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 …
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 …
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 …
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 …
Camelira: An Arabic Multi-Dialect Morphological Disambiguator,
2022
NYU Abu Dhabi
Camelira: An Arabic Multi-Dialect Morphological Disambiguator, Ossama Obeid, Go Inoue, Nizar Habash
Computer Vision Faculty Publications
We present Camelira, a web-based Arabic multi-dialect morphological disambiguation tool that covers four major variants of Arabic: Modern Standard Arabic, Egyptian, Gulf, and Levantine. Camelira offers a user-friendly web interface that allows researchers and language learners to explore various linguistic information, such as part-of-speech, morphological features, and lemmas. Our system also provides an option to automatically choose an appropriate dialect-specific disambiguator based on the prediction of a dialect identification component. Camelira is publicly accessible at http://camelira.camel-lab.com.
Pasta: Table-Operations Aware Fact Verification Via Sentence-Table Cloze Pre-Training,
2022
Renmin University of China
Pasta: Table-Operations Aware Fact Verification Via Sentence-Table Cloze Pre-Training, Zihui Gu, Ju Fan, Nan Tang, Preslav Nakov, Xiaoman Zhao, Xiaoyong Du
Natural Language Processing Faculty Publications
Fact verification has attracted a lot of research attention recently, e.g., in journalism, marketing, and policymaking, as misinformation and disinformation online can sway one's opinion and affect one's actions. While fact-checking is a hard task in general, in many cases, false statements can be easily debunked based on analytics over tables with reliable information. Hence, table-based fact verification has recently emerged as an important and growing research area. Yet, progress has been limited due to the lack of datasets that can be used to pre-train language models (LMs) to be aware of common table operations, such as aggregating a column …
Greener: Graph Neural Networks For News Media Profiling,
2022
Sofia University St. Kliment Ohridski
Greener: Graph Neural Networks For News Media Profiling, Panayot Panayotov, Utsav Shukla, Husrev T. Sencar, Mohamed Nabeel, Preslav Nakov
Natural Language Processing Faculty Publications
We study the problem of profiling news media on the Web with respect to their factuality of reporting and bias. This is an important but under-studied problem related to disinformation and “fake news” detection, but it addresses the issue at a coarser granularity compared to looking at an individual article or an individual claim. This is useful as it allows to profile entire media outlets in advance. Unlike previous work, which has focused primarily on text (e.g., on the articles published by the target website, or on the textual description in their social media profiles or in Wikipedia), here we …
Asdot: Any-Shot Data-To-Text Generation With Pretrained Language Models,
2022
Carnegie Mellon University
Asdot: Any-Shot Data-To-Text Generation With Pretrained Language Models, Jiannan Xiang, Zhengzhong Liu, Yucheng Zhou, Eric P. Xing, Zhiting Hu
Machine Learning Faculty Publications
Data-to-text generation is challenging due to the great variety of the input data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse predicates). Recent end-to-end neural methods thus require substantial training examples to learn to disambiguate and describe the data. Yet, real-world data-to-text problems often suffer from various data-scarce issues: one may have access to only a handful of or no training examples, and/or have to rely on examples in a different domain or schema. To fill this gap, we propose Any-Shot Data-to-Text (ASDOT), a new approach flexibly applicable to diverse settings by making efficient use of …
