scholarly journals From Theory of Rhetoric to the Practice of Language Use: The Case of Appeals to Ethos Elements

Argumentation ◽  
2022 ◽  
Author(s):  
Marcin Koszowy ◽  
Katarzyna Budzynska ◽  
Martín Pereira-Fariña ◽  
Rory Duthie
Keyword(s):  
Philosophy Of Language ◽  
Large Scale ◽  
Language Use ◽  
Practical Wisdom ◽  
Moral Virtue ◽  
Real Life ◽  
Argumentation Theory ◽  
Dialogue Systems ◽  
Theoretical Frameworks ◽  
Area Of Interest

AbstractIn their book Commitment in Dialogue, Walton and Krabbe claim that formal dialogue systems for conversational argumentation are “not very realistic and not easy to apply”. This difficulty may make argumentation theory less well adapted to be employed to describe or analyse actual argumentation practice. On the other hand, the empirical study of real-life arguments may miss or ignore insights of more than the two millennia of the development of philosophy of language, rhetoric, and argumentation theory. In this paper, we propose a novel methodology for adapting such theories to serve as applicable tools in the study of argumentation phenomena. Our approach is both theoretically-informed and empirically-grounded in large-scale corpus analysis. The area of interest are appeals to ethos, the character of the speaker, building upon Aristotle’s rhetoric. Ethotic techniques are used to influence the hearers through the communication, where speakers might establish, but also emphasise, weaken or undermine their own or others’ credibility and trustworthiness. Specifically, we apply our method to Aristotelian theory of ethos elements which identifies practical wisdom, moral virtue and goodwill as components of speakers’ character, which can be supported or attacked. The challenges we identified in this case and the solutions we proposed allow us to formulate general guidelines of how to exploit rich theoretical frameworks to the analysis of the practice of language use.

First grammar books in the Habsburg Monarchy: individual initiative and regulatory interference by the state (1760s–1770s)

2020 ◽  
Vol 2019 (2 (11)) ◽  
pp. 137-157
Author(s):  
Olga V. Khavanova ◽  
Keyword(s):  
Eighteenth Century ◽  
State Policy ◽  
Large Scale ◽  
Language Use ◽  
Linguistic Diversity ◽  
Mother Tongue ◽  
German Language ◽  
Habsburg Monarchy ◽  
Private Initiative ◽  
The One

The second half of the eighteenth century in the lands under the sceptre of the House of Austria was a period of development of a language policy addressing the ethno-linguistic diversity of the monarchy’s subjects. On the one hand, the sphere of use of the German language was becoming wider, embracing more and more segments of administration, education, and culture. On the other hand, the authorities were perfectly aware of the fact that communication in the languages and vernaculars of the nationalities living in the Austrian Monarchy was one of the principal instruments of spreading decrees and announcements from the central and local authorities to the less-educated strata of the population. Consequently, a large-scale reform of primary education was launched, aimed at making the whole population literate, regardless of social status, nationality (mother tongue), or confession. In parallel with the centrally coordinated state policy of education and language-use, subjects-both language experts and amateur polyglots-joined the process of writing grammar books, which were intended to ease communication between the different nationalities of the Habsburg lands. This article considers some examples of such editions with primary attention given to the correlation between private initiative and governmental policies, mechanisms of verifying the textbooks to be published, their content, and their potential readers. This paper demonstrates that for grammar-book authors, it was very important to be integrated into the patronage networks at the court and in administrative bodies and stresses that the Vienna court controlled the process of selection and financing of grammar books to be published depending on their quality and ability to satisfy the aims and goals of state policy.

Bullying, School Violence, and Climate in Evolving Contexts

2018 ◽  
Author(s):  
Ron Avi Astor ◽  
Rami Benbenisthty
Keyword(s):  
School Violence ◽  
School Safety ◽  
Media Coverage ◽  
Large Scale ◽  
Ecological Model ◽  
Empirical Studies ◽  
Practical Applications ◽  
Teacher Student ◽  
Area Of Interest ◽  
New Research

Since 2005, the bullying, school violence, and school safety literatures have expanded dramatically in content, disciplines, and empirical studies. However, with this massive expansion of research, there is also a surprising lack of theoretical and empirical direction to guide efforts on how to advance our basic science and practical applications of this growing scientific area of interest. Parallel to this surge in interest, cultural norms, media coverage, and policies to address school safety and bullying have evolved at a remarkably quick pace over the past 13 years. For example, behaviors and populations that just a decade ago were not included in the school violence, bullying, and school safety discourse are now accepted areas of inquiry. These include, for instance, cyberbullying, sexting, social media shaming, teacher–student and student–teacher bullying, sexual harassment and assault, homicide, and suicide. Populations in schools not previously explored, such as lesbian, gay, bisexual, transgender, and queer students and educators and military- and veteran-connected students, become the foci of new research, policies, and programs. As a result, all US states and most industrialized countries now have a complex quilt of new school safety and bullying legislation and policies. Large-scale research and intervention funding programs are often linked to these policies. This book suggests an empirically driven unifying model that brings together these previously distinct literatures. This book presents an ecological model of school violence, bullying, and safety in evolving contexts that integrates all we have learned in the 13 years, and suggests ways to move forward.

scholarly journals Neural methods for effective, efficient, and exposure-aware information retrieval

2021 ◽  
Vol 55 (1) ◽  
pp. 1-2
Author(s):  
Bhaskar Mitra
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Web Search ◽  
Real Life ◽  
Inverted Index ◽  
Information Need ◽  
Product Model ◽  
Performance Improvements ◽  
Deep Model

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

scholarly journals Robots for Elderly Care in the Home: A Landscape Analysis and Co-Design Toolkit

2021 ◽  
Author(s):  
Gianluca Bardaro ◽  
Alessio Antonini ◽  
Enrico Motta
Keyword(s):  
Large Scale ◽  
Daily Life ◽  
Elderly Care ◽  
Real Life ◽  
Robotic Platform ◽  
Holistic View ◽  
Personal Assistants ◽  
Healthcare Interventions ◽  
The Impact ◽  
The Eu

AbstractOver the last two decades, several deployments of robots for in-house assistance of older adults have been trialled. However, these solutions are mostly prototypes and remain unused in real-life scenarios. In this work, we review the historical and current landscape of the field, to try and understand why robots have yet to succeed as personal assistants in daily life. Our analysis focuses on two complementary aspects: the capabilities of the physical platform and the logic of the deployment. The former analysis shows regularities in hardware configurations and functionalities, leading to the definition of a set of six application-level capabilities (exploration, identification, remote control, communication, manipulation, and digital situatedness). The latter focuses on the impact of robots on the daily life of users and categorises the deployment of robots for healthcare interventions using three types of services: support, mitigation, and response. Our investigation reveals that the value of healthcare interventions is limited by a stagnation of functionalities and a disconnection between the robotic platform and the design of the intervention. To address this issue, we propose a novel co-design toolkit, which uses an ecological framework for robot interventions in the healthcare domain. Our approach connects robot capabilities with known geriatric factors, to create a holistic view encompassing both the physical platform and the logic of the deployment. As a case study-based validation, we discuss the use of the toolkit in the pre-design of the robotic platform for an pilot intervention, part of the EU large-scale pilot of the EU H2020 GATEKEEPER project.

scholarly journals Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Christos Makris ◽  
Georgios Pispirigos
Keyword(s):  
Social Networks ◽  
Graph Partitioning ◽  
Large Scale ◽  
Real Life ◽  
Information Networks ◽  
Digital Marketing ◽  
Partitioning Problems ◽  
Iterative Solutions ◽  
Community Extraction ◽  
Stability And Accuracy

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

scholarly journals “Am besten, du gehst.” Zur nähesprachlichen Verwendung des am-Superlativs im Vor-Vorfeld

Glottotheory ◽  
2020 ◽  
Vol 10 (1-2) ◽  
pp. 1-29
Author(s):  
Doris Höhmann
Keyword(s):  
Quantitative Analysis ◽  
Large Scale ◽  
Language Use ◽  
First Approximation ◽  
Core Part

AbstractThis paper investigates the use of the German am-superlative in colloquial utterances such as am besten, du gehst jetzt. In a first approximation, this construction can be described as elliptically used am-superlative occurring on the left sentence periphery within an Operator-Skopus-Struktur (Barden/Elstermann/Fiehler 2001). As will be shown in the empirical core part of the study, a qualitative-quantitative analysis, the pattern appears to be characterized by different overlapping and interplaying tendencies in language use (e.g. the selection and frequency of pronouns and sentence mood). The data used for the qualitative-quantitative analysis is taken mainly from a large-scale web corpus (deTenTen13).

scholarly journals ALVIC versus the Internet: Redesigning a Networked Virtual Environment Architecture

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Peter Quax ◽  
Jeroen Dierckx ◽  
Bart Cornelissen ◽  
Wim Lamotte
Keyword(s):  
Virtual Environment ◽  
Large Scale ◽  
Virtual Communities ◽  
Real Life ◽  
Point Of View ◽  
The Internet ◽  
Generic Framework ◽  
Short Period ◽  
Networked Virtual Environment ◽  
Near Future

The explosive growth of the number of applications based on networked virtual environment technology, both games and virtual communities, shows that these types of applications have become commonplace in a short period of time. However, from a research point of view, the inherent weaknesses in their architectures are quickly exposed. The Architecture for Large-Scale Virtual Interactive Communities (ALVICs) was originally developed to serve as a generic framework to deploy networked virtual environment applications on the Internet. While it has been shown to effectively scale to the numbers originally put forward, our findings have shown that, on a real-life network, such as the Internet, several drawbacks will not be overcome in the near future. It is, therefore, that we have recently started with the development of ALVIC-NG, which, while incorporating the findings from our previous research, makes several improvements on the original version, making it suitable for deployment on the Internet as it exists today.

scholarly journals Phenotypic discordance between primary and metastatic breast cancer in the large-scale real-life multicenter French ESME cohort

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Thomas Grinda ◽  
Natacha Joyon ◽  
Amélie Lusque ◽  
Sarah Lefèvre ◽  
Laurent Arnould ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Large Scale ◽  
Tumour Progression ◽  
Primary Tumour ◽  
Multivariable Analysis ◽  
Real Life ◽  
Metastatic Breast ◽  
Her2 Status ◽  
Risk Of Death

AbstractExpression of hormone receptor (HR) for estrogens (ER) and progesterone (PR) and HER2 remains the cornerstone to define the therapeutic strategy for breast cancer patients. We aimed to compare phenotypic profiles between matched primary and metastatic breast cancer (MBC) in the ESME database, a National real-life multicenter cohort of MBC patients. Patients with results available on both primary tumour and metastatic disease within 6 months of MBC diagnosis and before any tumour progression were eligible for the main analysis. Among the 16,703 patients included in the database, 1677 (10.0%) had available biopsy results at MBC diagnosis and on matched primary tumour. The change rate of either HR or HER2 was 27.0%. Global HR status changed (from positive = either ER or PR positive, to negative = both negative; and reverse) in 14.2% of the cases (expression loss in 72.5% and gain in 27.5%). HER2 status changed in 7.8% (amplification loss in 45.2%). The discordance rate appeared similar across different biopsy sites. Metastasis to bone, HER2+ and RH+/HER2- subtypes and previous adjuvant endocrine therapy, but not relapse interval were associated with an HR discordance in multivariable analysis. Loss of HR status was significantly associated with a risk of death (HR adjusted = 1.51, p = 0.002) while gain of HR and HER2 discordance was not. In conclusion, discordance of HR and HER2 expression between primary and metastatic breast cancer cannot be neglected. In addition, HR loss is associated with worse survival. Sampling metastatic sites is essential for treatment adjustment.

scholarly journals Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Sai Kiranmayee Samudrala ◽  
Jaroslaw Zola ◽  
Srinivas Aluru ◽  
Baskar Ganapathysubramanian
Keyword(s):  
Dimensionality Reduction ◽  
Organic Solar Cells ◽  
Large Scale ◽  
Parallel Implementation ◽  
High Dimensional Data ◽  
Real Life ◽  
Processing Parameters ◽  
High Dimensional ◽  
Morphology Evolution ◽  
Reduction Techniques

Dimensionality reduction refers to a set of mathematical techniques used to reduce complexity of the original high-dimensional data, while preserving its selected properties. Improvements in simulation strategies and experimental data collection methods are resulting in a deluge of heterogeneous and high-dimensional data, which often makes dimensionality reduction the only viable way to gain qualitative and quantitative understanding of the data. However, existing dimensionality reduction software often does not scale to datasets arising in real-life applications, which may consist of thousands of points with millions of dimensions. In this paper, we propose a parallel framework for dimensionality reduction of large-scale data. We identify key components underlying the spectral dimensionality reduction techniques, and propose their efficient parallel implementation. We show that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods. To further demonstrate applicability of our framework we perform dimensionality reduction of 75,000 images representing morphology evolution during manufacturing of organic solar cells in order to identify how processing parameters affect morphology evolution.

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