Reviewerly/exHarmony huggingface dataset:
Hierarchical dense recursive network for image super-resolution

Image super-resolution (SR) techniques with deep convolutional network (CNN) have achieved significant improvements compared to previous shallow-learning-based methods. Especially for dense connection based networks, these methods have yielded unprecedented achievements but bring the higher complexity and more parameters. To this end, this paper considers both reconstruction performance and efficiency, and advocates a novel hierarchical dense connection network (HDN) for image SR. First of all, we construct a hierarchical dense residual block (HDB) to promote the feature representation while saving the memory footprint with a hierarchical matrix structure design. In this way, it can provide additional interleaved pathways for information fusion and gradient optimization but with a shallower depth compare to the previous networks. In particular, a group of convolutional layers with small size (1 × 1) are embedded in HDB, releasing the computational burden and parameters by rescaling the feature dimensions. Furthermore, HDBs are connected to each other in a sharing manner, thereby allowing the network to fuse the features in different stages. At the final, the multi-scale features from these HDBs are integrated into global fusion module (GFM) for a global fusion and representation, and then the final profile-enriched residual map is obtained by realigning and sub-pixel upsampling the fusion maps. Extensive experimental results on benchmark datasets and really degraded images show that our model outperforms the state-of-the-art methods in terms of quantitative indicators and realistic visual effects, as well as enjoys a fast and accurate reconstruction.
Graph Structured Network for Image-Text Matching

Image-text matching has received growing interest since it bridges vision and language. The key challenge lies in how to learn correspondence between image and text. Existing works learn coarse correspondence based on object co-occurrence statistics, while failing to learn fine-grained phrase correspondence. In this paper, we present a novel Graph Structured Matching Network (GSMN) to learn fine-grained correspondence. The GSMN explicitly models object, relation and attribute as a structured phrase, which not only allows to learn correspondence of object, relation and attribute separately, but also benefits to learn fine-grained correspondence of structured phrase. This is achieved by node-level matching and structure-level matching. The node-level matching associates each node with its relevant nodes from another modality, where the node can be object, relation or attribute. The associated nodes then jointly infer fine-grained correspondence by fusing neighborhood associations at structure-level matching. Comprehensive experiments show that GSMN outperforms state-of-the-art methods on benchmarks, with relative Recall@1 improvements of nearly 7% and 2% on Flickr30K and MSCOCO, respectively. Code will be released at: https://github.com/CrossmodalGroup/GSMN.
Reinforcement learning for intelligent healthcare applications: A survey

Discovering new treatments and personalizing existing ones is one of the major goals of modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the realization of advanced intelligent systems able to learn about clinical treatments and discover new medical knowledge from the huge amount of data collected. Reinforcement Learning (RL), which is a branch of Machine Learning (ML), has received significant attention in the medical community since it has the potentiality to support the development of personalized treatments in accordance with the more general precision medicine vision. This report presents a review of the role of RL in healthcare by investigating past work, and highlighting any limitations and possible future contributions.
The impact of social capital and collaborative knowledge creation on e-business proactiveness and organizational agility in responding to the COVID-19 crisis

The purpose of this study is to explore the role of social capital and collaborative knowledge creation in achieving e-business proactiveness in responding to the COVID-19 crisis. An online survey was used to collect data from industries that had to continue working during the crisis, such as the pharmaceutical and cleaning materials sectors. The sample consisted of 198 managers. The findings show that social capital and collaborative knowledge creation have a significant role in achieving e-business proactiveness in responding to the pandemic. The results also show the positive impact of collaborative knowledge creation and e-business proactiveness on organizational agility during the crisis. The present study opens broad horizons for the exploration of emerging themes in information technology studies, including the role of collaborative knowledge creation and e-business proactiveness and their impact on organizational agility in responding to global pandemics. An understanding of the pivotal impact of social capital and collaborative knowledge creation on e-business proactiveness provides managers with valuable insights into managing the pressures of pandemics.
An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning

Speaker identity is one of the important characteristics of human speech. In voice conversion, we change the speaker identity from one to another, while keeping the linguistic content unchanged. Voice conversion involves multiple speech processing techniques, such as speech analysis, spectral conversion, prosody conversion, speaker characterization, and vocoding. With the recent advances in theory and practice, we are now able to produce human-like voice quality with high speaker similarity. In this article, we provide a comprehensive overview of the state-of-the-art of voice conversion techniques and their performance evaluation methods from the statistical approaches to deep learning, and discuss their promise and limitations. We will also report the recent Voice Conversion Challenges (VCC), the performance of the current state of technology, and provide a summary of the available resources for voice conversion research.
PypeIt: The Python Spectroscopic Data Reduction Pipeline

PypeIt is a Python package for semi-automated reduction of astronomical, spectroscopic data.Its algorithms build on decades-long development of previous data reduction pipelines by the developers (Bernstein et al., 2015;Bochanski et al., 2009).The reduction procedure -including a complete list of the input parameters and available functionality -is provided as online documentation hosted by Read the Docs, which is regularly updated.It is a set of commands designed to perform the reduction without any additional coding.In what follows, we provide a brief description of the algorithms, but refer the interested reader to the online documentation for the most up-to-date information.
Feature selection using bare-bones particle swarm optimization with mutual information

Feature selection (FS) is an important data processing method in pattern recognition and data mining. Due to not considering characteristics of the FS problem itself, traditional particle update mechanisms and swarm initialization strategies adopted in most particle swarm optimization (PSO) limit their performance on dealing with high-dimensional FS problems. Focused on it, this paper proposes a novel feature selection algorithm based on bare bones PSO (BBPSO) with mutual information. Firstly, an effective swarm initialization strategy based on label correlation is developed, making full use of the correlation between features and class labels to accelerate the convergence of swarm. Then, in order to enhance the exploitation performance of the algorithm, two local search operators, i.e., the supplementary operator and the deletion operator, are developed based on feature relevance-redundancy. Furthermore, an adaptive flip mutation operator is designed to help particles jump out of local optimal solutions. We apply the proposed algorithm to typical datasets based on the K-Nearest Neighbor classifier (K-NN), and compare it with eleven state-of-the-art algorithms, SFS, PTA, SGA, BPSO, PSO(4-2), HPSO-LS, Binary BPSO, NaFA, IBFA, KPLS-mRMR and SMBA-CSFS. The experimental results show that the proposed algorithm can achieve a feature subset with better performance, and is a highly competitive FS algorithm.
Fortified-Chain: A Blockchain-Based Framework for Security and Privacy-Assured Internet of Medical Things With Effective Access Control

The rapid developments in the Internet of Medical Things (IoMT) help the smart healthcare systems to deliver more sophisticated real-time services. At the same time, IoMT also raises many privacy and security issues. Also, the heterogeneous nature of these devices makes it challenging to develop a common security standard solution. Furthermore, the existing cloud-centric IoMT healthcare systems depend on cloud computing for electrical health records (EHR) and medical services, which is not suggestible for a decentralized IoMT healthcare systems. In this article, we have proposed a blockchain-based novel architecture that provides a decentralized EHR and smart-contract-based service automation without compromising with the system security and privacy. In this architecture, we have introduced the hybrid computing paradigm with the blockchain-based distributed data storage system to overcome blockchain-based cloud-centric IoMT healthcare system drawbacks, such as high latency, high storage cost, and single point of failure. A decentralized selective ring-based access control mechanism is introduced along with device authentication and patient records anonymity algorithms to improve the proposed system's security capabilities. We have evaluated the latency and cost effectiveness of data sharing on the proposed system using Blockchain. Also, we conducted a logical system analysis, which reveals that our architecture-based security and privacy mechanisms are capable of fulfilling the requirements of decentralized IoMT smart healthcare systems. Experimental analysis proves that our fortified-chain-based H-CPS needs insignificant storage and has a response time in the order of milliseconds as compared to traditional centralized H-CPS while providing decentralized automated access control, security, and privacy.
Residual Feature Distillation Network for Lightweight Image Super-Resolution

Recent advances in single image super-resolution (SISR) explored the power of convolutional neural network (CNN) to achieve a better performance. Despite the great success of CNN-based methods, it is not easy to apply these methods to edge devices due to the requirement of heavy computation. To solve this problem, various fast and lightweight CNN models have been proposed. The information distillation network is one of the state-of-the-art methods, which adopts the channel splitting operation to extract distilled features. However, it is not clear enough how this operation helps in the design of efficient SISR models. In this paper, we propose the feature distillation connection (FDC) that is functionally equivalent to the channel splitting operation while being more lightweight and flexible. Thanks to FDC, we can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distillation network (RFDN). RFDN uses multiple feature distillation connections to learn more discriminative feature representations. We also propose a shallow residual block (SRB) as the main building block of RFDN so that the network can benefit most from residual learning while still being lightweight enough. Extensive experimental results show that the proposed RFDN achieves a better trade-off against the state-of-the-art methods in terms of performance and model complexity. Moreover, we propose an enhanced RFDN (E-RFDN) and won the first place in the AIM 2020 efficient super-resolution challenge. Code will be available at https://github.com/njulj/RFDN .
<i>PMRSS:</i> Privacy-Preserving Medical Record Searching Scheme for Intelligent Diagnosis in IoT Healthcare

In medical field, previous patients’ cases are extremely private as well as intensely valuable to current disease diagnosis. Therefore, how to make full use of precious cases while not leaking out patients’ privacy is a leading and promising work especially in future privacy-preserving intelligent medical period. In this article, we investigate how to securely invoke patients’ records from past case-database while protecting the privacy of both current diagnosed patient and the case-database and construct a privacy-preserving medical record searching scheme based on ElGamal Blind Signature. In our scheme, by blinded the healthy data of the patient and the database of the iDoctor, respectively, the patient can securely make self-helped medical diagnosis by invoking past case-database and securely comparing the blinded abstracts of current data and previous records. Moreover, the patient can obtain target searching information intelligently at the same time he knows whether the abstracts match or not instead of obtaining it after matching. It greatly increases the timeliness of information acquisition and meets high-speed information sharing requirements, especially in 5G era. What's more, our proposed scheme achieves bilateral security, that is, whether the abstracts match or not, both of the privacy of the case-database and the private information of the current patient are well protected. Besides, it resists different levels of violent ergodic attacks by adjusting the number of zeros in a bit string according to different security requirements.
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content

Recent years have witnessed an explosion of user-generated content (UGC) videos shared and streamed over the Internet, thanks to the evolution of affordable and reliable consumer capture devices, and the tremendous popularity of social media platforms. Accordingly, there is a great need for accurate video quality assessment (VQA) models for UGC/consumer videos to monitor, control, and optimize this vast content. Blind quality prediction of in-the-wild videos is quite challenging, since the quality degradations of UGC content are unpredictable, complicated, and often commingled. Here we contribute to advancing the UGC-VQA problem by conducting a comprehensive evaluation of leading no-reference/blind VQA (BVQA) features and models on a fixed evaluation architecture, yielding new empirical insights on both subjective video quality studies and VQA model design. By employing a feature selection strategy on top of leading VQA model features, we are able to extract 60 of the 763 statistical features used by the leading models to create a new fusion-based BVQA model, which we dub the \textbf{VID}eo quality \textbf{EVAL}uator (VIDEVAL), that effectively balances the trade-off between VQA performance and efficiency. Our experimental results show that VIDEVAL achieves state-of-the-art performance at considerably lower computational cost than other leading models. Our study protocol also defines a reliable benchmark for the UGC-VQA problem, which we believe will facilitate further research on deep learning-based VQA modeling, as well as perceptually-optimized efficient UGC video processing, transcoding, and streaming. To promote reproducible research and public evaluation, an implementation of VIDEVAL has been made available online: \url{https://github.com/tu184044109/VIDEVAL_release}.
Blockchain technology applications for Industry 4.0: A literature-based review

Industry 4.0 involves innovations with upcoming digital technologies, and blockchain is one of them. Blockchain can be incorporated to improve security, privacy, and data transparency both for small and large enterprises. Industry 4.0 is a synthesis of the new production methods that allow manufacturers to achieve their target more rapidly. Research has been conducted on various Industry 4.0 technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big data, and Blockchain, and how they could create significant interruptions in recent years. These technologies provide various possibilities in the world of manufacturing and supply chain. Blockchain is a technology that has gained much recognition and can enhance the manufacturing and supply chain environment. Various fields now have fascinating insights into the advantages of blockchain. Several research articles on “Blockchain” and “Industry 4.0” from Google Scholar, Scopus, and other relevant sources are identified and reviewed for this study. This paper discusses the major potential of Blockchain in Industry 4.0. Various drivers, enablers, and associated capabilities of Blockchain technology for Industry 4.0 are discussed for insights. Different Industry 4.0 spheres/sub-domains for Blockchain technology realisation are also discussed. Finally, we have identified and studied fourteen significant applications of Blockchain in Industry 4.0. It is a range of new developments and hope for immense opportunities that are changing Industry 4.0. This technology would work to achieve amplified outcomes and work individually to enhance the process.
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines

As industries become automated and connectivity technologies advance, a wide range of systems continues to generate massive amounts of data. Many approaches have been proposed to extract principal indicators from the vast sea of data to represent the entire system state. Detecting anomalies using these indicators on time prevent potential accidents and economic losses. Anomaly detection in multivariate time series data poses a particular challenge because it requires simultaneous consideration of temporal dependencies and relationships between variables. Recent deep learning-based works have made impressive progress in this field. They are highly capable of learning representations of the large-scaled sequences in an unsupervised manner and identifying anomalies from the data. However, most of them are highly specific to the individual use case and thus require domain knowledge for appropriate deployment. This review provides a background on anomaly detection in time-series data and reviews the latest applications in the real world. Also, we comparatively analyze state-of-the-art deep-anomaly-detection models for time series with several benchmark datasets. Finally, we offer guidelines for appropriate model selection and training strategy for deep learning-based time series anomaly detection.
War Strategy Optimization Algorithm: A New Effective Metaheuristic Algorithm for Global Optimization

This paper proposes a new metaheuristic optimization algorithm based on ancient war strategy.The proposed War Strategy Optimization (WSO) is based on the strategic movement of army troops during the war.War strategy is modeled as an optimization process wherein each soldier dynamically moves towards the optimum value.The proposed algorithm models two popular war strategies, attack and defense strategies.The positions of soldiers on the battlefield are updated in accordance with the strategy implemented.To improve the algorithm's convergence and robustness, a novel weight updating mechanism and a weak soldier's relocation strategy are introduced.The proposed war strategy algorithm achieves good balance of the exploration and exploitation stages.A detailed mathematical model of the algorithm is presented.The efficacy of the proposed algorithm is tested on 50 benchmark functions and four engineering problems.The performance of the algorithm is compared with ten popular metaheuristic algorithms.The experimental results for various optimization problems prove the superiority of the proposed algorithm.
LECARM: Low-Light Image Enhancement Using the Camera Response Model

Low-light image enhancement algorithms can improve the visual quality of low-light images and support the extraction of valuable information for some computer vision techniques. However, existing techniques inevitably introduce color and lightness distortions when enhancing the images. To lower the distortions, we propose a novel enhancement framework using the response characteristics of cameras. First, we discuss how to determine a reasonable camera response model and its parameters. Then, we use the illumination estimation techniques to estimate the exposure ratio for each pixel. Finally, the selected camera response model is used to adjust each pixel to the desired exposure according to the estimated exposure ratio map. Experiments show that our method can obtain enhancement results with fewer color and lightness distortions compared with the several state-of-the-art methods.
Portfolio diversification across cryptocurrencies

Utilizing the empirical data of ten major cryptocurrencies, this article examines the investablitiy and role of diversification in cryptocurrency market, and evaluates the out-of-sample performance of commonly used asset allocation models across cryptocurrencies. We show that portfolio diversification across different cryptocurrencies can significantly improve the investment results. We also find robust evidence that the maximum utility model dominates the out-of-sample utility, although none of the models can consistently beat the naïve 1/N portfolio in Sharpe ratio.
Experimental explorations on short text topic mining between LDA and NMF based Schemes

Learning topics from short texts has become a critical and fundamental task for understanding the widely-spread streaming social messages, e.g., tweets, snippets and questions/answers. Up to date, there are two distinctive topic learning schemes: generative probabilistic graphical models and geometrically linear algebra approaches, with LDA and NMF being the representative works, respectively. Since these two methods both could uncover the latent topics hidden in the unstructured short texts, some interesting doubts are coming to our minds that which one is better and why? Are there any other more effective extensions? In order to explore valuable insights between LDA and NMF based learning schemes, we comprehensively conduct a series of experiments into two parts. Specifically, the basic LDA and NMF are compared with different experimental settings on several public short text datasets in the first part which would exhibit that NMF tends to perform better than LDA; in the second part, we propose a novel model called “Knowledge-guided Non-negative Matrix Factorization for Better Short Text Topic Mining” (abbreviated as KGNMF), which leverages external knowledge as a semantic regulator with low-rank formalizations, yielding up a time-efficient algorithm. Extensive experiments are conducted on three representative corpora with currently typical short text topic models to demonstrate the effectiveness of our proposed KGNMF. Overall, learning with NMF-based schemes is another effective manner in short text topic mining in addition to the popular LDA-based paradigms.