Reviewerly/exHarmony huggingface dataset:
Psychological Features of Individuals Prone to Cyber Victimization

The article discusses the results of the empirical research into the psychological features of individuals prone to cyber victimization. As a theoretical basis, this paper uses the analysis of the findings of the studies conducted by the Russian as well as foreign researchers, that indicate presence of the specific traits among victims of cyber crime. The focus group that took part in the research includes 78 adult users of the VK social network. The respondents who became victims of cyber scams more than twice over the past three years were identi-fied as the individuals prone to cyber victimization. The following techniques were used for the empirical data collection: Multifactorial personality questionnaire (16 PF) by R.B. Cattell, “Level of subjective control” test by V.F. Bazhin, E.A. Golynkina and A.M. Atkind and State-Trait anxiety inventory by C. Spielberger (adapted by Y. Khanin). The results of the conducted research show that one's tendency towards cyber victimization can be explained by the presence of certain specific emotional and interpersonal traits. The findings of this study could be used as major targets for designing preventive programs against crimes in cyber space.
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing

Semantic parsing is the task of producing structured meaning representations for natural language sentences.Recent research has pointed out that the commonly-used sequenceto-sequence (seq2seq) semantic parsers struggle to generalize systematically, i.e. to handle examples that require recombining known knowledge in novel settings.In this work, we show that better systematic generalization can be achieved by producing the meaning representation directly as a graph and not as a sequence.To this end we propose LAGr (Label Aligned Graphs), a general framework to produce semantic parses by independently predicting node and edge labels for a complete multi-layer input-aligned graph.The strongly-supervised LAGr algorithm requires aligned graphs as inputs, whereas weaklysupervised LAGr infers alignments for originally unaligned target graphs using approximate maximum-a-posteriori inference.Experiments demonstrate that LAGr achieves significant improvements in systematic generalization upon the baseline seq2seq parsers in both strongly-and weakly-supervised settings.
Numerical Evidence That the Power of Artificial Neural Networks Limits Strong AI

A famous definition of AI is based on the terms weak and strong AI from McCarthy. An open question is the characterization of these terms, i.e., the transition from weak to strong. Nearly no research results are known for this complex and important question. In this paper we investigate how the size and structure of a Neural Network (NN) limits the learnability of a training sample, and thus, can be used to discriminate weak and strong AI (domains). Furthermore, the size of the training sample is a primary parameter for the training effort estimation with the big O function. The needed training repetitions may also limit the learning tractability and will be investigated. The results are illustrated with an analysis of a feedforward NN and a training sample for language with 1,000 words including the effort for the training repetitions.
Design of Teaching Material Evaluation Incentive Mechanism based on Game Theory

As the carrier of knowledge and the basis of teaching, teaching materials affect the teaching quality of schools. However, the selection of teaching materials in colleges and universities is in a dilemma because of the variety of teaching materials and the uneven quality of teaching materials. Blockchain has gradually drawn attention from various fields, due to its secure storage, non-forgery and non-tamper features. The combination of blockchain and teaching material evaluation can provide fair and transparent grading and evaluation for teaching materials, which is of great reference value for colleges and universities to select teaching materials. This paper proposes a reputation incentive mechanism for teaching material evaluation blockchain based on game theory, analyzes the behavior of users in the scoring stage of teaching material evaluation, and gives the Nash equilibrium solution. Through the numerical simulation experiment, the effectiveness of the incentive mechanism and the reliability of the teaching materials’ evaluation results are proved.
Softmax Bottleneck Makes Language Models Unable to Represent Multi-mode Word Distributions

Neural language models (LMs) such as GPT-2 estimate the probability distribution over the next word by a softmax over the vocabulary.The softmax layer produces the distribution based on the dot products of a single hidden state and the embeddings of words in the vocabulary.However, we discover that this single hidden state cannot produce all probability distributions regardless of the LM size or training data size because the single hidden state embedding cannot be close to the embeddings of all the possible next words simultaneously when there are other interfering word embeddings between them.In this work, we demonstrate the importance of this limitation both theoretically and practically.Our work not only deepens our understanding of softmax bottleneck and mixture of softmax (MoS) but also inspires us to propose multi-facet softmax (MFS) to address the limitations of MoS.Extensive empirical analyses confirm our findings and show that against MoS, the proposed MFS achieves two-fold improvements in the perplexity of GPT-2 and BERT."The greater the ambiguity, the greater the pleasure."-Milan Kundera
Interpreting the Robustness of Neural NLP Models to Textual Perturbations

Modern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data.However, it is still unclear why models are less robust to some perturbations than others.In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence).We further give a causal justification for the learnability metric.We conduct extensive experiments with four prominent NLP models -TextRNN, BERT, RoBERTa and XLNetover eight types of textual perturbations on three datasets.We show that a model which is better at identifying a perturbation (higher learnability) becomes worse at ignoring such a perturbation at test time (lower robustness), providing empirical support for our hypothesis.
ProtoTEx: Explaining Model Decisions with Prototype Tensors

We present PROTOTEX, a novel white-box NLP classification architecture based on prototype networks (Li et al., 2018).PROTOTEX faithfully explains model decisions based on prototype tensors that encode latent clusters of training examples.At inference time, classification decisions are based on the distances between the input text and the prototype tensors, explained via the training examples most similar to the most influential prototypes.We also describe a novel interleaved training algorithm that effectively handles classes characterized by the absence of indicative features.On a propaganda detection task, PROTOTEX accuracy matches BART-large and exceeds BERTlarge with the added benefit of providing faithful explanations.A user study also shows that prototype-based explanations help non-experts to better recognize propaganda in online news.
Face Mask Wearing Detection Using Support Vector Machine (SVM)

As an effort to prevent the spread of the Covid-19, various countries have implemented health protocol policies such as work-from-home, social distancing, and face mask-wearing in public places. However, monitoring compliance with the policy is still difficult, especially for the face mask policy. It is still managed by humans and is costly. Thus, this research proposes a face mask-wearing detection using a soft-margin Support Vector Machine (SVM). There are three main stages: feature selection and preprocessing, model training, and evaluation. During the first stage, the dataset of 3833 images (1915 images with face masks and 1918 images without face masks) was prepared to be used in the training stage. The training stage was conducted using SVM added with the soft-margin objective to overcome images that could not be separated linearly. At the final stage, evaluation was conducted using a confusion matrix with 10 folds cross-validation. Based on the experiments, the proposed method shows a performance accuracy of 91.7%, a precision of 90.3%, recall of 93.5%, and an F-measure of 91.8%. Our method also worked fast, taking only 0.025 seconds to process a new image. It is 7.12 times faster than Deep Learning which requires 0.18 seconds for one classification.
The Efficacy of Using Subtitled Video in Teaching Reading to Indonesian English as Foreign Language (EFL) Students

This research aims at finding out the efficacy of using Subtitled Video in teaching reading especially stories to Indonesian English as Foreign Language (EFL) students. The main question of this research: Is the use of Subtitled Video effective in teaching reading stories to Indonesian EFL students?. The research can be an addition to the persisting teaching reading of English particularly the use of the Subtitled Video. This research was conducted at the tenth-grade students at SMA Kristen Mercusuar Kupang, an Indonesian High School. The total number of samples was 25 students. The research used a pre-experimental method with a pre-test and post-test design. The pre-test was given to find out the basic ability of the students in reading and the post-test given to find out the students’ improvement in reading after giving the treatment by using Subtitled Video as media. The findings show that using the Subtitled Video as media is effective in teaching reading to Indonesian EFL students. It is supported by the result of the significance test through SPSS 20 program that the P was 0.00. Therefore, it is recommended to use the Subtitled Video in developing students’ competence in reading of Indonesian EFL students.
Relationship between the Riemann and Lebesgue Integrals

Summary The goal of this article is to clarify the relationship between Riemann and Lebesgue integrals. In previous article [5], we constructed a one-dimensional Lebesgue measure. The one-dimensional Lebesgue measure provides a measure of any intervals, which can be used to prove the well-known relationship [6] between the Riemann and Lebesgue integrals [1]. We also proved the relationship between the integral of a given measure and that of its complete measure. As the result of this work, the Lebesgue integral of a bounded real valued function in the Mizar system [2], [3] can be calculated by the Riemann integral.
Similarity Is Not Entailment — Jointly Learning Similarity Transformation for Textual Entailment

Predicting entailment between two given texts is an important task upon which the performance of numerous NLP tasks depend on such as question answering, text summarization, and information extraction. The degree to which two texts are similar has been used extensively as a key feature in much previous work in predicting entailment. However, using similarity scores directly, without proper transformations, results in suboptimal performance. Given a set of lexical similarity measures, we propose a method that jointly learns both (a) a set of non-linear transformation functions for those similarity measures and, (b) the optimal non-linear combination of those transformation functions to predict textual entailment. Our method consistently outperforms numerous baselines, reporting a micro-averaged F-score of 46.48 on the RTE- 7 benchmark dataset. The proposed method is ranked 2-nd among 33 entailment systems participated in RTE-7, demonstrating its competitiveness over numerous other entailment approaches. Although our method is statistically comparable to the current state-of-the-art, we require less external knowledge resources.
Design of a Novel Information System for Semi-automated Management of Cybersecurity in Industrial Control Systems

There is an urgent need in many critical infrastructure sectors, including the energy sector, for attaining detailed insights into cybersecurity features and compliance with cybersecurity requirements related to their Operational Technology (OT) deployments. Frequent feature changes of OT devices interfere with this need, posing a great risk to customers. One effective way to address this challenge is via a semi-automated cyber-physical security assurance approach, which enables verification and validation of the OT device cybersecurity claims against actual capabilities, both pre- and post-deployment. To realize this approach, this article presents new methodology and algorithms to automatically identify cybersecurity-related claims expressed in natural language form in ICS device documents. We developed an identification process that employs natural language processing (NLP) techniques with the goal of semi-automated vetting of detected claims against their device implementation. We also present our novel NLP components for verifying feature claims against relevant cybersecurity requirements. The verification pipeline includes components such as automated vendor identification, device document curation, feature claim identification utilizing sentiment analysis for conflict resolution, and reporting of features that are claimed to be supported or indicated as unsupported. Our novel matching engine represents the first automated information system available in the cybersecurity domain that directly aids the generation of ICS compliance reports.
Accelerating the approach of dissipative quantum spin systems towards stationarity through global spin rotations

We consider open quantum systems whose dynamics is governed by a time-independent Markovian Lindblad master equation. Such systems approach their stationary state on a timescale that is determined by the spectral gap of the generator of the master equation dynamics. In a recent paper [Carollo et al., Phys. Rev. Lett. 127, 060401 (2021)] it was shown that under certain circumstances it was possible to exponentially accelerate the approach to stationarity by performing a unitary transformation of the initial state. This phenomenon can be regarded as the quantum version of the so-called Mpemba effect. The transformation of the initial state removes its overlap with the dynamical mode of the open system dynamics that possesses the slowest decay rate and, thus, determines the spectral gap. Whereas this transformation can be exactly constructed in some cases, it is, in practice, challenging to implement. Here we show that even far simpler transformations constructed by a global unitary spin rotation allow to exponentially speed up relaxation. We demonstrate this using simple dissipative quantum spin systems, which are relevant for current quantum simulation and computation platforms based on trapped atoms and ions.
The Effect of Using Whatsapp on EFL Students' Medical English Vocabulary Learning During the Covid-19 Pandemic

The role of social networking mobile applications such as WhatsApp in enhancing second language vocabulary learning among English language learners continues to be a subject of interest for many scholars. The current study aimed at examining medical English vocabulary learning among undergraduate students using WhatsApp compared to learning vocabulary via Blackboard platform during the Covid-19 pandemic. To this end, 108 medical students (51 males, 57 females) enrolled in a first semester English for a specific English course participated in the study. A quasi-experimental design was adopted for two groups. Fifty-three students were assigned to the WhatsApp group and 55 students were assigned to the Blackboard group. Data were collected using pretest-posttest design. Results of t-test scores revealed no significant difference between the WhatsApp and Blackboard groups on a vocabulary test. Results of survey that gauged students' perception of the use of WhatsApp as a platform for learning new vocabulary showed positive perceptions since participants thought that WhatsApp enhanced their learning experience.
Detection of Insulator Burst Position of Lightweight YOLOv5

Insulator is a key insulating device. With the development of artificial intelligence technology, traditional manual inspection has been gradually replaced by image inspection technology. Therefore, this paper proposes an improved algorithm based on YOLOv5 for detecting the burst position of lightweight insulators. Firstly, redundant convolutional layers are deleted and channels are clipped to make the model lightweight and occupy less memory. Secondly, adaptive attention modules are added between adjacent residual modules of the network to make the key feature targets gain more weight and enhance the learning feature capability of the network. Experimental results show that compared with the original model, the Map of the improved model is reduced by 0.52%, but the size of model memory is reduced by 69.4%, and the time of each round of training is increased by 20.8%.
TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data

Synthetic tabular data generation becomes crucial when real data is limited, expensive to collect, or simply cannot be used due to privacy concerns. However, producing good quality synthetic data is challenging. Several probabilistic, statistical, and generative adversarial networks (GANs) based approaches have been presented for synthetic tabular data generation. Once generated, evaluating the quality of the synthetic data is quite challenging. Some of the traditional metrics have been used in the literature but there is lack of a common, robust, and single metric. This makes it difficult to properly compare the effectiveness of different synthetic tabular data generation methods. In this paper we propose a new universal metric, TabSynDex, for robust evaluation of synthetic data. TabSynDex assesses the similarity of synthetic data with real data through different component scores which evaluate the characteristics that are desirable for "high quality" synthetic data. Being a single score metric, TabSynDex can also be used to observe and evaluate the training of neural network based approaches. This would help in obtaining insights that was not possible earlier. Further, we present several baseline models for comparative analysis of the proposed evaluation metric with existing generative models.
Artificial Intelligence in Promoting Teaching and Learning Transformation in Schools

With the development of science and technology, artificial intelligence technology has entered an unprecedented period of rapid development, and it is profoundly changing all areas of life. Various countries require implementation of intelligent education, but how artificial intelligent education promotes the development of school teaching is still lacking in systematic discussion. This paper discusses several aspects of artificial intelligence to promote teaching and learning reform, including artificial intelligence to promote the innovative development of teaching resources and teaching environment, teaching and learning methods, the development of school teaching management and teaching evaluation, and to promote the reform of subject structure and educational content. With a view to encouraging the application of artificial intelligence in promoting the transformation of teaching and learning, it is proposed that schools meet new requirements in the era of artificial intelligence.
Sharing generative models instead of private data: a simulation study on mammography patch classification

Early detection of breast cancer in mammography screening via deep-learning based computer-aided detection systems shows promising potential in improving the curability and mortality rates of breast cancer. However, many clinical centres are restricted in the amount and heterogeneity of available data to train such models to (i) achieve promising performance and to (ii) generalise well across acquisition protocols and domains. As sharing data between centres is restricted due to patient privacy concerns, we propose a potential solution: sharing trained generative models between centres as substitute for real patient data. In this work, we use three well known mammography datasets to simulate three different centres, where one centre receives the trained generator of Generative Adversarial Networks (GANs) from the two remaining centres in order to augment the size and heterogeneity of its training dataset. We evaluate the utility of this approach on mammography patch classification on the test set of the GAN-receiving centre using two different classification models, (a) a convolutional neural network and (b) a transformer neural network. Our experiments demonstrate that shared GANs notably increase the performance of both transformer and convolutional classification models and highlight this approach as a viable alternative to inter-centre data sharing.
Dynamic composition of services: an approach driven by the user's intention and context

With the continuous development of services in ubiquitous systems, service orientation is becoming increasingly important in its structuring. As a result, the design and the development of applications are gradually migrating from a traditional model to a service-oriented model. In this regard, we propose through this work an approach for web service (WS) composition guided by the context and the intention, by which we suggest an architecture for service composition that reduces the complexity of the generated artificial intelligence (AI) planning problem and to ensure the interoperability of any system independently of the domain planners. The general idea of this approach is to conceive an intentional-contextual metamodel that will be transformed into an OWL model using OMG standards, which will be used to map the WS composition problem into AI planning problems. This article describes the architectural, conceptual, and strategic aspects to deal with the WS composition problem.
Embracing AI-Based Education: Perceived Social Presence of Human Teachers and Expectations About Machine Teachers in Online Education

Technological advancements in education have turned the idea of machines as teachers into a reality. To better understand this phenomenon, the present study explores how college students develop expectations (or anticipations) about a machine teacher, particularly an AI teaching assistant. Specifically, the study examines whether students’ previous experiences with online courses taught by a human teacher would influence their expectations about AI teaching assistants in future online courses. An online survey was conducted to collect data from college students in the United States. Findings indicate that positively experienced social presence of a human teacher helps develop positive expectations about an AI teaching assistant. The study provides meaningful implications and contributions to our understanding of a machine agent in education