Databases and Information Systems Commons^™

Use Of Mental Models And Cognitive Maps To Understand Students’ Learning Challenges, Zixing Shen, Songxin Tan, Keng Siau

Research Collection School Of Computing and Information Systems

Mental models and cognitive maps have been used in college business education as an instructional design technique, assessment tool, and learning strategy. The authors propose a novel use of mental models and cognitive maps as a device to elicit students’ challenges in learning the domain knowledge of a course. Such usage is illustrated in a management information systems course. This student-focused approach can help instructor to better understand students’ learning challenges and enhance teaching effectiveness.

Stochastic Gradient Hamiltonian Monte Carlo With Variance Reduction For Bayesian Inference, Zhize Li, Tianyi Zhang, Shuyu Cheng, Jun Zhu, Jian Li

Research Collection School Of Computing and Information Systems

Gradient-based Monte Carlo sampling algorithms, like Langevin dynamics and Hamiltonian Monte Carlo, are important methods for Bayesian inference. In large-scale settings, full-gradients are not affordable and thus stochastic gradients evaluated on mini-batches are used as a replacement. In order to reduce the high variance of noisy stochastic gradients, Dubey et al. (in: Advances in neural information processing systems, pp 1154–1162, 2016) applied the standard variance reduction technique on stochastic gradient Langevin dynamics and obtained both theoretical and experimental improvements. In this paper, we apply the variance reduction tricks on Hamiltonian Monte Carlo and achieve better theoretical convergence results compared with …

Interpretable Fashion Matching With Rich Attributes, Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Understanding the mix-and-match relationships of fashion items receives increasing attention in fashion industry. Existing methods have primarily utilized the visual content to learn the visual compatibility and performed matching in a latent space. Despite their effectiveness, these methods work like a black box and cannot reveal the reasons that two items match well. The rich attributes associated with fashion items, e.g.,off-shoulder dress and black skinny jean, which describe the semantics of items in a human-interpretable way, have largely been ignored.This work tackles the interpretable fashion matching task, aiming to inject interpretability into the compatibility modeling of items. Specifically, given a …

Outcasts – In Search Of Identity, Syed Hasan Haider

MSJ Capstone Projects

The idea for this documentary came from a story published in the express tribune which talked about the people who are unable to vote in 2018 elections due to having Computerized National Identity Cards (CNICs) in the Ibrahim Hyderi locality in Karachi.

Not having a CNIC in Pakistan means that you are not able to participate in civic life and also not subscribe to basic facilitates like housing, water, gas and employment.

This documentary film looks at different cases and through the experience of some journalists what it is like to live as an undocumented citizen. The film also explores …

Research Methodology Adopted In Developing Tansse-L System, Ellen Kalinga

Tanzania Journal of Engineering and Technology (TJET)

Research methodology is among the very important part in a research work. It is the heart which describe the activities necessary for the completion of the research work. Research methodology provides a plan of investigation considered to obtain answers to research problems and it depends on the context of application. This paper presents the research methodology adopted when developing Tanzania Secondary Schools e-Learning (TanSSe-L) system, a learning management system (LMS) which was successfully developed through customization of Moodle open source LMS. TanSSe-L system is a context centered platform for secondary schools in Tanzania. TanSSe-L system was developed using a number …

Cyberbullying And Cybervictimization In Tanzanian Secondary Schools: Prevalence And Predictors, Hezron Zacharia Onditi, Jennifer Shapka

Journal of Humanities and Social Sciences

This study explored cyberbullying and cybervictimization, and the role of socio demographic and access to technology variables for Tanzanian adolescents. A self-report questionnaire was completed by secondary school students aged 14 to 18 (Form 1 to Form IV). Results provide evidence that online violence is increasingly becoming a problem of concern for Tanzanian adolescents. In particular, whereas 42% of the students reported to have cyberbullied others using electronic communication devices, 58% admitted having experienced cybervictimization. Also, results showed that students who spend more time online, share cellphones with others, and who access digital devices in a private location are more …

Data Mining And Machine Learning To Improve Northern Florida’S Foster Care System, Daniel Oldham, Nathan Foster, Mihhail Berezovski

Beyond: Undergraduate Research Journal

The purpose of this research project is to use statistical analysis, data mining, and machine learning techniques to determine identifiable factors in child welfare service records that could lead to a child entering the foster care system multiple times. This would allow us the capability of accurately predicting a case’s outcome based on these factors. We were provided with eight years of data in the form of multiple spreadsheets from Partnership for Strong Families (PSF), a child welfare services organization based in Gainesville, Florida, who is contracted by the Florida Department for Children and Families (DCF). This data contained a …

Multi-Resolution Models For Learning Multilevel Abstract Representation With Application To Information Retrieval, Tolgahan Cakaloglu

Theses and Dissertations

Deep language models learning a hierarchical representation proved to be a powerful tool for natural language processing, text mining, and information retrieval tasks. However, more specifically, representations that perform well for ad-hoc retrieval must capture semantic meaning at different levels of abstraction or context-scopes. The primary goal of ad-hoc retrieval is to find relevant documents satisfying the information need posted in a natural language query. It requires a good understanding of the query and all the documents in a corpus, which is difficult because the meaning of natural language texts depends on the context, syntax, and semantics. In this dissertation, …

Unsupervised Deep Structured Semantic Models For Commonsense Reasoning, Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Jing Jiang

Research Collection School Of Computing and Information Systems

Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.

Keylime, Eli William Partker

Computer Engineering

Josh, Matt and I knew we wanted to develop a mobile app for our senior project because that is what we found ourselves to be most passionate about during our time here at Cal Poly. We started to think of problems we wanted to solve using an application and we came up with a couple ideas but chose to expand on one. Students come to Cal Poly every year new to the area and the food options San Luis Obispo provides. Many of the restaurants in SLO offer a variety of deals to the community and most of them to …

Reach - A Community Service Application, Samuel Noel Magana

Computer Engineering

Communities are familiar threads that unite people through several shared attributes and interests. These commonalities are the core elements that link and bond us together. Many of us are part of multiple communities, moving in and out of them depending on our needs. These common threads allow us to support and advocate for each other when facing a common threat or difficult situation. Healthy and vibrant communities are fundamental to the operation of our society. These interactions within our communities define the way we as individuals interact with each other, and society at large. Being part of a community helps …

Radish: A Cross Platform Meal Prepping App For Beginner Weightlifters, Spoorthy S. Vemula, Tanay Gottigundala, Cory Baxes

Computer Science and Software Engineering

With the increasing ease of access and decreasing price of most food, obesity rates in the developing world have risen dramatically in recent years. As of March 23rd, 2019, obesity rates had reached 39.6%, a 6% increase in just 8 years. Research has shown that people with obesity have a significantly increased risk of heart disease, stroke, type 2 diabetes, and certain cancers, among other life-threatening diseases. In addition, 42% of people who begin weightlifting quit because it’s too difficult to follow a diet or workout regimen.

We created Radish in an attempt to tackle these problems. Radish makes it …

Dynamic Fusion With Intra-And Inter-Modality Attention Flow For Visual Question Answering, Peng Gao, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven C. H. Hoi, Xiaogang Wang, Hongsheng Li

Research Collection School Of Computing and Information Systems

Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fusing multi-modal features with intra- and inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities. It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering. We also show that the proposed dynamic intra-modality attention flow conditioned on the other modality can dynamically modulate the intramodality attention of the target modality, which is vital for multimodality feature fusion. Experimental evaluations on the …

Stabilized Svrg: Simple Variance Reduction For Nonconvex Optimization, Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang

Research Collection School Of Computing and Information Systems

Variance reduction techniques like SVRG provide simple and fast algorithms for optimizing a convex finite-sum objective. For nonconvex objectives, these techniques can also find a first-order stationary point (with small gradient). However, in nonconvex optimization it is often crucial to find a second-order stationary point (with small gradient and almost PSD hessian). In this paper, we show that Stabilized SVRG (a simple variant of SVRG) can find an $\epsilon$-second-order stationary point using only $\tilde{O}(n^{2/3}/\epsilon^2 + n/\epsilon^{1.5})$ stochastic gradients. To our best knowledge, this is the first second-order guarantee for a simple variant of SVRG. The running time almost matches the …

Self-Supervised Spatio-Temporal Representation Learning For Videos By Predicting Motion And Appearance Statistics, Jiangliu Wang, Jianbo Jiao, Linchao Bao, Shengfeng He, Yunhui Liu, Wei Liu

Research Collection School Of Computing and Information Systems

We address the problem of video representation learning without human-annotated labels. While previous efforts address the problem by designing novel self-supervised tasks using video data, the learned features are merely on a frame-by-frame basis, which are not applicable to many video analytic tasks where spatio-temporal features are prevailing. In this paper we propose a novel self-supervised approach to learn spatio-temporal features for video representation. Inspired by the success of two-stream approaches in video classification, we propose to learn visual features by regressing both motion and appearance statistics along spatial and temporal dimensions, given only the input video data. Specifically, we …

Learning Unsupervised Video Object Segmentation Through Visual Attention, Wenguan Wang, Hongmei Song, Shuyang Zhao, Jianbing Shen, Sanyuan Zhao, Steven C. H. Hoi, Haibin Ling

Research Collection Yong Pung How School Of Law

This paper conducts a systematic study on the role of visual attention in Unsupervised Video Object Segmentation (UVOS) tasks. By elaborately annotating three popular video segmentation datasets (DAVIS, Youtube-Objects and SegTrack V2) with dynamic eye-tracking data in the UVOS setting, for the first time, we quantitatively verified the high consistency of visual attention behavior among human observers, and found strong correlation between human attention and explicit primary object judgements during dynamic, task-driven viewing. Such novel observations provide an in-depth insight into the underlying rationale behind UVOS. Inspired by these findings, we decouple UVOS into two sub-tasks: UVOS-driven Dynamic Visual Attention …

Distributed Similarity Queries In Metric Spaces, Keyu Yang, Xin Ding, Yuanliang Zhang, Lu Chen, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Similarity queries, including range queries and k nearest neighbor (kNN) queries, in metric spaces have applications in many areas such as multimedia retrieval, computational biology and location-based services. With the growing volumes of data, a distributed method is required. In this paper, we propose an Asynchronous Metric Distributed System (AMDS), to support efficient metric similarity queries in the distributed environment. AMDS uniformly partitions the data with the pivot-mapping technique to ensure the load balancing, and employs publish/subscribe communication model to asynchronous process large scale of queries. The employment of asynchronous processing model also improves robustness and efficiency of AMDS. In …

View, Like, Comment, Post: Analyzing User Engagement By Topic At 4 Levels Across 5 Social Media Platforms For 53 News Organizations, Kholoud K. Aldous, Jisun An, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We evaluate the effects of the topics of social media posts on audiences across five social media platforms (i.e., Facebook, Instagram, Twitter, YouTube, and Reddit) at four levels of user engagement. We collected 3,163,373 social posts from 53 news organizations across five platforms during an 8month period. We analyzed the differences in news organization platform strategies by focusing on topic variations by organization and the corresponding effect on user engagement at four levels. Findings show that topic distribution varies by platform, although there are some topics that are popular across most platforms. User engagement levels vary both by topics and …

Salient Object Detection With Pyramid Attention And Salient Edges, Wenguan Wang, Shuyang Zhao, Jianbing Shen, Steven C. H. Hoi, Ali Borji

Research Collection Yong Pung How School Of Law

This paper presents a new method for detecting salient objects in images using convolutional neural networks (CNNs). The proposed network, named PAGE-Net, offers two key contributions. The first is the exploitation of an essential pyramid attention structure for salient object detection. This enables the network to concentrate more on salient regions while considering multi-scale saliency information. Such a stacked attention design provides a powerful tool to efficiently improve the representation ability of the corresponding network layer with an enlarged receptive field. The second contribution lies in the emphasis on the importance of salient edges. Salient edge information offers a strong …

Sliced Wasserstein Generative Models, Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc Van Gool

Research Collection School Of Computing and Information Systems

In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is challenging to approximate the WD of high-dimensional distributions. In contrast, the sliced Wasserstein distance (SWD) factorizes high-dimensional distributions into their multiple one-dimensional marginal distributions and is thus easier to approximate. In this paper, we introduce novel approximations of the primal and dual SWD. Instead of using a large number of random projections, as it is done by conventional SWD approximation methods, we propose to approximate SWDs with a small number of parameterized orthogonal projections …