138 research outputs found
Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles
The use of propaganda has spiked on mainstream and social media, aiming to
manipulate or mislead users. While efforts to automatically detect propaganda
techniques in textual, visual, or multimodal content have increased, most of
them primarily focus on English content. The majority of the recent initiatives
targeting medium to low-resource languages produced relatively small annotated
datasets, with a skewed distribution, posing challenges for the development of
sophisticated propaganda detection models. To address this challenge, we
carefully develop the largest propaganda dataset to date, ArPro, comprised of
8K paragraphs from newspaper articles, labeled at the text span level following
a taxonomy of 23 propagandistic techniques. Furthermore, our work offers the
first attempt to understand the performance of large language models (LLMs),
using GPT-4, for fine-grained propaganda detection from text. Results showed
that GPT-4's performance degrades as the task moves from simply classifying a
paragraph as propagandistic or not, to the fine-grained task of detecting
propaganda techniques and their manifestation in text. Compared to models
fine-tuned on the dataset for propaganda detection at different classification
granularities, GPT-4 is still far behind. Finally, we evaluate GPT-4 on a
dataset consisting of six other languages for span detection, and results
suggest that the model struggles with the task across languages. Our dataset
and resources will be released to the community.Comment: Accepted as a full paper at LREC-COLING 202
Large Language Models for Propaganda Span Annotation
The use of propagandistic techniques in online contents has increased in
recent years aiming to manipulate online audiences. Efforts to automatically
detect and debunk such content have been made addressing various modeling
scenarios. These include determining whether the content (text, image, or
multimodal) (i) is propagandistic, (ii) employs one or more propagandistic
techniques, and (iii) includes techniques with identifiable spans. Significant
research efforts have been devoted to the first two scenarios compared to the
latter. Therefore, in this study, we focus on the task of detecting
propagandistic textual spans. Specifically, we investigate whether large
language models (LLMs), such as GPT-4, can effectively perform the task.
Moreover, we study the potential of employing the model to collect more
cost-effective annotations. Our experiments use a large-scale in-house dataset
consisting of annotations from human annotators with varying expertise levels.
The results suggest that providing more information to the model as prompts
improves its performance compared to human annotations. Moreover, our work is
the first to show the potential of utilizing LLMs to develop annotated datasets
for this specific task, prompting it with annotations from human annotators
with limited expertise. We plan to make the collected span-level labels from
multiple annotators, including GPT-4, available for the community.Comment: propaganda, span detection, disinformation, misinformation, fake
news, LLMs, GPT-
Photosensitizer Using Visible Light: An Undergraduate Laboratory Experiment Utilizing an Affordable Photocatalytic Reactor
In this experiment, the visible light reactive photosensitizer (PS) derived from chlorophyllin sodium copper salt has been synthesized via a simple synthetic route. The enhanced photocatalytic activity for the decomposition of the pharmaceutical compound Diclofenac Potassium available as Voltfast sachets under visible light irradiation was demonstrated by comparing the photocatalytic decomposition of Diclofenac Potassium in the presence and absence of the new synthesized visible light photosensitizer under the same photocatalytic conditions. Based on the experimental results, higher activity was achieved for the sample composed of the new synthesized visible light photosensitizer. The photosensitized sample using the new derivative of chlorophyllin sodium copper salt exhibited approximately 21 times higher rate when compared with that of Chlorophyllin sodium copper salt sample. This photocatalytic activity can be attributed to the enhanced visible light harvesting of the new derivative of Chlorophyllin sodium copper salt.  
Minimally invasive approach for the management of right atrial angiosarcoma; A case report
Cardiac angiosarcoma is a rare primary cardiac tumor. Outcomes of minimally invasive resection of cardiac angiosarcoma are rarely reported in the literature
A male patient aged 28 years old presented with a right atrial mass compressing the superior vena cava and associated with pericardial effusion. Pericardiocentesis was done, and a preoperative workup revealed no distant metastasis. We planned excision of the mass through a right mini-thoracotomy approach. Intraoperatively, we found the mass invading the entire atrial wall thickness, and excision of the mass with a reconstruction of the right atrial wall was performed. Minimally invasive resection of atrial angiosarcoma could be feasible. Atrial angiosarcoma could present with vague signs and symptom
A novel optimized neutrosophic k-means using genetic algorithm for skin lesion detection in dermoscopy images
This paper implemented a new skin lesion detection method based on the genetic algorithm (GA) for optimizing the neutrosophic set (NS) operation to reduce the indeterminacy on the dermoscopy images. Then, k-means clustering is applied to segment the skin lesion regions
Doctor-Patient Communication A Requisite for Better Medication History Taking: Insight from Sudan
Despite the awareness of doctors about the significance of obtaining a comprehensive medication history for patients, they often neglect this in their practice, resulting in an incomplete patient medication list. The study aimed to investigate the role of communication skills as a crucial part of optimal pharmacotherapy. An observational, cross-sectional study was carried out at internal medicine department in a tertiary hospital, Wad Medani, Sudan. The research instrument was a form involved a checklist rating a doctor’s performance during the medical encounters. Among 94 medical doctors, 51% were males and 6.15 (SE) was the average years of experience. About 13% of participants received under-graduation training in communication skills, while 21% had it after post-graduation. Concerning communication skills evaluation, 61% of specialists, 29% of registrars, and 7% of house officers reported an excellent performance. Gender and doctors’ ranking in a medical team had a significant role in communication skills (P-value <0.05) with an overall adjusted R2 of 0.339. Specialists were the most knowledgeable and skillful in obtaining structured medication history; 67% reported an excellent performance. Communication skills had a remarkable impact in getting patient medication history (P-value: <0.05) with an overall adjusted R2 of 0. 763.The study concluded that; gender and doctors’ ranking in the medical team were the main predictors for doctors to be a good communicator. Communication skills have a significant role in medication history taking. There was a gap in knowledge and training in communication skills among internal medicine doctors specifically, “house officers.” This gap negatively contributed to obtaining a comprehensive patient medication history
When Seeing is Believing: A Framework for Reflective Conversations in Remote and Face-to-Face Coaching Approaches
Reflective conversations between teachers and coaches are critical to helping teachers improve their classroom instruction. Coaches who encourage teachers to see, think, and do are better able to facilitate meaningful reflective conversations with teachers. The See, Think, Do framework consists of six steps (observe, describe, process, analyze, draw conclusions, and plan) that can be used to help coaches engage in reflective conversations with teachers. The framework can be readily implemented in remote and face-to-face coaching modalities and in one-on-one and small group delivery formats. Suggestions and strategies for implementing the framework in ongoing coach-teacher conversations are provided
РОЛЬ ТРУДОВЫХ РЕСУРСОВ В РАЗВИТИИ ЭКОНОМИКИ БАХРЕЙНА
The article analyzes the experience of the development of labor resources in the Kingdom of Bahrain. The country is a member of the Cooperation Council for the Arab States of the Persian Gulf (GCC), which are characterized by high dependence on labor migrants, a large number of young people under 25 years old, and a low share of women's involvement in economic processes. Nevertheless, the experience of Bahrain is characterized by a unique approach to improving the quality of human capital. The multi-level principle of interaction between the state, society and business is embodied in the creation of the Tamkeen company, whose main goal is the development of national labor resources.El artículo analiza la experiencia del desarrollo de recursos laborales en el Reino de Bahrein. El país es miembro del Consejo de Cooperación para los Estados Árabes del Golfo Pérsico (CCG), que se caracteriza por una alta dependencia de trabajadores migrantes, un gran número de jóvenes menores de 25 años y una baja participación de las mujeres en Procesos economicos. Sin embargo, la experiencia de Bahrein se caracteriza por un enfoque único para mejorar la calidad del capital humano. El principio de interacción de varios niveles entre el estado, la sociedad y las empresas se materializa en la creación de la empresa Tamkeen, cuyo objetivo principal es el desarrollo de los recursos laborales nacionaleВ статье анализируется опыт развития трудовых ресурсов Королевства Бахрейн. Страна является членом Совета сотрудничества арабских государств Персидского залива (ССАГПЗ), который характеризуется высокой зависимостью от трудовых мигрантов, большим числом молодых людей в возрасте до 25 лет и низкой долей участия женщин в экономических процессах. Тем не менее, опыт Бахрейна характеризуется уникальным подходом к повышению качества человеческого капитала. Многоуровневый принцип взаимодействия государства, общества и бизнеса воплощен в создании компании "Тамкин", основной целью которой является развитие национальных трудовых ресурсов
Physicochemical characteristics and discolouration potentials of Pulpine mineral® and Pulpine NE®
ABSTRACT
Aim: To compare the physicochemical properties (solubility, pH, radiopacity and crown discoloration) of Pulpine mineral (PMIN) and Pulpine NE (PNE) with the conventional material, mineral trioxide aggregate (MTA).
Methodology: Specimens of the tested materials were prepared according to the manufacturer’s instructions using split Teflon ring molds. Solubility was evaluated by the percentage of material mass loss over 24 h and one week. The alkalinity was measured after each evaluation period using a pH meter. Other specimens were digitally radiographed on a size 2 sensor plate along with an aluminum step wedge to analyze the radiopacity by the Image J software. Finally, crown discoloration was assessed after applying the tested materials in the pulp chamber of sound human premolars using spectrophotometer. All data were statistically analyzed.
Results: Compared to MTA, both materials had significantly higher solubility and lower radiopacity (P<0.05). The alkalinity of PMIN was higher than that of MTA and PNE. Unlike PMIN, PNE and MTA caused crown discoloration.
Conclusions: PMIN exhibits promising results related to high alkalinity and adequate color stability but it needs modifications for radiopacity and solubility
LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking
The recent development and success of Large Language Models (LLMs)
necessitate an evaluation of their performance across diverse NLP tasks in
different languages. Although several frameworks have been developed and made
publicly available, their customization capabilities for specific tasks and
datasets are often complex for different users. In this study, we introduce the
LLMeBench framework, which can be seamlessly customized to evaluate LLMs for
any NLP task, regardless of language. The framework features generic dataset
loaders, several model providers, and pre-implements most standard evaluation
metrics. It supports in-context learning with zero- and few-shot settings. A
specific dataset and task can be evaluated for a given LLM in less than 20
lines of code while allowing full flexibility to extend the framework for
custom datasets, models, or tasks. The framework has been tested on 31 unique
NLP tasks using 53 publicly available datasets within 90 experimental setups,
involving approximately 296K data points. We open-sourced LLMeBench for the
community (https://github.com/qcri/LLMeBench/) and a video demonstrating the
framework is available online. (https://youtu.be/9cC2m_abk3A)Comment: Accepted as a demo paper at EACL 202
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