Reviewerly/exHarmony huggingface dataset:
Syntax-enhanced aspect-based sentiment analysis with multi-layer attention

As a key task of fine-grained sentiment analysis, aspect-based sentiment analysis aims to analyse people’s opinions at the aspect level from user-generated texts. Various sub-tasks have been defined according to different scenarios, extracting aspect terms, opinion terms, and the corresponding sentiment. However, most existing studies merely focus on a specific sub-task or a subset of sub-tasks, having many complicated models designed and developed. This hinders the practical applications of aspect-based sentiment analysis. Therefore, some unified frameworks are proposed to handle all the subtasks, but most of them suffer from two limitations. First, the syntactic features are neglected, but such features have been proven effective for aspect-based sentiment analysis. Second, very few efficient mechanisms are developed to leverage important syntactic features, e.g., dependency relations, dependency relation types, and part-of-speech tags. To address these challenges, in this paper, we propose a novel unified framework to handle all defined sub-tasks for aspect-based sentiment analysis. Specifically, based on the graph convolutional network, a multi-layer semantic model is designed to capture the semantic relations between aspect and opinion terms. Moreover, a multi-layer syntax model is proposed to learn explicit dependency relations from different layers. To facilitate the sub-tasks, the learned semantic features are propagated to the syntax model with better semantic guidance to learn the syntactic representations comprehensively. Different from the conventional syntactic model, the proposed framework introduces two attention mechanisms. One is to model dependency relation and type, and the other is to encode part-of-speech tags for detecting aspect and opinion term boundaries. Extensive experiments are conducted to evaluate the proposed novel unified framework, and the experimental results on four groups of real-world datasets explicitly demonstrate the superiority of the proposed framework over a range of baselines.
The tinder swindler: Analyzing public sentiments of romance fraud using machine learning and artificial intelligence

The Tinder Swindler is a Netflix documentary that documents the stories of three women who were swindled of their life savings by a man, Simon Leviev ("Leviev"), who presented himself as the son of a wealthy diamond oligarch. The paper's main objective is to understand the sentiments shared by the public on the microblogging website Twitter on Leviev's actions towards victims. This paper is anchored with Ervin Goffman's (1959) impression management (IM) theory to provide context and theoretical insights into the analysis. IM theory offers a cultural framework to address and understand how people mold their behaviour in social contexts to alter their audience's perceptions of themselves. Data for this project came from Twitter with the hashtag #tinderswindler. In total, 47876 tweets were collected between February 1st and March 18th. The findings reveal that most people who commented on Twitter had high neutral and positive but low negative sentiments about Leviev's actions in the Tinder Swindler. The neutral findings indicate that a significant proportion of individuals did not support Leviev's action, nor do they support the women who were defrauded. The positive sentiments reveal that Leviev's favourable self-presentation successfully persuaded people that his front-stage performance was real and that the performance resonated with Twitter users. The higher subjectivity for the negative sentiments means that the texts contain more factual information than personal opinion.
Quadratic estimation for stochastic systems in the presence of random parameter matrices, time-correlated additive noise and deception attacks

Networked systems usually face different random uncertainties that make the performance of the least-squares (LS) linear filter decline significantly. For this reason, great attention has been paid to the search for other kinds of suboptimal estimators. Among them, the LS quadratic estimation approach has attracted considerable interest in the scientific community for its balance between computational complexity and estimation accuracy. When it comes to stochastic systems subject to different random uncertainties and deception attacks, the quadratic estimator design has not been deeply studied. In this paper, using covariance information, the LS quadratic filtering and fixed-point smoothing problems are addressed under the assumption that the measurements are perturbed by a time-correlated additive noise, as well as affected by random parameter matrices and exposed to random deception attacks. The use of random parameter matrices covers a wide range of common uncertainties and random failures, thus better reflecting the engineering reality. The signal and observation vectors are augmented by stacking the original vectors with their second-order Kronecker powers; then, the linear estimator of the original signal based on the augmented observations provides the required quadratic estimator. A simulation example illustrates the superiority of the proposed quadratic estimators over the conventional linear ones and the effect of the deception attacks on the estimation performance.
Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm

Online shopping is a multi-billion-dollar industry worldwide. However, several challenges related to purchase intention can impact the sales of e-commerce. For example, e-commerce platforms are unable to identify which factors contribute to the high sales of a product. Besides, online sellers have difficulty finding products that align with customers’ preferences. Therefore, this work will utilize an artificial neural network to provide knowledge extraction for the online shopping industry or e-commerce platforms that might improve their sales and services. There are limited attempts to propose knowledge extraction with neural network models in the online shopping field, especially research revolving around online shoppers’ purchasing intentions. In this study, 2-satisfiability logic was used to represent the shopping attribute and a special recurrent artificial neural network named Hopfield neural network was employed. In reducing the learning complexity, a genetic algorithm was implemented to optimize the logical rule throughout the learning phase in performing a 2-satisfiability-based reverse analysis method, implemented during the learning phase as this method was compared. The performance of the genetic algorithm with 2-satisfiability-based reverse analysis was measured according to the selected performance evaluation metrics. The simulation suggested that the proposed model outperformed the existing model in doing logic mining for the online shoppers dataset.
Fast Facts: Platforms From Personalization to Centralization

In recent years, there has been a noticeable shift in the economies of search and information retrieval concerning how the products of giant platform companies consolidate and represent facts directly in their search results. Concomitantly, media, communication, and information scholars have recently refocused on how media technology companies variably create, collect, connect, and commercialize data related to facts about the world and how such processes have implications for how we know the world. Such approaches often counter popular narratives that seek to frame the problems of platforms in terms of personalization and personalized content. While research on the personalization afforded by media is widespread, platforms also engage in the centralization of facts by merging web data representing factual claims and offering answers directly in search engines and virtual assistants’ results and responses (what we refer to as “fast facts”). These processes considerably affect how knowledge is constructed and shared in a networked society. This special issue collects empirical investigative research on the platformization, exploitation, and centralization of facts while offering a variety of perspectives from which to study these developments, including semantic and infrastructural techniques. This article provides an overview of this field and contextualizes recent media studies on search and information retrieval in broader debates around facts and truth claims.
Hybrid Classical-Quantum Neural Network for Improving Space Weather Detection and Early Warning Alerts

Space weather events, such as solar flares and geomagnetic storms, can have significant impacts on space technologies and infrastructure. Traditional space weather detection methods are limited by their accuracy and speed, which can lead to missed or delayed warnings of these events. In this paper, we propose a Hybrid Classical-Quantum Neural Network (HCQNN) that leverages the principles of quantum computing to model and simulate space weather phenomena. The proposed HCQNN is capable of detecting space weather events with 99.9% accuracy and providing early warning alerts to mitigate potential impacts on space-based systems. Our findings indicate that the proposed approach has the potential to improve space weather detection and enhance the resiliency of critical space-based technologies. the proposed approach has the potential to reduce the economic and societal impacts of space weather events. This work contributes to the growing field of quantum computing applications in space science and technology and demonstrates the value of incorporating quantum computing principles into space weather detection and forecasting.
Applying Learning Systems Theory to Model Cognitive Unmanned Aerial Vehicles

With the increasing use of machine learning in autonomous systems, there is both an increasing need and interest in models of machine learning that are compatible with models of autonomous systems. Specifically, modern systems engineering processes rely on explicit models of the inputs, outputs, and interfaces of system components, as can be captured using systems modeling languages like SysML. While there is not a consensus standard reference architecture for machine learning, recently, abstract systems theory has been extended with learning theory to form abstract learning systems theory (ALST), thereby providing formal systems models of learning that are compatible with the majority of machine learning solution methods. In this paper, we apply ALST to provide explicit systems models for a cognitive path planning system in an unmanned aerial vehicle (UAV). Importantly, we consider levels of abstraction in the models of the cognitive system. We then discuss how these models enable systems engineering processes. This paper demonstrates an important application of systems theory that brings explicit systems models and model-based systems engineering into cognitive communications and aerospace applications.
Script2Graph: Auto-construct Screenplay Text World by Mining Contextual Information

With the rapid development of artificial intelligence and the continuous emergence of innovative technologies, its application in movie scenes has gained attention. Meanwhile, the application of natural language processing techniques in movie scripts has gradually attracted the attention of researchers, making text information extraction more intelligent and convenient. However, most of the previous works for unstructured script processing ignore the theoretical guidance of computational language and lack the framework of knowledge schema. Therefore, we propose Script2Graph method based on text world theory to solve unstructured text information extraction and knowledge organization, and realize the automatic construction of script text world. Our experiments demonstrate that the method is effective in extracting text world elements and using high-quality information in downstream applications.
Scalable Random Forest with Data-Parallel Computing

In the last years, there has been a significant increment in the quantity of data available and computational resources. This leads scientific and industry communities to pursue more accurate and efficient Machine Learning (ML) models. Random Forest is a well-known algorithm in the ML field due to the good results obtained in a wide range of problems. Our objective is to create a parallel version of the algorithm that can generate a model using data distributed across different processors that computationally scales on available resources. This paper presents two novel proposals for this algorithm with a data-parallel approach. The first version is implemented using the PyCOMPSs framework and its failure management mechanism, while the second variant uses the new PyCOMPSs nesting paradigm where the parallel tasks can generate other tasks within them. Both approaches are compared between them and against MLlib Apache Spark Random Forest with strong and weak scaling tests. Our findings indicate that while the MLlib implementation is faster when executed in a small number of nodes, the scalability of both new variants is superior. We conclude that the proposed data-parallel approaches to the Random Forest algorithm can effectively generate accurate and efficient models in a distributed computing environment and offer improved scalability over existing methods.
C2N-ABDP: Cluster-to-Node Attention-Based Differentiable Pooling

Graph neural networks have achieved state-of-the-art performance in various graph based tasks, including classification and regression at both node and graph level. In the context of graph classification, graph pooling plays an important role in reducing the number of graph nodes and allowing the graph neural network to learn a hierarchical representation of the input graph. However, most graph pooling methods fail to effectively preserve graph structure information and node feature information when reducing the number of nodes. At the same time, the existing hierarchical differentiable graph pooling methods cannot effectively calculate the importance of nodes and thus cannot effectively aggregate node information. In this paper, we propose an attention-based differentiable pooling method, which aggregates nodes into clusters when reducing the scale of the graph, uses singular value decomposition to calculate cluster information during the aggregation process, and captures node importance information through a novel attention mechanism. The experimental results show that our approach outperforms competitive models on benchmark datasets.
Human-Imperceptible Identification With Learnable Lensless Imaging

Lensless imaging protects visual privacy by capturing heavily blurred images that are imperceptible for humans to recognize the subject but contain enough information for machines to infer information. Unfortunately, protecting visual privacy comes with a reduction in recognition accuracy and vice versa. We propose a learnable lensless imaging framework that protects visual privacy while maintaining recognition accuracy. To make captured images imperceptible to humans, we designed several loss functions based on total variation, invertibility, and the restricted isometry property. We studied the effect of privacy protection with blurriness on the identification of personal identity via a quantitative method based on a subjective evaluation. Moreover, we validate our simulation by implementing a hardware realization of lensless imaging with photo-lithographically printed masks.
The Abu Dhabi Police Virtual Training Centre: A case study for building a virtual reality development capacity and capability

Abstract Virtual reality (VR) technology has revolutionized the way organizations are approaching the training of their workforce. By adopting elements of gamification, VR developers are now able to develop simulations that studies are beginning to demonstrate can teach vocational content in a more effective, efficient, and cost-effective manner, in comparison to traditional teaching approaches, such as classroom tuition for example. VR simulators are considered to be especially effective when used for replicating real-life training scenarios and this is the reason that they are beginning to be adopted by policing. To date, studies have examined their application in a small variety of policing scenarios but no literature has examined exactly how the police develop such VR training simulators, and this is the primary research gap that the study seeks to fill. This is achieved by using a single holistic case study methodology (Yin, 2018. Case Study Research: Design and Methods (6th edn). Thousand Oaks, CA: Sage) to examine the design process of nine simulators developed by the Abu Dhabi Police Virtual Training Centre (ADPVTC). By examining detailed design documents this article identifies the use of elements of the constructivist pedagogical approach to learning to underpin the development process used, and associated hardware and software specifications and outlines practical methods of scalability, portability, and mitigation of health and safety risks. In doing so, whilst also providing a blueprint, and the argument for police services to consider the implementation of their own internal VR development capacity and capability, we discuss the possible implications of this for policing. Limitations are also outlined, with the primary issue being the absence of a formal effectiveness evaluation of the products produced, particularly those used for training.
Making Classical (Threshold) Signatures Post-quantum for Single Use on a Public Ledger

The Bitcoin architecture heavily relies on the ECDSA signature scheme which is broken by quantum adversaries as the secret key can be computed from the public key in quantum polynomial time. To mitigate this attack, bitcoins can be paid to the hash of a public key (P2PKH). However, the first payment reveals the public key so all bitcoins attached to it must be spent at the same time (i.e. the remaining amount must be transferred to a new wallet). Some problems remain with this approach: the owners are vulnerable against rushing adversaries between the time the signature is made public and the time it is committed to the blockchain. Additionally, there is no equivalent mechanism for threshold signatures. Finally, no formal analysis of P2PKH has been done. In this paper, we formalize the security notion of a digital signature with a hidden public key and we propose and prove the security of a generic transformation that converts a classical signature to a post-quantum one that can be used only once. We compare it with P2PKH. Namely, our proposal relies on pre-image resistance instead of collision resistance as for P2PKH, so allows for shorter hashes. Additionally, we propose the notion of a delay signature to address the problem of the rushing adversary when used with a public ledger and discuss the advantages and disadvantages of our approach. We further extend our results to threshold signatures.
Necessary and sufficient conditions for the asymptotic normality of higher order Turing estimators

This paper establishes necessary and sufficient conditions for the asymptotic normality of higher order Turing estimators. It further gives several easy to verify sufficient conditions. These conditions are then used to show that the assumptions hold for large classes of distributions with regularly varying tails. This includes classes for which asymptotic normality had not been previously verified.
Bridging the Gap Between Model Explanations in Partially Annotated Multi-Label Classification

Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer vision. One baseline approach to this task is to assume unobserved labels as negative labels, but this assumption induces label noise as a form of false negative. To understand the negative impact caused by false negative labels, we study how these labels affect the model's explanation. We observe that the explanation of two models, trained with full and partial labels each, highlights similar regions but with different scaling, where the latter tends to have lower attribution scores. Based on these findings, we propose to boost the attribution scores of the model trained with partial labels to make its explanation resemble that of the model trained with full labels. Even with the conceptually simple approach, the multi-label classification performance improves by a large margin in three different datasets on a single positive label setting and one on a large-scale partial label setting. Code is available at https://github.com/youngwk/BridgeGapExplanationPAMC.
Hunting Sparsity: Density-Guided Contrastive Learning for Semi-Supervised Semantic Segmentation

Recent semi-supervised semantic segmentation methods combine pseudo labeling and consistency regularization to enhance model generalization from perturbation-invariant training. In this work, we argue that adequate supervision can be extracted directly from the geometry of feature space. Inspired by density-based unsupervised clustering, we propose to leverage feature density to locate sparse regions within feature clusters defined by label and pseudo labels. The hypothesis is that lower-density features tend to be under-trained compared with those densely gathered. Therefore, we propose to apply regularization on the structure of the cluster by tackling the sparsity to increase intra-class compactness in feature space. With this goal, we present a Density-Guided Contrastive Learning (DGCL) strategy to push anchor features in sparse regions toward cluster centers approximated by high-density positive keys. The heart of our method is to estimate feature density which is defined as neighbor compactness. We design a multi-scale density estimation module to obtain the density from multiple nearest-neighbor graphs for robust density modeling. Moreover, a unified training framework is proposed to combine label-guided self-training and density-guided geometry regularization to form complementary supervision on unlabeled data. Experimental results on PAS-CAL VOC and Cityscapes under various semi-supervised settings demonstrate that our proposed method achieves state-of-the-art performances. The project is available at https://github.com/Gavinwxy/DGCL.