Reviewerly/exHarmony huggingface dataset:
NONCLASSICAL TRUTH WITH CLASSICAL STRENGTH. A PROOF-THEORETIC ANALYSIS OF COMPOSITIONAL TRUTH OVER HYPE

Abstract Questions concerning the proof-theoretic strength of classical versus nonclassical theories of truth have received some attention recently. A particularly convenient case study concerns classical and nonclassical axiomatizations of fixed-point semantics. It is known that nonclassical axiomatizations in four- or three-valued logics are substantially weaker than their classical counterparts. In this paper we consider the addition of a suitable conditional to First-Degree Entailment—a logic recently studied by Hannes Leitgeb under the label HYPE . We show in particular that, by formulating the theory PKF over HYPE , one obtains a theory that is sound with respect to fixed-point models, while being proof-theoretically on a par with its classical counterpart KF . Moreover, we establish that also its schematic extension—in the sense of Feferman—is as strong as the schematic extension of KF , thus matching the strength of predicative analysis.
Fast heuristic approach for control of complex authentication systems

Abstract In secure networks, the authentication process, which verifies a user's identity, is a sensitive operation. Balancing the authentication process's security and usability while keeping operating costs low is a major challenge. We investigate this process in a secure system that has multiple authentication methods available. The goal is to find a fast and easy‐to‐implement approach to assign incoming requests to authentication methods in a way that balances cost, usability, and security. We model the system as a continuous‐time, infinite‐horizon Markov Decision Process. Then, we propose a new, fast heuristic approach to solve the problem, as the curse of dimensionality limits the effectiveness of exact methods. The proposed heuristic uses closed‐form approximations to define threshold policies that are easily implementable. Numerical experiments show that our approach yields robust, near‐optimal solutions, with high accuracy, for a wide range of problem instances.
Empirical Study of Tweets Topic Classification Using Transformer-Based Language Models

Social media opens up a great opportunity for policymakers to analyze and understand a large volume of online content for decision-making purposes. People’s opinions and experiences on social media platforms such as Twitter are extremely significant because of its volume, variety, and veracity. However, processing and retrieving useful information from natural language content is very challenging because of its ambiguity and complexity. Recent advances in Natural Language Understanding (NLU)-based techniques more specifically Transformer-based architecture solve sequence-to-sequence modeling tasks while handling long-range dependencies efficiently, and models based on transformers setting new benchmarks in performance across a wide variety of NLU-based tasks. In this paper, we applied transformer-based sequence modeling on short texts’ topic classification from tourist/user-posted tweets. Multiple BERT-like state-of-the-art sequence modeling approaches on topic/target classification tasks are investigated on the Great Barrier Reef tweet dataset and obtained findings can be valuable for researchers working on classification with large data sets and a large number of target classes.
Generation of Bright and Entangled Light from a Nondegenerate Three-Level Laser with Parametric Amplifier and Coupled to Thermal Reservoir

The detailed analysis of the two-mode quadrature squeezing and statistical properties of light generated by a nondegenerate three-level laser which has a parametric amplifier and coupled with a thermal reservoir is executed. The combination of the master equation and the stochastic differential equation is presented to study the nonclassical features of the light generated by the quantum system. Moreover, with the aid the resulting solutions together with the correlation properties of noise operators, we calculated the quadrature squeezing, entanglement, and mean number of photon pairs of the cavity light. It is found that the external small-amplitude driving radiation induces a strong correlation between the top and bottom states of three-level atoms to produce a high degree of squeezing. Moreover, the presence of a parametric amplifier is found to enhance the degree of squeezing of the cavity light. We have also established that an increase in the mean thermal photon number appears to degrade the squeezing, but enhances the mean number of photon pairs of the cavity light.
The Impact of Texting on Standard English: The Students’ Perspective

Overindulgence in social networking, in general, and texting, in particular, is much in practice. It is cutting across various population boundaries and has almost assumed an endemic proportion. Its consequential impact on the standard language has acquired greater importance. This paper aims to determine the perceptions and attitudes of English Second Language (ESL) learners at Aligarh Muslim University towards the consequences of texting on Standard English. The data were collected through a five-point scale questionnaire from ninety students who were enrolled at Aligarh Muslim University during the academic year 2010-2011. The respondents completed a 16-item questionnaire. The students from which the data were collected were grouped according to their levels. The results indicate the negative impact of this new usage of the language in breaking the rules of English language and influencing their literacy. Moreover, the questionnaire results from respondents' point of view show that regardless of their heavy use of texting, most respondents have a negative attitude towards texting and they viewed it as a threat to Standard English.
English to Thai Calligraphy Style Transfer Using Deep Learning

Designing a new font is time-consuming. There are about hundred or thousand characters that must be designed and edited. Adding a new set of characters in another language to an existing font is also not easy, as these characters should match the existing style. In this paper, a deep learning network architecture called U-Net is used for aiding the font style transfer of an existing set of characters in one language to another set of characters in another language. We experimented with 10 calligraphy fonts that contain English and Thai characters. On average, the algorithm produces images with a structure similarity index measure of 0.91 for English to Thai character style transfer.
Work from Home Faculty Performance Evaluation Amid Pandemic using Mamdani Fuzzy System

The start of the Novel Coronavirus-19 pandemic lead many higher education institutions to instantly dive into online-teaching and had to shift daily tasks appropriate to the new normal. This brings challenges to faculty, especially those who are not well skilled in the use of the Internet. Another challenge is on the side of the management. Faculty starts to work from home and tasks are done remotely without any visual supervision. This paper presents a simple mechanism for monitoring the daily and weekly performances of the faculty amid the pandemic before online teaching, and the used Mamdani Fuzzy Inference System to evaluate the faculty performance while working from home. There are three relevant input variables and one output variable, each qualitative variables were converted to linguistics variables. Matlab software was used to develop the Inference System, Triangular Membership function (MF) was used all variables, and crisp output was obtained using the centroid defuzzification. The result of the study shows an effective mechanism in monitoring faculty during the pandemic that meets the requirement of the administration. The fuzzy approach improves the unfair rate and improves the quality of performance evaluation instead of the traditional method.
Automatic coherence analysis of Dutch: testing the subjectivity hypothesis on a larger scale

With the increasing availability of large corpora, quantitative corpus analysis is becoming more and more popular as a method for conducting linguistic research. This paper uses a new research tool (cesar) that makes it possible to search syntactically annotated corpora without extensive programming knowledge to study the subjectivity patterns of four Dutch causal connectives. Analysing a large set of causal relations marked by four of the most frequent Dutch causal connectives ( daarom, dus, omdat and want), this case study aims to corroborate the subjectivity hypothesis established on the basis of smaller-scale studies that used manual annotation. The automatic analysis of the subjectivity patterns of Dutch causal connectives illustrates the usability of cesar in particular and the feasibility of automatic coherence analysis in general. In addition, it generates new insights into the subjectivity patterns of daarom, dus, omdat and want.
Axial Generation: A Concretism-Inspired Method for Synthesizing Highly Varied Artworks

Automated computer generation of aesthetically pleasing artwork has been the subject of research for several decades. The unsolved problem of interest is how to automatically please any audience without too much involvement of the said audience in the process of creation. Two-dimensional pictures have received a lot of attention however, 3D artwork has remained relatively unexplored. This paper introduces the Axial Generation Process (AGP), a versatile generation algorithm that can be employed to create both 2D and 3D items within the Concretism art style. A range of items generated through the AGP were evaluated against a set of formal aesthetic measures. This evaluation shows that the process is capable of generating visually varied items which generally exhibit a diverse range of values across the measures used, in both two and three dimensions.
Practical amplification for a single photon qudit encoded in three degrees of freedom

Abstract A single-photon qudit encoded in more than one degree of freedom (DOF) can effectively enlarge a single photon’s capacity and has great potential for applications in future high-capacity quantum communication fields. During the photon transmission in practical noisy channels, photon qudits may suffer from photon transmission loss, which largely limits their application. In this paper, we propose a practical noiseless linear amplification (NLA) protocol for protecting the single-photon qudit encoded in polarization and double-longitudinal momentum DOFs. This NLA adopts the imperfect entangled state generated from a practical spontaneous parametric down-conversion (SPDC) source as the auxiliary state and can be realized under current experimental conditions. After performing the NLA protocol, the fidelity of the target single-photon qudit can be increased and the encoded information in all DOFs can be well preserved. We show the adoption of the imperfect entangled state generated from SPDC source is superior to that of the imperfect single-photon state from <?CDATA $\mathbf{a}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi mathvariant="bold">a</mml:mi> </mml:mrow> </mml:math> practical heralded single-photon source. Our NLA protocol has application potential in current and future quantum communications.
CREATIVE SELF-REALIZATION OF STUDENTS OF RUSSIAN UNIVERSITIES IN DESIGN ACTIVITY

The article discusses the features and specifics of the implementation of project activities in the context of university education. It presents various points of view of scientific research on the issue of inclusion of project activities in the educational process of Russian universities. Some forums and projects are considered, such as: «Taurida», «Eurasia Global», «Territory of Smyslov», «Russian Student Spring», «My Legislative Initiative» and «My Education Initiative». These forums and projects are available for students of different levels and areas of training. Students of Russian universities take part in various forums and projects from international and world to cathedral and educational. The article analyzes the results of a study in which students from Russian universities in Moscow and Yekaterinburg accepted. The main method in the presented study is the project method. The research criteria were: motivational, value, emotional and communicative. The research results presented in the article prove the effectiveness of the inclusion of project activities in the context of university education. In the context of university education, project activity acts as a means of creative self-realization of students, increases motivation for educational activities, and promotes the development of personal and professional growth.
Right for the Right Reasons: Making Image Classification Intuitively Explainable

The effectiveness of Convolutional Neural Networks (CNNs) in classifying image data has been thoroughly demonstrated. In order to explain the classification to humans, methods for visualizing classification evidence have been developed in recent years. These explanations reveal that sometimes images are classified correctly, but for the wrong reasons, i.e., based on incidental evidence. Of course, it is desirable that images are classified correctly for the right reasons, i.e., based on the actual evidence. To this end, we propose a new explanation quality metric to measure object aligned explanation in image classification which we refer to as the ObAlEx metric. Using object detection approaches, explanation approaches, and ObAlEx, we quantify the focus of CNNs on the actual evidence. Moreover, we show that additional training of the CNNs can improve the focus of CNNs without decreasing their accuracy.
An Intelligent Fusion Algorithm and Its Application Based on Subgroup Migration and Adaptive Boosting

Imbalanced data and feature redundancies are common problems in many fields, especially in software defect prediction, data mining, machine learning, and industrial big data application. To resolve these problems, we propose an intelligent fusion algorithm, SMPSO-HS-AdaBoost, which combines particle swarm optimization based on subgroup migration and adaptive boosting based on hybrid-sampling. In this paper, we apply the proposed intelligent fusion algorithm to software defect prediction to improve the prediction efficiency and accuracy by solving the issues caused by imbalanced data and feature redundancies. The results show that the proposed algorithm resolves the coexisting problems of imbalanced data and feature redundancies, and ensures the efficiency and accuracy of software defect prediction.
Analysing Grammar Errors in Malay Language Learning

This study aims to describe the grammatical errors in an analysis of a Malay language course assignment. It focuses on several local students who enrolled on MPU3312 Malay Language course at a public university in Semester 1, 2020/2021 session. The study was conducted on 250 students, using scripts from their group assignment, namely Text Error Analysis Task (Tugasan Analisis Kesalahan Teks). A total of 25 samples of student assignments were analysed and described using Corder Error Analysis Theory. Findings show that students committed 79 types of grammatical errors. Errors in the orthographic (ortografi) aspect are the most common. Preposition (Kata sendi nama) errors and redundancy (kelewahan) were also distinctively found in the writing samples. There were also other types of grammatical errors identified, such as copular verb (kata pemeri), "ianya", abbreviation (singkatan), affix (imbuhan), and word choice (pemilihan kata). The study has brought an understanding of the nature of typical grammatical errors in Malay language writing at the university.
Alles eine Frage der Logik?! Erkenntnisse einer Mixed-Method-Studie zur Pkw-Nutzung in Berlin

Abstract. This paper is an empirical mixed-method study on car use in an urban context. It explores the questions: What mobility practices do people in the city display? What role does car use play in this context? What is the guiding, underlying logic behind personal car use? The findings help to understand mobility practices and their underlying logic. The central component is a user typology based on a quantitative survey and qualitative interviews. The study aims to present an empirical description of mobility practices and the guiding logic of different mobility types in Berlin. The findings offer starting points for user-specific measures to encourage people to use new mobility concepts instead of their personal car.
Community Matters more than Anonymity: Analysis of User Interactions on the Quora Q&amp;A Platform

Question-and-answer (Q&A) websites are one of the latest evolutions in crowdsourced knowledge aggregation. Q&A websites provide more diverse opinions, as they involve the entire community. Quora made its reputation out of enhancing the traditional Q&A model with popular aspects of social media and incites its users to provide their names, locations, and references. This model allows higher quality control - including anonymous content, but more importantly, it leads users to form communities based on other criteria (e.g. profession, city) than similar interests. In this paper, we study the interactions among Quorans to unveil how such communities emerge. We perform both quantitative and qualitative analysis on the user-generated content and relate this content to social and demographic features. We show that being anonymous significantly affects the answers' length and subjectivity. On the other hand, most of the user interactions relate to their geographic locations.
A Study of Distributed Representations for Figures of Research Articles

Figures of research articles are entities that can be directly used in many application systems to assist researchers, making the representation of figures a problem worth studying. In this paper, we study the effectiveness of distributed representations, learned using deep neural networks, for figures. We learn representations using both text and image data and compare different model architectures and loss functions for the task. Furthermore, to overcome the lack of training data for the task, we propose and study a novel weak supervision approach for learning embedding vectors and show that it is more effective than using some of the pre-trained neural models as suggested by recent works. Experimental results using figures from the ACL Anthology show that distributed representations for research figures can be more effective than the previously studied bag-of-words representations. Yet, combining the two approaches can further improve performance. Finally, the results also show that these representations, while effective in general, can be sensitive to the learning approach used and that using both image data and text and a simple model architecture is the most effective approach.
Recognition of Adavus in Bharatanatyam Dance

Digital heritage has been a challenging problem to preserve the cultural heritage like a dance. Digitization of dance requires systematic analyses and understanding the semantics. However, while several work has been reported in non-Indian Classical Dance (ICD); only few has been done in the ICD because of its complexity. Bharatanatyam is an important ICD form, and Adavus constitute the primary collections of postures and movements in Bharatanatyam. There are 15 classes of Adavus having 52 sub-classes between them. Adavus are the critical building blocks for Bharatanatyam and critical for understanding its artefacts and semantics. Hence, this paper attempts to recognize Adavus based on the sequence of Key Postures (KPs) that define and constitute them. Kinect captures dance performances. The videos are then manually segmented into sequences of Key Frames (KFs) corresponding to the expected KPs. For recognizing KP, we follow two approaches; a) skeleton videos and angles of skeleton bones as features, b) The HOG features from the RGB frames. In both approaches, we train SVMs and recognize the KPs using them. The classifier generated by SVM predicts the sequence of KPs involved in a given Adavu. Since KPs are the string-like encoding symbols of an Adavu, we use the predicted sequence and master sequence of KPs in Edit Distance to recognize the matching Adavu. We use a pre-existing annotated data set of Natta (with eight variants) and Mettu (with four variants) Adavus to achieve over 99% accuracy.
Systematic Literature Review of Profiling Analysis Personality from Social Media

Abstract The success or failure of a company is usually supported by the presence of reliable resources, especially human resources. Recruitment and placement of employees in the right position will have a significant impact on a company. Human nature and character are very diverse in their forms. DISC theory classifies personality into four types namely dominance, influence, steadiness, and compliance. The difference in the character of each type will of course affect the behavior style, how to deal with life pressures, and also how to communicate both directly and with social media. Through social media, a person can vent his feelings through the posts he uploaded. From these posts an analysis of the personality character he is possessed can be carried out. This systematic literature review is used to analyze and focus on techniques for conducting personality profiles through the use of social media. From this literature review of analysis, it can be obtained that the various personality classification methods and algorithms can provide a good level of accuracy.
Collective information security behaviour: a technology-driven framework

Purpose This paper aims to present the development of a framework for evaluating group behaviour in information security in practice. Design/methodology/approach Information security behavioural threshold analysis is used as the theoretical foundation for the proposed framework. The suitability of the proposed framework is evaluated based on two sets of qualitative measures (general frameworks and information security frameworks) which were identified from literature. The successful evaluation of the proposed framework, guided by the identified evaluation measures, is presented in terms of positive practical applications, as well as positive peer review and publication of the underlying theory. Findings A methodology to formalise a framework to analyse group behaviour in information security can successfully be applied in a practical environment. This application takes the framework from only a theoretical conceptualisation to an implementable solution to evaluate and positively influence information security group behaviour. Practical implications Behavioural threshold analysis is identified as a practical mechanism to evaluate information security group behaviour. The suggested framework, as implemented in a management decision support system (DSS), allows practitioners to assess the security behaviour and awareness in their organisation. The resulting information can be used to exert an influence for positive change in the information security of the organisation. Originality/value A novel conceptual mapping of two sets of qualitative evaluation measures is presented and used to evaluate the proposed framework. The resulting framework is made practical through its encapsulation in a DSS.