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Fair + Equitable November 2024, International Association Of Assessing Officers

Fair + Equitable

The November F+E issue cover story reviews the uncertain future of downtown office space.

The issue also includes articles on:

• Bridging the gap between large and small jurisdictions
• Garage apartments gaining support in small communities
• AVMs, AI topics at international symposium
• New website, database in November; system will require member profile update
• Reminder: Election begins Nov. 1
• Promote the value of membership in your office
• Meet the new IAAO instructors
• IAAO offering workshop on valuing properties with renewable energy resources
• Thought leader article blockchain technology
• Thank you to Library donors
• New IAAO designees
• In Memoriam

Massive Stars Exploding In A He-Rich Circumstellar Medium: X. Flash Spectral Features In The Type Ibn Sn 2019cj And Observations Of Sn 2018jmt, Z. Y. Wang, A. Pastorello, K. Maeda, A. Reguitti

Open Access archive

We present optical and near-infrared observations of two Type Ibn supernovae (SNe), SN 2018jmt and SN 2019cj. Their light curves have rise times of about ten days, reaching an absolute peak magnitude of Mg(SN 2018jmt) = - 19.07 ± 0.37 and MV(SN 2019cj) = - 18.94 ± 0.19 mag, respectively. The early-time spectra of SN 2018jmt are dominated by a blue continuum, accompanied by narrow (600- 1000 km s^{- 1}) HeI lines with the P-Cygni profile. At later epochs, the spectra become more similar to those of the prototypical SN Ibn 2006jc. At early phases, the spectra of …

Implementation Of Control Valves System On Microturbine Drone’S Rotational Motion, Muhammad Nor Ikmal Muhamad Nazri, Adi Azriff Basri, Ernnie Illyani Basri, Spoorthi Singh

Open Access archive

The drone technology has received a wide range of research focusing on improving flight dynamics, battery life, payload capacity and sensor technology. However, for the applications of using drone for high rise buildings are limited by battery life for longer flight with longer duration tasks. This work presents the implementation of a control valve systems for the rotational motion of a microturbine drone. The first objective of this project was to design and develop a control valve system for a microturbine with propeller. The second objective was to measure micro turbine’s performance output which consisted of revolution per minute (rpm) …

Enhancing Secured Data Sharing In Mobile Cloud Environments Using Self-Generative Schnorr Certificateless Signcryption, V. L. Sowmya, R. Shankar, Anitha Premkumar, Umi Salma Basha

Open Access archive

Mobile cloud computing uses cloud infrastructure to provide apps and services to mobile devices. In this scenario, cloud computing promotes data sharing among authorised users by providing access to resources stored on Cloud Servers (CS). Data sharing is the process of distributing data to various users or applications; however, providing secure access and data protection remains a major concern. To solve this, a novel technique known as Random Self-Generative Schnorr Certificateless Signcryption based Secured Data Sharing (RSGSCS-SDS) is presented to improve data confidentiality in mobile cloud environments. The RSGSCS-SDS approach begins with mobile. In order to access different services, cloud …

A Fisher Information Theory Of Aesthetic Preference For Complexity, Sébastien Berquet, Hassan Aleem, Norberto M. Grzywacz

Psychology: Faculty Publications and Other Works

When evaluating sensory stimuli, people tend to prefer those with not too little or not too much complexity. A recent theoretical proposal for this phenomenon is that preference has a direct link to the Observed Fisher Information that a stimulus carries about the environment. To make this theory complete, one must specify the model that the brain has about complexities in the world. Here, we develop this model by first obtaining the distributions of three indices of complexity measured as normalized Shannon Entropy in real-world images from seven environments. We then search for a parametric model that accounts for these …

Failing To See What's In Front Of Our Eyes: The Effect Of Cognitive Errors On Corporate Scandals, Nancy B. Rapoport

William & Mary Business Law Review

Why do we believe flim-flam artists so readily, especially in the business and commercial realm? This Article explores four reallife versions of “The Emperor’s New Clothes” fable—FTX, WeWork, Theranos, and a former bankruptcy judge’s hidden romance— and asks why people bought into the hype. Where were the warning signs, and how might we better spot those signs in the future?

Formalizing Fairness: Statistical Measures Of Parity For Recidivism Prediction Instruments, Joshua Song

Michigan Technology Law Review

Recidivism prediction instruments (RPI) are increasingly employed as an algorithmic arm of the criminal justice system to assist in bond, sentencing, and probation determinations. Due process and ethics guidelines require that RPIs, such as the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), apply a fair assessment in making recidivism predictions. To establish fair RPIs, we must first define standards of fairness and methods to measure bias in RPI outputs. However, defining algorithmic fairness can be cryptic and context-dependent. Scholars have proposed different criteria for what defines a fair RPI. Unfortunately, these criteria are conflicting and mutually exclusive; it is …

Ideal Enforcement: How Do We Achieve Optimal Enforcement Of Traffic Law As Ubiquitous Enforcement Becomes Technologically Conceivable?, Bryant Walker Smith, Jeffrey Michael, Johnathon Ehsani

Michigan Technology Law Review

Safety. Mobility. Technology. Data. Equity. Autonomy. Community. Justice. Power. Trust. These themes have resonated over the last few years across a wide range of specific issues—some that are new and others that have only recently received the broader attention that they demand. A coming age of “perfect enforcement,” in which both public and private actors use increasingly powerful, pervasive, and affordable technologies to automatically identify and penalize every deviation from public or private rules, captures all these issues: They meet—in the language of physics, they interfere, whether constructively or destructively—to affect both perception and reality. The potential role of technology …

Left, Then Right Internal Carotid Artery Dissection: A Case Report, Jeffrey M. Kalczynski, John Douds, Michael E. Silverman

SKMC Student Presentations and Publications

INTRODUCTION: We present a unique case of a patient who presented to the emergency department with stroke-like symptoms found to have a spontaneous, left-sided internal carotid artery dissection (ICAD).

CASE REPORT: The patient was treated successfully with thrombectomy and subsequently developed contralateral symptoms caused by a right-sided ICAD. This was managed with a second contra-lateral thrombectomy. The patient's course was complicated by persistent and mild hypotension, postulated to be secondary to bilateral carotid baroreceptor trauma from the dissections.

CONCLUSION: This case highlights the importance of close neurological monitoring for patients, preferably in a neurologic critical care setting, during and after …

Assessment Of The Impact Of Marine Pollution In Maputo Port, Mozambique, Lina Maria Elias Vasco

World Maritime University Dissertations

No abstract provided.

Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang

Research Collection School Of Computing and Information Systems

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task’s labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether …

Revisiting Conversation Discourse For Dialogue Disentanglement, Bobo Li, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Yinwei Wei, Tat-Seng Chua, Donghong Ji

Research Collection School Of Computing and Information Systems

Dialogue disentanglement aims to detach the chronologically ordered utterances into several independent sessions. Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attribute. In this article, we propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. First of all, in feature encoding stage, we construct the heterogeneous graph representations to model the various dialogue-specific discourse structural features, including the static speaker-role structures (i.e., speaker-utterance and speaker-mentioning structure) and the dynamic contextual structures (i.e., the utterance-distance and partial-replying structure). We then …

Improving Conversational Recommender System Via Contextual And Time-Aware Modeling With Less Domain-Specific Knowledge, Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of the external domain-specific information needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract the internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user …

A Comprehensive Survey On Relation Extraction: Recent Advances And New Frontiers, Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu

Research Collection School Of Computing and Information Systems

Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works …

Large Language Models For Software Engineering: A Systematic Literature Review, Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, Haoyu Wang

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages. To bridge this gap, we conducted a Systematic Literature Review (SLR) on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes. We selected and analyzed 395 research articles from January 2017 to January 2024 to answer four key Research Questions (RQs). In RQ1, we categorize different LLMs …

Balancing Visual Context Understanding In Dialogue For Image Retrieval, Zhaohui Wei, Lizi Liao, Xiaoyu Du, Xinguang Xiang

Research Collection School Of Computing and Information Systems

In the realm of dialogue-to-image retrieval, the primary challenge is to fetch images from a pre-compiled database that accurately reflect the intent embedded within the dialogue history. Existing methods often overemphasize inter-modal alignment, neglecting the nuanced nature of conversational context. Dialogue histories are frequently cluttered with redundant information and often lack direct image descriptions, leading to a substantial disconnect between conversational content and visual representation. This study introduces VCU, a novel framework designed to enhance the comprehension of dialogue history and improve cross-modal matching for image retrieval. VCU leverages large language models (LLMs) to perform a two-step extraction process. It …

Eyetraes : Fine-Grained, Low-Latency Eye Tracking Via Adaptive Event Slicing, Argha Sen, Panahetipola Mudiyanselage Nuwan Bandara, Ila Gokarn, Thivya Kandappu, Archan Misra

Research Collection School Of Computing and Information Systems

Eye-tracking technology has gained significant attention in recent years due to its wide range of applications in humancomputer interaction, virtual and augmented reality, and wearable health. Traditional RGB camera-based eye-tracking systems often struggle with poor temporal resolution and computational constraints, limiting their effectiveness in capturing rapid eye movements. To address these limitations, we propose EyeTrAES, a novel approach using neuromorphic event cameras for high-fidelity tracking of natural pupillary movement that shows significant kinematic variance. One of EyeTrAES’s highlights is the use of a novel adaptive windowing/slicing algorithm that ensures just the right amount of descriptive asynchronous event data accumulation within …

Strength Lies In Differences! Improving Strategy Planning For Non-Collaborative Dialogues Via Diversified User Simulation, Tong Zhang, Chen Huang, Yang Deng, Hongru Liang, Jia Liu, Zujie Wen, Wenqiang Lei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system’s objectives. This poses two main challenges for existing dialogue agents: 1) The inability to integrate user-specific characteristics into the strategic planning, and 2) The difficulty of training strategic planners that can be generalized to diverse users. To address these challenges, we propose TRIP to enhance the capability in tailored strategic planning, incorporating a user-aware strategic planning module and a population-based training paradigm. Through experiments on benchmark non-collaborative dialogue tasks, we demonstrate the effectiveness …

Unlocking Markets: A Multilingual Benchmark To Cross-Market Question Answering, Yifei Yuan, Yang Deng, Anders Sogaard, Mohammad Alliannejadi

Research Collection School Of Computing and Information Systems

Users post numerous product-related questions on e-commerce platforms, affecting their purchase decisions. Product-related question answering (PQA) entails utilizing product-related resources to provide precise responses to users. Wepropose a novel task of Multilingual Crossmarket Product-based Question Answering (MCPQA) and define the task as providing answers to product-related questions in a main marketplace by utilizing information from another resource-rich auxiliary marketplace in a multilingual context. We introduce a largescale dataset comprising over 7 million questions from 17 marketplaces across 11 languages. We then perform automatic translation on the Electronics category of our dataset, naming it as McMarket. We focus on two subtasks: …

Final 2024 Residential Metals Abatement Program (Rmap) Brown’S Gulch Borrow Submittal #1, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Enhancing Post Silicon Visibility Using Language Modelling Techniques, Nathaniel Joseph Fender

USF Tampa Graduate Theses and Dissertations

The debugging phase is a critical time in the development of a new system on chip product. Specifically, the post-silicon validation phase is one of the most important, as it allows engineers to test the behavior of a device in a real world setting. However, the issue of noisy or incomplete data is a frequent issue when attempting to debug an SoC design during this step. This thesis examines the utility of utilizing machine learning models for the purpose of repairing missing data in a system trace. We trained various models using the transformer architecture to identify missing data in …

Predicting Human Chess Moves With Large Language Models, Benjamin Kreiger

USF Tampa Graduate Theses and Dissertations

As artificial intelligence (AI) surpasses human performance in more tasks, the interest in leveraging and collaborating with this technology for greater productivity continues to grow. However, the black-box nature of current AI can make it difficult to interpret and unsuitable to perform tasks that are more complex and require human intuition. This has led to the pursuit of AI systems that can model individual behavior. Chess offers an ideal environment to explore this task due to its complexity, structure, and the abundance of data containing unique human decision-making examples. Given that a chess game can be fully represented with text, …

Ipydisp V2 Alias Dudutracker: A Web-Based Version, Komi Mensah Agboka, Elfatih M. Abdel-Rahman, Samira A. Mohamed, Sunday Ekesi

All Peer-Reviewed Publications

This study presents the updated version v2 of IpyDisp named DuduTracker which improves on the window-only-requirement of IpyDisp. The updated version is web-based that can be used in alternative operating systems like Ubuntu, Mac, Linux, and others. The update's effectiveness was also evaluated using a survey involving a diverse range of users including students, data analysts, and academic researchers from different age groups, geographical locations, and computer literacy levels. Areas for future enhancement were identified, primarily focused on making the software responsive to various screen types and improving certain interface aspects.

Syntheses And Crystal Structures Of The Imides 4-(2-Phenyl­Eth­Yl)- And 4-[2-(4-Hy­Droxy­Phen­Yl)Eth­Yl]-4-Aza­Tetra­Cyclo­[5.3.2.0,2,6.08,10]Dodec-11-Ene-3,5-Dione, Jonathan P. Bajko, Richard J. Staples, Shannon Biros

Open Access Publishing Support Funded Articles

The syntheses and characterization (NMR and XRD) of two substituted [2.2.2]bi­cyclo­octene ring systems are described here. The cyclo­hexene rings of these systems adopt a nearly perfect boat conformation according to analysis using Cremer–Pople parameters. Both structures contain a nearly planar imide ring that is oriented endo relative to a bridgehead cyclo­propyl ring. 4-(2-Phenyl­eth­yl)-4-aza­tetra­cyclo­[5.3.2.0^2,6.0^8,10]dodec-11-ene-3,5-dione, C₁₉H₁₉NO₂, is substituted with a phenylethyl group that hosts C—H⋯π inter­actions in the crystal. 4-[2-(4-Hy­droxy­phen­yl)eth­yl]-4-aza­tetra­cyclo­[5.3.2.0^2,6.0^8,10]dodec-11-ene-3,5-dione, C₁₉H₁₉NO₃, bears a 4-hy­droxy­phenylethyl group on the imide ring and contains O—H⋯O …

Safeguarding User-Centric Privacy In Smart Homes, Keyang Yu, Qi Li, Dong Chen, Liting Hu

Computer Science Faculty Research and Publications

Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new …

Techniques For Modeling Ocean Soundscapes: Detailed Description For Wind Contributionsa, Martin Siderius, Michael A. Ainslie, John Gebbie, Alexandra Schäfke, N Ross Chapman, Bruce Martin, Kay L. Gemba

Electrical and Computer Engineering Faculty Publications and Presentations

Wind over the ocean creates breaking waves that generate air-filled bubbles, which radiate underwater sound. This wind-generated sound is a significant component of the ocean soundscape, and models are essential for understanding and predicting its impact. Models for predicting sound pressure level (SPL) from wind have been studied for many years. However, the terminology and definitions behind modeling approaches have not been unified, and ambiguity has led to differences in predicted SPL. The 2022 Ambient Sound Modeling Workshop was organized to compare ambient sound modeling approaches from different researchers. The main goal of the workshop was to quantify differences in …

A Finite-Horizon Approach To Active Level Set Estimation, Phillip Kearns, Bruno Jedynak, John Lipor

Mathematics and Statistics Faculty Publications and Presentations

We consider the problem of active learning for level set estimation (LSE), where the goal is to localize all regions where a function of interest lies above/below a given threshold as quickly as possible. We present a finitehorizon search procedure to perform LSE in one dimension while optimally balancing both the final estimation error and the distance traveled during active learning for a fixed number of samples. A tuning parameter is used to trade off between the estimation accuracy and distance traveled. We show that the resulting optimization problem can be solved in closed form and that the resulting policy …

Targeted Study Of The Effect Of Yeast Strain On Volatile Compounds Produced In Sorghum Beer, Drew Budner, Joseph Carr, Brett Serafini, Samantha Tucker, Elisabeth Dieckman-Meyer, Lindsey Bell, Katherine A. Thompson-Witrick

Chemistry

An increase in the consumer demand and the availability of gluten-free products has led to several brewers investigating brewing with grains other than barley. The primary grain of choice has been sorghum. These new gluten-free beers have a unique flavor and aroma, which previous research has shown is the result of differences in concentration for key chemical compounds, including ethyl butyrate, butyl acetate, isoamyl acetate, ethyl caproate, hexyl acetate, 1-octanol, nonanal, ethyl octanoate, and ethyl decanoate. This study focused looked at the influence different strains of yeast had on the concentration of these key compounds. Beer was brewed using either …

Mxfda: A Comprehensive Toolkit For Functional Data Analysis Of Single-Cell Spatial Data., Julia Wrobel, Alex C. Soupir, Mitchell T. Hayes, Lauren C. Peres, Thao Vu, Andrew Leroux, Brooke L. Fridley

Manuscripts, Articles, Book Chapters and Other Papers

SUMMARY: Technologies that produce spatial single-cell (SC) data have revolutionized the study of tissue microstructures and promise to advance personalized treatment of cancer by revealing new insights about the tumor microenvironment. Functional data analysis (FDA) is an ideal analytic framework for connecting cell spatial relationships to patient outcomes, but can be challenging to implement. To address this need, we present mxfda, an R package for end-to-end analysis of SC spatial data using FDA. mxfda implements a suite of methods to facilitate spatial analysis of SC imaging data using FDA techniques.

AVAILABILITY AND IMPLEMENTATION: The mxfda R package is freely available …

A Site-Wise Reliability Analysis Of The Abcd Diffusion Fractional Anisotropy And Cortical Thickness: Impact Of Scanner Platforms, Yezhi Pan, L Elliot Hong, Ashley Acheson, Paul M Thompson, Neda Jahanshad, Alyssa H Zhu, Jiaao Yu, Chixiang Chen, Tianzhou Ma, Ho-Ling Liu, Jelle Veraart, Els Fieremans, Nicole R Karcher, Peter Kochunov, Shuo Chen

2020-Current year OA Pubs

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce nonbiological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation …