1,720,955 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
LLM Assisted Development
Get PDFAufgabenstellung
Das Ziel dieser Arbeit bestand darin, ein Tool oder eine Methode zu entwickeln, die es ermöglicht,
mittels spezialisierter Software zu erkennen, ob ein Prüfungsteilnehmer oder eine Prüfungsteilnehmerin
während einer Online-Prüfung betrügerische Handlungen begeht. Der Einsatz einer solchen Soft-
ware bietet den Vorteil, dass Online-Prüfungen effektiv durchgeführt werden können. Zudem
ermöglicht es, dass Programmierprüfungen nicht mehr manuell, sondern in einem kontrollierten
und überwachten Umfeld absolviert werden können. Der Fokus dieser Arbeit lag speziell auf
der Überwachung der Offline-Aktivitäten der Prüfungsteilnehmenden. Dies umfasst die Beob-
achtung von auffälligen Körperbewegungen und Sprachaktivitäten.
Vorgehen / Technologien
Unser Ansatz zielte darauf ab, die Benutzerfreundlichkeit unserer Software sowohl für Studie-
rende als auch für Lehrende zu maximieren und einen strukturierten Prozess zu etablieren.
Der Ablauf beginnt damit, dass der Studierende die Software aktiviert, die mittels einer Web-
cam ein Video aufzeichnet und über ein ausgewähltes Mikrofon den Ton erfasst. Nachdem die
Prüfungsaufgaben gelöst wurden, wird die Software beendet. Der Studierende erhält dadurch
eine Video- und Audiodatei, die er eigenständig an einen schulischen Server übermitteln kann.
Nachdem der Schulserver alle Audio und Videodateien erhalten hat. Kann der Professor mittels
einer Webseite die Video und Audiodateien analysieren. Während der Analyse wird bei jedem
Prüfungsteilnehmer die Video und Audiodatei analysiert. Dabei wurden bestimmte Kriterien
von uns ausgewählt auf welche sich die Videoanalyse stützen soll.
Gesichtserkennung: In jedem Frame des Videos soll überprüft werden, ob genau eine Person
anwesend ist. Dies ist wichtig, da Pr ̈ufungen in der Regel Einzelarbeiten sind. Sollte keine oder
mehr als eine Person erkannt werden, wird dies als Betrugsversuch gewertet.
Munderkennung: Das System muss ständig den Mund erkennen können. Dies dient dazu,
heimliche Gespräche aufzudecken, auch wenn das Mikrofon während der Prüfung absichtlich
ausgeschaltet ist. Ein Betrugsversuch wird deklariert, sobald der Mund sich öffnet, um die
Spracherkennung zu schützen.
Kopfneigungserkennung: Die Kopfposition wird kontinuierlich überwacht, um auffällige Be-
wegungen, die auf einen zweiten Bildschirm oder ein externes Gerät hinweisen könnten, als
Betrugsversuche zu identifizieren.
Iriserkennung: Diese Technik dient dazu, ungewöhnliches Augenschillen zu erkennen. Sie
soll Prüflinge identifizieren, die zwar ihren Kopf nicht bewegen, aber dennoch nicht auf die
Prüfungsaufgaben schauen.
Resultat
Derzeit ist eine vollständig automatisierte Proctoring-Lösung noch nicht realisierbar. Mensch-
liches Eingreifen bleibt notwendig, um zu entscheiden, ob in bestimmten Situationen Betrug
vorliegt. Unsere Software hat jedoch den Analyseprozess von Audio- und Videodateien erheblich
effizienter gestaltet. Als nächsten Schritt zur Verbesserung der Software könnte die Erfassung
von Online-Aktivitäten in Betracht gezogen werden
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
AI as a Teachers Assistant
Get PDFIntroduction:
Introduction: The digital transformation of higher
education has led to a significant shift in teaching
methods, with blended learning emerging as a
favored approach. In this paradigm, traditional faceto-face teaching is combined with online learning,
resulting in the well-known inverted classrooms. In
this setting, students begin with self-study, supported
by multimedia materials, followed by interactive faceto-face sessions.
However, experience shows that students may
struggle with self-study, highlighting the need for AIsupported learning assistants. These assistants
should provide students with personalized guidance,
feedback, and assessment, adapting to their
individual needs and learning styles. By leveraging AI,
primarily large language models (LLMs), these
assistants can help students progress towards
competency in an efficient and effective manner.
Approach: Three state-of-the-art LLMs (GPT-3.5,
GPT-4 and Mixtral-8x7B) are evaluated on three
subtasks based on lecture notes about political rights
in Switzerland:
- Generating questions
- Evaluating answers
- Providing feedback on the current study level
The models are fine-tuned and the quality of their
outputs were compared to their non-fine-tuned
equivalents.
A prototype application for a chat bot that supports
multiple languages, model selection from a graphical
user interface and an approach that combines chat
history and RAG is built. Model access is wrapped
under an abstracted class, allowing extensibility and
enabling rapid integration of new models.
Administrators assign documents and system
prompts to individual chat bots, giving them granular
control over their behavior and available information.
The documents get embedded with a locally hosted
Multilingual-E5-base instance.
Result: Our work has shown that while there is
potential in using LLMs as assistants for pre-study. It
was shown that existing chat models can work well on
text-based lecture scripts out of the box. However,
they could not interpret lecture scripts using images
and text. The vast range of possible user inputs in this
case makes finetuning challenging, as it would
require an enormous amount of specific training data.
Given that the non-finetuned versions already perform
well, finetuning with few datapoints actually worsened
the overall quality of the output.
The prototype serves as a blueprint for history-aware
and RAG-enabled chat bot applications. When
provided with a locally hosted Mixtral-8x7B instance,
the entire RAG process can be done locally. This
gives its users control over the use of their data and
ensures that classified information can be used to
enhance the chat bots.
In its current form, the prototype is primarily limited by
its resource consumption and therefore scalability
concerns
LLM Assisted Development
Get PDFA. Introduction
In recent years, Large Language Models (LLMs) have become valuable tools for developers with their ability to quickly scaffold code and act as an impactful accelerator for development teams around the world. With code being heavily standardized on a global scale and an abundance of training data freely available on platforms such as GitHub, it is easy to intuit possible reasons for their performance. While these assumptions hold true for popular languages such as Java, Python or C#, there has been little research in the usage of LLMs as development tools for less commonly used languages such as Haskell. With this project we aim to explore LLM based development support for Haskell and develop an environment, in which such research can be conducted in a quick and efficient manner.
B. Approach
Four stateoftheart models (Llama 2, Code Llama, GPT3.5 and GPT4) were evaluated based on their performance on tasks typically faced by an automated development support tool. The tasks were scoped and classified into three major categories:
Code Generation, Debugging and Testing. For each of these categories, quantifiable metrics for the evaluation of the model performance were defined. These criteria must be interpretable as quantifiable metrics to allow a comparative analysis between models. These metrics were then weighted according to their importance, based on the insight of experts in the field of Haskell development. Each task was executed with three sorting algorithms of varying cognitive complexity in sample implementations. Cognitive complexity was selected as the complexity measure after careful evaluation, to ensure that the ordering of the algorithms is based on complexity of interpretability. Utilizing cognitive complexity inspired us to frame LLMs as entities whose performance can be analysed through the lens of cognitive load theory. This enabled the differentiation between errors caused by the complexity of a provided algorithm (intrinsic load) and errors caused by unclear instructions (extraneous load). To accelerate the evaluation processes, a development environment, enabling both the automated testing of generated Haskell code using Jupyter notebooks and the utilization of cloud hosted models with modern LLM tooling like LangChain, was created.
C. Conclusion
Our work has shown that there are large gaps in output quality between each model. We have encountered outliers, but we are confident that these outliers were not caused by the intrinsic complexity of the algorithms provided and can be explained with extraneous complexity that lead to the model not understanding the task. This problem can be resolved with further prompt engineering and finetuning, which is why we are optimistic about the viability of models such as GPT4 or Code Llama as supporting tools for Haskell development. Using Chain of Thought Prompting, which leads models to break down given tasks into sequential subtasks, tends to increase the outputquality in general. However the sequential nature of it lead to inconsistencies in the compositions of Haskell’s higherorder functions. It lead the models to neglect critical nuances in function composition, resulting in erroneous code generation
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