Reviewerly/exHarmony huggingface dataset:
Influence of Perceived Risk of Blockchain Art Trading on User Attitude and Behavioral Intention

In the first half of 2020, the average sales volume of gallery operators declined due to the COVID-19 pandemic and art galleries faced a crisis relating to their sustainable operation. However, crypto art finance, which combines online sales with blockchain, is attracting a considerable amount of attention. Can the use of blockchain solve the problems encountered in today’s art trading market? Is it considered acceptable by participants in the art trading market? What factors affect the behavioral intentions of blockchain technology users? In this study, we discussed the relationship between perceived risks and the three external variables of trust, government support, and auction house initiative, as well as their impacts on user attitudes and behavioral intentions regarding blockchain. The results of this study verified key factors that will help to increase the use of blockchain and solve existing market issues. It will also promote the sustainable operation and development of art enterprises and the market.
Multi-cloud serverless function composition

Function-as-a-service (FaaS) is an emerging model based on serverless cloud computing technology. It builds on the microservice architecture, where developers implement specific functionality, deploy it to a cloud provider to be executed independently in its own containerised environment. In this paper, we present a software composition approach that orchestrates FaaS from various cloud providers to fulfil the requirements of an application. Our solution integrates a hierarchical planner and a constraint satisfaction solver. Specifically, we discuss the planning method, constraint satisfaction solver, and the coordination of selected functions during the execution. We also present an experiment where our approach is tested using functions in the cloud.
The Pragmatic Functions of Facebook Likes on Status Updates: Evidence from User Perceptions

This paper examines the pragmatic functions of the like button when users interact with status-updates on Facebook. To collect the data, the researcher asked a friend who happened to be a university professor to write a post on his Facebook account in which he requested his students, colleagues and friends to report to him in a comment when each of them tends to press the like button on a status and for what purpose. The total number of the collected comments is 218. Analysis of the data reveals that in the Jordanian context, the like button is not solely used to show appreciation as originally envisaged by Facebook. Further, the findings indicate that the use of the like button has eventually developed as a graphic pragmatic marker with eight functions and illocutions ranging in purpose from showing unreserved approval and admiration of content to showing mockery of published content. Keywords: Computer-Mediated Communication; Emojis, Gestures; Like; Pragmatic Functions.
An Efficient Human Instance-Guided Framework for Video Action Recognition

In recent years, human action recognition has been studied by many computer vision researchers. Recent studies have attempted to use two-stream networks using appearance and motion features, but most of these approaches focused on clip-level video action recognition. In contrast to traditional methods which generally used entire images, we propose a new human instance-level video action recognition framework. In this framework, we represent the instance-level features using human boxes and keypoints, and our action region features are used as the inputs of the temporal action head network, which makes our framework more discriminative. We also propose novel temporal action head networks consisting of various modules, which reflect various temporal dynamics well. In the experiment, the proposed models achieve comparable performance with the state-of-the-art approaches on two challenging datasets. Furthermore, we evaluate the proposed features and networks to verify the effectiveness of them. Finally, we analyze the confusion matrix and visualize the recognized actions at human instance level when there are several people.
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization

Neural architecture search (NAS) and network pruning are widely studied efficient AI techniques, but not yet perfect. NAS performs exhaustive candidate architecture search, incurring tremendous search cost. Though (structured) pruning can simply shrink model dimension, it remains unclear how to decide the per-layer sparsity automatically and optimally. In this work, we revisit the problem of layer-width optimization and propose Pruning-as-Search (PaS), an end-to-end channel pruning method to search out desired sub-network automatically and efficiently. Specifically, we add a depth-wise binary convolution to learn pruning policies directly through gradient descent. By combining the structural reparameterization and PaS, we successfully searched out a new family of VGG-like and lightweight networks, which enable the flexibility of arbitrary width with respect to each layer instead of each stage. Experimental results show that our proposed architecture outperforms prior arts by around 1.0% top-1 accuracy under similar inference speed on ImageNet-1000 classification task. Furthermore, we demonstrate the effectiveness of our width search on complex tasks including instance segmentation and image translation. Code and models are released.
Closing the loop: automatically identifying abnormal imaging results in scanned documents

Abstract Objectives Scanned documents (SDs), while common in electronic health records and potentially rich in clinically relevant information, rarely fit well with clinician workflow. Here, we identify scanned imaging reports requiring follow-up with high recall and practically useful precision. Materials and methods We focused on identifying imaging findings for 3 common causes of malpractice claims: (1) potentially malignant breast (mammography) and (2) lung (chest computed tomography [CT]) lesions and (3) long-bone fracture (X-ray) reports. We train our ClinicalBERT-based pipeline on existing typed/dictated reports classified manually or using ICD-10 codes, evaluate using a test set of manually classified SDs, and compare against string-matching (baseline approach). Results A total of 393 mammograms, 305 chest CT, and 683 bone X-ray reports were manually reviewed. The string-matching approach had an F1 of 0.667. For mammograms, chest CTs, and bone X-rays, respectively: models trained on manually classified training data and optimized for F1 reached an F1 of 0.900, 0.905, and 0.817, while separate models optimized for recall achieved a recall of 1.000 with precisions of 0.727, 0.518, and 0.275. Models trained on ICD-10-labelled data and optimized for F1 achieved F1 scores of 0.647, 0.830, and 0.643, while those optimized for recall achieved a recall of 1.0 with precisions of 0.407, 0.683, and 0.358. Discussion Our pipeline can identify abnormal reports with potentially useful performance and so decrease the manual effort required to screen for abnormal findings that require follow-up. Conclusion It is possible to automatically identify clinically significant abnormalities in SDs with high recall and practically useful precision in a generalizable and minimally laborious way.
Challenges of Integrating Blockchain with Internet of Things

There is no doubt that Internet of Things (IoT) and blockchain technology will a major impact in the automated futuristic world. Even though the usage of IoT is increasing rapidly, it is riddled with scalability, security, privacy and integrity issues. Even though blockchain was initially created for managing cryptocurrencies, its decentralised nature, higher security, integrity and privacy has led to being integrated with IoT in order to improve it. There are multiple challenges arising from this integration which increases the complexities. It is necessary to study these challenges involved in this integration before carrying it out. Hence, this paper has carried out a systematic study of the various challenges involved in IoT individually and also the advantages and challenges of integrating it with the blockchain system.
Fundamental Models of Education of the Future

Overcoming a discord with representations of the phenomenon lies in ontological roots of education of the future. Hence, problem-cognitive and memory-based models that shape approaches to the truth of the phenomenon and completeness of its image through epistemological and cultural-historical dimensions are identified as fundamental models of education of the future. With regard to problem-cogni­tive education, its conceptual foundations are formulated; differences from collec­tive, problematic and traditional (translational) methods of learning are shown; the content of paradigmatic relations is revealed: functions, forms, methods and imper­ative of being. When explicating the memory-based education, the “memory-based cognition” and “memory-based knowledge” concepts are defined. An epistemic thing containing a cultural implant and an epistemic collection is presented as a model of “memory-based knowledge”. Memory-based learning is described, and its relation to the “chronotope” concept introduced by A.A. Ukhtomsky is shown. The conclusion is drawn that the truth of being of education is achievable by leav­ing the track of centuries-old class-lesson (lecture-seminar) system and transition to a new ontological onset beginning – its anticipatory image is given by problem-cognitive and memory-based models of education.
A physical layer authentication mechanism for IoT devices

When Internet of Things (IoT) nodes access the network through wireless channels, the network is vulnerable to spoofing attacks and the Sybil attack. However, the connection of massive devices in IoT makes it difficult to manage and distribute keys, thus limiting the application of traditional high-level authentication schemes. Compared with the high-level authentication scheme, the physical layer authentication scheme realizes the lightweight authentication of users by comparing the wireless channel characteristics of adjacent packets. However, traditional physical layer authentication schemes still adopt the one-to-one authentication method, which will consume numerous network resources in the face of large-scale IoT node access authentication. In order to realize the secure access authentication of IoT nodes and regional intrusion detection with low resource consumption, we propose a physical layer authentication mechanism based on convolution neural network (CNN), which uses the deep characteristics of channel state information (CSI) to identify sending nodes in different locations. Specifically, we obtain the instantaneous CSI data of IoT sending nodes at different locations in the pre-set area, and then feed them into CNN for training to procure a model for IoT node authentication. With its powerful ability of data analysis and feature extraction, CNN can extract deep Spatio-temporal environment features of CSI data and bind them with node identities. Accordingly, an authentication mechanism which can distinguish the identity types of IoT nodes located in different positions is established to authenticate the identity of unknown nodes when they break into the pre-set area. Experimental results show that this authentication mechanism can still achieve 94.7% authentication accuracy in the case of a low signal-to-noise ratio (SNR) of 0 dB, which means a significant improvement in authentication accuracy and robustness.
Fast threshold ECDSA with honest majority1

ECDSA is a widely adopted digital signature standard. A number of threshold protocols for ECDSA have been developed that let a set of parties jointly generate the secret signing key and compute signatures, without ever revealing the signing key. Threshold protocols for ECDSA have seen recent interest, in particular due to the need for additional security in cryptocurrency wallets where leakage of the signing key is equivalent to an immediate loss of money. We propose a threshold ECDSA protocol secure against an active adversary in the honest majority model with abort. Our protocol is efficient in terms of both computation and bandwidth usage, and it allows the parties to pre-process parts of the signature, such that once the message to sign becomes known, they can compute a secret sharing of the signature very efficiently, using only local operations. We also show how to obtain guaranteed output delivery (and hence also fairness) in the online phase at the cost of some additional pre-processing work, i.e., such that it either aborts during the pre-processing phase, in which case nothing is revealed, or the signature is guaranteed to be delivered to all honest parties online.
Using Ethereum Smart Contracts to Store and Share COVID-19 Patient Data

Introduction The emergence and rapid spread of the coronavirus disease 2019 (COVID-19) pandemic have revealed the limitations in current healthcare systems to handle patient records securely and transparently, and novel protocols are required to address these shortcomings. An attractive option is the use of Ethereum smart contracts to secure the storage of medical records and concomitant data logs. Ethereum is an open-source platform that can be used to construct smart contracts, which are collections of code that allow transactions under certain parameters and are self-executable. Methods The present study developed a proof-of-concept smart contract that stores COVID-19 patient data such as the patient identifier (ID), variant, chest CT grade, and significant comorbidities. A sample, fictitious patient data for the purpose of testing was configured to a private network. A smart contract was created in the Ethereum state and tested by measuring the time to insert and query patient data. Results Testing with a private, Proof of Authority (PoA) network required only 191 milliseconds and 890 MB of memory per insertion to insert 50 records while inserting 350 records required 674 milliseconds and similar memory per insertion, as memory per insertion was nearly constant with the increasing number of records inserted. Retrieving required 912 MB for a query involving all three fields and no wildcards in a 350-record database. Only 883 MB was needed to procure a similar observation from a 50-record database. Conclusion This study exemplifies the use of smart contracts for efficient retrieval/insertion of COVID-19 patient data and provides a case use of secure and efficient data logging for sensitive COVID-19 data.
Vulnerability Due to Training Order in Split Learning

Split learning (SL) is a privacy-preserving distributed deep learning method used to train a collaborative model without the need for sharing of patient’s raw data between clients. In split learning, an additional privacy-preserving algorithm called no-peek algorithm can be incorporated, which is robust to adversarial attacks. The privacy benefits offered by split learning make it suitable for practice in the healthcare domain. However, the split learning algorithm is flawed as the collaborative model is trained sequentially; i.e., one client trains after the other. We point out that the model trained using the split learning algorithm gets biased toward the data of the clients used for training toward the end of a round. This makes SL algorithms highly susceptible to the order in which clients are considered for training. We demonstrate that the model trained using the data of all clients does not perform well on the client’s data which was considered earliest in a round for training the model. Moreover, we show that this effect becomes more prominent with the increase in the number of clients. We also demonstrate that the SplitFedv3 algorithm mitigates this problem while still leveraging the privacy benefits provided by split learning.
Real-Time Multi-Object Tracking of Pedestrians in a Video Using Convolution Neural Network and Deep SORT

Multi-Object tracking in video processing is a challenging task in computer vision. Typically, tracking the objects in a video is performed using binocular vision or top-down camera movements. These methods encounter the problem of rigid boundaries on the counting area application scene. To overcome this problem, in this paper, a novel object detection algorithm is proposed by revamping YOLOv3 and real-time multi-object tracking is done by employing Deep SORT to track the targets by using movement representation and data association algorithms. It is a tracking-by-detection algorithm. The proposed algorithm is experimented on different public datasets and own dataset of videos of real scenes relevant to pedestrian movements. The results of experiments indicate that the proposed algorithm yields higher accuracy and exhibits better performance in real-time object tracking as compared to other object tracking techniques in the literature and hence is a reliable object-tracking algorithm.
A Survey on Innovative Graph-Based Clustering Algorithms

Common text-clustering algorithms often require a given hyperparameter a-priori to specify the number of clusters as well as the presence of all documents beforehand. From practical point of view this is a shortcoming especially for applications that process dynamically changing corpora, e.g. like in search-engines. Novel graph-based clustering algorithms have been recently developed in order to group similar topics, represented by documents or terms, in clusters without the need of the users intervention. Within this paper a comparison between classical clustering algorithms and graph-based algorithms is made in order to evaluate the state of the art and find further optimizations especially for the SeqClu (sequential clustering algorithm) which is in focus of the authors work.
Self-supervised representation learning for detection of ACL tear injury in knee MR videos

The success of deep learning based models for computer vision applications requires large scale human annotated data which are often expensive to generate. Self-supervised learning, a subset of unsupervised learning, handles this problem by learning meaningful features from unlabeled image or video data. In this paper, we propose a self-supervised learning approach to learn transferable features from MR video clips by enforcing the model to learn anatomical features. The pretext task models are designed to predict the correct ordering of the jumbled image patches that the MR video frames are divided into. To the best of our knowledge, none of the supervised learning models performing injury classification task from MR video provide any explanation for the decisions made by the models and hence makes our work the first of its kind on MR video data. Experiments on the pretext task show that this proposed approach enables the model to learn spatial context invariant features which help for reliable and explainable performance in downstream tasks like classification of Anterior Cruciate Ligament tear injury from knee MRI. The efficiency of the novel Convolutional Neural Network proposed in this paper is reflected in the experimental results obtained in the downstream task.
Teaching and confronting digital extremism: contexts, challenges and opportunities

Purpose This paper aims to offer practical guidance on teaching about digital extremism – defined here as the intersection of digital disinformation campaigns with political extremism – by highlighting four pedagogical challenges: the danger of unintentionally “redpilling” students; the slippery slope to false equivalency and “bothsidesism” in turbulent partisan waters; the difficulty of separating empirical analyses from prescriptive debates circulating in popular media; and the trouble of getting students to understand digital extremism as a sociotechnical problem rather than as a social-or-technical problem. The conclusion proposes opportunities for educators to integrate practical approaches to confronting digital extremism with digital civics curricula. Design/methodology/approach This paper reviews pedagogical challenges and outlines a curricular program for teaching about digital extremism drawn from the author’s experience designing undergraduate courses and open teaching modules between 2016 and 2021. Findings Educators should shift focus from the substance of digital extremism to its tools – social media platforms’ surveillance and data-gathering methods, advertising technologies and monetized user-generated content, personalized recommendation algorithms and media manipulation strategies that amplify some narratives while suppressing others – and the media and political institutions that benefit most from it. Proposed lessons include: how digital extremists manipulate social media metadata; engagement with data creation and targeting practices; and analysis of information production, circulation and consumption exploring media manipulation tools and their effects. Originality/value This paper’s added values are the insights and practical recommendations for undergraduate educators teaching on a topic of urgent contemporary concern: digital extremism.
Averaged Errors as a Risk Factor for Intelligent Forecasting Systems Operation in the Power Industry

The paper discusses the operational risk in intelligent systems for forecasting time series. Typically, when developing and testing regression models based on machine learning, their accuracy is calculated over a long time interval, from several months to several years, and then is averaged. However, in the real-life operation of such systems, the customer is likely to draw a conclusion about the system efficiency based on the results of the first 2–4 weeks of operation. If one or several large errors appear on this short interval, they will not be averaged as it happens over a long one. As a result, there is a risk of failure in the intelligent forecasting system implementation due to the discrepancy between the calculated mean error and that obtained over a short time period at the start of operation. This study considers the problem of solar power plant generation short-term forecasting, analyzes the distribution of errors over short time periods, and substantiates the need to use more detailed accuracy metrics of machine learning models than the error values averaged over a long interval.
Experience Mode of Digital Media Art under Virtual Reality Technology

With the progress of the country’s comprehensive strength and scientific strength, the development of science and technology has brought tremendous changes to people’s lives and at the same time brought information dissemination and media methods to a new stage. This article integrates VR technology into your digital media art experience. This combination not only reduces the distance between the experience and the art but also allows the experiencers to better understand what the artist wants to convey in the artwork. Virtual reality technologies and experts have shifted from bystanders to participants and experiencers. This is a brand new experience. It dispels our previous experience based on visual experience and forms a new form of experience. The final results of the study showed that the scores of the three classes of cognitive experience were 4.8, 4.6, and 4.7, with an average score of 4.7. With a full score of 5, the scores in the three dimensions are very high, indicating that this digital media art interactive experience design has brought students a good sensory experience, interactive experience, and cognitive experience.