Reviewerly/exHarmony huggingface dataset:
Ternary Relational Semantics for the Variants of BN4 and E4 which Contain Routley and Meyer's Logic B

Six interesting variants of the logics BN4 and E4—which can be considered as the 4-valued logics of the relevant conditional and (relevant) entailment, respectively—were previously developed in the literature. All these systems are related to the family of relevant logics and contain Routley and Meyer's basic logic B, which is well-known to be specifically associated with the ternary relational semantics. The aim of this paper is to develop reduced general Routley-Meyer semantics for them. Strong soundness and completeness theorems are proved for each one of the logics.
Sentence Extraction-Based Machine Reading Comprehension for Vietnamese

The development of natural language processing (NLP) in general and machine reading comprehension in particular has attracted the great attention of the research community. In recent years, there are a few datasets for machine reading comprehension tasks in Vietnamese with large sizes, such as UIT-ViQuAD and UIT-ViNewsQA. However, the datasets are not diverse in answers to serve the research. In this paper, we introduce UIT-ViWikiQA, the first dataset for evaluating sentence extraction-based machine reading comprehension in the Vietnamese language. The UIT-ViWikiQA dataset is converted from the UIT-ViQuAD dataset, consisting of comprises 23.074 question-answers based on 5.109 passages of 174 Wikipedia Vietnamese articles. We propose a conversion algorithm to create the dataset for sentence extraction-based machine reading comprehension and three types of approaches for sentence extraction-based machine reading comprehension in Vietnamese. Our experiments show that the best machine model is XLM-R $$_{Large}$$ , which achieves an exact match (EM) of 85.97% and an F1-score of 88.77% on our dataset. Besides, we analyze experimental results in terms of the question type in Vietnamese and the effect of context on the performance of the MRC models, thereby showing the challenges from the UIT-ViWikiQA dataset that we propose to the language processing community.
The Chaos Theory and its Application

Abstract Chaos Theory is not only one of the most active branches of the dynamical system, but also one of the most important topics of the nonlinear scientific field. The main purpose of this thesis is to introduce and illustrate the basic concepts, characteristics, and various applications of Chaos Theory. Chaos Theory is widely applied in physics, biology, and economics fields etc., so the thesis will also exhibit interdisciplinary research on Chaos Theory. The conclusion is that the most important refinement of Chaos Theory is its sensitivity to initial values, and subtle changes will affect the final shape and state of the entire structure.
Main-Auxiliary Aggregation Strategy for Video Anomaly Detection

Surveillance video anomaly detection is to detect unusual events in videos. Different anomaly events have different anomalous properties. A single detector only can detect anomalies precisely on the property it focuses on. This paper proposes a Main-Auxiliary Aggregation Strategy (MAAS) to aggregate multiple detectors focusing on different properties. The MAAS consists of two branches: a main-detector branch and an auxiliary-detectors branch. We select and assign a detector to the main-detector branch to provide the basic anomaly detection ability. We assign the remaining detectors to the auxiliary branch to provide high-precision decisions on other properties. In the auxiliary branch,we set strict thresholds adaptively for each detector to detect strong-abnormal (SA) and strong-normal (SN) samples as high-precision decisions. Then,we design a 1D-Floodfill method to deduce more high-precision decisions according to events' temporal-continuity property. Finally,we design a soft-weight method to fuse the high-precision decisions in the auxiliary branch with the main detector to detect anomalies. We carry out experiments on multiple datasets. The experiments demonstrate the superiority of MAAS over the current state-of-the-art (SOTA) methods.
Conversational Search and Recommendation: Introduction to the Special Issue

introduction Share on Conversational Search and Recommendation: Introduction to the Special Issue Authors: Claudia Hauff Delft University of Technology, Van Mourik Broekmanweg, XE Delft, The Netherlands Delft University of Technology, Van Mourik Broekmanweg, XE Delft, The NetherlandsSearch about this author , Julia Kiseleva Microsoft, One Microsoft Way, Redmond, WA, United States Microsoft, One Microsoft Way, Redmond, WA, United StatesSearch about this author , Mark Sanderson RMIT University, Melbourne, Victoria, Australia RMIT University, Melbourne, Victoria, AustraliaSearch about this author , Hamed Zamani University of Massachusetts Amherst, Amherst, MA, United States University of Massachusetts Amherst, Amherst, MA, United StatesSearch about this author , Yongfeng Zhang Rutgers University, Piscataway, NJ, United States Rutgers University, Piscataway, NJ, United StatesSearch about this author Authors Info & Claims ACM Transactions on Information SystemsVolume 39Issue 4October 2021 Article No.: 38pp 1–6https://doi.org/10.1145/3465272Online:01 September 2021Publication History 0citation263DownloadsMetricsTotal Citations0Total Downloads263Last 12 Months263Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Raising Awareness of Data Sharing Consent Through Knowledge Graph Visualisation

Knowledge graphs facilitate systematic large-scale data analysis by providing both human and machine-readable structures, which can be shared across different domains and platforms. Nowadays, knowledge graphs can be used to standardise the collection and sharing of user information in many different sectors such as transport, insurance, smart cities and internet of things. Regulations such as the GDPR make sure that users are not taken advantage of when they share data. From a legal standpoint it is necessary to have the user’s consent to collect information. This consent is only valid if the user is aware about the information collected at all times. To increase this awareness, we present a knowledge graph visualisation approach, which informs users about the activities linked to their data sharing agreements, especially after they have already given their consent. To visualise the graph, we introduce a user-centred application which showcases sensor data collection and distribution to different data processors. Finally, we present the results of a user study conducted to find out whether this visualisation leads to more legal awareness and trust. We show that with our visualisation tool data sharing consent rates increase from 48% to 81.5%.
Comprehensive Study on Machine Learning Techniques for Software Bug Prediction

Software bugs are defects or faults in computer programs or systems that cause incorrect or unexpected operations.These negatively affect software quality, reliability, and maintenance cost; therefore many researchers have already built and developed several models for software bug prediction.Till now, a few works have been done which used machine learning techniques for software bug prediction.The aim of this paper is to present comprehensive study on machine learning techniques that were successfully used to predict software bug.Paper also presents a software bug prediction model based on supervised machine learning algorithms are Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF) and Logistic Regression (LR) on four datasets.We compared the results of our proposed models with those of the other studies.The results of this study demonstrated that our proposed models performed better than other models that used the same data sets.The evaluation process and the results of the study show that machine learning algorithms can be used effectively for prediction of bugs.
Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning

Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited.In this paper, we propose a novel contrastive learning framework that trains sentence embeddings to encode the relations in a graph structure.Given a sentence (unstructured text) and its graph, we use contrastive learning to impose relation-related structure on the tokenlevel representations of the sentence obtained with a CharacterBERT (El Boukkouri et al., 2020) model.The resulting relation-aware sentence embeddings achieve state-of-the-art results on the relation extraction task using only a simple KNN classifier, thereby demonstrating the success of the proposed method.Additional visualization by a tSNE analysis shows the effectiveness of the learned representation space compared to baselines.Furthermore, we show that we can learn a different space for named entity recognition, again using a contrastive learning objective, and demonstrate how to successfully combine both representation spaces in an entity-relation task.
Teaching Cyber Security Course in the Classrooms of NMIMS University

The paper aims at implementing new pedagogy and assessment practices from rigorous literature survey perusing quality papers and articles.The appropriate pedagogy and relevant assessment always go hand in hand.One cannot achieve effective teaching by compromising the other component.In engineering, pedagogy and assessment play extremely important roles.In recent years, engineering education has lost track of the big picture of what the curriculum has to be.In Computer Science Engineering, the course contents often change according to the demands for new technology in the market.Adhering to this fact, the courses must be designed either practical-based or case study based.Rote teaching-learning methods are not as effective as far as the curriculum design in the computing field is concerned.Cyber Security is one such course where students are expected to learn how to create a protective environment for computing and computing resources.The course must be designed in such a way that, students must learn how to identify the vulnerabilities in computing resources and methodologies to mitigate them.This course aims such that students must learn some popular attacks which help them to identify what are the possible ways from where the attacks could happen.The paper makes use of strategic assessment tools for the Cyber Security course (Taught to postgraduation students) and discusses outcomes through course outcome attainment analysis as per the threshold value of attainment set by the Computer Engineering Department to adhere to the accreditation standards.Through course attainment analysis it is observed that, Viva voce assessment tool is not suitable for evaluation of the course as it does not impose the technical details and working of Cyber Security concepts.The overall attainment was 55.55% which is 15% less than the threshold set by Computer Engineering Department.
Elementary Number Theory Problems. Part II

Summary In this paper problems 14, 15, 29, 30, 34, 78, 83, 97, and 116 from [6] are formalized, using the Mizar formalism [1], [2], [3]. Some properties related to the divisibility of prime numbers were proved. It has been shown that the equation of the form p 2 + 1 = q 2 + r 2 , where p , q , r are prime numbers, has at least four solutions and it has been proved that at least five primes can be represented as the sum of two fourth powers of integers. We also proved that for at least one positive integer, the sum of the fourth powers of this number and its successor is a composite number. And finally, it has been shown that there are infinitely many odd numbers k greater than zero such that all numbers of the form 2 2 n + k ( n = 1, 2, . . . ) are composite.
Keystroke Dynamics Patterns While Writing Positive and Negative Opinions

This paper deals with analysis of behavioural patterns in human–computer interaction. In the study, keystroke dynamics were analysed while participants were writing positive and negative opinions. A semi-experiment with 50 participants was performed. The participants were asked to recall the most negative and positive learning experiences (subject and teacher) and write an opinion about it. Keystroke dynamics were captured and over 50 diverse features were calculated and checked against the ability to differentiate positive and negative opinions. Moreover, classification of opinions was performed providing accuracy slightly above the random guess level. The second classification approach used self-report labels of pleasure and arousal and showed more accurate results. The study confirmed that it was possible to recognize positive and negative opinions from the keystroke patterns with accuracy above the random guess; however, combination with other modalities might produce more accurate results.
AutoGFS: Automated Group-based Feature Selection via Interactive Reinforcement Learning

Feature selection is a fundamental component of data mining, aiming to select optimal feature subsets for downstream task.Recently, an emerging feature selection method called reinforced feature selection applies reinforcement learning into feature selection.Reinforced Feature Selection (RFS) automates feature selection process and can effectively find the optimal subset.Generally, RFS can be categorized into single-agent RFS and multi-agent RFS.Single-agent RFS uses one reinforcement learning agent to select features, but its action space is exponentially-increasing with feature number and can merely obtain local optima.Multi-agent RFS uses multiple agents to select features; this method can achieve global optima, but it needs to optimize as many policy networks as feature number which costs huge computational resources and thus becomes computationally inefficient.This dilemma naturally leads to a research question: How can we synthesize the advantages of single-agent RFS and multi-agent RFS while avoiding their disadvantages?To answer this question, we propose a Group-based Interactive Reinforced Feature Selection (GIRFS) framework.This framework balances single-agent RFS and multi-agent RFS for better feature selection.Specifically, we formulate the feature selection problem into a group-based RFS problem.In this formulation, we first assign the given features into several groups based on feature similarity measurement.Then, we create agents for each group, where each agent decides to select/deselect features in its corresponding group.This design balances the size of action space and number of policy networks and thus makes RFS more effective and efficient.Moreover, to further improve learning efficiency, we propose a hierarchical teacher-like trainer to provide external action advice for agents.This trainer provides advice by intra-group selection and inter-group selection and fuses knowledge from mRMR and decision tree to help agents explore and learn.Finally, we present extensive experiments on real-world datasets to demonstrate the improved performances of our method.
A Database and Visualization of the Similarity of Contemporary Lexicons

Lexical similarity data, quantifying the “proximity” of languages based on the similarity of their lexicons, has been increasingly used to estimate the cross-lingual reusability of language resources, for tasks such as bilingual lexicon induction or cross-lingual transfer. Existing similarity data, however, originates from the field of comparative linguistics, computed from very small expert-curated vocabularies that are not supposed to be representative of modern lexicons. We explore a different, fully automated approach to lexical similarity computation, based on an existing 8-million-entry cognate database created from online lexicons orders of magnitude larger than the word lists typically used in linguistics. We compare our results to earlier efforts, and automatically produce intuitive visualizations that have traditionally been hand-crafted. With a new, freely available database of over 27 thousand language pairs over 331 languages, we hope to provide more relevant data to cross-lingual NLP applications, as well as material for the synchronic study of contemporary lexicons.
Searching for Dark Particles with Quantum Optics

We propose a way to use optical tools from quantum imaging and quantum communication to search for physics beyond the standard model. Spontaneous parametric down-conversion (SPDC) is a commonly used source of entangled photons in which pump photons convert to a signal-idler pair. We propose to search for “dark-SPDC” (dSPDC) events in which a new dark-sector particle replaces the idler. Though it does not interact, the presence of a dark particle can be inferred by the properties of the signal photon. Examples of dark states include axionlike particles and dark photons. We show that the presence of an optical medium opens the phase space of the down-conversion process, or decay, which would be forbidden in a vacuum. Search schemes are proposed that employ optical imaging and/or spectroscopy of the signal photons. The signal rates in our proposal scales with the second power of the small coupling to new physics, as opposed to light-shining-through-wall experiments, the signal of which scales with coupling to the fourth power. We analyze the characteristics of the optical media needed to enhance dSPDC and estimate the rate.1 MoreReceived 19 January 2021Revised 13 May 2021Accepted 28 July 2021DOI:https://doi.org/10.1103/PRXQuantum.2.030340Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasHypothetical particle physics modelsNonlinear opticsQuantum InformationAtomic, Molecular & OpticalParticles & Fields
Machine translation of English content: A comparative study of different methods

Abstract Based on neural machine translation, this article introduced the ConvS2S system and transformer system, designed a semantic sharing combined transformer system to improve translation quality, and compared the three systems on the NIST dataset. The results showed that the operation speed of the semantic sharing combined transformer system was the highest, reaching 3934.27 words per second; the BLEU value of the ConvS2S system was the smallest, followed by the transformer system and the semantic sharing combined transformer system. Taking NIST08 as an example, the BLEU values of the designed system were 4.74 and 1.49 higher than the other two systems. The analysis of examples showed that the semantic sharing combined transformer had higher translation quality. The experimental results show that the proposed system is reliable in English content translation and can be further promoted and applied in practice.
Literal2Feature: An Automatic Scalable RDF Graph Feature Extractor

The last decades have witnessed significant advancements in terms of data generation, management, and maintenance. This has resulted in vast amounts of data becoming available in a variety of forms and formats including RDF. As RDF data is represented as a graph structure, applying machine learning algorithms to extract valuable knowledge and insights from them is not straightforward, especially when the size of the data is enormous. Although Knowledge Graph Embedding models (KGEs) convert the RDF graphs to low-dimensional vector spaces, these vectors often lack the explainability. On the contrary, in this paper, we introduce a generic, distributed, and scalable software framework that is capable of transforming large RDF data into an explainable feature matrix. This matrix can be exploited in many standard machine learning algorithms. Our approach, by exploiting semantic web and big data technologies, is able to extract a variety of existing features by deep traversing a given large RDF graph. The proposed framework is open-source, well-documented, and fully integrated into the active community project Semantic Analytics Stack (SANSA). The experiments on real-world use cases disclose that the extracted features can be successfully used in machine learning tasks like classification and clustering.
Transformer-Based End-to-End Speech Recognition with Residual Gaussian-Based Self-Attention

Self-attention (SA), which encodes vector sequences according to their pairwise similarity, is widely used in speech recognition due to its strong context modeling ability. However, when applied to long sequence data, its accuracy is reduced. This is caused by the fact that its weighted average operator may lead to the dispersion of the attention distribution, which results in the relationship between adjacent signals ignored. To address this issue, in this paper, we introduce relative-position-awareness self-attention (RPSA). It not only maintains the global-range dependency modeling ability of self-attention, but also improves the localness modeling ability. Because the local window length of the original RPSA is fixed and sensitive to different test data, here we propose Gaussian-based self-attention (GSA) whose window length is learnable and adaptive to the test data automatically. We further generalize GSA to a new residual Gaussian self-attention (resGSA) for the performance improvement. We apply RPSA, GSA, and resGSA to Transformer-based speech recognition respectively. Experimental results on the AISHELL-1 Mandarin speech recognition corpus demonstrate the effectiveness of the proposed methods. For example, the resGSA-Transformer achieves a character error rate (CER) of 5.86% on the test set, which is relative 7.8% lower than that of the SA-Transformer. Although the performance of the proposed resGSA-Transformer is only slightly better than that of the RPSA-Transformer, it does not have to tune the window length manually.
Fast model‐based clustering of partial records

Partially recorded data are frequently encountered in many applications and usually clustered by first removing incomplete cases or features with missing values, or by imputing missing values, followed by application of a clustering algorithm to the resulting altered dataset. Here, we develop clustering methodology through a model-based approach using the marginal density for the observed values, assuming a finite mixture model of multivariate $t$ distributions. We compare our approximate algorithm to the corresponding full expectation-maximization (EM) approach that considers the missing values in the incomplete data set and makes a missing at random (MAR) assumption, as well as case deletion and imputation methods. Since only the observed values are utilized, our approach is computationally more efficient than imputation or full EM. Simulation studies demonstrate that our approach has favorable recovery of the true cluster partition compared to case deletion and imputation under various missingness mechanisms, and is at least competitive with the full EM approach, even when MAR assumptions are violated. Our methodology is demonstrated on a problem of clustering gamma-ray bursts and is implemented at https://github.com/emilygoren/MixtClust.