Reviewerly/exHarmony huggingface dataset:
Metric Learning for Anti-Compression Facial Forgery Detection

Detecting facial forgery images and videos is an increasingly important topic in multimedia forensics. As forgery images and videos are usually compressed into different formats such as JPEG and H264 when circulating on the Internet, existing forgery-detection methods trained on uncompressed data often suffer from significant performance degradation in identifying them. To solve this problem, we propose a novel anti-compression facial forgery detection framework, which learns a compression-insensitive embedding feature space utilizing both original and compressed forgeries. Specifically, our approach consists of three ideas: (i) extracting compression-insensitive features from both uncompressed and compressed forgeries using an adversarial learning strategy; (ii) learning a robust partition by constructing a metric loss that can reduce the distance of the paired original and compressed images in the embedding space; (iii) improving the accuracy of tampered localization with an attention-transfer module. Experimental results demonstrate that, the proposed method is highly effective in handling both compressed and uncompressed facial forgery images.
A <scp>Bayesian</scp> approach to calibrate system dynamics models using <scp>Hamiltonian Monte Carlo</scp>

Abstract Model calibration is an essential test that dynamic hypotheses must pass in order to serve as tools for decision‐making. In short, it is the search for a match between actual and simulated behaviours using parameter inference. Here, we approach such an inference process from a Bayesian perspective. Under this paradigm, we provide statements about the parameters (viewed as random variables) and data in probabilistic terms. These statements stem from a posterior distribution whose solution is often found via statistical simulation. However, the uptake of these methods within the system dynamics field has been somewhat limited, and state‐of‐the‐art algorithms have not been explored. Therefore, we introduce Hamiltonian Monte Carlo (HMC), an efficient algorithm that outperforms random‐walk methods in exploring complex parameter spaces. We apply HMC to calibrate an SEIR model and frame the process within a practical workflow. In doing so, we also recommend visualisation tools that facilitate the communication of results. © 2021 The Authors. System Dynamics Review published by John Wiley &amp; Sons Ltd on behalf of System Dynamics Society.
Fully Homomorphically Encrypted Deep Learning as a Service

Fully Homomorphic Encryption (FHE) is a relatively recent advancement in the field of privacy-preserving technologies. FHE allows for the arbitrary depth computation of both addition and multiplication, and thus the application of abelian/polynomial equations, like those found in deep learning algorithms. This project investigates how FHE with deep learning can be used at scale toward accurate sequence prediction, with a relatively low time complexity, the problems that such a system incurs, and mitigations/solutions for such problems. In addition, we discuss how this could have an impact on the future of data privacy and how it can enable data sharing across various actors in the agri-food supply chain, hence allowing the development of machine learning-based systems. Finally, we find that although FHE incurs a high spatial complexity cost, the run time is within expected reasonable bounds, while allowing for absolutely private predictions to be made, in our case for milk yield prediction with a Mean Absolute Percentage Error (MAPE) of 12.4% and an accuracy of 87.6% on average.
Simultaneous measurement stable and radiogenic Nd isotopic compositions by MC-ICP-MS with a single-step chromatographic extraction technique

A single TODGA resin column to isolate Nd from geological samples.
Review: Privacy-Preservation in the Context of Natural Language Processing

Data privacy is one of the highly discussed issues in recent years as we encounter data breaches and privacy scandals often. This raises a lot of concerns about the ways the data is acquired and the potential information leaks. There are opportunities where the privacy of the data could be violated when used in Artificial Intelligent (AI) models. A considerable portion of user-contributed data is in natural language, and in the past few years, many researchers have proposed NLP-based methods to address these data privacy challenges. To the best of our knowledge, this is the first interdisciplinary review discussing privacy preservation in the context of NLP. In this paper, we present a comprehensive review of previous research conducted to gather techniques and challenges of building and testing privacy-preserving systems in the context of Natural Language Processing (NLP). We group the different works under four categories: 1) Data privacy in the medical domain, 2) Privacy preservation in the technology domain, 3) Analysis of privacy policies, and 4) Privacy leaks detection in the text representation. This review compares the contributions and pitfalls of the various privacy violation detection and prevention works done using NLP techniques to help guide a path ahead.
Examining data visualization pitfalls in scientific publications

Abstract Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. To address common pitfalls in graphical representations, this paper focuses on identifying and understanding the root causes of misinformation in graphical representations. We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color, shape, size, and spatial orientation. Moreover, a text mining technique was applied to extract practical insights from common visualization pitfalls. Cochran’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color, shape, size, and spatial orientation. The findings showed that the pie chart is the most misused graphical representation, and size is the most critical issue. It was also observed that there were statistically significant differences in the proportion of errors among color, shape, size, and spatial orientation.
Graph-Based Tri-Attention Network for Answer Ranking in CQA

In community-based question answering (CQA) platforms, automatic answer ranking for a given question is critical for finding potentially popular answers in early times. The mainstream approaches learn to generate answer ranking scores based on the matching degree between question and answer representations as well as the influence of respondents. However, they encounter two main limitations: (1) Correlations between answers in the same question are often overlooked. (2) Question and respondent representations are built independently of specific answers before affecting answer representations. To address the limitations, we devise a novel graph-based tri-attention network, namely GTAN, which has two innovations. First, GTAN proposes to construct a graph for each question and learn answer correlations from each graph through graph neural networks (GNNs). Second, based on the representations learned from GNNs, an alternating tri-attention method is developed to alternatively build target-aware respondent representations, answer-specific question representations, and context-aware answer representations by attention computation. GTAN finally integrates the above representations to generate answer ranking scores. Experiments on three real-world CQA datasets demonstrate GTAN significantly outperforms state-of-the-art answer ranking methods, validating the rationality of the network architecture.
Forecasting induced seismicity in Oklahoma using machine learning methods

Oklahoma earthquakes in the past decade have been mostly associated with wastewater injection. Here we use a machine learning technique-the Random Forest to forecast induced seismicity rate in Oklahoma based on injection-related parameters. We split the data into training (2011.01-2015.05) and test (2015.06-2020.12) periods. The model forecasts seismicity rate during the test period based on input features, including operational parameters (injection rate and pressure), geological information (depth to basement), and modeled pore pressure and poroelastic stress. The results show overall good match with observed seismicity rate (adjusted [Formula: see text] of 0.75). The model shows that pore pressure rate and poroelastic stressing rates are the two most important features in forecasting. The absolute values of pore pressure and poroelastic stress, and the injection rate itself, are less important than the stressing rates. These findings further emphasize that temporal changes of stressing rates would lead to significant changes in seismicity rates.
Trapped-Ion Quantum Computer with Robust Entangling Gates and Quantum Coherent Feedback

Quantum computers are expected to achieve a significant speed-up over classical computers in solving a range of computational problems. Chains of ions held in a linear Paul trap are a promising platform for constructing such quantum computers, due to their long coherence times and high quality of control. Here, we report on the construction of a small five-qubit universal quantum computer using 88Sr+ ions in a radio-frequency (rf) trap. All basic operations, including initialization, quantum logic operations, and readout, are performed with high fidelity. Selective two-qubit and single-qubit gates, implemented using a narrow-line-width laser, comprise a universal gate set, allowing realization of any unitary on the quantum register. We review the main experimental tools and describe in detail unique aspects of the computer: the use of robust entangling gates and the development of a quantum coherent feedback system through electron-multiplying CCD camera acquisition. The latter is necessary for carrying out quantum error-correction protocols in future experiments.3 MoreReceived 8 November 2021Accepted 24 February 2022DOI:https://doi.org/10.1103/PRXQuantum.3.010347Published 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 AreasQuantum information architectures & platformsQuantum information processingQuantum information with trapped ionsAtomic, Molecular & OpticalQuantum Information
Cloud Computing as a Platform for Monetizing Data Services: A Two-Sided Game Business Model

We argue in this paper that the role of the cloud should be reshaped from being a passive virtual market to become an active platform for monetizing data. The objective is to enable the cloud to be an active platform that can help data providers reach a wider set of data consumers. This will allow these consumers to be exposed to a larger variety of data that benefits data analytic applications. To achieve this vision, we propose a novel game theoretical model, which consists of a mix of cooperative and competitive strategies. The players of the game are the data providers, cloud platform, and cloud users. The strategies of the players are modeled using the two-sided market theory that takes into consideration the network effects (externalities) among the players. Simulations conducted using Amazon and google clustered data show that the proposed model improves the total surplus of involved parties in terms of cloud resources provision and monetary profits compared to the current merchant model.
Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network

In recent years, with the gradual development of sports, the competition between athletes is becoming more and more fierce. The long training time and heavy body load of athletes lead to the increase of the incidence of sports injury, and the evaluation and analysis of athletes’ sports injury need a lot of manpower and material resources. In order to improve the calculation efficiency of sports injury estimation results and save the cost of estimation and analysis, we propose a sports injury estimation model based on the algorithm model of mutation fuzzy neural network. The sports injury model constructed in this paper can not only systematically evaluate and analyze the degree of sports injury of athletes, but also improve the accuracy and efficiency; at the same time, it has universality for the evaluation and analysis of the degree of sports injury. The construction of this model provides the theoretical basis of big data algorithm for the prevention of sports injury and the application of mutation fuzzy neural network in the field of sports.
Neuro-fuzzy network incorporating multiple lexicons for social sentiment analysis

We have proposed MultiLexANFIS which is an adaptive neuro-fuzzy inference system (ANFIS) that incorporates inputs from multiple lexicons to perform sentiment analysis of social media posts. We classify tweets into two classes: neutral and non-neutral; the latter class includes both positive and negative polarity. This type of classification will be considered for applications that aim to test the neutrality of content posted by the users in social media platforms. In our proposed model, features are extracted by integrating natural language processing with fuzzy logic; hence, it is able to deal with the fuzziness of natural language in a very efficient and automatic manner. We have proposed a novel set of 64 rules for the proposed neuro-fuzzy network that can classify tweets correctly by working on fuzzy features fetched from VADER, AFINN and SentiWordNet lexicons. The proposed novel rules are domain independent, i.e., we can extend these rules for any textual data that employs lexicons. The antecedent and consequent parameters of the ANFIS are optimized by gradient descent and least squares estimate algorithms, respectively, in an iterative manner. The key contributions of this paper are: (1) a novel neuro-fuzzy system: MultiLexANFIS that takes as its input the positive and negative sentiment scores of tweets computed from multiple lexicons—VADER, AFINN and SentiWordNet, in order to classify the tweets into neutral and non-neutral content, (2) a novel set of 64 rules for the Sugeno-type fuzzy inference system—MultiLexANFIS, (3) single-lexicon-based ANFIS variants to classify tweets when multiple lexicons are not available and (4) comparison of MultiLexANFIS with different fuzzy, non-fuzzy and deep learning state of the art on various benchmark datasets revealing the superiority of our proposed neuro-fuzzy system for social sentiment analysis.
Design of Educational Learning Management Cloud Process with Blockchain 4.0 based E-Portfolio

The development of technology in education has produced many innovations to support the learning process. The large variety of learning media is the digital competence of education, which is very much needed in higher education . However, from several universities, there are still many that do not have adequate digital competence. This study uses a survey method that will be calculated quantitatively through a Likert Scale calculation. The design of this cooperative education management system uses E-Portfolio Integration stored in cloud computing based on Blockchain technology. The Blockchain process will be helpful as a literary platform for researchers who will start research on Blockchain technology for digital education so that the results of digital competence cannot be taken over by third parties and are transparent, and this is the solution to the problems in this research. Based on the study results, it can be seen that the Student-Centered iLearning Blockchain (SCi-B) with E-Portfolio is supported by Blockchain technology that can meet the needs of universities and improve cooperative education on digital competence.
Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms

We introduce a quantum algorithm to compute the market risk of financial derivatives. Previous work has shown that quantum amplitude estimation can accelerate derivative pricing quadratically in the target error and we extend this to a quadratic error scaling advantage in market risk computation. We show that employing quantum gradient estimation algorithms can deliver a further quadratic advantage in the number of the associated market sensitivities, usually called <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>g</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi><mml:mi>k</mml:mi><mml:mi>s</mml:mi></mml:math>. By numerically simulating the quantum gradient estimation algorithms on financial derivatives of practical interest, we demonstrate that not only can we successfully estimate the greeks in the examples studied, but that the resource requirements can be significantly lower in practice than what is expected by theoretical complexity bounds. This additional advantage in the computation of financial market risk lowers the estimated logical clock rate required for financial quantum advantage from Chakrabarti et al. [Quantum 5, 463 (2021)] by a factor of ~7, from 50MHz to 7MHz, even for a modest number of greeks by industry standards (four). Moreover, we show that if we have access to enough resources, the quantum algorithm can be parallelized across 60 QPUs, in which case the logical clock rate of each device required to achieve the same overall runtime as the serial execution would be ~100kHz. Throughout this work, we summarize and compare several different combinations of quantum and classical approaches that could be used for computing the market risk of financial derivatives.
Privacy-preserving collaborative filtering algorithm based on local differential privacy

Mobile edge computing (MEC) is an emerging technolohgy that extends cloud computing to the edge of a network. MEC has been applied to a variety of services. Specially, MEC can help to reduce network delay and improve the service quality of recommendation systems. In a MEC-based recommendation system, users' rating data are collected and analyzed by the edge servers. If the servers behave dishonestly or break down, users' privacy may be disclosed. To solve this issue, we design a recommendation framework that applies local differential privacy (LDP) to collaborative filtering. In the proposed framework, users' rating data are perturbed to satisfy LDP and then released to the edge servers. The edge servers perform partial computing task by using the perturbed data. The cloud computing center computes the similarity between items by using the computing results generated by edge servers. We propose a data perturbation method to protect user's original rating values, where the Harmony mechanism is modified so as to preserve the accuracy of similarity computation. And to enhance the protection of privacy, we propose two methods to protect both users' rating values and rating behaviors. Experimental results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommendation methods.
Aspect-Based Sentiment Analysis Using Attribute Extraction of Hospital Reviews

The Covid pandemic has become a serious public health challenge for people across India and other nations. Nowadays, people rely on the online reviews being shared on different review sites to gather information about hospitals like the availability of beds, availability of ventilators, etc. However, since these reviews are large in number and are unstructured, patients struggle to get accurate and reliable information about the hospitals, due to which they end up taking admission into a hospital which might not be appropriate for the specific treatment they require. This paper employs the use of sentiment analysis to understand various online reviews of hospitals and provide valuable information to the patients. Approximately 30,000 + reviews were collected from more than 500 hospitals. The broad objective of the study is to give the patients a comprehensive and descriptive rating of the hospitals based on the online reviews given by different patients. In addition to providing a comprehensive summary, the study has conducted aspect-based analysis where it compares the hospitals based on four different aspects of the hospital viz. “Doctors’ services”, “Staff’s services”, “Hospital facilities”, and “Affordability”. The database containing aspect-based ratings of the hospitals will be of great value to the patients by allowing them to compare and select the best hospital based on the optimum fit of the aspects of their preference.
Evaluation of the Virtual Economic Effect of Tourism Product Emotional Marketing Based on Virtual Reality

Rapid development of Internet technology has led to great changes in all fields of life. Tourism industry has also gone to the Internet with the change of the times. Regarding the application of Internet technology in tourism, in fact, virtual tourism products are directly used to promote economic development. Online tourism stores have all kinds of tourism information in the database, realize network roaming, provide convenient information services for both the supply and demand of tourism, and can realize the rapid and stable sustainable development of tourism. This article mainly studies the evaluation of the virtual economic effect of emotional marketing of tourism products based on virtual reality (VR), the meaning algorithm of VR, etc., analyzes the virtual economy of tourism products based on VR, and helps tourism enterprises to grasp the needs of tourism consumers more accurately, so as to design products and services that are more in line with the demands of tourism consumers, the process of ultimately improving the user value. This study mainly uses the VR method, VR platform eye map processing, VR eye map regional detection and feature extraction method, and VR tourism product emotional marketing related experiments, to study the evaluation of the virtual economic effect of tourism product emotional marketing based on VR. The economic performance information of the virtual tourism industry is equivalent to the amount of these indicators, and the impact of the comprehensive evaluation on the economic performance of virtual tourism is quite equal. The results show that from 2017 to 2020, the virtual economic revenue of VR tourism product emotional marketing increased from 50 million to 1 billion. However, people give more importance to the tourism experience brought by virtual tourism products, and the reasonable consumption structure of virtual tourism hinders the improvement of tourism economic benefits. To promote the rationalization of the consumption structure of virtual tourism, the basic industry of tourism consumption is developed and the necessary design and processing is increased, in order to increase the supply of tourism products and to enrich tourism consumption.