Reviewerly/exHarmony huggingface dataset:
From Lost to Found

Design sharing sites provide UI designers with a platform to share their works and also an opportunity to get inspiration from others' designs. To facilitate management and search of millions of UI design images, many design sharing sites adopt collaborative tagging systems by distributing the work of categorization to the community. However, designers often do not know how to properly tag one design image with compact textual description, resulting in unclear, incomplete, and inconsistent tags for uploaded examples which impede retrieval, according to our empirical study and interview with four professional designers. Based on a deep neural network, we introduce a novel approach for encoding both the visual and textual information to recover the missing tags for existing UI examples so that they can be more easily found by text queries. We achieve 82.72% accuracy in the tag prediction. Through a simulation test of 5 queries, our system on average returns hundreds more results than the default Dribbble search, leading to better relatedness, diversity and satisfaction.
Ensemble model output statistics for the separation of direct and diffuse components from 1-min global irradiance

Separation models split diffuse and direct components of solar radiation from the global horizontal radiation. At the moment, all separation models only issue predictions that are deterministic (as opposed to probabilistic). Since the best prediction is necessarily probabilistic, a parametric post-processing framework called the ensemble model output statistics (EMOS) is introduced in this paper, to make probabilistic predictions. EMOS takes the diffuse fractions predicted by an ensemble of existing 1-min separation models, and outputs a predictive distribution, with parameters optimized by maximum likelihood estimation. Clearly, the EMOS-based separation modeling goes beyond the current literature, in terms of uncertainty quantification. Eight popular separation models from the literature, with different architectures, are used to demonstrate the predictive power of EMOS. Using 1-min high-quality radiometric data from seven stations in the USA and four stations in Europe, it is found that Yang2 is the best stand-alone model with an average RMSE of 21.8%, in terms of direct normal irradiance prediction, contrasting the 26.3% of the previously reported best model, namely, Engerer2. On the other hand, the EMOS post-processed predictions have an average RMSE of 20.8%, which is lower than that of the best stand-alone model. Moreover, EMOS is shown superior to simple model averaging, in terms of continuous ranked probability score and ignorance score.
Dynamic Heterogeneous Graph Neural Network for Real-time Event Prediction

Customer response prediction is critical in many industrial applications such as online advertising and recommendations. In particular, the challenge is greater for ride-hailing platforms such as Uber and DiDi, because the response prediction models need to consider historical and real-time event information in the physical environment, such as surrounding traffic and supply and demand conditions. In this paper, we propose to use dynamically constructed heterogeneous graph for each ongoing event to encode the attributes of the event and its surroundings. In addition, we propose a multi-layer graph neural network model to learn the impact of historical actions and the surrounding environment on the current events, and generate an effective event representation to improve the accuracy of the response model. We investigate this framework to two practical applications on the DiDi platform. Offline and online experiments show that the framework can significantly improve prediction performance. The framework has been deployed in the online production environment and serves tens of millions of event prediction requests every day.
Cyber Risk in Health Facilities: A Systematic Literature Review

The current world challenges include issues such as infectious disease pandemics, environmental health risks, food safety, and crime prevention. Through this article, a special emphasis is given to one of the main challenges in the healthcare sector during the COVID-19 pandemic, the cyber risk. Since the beginning of the Covid-19 pandemic, the World Health Organization has detected a dramatic increase in the number of cyber-attacks. For instance, in Italy the COVID-19 emergency has heavily affected cybersecurity; from January to April 2020, the total of attacks, accidents, and violations of privacy to the detriment of companies and individuals has doubled. Using a systematic and rigorous approach, this paper aims to analyze the literature on the cyber risk in the healthcare sector to understand the real knowledge on this topic. The findings highlight the poor attention of the scientific community on this topic, except in the United States. The literature lacks research contributions to support cyber risk management in subject areas such as Business, Management and Accounting; Social Science; and Mathematics. This research outlines the need to empirically investigate the cyber risk, giving a practical solution to health facilities. Keywords: cyber risk; cyber-attack; cybersecurity; computer security; COVID-19; coronavirus;information technology risk; risk management; risk assessment; health facilities; healthcare sector;systematic literature review; insurance
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data

In a lot of real-world data mining and machine learning applications, data are provided by multiple providers and each maintains private records of different feature sets about common entities. It is challenging to train these vertically partitioned data effectively and efficiently while keeping data privacy for traditional data mining and machine learning algorithms. In this paper, we focus on nonlinear learning with kernels,and propose a federated doubly stochastic kernel learning (FDSKL) algorithm for vertically partitioned data. Specifically, we use random features to approximate the kernel mapping function and use doubly stochastic gradients to update the solutions, which are all computed federatedly without the disclosure of data. Importantly, we prove that FDSKL has a sublinear convergence rate, and can guarantee the data security under the semi-honest assumption. Extensive experimental results on a variety of benchmark datasets show that FDSKL is significantly faster than state-of-the-art federated learning methods when dealing with kernels, while retaining the similar generalization performance.
HGMF: Heterogeneous Graph-based Fusion for Multimodal Data with Incompleteness

With the advances in data collection techniques, large amounts of multimodal data collected from multiple sources are becoming available. Such multimodal data can provide complementary information that can reveal fundamental characteristics of real-world subjects. Thus, multimodal machine learning has become an active research area. Extensive works have been developed to exploit multimodal interactions and integrate multi-source information. However, multimodal data in the real world usually comes with missing modalities due to various reasons, such as sensor damage, data corruption, and human mistakes in recording. Effectively integrating and analyzing multimodal data with incompleteness remains a challenging problem. We propose a Heterogeneous Graph-based Multimodal Fusion (HGMF) approach to enable multimodal fusion of incomplete data within a heterogeneous graph structure. The proposed approach develops a unique strategy for learning on incomplete multimodal data without data deletion or data imputation. More specifically, we construct a heterogeneous hypernode graph to model the multimodal data having different combinations of missing modalities, and then we formulate a graph neural network based transductive learning framework to project the heterogeneous incomplete data onto a unified embedding space, and multi-modalities are fused along the way. The learning framework captures modality interactions from available data, and leverages the relationships between different incompleteness patterns. Our experimental results demonstrate that the proposed method outperforms existing graph-based as well as non-graph based baselines on three different datasets.
Blind Image Quality Assessment by Natural Scene Statistics and Perceptual Characteristics

Opinion-unaware blind image quality assessment (OU BIQA) refers to establishing a blind quality prediction model without using the expensive subjective quality scores, which is a highly promising direction in the BIQA research. In this article, we focus on OU BIQA and propose a novel OU BIQA method. Specifically, in our proposed method, we deeply investigate the natural scene statistics (NSS) and the perceptual characteristics of the human brain for visual perception. Accordingly, a set of quality-aware NSS and perceptual characteristics-related features are designed to characterize the image quality effectively. For inferring the image quality, we learn a pristine multivariate Gaussian (MVG) model on a collection of pristine images, which serves as the reference information for quality evaluation. At last, the quality of a new given image is defined by measuring the divergence between its MVG model and the learned pristine MVG model. Thorough experiments performed on seven popular image databases demonstrate that the proposed OU BIQA method delivers superior performance to the state-of-the-art OU BIQA methods. The Matlab source code of the proposed method will be made publicly available at https://github.com/YT2015?tab=;repositories.
A Novel Simulated Annealing Based Strategy for Balanced UAV Task Assignment and Path Planning

The unmanned aerial vehicle (UAV) has drawn increasing attention in recent years, especially in executing tasks such as natural disaster rescue and detection, and battlefield cooperative operations. Task assignment and path planning for multiple UAVs in the above scenarios are essential for successful mission execution. But, effectively balancing tasks to better excavate the potential of UAVs remains a challenge, as well as efficiently generating feasible solutions from the current one in constrained explosive solution spaces with the increase in the scale of optimization problems. This paper proposes an efficient approach for task assignment and path planning with the objective of balancing the tasks among UAVs and achieving satisfactory temporal resolutions. To be specific, we add virtual nodes according to the number of UAVs to the original model of the vehicle routing problem (VRP), thus make it easier to form a solution suitable for heuristic algorithms. Besides, the concept of the universal distance matrix is proposed to transform the temporal constraints to spatial constraints and simplify the programming model. Then, a Swap-and-Judge Simulated Annealing (SJSA) algorithm is therefore proposed to improve the efficiency of generating feasible neighboring solutions. Extensive experimental and comparative studies on different scenarios demonstrate the efficiency of the proposed algorithm compared with the exact algorithm and meta-heuristic algorithms. The results also inspire us about the characteristics of a population-based algorithm in solving combinatorial discrete optimization problems.
Artificial intelligence tools and perspectives of university librarians: An overview

The main purpose of this paper is to assess and examine the possible application of Artificial Intelligence (AI) tools in Pakistani academic libraries, particularly those areas of library technical and library user services where AI could be applied in the near future. A secondary purpose is to bring the library perspective on AI to the forefront of the scholarly world. This is a self-exploratory study, in which a qualitative approach interview has been conducted with 10 chief librarians/library heads (5 public + 5 private sectors) from universities regarding their views on the adoption of artificial intelligence tools in Pakistani academic libraries. Results are tabulated in a descriptive format. Librarians are aware of AI technologies. Services based on Natural Language Processing (NLP) are used in libraries, e.g. Google Assistant, Voice Searching, and Google Translate. Pattern recognition methods, such as text data mining, are also used to retrieve library material and conduct online searching. Big data is accessed via services such as cloud computing, OneDrive, and Google Drive. There is a very low level of awareness of robotics and chatbots. This study provides librarians with suggestions as to how AI tools could be used in libraries which either have yet to adopt AI technologies or wish to implement more advanced tools. Pakistani library schools could collaborate with computer science departments to establish AI Labs in the respective library and information science (LIS) departments/libraries. AI challenges funding and technological skills are the key problem to implement with AI in the University Libraries.
EraseNet: End-to-End Text Removal in the Wild

Scene text removal has attracted increasing research interests owing to its valuable applications in privacy protection, camera-based virtual reality translation, and image editing. However, existing approaches, which fall short on real applications, are mainly because they were evaluated on synthetic or unrepresentative datasets. To fill this gap and facilitate this research direction, this paper proposes a real-world dataset called SCUT-EnsText that consists of 3,562 diverse images selected from public scene text reading benchmarks, and each image is scrupulously annotated to provide visually plausible erasure targets. With SCUT-EnsText, we design a novel GANbased model termed EraseNet that can automatically remove text located on the natural images. The model is a two-stage network that consists of a coarse-erasure sub-network and a refinement sub-network. The refinement sub-network targets improvement in the feature representation and refinement of the coarse outputs to enhance the removal performance. Additionally, EraseNet contains a segmentation head for text perception and a local-global SN-Patch-GAN with spectral normalization (SN) on both the generator and discriminator for maintaining the training stability and the congruity of the erased regions. A sufficient number of experiments are conducted on both the previous public dataset and the brand-new SCUT-EnsText. Our EraseNet significantly outperforms the existing state-of-the-art methods in terms of all metrics, with remarkably superior higherquality results. The dataset and code will be made available at https://github.com/HCIILAB/SCUT-EnsText.
An optimally weighted user- and item-based collaborative filtering approach to predicting baseline data for Friedreich’s Ataxia patients

In this paper, a modified collaborative filtering (MCF) algorithm with improved performance is developed for recommendation systems with application in predicting baseline data of Friedreich’s Ataxia (FRDA) patients. The proposed MCF algorithm combines the individual merits of both the user-based collaborative filtering (UBCF) method and the item-based collaborative filtering (IBCF) method, where both the positively and negatively correlated neighbors are taken into account. The weighting parameters are introduced to quantify the degrees of utilizations of the UBCF and IBCF methods in the rating prediction, and the particle swarm optimization algorithm is applied to optimize the weighting parameters in order to achieve an adequate tradeoff between the positively and negatively correlated neighbors in terms of predicting the rating values. To demonstrate the prediction performance of the proposed MCF algorithm, the developed MCF algorithm is employed to assist with the baseline data collection for the FRDA patients. The effectiveness of the proposed MCF algorithm is confirmed by extensive experiments and, furthermore, it is shown that our algorithm outperforms some conventional approaches.
Towards real-time demand-aware sequential POI recommendation

Next point-of-interest (POI) recommendation has gained growing attention in recent years due to the emergence of location-based social networks (LBSN) services. Most existing approaches focus on learning user’s preferences to POIs from check-in records and recommend a POI to visit next given his/her previously visited POIs. However, the user’s visiting behavior is not only driven by user preferences in real-world scenarios. The real-time demand is another crucial factor to determine the user’s visiting behaviors, which is usually neglected in established approaches. In this paper, we propose a new next point-of-interest (POI) recommendation method, called DSPR, by exploring user’s preferences and real-time demand simultaneously. To model the real-time demand, different kinds of contextual information are exploited, such as absolute time, POI–POI transition time/distance, and the types of POIs. By incorporating user’s preferences, these contextual factors are further modeled and learned automatically with an attention-based recurrent neural network model to support the final next POI recommendation. Experiments on three real-world check-in datasets show that DSPR has better recommendation performance compared with many state-of-the-art methods.
Artificial intelligence for photonics and photonic materials

Artificial intelligence (AI) is the most important new methodology in scientific research since the adoption of quantum mechanics and it is providing exciting results in numerous fields of science and technology. In this review we summarize research and discuss future opportunities for AI in the domains of photonics, nanophotonics, plasmonics and photonic materials discovery, including metamaterials.
Text extraction using OCR: A Systematic Review

In the digital era, almost everything is automated, and information is stored and communicated in digital forms. However, there are several situations where the data is not digitized, and it might become essential to extract text from those to store in digitized form. The latest technology such as Text recognition software has completely revolutionized the process of text extraction using Optical Character Recognition. Therefore, this paper introduces the concept, explains the process of extraction, presents the latest techniques, technologies, and current research in the area. Such a review will help other researchers in the field to get an overview of the technology.
The future of precision medicine: towards a more predictive personalized medicine

Precision medicine can be defined as personalized medicine enhanced by technology. In the past, medicine has, in some cases, been personalized. For example, some drugs are dosed on an individualized basis based on age, body-mass index, comorbidities and other clinical parameters. However, overall, medicine has largely followed the 'one-size-fits-all' paradigm as exemplified in the treatment of essential hypertension or type 2 diabetes mellitus. What has changed in the past few years is that technologies such as high throughput sequencing, mass spectrometry, microfluidics, and imaging can help conduct a multitude of complex measurements on clinical samples. Aided by analytics, these technologies have been providing an increasingly detailed picture of molecular and cellular alterations underlying numerous diseases and have revealed tremendous variability between individuals and patients at the molecular and cellular level. These findings have motivated a more personalized or 'precision' approach to medicine, in which molecular and cellular markers help tailor patient management to each individual. Here we provide an overview of the key factors driving adoption of precision medicine and highlight current research that may soon make precision medicine more predictive.
Poly-GAN: Multi-conditioned GAN for fashion synthesis

We present Poly-GAN, a novel conditional GAN architecture that is motivated by Fashion Synthesis, an application where garments are automatically placed on images of human models at an arbitrary pose. Poly-GAN allows conditioning on multiple inputs and is suitable for many tasks, including image alignment, image stitching and inpainting. Existing fashion synthesis methods have a similar pipeline where three different networks are used to first align garments with the human pose, then perform stitching of the aligned garment and finally refine the results. Poly-GAN is the first instance where a common architecture is used to perform all three tasks. Our novel architecture enforces the conditions at all layers of the encoder and utilizes skip connections from the coarse layers of the encoder to the respective layers of the decoder. Poly-GAN is able to perform a spatial transformation of the garment based on the RGB skeleton of the model at an arbitrary pose. Additionally, Poly-GAN can perform image stitching, regardless of the garment orientation, and inpainting on the garment mask when it contains irregular holes. Our system achieves state-of-the-art quantitative results on Structural Similarity Index metric and Inception Score metric using the DeepFashion dataset.
Application of interval type-2 fuzzy logic systems to gas turbine fault diagnosis

Several approaches have been employed for gas turbine Fault Detection and Identification (FDI), since a reliable FDI system with minimum false alarm rate can effectively reduce maintenance cost and downtime. This paper introduces the application of Interval Type-2 Fuzzy Logic Systems (IT2FLSs) to gas turbine fault diagnosis for the first time. The proposed FDI system is composed of a bank of IT2FLSs, trained for state detection and health assessment of an industrial gas turbine at various operating conditions. For this purpose, train and test data are generated by applying mechanical fault signatures to gas turbine's mathematical model. Fuzzy Rule Base is then developed by means of Interval Type-2 Fuzzy C-Means (IT2FCM) clustering, and parameters of the IT2FLSs are optimized using a metaheuristic algorithm. Finally, the performance of the IT2FL based FDI system is compared to several classification techniques. It is concluded that as a compromise among the objectives of online applicability, accuracy, reliability against measurement uncertainty, incipient fault diagnosis, robustness against abrupt sensor failure and generalization capacity, the proposed method demonstrates a promising performance.
Improving data access democratizes and diversifies science

Significance Data access is critical to empirical research, but past work on open access is largely restricted to the life sciences and has not directly analyzed the impact of data access restrictions. We analyze the impact of improved data access on the quantity, quality, and diversity of scientific research. We focus on the effects of a shift in the accessibility of satellite imagery data from Landsat, a NASA program that provides valuable remote-sensing data. Our results suggest that improved access to scientific data can lead to a large increase in the quantity and quality of scientific research. Further, better data access disproportionately enables the entry of scientists with fewer resources, and it promotes diversity of scientific research.