A Robust Random Forest Prediction Model For Mother-To-Child Hiv Transmission Based On Individual Medical History,
2022
1Directorate of Information and Communication Technology, Muhimbili University of Health and Allied Sciences, Dar es Salaam
A Robust Random Forest Prediction Model For Mother-To-Child Hiv Transmission Based On Individual Medical History, Rebecca Chaula
Tanzania Journal of Engineering and Technology (TJET)
Human Immunodeficiency Virus (HIV) continues to be a leading cause of mortality and reduces manpower throughout the world. HIV transmission from mother to child is still a global challenge in health research. According to UNAIDS, in every 7 girls, 6 are found to be newly infected among adolescents whereby 15-24 years are likely to be living with HIV which is the maternal age and likely to transfer to the child. Machine learning methods have been used to predict HIV/AIDS transmission from mother to child but left behind some important considerations including the use of patient-level information and techniques in balancing …
Iot-Based Smart Fishing Gear For Sustainability Of The Tanzania Blue Economy,
2022
University of Dar es salaam
Iot-Based Smart Fishing Gear For Sustainability Of The Tanzania Blue Economy, Abdi Abdalla
Tanzania Journal of Engineering and Technology (TJET)
The fishing industry engages many Tanzanians and is among the leading sectors of Blue Economy in the country. However, fishing practices are small-scale with poor and insufficient number of fishing facilities, hence limiting productivity and efficacy. Studies argue that the low level of technology currently used in the country could possibly be an impeding factor. Specifically, fishers use non interactive gears that cannot instantaneously update status and alert them whenever the gears are ready for collection. In such scenarios, fishers not only waste their time but also scarce resources such as fuel to facilitate trips to and from the fishing …
Polarmix: A General Data Augmentation Technique For Lidar Point Clouds,
2022
School of Computer Science and Engineering
Polarmix: A General Data Augmentation Technique For Lidar Point Clouds, Aoran Xiao, Jiaxing Huang, Dayan Guan, Kaiwen Cui, Shijian Lu, Ling Shao
Computer Vision Faculty Publications
LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial to many autonomous detection and navigation tasks. Though many 3D deep architectures have been developed, efficient collection and annotation of large amounts of point clouds remain one major challenge in the analytics and understanding of point cloud data. This paper presents PolarMix, a point cloud augmentation technique that is simple and generic but can mitigate the data constraint effectively across different perception tasks and scenarios. PolarMix enriches point cloud distributions and preserves point cloud fidelity via two cross-scan …
Towards Improving Calibration In Object Detection Under Domain Shift,
2022
Information Technology University
Towards Improving Calibration In Object Detection Under Domain Shift, Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz, Mohsen Ali
Computer Vision Faculty Publications
With deep neural network based solution more readily being incorporated in real-world applications, it has been pressing requirement that predictions by such models, especially in safety-critical environments, be highly accurate and well-calibrated. Although some techniques addressing DNN calibration have been proposed, they are only limited to visual classification applications and in-domain predictions. Unfortunately, very little to no attention is paid towards addressing calibration of DNN-based visual object detectors, that occupy similar space and importance in many decision making systems as their visual classification counterparts. In this work, we study the calibration of DNN-based object detection models, particularly under domain shift. …
Intermediate Prototype Mining Transformer For Few-Shot Semantic Segmentation,
2022
Northwestern Polytechnical University
Intermediate Prototype Mining Transformer For Few-Shot Semantic Segmentation, Yuanwei Liu, Nian Liu, Xiwen Yao, Junwei Han
Computer Vision Faculty Publications
Few-shot semantic segmentation aims to segment the target objects in query under the condition of a few annotated support images. Most previous works strive to mine more effective category information from the support to match with the corresponding objects in query. However, they all ignored the category information gap between query and support images. If the objects in them show large intra-class diversity, forcibly migrating the category information from the support to the query is ineffective. To solve this problem, we are the first to introduce an intermediate prototype for mining both deterministic category information from the support and adaptive …
Explainable Ai Helps Bridge The Ai Skills Gap: Evidence From A Large Bank,
2022
Carnegie Mellon University
Explainable Ai Helps Bridge The Ai Skills Gap: Evidence From A Large Bank, Selina Carter, Jonathan Hersh
Economics Faculty Articles and Research
Advances in machine learning have created an “AI skills gap” both across and within firms. As AI becomes embedded in firm processes, it is unknown how this will impact the digital divide between workers with and without AI skills. In this paper we ask whether managers trust AI to predict consequential events, what manager characteristics are associated with increasing trust in AI predictions, and whether explainable AI (XAI) affects users’ trust in AI predictions. Partnering with a large bank, we generated AI predictions for whether a loan will be late in its final disbursement. We embedded these predictions into a …
A Call For Research: Ethical Dilemmas Of Autonomous Vehicle Manufacturers,
2022
Christopher Newport University
A Call For Research: Ethical Dilemmas Of Autonomous Vehicle Manufacturers, Remy Harwood
Cybersecurity Undergraduate Research Showcase
While autonomous vehicles accounted for about 31.4 million vehicles on the road in 2019 (Placek). They have continued to flood the market and have a projected growth to 58 million in just 8 years from now (Placek) As well as a market cap in the billions of dollars. Even the comparatively new AV company Tesla has over 3 times the market cap value of the leading non AV brand Toyota (Market Cap) who are also working toward AVs as well, like their level 2 teammate driver assistance. Following Moore’s Law, as technology continues to improve, their social impact and ethical …
Assessing Wood Failure In Plywood By Deep Learning/Semantic Segmentation,
2022
Mississippi State University
Assessing Wood Failure In Plywood By Deep Learning/Semantic Segmentation, Ramon Ferreira Oliveira
Theses and Dissertations
The current method for estimating wood failure is highly subjective. Various techniques have been proposed to improve the current protocol, but none have succeeded. This research aims to use deep learning/semantic segmentation using SegNet architecture to estimate wood failure in four types of three-ply plywood from mechanical shear strength specimens. We trained and tested our approach on custom and commercial plywood with bio-based and phenol-formaldehyde adhesives. Shear specimens were prepared and tested. Photographs of 255 shear bonded areas were taken. Forty photographs were used to solicit visual estimates from five human evaluators, and the remaining photographs were used to train …
Iitd At The Wanlp 2022 Shared Task: Multilingual Multi-Granularity Network For Propaganda Detection,
2022
Indian Institute of Technology Delhi
Iitd At The Wanlp 2022 Shared Task: Multilingual Multi-Granularity Network For Propaganda Detection, Shubham Mittal, Preslav Nakov
Natural Language Processing Faculty Publications
We present our system for the two subtasks of the shared task on propaganda detection in Arabic, part of WANLP'2022. Subtask 1 is a multi-label classification problem to find the propaganda techniques used in a given tweet. Our system for this task uses XLM-R to predict probabilities for the target tweet to use each of the techniques. In addition to finding the techniques, Subtask 2 further asks to identify the textual span for each instance of each technique that is present in the tweet; the task can be modeled as a sequence tagging problem. We use a multi-granularity network with …
Transfer Of Personality Through Text Style,
2022
Technological University Dublin
Transfer Of Personality Through Text Style, Michael O'Mahony, Robert Ross
Other resources
The style of generated text is how something is said rather than what is said. We hypothesize that changing the style of generated text can change the perceived personality of the text generation agent. Dialogue systems that aim to imitate a human agent can appear to have a consistent personality through a consistent, controllable style of conversation. Some recent work on the style of generated text [1] performs impressively in the small number of domains selected for their experiments using transformer and LSTM-based models. Lin et al. [1] used weak supervised learning as their data set lacks parallel data. The …
Hate-Clipper: Multimodal Hateful Meme Classification Based On Cross-Modal Interaction Of Clip Features,
2022
Mohamed Bin Zayed University of Artificial Intelligence
Hate-Clipper: Multimodal Hateful Meme Classification Based On Cross-Modal Interaction Of Clip Features, Gokul Karthik Kumar, Karthik Nandakumar
Computer Vision Faculty Publications
Hateful memes are a growing menace on social media. While the image and its corresponding text in a meme are related, they do not necessarily convey the same meaning when viewed individually. Hence, detecting hateful memes requires careful consideration of both visual and textual information. Multimodal pretraining can be beneficial for this task because it effectively captures the relationship between the image and the text by representing them in a similar feature space. Furthermore, it is essential to model the interactions between the image and text features through intermediate fusion. Most existing methods either employ multimodal pre-training or intermediate fusion, …
Actively Guided Cansats For Assisting Localization And Mapping In Unstructured And Unknown Environments,
2022
Kennesaw State University
Actively Guided Cansats For Assisting Localization And Mapping In Unstructured And Unknown Environments, Cary Chun, M. Hassan Tanveer
Symposium of Student Scholars
When navigating in unknown and unstructured environments, Unmanned Arial Vehicles (UAVs) can struggle when attempting to preform Simultaneous Localization and Mapping (SLAM) operations. Particularly challenging circumstance arise when an UAV may need to land or otherwise navigate through treacherous environments. As the primary UAV may be too large and unwieldly to safely investigate in these types of situations, this research effort proposes the use of actively guided CanSats for assisting in localization and mapping of unstructured environments. A complex UAV could carry multiple of these SLAM capable CanSats, and when additional mapping and localization capabilities where required, the CanSat would …
A High-Accuracy Detection System: Based On Transfer Learning For Apical Lesions On Periapical Radiograph,
2022
Chang Gung Memorial Hospital, Taoyuan City, Taiwan
A High-Accuracy Detection System: Based On Transfer Learning For Apical Lesions On Periapical Radiograph, Yueh Chuo, Wen-Ming Lin, Tsung-Yi Chen, Mei-Ling Chan, Yu-Sung Chang, Yan-Ru Lin, Yuan-Jin Lin, Yu-Han Shao, Chiung-An Chen, Patricia Angela R. Abu
Department of Information Systems & Computer Science Faculty Publications
Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold …
College Teaching And Ai,
2022
Loyola University Chicago
College Teaching And Ai, Leo Irakliotis
Computer Science: Faculty Publications and Other Works
Artificial Intelligence will reshape the way we assess student learning in ways that no one has prepared us for.
The Interaction Of Normalisation And Clustering In Sub-Domain Definition For Multi-Source Transfer Learning Based Time Series Anomaly Detection,
2022
ADAPT Centre, Trinity College Dublin
The Interaction Of Normalisation And Clustering In Sub-Domain Definition For Multi-Source Transfer Learning Based Time Series Anomaly Detection, Matthew Nicholson, Rahul Agrahari, Clare Conran, Haythem Assem, John D. Kelleher
Articles
This paper examines how data normalisation and clustering interact in the definition of sub-domains within multi-source transfer learning systems for time series anomaly detection. The paper introduces a distinction between (i) clustering as a primary/direct method for anomaly detection, and (ii) clustering as a method for identifying sub-domains within the source or target datasets. Reporting the results of three sets of experiments, we find that normalisation after feature extraction and before clustering results in the best performance for anomaly detection. Interestingly, we find that in the multi-source transfer learning scenario clustering on the target dataset and identifying subdomains in the …
Artificial Intelligence In The Medical Field: Medical Review Sentiment Analysis,
2022
Northern Illinois University
Artificial Intelligence In The Medical Field: Medical Review Sentiment Analysis, Nicholas Podlesak
Honors Capstones
In this research project, natural language processing techniques’ ability to accurately classify medical text was measured to reinforce the relevance of artificial intelligence in the medical field. Sentiment analyses (analyses to determine whether the text was positive or negative) were performed on the prescription drug reviews in an open-source dataset using four different models: lexical, a neural network, a support vector machine, and a logistic regression model. Each model’s effectiveness was gauged by its ability to correctly classify unlabeled drug reviews (i.e., a percentage representing accuracy). The machine learning models were able to accurately classify the text, while the lexical …
Predicting Publication Of Clinical Trials Using Structured And Unstructured Data: Model Development And Validation Study,
2022
The University of Melbourne
Predicting Publication Of Clinical Trials Using Structured And Unstructured Data: Model Development And Validation Study, Siyang Wang, Simon Šuster, Timothy Baldwin, Karin Verspoor
Natural Language Processing Faculty Publications
Background: Publication of registered clinical trials is a critical step in the timely dissemination of trial findings. However, a significant proportion of completed clinical trials are never published, motivating the need to analyze the factors behind success or failure to publish. This could inform study design, help regulatory decision-making, and improve resource allocation. It could also enhance our understanding of bias in the publication of trials and publication trends based on the research direction or strength of the findings. Although the publication of clinical trials has been addressed in several descriptive studies at an aggregate level, there is a lack …
Assisting The Human Fact-Checkers: Detecting All Previously Fact-Checked Claims In A Document,
2022
Cornell University
Assisting The Human Fact-Checkers: Detecting All Previously Fact-Checked Claims In A Document, Shaden Shaar, Nikola Georgiev, Firoj Alam, Giovanni Da San Martino, Aisha Mohamed, Preslav Nakov
Natural Language Processing Faculty Publications
Given the recent proliferation of false claims online, there has been a lot of manual fact-checking effort. As this is very time-consuming, human fact-checkers can benefit from tools that can support them and make them more efficient. Here, we focus on building a system that could provide such support. Given an input document, it aims to detect all sentences that contain a claim that can be verified by some previously fact-checked claims (from a given database). The output is a re-ranked list of the document sentences, so that those that can be verified are ranked as high as possible, together …
Overview Of The Wanlp 2022 Shared Task On Propaganda Detection In Arabic,
2022
Qatar Computing Research Institute
Overview Of The Wanlp 2022 Shared Task On Propaganda Detection In Arabic, Firoj Alam, Hamdy Mubarak, Wajdi Zaghouani, Giovanni Da San Martino, Preslav Nakov
Natural Language Processing Faculty Publications
Propaganda is the expression of an opinion or an action by an individual or a group deliberately designed to influence the opinions or the actions of other individuals or groups with reference to predetermined ends, which is achieved by means of well-defined rhetorical and psychological devices. Propaganda techniques are commonly used in social media to manipulate or to mislead users. Thus, there has been a lot of recent research on automatic detection of propaganda techniques in text as well as in memes. However, so far the focus has been primarily on English. With the aim to bridge this language gap, …
Supervised Acoustic Embeddings And Their Transferability Across Languages,
2022
United Arab Emirates University
Supervised Acoustic Embeddings And Their Transferability Across Languages, Sreepratha Ram, Hanan Aldarmaki
Natural Language Processing Faculty Publications
In speech recognition, it is essential to model the phonetic content of the input signal while discarding irrelevant factors such as speaker variations and noise, which is challenging in low-resource settings. Self-supervised pretraining has been proposed as a way to improve both supervised and unsupervised speech recognition, including frame-level feature representations and Acoustic Word Embeddings (AWE) for variable-length segments. However, self-supervised models alone cannot learn perfect separation of the linguistic content as they are trained to optimize indirect objectives. In this work, we experiment with different pre-trained self-supervised features as input to AWE models and show that they work best …
