On Predicting Esg Ratings Using Dynamic Company Networks,
2023
Singapore Management University
On Predicting Esg Ratings Using Dynamic Company Networks, Gary Ang, Zhiling Guo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Environmental, social and governance (ESG) considerations play an increasingly important role due to the growing focus on sustainability globally. Entities, such as banks and investors, utilize ESG ratings of companies issued by specialized rating agencies to evaluate ESG risks of companies. The process of assigning ESG ratings by human analysts is however laborious and time intensive. Developing methods to predict ESG ratings could alleviate such challenges, allow ESG ratings to be generated in a more timely manner, cover more companies, and be more accessible. Most works study the effects of ESG ratings on target variables such as stock prices or …
Generative Model-Based Testing On Decision-Making Policies,
2023
Kyushu University
Generative Model-Based Testing On Decision-Making Policies, Zhuo Li, Xiongfei Wu, Derui Zhu, Mingfei Cheng, Siyuan Chen, Fuyuan Zhang, Xiaofei Xie, Lei Ma, Jianjun Zhao
Research Collection School Of Computing and Information Systems
The reliability of decision-making policies is urgently important today as they have established the fundamentals of many critical applications, such as autonomous driving and robotics. To ensure reliability, there have been a number of research efforts on testing decision-making policies that solve Markov decision processes (MDPs). However, due to the deep neural network (DNN)-based inherit and infinite state space, developing scalable and effective testing frameworks for decision-making policies still remains open and challenging.In this paper, we present an effective testing framework for decision-making policies. The framework adopts a generative diffusion model-based test case generator that can easily adapt to different …
Personalized Fashion Outfit Generation With User Coordination Preference Learning,
2023
Singapore Management University
Personalized Fashion Outfit Generation With User Coordination Preference Learning, Yujuan Ding, P.Y. Mok, Yunshan Ma, Yi Bin
Research Collection School Of Computing and Information Systems
This paper focuses on personalized outfit generation, aiming to generate compatible fashion outfits catering to given users. Personalized recommendation by generating outfits of compatible items is an emerging task in the recommendation community with great commercial value but less explored. The task requires to explore both user-outfit personalization and outfit compatibility, any of which is challenging due to the huge learning space resulted from large number of items, users, and possible outfit options. To specify the user preference on outfits and regulate the outfit compatibility modeling, we propose to incorporate coordination knowledge in fashion. Inspired by the fact that users …
Rosas: Deep Semi-Supervised Anomaly Detection With Contamination-Resilient Continuous Supervision,
2023
Singapore Management University
Rosas: Deep Semi-Supervised Anomaly Detection With Contamination-Resilient Continuous Supervision, Hongzuo Xu, Yijie Wang, Guansong Pang, Songlei Jian, Ning Liu, Yongjun Wang
Research Collection School Of Computing and Information Systems
Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method …
Continual Collaborative Filtering Through Gradient Alignment,
2023
Singapore Management University
Continual Collaborative Filtering Through Gradient Alignment, Dinh Hieu Do, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
A recommender system operates in a dynamic environment where new items emerge and new users join the system, resulting in ever-growing user-item interactions over time. Existing works either assume a model trained offline on a static dataset (requiring periodic re-training with ever larger datasets); or an online learning setup that favors recency over history. As privacy-aware users could hide their histories, the loss of older information means that periodic retraining may not always be feasible, while online learning may lose sight of users' long-term preferences. In this work, we adopt a continual learning perspective to collaborative filtering, by compartmentalizing users …
When Routing Meets Recommendation: Solving Dynamic Order Recommendations Problem In Peer-To-Peer Logistics Platforms,
2023
Singapore Management University
When Routing Meets Recommendation: Solving Dynamic Order Recommendations Problem In Peer-To-Peer Logistics Platforms, Zhiqin Zhang, Waldy Joe, Yuyang Er, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Peer-to-Peer (P2P) logistics platforms, unlike traditional last-mile logistics providers, do not have dedicated delivery resources (both vehicles and drivers). Thus, the efficiency of such operating model lies in the successful matching of demand and supply, i.e., how to match the delivery tasks with suitable drivers that will result in successful assignment and completion of the tasks. We consider a Same-Day Delivery Problem (SDDP) involving a P2P logistics platform where new orders arrive dynamically and the platform operator needs to generate a list of recommended orders to the crowdsourced drivers. We formulate this problem as a Dynamic Order Recommendations Problem (DORP). …
Graph-Level Anomaly Detection Via Hierarchical Memory Networks,
2023
Singapore Management University
Graph-Level Anomaly Detection Via Hierarchical Memory Networks, Chaoxi Niu, Guansong Pang, Ling Chen
Research Collection School Of Computing and Information Systems
Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole. To tackle this challenge, we propose a novel approach called Hierarchical Memory Networks (HimNet), which learns hierarchical memory modules---node and graph memory modules---via a graph autoencoder network architecture. The node-level memory module is trained to model fine-grained, internal graph interactions among nodes for detecting locally abnormal graphs, while the …
Real: A Representative Error-Driven Approach For Active Learning,
2023
Singapore Management University
Real: A Representative Error-Driven Approach For Active Learning, Cheng Chen, Yong Wang, Lizi Liao, Yueguo Chen, Xiaoyong Du
Research Collection School Of Computing and Information Systems
Given a limited labeling budget, active learning (al) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, al typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, it does not consider erroneous instances with their neighborhood error density, which have great potential to improve the model performance. To address this limitation, we propose Real, a novel approach to select data instances with Representative Errors for Active Learning. It identifies minority predictions as pseudo errors within a cluster and allocates an adaptive sampling budget for …
The Power Of Identity Cues In Text-Based Customer Service: Evidence From Twitter,
2023
Singapore Management University
The Power Of Identity Cues In Text-Based Customer Service: Evidence From Twitter, Yang Gao, Huaxia Rui, Shujing Sun
Research Collection School Of Computing and Information Systems
Text-based customer service is emerging as an important channel through which companies can assist customers. However, the use of few identity cues may cause customers to feel limited social presence and even suspect the human identity of agents, especially in the current age of advanced algorithms. Does such a lack of social presence affect service interactions? We studied this timely question by evaluating the impact of customers’ perceived social presence on service outcomes and customers’ attitudes toward agents. Our identification strategy hinged on Southwest Airlines’ sudden requirement to include a first name in response to service requests on Twitter, which …
Literature Review In The Generative Ai Era: How To Make A Compelling Contribution,
2023
University of New South Wales
Literature Review In The Generative Ai Era: How To Make A Compelling Contribution, Shan L. Pan, Rohit Nishant, Tuure Tuunanen, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
As we write this editorial for this special issue, we are amidst the significant technological changes that are continuing to shape society. Since the emergence of ChatGPT in November 2022, humanity has become aware of the potential of generative AI (i.e., AI that can generate content) and large language models (LLMs) (i.e., AI models trained on a massive corpus of unstructured data). There is growing debate and discussion about the promise and perils of generative AI for the future of work, and academia is not immune. Premier journals in the IS domain, such as Information Systems Research, have published editorials …
Models And Algorithms For Promoting Diverse And Fair Query Results,
2023
New Jersey Institute of Technology
Models And Algorithms For Promoting Diverse And Fair Query Results, Md Mouinul Islam
Dissertations
Ensuring fairness and diversity in search results are two key concerns in compelling search and recommendation applications. This work explicitly studies these two aspects given multiple users' preferences as inputs, in an effort to create a single ranking or top-k result set that satisfies different fairness and diversity criteria. From group fairness standpoint, it adapts demographic parity like group fairness criteria and proposes new models that are suitable for ranking or producing top-k set of results. This dissertation also studies equitable exposure of individual search results in long tail data, a concept related to individual fairness. First, the dissertation focuses …
Diversification And Fairness In Top-K Ranking Algorithms,
2023
New Jersey Institute of Technology
Diversification And Fairness In Top-K Ranking Algorithms, Mahsa Asadi
Dissertations
Given a user query, the typical user interfaces, such as search engines and recommender systems, only allow a small number of results to be returned to the user. Hence, figuring out what would be the top-k results is an important task in information retrieval, as it helps to ensure that the most relevant results are presented to the user. There exists an extensive body of research that studies how to score the records and return top-k to the user. Moreover, there exists an extensive set of criteria that researchers identify to present the user with top-k results, and result diversification …
Human-Ai Complex Task Planning,
2023
New Jersey Institute of Technology
Human-Ai Complex Task Planning, Sepideh Nikookar
Dissertations
The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. …
Data-Driven 2d Materials Discovery For Next-Generation Electronics,
2023
New Jersey Institute of Technology
Data-Driven 2d Materials Discovery For Next-Generation Electronics, Zeyu Zhang
Dissertations
The development of material discovery and design has lasted centuries in human history. After the concept of modern chemistry and material science was established, the strategy of material discovery relies on the experiments. Such a strategy becomes expensive and time-consuming with the increasing number of materials nowadays. Therefore, a novel strategy that is faster and more comprehensive is urgently needed. In this dissertation, an experiment-guided material discovery strategy is developed and explained using metal-organic frameworks (MOFs) as instances. The advent of 7r-stacked layered MOFs, which offer electrical conductivity on top of permanent porosity and high surface area, opened up new …
Public Biological Databases And The Sui Generis Database Right,
2023
University of Toronto, Canada
Public Biological Databases And The Sui Generis Database Right, Alexander Bernier, Christian Busse, Tania M. Bubela
Office of the Provost
The sui generis database right is an intellectual property right created in the European Union to stimulate investment in the curation of databases. Since its inception, communities engaged in research and development efforts have questioned its potential to incentivise database production, and posit that it stifles productive downstream uses of existing datasets. European courts have restricted the right’s ambit through a restrictive interpretation of the circumstances in which it applies, which we argue, enables downstream use of biological databases. Nonetheless, residual ambiguities about potential infringement of the right exist. The prospect of unintentional infringement can frustrate downstream innovation. These ambiguities …
Forecasting Stock Indices With The Covid-19 Infection Rate As An Exogenous Variable,
2023
University of Louisiana at Lafayette
Forecasting Stock Indices With The Covid-19 Infection Rate As An Exogenous Variable, Mohammad Saha A. Patwary
School of Computing and Informatics
Forecasting stock market indices is challenging because stock prices are usually nonlinear and non- stationary. COVID-19 has had a significant impact on stock market volatility, which makes forecasting more challenging. Since the number of confirmed cases significantly impacted the stock price index; hence, it has been considered a covariate in this analysis. The primary focus of this study is to address the challenge of forecasting volatile stock indices during Covid-19 by employing time series analysis. In particular, the goal is to find the best method to predict future stock price indices in relation to the number of COVID-19 infection rates. …
On Digital Productivity Base Of Policies For Cross-Border Data Flows Between Rcep Parties And Its Influences—Taking Digital Integration Index As A Reference,
2023
Law School, Yunnan University, Kunming 650500, China
Faculty of Law, University of Fribourg, Fribourg CH1700, Switzerland
On Digital Productivity Base Of Policies For Cross-Border Data Flows Between Rcep Parties And Its Influences—Taking Digital Integration Index As A Reference, Gui Huang, Ru Tao
Bulletin of Chinese Academy of Sciences (Chinese Version)
This study reviews the newest legislation and policies of Regional Comprehensive Economic Partnership (RCEP) participating countries on cross-border data flow, and then categorized them according to the ban on data transfer, local storage of data, permission-based regulation, and standards-based regulation. By referring to the indexes in the ASEAN Digital Integration Index, the subject and object factors of digital productivity in RCEP parities are sorted out, as well as the status quo of digital economy. Through the introduction of data value chain theory, the decisive impact of digital productivity factors on the policy formulation of cross-border data flow is expounded; by …
Paradigm Review Of Data Localization In India And Its Implications For China,
2023
School of Law, Henan University of Economics and Law, Zhengzhou 450046, China
Paradigm Review Of Data Localization In India And Its Implications For China, Ying Fan
Bulletin of Chinese Academy of Sciences (Chinese Version)
Data localization is a focal point of global data governance and its impact on global data governance is no longer confined to a single country. Over the years, India has followed a unique policy framework in terms of cross-border data flows and data localization, and its insistence on data sovereignty reflects its position in the international arena. This study uses the Indian data localization paradigm as a research base to discuss the common phenomenon of disconnect between policy motivations and practical effects of data localization, and as an entry point to introduce the latest Indian research findings in this area. …
Research On Multi-Source Heterogeneous Big Data Fusion Based On Wsr,
2023
School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China
Research On Multi-Source Heterogeneous Big Data Fusion Based On Wsr, Aihua Li, Weijia Xu, Yong Shi
Bulletin of Chinese Academy of Sciences (Chinese Version)
In the era of multi-source heterogeneous big data, big data presents new features such as cross, diversity and variability. The applications of big data in a wider range of fields have new requirements for data fusion. Under this background, the connotation of data fusion is enriched and expanded. The generalized data fusion includes the fusion of data resources, the fusion of model methods, and the fusion of decision-makers' knowledge and experience. This study analyzes the characteristics of multi-source heterogeneous data fusion at three different fusion levels: data level, information level and decision level, and discusses challenges for data fusion in …
Cybersecurity Safeguards: What Cybersecurity Safeguards Could Have Prevented The Intelligence/Data Breach By A Member Of The Air National Guard,
2023
Boise State University
Cybersecurity Safeguards: What Cybersecurity Safeguards Could Have Prevented The Intelligence/Data Breach By A Member Of The Air National Guard, Christopher Curtis Royal
Cyber Operations and Resilience Program Graduate Projects
Jack Teixeira, a 21-year-old IT specialist Air National Guard found himself on the wrong side of the US law after sharing what is considered classified and extremely sensitive information about USA's operations and role in Ukraine and Russia war. Like other previous cases of leakage of classified intelligence, the case of Teixeira raises concerns about the weaknesses and vulnerability of federal agencies' IT systems and security protocols governing accessibility to classified documents. Internal leakages of such classified documents hurt national security and can harm the country, especially when such secretive intelligence finds its way into the hands of enemies. Unauthorized …
