Reviewerly/exHarmony huggingface dataset:
Blockchain’s impact on platform supply chains: transaction cost and information transparency perspectives

This study examines the impact of blockchain technology on platform supply chains from the perspectives of information transparency and transaction cost. We build a hybrid-format supply chain in which one supplier adopts an agency selling format and sells a product through the platform by paying a commission fee, while the other supplier adopts a reselling format. A game theory model is adopted to solve the key question: What is the best blockchain strategy for each supply chain member? Our research shows that blockchain technology can be a powerful weapon for platforms to adjust prices and control the market. As for the two suppliers in our supply chain, they will become better off with blockchain technology due to the elimination of information asymmetry and transaction costs. Moreover, the optimal strategies for platforms strongly depend on transaction cost, commission rate, and blockchain’s operation cost. Furthermore, we also provide a side-payment contract to help different players achieve Pareto improvement, thus reaching a win-win-win outcome. Our findings not only provide guidance for the adoption of blockchains in the platform economy but also complement the gap in research on information sharing.
Investor sentiment and Bitcoin relationship: A quantile-based analysis

This paper applies a quantile-based analysis to investigate the causal relationships between Bitcoin and investor sentiment by considering the possible effects of the ongoing COVID-19 pandemic. Such an analysis allows investigating the predictive power of investor sentiment (Bitcoin) on Bitcoin (investor sentiment) at different levels of the distributions. Results emphasize that only Bitcoin returns/volatility have significant predictive power on the investor sentiment whether investors are fear or greed before and over the COVID-19 period. Moreover, the COVID-19 crisis has no effect on the causal relationship between the two variables. Further analysis shows an asymmetric causality observed only during the pandemic period. Furthermore, the quantile autoregressive regression model shows a significant positive relationship between investor sentiment and Bitcoin returns.
Phobia Exposure Therapy Using Virtual and Augmented Reality: A Systematic Review

A specific phobia is a common anxiety-related disorder that can be treated efficiently using different therapies including exposure therapy or cognitive therapy. One of the most famous methods to treat a specific phobia is exposure therapy. Exposure therapy involves exposing the target patient to the anxiety source or its context without the intention to cause any danger. One promising track of research lies in VR exposure therapy (VRET) and/or AR exposure therapy (ARET), where gradual exposure to a negative stimulus is used to reduce anxiety. In order to review existing works in this field, a systematic search was completed using the following databases: PubMed, ProQuest, Scopus, Web of Science, and Google Scholar. All studies that present VRET and/or ARET solutions were selected. By reviewing the article, each author then applied the inclusion and exclusion criteria, and 18 articles were selected. This systematic review aims to investigate the previous studies that used either VR and/or AR to treat any type of specific phobia in the last five years. The results demonstrated a positive outcome of virtual reality exposure treatment in the treatment of most phobias. In contrast, some of these treatments did not work for a few specific phobias in which the standard procedures were more effective. Besides, the study will also discuss the best of both technologies to treat a specific phobia. Furthermore, this review will present the limitations and future enhancements in this field.
Influence-Aware Attention Networks for Anomaly Detection in Surveillance Videos

Detecting anomalies in videos is a fundamental issue in public security. The majority of existing deep learning methods often perform anomaly detection based on the behavior or the trajectory of a single target. However, due to the overlaps of the crowd and the low-resolution of monitoring images, the segmentation of population is hard to implement and the features cannot be learned thoroughly, which make the methods be easily disturbed by visual elements and thus may lead to false detection sometimes. To tackle these problems, we propose the influence-aware attention to learn the representative attributes of the whole crowd. Walking pedestrians can be divided into numbers of flows, and in this paper, we aim to measure the consistency of movement patterns in the same stream and the interactions between different streams. Meanwhile, great importance is given to the relation between pedestrians and the circumstance for certain anomalies occur as a result of environmental issues. Specifically, the influence-aware attention module is composed of the motion attention and the location attention, which is designed to quantify the relations in the scene from spatial and temporal aspects. For the lack of abnormal samples, we utilize a dual generator-based framework to learn interactions among normal scenes. Experimental results on six benchmarks verify the effectiveness and robustness of our proposed method.
Blockchain-assisted searchable attribute-based encryption for e-health systems

The Internet of Things (IoT) is becoming increasingly popular for transmitting massive data in a network. With a limited supply of IoT devices, utilizing cloud storage is considered to be an optimal way of processing data. Although cloud computing provides an efficient means of data storage and secure sharing in resource-limited devices, it is still a challenge to enable end users to publish search queries and implement fine-grained access control over a ciphertext at the same time. Blockchain-assisted searchable attribute-based encryption (SABE) can achieve data confidentiality and fine-grained access control. In our work, we present blockchain-assisted SABE for e-health systems. Our framework not only combines the advantages of attribute-based encryption (ABE) and blockchain technology but also supports hidden policies. A rigorous security proof shows that our scheme can achieve the known security under a chosen keyword attack. An experiment indicates that the proposal is suitable for e-health systems.
An improved algorithm for practical byzantine fault tolerance to large-scale consortium chain

Consortium chain can better combine blockchain technology with market business, so it is adopted by all walks of life and develops at a large scale. Practical Byzantine Fault Tolerance (PBFT) is more suitable for consortium chain, which are partially decentralized, resistant to Byzantine nodes and strong consistency. However, the limited network scale that PBFT can support is not conducive to the large-scale development of consortium chain. Based on the analysis of the working principle and consensus mechanism of PBFT, this study proposed an algorithm to improve PBFT: feature grouping and credit optimization Byzantine Fault Tolerance (FCBFT). In this algorithm, a feature grouping model is proposed to optimize the node structure of large-scale consortium chain, which divides large-scale network nodes into different institutions to form independent consensus groups by feature grouping. On this basis, a reputation score reward mechanism is proposed to improve the consensus efficiency of large-scale consortium chains. It introduces a reputation score calculation formula to select high-reputation primary nodes. At the same time, a replacement cycle is established to replace high-reputation nodes with low-reputation nodes, so as to optimize the consensus efficiency of the consortium chain. The experimental results show that FCBFT has shorter delay and higher throughput (TPS) than PBFT, which becomes more obvious as the number of nodes increases. After the number of nodes exceeds the threshold, the TPS of PBFT drops rapidly, while FCBFT can maintain high efficiency and stability. FCBFT's block generation speed is higher than PBFT, and its relative growth rate has steadily increased.
Emerging Concepts Using Blockchain and Big Data

Blockchain and big data are the emerging technologies that are on the highest agendas of the firms. These are significantly expected to transform the ways in which the business as well as the firm run. These are on the verge of increasing the expectation of the distributed ledgers, which would keep the firms away from struggling challenges. The concept of big data and Blockchain has been used up by various other concepts that would help secure and interpret the information. The ideal solutions offered by these technologies shall address the challenges of big data management as well as for analytics. In addition to that, Blockchain provides its own consensus method, which is the primary means to create an audit trail. This enables users to verify all transactions. The audit trail is a means of verifying the correctness and integrity of every transaction, regardless of who owns the asset. The Blockchain can also verify that different parties of a transaction are following an agreement and not breaking the agreement. Moreover, there have been continuous arguments in the concept of Blockchain at which the bitcoin is fundamental and there are several popular blockchain approaches developed which would deliver performance, security as well as privacy. Apart from this, the use of Blockchain plays a major role in adding an extra data layer for the big data analytics process. Big data is considered secure which cannot be further forged with the network architecture. The current paper shall be discussing the emerging concepts that are using Blockchain as well as big data.
Everybody’s Talkin’: Let Me Talk as You Want

We present a method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video. This method is unique because it is highly dynamic. It does not assume a person-specific rendering network yet capable of translating one source audio into one random chosen video output within a set of speech videos. Instead of learning a highly heterogeneous and nonlinear mapping from audio to the video directly, we first factorize each target video frame into orthogonal parameter spaces, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , expression, geometry, and pose, via monocular 3D face reconstruction. Next, a recurrent network is introduced to translate source audio into expression parameters that are primarily related to the audio content. The audio-translated expression parameters are then used to synthesize a photo-realistic human subject in each video frame, with the movement of the mouth regions precisely mapped to the source audio. The geometry and pose parameters of the target human portrait are retained, therefore preserving the context of the original video footage. Finally, we introduce a novel video rendering network and a dynamic programming method to construct a temporally coherent and photo-realistic video. Extensive experiments demonstrate the superiority of our method over existing approaches. Our method is end-to-end learnable and robust to voice variations in the source audio.
Explainable autonomous robots: a survey and perspective

Advanced communication protocols are critical for the coexistence of autonomous robots and humans. Thus, the development of explanatory capabilities in robots is an urgent first step toward realizing autonomous robots. This survey provides an overview of the various types of ‘explainability’ discussed in the machine learning literature. The definition of ‘explainability’ in the context of autonomous robots is then discussed by exploring the question: ‘What is an explanation?’ We further conduct a survey based on this definition and present relevant topics for future research in this paper.
Privacy-preserving Naive Bayes classification in semi-fully distributed data model

In recent years, issues of privacy preservation in data mining and machine learning have received more and more attention from the research community. Privacy-preserving data mining and machine learning solutions enable data holders to jointly discover knowledge and valuable information, as well as construct machine learning models without privacy concerns. In this paper, we address the distressing problem of privacy-preservation for a novel data model called the semi-fully distributed setting. Differently from the existing scenarios, each record of the dataset in this data model is composed of three parts, in which the first part is privately kept by a data user, the second one is securely stored by the miner, and the rest is publicly known by both the miner and the data user. For this new data model, we propose a privacy-preserving Naive Bayes classification solution based on secure multi-party computation. Our proposed solution not only achieves a high level of privacy but also guarantees the accuracy of the classification model. The experimental results show that the new proposal is efficient in real-life applications. Furthermore, our pioneering study paves the way for new researches into privacy preservation issues for the semi-fully distributed data model.
Extreme-learning-machine-based robust integral terminal sliding mode control of bicycle robot

In this paper, an extreme-learning-machine (ELM)-based robust integral terminal sliding mode (ITSM) control scheme is developed for a bicycle robot (BR) to achieve balancing target. First, the bicycle robot equipped with a reaction wheel is formulated by a second-order mathematical model with uncertainties. Then, an ITSM controller is designed for the balancing control of the BR, where an ELM scheme is designed as a compensator for estimating lumped uncertainties of the system. The stability proof of the closed-loop control system is presented based on Lyapunov theory. Comparative experimental results are demonstrated to verify the superior balancing performance of the proposed control.
Disease prediction with edge-variational graph convolutional networks

The need for computational models that can incorporate imaging data with non-imaging data while investigating inter-subject associations arises in the task of population-based disease analysis. Although off-the-shelf deep convolutional neural networks have empowered representation learning from imaging data, incorporating data of different modalities complementarily in a unified model to improve the disease diagnostic quality is still challenging. In this work, we propose a generalizable graph-convolutional framework for population-based disease prediction on multi-modal medical data. Unlike previous methods constructing a static affinity population graph in a hand-crafting manner, the proposed framework can automatically learn to build a population graph with variational edges, which we show can be optimized jointly with spectral graph convolutional networks. In addition, to estimate the predictive uncertainty related to the constructed graph, we propose Monte-Carlo edge dropout uncertainty estimation. Experimental results on four multi-modal datasets demonstrate that the proposed method can substantially improve the predictive accuracy for Autism Spectrum Disorder, Alzheimer's disease, and ocular diseases. A sufficient ablation study with in-depth discussion is conducted to evaluate the effectiveness of each component and the choice of algorithmic details of the proposed method. The results indicate the potential and extendability of the proposed framework in leveraging multi-modal data for population-based disease prediction.
Cross-Lingual Few-Shot Hate Speech and Offensive Language Detection Using Meta Learning

Automatic detection of abusive online content such as hate speech, offensive language, threats, etc. has become prevalent in social media, with multiple efforts dedicated to detecting this phenomenon in English.However, detecting hatred and abuse in low-resource languages is a non-trivial challenge.The lack of sufficient labeled data in low-resource languages and inconsistent generalization ability of transformer-based multilingual pre-trained language models for typologically diverse languages make these models inefficient in some cases.We propose a meta learning-based approach to study the problem of few-shot hate speech and offensive language detection in low-resource languages that will allow hateful or offensive content to be predicted by only observing a few labeled data items in a specific target language.We investigate the feasibility of applying a meta learning approach in cross-lingual few-shot hate speech detection by leveraging two meta learning models based on optimization-based and metric-based (MAML and Proto-MAML) methods.To the best of our knowledge, this is the first effort of this kind.To evaluate the performance of our approach, we consider hate speech and offensive language detection as two separate tasks and make two diverse collections of different publicly available datasets comprising 15 datasets across 8 languages for hate speech and 6 datasets across 6 languages for offensive language.Our experiments show that meta learning-based models outperform transfer learning-based models in a majority of cases, and that Proto-MAML is the best performing model, as it can quickly generalize and adapt to new languages with only a few labeled data points (generally, 16 samples per class yields an effective performance) to identify hateful or offensive content.
SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images

Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due to the intrinsic variations in size, intensity, and morphology of different types of nuclei. Herein, we propose a self-guided ordinal regression neural network for simultaneous nuclear segmentation and classification that can exploit the intrinsic characteristics of nuclei and focus on highly uncertain areas during training. The proposed network formulates nuclei segmentation as an ordinal regression learning by introducing a distance decreasing discretization strategy, which stratifies nuclei in a way that inner regions forming a regular shape of nuclei are separated from outer regions forming an irregular shape. It also adopts a self-guided training strategy to adaptively adjust the weights associated with nuclear pixels, depending on the difficulty of the pixels that is assessed by the network itself. To evaluate the performance of the proposed network, we employ large-scale multi-tissue datasets with 276349 exhaustively annotated nuclei. We show that the proposed network achieves the state-of-the-art performance in both nuclei segmentation and classification in comparison to several methods that are recently developed for segmentation and/or classification.
C-FDRL: Context-Aware Privacy-Preserving Offloading Through Federated Deep Reinforcement Learning in Cloud-Enabled IoT

Recently, artificial intelligence approaches are widely suggested to optimize numerous offloading task-scheduling purposes. However, they confront difficulties in maintaining data privacy regarding the context of the data offloading during the course of offloading in the different stages. To address this problem, in this article we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C-fDRL</monospace> , a framework to provide context-aware federated deep reinforcement learning (fDRL) to maintain the context-aware privacy of the task offloading. We perform this in three stages (CloudAI, EdgeAI, and DeviceAI) of the overall system. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C-fDRL</monospace> checks whether the privacy of high-context-aware data with the task being offloaded is maintained locally at the DeviceAI, and low-context-aware data distributedly at the EdgeAI. When there is an offloading task request or a user needs to offload the data, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C-fDRL</monospace> uses a context-aware data management approach to decouple the context-aware (privacy) data from the tasks. This separates the context-aware data from the task for local computation and allows a new scheduling technique called “context-aware multilevel scheduler.” This places high-context-aware data on local devices and low-context-aware data at the edge device for computation before the actual task execution. We performed experiments to evaluate the data privacy with the offloading tasks and the federated DRL. The results show that the proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C-fDRL</monospace> performs better than the existing framework.
Sentimental Analysis of Twitter Data using Machine Learning Algorithms

As social media is becoming a necessity for communication, a lot of data is available on this platform, which could be helpful for analysis. At a certain time, we use Twitter to tweet almost about similar topics, different emotions. In this article, sentimental analysis is proposed to get an idea about what people have in their minds or get people’s emotions. It segregates every tweet to its appropriate emotion. The emotion might be either positive or negative. The proposed methodology has two steps, namely preprocessing and classification. The corpus is created after all necessary preprocessing. The classification algorithms such as Logistic Regression, Linear SVC, Random Forest Classifier, Bernoulli NB, Decision Tree Classifier, Voting Classifier, and KNN Classifier are used for classification. Twitter 2020 and 2021 data has been taken for experimentation. The performance of Linear SVC shows a higher accuracy on training data, and Linear Regression shows higher accuracy in testing data.
Duplicated Reality for Co-located Augmented Reality Collaboration

When two or more users attempt to collaborate in the same space with Augmented Reality, they often encounter conflicting intentions regarding the occupation of the same working area and self-positioning around such without mutual interference. Augmented Reality is a powerful tool for communicating ideas and intentions during a co-assisting task that requires multi-disciplinary expertise. To relax the constraint of physical co-location, we propose the concept of Duplicated Reality, where a digital copy of a 3D region of interest of the users' environment is reconstructed in real-time and visualized in-situ through an Augmented Reality user interface. This enables users to remotely annotate the region of interest while being co-located with others in Augmented Reality. We perform a user study to gain an in-depth understanding of the proposed method compared to an in-situ augmentation, including collaboration, effort, awareness, usability, and the quality of the task. The result indicates almost identical objective and subjective results, except a decrease in the consulting user's awareness of co-located users when using our method. The added benefit from duplicating the working area into a designated consulting area opens up new interaction paradigms to be further investigated for future co-located Augmented Reality collaboration systems.