Reviewerly/exHarmony huggingface dataset:
Understanding the Behaviors of Toxic Accounts on Reddit

Toxic comments are the top form of hate and harassment experienced online. While many studies have investigated the types of toxic comments posted online, the effects that such content has on people, and the impact of potential defenses, no study has captured the behaviors of the accounts that post toxic comments or how such attacks are operationalized. In this paper, we present a measurement study of 929K accounts that post toxic comments on Reddit over an 18 month period. Combined, these accounts posted over 14 million toxic comments that encompass insults, identity attacks, threats of violence, and sexual harassment. We explore the impact that these accounts have on Reddit, the targeting strategies that abusive accounts adopt, and the distinct patterns that distinguish classes of abusive accounts. Our analysis informs the nuanced interventions needed to curb unwanted toxic behaviors online.
HISum: Hyperbolic Interaction Model for Extractive Multi-Document Summarization

Extractive summarization helps provide a short description or a digest of news or other web texts. It enhances the reading experience of users, especially when they are reading on small displays (e.g., mobile phones). Matching-based methods are recently proposed for the extractive summarization task, which extracts a summary from a global view via a document-summary matching framework. However, these methods only calculate similarities between candidate summaries and the entire document embeddings, insufficiently capturing interactions between different contextual information in the document to accurately estimate the importance of candidates. In this paper, we propose a new hyperbolic interaction model for extractive multi-document summarization (HISum). Specifically, HISum first learns document and candidate summary representations in the same hyperbolic space to capture latent hierarchical structures and then estimates the importance scores of candidates by jointly modeling interactions between each candidate and the document from global and local views. Finally, the importance scores are used to rank and extract the best candidate as the extracted summary. Experimental results on several benchmarks show that HISum outperforms the state-of-the-art extractive baselines1.
GELTOR: A Graph Embedding Method based on Listwise Learning to Rank

Similarity-based embedding methods have introduced a new perspective on graph embedding by conforming the similarity distribution of latent vectors in the embedding space to that of nodes in the graph; they show significant effectiveness over conventional embedding methods in various machine learning tasks. In this paper, we first point out the three drawbacks of existing similarity-based embedding methods: inaccurate similarity computation, conflicting optimization goal, and impairing in/out-degree distributions. Then, motivated by these drawbacks, we propose AdaSim*, a novel similarity measure for graphs that is conducive to the similarity-based graph embedding. We finally propose GELTOR, an effective embedding method that employs AdaSim* as a node similarity measure and the concept of learning-to-rank in the embedding process. Contrary to existing methods, GELTOR does not learn the similarity scores distribution; instead, for any target node, GELTOR conforms the ranks of its top-t similar nodes in the embedding space to their original ranks based on AdaSim* scores. We conduct extensive experiments with six real-world datasets to evaluate the effectiveness of GELTOR in graph reconstruction, link prediction, and node classification tasks. Our experimental results show that (1) AdaSim* outperforms AdaSim, RWR, and MCT in computing nodes similarity in graphs, (2) our GETLOR outperforms existing state-of-the-arts and conventional embedding methods in most cases of the above machine learning tasks, thereby implying that learning-to-rank is beneficial to graph embedding.
Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning

Temporal Knowledge graph (TKG) reasoning aims to predict missing facts based on historical TKG data. Most of the existing methods are incapable of explicitly modeling the long-term time dependencies from history and neglect the adaptive integration of the long- and short-term information. To tackle these problems, we propose a novel method that utilizes a designed Hierarchical Relational Graph Neural Network to learn the Long- and Short-term representations for TKG reasoning, namely HGLS. Specifically, to explicitly associate entities in different timestamps, we first transform the TKG into a global graph. Based on the built graph, we design a Hierarchical Relational Graph Neural Network that executes in two levels: The sub-graph level is to capture the semantic dependencies within concurrent facts of each KG. And the global-graph level aims to model the temporal dependencies between entities. Furthermore, we design a module to extract the long- and short-term information from the output of these two levels. Finally, the long- and short-term representations are fused into a unified one by Gating Integration for entity prediction. Extensive experiments on four datasets demonstrate the effectiveness of HGLS.
Analogy between Boltzmann Machines and Feynman Path Integrals

Machine learning has had a significant impact on multiple areas of science, technology, health, and computer and information sciences. Through the advent of quantum computing, quantum machine learning has developed as a new and important avenue for the study of complex learning problems. Yet there is substantial debate and uncertainty in regard to the foundations of machine learning. Here, we provide a detailed exposition of the mathematical connections between a general machine learning approach called Boltzmann machines and Feynman's description of quantum and statistical mechanics. In Feynman's description, quantum phenomena arise from an elegant, weighted sum over (or superposition of) paths. Our analysis shows that Boltzmann machines and neural networks have a similar mathematical structure. This allows the interpretation that the hidden layers in Boltzmann machines and neural networks are discrete versions of path elements and allows a path integral interpretation of machine learning similar to that in quantum and statistical mechanics. Since Feynman paths are a natural and elegant depiction of interference phenomena and the superposition principle germane to quantum mechanics, this analysis allows us to interpret the goal in machine learning as finding an appropriate combination of paths, and accumulated path-weights, through a network, that cumulatively captures the correct properties of an x-to-y map for a given mathematical problem. We are forced to conclude that neural networks are naturally related to Feynman path-integrals and hence may present one avenue to be considered as quantum problems. Consequently, we provide general quantum circuit models applicable to both Boltzmann machines and Feynman path integrals.
Using interactive learning in future aviation professionals teaching English

The article aims to find out whether interactive learning in ESP language course for future aviation professionals introduction can raise students’ level of motivation. This study was conducted during the 2019-2020 academic year at National Aviation University (Kyiv, Ukraine). Testings to determine students’ level of proficiency according to Language Proficiency Rating Scale ( ICAO, Level 4) were conducted in September and May of the same year of education. With the purpose to learn the motivational effect from interactive learning introduction students were asked to respond to the questionnaire based on Attitude/Motivation Test Battery simultaneously with language testing. The analyses of the data revealed the positive effect of the interactive learning introduction in future aviation professionals’ training course on the motivational level as well as language competence level. A greater impact on students’ instrumental motivation is observed, which can be explained by the use of professionally-oriented training materials, while students’ integrative motivation also has a positive effect. According to the results of the experiment, some other conclusions are made.
An Ecosystem Approach to Building a Career Trajectory for Students

Based on the revealed contradictions between the needs of the labor market and the level of qualification of personnel, the article attempts to comprehend the current situation of integration of the potentials of the educational and professional communities in building the trajectory of career growth of students. The current mechanisms of building an ecosystem of professional education organizations, general education organizations and the business community are considered. The “ecosystem of personnel partnership” is discussed as a social environment that is formed for the purpose of productive development of the potential of students. It is a collaboration of teachers, employees of manufacturing companies and organizations, people living in this territory, education authorities and the labor market. The article analyzes the possibilities of the digital educational space as a link in the process of community integration and professional self-determination and self-realization of young people. The main technologies implemented in the integration process are systematized.
Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement

The detection of entanglement provides a definitive proof of quantumness. Its ascertainment might be challenging for hot or macroscopic objects, where entanglement is typically weak, but nevertheless present. Here we propose a platform for measuring entanglement by connecting the objects of interest to an uncontrolled quantum network, whose emission (readout) is trained to learn and sense the entanglement of the former. First, we demonstrate the platform and its features with generic quantum systems. As the network effectively learns to recognise quantum states, it is possible to sense the amount of entanglement after training with only non-entangled states. Furthermore, by taking into account measurement errors, we demonstrate entanglement sensing with precision that scales beyond the standard quantum limit and outperforms measurements performed directly on the objects. Finally, we utilise our platform for sensing gravity-induced entanglement between two masses and predict an improvement of two orders of magnitude in the precision of entanglement estimation compared to existing techniques.
Blockchain-enabled secure and efficient data sharing scheme for trust management in healthcare smartphone network

The Internet of Medical Things (IoMT) is an extended genre of the Internet of Things (IoT) where the Things collaborate to provide remote patient health monitoring, also known as the Internet of Health (IoH). Smartphones and IoMTs are expected to maintain secure and trusted confidential patient record exchange while managing the patient remotely. Healthcare organizations deploy Healthcare Smartphone Networks (HSN) for personal patient data collection and sharing among smartphone users and IoMT nodes. However, attackers gain access to confidential patient data via infected IoMT nodes on the HSN. Additionally, attackers can compromise the entire network via malicious nodes. This article proposes a Hyperledger blockchain-based technique to identify compromised IoMT nodes and safeguard sensitive patient records. Furthermore, the paper presents a Clustered Hierarchical Trust Management System (CHTMS) to block malicious nodes. In addition, the proposal employs Elliptic Curve Cryptography (ECC) to protect sensitive health records and is resilient against Denial-Of-Service (DOS) attacks. Finally, the evaluation results show that integrating blockchains into the HSN system improved detection performance compared to the existing state of the art. Therefore, the simulation results indicate better security and reliability when compared to conventional databases.
Building Community Resiliency through Immersive Communal Extended Reality (CXR)

Situated and shared experiences can motivate community members to plan shared action, promoting community engagement. We deployed and evaluated a communal extended-reality (CXR) bus tour that depicts the possible impacts of flooding and climate change. This paper describes the results of seven community engagement sessions with a total of N = 74 members of the Roosevelt Island community. We conducted pre- and post-bus tour focus groups to understand how the tour affected these community members’ awareness and motivation to take action. We found that the unique qualities of immersive, situated, and geo-located virtual reality (VR) on a bus made climate change feel real, brought the consequences of climate change closer to home, and highlighted existing community resources to address the issue. Our results showed that the CXR experience helped to simulate a physical emergency state, which empowered the community to translate feelings of hopelessness into creative and actionable ideas. Our finding exemplifies that geo-located VR on a bus can be a powerful tool to motivate innovations and collective action. Our work is a first-of-its-kind empirical contribution showing that CXR experiences can inspire action. It offers a proof-of-concept of a large-scale community engagement process featuring simulated communal experiences, leading to creative ideas for a bottom-up community resiliency plan.
Grid-Based Low Computation Image Processing Algorithm of Maritime Object Detection for Navigation Aids

Several cameras are mounted on navigation aid buoys and these cameras can be used for accident prevention systems by processing the images captured. The currently existing image processing algorithms were originally designed for accident prevention on land—for example, CCTV (closed-circuit television)—which are performance oriented. However, when it comes to ocean-based images, navigation aids are usually located at sea and the cameras must be battery operated, and consequently, the energy efficiency of image processing is a major concern. Therefore, this paper proposed a novel approach to the detection of images in an ocean environment with a significantly lower computation. The new algorithm clustered pixels to grids and dealt with grids using greyscale rather than the particular color values of each pixel. Simulation-based experiments demonstrated that the grid-based algorithm provided five-times faster image processing in order to detect an object and achieved an up to 2.5 higher detection rate when compared with existing algorithms using ocean images.
RobustDespeckling: Robust speckle noise reduction method using multi-scale and kernel fisher discriminant analysis

In medical ultrasound imaging, speckle noise is an important property since it usually involves worsening the image resolution along with contrast, thus reducing the diagnostic significance of the imaging modality. The "RobustDespeckling" method, which has been proposed in this study to reduce speckle noise, involves extracting the noise-only signal from the image signal through applying the bidimesional empirical mode decomposition (BEMD) method and wavelet transform (WT) in the encapsulated way. Then, wavelet coefficients are thresholded to filter the noisy coefficients by an optimal threshold parameter that is estimated from kernel fisher discriminant analysis (KFDA). Finally, BEMD and WT processes are employed to obtain a de-noised image. The investigation is conducted by way of upsampling and downsampling operations of ultrasound images to analyze the efficiency of different de-noising techniques including the proposed “RobustDespeckling” method. The visual observation and statistical analysis signify that the “RobustDespeckling” method is robust in minimizing the speckle noise of ultrasound images over existing de-noising techniques. Consequently, the method is more competent to preserve the clarity of the edge without losing the significant image detail.
Implementing Artificial Intelligence

This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients. Finally, we describe how artificial intelligence models can unleash researchers' minds by proposing new strategies, by increasing the quality of clinical practice, and by questioning current knowledge and understanding.
DeepSentiParsBERT: A Deep Learning Model for Persian Sentiment Analysis Using ParsBERT

Social media has provided a platform for sharing opinions and feelings on a variety of topics. Automated analysis of these opinions is of particular importance to business organizations for improving their products and services. In recent years, deep learning techniques have become very popular due to their high efficiency. Several DNN models have been proposed for the task of sentiment analysis and their performance is promising. In this paper, a new deep architecture consisting of ParsBERT and Bidirectional LSTM models (DeepSentiParsBERT) is proposed for the sentiment analysis of Persian texts. Results from comparison with the most recent state-of-the-art models show the superiority of DeepSentiParsBERT on the Digikala corpus (91.57% F1-Score).
A Fuzzy Integral Approach for Ensembling Unsupervised Feature Selection Algorithms

Feature selection is an effective technique for decreasing data dimensionality by selecting a significant feature set. Gathering label information can be time-consuming and expensive, as labeled instances are not always available. Therefore, unsupervised learning importance has emerged. In this article, a new unsupervised feature selection is presented based on an ensemble strategy. The ensemble of multiple feature selection methods is performed using fuzzy integral operators. The comparisons are made against various feature selection methods in the literature to show the better performance of the proposed method. These comparisons are conducted based on classification accuracy and run-time.
Additive Feature Attribution Explainable Methods to Craft Adversarial Attacks for Text Classification and Text Regression

Deep learning (DL) models have significantly improved the performance of text classification and text regression tasks. However, DL models are often strikingly vulnerable to adversarial attacks. Many researchers have aimed to develop adversarial attacks against DL models in realistic black-box settings (i.e., assuming no model knowledge is accessible to attackers). These attacks typically operate with a two-phase framework: (1) sensitivity estimation through gradient-based or deletion-based methods to evaluate the sensitivity of each token to the prediction of the target model, and (2) perturbation execution to craft adversarial examples based on the estimated token sensitivity. However, gradient-based and deletion-based methods used to estimate sensitivity often face issues of capturing token directionality and overlapping token sensitivities, respectively. In this study, we propose a novel eXplanation-based method for Adversarial Text Attacks (XATA) that leverages additive feature attribution explainable methods, namely LIME or SHAP, to measure the sensitivity of input tokens when crafting black-box adversarial attacks on DL models performing text classification or text regression. We evaluated XATA's attack performance on DL models executing text classification on the IMDB Movie Review, Yelp Reviews-Polarity, and Amazon Reviews-Polarity datasets and DL models conducting text regression on the My Personality, Drug Review, and CommonLit Readability datasets. The proposed XATA outperformed the existing gradient-based and deletion-based adversarial attack baselines in both tasks. These findings indicate that the ever-growing research focused on improving the explainability of DL models with additive feature attribution explainable methods can provide attackers with weapons to launch targeted adversarial attacks.
A Self-Sensing Shape Memory Alloy Actuator Physical Reservoir Computer

A self-sensing shape memory alloy actuator is harnessed as a computational resource by utilizing it as a physical reservoir computer. Physical reservoir computing is a machine learning technique that takes advantage of the dynamics of a physical system for computation. Compared to recurrent neural networks, this architecture can be both fast and efficient with a cheaper training procedure. A shape memory alloy actuator is designed, fabricated, and tested for processing information. Voltage variation along the shape memory alloy wire is used as the reservoir's nodes. The physical reservoir is then used to predict the future trajectory of the actuator's end effector under various driving signals. This self-prediction method is also reconfigurable, as demonstrated by training the reservoir for one waveform but testing it for a different one. A nonlinear autoregressive moving average prediction task was also used to highlight the physical reservoir computer's abilities. Following this methodology, the soft actuator can be used for actuation and computation at the same time without altering its design.