Reviewerly/exHarmony huggingface dataset:
CanvasVAE: Learning to Generate Vector Graphic Documents

Vector graphic documents present visual elements in a resolution free, compact format and are often seen in creative applications. In this work, we attempt to learn a generative model of vector graphic documents. We define vector graphic documents by a multi-modal set of attributes associated to a canvas and a sequence of visual elements such as shapes, images, or texts, and train variational auto-encoders to learn the representation of the documents. We collect a new dataset of design templates from an online service that features complete document structure including occluded elements. In experiments, we show that our model, named CanvasVAE, constitutes a strong baseline for generative modeling of vector graphic documents.
Improving Chinese Named Entity Recognition by Large-Scale Syntactic Dependency Graph

Named entity recognition (NER) isa preliminary task in natural language processing (NLP). Recognizing Chinese named entities from unstructured texts is challenging due to the lack of word boundaries. Even if performing Chinese Word Segmentation (CWS) could help to determine word boundaries, it is still difficult to determine which words should be clustered together for entity identification, since entities are often composed of multiple-segmented words. As dependency relationships between segmented words could help to determine entity boundaries, it is crucial to employ information related to syntactic dependency relationships to improve NER performance. In this paper, we propose a novel NER model to learn information about syntactic dependency graphs with graph neural networks, and merge learned information into the classic Bidirectional Long Short-Term Memory (BiLSTM) - Conditional Random Field (CRF) NER scheme. In addition, we extract various kinds of task-specific hidden information from multiple CWS and part-of-speech (POS) tagging tasks, to further improve the NER model. We finally leverage multiple self-attention components to integrate multiple kinds of extracted information for named entity identification. Experimental results on three public benchmark datasets show that our model outperforms the state-of-the-art baselines in most scenarios.
Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation

Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the data sparsity and cold-start problem in recommender systems. In this paper, we focus on the Review-based Non-overlapped Recommendation (RNCDR) problem. The problem is commonly-existed and challenging due to two main aspects, i.e, there are only positive user-item ratings on the target domain and there is no overlapped user across different domains. Most previous CDR approaches cannot solve the RNCDR problem well, since (1) they cannot effectively combine review with other information (e.g., ID or ratings) to obtain expressive user or item embedding, (2) they cannot reduce the domain discrepancy on users and items. To fill this gap, we propose Collaborative Filtering with Attribution Alignment model (CFAA), a cross-domain recommendation framework for the RNCDR problem. CFAA includes two main modules, i.e., rating prediction module and embedding attribution alignment module. The former aims to jointly mine review, one-hot ID, and multi-hot historical ratings to generate expressive user and item embeddings. The later includes vertical attribution alignment and horizontal attribution alignment, tending to reduce the discrepancy based on multiple perspectives. Our empirical study on Douban and Amazon datasets demonstrates that CFAA significantly outperforms the state-of-the-art models under the RNCDR setting.
Utilising high-fidelity 3D building model for analysing the rooftop solar photovoltaic potential in urban areas

There are multiple approaches of estimating solar power generation by rooftop solar photovoltaic (PV) systems. Methods processed using GIS as well as 3D models provide the most reliable and accurate results. However, with the restriction of a limited detail 3D model area, estimations on a regional scale are hard to achieve. The regional power potential can be predicted by identifying the correlation of rooftop areas and the shadowing effect changing between the Level of Detail (LOD)1 and LOD2 models in the case study areas. This research considers the aspects and angles of the rooftops, to try and not only figure out the optimal PV installation conditions but also to make a general estimation for optimal angle, 2-axis solar tracking as well as total solar radiation received scenarios. The results of the research indicate that the optimal annual angle is 177° and 13° in aspect and slope degree, respectively, and the improvements of power generation under those three scenarios increase from about 2 to 16% in the test area, respectively. This research also showed that the power potential in partial small areas had the same tendency as the whole region even though each part of the small area had uneven city pattern. Additionally, different from most of the study, north-facing rooftops had been proved to have the strong potential to be developed as a solar power PV panel site and should not be neglected from the whole PV system.
TDPN: Texture and Detail-Preserving Network for Single Image Super-Resolution

Single image super-resolution (SISR) using deep convolutional neural networks (CNNs) achieves the state-of-the-art performance. Most existing SISR models mainly focus on pursuing high peak signal-to-noise ratio (PSNR) and neglect textures and details. As a result, the recovered images are often perceptually unpleasant. To address this issue, in this paper, we propose a texture and detail-preserving network (TDPN), which focuses not only on local region feature recovery but also on preserving textures and details. Specifically, the high-resolution image is recovered from its corresponding low-resolution input in two branches. First, a multi-reception field based branch is designed to let the network fully learn local region features by adaptively selecting local region features in different reception fields. Then, a texture and detail-learning branch supervised by the textures and details decomposed from the ground-truth high resolution image is proposed to provide additional textures and details for the super-resolution process to improve the perceptual quality. Finally, we introduce a gradient loss into the SISR field and define a novel hybrid loss to strengthen boundary information recovery and to avoid overly smooth boundary in the final recovered high-resolution image caused by using only the MAE loss. More importantly, the proposed method is model-agnostic, which can be applied to most off-the-shelf SISR networks. The experimental results on public datasets demonstrate the superiority of our TDPN on most state-of-the-art SISR methods in PSNR, SSIM and perceptual quality. We will share our code on https://github.com/tocaiqing/TDPN.
A Tensor-Based Truthful Incentive Mechanism for Blockchain-Enabled Space-Air-Ground Integrated Vehicular Crowdsensing

Space-Air-Ground Integrated Network (SAGIN) as an efficient newly integration network could provide more comprehensive network services to meet the multifarious quality of service requirements in different Intelligent Transportation Systems (ITS). By taking advantage of SAGIN, Space-Air-Ground Integrated Vehicular Crowdsensing (SAGI-VCS) would have great potential and the services regarding ITS could be facilitated. However, centralized SAGI-VCS is usually vulnerable to malicious attacks and the trust issues are one of the main reasons that hinder its further development. Blockchain as a distributed hyperledger shows a vital potential to solve the trust problem of multiple participants who do not trust each other and tackle the security issues in SAGI-VCS. Additionally, selfishness is another factor that prevents vehicles from participating in SAGI-VCS. The vast majority of existing incentives for vehicular crowdsensing only focus on the terrestrial networks which cannot be directly used in SAGI-VCS. Meanwhile, the redundant winner phenomenon and the multi-attributes of participants are less considered by them. Toward this end, we first illustrate a blockchain-enabled service architecture for SAGI-VCS and then construct a unified representation model. Afterwards, a tensor computing based truthful incentive mechanism TensorBC for blockchain-enabled SAGI-VCS is proposed to motivate vehicles to participate in completing tasks, ensure the security of the whole process and maximize the social welfare. TensorBC not only can eliminate the redundant winner phenomenon, but also can guarantee the economic properties such as truthfulness, individual rationality and profitability. Finally, both the rigorous theoretical analysis and extensive experimental results show that TensorBC could achieve a better performance.
State Graph Reasoning for Multimodal Conversational Recommendation

Conversational recommendation system (CRS) attracts increasing attention in various application domains such as retail and travel. It offers an effective way to capture users' dynamic preferences with multi-turn conversations. However, most current studies center on the recommendation aspect while over-simplifying the conversation process. The negligence of complexity in data structure and conversation flow hinders their practicality and utility. In reality, there exist various relationships among slots and values, while users' requirements may dynamically adjust or change. Moreover, the conversation often involves visual modality to facilitate the conversation. These actually call for a more advanced internal state representation of the dialogue and a proper reasoning scheme to guide the decision making process. In this paper, we explore multiple facets of multimodal conversational recommendation and try to address the above mentioned challenges. In particular, we represent the structured back-end database as a multimodal knowledge graph which captures the various relations and evidence in different modalities. The user preferences expressed via conversation utterances will then be gradually updated to the state graph with clear polarity. Based on these, we train an end-to-end State Graph-based Reasoning model SGR to perform reasoning over the whole state graph. The prediction of our proposed model benefits from the structure of the graph. It not only allows for zero-shot reasoning for items unseen in training conversations, but also provides a natural way to explain the policies. Extensive experiments show that our model achieves better performance compared with existing methods.
3D data computation and visualization

This editorial introduces the Virtual Special Issue: “3D data computation and visualization” that was handled in 2021.
Secure and transparent pharmaceutical supply chain using permissioned blockchain network

This paper proposes a secure and transparent pharmaceutical supply chain system using a permissioned blockchain network. This work focuses on (i) Enhancing the security of origin information across the supply chain with proper identification and authentication among the supplier, distributor, and other members of the system, (ii) Secure storing of the Supply Drugs Information (SDI) in the blockchain in a distributed manner, (iii) Traceability and transparent sharing of information across supply chain system. This proposed work also performs automatic transactions between suppliers and the distributors, tracking the provenance, adequate verification, and reducing supply chains’ risk. The proposed system has been implemented using Hyperledger fabric and evaluated using Hyperledger Calliper to test its performance. Finally, we analyzed the proposed work's performance in terms of throughput, transaction latency, and network load. The results have shown that better transaction processing power and security than other existing systems of the blockchain.
EMRI: A scalable and secure Blockchain-based IoMT framework for healthcare data transaction

Internet of medical things (IoMT) is one of the latest healthcare monitoring and data processing improvements in the medical industry around the globe. The internet of medical things (IoMT) provides a smart integrated platform for online healthcare services and securities for electronic health records. With the help of the Internet IoMT is capable of remotely monitoring, collecting, and processing patient's healthcare data without physical movement to the hospitals. However with the centralized structure of the IoT platform invites scalability, privacy, and security concerns. Blockchain is a decentralized distributed ledger technology that enables a peer-to-peer network for communication of non-trusted members around the globe. The decentralized and immutable record-keeping feature of Blockchain technology has the potential to increase the scalability, security, and privacy of healthcare data. With the powerful features of Blockchain technology in this paper, we proposed a smart contract-based novel framework named Electronic Medical Record Infrastructure (EMRI) for privacy preservation of patents information and enabling scalable and secure communication. We also outline the challenges, advantages, and future direction of this framework.
Image visualization: Dynamic and static images generate users’ visual cognitive experience using eye-tracking technology

This paper discusses the influence of dynamic images and traditional static images on user perception in web interface visualizations of products. First, thirty graduate students in industrial design participated in an eye-tracking experiment, performing visual imagery (VI) tasks of the product images with different presentation formats and durations. The results of eye movement experiment show that the visual cognitive effect was better for the dynamic images than the static image, and the efficiency of visual search was improved. However, the emotional experience of viewing dynamic images was substantially affected by the presentation time. Secondly, there were significant differences in the cognitive level and emotional experience of the users between the dynamic images with different presentation times. The optimal perception experience was observed at a presentation time of 9000 ms, indicating that the subjective responses of the users’ questionnaire survey did not represent the actual cognitive needs of the users. This study provides a scientific basis for product designers to achieve an improved browsing experience of their products.
Learning task-specific discriminative embeddings for few-shot image classification

Recently, few-shot learning has attracted more and more attention. Generally, the fine-tuning-based few-shot learning framework contains two stages: i) In the pre-training stage, using base data to train the feature extractor; ii) In the meta-testing stage, using a well-trained feature extractor to extract embedding features of novel data and designing a base learner to predict the labels. Due to the diverse categories of base and novel data, it is challenging for the feature extractor trained in the pre-training stage to adapt to novel data, which will result in an embedding-mismatch problem. This paper proposes Task-specific Discriminative Embeddings for Few-shot Learning (TDE-FSL) to solve the embedding-mismatch problem. Specifically, we embed the dictionary learning method into the few-shot learning framework to map the feature embeddings to a more discriminative subspace to adapt to the specific task. Moreover, we extend the self-training framework to our approach to fully utilize the unlabeled data. Finally, we evaluate the TDE-FSL on five benchmark image datasets, such as mini-Imagenet, tiered-Imagenet, CIFAR-FS, FC100, and CUB dataset. The experimental results show that the performance of our proposed TDE-FSL achieves a significant improvement.
LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion

The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi–Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB.
The Digital Divide of Know-How and Use of Digital Technologies in Higher Education: The Case of a College in Latin America in the COVID-19 Era

To determine whether or not digital inequalities exist, the frequency, duration, satisfaction, importance, and perceived competence of eighteen groups of digital tools during the COVID-19 pandemic confinement were examined. An online survey was administered to 2882 Latin American university students (49% female; M = 21.3 years). The following items are checked: (1) increased digital inequalities during the pandemic; (2) adequate reliability and validity of the Digital Technology Survey (DTS) instrument; (3) patterns of digital inequalities to the detriment of men, lower strata and unemployed people; and (4) evidence that the importance of digital activities as a causal factor on satisfaction with such digital activities as an outcome is mediated by the purpose of use and communication recipients, but not by strata or employment status, nor moderated by gender. The results are discussed in the light of previous studies, the limitations of the study and future perspectives.
EvoSTGAT: Evolving spatiotemporal graph attention networks for pedestrian trajectory prediction

Predicting pedestrian trajectory is an essential task in many applications. While previous studies based on graphs seek to model spatiotemporal information among pedestrian interactions, most of them neglect the recursive and continuous relations between neighboring time points. In this paper, we propose an evolving spatiotemporal graph attention network to predict future trajectories of pedestrians. This model considers the evolving relations of social interactions between contiguous time points and uses coordinates. The interaction is modeled by an evolving and dynamic attention mechanism. The social influence of each pedestrians of current frame is evolved from that of last frame and will be utilized to generate the social influence of next frame. The proposed model was tested on two challenging datasets and the experimental results prove the strength of the model.
Unified Performance Evaluation Method for Perceptual Image Hashing

In recent decades, a large number of perceptual image hashing schemes have been designed to secure the authenticity and integrity of digital images. However, the feasible criterion to evaluate the performances of hashing schemes has not been developed yet. To this end, a unified performance evaluation method for perceptual image hashing schemes is proposed in this paper. The proposed evaluation method contains six modules: robustness, discrimination, tampering detection, security, computational efficiency and hash length. The order relationship analysis (ORA) is employed to assign the score proportion of each module in accordance with the relative importance of performance, which allows the customizability of user. The real scores of modules and the outputted final score can reflect the performances of perceptual image hashing schemes intuitively and convincingly. Experimental results demonstrate that the proposed evaluation method is practical and effective for the complete and comprehensive evaluation of perceptual image hashing schemes.
Dynamic and adaptive fault-tolerant asynchronous federated learning using volunteer edge devices

The number of devices, from smartphones to IoT hardware, interconnected via the Internet is growing all the time. These devices produce a large amount of data that cannot be analyzed in any data center or stored in the cloud, and it might be private or sensitive, thus precluding existing classic approaches. However, studying these data and gaining insights from them is still of great relevance to science and society. Recently, two related paradigms try to address the above problems. On the one hand, edge computing (EC) suggests to increase processing on edge devices. On the other hand, federated learning (FL) deals with training a shared machine learning (ML) model in a distributed (non-centralized) manner while keeping private data locally on edge devices. The combination of both is known as federated edge learning (FEEL). In this work, we propose an algorithm for FEEL that adapts to asynchronous clients joining and leaving the computation. Our research focuses on adapting the learning when the number of volunteers is low and may even drop to zero. We propose, implement, and evaluate a new software platform for this purpose. We then evaluate its results on problems relevant to FEEL. The proposed decentralized and adaptive system architecture for asynchronous learning allows volunteer users to yield their device resources and local data to train a shared ML model. The platform dynamically self-adapts to variations in the number of collaborating heterogeneous devices due to unexpected disconnections (i.e., volunteers can join and leave at any time). Thus, we conduct comprehensive empirical analysis in a static configuration and highly dynamic and changing scenarios. The public open-source platform enables interoperability between volunteers connected using web browsers and Python processes. We show that our platform adapts well to the changing environment getting a numerical accuracy similar to today’s configurations using a given number of homogeneous (hardware and software) computers as a static platform for learning. We demonstrate the fault-tolerance of the platform in self-recovering from unexpected disconnections of volunteer devices. We then prove that EC, coupled with FL, can lead to scientific tools that can be practical involving real users for final competitive numerical results in real problems for science and society.