minimizing the distribution discrepancy and show that our method can help ease this issue by minimizing the most similar stuff and instance features between the source and the target domains. We conduct extensive experiments in two unsupervised domain adaptation tasks, i.e. GTA5 → Cityscapes and SYNTHIA → Cityscapes, and achieve the new state-of-the-art segmentation accuracy.
Gradually Vanishing Bridge for Adversarial Domain Adaptation

In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is considered to be directly minimized in existing solutions, which is difficult to achieve in practice. Some methods alleviate the difficulty by explicitly modeling domain-invariant and domain-specific parts in the representations, but the adverse influence of the explicit construction lies in the residual domain-specific characteristics in the constructed domain-invariant representations. In this paper, we equip adversarial domain adaptation with Gradually Vanishing Bridge (GVB) mechanism on both generator and discriminator. On the generator, GVB could not only reduce the overall transfer difficulty, but also reduce the influence of the residual domain-specific characteristics in domain-invariant representations. On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process. Experiments on three challenging datasets show that our GVB methods outperform strong competitors, and cooperate well with other adversarial methods. The code is available at https://github.com/cuishuhao/GVB.
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration

Filter pruning has been widely applied to neural network compression and acceleration.Existing methods usually utilize pre-defined pruning criteria, such as p -norm, to prune unimportant filters.There are two major limitations to these methods.First, prevailing methods fail to consider the variety of filter distribution across layers.To extract features of the coarse level to the fine level, the filters of different layers have various distributions.Therefore, it is not suitable to utilize the same pruning criteria to different functional layers.Second, prevailing layer-by-layer pruning methods process each layer independently and sequentially, failing to consider that all the layers in the network collaboratively make the final prediction.In this paper, we propose Learning Filter Pruning Criteria (LFPC) to solve the above problems.Specifically, we develop a differentiable pruning criteria sampler.This sampler is learnable and optimized by the validation loss of the pruned network obtained from the sampled criteria.In this way, we could adaptively select the appropriate pruning criteria for different functional layers.Besides, when evaluating the sampled criteria, LFPC comprehensively considers the contribution of all the layers at the same time.Experiments validate our approach on three image classification benchmarks.Notably, on ILSVRC-2012, our LFPC reduces more than 60% FLOPs on ResNet-50 with only 0.83% top-5 accuracy loss.
MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data

Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress in this task, usually relying on large amounts of training data. Due to the nature of scarcity for medical images, it is important to effectively aggregate data from multiple sites for robust model training, to alleviate the insufficiency of single-site samples. However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training. In this paper, we propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations, leveraging multiple sources of data. To compensate for the inter-site heterogeneity of different MRI datasets, we develop Domain-Specific Batch Normalization layers in the network backbone, enabling the network to estimate statistics and perform feature normalization for each site separately. Considering the difficulty of capturing the shared knowledge from multiple datasets, a novel learning paradigm, i.e., Multi-site-guided Knowledge Transfer, is proposed to enhance the kernels to extract more generic representations from multi-site data. Extensive experiments on three heterogeneous prostate MRI datasets demonstrate that our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains K sub-centers and training samples only need to be close to any of the K positive sub-centers. Sub-center ArcFace encourages one dominant sub-class that contains the majority of clean faces and non-dominant sub-classes that include hard or noisy faces. Based on this self-propelled isolation, we boost the performance through automatically purifying raw web faces under massive real-world noise. Besides discriminative feature embedding, we also explore the inverse problem, mapping feature vectors to face images. Without training any additional generator or discriminator, the pre-trained ArcFace model can generate identity-preserved face images for both subjects inside and outside the training data only by using the network gradient and Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace can enhance the discriminative feature embedding as well as strengthen the generative face synthesis.
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding

Emerging interests have been brought to recognize previously unseen objects given very few training examples, known as few-shot object detection (FSOD). Recent researches demonstrate that good feature embedding is the key to reach favorable few-shot learning performance. We observe object proposals with different Intersection-of-Union (IoU) scores are analogous to the intra-image augmentation used in contrastive visual representation learning. And we exploit this analogy and incorporate supervised contrastive learning to achieve more robust objects representations in FSOD. We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects. We notice the degradation of average precision (AP) for rare objects mainly comes from misclassifying novel instances as confusable classes. And we ease the misclassification issues by promoting instance level intraclass compactness and inter-class variance via our contrastive proposal encoding loss (CPE loss). Our design outperforms current state-of-the-art works in any shot and all data splits, with up to +8.8% on standard benchmark PASCAL VOC and +2.7% on challenging COCO benchmark. Code is available at: https://github.com/MegviiDetection/FSCE.
Blockchain-Based Distributed Framework for Automotive Industry in a Smart City

The digitalization and massive adoption of advanced technologies in the automotive industry not only transform the equipment manufacturer's operating mode, but also change the current business models. The increased adoption of autonomous cars is expected to disrupt government regulations, manufacturing, insurance, and maintenance services. Moreover, providing integrated, personalized, and on-demand services have shared, connected, and autonomous cars in the smart city for a sustainable ecosystem. To address these issues in this paper, we propose a blockchain-based distributed framework for the automotive industry in the smart city. The proposed framework includes a novel miner node selection algorithm for the blockchain-based distributed network architecture. To evaluate the feasibility of the proposed framework, we simulated the proposed model on a private Ethereum blockchain platform using captured dataset of mined blocks from litecoinpool.org. The simulation results show the proof-of-concept of the proposed model that can be used for wide range of future smart applications.
An improved tiny-yolov3 pedestrian detection algorithm

The existing real-time pedestrian detection method often loses part of the detection accuracy. For this reason, we proposed a real-time pedestrian detection algorithm based on tiny-yolov3. The proposed method uses K-means clustering on our training set to find the best priors. We improved the network structure of tiny-yolov3 to make it more accurate in pedestrian detection. From the experimental results, the proposed method has higher detection accuracy under the premise of satisfying real-time performance.
Explaining Decision-Making Algorithms through UI

Increasingly, algorithms are used to make important decisions across society. However, these algorithms are usually poorly understood, which can reduce transparency and evoke negative emotions. In this research, we seek to learn design principles for explanation interfaces that communicate how decision-making algorithms work, in order to help organizations explain their decisions to stakeholders, or to support users' "right to explanation". We conducted an online experiment where 199 participants used different explanation interfaces to understand an algorithm for making university admissions decisions. We measured users' objective and self-reported understanding of the algorithm. Our results show that both interactive explanations and "white-box" explanations (i.e. that show the inner workings of an algorithm) can improve users' comprehension. Although the interactive approach is more effective at improving comprehension, it comes with a trade-off of taking more time. Surprisingly, we also find that users' trust in algorithmic decisions is not affected by the explanation interface or their level of comprehension of the algorithm.
Revisiting collaboration through mixed reality: The evolution of groupware

Collaborative Mixed Reality (MR) systems are at a critical point in time as they are soon to become more commonplace. However, MR technology has only recently matured to the point where researchers can focus deeply on the nuances of supporting collaboration, rather than needing to focus on creating the enabling technology. In parallel, but largely independently, the field of Computer Supported Cooperative Work (CSCW) has focused on the fundamental concerns that underlie human communication and collaboration over the past 30-plus years. Since MR research is now on the brink of moving into the real world, we reflect on three decades of collaborative MR research and try to reconcile it with existing theory from CSCW, to help position MR researchers to pursue fruitful directions for their work. To do this, we review the history of collaborative MR systems, investigating how the common taxonomies and frameworks in CSCW and MR research can be applied to existing work on collaborative MR systems, exploring where they have fallen behind, and look for new ways to describe current trends. Through identifying emergent trends, we suggest future directions for MR, and also find where CSCW researchers can explore new theory that more fully represents the future of working, playing and being with others.
In bot we trust: A new methodology of chatbot performance measures

Chatbots are used frequently in business to facilitate various processes, particularly those related to customer service and personalization. In this article, we propose novel methods of tracking human-chatbot interactions and measuring chatbot performance that take into consideration ethical concerns, particularly trust. Our proposed methodology links neuroscientific methods, text mining, and machine learning. We argue that trust is the focal point of successful human-chatbot interaction and assess how trust as a relevant category is being redefined with the advent of deep learning supported chatbots. We propose a novel method of analyzing the content of messages produced in human-chatbot interactions, using the Condor Tribefinder system we developed for text mining that is based on a machine learning classification engine. Our results will help build better social bots for interaction in business or commercial environments.
DENSE-INception U-net for medical image segmentation

Convolutional neural networks (CNNs) play an important role in the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture is one of the most famous fully convolutional network architectures for medical semantic segmentation tasks. Recent work shows that the U-net network can be substantially deeper thus resulting in improved performance on segmentation tasks. Though adding more layers directly into network is a popular way to make a network deeper, it may lead to gradient vanishing or redundant computation during training. A novel CNN architecture is proposed that integrates the Inception-Res module and densely connecting convolutional module into the U-net architecture. The proposed network model consists of the following parts: firstly, the Inception-Res block is designed to increase the width of the network by replacing the standard convolutional layers; secondly, the Dense-Inception block is designed to extract features and make the network more deep without additional parameters; thirdly, the down-sampling block is adopted to reduce the size of feature maps to accelerate learning and the up-sampling block is used to resize the feature maps. The proposed model is tested on images of blood vessel segmentations from retina images, the lung segmentation of CT Data from the benchmark Kaggle datasets and the MRI scan brain tumor segmentation datasets from MICCAI BraTS 2017. The experimental results show that the proposed method can provide better performance on these two tasks compared with the state-of-the-art algorithms. The results reach an average Dice score of 0.9857 in the lung segmentation. For the blood vessel segmentation, the results reach an average Dice score of 0.9582. For the brain tumor segmentation, the results reach an average Dice score of 0.9867. The experiments highlighted that combining the inception module with dense connections in the U-Net architecture is a promising approach for semantic medical image segmentation.
Prototype Mixture Models for Few-Shot Semantic Segmentation

Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose. Using a single prototype acquired directly from the support image to segment the query image causes semantic ambiguity. In this paper, we propose prototype mixture models (PMMs), which correlate diverse image regions with multiple prototypes to enforce the prototype-based semantic representation. Estimated by an Expectation-Maximization algorithm, PMMs incorporate rich channel-wised and spatial semantics from limited support images. Utilized as representations as well as classifiers, PMMs fully leverage the semantics to activate objects in the query image while depressing background regions in a duplex manner. Extensive experiments on Pascal VOC and MS-COCO datasets show that PMMs significantly improve upon state-of-the-arts. Particularly, PMMs improve 5-shot segmentation performance on MS-COCO by up to 5.82% with only a moderate cost for model size and inference speed (Code is available at github.com/Yang-Bob/PMMs .).
Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance

Whale Optimization Algorithm (WOA) is a popular swarm-based algorithm with some spotted defects in its generated patterns during the searching phases. In this study, an enhanced WOA-based method is proposed in order to overcome the drawbacks of slow convergence speed and easy falling of WOA into the local optimum. The designed variant is called enhanced WOA (EWOA), which combines two strategies at the same time. First, a new communication mechanism (CM) is embedded into the basic WOA to promote the global optimal search ability and the exploitation tendency of the WOA. Then, the Biogeography-based Optimization (BBO) algorithm is partially utilized to harmonize the exploration and exploitation trends. A representative set of comprehensive benchmark cases and three engineering cases are utilized to verify the advantages of the proposed EWOA. The experimental results show that the exploration ability, exploitation ability, state of the balance, and convergence style of the algorithm has been improved significantly. Based on results, the proposed EWOA is a promising and excellent algorithm, and it has achieved better solution quality and faster convergence rate compared with other most advanced algorithms. For access to material and guide for users of this paper, we host an online page at https://aliasgharheidari.com.
Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends

Cloud computing is a recently looming-evoked paradigm, the aim of which is to provide on-demand, pay-as-you-go, internet-based access to shared computing resources (hardware and software) in a metered, self-service, dynamically scalable fashion. A related hot topic at the moment is task scheduling, which is well known for delivering critical cloud service performance. However, the dilemmas of resources being underutilized (underloaded) and overutilized (overloaded) may arise as a result of improper scheduling, which in turn leads to either wastage of cloud resources or degradation in service performance, respectively. Thus, the idea of incorporating meta-heuristic algorithms into task scheduling emerged in order to efficiently distribute complex and diverse incoming tasks (cloudlets) across available limited resources, within a reasonable time. Meta-heuristic techniques have proven very capable of solving scheduling problems, which is fulfilled herein from a cloud perspective by first providing a brief on traditional and heuristic scheduling methods before diving deeply into the most popular meta-heuristics for cloud task scheduling followed by a detailed systematic review featuring a novel taxonomy of those techniques, along with their advantages and limitations. More specifically, in this study, the basic concepts of cloud task scheduling are addressed smoothly, as well as diverse swarm, evolutionary, physical, emerging, and hybrid meta-heuristic scheduling techniques are categorized as per the nature of the scheduling problem (i.e., single- or multi-objective), the primary objective of scheduling, task-resource mapping scheme, and scheduling constraint. Armed with these methods, some of the most recent relevant literature are surveyed, and insights into the identification of existing challenges are presented, along with a trail to potential solutions. Furthermore, guidelines to future research directions drawn from recently emerging trends are outlined, which should definitely contribute to assisting current researchers and practitioners as well as pave the way for newbies excited about cloud task scheduling to pursue their own glory in the field.