Digital Commons Network^™

A Review Of Solar Hybrid Photovoltaic-Thermal (Pv-T) Collectors And Systems, Todd Otanicar

Mechanical and Biomedical Engineering Faculty Publications and Presentations

In this paper, we provide a comprehensive overview of the state-of-the-art in hybrid PV-T collectors and the wider systems within which they can be implemented, and assess the worldwide energy and carbon mitigation potential of these systems. We cover both experimental and computational studies, identify opportunities for performance enhancement, pathways for collector innovation, and implications of their wider deployment at the solar-generation system level. First, we classify and review the main types of PV-T collectors, including air-based, liquid-based, dual air–water, heat-pipe, building integrated and concentrated PV-T collectors. This is followed by a presentation of performance enhancement opportunities and pathways for …

Water Current, Volume 55, No. 2, Summer 2023

Water Current Newsletter

No abstract provided.

Droughtscape, Summer 2023, National Drought Mitigation Center

DroughtScape: Quarterly Newsletter of the National Drought Mitigation Center (2007–2023)

DroughtScape, Summer 2023

Social Troubleshooting Workshops: Upskilling Students' Soft And Self-Reflection Skills, Sandra Schulz, Rita Garcia, Christoph Treude

Research Collection School Of Computing and Information Systems

This poster focuses on workshops to support students’ soft and selfreflection skills during collaborative learning. These workshops intend to help reduce anxiety during group work and to promote inclusive and equitable collaborative learning environments. Unfortunately, single-paced instructional approaches are typically applied in learning environments [3] and do not consider students’ needs when learning nor provide soft-skills guidance that encourages equal participation. The workshops offer educator and student support for equitable group work through upskilling students’ soft skills, such as leadership and communication, that promote better teamwork. By assisting students in developing and practising soft and self-reflection skills, they might have …

A Comprehensive Study On Quality Assurance Tools For Java, Han Liu, Sen Chen, Ruitao Feng, Chengwei Liu, Kaixuan Li, Zhengzi Xu, Liming Nie, Yang Liu, Yixiang Chen

Research Collection School Of Computing and Information Systems

Quality assurance (QA) tools are receiving more and more attention and are widely used by developers. Given the wide range of solutions for QA technology, it is still a question of evaluating QA tools. Most existing research is limited in the following ways: (i) They compare tools without considering scanning rules analysis. (ii) They disagree on the effectiveness of tools due to the study methodology and benchmark dataset. (iii) They do not separately analyze the role of the warnings. (iv) There is no large-scale study on the analysis of time performance. To address these problems, in the paper, we systematically …

A Unified Multi-Task Learning Framework For Multi-Goal Conversational Recommender Systems, Yang Deng, Wenxuan Zhang, Weiwen Xu, Wenqiang Lei, Tat-Seng Chua, Wai Lam

Research Collection School Of Computing and Information Systems

Question generation (QG) aims to automatically generate fluent and relevant questions, where the two most mainstream directions are generating questions from unstructured contextual texts (CQG), such as news articles, and generating questions from structured factoid texts (FQG), such as knowledge graphs or tables. Existing methods for these two tasks mainly face challenges of limited internal structural information as well as scarce background information, while these two tasks can benefit each other for alleviating these issues. For example, when meeting the entity mention “United Kingdom” in CQG, it can be inferred that it is a country in European continent based on …

Testing Automated Driving Systems By Breaking Many Laws Efficiently, Xiaodong Zhang, Wei Zhao, Yang Sun, Jun Sun, Yulong Shen, Xuewen Dong, Zijiang Yang

Research Collection School Of Computing and Information Systems

An automated driving system (ADS), as the brain of an autonomous vehicle (AV), should be tested thoroughly ahead of deployment. ADS must satisfy a complex set of rules to ensure road safety, e.g., the existing traffic laws and possibly future laws that are dedicated to AVs. To comprehensively test an ADS, we would like to systematically discover diverse scenarios in which certain traffic law is violated. The challenge is that (1) there are many traffic laws (e.g., 13 testable articles in Chinese traffic laws and 16 testable articles in Singapore traffic laws, with 81 and 43 violation situations respectively); and …

Cheer: Centrality-Aware High-Order Event Reasoning Network For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Yan Zhang, Zhiwei Liu

Research Collection School Of Computing and Information Systems

Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one or two “central” events that prevail throughout the document, with most other events serving as either their cause or consequence. In this paper, we manually annotate central events for a systematical investigation and propose a novel DECI model, CHEER, which performs high-order reasoning while considering event centrality. First, we summarize a general GNN-based DECI model and provide a unified view for better understanding. Second, we …

Cone: An Efficient Coarse-To-Fine Alignment Framework For Long Video Temporal Grounding, Zhijian Hou, Wanjun Zhong, Lei Ji, Difei Gao, Kun Yan, Wing-Kwong Chan, Chong-Wah Ngo, Mike Z. Shou, Nan. Duan

Research Collection School Of Computing and Information Systems

This paper tackles an emerging and challenging problem of long video temporal grounding (VTG) that localizes video moments related to a natural language (NL) query. Compared with short videos, long videos are also highly demanded but less explored, which brings new challenges in higher inference computation cost and weaker multi-modal alignment. To address these challenges, we propose CONE, an efficient COarse-to-fiNE alignment framework. CONE is a plug-and-play framework on top of existing VTG models to handle long videos through a sliding window mechanism. Specifically, CONE (1) introduces a query-guided window selection strategy to speed up inference, and (2) proposes a …

Fine-Grained Domain Adaptive Crowd Counting Via Point-Derived Segmentation, Yongtuo Liu, Dan Xu, Sucheng Ren, Hanjie Wu, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods achieve promising results, they typically regard each crowd image as a whole and reduce domain discrepancies in a holistic manner, thus limiting further improvement of domain adaptation performance. To this end, we propose to untangle domain-invariant crowd and domain-specific background from crowd images and design a fine-grained domain adaption method for crowd counting. Specifically, to disentangle crowd from background, we propose to learn crowd segmentation from point-level crowd counting annotations in a …

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 …

Recognizing Hand Gestures Using Solar Cells, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, B. Mushfika Upama, Ashraf Uddin, Youseef, Moustafa

Research Collection School Of Computing and Information Systems

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its discernible signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for …

Singapore's Ai Applications In The Public Sector: Six Examples, Steven M. Miller

Research Collection School Of Computing and Information Systems

Steven M. Miller describes six instances in which Singapore has applied AI in the public sector, illustrating different ways of improving its engagement with the public by making government services more accessible, anywhere, anytime, and speeding its responses to public processes and feedback. He illustrates how its leaders made the city a living lab for AI use, and what they learned.

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 …

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 …

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 …

Take A Break In The Middle: Investigating Subgoals Towards Hierarhical Script Generation, Xinze Li, Yixin Cao, Muhao Chen, Aixin Sun

Research Collection School Of Computing and Information Systems

Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of cognitive theory. Instead of a simple flat structure, the steps are typically organized hierarchically — Human often decompose a complex task into subgoals, where each subgoal can be further decomposed into steps. To establish the benchmark, we contribute a new dataset, propose several baseline methods, and set up evaluation metrics. Both automatic and human evaluation verify the high-quality of dataset, as well as the effectiveness of incorporating subgoals …

Synthesizing Speech Test Cases With Text-To-Speech? An Empirical Study On The False Alarms In Automated Speech Recognition Testing, Julia Kaiwen Lau, Kelvin Kai Wen Kong, Julian Hao Yong, Per Hoong Tan, Zhou Yang, Zi Qian Yong, Joshua Chern Wey Low, Chun Yong Chong, Mei Kuan Lim, David Lo

Research Collection School Of Computing and Information Systems

Recent studies have proposed the use of Text-To-Speech (TTS) systems to automatically synthesise speech test cases on a scale and uncover a large number of failures in ASR systems. However, the failures uncovered by synthetic test cases may not reflect the actual performance of an ASR system when it transcribes human audio, which we refer to as false alarms. Given a failed test case synthesised from TTS systems, which consists of TTS-generated audio and the corresponding ground truth text, we feed the human audio stating the same text to an ASR system. If human audio can be correctly transcribed, an …

Mitigating Adversarial Attacks On Data-Driven Invariant Checkers For Cyber-Physical Systems, Rajib Ranjan Maiti, Cheah Huei Yoong, Venkata Reddy Palleti, Arlindo Silva, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

The use of invariants in developing security mechanisms has become an attractive research area because of their potential to both prevent attacks and detect attacks in Cyber-Physical Systems (CPS). In general, an invariant is a property that is expressed using design parameters along with Boolean operators and which always holds in normal operation of a system, in particular, a CPS. Invariants can be derived by analysing operational data of various design parameters in a running CPS, or by analysing the system's requirements/design documents, with both of the approaches demonstrating significant potential to detect and prevent cyber-attacks on a CPS. While …

Transfer-Once-For-All: Ai Model Optimization For Edge, Achintya Kundu, Laura Wynter, Rhui Dih Lee, Luis Angel Bathen

Research Collection School Of Computing and Information Systems

Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches use evolutionary search to extract models of different sizes from a supernet trained on a very large data set, and then fine-tune the extracted models on the typically small, real-world data set of interest. The computational cost of training thus grows linearly with the number of different model deployment scenarios. Hence, we propose Transfer-Once-For-All (TOFA) for supernet-style training on small data sets with constant computational training cost over any number of edge …

A Review On The Effects Of Chanting And Solfeggio Frequencies On Well-Being, Xuyu Yang, Fiona Fui-Hoon Nah, Fen Lin

Research Collection School Of Computing and Information Systems

This paper presents our literature review on how chanting and solfeggio frequencies affect brain activity, enhance well-being, and inform the design of sound therapy. We call for more scientific research to investigate and better understand the effects of chanting and solfeggio frequencies on well-being.

Norfolk Integrated Waste-Energy Framework, Emma Kurtz, Abigail Schroeder, Jonah Mcdowell

Project Energy Nebraska: Student Projects

Environmental Engagement in the Community projects, spring 2023 A poster titled Norfolk Integrated Waste-Energy Framework for client City of Norfolk, Nebraska and Nebraska Public Power District.

Faculty sponsor: David Gosselin

Automated Fiber Placement Through Thickness Defect Stacking Optimization, Noah Christopher Swingle

Theses and Dissertations

In its 2022 commercial market outlook, Boeing forecasted an 80% increase in the global fleet through 2041 compared to 2019 pre-pandemic levels. This sharp rise in demand will drive pressure onto airframe manufacturers to ramp up production and find more efficient ways to design and manufacture airplanes. Complicating this challenge is the industry’s recent transformation from traditional metal-based airframes towards hybrid composite-metal aircraft. While composites have been used in aviation for decades, aircraft manufacturers are still struggling to design and manufacture quality parts at a high rate. Automated Fiber Placement (AFP) is one of the main manufacturing techniques used to …

Improving Outcomes For Older Struggling Readers In Special Education Resource, Kathryn Stone

Education Theses and Dissertations

This research aimed to increase the reading achievement of sixth-grade special education students receiving interventions in a resource setting. This problem of practice was created in response to the theory of change made by a Network Improvement Community (NIC) rooted in the lack of professional development offered by the school district for special education teachers in teaching students foundational reading skills. The research questions used for this study this evaluation were 1) What percent of students made progress in each reporting category (word study, grammar, and comprehension) in the Lexia PowerUp literacy program? 2) To what extent did the teachers …

To What Extent Do 3- To -6-Year-Olds Demonstrate Connection To Nature At A Montessori School In The Upper Midwest, Natasha Yates

School of Education and Leadership Student Capstone Theses and Dissertations

Yates, N. (2023). To What Extent Do 3- to -6-Year-Olds Demonstrate Connection to Nature at a Montessori School in the Upper Midwest.

Young children are born into a world today with a drastically changing environmental climate. When young people develop an emotional attachment and sense of identity with nature, they may be more likely to behave in less destructive ways towards the planet and possibly live with a sense of responsibility and respect for nature. This mixed method study aimed to measure to what extent 3- to 6-year-olds demonstrated environmental sensitivity, awareness, and preferences at a nature-based Montessori school in …

Density-Mediated Spin Correlations Drive Edge-To-Bulk Flow Transition In Active Chiral Matter, Alexander P. Petroff, Christopher Whittington, Arshad Kudrolli

Physics

We demonstrate that edge currents develop in active chiral matter due to boundary shielding over a wide range of densities corresponding to a gas, fluid, and crystal. The system is composed of spinning disk-shaped grains with chirally arranged tilted legs confined in a circular vibrating chamber. The edge currents are shown to increasingly drive circulating bulk flows with area fraction as percolating clusters develop due to increasing spin-coupling between neighbors mediated by frictional contacts. Edge currents are observed even in the dilute limit. While, at low area fraction, the average flux vanishes except within a distance that is of the …

Er Translocon Inhibitor Ipomoeassin F Inhibits Triple-Negative Breast Cancer Growth Via Blocking Er Molecular Chaperones, Shishi Tao, Eun Ju Yang, Guanghui Zong, Pui Kei Mou, Guowen Ren, Liang Chen, Ho Jeong Kwon, Jianhong Zhou, Zhijian Hu, Arman Khosravi, Qingyang Zhang, Yuchun Du, Wei Q. Shi, Joong Sup Shim

Biological Sciences Faculty Publications and Presentations

Triple-negative breast cancer (TNBC) is an aggressive type of breast cancer where no effective therapy has been developed. Here, we report that the natural product ER translocon inhibitor ipomoeassin F is a selective inhibitor of TNBC cell growth. A proteomic analysis of TNBC cells revealed that ipomoeassin F significantly reduced the levels of ER molecular chaperones, including PDIA6 and PDIA4, and induced ER stress, unfolded protein response (UPR) and autophagy in TNBC cells. Mechanistically, ipomoeassin F, as an inhibitor of Sec61α-containing ER translocon, blocks ER translocation of PDIA6, inducing its proteasomal degradation. Silencing of PDIA6 or PDIA4 by RNA interferences …

Accurate Covariance Estimation For Pose Data From Iterative Closest Point Algorithm, Rick H. Yuan, Clark N. Taylor, Scott L. Nykl

Faculty Publications

One of the fundamental problems of robotics and navigation is the estimation of the relative pose of an external object with respect to the observer. A common method for computing the relative pose is the iterative closest point (ICP) algorithm, where a reference point cloud of a known object is registered against a sensed point cloud to determine relative pose. To use this computed pose information in downstream processing algorithms, it is necessary to estimate the uncertainty of the ICP output, typically represented as a covariance matrix. In this paper, a novel method for estimating uncertainty from sensed data is …

Antitrust And Sustainability: A Landscape Analysis, Columbia Center On Sustainable Investment, Sabin Center For Climate Change Law

Columbia Center on Sustainable Investment

Competition policy and antitrust law are experiencing a global renaissance. New market realities such as digital market gatekeepers, the financialization of firms, highly concentrated markets, a rising labor movement, industrial policy, and trade wars, among others, are radically reshaping how this policy area is understood and applied.

Sustainability concerns have also been a driving force for reconstituting antitrust to meet twenty-first century challenges. It is now widely accepted that competition policy – both its aims and its enforcement – has wider societal impacts beyond competition, including effects on democracy, economic inequality, growth and innovation, racial and gender imbalances, privacy, geopolitical …

An Evolutionary Comparative Study Of Congenital Stationary Night Blindness-Associated Trpm1 Genetic Variants Of Uncertain Significance In Horses And Humans Utilizing Caenorhabditis Elegans, Gabrielle Davis

Theses

Congenital stationary night blindness (CSNB) is a heterogeneous collection of genetic diseases affecting the eyes and vision in horses and humans. Current research has implicated several genetic mutations impacting different genes involved in phototransduction and signal transmission, including TRPM1. In horses, genetic mutations in TRPM1 also result in a leopard spotting pattern or leopard complex. The goal of this study is to examine the potential impact of CSNB associated TRPM1 missense variants of uncertain significance (VUS). Previous research in Caenorhabditis elegans have revealed an orthologous TRPM1 gene known as gon-2 that allows for comparative studies. The evolutionary relationship of …