Reviewerly/exHarmony huggingface dataset:
Linked open data portals: functionalities and user experience in semantic catalogues

Purpose This study seeks to understand the current state of the development of linked open data (LOD) bibliographic portals to discuss their functionalities, contributions, value-adds and user experience. Design/methodology/approach A set of evaluative aspects grouped into three analysis dimensions was established: collections, tools—technologies and standards used—and web user interface. As the object of the study, four projects of diverse nature and volume were selected to help provide a better understanding of the trends in the solutions provided for the end user when accessing linked data collections. Findings Publishing LOD through visual interfaces maximises information enrichment, contextualisation and discovery, in addition to improving user experience, because of both increased navigation capabilities and interrelationships between data. These more flexible environments have metamorphosised the visualisation of bibliographic information. However, aspects that needed improvement were observed, primarily relating to (1) a more intuitive interaction, (2) possibilities of greater personalisation, (3) enhanced communication with the user to favour user engagement and (4) experimental spaces of data reuse. Research limitations/implications Further quantitative and qualitative studies should be conducted to improve these portals, assess their adaptation to the behaviour of the user and their influence on the use of library collections. Originality/value This article investigates the potential of semantic technologies in bibliographic data portals, proposes a methodological model for their evaluation and advances conclusions about the usability and user experience that these platforms provide, compared to classic catalogues.
Generation of contrast-enhanced CT with residual cycle-consistent generative adversarial network (Res-CycleGAN)

Contrast-enhanced computed tomography (CECT) has been commonly used in clinical practice of radiotherapy for enhanced tumor and organs at risk (OARs) delineation since it provides additional visualization of soft tissue and vessel anatomy. However, the additional CECT scan leads to increased radiation dose, prolonged scan time, risks of contrast induced nephropathy (CIN), potential requirement of image registration to non-contrast simulation CT, as well as elevated cost, etc. Hypothesizing that the non-contrast simulation CT contains sufficient features to differentiate blood and other soft tissues, in this study, we propose a novel deep learning-based method for generation of CECT images from non-contrast CT. The method exploits a cycle-consistent generative adversarial network (CycleGAN) framework to learn a mapping from non-contrast CT to CECT. A residual U-Net was employed as the generator of the CycleGAN to force the model to learn the specific difference between the non-contrast CT and CECT. The proposed algorithm was evaluated with 20 sets of abdomen patient data with a manor of five-fold cross validation. Each patient was scanned at the same position with non-contrast simulation CT and CECT. The CECT images were treated as training target in training and ground truth in testing. The non-contrast simulation CT served as the input. The preliminary results of visual and quantitative inspections suggest that the proposed method could effectively generate CECT images from non-contrast CT. This method could improve anatomy definition and contouring in radiotherapy without additional clinic efforts in CECT scanning.
Overview of image colorization and its applications

Image colorization is a computer-assisted technology to colorize grayscale images. With the rapid development of information technology and increasing image data, the study of image colorization has also become particularly important. Through the summary and analysis of related literature in recent years, the paper presents a review about the techniques of image colorization. In addition, we also divide image colorization methods into three categories, then describe their representative methods and applications, and discuss their advantages and limitations in future development trends. The results show specially that model based colorization has a better colorization effect than other methods, whether in terms of image quality or real-time performance.
Single-frame super-resolution for remote sensing images based on improved deep recursive residual network

Abstract Single-frame image super-resolution (SISR) technology in remote sensing is improving fast from a performance point of view. Deep learning methods have been widely used in SISR to improve the details of rebuilt images and speed up network training. However, these supervised techniques usually tend to overfit quickly due to the models’ complexity and the lack of training data. In this paper, an Improved Deep Recursive Residual Network (IDRRN) super-resolution model is proposed to decrease the difficulty of network training. The deep recursive structure is configured to control the model parameter number while increasing the network depth. At the same time, the short-path recursive connections are used to alleviate the gradient disappearance and enhance the feature propagation. Comprehensive experiments show that IDRRN has a better improvement in both quantitation and visual perception.
Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation

In this paper, we tackle the task of Definition Generation (DG) in Chinese, which aims at automatically generating a definition for a word.Most existing methods take the source word as an indecomposable semantic unit.However, in parataxis languages like Chinese, word meanings can be composed using the word formation process, where a word ("桃 花", peachblossom) is formed by formation components ("桃", peach; "花", flower) using a formation rule (Modifier-Head).Inspired by this process, we propose to enhance DG with word formation features.We build a formation-informed dataset and propose a model DeFT, which Decomposes words into formation features, dynamically Fuses different features through a gating mechanism, and generaTes word definitions.Experimental results show that our method is both effective and robust. 1
Knowledge-aware Named Entity Recognition with Alleviating Heterogeneity

Named Entity Recognition (NER) is a fundamental and important research topic for many downstream NLP tasks, aiming at detecting and classifying named entities (NEs) mentioned in unstructured text into pre-defined categories. Learning from labeled data only is far from enough when it comes to domain-specific or temporally-evolving entities (medical terminologies or restaurant names). Luckily, open-source Knowledge Bases (KBs) (Wikidata and Freebase) contain NEs that are manually labeled with predefined types in different domains, which is potentially beneficial to identify entity boundaries and recognize entity types more accurately. However, the type system of a domain-specific NER task is typically independent of that of current KBs and thus exhibits heterogeneity issue inevitably, which makes matching between the original NER and KB types (Person in NER potentially matches President in KBs) less likely, or introduces unintended noises without considering domain-specific knowledge (Band in NER should be mapped to Out_of_Entity_Types in the restaurant-related task). To better incorporate and denoise the abundant knowledge in KBs, we propose a new KB-aware NER framework (KaNa), which utilizes type-heterogeneous knowledge to improve NER. Specifically, for an entity mention along with a set of candidate entities that are linked from KBs, KaNa first uses a type projection mechanism that maps the mention type and entity types into a shared space to homogenize the heterogeneous entity types. Then, based on projected types, a noise detector filters out certain less-confident candidate entities in an unsupervised manner. Finally, the filtered mention-entity pairs are injected into a NER model as a graph to predict answers. The experimental results demonstrate KaNa's state-of-the-art performance on five public benchmark datasets from different domains.
3D Virtual Animation Instant Network Communication System Design

This project uses Openfire to implement a virtual 3D animation instant messaging system, which is easier to use and more expandable. The main work of the client is to implement the Extensible Messaging and Presence Protocol (XMPP) and use XMPP to transmit data to the server side and receive data from the server side, while Openfire is built by the server side to use. To address the problem that the current mainstream face key point localization model is less robust to complex environments, this project adopts a deep learning-based approach to design and implement the face key point localization model, through data preprocessing, model design, and model training, to achieve a robust model that can locate 68 face key points and complete the migration of the model to mobile. The current video communication often suffers from delay and lag, so this project uses face key point data instead of video stream data transmission to reduce the pressure on the network. This topic also uses voice coding and decoding, noise reduction, echo cancellation, and other processing to solve the problems of noise interference and echo interference in voice transmission. This paper also introduces the creation, import, and loading of 3D virtual models, and explains how to use face key point association to drive 3D animation models, how to make the drive smoother and more natural, and using individual face key points as an example.
Multimodal Item Categorization Fully Based on Transformer

The Transformer has proven to be a powerful feature extraction method and has gained widespread adoption in natural language processing (NLP).In this paper we propose a multimodal item categorization (MIC) system solely based on the Transformer for both text and image processing.On a multimodal product data set collected from a Japanese ecommerce giant, we tested a new image classification model based on the Transformer and investigated different ways of fusing bi-modal information.Our experimental results on real industry data showed that the Transformerbased image classifier has performance on par with ResNet-based classifiers and is four times faster to train.Furthermore, a cross-modal attention layer was found to be critical for the MIC system to achieve performance gains over text-only and image-only models.
Are data repositories fettered? A survey of current practices, challenges and future technologies

Purpose The purpose of this study is to explore current practices, challenges and technological needs of different data repositories. Design/methodology/approach An online survey was designed for data repository managers, and contact information from the re3data, a data repository registry, was collected to disseminate the survey. Findings In total, 189 responses were received, including 47% discipline specific and 34% institutional data repositories. A total of 71% of the repositories reporting their software used bespoke technical frameworks, with DSpace, EPrint and Dataverse being commonly used by institutional repositories. Of repository managers, 32% reported tracking secondary data reuse while 50% would like to. Among data reuse metrics, citation counts were considered extremely important by the majority, followed by links to the data from other websites and download counts. Despite their perceived usefulness, repository managers struggle to track dataset citations. Most repository managers support dataset and metadata quality checks via librarians, subject specialists or information professionals. A lack of engagement from users and a lack of human resources are the top two challenges, and outreach is the most common motivator mentioned by repositories across all groups. Ensuring findable, accessible, interoperable and reusable (FAIR) data (49%), providing user support for research (36%) and developing best practices (29%) are the top three priorities for repository managers. The main recommendations for future repository systems are as follows: integration and interoperability between data and systems (30%), better research data management (RDM) tools (19%), tools that allow computation without downloading datasets (16%) and automated systems (16%). Originality/value This study identifies the current challenges and needs for improving data repository functionalities and user experiences. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0204
Efficient Video Super-Resolution via Hierarchical Temporal Residual Networks

Super-Resolving (SR) video is more challenging compared with image super-resolution because of the demanding computation time.To enlarge a low-resolution video, the temporal relationship among frames must be fully exploited.We can model video SR as a multi-frame SR problem and use deep learning methods to estimate the spatial and temporal information.This paper proposes a lighter residual network, based on a multi-stage back projection for multi-frame SR.We improve the back projection based residual block by adding weights for adaptive feature tuning, and add global & local connections to explore deeper feature representation.We jointly learn spatial-temporal feature maps by using the proposed Spatial Convolution Packing scheme as an attention mechanism to extract more information from both spatial and temporal domains.Different from others, our proposed network can input multiple low-resolution frames to obtain multiple super-resolved frames simultaneously.We can then further improve the video SR quality by self-ensemble enhancement to meet videos with different motions and distortions.Results of much experimental work show that our proposed approaches give large improvement over other state-of-the-art video SR methods.Compared to recent CNN based video SR works, our approaches can save, up to 60% computation time and achieve 0.6 dB PSNR improvement.
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners. However, their effectiveness depends mainly on scaling the model parameters and prompt design, hindering their implementation in most real-world applications. This study proposes a novel pluggable, extensible, and efficient approach named DifferentiAble pRompT (DART), which can convert small language models into better few-shot learners without any prompt engineering. The main principle behind this approach involves reformulating potential natural language processing tasks into the task of a pre-trained language model and differentially optimizing the prompt template as well as the target label with backpropagation. Furthermore, the proposed approach can be: (i) Plugged to any pre-trained language models; (ii) Extended to widespread classification tasks. A comprehensive evaluation of standard NLP tasks demonstrates that the proposed approach achieves a better few-shot performance. Code is available in https://github.com/zjunlp/DART.
Square-root filtering via covariance SVD factors in the accurate continuous-discrete extended-cubature Kalman filter

This paper continues our research devoted to an accurate nonlinear Bayesian filters' design. Our solution implies numerical methods for solving ordinary differential equations (ODE) when propagating the mean and error covariance of the dynamic state. The key idea is that an accurate implementation strategy implies the methods with a discretization error control involved. This means that the filters' moment differential equations are to be solved accurately, i.e. with negligible error. In this paper, we explore the continuous-discrete extended-cubature Kalman filter that is a hybrid method between Extended and Cubature Kalman filters (CKF). Motivated by recent results obtained for the continuous-discrete CKF in Bayesian filtering realm, we propose the numerically stable (to roundoff) square-root approach within a singular value decomposition (SVD) for the hybrid filter. The new method is extensively tested on a few application examples including stiff systems.
An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing

With existing power prediction algorithms, it is difficult to satisfy the requirements for prediction accuracy and time when PV output power fluctuates sharply within seconds, so this paper proposes a high-precision and ultra-fast PV power prediction algorithm. Firstly, in order to shorten the optimization time and improve the optimization accuracy, the single-iteration Gray Wolf Optimization (SiGWO) method is used to simplify the iteration process of the hyperparameters of Least Squares Support Vector Machine (LSSVM), and then the hybrid local search algorithm composed of Iterative Local Search (ILS) and Self-adaptive Differential Evolution (SaDE) is used to improve the accuracy of hyperparameters, so as to achieve high-precision and ultra-fast PV power prediction. The power prediction model is established, and the proposed algorithm is applied in a test experiment which can complete the power prediction within 3 s, and the RMSE is only 0.44%. Finally, combined with the PV-storage advanced smoothing control strategy, it is verified that the performance of the proposed algorithm can satisfy the system’s requirements for prediction accuracy and time under the condition of power mutation in a PV power generation system.
Efficient Framework for Genetic Algorithm-Based Correlation Power Analysis

Various Artificial Intelligence (AI) techniques are combined with classic side-channel methods to improve the efficiency of attacks. Among them, Genetic-Algorithms-based Correlation Power Analysis (GA-CPA) is proposed to launch attacks on hardware cryptosystems to extract the secret key efficiently. However, the convergence efficiency of GA-CPA is unsatisfactory due to two problems: the randomly generated initial population generally have low fitness, and the mutation operation in each iteration hardly produces high-quality individuals because of the confusion and diffusion characteristics of S-boxes. In this paper, we propose an analysis framework of GA-CPA which focuses on solving these two problems. First, we explore the list of candidate key bytes which is the result of Correlation Power Analysis (CPA) on a limited number of power traces, so that the population can be initialized with high quality candidates. Second, we improve the mutation operation by guiding the candidate key to mutate in a higher-fitness direction instead of randomly. Third, we make full use of the fitness calculation method and combine it with key enumeration algorithms to further improve the efficiency of key recovery. Simulation experimental results show that our method reduces the number of traces by 33.3% and 43.9% compared to CPA with key enumeration and GA-CPA respectively when the success rate is fixed to 90%. Real experiments performed on SAKURA-G confirm that the number of traces required in our method is much less than the numbers of traces required in CPA and GA-CPA. Besides, we adjust our method to deal with DPA contest v1 dataset, and achieve a better result of 40.76 traces than the winning proposal of 42.42 traces. The computation cost of our proposal is nearly 16.7% of the winner.
BlockHDFS: Blockchain-integrated Hadoop distributed file system for secure provenance traceability

Hadoop Distributed File System (HDFS) is one of the widely used distributed file systems in big data analysis for frameworks such as Hadoop. HDFS allows one to manage large volumes of data using low-cost commodity hardware. However, vulnerabilities in HDFS can be exploited for nefarious activities. This reinforces the importance of ensuring robust security to facilitate file sharing in Hadoop as well as having a trusted mechanism to check the authenticity of shared files. This is the focus of this paper, where we aim to improve the security of HDFS using a blockchain-enabled approach (hereafter referred to as BlockHDFS). Specifically, the proposed BlockHDFS uses the enterprise-level Hyperledger Fabric platform to capitalize on files' metadata for building trusted data security and traceability in HDFS.
IRLNet: A Short-Time and Robust Architecture for Automatic Modulation Recognition

Automatic modulation recognition with deep learning (DL) is challenging in distinguishing high-order modulation modes and balancing complexity against recognition accuracy. In this paper, we propose a novel dual-path modulation recognition framework named IRLNet, which consists of the improved residual stacks (IRS) and long short-term memory (LSTM). The IRS maintains the more initial residual information, learns the signal features in deep and shallow, and achieves various degrees of feature extraction. The model learns from the time domain I/Q, amplitude and phase information presented in the training data. The simulation results on RadioML2016.10B show that IRLNet performs stable in the training stage and has low spatial-temporal complexity. It also achieves a recognition accuracy of more than 93% at high signal-to-noise ratios (SNRs). Transfer learning is introduced to improve the efficiency of retraining, and robustness is proved by transferring the model to RadioML 2018.01A and HisarMod 2019.1. The simulation result shows that the training time is greatly shortened by about 25.6% in RadioML 2018.01A by introducing transfer learning. Moreover, IRLNet improves the confusion of high-order modulations and achieves a recognition accuracy of more than 90% in high SNRs.
An Introduction To Quantum Machine Learning Techniques

This paper presents an overview of quantum machine learning (QML) techniques, their approach, and their landscape. QML is a rapidly evolving field of study which has recently generated lots of interest due to improvements it promises over the classical machine learning approaches. This paper presents an overview of the application of quantum information theory on machine learning algorithms. It also discusses various designs, techniques, and algorithms of classical machine learning and quantum information theory and how they can be fused with each other. The goal of this paper is to analyze algorithms and techniques used in QML. The paper also presents some of the promising algorithms used in QML, which can be executed on a real quantum computer over cloud computing platforms with the aim to evaluate its performance based on given parameters.