Reviewerly/exHarmony huggingface dataset:
Anonymous Broadcast Authentication for Securely Remote-Controlling IoT Devices

In this paper, we present a basic system for controlling IoT devices in remote environments with the following requirements: (1) in a situation where an operation center broadcasts information to IoT devices, e.g., wireless environment, only the designated devices can identify operations sent from the center; (2) the devices can detect manipulation of the broadcast information and hence prevents maliciously generated operations from being executed. We formalize a model of the basic system and its essential requirements and propose anonymous broadcast authentication (ABA) as its core cryptographic primitive. We formally define the syntax and security notions for ABA and show provably-secure ABA constructions.
A Blockchain-based Educational Digital Assets Management System

A variety of heterogeneous information is produced after the application of various emerging technologies in education, which is of great research value as educational assets. The traditional educational asset management system has emerged. In view of the disadvantages of traditional system centralization process, people begin to use decentralized blockchain technology for innovation. This paper proposes an educational digital asset management system architecture based on blockchain 3.0 technology system, which forms various data formed by students in and out of class learning process, courseware and experience data generated by teachers in the process of teaching into educational digital assets, and carries out the right confirmation and storage on the blockchain. The system can use a variety of heterogeneous data in digital assets to analyze the development status of school users, achieve the teaching purpose of teaching students according to their aptitude and different people.
Evaluating privacy of individuals in medical data

Although data protection is compulsory when personal data is shared, there is no systematic method available to evaluate to what extent each individual is at risk of a privacy breach. We use a collection of measures that quantify how much information is needed to uncover sensitive information. Combined with visualization techniques, our approach can be used to perform a detailed privacy analysis of medical data. Because privacy is evaluated per variable, these adjustments can be made while incorporating how likely it is that these variables will be exploited to uncover sensitive information in practice, as is mandatory in the European Union. Additionally, the analysis of privacy can be used to evaluate to what extent knowledge on specific variables in the data can contribute to privacy breaches, which can subsequently guide the use of anonymization techniques, such as generalization.
Multimedia Data Protection Using Hybridized Crystal Payload Algorithm With Chicken Swarm Optimization

This research consists of three phase. The first model includes a crystal payload encryption method watermarking scheme and an attack-free encryption scheme called international data encryption algorithm (IDEA). The second model is a binary grey scale image in chicken swarm optimization (CSO) applied to copyright production parameter optimized swarm intelligence domain-based approach, which is compared to conventional approaches. The work performance has been evaluated for conventional machine learning approach using MATLAB. The simulation results show that proposed hybridized crystal payload algorithm with chicken swarm optimization (HCPECSO) scheme achieves a high copyright production with the lowest mean square error values and highest peak signal noise ration when compared with the existing approaches schemes like machine learning SVM, logistic regression, and neural network. The proposed HCPECSO attained less processing time of 32.33s and processing cost compared to existing schemes.
Tuning machine learning dropout for subsurface uncertainty model accuracy

Machine learning hyperparameter tuning generally relies on minimizing the prediction error and maximizing the accuracy at withheld testing data locations. However, for many subsurface applications, prediction accuracy is not sufficient, and we must consider the goodness, accuracy, and precision, of the entire uncertainty model. Bayesian deep neural networks provide a robust framework to model uncertainty. But at a high computational cost and difficulty to scale in high-dimensional parameter spaces. Dropout offers a potential alternative for calculating prediction model realizations to represent the uncertainty model. We propose a method to compare the entire uncertainty model based on the prediction realizations and the addition of a single objective function, known as uncertainty model goodness, to the loss function to integrate model uncertainty for improved tuning of the dropout frequency in deep learning models. We test our method with a subsurface flow surrogate model based on an image-to-image regression problem. Implementing the proposed method with the uncertainty model goodness results in accurate and precise uncertainty models using the tuned dropout hyperparameter. We test the robustness and generalization of the proposed method with out-of-distribution testing data.
Remote clinical trials: A timely opportunity for a virtual reality approach and its potential application in neurology

The COVID‐19 pandemic boosted the expansion and development of new remote models of care and clinical research modalities. Health systems are going to implement telemedical innovations in the near future. Virtual clinical trials (VCT), also known as remote or decentralized ones, may profoundly change the way how clinical studies are conducted, for the benefit of patients with chronic and neurological diseases who are often fragile and may have limited access to traditional healthcare facilities. Despite significant progress, several limitations still need to be addressed to implement telemedicine technologies for VCT. The information and communication technology (ICT) devices (e.g., mobile apps and wearables) may be applied to VCTs but show some practical issues that may hamper the compliance with rigorous research criteria and protocols. We herewith discuss the advantages and disadvantages of virtual reality (VR) in combination with other ICT devices and solutions to improve the conduction of VCT in patients with neurological disorders. The so‐called “digital divide,” that is, the gap between people who can and those who cannot access high‐speed and broadband internet connections, and issues related to VR, such as VR sickness, should be addressed to improve larger VCT participation to neurological patients.
An Efficient Double Parameter Elliptic Curve Digital Signature Algorithm for Blockchain

The classic Elliptic curve digital Signature Algorithm (ECDSA) uses one inversion operation in the process of signature and verification, which greatly reduces the efficiency of digital signatures. Up to now, most research schemes improve efficiency by reducing reverse operations, but they fail to attach importance to such issues as forgery signature attack. At the same time, in the blockchain, the weak randomness of ECDSA will lead to the attack of forging random numbers, which is a potential problem of digital currency transactions. In consideration of this reason, in this article, an improved provably secure elliptic curve digital signature scheme is constructed. First, the new scheme introduces double parameters in the signature process, that can effectively resist the weak randomness attack of ECDSA in Bitcoin, and can be applied to blockchain digital currency trading systems. Second, in the random oracle model, it is provably indistinguishable against Elliptic Curve Discrete Logarithm Problem (ECDLP) under the super type I and type II adversary. Third, the new scheme avoids the inverse operation in the signature and verification phase. Compared with the ECDSA, the running speed is optimized by 50.1%. Similarly, the proposed scheme has higher computational efficiency than other existing algorithms.
Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression

The video-based point cloud compression (V-PCC, ISO/IEC 23090-5) is the state-of-the-art international standard for compressing dynamic point clouds developed by the moving picture experts group (MPEG). It has been achieved good rate-distortion (RD) performance by employing the 2D-based dynamic point cloud compression. As a brief look, V-PCC first converts the 3D input point cloud into a set of 2D patches followed by a packing process. The packing process then maps the patches into a 2D grid. Such a way allows compressing the patches utilizing the existing video coding standards. Besides the RD performance, complexity is another vital factor to consider in performance evaluations. In the V-PCC encoder, the self-time accounts for on average 15.9% and a maximum of 48.2% of the total-time, which can be a hindrance to realizing real-time V-PCC applications. One of the most computationally intensive modules of V-PCC is the grid-based refining segmentation (G-RS). Thus this paper proposes a fast G-RS method that can adaptively select voxels that need the refining segmentation. More concretely, the proposed method classifies the voxels based on the projection plane indices of 3D points and only applies the refining process to the selected voxels. Experimental results demonstrate that the proposed method reduces the complexity of the refining steps in G-RS, on average, by 60.7% and 62.5% without coding efficiency loss compared to the test model for category 2 (TMC2) version 12.0 reference software under the random access (RA) and all-intra (AI) configurations, respectively.
Technological Platforms for Inclusive Practice at University: A Qualitative Analysis from the Perspective of Spanish Faculty Members

Currently, the development of new virtual environments as a complementary tool to face-to-face teaching and the increased presence of students with disabilities at university classrooms are changing the landscape of university teaching. This article analyses the actions of faculty members who carry out inclusive practices in the context of technological platforms. The research was based on the assumptions of the qualitative paradigm, using individual semi-structured interviews with 119 faculty members from 10 Spanish public universities. The results show the reasons for inclusive learning with technological platforms, the use that faculty members make of these platforms in their inclusive educational practices, and the influence of these on the learning of students, especially students with disabilities. The conclusions give a good account of the conditions that determine the pedagogical use that faculty members make of virtual environments to facilitate the inclusion of students.
Experimental demonstration of one-shot coherence distillation: realizing N-dimensional strictly incoherent operations

We experimentally investigate the one-shot distillation of quantum coherence, which focuses on the transformations from a single copy of a given state into maximally coherent states under various incoherent operations. We present a general proposal to realize a type of strictly incoherent operations (SIOs) that can act on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> </mml:math> -dimensional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mo stretchy="false">(</mml:mo> <mml:mi>N</mml:mi> <mml:mo>≥<!-- ≥ --></mml:mo> <mml:mn>2</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> states. This proposal is suitable for a variety of quantum devices. Based on a linear optical setup, we experimentally demonstrate that the proposed SIO successfully implements pure-to-pure state transformations, and thus the one-shot coherence distillation process is accomplished. The experimental data agree well with the theoretical results and clearly indicate the distillation fidelities, which bound the regions of different coherence distillation rates versus different superposition parameters of input states and different distillation errors.
Papers101: Supporting the Discovery Process in the Literature Review Workflow for Novice Researchers

A literature review is a critical task in performing research. However, even browsing an academic database and choosing must-read items can be daunting for novice researchers. In this paper, we introduce Papers101, an interactive system that supports novice researchers' discovery of papers relevant to their research topics. Prior to system design, we performed a formative study to investigate what difficul-ties novice researchers often face and how experienced researchers address them. We found that novice researchers have difficulty in identifying appropriate search terms, choosing which papers to read first, and ensuring whether they have examined enough candidates. In this work, we identified key requirements for the system dedicated to novices: prioritizing search results, unifying the contexts of multiple search results, and refining and validating the search queries. Accordingly, Papers101 provides an opinionated perspective on selecting important metadata among papers. It also visualizes how the priority among papers is developed along with the users' knowledge discovery process. Finally, we demonstrate the potential usefulness of our system with the case study on the metadata collection of papers in visualization and HCI community.
DARING: Differentiable Causal Discovery with Residual Independence

Discovering causal structure among a set of variables is a crucial task in various scientific and industrial scenarios. Given finite i.i.d. samples from a joint distribution, causal discovery is a challenging combinatorial problem in nature. The recent development in functional causal models, especially the NOTEARS provides a differentiable optimization framework for causal discovery. They formulate the structure learning problem as a task of maximum likelihood estimation over observational data (i.e., variable reconstruction) with specified structural constraints such as acyclicity and sparsity. Despite its success in terms of scalability, we find that optimizing the objectives of these differentiable methods is not always consistent with the correctness of learned causal graph especially when the variables carry heterogeneous noises (i.e., different noise types and noise variances) in real data from wild environments. In this paper, we provide the justification that their proneness to erroneous structures is mainly caused by the over-reconstruction problem, i.e., the noises of variables are absorbed into the variable reconstruction process, leading to the dependency among variable reconstruction residuals, and thus raise structure identifiability problems according to FCM theories. To remedy this, we propose a novel differentiable method DARING by imposing explicit residual independence constraint in an adversarial way. Extensive experimental results on both simulation and real data show that our proposed method is insensitive to the heterogeneity of external noise, and thus can significantly improve the causal discovery performances.
An evaluation and annotation methodology for product category matching in e-commerce

Product category matching is an important task in digital marketplaces and e-commerce, helping to power better search and recommendations in an online context. While variants of the problem have received some attention in academia, there is no documented guidance on how to efficiently acquire annotations for evaluating multiple (current and future) models, many of which rely on modern machine learning techniques such as neural representation learning. In this paper, we motivate and formalize the problem of product category matching in e-commerce, and present a rigorously designed set of guidelines and methodology for acquiring annotations in a cost-effective and reliable manner. We also present a methodology for using the annotations to compare solutions of two or more product category matching methods, including comparing models both before and after annotation. Three widely used e-commerce product category taxonomies, and multiple metrics, are used to demonstrate the utility of our proposals.
A statistical distribution recalibration method of soft labels to improve domain adaptation for cross-location and cross-machine fault diagnosis

Unsupervised domain adaptation has achieved certain success in recent cross-domain fault diagnosis research. As a widely used transfer strategy, the distribution alignment often occurs with the problems of too few valid alignment samples, too low confidence of predicted labels, and the inadequate alignment of marginal or conditional distributions. Therefore, this paper proposes a statistical distribution recalibration method of soft labels (SDRS). First, SDRS defines the valid samples and confusion interval in the statistical distribution of per-class predicted probabilities. Then, from the perspective of binary classification, a recalibration space in the confusion interval is further optimized by a center distance metric, to improve predicted confidence and valid distribution alignment. Built on SDRS, a novel cross-domain fault diagnosis approach named SDRS-DAN is constructed, where dynamic distribution adaptation is used to match and adjust the marginal and conditional distribution discrepancies adaptively. Extensive experiments prove the effectiveness of SDRS-DAN in cross-location and cross-machine scenarios. • The proposed SDRS improves the domain adaptation of unsupervised fault diagnosis. • The SDRS provides more valid samples for distribution alignment of fault data. • The recalibration space of SDRS can be optimized by center distance metric. • SDRS-DAN method obtains higher accuracy on cross-machine fault diagnosis tasks. • The dynamic adaptation in SDRS-DAN can adjust the distribution alignment better.
A Weight Possibilistic Fuzzy C-Means Clustering Algorithm

Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. Especially, parameters in FCM have influence on clustering results. However, a lot of FCM algorithm did not solve the problem, that is, how to set parameters. In this study, we present a kind of method for computing parameters values according to role of parameters in the clustering process. New parameters are assigned to membership and typicality so as to modify objective function, on the basis of which Lagrange equation is constructed and iterative equation of membership is acquired, so does the typicality and center equation. At last, a new possibilistic fuzzy C-means based on the weight parameter algorithm (WPFCM) was proposed. In order to test the efficiency of the algorithm, some experiments on different datasets are conducted to compare WPFCM with FCM, possibilistic C-means (PCM), and possibilistic fuzzy C-means (PFCM). Experimental results show that iterative times of WPFCM are less than FCM about 25% and PFCM about 65% on dataset X12. Resubstitution errors of WPFCM are less than FCM about 19% and PCM about 74% and PFCM about 10% on the IRIS dataset.
Know what you don't need: Single-Shot Meta-Pruning for attention heads

Deep pre-trained Transformer models have achieved state-of-the-art results over a variety of natural language processing (NLP) tasks. By learning rich language knowledge with millions of parameters, these models are usually overparameterized and significantly increase the computational overhead in applications. It is intuitive to address this issue by model compression. In this work, we propose a method, called Single-Shot Meta-Pruning, to compress deep pre-trained Transformers before fine-tuning. Specifically, we focus on pruning unnecessary attention heads adaptively for different downstream tasks. To measure the informativeness of attention heads, we train our Single-Shot Meta-Pruner (SMP) with a meta-learning paradigm aiming to maintain the distribution of text representations after pruning. Compared with existing compression methods for pre-trained models, our method can reduce the overhead of both fine-tuning and inference. Experimental results show that our pruner can selectively prune 50% of attention heads with little impact on the performance on downstream tasks and even provide better text representations. The source code is available at https://github.com/thunlp/SMP.
What Happens Behind the Scene? Towards Fraud Community Detection in E-Commerce from Online to Offline

Fraud behavior poses a severe threat to e-commerce platforms and anti-fraud systems have become indispensable infrastructure of these platforms. Recently, there have been a large number of fraud detection models proposed to monitor online purchasing transactions and extract hidden fraud patterns. Thanks to these fraud detection models, we have observed a significant reduction of committed frauds in the last several years. However, there have been an increasing number of malicious sellers on e-commerce platforms, according to our recent statistics, who purposely circumvent these online fraud detection systems by transferring their fake purchasing behaviors from online to offline. This way, the effectiveness of our existing fraud detection system built based upon online transactions is compromised. To solve this problem, we study in this paper a new problem, called offline fraud community detection, which can greatly strengthen our existing fraud detection systems. We propose a new FRaud COmmunity Detection from Online to Offline (FRODO) framework which combines the strength of both online and offline data views, especially the offline spatial-temporal data, for fraud community discovery. Moreover, a new Multi-view Heterogeneous Graph Neural Network model is proposed within our new FRODO framework which can find anomalous graph patterns such as biclique communities through only a small number of black seeds, i.e., a small number of labeled fraud users. The seeds are processed by a streamlined pipeline of three components comprised of label propagation for a high coverage, multi-view heterogeneous graph neural networks for high-risky fraud user recognition, and spatial-temporal network reconstruction and mining for offline fraud community detection. The extensive experimental results on a large real-life Taobao network, with 20 millions of users, 5 millions of product items and 30 millions of transactions, demonstrate the good effectiveness of the proposed methods.
FedSVRG Based Communication Efficient Scheme for Federated Learning in MEC Networks

Recently, a novel machine learning technique, federated learning, attracts ever-increasing interests from academia to industry. The main idea of federated learning is to collaboratively train a global optimal machine learning model among all the participants. During the process of parameter updating, the communication cost of the system or network can be extremely huge with a large number of iterations and participants. Although the edge computing paradigm can decrease the latency to a certain extent, how to obtain further delay reduction is still a challenge. Therefore, to address the problem, we firstly model the corresponding problem into a finite-sum optimization problem. Then, we propose a federated stochastic variance reduced gradient based method to decrease the number of iterations between the participants and server from the system perspective, and guarantee the accuracy at the same time. Meanwhile, the corresponding convergence analysis is provided. Finally, we test our proposed method on the linear regression problem and the logistic regression problem. The simulation results show that our proposed method can reduce the communication cost significantly compared with general stochastic gradient descent based federated learning.