Overview Of The Clpsych 2022 Shared Task: Capturing Moments Of Change In Longitudinal User Posts, 2022 The Alan Turing Institute, UK
Overview Of The Clpsych 2022 Shared Task: Capturing Moments Of Change In Longitudinal User Posts, Adam Tsakalidis, Jenny Chim, Iman Munire Bilal, Ayah Zirikly, Dana Atzil-Slonim, Federico Nanni, Philip Resnik, Manas Gaur, Kaushik Roy, Becky Inkster, Jeff Leintz, Maria Liakata
We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of Moments of Change in longitudinal posts by individuals on social media and its connection with information regarding mental health . This year's task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sensitive evaluation metrics. The Shared Task consisted of two subtasks: (a) the main task of capturing changes in an individual's mood (drastic changes-`Switches'- and gradual changes -`Escalations'- on the basis of textual content shared online; and subsequently (b ...
Negational Symmetry Of Quantum Neural Networks For Binary Pattern Classification, 2022 University of Oxford, United Kingdom
Negational Symmetry Of Quantum Neural Networks For Binary Pattern Classification, Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric P. Xing
Machine Learning Faculty Publications
Although quantum neural networks (QNNs) have shown promising results in solving simple machine learning tasks recently, the behavior of QNNs in binary pattern classification is still underexplored. In this work, we find that QNNs have an Achilles’ heel in binary pattern classification. To illustrate this point, we provide a theoretical insight into the properties of QNNs by presenting and analyzing a new form of symmetry embedded in a family of QNNs with full entanglement, which we term negational symmetry. Due to negational symmetry, QNNs can not differentiate between a quantum binary signal and its negational counterpart. We empirically evaluate the ...
Irrelevant Pixels Are Everywhere: Find And Exclude Them For More Efficient Computer Vision, 2022 Purdue University
Irrelevant Pixels Are Everywhere: Find And Exclude Them For More Efficient Computer Vision, Caleb Tung, Abhinav Goel, Xiao Hu, Nick Eliopoulos, Emmanuel Amobi, George K. Thiruvathukal, Vipin Chaudhary, Yung-Hisang Lu
Computer Science: Faculty Publications and Other Works
Computer vision is often performed using Convolutional Neural Networks (CNNs). CNNs are compute-intensive and challenging to deploy on power-constrained systems such as mobile and Internet-of-Things (IoT) devices. CNNs are compute-intensive because they indiscriminately compute many features on all pixels of the input image. We observe that, given a computer vision task, images often contain pixels that are irrelevant to the task. For example, if the task is looking for cars, pixels in the sky are not very useful. Therefore, we propose that a CNN be modified to only operate on relevant pixels to save computation and energy. We propose a ...
Are You Really Muted?: A Privacy Analysis Of Mute Buttons In Video Conferencing Apps, 2022 University of Wisconsin - Madison
Are You Really Muted?: A Privacy Analysis Of Mute Buttons In Video Conferencing Apps, Yucheng Yang, Jack West, George K. Thiruvathukal, Neil Klingensmith, Kassem Fawaz
Computer Science: Faculty Publications and Other Works
In the post-pandemic era, video conferencing apps (VCAs) have converted previously private spaces — bedrooms, living rooms, and kitchens — into semi-public extensions of the office. And for the most part, users have accepted these apps in their personal space, without much thought about the permission models that govern the use of their personal data during meetings. While access to a device’s video camera is carefully controlled, little has been done to ensure the same level of privacy for accessing the microphone. In this work, we ask the question: what happens to the microphone data when a user clicks the mute ...
A Mean-Field Markov Decision Process Model For Spatial Temporal Subsidies In Ride-Sourcing Markets, 2022 Singapore Management University
A Mean-Field Markov Decision Process Model For Spatial Temporal Subsidies In Ride-Sourcing Markets, Zheng Zhu, Jintao Ke, Hai Wang
Research Collection School Of Computing and Information Systems
Ride-sourcing services are increasingly popular because of their ability to accommodate on-demand travel needs. A critical issue faced by ride-sourcing platforms is the supply-demand imbalance, as a result of which drivers may spend substantial time on idle cruising and picking up remote passengers. Some platforms attempt to mitigate the imbalance by providing relocation guidance for idle drivers who may have their own self-relocation strategies and decline to follow the suggestions. Platforms then seek to induce drivers to system-desirable locations by offering them subsidies. This paper proposes a mean-field Markov decision process (MF-MDP) model to depict the dynamics in ride-sourcing markets ...
Nonparametric Contextual Reasoning For Question Answering Over Large Knowledge Bases, 2022 University of Massachusetts Amherst
Nonparametric Contextual Reasoning For Question Answering Over Large Knowledge Bases, Rajarshi Das
Question answering (QA) over knowledge bases provides a user-friendly way of accessing the massive amount of information stored in them. We have experienced tremendous progress in the performance of QA systems, thanks to the recent advancements in representation learning by deep neural models. However, such deep models function as black boxes with an opaque reasoning process, are brittle, and offer very limited control (e.g. for debugging an erroneous model prediction). It is also unclear how to reliably add or update knowledge stored in their model parameters.
This thesis proposes nonparametric models for question answering that disentangle logic from knowledge ...
Machine Learning With Kay, 2022 Technological University Dublin
Machine Learning With Kay, Lasith Niroshan, James Carswell
Computational power is very important when training Deep Learning (DL) models with large amounts of data (Wooldridge, 2021). Hence, High-Performance Computing (HPC) can be leveraged to reduce computational cost, and the Irish Centre for High-End Computing (ICHEC) provides significant infrastructure and services for research and development to both academia and industry. A portion of ICHEC's HPC system has been allocated for institutional access, and this paper presents a case study of how to use Kay (Ireland's national supercomputer) in the remote sensing domain. Specifically, this study uses clusters of Kay Graphics Processing Units (GPUs) for training DL models ...
An Empirical Study On Sampling Approaches For 3d Image Classification Using Deep Learning, 2022 Rowan University
An Empirical Study On Sampling Approaches For 3d Image Classification Using Deep Learning, Nicholas Michelette
Theses and Dissertations
A 3D classification method requires more training data than a 2D image classification method to achieve good performance. These training data usually come in the form of multiple 2D images (e.g., slices in a CT scan) or point clouds (e.g., 3D CAD modeling) for volumetric object representation. The amount of data required to complete this higher dimension problem comes with the cost of requiring more processing time and space. This problem can be mitigated with data size reduction (i.e., sampling). In this thesis, we empirically study and compare the classification performance and deep learning training time of ...
Single-Pass Inline Pipeline 3d Reconstruction Using Depth Camera Array, 2022 University of Nebraska-Lincoln
Single-Pass Inline Pipeline 3d Reconstruction Using Depth Camera Array, Zhexiong Shang, Zhigang Shen
Faculty Publications in Construction Engineering & Management
A novel inline inspection (ILI) approach using depth cameras array (DCA) is introduced to create high-fidelity, dense 3D pipeline models. A new camera calibration method is introduced to register the color and the depth information of the cameras into a unified pipe model. By incorporating the calibration outcomes into a robust camera motion estimation approach, dense and complete 3D pipe surface reconstruction is achieved by using only the inline image data collected by a self-powered ILI rover in a single pass through a straight pipeline. The outcomes of the laboratory experiments demonstrate one-millimeter geometrical accuracy and 0.1-pixel photometric accuracy ...
Where Is The Author: The Copyright Protection For Ai-Generated Works, 2022 Maurer School of Law - Indiana University
Where Is The Author: The Copyright Protection For Ai-Generated Works, Chieh Huang
Maurer Theses and Dissertations
The two groups of the human-or-machine questions, whether AI-generated works are copyrightable and whether AI-generated works have human authors, are revisiting the current copyright law with the emergence of AI-generated works. These revisiting questions reveal that the current authorship requirement fails to provide a clear and operable standard on evaluating a human contributor’s intellectual labor for creative output. Such a defect of the current authorship requirement has to be fixed to respond to the technological change of artificial intelligence and the burgeoning prevalence of AI- or advanced computer program-generated works.
This dissertation’s main goal is to fix the ...
Multiple Object Tracking For Marine Science, 2022 California Polytechnic State University, San Luis Obispo
Multiple Object Tracking For Marine Science, Nicholas A. Wachter
Computer Science and Software Engineering
As drone and computer vision technology has been improving, many fields of study have been quick to utilize it to improve the accuracy and ease of data collection. The combination of the two technologies is perfect for surveying large areas and identifying features of interest. Marine science utilizes these technologies for activities such as animal tracking and population counting. I am working with the Drones for Marine Science research group at Cal Poly who want to build a fleet of drones that will fly out over the ocean to identify and track various marine animals. My role will be to ...
Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, 2022 CUNY Graduate Center
Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges ...
Problematic Ai — When Should We Use It?, 2022 William & Mary Law School
Problematic Ai — When Should We Use It?, Fredric Lederer
No abstract provided.
Incorporating Spatial Relationship Information In Signal-To-Text Processing, 2022 Mississippi State University
Incorporating Spatial Relationship Information In Signal-To-Text Processing, Jeremy Elon Davis
Theses and Dissertations
This dissertation outlines the development of a signal-to-text system that incorporates spatial relationship information to generate scene descriptions. Existing signal-to-text systems generate accurate descriptions in regards to information contained in an image. However, to date, no signalto- text system incorporates spatial relationship information. A survey of related work in the fields of object detection, signal-to-text, and spatial relationships in images is presented first. Three methodologies followed by evaluations were conducted in order to create the signal-to-text system: 1) generation of object localization results from a set of input images, 2) derivation of Level One Summaries from an input image, and ...
Prospects For Legal Analytics: Some Approaches To Extracting More Meaning From Legal Texts, 2022 University of Cincinnati College of Law
Prospects For Legal Analytics: Some Approaches To Extracting More Meaning From Legal Texts, Kevin D. Ashley
University of Cincinnati Law Review
No abstract provided.
A Survey On Cache-Aided Noma For 6g Networks, 2022 The Department of Electronics and Communication Engineering, National Institute of Technology, Chhattisgarh, Raipur, 492010, India
A Survey On Cache-Aided Noma For 6g Networks, Dipen Bepari, Soumen Mondal, Aniruddha Chandra, Rajeev Shukla, Yuanwei Liu, Mohsen Guizani, Arumugam Nallanathan
Machine Learning Faculty Publications
Contrary to orthogonal multiple-access (OMA), non-orthogonal multiple-access (NOMA) schemes can serve a pool of users without exploiting the scarce frequency or time domain resources. This is useful in meeting the sixth generation (6G) network requirements, such as, low latency, massive connectivity, users' fairness, and high spectral efficiency. On the other hand, content caching restricts duplicate data transmission by storing popular contents in advance at the network edge which reduces 6G data traffic. In this survey, we focus on cache-aided NOMA-based wireless networks which can reap the benefits of both cache and NOMA; switching to NOMA from OMA enables cache-aided networks ...
Segmentation With Super Images: A New 2d Perspective On 3d Medical Image Analysis, 2022 Mohamed bin Zayed University of Artificial Intelligence
Segmentation With Super Images: A New 2d Perspective On 3d Medical Image Analysis, Ikboljon Sobirov, Numan Saeed, Mohammad Yaqub
Computer Vision Faculty Publications
Deep learning is showing an increasing number of audience in medical imaging research. In the segmentation task of medical images, we oftentimes rely on volumetric data, and thus require the use of 3D architectures which are praised for their ability to capture more features from the depth dimension. Yet, these architectures are generally more ineffective in time and compute compared to their 2D counterpart on account of 3D convolutions, max pooling, up-convolutions, and other operations used in these networks. Moreover, there are limited to no 3D pretrained model weights, and pretraining is generally challenging. To alleviate these issues, we propose ...
Evolution Of Combined Arms Tactics In Heterogeneous Multi-Agent Teams, 2022 Air Force Institute of Technology
Evolution Of Combined Arms Tactics In Heterogeneous Multi-Agent Teams, Robert J. Wilson, David W. King, Gilbert L. Peterson
Multi-agent systems research is concerned with the emergence of system-level behaviors from relatively simple agent interactions. Multi-agent systems research to date is primarily concerned with systems of homogeneous agents, with member agents both physically and behaviorally identical. Systems of heterogeneous agents with differing physical or behavioral characteristics may be able to accomplish tasks more efficiently than homogeneous teams, via cooperation between mutually complementary agent types. In this article, we compare the performance of homogeneous and heterogeneous teams in combined arms situations. Combined arms theory proposes that the application of heterogeneous forces, en masse, can generate effects far greater than outcomes ...
Factored Beliefs For Machine Agents In Decentralized Partially Observable Markov Decision Processes, 2022 Air Force Institute of Technology
Factored Beliefs For Machine Agents In Decentralized Partially Observable Markov Decision Processes, Joshua Lapso, Gilbert L. Peterson
A shared mental model (SMM) is a foundational structure in high performing, task-oriented teams and aid humans in determining their teammate's goals and intentions. Higher levels of mental alignment between teammates can reduce the direct dialogue required for team success. For decision-making teams, a transactive memory system (TMS) offers team members a map of specialized knowledge, indicating source of knowledge and the source's credibility. SMM and TMS formulations aid human-agent team performance in their intended team types. However, neither improve team performance with a project team--one that requires both behavioral and knowledge integration. We present a hybrid cognitive ...
A Machine Learning Approach For Reconnaissance Detection To Enhance Network Security, 2022 East Tennessee State University
A Machine Learning Approach For Reconnaissance Detection To Enhance Network Security, Rachel Bakaletz
Electronic Theses and Dissertations
Before cyber-crime can happen, attackers must research the targeted organization to collect vital information about the target and pave the way for the subsequent attack phases. This cyber-attack phase is called reconnaissance or enumeration. This malicious phase allows attackers to discover information about a target to be leveraged and used in an exploit. Information such as the version of the operating system and installed applications, open ports can be detected using various tools during the reconnaissance phase. By knowing such information cyber attackers can exploit vulnerabilities that are often unique to a specific version.
In this work, we develop an ...