Reviewerly/exHarmony huggingface dataset:
Learning 3D Human Shape and Pose from Dense Body Parts

Reconstructing 3D human shape and pose from monocular images is challenging despite the promising results achieved by the most recent learning-based methods. The commonly occurred misalignment comes from the facts that the mapping from images to the model space is highly non-linear and the rotation-based pose representation of the body model is prone to result in the drift of joint positions. In this work, we investigate learning 3D human shape and pose from dense correspondences of body parts and propose a Decompose-and-aggregate Network (DaNet) to address these issues. DaNet adopts the dense correspondence maps, which densely build a bridge between 2D pixels and 3D vertexes, as intermediate representations to facilitate the learning of 2D-to-3D mapping. The prediction modules of DaNet are decomposed into one global stream and multiple local streams to enable global and fine-grained perceptions for the shape and pose predictions, respectively. Messages from local streams are further aggregated to enhance the robust prediction of the rotation-based poses, where a position-aided rotation feature refinement strategy is proposed to exploit spatial relationships between body joints. Moreover, a Part-based Dropout (PartDrop) strategy is introduced to drop out dense information from intermediate representations during training, encouraging the network to focus on more complementary body parts as well as neighboring position features. The efficacy of the proposed method is validated on both indoor and real-world datasets including Human3.6M, UP3D, COCO, and 3DPW, showing that our method could significantly improve the reconstruction performance in comparison with previous state-of-the-art methods. Our code is publicly available at https://hongwenzhang.github.io/dense2mesh.
EfficientFCN: Holistically-Guided Decoding for Semantic Segmentation

Both performance and efficiency are important to semantic segmentation. State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated convolutions in the backbone networks to extract high-resolution feature maps for achieving high-performance segmentation performance. However, due to many convolution operations are conducted on the high-resolution feature maps, such dilatedFCN-based methods result in large computational complexity and memory consumption. To balance the performance and efficiency, there also exist encoder-decoder structures that gradually recover the spatial information by combining multi-level feature maps from the encoder. However, the performances of existing encoder-decoder methods are far from comparable with the dilatedFCN-based methods. In this paper, we propose the EfficientFCN, whose backbone is a common ImageNet pretrained network without any dilated convolution. A holistically-guided decoder is introduced to obtain the high-resolution semantic-rich feature maps via the multi-scale features from the encoder. The decoding task is converted to novel codebook generation and codeword assembly task, which takes advantages of the high-level and low-level features from the encoder. Such a framework achieves comparable or even better performance than state-of-the-art methods with only 1/3 of the computational cost. Extensive experiments on PASCAL Context, PASCAL VOC, ADE20K validate the effectiveness of the proposed EfficientFCN.
High-Fidelity Synthesis with Disentangled Representation

Learning disentangled representation of data without supervision is an important step towards improving the interpretability of generative models. Despite recent advances in disentangled representation learning, existing approaches often suffer from the trade-off between representation learning and generation performance (i.e., improving generation quality sacrifices disentanglement performance). We propose an Information-Distillation Generative Adversarial Network (ID-GAN), a simple yet generic framework that easily incorporates the existing state-of-the-art models for both disentanglement learning and high-fidelity synthesis. Our method learns disentangled representation using VAE-based models, and distills the learned representation with an additional nuisance variable to the separate GAN-based generator for high-fidelity synthesis. To ensure that both generative models are aligned to render the same generative factors, we further constrain the GAN generator to maximize the mutual information between the learned latent code and the output. Despite the simplicity, we show that the proposed method is highly effective, achieving comparable image generation quality to the state-of-the-art methods using the disentangled representation. We also show that the proposed decomposition leads to an efficient and stable model design, and we demonstrate photo-realistic high-resolution image synthesis results ( $$1024\times 1024$$ pixels) for the first time using the disentangled representations. Our code is available at https://www.github.com/1Konny/idgan .
Aspect-level sentiment analysis using context and aspect memory network

With the popularity of social networks, sentiment analysis has become one of the hottest topics in natural language processing (NLP). As the development of research on the fine-grained sentiment analysis, more and more researchers pay attention to aspect-level sentiment analysis. It aims to identify the same or different sentiment polarity in different aspects of the context. In this paper, a context and aspect memory network (CAMN) method is proposed to solve the problem of aspect level sentiment analysis. In this method, deep memory network, bi-directional long short-term memory network and multi-attention mechanism are introduced to better capture the sentiment features in short texts. It includes two strategies: one is to use the self-attention mechanism (i.e., CAMN-SA) to calculate the context relevance; the other is to use the encoder-decoder attention mechanism (i.e., CAMN-ED) to calculate the context and aspect relevance. In order to verify the function of each component in the proposed method, and to test the effect of different hops on the memory network, we conduct many experiments on three real-world datasets to compare the baseline models with our proposed method. Experimental results show that our proposed method can achieve better performance than the baseline models.
Learning Visual Commonsense for Robust Scene Graph Generation

Scene graph generation models understand the scene through object and predicate recognition, but are prone to mistakes due to the challenges of perception in the wild. Perception errors often lead to nonsensical compositions in the output scene graph, which do not follow real-world rules and patterns, and can be corrected using commonsense knowledge. We propose the first method to acquire visual commonsense such as affordance and intuitive physics automatically from data, and use that to improve the robustness of scene understanding. To this end, we extend Transformer models to incorporate the structure of scene graphs, and train our Global-Local Attention Transformer on a scene graph corpus. Once trained, our model can be applied on any scene graph generation model and correct its obvious mistakes, resulting in more semantically plausible scene graphs. Through extensive experiments, we show our model learns commonsense better than any alternative, and improves the accuracy of state-of-the-art scene graph generation methods.
Learning Semantic Neural Tree for Human Parsing

In this paper, we design a novel semantic neural tree for human parsing, which uses a tree architecture to encode physiological structure of human body, and design a coarse to fine process in a cascade manner to generate accurate results. Specifically, the semantic neural tree is designed to segment human regions into multiple semantic sub-regions (e.g., face, arms, and legs) in a hierarchical way using a new designed attention routing module. Meanwhile, we introduce the semantic aggregation module to combine multiple hierarchical features to exploit more context information for better performance. Our semantic neural tree can be trained in an end-to-end fashion by standard stochastic gradient descent (SGD) with back-propagation. Several experiments conducted on four challenging datasets for both single and multiple human parsing, i.e., LIP, PASCAL-Person-Part, CIHP and MHP-v2, demonstrate the effectiveness of the proposed method. Code can be found at https://isrc.iscas.ac.cn/gitlab/research/sematree .
A survey of approaches for implementing optical neural networks

Conventional neural networks are software simulations of artificial neural networks (ANNs) implemented on von Neumann machines. This technology has recently encountered bottlenecks in terms of computing speed and energy consumption, leading to increased research interest in optical neural networks (ONNs), which are expected to become the basis for the next generation of artificial intelligence. To provide a better understanding of ONNs and to motivate further developments in this field, previous studies of ONN are reviewed in this article. Our work mainly focuses on the mathematical operations that are decomposed from theoretical models of ANNs and their corresponding optical implementations; these include matrix multiplication, nonlinear activation, convolution, and learning algorithms realized via optical approaches. Some fundamental information about ANNs is also introduced to make this work friendlier to non-experts.
Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image

We introduce the problem of perpetual view generation— long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image. This is a challenging problem that goes far beyond the capabilities of current view synthesis methods, which quickly degenerate when presented with large camera motions. Methods for video generation also have limited ability to produce long sequences and are often agnostic to scene geometry. We take a hybrid approach that integrates both geometry and image synthesis in an iterative ‘render, refine and repeat’ framework, allowing for long-range generation that cover large distances after hundreds of frames. Our approach can be trained from a set of monocular video sequences. We propose a dataset of aerial footage of coastal scenes, and compare our method with recent view synthesis and conditional video generation baselines, showing that it can generate plausible scenes for much longer time horizons over large camera trajectories compared to existing methods. Project page at https://infinite-nature.github.io.
Is blockchain tourism’s new hope?

Purpose Blockchain is a disruptive technology enabling distributed, encrypted, smart and secure peer-to-peer transactions. The fragmented nature of the tourism industry with a high number of contracts and transactions between several parties has security issues, disputes and delay. Although these motivate the use of blockchain, scholars have barely begun to systematically assess the value proposition of blockchain in the hospitality and tourism industry. The purpose of this paper is to examine the impacts, opportunities and challenges of blockchain in the tourism and hospitality sector. The authors present early use cases of blockchain in the tourism industry. Design/methodology/approach The authors conducted a multiple case-study approach and grounded this study based on the technology acceptance management literature with context-specific variables that are pivotal to the study of this topic. Findings This paper outlines the useful features of blockchain in the tourism industry in seven major streams and raises four future research questions. This review will enable hypotheses to be set out for consumers and producers involved in tourism to uncover potential motives and barriers to embracing blockchain. Research limitations/implications Blockchain is a relatively new technology, and given that all use cases are limited to the proof of concept and have not been fully adopted by the industry, empirical assessment of case studies is not feasible yet. Originality/value As blockchain is becoming more known and used in the tourism industry, there is a need to identify challenges, issues and concerns relevant for this industry moving forward. This paper fulfils this need and provides directions for future research.
Optimizing Latent Distributions for Non-Adversarial Generative Networks

The generator in generative adversarial networks (GANs) is driven by a discriminator to produce high-quality images through an adversarial game. At the same time, the difficulty of reaching a stable generator has been increased. This paper focuses on non-adversarial generative networks that are trained in a plain manner without adversarial loss. The given limited number of real images could be insufficient to fully represent the real data distribution. We therefore investigate a set of distributions in a Wasserstein ball centred on the distribution induced by the training data and propose to optimize the generator over this Wasserstein ball. We theoretically discuss the solvability of the newly defined objective function and develop a tractable reformulation to learn the generator. The connections and differences between the proposed non-adversarial generative networks and GANs are analyzed. Experimental results on real-world datasets demonstrate that the proposed algorithm can effectively learn image generators in a non-adversarial approach, and the generated images are of comparable quality with those from GANs.
Ultrasonic thyroid nodule detection method based on U-Net network

Aiming at the time consuming processing of existing thyroid nodule detection and difficulty in feature extraction, U-Net-based thyroid nodule detection is proposed to perform computed aided diagnosis. This paper proposes a mark-guided ultrasound deep network segmentation model of thyroid nodules. By comparing with VGG19, Inception V3, DenseNet 161, segmentation accuracy, segmentation edge and network operation time, it is found that the algorithm in this paper has relative advantages. U-Net network-based ultrasound thyroid nodules segmented the nodule area overlapped with the manually depicted nodule area close to 100%, the segmentation accuracy rate was as high as 0.9785, and the U-Net segmentation result was closer to the manually depicted nodule. The accuracy of U-Net segmentation of the thyroid is about 3% higher than the other three networks. The segmentation of nodules based on U-Net proposed in this paper significantly improves the segmentation accuracy of thyroid nodules with a small training data set, and provides a comprehensive reference for clinical diagnosis and treatment.
D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations

This work proposes a weakly-supervised temporal action localization framework, called D2-Net, which strives to temporally localize actions using video-level supervision. Our main contribution is the introduction of a novel loss formulation, which jointly enhances the discriminability of latent embeddings and robustness of the output temporal class activations with respect to foreground-background noise caused by weak supervision. The proposed formulation comprises a discriminative and a denoising loss term for enhancing temporal action localization. The discriminative term incorporates a classification loss and utilizes a top-down attention mechanism to enhance the separability of latent foreground-background embeddings. The denoising loss term explicitly addresses the foreground-background noise in class activations by simultaneously maximizing intra-video and inter-video mutual information using a bottom-up attention mechanism. As a result, activations in the foreground regions are emphasized whereas those in the background regions are suppressed, thereby leading to more robust predictions. Comprehensive experiments are performed on multiple benchmarks, including THUMOS14 and ActivityNet1.2. Our D2-Net performs favorably in comparison to the existing methods on all datasets, achieving gains as high as 2.3% in terms of mAP at IoU=0.5 on THUMOS14. Source code is available at https://github.com/naraysa/D2-Net.
Sledge

Emerging IoT applications with real-time latency constraints require new data processing systems operating at the Edge. Serverless computing offers a new compelling paradigm, where a user can execute a small application without handling the operational issues of server provisioning and resource management. Despite a variety of existing commercial and open source serverless platforms (utilizing VMs and containers), these solutions are too heavy-weight for a resource-constrained Edge systems (due to large memory footprint and high invocation time). Moreover, serverless workloads that focus on per-client, short-running computations are not an ideal fit for existing general purpose computing systems.
Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks

5G heterogeneous networks have become a promising platform to connect a growing number of Internet-of-Things (IoT) devices and accommodate a wide variety of vertical services. IoT has not been limited to traditional sensing systems since the introduction of 5G, but also includes a range of autonomous moving platforms, e.g., autonomous flying vehicles, autonomous underwater vehicles, autonomous surface vehicles as well as autonomous land vehicles. These platforms can be used as an effective means to connect air, space, ground, and sea mobile networks for providing a wider diversity of Internet services. Deep learning has been widely used to extract useful information from network big data for enhancing network quality-of-service and user quality-of-experience. Privacy preservation for user and network data is a burning concern in 5G heterogeneous networks due to various attacks in this environment. In this paper, we conduct an in-depth investigation on how deep learning can cope with privacy preservation issues in 5G heterogeneous networks, in terms of heterogeneous radio access networks (RANs), beyond-RAN networks, and end-to-end network slices, followed by a set of key research challenges and open issues that aim to guide future research.
Learning Point-Guided Localization for Detection in Remote Sensing Images

Object detection in remote sensing images is challenging due to the dense distribution and arbitrary angle of the objects. It is a consensus that the oriented bounding box (OBB) is more suitable to represent the aerial objects. However, there are some extreme cases in regression-based OBB detection that make the regression target discontinuous, resulting in the poor performance. In this article, an analysis of the formats of OBB and the problems in its regression is presented, following with an exploration of transform localization from regression to keypoint estimation, which could be applied to avoid the problem of discontinuous regression target. Our novel method is called Object-wise Point-guided Localization Detector (OPLD). Continuously, a new prediction of center-point is introduced to refine the results, as the truncation problem caused by the cut graph. Lastly, in order to figure the problem of inconsistency between the localization quality and the classification score, both the endpoint scores and the classification score are adopted weighting as a result score. Experimental results are based on two widely used datasets, i.e., DOTA and HRSC2016. OPLD achieve 76.43% mAP and 78.35% mAP in OBB and horizontal bounding boxes tasks of DOTA-v1.0, which achieves state-of-the-art performance, respectively. Project page at https://github.com/yf19970118/OPLD-Pytorch.
The differential impact of corporate blockchain-development as conditioned by sentiment and financial desperation

This paper investigates how companies can utilise Twitter social media-derived sentiment as a method of generating short-term corporate value from statements based on initiated blockchain-development. Results indicate that investors were subjected to a very sophisticated form of asymmetric information designed to propel sentiment and market euphoria, that translates into increased access to leverage on the part of speculative firms. Technological-development firms are found to financially behave in a profoundly different fashion to reactionary-driven firms which have no background in ICT technological development, and who experience an estimated increased one-year probability of default of 170 bps. Rating agencies are found to have under-estimated the risk on-boarded by these speculative firms, failing to identify that they should be placed under an increased degree of scrutiny. Unfiltered market sentiment information, regulatory unpreparedness and mis-pricing by trusted market observers has resulted in a situation where investors and lenders have been compromised by direct exposure to an asset class becoming known for law-breaking activity, financial losses and frequent reputational damage.
Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks

Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied. The trust information in social networks among users is an important factor for improving recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. However, the prediction performance of matrix factorization based methods largely depends on the matrixes initialization of users and items. To address this challenge, we develop a novel trust-aware approach based on deep learning to alleviate the initialization dependence. First, we propose two deep matrix factorization (DMF) techniques, i.e., linear DMF and non-linear DMF to extract features from the user-item rating matrix for improving the initialization accuracy. The trust relationship is integrated into the DMF model according to the preference similarity and the derivations of users on items. Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix factorized from the user-item rating matrix. The community regularization is integrated in the joint optimization function to take neighbours’ effects into consideration. The results of DMF are applied to initialize the updating variables of Deep-MDAE in order to further improve the recommendation performance. Finally, we validate that the proposed approach outperforms state-of-the-art baselines for recommendation, especially for the cold-start users.
The Quantum software lifecycle

Quantum computing is an emerging paradigm that enables to solve a variety of problems more efficiently than it is possible on classical computers. As the first quantum computers are available, quantum algorithms can be implemented and executed on real quantum hardware. However, the capabilities of today’s quantum computers are very limited and quantum computations are always disturbed by some error. Thus, further research is needed to develop or improve quantum algorithms, quantum computers, or required software tooling support. Due to the interdisciplinary nature of quantum computing, a common understanding of how to develop and execute a quantum software application is needed. However, there is currently no methodology or lifecycle comprising all relevant phases that can occur during the development and execution process. Hence, in this paper, we introduce the quantum software lifecycle consisting of ten phases a gate-based quantum software application should go through. We analyze the purpose of each phase, the available methods and tools that can be applied, and the open problems or research questions. Therefore, the lifecycle can be used as a baseline for discussions and future research.