Databases and Information Systems Commons^™

Automatic Recall Of Software Lessons Learned For Software Project Managers, Tamer Mohamed Abdellatif Mohamed, Luiz Fernando Capretz, Danny Ho

Electrical and Computer Engineering Publications

Context: Lessons learned (LL) records constitute the software organization memory of successes and failures. LL are recorded within the organization repository for future reference to optimize planning, gain experience, and elevate market competitiveness. However, manually searching this repository is a daunting task, so it is often disregarded. This can lead to the repetition of previous mistakes or even missing potential opportunities. This, in turn, can negatively affect the organization’s profitability and competitiveness.

Objective: We aim to present a novel solution that provides an automatic process to recall relevant LL and to push those LL to project managers. This will dramatically …

Characterizing Instructional Leader Interactions In A Social Learning Management System Using Social Network Analysis, Orven E. Llantos, Ma. Regina Justina E. Estuar

Department of Information Systems & Computer Science Faculty Publications

Online learning environments are designed with specific users and respective roles in mind. However, for social systems that thrive on the interaction between and among users, important features are developed based on relationships that evolve over time. Typical learning management systems are designed with the teacher and the student as primary users of the system. my.eskwela is a social learning management system that has been designed for use in public schools in the Philippines with the inclusion of an additional user, the school administrator. Although administrators influence on student learning through mediated effects of instructional leadership, pieces of literature are …

Medtruth: A Semi-Supervised Approach To Discovering Knowledge Condition Information From Multi-Source Medical Data, Yang Deng, Yaliang Li, Ying Shen, Nan Du, Wei Fan, Min Yang, Kai Lei

Research Collection School Of Computing and Information Systems

Knowledge Graph (KG) contains entities and the relations between entities. Due to its representation ability, KG has been successfully applied to support many medical/healthcare tasks. However, in the medical domain, knowledge holds under certain conditions. Such conditions for medical knowledge are crucial for decisionmaking in various medical applications, which is missing in existing medical KGs. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. Electronic Medical Records (EMRs) are systematized collection of clinical data and contain detailed information about patients, thus EMRs can be a good resource to discover medical knowledge conditions. Unfortunately, the …

An Online Wideband Acoustic Immittance (Wai) Database Andcorresponding Website: Resource Review, Susan E. Voss

Engineering: Faculty Publications

Wideband acoustic immittance (WAI) measures, which include absorbance, power reflectance, impedance, and other related quantities, offer an objective, noninvasive diagnostic tool for some middle-ear pathologies. An online database for normative adult WAI measures has been designed and implemented in MySQL, with the goal of enabling researchers to share and analyze data across studies.

Access database through ScholarWorks here: https://scholarworks.smith.edu/dds_data/6

Or directly here: http://www.science.smith.edu/wai-database/

Traceable Dynamic Public Auditing With Identity Privacy Preserving For Cloud Storage, Yinghui Zhang, Tiantian Zhang, Rui Guo, Shengmin Xu, Dong Zheng

Research Collection School Of Computing and Information Systems

In cloud computing era, an increasing number of resource-constrained users outsource their data to cloud servers. Due to the untrustworthiness of cloud servers, it is important to ensure the integrity of outsourced data. However, most of existing solutions still have challenging issues needing to be addressed, such as the identity privacy protection of users, the traceability of users, the supporting of dynamic user operations, and the publicity of auditing. In order to tackle these issues simultaneously, in this paper, we propose a traceable dynamic public auditing scheme with identity privacy preserving for cloud storage. In the proposed scheme, a single …

Estimating Glycemic Impact Of Cooking Recipes Via Online Crowdsourcing And Machine Learning, Helena Lee, Palakorn Achananuparp, Yue Liu, Ee-Peng Lim, Lav R. Varshney

Research Collection School Of Computing and Information Systems

Consumption of diets with low glycemic impact is highly recommended for diabetics and pre-diabetics as it helps maintain their blood glucose levels. However, laboratory analysis of dietary glycemic potency is time-consuming and expensive. In this paper, we explore a data-driven approach utilizing online crowdsourcing and machine learning to estimate the glycemic impact of cooking recipes. We show that a commonly used healthiness metric may not always be effective in determining recipes suitable for diabetics, thus emphasizing the importance of the glycemic-impact estimation task. Our best classification model, trained on nutritional and crowdsourced data obtained from Amazon Mechanical Turk (AMT), can …

Automated Theme Search In Ico Whitepapers, Chuanjie Fu, Andrew Koh, Paul Griffin

Research Collection School Of Computing and Information Systems

The authors explore how topic modeling can be used to automate the categorization of initial coin offerings (ICOs) into different topics (e.g., finance, media, information, professional services, health and social, natural resources) based solely on the content within the whitepapers. This tool has been developed by fitting a latent Dirichlet allocation (LDA) model to the text extracted from the ICO whitepapers. After evaluating the automated categorization of whitepapers using statistical and human judgment methods, it is determined that there is enough evidence to conclude that the LDA model appropriately categorizes the ICO whitepapers. The results from a two-population proportion test …

Low-Resource Name Tagging Learned With Weakly Labeled Data, Yixin Cao, Zikun Hu, Tat-Seng Chua, Zhiyuan Liu, Heng Ji

Research Collection School Of Computing and Information Systems

Name tagging in low-resource languages or domains suffers from inadequate training data. Existing work heavily relies on additional information, while leaving those noisy annotations unexplored that extensively exist on the web. In this paper, we propose a novel neural model for name tagging solely based on weakly labeled (WL) data, so that it can be applied in any low-resource settings. To take the best advantage of all WL sentences, we split them into high-quality and noisy portions for two modules, respectively: (1) a classification module focusing on the large portion of noisy data can efficiently and robustly pretrain the tag …

Automatic Generation Of Pull Request Descriptions, Zhongxin Liu, Xin Xia, Christoph Treude, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Enabled by the pull-based development model, developers can easily contribute to a project through pull requests (PRs). When creating a PR, developers can add a free-form description to describe what changes are made in this PR and/or why. Such a description is helpful for reviewers and other developers to gain a quick understanding of the PR without touching the details and may reduce the possibility of the PR being ignored or rejected. However, developers sometimes neglect to write descriptions for PRs. For example, in our collected dataset with over 333K PRs, more than 34% of the PR descriptions are empty. …

Stylistic Features Usage: Similarities And Differences Using Multiple Social Networks, Kholoud Khalil Aldous, Jisun An, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

User engagement on social networks is essential for news outlets where they often distribute online content. News outlets simultaneously leverage multiple social media platforms to reach their overall audience and to increase marketshare. In this research, we analyze ten common stylistic features indicative of user engagement for news postings on multiple social media platforms. We display the stylistic features usage differences of news posts from various news sources. Results show that there are differences in the usage of stylistic features across social media platforms (Facebook, Instagram, Twitter, and YouTube). Online news outlets can benefit from these findings in building guidelines …

Semi-Supervised Entity Alignment Via Joint Knowledge Embedding Model And Cross-Graph Model, Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages, which may benefit many knowledge-driven applications. It is challenging due to the heterogeneity of KGs and limited seed alignments. In this paper, we propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model (KECG). It can make better use of seed alignments to propagate over the entire graphs with KG-based constraints. Specifically, as for the knowledge embedding model, we utilize TransE to implicitly complete two KGs towards consistency and learn relational constraints between entities. As for the cross-graph model, we extend Graph …

Special Issue On Multimedia Recommendation And Multi-Modal Data Analysis, Xiangnan He, Zhenguang Liu, Hanwang Zhang, Chong-Wah Ngo, Svebor Karaman, Yongfeng Zhang

Research Collection School Of Computing and Information Systems

Rich multimedia contents are dominating the Web. In popular social media platforms such as FaceBook, Twitter, and Instagram, there are over millions of multimedia contents being created by users on a daily basis. In the meantime, multimedia data consist of data in multiple modalities, such as text, images, audio, and so on. Users are heavily overloaded by the massive multi-modal data, and it becomes critical to explore advanced techniques for heterogeneous big data analytics and multimedia recommendation. Traditional multimedia recommendation and data analysis technologies cannot well address the problem of understanding users’ preference in the feature-rich multimedia contents, and have …

Visualizing The Invisible: Occluded Vehicle Segmentation And Recovery, Xiaosheng Yan, Feigege Wang, Wenxi Liu, Yuanlong Yu, Shengfeng He, Jia Pan

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, firstly, to improve the quality of the segmentation completion, we present two coupled discriminators that introduce an auxiliary 3D model pool for sampling authentic silhouettes as adversarial samples. In addition, we propose a two-path structure with a shared network to enhance the appearance recovery capability. By iteratively performing the segmentation completion and the appearance recovery, the results will be progressively refined. To evaluate our method, we present a dataset, Occluded Vehicle …

Ridesourcing Systems: A Framework And Review, Hai Wang, Hai Yang

Research Collection School Of Computing and Information Systems

With the rapid development and popularization of mobile and wireless communication technologies, ridesourcing companies have been able to leverage internet-based platforms to operate e-hailing services in many cities around the world. These companies connect passengers and drivers in real time and are disruptively changing the transportation indus- try. As pioneers in a general sharing economy context, ridesourcing shared transportation platforms consist of a typical two-sided market. On the demand side, passengers are sensi- tive to the price and quality of the service. On the supply side, drivers, as freelancers, make working decisions flexibly based on their income from the platform …

Characterizing And Predicting Repeat Food Consumption Behavior For Just-In-Time Interventions, Yue Liu, Helena Huey Chong Lee, Palakorn Achananuparp, Ee-Peng Lim, Tzu-Ling Cheng, Shou-De Lin

Research Collection School Of Computing and Information Systems

Human beings are creatures of habit. In their daily life, people tend to repeatedly consume similar types of food items over several days and occasionally switch to consuming different types of items when the consumptions become overly monotonous. However, the novel and repeat consumption behaviors have not been studied in food recommendation research. More importantly, the ability to predict daily eating habits of individuals is crucial to improve the effectiveness of food recommender systems in facilitating healthy lifestyle change. In this study, we analyze the patterns of repeat food consumptions using large-scale consumption data from a popular online fitness community …

Ad-Link: An Adaptive Approach For User Identity Linkage, Xin Mu, Wei Xie, Ka Wei, Roy Lee, Feida Zhu, Ee Peng Lim

Research Collection School Of Computing and Information Systems

User identity linkage (UIL) refers to linking accounts of the same user across different online social platforms. The state-of-the-art UIL methods usually perform account matching using user account’s features derived from the profile attributes, content and relationships. They are however static and do not adapt well to fast-changing online social data due to: (a) new content and activities generated by users; as well as (b) new platform functions introduced to users. In particular, the importance of features used in UIL methods may change over time and new important user features may be introduced. In this paper, we proposed AD-Link, a …

Aspect And Opinion Aware Abstractive Review Summarization With Reinforced Hard Typed Decoder, Yufei Tian, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we study abstractive review summarization. Observing that review summaries often consist of aspect words, opinion words and context words, we propose a two-stage reinforcement learning approach, which first predicts the output word type from the three types, and then leverages the predicted word type to generate the final word distribution. Experimental results on two Amazon product review datasets demonstrate that our method can consistently outperform several strong baseline approaches based on ROUGE scores.

Data Curation Workshop: Tips And Tools For Today, Matthew M. Benzing

Charleston Library Conference

The current state of research data is like a disorganized photo collection: a mix of formats scattered across different media without a lot of authority control. That is changing as the need to make data available to researchers across the world is becoming recognized. Researchers know that their data needs to be maintained and made accessible, but often they do not have the time or the inclination to get involved in all of the details. This provides an excellent opportunity for librarians. Data curation is the process of preparing data to be made available in a repository with the goal …

On Finding Two Posets That Cover Given Linear Orders, Ivy Ordanel, Proceso L. Fernandez Jr, Henry Adorna

Department of Information Systems & Computer Science Faculty Publications

The Poset Cover Problem is an optimization problem where the goal is to determine a minimum set of posets that covers a given set of linear orders. This problem is relevant in the field of data mining, specifically in determining directed networks or models that explain the ordering of objects in a large sequential dataset. It is already known that the decision version of the problem is NP-Hard while its variation where the goal is to determine only a single poset that covers the input is in P. In this study, we investigate the variation, which we call the 2-Poset …

Ml4iot: A Framework To Orchestrate Machine Learning Workflows On Internet Of Things Data, Jose Miguel Alves, Leonardo Honorio, Miriam A M Capretz

Electrical and Computer Engineering Publications

Internet of Things (IoT) applications generate vast amounts of real-time data. Temporal analysis of these data series to discover behavioural patterns may lead to qualified knowledge affecting a broad range of industries. Hence, the use of machine learning (ML) algorithms over IoT data has the potential to improve safety, economy, and performance in critical processes. However, creating ML workflows at scale is a challenging task that depends upon both production and specialized skills. Such tasks require investigation, understanding, selection, and implementation of specific ML workflows, which often lead to bottlenecks, production issues, and code management complexity and even then may …