Reviewerly/exHarmony huggingface dataset:
HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein–Procrustes Learning for Unsupervised Network Alignment

Network alignment (NA) that identifies equivalent nodes across networks is an effective tool for integrating knowledge from multiple networks. The state-of-the-art NA methods learn inter-network node similarities based on labeled anchor links, which are costly, time-consuming, and difficult to acquire. Therefore, a few unsupervised network alignment (UNA) methods propose solving NA problems without anchor links. However, most existing UNA methods rely on discriminative attributes to capture nodes’ similarities and are hard to obtain optimal one-to-one alignments. Toward these issues, this article proposes a novel method named HackGAN to solve the UNA problem solely based on the structural information. Specifically, HackGAN represents nodes with embeddings based on an unsupervised graph neural network (GNN) to capture their global and local structural features. After that, it initializes mapping functions to transform the embedding spaces of different networks into the same vector space by iteratively solving the Wasserstein–Procrustes problem. The mapping functions are then refined by an adversarial model with cycle-consistency and Sinkhorn distance losses to obtain optimized one-to-one mappings. Based on the distances between mapped embeddings, accurate and robust results are obtained with a collective alignment algorithm. Experimental comparisons on both synthetic and real-world datasets demonstrate the superiority of HackGAN.
Improvement of the Seagull Optimization Algorithm and Its Application in Path Planning

Abstract Seagull Optimization Algorithm (SOA) is an emerging intelligent optimization algorithm proposed in recent years. This paper proposes an improvement in the SOA based on Levy flight(LSOA), which aims to solve the problems of decreasing exploration ability and easing to fall into local extreme values in the late stage of SOA. LSOA could increase the diversity of the seagull population, improve the exploration ability of SOA. Then selecting the classic 23 benchmark functions for simulation experiments and comparing with the other five intelligent algorithms in recent years. The result shows that LSOA has improved stability, convergence speed, and search accuracy, effectively avoiding local optimum conditions, with broad research prospects. Finally, LSOA is applied to the path planning problem, proving the algorithm’s feasibility and having a good optimization effect.
Benchmarking single-photon sources from an auto-correlation measurement

Here we argue that the probability that a given source produces exactly a single photon is a natural quantity to benchmark single-photon sources as it certifies the absence of multi-photon components and quantifies the efficiency simultaneously. Moreover, this probability can be bounded simply from an auto-correlation measurement -- a balanced beam splitter and two photon detectors. Such a bound gives access to various non-classicality witnesses that can be used to certify and quantify Wigner-negativity, in addition to non-Gaussianity and P-negativity of the state produced by the source. We provide tools that can be used in practice to account for an imperfect beam splitter, non-identical and non-unit detection efficiencies, dark counts and other imperfections, to take finite statistical effects into account without assuming that identical states are produced in all rounds, and optionally allow one to remove the detector inefficiencies from the analysis. We demonstrate the use of the proposed benchmark, non-classicality witness and measure using a heralded single-photon source based on spontaneous parametric down-conversion. We report on an average probability that a single photon is produced $\geq 55\%$ and an average measure of the Wigner negativity $\geq 0.004$ with a confidence level of $1-10^{-10}$.
Indigenous perspectives on the biodigital convergence

The biodigital convergence describes the intersection, and in some cases merging, of biological and digital technologies. Biodigital technologies include mRNA vaccines used to treat COVID-19, digitally controlled surveillance insects, microorganisms genetically engineered to produce medicinal compounds, and more. While significant scholarship has been paid to the ethical dimensions of biodigital technologies from a Western standpoint, little attention has focused on Indigenous views on the biodigital convergence. In this article, we explore the biodigital convergence from a Haudenosaunee perspective and suggest that insights from Indigenous philosophical traditions—specifically regarding relationality and territory—may be necessary for humanity to adapt to the profound and existential changes implicit in the biodigital convergence.
Intentional Head-Motion Assisted Locomotion for Reducing Cybersickness

We present an efficient locomotion technique that can reduce cybersickness through aligning the visual and vestibular induced self-motion illusion. Our locomotion technique stimulates proprioception consistent with the visual sense by intentional head motion, which includes both the head's translational movement and yaw rotation. A locomotion event is triggered by the hand-held controller together with an intended physical head motion simultaneously. Based on our method, we further explore the connections between the level of cybersickness and the velocity of self motion through a series of experiments. We first conduct Experiment 1 to investigate the cybersickness induced by different translation velocities using our method and then conduct Experiment 2 to investigate the cybersickness induced by different angular velocities. Our user studies from these two experiments reveal a new finding on the correlation between translation/angular velocities and the level of cybersickness. The cybersickness is greatest at the lowest velocity using our method, and the statistical analysis also indicates a possible U-shaped relation between the translation/angular velocity and cybersickness degree. Finally, we conduct Experiment 3 to evaluate the performances of our method and other commonly-used locomotion approaches, i.e., joystick-based steering and teleportation. The results show that our method can significantly reduce cybersickness compared with the joystick-based steering and obtain a higher presence compared with the teleportation. These advantages demonstrate that our method can be an optional locomotion solution for immersive VR applications using commercially available HMD suites only.
Self-Supervised Metric Learning With Graph Clustering For Speaker Diarization

In this paper, we propose a novel algorithm for speaker diarization using metric learning for graph based clustering. The graph clustering algorithms use an adjacency matrix consisting of similarity scores. These scores are computed between speaker embeddings extracted from pairs of audio segments within the given recording. In this paper, we propose an approach that jointly learns the speaker embeddings and the similarity metric using principles of self-supervised learning. The metric learning network implements a neural model of the probabilistic linear discriminant analysis (PLDA). The self-supervision is derived from the pseudo labels obtained from a previous iteration of clustering. The entire model of representation learning and metric learning is trained with a binary cross entropy loss. By combining the self-supervision based metric learning along with the graph-based clustering algorithm, we achieve significant relative improvements of 60% and 7% over the x-vector PLDA agglomerative hierarchical clustering (AHC) approach on AMI and the DIHARD datasets respectively in terms of diarization error rates (DER).
Adversarial Machine Learning

Recent innovations in machine learning enjoy a remarkable rate of adoption across a broad spectrum of applications, including cyber-security. While previous chapters study the application of machine learning solutions to cyber-security, in this chapter we present adversarial machine learning: a field of study concerned with the security of machine learning algorithms when faced with attackers. Likewise, adversarial machine learning enjoys remarkable interest from the community, with a large body of works that either propose attacks against machine learning algorithms, or defenses against adversarial attacks. In particular, adversarial attacks have been mounted in almost all applications of machine learning. Here, we aim to systematize adversarial machine learning, with a pragmatic focus on common computer security applications. Without assuming a strong background in machine learning, we also introduce the basic building blocks and fundamental properties of adversarial machine learning. This study is therefore accessible both to a security audience without in-depth knowledge of machine learning and to a machine learning audience.
On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations

Text classification is a fundamental Natural Language Processing task that has a wide variety of applications, where deep learning approaches have produced state-of-the-art results. While these models have been heavily criticized for their black-box nature, their robustness to slight perturbations in input text has been a matter of concern. In this work, we carry out a data-focused study evaluating the impact of systematic practical perturbations on the performance of the deep learning based text classification models like CNN, LSTM, and BERT-based algorithms. The perturbations are induced by the addition and removal of unwanted tokens like punctuation and stop-words that are minimally associated with the final performance of the model. We show that these deep learning approaches including BERT are sensitive to such legitimate input perturbations on four standard benchmark datasets SST2, TREC-6, BBC News, and tweet_eval. We observe that BERT is more susceptible to the removal of tokens as compared to the addition of tokens. Moreover, LSTM is slightly more sensitive to input perturbations as compared to CNN based model. The work also serves as a practical guide to assessing the impact of discrepancies in train-test conditions on the final performance of models.
Optimal Transportation Particle Filter for Linear Filtering Systems With Correlated Noises

Dedicate to Professor Thomas Kailath on the occasion of his 87 Birthday. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In this article, we derive an optimal transportation particle filter for linear time-varying systems with correlated noises. This method can be regarded as the extension of the feedback particle filter with an optimal transportation structure. However, the particles in our method are evolved in a deterministic way, while we need to generate random particles in a feedback particle filter. Consequently, we only need a very few particles to obtain the satisfying results, and this property is especially significant for high-dimensional problems. The error analysis of our method and the feedback particle filter has been carried out when the system is time invariant. Compared with the feedback particle filter and the ensemble Kalman filter, our method shows great efficiency in numerical experiments, including both the scalar and high-dimensional cases.
A Personalized Privacy-Preserving Scheme for Federated Learning

Federated learning (FL), which is a state-of-the-art distributed machine learning (DML) model, brings the spare resources of mobile devices into full play and provides strong security guarantee to local sensitive data by local differential privacy. However, the introduction of noise data leads to an unignored reduction of model utility. In this paper, we consider the heterogeneity of privacy requirement for various participants in FL and propose a novel federated learning scheme (PGC-LDP) that lets users personally choose their privacy level based on federated stochastic gradient descent algorithm with local differential privacy. In the scheme, we design a new algorithm based on Nguy&#x00EA;n&#x0027;s solution in client side and optimize aggregation method in server side. Moreover, we theoretically analyze the privacy guarantee and verify the utility of PGC-LDP on real-world dataset.
Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction

Real-world Electronic Health Records (EHRs) are often plagued by a high rate of missing data. In our EHRs, for example, the missing rates can be as high as 90% for some features, with an average missing rate of around 70% across all features. We propose a Time-Aware Dual-Cross-Visit missing value imputation method, named TA-DualCV, which spontaneously leverages multivariate dependencies across features and longitudinal dependencies both within- and cross-visit to maximize the information extracted from limited observable records in EHRs. Specifically, TA-DualCV captures the latent structure of missing patterns across measurements of different features and it also considers the time continuity and capture the latent temporal missing patterns based on both time-steps and irregular time-intervals. TA-DualCV is evaluated using three large real-world EHRs on two types of tasks: an unsupervised imputation task by varying mask rates up to 90% and a supervised 24-hour early prediction of septic shock using Long Short-Term Memory (LSTM). Our results show that TA-DualCV performs significantly better than all of the existing state-of-the-art imputation baselines, such as DETROIT and TAME, on both types of tasks.
Conditional Random Fields for Multiview Sequential Data Modeling

Recently, multiview learning has been increasingly focused on machine learning. However, most existing multiview learning methods cannot directly deal with multiview sequential data, in which the inherent dynamical structure is often ignored. Especially, most traditional multiview machine learning methods assume that the items at different time slices within a sequence are independent of each other. In order to solve this problem, we propose a new multiview discriminant model based on conditional random fields (CRFs) to model multiview sequential data, called multiview CRF. It inherits the advantages of CRFs that build a relationship between items in each sequence. Moreover, by introducing specific features designed on the CRFs for multiview data, the multiview CRF not only considers the relationship among different views but also captures the correlation between the features from the same view. Particularly, some features can be reused or divided into different views to build an appropriate size of feature space. This helps to avoid underfitting problems caused by too small feature space or overfitting problems caused by too large feature space. In order to handle large-scale data, we use the stochastic gradient method to speed up our model. The experimental results on the text and video data illustrate the superiority of the proposed model.
Opinion Mining of Customer Reviews Using Supervised Learning Algorithms

Nowadays, a lot of people express their opinions on various topics using social networking sites. Twitter has become a famous social networking site where people can express their opinions to the point and so it has become a great source for opinion mining. In this research, the goal was to train and build a model that can automatically and accurately categorize the opinion of customer tweet reviews about popular cell phone brands. We have used python TextBlob library for getting the polarity values of all the tweet reviews of the dataset. We have also used Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree and Random Forest algorithms along with Bag of Words and TF-IDF vectorizers separately to train and build the model that will categorize the customer tweet reviews into five opinion categories: Strongly Negative, Weakly Negative, Neutral, Weakly Positive and Strongly Positive. We have observed that SVM and Logistic Regression algorithms have outperformed other algorithms with 88% accuracy using Bag of Words vectorizer while SVM algorithm has outperformed other algorithms with 87% accuracy using TF-IDF vectorizer.
HRG: A Hybrid Retrieval and Generation Model in Multi-turn Dialogue

In multi-turn dialogue generation, the generated response should consider the content before the current turn of dialogue. Due to multiple turns, it is difficult to maintain the context consistency by using only a few previous turns of the dialogue indiscriminately. Except for the context information, we can retrieve additional candidates from historical contexts, according to semantic similarity. Therefore, in this paper, we integrate the historical information into the generative model called HRG. The HRG model can generate a response by using both context information and retrieved historical candidates, which contain richer information such as theme and latent information. We encode contexts, current turn and historical information separately to find the most important turns and give the current turn a higher level of attention. Then we propose a hierarchical fusion encoder to integrate the retrieval information through a KL divergence gate dynamically. Finally, we conduct experiments on the Ubuntu large-scale English multi-turn dialogue community dataset and Daily dialogue dataset. The results show that our hybrid model performs well on both automatic evaluation and human evaluation compared with the existing baseline models.
Improving the Performance of Feature Selection Methods with Low-Sample-Size Data

Abstract Feature selection refers to a critical preprocessing of machine learning to remove irrelevant and redundant data. According to feature selection methods, sufficient samples are usually required to select a reliable feature subset, especially considering the presence of outliers. However, sufficient samples cannot always be ensured in several real-world applications (e.g. neuroscience, bioinformatics and psychology). This study proposed a method to improve the performance of feature selection methods with ultra low-sample-size data, which is named feature selection based on data quality and variable training samples (QVT). Given that none of feature selection methods can perform optimally in all scenarios, QVT is primarily characterized by its versatility, because it can be implemented in any feature selection method. Furthermore, compared to the existing methods which tried to extract a stable feature subset for low-sample-size data by increasing the sample size or using more complicated algorithm, QVT tried to get improvement using the original data. An experiment was performed using 20 benchmark datasets, three feature selection methods and three classifiers to verify the feasibility of QVT; the results showed that using features selected by QVT is capable of achieving higher classification accuracy than using the explicit feature selection method, and significant differences exist.
Architectural View of Non-face-to-face Experiential Learning Through the Immersive Technologies

The significant disruption caused by the COVID-19 pandemic prohibits the face-to-face teaching-learning process. This pandemic forced the students to utilize online i.e. non-face-to-face mode of education via different platforms available on the internet. The use of the internet allows students to use e-learning resources to learn things from anywhere and at any time. The ease of use makes these systems more favorable amongst the learner. The traditional way of face-to-face teaching-learning with the utmost learning rate is no longer beneficial in the pandemic situation. Many of the teaching institutes use online aids for content delivery. No doubt, all these platforms are far better to provide knowledge and educate the students, but these platforms do not focus on the active participation of students in an online class like teacher observe the students concentration in a physical classroom. Monitoring the engagement of students in the online mode of education seems to be difficult, but can be achieved through the use of immersive technology. This paper provides an architectural view of the online teaching-learning system with the use of Virtual Reality to achieve better engagement of students in the virtual classroom through immersion.
Fides: Distributed Cyber-Physical Contracts

Current work in the field of smart contracts is primarily aimed at developers and directly connected to an underlying cryptocurrency. Those self-enforcing contracts are suitable for financial applications, but often disregard regular agreements that do not rely on digital money or are difficult to specify in the form of program code. In order to promote social interaction and self-organization for all types of users, we present Fides, a framework for creating contracts based on natural language that focuses on security and privacy. The use of natural language, detached from the actual payment process, allows everyone to create digital contracts inside a decentralized peer-to-peer network without relying on an inefficient Blockchain solution. These agreements are not only intended for interactions between humans, but can also be established between devices by automation.
Cloud Security Analysis Based on Virtualization Technology

The experimental results demonstrated that, With the development of cloud computing, more and more people use cloud computing to do all kinds of things. However, for cloud computing, the most important thing is to ensure the stability of user data and improve security at the same time. From an analysis of the experimental results, it can be found that Cloud computing makes extensive use of technical means such as computing virtualization, storage system virtualization and network system virtualization, abstracts the underlying physical facilities into external unified interfaces, maps several virtual networks with different topologies to the underlying infrastructure, and provides differentiated services for external users. By comparing and analyzing the experimental results, it is clear that virtualization technology will be the main way to solve cloud computing security. Virtualization technology introduces a virtual layer between software and hardware, provides an independent running environment for applications, shields the dynamics, distribution and differences of hardware platforms, supports the sharing and reuse of hardware resources, provides each user with an independent and isolated computer environment, and facilitates the efficient and dynamic management and maintenance of software and hardware resources of the whole system. Applying virtualization technology to cloud security reduces the hardware cost and management cost of "cloud security" enterprises to a certain extent, and improves the security of "cloud security" technology to a certain extent. This paper will outline the basic cloud computing security methods, and focus on the analysis of virtualization cloud security technology