Reviewerly/exHarmony huggingface dataset:
Emergency University Teaching in the COVID-19 Health Crisis

The pandemic and health crisis emerges in Chile shortly after the so-called “social outbreak” under a complex political and social climate. Conditions arose out of forced confinement and paralysis of various daily activities and the productive sector; removing the socioeconomic structure, where the educational system has not been spared. In this uncertain climate, teachers of different educational levels have made substantial efforts to give continuity to the teaching-learning processes, trying to support their students and carry out emergency teaching. This work collects some reflections that arise from the teaching practice and various studies reviewed in the university environment to identify those challenges and opportunities under the context of COVID-19. From these considerations, the need arises to rethink the educational model and move towards more flexible systems, where the principles of equity and inclusion guide the teaching practices and the training of future teachers.
Region-Based Merging of Open-Domain Terminological Knowledge

This paper introduces a novel method for merging open-domain terminological knowledge. It takes advantage of the Region Connection Calculus (RCC5), a formalism used to represent regions in a topological space and to reason about their set-theoretic relationships. To this end, we first propose a faithful translation of terminological knowledge provided by several and potentially conflicting sources into region spaces. The merging is then performed on these spaces, and the result is translated back into the underlying language of the input sources. Our approach allows us to benefit from the expressivity and the flexibility of RCC5 while dealing with conflicting knowledge in a principled way.
Hyperintensional Partial Meet Contractions

Formal frameworks for Epistemology need to have enough logical structure to enable interesting conclusions regarding epistemic phenomena and to be expressive enough to model competing positions in the philosophical and logical literature. While beliefs are commonly accepted as hyperintensional attitudes, i.e., epistemic attitudes, which may differ even towards necessarily equivalent sentences, most work on standard epistemic logic has relied on idealised and intensional agents. This is particularly true in the area of AGM-inspired Belief Change. Although a few recent studies investigate hyperintensional models of belief change, few have been well connected to the AGM framework, the main paradigm in the area. This work investigates hyperintensional notions of belief base contraction and belief set contraction, as studied in the AGM framework, and its connections to partial meet contractions. We also provide suitable representation theorems, characterising the constructions by means of rationality postulates.
Computing Stable Conclusions under the Weakest-Link Principle in the ASPIC+ Argumentation Formalism

Rephrasing argumentation semantics in terms of subsets of defeasible elements allows for gaining new insights for reasoning about acceptance in established fragments of the central structured argumentation formalism of ASPIC+. We provide a non-trivial generalization of these recent results, capturing preferences in ASPIC+. In particular, considering preferences under the weakest-link principle, we show that the stable semantics can be phrased in terms of subsets of defeasible elements. We employ the rephrasing for establishing both complexity results and practical algorithms for reasoning about acceptance in this variant of ASPIC+. Justified by completeness for the second level of the polynomial hierarchy, we develop an iterative answer set solving based approach to reasoning about acceptance under the so-called elitist lifting in ASPIC+ frameworks. Our implementation of the approach scales well in practice.
Public and Private Affairs in Strategic Reasoning

Do agents know each others’ strategies? In multi-process software construction, each process has access to the processes already constructed; but in typical human-robot interactions, a human may not announce its strategy to the robot (indeed, the human may not even know their own strategy). This question has often been overlooked when modeling and reasoning about multi-agent systems. In this work, we study how it impacts strategic reasoning. To do so we consider Strategy Logic (SL), a well-established and highly expressive logic for strategic reasoning. Its usual semantics, which we call “white-box semantics”, models systems in which agents “broadcast” their strategies. By adding imperfect information to the evaluation games for the usual semantics, we obtain a new semantics called “black-box semantics”, in which agents keep their strategies private. We consider the model-checking problem and show that the black-box semantics has much lower complexity than white-box semantics for an important fragment of Strategy Logic.
Automating Reasoning with Standpoint Logic via Nested Sequents

Standpoint logic is a recently proposed formalism in the context of knowledge integration, which advocates a multi-perspective approach permitting reasoning with a selection of diverse and possibly conflicting standpoints rather than forcing their unification. In this paper, we introduce nested sequent calculi for propositional standpoint logics---proof systems that manipulate trees whose nodes are multisets of formulae---and show how to automate standpoint reasoning by means of non-deterministic proof-search algorithms. To obtain worst-case complexity-optimal proof-search, we introduce a novel technique in the context of nested sequents, referred to as "coloring," which consists of taking a formula as input, guessing a certain coloring of its subformulae, and then running proof-search in a nested sequent calculus on the colored input. Our technique lets us decide the validity of standpoint formulae in CoNP since proof-search only produces a partial proof relative to each permitted coloring of the input. We show how all partial proofs can be fused together to construct a complete proof when the input is valid, and how certain partial proofs can be transformed into a counter-model when the input is invalid. These "certificates" (i.e. proofs and counter-models) serve as explanations of the (in)validity of the input.
Pengembangan Media Pembelajaran Sembia Sosis Berbasis Android Untuk Siswa Sekolah Dasar

The purpose of this research is to produce an android-based learning media that can be used by sixth-grade elementary school (SD) students with the content of science subjects on theme 9 with material on the solar system. The background of this research is the boredom of students in implementing Distance Learning (PJJ) so that it requires interesting learning media that can be used anytime and anywhere offline. This research is a development research (R&amp;D) using the ADDIE model procedure. Analysis of the data used is quantitative data analysis. The validation used is media and material validation carried out by experts in their fields. The respondents who were involved in testing the feasibility of the media included class VI teachers, and 20 elementary school students in class VI. The results of this study indicate that the Sembia Sosis (Happy Easy Learning Natural Sciences material for Solar Systems) application which was developed has the characteristics of a file in the form of an application package (apk) that can be installed and run using android that supports students to study independently. Based on the results of expert validation and respondents, overall it was concluded that the Sembia Sosis application suitable for use as a learning medium.
Dynamic Obstacle Avoidance of Quadcopters with Monocular Camera Based on Image-based Visual Servo

This paper focuses on avoiding a dynamic obstacle for Micro-aerial vehicles (MAVs). Instead of estimating the depth or the 3D model of the obstacle, our method is only based on images. First, a geometric model is established, which describes the relationship between a quadcopter and a dynamic obstacle by a monocular camera. Then, based on the established geometric model and the dynamics of the quadcopter, the avoidance control command, namely the angular velocity and the thrust, are generated in real-time. Finally, both the hardware-in-the-loop simulation and the flight experiment are demonstrated to verify the effectiveness of the algorithm.
Supporting drone mission planning and risk assessment with interactive representations of operational parameters

Drone missions in urban areas or at long range offer new opportunities for drone operators but require approval from the authorities due to the potential risks.Obtaining authorization is complex and time-consuming since operators must produce and often iterate on a concept of operation and risk analysis documents to accommodate the authorities' expectations.Our goal is to support the drone mission planning process to make it more efficient so that more and safer operations can be designed and authorized.In this paper, we describe our work designing and assessing interactive representations of operational parameters on maps to enhance thinking and accelerate the process from early concept definition to mission execution.We first collaborated with an operator during a longitudinal study that enabled us to refine and explore our concepts and prototype with real-world use cases.We then collected the feedback from authorities to evaluate the ability of our prototype to match the needs and requirements beyond our first study.Our results indicate that visually representing the concept of operations makes its description more accurate and easier to understand.We also found that exploring the impact of the operational parameters on safety actively supports risk identification and the formulation of adequate mitigation.We believe that our work can inspire the design of future safety support systems and may also contribute to supporting the collaborative process between operators and authorities needed for drone operations in the Specific category.
Challenges in Measuring the Internet for the Public Interest

ABSTRACT The goal of this article is to offer framing for conversations about the role of measurement in informing public policy about the Internet. We review different stakeholders’ approaches to measurements and associated challenges, including the activities of U.S. government agencies. We show how taxonomies of existing harms can facilitate the search for clarity along the fraught path from identifying to measuring harms. Looking forward, we identify barriers to advancing our empirical grounding of Internet infrastructure to inform policy, societal challenges that create pressure to overcome these barriers, and steps that could facilitate measurement to support policymaking.
Learning from Explanations With Maximum Likelihood Inverse Reinforcement Learning

Abstract Our research effort takes inspiration from human social learning mechanisms to focus on situations in which an expert guides a learner through explanations. The proposed approach incorporates explanations into maximum likelihood inverse reinforcement learning. We computationally evaluate explanations against other teaching signals (reward, demonstration and explanation) in three navigational scenarios. The generated explanations are also evaluated in a user study with 150 participants. The user study investigates participants' preferences between the different types of teaching signals and the impact of contextual situations, i.e., distance from the task's goal, on their preferences. Our simulations' results show that explanations lead to better performance compared to reward and demonstration signals, and that explanations are preferred by human teachers in situations where the goal is far from the learner.
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection

Unsupervised time series anomaly detection is instrumental in monitoring and alarming potential faults of target systems in various domains. Current state-of-the-art time series anomaly detectors mainly focus on devising advanced neural network structures and new reconstruction/prediction learning objectives to learn data normality (normal patterns and behaviors) as accurately as possible. However, these one-class learning methods can be deceived by unknown anomalies in the training data (i.e., anomaly contamination). Further, their normality learning also lacks knowledge about the anomalies of interest. Consequently, they often learn a biased, inaccurate normality boundary. This paper proposes a novel one-class learning approach, named calibrated one-class classification, to tackle this problem. Our one-class classifier is calibrated in two ways: (1) by adaptively penalizing uncertain predictions, which helps eliminate the impact of anomaly contamination while accentuating the predictions that the one-class model is confident in, and (2) by discriminating the normal samples from native anomaly examples that are generated to simulate genuine time series abnormal behaviors on the basis of original data. These two calibrations result in contamination-tolerant, anomaly-informed one-class learning, yielding a significantly improved normality modeling. Extensive experiments on six real-world datasets show that our model substantially outperforms twelve state-of-the-art competitors and obtains 6% - 31% F1 score improvement. The source code is available at \url{https://github.com/xuhongzuo/couta}.
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning

Currently, graph learning models are indispensable tools to help researchers explore graph-structured data. In academia, using sufficient training data to optimize a graph model on a single device is a typical approach for training a capable graph learning model. Due to privacy concerns, however, it is infeasible to do so in real-world scenarios. Federated learning provides a practical means of addressing this limitation by introducing various privacy-preserving mechanisms, such as differential privacy (DP) on the graph edges. However, although DP in federated graph learning can ensure the security of sensitive information represented in graphs, it usually causes the performance of graph learning models to degrade. In this paper, we investigate how DP can be implemented on graph edges and observe a performance decrease in our experiments. In addition, we note that DP on graph edges introduces noise that perturbs graph proximity, which is one of the graph augmentations in graph contrastive learning. Inspired by this, we propose leveraging graph contrastive learning to alleviate the performance drop resulting from DP. Extensive experiments conducted with four representative graph models on five widely used benchmark datasets show that contrastive learning indeed alleviates the models' DP-induced performance drops.
3D-BCNN-Based Image Encryption With Finite Computing Precision

To resolve the finite computing precision problem in chaotic cryptography, and the security and efficiency drawbacks of deep learning-based image cryptosystems, an image encryption framework with low computing precision is presented based on three-dimensional Boolean convolution neural network (3D-BCNN). Unlike traditional CNN, the proposed 3D-BCNN is composed of only convolutional layers, in which a cross-channel 3-D Boolean convolution operation is devised without training and parameter optimization. To strengthen the security and sensitivity of the cryptosystem, the kernels and convolutional matrices are generated by the combined prime modulo multiplicative linear congruence generators, SHA-1, and a plain-image. All operations of 3D-BCNN can be conducted on devices with 8-bit word length, so the proposed encryption scheme could work well with low computing precision. Simulation results demonstrate that, with the largest computing accuracy 2−8, the proposed encryption scheme has high security and low time complexity, and can withstand various attacks.
Students' Perception on the Use of Kahoot as a Learning Media

This study analyzes students' perceptions of Kahoot as a learning evaluation medium. This type of research is descriptive quantitative. The sample in this study was 68 grade 8 junior high school students. Data was collected through a questionnaire with the help of Google Form. Then the data is interpreted and analyzed to see the usefulness of Kahoot! in learning. The results showed that as many as 71.6% of students agreed that Kahoot increased student motivation in the good category; 91.7% of students agree that Kahoot improves learning effectiveness in the very good category; and 81.2% of students agree that Kahoot improves students' knowledge in the good category. Thus students' perceptions of learning motivation, learning effectiveness, and student knowledge can be increased with Kahoot. Therefore, it is recommended that teachers use Kahoot as an alternative media to help teachers evaluate learning
Challenges of Deaf Students in Online Learning at Universities

This study describes the challenges faced by deaf students in online learning, focusing on three aspects: internet access and use of media, volunteer assistance, and interaction in online learning. This study uses a qualitative approach with a descriptive method. Sources of data in this study were deaf students and volunteers. Data collection techniques using interviews and observation. Data were analyzed using Miles and Huberman's interactive data analysis model, which consisted of data reduction, presentation, and conclusion drawing. The results of the study describe that 1) deaf students have problems with internet networks and quotas so that they are not optimal in attending lectures, besides that deaf students also experience problems in using learning media used in lectures, for example, in using zoom, and e-learning SIMARI, students Deaf people still have difficulty using some of the features contained in e-learning. 2) volunteers who are tasked with assisting deaf students are active students. The practice of mentoring is not optimal because volunteers also learn from their homes. In addition, the distance between the houses of deaf students and volunteers is quite far, and some are even in different cities. This will further hinder the process of assistance. 3) the lack of assistance by volunteers also has an impact on the interaction of deaf students with lecturers and deaf students with regular students during online learning. This is because not all lecturers can communicate using sign language
The Implied Meaning of Thanking Expressions in Students’ Drama Performance: “The Pursuit of Happyness “Drama

This study investigated thanking expressions used in a drama performance at the English Department of Pasir Pengaraian. Thanking expression has an essential role in giving a sense of communication. The research design was descriptive qualitative, especially in phenomenology. The data was collected from 29 sixth-semester students. The data were collected by using non-participant observation. This research data was analyzed by using Hymes' theory (1972) and Yule (1996). Based on the research, there were 60 thanking expressions used in this drama. The result of data showed some implied meanings of thanking expressions in this drama. There were 14 data (27%) as acknowledging a major favor, 8 data (15%) as acknowledging a favor, 4 data (7%) as assuring a person of one's gratitude, 3 data (6%) as dismissing a person's service, 15 data (29%) as closing the conversation, 6 data (10%) as accepting an offer, 1 data (2 %) as making the hearer feel good (phatic function), and 1 data (2 %) as ending the conversation. Thus, the implied meaning of thanking expression would be a particular function to fill gab language and culture in teaching and learning, especially in drama performance
Implementation Indonesian Language Learning through Critical Discourse Analysis of Verbal Abuse in the 2019 Presidential Election

This study aims to describe the sources of critical discourse analysis of verbal abuse through social media during the 2019 presidential election of the Republic of Indonesia into Indonesian language learning. This is intended so that students have space to improve critical thinking skills. A qualitative approach with phenomenological methods is used in describing the results of the study. This research has two stages, namely: first, analyzing critical discourse on social media during the 2019 presidential election, and researchers implementing it in Indonesian language learning in universities. Collecting data using observation, interview, and documentation techniques. The results of the study describe that 2019 was a big event for Indonesia. As for the Indonesian presidential candidates in 2019, there are two people, namely; Ir. Joko Widodo (sequence 1) and Prabowo Subianto (sequence number 2). Social media is busy spreading news without any filtering of the information. Based on the reduction of documentation data, it was found that verbal abuse that occurred during the 2019 presidential election of the Republic of Indonesia, namely; a) insulting; b) condescending; c) accusing; and d) expel/dismiss. The practice of learning critical discourse analysis in verbal abuse in the 2019 Indonesian republican elections was held on 7 and 14 October 2021. The topic of learning was reading and writing literacy. Learning is presented by applying the cooperative learning model to see how students are able to express opinions
Model of Teaching English Speaking Practiced for English Department Students at IAIN Palopo

The objectives of the research are to discover models of teaching speaking practiced at IAKN Palopo, students’ perceptions of teaching models, teaching speaking models to students’ speaking achievement.The researcher employed qualitative methods for the study. The data resources for the research chosen through random sampling lectures at IAIN Palopo. The instruments for the research consist of observations, interviews in analyzing the data, the researcher employed a qualitative method.The results show that the models of teaching speaking practiced at IAKN Palopo is presentation, practice, production model (PPP) the model divided into three phases as follow: Presentation, refers to new topic and contextualizing, teaching Demonstration (cookies activities), teaching English based on classroom situation. Practice refers to learners practice the structure using accurate reproduction techniques including choral and individual repetition and cue-response drills, students did demonstration based on lecturer’s instruction and Students’ perception toward the model is good, there is positive indicator perception because almost students said, happy, lecturer as a good figure in teaching speaking, simple material.