Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDiscussion on the relationship between elders' daily conversations and cognitive executive function: using word vectors and regression models
مناقشة حول العلاقة بين المحادثات اليومية الشيوخ والوظيفة التنفيذية المعرفية: استخدام ناقلات Word ونماذج الانحدار
296
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نظرا لأن متوسط العمر المتوقع للشعب الصيني يرتفع، أصبحت مشاكل الرعاية الصحية للمسنين أكثر تنوعا، ويتزايد الطلب على الرعاية الطويلة الأجل أيضا.لذلك، كيفية مساعدة كبار السن لديهم نوعية جيدة للحياة والحفاظ على كرامتهم هو ما نحتاج إلى التفكير فيه.يعتزم هذا البحث استكشاف خصائص اللغة الطبيعية من الشيخوخة العاديين من خلال نموذج عميق.أولا، نجمع المعلومات من خلال مجموعات التركيز حتى يتمكن الشيوخ من التفاعل بشكل طبيعي مع المشاركين الآخرين في هذه العملية.ثم، من خلال نموذج ناقلات الكلمة ونموذج الانحدار، يتم إنشاء نموذج التنبؤ بالوظائف التنفيذية المستندة إلى بيانات الحوار للمساعدة في فهم مسار تدهور الوظيفة التنفيذية وإنشاء تحذير مبكر.
As the average life expectancy of Chinese people rises, the health care problems of the elderly are becoming more diverse, and the demand for long-term care is also increasing. Therefore, how to help the elderly have a good quality of life and maintain their dignity is what we need to think about. This research intends to explore the characteristics of natural language of normal aging people through a deep model. First, we collect information through focus groups so that the elders can naturally interact with other participants in the process. Then, through the word vector model and regression model, an executive function prediction model based on dialogue data is established to help understand the degradation trajectory of executive function and establish an early warning.
References used
https://aclanthology.org/
rate research
Read More
This study recruited 51 elders aged 53-74 to discuss their daily activities in focus groups. The transcribed discourse was analyzed using the Chinese version of LIWC (Lin et al., 2020; Pennebaker et al., 2015) for cognitive complexity and dynamic lan
guage as well as content words related to elders' daily activities. The interruption behavior during the conversation was also coded and analyzed. After controlling for education, gender and age, the results showed that cognitive flexibility performance was accompanied by the increasing adoption of dynamic language, insight words and family words. These findings serve as the basis for predicting elders' cognitive flexibility through their daily language use.
Obesity is a widespread problem in all societies
and it is accompanied with short sleep duration on the recent years
especially after Media and communication has spanned, so many
people will stay awake with TV and internet. For that, many
researc
hes were performed to catch evidence about the relation
between short sleep duration and overweight .
One of the long-standing challenges in lexical semantics consists in learning representations of words which reflect their semantic properties. The remarkable success of word embeddings for this purpose suggests that high-quality representations can
be obtained by summarizing the sentence contexts of word mentions. In this paper, we propose a method for learning word representations that follows this basic strategy, but differs from standard word embeddings in two important ways. First, we take advantage of contextualized language models (CLMs) rather than bags of word vectors to encode contexts. Second, rather than learning a word vector directly, we use a topic model to partition the contexts in which words appear, and then learn different topic-specific vectors for each word. Finally, we use a task-specific supervision signal to make a soft selection of the resulting vectors. We show that this simple strategy leads to high-quality word vectors, which are more predictive of semantic properties than word embeddings and existing CLM-based strategies.
In this paper, we use domain generalization to improve the performance of the cross-device speaker verification system. Based on a trainable speaker verification system, we use domain generalization algorithms to fine-tune the model parameters. First
, we use the VoxCeleb2 dataset to train ECAPA-TDNN as a baseline model. Then, use the CHT-TDSV dataset and the following domain generalization algorithms to fine-tune it: DANN, CDNN, Deep CORAL. Our proposed system tests 10 different scenarios in the NSYSU-TDSV dataset, including a single device and multiple devices. Finally, in the scenario of multiple devices, the best equal error rate decreased from 18.39 in the baseline to 8.84. Successfully achieved cross-device identification on the speaker verification system.
this study aimed to use derived indicators for development of financial system by using
factor analysis to fully understand the level of financial development in Syria and it's
relationship with the economic growth.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
