Reviewerly/exHarmony huggingface dataset:
Emerging-market consumers’ interactions with banking chatbots

Chatbot development and adoption is a growing challenge in emerging markets, which are characterised by a young population structure, low-level internet penetration and a high degree of institutional adversity. The purpose of this study is to explore how consumers in emerging markets interact and engage with banking chatbots when conducting bank transactions. Based on qualitative research using semi-structured interviews with 36 Nigerian residents, this study demonstrates that the four factors of the unified theory of acceptance and use of technology (UTAUT) can explain how emerging-market consumers interact and engage with banking chatbots. While age and technological experience are significant components in facilitating the use of chatbots, perceived expertise, responsiveness and security were found to be particularly important to users. This study delves into user experiences with conversational interfaces, specifically focusing on emerging markets.
Psychological benefits of using social virtual reality platforms during the covid-19 pandemic: The role of social and spatial presence

Social virtual reality (VR) platforms are an emergent phenomenon, with growing numbers of users utilizing them to connect with others while experiencing feelings of presence ("being there"). This article examines the associations between feelings of presence and the activities performed by users, and the psychological benefits obtained in terms of relatedness, self-expansion, and enjoyment, in the context of the covid-19 pandemic. The results of a survey conducted among users (N = 220) indicate that feelings of spatial presence predict these three outcomes, while social presence predicts relatedness and enjoyment, but not self-expansion. Socialization activities like meeting friends in VR are associated with relatedness and enjoyment, while playful and creative activities allow for self-expansion. Moreover, the perceived impact of social distancing measures was associated with an increase in use, suggesting the utility of these platforms to help users meeting particularly frustrated psychological needs. These results provide a first quantitative account of the potential positive effects of social VR platforms on users' wellbeing and encourage further research on the topic.
Systematic Literature Review of Challenges in Blockchain Scalability

Blockchain technology is fast becoming the most transformative technology of recent times and has created hype and optimism, gaining much attention from the public and private sectors. It has been widely deployed in decentralized crypto currencies such as Bitcoin and Ethereum. Bitcoin is the success story of a public blockchain application that propelled intense research and development into blockchain technology. However, scalability remains a crucial challenge. Both Bitcoin and Ethereum are encountering low-efficiency issues with low throughput, high transaction latency, and huge energy consumption. The scalability issue in public Blockchains is hindering the provision of optimal solutions to businesses and industries. This paper presents a systematic literature review (SLR) on the public blockchain scalability issue and challenges. The scope of this SLR includes an in-depth investigation into the scalability problem of public blockchain, associated fundamental factors, and state-of-art solutions. This project managed to extract 121 primary papers from major scientific databases such as Scopus, IEEE explores, Science Direct, and Web of Science. The synthesis of these 121 articles revealed that scalability in public blockchain is not a singular term. A variety of factors are allied to it, with transaction throughput being the most discussed factor. In addition, other interdependent vita factors include storages, block size, number of nodes, energy consumption, latency, and cost. Generally, each term is somehow directly or indirectly reliant on the consensus model embraced by the blockchain nodes. It is also noticed that the contemporary available consensus models are not efficient in scalability and thus often fail to provide good QoS (throughput and latency) for practical industrial applications. Our findings exemplify that the Internet of Things (IoT) would be the leading application of blockchain in industries such as energy, finance, resource management, healthcare, education, and agriculture. These applications are, however, yet to achieve much-desired outcomes due to scalability issues. Moreover, Onchain and offchain are the two major categories of scalability solutions. Sagwit, block size expansion, sharding, and consensus mechanisms are examples of onchain solutions. Offchain, on the other hand, is a lighting network.
Breast cancer prediction using a hybrid method based on Butterfly Optimization Algorithm and Ant Lion Optimizer

The design and development of a computer-based system for breast cancer detection are largely reliant on feature selection techniques. These techniques are used to reduce the dimensionality of the feature space by removing irrelevant or redundant features from the original set. This article presents a hybrid feature selection method that is based on the Butterfly optimization algorithm (BOA) and the Ant Lion optimizer (ALO) to form a hybrid BOAALO method. The optimal subset of features chosen by BOAALO is utilized to predict the benign or malignant status of breast tissue using three classifiers: artificial neural network, adaptive neuro-fuzzy inference system, and support vector machine. The goodness of the proposed method is tested using 651 mammogram images. The results show that BOAALO outperforms the original BOA and ALO in terms of accuracy, sensitivity, specificity, kappa value, type-I, and type-II error as well as the receiver operating characteristics curve. Additionally, the suggested method's robustness is assessed and compared to five well-known methods using a benchmark dataset. The experimental findings demonstrate that BOAALO achieves a high degree of accuracy with a minimum number of features. These results support the suggested method's applicability for breast cancer diagnosis.
XVFI: eXtreme Video Frame Interpolation

In this paper, we firstly present a dataset (X4K1000FPS) of 4K videos of 1000 fps with the extreme motion to the research community for video frame interpolation (VFI), and propose an extreme VFI network, called XVFI-Net, that first handles the VFI for 4K videos with large motion. The XVFI-Net is based on a recursive multi-scale shared structure that consists of two cascaded modules for bidirectional optical flow learning between two input frames (BiOF-I) and for bidirectional optical flow learning from target to input frames (BiOF-T). The optical flows are stably approximated by a complementary flow reversal (CFR) proposed in BiOF-T module. During inference, the BiOF-I module can start at any scale of input while the BiOF-T module only operates at the original input scale so that the inference can be accelerated while maintaining highly accurate VFI performance. Extensive experimental results show that our XVFI-Net can successfully capture the essential information of objects with extremely large motions and complex textures while the state-of-the-art methods exhibit poor performance. Furthermore, our XVFI-Net framework also performs comparably on the previous lower resolution benchmark dataset, which shows a robustness of our algorithm as well. All source codes, pre-trained models, and proposed X4K1000FPS datasets are publicly available at https://github.com/JihyongOh/XVFI.
Networking and knowledge creation: Social capital and collaborative innovation in responding to the COVID-19 crisis

This study empirically explores the role of social capital in creating collaborative innovation and collective intelligence and maintaining organizational sustainability in the unprecedented COVID-19 crisis. Data were collected from a sample of 289 managers, directors and heads of departments of top 50 manufacturing firms in Jordan and analyzed using Smart-PLS-SEM. The results indicate that social capital significantly impacts collaborative innovation, collective intelligence and organization sustainability during the COVID-19 crisis. They also reveal that collective intelligence significantly impacts collaborative innovation and organization sustainability. This study enriches the literature on social capital, collaborative innovation and collective intelligence. It elucidates the role of such dynamic capabilities in maintaining both organizational sustainability and the chance of recovery from unprecedented crises.
Underwater Image Enhancement Quality Evaluation: Benchmark Dataset and Objective Metric

Due to the attenuation and scattering of light by water, there are many quality defects in raw underwater images such as color casts, decreased visibility, reduced contrast, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> . Many different underwater image enhancement (UIE) algorithms have been proposed to enhance underwater image quality. However, how to fairly compare the performance among UIE algorithms remains a challenging problem. So far, the lack of comprehensive human subjective user study with large-scale benchmark dataset and reliable objective image quality assessment (IQA) metric makes it difficult to fully understand the true performance of UIE algorithms. We in this paper make efforts in both subjective and objective aspects to fill these gaps. Firstly, we construct a new Subjectively-Annotated UIE benchmark Dataset (SAUD) which simultaneously provides real-world raw underwater images, readily available enhanced results by representative UIE algorithms, and subjective ranking scores of each enhanced result. Secondly, we propose an effective No-reference (NR) Underwater Image Quality metric (NUIQ) to automatically evaluate the visual quality of enhanced underwater images. Experiments on the constructed SAUD dataset demonstrate the superiority of our proposed NUIQ metric, achieving higher consistency with subjective rankings than 22 mainstream NR-IQA metrics. The dataset and source code will be made available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yia-yuese/SAUD-Dataset</uri> .
Predicting Workflow Task Execution Time in the Cloud Using A Two-Stage Machine Learning Approach

Many techniques such as scheduling and resource provisioning rely on performance prediction of workflow tasks for varying input data.However, such estimates are difficult to generate in the cloud.This paper introduces a novel two-stage machine learning approach for predicting workflow task execution times for varying input data in the cloud.In order to achieve high accuracy predictions, our approach relies on parameters reflecting runtime information and two stages of predictions.Empirical results for four real world workflow applications and several commercial cloud providers demonstrate that our approach outperforms existing prediction methods.In our experiments, our approach respectively achieves a best-case and worst-case estimation error of 1.6 and 12.2 percent, while existing methods achieved errors beyond 20 percent (for some cases even over 50 percent) in more than 75 percent of the evaluated workflow tasks.In addition, we show that the models predicted by our approach for a specific cloud can be ported with low effort to new clouds with low errors by requiring only a small number of executions.
Stabilized Cat in a Driven Nonlinear Cavity: A Fault-Tolerant Error Syndrome Detector

In quantum error correction, information is encoded in a high-dimensional system to protect it from the environment. A crucial step is to use natural, two-body operations with an ancilla to extract information about errors without causing backaction on the encoded information. Essentially, ancilla errors must not propagate to the encoded system and induce errors beyond those which can be corrected. The current schemes for achieving this fault tolerance to ancilla errors come at the cost of increased overhead requirements. An efficient way to extract error syndromes in a fault-tolerant manner is by using a single ancilla with a strongly biased noise channel. Typically, however, required elementary operations can become challenging when the noise is extremely biased. We propose to overcome this shortcoming by using a bosonic-cat ancilla in a parametrically driven nonlinear oscillator. Such a cat qubit experiences only bit-flip noise, while the phase flips are exponentially suppressed. To highlight the flexibility of this approach, we illustrate the syndrome extraction process in a variety of codes such as qubit-based toric, bosonic-cat, and Gottesman-Kitaev-Preskill codes. Our results open a path for realizing hardware-efficient, fault-tolerant error syndrome extraction.11 MoreReceived 7 August 2018Revised 16 July 2019DOI:https://doi.org/10.1103/PhysRevX.9.041009Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMeasurement-based quantum computingQuantum channelsQuantum error correctionQuantum gatesQuantum information processing with continuous variablesQuantum memoriesSurface code quantum computingTopological quantum computingQuantum Information
Cross Subspace Alignment and the Asymptotic Capacity of $X$ -Secure $T$ -Private Information Retrieval

X-secure and T-private information retrieval (XSTPIR) is a form of private information retrieval where data security is guaranteed against collusion among up to X servers and the user's privacy is guaranteed against collusion among up to T servers. The capacity of XSTPIR is characterized for an arbitrary number of servers N and arbitrary security and privacy thresholds X and T, in the limit as the number of messages K → ∞. Capacity is also characterized for any number of messages if either N = 3, X = T = 1 or if N ≤ X +T. Insights are drawn from these results, about aligning versus decoding noise, dependence of PIR rate on field size, and robustness to symmetric security constraints. In particular, the idea of cross subspace alignment, i.e., introducing a subspace dependence between Reed-Solomon code parameters, emerges as the optimal way to align undesired terms while keeping desired terms resolvable.
Continuous Gesture Segmentation and Recognition Using 3DCNN and Convolutional LSTM

Continuous gesture recognition aims at recognizing the ongoing gestures from continuous gesture sequences and is more meaningful for the scenarios, where the start and end frames of each gesture instance are generally unknown in practical applications. This paper presents an effective deep architecture for continuous gesture recognition. First, continuous gesture sequences are segmented into isolated gesture instances using the proposed temporal dilated Res3D network. A balanced squared hinge loss function is proposed to deal with the imbalance between boundaries and nonboundaries. Temporal dilation can preserve the temporal information for the dense detection of the boundaries at fine granularity, and the large temporal receptive field makes the segmentation results more reasonable and effective. Then, the recognition network is constructed based on the 3-D convolutional neural network (3DCNN), the convolutional long-short-term-memory network (ConvLSTM), and the 2-D convolutional neural network (2DCNN) for isolated gesture recognition. The “3DCNN-ConvLSTM-2DCNN” architecture is more effective to learn long-term and deep spatiotemporal features. The proposed segmentation and recognition networks obtain the Jaccard index of 0.7163 on the Chalearn LAP ConGD dataset, which is 0.106 higher than the winner of 2017 ChaLearn LAP Large-Scale Continuous Gesture Recognition Challenge.
MRI brain tumor segmentation based on texture features and kernel sparse coding

An automatic brain tumor segmentation method based on texture feature and kernel sparse coding from FLAIR (fluid attenuated inversion recovery) contrast-enhanced MRIs (magnetic resonance imaging) is presented in this paper. First, the MRIs are pre-processed to reduce noise, enhance contrast and correct the intensity non-uniformity. Then sparse coding is performed on the first order and second order statistical eigenvector extracted from original MRIs which is a patch of 3 × 3 around the voxel. The kernel dictionary learning is used to extract the non-linear features to construct two adaptive dictionaries for healthy and pathologically tissues respectively. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels, then the linear discrimination method is used to classify the target pixels. In the end, the flood-fill operation is used to improve the segmentation quality. The results demonstrate that the method based on kernel sparse coding has better capacity and higher segmentation accuracy with low computation cost.
Domain Generalization with Domain-Specific Aggregation Modules

Visual recognition systems are meant to work in the real world. For this to happen, they must work robustly in any visual domain, and not only on the data used during training. Within this context, a very realistic scenario deals with domain generalization, i.e. the ability to build visual recognition algorithms able to work robustly in several visual domains, without having access to any information about target data statistic. This paper contributes to this research thread, proposing a deep architecture that maintains separated the information about the available source domains data while at the same time leveraging over generic perceptual information. We achieve this by introducing domain-specific aggregation modules that through an aggregation layer strategy are able to merge generic and specific information in an effective manner. Experiments on two different benchmark databases show the power of our approach, reaching the new state of the art in domain generalization.
An Iterative Image Dehazing Method With Polarization

This paper presents a joint dehazing and denoising scheme for an image taken in hazy conditions. Conventional image dehazing methods may amplify the noise depending on the distance and density of the haze. To suppress the noise and improve the dehazing performance, an imaging model is modified by adding the process of amplifying the noise in hazy conditions. This model offers depth-chromaticity compensation regularization for the transmission map and chromaticity-depth compensation regularization for dehazing the image. The proposed iterative image dehazing method with polarization uses these two joint regularization schemes and the relationship between the transmission map and dehazed image. The transmission map and irradiance image are used to promote each other. To verify the effectiveness of the algorithm, polarizing images of different scenes in different days are collected. Different algorithms are applied to the original images. Experimental results demonstrate that the proposed scheme increases visibility in extreme weather conditions without amplifying the noise.
Fractional-order orthogonal Chebyshev Moments and Moment Invariants for image representation and pattern recognition

In this paper, we present a new set of fractional-order orthogonal moments, named Fractional-order Chebyshev Moments (FCM). We initially introduce the necessary relations and properties to define the FCM in the Cartesian coordinates. Then, we provide the theoretical framework to construct the Fractional-order Chebyshev Moment Invariants (FCMI), which are invariants with respect to rotation, scaling and translation transforms. In addition, we devoted a substantial attention to enhance their computational time and numerical accuracy. Consequently, the numerical experiments are carried out to demonstrate the validity of the introduced fractional-order moments and moment invariants in comparison with the classical methods, with regard to image representation capability and object recognition accuracy on several publicly available databases. The presented theoretical and experimental results demonstrate the efficiency and the superiority of the proposed method.
Protecting Trajectory From Semantic Attack Considering ${k}$ -Anonymity, ${l}$ -Diversity, and ${t}$ -Closeness

Nowadays, human trajectories are widely collected and utilized for scientific research and business purpose. However, publishing trajectory data without proper handling might cause severe privacy leakage. A large body of works is dedicated to merging one’s trajectory with others’, so as to avoid any individual trajectory being re-identified. Yet their solutions do not provide enough protection since they cannot prevent semantic attack, which means the attackers are able to acquire individual’s private information by using the semantics features of frequently visited locations in the trajectory even without re-identification. In this paper, we are the first to recognize the semantic attack, which is another severe privacy problem in publishing trajectory datasets. We propose an algorithm providing strong privacy protection against both the semantic and re-identification attack while reserving high data utility. Extensive evaluations based on two real-world datasets demonstrate that our solution improves the quality of privacy protection by three times, sacrificing only 36% and 10% of spatial and temporal resolution, respectively.
Users' continued participation behavior in social Q&amp;A communities: A motivation perspective

Social Q&A communities are becoming increasingly popular, but literature on users' continued participation is still relatively limited. Based on theory of planned behavior, this paper aims to answer two research questions: (1) what are the factors that motivate users to continuously participate in online social Q&A communities? (2) How do the factors differ across lurkers, askers and answerers? An online survey was performed on a Chinese social Q&A community. And motivational factors are selected from three perspectives: psychological, social and functional. Results indicate that commitment, shared language and shared vision have positive influence on both lurkers' and answerers' attitude towards continued participation. Concerning the functional dimension, hypotheses about influence of network externalities on users' perceived usefulness are partially supported. And about the psychological dimension, motivations differ greatly between lurkers and answerers.