Online Therapy’s Influences on College Student’s Emotional Health

Based on previous research, online therapy has been find as effective as traditional face-to-face therapy in reducing emotional health-related symptoms. Still, people tend to prefer face-to-face therapy more. Using theories from previous research, we adopted the “Depression, Anxiety, and Stress Scale” (DASS-21) to study our hypothesis that effective scores on DASS-21 will not differ between the students receiving the online therapy and face-to-face therapy. Also, the “Satisfaction with Treatment Questionnaire” has been used to study our hypothesis that students will prefer face-to-face therapy. This study examines the effectiveness of online therapy and the population’s preferences while focusing on Trinity college students. This study found no significant difference between the DASS-21 score, self-reported change, and level of satisfaction between online therapy and face-to-face therapy groups. Our finding also showed no significant difference between the DASS-21 score, self-reported change, and level of satisfaction between male and female groups. These findings support the first hypothesis that effective scores on DASS-21 will not differ between the students receiving the online therapy and face-to-face therapy. In contrast, the second hypothesis that students will prefer face-to-face therapy has not been supported

Research on the Path of Teaching Staff Construction in Independent Colleges

Independent college is an important part of higher education in China, and it is also the guarantee of providing applied talents for China’s economic and social development. Therefore, the construction of teaching staff in independent colleges is particularly important, and its comprehensive strength of teachers directly affects the quality of personnel training in independent colleges. Compared with public colleges, the overall faculty of independent colleges is still relatively weak, which is not conducive to the overall promotion of the school-running level of independent colleges. In order to better promote the construction of teachers in independent colleges, Promote the improvement of the school-running strength and teachers’ level of independent colleges, Taking the construction of teaching staff in an independent college in Zhejiang Province as an example, On the basis of a profound analysis of the shortcomings faced by the construction of the teaching staff in this college, this paper puts forward some countermeasures to improve the construction of the teaching staff in this independent college, in order to make suggestions for the construction of the talent team in this independent college and provide case reference for the construction of the teaching staff in other independent colleges of the same type in Chin

Selected results from clustering and analyzing stock market trade data

Master of ScienceDepartment of StatisticsMichael HigginsThe amount of data generated from stock market trading is massive. For example, roughly 10 million trades are performed each day on the NASDAQ stock exchange. A significant proportion of these trades are made by high-frequency traders. These entities make on the order of thousands or more trades a day. However, the stock-market factors that drive the decisions of high-frequency traders are poorly understood. Recently, hybridized threshold clustering (HTC) has been proposed as a way of clustering large-to-massive datasets. In this report, we use three months of NASDAQ HFT data---a dataset containing information on all trades of 120 different stocks including identifiers on whether the buyer and/or seller were high-frequency traders---to investigate the trading patterns of high-frequency traders, and we explore the use of HTC to identify these patterns. We find that, while HTC can be successfully performed on the NASDAQ HFT dataset, the amount of information gleaned from this clustering is limited. Instead, we show that an understanding of the habits of high-frequency traders may be gained by looking at \textit{janky} trades---those in which the number of shares traded is not a multiple of 10. We demonstrate evidence that janky trades are more common for high-frequency traders. Additionally, we suggest that a large number of small, janky trades may help signal that a large trade will happen shortly afterward

Pre-training Language Models for Comparative Reasoning

Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions, which constitutes a fundamental cognitive ability. In this paper, we propose a novel framework to pre-train language models for enhancing their abilities of comparative reasoning over texts. While there have been approaches for NLP tasks that require comparative reasoning, they suffer from costly manual data labeling and limited generalizability to different tasks. Our approach introduces a novel method of collecting scalable data for text-based entity comparison, which leverages both structured and unstructured data. Moreover, we present a framework of pre-training language models via three novel objectives on comparative reasoning. Evaluation on downstream tasks including comparative question answering, question generation, and summarization shows that our pre-training framework significantly improves the comparative reasoning abilities of language models, especially under low-resource conditions. This work also releases the first integrated benchmark for comparative reasoning.Comment: EMNLP 2023 - Camera Ready. Typos fixe

A Unified Encoder-Decoder Framework with Entity Memory

Entities, as important carriers of real-world knowledge, play a key role in many NLP tasks. We focus on incorporating entity knowledge into an encoder-decoder framework for informative text generation. Existing approaches tried to index, retrieve, and read external documents as evidence, but they suffered from a large computational overhead. In this work, we propose an encoder-decoder framework with an entity memory, namely EDMem. The entity knowledge is stored in the memory as latent representations, and the memory is pre-trained on Wikipedia along with encoder-decoder parameters. To precisely generate entity names, we design three decoding methods to constrain entity generation by linking entities in the memory. EDMem is a unified framework that can be used on various entity-intensive question answering and generation tasks. Extensive experimental results show that EDMem outperforms both memory-based auto-encoder models and non-memory encoder-decoder models.Comment: Accepted by the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022

Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

Predicting the direction of the stock market has always been a huge challenge. Also, the way of forecasting the stock market reduces the risk in the financial market, thus ensuring that brokers can make normal returns. Despite the complexities of the stock market, the challenge has been increasingly addressed by experts in a variety of disciplines, including economics, statistics, and computer science. The introduction of machine learning, in-depth understanding of the prospects of the financial market, thus doing many experiments to predict the future so that the stock price trend has different degrees of success. In this paper, we propose a method to predict stocks from different industries and markets, as well as trend prediction using traditional machine learning algorithms such as linear regression, polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks: long and short time memory (LSTM) and spoken short-term memory