Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformer-based solutions using transfer learning and instruction ﬁne-tuning conditional on context input data for downstream NLP tasks in the domain of job application pain points

The increasing availability of pretrained transformer models has made transfer learning a more viable solution towards downstream NLP tasks. Fine-tuning a pretrained model on data examples directly related to the downstream task can result in better performance with less labels because of the size of the corpus the pretrained model was trained under (typically in the range of several billions of tokens). In this study, we assess this claim by considering a set of downstream NLP tasks within the domain of job application pain points. In identifying these pain points, ﬁve NLP tasks were developed with the underlying goal to ofﬂoad the manual effort job applicants face in the end-to-end job application process. Tasks 1 and 2 were solved using a masked language model (BERT and DistilRoBERTa) with respective accuracies of 78.3%, and 48% obtained in the ﬁne-tuned models. Tasks 3, 4, and 5 were solved using instruction ﬁne-tuning with FLAN-T5. The models produced semantic textual similarity scores of 0.34436, 0.14566, and 0.43527 respectively. Tasks 1 and 2 showed an improvement over the baseline models because of the inclusion of niche domain-speciﬁc words that could be accounted for in the overall vocabulary, while Tasks 3, 4, and 5 did not show an improvement over the baseline (gpt-3.5-turbo) most likely because of the few training examples used during ﬁne-tuning which could not compete with GPT’s large language model.
CSE 571 Winter 2019 Project Report Local Collision Avoidance for a Nano-drone

Navigation in the real world is one of the most important characteristics of robots, particularly Unmanned Aerial Vehicles (UAVs). Automatic navigation involves careful motion planning to avoid collision with all obstacles in the navigation environment [1], and the problem becomes more challenging when there are dynamic obstacles in addition to static obstacles along the path of motion. In this project, we design an effective local collision avoidance system for a nano-drone that allows it to navigate safely through static and dynamic objects in a custom indoor environment. For static objects, we pre-compute the environment grid map using sensor data from the drone lasers and use a shortest path algorithm to get the optimal motion plan. For dynamic objects, we track the laser data input during motion and accordingly update the map and the plan when obstacles are encountered.
Knowledge Graph-Based Approach For Dynamic Ontology Generation

-A knowledge graph is a data model representing real-world entities and relationships in a machine-readable format providing a comprehensive view of a specific domain or multiple domains. Dynamic ontology is a concept that refers to the idea that the fundamental nature of reality changes over time. Dynamic ontology generation using a knowledge graph automatically creates a new ontology or updates an existing ontology. This process keeps the ontology up-to-date with the changing real-world information. The most important part of dynamic ontology generation is integrating the domain ontology into the knowledge graph and updating the knowledge graph accordingly. The primary purpose of the research is to develop a dynamic environment that integrates the domain ontology with the knowledge graph. The main problem here is the dynamic integration of new data and concepts with changes in real-world information. To achieve this task graph-based ontology mapping framework is developed with matrix-based graph merging, graph clustering, cluster label propagation and matrix-based ontology mapping. The new mapping algorithm was tested with real-time dynamic data and compared with existing systems. The proposed approach outperforms the existing system in accuracy, relevancy and introducing new concepts to the ontology.
EnchantedBrush: Animating in Mixed Reality for Storytelling and Communication

Recent progress in head-worn 3D displays has made mixed-reality storytelling, which allows digital art to interact with the physical surroundings, a new and promising medium to visualize ideas and bring sketches to life. While previous works have introduced dynamic sketching and animation in 3D spaces, visual and audio effects typically need to be manually specified. We present EnchantedBrush, a novel mixed-reality approach for creating animated storyboards with automatic motion and sound effects in real-world environments. People can create animations that interact with their physical surroundings using a set of interactive motion and sound brushes. We evaluated our approach with 12 participants, including professional *e-mail: eveli@dgp.toronto. edu †e-mail: annranqi1024@g.ecc. u-tokyo.ac. jp ‡e-mail: mauricio@dgp.toronto. edu §e-mail: tovi@dgp.toronto. edu artists. The results suggest that EnchantedBrush facilitates storytelling and communication, and utilizing the physical environment eases animation authoring and simplifies story creation.
Identifying Depressive Tweets using Natural Language Processing (NLP) Frameworks

The number of patients diagnosed with depression yearly is a growing concern among mental health advocates. Consequently, the ef ect of this ailment is detrimental to not only the patient but also family members, as well as their jobs or school. Many factors, ranging from hereditary conditions to life-altering experiences, can trigger depression, and symptoms vary between individuals. Hence, the disparity of symptoms in diagnosing depression makes it dif icult to identify early on. Fortunately, the prevalence of social media platforms has led to individuals posting updates on various aspects of their lives, particularly their mental health. These platforms now provide valuable data sources for mental health researchers, aiding in the timely diagnosis of depression. In this research, we use sentiment analysis to identify depressed tweets from random tweets. We used six natural language processing frameworks for our classification. They are BERT, XLNet, ALBERT, DeBERTa, RoBERTa, and ELECTRA. Our results show that BERT performs best with an accuracy of 99%, while ALBERT is the model with the lowest accuracy rate at 87%. This research shows that by leveraging NLP frameworks, we can successfully utilize machine learning for the early detection of depression and help diagnose individuals struggling with this ailment.
MAKE BETTER FOOD DECISIONS

People are often given options on restaurants to eat at and are also given the ratings of those restaurants. However, the ratings can sometimes be rather similar and hard to choose from, and it can also be hard to find a restaurant that suits a person’s special needs, and people often eat at a singular place once they find a good restaurant; we want to change that by trying to encourage people to try new restaurants. While our idea isn’t original, we still decided to add it to the list of probably hundreds of sites out there that do the same thing. We made a restaurant recommendation with one purpose in mind, to gather data from restaurants and share that data with everyone. We used many methods to get that data, create a user interface, and add that data to the site so everyone can use it. This project had originated from another idea for a Roblox sniping site which I was told was a bit too advanced and was suggested something similar in design. A restaurant recommender and a Roblox sniping site are similar in the way they both use web scraping. Web scraping is the ability of a website to get the code from other sites. Our restaurant recommender gets data from Yelp to add to our database in the way that a Roblox sniping site can get information from the Roblox catalog to display to people to see when there’s a good bargain. The restaurant recommender uses the data it gets to give recommendations for a better restaurant and it gives the 3 worst reviews on the restaurant, which are to highlight some of the potential flaws of the input restaurant. The main pieces of data the recommender gets are the restaurant genre for people to see what kind of restaurant it is, the restaurant region to see what type of food the restaurant serves, the restaurant type to see if its a bar or restaurant or what type it is, the restaurant’s overall rating to see how good the restaurant is, and the Yelp page of the restaurant, for a deeper look into the restaurant itself. We then use the data to get a better restaurant. We use the restaurant type, region, and genre to find a similar restaurant, and we use the rating to find a restaurant with a better rating. We used many libraries and coding languages to build our site. We used HTML and CSS to build the user interface, we used Python to run the server we were using and build the web scraper, and then we used csv for the database containing all the data. We used beautiful soup to organize the data, and we used requests to get user input. We used pandas for the data analysis and we used sklearn to build the predictor for a better restaurant.
MEDI-BERPT: A Novel Multitask Approach to Streamlining Chinese Healthcare

In the pursuit of revolutionizing the medical industry, Large Language Models (LLMs) have faced obstacles due to stringent quality requirements and numerous ethical concerns. Previous medical NLP approaches focused on building single-task models that were dedicated to simple classification or to generating doctor-like responses. Our study presents a multi-task trans-former encoder-decoder model that takes a patient’s symptom descriptions and generates predictions for both the appropriate medical department and an initial diagnosis or a follow-up question. The primary objective is to evaluate the extent to which the inclusion of a secondary task, specifically the doctor-diagnosis generation, enhances the performance of the medical department classification task. We leverage a dataset comprising 3 million doctor-patient conversations scraped from Chinese online medical forums (MedDialog) to fine-tune BERT and GPT-2 models, establishing baseline performances. Our findings demonstrate that incorporating a secondary objective enables the model to capture more nuanced relationships between the doctor’s response and the patient’s symptom description, consequently enhancing the medical department classification process. The proposed multi-objective transformer encoder-decoder model outperforms the original BERT encoder with an accuracy of 91% compared to 84% in predicting the top 10 most relevant medical departments. Nonethe-less, our analysis highlights significant limitations in attempting to generate a doctor’s response based solely on the patient’s symptom description, underscoring the importance of developing AI-assisted tools that support patients within the medical system, rather than seeking to replace human expertise.
Ethnicity, Gender,and Language: Comparing the Performance of Emotion Recognition Systems in Different Modalities (Non-verbal and Verbal) with Emphasis on Bengali Data

Emotions are the windows to the nonverbal communication framework employed by the animal kingdom since inception. Humanity is not an exception to this practice. Often, throughout history, people have concentrated on the verbal aspect of the communication but recently, with the advances in Machine Learning and Artificial Intelligence, researchers have tried to delve into the non-verbal realm to try detecting the emotions, their nuances and their role in effectively understanding communication.Teaching a machine that understands only bits, to gauge the emotions of a subject is the beauty of the modern Machine learning algorithms and frameworks. The technology is still at intermediate stages and needs further analysis and research on the same. This dissertation was aimed at evaluating the performance of the different emotion recognition systems across different modalities (facial expressions, audio and text). The study also analyzed how the emotion recognition systems performed in different contexts: posed vs spontaneous, ethnicity and gender. It also analyzed how the Emotion Recognition Systems(ERs) process the physical correlates or landmarks of the modalities (Action Units for face; F0,intensity, loudness, jitter,etc for audio). Similarly how the FERs process the action units were also analyzed. Both the Affectiva and Emotient reported dominant action units(AU) very similar to what has been reported in literature. Statistically significant differences were observed between the distribution of both the facial emotion recognition and speech systems.Differences were also observed in the system outputs when compared across categories like ethnicity and gender. The findings in this dissertation corroborated several studies on posed vs spontaneity, cross-culture variations. For the speech emotion recognition systems it was observed that the inter-annotator agreement rapidly decreased when the systems were trained on databases they have not seen before.For Automatic Speech Recognition (or ASR), it has been observed that Desript outperformed Google Cloud as the preferred service where GCP showed a Word Error Rate of about 80 percent. On the other hand Descript WER rate was around 28 percent. A 3-lexicon system was used to properly extract sentiments from the transcripts and the transcripts were also analyzed across four levels with a bag of words model. The dissertation also aims to introduce a preliminary model of multi-modal emotion detection and fusion as a part of future work
On the virtual waiting time in an M/G/1 retrial queue

This paper deals with the stationary distribution of the virtual waiting time, i.e. the time between the arrival and the beginning of service of a customer in a single-server queue that operates as follows. If the server is busy at an arrival time, the customer is rejected. This customer attempts service again after some random delay and continues to do so until the first time at which the server is idle. At this time, the customer is served and leaves the system after service completion. Interarrival times and delays are assumed to be two independent sequences of i.i. d. exponentially distributed random variables. Service times are also i.i. d. , generally distributed, and independent of the previous sequences.
In-Timestep Remeshing for Contacting Elastodynamics

We propose In-Timestep Remeshing, a fully coupled, adaptive meshing algorithm for contacting elastodynamics where remeshing steps are tightly integrated, implicitly, within the timestep solve. Our algorithm refines and coarsens the domain automatically by measuring physical energy changes within each ongoing timestep solve. This provides consistent, degree-of-freedom-efficient, productive remeshing that, by construction, is physics-aware and so avoids the errors, over-refinements, artifacts, per-example hand-tuning, and instabilities commonly encountered when remeshing with timestepping methods. Our in-timestep computation then ensures that each simulation step's output is both a converged stable solution on the updated mesh and a temporally consistent trajectory with respect to the model and solution of the last timestep. At the same time, the output is guaranteed safe (intersection- and inversion-free) across all operations. We demonstrate applications across a wide range of extreme stress tests with challenging contacts, sharp geometries, extreme compressions, large timesteps, and wide material stiffness ranges - all scenarios well-appreciated to challenge existing remeshing methods.
Exploring Strategies for Improved Performance in Multi-Task Learning with Pretrained-BERT

We proposes a multi-task learning approach utilizing pretrained-BERT for Sentiment Analysis, Paraphrase Detection, and Semantic Textual Similarity. The approach involves a three-step process: BERT Further Pre-training, Classifier Pre-training, and Multi-Task Fine-tuning. To improve performance on the target tasks, two strategies were implemented: advanced model structures and further pretraining BERT with task-specific datasets. Experimental results demonstrate that pretraining BERT with in-domain data significantly enhances performance on target tasks while maintaining performance on other tasks. Moreover, the use of a complex classification head did not enhance target task performance without sufficient training data.
Does Learning Syntax Help Models Learn Language?

Papadimitriou and Jurafsky (2020) showed that LSTMs trained on nonlinguistic structural data performed significantly better than random baselines when evaluated zero-shot on language tasks, which suggests that models can learn generalizable structure independent of specific vocabularies. In this paper, I replicate that finding for transformer models and introduce a new synthetic corpus that captures a different type of structure (i
India at 75: Replete with Contradictions, Brimming with Opportunities, Saddled with Challenges

ABSTRACT: I present a perspective on where the Indian economy stands right now. I acknowledge the contradictions that have arisen given the divergent growth path of urban, formal or (stock-market) listed India relative to rural, informal or unlisted India. I also focus on the country's immense opportunities in expanding the digital footprint of finance to last-mile borrowers. I present novel facts on the rising industrial concentration, drawing out its historical evolution, the channels that have caused it to rise recently, and its implications for product price markups and inflation. I recommend that to restore industrial balance, India increase overall competition by reducing import tariffs and reduce the pricing power of its largest conglomerates. I also propose that to restore macroeconomic balance, India reduce fiscal deficit and public sector borrowing requirements as well as rein in inflation, address gaps in skills and education, and restore female labor force participation.
Defining a Cyberinfrastructure for Plasma Science and Space Weather Simulations

This paper presents a novel cyberinfrastructure designed to facilitate advanced plasma and space weather simulations across various high performance computing platforms through a user friendly web-based interface. The interface offers a comprehensive, open source solution with workflow control that enables the modeling of plasma systems from terrestrial magnetospheres to laboratory experiments in weakly collisional or collisionless plasma regimes. The discussion covers the supported use cases that cater to a diverse user community, an overview of the cyberinfrastructure software components, and community building efforts.
Interactively generating 3D models from contour drawings

We introduce a system for generating 3D free-form models from contour sketches. Our proof-of-concept system executes several steps: classi(cid:12)cation of contours, completion of hidden contours, and inﬂation. These take us from a 2D contour drawing to a 3D mesh matching this contour. The main focus of this work was on so-called cusp and tee hidden contours. We can handle several classes of drawings, and the shapes returned by the system look like plausible interpretations of the sketches.
Contrastive Learning for Generalizable Sentence Embeddings

Recent works have proven contrastive learning an effective framework for producing generalizable sentence embeddings. In this project, supervised and unsupervised contrastive training is explored in conjunction with task-specific supervision to produce high-quality sentence embeddings for multiple tasks: sentiment classification, paraphrase detection, and semantic textual similarity prediction. In unsupervised settings, a fusion training approach is adopted, where the same batch of STS data is trained first against the supervised classification objective and then against the unsupervised contrastive objective. It leads to the best overall score of 0.520 on the dev sets, among all unsupervised methods explored in this project. In supervised settings, the paraphrases in the Quora dataset are used as positive pairs. During task-specific fine-tuning, it is helpful to add MLP networks on top of BERT embeddings as predition heads, while sharing the first layer between paraphrase and STS tasks. Multi-task average loss turns out to work the best, as compared to alternative approaches like gradient surgery or round robin training. Finally, A model ensemble combines the best-performing models on each single task and give the best results on the test sets: a sentiment classification accuracy of 0.521, a paraphrase detection accuracy of 0.830, a semantic textual similarity of 0.753, and an overall accuracy of 0.701. In summary, contrastive learning allows flexible construction of instance pairs from various kinds of datasets. It is demonstrated to be an effective pre-training method for sentence embeddings across different tasks.
Exploring how Determinants of the Gender Wage Gap in the United States have Changed Women’s Wages over time, Highlighting Changes During the COVID-19 Period

Using IPUMS Current Population Survey (CPS) and CPS Annual Social and Economic Supplement (ASEC) microdata, new empirical evidence is presented on the changing levels and trends in the gender wage gap from 2009 to 2022, including a COVID-19 specific analysis. Based on the empirical research study performed in this paper, three main conclusions can be identified. First, when controlling for standard wage explanatory variables, the gender wage gap reduced during COVID-19, as women increased their wages by 3.12% relative to men. This is opposed to predictions from Alon et al. (2020) who projected the wage premium for men would rise during COVID-19. Second, we find that the gender wage gap is primarily driven by marital status, followed closely by child status. Married women have the largest wage gap out of the tested factors, with single women having 19% higher wages when controlling for married women, and childless women having 15% higher wages when controlling for women with children of any age. Finally, we see that the growth in the gender wage gap during COVID-19 was primarily caused by child status compared to marital status, as the wage gap fell more for women with no children than for single women. Future research will be proposed for expansions of the empirical study in this paper.
Exploring the Spectrum of Mask Supervision for Unpaired Image-to-Image Translation Aaron Gokaslan

Current unsupervised image-to-image translation techniques have proven to struggle when faced with domains exhibiting signiﬁcant shape changes. Recently proposed approaches enable this property but require instance-level segmentations which limit their domain applicability due to the cost of acquiring this supervision. We explore and analyze the effect of different forms of supervised masks for image-to-image translation under signiﬁcant shape change. We show qualitatively and quantitatively that imprecise su-pervisions can produce appealing results, sometimes comparable to the full-supervision case.