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Artificial Intelligence and Robotics

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Articles 4111 - 4140 of 11288

Full-Text Articles in Computer Sciences

A Social Profile-Based E-Learning Model, Xola Ntlangula Sep 2023

A Social Profile-Based E-Learning Model, Xola Ntlangula

African Conference on Information Systems and Technology

Many High Education Institutions (HEIs) have migrated to blended or complete online learning to cater for less interruption with learning. As such, there is a growing demand for personalized e-learning to accommodate the diversity of students' needs. Personalization can be achieved using recommendation systems powered by artificial intelligence. Although using student data to personalize learning is not a new concept, collecting and identifying appropriate data is necessary to determine the best recommendations for students. By reviewing the existing data collection capabilities of the e-learning platforms deployed by public universities in South Africa, we were able to establish the readiness of …

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Impact Of Green Building Certification On The Rent Of Commercial Properties: A Review, Thebuwena Arachchige Chandana Hemantha Jayakody, Anthony Vaz Sep 2023

Impact Of Green Building Certification On The Rent Of Commercial Properties: A Review, Thebuwena Arachchige Chandana Hemantha Jayakody, Anthony Vaz

Journal of Informatics and Web Engineering

The world is currently facing two major problems, namely, increasing energy costs and global warming. As a result, it is crucial to take proactive measures to effectively address and mitigate the detrimental impacts arising from elevated energy costs, the pressing issue of global warming, and various types of environmental degradation. As a reaction, international organizations are advocating for the development of eco-friendly, sustainable, or green buildings as a strategy to reduce the harmful effects of the construction sector on the environment. While green development may entail higher costs for developers, it is imperative to evaluate the return on investment from …

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Dropout Prediction Model For College Students In Moocs Based On Weighted Multi-Feature And Svm, Zhang Yujiao, Ang Ling Weay, Shi Shaomin, Sellappan Palaniappan Sep 2023

Dropout Prediction Model For College Students In Moocs Based On Weighted Multi-Feature And Svm, Zhang Yujiao, Ang Ling Weay, Shi Shaomin, Sellappan Palaniappan

Journal of Informatics and Web Engineering

Due to the COVID -19 pandemic, MOOCs have become a popular form of learning for college students. However, unlike traditional face-to-face courses, MOOCs offer little faculty supervision, which may result in students being insufficiently motivated to continue learning, ultimately leading to a high dropout rate. Consequently, the problem of high dropout rates in MOOCs requires urgent attention in MOOC research. Predicting dropout rates is the first step to address this problem, and MOOCs have a large amount of behavioral data that can be used for such predictions. Most existing models for predicting MOOC dropout based on behavioral data assign equal …

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Predicting Travel Insurance Purchases In An Insurance Firm Through Machine Learning Methods After Covid-19, Shiuh Tong Lim, Joe Yee Yuan, Khai Wah Khaw, Xinying Chew Sep 2023

Predicting Travel Insurance Purchases In An Insurance Firm Through Machine Learning Methods After Covid-19, Shiuh Tong Lim, Joe Yee Yuan, Khai Wah Khaw, Xinying Chew

Journal of Informatics and Web Engineering

Travel insurance serves as a crucial financial safeguard, offering coverage against unforeseen expenses and losses incurred during travel. With the advent of the proliferation of insurance types and the amplified demand for Covid-related coverage, insurance companies face the imperative task of accurately predicting customers’ likelihood to purchase insurance. This can assist the insurance providers in focusing on the most lucrative clients and boosting sales. By employing advanced machine learning techniques, this study aims to forecast the consumer segments most inclined to acquire travel insurance, allowing targeted strategies to be developed. A comprehensive analysis was carried out on a Kaggle dataset …

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A Cost-Based Dual Convnet-Attention Transfer Learning Model For Ecg Heartbeat Classification, Johnson Olanrewaju Victor, Xinying Chew, Khai Wah Khaw, Ming Ha Lee Sep 2023

A Cost-Based Dual Convnet-Attention Transfer Learning Model For Ecg Heartbeat Classification, Johnson Olanrewaju Victor, Xinying Chew, Khai Wah Khaw, Ming Ha Lee

Journal of Informatics and Web Engineering

The heart is a very crucial organ of the body. Concerted efforts are constantly put forward to provide adequate monitoring of the heart. A heart disorder is reported to cause a lot of hidden ailments resulting in numerous deaths. Early heart monitoring using an electrocardiogram (ECG) through the advancement of computer-aided diagnostic (CAD) systems is widely used. Meanwhile, the use of human reading of ECG results are faced with many challenges of inaccurate and unreliable interpretations. Over two decades, studies provided artificial intelligence (AI) technique using machine learning (ML) algorithms as a fast and reliable technique for ECG heartbeat classification. …

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The Assistance Of Eye Blink Detection For Two- Factor Authentication, Wei-Hoong Chuah, Siew-Chin Chong, Lee-Ying Chong Sep 2023

The Assistance Of Eye Blink Detection For Two- Factor Authentication, Wei-Hoong Chuah, Siew-Chin Chong, Lee-Ying Chong

Journal of Informatics and Web Engineering

This paper discusses the implementation of a blink detection method using 68 facial markers and the eye aspect ratio (EAR) to provide strong protection for access systems. It investigates the importance of 68 facial markers and explores how to use eye landmarks to calculate the eye aspect ratio. Access systems, which should have good security measures and be difficult to decipher, are typically safeguarded by passwords or multi-factor verification, such as passwords combined with facial recognition. However, these methods have inherent weaknesses, including the risk of shoulder surfing with passwords and the potential to be deceived by fake face images …

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A Marker Free Visual-Based Home Rehabilitation Framework, Roy Kwang Yang Chang, Kok Swee Sim, Siong Hoe Lau Sep 2023

A Marker Free Visual-Based Home Rehabilitation Framework, Roy Kwang Yang Chang, Kok Swee Sim, Siong Hoe Lau

Journal of Informatics and Web Engineering

Adhesive capsulitis or more commonly known as frozen shoulder, is a familiar occurrence for adults aged above 40 caused by the inflammation of the connective tissues surrounding the shoulder joint. There are different severity of adhesive capsulitis but patients afflicted with frozen shoulder typically will experience stiffness, severe pain, and reduced range of motion (ROM) for the shoulder. No matter the course of treatment being non-steroidal anti-inflammatory drugs (NSAIDs) or steroid injections, which can help reduce the inflammation and reduce pain, in order to restore ROM for the afflicted shoulder joint, rehabilitation exercises need to be performed. Even without the …

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Genregait: Gender Recognition Using Gait Features, Yue Fong Ti, Tee Connie, Michael Kah Ong Goh Sep 2023

Genregait: Gender Recognition Using Gait Features, Yue Fong Ti, Tee Connie, Michael Kah Ong Goh

Journal of Informatics and Web Engineering

Gender recognition based on gait features has gained significant interest due to its wide range of applications in various fields. This paper proposes GenReGait, a robust method for gender recognition utilizing gait features. Gait, the unique walking pattern of individuals, contains distinct gender-specific characteristics, such as stride length, step frequency, and body posture, making it a promising modality for gender estimation. The proposed GenReGait method begins by extracting landmark positions on the human body using a human keypoint estimation technique. These landmarks serve as informative cues for estimating gender based on their spatial and temporal characteristics. However, environmental factors can …

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Ensuring Privacy And Security On Banking Websites In Malaysia: A Cookies Scanner Solution, Yi Hong Tay, Shih Yin Ooi, Ying Han Pang, Ying Huey Gan, Sook Ling Lew Sep 2023

Ensuring Privacy And Security On Banking Websites In Malaysia: A Cookies Scanner Solution, Yi Hong Tay, Shih Yin Ooi, Ying Han Pang, Ying Huey Gan, Sook Ling Lew

Journal of Informatics and Web Engineering

In this new era of science and technology, data can be said to be an extremely valuable asset for individuals, corporations, and even countries. Different parties attempt to obtain users' data occasionally, and the collection of web cookies is a prominent example. When users use a computer network, their data will be saved by the web server as cookies, including their private information. As people with bad intentions obtain this information, they can use it to commit cybercrimes and cause losses to the information owners. Thus, cookies management is vital for web users to protect their data. This paper proposes …

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Traffic Impact Assessment System Using Yolov5 And Bytetrack, Jin Jie Ng, Kah Ong Michael Goh, Connie Tee Sep 2023

Traffic Impact Assessment System Using Yolov5 And Bytetrack, Jin Jie Ng, Kah Ong Michael Goh, Connie Tee

Journal of Informatics and Web Engineering

Monitoring software for traffic is not too much in this era of digital. Even cheaper is decent traffic monitoring software. You can gauge the quality of the software. It should be possible to assess the code's performance outside of a test environment. The most useful metrics are frequently those that support the program's ability to fulfil business requirements. Therefore, this project is planning to develop a traffic assessment system. The main purpose of development is to improve heavy traffic in this country – Malaysia. This system includes function vehicle detection using YOLOv5, vehicle counting with a different type (such as …

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Engaging Learning Experience: Enhancing Productivity Software Lessons With Screencast Videos, Usha Vellappan, Lim Liyen, Lim Su Yin Sep 2023

Engaging Learning Experience: Enhancing Productivity Software Lessons With Screencast Videos, Usha Vellappan, Lim Liyen, Lim Su Yin

Journal of Informatics and Web Engineering

The use of screencast videos can improve the effectiveness of the teaching and learning process, whether it is face-to-face or online. Screencast videos are digital resources that capture the computer screen and create an audio-visual experience, and they can be shared online to aid the learning process. It eliminates the need for educators to repeat information multiple times and creates an uninterrupted personalised learning environment for the students. This method of learning gives students a more personalised sense, as if they were given one-on-one guidance from the educator, with students having access to the educator and receiving immediate feedback during …

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Enhancing Migraine Management System Through Weather Forecasting For A Better Daily Life, Wen-Xuan Ong, Sin-Ban Ho, Chuie-Hong Tan Sep 2023

Enhancing Migraine Management System Through Weather Forecasting For A Better Daily Life, Wen-Xuan Ong, Sin-Ban Ho, Chuie-Hong Tan

Journal of Informatics and Web Engineering

A migraine is a severe, throbbing, or pulsing headache that typically affects one side of the head. A migraine attack can be so painful that it interferes with daily activities and can last for hours or even days. Migraine is a common health issue that affects approximately 1 in every 5 women and 1 in every 15 men. Additionally, millions of people worldwide suffer from migraine attacks due to the inability to anticipate or adapt to their environment. In today's globalized world, mobile phones have become a necessity for the general public, enabling communication, internet shopping, food purchases, and even …

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Multi-Label Classification With Deep Learning For Retail Recommendation, Zhi Yuan Poo, Choo Yee Ting, Yuen Peng Loh, Khairil Imran Ghauth Sep 2023

Multi-Label Classification With Deep Learning For Retail Recommendation, Zhi Yuan Poo, Choo Yee Ting, Yuen Peng Loh, Khairil Imran Ghauth

Journal of Informatics and Web Engineering

Selecting the right retail business for a location is crucial for the success of a business because it determines the likelihood of favourable return on investment. One common approach used in retail recommendation is multi-class classification, where retail businesses are categorized into different classes or categories based on various features or attributes. Existing research in the field of retail recommendation has extensively proposed and evaluated different algorithms, techniques, and approaches for multi-class classification in the context of retail recommendation, however, limited work has been focusing on formulating retail recommendation as a multi-label problem. This is because in retail recommendation, one …

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Workplace Preference Analytics Among Graduates, Sin-Yin Ong, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham Sep 2023

Workplace Preference Analytics Among Graduates, Sin-Yin Ong, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham

Journal of Informatics and Web Engineering

Graduates often find themselves difficult to secure a job after completing their education at universities or colleges. In this light, researchers have proposed various solutions to address this challenge. However, most of the work has largely focused on academic profile and personality traits; very few have highlighted the importance of workplace location characteristics. To address this challenge, this study has employed feature selection and machine learning approach to help graduates identify desired company type and sector based on their preferences and preferred location. The data used in this study was obtained from the Ministry of Higher Education Graduates Tracer Study's …

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Building Cyber Resilience: Key Factors For Enhancing Organizational Cyber Security, Thavaselvi Munusamy, Touraj Khodadi Sep 2023

Building Cyber Resilience: Key Factors For Enhancing Organizational Cyber Security, Thavaselvi Munusamy, Touraj Khodadi

Journal of Informatics and Web Engineering

The increasingly pervasive influence of technology on a global scale, coupled with the accelerating pace of organizations operating in cyberspace, has intensified the need for adequate protection against the risks posed by cyber threats. This paper aims to identify cyber resilience management attributes that can enable organizations to sustain and continually adapt in the face of evolving cyber risks and threats. The researcher explores the intersections between cybersecurity and resilience by reviewing existing frameworks, models, studies, and surveys. This study establishes the attributes of resilience with the integration of resilience theory and security theory, along with their position in the …

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Qr Food Ordering System With Data Analytics, Chee-Chun Wong, Lee- Ying Chong, Siew-Chin Chong, Check-Yee Law Sep 2023

Qr Food Ordering System With Data Analytics, Chee-Chun Wong, Lee- Ying Chong, Siew-Chin Chong, Check-Yee Law

Journal of Informatics and Web Engineering

As the epidemic starts to slow down and Malaysians are more confident about containing the outbreak with the norm of vaccination, diners have been aching to return to dining rooms, with many restaurants functioning at full capacity, but staffing is an entirely different story. As restaurateurs try to keep their businesses running at full speed and solve limited staff issues, there is only one solution: process automation. This paper aims to design a food ordering system that covers the benefits of automating the ordering process using the QR code and provides visualised insightful information based on the business data. Customers …

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Utilizing Fuzzy Algorithm For Understanding Emotional Intelligence On Individual Feedback, Elham Abdulwahab Anaam, Su-Cheng Haw, Kok-Why Ng, Palanichamy Naveen, Rasha Thabit Sep 2023

Utilizing Fuzzy Algorithm For Understanding Emotional Intelligence On Individual Feedback, Elham Abdulwahab Anaam, Su-Cheng Haw, Kok-Why Ng, Palanichamy Naveen, Rasha Thabit

Journal of Informatics and Web Engineering

Although previous studies looked at how employees should seek assistance, the issue is the researchinvestigation into how behavioral intelligence affects employee satisfaction is limited. This study examines several significant usages and developments of fuzzy mental modelling. The primary objective of the current section is to provide an innovative technique for modelling an emotion-based acceleration of the compressor for individuals. Methodologies of experiential thinking postulate that our comprehension of facial emotional reactions depends significantly on facial behavior imitation and the reactions as opportunities. Considering the theoretical foundations of combined logical reasoning. In addition, the hypothesis of probability, it additionally is not …

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Face And Facial Expressions Recognition System For Blind People Using Resnet50 Architecture And Cnn, Jia-Rou Lee, Kok-Why Ng, Yih-Jian Yoong Sep 2023

Face And Facial Expressions Recognition System For Blind People Using Resnet50 Architecture And Cnn, Jia-Rou Lee, Kok-Why Ng, Yih-Jian Yoong

Journal of Informatics and Web Engineering

Many blind individuals have difficulties in recognizing people’s facial expression which may impact their social interaction. With the recognition, the blind individuals can accurately interpret and respond to the emotions. There is a lack in the existing application with the combination of face and facial expressions recognition. The blind individuals have to rely on multiple applications to accomplish the same task, making it difficult and time-consuming for them to use. The paper aims to recognize faces and facial expressions for blind individuals and provides feedback in real-time. Three face detection algorithms of Haar Cascade Classifier, Dlib, and RetinaFace are compared. …

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A Multi-Scale Feature Attention Image Recognition Algorithm, Xin Ming Yuan, Ang Ling Weay, Sellappan Palaniappan Sep 2023

A Multi-Scale Feature Attention Image Recognition Algorithm, Xin Ming Yuan, Ang Ling Weay, Sellappan Palaniappan

Journal of Informatics and Web Engineering

The success of image classification using small samples is contingent on neural network models' capability to derive image representations from the data. A proposed solution is a small-sample image classification system that leverages attention mechanisms and meta-learning to capture more comprehensive image information. Due to its ability to efficiently suppress irrelevant characteristics and accentuate pertinent ones, this technique may extract more robust multiscale features and enhance classification performance through meta-learning.In this paper, the effectiveness of the multi-scale attention network is verified on two datasets, namely, Mini-ImageNet and Tiered-ImageNet, and the accuracy of the method is 58.54% for 5-way 1shot and …

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Aira: An Intelligent Recommendation Agent Application For Movies, Ayesha Anees Zaveri, Ramsha Mashood, Sarama Shehmir, Misbah Parveen, Naveera Sami, Mobeen Nazar Sep 2023

Aira: An Intelligent Recommendation Agent Application For Movies, Ayesha Anees Zaveri, Ramsha Mashood, Sarama Shehmir, Misbah Parveen, Naveera Sami, Mobeen Nazar

Journal of Informatics and Web Engineering

An intelligent Recommendation App has been developed to assist caregivers. This project's primary objective is to assist parents in determining whether a particular movie/cartoon/drama is adequate for their children by providing ratings that will assist them in identifying age-appropriate content. This application will provide reliable evaluations, reviews, and recommendations to parents. Each rating and review are based on fundamental, essential child development principles. Intelligent Recommendation Agent aids families in making intelligent media selections. It provides the most extensive and reliable database of learning ratings, age recommendations, and content evaluations for films, television series, and dramas. In addition, there will be …

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Analysing Gamma Frequency Components In Eeg Signals: A Comprehensive Extraction Approach, Tanvir Hasib, Vijayakumar Vengadasalam Sep 2023

Analysing Gamma Frequency Components In Eeg Signals: A Comprehensive Extraction Approach, Tanvir Hasib, Vijayakumar Vengadasalam

Journal of Informatics and Web Engineering

Gamma band activity is a high-frequency (30-100 Hz) oscillation of the electroencephalogram (EEG) that has been linked to a variety of cognitive processes including attention, memory and learning. However, extracting gamma band activity from EEG data can be challenging due to the relatively low signal-to-noise ratio of gamma band signals and the presence of other frequency bands such as beta and alpha. In this paper, we present a method for extracting gamma band activity from EEG data. We evaluated our method on a dataset of EEG data recorded from dyslexic patients. We found that our method was able to successfully …

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Bare-Bones Based Salp Swarm Algorithm For Text Document Clustering, Mohammed Azmi Al-Betar, Ammar Kamal Abasi, Ghazi Al-Naymat, Kamran Arshad, Sharif Naser Makhadmeh Sep 2023

Bare-Bones Based Salp Swarm Algorithm For Text Document Clustering, Mohammed Azmi Al-Betar, Ammar Kamal Abasi, Ghazi Al-Naymat, Kamran Arshad, Sharif Naser Makhadmeh

Machine Learning Faculty Publications

Text Document Clustering (TDC) is a challenging optimization problem in unsupervised machine learning and text mining. The Salp Swarm Algorithm (SSA) has been found to be effective in solving complex optimization problems. However, the SSA’s exploitation phase requires improvement to solve the TDC problem effectively. In this paper, we propose a new approach, known as the Bare-Bones Salp Swarm Algorithm (BBSSA), which leverages Gaussian search equations, inverse hyperbolic cosine control strategies, and greedy selection techniques to create new individuals and guide the population towards solving the TDC problem. We evaluated the performance of the BBSSA on six benchmark datasets from …

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A Study On Feature Selection Using Multi-Domain Feature Extraction For Automated K-Complex Detection, Yabing Li, Xinglong Dong, Kun Song, Xiangyun Bai, Hongye Li, Fakhreddine Karray Sep 2023

A Study On Feature Selection Using Multi-Domain Feature Extraction For Automated K-Complex Detection, Yabing Li, Xinglong Dong, Kun Song, Xiangyun Bai, Hongye Li, Fakhreddine Karray

Machine Learning Faculty Publications

Background: K-complex detection plays a significant role in the field of sleep research. However, manual annotation for electroencephalography (EEG) recordings by visual inspection from experts is time-consuming and subjective. Therefore, there is a necessity to implement automatic detection methods based on classical machine learning algorithms. However, due to the complexity of EEG signal, current feature extraction methods always produce low relevance to k-complex detection, which leads to a great performance loss for the detection. Hence, finding compact yet effective integrated feature vectors becomes a crucially core task in k-complex detection. Method: In this paper, we first extract multi-domain features based …

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Disease Progression Modelling Of Alzheimer's Disease Using Probabilistic Principal Components Analysis, Martin Saint-Jalmes, Victor Fedyashov, Daniel Beck, Timothy Baldwin, Noel G. Faux, Pierrick Bourgeat, Jurgen Fripp, Colin L. Masters, Benjamin Goudey Sep 2023

Disease Progression Modelling Of Alzheimer's Disease Using Probabilistic Principal Components Analysis, Martin Saint-Jalmes, Victor Fedyashov, Daniel Beck, Timothy Baldwin, Noel G. Faux, Pierrick Bourgeat, Jurgen Fripp, Colin L. Masters, Benjamin Goudey

Natural Language Processing Faculty Publications

The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the …

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Overview Of The Clef-2023 Checkthat! Lab Task 1 On Check-Worthiness Of Multimodal And Multigenre Content, Firoj Alam, Alberto Barrón-Cedeño, Gullal S. Cheema, Gautam Kishore Shahi, Sherzod Hakimov, Maram Hasanain, Chengkai Li, Rubén Míguez, Hamdy Mubarak, Wajdi Zaghouani, Preslav Nakov Sep 2023

Overview Of The Clef-2023 Checkthat! Lab Task 1 On Check-Worthiness Of Multimodal And Multigenre Content, Firoj Alam, Alberto Barrón-Cedeño, Gullal S. Cheema, Gautam Kishore Shahi, Sherzod Hakimov, Maram Hasanain, Chengkai Li, Rubén Míguez, Hamdy Mubarak, Wajdi Zaghouani, Preslav Nakov

Natural Language Processing Faculty Publications

We present an overview of CheckThat! Lab’s 2023 Task 1, which is part of CLEF-2023. Task 1 asks to determine whether a text item, or a text coupled with an image, is check-worthy. This task places a special emphasis on COVID-19, political debates and transcriptions, and it is conducted in three languages: Arabic, English, and Spanish. A total of 15 teams participated, and most submissions managed to achieve significant improvements over the baselines using Transformer-based models. Out of these, seven teams participated in the multimodal subtask (1A), and 12 teams participated in the Multigenre subtask (1B), collectively submitting 155 official …

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Gpachov At Checkthat! 2023: A Diverse Multi-Approach Ensemble For Subjectivity Detection In News Articles, Georgi Pachov, Dimitar Dimitrov, Ivan Koychev, Preslav Nakov Sep 2023

Gpachov At Checkthat! 2023: A Diverse Multi-Approach Ensemble For Subjectivity Detection In News Articles, Georgi Pachov, Dimitar Dimitrov, Ivan Koychev, Preslav Nakov

Natural Language Processing Faculty Publications

The wide-spread use of social networks has given rise to subjective, misleading, and even false information on the Internet. Thus, subjectivity detection can play an important role in ensuring the objectiveness and the quality of a piece of information. This paper presents the solution built by the Gpachov team for the CLEF-2023 CheckThat! lab Task 2 on subjectivity detection. Three different research directions are explored. The first one is based on fine-tuning a sentence embeddings encoder model and dimensionality reduction. The second one explores a sample-efficient few-shot learning model. The third one evaluates fine-tuning a multilingual transformer on an altered …

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Enriched Pre-Trained Transformers For Joint Slot Filling And Intent Detection, Momchil Hardalov, Ivan Koychev, Preslav Nakov Sep 2023

Enriched Pre-Trained Transformers For Joint Slot Filling And Intent Detection, Momchil Hardalov, Ivan Koychev, Preslav Nakov

Natural Language Processing Faculty Publications

Detecting the user's intent and finding the corresponding slots among the utterance's words are important tasks in natural language understanding. Their interconnected nature makes their joint modeling a standard part of training such models. Moreover, data scarceness and specialized vocabularies pose additional challenges. Recently, the advances in pre-trained language models, namely contextualized models such as ELMo and BERT have revolutionized the field by tapping the potential of training very large models with just a few steps of fine-tuning on a task-specific dataset. Here, we leverage such models, and we design a novel architecture on top of them. Moreover, we propose …

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Grammatical Error Correction: A Survey Of The State Of The Art, Christopher Bryant, Zheng Yuan, Muhammad Reza Qorib, Hannan Cao, Hwee Tou Ng, Ted Briscoe Sep 2023

Grammatical Error Correction: A Survey Of The State Of The Art, Christopher Bryant, Zheng Yuan, Muhammad Reza Qorib, Hannan Cao, Hwee Tou Ng, Ted Briscoe

Natural Language Processing Faculty Publications

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject–verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors, respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems, which represent the current dominant state of the art. In this survey paper, we condense …

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Thermodynamics Of Learning With Parametric Probabilistic Models, Shervin Sadat Parsi Sep 2023

Thermodynamics Of Learning With Parametric Probabilistic Models, Shervin Sadat Parsi

Dissertations, Theses, and Capstone Projects

This study delves into the learning process within the Probabilistic Parametric Models (PPMs) framework from a unique thermodynamic perspective. By exploring the core concepts of thermodynamics and its innate connection with information theory, we showcase how this interdisciplinary approach can effectively contribute to the domain of machine learning. In the initial chapter, we establish the link between the learning problem in PPMs and a thermodynamic process by reframing various elements of the learning process within the context of thermodynamics. We introduce novel information-theoretic measurements that provide insights into the information learned in both the parameter space and the overall performance …

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Out-Of-Distribution Generalization Of Deep Learning To Illuminate Dark Protein Functional Space, Tian Cai Sep 2023

Out-Of-Distribution Generalization Of Deep Learning To Illuminate Dark Protein Functional Space, Tian Cai

Dissertations, Theses, and Capstone Projects

Dark protein illumination is a fundamental challenge in drug discovery where majority human proteins are understudied, i.e. with only known protein sequence but no known small molecule binder. It's a major road block to enable drug discovery paradigm shift from single-targeted which looks to identify a single target and design drug to regulate the single target to multi-targeted in a Systems Pharmacology perspective. Diseases such as Alzheimer's and Opioid-Use-Disorder plaguing millions of patients call for effective multi-targeted approach involving dark proteins. Using limited protein data to predict dark protein property requires deep learning systems with OOD generalization capacity. Out-of-Distribution (OOD) …

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