Reviewerly/exHarmony huggingface dataset:
Political Discussion is Abundant in Non-political Subreddits (and Less Toxic)

Research on online political communication has primarily focused on content in explicitly political spaces. In this work, we set out to determine the amount of political talk missed using this approach. Focusing on Reddit, we estimate that nearly half of all political talk takes place in subreddits that host political content less than 25% of the time. In other words, cumulatively, political talk in non-political spaces is abundant. We further examine the nature of political talk and show that political conversations are less toxic in non-political subreddits. Indeed, the average toxicity of political comments replying to a out-partisan in non-political subreddits is less than even the toxicity of co-partisan replies in explicitly political subreddits.
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to privacy concerns, regulation restrictions, and commercial competitions. Federated learning (FL), a trending distributed learning paradigm, provides possibilities to solve this challenge while preserving data privacy. Despite recent advances in vision and language domains, there is no suitable platform for the FL of GNNs. To this end, we introduce FedGraphNN, an open FL benchmark system that can facilitate research on federated GNNs. FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support. Particularly for the datasets, we collect, preprocess, and partition 36 datasets from 7 domains, including both publicly available ones and specifically obtained ones such as hERG and Tencent. Our empirical analysis showcases the utility of our benchmark system, while exposing significant challenges in graph FL: federated GNNs perform worse in most datasets with a non-IID split than centralized GNNs; the GNN model that attains the best result in the centralized setting may not maintain its advantage in the FL setting. These results imply that more research efforts are needed to unravel the mystery behind federated GNNs. Moreover, our system performance analysis demonstrates that the FedGraphNN system is computationally efficient and secure to large-scale graphs datasets. We maintain the source code at https://github.com/FedML-AI/FedGraphNN.
Towards a Lightweight, Hybrid Approach for Detecting DOM XSS Vulnerabilities with Machine Learning

Client-side cross-site scripting (DOM XSS) vulnerabilities in web applications are common, hard to identify, and difficult to prevent. Taint tracking is the most promising approach for detecting DOM XSS with high precision and recall, but is too computationally expensive for many practical uses.
Perceiving Humans: From Monocular 3D Localization to Social Distancing

Perceiving humans in the context of Intelligent Transportation Systems (ITS) often relies on multiple cameras or expensive LiDAR sensors. In this work, we present a new cost-effective vision-based method that perceives humans’ locations in 3D and their body orientation from a single image. We address the challenges related to the ill-posed monocular 3D tasks by proposing a neural network architecture that predicts confidence intervals in contrast to point estimates. Our neural network estimates human 3D body locations and their orientation with a measure of uncertainty. Our proposed solution (i) is privacy-safe, (ii) works with any fixed or moving cameras, and (iii) does not rely on ground plane estimation. We demonstrate the performance of our method with respect to three applications: locating humans in 3D, detecting social interactions, and verifying the compliance of recent safety measures due to the COVID-19 outbreak. We show that it is possible to rethink the concept of “social distancing” as a form of social interaction in contrast to a simple location-based rule. We publicly share the source code towards an open science mission.
LOME: Large Ontology Multilingual Extraction

We present LOME, a system for performing multilingual information extraction.Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser.It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events.By doing so, the system constructs an event and entity focused knowledge graph.We can further apply third-party modules for other types of annotation, like relation extraction.Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art.We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data.LOME is available as a Docker container on Docker Hub.In addition, a lightweight version of the system is accessible as a web demo.
Application and Exploration of Artificial Intelligence and Edge Computing in Long-Distance Education on Mobile Network

In response to the demand for high-quality electronic information talents in the mobile network industry, in the situation of artificial intelligence (AI) to promote technological innovation, this paper conducts an overall design in the target system, curriculum system, teaching platform, teaching mode and teaching case. The practice education mode of teaching practice, engineering practice, innovation practice, and enterprise practice, which aims to improve students’ ability to solve complex engineering problems, is constructed. The mode breaks geographical boundaries between schools and enterprises to build the through-through experimental teaching course system based on artificial intelligence and edge computing and design a medical image intelligent analysis system project case based on Mobile Edge Computing (MEC), which improves students’ practical ability, engineering design ability, scientific research innovation ability, enterprise practice ability and mobile network application capabilities. At the same time, the hardware portability of the edge computing platform provides good conditions for long-distance education and the mobile network. This method is a beneficial attempt to cultivate high-level, diversified, and creative electronic information talents.
PSO based data clustering with a different perception

Generally, the Particle Swarm Optimization (PSO) algorithm has two memory dimensions: cognitive and social. In this study, a new dimension called family memory has been introduced to enhance PSO-based clustering's performance wherein not only the data were clustered but also the particles. Earlier studies on PSO-based clustering had used uniform distribution to create the particles' coordinates. In contrast, a multi-normal distribution-based swarm initialization was proposed in this study wherein K-means clustering outcomes were used as an input, which ensured the relationship within a particle's coordinates. The performance improvement through each modification to the Conventional PSO (CPSO) algorithm was demonstrated, and the statistical tests proved the same. Based on the two proposed changes, a modified PSO (MPSO) algorithm was developed. To extend the validation, both the proposed changes were incorporated into four different PSO variants, and the performance improvements were tested. Further, the MPSO algorithm was compared with the four different clustering algorithms (three algorithms are evolutionary type, and one is a non-evolutionary type) present in the recent literature. Statistical tests for the same proved the MPSO’s significance. As an application, the MPSO algorithm was applied to Multi-criteria Inventory classification (MCIC) data. Its classification was compared with the nineteen recent inventory models’ classification using the total relevant cost, fill rate, total safety stock cost, and the validity indices. Besides, a new index has been developed to identify the most similar models to MPSO.
Improvement of Adaptive Learning Service Recommendation Algorithm Based on Big Data

Abstract In view of the problem that the traditional learning service recommendation does not fully consider the distinct differences between individuals, it is easy to lead to the contradiction between unchanging learning resources and learners’ personalized learning needs that are constantly improving, so an adaptive learning service recommendation improvement algorithm based on big data is proposed. Idea is based on adaptive learning platform and function modules. We consider the individual differences between students, to students as the center, collect students’ personalized learning demand data, and according to the data information to build student demand model. On the basis of using data mining methods for clustering recommendation service resources in learning, the adaptive recommend according to students’ individual need is proposed. The experimental results show that the adaptive learning service recommendation algorithm based on big data has high recommendation accuracy, coverage rate and recall rate, which is of great significance in the actual learning service recommendation.
imodels: a python package for fitting interpretable models

imodels is a Python package for concise, transparent, and accurate predictive modeling.It provides users a simple interface for fitting and using state-of-the-art interpretable models, all compatible with scikit-learn (Pedregosa et al., 2011).These models can often replace black-box models while improving interpretability and computational efficiency, all without sacrificing predictive accuracy.In addition, the package provides a framework for developing custom tools and rule-based models for interpretability.
Research on Blockchain-Based E-Bidding System

In the field of modern bidding, electronic bidding leads a new trend of development, convenience and efficiency and other significant advantages effectively promote the reform and innovation of China’s bidding field. Nowadays, most systems require a strong and trusted third party to guarantee the integrity and security of the system. However, with the development of blockchain technology and the rise of privacy protection, researchers has begun to emphasize the core concept of decentralization. This paper introduces a decentralized electronic bidding system based on blockchain and smart contract. The system uses blockchain to replace the traditional database and uses chaincode to process business logic. In data interaction, encryption techniques such as zero-knowledge proof based on graph isomorphism are used to improve privacy protection, which improves the anonymity of participants, the privacy of data transmission, and the traceability and verifiable of data. Compared with other electronic bidding systems, this system is more secure and efficient, and has the nature of anonymous operation, which fully protects the privacy information in the bidding process.
Improving the accuracy and efficiency of quantum connected moments expansions<sup>*</sup>

The still-maturing noisy intermediate-scale quantum (NISQ) technology faces strict limitations on the algorithms that can be implemented efficiently. In quantum chemistry, the variational quantum eigensolver (VQE) algorithm has become ubiquitous, using the functional form of the ansatz as a degree of freedom, whose parameters are found variationally in a feedback loop between the quantum processor and its conventional counterpart. Alternatively, a promising new avenue has been unraveled by the quantum variants of techniques grounded on expansions of the moments of the Hamiltonian, among which two stand out: the connected moments expansion (CMX) [Phys. Rev. Lett. 58, 53 (1987)] and the Peeters-Devreese-Soldatov (PDS) functional [J. Phys. A 17, 625 (1984); Int. J. Mod. Phys. B 9, 2899], the latter based on the standard moments <$H^k$>. Contrasting with VQE-based methods and provided the quantum circuit prepares a state with non-vanishing overlap with the true ground state, CMX often converges to the ground state energy, while PDS is guaranteed to converge by virtue of being variational. However, for a finite CMX/PDS order, the circuit may significantly impact the energy accuracy. Here we use the ADAPT-VQE algorithm to test shallow circuit construction strategies that are not expected to impede their implementation in the present quantum hardware while granting sizable accuracy improvement in the computed ground state energies. We also show that we can take advantage of the fact that the terms in the connected moments are highly recurring in different powers, incurring a sizable reduction in the number of necessary measurements. By coupling this measurement caching with a threshold that determines whether a given term is to be measured based on its associated scalar coefficient, we observe a further reduction in the number of circuit implementations while allowing for tunable accuracy.
MouseView.js: Reliable and valid attention tracking in web-based experiments using a cursor-directed aperture

Psychological research is increasingly moving online, where web-based studies allow for data collection at scale. Behavioural researchers are well supported by existing tools for participant recruitment, and for building and running experiments with decent timing. However, not all techniques are portable to the Internet: While eye tracking works in tightly controlled lab conditions, webcam-based eye tracking suffers from high attrition and poorer quality due to basic limitations like webcam availability, poor image quality, and reflections on glasses and the cornea. Here we present MouseView.js, an alternative to eye tracking that can be employed in web-based research. Inspired by the visual system, MouseView.js blurs the display to mimic peripheral vision, but allows participants to move a sharp aperture that is roughly the size of the fovea. Like eye gaze, the aperture can be directed to fixate on stimuli of interest. We validated MouseView.js in an online replication (N = 165) of an established free viewing task (N = 83 existing eye-tracking datasets), and in an in-lab direct comparison with eye tracking in the same participants (﻿N = 50). Mouseview.js proved as reliable as gaze, and produced the same pattern of dwell time results. In addition, dwell time differences from MouseView.js and from eye tracking correlated highly, and related to self-report measures in similar ways. The tool is open-source, implemented in JavaScript, and usable as a standalone library, or within Gorilla, jsPsych, and PsychoJS. In sum, MouseView.js is a freely available instrument for attention-tracking that is both reliable and valid, and that can replace eye tracking in certain web-based psychological experiments.
Laser-light speckle formation for deflection-shape identification using digital image correlation

The application of a speckle pattern to the surface of a structure is usually required for Digital Image Correlation (DIC)-based displacement identification. A speckle pattern generally requires the modification (e.g., painting) of the structure's surface. An improved method of laser-light speckle formation for DIC measurement of relatively large structures is proposed, where the speckle pattern is formed by laser-light interference as a consequence of the surface roughness and the laser illumination, leaving the surface of the object untouched. The interference phenomena caused by the light reflecting from the rough surface create bright spots that move with the observed structure. The size of the speckles depends on the surface roughness, the experimental setup and the optical system. The proposed method is researched using three experimental cases: two rigid-body motion measurements and a full-field operating-deflection-shape measurement. The experiments show that motion can be identified even when the amplitudes of the oscillation are as low as 0.1 μm (on a scale of 1/1000 of a pixel). While the laser speckle accuracy is promising, it was found to be less accurate than painted speckle-pattern measurements, but with further development it could become a viable alternative.
RSP4J: An API for RDF Stream Processing

The RDF Stream Processing (RSP) community has proposed several models and languages for continuously querying and reasoning over RDF streams over the last decade. They each have their semantics, making them hard to compare. The variety of approaches has fostered both empirical and theoretical research and led to the design of RSPQL, i.e., a unifying model for RSP. However, an RSP API for the development under RSPQL semantics was still missing. RSP community would benefit from an RSP API because it can foster comparable and reproducible research by providing programming abstractions based on RSPQL semantics. Moreover, it can encourage further development and in-use research. Finally, it can stimulate practical activities such as tutorials, lectures, and challenges, e.g., during the Stream Reasoning Workshop.
SoK

One fundamental limitation of blockchain-based smart contracts is that they execute in a closed environment. Thus, they only have access to data and functionality that is already on the blockchain, or is fed into the blockchain. Any interactions with the real world need to be mediated by a bridge service, which is called an oracle. As decentralized applications mature, oracles are playing an increasingly prominent role. With their evolution comes more attacks, necessitating greater attention to their trust model. In this systemization of knowledge paper (SoK), we dissect the design alternatives for oracles, showcase attacks, and discuss attack mitigation strategies.
Comprehensive Survey and Research Directions on Blockchain IoT Access Control

The Internet of Things (IoT) is a widely used technology in the last decade in different applications.The Internet of things is wirelessly or wired to communicate, store, compute and track various real-time scenarios.This survey mainly discussed the core problems of Internet of things security and access control to unauthorized users and security requirements for IoT.The Internet of things is a heterogeneous device and has low memory, less processing power because of the small sizes.Nowadays, IoT systems are not sure and powerless to protect themselves against cyber attacks.It is mainly due to inadequate space in IoT gadgets, immature standards, and the lack of protected hardware and software design, development, and deployment.To meet IoT requirements, the authors discussed the limitations of traditional access control.Then the authors examined the potential to spread access control by implementing the safe architecture accommodated by the Blockchain.The authors also addressed how to use the Blockchain to work with and resolve some of the standards relevant to IoT security issues.In the end, an analysis of this survey shows future, open-ended problems, and challenges.It offers how the Blockchain potentially ensures reliable, scalable, and more efficient security solutions for IoT and further research work.
Vehicle classification using a real-time convolutional structure based on DWT pooling layer and SE blocks

Real-time vehicle type classification has become more popular and significant in recent years owing to its potential applications. In this paper, a real-time Convolutional Neural Network is proposed for vehicle type classification. This novel CNN structure is a combination of classic CNN layers along with squeeze-and-excitation (SE) modules and Haar wavelet as the pooling layer. This structure improves the performance of the CNN classifier by emphasizing informative feature maps and decreasing the entropy of the network. A cross-entropy loss function is proposed for better performance. A new pooling method is also introduced based on the Haar transform. Network parameters and the number of layers are selected in such a way to be real-time. Experimental results on two vehicle datasets show that the overall performance of this model, including recognition time and accuracy, is better than the others. For example, using DWT instead of Max-pooling improved the recognition rate on the IRVD dataset from 97.12% to 99.06%. The number of training parameters of the proposed model is about 5 million which is much less than popular networks like VGG (128 m), ResNet152 (58 m), DarkNet (40 m), and Inception-V3 (24 m). This made it very faster so that its recognition time is only 42 ms on CPU which is suitable for real-time applications.
Cross-lingual Cross-modal Pretraining for Multimodal Retrieval

Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English.Their success, however, heavily depends on the availability of many annotated image-caption datasets for pretraining, where the texts are not necessarily in English.Although we can utilize machine translation (MT) tools to translate non-English text to English, the performance still largely relies on MT's quality and may suffer from high latency problems in realworld applications.This paper proposes a new approach to learn cross-lingual cross-modal representations for matching images and their relevant captions in multiple languages.We seamlessly combine cross-lingual pretraining objectives and cross-modal pretraining objectives in a unified framework to learn image and text in a joint embedding space from available English image-caption data, monolingual and parallel corpus.We show that our approach achieves SOTA performance in retrieval tasks on two multimodal multilingual image caption benchmarks: Multi30k with German captions and MSCOCO with Japanese captions.
A scalable association rule learning heuristic for large datasets

Abstract Many algorithms have proposed to solve the association rule learning problem. However, most of these algorithms suffer from the problem of scalability either because of tremendous time complexity or memory usage, especially when the dataset is large and the minimum support ( minsup ) is set to a lower number. This paper introduces a heuristic approach based on divide-and-conquer which may exponentially reduce both the time complexity and memory usage to obtain approximate results that are close to the accurate results. It is shown from comparative experiments that the proposed heuristic approach can achieve significant speedup over existing algorithms.
GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph

The rise of digitization of cultural documents offers large-scale contents, opening the road for development of AI systems in order to preserve, search, and deliver cultural heritage. To organize such cultural content also means to classify them, a task that is very familiar to modern computer science. Contextual information is often the key to structure such real world data, and we propose to use it in form of a knowledge graph. Such a knowledge graph, combined with content analysis, enhances the notion of proximity between artworks so it improves the performances in classification tasks. In this paper, we propose a novel use of a knowledge graph, that is constructed on annotated data and pseudo-labeled data. With label propagation, we boost artwork classification by training a model using a graph convolutional network, relying on the relationships between entities of the knowledge graph. Following a transductive learning framework, our experiments show that relying on a knowledge graph modeling the relations between labeled data and unlabeled data allows to achieve state-of-the-art results on multiple classification tasks on a dataset of paintings, and on a dataset of Buddha statues. Additionally, we show state-of-the-art results for the difficult case of dealing with unbalanced data, with the limitation of disregarding classes with extremely low degrees in the knowledge graph.