OpenRaiser/Intern-Atlas huggingface dataset:
Fake Reviewer Group Detection in Online Review Systems
Venue: arXiv cs.SI 2021
Year: 2021

Online review systems are important components in influencing customers' purchase decisions. To manipulate a product's reputation, many stores hire large numbers of people to produce fake reviews to mislead customers. Previous methods tackle this problem by detecting malicious individuals, ignoring the fact that the spam activities are often formed in groups, where individuals work collectively to write fake reviews. Fake reviewer group detection, however, is more challenging due to the difficulties in capturing the underlying relationships in groups. In this work, we present an unsupervised and end-to-end approach for fake reviewer group detection in online reviews. Specifically, our method can be summarized into two procedures. First, cohensive groups are detected with modularity-based graph convolutional networks. Then the suspiciousness of each group is measured by several anomaly indicators from both individual and group levels. The fake reviewer groups can be finally detected through suspiciousness. Extensive experiments are conducted on real-world datasets, and the results show that our proposed method is effective in detecting fake reviewer groups compared with the state-of-the-art baselines.
Stacked Generative Machine Learning Models for Fast Approximations of Steady-State Navier-Stokes Equations
Venue: arXiv cs.CE 2021
Year: 2021

Computational fluid dynamics (CFD) simulations are broadly applied in engineering and physics. A standard description of fluid dynamics requires solving the Navier-Stokes (N-S) equations in different flow regimes. However, applications of CFD simulations are computationally-limited by the availability, speed, and parallelism of high-performance computing. To improve computational efficiency, machine learning techniques have been used to create accelerated data-driven approximations for CFD. A majority of such approaches rely on large labeled CFD datasets that are expensive to obtain at the scale necessary to build robust data-driven models. We develop a weakly-supervised approach to solve the steady-state N-S equations under various boundary conditions, using a multi-channel input with boundary and geometric conditions. We achieve state-of-the-art results without any labeled simulation data, but using a custom data-driven and physics-informed loss function by using and small-scale solutions to prime the model to solve the N-S equations. To improve the resolution and predictability, we train stacked models of increasing complexity generating the numerical solutions for N-S equations. Without expensive computations, our model achieves high predictability with a variety of obstacles and boundary conditions. Given its high flexibility, the model can generate a solution on a 64 x 64 domain within 5 ms on a regular desktop computer which is 1000 times faster than a regular CFD solver. Translation of interactive CFD simulation on local consumer computing hardware enables new applications in real-time predictions on the internet of things devices where data transfer is prohibitive and can increase the scale, speed, and computational cost of boundary-value fluid problems.
Decoding Visual Imagery from EEG Signals using Visual Perception Guided Network Training Method
Venue: arXiv cs.HC 2021
Year: 2021

An electroencephalogram is an effective approach that provides a bidirectional pathway between user and computer in a non-invasive way. In this study, we adopted the visual perception data for training the visual imagery decoding network. We proposed a visual perception-guided network training approach for decoding visual imagery. Visual perception decreases the power of the alpha frequency range of the visual cortex over time when the user performed the task, and visual imagery increases the power of the alpha frequency range of the visual cortex over time as the user performed with the task. Generated brain signals when the user performing visual imagery and visual perception have opposite brain activity tendencies, and we used these characteristics to design the proposed network. When using the proposed method, the average classification performance of visual imagery with the visual perception data was 0.7008. Our results provide the possibility of using the visual perception data as a guide of the visual imagery classification network training.
Predicting User Code-Switching Level from Sociological and Psychological Profiles
Venue: arXiv cs.CL 2021
Year: 2021

Multilingual speakers tend to alternate between languages within a conversation, a phenomenon referred to as "code-switching" (CS). CS is a complex phenomenon that not only encompasses linguistic challenges, but also contains a great deal of complexity in terms of its dynamic behaviour across speakers. This dynamic behaviour has been studied by sociologists and psychologists, identifying factors affecting CS. In this paper, we provide an empirical user study on Arabic-English CS, where we show the correlation between users' CS frequency and character traits. We use machine learning (ML) to validate the findings, informing and confirming existing theories. The predictive models were able to predict users' CS frequency with an accuracy higher than 55%, where travel experiences and personality traits played the biggest role in the modeling process.
Spark Optimization of Linear Codes for Reliable Data Delivery by Relay Drones
Venue: arXiv cs.IT 2021
Year: 2021

Data gathering operations in remote locations often rely on relay drones, which collect, store and deliver transmitted information to a ground control station. The probability of the ground control station successfully reconstructing the gathered data can be increased if random linear coding (RLC) is used, especially when feedback channels between the drones and the transmitter are not available. RLC decoding can be complemented by partial packet recovery (PPR), which utilizes sparse recovery principles to repair erroneously received data before RLC decoding takes place. We explain that the spark of the transpose of the parity-check matrix of the linear code, that is, the smallest number of linearly-dependent columns of the matrix, influences the effectiveness of PPR. We formulate a spark optimization problem and obtain code designs that achieve a gain over PPR-assisted RLC, in terms of the probability that the ground control station will decode the delivered data.
RIS-Aided Cell-Free Massive MIMO Systems: Joint Design of Transmit Beamforming and Phase Shifts
Venue: arXiv cs.IT 2021
Year: 2021

This paper studies RIS-aided cell-free massive MIMO systems, where multiple RISs are deployed to assist the communication between multiple access points (APs) and multiple users, with either continuous or discrete phase shifts at the RISs. We formulate the max-min fairness problem that maximizes the minimum achievable rate among all users by jointly optimizing the transmit beamforming at active APs and the phase shifts at passive RISs, subject to power constraints at the APs. To address such a challenging problem, we first study the special single-user scenario and propose an algorithm that can transform the optimization problem into semidefinite program (SDP) or integer linear program (ILP) for the cases of continuous and discrete phase shifts, respectively. By solving the resulting SDP and ILP, we first obtain the optimal phase shifts, and then design the optimal transmit beamforming accordingly. To solve the optimization problem for the multi-user scenario and continuous phase shifts at RISs, we extend the single-user algorithm and propose an alternating optimization algorithm, which can first decompose the max-min fairness problem into two subproblems related to transmit beamforming and phase shifts, and then transform the two subproblems into second-order-cone program and SDP, respectively. For the multi-user scenario and discrete phase shifts, the max-min fairness problem is shown to be a mixed-integer non-linear program (MINLP). To tackle it, we design a ZF-based successive refinement algorithm, which can find a suboptimal transmit beamforming and phase shifts by means of alternating optimization. Numerical results show that compared with benchmark schemes of random phase shifts and without using RISs, the proposed algorithms can significantly increase the minimum achievable rate among all users, especially when the number of reflecting elements at each RIS is large.
Unraveling Social Perceptions & Behaviors towards Migrants on Twitter
Venue: arXiv cs.LG 2021
Year: 2021

We draw insights from the social psychology literature to identify two facets of Twitter deliberations about migrants, i.e., perceptions about migrants and behaviors towards mi-grants. Our theoretical anchoring helped us in identifying two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users towards migrants. We have employed unsuper-vised and supervised approaches to identify these perceptions and behaviors. In the domain of applied NLP, our study of-fers a nuanced understanding of migrant-related Twitter de-liberations. Our proposed transformer-based model, i.e., BERT + CNN, has reported an F1-score of 0.76 and outper-formed other models. Additionally, we argue that tweets con-veying antipathy or animosity can be broadly considered hate speech towards migrants, but they are not the same. Thus, our approach has fine-tuned the binary hate speech detection task by highlighting the granular differences between perceptual and behavioral aspects of hate speeches.
On the Dynamics of Hopfield Neural Networks on Unit Quaternions
Venue: arXiv cs.NE 2021
Year: 2021

In this paper, we first address the dynamics of the elegant multi-valued quaternionic Hopfield neural network (MV-QHNN) proposed by Minemoto and collaborators. Contrary to what was expected, we show that the MV-QHNN, as well as one of its variation, does not always come to rest at an equilibrium state under the usual conditions. In fact, we provide simple examples in which the network yields a periodic sequence of quaternionic state vectors. Afterward, we turn our attention to the continuous-valued quaternionic Hopfield neural network (CV-QHNN), which can be derived from the MV-QHNN by means of a limit process. The CV-QHNN can be implemented more easily than the MV-QHNN model. Furthermore, the asynchronous CV-QHNN always settles down into an equilibrium state under the usual conditions. Theoretical issues are all illustrated by examples in this paper.
Tree-Based Dynamic Classifier Chains
Venue: arXiv cs.LG 2021
Year: 2021

Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of the labels. While in theory, any order is sufficient, in practice, this order has a substantial impact on the quality of the final prediction. Dynamic classifier chains denote the idea that for each instance to classify, the order in which the labels are predicted is dynamically chosen. The complexity of a naive implementation of such an approach is prohibitive, because it would require to train a sequence of classifiers for every possible permutation of the labels. To tackle this problem efficiently, we propose a new approach based on random decision trees which can dynamically select the label ordering for each prediction. We show empirically that a dynamic selection of the next label improves over the use of a static ordering under an otherwise unchanged random decision tree model. % and experimental environment. In addition, we also demonstrate an alternative approach based on extreme gradient boosted trees, which allows for a more target-oriented training of dynamic classifier chains. Our results show that this variant outperforms random decision trees and other tree-based multi-label classification methods. More importantly, the dynamic selection strategy allows to considerably speed up training and prediction.
GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network
Venue: arXiv cs.CV 2021
Year: 2021

Depth estimation is a challenging task of 3D reconstruction to enhance the accuracy sensing of environment awareness. This work brings a new solution with a set of improvements, which increase the quantitative and qualitative understanding of depth maps compared to existing methods. Recently, a convolutional neural network (CNN) has demonstrated its extraordinary ability in estimating depth maps from monocular videos. However, traditional CNN does not support topological structure and they can work only on regular image regions with determined size and weights. On the other hand, graph convolutional networks (GCN) can handle the convolution on non-Euclidean data and it can be applied to irregular image regions within a topological structure. Therefore, in this work in order to preserve object geometric appearances and distributions, we aim at exploiting GCN for a self-supervised depth estimation model. Our model consists of two parallel auto-encoder networks: the first is an auto-encoder that will depend on ResNet-50 and extract the feature from the input image and on multi-scale GCN to estimate the depth map. In turn, the second network will be used to estimate the ego-motion vector (i.e., 3D pose) between two consecutive frames based on ResNet-18. Both the estimated 3D pose and depth map will be used for constructing a target image. A combination of loss functions related to photometric, projection, and smoothness is used to cope with bad depth prediction and preserve the discontinuities of the objects. In particular, our method provided comparable and promising results with a high prediction accuracy of 89% on the publicly KITTI and Make3D datasets along with a reduction of 40% in the number of trainable parameters compared to the state of the art solutions. The source code is publicly available at https://github.com/ArminMasoumian/GCNDepth.git
Open or Sneaky? Fast or Slow? Light or Heavy?: Investigating Security Releases of Open Source Packages
Venue: arXiv cs.CR 2021
Year: 2021

Vulnerabilities in open source packages can be a security risk for the client projects that use these packages as dependencies. When a new vulnerability is discovered in a package, the package should quickly release a fix in a new version, referred to as security release in this study. The security release should be well-documented and require minimal migration effort to facilitate fast adoption by the client projects. However, to what extent the open source packages follow these recommendations is not known. The goal of this study is to aid software practitioners and researchers in understanding the current practice of releasing security fixes by open source packages and identifying areas for improvement through an empirical study of security releases. Specifically, in this paper, we study (1) the time lag between fix and release; (2) how security fixes are documented in the release notes; (3) code change characteristics (size and semantic versioning) of the release; and (4) the time lag between the release and an advisory publication on Snyk or NVD (two popular vulnerability databases) for security releases over a dataset of 4,377 security advisories across seven package ecosystems. We find that the median security release is available in under 4 days of the corresponding fix and contains 134 lines of code (LOC) change. Further, we find that 61.5% of the security releases come with a release note that documents the corresponding security fix. However, Snyk and NVD may take a median of 25 days (from the release) to publish an advisory for these security releases, possibly resulting in delayed notification to the client projects. Based on our findings, we make four recommendations for the package maintainers and the ecosystem administrators, such as using private fork for security fixes and standardizing the practice for announcing security releases.
Stabilized finite element methods for a fully-implicit logarithmic reformulation of the Oldroyd-B constitutive law
Venue: arXiv cs.CE 2021
Year: 2021

Logarithmic conformation reformulations for viscoelastic constitutive laws have alleviated the high Weissenberg number problem, and the exploration of highly elastic flows became possible. However, stabilized formulations for logarithmic conformation reformulations in the context of finite element methods have not yet been widely studied. We present stabilized formulations for the logarithmic reformulation of the Oldroyd-B model by Saramito (2014) based on the Variational Multiscale framework and Galerkin/Least-Squares method. The reformulation allows the use of Newton's method due to its fully-implicit nature for solving the steady-state problem directly. The proposed stabilization methods cure instabilities of convection and compatibility while preserving a three-field problem. The spatial accuracy of the formulations is assessed with the four-roll periodic box, revealing comparable accuracy between the methods. The formulations are validated with benchmark flows past a cylinder and through a 4:1 contraction. We found an excellent agreement to benchmark results in the literature. The algebraic sub-grid scale method is highly robust, indicated by the high limiting Weissenberg numbers in the benchmark flows.
The whole and the parts: the MDL principle and the a-contrario framework
Venue: arXiv cs.CV 2021
Year: 2021

This work explores the connections between the Minimum Description Length (MDL) principle as developed by Rissanen, and the a-contrario framework for structure detection proposed by Desolneux, Moisan and Morel. The MDL principle focuses on the best interpretation for the whole data while the a-contrario approach concentrates on detecting parts of the data with anomalous statistics. Although framed in different theoretical formalisms, we show that both methodologies share many common concepts and tools in their machinery and yield very similar formulations in a number of interesting scenarios ranging from simple toy examples to practical applications such as polygonal approximation of curves and line segment detection in images. We also formulate the conditions under which both approaches are formally equivalent.
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not
Venue: arXiv cs.LG 2021
Year: 2021

Farquhar et al. [2021] show that correcting for active learning bias with underparameterised models leads to improved downstream performance. For overparameterised models such as NNs, however, correction leads either to decreased or unchanged performance. They suggest that this is due to an "overfitting bias" which offsets the active learning bias. We show that depth uncertainty networks operate in a low overfitting regime, much like underparameterised models. They should therefore see an increase in performance with bias correction. Surprisingly, they do not. We propose that this negative result, as well as the results Farquhar et al. [2021], can be explained via the lens of the bias-variance decomposition of generalisation error.
Exploring Latent Dimensions of Crowd-sourced Creativity
Venue: arXiv cs.CV 2021
Year: 2021

Recently, the discovery of interpretable directions in the latent spaces of pre-trained GANs has become a popular topic. While existing works mostly consider directions for semantic image manipulations, we focus on an abstract property: creativity. Can we manipulate an image to be more or less creative? We build our work on the largest AI-based creativity platform, Artbreeder, where users can generate images using pre-trained GAN models. We explore the latent dimensions of images generated on this platform and present a novel framework for manipulating images to make them more creative. Our code and dataset are available at http://github.com/catlab-team/latentcreative.
Public Release and Validation of SPEC CPU2017 PinPoints
Venue: arXiv cs.PF 2021
Year: 2021

Phase-based statistical sampling methods such as SimPoints have proven to be effective at dramatically reducing the long time for architectural simulators to run large workloads such as SPEC CPU2017. However, generating and validating them is a long and tenuous process. While checkpoints of program phases, or "pinballs", of SPEC CPU2017 have been collected by other researchers and shared with the research community, they are outdated and produce errors when used with the latest versions of the Sniper architectural simulator. To facilitate our own research as well as contribute to the community, we collect and validate our own pinballs for the SPEC CPU2017 SPECspeed suite and release them to the public domain. In this work we document our methodology, the hardware and software details of the collection process, and our validation results. In terms of CPI, our pinballs have an average error rate of 12% when compared with the native whole-program benchmark execution.
On The Reliability Of Machine Learning Applications In Manufacturing Environments
Venue: arXiv cs.LG 2021
Year: 2021

The increasing deployment of advanced digital technologies such as Internet of Things (IoT) devices and Cyber-Physical Systems (CPS) in industrial environments is enabling the productive use of machine learning (ML) algorithms in the manufacturing domain. As ML applications transcend from research to productive use in real-world industrial environments, the question of reliability arises. Since the majority of ML models are trained and evaluated on static datasets, continuous online monitoring of their performance is required to build reliable systems. Furthermore, concept and sensor drift can lead to degrading accuracy of the algorithm over time, thus compromising safety, acceptance and economics if undetected and not properly addressed. In this work, we exemplarily highlight the severity of the issue on a publicly available industrial dataset which was recorded over the course of 36 months and explain possible sources of drift. We assess the robustness of ML algorithms commonly used in manufacturing and show, that the accuracy strongly declines with increasing drift for all tested algorithms. We further investigate how uncertainty estimation may be leveraged for online performance estimation as well as drift detection as a first step towards continually learning applications. The results indicate, that ensemble algorithms like random forests show the least decay of confidence calibration under drift.