Physical Sciences and Mathematics Commons^™

Building Legal Datasets, Jerrold Soh

Research Collection Yong Pung How School Of Law

Data-centric AI calls for better, not just bigger, datasets. As data protection laws with extra-territorial reach proliferate worldwide, ensuring datasets are legal is an increasingly crucial yet overlooked component of “better”. To help dataset builders become more willing and able to navigate this complex legal space, this paper reviews key legal obligations surrounding ML datasets, examines the practical impact of data laws on ML pipelines, and offers a framework for building legal datasets.

Research Information Management In The United States: Part One, Findings And Recommendations, Rebecca Bryant, Jan Fransen, Pablo De Castro, Brenna Helmstutler, David Scherer

Copyright, Fair Use, Scholarly Communication, etc.

Research information management (RIM) is a rapidly growing area of investment in US research universities. RIM systems that support the collection and use of research outputs metadata have been in place for many years. Globally, the RIM ecosystem is quite mature in locales where national research assessment exercises like the United Kingdom’s Research Excellence Framework (REF) and the Excellence in Research for Australia (ERA) require institutions to collect and report on the outputs of institutional research. A pan-European community of practice is led by euroCRIS.

This report describes six discrete RIM use cases detailed in the companion report:

• Faculty …

A Global Set Of Subduction Zone Earthquake Scenarios And Recurrence Intervals Inferred From Geodetically Constrained Block Models Of Interseismic Coupling Distributions, Shannon E. Graham, John P. Loveless, Brendan J. Meade

Geosciences: Faculty Publications

The past 100 years have seen the occurrence of five (Formula presented.) earthquakes and 94 (Formula presented.) earthquakes. Here we assess the potential for future great earthquakes using inferences of interseismic subduction zone coupling from a global block model incorporating both tectonic plate motions and earthquake cycle effects. Interseismic earthquake cycle effects are represented using a first-order quasistatic elastic approximation and include (Formula presented.) of interacting fault system area across the globe. We use estimated spatial variations in decadal-duration coupling at 15 subduction zones and the Himalayan range front to estimate the locations and magnitudes of potential seismic events using …

Protein Deep Profile And Model Predictions For Identifying The Causal Genes Of Male Infertility Based On Deep Learning, Fang Xu, Ganggang Guo, Feida Zhu, Xiaojun Tan, Liqing Fan

Research Collection School Of Computing and Information Systems

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. Millions of genes have been sequenced in data-driven genomics era, but their causal relationships with disease phenotypes remain limited, due to the difficulty of elucidating underlying causal genes by laboratory-based strategies. Here, we proposed an innovative deep learning computational modeling alternative (DPPCG framework) for identifying causal (coding) genes for a specific disease phenotype. In terms of male infertility, we introduced proteins as intermediate cell variables, leveraging integrated deep knowledge representations (Word2vec, ProtVec, Node2vec, and Space2vec) quantitatively represented as ‘protein deep profiles’. We …

Wav-Bert: Cooperative Acoustic And Linguistic Representation Learning For Low-Resource Speech Recognition, Guolin Zheng, Yubei Xiao, Ke Gong, Pan Zhou, Xiaodan Liang, Liang Lin

Research Collection School Of Computing and Information Systems

Unifying acoustic and linguistic representation learning has become increasingly crucial to transfer the knowledge learned on the abundance of high-resource language data for low-resource speech recognition. Existing approaches simply cascade pre-trained acoustic and language models to learn the transfer from speech to text. However, how to solve the representation discrepancy of speech and text is unexplored, which hinders the utilization of acoustic and linguistic information. Moreover, previous works simply replace the embedding layer of the pre-trained language model with the acoustic features, which may cause the catastrophic forgetting problem. In this work, we introduce Wav-BERT, a cooperative acoustic and linguistic …

Cs-Light: Camera Sensing Based Occupancy-Aware Robust Smart Building Lighting Control, Anuradha Ravi, Kasun Pramuditha Gamlath, Siyan Hu, Archan Misra

Research Collection School Of Computing and Information Systems

We describe the practical development of a smart lighting control system, CS-Light, that uses a preexisting surveillance camera infrastructure as the sole sensing substrate. At a high level, the camera feeds are used to both (a) estimate the illuminance of individual, fine-grained (roughly 12m2) sub-regions, and (b) identify sub-regions that have non-transient human occupancy. Subsequently, these estimates are used to perform fine-grained (non-binary) power optimization of a set of LED luminaires, collectively minimizing energy consumption while assuring comfort to human occupants. The key to our approach is the ability to tackle the challenging problem of translating the luminance (pixel intensity) …

Learning To Teach And Learn For Semi-Supervised Few-Shot Image Classification, Xinzhe Li, Jianqiang Huang, Yaoyao Liu, Qin Zhou, Shibao Zheng, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

This paper presents a novel semi-supervised few-shot image classification method named Learning to Teach and Learn (LTTL) to effectively leverage unlabeled samples in small-data regimes. Our method is based on self-training, which assigns pseudo labels to unlabeled data. However, the conventional pseudo-labeling operation heavily relies on the initial model trained by using a handful of labeled data and may produce many noisy labeled samples. We propose to solve the problem with three steps: firstly, cherry-picking searches valuable samples from pseudo-labeled data by using a soft weighting network; and then, cross-teaching allows the classifiers to teach mutually for rejecting more noisy …

Predicting Anti-Asian Hateful Users On Twitter During Covid-19, Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, Yong-Yeol Ahn

Research Collection School Of Computing and Information Systems

We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attracting many researchers. Here, we apply natural language processing techniques to characterize social media users who began to post anti-Asian hate messages during COVID-19. We compare two user groups—those who posted anti-Asian slurs and those who did not—with respect to a rich set of features measured with data prior to COVID-19 and show that it is possible to predict who later publicly posted anti-Asian …

Incbl: Incremental Bug Localization, Zhou Yang, Jieke Shi, Wang Shaowei, David Lo

Research Collection School Of Computing and Information Systems

Numerous efforts have been invested in improving the effectiveness of bug localization techniques, whereas little attention is paid to making these tools run more efficiently in continuously evolving software repositories. This paper first analyzes the information retrieval model behind a classic bug localization tool, BugLocator, and builds a mathematical foundation illustrating that the model can be updated incrementally when codebase or bug reports evolve. Then, we present IncBL, a tool for Incremental Bug Localization in evolving software repositories. IncBL is evaluated on the Bugzbook dataset, and the results show that IncBL can significantly reduce the running time by 77.79% on …

Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability, Xin Lv, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Yichi Zhang, Zelin Dai

Research Collection School Of Computing and Information Systems

Multi-hop reasoning has been widely studied in recent years to obtain more interpretable link prediction. However, we find in experiments that many paths given by these models are actually unreasonable, while little work has been done on interpretability evaluation for them. In this paper, we propose a unified framework to quantitatively evaluate the interpretability of multi-hop reasoning models so as to advance their development. In specific, we define three metrics, including path recall, local interpretability, and global interpretability for evaluation, and design an approximate strategy to calculate these metrics using the interpretability scores of rules. We manually annotate all possible …

Where2change: Change Request Localization For App Reviews, Tao Zhang, Jiachi Chen, Xian Zhan, Xiapu Luo, David Lo, He Jiang

Research Collection School Of Computing and Information Systems

Million of mobile apps have been released to the market. Developers need to maintain these apps so that they can continue to benefit end users. Developers usually extract useful information from user reviews to maintain and evolve mobile apps. One of the important activities that developers need to do while reading user reviews is to locate the source code related to requested changes. Unfortunately, this manual work is costly and time consuming since: (1) an app can receive thousands of reviews, and (2) a mobile app can consist of hundreds of source code files. To address this challenge, Palomba et …

A Bert-Based Two-Stage Model For Chinese Chengyu Recommendation, Minghuan Tan, Jing Jiang, Bingtian Dai

Research Collection School Of Computing and Information Systems

In Chinese, Chengyu are fixed phrases consisting of four characters. As a type of idioms, their meanings usually cannot be derived from their component characters. In this paper, we study the task of recommending a Chengyu given a textual context. Observing some of the limitations with existing work, we propose a two-stage model, where during the first stage we re-train a Chinese BERT model by masking out Chengyu from a large Chinese corpus with a wide coverage of Chengyu. During the second stage, we fine-tune the retrained, Chengyu-oriented BERT on a specific Chengyu recommendation dataset. We evaluate this method on …

Probablistic Verification Of Neural Networks Against Group Fairness, Bing Sun, Jun Sun, Ting Dai, Lijun Zhang

Research Collection School Of Computing and Information Systems

Fairness is crucial for neural networks which are used in applications with important societal implication. Recently, there have been multiple attempts on improving fairness of neural networks, with a focus on fairness testing (e.g., generating individual discriminatory instances) and fairness training (e.g., enhancing fairness through augmented training). In this work, we propose an approach to formally verify neural networks against fairness, with a focus on independence-based fairness such as group fairness. Our method is built upon an approach for learning Markov Chains from a user-provided neural network (i.e., a feed-forward neural network or a recurrent neural network) which is guaranteed …

Figcps: Effective Failure-Inducing Input Generation For Cyber-Physical Systems With Deep Reinforcement Learning, Shaohua Zhang, Shuang Liu, Jun Sun, Yuqi Chen, Wenzhi Huang, Jinyi Liu, Jian Liu, Jianye Hao

Research Collection School Of Computing and Information Systems

Cyber-Physical Systems (CPSs) are composed of computational control logic and physical processes, that intertwine with each other. CPSs are widely used in various domains of daily life, including those safety-critical systems and infrastructures, such as medical monitoring, autonomous vehicles, and water treatment systems. It is thus critical to effectively test them. However, it is not easy to obtain test cases which can fail the CPS. In this work, we propose a failure-inducing input generation approach FIGCPS for CPS, which requires no knowledge of the CPS under test or any history logs of the CPS which are usually hard to obtain. …

Using Role Play To Develop An Empathetic Mindset In Executive Education, Anuradha Ravi, Kasun Gamlath, Siyan Hu, Archan Misra

Research Collection School Of Computing and Information Systems

We describe the practical development of a smart lighting control system, CS-Light, that uses a preexisting surveillance camera infrastructure as the sole sensing substrate. At a high level, the camera feeds are used to both (a) estimate the illuminance of individual, fine-grained (roughly 12m2) sub-regions, and (b) identify sub-regions that have non-transient human occupancy. Subsequently, these estimates are used to perform fine-grained (non-binary) power optimization of a set of LED luminaires, collectively minimizing energy consumption while assuring comfort to human occupants. The key to our approach is the ability to tackle the challenging problem of translating the luminance (pixel intensity) …

Expediting The Accuracy-Improving Process Of Svms For Class Imbalance Learning, Bin Cao, Yuqi Liu, Chenyu Hou, Jing Fan, Baihua Zheng, Jianwei Jin

Research Collection School Of Computing and Information Systems

To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., DEC (Different Error Costs) and FSVM-CIL (Fuzzy SVM for Class Imbalance Learning). They relocate the hyperplane by adjusting the costs associated with misclassifying samples. However, the error costs are determined either empirically or by performing an exhaustive search in the parameter space. Both strategies can not guarantee effectiveness and efficiency simultaneously. In this paper, we propose ATEC, a solution that can efficiently find a preferable hyperplane by automatically tuning the error cost for between-class samples. ATEC distinguishes itself from all …

Factual Consistency Evaluation For Text Summarization Via Counterfactual Estimation, Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding

Research Collection School Of Computing and Information Systems

Despite significant progress has been achieved in text summarization, factual inconsistency in generated summaries still severely limits its practical applications. Among the key factors to ensure factual consistency, a reliable automatic evaluation metric is the first and the most crucial one. However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice. In light of these challenges, we propose a novel metric to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship …

Aspect-Based Sentiment Analysis In Question Answering Forums, Wenxuan Zhang, Yang Deng, Xin Li, Lidong Bing, Wai Lam

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews. Recently, another kind of opinion sharing platform, namely question answering (QA) forum, has received increasing popularity, which accumulates a large number of user opinions towards various aspects. This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair. Unlike review sentences, a QA pair is composed of two parallel sentences, which requires interaction modeling to align the aspect mentioned in the question …

Aspect Sentiment Quad Prediction As Paraphrase Generation, Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, Wai Lam

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. Existing studies usually consider the detection of partial sentiment elements, instead of predicting the four elements in one shot. In this work, we introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, which can reveal a more comprehensive and complete aspect-level sentiment structure. We further propose a novel Paraphrase modeling paradigm to cast the ASQP task to a …

Efficient Online-Friendly Two-Party Ecdsa Signature, Haiyang Xue, Ho Man Au, Xiang Xie, Hon Tsz Yuen, Handong Cui

Research Collection School Of Computing and Information Systems

Two-party ECDSA signatures have received much attention due to their widespread deployment in cryptocurrencies. Depending on whether or not the message is required, we could divide two-party signing into two different phases, namely, offline and online. Ideally, the online phase should be made as lightweight as possible. At the same time, the cost of the offline phase should remain similar to that of a normal signature generation. However, the existing two-party protocols of ECDSA are not optimal: either their online phase requires decryption of a ciphertext, or their offline phase needs at least two executions of multiplicative-to-additive conversion which dominates …

The Solar Memory From Hours To Decades, Markus J. Aschwanden, Jay R. Johnson

Faculty Publications

Waiting-time distributions allow us to distinguish at least three different types of dynamical systems, including (i) linear random processes (with no memory); (ii) nonlinear, avalanche-type, nonstationary Poisson processes (with memory during the exponential growth of the avalanche rise time); and (iii) chaotic systems in the state of a nonlinear limit cycle (with memory during the oscillatory phase). We describe the temporal evolution of the flare rate λ(t) ∝ t p with a polynomial function, which allows us to distinguish linear (p ≈ 1) from nonlinear (p  2) events. The power-law slopes α of the observed waiting times (with full …

Searches For Continuous Gravitational Waves From Young Supernova Remnants In The Early Third Observing Run Of Advanced Ligo And Virgo, R. Abbott, T. D. Abbott, S. Abraham, Teviet Creighton, Mario C. Diaz, Soma Mukherjee, Volker Quetschke, Karla E. Ramirez, Wenhui Wang

Physics & Astronomy Faculty Publications

We present results of three wide-band directed searches for continuous gravitational waves from 15 young supernova remnants in the first half of the third Advanced LIGO and Virgo observing run. We use three search pipelines with distinct signal models and methods of identifying noise artifacts. Without ephemerides of these sources, the searches are conducted over a fRequency band spanning from 10 to 2 kHz. We find no evidence of continuous gravitational radiation from these sources. We set upper limits on the intrinsic signal strain at 95% confidence level in sample subbands, estimate the sensitivity in the full band, and derive …

Constraints From Ligo O3 Data On Gravitational-Wave Emission Due To R-Modes In The Glitching Pulsar Psr J0537–6910, R. Abbott, T. D. Abbott, S. Abraham, F. Acernese, K. Ackley, Teviet Creighton, Mario C. Diaz, Soma Mukherjee, Volker Quetschke, Karla E. Ramirez, Wenhui Wang

Physics & Astronomy Faculty Publications

We present a search for continuous gravitational-wave emission due to r-modes in the pulsar PSR J0537-6910 using data from the LIGO-Virgo Collaboration observing run O3. PSR J0537-6910 is a young energetic X-ray pulsar and is the most frequent glitcher known. The inter-glitch braking index of the pulsar suggests that gravitational-wave emission due to r-mode oscillations may play an important role in the spin evolution of this pulsar. Theoretical models confirm this possibility and predict emission at a level that can be probed by ground-based detectors. In order to explore this scenario, we search for r-mode emission in the epochs between …

Galaxy And Mass Assembly (Gama): The Merging Potential Of Brightest Group Galaxies, K. Banks, S. Brough, Benne Holwerda, A. M. Hopkins, Á. R. López-Sánchez, S. Phillipps, K. A. Pimbblet, A. S. G. Robotham

Faculty and Staff Scholarship

Using a volume-limited sample of 550 groups from the Galaxy And Mass Assembly Galaxy Group Catalogue spanning the halo mass range , we investigate the merging potential of central Brightest Group Galaxies (BGGs). We use spectroscopically confirmed close-companion galaxies as an indication of the potential stellar mass buildup of low-redshift BGGs, z ≤ 0.2. We identify 17 close-companion galaxies with projected separations rp < 30 kpc, relative velocities Δv ≤ 300 km s−1, and stellar mass ratios MBGG/MCC ≤ 4 relative to the BGG. These close-companion galaxies yield a total pair fraction of 0.03 ± 0.01. Overall, we find that BGGs …

Wildlife Responses To Livestock Guard Dogs And Domestic Sheep On Open Range, Daniel Kinka, Jeffrey T. Schultz, Julie K. Young

United States Department of Agriculture Wildlife Services: Staff Publications

Domestic sheep (Ovis aries) are an attractant to carnivores; however, sheep are often accompanied by humans and livestock guardian dogs (LGDs; Canis familiaris), which defend sheep from depredation. Sheep also compete directly with wildlife for grazing resources. Although practiced for millennia in much of the world outside North America, the effect that transhumance has on wildlife is poorly understood. To test the effect of sheep bands (sheep, humans, and LGDs) on wildlife, we modeled the detection probability of wild mammals relative to the presence of sheep bands in the Northwestern United States. Sheep band presence was associated …

Mongooses (Urva Auropunctata) As Reservoir Hosts Of Leptospira Species In The United States Virgin Islands, 2019–2020, Hannah M. Cranford, A. Springer Browne, Karen Lecount, Tammy Anderson, Camila Hamond, Linda Schlater, Tod Stuber, Valicia J. Burke-France, Marissa Taylor, Cosme J. Harrison, Katia Y. Matias, Alexandra Medley, John Rossow, Nicholas Wiese, Leanne Jankelunas, Leah De Wilde, Michelle Mehalick, Gerard L. Blanchard, Keith R. Garcia, Alan S. Mckinley, Claudia D. Lombard, Nicole F. Angeli, David Horner, Thomas Kelley, David J. Worthington, Jennifer Valiulis, Bethany Bradford, Are Berentsen, Johanna S. Salzer, Renee Galloway, Ilana J. Schafer, Kristine Bisgard, Joseph Roth, Brett R. Ellis, Esther M. Ellis, Jarlath E. Nally

United States Department of Agriculture Wildlife Services: Staff Publications

During 2019–2020, the Virgin Islands Department of Health investigated potential animal reservoirs of Leptospira spp., the bacteria that cause leptospirosis. In this cross-sectional study, we investigated Leptospira spp. exposure and carriage in the small Indian mongoose (Urva auropunctata, syn: Herpestes auropunctatus), an invasive animal species. This study was conducted across the three main islands of the U.S. Virgin Islands (USVI), which are St. Croix, St. Thomas, and St. John. We used the microscopic agglutination test (MAT), fluorescent antibody test (FAT), real-time polymerase chain reaction (lipl32 rt-PCR), and bacterial culture to evaluate serum and kidney specimens and compared the …

Preparation And Evaluation Of Crocetin-Coated Biodegradable Polymer On Top Of Magnetite Nanoparticles For Their Anticancer Effects, Sulafa Saeed Abdelhalim

Theses

Liver cancer is still one of the leading causes of cancer-related deaths worldwide. This is due to many reasons including lack of effective drugs, late diagnosis of this type of cancer due to the overlapping of symptoms with many other liver diseases, and lack of effective screening tests. Targeted drug delivery systems offer many promising advantages compared to conventional chemotherapy. Targeted delivery will mitigate the bad side effects of chemotherapy such as drug resistance, low therapeutic value since the drug mostly will be administered through an IV affecting healthy and cancer cells alike, and that leads us to an important …

Prediction Of Dry Matter Intake Based On Ruminal Degradation From Milking Cows Grazing Coast-Cross Grass, T. T. Berchielli, J. P. G. Soares, L. J. M. Aroeira, C. L. Furlan, A. K. D. Salman, R. N. Da Silveira, E. B. Malheiros

IGC Proceedings (1977-2023)

Dry matter intake (DMI) of coast-cross grazing by crossbred Holstein-Zebu and Zebu lactating cows was calculated using in vitro dry matter digestibility from extrusa (four esophageal fistulated cows) and fecal output estimate with mordent chromium. Pasture was rotationally grazed with three days grazing period and 27 days resting period, adopting a stocking rate of 1.6 and 3.2 cows/ha, during the dry and rainy season respectively. Voluntary DMI was estimated from degradation characteristics using different equations. Predicted coast-cross DMI varied with models. The prediction of tropical forages dry matter intake from equations based in ruminal degradation parameters needs further investigation before …

Forage Quality, Yield And Palatability Of Quackgrass (Elytrigia Repens (L.) Nevski), T. Yagi, R. Meguro, E. Fukuda

IGC Proceedings (1977-2023)

Quackgrass (Elytrigia repens (L.) Nevski) is a competitive perennial invader of pastures and hay meadows which is frequently harvested as forage in mixtures with desired forage species. Field experiments were conducted to compare quackgrass with cool-season perennial grasses grown under the same soil and climatic conditions, in terms of forage quality, productivity, and palatability. The forage quality of the hays was influenced by the grass species. Quackgrass showed forage crude protein (CP) concentration that was equal to those of perennial ryegrass (Lolium perenne), reed canarygrass (Phalaris arundinacea) and Kentucky bluegrass (Poa pratensis), and …

In Vitro Gas Production Technique To Predict Dmd Of Ensiled Forage Ruminant Based Diets, M. Wawrzkiewicz, J. L. Danelón, G. Jaurena

IGC Proceedings (1977-2023)

The in vitro gas production technique was used as a tool to develop an improved prediction model of dry matter digestibility of ensiled forage based diets. Eleven diets were tested through conventional experiments of in vivo dry matter digestibility (DMD). The same diets were evaluated by the in vitro gas production technique using a gas pressure transducer . The parameters of the model y = A - B Q^t Z^√t were calculated with data from the accumulated gas curves, i.e. y=cumulative gas production (ml), Q=e^-b, Z=e^-c, B=e^bT+c√T , being …