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Articles 1 - 30 of 315

Full-Text Articles in Physical Sciences and Mathematics

Partial Annotation-Based Video Moment Retrieval Via Iterative Learning, Wei Ji, Renjie Liang, Lizi Liao, Hao Fei, Fuli Feng Nov 2023

Partial Annotation-Based Video Moment Retrieval Via Iterative Learning, Wei Ji, Renjie Liang, Lizi Liao, Hao Fei, Fuli Feng

Research Collection School Of Computing and Information Systems

Given a descriptive language query, Video Moment Retrieval (VMR) aims to seek the corresponding semantic-consistent moment clip in the video, which is represented as a pair of the start and end timestamps. Although current methods have achieved satisfying performance, training these models heavily relies on the fully-annotated VMR datasets. Nonetheless, precise video temporal annotations are extremely labor-intensive and ambiguous due to the diverse preferences of different annotators.Although there are several works trying to explore weakly supervised VMR tasks with scattered annotated frames as labels, there is still much room to improve in terms of accuracy. Therefore, we design a new …

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Adapting The Adapters For Code-Switching In Multilingual Asr, Atharva Kulkarni, Ajinkya Kulkarni, Miguel Couceiro, Hanan Al Darmaki Oct 2023

Adapting The Adapters For Code-Switching In Multilingual Asr, Atharva Kulkarni, Ajinkya Kulkarni, Miguel Couceiro, Hanan Al Darmaki

Natural Language Processing Faculty Publications

Recently, large pre-trained multilingual speech models have shown potential in scaling Automatic Speech Recognition (ASR) to many low-resource languages. Some of these models employ language adapters in their formulation, which helps to improve monolingual performance and avoids some of the drawbacks of multi-lingual modeling on resource-rich languages. However, this formulation restricts the usability of these models on code-switched speech, where two languages are mixed together in the same utterance. In this work, we propose ways to effectively fine-tune such models on code-switched speech, by assimilating information from both language adapters at each language adaptation point in the network. We also …

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Deep Weakly-Supervised Anomaly Detection, Guansong Pang, Chunhua Shen, Huidong Jin, Anton Van Den Hengel Aug 2023

Deep Weakly-Supervised Anomaly Detection, Guansong Pang, Chunhua Shen, Huidong Jin, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples only (i.e., seen anomalies), and consequently they fail to generalize to those that are not, i.e., new types/classes of anomaly unseen during training. To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two …

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Few-Shot Event Detection: An Empirical Study And A Unified View, Yubo Ma, Zehao Wang, Yixin Cao, Aixin Sun Jul 2023

Few-Shot Event Detection: An Empirical Study And A Unified View, Yubo Ma, Zehao Wang, Yixin Cao, Aixin Sun

Research Collection School Of Computing and Information Systems

Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e.g., various motivations, tasks, and experimental settings, that hinder the understanding of models for future progress. This paper presents a thorough empirical study, a unified view of ED models, and a better unified baseline. For fair evaluation, we compare 12 representative methods on three datasets, which are roughly grouped into prompt-based and prototype-based models for detailed analysis. Experiments consistently demonstrate that prompt-based methods, including ChatGPT, still significantly trail prototype-based methods in terms of overall performance. To investigate their superior performance, we break down their design elements along …

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Plan-And-Solve Prompting: Improving Zero-Shot Chain-Of-Thought Reasoning By Large Language Models, Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim Jul 2023

Plan-And-Solve Prompting: Improving Zero-Shot Chain-Of-Thought Reasoning By Large Language Models, Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning demonstrations which enable LLMs to explicitly generate reasoning steps and improve their reasoning task accuracy. To eliminate the manual effort, Zeroshot-CoT concatenates the target problem statement with “Let’s think step by step” as an input prompt to LLMs. Despite the success of Zero-shot-CoT, it still suffers from three pitfalls: calculation errors, missing-step errors, and semantic misunderstanding errors. To address the missing-step errors, we propose Planand-Solve (PS) Prompting. It …

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Handling Realistic Label Noise In Bert Text Classification, Maha Tufail Agro, Hanan Al Darmaki May 2023

Handling Realistic Label Noise In Bert Text Classification, Maha Tufail Agro, Hanan Al Darmaki

Natural Language Processing Faculty Publications

Label noise refers to errors in training labels caused by cheap data annotation methods, such as web scraping or crowd-sourcing, which can be detrimental to the performance of supervised classifiers. Several methods have been proposed to counteract the effect of random label noise in supervised classification, and some studies have shown that BERT is already robust against high rates of randomly injected label noise. However, real label noise is not random; rather, it is often correlated with input features or other annotator-specific factors. In this paper, we evaluate BERT in the presence of two types of realistic label noise: feature-dependent …

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High-Performance Domain-Specific Library For Hydrologic Data Processing, Kalyan Bhetwal May 2023

High-Performance Domain-Specific Library For Hydrologic Data Processing, Kalyan Bhetwal

Boise State University Theses and Dissertations

Hydrologists must process many gigabytes of data for hydrologic simulations, which takes time and resources degrading performance. The performance issues are caused mainly by domain scientists’ preference for using Python, which trades performance for productivity. In my thesis, I demonstrate that using the static compilation technique to compile Python to generate C code along with several optimizations reduces time and resources for hydrologic data processing. I developed a Domain Specific Library (DSL) which is a subset of Python and compiles to Sparse Polyhedral Framework - Intermediate Representation (SPF-IR), which allows opportunities for optimizations like read reduction fusion which are not …

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Mixed Fattening Of Steers And Lambs On Improved Grasslands In Uruguay: Ii. Animal Performance And Productivity, D. F. Risso, Fabio Montossi, E. J. Berretta, R. Cuadro, I. De Barbieri, R. San Julián, A. Dighiero, A. Zarza Apr 2023

Mixed Fattening Of Steers And Lambs On Improved Grasslands In Uruguay: Ii. Animal Performance And Productivity, D. F. Risso, Fabio Montossi, E. J. Berretta, R. Cuadro, I. De Barbieri, R. San Julián, A. Dighiero, A. Zarza

IGC Proceedings (1997-2023)

In cow-calf operations in Uruguay, mixed cattle and sheep grazing on rangelands is predominant, while fattening is a specialised process. Within certain limits of the lamb/steer ratio and stocking rate, a complementary grazing effect occurs under mixed grazing, improving net results (Nolan & Connolly, 1977; Risso et al., 2002). These trials characterise animal performance under such management.

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Heart: Motion-Resilient Heart Rate Monitoring With In-Ear Microphones, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Mascolo Mar 2023

Heart: Motion-Resilient Heart Rate Monitoring With In-Ear Microphones, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Mascolo

Research Collection School Of Computing and Information Systems

With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate (HR) detection systems. HR is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable HR monitoring with wearable devices has therefore gained increasing attention in recent years. Existing HR detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient HR monitoring. …

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Farm Performance From Holstein-Friesian Cows Of Three Genetic Strains On Grazed Pasture, K. A. Macdonald, B. S. Thorrold, C. B. Glassey, J. A. S. Lancaster, G. A. Verkerk, J. E. Pryce, C. W. Holmes Feb 2023

Farm Performance From Holstein-Friesian Cows Of Three Genetic Strains On Grazed Pasture, K. A. Macdonald, B. S. Thorrold, C. B. Glassey, J. A. S. Lancaster, G. A. Verkerk, J. E. Pryce, C. W. Holmes

IGC Proceedings (1997-2023)

Dairy selection objectives and farm production systems in USA and Europe are different from those in New Zealand (NZ). The use of overseas semen in NZ in the last 20 years has changed the genetics of the former NZ Holstein-Friesian (HF) strain. This trial was designed to demonstrate the genetic progress in the NZ HF dairy herd in the last 25 years and how high production potential North American HF cows perform under pasture-based feeding systems.

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Effects Of Rumen Fill On Intake And Milk Production In Dairy Cows Fed Perennial Ryegrass, A. V. Chaves, A. Boudon Feb 2023

Effects Of Rumen Fill On Intake And Milk Production In Dairy Cows Fed Perennial Ryegrass, A. V. Chaves, A. Boudon

IGC Proceedings (1997-2023)

Physical limitation often limits dry matter intake (DMI) of high producing cows or cows fed high forage diets. The extent to which DMI is regulated by distention in the rumen depends upon the cow’s energy requirement and filling effects of the diet offered (Allen, 2000). The objective here was to challenge middle lactation dairy cows with rumen fill (rumen inert bulk – RIB) feeding ryegrass fresh cut (indoors) or grazed to determine whether RIB affects intake and milk production.

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Performance Of Meat Goats Grazing Winter Annual Grasses In The Piedmont Of The Southeastern Usa, J-M. Luginbuhl, J. P. Mueller Feb 2023

Performance Of Meat Goats Grazing Winter Annual Grasses In The Piedmont Of The Southeastern Usa, J-M. Luginbuhl, J. P. Mueller

IGC Proceedings (1997-2023)

In the Southeastern United States, meat goats (Capra hircus hircus) are becoming increasingly important contributors to the income of many small producers. Meat goats perform well in grazing situations if grazing management practices match their grazing behavior. Nevertheless, little research data are available from the region specifically directed toward forage feeding programs for goats reared for meat production. Hart et al. (1993) reported that growing Alpine, Angora and Nubian kids grazed on high quality Triticum aestivum forage gained 50 g/d, whereas Kiesling et al. (1994) reported gains ranging from 65 to 141 g/d in growing Angora …

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Performance Analysis Of Zero Trust In Cloud Native Systems, Simone Rodigari Jan 2023

Performance Analysis Of Zero Trust In Cloud Native Systems, Simone Rodigari

Theses

Critical applications demand strong security implementations, low latency and high availability at constant rates, however, the performance of a software system is affected by the implementation of security. This research measures the performance overhead and possible mitigation in cloud native systems secured with a service mesh, which allows enabling security policies for the authentication, authorization and encryption of traffic within distributed systems. The side-car proxy is a core component of this architecture, acting as a policy enforcement point and intercepting networking communication from/to applications part of the mesh, consequently affecting the performance of applications hosted in the cloud. Physical resources …

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Optimization Of Ported Cfd Kernels On Intel Data Center Gpu Max 1550 Using Oneapi Esimd, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Christoph Bauinger, Xiao Zhu Jan 2023

Optimization Of Ported Cfd Kernels On Intel Data Center Gpu Max 1550 Using Oneapi Esimd, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Christoph Bauinger, Xiao Zhu

Computer Science Faculty Publications

We describe our experience porting FUN3D’s CUDA-optimized kernels to Intel oneAPI SYCL.We faced several challenges, including foremost the suboptimal performance of the oneAPI code on Intel’s new data center GPU. Suboptimal performance of the oneAPI code was due primarily to high register spills, memory latency, and poor vectorization. We addressed these issues by implementing the kernels using Intel oneAPI’s Explicit SIMD SYCL extension (ESIMD) API. The ESIMD API enables the writing of explicitly vectorized kernel code, gives more precise control over register usage and prefetching, and better handles thread divergence compared to SYCL. The ESIMD code outperforms the optimized SYCL …

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Comparative Analysis Of Fullstack Development Technologies: Frontend, Backend And Database, Qozeem Odeniran Jan 2023

Comparative Analysis Of Fullstack Development Technologies: Frontend, Backend And Database, Qozeem Odeniran

Electronic Theses and Dissertations

Accessing websites with various devices has brought changes in the field of application development. The choice of cross-platform, reusable frameworks is very crucial in this era. This thesis embarks in the evaluation of front-end, back-end, and database technologies to address the status quo. Study-a explores front-end development, focusing on angular.js and react.js. Using these frameworks, comparative web applications were created and evaluated locally. Important insights were obtained through benchmark tests, lighthouse metrics, and architectural evaluations. React.js proves to be a performance leader in spite of the possible influence of a virtual machine, opening the door for additional research. Study b …

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The Planets, Reimagined: Translating Science Into Music, Kaitlyn Wincup Dec 2022

The Planets, Reimagined: Translating Science Into Music, Kaitlyn Wincup

Honors Projects

Inspired by Gustav Holst’s The Planets, this project analyzed the qualitative properties of the planets in our Solar System and translated them into a composition, created by Connor Gibbs, to represent an overall aural depiction of each planet. Where Holst created an astrological depiction of each of the planets, this piece is an astronomical depiction that broadens the perspectives of its listeners.

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Visual Object Tracking With Discriminative Filters And Siamese Networks: A Survey And Outlook, Sajid Javed, Martin Danelljan, Fahad Shahbaz Khan, Muhammad Haris Khan, Michael Felsberg, Jiri Matas Oct 2022

Visual Object Tracking With Discriminative Filters And Siamese Networks: A Survey And Outlook, Sajid Javed, Martin Danelljan, Fahad Shahbaz Khan, Muhammad Haris Khan, Michael Felsberg, Jiri Matas

Computer Vision Faculty Publications

Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress. Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in …

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An Attempt To Develop A Measurement Tool For Interpretation Performance Of Tourist Guides, Gizem Capar, Dilek Atci Oct 2022

An Attempt To Develop A Measurement Tool For Interpretation Performance Of Tourist Guides, Gizem Capar, Dilek Atci

University of South Florida (USF) M3 Publishing

The search for different experiences in touristic visits brings the necessity of differentiating the tours for tour guides with. Interpretation lies at the heart of this differentiation. This research aims to examine the structure of interpretation performance of tour guides empirically within the framework of E.R.O.T/T.O.R.E model. For this purpose, in line with the literature firstly conceptual structure of interpretation performance and interpretative guiding was determined, then expert opinion was sought with the expression pool consisting of draft statements. After expertising process, the measurement tool was first applied on a sample of 191 participants. For preliminary analysis the performance of …

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Holistic Performance Analysis And Optimization Of Unified Virtual Memory, Tyler Allen Aug 2022

Holistic Performance Analysis And Optimization Of Unified Virtual Memory, Tyler Allen

All Dissertations

The programming difficulty of creating GPU-accelerated high performance computing (HPC) codes has been greatly reduced by the advent of Unified Memory technologies that abstract the management of physical memory away from the developer. However, these systems incur substantial overhead that paradoxically grows for codes where these technologies are most useful. While these technologies are increasingly adopted for use in modern HPC frameworks and applications, the performance cost reduces the efficiency of these systems and turns away some developers from adoption entirely. These systems are naturally difficult to optimize due to the large number of interconnected hardware and software components that …

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Co-Advise: Cross Inductive Bias Distillation, Sucheng Ren, Zhengqi Gao, Tiany Hua, Zihui Xue, Yonglong Tian, Shengfeng He, Hang Zhao Jun 2022

Co-Advise: Cross Inductive Bias Distillation, Sucheng Ren, Zhengqi Gao, Tiany Hua, Zihui Xue, Yonglong Tian, Shengfeng He, Hang Zhao

Research Collection School Of Computing and Information Systems

The inductive bias of vision transformers is more relaxed that cannot work well with insufficient data. Knowledge distillation is thus introduced to assist the training of transformers. Unlike previous works, where merely heavy convolution-based teachers are provided, in this paper, we delve into the influence of models inductive biases in knowledge distillation (e.g., convolution and involution). Our key observation is that the teacher accuracy is not the dominant reason for the student accuracy, but the teacher inductive bias is more important. We demonstrate that lightweight teachers with different architectural inductive biases can be used to co-advise the student transformer with …

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Comparative Study Of Snort 3 And Suricata Intrusion Detection Systems, Cole Hoover May 2022

Comparative Study Of Snort 3 And Suricata Intrusion Detection Systems, Cole Hoover

Computer Science and Computer Engineering Undergraduate Honors Theses

Network Intrusion Detection Systems (NIDS) are one layer of defense that can be used to protect a network from cyber-attacks. They monitor a network for any malicious activity and send alerts if suspicious traffic is detected. Two of the most common open-source NIDS are Snort and Suricata. Snort was first released in 1999 and became the industry standard. The one major drawback of Snort has been its single-threaded architecture. Because of this, Suricata was released in 2009 and uses a multithreaded architecture. Snort released Snort 3 last year with major improvements from earlier versions, including implementing a new multithreaded architecture …

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Visual Attention Methods In Deep Learning: An In-Depth Survey, Mohammed Hassanin, Anwar Saeed, Ibrahim Radwan, Fahad Shahbaz Khan, Ajmal Mian Apr 2022

Visual Attention Methods In Deep Learning: An In-Depth Survey, Mohammed Hassanin, Anwar Saeed, Ibrahim Radwan, Fahad Shahbaz Khan, Ajmal Mian

Computer Vision Faculty Publications

Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data. Deep learning has employed attention to boost performance for many applications. Interestingly, the same attention design can suit processing different data modalities and can easily be incorporated into large networks. Furthermore, multiple complementary attention mechanisms can be incorporated in one network. Hence, attention techniques have become extremely attractive. However, the literature lacks a comprehensive survey specific to attention techniques to guide researchers in employing attention in their deep models. …

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Mucot: Multilingual Contrastive Training For Question-Answering In Low-Resource Languages, Gokul Karthik Kumar, Abhishek Singh Gehlot, Sahal Shaji Mullappilly, Karthik Nandakumar Apr 2022

Mucot: Multilingual Contrastive Training For Question-Answering In Low-Resource Languages, Gokul Karthik Kumar, Abhishek Singh Gehlot, Sahal Shaji Mullappilly, Karthik Nandakumar

Computer Vision Faculty Publications

Accuracy of English-language Question Answering (QA) systems has improved significantly in recent years with the advent of Transformer-based models (e.g., BERT). These models are pre-trained in a self-supervised fashion with a large English text corpus and further fine-tuned with a massive English QA dataset (e.g., SQuAD). However, QA datasets on such a scale are not available for most of the other languages. Multi-lingual BERT-based models (mBERT) are often used to transfer knowledge from high-resource languages to low-resource languages. Since these models are pre-trained with huge text corpora containing multiple languages, they typically learn language-agnostic embeddings for tokens from different languages. …

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Unsupervised Automatic Speech Recognition: A Review, Hanan Aldarmaki, Asad Ullah, Sreepratha Ram, Nazar Zaki Apr 2022

Unsupervised Automatic Speech Recognition: A Review, Hanan Aldarmaki, Asad Ullah, Sreepratha Ram, Nazar Zaki

Natural Language Processing Faculty Publications

Automatic Speech Recognition (ASR) systems can be trained to achieve remarkable performance given large amounts of manually transcribed speech, but large labeled data sets can be difficult or expensive to acquire for all languages of interest. In this paper, we review the research literature to identify models and ideas that could lead to fully unsupervised ASR, including unsupervised sub-word and word modeling, unsupervised segmentation of the speech signal, and unsupervised mapping from speech segments to text. The objective of the study is to identify the limitations of what can be learned from speech data alone and to understand the minimum …

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Faster Multidimensional Data Queries On Infrastructure Monitoring Systems, Yinghua Qin, Gheorghi Guzun Feb 2022

Faster Multidimensional Data Queries On Infrastructure Monitoring Systems, Yinghua Qin, Gheorghi Guzun

Faculty Research, Scholarly, and Creative Activity

The analytics in online performance monitoring systems have often been limited due to the query performance of large scale multidimensional data. In this paper, we introduce a faster query approach using the bit-sliced index (BSI). Our study covers multidimensional grouping and preference top-k queries with the BSI, algorithms design, time complexity evaluation, and the query time comparison on a real-time production performance monitoring system. Our research work extended the BSI algorithms to cover attributes filtering and multidimensional grouping. We evaluated the query time with the single attribute, multiple attributes, feature filtering, and multidimensional grouping. To compare with the existing prior …

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Challenges In Covid-19 Chest X-Ray Classification: Problematic Data Or Ineffective Approaches?, Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarrete, Ibrahim Almakky, Mohammad Yaqub Jan 2022

Challenges In Covid-19 Chest X-Ray Classification: Problematic Data Or Ineffective Approaches?, Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarrete, Ibrahim Almakky, Mohammad Yaqub

Computer Vision Faculty Publications

The value of quick, accurate, and confident diagnoses cannot be undermined to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developing deep learning methods to classify and detect COVID-19 infections from chest radiography images. However, recently some questions have been raised surrounding the clinical viability and effectiveness of such methods. In this work, we carry out extensive experiments on a large COVID-19 chest X-ray dataset to investigate the challenges faced with creating reliable solutions from both the data and machine learning perspectives. Accordingly, we offer an in-depth discussion into the challenges faced …

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Is Contrastive Learning Suitable For Left Ventricular Segmentation In Echocardiographic Images?, Mohamed Saeed, Rand Muhtaseb, Mohammad Yaqub Jan 2022

Is Contrastive Learning Suitable For Left Ventricular Segmentation In Echocardiographic Images?, Mohamed Saeed, Rand Muhtaseb, Mohammad Yaqub

Computer Vision Faculty Publications

Contrastive learning has proven useful in many applications where access to labelled data is limited. The lack of annotated data is particularly problematic in medical image segmenta-tion as it is difficult to have clinical experts manually annotate large volumes of data. One such task is the segmentation of cardiac structures in ultrasound images of the heart. In this paper, we argue whether or not contrastive pretraining is helpful for the segmentation of the left ventricle in echocardiography images. Furthermore, we study the effect of this on two segmentation networks, DeepLabV3, as well as the commonly used segmentation net-work, UNet. Our …

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Strategies For Adoption Of Innovative Information Technology For Business Performance Improvement, James Melvin Smith Jan 2022

Strategies For Adoption Of Innovative Information Technology For Business Performance Improvement, James Melvin Smith

Walden Dissertations and Doctoral Studies

Business leaders do not often consider investing in informational technology (IT) to increase performance. These business leaders lack knowledge of innovative IT adoption strategies to expand their revenue and reduce costs to sustain a competitive advantage. Grounded in the technology-organization-environment (TOE) theory, the purpose of this qualitative multiple case study was to explore IT innovation adoption strategies business leaders use to increase performance. The participants were four successful business leaders selected from finalists for international entrepreneurial awards presented annually in the Mid-Atlantic region of the United States. The data were collected using semistructured telephone interviews, media releases, and online publications. …

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Predicting Pair Success In A Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis, Maureen M. Villamor, Maria Mercedes T. Rodrigo Jan 2022

Predicting Pair Success In A Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis, Maureen M. Villamor, Maria Mercedes T. Rodrigo

Department of Information Systems & Computer Science Faculty Publications

Pair programming is a model of collaborative learning. It has become a well-known pedagogical practice in teaching introductory programming courses because of its potential benefits to students. This study aims to investigate pair patterns in the context of pair program tracing and debugging to determine what characterizes collaboration and how these patterns relate to success, where success is measured in terms of performance task scores. This research used eye-tracking methodologies and techniques such as cross-recurrence quantification analysis. The potential indicators for pair success were used to create a model for predicting pair success. Findings suggest that it is possible to …

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Quality Of Ammoniated Brachiaria Decumbens Hay, L. O. Fernandes, Ricardo A. Reis, L. R. A. Rodrigues Dec 2021

Quality Of Ammoniated Brachiaria Decumbens Hay, L. O. Fernandes, Ricardo A. Reis, L. R. A. Rodrigues

IGC Proceedings (1997-2023)

The experiment was conducted at UNESP-Jaboticabal to evaluate the quality of Brachiaria decumbens hays harvested after seed ripening. The hays were submitted to the following treatments: control, anhydrous ammonia (3.0% NH3 in the DM), and urea (5.0% in the DM). The chemical composition, digestibility and the performance of steers were evaluated using a complete randomized block design with three treatments and six replications. The ammoniation either with NH3 or urea increased (P< 0.05) the CP content, and the IVDMD. The NH3 treatment reduced (P < 0.05) the contents of NDF and ADF, and the urea application reduced (P < 0.05) the contents of hemicellulose and lignin. The ammoniation did not affect (P > 0.05) the neutral detergent insoluble nitrogen, and acid detergent insoluble nitrogen values. Steers receiving Brachiaria hay plus soybean meal (1.08 kg DM.day), hay …

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