Databases and Information Systems Commons^™

Debunking Rumors On Twitter With Tree Transformer, Jing Ma, Wei Gao

Research Collection School Of Computing and Information Systems

Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by "word-of-post" through social media conversations. Conversation tree encodes important information indicative of the credibility of rumor. Existing conversation-based techniques for rumor detection either just strictly follow tree edges or treat all the posts fully-connected during feature learning. In this paper, we propose a novel detection model based on tree transformer to better utilize user interactions in the dialogue where post-level self-attention plays the key role for aggregating the intra-/inter-subtree stances. Experimental results on the TWITTER and PHEME datasets show that the proposed approach consistently improves …

A Bert-Based Dual Embedding Model For Chinese Idiom Prediction, Minghuan Tan, Jing Jiang

Research Collection School Of Computing and Information Systems

Chinese idioms are special fixed phrases usually derived from ancient stories, whose meanings are oftentimes highly idiomatic and non-compositional. The Chinese idiom prediction task is to select the correct idiom from a set of candidate idioms given a context with a blank. We propose a BERT-based dual embedding model to encode the contextual words as well as to learn dual embeddings of the idioms. Specifically, we first match the embedding of each candidate idiom with the hidden representation corresponding to the blank in the context. We then match the embedding of each candidate idiom with the hidden representations of all …

Digital Social Listening On Conversations About Sexual Harassment, Xuesi Sim, Ern Rae Chang, Yu Xiang Ong, Jie Ying Yeo, Christine Bai Shuang Yan, Eugene Wen Jia Choy, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In light of the #MeToo movement and publicized sexual harassment incidents in Singapore in recent years, we built an analytics pipeline for performing digital social listening on conversations about sexual harassment for AWARE (Association of Women for Action and Research). Our social network analysis results identified key influencers that AWARE can engage for sexual harassment awareness campaigns. Further, our analysis results suggest new hashtags that AWARE can use to run social media campaigns and achieve greater reach.

Learning To Dispatch For Job Shop Scheduling Via Deep Reinforcement Learning, Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Xu Chi

Research Collection School Of Computing and Information Systems

Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance. In this paper, we propose to automatically learn PDRs via an end-to-end deep reinforcement learning agent. We exploit the disjunctive graph representation of JSSP, and propose a Graph Neural Network based scheme to embed the states encountered during solving. The resulting policy network is size-agnostic, effectively enabling generalization on large-scale instances. Experiments show that the agent can learn high-quality PDRs from scratch with elementary raw …

Sharper Generalisation Bounds For Pairwise Learning, Yunwen Lei, Antoine Ledent, Marius Kloft

Research Collection School Of Computing and Information Systems

Pairwise learning refers to learning tasks with loss functions depending on a pair of training examples, which includes ranking and metric learning as specific examples. Recently, there has been an increasing amount of attention on the generalization analysis of pairwise learning to understand its practical behavior. However, the existing stability analysis provides suboptimal high-probability generalization bounds. In this paper, we provide a refined stability analysis by developing generalization bounds which can be √nn-times faster than the existing results, where nn is the sample size. This implies excess risk bounds of the order O(n−1/2) (up to a logarithmic factor) for both …

Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam In Deep Learning, Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven C. H. Hoi, Weinan E

Research Collection School Of Computing and Information Systems

It is not clear yet why ADAM-alike adaptive gradient algorithms suffer from worse generalization performance than SGD despite their faster training speed. This work aims to provide understandings on this generalization gap by analyzing their local convergence behaviors. Specifically, we observe the heavy tails of gradient noise in these algorithms. This motivates us to analyze these algorithms through their Lévy-driven stochastic differential equations (SDEs) because of the similar convergence behaviors of an algorithm and its SDE. Then we establish the escaping time of these SDEs from a local basin. The result shows that (1) the escaping time of both SGD …

Blockchain-Based Public Auditing And Secure Deduplication With Fair Arbitration, Haoran Yuan, Xiaofeng Chen, Jianfeng Wang, Jiaming Yuan, Hongyang Yan, Willy Susilo

Research Collection School Of Computing and Information Systems

Data auditing enables data owners to verify the integrity of their sensitive data stored at an untrusted cloud without retrieving them. This feature has been widely adopted by commercial cloud storage. However, the existing approaches still have some drawbacks. On the one hand, the existing schemes have a defect of fair arbitration, i.e., existing auditing schemes lack an effective method to punish the malicious cloud service provider (CSP) and compensate users whose data integrity is destroyed. On the other hand, a CSP may store redundant and repetitive data. These redundant data inevitably increase management overhead and computational cost during the …

Business Practice Of Social Media - Platform And Customer Service Adoption, Shujing Sun, Yang Gao, Huaxia Rui

Research Collection School Of Computing and Information Systems

This paper examines the key drivers in business adoptions of the platform and customer service within the context of social media. We carry out the empirical analyses using the decision trajectories of the international airline industry on Twitter. We find that a firm's decision-making is subject to both peer influence and consumer pressure. Regarding peer influence, we find that the odds of both adoptions increase when the extent of peers' adoption increases. We also identify the distinctive role of consumers. Specifically, before the platform adoption, firms learn about potential consequences from consumer reactions to peers' adoptions. Upon the platform adoption, …

Eelgrass (Zostera Marina) Population Decline In Morro Bay, Ca: A Meta-Analysis Of Herbicide Application In San Luis Obispo County And Morro Bay Watershed, Tyler King Sinnott

Master's Theses

The endemic eelgrass (Zostera marina) community of Morro Bay Estuary, located on the central coast of California, has experienced an estimated decline of 95% in occupied area (reduction of 344 acres to 20 acres) from 2008 to 2017 for reasons that are not yet definitively clear. One possible driver of degradation that has yet to be investigated is the role of herbicides from agricultural fields in the watershed that feeds into the estuary. Thus, the primary research goal of this project was to better understand temporal and spatial trends of herbicide use within the context of San Luis …

Ppmexplorer: Using Information Retrieval, Computer Vision And Transfer Learning Methods To Index And Explore Images Of Pompeii, Cindy Roullet

Graduate Theses and Dissertations

In this dissertation, we present and analyze the technology used in the making of PPMExplorer: Search, Find, and Explore Pompeii. PPMExplorer is a software tool made with data extracted from the Pompei: Pitture e Mosaic (PPM) volumes. PPM is a valuable set of volumes containing 20,000 historical annotated images of the archaeological site of Pompeii, Italy accompanied by extensive captions. We transformed the volumes from paper, to digital, to searchable. PPMExplorer enables archaeologist researchers to conduct and check hypotheses on historical findings. We present a theory that such a concept is possible by leveraging computer generated correlations between artifacts using …

Secure Unlinkability Schemes For Privacy Preserving Data Publishing In Weighted Social Networks, Chong Kah Meng

Student Works (2020-2029)

Preserving privacy of users has been one of the important research issues in social networks. Social networks contain sensitive personal information that are often released for business and research purposes. The privacy of a user can be breached if the data are not released in an anonymized form. In this thesis, we address edge weight disclosure, link disclosure and identity disclosure problems in publishing weighted network data. To counter these privacy risks while preserving high utility of the published data, we define two key privacy properties, namely edge weight unlinkability and node unlinkability. We design two novel anonymization schemes namely …

Implementation Of Smartphone Navigation Features By Combined Forces In Determining The Hazards Of Terrorism In Poso, Mahturai Rian Fitra Mrf, Arthur Josias Simon Runturambi Ajsr

Journal of Terrorism Studies

The presence of armed terrorist groups in Poso can threaten security conditions in the country because their activities are considered quite dangerous for the surrounding community. This terrorist group did not hesitate to kill civilians who tried to deny its existence. Therefore, various joint military operations have been launched to crush this armed terrorist group, such as Camar Maleo and Tinombala. However, until now this terrorist group is difficult to destroy, due to the condition of the operating area in the form of dense tropical rainforest and steep slopes. This makes it difficult for troops to carry out chases and …

Barriers And Drivers Influencing The Growth Of E-Commerce In Uzbekistan, Madinakhon Tursunboeva

Theses and Dissertations

Electronic commerce (e-commerce) has become a major retail channel for businesses in developed countries. However, it is still considered an innovation in developing countries. Specifically, e-commerce in Uzbekistan is in the early stages of emergence despite its advance in recent years in terms of Internet penetration, a strong retail sector, new national regulations, and a young population. The study aimed to identify barriers and drivers influencing e-commerce growth in Uzbekistan. A Delphi research design was utilized to answer the research questions of the study, which categorized and ranked factors that Uzbekistani entrepreneurs are facing when engaging in e-commerce processes. A …

An Analysis Of Technological Components In Relation To Privacy In A Smart City, Kayla Rutherford, Ben Lands, A. J. Stiles

James Madison Undergraduate Research Journal (JMURJ)

A smart city is an interconnection of technological components that store, process, and wirelessly transmit information to enhance the efficiency of applications and the individuals who use those applications. Over the course of the 21st century, it is expected that an overwhelming majority of the world’s population will live in urban areas and that the number of wireless devices will increase. The resulting increase in wireless data transmission means that the privacy of data will be increasingly at risk. This paper uses a holistic problem-solving approach to evaluate the security challenges posed by the technological components that make up a …

An Introduction To Seshat: Global History Databank, Peter Turchin, Harvey Whitehouse, Pieter François, Daniel Hoyer, Abel Alves, John Baines, David Baker, Marta Bartkowiak, Jennifer Bates, James Bennett, Julye Bidmead, Peter Bol, Alessandro Ceccarelli, Kostis Christakis, David Christian, Alan Covey, Franco De Angelis, Timothy K. Earle, Neil R. Edwards, Gary Feinman, Stephanie Grohmann, Philip B. Holden, Árni Júlíusson, Andrey Korotayev, Axel Kristinsson, Jennifer Larson, Oren Litwin, Victor Mair, Joseph G. Manning, Patrick Manning, Arkadiusz Marciniak, Gregory Mcmahon, John Miksic, Juan Carlos Moreno Garcia, Ian Morris, Ruth Mostern, Daniel Mullins, Oluwole Oyebamiji, Peter Peregrine, Cameron Petrie, Johannes Preiser-Kapeller, Peter Rudiak-Gould, Paula Sabloff, Patrick Savage, Charles Spencer, Miriam Stark, Barend Ter Haar, Stefan Thurner, Vesna Wallace, Nina Witoszek, Liye Xie

Religious Studies Faculty Articles and Research

This article introduces the Seshat: Global History Databank, its potential, and its methodology. Seshat is a databank containing vast amounts of quantitative data buttressed by qualitative nuance for a large sample of historical and archaeological polities. The sample is global in scope and covers the period from the Neolithic Revolution to the Industrial Revolution. Seshat allows scholars to capture dynamic processes and to test theories about the co-evolution (or not) of social scale and complexity, agriculture, warfare, religion, and any number of such Big Questions. Seshat is rapidly becoming a massive resource for innovative cross-cultural and cross-disciplinary research. Seshat is …

Digital Identity: A Human-Centered Risk Awareness Study, Toufic N. Chebib

USF Tampa Graduate Theses and Dissertations

Cybersecurity threats and compromises have been at the epicenter of media attention; their risk and effect on people’s digital identity is something not to be taken lightly. Though cyber threats have affected a great number of people in all age groups, this study focuses on 55 to 75-year-olds, as this age group is close to retirement or already retired. Therefore, a notable compromise impacting their digital identity can have a major impact on their life.

To help guide this study, the following research question was formulated, “What are the risk perceptions of individuals, between the ages of 55 and 75 …

A Framework To Determine The Impact Of Poor Data Quality On The Reliability Of Iot Sensor-Based Real-Time Decision-Making, Arnold Rego

Theses and Dissertations

Today, more and more systems make real-time decisions based on the data received from sensors. But it is inevitable that sensors will fail, or send bad data – leading to faulty real-time decision-making. Using this poor-quality data in real-time decision-making can lead to deadly consequences. Traditional Data Quality (DQ) foundations call for “fixing” the poor DQ before the data can be used. However, time is often of the essence in a real-time decision-making environment, leaving little or no time to “fix” poor-quality data before it can be used in the decision-making process. The primary objective in real-time decision-making is not …

A Framework For Identifying Host-Based Artifacts In Dark Web Investigations, Arica Kulm

Masters Theses & Doctoral Dissertations

The dark web is the hidden part of the internet that is not indexed by search engines and is only accessible with a specific browser like The Onion Router (Tor). Tor was originally developed as a means of secure communications and is still used worldwide for individuals seeking privacy or those wanting to circumvent restrictive regimes. The dark web has become synonymous with nefarious and illicit content which manifests itself in underground marketplaces containing illegal goods such as drugs, stolen credit cards, stolen user credentials, child pornography, and more (Kohen, 2017). Dark web marketplaces contribute both to illegal drug usage …

Cost-Sensitive Deep Forest For Price Prediction, Chao Ma, Zhenbing Liu, Zhiguang Cao, Wen Song, Jie Zhang, Weiliang Zeng

Research Collection School Of Computing and Information Systems

For many real-world applications, predicting a price range is more practical and desirable than predicting a concrete value. In this case, price prediction can be regarded as a classification problem. Although deep forest is recognized as the best solution to many classification problems, a crucial issue limits its direct application to price prediction, i.e., it treated all the misclassifications equally no matter how far away they are from the real classes, since their impacts on the accuracy are the same. This is unreasonable to price prediction as the misclassification should be as close to the real price range as possible …

Capitalising Product Associations In A Supermarket Retail Environment, Michelle L. F. Cheong, Yong Qing Chia

Research Collection School Of Computing and Information Systems

This paper explores methods to capitalise on retail companies’ transactional databases, to mine meaningful product associations, and to design product placement strategies as a means to drive sales. We implemented three in-store initiatives based on our hypotheses – placing products with high associations together will induce an increase in sales of consequent; introducing an antecedent that is new to store will bring about a similar impact on sales of consequent based on established product association rules uncovered from other stores. Sales tracking over twelve weeks revealed that there were improvements in sales of consequents across all three initiatives performed in-store.

Exploring And Evaluating Attributes, Values, And Structures For Entity Alignment, Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, Zhiyuan Liu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performance by modeling the KG structure defined by relation triples. However, attribute triples can also provide crucial alignment signal but have not been well explored yet. In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently. Besides, the performances of current EA methods are overestimated because of the name-bias of existing EA datasets. To make an objective …

Efficient And Fair Data Valuation For Horizontal Federated Learning, Shuyue Wei, Yongxin Tong, Zimu Zhou, Tianshu Song

Research Collection School Of Computing and Information Systems

Availability of big data is crucial for modern machine learning applications and services. Federated learning is an emerging paradigm to unite different data owners for machine learning on massive data sets without worrying about data privacy. Yet data owners may still be reluctant to contribute unless their data sets are fairly valuated and paid. In this work, we adapt Shapley value, a widely used data valuation metric to valuating data providers in federated learning. Prior data valuation schemes for machine learning incur high computation cost because they require training of extra models on all data set combinations. For efficient data …

A Survey Of Typical Attributed Graph Queries, Yanhao Wang, Yuchen Li, Ju Fan, Chang Ye, Mingke Chai

Research Collection School Of Computing and Information Systems

Graphs are commonly used for representing complex structures such as social relationships, biological interactions, and knowledge bases. In many scenarios, graphs not only represent topological relationships but also store the attributes that denote the semantics associated with their vertices and edges, known as attributed graphs. Attributed graphs can meet demands for a wide range of applications, and thus a variety of queries on attributed graphs have been proposed. However, these diverse types of attributed graph queries have not been systematically investigated yet. In this paper, we provide an extensive survey of several typical types of attributed graph queries. We propose …

Answerfact: Fact Checking In Product Question Answering, Wenxuan Zhang, Yang Deng, Jing Ma, Wai Lam

Research Collection School Of Computing and Information Systems

Product-related question answering platforms nowadays are widely employed in many E-commerce sites, providing a convenient way for potential customers to address their concerns during online shopping. However, the misinformation in the answers on those platforms poses unprecedented challenges for users to obtain reliable and truthful product information, which may even cause a commercial loss in E-commerce business. To tackle this issue, we investigate to predict the veracity of answers in this paper and introduce AnswerFact, a large scale fact checking dataset from product question answering forums. Each answer is accompanied by its veracity label and associated evidence sentences, providing a …

Cache-Based Optimization For Block Commit Of Hyperledger Fabric, Qinqi Xu, Yimin Lin, Qingshan Jiang, Mengqiu Zhang

Research Collection Lee Kong Chian School Of Business

Blockchain technology has been applied to various domains in recent years, and Hyperledger Fabric is one of the most popular blockchain systems. This paper conducts a fine analysis on the block commit of Hyperledger Fabric through latency evaluation as well as flame graph. To overcome the bottlenecks pointed out by the analysts, this paper proposes two cache-based schemata. In particular, we add a cache for index and state storage to accelerate commit. We also cache deserialized block globally for block delivery to avoid repeat operations. The experiments reveal performance improvement of 2×. Notably, our optimizations can also be implemented easily …

Exploring The Impact Of Covid-19 On Aviation Industry: A Text Mining Approach, Gottipati Swapna, Kyong Jin Shim, Weiling Angeline Jiang, Sheng Wei Andre Justin Lee

Research Collection School Of Computing and Information Systems

Our study presents a comprehensive analysis of news articles from FlightGlobal website during the first half of 2020. Our analyses reveal useful insights on themes and trends concerning the aviation industry during the COVID-19 period. We applied text mining and NLP techniques to analyse the articles for extracting the aviation themes and article sentiments (positive and negative). Our results show that there is a variation in the sentiment trends for themes aligned with the real-world developments of the pandemic. The article sentiment analysis can offer industry players a quick sense of the nature of developments in the industry. Our article …

Cross-Thought For Sentence Encoder Pre-Training, Shuohang Wang, Yuwei Fang, Siqi Sun, Zhe Gan, Yu Cheng, Jingjing Liu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering. Instead of using the original signals of full sentences, we train a Transformer-based sequence encoder over a large set of short sequences, which allows the model to automatically select the most useful information for predicting masked words. Experiments on question answering and textual entailment tasks demonstrate that our pre-trained encoder can outperform state-of-the-art encoders trained with continuous sentence signals as well as traditional masked language modeling baselines. Our proposed approach also …

Coupled Hierarchical Transformer For Stance-Aware Rumor Verification In Social Media Conversations, Jianfei Yu, Jing Jiang, Ling Min Serena Khoo, Hai Leong Chieu, Rui Xia

Research Collection School Of Computing and Information Systems

The prevalent use of social media enables rapid spread of rumors on a massive scale, which leads to the emerging need of automatic rumor verification (RV). A number of previous studies focus on leveraging stance classification to enhance RV with multi-task learning (MTL) methods. However, most of these methods failed to employ pre-trained contextualized embeddings such as BERT, and did not exploit inter-task dependencies by using predicted stance labels to improve the RV task. Therefore, in this paper, to extend BERT to obtain thread representations, we first propose a Hierarchical Transformer1 , which divides each long thread into shorter subthreads, …

Leveraging Profanity For Insincere Content Detection: A Neural Network Approach, Swapna Gottipati, Annabel Tan, David Jing Shan Chow, Joel Wee Kiat Lim

Research Collection School Of Computing and Information Systems

Community driven social media sites are rich sources of knowledge and entertainment and at the same vulnerable to the flames or toxic content that can be dangerous to various users of these platforms as well as to the society. Therefore, it is crucial to identify and remove such content to have a better and safe online experience. Manually eliminating flames is tedious and hence many research works focus on machine learning or deep learning models for automated methods. In this paper, we primarily focus on detecting the insincere content using neural network-based learning methods. We also integrated the profanity features …

Learning Personal Conscientiousness From Footprints In E-Learning Systems, Lo Pang-Yun Ting, Shan Yun Teng, Kun Ta Chuang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Personality inference has received widespread attention for its potential to infer psychological well being, job satisfaction, romantic relationship success, and professional performance. In this research, we focus on Conscientiousness, one of the well studied Big Five personality traits, which determines if a person is self-disciplined, organized, and hard-working. Research has shown that Conscientiousness is related to a person's academic and workplace success. For an expert to evaluate a person's Conscientiousness, long-term observation of the person's behavior at work place or at home is usually required. To reduce this evaluation effort as well as to cope with the increasing trend of …