Reviewerly/exHarmony huggingface dataset:
Representing and Reasoning with Multi-Stakeholder Qualitative Preference Queries

Many decision-making scenarios, e.g., public policy, healthcare, business, and disaster response, require accommodating the preferences of multiple stakeholders. We offer the first formal treatment of reasoning with multi-stakeholder qualitative preferences in a setting where stakeholders express their preferences in a qualitative preference language, e.g., CP-net, CI-net, TCP-net, CP-Theory. We introduce a query language for expressing queries against such preferences over sets of outcomes that satisfy specified criteria, e.g., ψ1PAψ2 (read loosely as the set of outcomes satisfying ψ1 that are preferred over outcomes satisfying ψ2 by a set of stakeholders A). Motivated by practical application scenarios, we introduce and analyze several alternative semantics for such queries, and examine their interrelationships. We provide a provably correct algorithm for answering multi-stakeholder qualitative preference queries using model checking in alternation-free μ-calculus. We present experimental results that demonstrate the feasibility of our approach.
From Intermediate Representations to Explanations: Exploring Hierarchical Structures in NLP

Interpretation methods for learned models used in natural language processing (NLP) applications usually provide support for local (specific) explanations, such as quantifying the contribution of each word to the predicted class. But they typically ignore the potential interaction amongst those word tokens. Unlike currently popular methods, we propose a deep model which uses feature attribution and identification of dependencies to support the learning of interpretable representations that will support creation of hierarchical explanations. In addition, hierarchical explanations provide a basis for visualizing how words and phrases are combined at different levels of abstraction, which enables end-users to better understand the prediction process of a deep network. Our study uses multiple well-known datasets to demonstrate the effectiveness of our approach, and provides both automatic and human evaluation.
CAFIN: Centrality Aware Fairness Inducing IN-Processing for Unsupervised Representation Learning on Graphs

Unsupervised Representation Learning on graphs is gaining traction due to the increasing abundance of unlabelled network data and the compactness, richness, and usefulness of the representations generated. In this context, the need to consider fairness and bias constraints while generating the representations has been well-motivated and studied to some extent in prior works. One major limitation of most of the prior works in this setting is that they do not aim to address the bias generated due to connectivity patterns in the graphs, such as varied node centrality, which leads to a disproportionate performance across nodes. In our work, we aim to address this issue of mitigating bias due to inherent graph structure in an unsupervised setting. To this end, we propose CAFIN, a centrality-aware fairness-inducing framework that leverages the structural information of graphs to tune the representations generated by existing frameworks. We deploy it on GraphSAGE (a popular framework in this domain) and showcase its efficacy on two downstream tasks – Node Classification and Link Prediction. Empirically, CAFIN consistently reduces the performance disparity across popular datasets (varying from 18 to 80% reduction in performance disparity) from various domains while incurring only a minimal cost of fairness.
Read Key Points: Dialogue-Grounded Knowledge Points Generation with Multi-Level Salience-Aware Mixture

Knowledge-grounded dialogue (KGD) has become increasingly essential for online services, enabling individuals to obtain desired information. While KGD contains knowledge information, most knowledge points are fragmented and repeated in dialogues, making it difficult for users to quickly grasp complete and key information from a collection of sessions. In this paper, we propose a novel task of dialogue-grounded knowledge points generation (DialKPG) to condense a collection of sessions on a topic into succinct and complete knowledge points. To enable empirical study, we create TopicDial and OpenDial corpus based on two existing knowledge-grounded dialogue corpus FaithDial and OpenDialKG by a Three-Stage Annotation Framework, and establish a novel approach for DialKPG task, namely MSAM (Multi-Level Salience-Aware Mixture). MSAM explicitly incorporates salient information at the token-level, utterance-level, and session-level to better guide knowledge points generation. Extensive experiments have verified the effectiveness of our method over competitive baselines. Furthermore, our analysis shows that the proposed model is particularly effective at handling long inputs and multiple sessions due to its strong capability of duplicated elimination and knowledge integration.
A Model-Theoretic Approach to Belief Revision in Multi-Agent Belief Logic and Its Syntactic Characterizations

Belief change studies how an agent modifies her beliefs on receiving new information. However, so far most research on belief change works on beliefs represented in propositional logic. There have been many works on integrating belief revision with reasoning about actions, and some works extending belief change from propositional logic to epistemic logics. In this paper, we study revision on beliefs of a third person represented with the multi-agent KD45 logic. Our formal technique is analogous to that of distance-based belief revision in propositional logic: to revise a KB by a formula, select from models of the formula those that are closest to models of the KB. To this end, a challenge is that in modal logics, a formula may have infinitely many Kripke models. To tackle this, we propose a variant of Moss’ canonical formulas called alternating canonical formulas, treat them as models for formulas, and define a notion of distance between them, based on the Hausdorff distance between two sets. We show that our revision satisfies all of the AGM postulates. To give syntactic characterizations of our revision, we make use of a normal form for KD45n called alternating cover disjunctive formulas (ACDFs). We give syntactic characterizations firstly on fragments of ACDFs called proper ACDFs and alternating cover conjunctive formulas (ACCFs), and finally on the whole ACDFs.
VERBAL CREATIVITY ATTRIBUTES OF MODERN STUDENTS

Verbal creativity is an important psychological condition for developing creative aspects of professional competence in students. The empirical study revealed insufficient level of verbal creativity development with regard to the criteria of authenticity and originality. The analysis of association content allowed to reveal psychological resources for the development of verbal creative thinking.
PEDAGOGICAL CONDITIONS AND CAPABILITIES FOR DEVELOPING A RESPONSIBLE ATTITUDE TO DECISION-MAKING IN FUTURE OFFICERS

The article identifies the pedagogical capabilities necessary for developing a responsible attitude to making managerial decisions in future officers. These capabilities, in turn, help to outline the pedagogical conditions in a military university that ensure effective development. The results of experimental research demonstrate how well-targeted use of pedagogical conditions and capabilities of a military university makes it possible to increase the level of managerial responsibility of cadets.
SCIENTIFIC AND METHODOLOGICAL SUPPORT OF UNIVERSITY LIBRARIES AS A FACTOR IN PREPARING STUDENTS FOR HOMEROOM TEACHER RESPONSIBILITIES

The article explores the availability in university libraries of scholarly and methodological literature needed to prepare students for work as homeroom teachers. By analyzing collections of scholarly and methodological literature, the author identifies the dynamics of educational issues discourse, shows the correlation between educational content at different stages of development of the country, identifies the conditions for using accumulated library sources in training homeroom teachers. In conclusion, the author determines the prospects of scolarly and methodological support for training homeroom teachers.
No-Code Approach for Creating Virtual Stands for Professional Research

Virtual reality (VR) is the computer-generated simulation of an environment that can be explored and manipulated in 360 degrees using special VR equipment. It is of great interest in educational institutions because of the possibilities it enables and the novelty of the technology. However, there are still obstacles to large-scale deployment of this technology, such as a lack of customization and flexibility. This paper presents a new approach to enhancing the use of virtual reality technology in education and research. The main goal is to help specialists with no programming knowledge construct new laboratory stands. The approach considers that real-life laboratory stands are decomposed into atomic components, and programmers create analogs for these components in the virtual environment. Later on, an intuitive and user-friendly virtual environment is provided, where no programming knowledge is needed to combine the virtual components into a laboratory stand. Using this approach, new virtual components can be continuously developed to cover more and more education fields.
METODE MONTESSORY UNTUK MENINGKATKAN SELF ESTEEM PADA SISWA SLOW LEARNER

Slow learner students are students who experience delays in the learning process. This delay affects other abilities such as adaptation, communication and personality which can have an impact on self-esteem. Students with high self-esteem make them able to think positively about themselves and be more confident. One form of handling to increase self-esteem is using the Montessory method. This method helps students explore themselves through play media. The purpose of this research is to identify the needs of slow learner students with low self-esteem towards the Montessory method in learning.
q-Deformed and $$\lambda $$-Parametrized Hyperbolic Tangent Based Banach Space Valued Ordinary and Fractional Neural Network Approximation

Here we study the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative of fractional derivatives. Our operators are defined by using a density function generated by a q-deformed and $$\lambda $$ -parametrized hyperbolic tangent function, which is a sigmoid function. The approximations are pointwise and of the uniform norm. The related Banach space valued feed-forward neural networks are with one hidden layer.
Abstract Ordinary and Fractional Neural Network Approximations Based on Richard’s Curve

Here we perform the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative or fractional derivatives. Our operators are defined by using a density function generated by the Richards’s curve, which is generalized logistic function. The approximations are pointwise and of the uniform norm. The related Banach space valued feed-forward neural networks are with one hidden layer. It follows [14].
Banach Space Valued Multivariate Multi Layer Neural Network Approximation Based on q-Deformed Hyperbolic Tangent Activation Function

Here we study the multivariate quantitative approximation of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N}$$ , $$N\in \mathbb {N},$$ by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We research also the case of approximation by iterated multilayer neural network operators of the last four types.
Parametrized Hyperbolic Tangent Based Banach Space Valued Basic and Fractional Neural Network Approximations

Here we examine the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative of fractional derivatives. Our operators are defined by using a density function generated by a parametrized hyperbolic tangent function, which is a sigmoid function.
Banach Space Valued Ordinary and Fractional Neural Network Approximations Based on q-Deformed Hyperbolic Tangent Activation Function

Here we research the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative of fractional derivatives. Our operators are defined by using a density function generated by a q-deformed hyperbolic tangent function, which is a sigmoid function. The approximations are pointwise and of the uniform norm. The related Banach space valued feed-forward neural networks are with one hidden layer.
An Invention of Smart Glove to Help the Elderly Patients

Aging is characterized as the progressive decline of physiological capabilities required for life and reproduction over time. Everyone in the world should take care of old patients and treat them the way they should be handled. Most older people are encountering health problems as they age and are being placed in homes and orphanages. Elders are unable to visit doctors on a regular basis to assess their health state. This article proposes a remedy for the elderly patients in the area by aiding them and encouraging them to be unafraid. The suggested system takes use of many characteristics such as GPS and GSM modules, a pulse oximeter, an accelerometer, a touch sensor, and Bluetooth that communicate with the Arduino. The suggested smart glove is used to assess temperature, detect falls, and monitor pulse rate in elderly individuals who are experiencing health problems. In this context, for client health care, the proposed system is based on supported mobile health technology and employs a smart glove to authorize caregivers or physicians to line up as well as examine the history of the patient's physiological data during his or her absence from the clinical center or home. These statistics will assist them in caring for older patients during challenging times for their survival. Furthermore, this smart glove is quite important for women's protection in order to keep them protected from harm.
Detecting Thermal Anomalies In Lahendong Geothermal Prospect Using Aster TIR and Landsat 8

This study intends to determine geothermal anomalies area using remote sensing data in the form of Landsat 8 and ASTER satellite imagery data which have Thermal Infrared Sensor (TIRS). Through pre-processing like georeferencing, radiometric calibration, and atmospheric correction, the Landsat 8 TIRS and ASTER data were wont to invert the land surface temperature of the study area during the daytime and night time using the inversion of planck function and emissivity separation algorithm. Result shows the land surface temperatures during daytime and night time of four natural land cover —water, vegetation, built up area, and bare soil—were classified and analyzed. According to the results, vegetation and bare soil show relatively thermal anomalies during the day and comparatively cold anomalies during the night. Otherwise water shows relatively cold anomalies during the day and relatively thermal anomalies during the night. Meanwhile built up area shows relatively thermal anomalies during the day and cold anomalies during the night. Superimposed and calculating mean of the night and day surface temperature can adequately eliminate the relatively cold/thermal anomalies of land cover caused by solar radiation, thus effectively highlighting geothermal anomalies. Thus, Nine geothermal anomalies areas were successfully extracted.
The Review of Human Activity Recognition Survey

Human Action Recognition is widely used in multiple&#x0D; fields to recognize activities and extract spatial and temporal&#x0D; information. This paper focuses on analyzing various methods as&#x0D; well as provides extensive knowledge of the foundational concepts&#x0D; of the HAR. A dataset is also very crucial for any research study&#x0D; so we discussed the popular dataset and its features in this paper.
Influence of Service Quality on Student Loyalty at Budi Bhakti Tutoring

Education is considered one of the important primary human needs. Therefore, people are competing to get the best educational place. This phenomenon encourages parents to register their children with tutoring institutions to get additional classes. Tutoring in Indonesia is included in the Course and Training Institute (LKP). During the pandemic, LKP's growth declined due to the loss of students. The decrease in student enrollment also occurred in one of the tutoring institutions in Bandung Regency, namely the Budi Bakti Tutoring Institute. In services, service quality is an essential factor in the competitiveness and sustainability of institutions. The purpose of this study is to assess the level of service quality at Budi Bakti Tutoring to retain existing students and gain new students by measuring the five dimensions of service quality perceived value, student satisfaction, and student loyalty. The research method used is a quantitative approach. In this approach, data collection was carried out on 222 samples of students and alumni of Budi Bakti Tutoring through the distribution of questionnaires and then analyzed with PLS-SEM. The results showed that there was a positive influence between service quality on perceived value, student satisfaction, and student loyalty. This research is expected to improve the quality of service at Budi Bakti Tutoring.
Self-evaluation in Distance Education of Biology Program FKIP-Universitas Terbuka

The purpose of this research is expected to help the S1 Biology Education Study Program FKIP-UT in improving academic and administrative services for users. In detail, the objectives of this study are: (1) The effectiveness of the services that have been provided by the Biology Education S1 Study Program FKIP-UT to service users; (2) Student services of the S1 Biology Education Study Program FKIP-UT; (3) Service user satisfaction of the S1 Biology Education Study Program FKIP-UT; (4) Characteristics of the learning process of the S1 Biology Education Study Program FKIP –UT; (5) Learning experience of service users of the S1 Biology Education Study Program FKIP-UT. The research used quantitative and qualitative methods. Quantitative methods are carried out by surveying all graduates/ students using questionnaires [letters, forms]. While the qualitative method was carried out for limited respondents who were randomly selected proportionally through [online] interviews. The results of this study include data analysis of the effectiveness of services that have been provided by the Biology Education S1 Study Program FKIP-UT to service users, as many as 62.4% are satisfied with the Subject Material Books used, 48.5% are online tutorials, 41.3% Webinars and 56.2% are final exams. As many as 62% of respondents already know the student services of the S1 Biology Education Study Program FKIP-UT regarding reasoning, interests and talents, 60% regarding career guidance and 45.8% regarding welfare and easy access. Satisfaction with the learning experience in the S1 Biology Education Study Program, respondents were satisfied and very satisfied in discussions, tutorial assignments, practices and practicums. With respondents who are dissatisfied with academic and administrative services, it is necessary to improve services and socialization for those who have difficulty accessing services.
Scoring Coreference Chains with Split-Antecedent Anaphors

Anaphoric reference is an aspect of language interpretation covering a variety of types of interpretation beyond the simple case of identity reference to entities introduced via nominal expressions covered by the traditional coreference task in its most recent incarnation in ONTONOTES and similar datasets. One of these cases that go beyond simple coreference is anaphoric reference to entities that must be added to the discourse model via accommodation, and in particular split-antecedent references to entities constructed out of other entities, as in split-antecedent plurals and in some cases of discourse deixis. Although this type of anaphoric reference is now annotated in many datasets, systems interpreting such references cannot be evaluated using the Reference coreference scorer Pradhan et al. (2014). As part of the work towards a new scorer for anaphoric reference able to evaluate all aspects of anaphoric interpretation in the coverage of the Universal Anaphora initiative, we propose in this paper a solution to the technical problem of generalizing existing metrics for identity anaphora so that they can also be used to score cases of split-antecedents. This is the first such proposal in the literature on anaphora or coreference, and has been successfully used to score both split-antecedent plural references and discourse deixis in the recent CODI/CRAC anaphora resolution in dialogue shared tasks.
Learning Logic Programs by Combining Programs

The goal of inductive logic programming is to induce a logic program (a set of logical rules) that generalises training examples. Inducing programs with many rules and literals is a major challenge. To tackle this challenge, we introduce an approach where we learn small non-separable programs and combine them. We implement our approach in a constraint-driven ILP system. Our approach can learn optimal and recursive programs and perform predicate invention. Our experiments on multiple domains, including game playing and program synthesis, show that our approach can drastically outperform existing approaches in terms of predictive accuracies and learning times, sometimes reducing learning times from over an hour to a few seconds.