Reviewerly/exHarmony huggingface dataset:
Towards Formally Grounded Evaluation Measures for Semantic Parsing-based Knowledge Graph Question Answering

Knowledge graph question answering (KGQA) is important to make structured information accessible without formal query language expertise on the part of the users. The semantic parsing (SP) flavor of this task maps a natural language question to a formal query that is machine executable, such as SPARQL. The SP-KGQA task is currently evaluated by adopting measures from other tasks, such as information retrieval and machine translation. However, this adoption typically occurs without fully considering the desired behavior of SP-KGQA systems. To address this, we articulate task-specific desiderata, then develop novel SP-KGQA measures based on a probabilistic framework. We use the desiderata to formulate a set of axioms for SP-KGQA measures and conduct an axiomatic analysis that reveals insufficiencies of established measures previously used to report SP-KGQA performance. We also perform experimental evaluations, using synthetic and state-of-the-art neural machine translation approaches. The results highlight the importance of grounded alternative SP-KGQA measures.
A robotic knowledge base to model and update real-world information from indoor environments

Robotic systems operating in the real world would benefit from a clear semantic model to understand their interactions with the real world. Such semantics are typically captured in an ontology. Unfortunately, the underlying model of existing ontologies requires many work-arounds before it can be used to capture general knowledge about objects and interactions in the real physical world. To remove such work-arounds, this article adopts the richer hypergraph model. It is used to develop an ontology, which is further implemented as the knowledge base of an actual robotic system performing search operations. Also, actual information extracted from the robot's sensors is used to update its knowledge base logically and sensibly.
Use Cases and Detailed Explanation of Enterprise-Level Blockchain

Ethereum, Hyperledger, and other open-source blockchain platforms are still in the process of further development and improvement. There are a large number of problems in consensus efficiency, privacy protection, large-scale storage, regulatory access, which hinder the implementation and commercialization of applications on open-source platform projects. Hyperchain is an alliance chain platform explicitly designed for enterprise-level applications with complete functions and leading technologies. Many financial institutions so far have developed blockchain projects as the Hyperchain platform as a basis, directly connecting with the banking system. Some have already been implemented and promoted commercially. The previous chapters have reviewed the core principles and development practices of Hyperchain in detail. This chapter will present two examples of Hyperchain-based enterprise-level blockchain application projects closer to reality: accounts receivable system and travel ride-sharing platform.
Hyperchain Application Development Fundamentals

Chapter 6 provides an introduction to the core system components of the Hyperchain platform, including detailed descriptions of features such as authentication licensing mechanisms for members, security and privacy of business transaction data, high transaction throughput and low transaction latency, a secure and complete smart contract engine, and interoperability for high user experience. As a federated blockchain technology infrastructure platform that satisfies the needs of industry applications, Hyperchain absorbs the most cutting-edge technologies from the blockchain open-source community and research fields, integrates high-performance and reliable consensus algorithms, compatible with a variety of smart contract development languages, and good execution environments from the open-source community; in the meantime, Hyperchain also enhances key features such as bookkeeping authorization mechanism and transaction data encryption, and offers a powerful visual web management console to efficiently manage blockchain nodes, ledgers, transactions, and smart contracts.
Ethereum Application and Case Studies

As a core technology that triggers a wave of disruptive revolution, the application of blockchain technology in the financial sector will potentially change the conventional transaction process and record-keeping methods, and thus significantly reducing costs and improving efficiency. Due to blockchain’s security, transparency, and non-tampering characteristics, the trust model between financial systems will no longer rely on intermediaries, and many businesses will be “decentralized” to realize real-time digital transactions. Ethereum is the well-known Turing-complete open-source blockchain platform where all computable logic operations can be implemented based on Ethereum smart contracts. Numerous innovative blockchain applications and innovative products of startups are growing up on it. The previous chapters have reviewed the core principles and development practices of Ethereum in detail. This chapter will concentrate more on the actual procedure by introducing two Ethereum-based practical applications: generic points system and electronic coupon system. The generic points system develops DApp applications directly on the underlying platform of Ethereum and calls the methods of smart contracts through the web3.js interface provided by Ethereum to send transactions or read data and display them to users on web pages, while the electronic coupon system, the overall architecture of which is a Java Web Project with the SSM framework, introduces blockchain technology at the application layer, independently encapsulates the interface tools for interaction with the Ethereum platform, uses the platform and the database to cooperate and store data, and develops smart contracts to record the actual backend business logic as transactions on the blockchain.
In-Depth Interpretation of Hyperledger Fabric

Bitcoin is considered by many to be the powerful emerging platform of the 1.0 era of blockchain technology. With the birth of the Ethereum platform, primarily characterized by smart contracts, blockchain has entered the 2.0 era. In contrast, the Hyperledger Fabric blockchain technology, an open-source platform, marks the arrival of the 3.0 era of blockchain technology. The latest release of Fabric v1.4.1 (LTS) proposes many new design concepts, adds many new features, provides a highly modular and configurable architecture, supports common programming languages (such as Java, Go, and Node.js) for writing smart contracts, and supports unpluggable consensus protocols, making the development of enterprise-level applications based on this platform a reality, and the platform captures growing attention. This chapter will take readers into the world of Hyperledger Fabric and explore the basic operating principles to deepen their understanding of the platform and lay the foundation for subsequent study of Fabric-based application development technologies.
In-Depth Interpretation of Ethereum

Ethereum is a Turing-Complete open-source platform that has attracted a large number of decentralized application developers. This chapter will give a shallow-to-deep explanation of Ethereum, mainly including related concepts, such as nodes, mining, accounts, gas, and messages; Ethereum core principles, including consensus mechanism, EVM, data storage, and cryptographic algorithms; Ethereum smart contracts, including the syntax structure of smart contracts, compilation deployment, and testing; major events dissection, the DAO event and the main problems of current Ethereum, such as consensus efficiency issues and privacy protection issues, and the upcoming Ethereum 2.0.
Anatomy of the Core: Principles of Enterprise-Level Blockchain Platform

Enterprise blockchain, also known as federated blockchain, provides enterprise-level blockchain network solutions to address the blockchain technology requirements of large companies, government agencies, and industrial alliances. Each node of a federated chain usually corresponds to an organization of entities, and nodes must be authorized to join and withdraw. The various organizations form a coalition of stakeholders to maintain the blockchain network health.
Innovative Educational Communication in the Global Digital Environment: Trans-Disciplinary Models, Tools, and Mechanisms

The objective of the inquiry is on innovative communication in the light of identifying trans-disciplinary aspects of phylogeny, sociogenesis, and technogenesis of educational communities, which determines the identification of this communicative sphere as an integrative structure determined by the internal unity of its micro- and macro-structures, which in the plane of the sign substrate synthesizes the features of existential, cognitive, anthropological and technological substance. Applied trans-disciplinary lens of the phenomenological approach to the study contributes to the solution of holistic modeling of processes and results of updating models and mechanisms of the highly dynamic communication system of education in the digital environment as a whole and its individual formats at the beginning of the XXI century in particular. The object of research is innovative educational communication in the global digital environment. The study focus is on innovative language and technology models, digital tools, and mechanisms of educational communication in the digital realm. The empirical basis of the research is the practices of innovative communication related to different substrates, formats, objects, and phenomena of acquiring new knowledge and education in the digital realm. The study results allow to provide a transdisciplinary synthesis (across communicative theory, information theory, philosophy, education and e-learning studies, semiotics, digital humanities).
<i>Cryptocurrencies and the Regulatory Challenge</i>, ALLAN C.HUTCHINSON, London: Routledge, 2021, 156 pp., £120.00

Journal of Law and SocietyVolume 49, Issue 3 p. 623-628 BOOK REVIEW Cryptocurrencies and the Regulatory Challenge, ALLAN C. HUTCHINSON, London: Routledge, 2021, 156 pp., £120.00 OONAGH MCDONALD CBE, Corresponding Author OONAGH MCDONALD CBE oonagh.mcdonaldcbe@gmail.com Email: oonagh.mcdonaldcbe@gmail.comSearch for more papers by this author OONAGH MCDONALD CBE, Corresponding Author OONAGH MCDONALD CBE oonagh.mcdonaldcbe@gmail.com Email: oonagh.mcdonaldcbe@gmail.comSearch for more papers by this author First published: 29 August 2022 https://doi.org/10.1111/jols.12383Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume49, Issue3September 2022Pages 623-628 RelatedInformation
A Comparative Study of Traditional Light Field Methods and NeRF

Neural Radiance Fields (NeRF) is a recent technology which had a large impact in computer vision, promising to generate high quality novel views and corresponding disparity map, all using a fairly small number of input images. In effect, they are a new way to represent a light field. In this paper, we compare NeRF with traditional light field methods for novel view synthesis and depth estimation, in an attempt to quantify the advantages brought by NeRF, and to put these results in perspective with the way both paradigms are used practically. We provide qualitative and quantitative comparisons, discuss them and highlight some aspects of working with NeRF depending on the type of light field data used.
Performance Analysis of Sign Language Recognition System Using Hybrid Feature Descriptor

The main objective of this paper is to analyze the classifier performance for the recognition of sign language using hybrid feature descriptor. Techniques for the recognition of sign language are becoming highly important because these recognition systems help to reduce the communication barrier between sensory impaired people and normal human beings. There are various methods are used for the same but there is still a need for robust methods to be Deployed to upgrade the recognition results. The sign Language system is used for the interpretation of hand gestures that are used by the sensory impaired persons. For analyzing the output of various classifiers for evaluating the best one, for the recognition of sign language, the proposed framework considers accuracy as the main parameter. The hybridization of features has been explored to fulfill the objective of this work. In this study, at the preprocessing stage (First stage of recognition process), two main parameters (PSNR, MSE) have been demonstrated for different filters for finding the appropriate filter to represent the effectiveness of a recognition model. The result of the work showed that the Median filter has a higher PSNR value. The finding of this article depicts that the Support Vector Machine has higher recognition accuracy as compared to the other classifiers using hybridization of features.
Multi-Scale Transformer with Explicit Boundary Constraint for Semantic Segmentation

Semantic segmentation is a dense pixel-level prediction task, which requires both strong classification and locality ability simultaneously. Previous methods tackle this challenging problem by enlarging receptive field, incorporating contextual cues, fusing multi-level features, etc. Recently, several pure Transformer methods push this forward by its intrinsic characteristics of global relation modeling between any pixels. The receptive field is enlarged to the whole image as while. However, current pure Transformer methods still has shortcomings, such as poor ability for pixel-level locality because of single-scale image patch splitting, the simple upsampling of bilinear interpolation and discrete positional encoding. To this end, we propose a pure multi-scale Transformer architecture with pixel-shuffle upsampling kernel and semantically continuous positional encoding. In addition, current Transformers naturally lack the ability to encode object shape due to the one-dimensional processing. Hence we propose to explicitly enforce the boundary constraints on the decoder. Experimental results demonstrate our method achieves state-of-art results on both Cityscapes and ADE20K datasets.
HairGAN: Spatial-Aware Palette GAN for Hair Color Transfer

Hair color transfer aims to transfer the hair color from a reference image to an original image while maintaining the hair structure of the original image. However, due to the complex hair structure and the misalignment of hair regions between the original image and the reference image, existing methods cannot complete this task well. To address these issues, we propose a hair color transfer GAN (HairGAN). It first utilizes off-the-shelf network to extract the hair region from the original image and transfer it into intermediary hair color. Then, Spatial-Aware Palette Alignment (SAPA) module is introduced to align the hair regions in the original image and the reference image. Furthermore, HairGAN employs cycle consistency reconstruction module to ensure the global consistency between original image and transferred image. We collect a dataset containing 2850 images and a few paired samples for hair color transfer. Extensive experiments show that HairGAN could generate high-quality transferred results.
EvoDNN - Evolving Weights, Biases, and Activation Functions in a Deep Neural Network

Classification, such as classifying cell samples into cancer (malignant) or normal (benign), or the classification of genome regions into functional regions (for example coding regions or promoter regions) are important problems in Computational Biology. For such tasks, we have previously designed an evolutionary deep neural network that in addition to evolving the neuron's weights and biases also evolves (learns) the activation functions for each neuron and called this approach EvoDNN. EvoDNN can employ activation functions that are non-differentiable as it does not rely on back-propagation. This feature is adding flexibility in terms of activation functions EvoDNN can employ. The work presented here extends our previous work on EvoDNN by analyzing a more extensive set of data sets and studying variations of the internal topology of the EvoDNN model. In addition, we study the effect of evolving the weights and biases only while holding the activation function fixed and demonstrate that evolving activation functions indeed provides better performance. We also compare our model to several other popular models and demonstrate superior performance on several data sets. The current code for EvoDNN is available at https://github.com/Payuing/evoDNN.
A Matrix Software Library Method for the Ground System of Space Data Center

A matrix software library is presented to rapidly establish the ground complex system of the Space Data Center, which is designed for the management of several hundred satellites. Firstly, the research of domestic and foreign study is analyzed. Secondly, the four-layer structure is introduced for Space Data Center system based on virtualized cloud computation that could divide the software with the hardware and the application effectively. Thirdly, the centralized hardware platform architecture is designed to decouple the operation with hardware, and the micro module Server room is used to manage the system efficiently. And then, the matrix software library method is presented based on the classification of the complex software system, and the rapid establishment process is introduced step by step for the management system of large number of satellites, which could load and unload the system on demand. This method could solve the problem of the multi-series and great number required by the satellite management for Space Data Processing Center. The system cost of the management and development is reduced effectively and efficiently. And the system operation and maintenance are reduced obviously.
Towards Underwater Image Restoration: A Physical-Accurate Pipeline and a Large Scale Full-Reference Benchmark

Underwater images always present low-quality features such as low contrast, blurred edges and color distortion, which brings great challenges to high-level underwater vision tasks. In this paper, a novel underwater image restoration method, namely MonoUIR (Monocular Underwater Image Restoration), is proposed, which is based on a more physical-accurate imaging model compared to existing schemes. And with the monocular depth estimation, MonoUIR has no dependence on extra ranging equipment or specific shooting operations. Experimental results demonstrate that MonoUIR overwhelmingly outperforms other physical model-based competitors. In addition, the Real-world Undersea Color Board (RUCB) dataset is established, providing the illconditioned underwater images collected in the East China Sea and the corresponding high-quality references. To our knowledge, this is the first full-reference underwater benchmark dataset collected entirely in a real-world marine environment, which will further support the fullreference evaluation of underwater image restoration approaches. The source code and the dataset are available at https://TongJiayan.github.io/MonoUIR-Homepage.
Source-ID-Tracker: Source Face Identity Protection in Face Swapping

Swapping faces with deep learning technology to generate realistic fake videos/images (a.k.a, deepfakes) has drawn great public con-cerns recently. Numerous approaches have been proposed to iden-tify fake contents; however, less work has been dedicated to pro-tecting the source faces in an active way. In this paper, we stand for a legitimate faceswap service provider and present an approach called Source-ID- Tracker (SIDT), which aims to protect the identity of source faces in deepfakes from malicious uses. As a plug-in, the encoder of SIDT implicitly embeds a source face image into a deep-fake image while ensuring the resultant encoded image is visually indistinguishable from the deepfake image. After sharing through social media, the embedded source face and its identity can still be recovered with a decoder. Experimental results show that the pro-posed model achieves a promising performance, in terms of reconstruction quality and attribution inference accuracy, in revealing the hidden source face.
A model for the identification of the functional structures of unstructured abstracts in the social sciences

Purpose Reorganising unstructured academic abstracts according to a certain logical structure can help scholars not only extract valid information quickly but also facilitate the faceted search of academic literature. This study aims to build a high-performance model for identifying of the functional structures of unstructured abstracts in the social sciences. Design/methodology/approach This study first investigated the structuring of abstracts in academic articles in the field of social sciences, using large-scale statistical analyses. Then, the functional structures of sentences in the abstract in a corpus of more than 3.5 million abstracts were identified from sentence classification and sequence tagging by using several models based on either machine learning or a deep learning approach, and the results were compared. Findings The results demonstrate that the functional structures of sentences in abstracts in social science manuscripts include the background, purpose, methods, results and conclusions. The experimental results show that the bidirectional encoder representation from transformers exhibited the best performance, the overall F1 score of which was 86.23%. Originality/value The data set of annotated social science abstract is generated and corresponding models are trained on the basis of the data set, both of which are available on Github ( https://github.com/Academic-Abstract-Knowledge-Mining/SSCI_Abstract_Structures_Identification ). Based on the optimised model, a Web application for the identification of the functional structures of abstracts and their faceted search in social sciences was constructed to enable rapid and convenient reading, organisation and fine-grained retrieval of academic abstracts.
Conflicting Statements Detection Using Bi-Directional LSTM

As data sources become more and more diversified, the importance of data mismatch detection increases. Contradiction is a semantic relation where two sentences cannot be true simultaneously. Conflicts can happen because of lexical features, such as negation, antonyms or numerical mismatch, or can be adapted upon time or potentially physiological status. There are many methods to detect conflicting statements. The method based on Deep Learning, Artificial Neural Networks (ANN), Bi-directional long short-term memory, and Global vectors for word representation (GloVe) is used for conflicting text statements detection. The Bidirectional Long short-term memory (LSTM) model is built to increase the accuracy of detecting conflicting statements. GloVe is a count model, looking at how frequently a word appears in another word context (co-occurrence probabilities), within the whole corpus. Thus, the GloVe model learns the word representation vector by performing dimensionality reduction on a co-occurrence counts matrix. Google colab is used for implementing this. Tensorflow machine learning framework is utilized for training and testing the predictive model for confliction detection in texts. The main aim is to build a system to identify inconsistencies and defiance in text. And also improve the performance of the model which can be evaluated using confusion matrix and accuracy.