Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Sources of Healthcare Associated Infections

Healthcare acquired infections (HAIs) (e.g., Methicillin-resistant Staphylococcus aureus infection) have complex transmission pathways, spreading not just via direct person-to-person contacts, but also via contaminated surfaces. Prior work in mathematical epidemiology has led to a class of models – which we call load sharing models – that provide a discrete-time, stochastic formalization of HAI-spread on temporal contact networks. The focus of this paper is the source detection problem for the load sharing model. The source detection problem has been studied extensively in SEIR type models, but this prior work does not apply to load sharing models. We show that a natural formulation of the source detection problem for the load sharing model is computationally hard, even to approximate. We then present two alternate formulations that are much more tractable. The tractability of our problems depends crucially on the submodularity of the expected number of infections as a function of the source set. Prior techniques for showing submodularity, such as the "live graph" technique are not applicable for the load sharing model and our key technical contribution is to use a more sophisticated "coupling" technique to show the submodularity result. We propose algorithms for our two problem formulations by extending existing algorithmic results from submodular optimization and combining these with an expectation propagation heuristic for the load sharing model that leads to orders-of-magnitude speedup. We present experimental results on temporal contact networks based on fine-grained EMR data from three different hospitals. Our results on synthetic outbreaks on these networks show that our algorithms outperform baselines by up to 5.97 times. Furthermore, case studies based on hospital outbreaks of Clostridioides difficile infection show that our algorithms identify clinically meaningful sources.
Model Predictive Control of Linear Induction Motor Drive with End Effect Consideration

Article History: Received 28 June 2022 Reviewed 29 July 2022 Revised 01 August 2022 Accepted 22 October 2022 Background and Objectives: Linear Induction Motors (LIMs) are favorite machines utilized in various industrial applications. But, due to the end effect phenomena, control of a LIM drive is more complicated than rotational machine drives. Therefore, selecting the proper control strategy for a LIM drive has been a significant challenge for the researchers. Methods: This paper concentrates on a new Model Predictive Control (MPC) of LIM drives which considers the end effect. Accordingly, the discrete-time model of the LIM with end effect is extracted, and the required flowchart used for the MPC of LIM drive has been presented in this paper. Results: To study the effectiveness of the suggested strategy, simulation results of a LIM drive with MPC are presented and compared to the traditional Indirect Field Oriented Control (IFOC) of LIM drive. Simulations have been carried out using Matlab. The end effect has been considered in the LIM model and control strategies. Conclusion: Simulation results validate that the suggested MPC of LIM drive yields excellent dynamic characteristics such as fast speed response with no overshoot. Moreover, in comparison to the traditional IFOC method, the suggested MPC strategy offers lower current ripple and lower electromagnetic force ripple, and therefore, it is suitable for industrial drive applications.
Using Named Entity Recognition to Supplement The Ocean Cleanup’s Global Beached Plastics Dataset

This project seeks to take advantage of recently emerging NLP capabilities and the proliferation of social media and internet news outlet text data to assist the global plastic modeling and cleanup efforts of The Ocean Cleanup. For this project, we built a Named Entity Extraction (NER) tool that, when provided unstructured text data about beach cleanup efforts from various internet sources, can identify and extract key data points from the text and transform them into a tabular format that is usable by The Ocean Cleanup for downstream analysis tasks. We experimented with several NER model architectures, fine-tuning them to perform on our task. Our best model is able to identify key data types for trash collection with an F1 score of 94.75, which is comparable to existing state-of-the-art NER model performance. This result gives us confidence that our tool can be used to reliably extract high-quality data from raw internet text for use by The Ocean Cleanup.
Optimizing Multi-Task Classification Finetuning in BERT: a Multi-Pronged Approach

The development of BERT (Devlin et al., 2018) was a state-of-the-art performance improvement in several natural language processing tasks. In this paper, our intent was to extend this performance to multitask use cases: paraphrase detection, semantic textual similarity, and sentiment analysis. In exploring improvements to our model we focus on six distinct optimizations: multitask finetuning for training all 3 tasks together with an aggregated loss function, using cosine similarity on the comparison task of predicting similarity, using ReLU layers in our architecture to improve performance, parameter optimizations for dropout and weight decay distinctly chosen to address fitting issues in different tasks, loss coefficient refinement to reweight training emphasis, and reworking attention to include linear biases and slope optimizations. Our primary findings were that cosine similarity layered with ReLU activation improved accuracy in comparison tasks, multitask finetuning massively increased performance when performing multitask learning, and that tuning dropout and loss function coefficients effectively combated overfitting and increased overall accuracy in multitask systems. We also found that more work must be done on linear biases in attention calculations to have them improve results in models like BERT.
The
possibility of predicting the performance of advanced ski elements based on the
performance of basic ski elements

The aim of this research was to predict the success of the performance of advanced ski elements based on the level of performance of basic ski elements, with the ultimate purpose of improving training programmes for alpine ski beginners. The sample of participants consisted of 250 students of the Faculty of Kinesiology University of Zagreb who attended their mandatory classes of the university study subject of Skiing in the academic year 2021/22. The sample of variables consisted of the grades the students earned for their performance of four exam elements, two basic ski elements and the other two belonging to the advanced ski elements group. The basic ski elements were uphill turn (UT), for which the mean of two grades was taken for further analysis due to its performance to both sides – left and right, and snowplough turn (SPT). Advanced ski elements were parallel turn (PT) and short turn (ST). Besides descriptive statistics for each variable, two ordinal logistic regression models were constructed for determining the relationship between the performance of short turn based on the level of performance of snowplough turn, on the one hand, and on the other, between the performance of parallel turn based on the performance of uphill turn. The results of the first ordinal logistic regression analysis (short turn performance based on snowplough performance) showed the existence of a statistically significant linear association of the two ski elements performance (b=2.15, SE=0.72, p=.03). Similar results were obtained in the second ordinal logistic regression analysis for the other pair of ski elements (parallel turn performance based on uphill turn performance): b=2.36, SE=0.24, p<. 001. Well acquired dynamic motor stereotypes, reflected in good performance of basic ski elements, are the basis for good performance of advanced ski elements, therefore skipping any step in the teaching process based on progression could have a negative impact on achieving the desired goals.
T ������� : Isolated, Transparent & Confidential Execution of Arbitrary Code in ARM’s TrustZone

Protecting the con � dentiality of applications on commodity operating systems, both on desktop and mobile devices, is challenging: attackers have unrestricted control over an application’s processes and thus direct access to any of the application’s assets. However, the application’s code itself can be of great commercial value, for example in the case of proprietary code or additional functionality obtained as downloadable content and via in-app purchases, which are widely used to monetize free applications through premium content. Developers still rely heavily on obfuscation to protect their own code from unauthorized tampering or copying, providing an obstacle for an attacker, but not preventing compromise. In this paper, we present T ������� , an approach to o � er a practical and transparent primitive to implement code con � dentiality by extending ARM’s TrustZone, a TEE that so far provides limited functionality to application developers. T ������� allows developers to easily designate part of their code as con � dential through source code annotations. At compile time, T ������� automatically partitions the application into regular application code, executed in the “normal world,” and the invisible code , transparently executed in the “secure world. ” T ������� tightly couples and secures
Hate Speech Detection Using Natural Language Processing

There is a pronounced usage of Hate Speech in Online Social Networks, with potential harm to society and targeted hatred towards communities and individuals. While automated AI based Hate Speech detection mechanisms are in place in these online platforms, detection and classification of Hate Speech remains a hard problem with high false positive rates, wherein offensive but non-hate content is incorrectly classified as hate speech, along with new hate words and variants introduced constantly. In this paper, we tackle the problem of Hate Speech Detection by comparing the ternary (hate, offensive, non-hate) classification performance and model complexity of four different Deep Neural Network models - CNN (baseline), Bi-Directional LSTM with Attention, Pretrained BERT and Finetuned RoBERTa Transformer models - with Accuracy, F1-score and Matthews Correlation Coefficient (MCC) metrics on the validation set. While the baseline CNN model achieves an accuracy of 0.77, the Bi-Directional LSTM, BERT and finetuned RoBERTa models achieve an accuracy of 0.90-0.92. Further, the best MCC performance is achieved by the Transformer models, with pretrained BERT and finetuned RoBERTa models scoring at 0.75 and 0.76 respectively.
Weight-Balancing Fixes and Flows for Deep Learning

Feedforward neural networks with homogeneous activation functions possess an internal symmetry: the functions they compute do not change when the incoming and outgoing weights at any hidden unit are rescaled by reciprocal positive values. This paper makes two contributions to our understanding of these networks. The first is to describe a simple procedure, or fix , for balancing the weights in these networks: this procedure computes multiplicative rescaling factors—one at each hidden unit—that rebalance the weights of these networks without changing the end-to-end functions that they compute. Specifically, given an initial network with arbitrary weights, the procedure determines the functionally equivalent network whose weight matrix is of minimal ℓ p,q -norm; the weights at each hidden unit are said to be balanced when this norm is stationary with respect to rescaling transformations. The optimal rescaling factors are computed in an iterative fashion via simple multiplicative updates, and the updates are notable in that (a) they do not require the tuning of learning rates, (b) they operate in parallel on the rescaling factors at all hidden units, and (c) they converge monotonically to a global minimizer of the ℓ p,q -norm. The paper’s second contribution is to analyze the optimization landscape for learning in these networks. We suppose that the network’s loss function consists of two terms—one that is invariant to rescaling transformations, measuring predictive accuracy, and another (a regularizer) that breaks this invariance, penalizing large weights. We show how to derive a weight-balancing flow such that the regularizer remains minimal with respect to rescaling transformations as the weights descend in the loss function. These dynamics reduce to an ordinary gradient flow for ℓ 2 -norm regularization, but not otherwise. In this way our analysis suggests a canonical pairing of alternative flows and regularizers.
Avgust: A Tool for Generating Usage-Based Tests from Videos of App Executions

Creating UI tests for mobile applications is a difficult and time-consuming task. As such, there has been a considerable amount of work carried out to automate the generation of mobile tests-largely focused upon the goals of maximizing code coverage or finding crashes. However, comparatively fewer automated techniques have been proposed to generate a highly sought after type of test: usage-based tests. These tests exercise targeted app functionalities for activities such as regression testing. In this paper, we present the Avgusttool for automating the construction of usage-based tests for mobile apps. Avgustlearns usage patterns from videos of app executions collected by beta testers or crowd-workers, translates these into an app-agnostic state-machine encoding, and then uses this encoding to generate new test cases for an unseen target app. We evaluated Avguston 374 videos of use cases from 18 popular apps and found that it can successfully exercise the desired usage in 69% of the tests. Avgustis an open-source tool available at https://github.com/felicitia/UsageTesting-Repo/tree/demo. A video illustrating the capabilities of Avgustcan be found at: https://youtu.be/LPICxVd0YAg
Mechanisms and Model-Based Functional Magnetic Resonance Imaging

Mechanistic explanations satisfy widely held norms of explanation: the ability to manipulate and answer counterfactual questions about the explanandum phenomenon. A currently debated issue is whether any nonmechanistic explanations can satisfy these explanatory norms. Weiskopf argues that the models of object recognition and categorization, JIM, SUSTAIN, and ALCOVE, are not mechanistic yet satisfy these norms of explanation. In this article I argue that these models are mechanism sketches. My argument applies recent research using model-based functional magnetic resonance imaging, a novel neuroimaging method whose significance for current debates on psychological models and mechanistic explanation has yet to be explored.
Multi Distribution Dense Information Retrieval

In this project, we propose the novel problem of multi-distribution information retrieval where given a query we need to retrieve passages from different corpora, each drawn from a different distribution. Some of these distributions might not be known at train time. This is a very natural setting that arises for queries such as "Is Mbappé younger than me? " which require retrieval from public and private data sources – personal information, medical data or data private to organizations. Due to the lack of existing benchmarks on this novel setting, we design benchmarks for this task by adapting existing multi-hop question answering and entity matching datasets. Specifically, we create one question answering and two entity matching based datasets for evaluating multi-distribution retrieval. We propose simple methods for this task which allocate the fixed retrieval budget (top-k passages) strategically across domains to prevent the known domains from consuming most of the budget. We show that our methods lead to 8+ point improvements in Recall@100 over three datasets and that improvements are consistent when fine-tuning different base models. 1
CAPatch: Physical Adversarial Patch against Image Captioning Systems

The fast-growing surveillance systems will make image cap-tioning, i.e. , automatically generating text descriptions of images, an essential technique to process the huge volumes of videos efﬁciently, and correct captioning is essential to ensure the text authenticity. While prior work has demonstrated the feasibility of fooling computer vision models with adversarial patches, it is unclear whether the vulnerability can lead to incorrect captioning, which involves natural language processing after image feature extraction. In this paper, we design CAPatch , a physical adversarial patch that can result in mistakes in the ﬁnal captions, i.e. , either create a completely different sentence or a sentence with keywords missing, against multi-modal image captioning systems. To make CAPatch effective and practical in the physical world, we propose a detection assurance and attention enhancement method to increase the impact of CAPatch and a robustness improvement method to address the patch distortions caused by image printing and capturing. Evaluations on three commonly-used image captioning systems (Show-and-Tell, Self-critical Sequence Training: Att2in, and Bottom-up Top-down) demonstrate the effectiveness of CAPatch in both the digital and physical worlds, whereby volunteers wear printed patches in various scenarios, clothes, lighting conditions. With a size of 5% of the image, physically-printed CAPatch can achieve continuous attacks with an attack success rate higher than 73.1% over a video recorder.
HEART DISEASE PREDICTION USING MACHINE LEARNING AND DEEP LEARNING

Heart disease is most common disease reported currently in the United States among both the genders and according to official statistics about fifty percent of the American population is suffering from some form of cardiovascular disease. This paper performs chi square tests and linear regression analysis to predict heart disease based on the symptoms like chest pain and dizziness. This paper will help healthcare sectors to provide better assistance for patients suffering from heart disease by predicting it in beginning stage of disease. Chi square test is conducted to identify whether there is a relation between chest pain and heart disease cases in the United States by analyzing heart disease dataset from IEEE Data Port. The test results and analysis show that males in the United States are most likely to develop heart disease with the symptoms like chest pain, dizziness, shortness of breath, fatigue, and nausea. This test also shows that there is a week corelation of 0.5 is identified which shows people with all ages including teens can face heart diseases and its prevalence increase with age. Also, the tests indicate that 90 percent of the participant who are facing severe chest pain is suffering from heart disease where majority of the successful heart disease identified is in males and only 10 percent participants are identified as healthy. The evaluated p-values are much greater than the statistical threshold of 0.05 which concludes factors like sex, Exercise angina, Cholesterol, old peak, ST_Slope, obesity, and blood sugar play significant role in onset of cardiovascular disease. We have tested the dataset with prediction model built on logistic regression and observed an accuracy of 85.12 percent.
Haiku Generation with Large Language Models

Generating poetry and other creative open-ended texts has been a long-standing challenge in the field of Natural Language Processing. In this paper, we present an approach to fine-tuning Large Language Models, specifically OpenAI’s GPT-3 and Meta’s OPT-125M, to generate haikus, a traditional form of Japanese poetry that consists of three lines and a total of 17 syllables. Our goal is to generate haikus that not only follow the traditional syllabic structure but also convey poetic imagery and evoke an emotional response in the reader. We evaluate our approach based on perplexity metrics in addition to qualitative human evaluation. We believe that our approach opens up new possibilities for using Large Language Models to generate creative and emotionally resonant text; our results show us that while it can be difficult for models to learn the structure of the haiku, they are surprisingly poetic after being fine-tuned on tens of thousands of haikus and using certain sampling and prompting techniques
Automating English Language Proficiency Assessments

We have seen the proliferation in English proficiency assessments for non-native speakers–especially in writing and speaking–for academic and professional opportunities. While human graders have been the gold standard for evaluation, automated tools provide a scalable, objective alternative. We propose transformer models for written and spoken proficiency (based on DistilBERT and Wav2Vec2, respectively) finetuned on datasets from ETS, ICNALE, ELL, and Speechocean. We evaluated various models for both essay and speech assessments and found that simple models built on top of the transformers perform best. Our best models achieve 0.4254 and 0.5718 test R 2 for the written and spoken assessment total scores, respectively. Our predicted written and spoken assessment scores have correlations of 0.66 and 0.77 with the corresponding human-graded scores. The results of our study suggest that transformer models have the potential to be a reliable and efficient alternative to human graders for English proficiency assessments.
Unitary Scalarization or Gradient Surgery? Best Practices for Multitask Fine-Tuning

In an effort to build efficient deep learning networks which can be broadly applied to a given field of tasks, many researchers have turned to multitask learning (MTL), where a network is simultaneously trained on multiple tasks with their own loss functions. A simple and efficient way to perform MTL is unitary scalarization, where the multitask loss function is a weighted sum of the loss functions for individual tasks. Some research suggests that more complex multitask optimization methods, such as gradient surgery, lead to more reliable learning by reducing destructive interference between the gradients of different tasks. However, others argue that these methods are unneeded and any observed benefit is just a regularization effect, which can be achieved through more efficient methods. In this work, we extend past research on this topic (which has primarily been performed for image recognition tasks) to the context of natural language processing. By finetuning BERT sentence embeddings on three simple downstream tasks, using both unitary scalarization and gradient surgery, we find that gradient surgery provides minimal benefit over simple unitary scalarization, making it difficult to justify the additional cost in time and memory. However, it may lead to faster learning when model architecture is such that similar embedding features must simultaneously be applied to two different tasks.