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Articles 571 - 600 of 15936
Full-Text Articles in Entire DC Network
A Partition Cover Approach To Tokenization, Jia Peng Lim, Shawn Tan, Davin Choo, Hady Wirawan Lauw
A Partition Cover Approach To Tokenization, Jia Peng Lim, Shawn Tan, Davin Choo, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Tokenization is the process of encoding strings into tokens of a fixed vocabulary size, and is widely utilized in Natural Language Processing applications. The leading tokenization algorithm today is Byte Pair Encoding (BPE), which formulates the tokenization problem as a compression problem and tackles it by performing sequences of merges. In this work, we formulate tokenization as an optimization objective, show that it is NP-hard via a simple reduction from vertex cover, and propose a polynomial-time greedy algorithm GreedTok. Our formulation naturally relaxes to the well-studied weighted maximum coverage problem which has a simple -approximation algorithm GreedWMC. Through empirical evaluations …
Designing Payment Models For The Poor, Bhavani Shanker Uppari, Sasa Zorc
Designing Payment Models For The Poor, Bhavani Shanker Uppari, Sasa Zorc
Research Collection Lee Kong Chian School Of Business
Millions of people in poverty lack access to basic services such as energy, clean water, and cooking gas. Private firms are increasingly delivering these services—for example, by offering solar home systems or clean cooking packages with remote lockout capabilities—through pay-as-you-go (PAYGo) contracts that give consumers flexibility over payment timing and amount to match their erratic cash flows. Because firms cannot observe consumer liquidity, however, consumers may misuse this flexibility and prioritize other needs over repayment. We study how to design contracts that preserve flexibility while creating incentives for timely repayment using an optimal contracting approach. The optimal contract summarizes each …
The Power And Peril Of First Offers In Negotiations: A Conceptual, Meta-Analytic, And Experimental Synthesis, H.M. Petrowsky, L. Boecker, Y.A. Escher, M.L. Frech, M. Friese, A.D. Galinsky, B. Gunia, A.J. Lee, Michael Schaerer, M. Schweinsberg, M. Soliman, R. Swaab, E.S. Troll, M. Weber, D.D. Loschelder
The Power And Peril Of First Offers In Negotiations: A Conceptual, Meta-Analytic, And Experimental Synthesis, H.M. Petrowsky, L. Boecker, Y.A. Escher, M.L. Frech, M. Friese, A.D. Galinsky, B. Gunia, A.J. Lee, Michael Schaerer, M. Schweinsberg, M. Soliman, R. Swaab, E.S. Troll, M. Weber, D.D. Loschelder
Research Collection Lee Kong Chian School Of Business
Is it advantageous to make the first offer and to do so ambitiously? Although initial studies suggested clear advantages across cultures and contexts, recent findings have challenged the robustness of this first-mover advantage. A preregistered meta-analysis of 374 effects from 90 studies (Study 1; N = 16,334) revealed three beneficial effects of making the first offer: (a) a general first-mover advantage (g = 0.42, m = 80), (b) a positive correlation between first-offer magnitude and agreement value (r = 0.62, g = 1.56, m = 53), and (c) an advantage of ambitious (vs. moderate) first offers on agreement value (g …
Managing The Personalization Paradox: Lessons From Spotify’S Ai Dj, Anirban Mukherjee, Hannah H. Chang, Jonathan Wibowo
Managing The Personalization Paradox: Lessons From Spotify’S Ai Dj, Anirban Mukherjee, Hannah H. Chang, Jonathan Wibowo
Research Collection Lee Kong Chian School Of Business
This case study uses Spotify’s AI DJ to examine the “personalization paradox,” where product features designed to deepen user connection can instead cause alienation. An analysis of over 1,400 user comments identifies three core frictions: a perceived loss of user agency, mismanaged expectations for the human-like AI, and a scalability bottleneck that led to repetitive content. Students are placed in the role of a product manager tasked with developing a prioritized roadmap to address these issues, forcing them to grapple with the critical trade-offs between technological capability and user psychology.
Temporal And Heterogeneous Graph Neural Network For Remaining Useful Life Prediction, Zhihao Wen, Yuan Fang, Pengcheng Wei, Fayao Liu, Zhenghua Chen, Min Wu
Temporal And Heterogeneous Graph Neural Network For Remaining Useful Life Prediction, Zhihao Wen, Yuan Fang, Pengcheng Wei, Fayao Liu, Zhenghua Chen, Min Wu
Research Collection School Of Computing and Information Systems
Predicting remaining useful life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant stream of time-series sensory data from such systems, deep learning (DL) models have risen to prominence at identifying complex, nonlinear temporal dependencies in these data. In addition to the temporal dependencies of individual sensors, spatial dependencies emerge as important correlations among these sensors, which can be naturally modeled by a temporal graph that describes time-varying spatial relationships. However, the majority of existing studies have relied on capturing discrete snapshots of this temporal …
Bridging The (U)Rban And (I)Ndividual To Transform Cities, Lily Kong
Bridging The (U)Rban And (I)Ndividual To Transform Cities, Lily Kong
Asian Management Insights
Urban transformation efforts are often focused on infrastructure and technology issues, overlooking how the individual experiences the city. An interview with SMU President Professor Lily Kong.
Foreign Director Exit In The Midst Of Deteriorating Bilateral Political Relations, Xiaocong Tian, Yuehua Xu, Daphne W. Yiu
Foreign Director Exit In The Midst Of Deteriorating Bilateral Political Relations, Xiaocong Tian, Yuehua Xu, Daphne W. Yiu
Research Collection Lee Kong Chian School Of Business
Amid global geopolitical realism pushing for a race of hegemonic rivalry nowadays, an outflow of global human talents is a pressing organizational concern. Our study draws attention to the underexamined phenomenon of the exit of foreign directors from their host countries during geopolitical tensions. Theorizing from a sensemaking logic, we posit that the deterioration of bilateral political relations serves as an unexpected event that activates foreign directors’ schemas for dual identity conflict, propelling them to react behaviorally to such identity threats by exiting the board in the host country. In addition, we further posit that the sensemaking process is contingent …
Internet Of Things In Intralogistics: Applications And Emerging Research, Rene De Koster, Debjit Roy, Yun Fong Lim, Subodha Kumar
Internet Of Things In Intralogistics: Applications And Emerging Research, Rene De Koster, Debjit Roy, Yun Fong Lim, Subodha Kumar
Research Collection Lee Kong Chian School Of Business
Managing the performance of intralogistics operations, that is logistics operations within facilities such as manufacturing plants, order fulfillment warehouses, ports and terminals, and retail stores, is critical in fulfilling customer expectations. Traditional decision-making for intralogistics operations is based on historical data, typically collected over long-range intervals with significant processing delays. However, nowadays, Internet of Things (IoT) applications are used to gather detailed real-time data to make dynamic decisions. These new data sources provide challenges and opportunities for operations management. We provide an overview of prominent IoT technologies in four domains: manufacturing, warehousing, ports and terminals, retail, and other emerging areas. …
Accelerating Expertise In Professional Decision Making: The Effects Of A Simple Rules Training, Gavin Raj Maistry, Jochen Reb, Shenghua Luan, Thomas Menkhoff
Accelerating Expertise In Professional Decision Making: The Effects Of A Simple Rules Training, Gavin Raj Maistry, Jochen Reb, Shenghua Luan, Thomas Menkhoff
Research Collection Lee Kong Chian School Of Business
Traditional approaches to professional decision making hold analytical expertise as superior to qualitative judgment. However, this view is increasingly challenged by work on heuristics as effective decision strategies under conditions of uncertainty. Building on this insight, we developed a script-based simple rules training program in the context of insurance underwriting. We then conducted a quasi-experimental intervention study with 220 participants of varying levels of experience. The study compared performance in the training condition with that in an active control condition on a work sample test given before and after the intervention. The results show that training in simple rules could …
Technology Discontinuance: A Systematic Literature Review And Research Agenda, Birgit Altrichter, Sabine Benoit
Technology Discontinuance: A Systematic Literature Review And Research Agenda, Birgit Altrichter, Sabine Benoit
Research Collection Lee Kong Chian School Of Business
Technology discontinuance by users who quit digital services such as social media or lose interest in their devices after a few weeks is a common phenomenon. Yet it has received little research attention; the few studies that address it are scattered across disciplines. Research into customer churn is more prevalent, but it implies switching providers rather than halting usage altogether. By moving beyond discipline-based knowledge boundaries and conducting a systematic literature review, the current article proposes a technology discontinuance framework, which details seven technology discontinuance types (overuse, overload, proficiency, disenchantment, social, circumstantial, and solution) and their drivers. For example, guilt …
Price Dislocations: Insights From Trade Repository Data, Albert Menkveld, Lucas Saru, Shihao Yu
Price Dislocations: Insights From Trade Repository Data, Albert Menkveld, Lucas Saru, Shihao Yu
Research Collection Lee Kong Chian School Of Business
This paper identifies price dislocation events in EuroSTOXX 50 futures, i.e., periods marked by high absolute returns. Combining public limit order book data with confidential trade repository data collected under the European Market Infrastructure Regulation (EMIR), we analyze market conditions around such dislocations. We find that price dislocations are accompanied by an increase in trading volume, and in the number of trades. EMIR data enables us to identify who participates in these trades, which allows us to tell if the volume increase is driven by fewer investors trading more, i.e., a more concentrated market, or by more investors participating. The …
Explainable Sentiment Analysis With Deepseek-R1: Performance, Efficiency, And Few-Shot Learning, Donghao Huang, Zhaoxia Wang
Explainable Sentiment Analysis With Deepseek-R1: Performance, Efficiency, And Few-Shot Learning, Donghao Huang, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
Large language models (LLMs) have transformed sentiment analysis, yet balancing accuracy, efficiency, and explainability remains a critical challenge. This study presents the first comprehensive evaluation of DeepSeek-R1—an open-source reasoning model—against OpenAI’s GPT-4o and GPT-4o-mini. We test the full 671B model and its distilled variants, systematically documenting few-shot learning curves. Our experiments show DeepSeek-R1 achieves a 91.39% F1 score on 5-class sentiment and 99.31% accuracy on binary tasks with just 5 shots, an eightfold improvement in few-shot efficiency over GPT-4o. Architecture-specific distillation effects emerge, where a 32B Qwen2.5-based model outperforms the 70B Llama-based variant by 6.69 percentage points. While its reasoning …
Does Generative Ai Facilitate Investor Trading? Early Evidence From Chatgpt Outages, Qiang Cheng, Pengkai Lin, Yue Zhao
Does Generative Ai Facilitate Investor Trading? Early Evidence From Chatgpt Outages, Qiang Cheng, Pengkai Lin, Yue Zhao
Research Collection School Of Accountancy
In this paper, we use ChatGPT outages to provide early evidence on whether investors rely on generative artificial intelligence (GenAI) to perform professional tasks and the associated impact on stock price informativeness. We document a significant decline in stock trading volume during ChatGPT outages. The effect is stronger for firms with corporate news released immediately before or during the outages and for firms with higher ownership held by transient institutional investors. We then document declines in short-run price impact and return variance during the outage periods, consistent with reduced informed trading. Lastly, we document a positive effect of GenAI-assisted trading …
Discussion Of "Csr And Negative Corporate Events: The Moderating Role Of Managerial Overconfidence", Hye Sun Chang
Discussion Of "Csr And Negative Corporate Events: The Moderating Role Of Managerial Overconfidence", Hye Sun Chang
Research Collection School Of Accountancy
Corporate social responsibility (CSR) refers to the notion that firms are accountable not only to shareholders but also to a broader set of stakeholders including customers, employees, creditors, and the communities in which they operate. Although CSR has long been part of corporate discourse, its prominence has grown with the rise of environmental, social, and governance (ESG) concerns. Today’s consumers and stakeholders are increasingly attentive to issues such as climate change, economic inequality, labor rights, and social justice, prompting firms to align their strategies with evolving societal expectations. Building on this increasingly salient backdrop, Chu et al. (2026), hereafter CCT, …
Chain Of Strategy Optimization Makes Large Language Models Better Emotional Supporter, Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu
Chain Of Strategy Optimization Makes Large Language Models Better Emotional Supporter, Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu
Research Collection School Of Computing and Information Systems
The growing emotional stress in modern society has increased the demand for Emotional Support Conversations (ESC). While Large Language Models (LLMs) show promise for ESC, they face two key challenges: (1) low strategy selection accuracy, and (2) preference bias, limiting their adaptability to users’ emotional needs. Existing supervised fine-tuning (SFT) struggles to address these issues, as it rigidly trains models on single gold-standard responses without modeling nuanced strategy trade-offs. To overcome these limitations, we propose a novel two-stage framework that optimizes strategy selection preferences at each dialogue turn. We first leverage Monte Carlo Tree Search to construct ESC-Pro, a high-quality …
Security Modelling For Cyber-Physical Systems: A Systematic Literature Review, Shao Fei Huang, Christopher M. Poskitt, Lwin Khin Shar
Security Modelling For Cyber-Physical Systems: A Systematic Literature Review, Shao Fei Huang, Christopher M. Poskitt, Lwin Khin Shar
Research Collection School Of Computing and Information Systems
Cyber-physical systems are at the intersection of digital technology and engineering domains, rendering them high-value targets of sophisticated and well-funded cybersecurity threat actors. Prominent cybersecurity attacks on CPS have brought attention to the vulnerability of these systems and the inherent weaknesses of critical infrastructure reliant on them. Security modelling for CPS is an important mechanism to systematically identify and assess vulnerabilities, threats, and risks throughout system life cycles, and to ultimately ensure system resilience, safety, and reliability. This survey delves into state-of-the-art research on CPS security modelling, encompassing both threat and attack modelling. While these terms are sometimes used interchangeably, …
Disc: Decentralized Identity System With Self-Sovereign Credential Aggregation, Yang Yang, Wai Keung Ching, Minming Huang, Supachate Innet, Guomin Yang, Hwee Hwa Pang, Robert H. Deng
Disc: Decentralized Identity System With Self-Sovereign Credential Aggregation, Yang Yang, Wai Keung Ching, Minming Huang, Supachate Innet, Guomin Yang, Hwee Hwa Pang, Robert H. Deng
Research Collection School Of Computing and Information Systems
The evolution of decentralized identity (DID) and self-sovereign identity (SSI) frameworks, as endorsed by W3C Verifiable Credentials (VC) and eIDAS 2.0, underscores the need for secure, efficient, and privacy-preserving credential management. However, existing credential systems often depend on centralized issuers, lack efficient aggregation mechanisms, or fail to ensure unlinkability across authentication sessions. To address these challenges, we propose DISC (Decentralized Identity System with Self-Sovereign Credential Aggregation), a novel credential system that enables multi-authority credential issuance, user-controlled credential aggregation, and unlinkable authentication. DISC allows users to aggregate credentials from multiple issuers while maintaining constant-size authentication tokens and supporting batch verification for …
Towards Goal-Oriented Intelligent Tutoring Systems In Online Education, Yang Deng, Zifeng Ren, An Zhang, Tat-Seng Chua
Towards Goal-Oriented Intelligent Tutoring Systems In Online Education, Yang Deng, Zifeng Ren, An Zhang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Interactive Intelligent Tutoring Systems (ITSs) enhance the learning experience in online education byfostering effective learning through interactive problem-solving. However, many current ITS models do notfully incorporate proactive engagement strategies that optimize educational resources through thoughtfulplanning and assessment. In this work, we propose a novel and practical task of Goal-oriented IntelligentTutoring Systems (GITS), designed to help students achieve proficiency in specific concepts through a tailoredsequence of exercises and evaluations. We introduce a novel graph-based reinforcement learning framework,named Planning-Assessment-Interaction (PAI), to tackle the challenges of goal-oriented policy learningwithin GITS. This framework utilizes cognitive structure information to refine state representation andguide the selection …
Associations Between Perceived Stress And Glycemic Measures: Gender And Age As Moderators, Ming-Hong Tsai, Charlene Enhui Goh, Michelle H. Lee, Francine Seah, Maybritte Lim, Eveline Febriana, Jiahui Fu, Julie K. Yip, Philip Presham, Sue-Anne Ee Shiow Toh
Associations Between Perceived Stress And Glycemic Measures: Gender And Age As Moderators, Ming-Hong Tsai, Charlene Enhui Goh, Michelle H. Lee, Francine Seah, Maybritte Lim, Eveline Febriana, Jiahui Fu, Julie K. Yip, Philip Presham, Sue-Anne Ee Shiow Toh
Research Collection School of Social Sciences
Introduction Prediabetes presents an opportunity for early intervention. Growing evidence suggests that psychological stress may contribute to glucose dysregulation, but the findings are inconsistent. This study aimed to clarify the association between perceived stress and glycemic measures, by first testing gender as a moderator, and then examining age as a moderator within each gender group. Research design and methods We analyzed cross-sectional data from 470 diabetes-free adults in Singapore. Participants completed the 10-item Perceived Stress Scale, comprising two subscales: perceived helplessness and perceived self-efficacy. Glycemic measures included fasting plasma glucose, glycated hemoglobin, and 2-hour plasma glucose (2h-PG) following an oral …
Local-Thinking Bias, Ed Dehaan, Charles M. C. Lee, Wei Ting Loh
Local-Thinking Bias, Ed Dehaan, Charles M. C. Lee, Wei Ting Loh
Research Collection School Of Accountancy
Local-thinking bias, wherein agents overweight information that comes readily to mind, is a prominent finding in cognitive psychology. In this study, we investigate local-thinking bias in the context of sell-side analysts and measure each analyst’s “local” information as news stemming from their individual coverage portfolio. Tests examining multiple analysts forecasting on the same focal firm at the same time find that individual analysts overweight idiosyncratic local news and underweight news from economically linked firms that are not in their coverage portfolios. Market prices track the analyst bias from local news, leading to predictable and economically significant return reversal patterns in …
When Deep Learning Meets Information Retrieval-Based Bug Localization: A Survey, Feifei Niu, Chuanyi Li, Kui Liu, Xin Xia, David Lo
When Deep Learning Meets Information Retrieval-Based Bug Localization: A Survey, Feifei Niu, Chuanyi Li, Kui Liu, Xin Xia, David Lo
Research Collection School Of Computing and Information Systems
Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. …
Usefulness And Diminishing Returns: Evaluating Social Information In Recommender Systems, Qing Meng, Huiyu Min, Ming Shan Hee, Roy Ka-Wei Lee, Bing Tian Dai, Shuai Xu
Usefulness And Diminishing Returns: Evaluating Social Information In Recommender Systems, Qing Meng, Huiyu Min, Ming Shan Hee, Roy Ka-Wei Lee, Bing Tian Dai, Shuai Xu
Research Collection School Of Computing and Information Systems
Social recommendation, which leverages users’ social information to predict users’ preferences, is a popular branch of recommender systems. Many existing studies have attempted to advance the performance of collaborative filtering methods by leveraging the user-user matrix to enhance user embedding learning with user’s social connections. While the existing social recommender systems have demonstrated good performance in various recommendation tasks, the extent of social information usefulness in recommender systems remains unclear. This paper addresses the research gap by designing experiments to answer three research questions: (i) How useful is social information in varying user-item data sparsity? (ii) How much social information …
Distillcaps: Enhancing Audio-Language Alignment In Captioning Via Retrieval-Augmented Knowledge Distillation, Thinh Pham, Nghiem Diep, Lizi Liao, Binh Nguyen
Distillcaps: Enhancing Audio-Language Alignment In Captioning Via Retrieval-Augmented Knowledge Distillation, Thinh Pham, Nghiem Diep, Lizi Liao, Binh Nguyen
Research Collection School Of Computing and Information Systems
Automated audio captioning (AAC) benefits from incorporatingexternal context to interpret complex sounds, but doing so withretrieval-augmented generation (RAG) at inference is sometimesinfeasible due to data availability or incurs significant latency andcomplexity. We propose DistillCaps, a novel training-time frame-work that leverages RAG to guide knowledge distillation for im-proved audio-language alignment, while lessening the relianceon retrieval during inference. In our framework, a RAG-equippedteacher model retrieves relevant textual information (e.g., simi-lar captions) for each audio clip and uses it for training to gener-ate context-enriched captions. Simultaneously, a student model istrained to imitate this teacher, learning to produce high-qualitycaptions from audio alone. We further …
Unified Molecule Pre-Training With Flexible 2d And 3d Modalities: Single And Paired Modality Integration, Tengwei Song, Min Wu, Yuan Fang
Unified Molecule Pre-Training With Flexible 2d And 3d Modalities: Single And Paired Modality Integration, Tengwei Song, Min Wu, Yuan Fang
Research Collection School Of Computing and Information Systems
Molecular representation learning plays a crucial role in advancing applications such as drug discovery and material design. Existing work leverages 2D and 3D modalities of molecular information for pre-training, aiming to capture comprehensive structural and geometric insights. However, these methods require paired 2D and 3D molecular data to train the model effectively and prevent it from collapsing into a single modality, posing limitations in scenarios where a certain modality is unavailable or computationally expensive to generate. To overcome this limitation, we propose FlexMol, a flexible molecule pre-training framework that learns unified molecular representations while supporting single-modality input. Specifically, inspired by …
Damslnet: Dual-Attention Multi-Scale Lightweight Network For Plant Disease Classification, Linfan Deng, Juan Qin, Kun Li, Jinhua Zhu, Zhaoxia Wang
Damslnet: Dual-Attention Multi-Scale Lightweight Network For Plant Disease Classification, Linfan Deng, Juan Qin, Kun Li, Jinhua Zhu, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
Accurately identifying crop diseases plays a crucial role in advancing intelligent and modern agricultural production. Deep learning techniques have performed robust performance in classifying plant disease images. However, current studies face the challenge that many plant disease datasets are generated in controlled environments, leading to reduced model performance in real-world agricultural settings. This paper aims to provide a lightweight model that can accurately classify plant diseases in natural environments. Specifically, this paper investigates the Dual-Attention Multi-Scale Lightweight Network (DAMSLNet), which combines dual-attention-based multi-scale feature extraction and deep information fusion, to classify plant diseases. At the front end, the model employs …
Branch-And-Cut-And-Price For Agile Earth Observation Satellite Scheduling, Guansheng Peng, Jianjiang Wang, Guopeng Song, Aldy Gunawan, Lining Xing, Pieter Vansteenwegen
Branch-And-Cut-And-Price For Agile Earth Observation Satellite Scheduling, Guansheng Peng, Jianjiang Wang, Guopeng Song, Aldy Gunawan, Lining Xing, Pieter Vansteenwegen
Research Collection School Of Computing and Information Systems
The Agile Earth Observation Satellite scheduling selects and sequences satellite observations of possible targets on the Earth’s surface, each with a specific profit and multiple time windows. The objective is to maximize the collected profit of all observations completed under some operational constraints. The problem can be modeled as a variant of the Team Orienteering Problem with Time Windows (TOPTW). The key differences with the regular TOPTW are twofold: first, a time-dependent transition time is required for each pair of consecutive observations to adjust the camera’s look angles. Second, the time windows of each target vary during different observation cycles, …
Deep Reinforcement Learning For Solving The Stochastic E-Waste Collection Problem, Dang Viet Anh Nguyen, Aldy Gunawan, Mustafa Misir, Kwan Hui Lim, Pieter Vansteenwegen
Deep Reinforcement Learning For Solving The Stochastic E-Waste Collection Problem, Dang Viet Anh Nguyen, Aldy Gunawan, Mustafa Misir, Kwan Hui Lim, Pieter Vansteenwegen
Research Collection School Of Computing and Information Systems
With the growing influence of the internet and information technology, Electrical and Electronic Equipment (EEE) has become a gateway to technological innovations. However, discarded devices, also called e-waste, pose a significant threat to the environment and human health if not properly treated, disposed of, or recycled. In this study, we extend a novel model for the e-waste collection in an urban context: the Heterogeneous VRP with Multiple Time Windows and Stochastic Travel Times (HVRP-MTWSTT). We propose a solution method that employs deep reinforcement learning to guide local search heuristics (DRL-LSH). The contributions of this paper are as follows: (1) HVRP-MTWSTT …
The Impact Of Sanctions On Github Developers And Activities, Youmei Fan, Ani Hovhannisyan, Hideaki Hata, Christoph Treude, Raula G. Kula
The Impact Of Sanctions On Github Developers And Activities, Youmei Fan, Ani Hovhannisyan, Hideaki Hata, Christoph Treude, Raula G. Kula
Research Collection School Of Computing and Information Systems
The GitHub platform has fueled the creation of truly global software, enabling contributions from developers across various geographical regions of the world. As software becomes more entwined with global politics and social regulations, it becomes similarly subject to government sanctions. In 2019, GitHub restricted access to certain services for users in specific locations but rolled back these restrictions for some communities (e.g., the Iranian community) in 2021. We conducted a largescale empirical study, collecting approximately 156 thousand user profiles and their 41 million activity points from 2008 to 2022, to understand the response of developers. Our results indicate that many …
The Lexicographic Preference And The Tops-Only Property, Xinning Geng
The Lexicographic Preference And The Tops-Only Property, Xinning Geng
Dissertations and Theses Collection (Open Access)
This paper studies the structure of the lexicographic preference domain in the context of strategy proof Random Social Choice Functions (RSCFs). Classical sufficient conditions for the tops-only property—namely, the Interior and Exterior Properties—do not hold in lexicographic domains. Nevertheless, these domains still exhibit the tops-only property. We first provide a structural explanation of why the Interior and Exterior Properties fail in this setting. We then introduce two new structural conditions, Property 1 and Property 2, and show that any two-dimensional lexicographic preference domain satisfying these conditions is a tops-only domain. Finally, we prove that the two-dimensional lexicographic domain meets both …
Graph Perturbations For Robust Knowledge Discovery And Retrieval, Hanhua Xiao
Graph Perturbations For Robust Knowledge Discovery And Retrieval, Hanhua Xiao
Dissertations and Theses Collection (Open Access)
Graph perturbation, rooted in classical perturbation theory, studies how small topology edits, i.e., adding or deleting edges, affects graph properties (e.g., density, centrality). This fundamental problem underpins applications like bioinformatics, privacy preservation and system defense. While much prior work targets perturbations that influence global graph statistics or model outputs, comparatively little addresses robustness for knowledge discovery and information retrieval. In these settings, graphs are attributed: nodes carry real-world semantics (e.g., locations, people) and edges encode interactions or relationships. This thesis proposes new formulations and algorithms that generate and leverage graph perturbations to make knowledge discovery and retrieval more robust. Specifically, …