Reviewerly/exHarmony huggingface dataset:
Narrative Planning for Belief and Intention Recognition

Planning algorithms generate sequences of actions that achieve a goal, but they can also be used in reverse: to infer the goals that led to a sequence of actions. Traditional plan-based goal recognition assumes agents are rational and the environment is fully observable. Recent narrative planning models represent agents as believable rather than perfectly rational, meaning their actions need to be justified by their goals, but they may act in ways that are not optimal, and they may possess incorrect beliefs about the environment. In this work we propose a technique for inferring the goals and beliefs of agents in this context, where rationality and omniscience are not assumed. We present two evaluations that investigate the effectiveness of this approach. The first uses partial observation sequences and shows how this impacts the algorithm’s accuracy. The second uses human data and compares the algorithm’s inferences to those made by humans.
Using Traces from IoT Devices to Solve Criminal Cases

During a criminal investigation, the evidence collection process produces an enormous amount of data. These data are present in many medias and IoT devices that are extracted as crime evidences (USB flash drives, smartphones, hard drives, computers, drones, smartwatches, AI speakers, sensors etc). Due to this data volume, the manual analysis is slow and costly. This work fulfills this gap by presenting a data extraction and processing platform for crime evidence analysis. Our proposed platform leverages a lambda architecture and uses a set of tools and frameworks such as Hadoop HDFS, Kafka, Spark and Docker to analyze a big volume of data at an acceptable time. We also present an example of the proposed platform in use by the State Attorney Office of Rio Grande do Norte (Brazil), where some evaluative tests have been carried out.
Defend Your Enemy. A Qualitative Study on Defending Political Opponents Against Hate Speech Online

Both hate speech and disinformation negatively influence the internet's potential for public deliberation and lead to polarization between political groups. In this paper, we examine the potential of counter speech to bolster public deliberation and reduce polarization. In two focus groups, we interview participants on what motivates them to engage in counter speech in general as well as counter speech favoring political adversaries. Firstly, we find a sharp distinction between participants who avoid engaging with hate speech and participants who actively engage with hate speech in order to combat it. Thus, the most important predictor for counter speech favoring adversaries is an individual's propensity for counter speech in general. In turn, motivations for counter speech in general are a strong sense of morality, a perception of the internet as an important space for public deliberation, and a sense of responsibility to enforce rules for a fair debate. Many of those participants view their online activitiy as a form of activism. Additionally, individuals engaging in counter speech hope to positively influence not necessarily the hater, but the broader audience.
Hybrid Features for Twitter Sentiment Analysis

The Twitter platform is one of the most popular social media environments that gathers concise messages regarding the topics of the moment expressed by its users. Processing sentiments from tweets is a challenging task due to the natural language complexity, misspelling and short forms of words. The goal of this article is to present a hybrid feature for Twitter Sentiment Analysis, focused on information gathered from this social media. The baseline perspective is presented based on different scenarios that take into consideration preprocessing techniques, data representations, methods and evaluation measures. Also, several interesting features are detailed described: the hashtag-based, the fused one, and the raw text feature. All these perspectives are highlighted for proving the high importance and impact that the analysis of tweets has on social studies and society in general. We conducted several experiments that include all these features, with two granularity tweets (word and bigram) on Sanders dataset. The results reveal the idea that best polarity classification performances are produced by the fused feature, but overall the raw feature is better than other approaches. Therefore, the domain-specific features (in this case Twitter hashtags) represent information that can be an important factor for the polarity classification task, not exploited enough.
Real-Time Theoretical and Experimental Dynamic Mode Shapes for Structural Analysis Using Augmented Reality

Researchers conduct dynamic experiments to quantify changes in frequencies and mode shapes of interest for the safety and performance of structures. Even with sensor data and advanced computing techniques, valuable information of the experiment is always best observed with physical presence during the experiment. However, as of today, there exists no method for engineers to observe the dynamics experiments in the physical field and conduct dynamic analysis real-time. This paper describes a software application that conducts dynamic analysis in order to resolve this issue. The research consists of theoretical and experimental modal analyses of a cantilever beam using Augmented Reality (AR). The physical experiment involves a continuous aluminum beam attached to a shake table. First, a theoretical modal analysis is performed using Euler-Bernoulli equations in order to determine the modal parameters of this beam. A software application is built for the AR headset which contains a scaled hologram of the beam and depicts the first three theoretical mode shapes. This hologram is anchored to shake table to superimpose the physical beam. Using sinusoidal vibrations, the shake table can then excite the experimental mode shapes. The hologram and the actual beam are overlaid to compare real time structural data with theoretical mode shapes. By incorporating AR visualization of mode shapes, this prototype enables a new interface for structural analysis. Use of AR in the field of structural dynamics can expand human cognition by introducing a new interface for humans to interact with.
Design Justice and User Interface Design

In this keynote talk, Dr. Costanza-Chock will explore the theory and practice of design justice, discuss how design affordances, disaffordances, and dysaffordances distribute benefits and burdens unequally according to users? location within the matrix of domination (white supremacy, heteropatriarchy, ableism, capitalism, and settler colonialism), and invite us to consider how user interface designers can intentionally contribute to building ?a better world?, a world where many worlds fit; linked worlds of collective liberation and ecological sustainability.
Educational And Methodological Activity Of The University In The Digitalization Context

The article deals with the educational and methodological aspects of higher education digitalization. The current digitalization of the economy and education opens up new opportunities for universities, but also entails competition from other forms of education, in particular, mass online courses (MOOCs). A comparative analysis of the educational and methodological works’ peculiarities in the context of traditional teaching and use of MOOCs is carried out in the article. For both options, models of the teaching and learning cycle, including the development of learning and teaching support materials, its implementation in the educational setting, analysis and modification are built. By a comparative analysis of the traditional model and the MOOC model, it is shown that the significant advantages of MOOCs are the following: acceleration of the teaching and learning cycle; possibility of improving learning and teaching support materials based on the analysis of educational data; shifting the focus of analysis from teaching and learning documentation to its immediate use in the educational setting. Effective implementation of new forms of education in the universities is possible only with the reconsideration of traditional approaches to teaching and methodological work.
Self-Test As A Quality Assessment Mechanism Of University Education Programs: Modern Requirements

In this article we list the results of the study of the self-examination procedure conducted by higher education institutions (further - universities) and/or their structural units in relation to the various majors (educational programs) they implement. The study examines and establishes the main clusters (spheres) of self-examination, analysing which we assess the effectiveness, the correct implementation of a particular educational program (major) and its availability, demand, compliance with national and international educational standards at the state and public levels. The self-examination procedure, which involves systemic monitoring, collection and study of the received statistics on the major (educational programs), allows the university (or its structural unit) to establish the main positive aspects in the implementation of educational programs, assess the quality of the training offered to students, its compliance with modern requirements, its demand by society, as well as identify negative points, disadvantages and weaknesses that need to be modernized, improved and refined. At the same time, all these procedures can be carried out by the university (or its structural unit) without interference from any third-party organizations, state and non-state structures and institutions. Thus, in the course of the study, we come to the conclusion that having universities and/or their structural units self-examine on a systematic, planned basis, taking into account current trends and trends in the field of higher education and education as a public sphere of activity in general is highly efficient and necessary.
Faktor-Faktor Keberhasilan Pembelajaran Bahasa: Perspektif Intake Factors

This study aims to identify the factors that determine the success of a learner in language learning. The study used library research methods through procedures, namely data collection, reading and recording data, and data processing. Data collection technique used was documentation with instruments in the form of data cards. Data analysis technique used content analysis. The results showed that the success of learners in language learning was influenced by several factors, namely factors related to students, factors related to teachers, factors related to the learning environment, and factors related to the social environment. Therefore, language teachers must identify the factors that can influence the success of language learning in their class and accommodate all the factors that influence the success of language learning, otherwise the factors that influence failure must be avoided.
Target Audience Response Analysis in Out-of-home Advertising Using Computer Vision

In this paper we aim to analyze the response of the target audience to out-of-home advertising panels mounted in display windows. We track people passing in front of the panels and, if they approach the panel, looking directly at it, we analyze their facial movements, to see whether the advert appeals to them or not. The approach is based on video data gathering using a static IP camera, storage and analysis, aiming to deliver real-time results. We use neural networks and support vector machines to determine facial microexpression. Results are correlated with information about the adverts displayed to be of use to advertising providers.
Technological and ethical aspects of autonomous driving in a multicultural society

At the beginning of a new decade, major car manufacturers are heading towards the finish line of the autonomous driving race. Whereby advanced driver assisted systems and electric mobility are already implemented and gaining an ever-increasing market share, autonomous cars are mostly still in the testing and development phase. Are humans ready for full autonomy on the road, or will the society resist the data-driven artificial intelligence approach of driving? This paper contributes to the acceleration of autonomous driving innovation and understanding of the underlying concepts. It will expose, explain, and uncover most technological and ethical aspects around autonomous, driverless cars considering compliance and regulatory affairs, which are inevitably impacting today's and tomorrow's society.
MENINGKATKAN KEMAMPUAN MENULIS TEKS EXPOSISI ANALITIS PESERTA DIDIK PELAJARAN BAHASA INGGRIS MELALUI METODE STAD

IMPROVING STUDENTS’ ABILITY IN WRITING ANALYTICAL EXPOSITION TEXT OF ENGLISH LESSONS THROUGH STAD METHODOne of the writing skill was writing analytical exposition text. The teacher saw that the test results of student ability in writing analytical exposition text were disappointing. The dominant students' scores were low. For this reason, the teacher reviewed the use of the learning model that had been applied so that the activities of students and their learning outcomes increased. The purpose of this study was to determine the learning outcomes of students in English lessons on writing analytical exposition text through the cooperative learning using Students Team Achievement Division (STAD) type in class XI IPA 4 odd semester 2017-2018 academic year of SMA Negeri 9 Bogor. After conducting the research, the results showed that the STAD type of learning model was proven to improve students learning outcomes and made them more enthusiastic in learning English. Before using this learning model, students only obtained an average value of 6, 81 then an increase occurred after uing the STAD type of learning model. On the first cycle the class average was 81.50 and became 86.28 on the second cycle. From the description above, the researcher concluded that applying the cooperative learning model using Student Team Achievement Division type could change the learning situation of students to be active and excited so that there was an increase in student learning outcomes. Therefore, the researcher suggested that the use of the cooperative learning model using STAD type had to be applied as a variation of another model in teaching English material in schools.
A Machine-Learning Approach for Time-Optimal Trajectory Generation for UAV's

This paper presents a data-driven approach towards time-optimal trajectory generation for Unmanned Aerial Vehicles (UAV's) using a machine-learned trajectory generation mechanism for point-to-point time-optimal trajectories on-the-fly. To train this machine-learned black box trajectory generator off-line, a model-based optimization problem is first constructed for point-to-point time-optimal trajectory generation, with physical constraints on inputs, states, and rates. The formulated optimization problem is then solved off-line for a range of initial and terminal flight states to generate point-to-point data-sets that consist of the optimal state and input trajectories. This information is compressed by parameterizing the input and state trajectories using a set of basis functions. This data is then used to train the neural network-based trajectory planner. The output of the neural network is the basis function coefficient sets for the state and input trajectories (and the total flight time) which can then be used to reconstruct the flight trajectory. Once the neural network is trained, the data-driven on-board trajectory generator is ready to be deployed on the UAV for on-board planning. This approach is demonstrated for two scenarios: (1) the input to the neural network being the initial and terminal flight states and (2) the input to the neural network being initial and terminal flight states as well as physical constraints. To validate the performance of the machine-learned black-box trajectory generator, the root mean squared error between the neural network generated trajectories and the trajectories obtained from solving the optimization problem directly is statistically evaluated. These trajectories are also tested for violation of path constraints (which are not included explicitly in the training or input to the black box planner) by evaluating the mean constraint violation for each path-constrained variable.
Walter Burley on Co-Signification in Opaque Contexts

Foreshadowing in many ways theories of direct reference popular today, Walter Burley (died c. 1345) favors a theory of direct signification, according to which names directly signify things in the world. But he recognizes that opaque contexts, such as propositional attitude reports, represent a challenge to that theory. In response, Burley develops a sophisticated account of our noetic states, one according to which those states can be individuated more finely than in terms of their contents. Paired with a certain semantic analysis of propositional attitude reports, that account of our noetic states is ready-made to accommodate Burley’s commitment to direct signification even in the face of opacity considerations.
Affordable Digital Signage with Raspberry Pi

Digital Signage is a great way to inform library users about programs, events, services, and other library news. Unfortunately, digital signage can be difficult to implement and come with pricey monthly charges. When looking for ways to implement versatile digital signage, Fort Hays State University’s Forsyth Library and Learning Commons discovered an affordable and easy to manage solution - the Raspberry Pi. In this paper, you will discover what Raspberry Pis are and how to purchase and install them. You’ll also learn about several methods for creating messages.
High Precision Deep Learning-Based Tabular Position Detection

Documents are constantly being processed within supply chains in various industries throughout the globe. Within those documents, often times the most important content is stored in tabular format. Therefore an automated technique for supply chain document processing is highly desired. Deep learning approaches show promise to deliver an end-to-end extraction model. However, it has been shown that tabular detection accuracy is not always correlated to tabular localization accuracy. Portions of the desired tabular information can easily be cropped out due to a lack of localization accuracy. In this paper, we propose a two stage convolutional neural network-based deep learning framework to improve tabular localization accuracy. We use pre-trained backbone network ResNet-50 and then apply transfer learning to fit our application. One of our main contributions is the introduction of the KL loss function into Faster-RCNN. Once the bounding box variances are acquired from the KL loss function, we introduce a voting procedure with soft-non-maximum suppression (Soft-NMS) to improve localization performance. The proposed framework is trained and evaluated on public and private datasets that span from scientific documents to various electronic components. Our test results show that the precision of tabular detection can be improved by 1.2% while achieving the same recall as other state-of-the-art models on the public ICDAR2013 dataset. Furthermore, a large improvement in precision has been achieved at extremely high intersection over union (IoU) thresholds (i.e. 95%). Thus, 10.9% higher precision is achieved at 95% IoU for ICDAR2013. For another public dataset, namely ICDAR2017, 8.4% higher precision is achieved at 95% IoU .
An Evaluation Model of English Teaching Effectiveness based on Online Education

In order to overcome the problems of long construction time, poor critical reasonableness of indexes and low accuracy of the model in the construction of the current evaluation model of English teaching effectiveness, this paper proposes an evaluation model of English teaching effectiveness based on online education, which designs an online education platform and obtains relevant information of English teaching effectiveness evaluation through online education platform. Principal component analysis (PCA) is used to reduce the dimension of the relevant information and extract the principal components in the information. The evaluation index system of students, peer teachers and supervising experts is constructed. The evaluation model of English teaching effectiveness is constructed through three parts: attribute recognition, multi-index attribute comprehensive measurement analysis and single-index attribute measurement analysis. The experimental results show that the proposed method has high efficiency, good index rationality and high model accuracy.
Psycholinguistic Aspect Of Dramaturgic And Educational Discourse Interaction

The article shows that the involvement of psycholinguistic analysis of educational discourse in the analysis of dramaturgic discourse during their interaction makes it possible to identify the components that are characteristic of both dramatic and educational discourses.The study of educational discourse in the context of dramaturgic discourse allows us to analyze these factors on the material of educational textbooks, making it possible to identify aspects of interpretations, setting many potential readings for the production as a whole using an example of a specific play.The dramaturgic discourse openness is of great interest regarding the mainstream of both direct acting decisions and other functions that each of the participants in dramaturgic and educational discourses performs according to the roles assigned to them.The components of the discourse are its participants, who in turn change their status when discourses enter into interaction.When moving from one discourse to another, its participant establishes new references, offers his inferences, which leads to unexpected staged decisions, forming links that reflect the relationship of the recipient and participant as well as the author of the staged work.The integrity and interconnection of the components of the dramaturgic discourse with the educational discourse makes it possible to simultaneously perform several actions: psycholinguistic analysis of the texts of the plays, recommendations of a teacher and direct play of actors in different interpretations.It is necessary to take into account pre-production and staging processes, in which a community of discourse-forming factors is generally observed.
Overview of Machine Learning and Deep Learning Concepts

In this chapter, we focus on exploring the realms of machine learning and deep learning algorithms. Datasets from the previous chapter are used in this chapter as well. In addition, we introduce some new public datasets to build machine learning and deep learning models. In the previous chapter, you learned the basic concepts of Python programming. In this chapter, we start using Python programming and related libraries to explore the data and doing data wrangling to clean it so that it can be used as an input for different machine learning models. The chapter is divided into seven broad sections, starting with the overview of machine learning.
Cortico-Hippocampal Computational Modeling Using Quantum-Inspired Neural Networks

Many current computational models that aim to simulate cortical and hippocampal modules of the brain depend on artificial neural networks. However, such classical or even deep neural networks are very slow, sometimes taking thousands of trials to obtain the final response with a considerable amount of error. The need for a large number of trials at learning and the inaccurate output responses are due to the complexity of the input cue and the biological processes being simulated. This article proposes a computational model for an intact and a lesioned cortico-hippocampal system using quantum-inspired neural networks. This cortico-hippocampal computational quantum-inspired (CHCQI) model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. The proposed model is used to simulate various classical conditioning tasks related to biological processes. The output of the simulated tasks yielded the desired responses quickly and efficiently compared with other computational models, including the recently published Green model.