Reviewerly/exHarmony huggingface dataset:
The Open Innovation in Science research field: a collaborative conceptualisation approach

Openness and collaboration in scientific research are attracting increasing attention from scholars and practitioners alike. However, a common understanding of these phenomena is hindered by disciplinary boundaries and disconnected research streams. We link dispersed knowledge on Open Innovation, Open Science, and related concepts such as Responsible Research and Innovation by proposing a unifying Open Innovation in Science (OIS) Research Framework. This framework captures the antecedents, contingencies, and consequences of open and collaborative practices along the entire process of generating and disseminating scientific insights and translating them into innovation. Moreover, it elucidates individual-, team-, organisation-, field-, and society‐level factors shaping OIS practices. To conceptualise the framework, we employed a collaborative approach involving 47 scholars from multiple disciplines, highlighting both tensions and commonalities between existing approaches. The OIS Research Framework thus serves as a basis for future research, informs policy discussions, and provides guidance to scientists and practitioners.
Investment decision and coordination of green agri-food supply chain considering information service based on blockchain and big data

Researches about the fusion application of Big Data and blockchain have appeared for a long time, many information service providers have launched information service business based on Big Data and blockchain (hereafter, ISBD). However, in the green agri-food area, the ISBD application does not popularized. A vital reason is that many decision makers do not know how to make an optimal investment decision and coordinate chain members after adopting ISBD. The core of this problem is to study the issue of investment decision and coordination in a green agri-food supply chain. To solve this problem, firstly, combining with the status of Chinese agricultural development, we proposed a more suitable supply chain structure in the fusion application environment of Big Data and blockchain. Then, we chose a green agri-food supply chain with one producer and one retailer as research object and revised the demand function. Afterwards, considering the changes of agri-food freshness and greenness, we built and analysed the benefit models of producer and retailer before and after using ISBD, and then a cost-sharing and revenue-sharing contract was put forward to coordinate the supply chain. Findings: 1) When the total investment cost payed by producer and retailer is in a certain range, using ISBD will help chain members gain more benefits. 2) If chain members want to gain more benefits after using ISBD, they should try their best to optimize costs by extracting valuable information. Results can offer a theoretical guidance for producer and retailer in investing in ISBD, pricing decision and supply chain coordination after applying ISBD.
Application of genetic algorithm and BP neural network in supply chain finance under information sharing

The supply chain finance industry will generate the flow of funds and commodities when providing financing services to small and medium-sized enterprises (SMEs). At this time, banks will face multiple risks such as policy, operation, market and credit. The investigation on supply chain finance under information sharing from the aspect of credit risk assessment will be conducted. The genetic algorithm combined with support vector machine and BP neural network is selected to evaluate the credit risk of supply chain finance. In the support vector machine method, the parameter selection method adopts genetic algorithm. In the included data, the gap in growth capacity of SMEs is relatively large. The standard deviations of main business, net profit and total assets are all above 30%, and the standard deviations of current ratio and quick ratio are small, which means that the two are more stable and healthier. In addition, among all the investigated enterprises, the cost gap is large, and the standard deviation of the inventory decline price reserve is small, which means that most enterprises have good inventory quality. After classification, 32 high-quality enterprises, 46 neutral enterprises and 55 risk enterprises are obtained in the total sample. In the test sample, there are 21 high-quality enterprises, 12 neutral enterprises, and 26 risk enterprises. The overall classification accuracy of the support vector machine method optimized by genetic algorithm is relatively lower than that of the BP neural network method. The classification accuracy of the support vector machine method optimized by genetic algorithm is 76.27%, and the classification accuracy of BP neural network method is 89.83%. The supply chain financial risk assessment of SMEs is mainly explored from the perspective of banks. The results can provide theoretical support for reducing the probability of bank's profit damage and increasing the bank's profitability.
Sentiment Lexicon Expansion using Word2vec and fastText for Sentiment Prediction in Tamil texts

Sentiment Analysis is the process of identifying and categorising the sentiments expressed in a text into positive or negative. The words which carry the sentiments are the keys in sentiment prediction. The SentiWordNet is the sentiment lexicon used to determine the sentiment of texts. There are huge number of sentiment terms that are not in the SentiWordNet limit the performance of Sentiment Analysis. Gathering and grouping such sentiment words manually is a tedious task. In this paper we propose a sentiment lexicon expansion method using Word2vec and fastText word embeddings along with rule-based Sentiment Analysis method. We expand the sentiment lexicon from the initial seed list of 2951 positive and 5598 negative words in two steps: (i) Gathering related words using Word2vec word embedding and (ii) Gathering lexically similar words using fastText word embedding. Our final lexicons UJ_Lex_Pos and UJ_Lex_Neg ended up with 10537 positive and 12664 negative words respectively which are labelled using Word2vec word embedding. Furthermore the rule-based Sentiment Analysis method uses expanded lexicons (UJ_Lex_Pos and UJ_Lex_Neg), lists of conjunctions and negational words to predict the sentiments expressed in Tamil texts. The method is evaluated on UJ_MovieReviews and an accuracy of 88 0.14% is obtained.
QoS Prediction for Service Recommendation With Features Learning in Mobile Edge Computing Environment

In recent years, deep neural networks have achieved exciting results in a variety of tasks, and many fields try to introduce neural network techniques. In mobile edge computing, there are not many attempts that build neural network models in service recommendation or QoS (quality-of-service) prediction. The method proposed in this article is an attempt to employ neural network technique for QoS prediction. Compared to the pure use of QoS records, the exploration for context information in QoS prediction also still needs a lot of efforts. But an increasing number of features are highly likely to result in overfitting problem, especially in the case that the data size is small. To solve those problems, in this article, we propose several new techniques, including denoising auto-encoder with fuzzy clustering (DAFC) and recombination embedding network, focusing on how to use context information and how to alleviate overfitting problem. DAFC uses the denoising auto-encoder, which helps the fuzzy clustering algorithm overcome the defect that the performance is easy to be impacted by the number of clusters. Extensive experiments under different data densities show that these two network structures indeed improve the performance and reduce the overfitting problem.
A deep neural network based multi-task learning approach to hate speech detection

With the advent of the internet and numerous social media platforms, citizens now have enormous opportunities to express and share their opinions on various societal and political issues. This phenomenal growth of the internet, social media networks, and messaging platforms provide plenty of opportunities for building intelligent systems, but these are also being heavily misused by certain groups who often disseminate offensive, racial, and hate speeches. Hence, detecting hate speech at the right time plays a crucial role as its spread might affect social fabrics. In recent times, although a few benchmark datasets have emerged for hate speech detection, these are limited in volume and also do not follow any uniform annotation schema. In this paper, a deep multi-task learning (MTL) framework is proposed to leverage useful information from multiple related classification tasks in order to improve the performance of the individual task. The proposed multi-task model is based on the shared-private scheme that assigns shared and private layers to capture the shared-features and task-specific features from five classification tasks. Experiments1 on the 5 datasets show that the proposed framework attains encouraging performance in terms of macro-F1 and weighted-F1.
A new topic modeling based approach for aspect extraction in aspect based sentiment analysis: SS-LDA

With the widespread use of social networks, blogs, forums and e-commerce web sites, the volume of user generated textual data is growing exponentially. User opinions in product reviews or in other textual data are crucial for manufacturers, retailers and providers of the products and services. Therefore, sentiment analysis and opinion mining have become important research areas. In user reviews mining, topic modeling based approaches and Latent Dirichlet Allocation (LDA) are significant methods that are used in extracting product aspects in aspect based sentiment analysis. However, LDA cannot be directly applied on user reviews and on other short texts because of data sparsity problem and lack of co-occurrence patterns. Several studies have been published for the adaptation of LDA for short texts. In this study, a novel method for aspect based sentiment analysis, Sentence Segment LDA (SS-LDA) is proposed. SS-LDA is a novel adaptation of LDA algorithm for product aspect extraction. The experimental results reveal that SS-LDA is quite competitive in extracting products aspects.
PhysCap

Marker-less 3D human motion capture from a single colour camera has seen significant progress. However, it is a very challenging and severely ill-posed problem. In consequence, even the most accurate state-of-the-art approaches have significant limitations. Purely kinematic formulations on the basis of individual joints or skeletons, and the frequent frame-wise reconstruction in state-of-the-art methods greatly limit 3D accuracy and temporal stability compared to multi-view or marker-based motion capture. Further, captured 3D poses are often physically incorrect and biomechanically implausible, or exhibit implausible environment interactions (floor penetration, foot skating, unnatural body leaning and strong shifting in depth), which is problematic for any use case in computer graphics. We, therefore, present PhysCap , the first algorithm for physically plausible, real-time and marker-less human 3D motion capture with a single colour camera at 25 fps. Our algorithm first captures 3D human poses purely kinematically. To this end, a CNN infers 2D and 3D joint positions, and subsequently, an inverse kinematics step finds space-time coherent joint angles and global 3D pose. Next, these kinematic reconstructions are used as constraints in a real-time physics-based pose optimiser that accounts for environment constraints ( e.g. , collision handling and floor placement), gravity, and biophysical plausibility of human postures. Our approach employs a combination of ground reaction force and residual force for plausible root control, and uses a trained neural network to detect foot contact events in images. Our method captures physically plausible and temporally stable global 3D human motion, without physically implausible postures, floor penetrations or foot skating, from video in real time and in general scenes. PhysCap achieves state-of-the-art accuracy on established pose benchmarks, and we propose new metrics to demonstrate the improved physical plausibility and temporal stability.
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles. By intelligently aggregating the information received from multiple nearby vehicles, we can observe the same scene from different viewpoints. This allows us to see through occlusions and detect actors at long range, where the observations are very sparse or non-existent. We also show that our approach of sending compressed deep feature map activations achieves high accuracy while satisfying communication bandwidth requirements.
Comparison of Faster-RCNN, YOLO, and SSD for Real-Time Vehicle Type Recognition

This paper studies a method to recognize vehicle types based on deep learning model. Faster-RCNN, YOLO, and SSD, which can be processed in real-time and have relatively high accuracy, are presented in this paper. We trained each algorithm through an automobile training dataset and analyzed the performance to determine what is the optimized model for vehicle type recognition. The Yolov4 model outperforms other methods, showing 93% accuracy in recognizing the vehicle model.
Leveraging “human-likeness” of robotic service at restaurants

Despite the rise of human-robot interaction research, the mixed findings of human-likeness in consumer evaluation exist. Focusing on the restaurant sector, this research investigates how service robots’ varying levels of human-likeness of attributes (i.e., visual, vocal and verbal) influence consumption outcomes (e.g., service encounter evaluation, revisit intentions and positive word of mouth intentions) and the underlying mechanisms through cognition (i.e., perceived credibility) and positive emotion per Appraisal Theory. Drawing on a consumer experiment involving a total of 587 participants, results suggest that humanlike voice emerges as a dominant attribute affecting all three consumption outcomes. Humanlike language style positively affects service encounter evaluation but barely affects the other two outcomes. The significant effect of humanlike voice on three consumption outcomes is only explained by positive emotion whereas the effect of humanlike language style on service encounter evaluation is explained by both cognition (i.e., perceived credibility) and emotion.
Dynamic Selection Network for Image Inpainting

Image inpainting is a challenging computer vision task that aims to fill in missing regions of corrupted images with realistic contents. With the development of convolutional neural networks, many deep learning models have been proposed to solve image inpainting issues by learning information from a large amount of data. In particular, existing algorithms usually follow an encoding and decoding network architecture in which some operations with standard schemes are employed, such as static convolution, which only considers pixels with fixed grids, and the monotonous normalization style (e.g., batch normalization). However, these techniques are not well-suited for the image inpainting task because the random corrupted regions in the input images tend to mislead the inpainting process and generate unreasonable content. In this paper, we propose a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic selection network (DSNet)</b> to solve this problem in image inpainting tasks. The principal idea of the proposed DSNet is to distinguish the corrupted region from the valid ones throughout the entire network architecture, which may help make full use of the information in the known area. Specifically, the proposed DSNet has two novel dynamic selection modules, namely, the validness migratable convolution (VMC) and regional composite normalization (RCN) modules, which share a dynamic selection mechanism that helps utilize valid pixels better. By replacing vanilla convolution with the VMC module, spatial sampling locations are dynamically selected in the convolution phase, resulting in a more flexible feature extraction process. Besides, the RCN module not only combines several normalization methods but also normalizes the feature regions selectively. Therefore, the proposed DSNet can illustrate realistic and fine-detailed images by adaptively selecting features and normalization styles. Experimental results on three public datasets show that our proposed method outperforms state-of-the-art methods both quantitatively and qualitatively.
Rise of the Machines? Customer Engagement in Automated Service Interactions

Artificial intelligence (AI) is likely to spawn revolutionary transformational effects on service organizations, including by impacting the ways in which firms engage with their customers. In parallel, customer engagement (CE), which reflects customer interactions with brands, offerings, or firms, has risen to the top of many managers’ strategic wish lists in the last decade. However, despite literature-based advances made in both areas, AI and CE are largely investigated in isolation to date, yielding a paucity of insight into their interface. In response to this gap, this Special Issue offers a pioneering exploration of CE in automated or AI-based service interactions. Our editorial first reviews AI’s Industry 4.0 underpinnings, followed by an important AI typology that comprises robotic process automation (RPA), machine learning (ML), and deep learning (DL) applications. We then offer a high-level synopsis of existing CE research, followed by the development of a set of integrative propositions of CE in automated service interactions. Next, we introduce the Special Issue papers, which feature particular RPA, ML, or DL applications. We conclude with an overview of further research avenues in this growing area, which has the potential to develop into a powerful service research substream in the coming years.
Toward Resource-Efficient Federated Learning in Mobile Edge Computing

Federated learning is a newly emerged distributed deep learning paradigm, where the clients separately train their local neural network models with private data and then jointly aggregate a global model at the central server. Mobile edge computing is aimed at deploying mobile applications at the edge of wireless networks. Federated learning in mobile edge computing is a prospective distributed framework to deploy deep learning algorithms in many application scenarios. The bottleneck of federated learning in mobile edge computing is the intensive resources of mobile clients in computation, bandwidth, energy, and data. This article first illustrates the typical use cases of federated learning in mobile edge computing, and then investigates the state-of-the-art resource optimization approaches in federated learning. The resource-efficient techniques for federated learning are broadly divided into two classes: the black-box and white-box approaches. For black-box approaches, the techniques of training tricks, client selection, data compensation, and hierarchical aggregation are reviewed. For white-box approaches, the techniques of model compression, knowledge distillation, feature fusion, and asynchronous update are discussed. After that, a neural-structure-aware resource management approach with module-based federated learning is proposed, where mobile clients are assigned with different subnetworks of the global model according to the status of their local resources. Experiments demonstrate the superiority of our approach in elastic and efficient resource utilization.
Image synthesis with adversarial networks: A comprehensive survey and case studies

Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, transforming an object or person to a desired shape become a well-studied research in the GANs. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training. Given the current fast GANs development, in this survey, we provide a comprehensive review of adversarial models for image synthesis. We summarize the synthetic image generation methods, and discuss the categories including image-to-image translation, fusion image generation, label-to-image mapping, and text-to-image translation. We organize the literature based on their base models, developed ideas related to architectures, constraints, loss functions, evaluation metrics, and training datasets. We present milestones of adversarial models, review an extensive selection of previous works in various categories, and present insights on the development route from the model-based to data-driven methods. Further, we highlight a range of potential future research directions. One of the unique features of this review is that all software implementations of these GAN methods and datasets have been collected and made available in one place at https://github.com/pshams55/GAN-Case-Study.
Efficient Estimation of Pauli Observables by Derandomization

We consider the problem of jointly estimating expectation values of many Pauli observables, a crucial subroutine in variational quantum algorithms. Starting with randomized measurements, we propose an efficient derandomization procedure that iteratively replaces random single-qubit measurements by fixed Pauli measurements; the resulting deterministic measurement procedure is guaranteed to perform at least as well as the randomized one. In particular, for estimating any $L$ low-weight Pauli observables, a deterministic measurement on only of order $\mathrm{log}(L)$ copies of a quantum state suffices. In some cases, for example, when some of the Pauli observables have high weight, the derandomized procedure is substantially better than the randomized one. Specifically, numerical experiments highlight the advantages of our derandomized protocol over various previous methods for estimating the ground-state energies of small molecules.
A Survey on Knowledge Graph Embeddings for Link Prediction

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform the link-prediction task based on various representation techniques. Among them, KG-embedding models have significantly advanced the state of the art in the past few years. In this paper, we provide a comprehensive survey on KG-embedding models for link prediction in knowledge graphs. We first provide a theoretical analysis and comparison of existing methods proposed to date for generating KG embedding. Then, we investigate several representative models that are classified into five categories. Finally, we conducted experiments on two benchmark datasets to report comprehensive findings and provide some new insights into the strengths and weaknesses of existing models.