Physical Sciences and Mathematics Commons^™

Rethinking Education In The Age Of Ai: The Importance Of Developing Durable Skills In The Industry 4.0, James Hutson, Jason Ceballos

Faculty Scholarship

This article discusses the pressing need to integrate artificial intelligence (AI) into education to facilitate customizable, individualized, and on-demand learning pathways. At the same time, while AI has the potential to expand the learner base and improve learning outcomes, the development of NACE Competencies and durable skills – communication, critical thinking, creativity, leadership, adaptability, and emotional intelligence - must be purposefully integrated in curriculum design now more than ever. Recent studies have shown that AI-driven learning pathways can achieve outcomes more quickly, but this comes at the cost of the development of durable skills. Therefore, traditional student-to-student and student-to-teacher interactions …

Socialz: Multi-Feature Social Fuzz Testing, Francisco Zanartu, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Online social networks have become an integral aspect of our daily lives and play a crucial role in shaping our relationships with others. However, bugs and glitches, even minor ones, can cause anything from frustrating problems to serious data leaks that can have farreaching impacts on millions of users. To mitigate these risks, fuzz testing, a method of testing with randomised inputs, can provide increased confidence in the correct functioning of a social network. However, implementing traditional fuzz testing methods can be prohibitively difficult or impractical for programmers outside of the network’s development team. To tackle this challenge, we present …

Barriers And Self-Efficacy: A Large-Scale Study On The Impact Of Oss Courses On Student Perceptions, Larissa Salerno, Simone De França Tonhão, Igor Steinmacher, Christoph Treude

Research Collection School Of Computing and Information Systems

Open source software (OSS) development offers a unique opportunity for students in Software Engineering to experience and participate in large-scale software development, however, the impact of such courses on students’ self-efficacy and the challenges faced by students are not well understood. This paper aims to address this gap by analyzing data from multiple instances of OSS development courses at universities in different countries and reporting on how students’ self-efficacy changed as a result of taking the course, as well as the barriers and challenges faced by students

Towards Robust Personalized Dialogue Generation Via Order-Insensitive Representation Regularization, Liang Chen, Hongru Wang, Yang Deng, Wai-Chung Kwan, Zezhong Wang, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Generating persona consistent dialogue response is important for developing an intelligent conversational agent. Recent works typically fine-tune large-scale pre-trained models on this task by concatenating persona texts and dialogue history as a single input sequence to generate the target response. While simple and effective, our analysis shows that this popular practice is seriously affected by order sensitivity where different input orders of persona sentences significantly impact the quality and consistency of generated response, resulting in severe performance fluctuations (i.e., 29.4% on GPT2 and 83.2% on BART). To mitigate the order sensitivity problem, we propose a model-agnostic framework, ORder Insensitive Generation …

Seed Selection For Testing Deep Neural Networks, Yuhan Zhi, Xiaofei Xie, Chao Shen, Jun Sun, Xiaoyu Zhang, Xiaohong Guan

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been applied in many applications. Meanwhile, the quality of DL systems is becoming a big concern. To evaluate the quality of DL systems, a number of DL testing techniques have been proposed. To generate test cases, a set of initial seed inputs are required. Existing testing techniques usually construct seed corpus by randomly selecting inputs from training or test dataset. Till now, there is no study on how initial seed inputs affect the performance of DL testing and how to construct an optimal one. To fill this gap, we conduct the first systematic study to evaluate …

Proactive Conversational Agents In The Post-Chatgpt World, Lizi Liao, Grace Hui Yang, Chirag Shah

Research Collection School Of Computing and Information Systems

ChatGPT and similar large language model (LLM) based conversational agents have brought shock waves to the research world. Although astonished by their human-like performance, we find they share a significant weakness with many other existing conversational agents in that they all take a passive approach in responding to user queries. This limits their capacity to understand the users and the task better and to offer recommendations based on a broader context than a given conversation. Proactiveness is still missing in these agents, including their ability to initiate a conversation, shift topics, or offer recommendations that take into account a more …

Forward/Backward And Content Private Dsse For Spatial Keyword Queries, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Yinbin Miao, Yang Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Spatial keyword queries are attractive techniques that have been widely deployed in real-life applications in recent years, such as social networks and location-based services. However, existing solutions neither support dynamic update nor satisfy the privacy requirements in real applications. In this article, we investigate the problem of Dynamic Searchable Symmetric Encryption (DSSE) for spatial keyword queries. First, we formulate the definition of DSSE for spatial keyword queries (namely, DSSESKQ) and extend the DSSE leakage functions to capture the leakages in DSSESKQ. Then, we present a practical DSSESKQ construction based on geometric prefix encoding inverted-index and encrypted bitmap. Rigorous security analysis …

Information Screening Whilst Exploiting! Multimodal Relation Extraction With Feature Denoising And Multimodal Topic Modeling, Shengqiong Wu, Hao Fei, Yixin Cao, Lidong Bing, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Existing research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation. To combat that, we propose a novel framework that simultaneously implements the idea of internal-information screening and external-information exploiting. First, we represent the fine-grained semantic structures of the input image and text with the visual and textual scene graphs, which are further fused into a unified cross-modal graph (CMG). Based on CMG, we perform structure refinement with the guidance of the graph information bottleneck principle, actively denoising the less-informative features. Next, we perform topic modeling over the input image and text, incorporating latent multimodal …

Lessening Student Anxiety With Docker, Kourtnee Fernalld

Theses and Dissertations

Remote learning during the COVID-19 pandemic transformed the educational landscape for hands-on Computer Science courses. This paradigm shift accelerated the transition from traditional in-person programming labs to decentralized student-provided resources. Even as students returned to in-person learning, many continued to rely on their personal computers rather than embracing university-provided labs. However, this shift to decentralized, heterogeneous environments introduces various information technology and instructional challenges. The recent emergence of lightweight, container-based virtualization presents a unique opportunity to address these challenges by offering standardized environments on decentralized platforms. To investigate this opportunity, we implemented lightweight virtualization for three undergraduate computer science courses …

Obscuration Analysis Of Camera Imagery For Aviation Applications, Patrick James Roelant

Theses and Dissertations

Image feature detection is a potent tool with many applications, such as fog identification, roadway conditions, etc. As part of the recent surge in machine learning applications, cloud detection has also become an increasingly engaged area of research. Identifying low clouds is especially useful with respect to aviation, particularly in regions of complex topography prone to visibility-related hazards such as haze or fog. To address this issue, a threshold-based semi-automated algorithm was developed and tested to determine whether or not an image is obscured by fog or haze. Images were obtained from a ground-based camera network in Southern California, the …

Security Of Text To Image Conversions, Zobaida Alssadi

Theses and Dissertations

The use of images and icons to represent news or narratives has grown in popularity. Still, one critical problem is that they are not equivalent to language, making them vulnerable to adversary attacks. This study examines the impact of image-poisoning attacks based on polysemantic words and of image attacks based on cultural differences when converting text to images. Such attacks can lead to the loss of important information and create confusion and incorrect interpretations of the intended meaning, misinforming the general public. The study specifically focuses on possible effects in a news and story context. This study highlights the significance …

Forecasting >100 Mev Sep Events And Intensity Based On Cme And Other Solar Activities Using Machine Learning, Daniel Lee Griessler

Theses and Dissertations

There is a severe risk for astronauts and machinery from high intensity Solar Energetic Particle (SEP) events which can be mitigated through accurate forecast of their presence and peak intensity. By using characteristics of CME and other space weather phenomena, machine learning techniques have the potential to classify and predict the peak intensity of SEP events. The extreme scarcity of SEP events in current datasets poses a challenge to traditional machine learning techniques. In this work, we first demonstrate classifier machine learning techniques that can achieve an F1 score of 0.800 in forecasting SEP events. We then propose techniques for …

Using Asos Ceilings And Mesonet Relative Humidity To Improve General Aviation Flight Planning And Decision Making In Complex Terrain, Connor Hayden Welch

Theses and Dissertations

Despite the increasing availability of weather products and access to data, the issue of weather representativeness, especially in relation to terrain, persists in the aviation industry. Data-sparse regions pose a particular challenge, requiring focused research efforts to address this issue and reduce accident and fatality rates within the general aviation (GA) community. This thesis aims to tackle the specific problem of representing visibility conditions, with a focus on obscuration conditions in elevated terrain.

To achieve this goal, data from Automated Surface Observing System (ASOS) ceilometers and nearby mesonet relative humidity (RH) were analyzed to establish a relationship between the lowest …

Integrable Discretizations For A Generalized Sine-Gordon Equation And The Reductions To The Sine-Gordon Equation And The Short Pulse Equation, Han-Han Sheng, Bao-Feng Feng, Guo-Fu Yu

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this paper, we propose fully discrete analogues of a generalized sine-Gordon (gsG) equation utx=(1+ν∂2x)sinu. The bilinear equations of the discrete KP hierarchy and the proper definition of discrete hodograph transformations are the keys to the construction. Then we derive semi-discrete analogues of the gsG equation from the fully discrete gsG equation by taking the temporal parameter b→0. Especially, one full-discrete gsG equation is reduced to a semi-discrete gsG equation in the case of ν=−1 (Feng {\it et al. Numer. Algorithms} 2023). Furthermore, N-soliton solutions to the semi- and fully discrete analogues of the gsG equation in the determinant form …

Iowa Waste Reduction Center Newsletter, July 2023, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

In this issue:

--- Registration Now Open for the IRA/ISOSWO Conference
--- 20 Years of STAR4D
--- Industry News

Covering By Planks And Avoiding Zeros Of Polynomials, Alexey Glazyrin, Roman Karasev, Alexandr Polyanskii

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

We note that the recent polynomial proofs of the spherical and complex plank covering problems by Zhao and Ortega-Moreno give some general information on zeros of real and complex polynomials restricted to the unit sphere. As a corollary of these results, we establish several generalizations of the celebrated Bang plank covering theorem. We prove a tight polynomial analog of the Bang theorem for the Euclidean ball and an even stronger polynomial version for the complex projective space. Specifically, for the ball, we show that for every real nonzero d-variate polynomial P of degree n⁠, there exists a point in the …

Lossy Kernelization Of Same-Size Clustering, Sayan Bandyapadhyay, Fedor V. Fomin, Petr A. Golovach, Nidhi Purohit, Kirill Simonov

Computer Science Faculty Publications and Presentations

In this work, we study the k-median clustering problem with an additional equal-size constraint on the clusters from the perspective of parameterized preprocessing. Our main result is the first lossy (2-approximate) polynomial kernel for this problem parameterized by the cost of clustering. We complement this result by establishing lower bounds for the problem that eliminate the existence of an (exact) kernel of polynomial size and a PTAS.

Classification Of Drainage Crossings On High-Resolution Digital Elevation Models: A Deep Learning Approach, Di Wu, Ruopu Li, Banafsheh Rekabdar, Claire Talbert, Michael Edidem, Guangxing Wang

Computer Science Faculty Publications and Presentations

High-Resolution Digital Elevation Models (HRDEMs) have been used to delineate fine-scale hydrographic features in landscapes with relatively level topography. However, artificial flow barriers associated with roads are known to cause incorrect modeled flowlines, because these barriers substantially increase the terrain elevation and often terminate flowlines. A common practice is to breach the elevation of roads near drainage crossing locations, which, however, are often unavailable. Thus, developing a reliable drainage crossing dataset is essential to improve the HRDEMs for hydrographic delineation. The purpose of this research is to develop deep learning models for classifying the images that contain the locations of …

Generation Of Cardiac Chamber Models Using Interpretable Generative Neural Networks For Electrophysiology Studies, Sunil Mathew

Dissertations (1934 -)

An Electrophysiology study is conducted to diagnose and treat heart rhythm disorders, such as arrhythmias (abnormal heartbeat) like atrial fibrillation. A catheter is inserted into the chamber of interest to acquire 3D location and electrical information to create an electroanatomical map. This dissertation explores the design of a mapping system based on interpretable generative neural networks for generating patient specific cardiac models. Chapter 1 provides an introduction to electroanatomical mapping, the need for interpretability in neural networks and other relevant topics that are discussed in detail in the chapters that follow. Neural networks are often very large models with millions …

Designing Human-Centered Algorithms For The Public Sector: A Case Study Of The U.S. Child Welfare System, Devansh Saxena

Dissertations (1934 -)

Public sector agencies in the United States are increasingly seeking to emulate business models of the private sector centered in efficiency, cost reduction, and innovation through the adoption of algorithmic systems. These data-driven systems purportedly improve decision-making; however, the public sector poses its own unique challenges where policies, practices, and organizational constraints mediate all decisions. Algorithms that do not account for these pertinent aspects of professional practice frustrate practitioners, diminish the quality of human discretionary work, and amplify biases in decision-making. A human-centered research agenda can help us develop algorithms centered in social-ecological theories that support the decision-making processes of …

Impact Of Difficult Negatives On Twitter Crisis Detection, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint

Research Collection School Of Computing and Information Systems

Twitter has become an alternative information source during a crisis. However, the short, noisy nature of tweets hinders information extraction. While models trained with standard Twitter crisis datasets accomplished decent performance, it remained a challenge to generalize to unseen crisis events. Thus, we proposed adding “difficult” negative examples during training to improve model generalization for Twitter crisis detection. Although adding random noise is a common practice, the impact of difficult negatives, i.e., negative data semantically similar to true examples, was never examined in NLP. Most of existing research focuses on the classification task, without considering the primary information need of …

Plan-And-Solve Prompting: Improving Zero-Shot Chain-Of-Thought Reasoning By Large Language Models, Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning demonstrations which enable LLMs to explicitly generate reasoning steps and improve their reasoning task accuracy. To eliminate the manual effort, Zeroshot-CoT concatenates the target problem statement with “Let’s think step by step” as an input prompt to LLMs. Despite the success of Zero-shot-CoT, it still suffers from three pitfalls: calculation errors, missing-step errors, and semantic misunderstanding errors. To address the missing-step errors, we propose Planand-Solve (PS) Prompting. It …

An Efficient Hybrid Genetic Algorithm For The Quadratic Traveling Salesman Problem, Quang Anh Pham, Hoong Chuin Lau, Minh Hoang Ha, Lam Vu

Research Collection School Of Computing and Information Systems

The traveling salesman problem (TSP) is the most well-known problem in combinatorial optimization which hasbeen studied for many decades. This paper focuses on dealing with one of the most difficult TSP variants named thequadratic traveling salesman problem (QTSP) that has numerous planning applications in robotics and bioinformatics.The goal of QTSP is similar to TSP which finds a cycle visiting all nodes exactly once with minimum total costs. However, the costs in QTSP are associated with three vertices traversed in succession (instead of two like in TSP). This leadsto a quadratic objective function that is much harder to solve.To efficiently solve …

Adaptive Split-Fusion Transformer, Zixuan Su, Jingjing Chen, Lei Pang, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Neural networks for visual content understanding have recently evolved from convolutional ones to transformers. The prior (CNN) relies on small-windowed kernels to capture the regional clues, demonstrating solid local expressiveness. On the contrary, the latter (transformer) establishes long-range global connections between localities for holistic learning. Inspired by this complementary nature, there is a growing interest in designing hybrid models which utilize both techniques. Current hybrids merely replace convolutions as simple approximations of linear projection or juxtapose a convolution branch with attention without considering the importance of local/global modeling. To tackle this, we propose a new hybrid named Adaptive Split-Fusion Transformer …

Discriminative Reasoning With Sparse Event Representation For Document-Level Event-Event Relation Extraction, Changsen Yuan, Heyan Huang, Yixin Cao, Yonggang Wen

Research Collection School Of Computing and Information Systems

Document-level Event-Event Relation Extraction (DERE) aims to extract relations between events in a document. It challenges conventional sentence-level task (SERE) with difficult long-text understanding. In this paper, we propose a novel DERE model (SENDIR) for better document-level reasoning. Different from existing works that build an event graph via linguistic tools, SENDIR does not require any prior knowledge. The basic idea is to discriminate event pairs in the same sentence or span multiple sentences by assuming their different information density: 1) low density in the document suggests sparse attention to skip irrelevant information. Our module 1 designs various types of attention …

Binalign: Alignment Padding Based Compiler Provenance Recovery, Maliha Ismail, Yan Lin, Donggyun Han, Debin Gao

Research Collection School Of Computing and Information Systems

Compiler provenance is significant in investigating the source-level indicators of binary code, like development-environment, source compiler, and optimization settings. Not only does compiler provenance analysis have important security applications in malware and vulnerability analysis, but it is also very challenging to extract useful artifacts from binary when high-level language constructs are missing. Previous works applied machine-learning techniques to predict the source compiler of binaries. However, most of the work is done on the binaries compiled on Linux operating system. We highlight the importance and need to explore Windows compilers and the complicated binaries compiled on the latest versions of these …

Diaasq: A Benchmark Of Conversational Aspect-Based Sentiment Quadruple Analysis, Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji

Research Collection School Of Computing and Information Systems

The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We deliberately develop a neural model to benchmark …

Finding Causally Different Tests For An Industrial Control System, Christopher M. Poskitt, Yuqi Chen, Jun Sun, Yu Jiang

Research Collection School Of Computing and Information Systems

Industrial control systems (ICSs) are types of cyber-physical systems in which programs, written in languages such as ladder logic or structured text, control industrial processes through sensing and actuating. Given the use of ICSs in critical infrastructure, it is important to test their resilience against manipulations of sensor/actuator inputs. Unfortunately, existing methods fail to test them comprehensively, as they typically focus on finding the simplest-to-craft manipulations for a testing goal, and are also unable to determine when a test is simply a minor permutation of another, i.e. based on the same causal events. In this work, we propose a guided …

Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai

Research Collection School Of Computing and Information Systems

Recent works using deep reinforcement learning (RL) to solve routing problems such as the capacitated vehicle routing problem (CVRP) have focused on improvement learning-based methods, which involve improving a given solution until it becomes near-optimal. Although adequate solutions can be achieved for small problem instances, their efficiency degrades for large-scale ones. In this work, we propose a newimprovement learning-based framework based on imitation learning where classical heuristics serve as experts to encourage the policy model to mimic and produce similar or better solutions. Moreover, to improve scalability, we propose Clockwise Clustering, a novel augmented framework for decomposing large-scale CVRP into …

A Lightweight Privacy-Preserving Path Selection Scheme In Vanets, Guojun Wang, Huijie Yang

Research Collection School Of Computing and Information Systems

With the rapid development of edge computing, artificial intelligence and other technologies, intelligent transportation services in the vehicular ad hoc networks (VANETs) such as in-vehicle navigation and distress alert are increasingly being widely used in life. Currently, road navigation is an essential service in the vehicle network. However, when a user employs the road navigation service, his private data maybe exposed to roadside nodes. Meanwhile, when the trusted authorization sends the navigation route data to the user, the user can obtain all the road data. Especially, other unrequested data might be related to the military. Therefore, how to achieve secure …